{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" }, "colab": { "name": "explore_data.ipynb", "provenance": [], "include_colab_link": true } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "code", "metadata": { "id": "5fyVNZ6AkS2C", "colab_type": "code", "colab": {} }, "source": [ "import numpy as np\n", "import pandas as pd\n", "from matplotlib import pyplot as plt\n", "import seaborn as sns\n", "\n", "\n", "pd.set_option('display.max_columns', 500)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "rPRtOZBUkS2O", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 383 }, "outputId": "dc67397c-e95f-493b-fcd6-9cf3f3de327c" }, "source": [ "# retrieved from https://www.kaggle.com/rajeevw/ufcdata\n", "# on 11/15/2019\n", "dat_df = pd.read_csv(\"https://raw.githubusercontent.com/ekoly/DS-Unit-1-Build/master/csv/data.csv\")\n", "dat_df.head()" ], "execution_count": 139, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
R_fighterB_fighterRefereedatelocationWinnertitle_boutweight_classno_of_roundsB_current_lose_streakB_current_win_streakB_drawB_avg_BODY_attB_avg_BODY_landedB_avg_CLINCH_attB_avg_CLINCH_landedB_avg_DISTANCE_attB_avg_DISTANCE_landedB_avg_GROUND_attB_avg_GROUND_landedB_avg_HEAD_attB_avg_HEAD_landedB_avg_KDB_avg_LEG_attB_avg_LEG_landedB_avg_PASSB_avg_REVB_avg_SIG_STR_attB_avg_SIG_STR_landedB_avg_SIG_STR_pctB_avg_SUB_ATTB_avg_TD_attB_avg_TD_landedB_avg_TD_pctB_avg_TOTAL_STR_attB_avg_TOTAL_STR_landedB_longest_win_streakB_lossesB_avg_opp_BODY_attB_avg_opp_BODY_landedB_avg_opp_CLINCH_attB_avg_opp_CLINCH_landedB_avg_opp_DISTANCE_attB_avg_opp_DISTANCE_landedB_avg_opp_GROUND_attB_avg_opp_GROUND_landedB_avg_opp_HEAD_attB_avg_opp_HEAD_landedB_avg_opp_KDB_avg_opp_LEG_attB_avg_opp_LEG_landedB_avg_opp_PASSB_avg_opp_REVB_avg_opp_SIG_STR_attB_avg_opp_SIG_STR_landedB_avg_opp_SIG_STR_pctB_avg_opp_SUB_ATTB_avg_opp_TD_attB_avg_opp_TD_landedB_avg_opp_TD_pctB_avg_opp_TOTAL_STR_attB_avg_opp_TOTAL_STR_landedB_total_rounds_foughtB_total_time_fought(seconds)B_total_title_boutsB_win_by_Decision_MajorityB_win_by_Decision_SplitB_win_by_Decision_UnanimousB_win_by_KO/TKOB_win_by_SubmissionB_win_by_TKO_Doctor_StoppageB_winsB_StanceB_Height_cmsB_Reach_cmsB_Weight_lbsR_current_lose_streakR_current_win_streakR_drawR_avg_BODY_attR_avg_BODY_landedR_avg_CLINCH_attR_avg_CLINCH_landedR_avg_DISTANCE_attR_avg_DISTANCE_landedR_avg_GROUND_attR_avg_GROUND_landedR_avg_HEAD_attR_avg_HEAD_landedR_avg_KDR_avg_LEG_attR_avg_LEG_landedR_avg_PASSR_avg_REVR_avg_SIG_STR_attR_avg_SIG_STR_landedR_avg_SIG_STR_pctR_avg_SUB_ATTR_avg_TD_attR_avg_TD_landedR_avg_TD_pctR_avg_TOTAL_STR_attR_avg_TOTAL_STR_landedR_longest_win_streakR_lossesR_avg_opp_BODY_attR_avg_opp_BODY_landedR_avg_opp_CLINCH_attR_avg_opp_CLINCH_landedR_avg_opp_DISTANCE_attR_avg_opp_DISTANCE_landedR_avg_opp_GROUND_attR_avg_opp_GROUND_landedR_avg_opp_HEAD_attR_avg_opp_HEAD_landedR_avg_opp_KDR_avg_opp_LEG_attR_avg_opp_LEG_landedR_avg_opp_PASSR_avg_opp_REVR_avg_opp_SIG_STR_attR_avg_opp_SIG_STR_landedR_avg_opp_SIG_STR_pctR_avg_opp_SUB_ATTR_avg_opp_TD_attR_avg_opp_TD_landedR_avg_opp_TD_pctR_avg_opp_TOTAL_STR_attR_avg_opp_TOTAL_STR_landedR_total_rounds_foughtR_total_time_fought(seconds)R_total_title_boutsR_win_by_Decision_MajorityR_win_by_Decision_SplitR_win_by_Decision_UnanimousR_win_by_KO/TKOR_win_by_SubmissionR_win_by_TKO_Doctor_StoppageR_winsR_StanceR_Height_cmsR_Reach_cmsR_Weight_lbsB_ageR_age
0Henry CejudoMarlon MoraesMarc Goddard2019-06-08Chicago, Illinois, USARedTrueBantamweight50.04.00.09.2000006.0000000.2000000.00000062.60000020.6000002.6000002.00000048.60000011.2000000.8000007.65.4000000.4000000.00000065.4022.6000000.4660000.4000000.800000.2000000.10000066.40000023.6000004.01.06.4000004.0000001.0000000.6000051.20000017.4000000.6000000.20000039.6000009.4000000.2000006.800004.8000000.0000000.00000052.80000018.200000.2360000.0000001.0000000.4000000.10000053.80000019.2000009.0419.4000000.00.01.00.02.01.00.04.0Orthodox167.64170.18135.00.04.00.021.90000016.40000017.00000011.00000075.00000026.5000009.4000006.50000074.20000023.900.4005.3000003.7000001.2000000.000000101.40000044.0000000.4660000.1000005.3000001.9000000.458000129.90000069.1000004.02.013.3000008.8000007.5000005.10000090.50000026.8000000.8000000.30000076.10000017.3000000.1000009.4000006.1000000.0000000.00000098.80000032.2000000.3360000.0000000.9000000.1000000.050000110.50000043.30000027.0742.603.00.02.04.02.00.00.08.0Orthodox162.56162.56135.031.032.0
1Valentina ShevchenkoJessica EyeRobert Madrigal2019-06-08Chicago, Illinois, USARedTrueWomen's Flyweight50.03.00.014.6000009.10000011.8000007.300000124.70000042.1000002.4000001.900000112.00000032.0000000.00000012.310.2000000.8000000.000000138.9051.3000000.3990000.7000001.000000.5000000.225000158.70000069.6000003.06.013.0000009.30000012.8000009.60000101.70000032.0000008.1000006.90000097.70000030.8000000.10000011.900008.4000001.4000000.000000122.60000048.500000.4080000.7000002.3000000.9000000.231000151.50000075.40000029.0849.0000000.00.02.01.00.00.01.04.0Orthodox167.64167.64125.00.02.00.012.0000007.7142869.2857146.85714388.14285736.14285718.42857116.42857184.57142937.000.00019.28571414.7142861.7142860.142857115.85714359.4285710.5757140.4285715.1428572.4285710.601429161.571429102.8571432.02.024.57142914.14285710.5714297.85714398.57142932.5714296.4285714.28571461.85714312.4285710.00000029.14285718.1428571.1428570.000000115.57142944.7142860.4371430.2857143.2857140.8571430.147143158.14285782.28571425.01062.002.00.01.02.00.02.00.05.0Southpaw165.10167.64125.032.031.0
2Tony FergusonDonald CerroneDan Miragliotta2019-06-08Chicago, Illinois, USARedFalseLightweight30.03.00.015.35483911.3225816.7419354.38709784.74193538.5806455.5161293.80645267.64516123.2580650.64516114.012.1935480.9354840.09677497.0046.7741940.4961290.3548392.161290.6774190.295484103.70967752.5483878.08.017.90322611.8709688.4193555.8387184.54838738.0645161.7419350.93548467.64516125.4838710.2258069.161297.4838710.0322580.03225894.70967744.838710.4532260.0967742.0967740.2258060.063548100.38709749.77419468.0581.8709681.00.00.07.010.06.00.023.0Orthodox185.42185.42155.00.011.00.013.8666678.6666672.8666671.733333116.13333349.4666675.3333334.26666796.73333335.600.20013.73333311.2000000.3333330.133333124.33333355.4666670.4300001.0000000.9333330.4000000.277333133.00000063.40000011.01.014.4666678.1333332.8000000.73333391.06666732.2000004.8666672.80000078.26666723.2000000.2666676.0000004.4000000.3333330.13333398.73333335.7333330.3400000.0666672.8666670.6666670.131333102.13333338.60000033.0604.402.00.01.03.03.06.01.014.0Orthodox180.34193.04155.036.035.0
3Jimmie RiveraPetr YanKevin MacDonald2019-06-08Chicago, Illinois, USABlueFalseBantamweight30.04.00.017.00000014.00000013.75000011.000000109.50000048.75000013.00000010.500000116.25000053.7500000.5000003.02.5000000.5000000.250000136.2570.2500000.5500000.2500002.500001.2500000.287500154.75000086.7500004.00.012.2500006.0000006.0000003.7500094.25000026.7500001.7500001.25000082.50000021.5000000.2500007.250004.2500000.0000000.000000102.00000031.750000.3375000.0000004.5000000.7500000.097500104.75000034.2500009.0652.0000000.00.00.02.02.00.00.04.0Switch170.18170.18135.01.00.00.018.25000010.2500005.8750004.125000104.87500041.0000001.0000000.62500080.50000024.000.37513.00000011.5000000.1250000.000000111.75000045.7500000.3662500.0000002.2500000.6250000.103750117.37500050.7500005.02.020.25000013.3750006.8750005.625000103.12500038.5000000.8750000.75000077.37500020.3750000.12500013.25000011.1250000.0000000.000000110.87500044.8750000.4462500.0000002.3750000.0000000.000000115.12500048.87500020.0690.250.00.01.04.01.00.00.06.0Orthodox162.56172.72135.026.029.0
4Tai TuivasaBlagoy IvanovDan Miragliotta2019-06-08Chicago, Illinois, USABlueFalseHeavyweight30.01.00.017.00000014.5000002.5000002.000000201.00000059.5000000.0000000.000000184.50000045.0000000.0000002.02.0000000.0000000.000000203.5061.5000000.3100000.0000000.000000.0000000.000000204.00000062.0000001.01.042.50000023.5000000.5000000.50000205.00000089.5000000.0000000.000000152.50000056.5000000.00000010.5000010.0000000.0000000.000000205.50000090.000000.4300000.0000000.5000000.0000000.000000205.50000090.0000008.01200.0000000.00.00.01.00.00.00.01.0Southpaw180.34185.42250.01.00.00.07.7500006.75000011.0000007.25000050.75000024.7500000.5000000.50000050.75000022.750.5003.7500003.0000000.2500000.00000062.25000032.5000000.5450000.0000000.5000000.0000000.00000063.50000032.7500003.01.06.2500004.7500004.5000003.50000042.75000016.2500007.7500002.75000043.25000014.0000000.2500005.5000003.7500000.7500000.00000055.00000022.5000000.3975000.0000001.0000000.0000000.00000060.50000027.7500007.0440.750.00.00.01.02.00.00.03.0Southpaw187.96190.50264.032.026.0
\n", "
" ], "text/plain": [ " R_fighter B_fighter Referee date \\\n", "0 Henry Cejudo Marlon Moraes Marc Goddard 2019-06-08 \n", "1 Valentina Shevchenko Jessica Eye Robert Madrigal 2019-06-08 \n", "2 Tony Ferguson Donald Cerrone Dan Miragliotta 2019-06-08 \n", "3 Jimmie Rivera Petr Yan Kevin MacDonald 2019-06-08 \n", "4 Tai Tuivasa Blagoy Ivanov Dan Miragliotta 2019-06-08 \n", "\n", " location Winner title_bout weight_class no_of_rounds \\\n", "0 Chicago, Illinois, USA Red True Bantamweight 5 \n", "1 Chicago, Illinois, USA Red True Women's Flyweight 5 \n", "2 Chicago, Illinois, USA Red False Lightweight 3 \n", "3 Chicago, Illinois, USA Blue False Bantamweight 3 \n", "4 Chicago, Illinois, USA Blue False Heavyweight 3 \n", "\n", " B_current_lose_streak B_current_win_streak B_draw B_avg_BODY_att \\\n", "0 0.0 4.0 0.0 9.200000 \n", "1 0.0 3.0 0.0 14.600000 \n", "2 0.0 3.0 0.0 15.354839 \n", "3 0.0 4.0 0.0 17.000000 \n", "4 0.0 1.0 0.0 17.000000 \n", "\n", " B_avg_BODY_landed B_avg_CLINCH_att B_avg_CLINCH_landed \\\n", "0 6.000000 0.200000 0.000000 \n", "1 9.100000 11.800000 7.300000 \n", "2 11.322581 6.741935 4.387097 \n", "3 14.000000 13.750000 11.000000 \n", "4 14.500000 2.500000 2.000000 \n", "\n", " B_avg_DISTANCE_att B_avg_DISTANCE_landed B_avg_GROUND_att \\\n", "0 62.600000 20.600000 2.600000 \n", "1 124.700000 42.100000 2.400000 \n", "2 84.741935 38.580645 5.516129 \n", "3 109.500000 48.750000 13.000000 \n", "4 201.000000 59.500000 0.000000 \n", "\n", " B_avg_GROUND_landed B_avg_HEAD_att B_avg_HEAD_landed B_avg_KD \\\n", "0 2.000000 48.600000 11.200000 0.800000 \n", "1 1.900000 112.000000 32.000000 0.000000 \n", "2 3.806452 67.645161 23.258065 0.645161 \n", "3 10.500000 116.250000 53.750000 0.500000 \n", "4 0.000000 184.500000 45.000000 0.000000 \n", "\n", " B_avg_LEG_att B_avg_LEG_landed B_avg_PASS B_avg_REV B_avg_SIG_STR_att \\\n", "0 7.6 5.400000 0.400000 0.000000 65.40 \n", "1 12.3 10.200000 0.800000 0.000000 138.90 \n", "2 14.0 12.193548 0.935484 0.096774 97.00 \n", "3 3.0 2.500000 0.500000 0.250000 136.25 \n", "4 2.0 2.000000 0.000000 0.000000 203.50 \n", "\n", " B_avg_SIG_STR_landed B_avg_SIG_STR_pct B_avg_SUB_ATT B_avg_TD_att \\\n", "0 22.600000 0.466000 0.400000 0.80000 \n", "1 51.300000 0.399000 0.700000 1.00000 \n", "2 46.774194 0.496129 0.354839 2.16129 \n", "3 70.250000 0.550000 0.250000 2.50000 \n", "4 61.500000 0.310000 0.000000 0.00000 \n", "\n", " B_avg_TD_landed B_avg_TD_pct B_avg_TOTAL_STR_att B_avg_TOTAL_STR_landed \\\n", "0 0.200000 0.100000 66.400000 23.600000 \n", "1 0.500000 0.225000 158.700000 69.600000 \n", "2 0.677419 0.295484 103.709677 52.548387 \n", "3 1.250000 0.287500 154.750000 86.750000 \n", "4 0.000000 0.000000 204.000000 62.000000 \n", "\n", " B_longest_win_streak B_losses B_avg_opp_BODY_att B_avg_opp_BODY_landed \\\n", "0 4.0 1.0 6.400000 4.000000 \n", "1 3.0 6.0 13.000000 9.300000 \n", "2 8.0 8.0 17.903226 11.870968 \n", "3 4.0 0.0 12.250000 6.000000 \n", "4 1.0 1.0 42.500000 23.500000 \n", "\n", " B_avg_opp_CLINCH_att B_avg_opp_CLINCH_landed B_avg_opp_DISTANCE_att \\\n", "0 1.000000 0.60000 51.200000 \n", "1 12.800000 9.60000 101.700000 \n", "2 8.419355 5.83871 84.548387 \n", "3 6.000000 3.75000 94.250000 \n", "4 0.500000 0.50000 205.000000 \n", "\n", " B_avg_opp_DISTANCE_landed B_avg_opp_GROUND_att B_avg_opp_GROUND_landed \\\n", "0 17.400000 0.600000 0.200000 \n", "1 32.000000 8.100000 6.900000 \n", "2 38.064516 1.741935 0.935484 \n", "3 26.750000 1.750000 1.250000 \n", "4 89.500000 0.000000 0.000000 \n", "\n", " B_avg_opp_HEAD_att B_avg_opp_HEAD_landed B_avg_opp_KD B_avg_opp_LEG_att \\\n", "0 39.600000 9.400000 0.200000 6.80000 \n", "1 97.700000 30.800000 0.100000 11.90000 \n", "2 67.645161 25.483871 0.225806 9.16129 \n", "3 82.500000 21.500000 0.250000 7.25000 \n", "4 152.500000 56.500000 0.000000 10.50000 \n", "\n", " B_avg_opp_LEG_landed B_avg_opp_PASS B_avg_opp_REV B_avg_opp_SIG_STR_att \\\n", "0 4.800000 0.000000 0.000000 52.800000 \n", "1 8.400000 1.400000 0.000000 122.600000 \n", "2 7.483871 0.032258 0.032258 94.709677 \n", "3 4.250000 0.000000 0.000000 102.000000 \n", "4 10.000000 0.000000 0.000000 205.500000 \n", "\n", " B_avg_opp_SIG_STR_landed B_avg_opp_SIG_STR_pct B_avg_opp_SUB_ATT \\\n", "0 18.20000 0.236000 0.000000 \n", "1 48.50000 0.408000 0.700000 \n", "2 44.83871 0.453226 0.096774 \n", "3 31.75000 0.337500 0.000000 \n", "4 90.00000 0.430000 0.000000 \n", "\n", " B_avg_opp_TD_att B_avg_opp_TD_landed B_avg_opp_TD_pct \\\n", "0 1.000000 0.400000 0.100000 \n", "1 2.300000 0.900000 0.231000 \n", "2 2.096774 0.225806 0.063548 \n", "3 4.500000 0.750000 0.097500 \n", "4 0.500000 0.000000 0.000000 \n", "\n", " B_avg_opp_TOTAL_STR_att B_avg_opp_TOTAL_STR_landed B_total_rounds_fought \\\n", "0 53.800000 19.200000 9.0 \n", "1 151.500000 75.400000 29.0 \n", "2 100.387097 49.774194 68.0 \n", "3 104.750000 34.250000 9.0 \n", "4 205.500000 90.000000 8.0 \n", "\n", " B_total_time_fought(seconds) B_total_title_bouts \\\n", "0 419.400000 0.0 \n", "1 849.000000 0.0 \n", "2 581.870968 1.0 \n", "3 652.000000 0.0 \n", "4 1200.000000 0.0 \n", "\n", " B_win_by_Decision_Majority B_win_by_Decision_Split \\\n", "0 0.0 1.0 \n", "1 0.0 2.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "\n", " B_win_by_Decision_Unanimous B_win_by_KO/TKO B_win_by_Submission \\\n", "0 0.0 2.0 1.0 \n", "1 1.0 0.0 0.0 \n", "2 7.0 10.0 6.0 \n", "3 2.0 2.0 0.0 \n", "4 1.0 0.0 0.0 \n", "\n", " B_win_by_TKO_Doctor_Stoppage B_wins B_Stance B_Height_cms B_Reach_cms \\\n", "0 0.0 4.0 Orthodox 167.64 170.18 \n", "1 1.0 4.0 Orthodox 167.64 167.64 \n", "2 0.0 23.0 Orthodox 185.42 185.42 \n", "3 0.0 4.0 Switch 170.18 170.18 \n", "4 0.0 1.0 Southpaw 180.34 185.42 \n", "\n", " B_Weight_lbs R_current_lose_streak R_current_win_streak R_draw \\\n", "0 135.0 0.0 4.0 0.0 \n", "1 125.0 0.0 2.0 0.0 \n", "2 155.0 0.0 11.0 0.0 \n", "3 135.0 1.0 0.0 0.0 \n", "4 250.0 1.0 0.0 0.0 \n", "\n", " R_avg_BODY_att R_avg_BODY_landed R_avg_CLINCH_att R_avg_CLINCH_landed \\\n", "0 21.900000 16.400000 17.000000 11.000000 \n", "1 12.000000 7.714286 9.285714 6.857143 \n", "2 13.866667 8.666667 2.866667 1.733333 \n", "3 18.250000 10.250000 5.875000 4.125000 \n", "4 7.750000 6.750000 11.000000 7.250000 \n", "\n", " R_avg_DISTANCE_att R_avg_DISTANCE_landed R_avg_GROUND_att \\\n", "0 75.000000 26.500000 9.400000 \n", "1 88.142857 36.142857 18.428571 \n", "2 116.133333 49.466667 5.333333 \n", "3 104.875000 41.000000 1.000000 \n", "4 50.750000 24.750000 0.500000 \n", "\n", " R_avg_GROUND_landed R_avg_HEAD_att R_avg_HEAD_landed R_avg_KD \\\n", "0 6.500000 74.200000 23.90 0.400 \n", "1 16.428571 84.571429 37.00 0.000 \n", "2 4.266667 96.733333 35.60 0.200 \n", "3 0.625000 80.500000 24.00 0.375 \n", "4 0.500000 50.750000 22.75 0.500 \n", "\n", " R_avg_LEG_att R_avg_LEG_landed R_avg_PASS R_avg_REV R_avg_SIG_STR_att \\\n", "0 5.300000 3.700000 1.200000 0.000000 101.400000 \n", "1 19.285714 14.714286 1.714286 0.142857 115.857143 \n", "2 13.733333 11.200000 0.333333 0.133333 124.333333 \n", "3 13.000000 11.500000 0.125000 0.000000 111.750000 \n", "4 3.750000 3.000000 0.250000 0.000000 62.250000 \n", "\n", " R_avg_SIG_STR_landed R_avg_SIG_STR_pct R_avg_SUB_ATT R_avg_TD_att \\\n", "0 44.000000 0.466000 0.100000 5.300000 \n", "1 59.428571 0.575714 0.428571 5.142857 \n", "2 55.466667 0.430000 1.000000 0.933333 \n", "3 45.750000 0.366250 0.000000 2.250000 \n", "4 32.500000 0.545000 0.000000 0.500000 \n", "\n", " R_avg_TD_landed R_avg_TD_pct R_avg_TOTAL_STR_att R_avg_TOTAL_STR_landed \\\n", "0 1.900000 0.458000 129.900000 69.100000 \n", "1 2.428571 0.601429 161.571429 102.857143 \n", "2 0.400000 0.277333 133.000000 63.400000 \n", "3 0.625000 0.103750 117.375000 50.750000 \n", "4 0.000000 0.000000 63.500000 32.750000 \n", "\n", " R_longest_win_streak R_losses R_avg_opp_BODY_att R_avg_opp_BODY_landed \\\n", "0 4.0 2.0 13.300000 8.800000 \n", "1 2.0 2.0 24.571429 14.142857 \n", "2 11.0 1.0 14.466667 8.133333 \n", "3 5.0 2.0 20.250000 13.375000 \n", "4 3.0 1.0 6.250000 4.750000 \n", "\n", " R_avg_opp_CLINCH_att R_avg_opp_CLINCH_landed R_avg_opp_DISTANCE_att \\\n", "0 7.500000 5.100000 90.500000 \n", "1 10.571429 7.857143 98.571429 \n", "2 2.800000 0.733333 91.066667 \n", "3 6.875000 5.625000 103.125000 \n", "4 4.500000 3.500000 42.750000 \n", "\n", " R_avg_opp_DISTANCE_landed R_avg_opp_GROUND_att R_avg_opp_GROUND_landed \\\n", "0 26.800000 0.800000 0.300000 \n", "1 32.571429 6.428571 4.285714 \n", "2 32.200000 4.866667 2.800000 \n", "3 38.500000 0.875000 0.750000 \n", "4 16.250000 7.750000 2.750000 \n", "\n", " R_avg_opp_HEAD_att R_avg_opp_HEAD_landed R_avg_opp_KD R_avg_opp_LEG_att \\\n", "0 76.100000 17.300000 0.100000 9.400000 \n", "1 61.857143 12.428571 0.000000 29.142857 \n", "2 78.266667 23.200000 0.266667 6.000000 \n", "3 77.375000 20.375000 0.125000 13.250000 \n", "4 43.250000 14.000000 0.250000 5.500000 \n", "\n", " R_avg_opp_LEG_landed R_avg_opp_PASS R_avg_opp_REV R_avg_opp_SIG_STR_att \\\n", "0 6.100000 0.000000 0.000000 98.800000 \n", "1 18.142857 1.142857 0.000000 115.571429 \n", "2 4.400000 0.333333 0.133333 98.733333 \n", "3 11.125000 0.000000 0.000000 110.875000 \n", "4 3.750000 0.750000 0.000000 55.000000 \n", "\n", " R_avg_opp_SIG_STR_landed R_avg_opp_SIG_STR_pct R_avg_opp_SUB_ATT \\\n", "0 32.200000 0.336000 0.000000 \n", "1 44.714286 0.437143 0.285714 \n", "2 35.733333 0.340000 0.066667 \n", "3 44.875000 0.446250 0.000000 \n", "4 22.500000 0.397500 0.000000 \n", "\n", " R_avg_opp_TD_att R_avg_opp_TD_landed R_avg_opp_TD_pct \\\n", "0 0.900000 0.100000 0.050000 \n", "1 3.285714 0.857143 0.147143 \n", "2 2.866667 0.666667 0.131333 \n", "3 2.375000 0.000000 0.000000 \n", "4 1.000000 0.000000 0.000000 \n", "\n", " R_avg_opp_TOTAL_STR_att R_avg_opp_TOTAL_STR_landed R_total_rounds_fought \\\n", "0 110.500000 43.300000 27.0 \n", "1 158.142857 82.285714 25.0 \n", "2 102.133333 38.600000 33.0 \n", "3 115.125000 48.875000 20.0 \n", "4 60.500000 27.750000 7.0 \n", "\n", " R_total_time_fought(seconds) R_total_title_bouts \\\n", "0 742.60 3.0 \n", "1 1062.00 2.0 \n", "2 604.40 2.0 \n", "3 690.25 0.0 \n", "4 440.75 0.0 \n", "\n", " R_win_by_Decision_Majority R_win_by_Decision_Split \\\n", "0 0.0 2.0 \n", "1 0.0 1.0 \n", "2 0.0 1.0 \n", "3 0.0 1.0 \n", "4 0.0 0.0 \n", "\n", " R_win_by_Decision_Unanimous R_win_by_KO/TKO R_win_by_Submission \\\n", "0 4.0 2.0 0.0 \n", "1 2.0 0.0 2.0 \n", "2 3.0 3.0 6.0 \n", "3 4.0 1.0 0.0 \n", "4 1.0 2.0 0.0 \n", "\n", " R_win_by_TKO_Doctor_Stoppage R_wins R_Stance R_Height_cms R_Reach_cms \\\n", "0 0.0 8.0 Orthodox 162.56 162.56 \n", "1 0.0 5.0 Southpaw 165.10 167.64 \n", "2 1.0 14.0 Orthodox 180.34 193.04 \n", "3 0.0 6.0 Orthodox 162.56 172.72 \n", "4 0.0 3.0 Southpaw 187.96 190.50 \n", "\n", " R_Weight_lbs B_age R_age \n", "0 135.0 31.0 32.0 \n", "1 125.0 32.0 31.0 \n", "2 155.0 36.0 35.0 \n", "3 135.0 26.0 29.0 \n", "4 264.0 32.0 26.0 " ] }, "metadata": { "tags": [] }, "execution_count": 139 } ] }, { "cell_type": "code", "metadata": { "id": "Zz9VuFqhkS2P", "colab_type": "code", "colab": {} }, "source": [ "red = [s for s in dat_df.columns if s.startswith(\"R_\")] + [\"Winner\", \"date\", \"weight_class\"]\n", "blue = [s for s in dat_df.columns if s.startswith(\"B_\")] + [\"Winner\", \"date\", \"weight_class\"]" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "RDx8cswDkS2R", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 617 }, "outputId": "f6f961c5-3cf6-42c7-8747-f8d07666db4b" }, "source": [ "dat_df[red].groupby(\"R_fighter\").agg(\"mean\")" ], "execution_count": 141, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
R_current_lose_streakR_current_win_streakR_drawR_avg_BODY_attR_avg_BODY_landedR_avg_CLINCH_attR_avg_CLINCH_landedR_avg_DISTANCE_attR_avg_DISTANCE_landedR_avg_GROUND_attR_avg_GROUND_landedR_avg_HEAD_attR_avg_HEAD_landedR_avg_KDR_avg_LEG_attR_avg_LEG_landedR_avg_PASSR_avg_REVR_avg_SIG_STR_attR_avg_SIG_STR_landedR_avg_SIG_STR_pctR_avg_SUB_ATTR_avg_TD_attR_avg_TD_landedR_avg_TD_pctR_avg_TOTAL_STR_attR_avg_TOTAL_STR_landedR_longest_win_streakR_lossesR_avg_opp_BODY_attR_avg_opp_BODY_landedR_avg_opp_CLINCH_attR_avg_opp_CLINCH_landedR_avg_opp_DISTANCE_attR_avg_opp_DISTANCE_landedR_avg_opp_GROUND_attR_avg_opp_GROUND_landedR_avg_opp_HEAD_attR_avg_opp_HEAD_landedR_avg_opp_KDR_avg_opp_LEG_attR_avg_opp_LEG_landedR_avg_opp_PASSR_avg_opp_REVR_avg_opp_SIG_STR_attR_avg_opp_SIG_STR_landedR_avg_opp_SIG_STR_pctR_avg_opp_SUB_ATTR_avg_opp_TD_attR_avg_opp_TD_landedR_avg_opp_TD_pctR_avg_opp_TOTAL_STR_attR_avg_opp_TOTAL_STR_landedR_total_rounds_foughtR_total_time_fought(seconds)R_total_title_boutsR_win_by_Decision_MajorityR_win_by_Decision_SplitR_win_by_Decision_UnanimousR_win_by_KO/TKOR_win_by_SubmissionR_win_by_TKO_Doctor_StoppageR_winsR_Height_cmsR_Reach_cmsR_Weight_lbsR_age
R_fighter
Aaron Phillips1.0000000.0000000.014.00000012.0000006.0000003.00000026.0000009.0000008.0000006.00000023.0000005.0000000.0000003.0000001.0000001.0000001.00000040.00000018.0000000.4500001.0000000.0000000.0000000.000000137.000000109.0000000.0000001.00000013.0000008.0000006.0000004.00000031.00000012.00000031.00000021.00000053.00000028.0000000.0000002.0000001.0000007.0000001.00000068.00000037.0000000.5400001.0000008.0000005.0000000.620000129.00000095.0000003.00900.0000000.00.00.0000.0000000.0000000.0000000.00.000000175.26177.80135.025.000000
Aaron Riley1.2500000.0000000.013.39583311.00000022.96875013.67708364.19791721.1562502.9583332.16666763.79166716.8437500.00000012.9375009.1562500.7187500.00000090.12500037.0000000.3944790.2604172.7187500.8958330.306354119.27083364.0312500.7500003.50000012.95833310.81250021.94791712.41666778.22916726.6354175.3645832.75000088.61458328.7291670.3958333.9687502.2604170.0000000.000000105.54166741.8020830.3928130.0000003.0520831.7291670.244792121.03125056.96875010.50585.6041670.00.00.0001.5000000.0000000.0000000.01.500000172.72175.26155.029.000000
Aaron Rosa1.0000000.0000000.016.00000013.00000019.00000011.000000160.00000062.0000000.0000000.000000158.00000056.0000000.0000005.0000004.0000000.0000000.000000179.00000073.0000000.4000000.0000001.0000000.0000000.000000258.000000135.0000000.0000001.00000019.00000016.00000027.00000022.000000154.00000059.0000007.0000007.000000146.00000053.0000000.00000023.00000019.0000001.0000000.000000188.00000088.0000000.4600000.0000004.0000001.0000000.250000304.000000185.0000003.00686.0000000.00.00.0000.0000000.0000000.0000000.00.000000193.04198.12205.028.000000
Aaron Simpson0.2500001.1250000.09.4337876.76848116.58639512.10912732.24761911.25640615.8888899.39620247.77335618.9237530.6201817.5157607.0695010.7331070.00000064.72290232.7617350.5387780.2409866.4201812.5106010.62087193.13242657.4824262.2500001.3750003.9747733.02363917.06434210.21320933.7747179.8709181.4590141.25629343.47131514.3904760.0000004.8519843.9263040.0301590.06842452.29807321.3404200.3616840.2082771.5757370.3013040.06082764.85510233.19070312.25507.1858280.00.00.6251.5000001.6250000.0000000.03.750000182.88185.42170.036.125000
Abdul Razak Alhassan0.0000001.5000000.01.0000000.6250004.5000003.37500030.87500012.6250002.5000001.87500035.25000016.2500002.1250001.6250001.0000000.0000000.12500037.87500017.8750000.4950000.0000000.3750000.1250000.04125040.50000019.7500001.5000000.5000003.6250002.8750004.6250003.62500030.5000007.8750003.3750002.12500033.8750009.7500000.0000001.0000001.0000000.3750000.00000038.50000013.6250000.2500000.0000001.6250000.8750000.14500047.00000019.8750003.50206.6250000.00.00.0000.0000002.0000000.0000000.02.000000177.80185.42170.032.000000
............................................................................................................................................................................................................
Zach Makovsky1.0000000.3333330.07.6111113.3888892.5555561.22222265.38888922.5000006.9444444.55555659.94444418.5555560.0000007.3333336.3333332.7222220.72222274.88888928.2777780.3866670.50000012.9444444.5555560.37666793.44444444.7777781.6666671.3333338.0555565.77777812.5555568.50000060.61111116.7222224.4444443.05555662.88888917.2777780.0000006.6666675.2222221.7777781.00000077.61111128.2777780.3638890.5000003.4444441.0000000.247778106.27777854.77777810.00900.0000000.00.00.0002.0000000.0000000.0000000.02.000000162.56162.56125.031.333333
Zak Cummings0.3333330.6666670.07.4811904.4776198.0766674.57047665.84547617.0497625.5047623.02761966.34857115.6478570.2338105.5971434.5223811.2821430.17357179.42690524.6478570.3395950.6176192.1702381.0816670.34478890.75238134.5109521.6666671.6666675.8435713.7945242.8028571.78119044.18357118.8383333.4980953.21761939.86452416.4202380.0000004.7764293.6223810.9678570.19357150.48452423.8371430.5841690.5207142.2147620.5257140.28105058.70309531.82881012.00587.8754760.00.00.0001.6666670.6666671.6666670.04.000000182.88190.50185.031.666667
Zak Ottow1.0000000.0000000.09.8333335.1666671.3333330.83333367.16666725.5000006.3333333.50000057.66666717.8333330.3333337.3333336.8333331.1666670.16666774.83333329.8333330.3866670.3333331.6666670.5000000.15166796.16666748.6666671.0000003.0000007.1666673.8333333.6666673.16666751.66666719.1666676.5000004.50000043.83333314.3333330.33333310.8333338.6666670.8333330.16666761.83333326.8333330.4400000.1666672.1666670.8333330.33000079.16666742.50000013.00587.3333330.00.02.0000.0000001.0000000.0000000.03.000000180.34182.88170.031.000000
Zhang Lipeng0.3333331.0000000.05.5555562.4444445.3333334.38888923.1666678.2777783.0000001.83333318.2222225.1666670.0000007.7222226.8888893.2777780.00000031.50000014.5000000.4416671.5000008.0000003.0000000.38333393.83333363.9444441.6666670.3333338.9444443.72222211.6666677.27777842.27777813.94444415.4444447.11111141.66666712.5000000.00000018.77777812.1111111.8333331.50000069.38888928.3333330.4016670.0000001.8333330.4444440.048889156.83333396.4444446.00900.0000001.00.01.0000.6666670.0000000.0000000.01.666667180.34180.34155.024.333333
Zubaira Tukhugov0.0000001.5000000.013.5000008.00000012.0000005.00000067.75000028.5000008.2500002.75000073.50000027.2500000.0000001.0000001.0000000.0000000.00000088.00000036.2500000.4175000.0000004.5000002.2500000.37500094.00000041.5000001.5000000.0000009.2500003.0000006.0000002.25000074.25000021.0000000.0000000.00000058.50000012.0000000.00000012.5000008.2500000.0000000.00000080.25000023.2500000.3025000.0000000.0000000.0000000.00000080.25000023.2500003.50740.2500000.00.00.0001.0000000.5000000.0000000.01.500000172.72172.72145.023.500000
\n", "

1334 rows × 67 columns

\n", "
" ], "text/plain": [ " R_current_lose_streak R_current_win_streak R_draw \\\n", "R_fighter \n", "Aaron Phillips 1.000000 0.000000 0.0 \n", "Aaron Riley 1.250000 0.000000 0.0 \n", "Aaron Rosa 1.000000 0.000000 0.0 \n", "Aaron Simpson 0.250000 1.125000 0.0 \n", "Abdul Razak Alhassan 0.000000 1.500000 0.0 \n", "... ... ... ... \n", "Zach Makovsky 1.000000 0.333333 0.0 \n", "Zak Cummings 0.333333 0.666667 0.0 \n", "Zak Ottow 1.000000 0.000000 0.0 \n", "Zhang Lipeng 0.333333 1.000000 0.0 \n", "Zubaira Tukhugov 0.000000 1.500000 0.0 \n", "\n", " R_avg_BODY_att R_avg_BODY_landed R_avg_CLINCH_att \\\n", "R_fighter \n", "Aaron Phillips 14.000000 12.000000 6.000000 \n", "Aaron Riley 13.395833 11.000000 22.968750 \n", "Aaron Rosa 16.000000 13.000000 19.000000 \n", "Aaron Simpson 9.433787 6.768481 16.586395 \n", "Abdul Razak Alhassan 1.000000 0.625000 4.500000 \n", "... ... ... ... \n", "Zach Makovsky 7.611111 3.388889 2.555556 \n", "Zak Cummings 7.481190 4.477619 8.076667 \n", "Zak Ottow 9.833333 5.166667 1.333333 \n", "Zhang Lipeng 5.555556 2.444444 5.333333 \n", "Zubaira Tukhugov 13.500000 8.000000 12.000000 \n", "\n", " R_avg_CLINCH_landed R_avg_DISTANCE_att \\\n", "R_fighter \n", "Aaron Phillips 3.000000 26.000000 \n", "Aaron Riley 13.677083 64.197917 \n", "Aaron Rosa 11.000000 160.000000 \n", "Aaron Simpson 12.109127 32.247619 \n", "Abdul Razak Alhassan 3.375000 30.875000 \n", "... ... ... \n", "Zach Makovsky 1.222222 65.388889 \n", "Zak Cummings 4.570476 65.845476 \n", "Zak Ottow 0.833333 67.166667 \n", "Zhang Lipeng 4.388889 23.166667 \n", "Zubaira Tukhugov 5.000000 67.750000 \n", "\n", " R_avg_DISTANCE_landed R_avg_GROUND_att \\\n", "R_fighter \n", "Aaron Phillips 9.000000 8.000000 \n", "Aaron Riley 21.156250 2.958333 \n", "Aaron Rosa 62.000000 0.000000 \n", "Aaron Simpson 11.256406 15.888889 \n", "Abdul Razak Alhassan 12.625000 2.500000 \n", "... ... ... \n", "Zach Makovsky 22.500000 6.944444 \n", "Zak Cummings 17.049762 5.504762 \n", "Zak Ottow 25.500000 6.333333 \n", "Zhang Lipeng 8.277778 3.000000 \n", "Zubaira Tukhugov 28.500000 8.250000 \n", "\n", " R_avg_GROUND_landed R_avg_HEAD_att R_avg_HEAD_landed \\\n", "R_fighter \n", "Aaron Phillips 6.000000 23.000000 5.000000 \n", "Aaron Riley 2.166667 63.791667 16.843750 \n", "Aaron Rosa 0.000000 158.000000 56.000000 \n", "Aaron Simpson 9.396202 47.773356 18.923753 \n", "Abdul Razak Alhassan 1.875000 35.250000 16.250000 \n", "... ... ... ... \n", "Zach Makovsky 4.555556 59.944444 18.555556 \n", "Zak Cummings 3.027619 66.348571 15.647857 \n", "Zak Ottow 3.500000 57.666667 17.833333 \n", "Zhang Lipeng 1.833333 18.222222 5.166667 \n", "Zubaira Tukhugov 2.750000 73.500000 27.250000 \n", "\n", " R_avg_KD R_avg_LEG_att R_avg_LEG_landed R_avg_PASS \\\n", "R_fighter \n", "Aaron Phillips 0.000000 3.000000 1.000000 1.000000 \n", "Aaron Riley 0.000000 12.937500 9.156250 0.718750 \n", "Aaron Rosa 0.000000 5.000000 4.000000 0.000000 \n", "Aaron Simpson 0.620181 7.515760 7.069501 0.733107 \n", "Abdul Razak Alhassan 2.125000 1.625000 1.000000 0.000000 \n", "... ... ... ... ... \n", "Zach Makovsky 0.000000 7.333333 6.333333 2.722222 \n", "Zak Cummings 0.233810 5.597143 4.522381 1.282143 \n", "Zak Ottow 0.333333 7.333333 6.833333 1.166667 \n", "Zhang Lipeng 0.000000 7.722222 6.888889 3.277778 \n", "Zubaira Tukhugov 0.000000 1.000000 1.000000 0.000000 \n", "\n", " R_avg_REV R_avg_SIG_STR_att R_avg_SIG_STR_landed \\\n", "R_fighter \n", "Aaron Phillips 1.000000 40.000000 18.000000 \n", "Aaron Riley 0.000000 90.125000 37.000000 \n", "Aaron Rosa 0.000000 179.000000 73.000000 \n", "Aaron Simpson 0.000000 64.722902 32.761735 \n", "Abdul Razak Alhassan 0.125000 37.875000 17.875000 \n", "... ... ... ... \n", "Zach Makovsky 0.722222 74.888889 28.277778 \n", "Zak Cummings 0.173571 79.426905 24.647857 \n", "Zak Ottow 0.166667 74.833333 29.833333 \n", "Zhang Lipeng 0.000000 31.500000 14.500000 \n", "Zubaira Tukhugov 0.000000 88.000000 36.250000 \n", "\n", " R_avg_SIG_STR_pct R_avg_SUB_ATT R_avg_TD_att \\\n", "R_fighter \n", "Aaron Phillips 0.450000 1.000000 0.000000 \n", "Aaron Riley 0.394479 0.260417 2.718750 \n", "Aaron Rosa 0.400000 0.000000 1.000000 \n", "Aaron Simpson 0.538778 0.240986 6.420181 \n", "Abdul Razak Alhassan 0.495000 0.000000 0.375000 \n", "... ... ... ... \n", "Zach Makovsky 0.386667 0.500000 12.944444 \n", "Zak Cummings 0.339595 0.617619 2.170238 \n", "Zak Ottow 0.386667 0.333333 1.666667 \n", "Zhang Lipeng 0.441667 1.500000 8.000000 \n", "Zubaira Tukhugov 0.417500 0.000000 4.500000 \n", "\n", " R_avg_TD_landed R_avg_TD_pct R_avg_TOTAL_STR_att \\\n", "R_fighter \n", "Aaron Phillips 0.000000 0.000000 137.000000 \n", "Aaron Riley 0.895833 0.306354 119.270833 \n", "Aaron Rosa 0.000000 0.000000 258.000000 \n", "Aaron Simpson 2.510601 0.620871 93.132426 \n", "Abdul Razak Alhassan 0.125000 0.041250 40.500000 \n", "... ... ... ... \n", "Zach Makovsky 4.555556 0.376667 93.444444 \n", "Zak Cummings 1.081667 0.344788 90.752381 \n", "Zak Ottow 0.500000 0.151667 96.166667 \n", "Zhang Lipeng 3.000000 0.383333 93.833333 \n", "Zubaira Tukhugov 2.250000 0.375000 94.000000 \n", "\n", " R_avg_TOTAL_STR_landed R_longest_win_streak R_losses \\\n", "R_fighter \n", "Aaron Phillips 109.000000 0.000000 1.000000 \n", "Aaron Riley 64.031250 0.750000 3.500000 \n", "Aaron Rosa 135.000000 0.000000 1.000000 \n", "Aaron Simpson 57.482426 2.250000 1.375000 \n", "Abdul Razak Alhassan 19.750000 1.500000 0.500000 \n", "... ... ... ... \n", "Zach Makovsky 44.777778 1.666667 1.333333 \n", "Zak Cummings 34.510952 1.666667 1.666667 \n", "Zak Ottow 48.666667 1.000000 3.000000 \n", "Zhang Lipeng 63.944444 1.666667 0.333333 \n", "Zubaira Tukhugov 41.500000 1.500000 0.000000 \n", "\n", " R_avg_opp_BODY_att R_avg_opp_BODY_landed \\\n", "R_fighter \n", "Aaron Phillips 13.000000 8.000000 \n", "Aaron Riley 12.958333 10.812500 \n", "Aaron Rosa 19.000000 16.000000 \n", "Aaron Simpson 3.974773 3.023639 \n", "Abdul Razak Alhassan 3.625000 2.875000 \n", "... ... ... \n", "Zach Makovsky 8.055556 5.777778 \n", "Zak Cummings 5.843571 3.794524 \n", "Zak Ottow 7.166667 3.833333 \n", "Zhang Lipeng 8.944444 3.722222 \n", "Zubaira Tukhugov 9.250000 3.000000 \n", "\n", " R_avg_opp_CLINCH_att R_avg_opp_CLINCH_landed \\\n", "R_fighter \n", "Aaron Phillips 6.000000 4.000000 \n", "Aaron Riley 21.947917 12.416667 \n", "Aaron Rosa 27.000000 22.000000 \n", "Aaron Simpson 17.064342 10.213209 \n", "Abdul Razak Alhassan 4.625000 3.625000 \n", "... ... ... \n", "Zach Makovsky 12.555556 8.500000 \n", "Zak Cummings 2.802857 1.781190 \n", "Zak Ottow 3.666667 3.166667 \n", "Zhang Lipeng 11.666667 7.277778 \n", "Zubaira Tukhugov 6.000000 2.250000 \n", "\n", " R_avg_opp_DISTANCE_att R_avg_opp_DISTANCE_landed \\\n", "R_fighter \n", "Aaron Phillips 31.000000 12.000000 \n", "Aaron Riley 78.229167 26.635417 \n", "Aaron Rosa 154.000000 59.000000 \n", "Aaron Simpson 33.774717 9.870918 \n", "Abdul Razak Alhassan 30.500000 7.875000 \n", "... ... ... \n", "Zach Makovsky 60.611111 16.722222 \n", "Zak Cummings 44.183571 18.838333 \n", "Zak Ottow 51.666667 19.166667 \n", "Zhang Lipeng 42.277778 13.944444 \n", "Zubaira Tukhugov 74.250000 21.000000 \n", "\n", " R_avg_opp_GROUND_att R_avg_opp_GROUND_landed \\\n", "R_fighter \n", "Aaron Phillips 31.000000 21.000000 \n", "Aaron Riley 5.364583 2.750000 \n", "Aaron Rosa 7.000000 7.000000 \n", "Aaron Simpson 1.459014 1.256293 \n", "Abdul Razak Alhassan 3.375000 2.125000 \n", "... ... ... \n", "Zach Makovsky 4.444444 3.055556 \n", "Zak Cummings 3.498095 3.217619 \n", "Zak Ottow 6.500000 4.500000 \n", "Zhang Lipeng 15.444444 7.111111 \n", "Zubaira Tukhugov 0.000000 0.000000 \n", "\n", " R_avg_opp_HEAD_att R_avg_opp_HEAD_landed R_avg_opp_KD \\\n", "R_fighter \n", "Aaron Phillips 53.000000 28.000000 0.000000 \n", "Aaron Riley 88.614583 28.729167 0.395833 \n", "Aaron Rosa 146.000000 53.000000 0.000000 \n", "Aaron Simpson 43.471315 14.390476 0.000000 \n", "Abdul Razak Alhassan 33.875000 9.750000 0.000000 \n", "... ... ... ... \n", "Zach Makovsky 62.888889 17.277778 0.000000 \n", "Zak Cummings 39.864524 16.420238 0.000000 \n", "Zak Ottow 43.833333 14.333333 0.333333 \n", "Zhang Lipeng 41.666667 12.500000 0.000000 \n", "Zubaira Tukhugov 58.500000 12.000000 0.000000 \n", "\n", " R_avg_opp_LEG_att R_avg_opp_LEG_landed R_avg_opp_PASS \\\n", "R_fighter \n", "Aaron Phillips 2.000000 1.000000 7.000000 \n", "Aaron Riley 3.968750 2.260417 0.000000 \n", "Aaron Rosa 23.000000 19.000000 1.000000 \n", "Aaron Simpson 4.851984 3.926304 0.030159 \n", "Abdul Razak Alhassan 1.000000 1.000000 0.375000 \n", "... ... ... ... \n", "Zach Makovsky 6.666667 5.222222 1.777778 \n", "Zak Cummings 4.776429 3.622381 0.967857 \n", "Zak Ottow 10.833333 8.666667 0.833333 \n", "Zhang Lipeng 18.777778 12.111111 1.833333 \n", "Zubaira Tukhugov 12.500000 8.250000 0.000000 \n", "\n", " R_avg_opp_REV R_avg_opp_SIG_STR_att \\\n", "R_fighter \n", "Aaron Phillips 1.000000 68.000000 \n", "Aaron Riley 0.000000 105.541667 \n", "Aaron Rosa 0.000000 188.000000 \n", "Aaron Simpson 0.068424 52.298073 \n", "Abdul Razak Alhassan 0.000000 38.500000 \n", "... ... ... \n", "Zach Makovsky 1.000000 77.611111 \n", "Zak Cummings 0.193571 50.484524 \n", "Zak Ottow 0.166667 61.833333 \n", "Zhang Lipeng 1.500000 69.388889 \n", "Zubaira Tukhugov 0.000000 80.250000 \n", "\n", " R_avg_opp_SIG_STR_landed R_avg_opp_SIG_STR_pct \\\n", "R_fighter \n", "Aaron Phillips 37.000000 0.540000 \n", "Aaron Riley 41.802083 0.392813 \n", "Aaron Rosa 88.000000 0.460000 \n", "Aaron Simpson 21.340420 0.361684 \n", "Abdul Razak Alhassan 13.625000 0.250000 \n", "... ... ... \n", "Zach Makovsky 28.277778 0.363889 \n", "Zak Cummings 23.837143 0.584169 \n", "Zak Ottow 26.833333 0.440000 \n", "Zhang Lipeng 28.333333 0.401667 \n", "Zubaira Tukhugov 23.250000 0.302500 \n", "\n", " R_avg_opp_SUB_ATT R_avg_opp_TD_att \\\n", "R_fighter \n", "Aaron Phillips 1.000000 8.000000 \n", "Aaron Riley 0.000000 3.052083 \n", "Aaron Rosa 0.000000 4.000000 \n", "Aaron Simpson 0.208277 1.575737 \n", "Abdul Razak Alhassan 0.000000 1.625000 \n", "... ... ... \n", "Zach Makovsky 0.500000 3.444444 \n", "Zak Cummings 0.520714 2.214762 \n", "Zak Ottow 0.166667 2.166667 \n", "Zhang Lipeng 0.000000 1.833333 \n", "Zubaira Tukhugov 0.000000 0.000000 \n", "\n", " R_avg_opp_TD_landed R_avg_opp_TD_pct \\\n", "R_fighter \n", "Aaron Phillips 5.000000 0.620000 \n", "Aaron Riley 1.729167 0.244792 \n", "Aaron Rosa 1.000000 0.250000 \n", "Aaron Simpson 0.301304 0.060827 \n", "Abdul Razak Alhassan 0.875000 0.145000 \n", "... ... ... \n", "Zach Makovsky 1.000000 0.247778 \n", "Zak Cummings 0.525714 0.281050 \n", "Zak Ottow 0.833333 0.330000 \n", "Zhang Lipeng 0.444444 0.048889 \n", "Zubaira Tukhugov 0.000000 0.000000 \n", "\n", " R_avg_opp_TOTAL_STR_att R_avg_opp_TOTAL_STR_landed \\\n", "R_fighter \n", "Aaron Phillips 129.000000 95.000000 \n", "Aaron Riley 121.031250 56.968750 \n", "Aaron Rosa 304.000000 185.000000 \n", "Aaron Simpson 64.855102 33.190703 \n", "Abdul Razak Alhassan 47.000000 19.875000 \n", "... ... ... \n", "Zach Makovsky 106.277778 54.777778 \n", "Zak Cummings 58.703095 31.828810 \n", "Zak Ottow 79.166667 42.500000 \n", "Zhang Lipeng 156.833333 96.444444 \n", "Zubaira Tukhugov 80.250000 23.250000 \n", "\n", " R_total_rounds_fought R_total_time_fought(seconds) \\\n", "R_fighter \n", "Aaron Phillips 3.00 900.000000 \n", "Aaron Riley 10.50 585.604167 \n", "Aaron Rosa 3.00 686.000000 \n", "Aaron Simpson 12.25 507.185828 \n", "Abdul Razak Alhassan 3.50 206.625000 \n", "... ... ... \n", "Zach Makovsky 10.00 900.000000 \n", "Zak Cummings 12.00 587.875476 \n", "Zak Ottow 13.00 587.333333 \n", "Zhang Lipeng 6.00 900.000000 \n", "Zubaira Tukhugov 3.50 740.250000 \n", "\n", " R_total_title_bouts R_win_by_Decision_Majority \\\n", "R_fighter \n", "Aaron Phillips 0.0 0.0 \n", "Aaron Riley 0.0 0.0 \n", "Aaron Rosa 0.0 0.0 \n", "Aaron Simpson 0.0 0.0 \n", "Abdul Razak Alhassan 0.0 0.0 \n", "... ... ... \n", "Zach Makovsky 0.0 0.0 \n", "Zak Cummings 0.0 0.0 \n", "Zak Ottow 0.0 0.0 \n", "Zhang Lipeng 1.0 0.0 \n", "Zubaira Tukhugov 0.0 0.0 \n", "\n", " R_win_by_Decision_Split R_win_by_Decision_Unanimous \\\n", "R_fighter \n", "Aaron Phillips 0.000 0.000000 \n", "Aaron Riley 0.000 1.500000 \n", "Aaron Rosa 0.000 0.000000 \n", "Aaron Simpson 0.625 1.500000 \n", "Abdul Razak Alhassan 0.000 0.000000 \n", "... ... ... \n", "Zach Makovsky 0.000 2.000000 \n", "Zak Cummings 0.000 1.666667 \n", "Zak Ottow 2.000 0.000000 \n", "Zhang Lipeng 1.000 0.666667 \n", "Zubaira Tukhugov 0.000 1.000000 \n", "\n", " R_win_by_KO/TKO R_win_by_Submission \\\n", "R_fighter \n", "Aaron Phillips 0.000000 0.000000 \n", "Aaron Riley 0.000000 0.000000 \n", "Aaron Rosa 0.000000 0.000000 \n", "Aaron Simpson 1.625000 0.000000 \n", "Abdul Razak Alhassan 2.000000 0.000000 \n", "... ... ... \n", "Zach Makovsky 0.000000 0.000000 \n", "Zak Cummings 0.666667 1.666667 \n", "Zak Ottow 1.000000 0.000000 \n", "Zhang Lipeng 0.000000 0.000000 \n", "Zubaira Tukhugov 0.500000 0.000000 \n", "\n", " R_win_by_TKO_Doctor_Stoppage R_wins R_Height_cms \\\n", "R_fighter \n", "Aaron Phillips 0.0 0.000000 175.26 \n", "Aaron Riley 0.0 1.500000 172.72 \n", "Aaron Rosa 0.0 0.000000 193.04 \n", "Aaron Simpson 0.0 3.750000 182.88 \n", "Abdul Razak Alhassan 0.0 2.000000 177.80 \n", "... ... ... ... \n", "Zach Makovsky 0.0 2.000000 162.56 \n", "Zak Cummings 0.0 4.000000 182.88 \n", "Zak Ottow 0.0 3.000000 180.34 \n", "Zhang Lipeng 0.0 1.666667 180.34 \n", "Zubaira Tukhugov 0.0 1.500000 172.72 \n", "\n", " R_Reach_cms R_Weight_lbs R_age \n", "R_fighter \n", "Aaron Phillips 177.80 135.0 25.000000 \n", "Aaron Riley 175.26 155.0 29.000000 \n", "Aaron Rosa 198.12 205.0 28.000000 \n", "Aaron Simpson 185.42 170.0 36.125000 \n", "Abdul Razak Alhassan 185.42 170.0 32.000000 \n", "... ... ... ... \n", "Zach Makovsky 162.56 125.0 31.333333 \n", "Zak Cummings 190.50 185.0 31.666667 \n", "Zak Ottow 182.88 170.0 31.000000 \n", "Zhang Lipeng 180.34 155.0 24.333333 \n", "Zubaira Tukhugov 172.72 145.0 23.500000 \n", "\n", "[1334 rows x 67 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 141 } ] }, { "cell_type": "code", "metadata": { "id": "5mXWWKaAkS2T", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 774 }, "outputId": "8fc13b7a-3eb2-43cf-a3bb-b134f27d592f" }, "source": [ "# retrieved from https://www.kaggle.com/rajeevw/ufcdata\n", "# on 11/15/2019\n", "raw_dat_df = pd.read_csv(\"https://raw.githubusercontent.com/ekoly/DS-Unit-1-Build/master/csv/raw_total_fight_data.csv\", sep=\";\")\n", "raw_dat_df" ], "execution_count": 142, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
R_fighterB_fighterR_KDB_KDR_SIG_STR.B_SIG_STR.R_SIG_STR_pctB_SIG_STR_pctR_TOTAL_STR.B_TOTAL_STR.R_TDB_TDR_TD_pctB_TD_pctR_SUB_ATTB_SUB_ATTR_PASSB_PASSR_REVB_REVR_HEADB_HEADR_BODYB_BODYR_LEGB_LEGR_DISTANCEB_DISTANCER_CLINCHB_CLINCHR_GROUNDB_GROUNDwin_bylast_roundlast_round_timeFormatRefereedatelocationFight_typeWinner
0Henry CejudoMarlon Moraes0090 of 17157 of 11952%47%99 of 18259 of 1211 of 40 of 225%0%10100073 of 15035 of 8913 of 167 of 84 of 515 of 2245 of 11854 of 11619 of 232 of 226 of 301 of 1KO/TKO34:515 Rnd (5-5-5-5-5)Marc GoddardJune 08, 2019Chicago, Illinois, USAUFC Bantamweight Title BoutHenry Cejudo
1Valentina ShevchenkoJessica Eye108 of 112 of 1272%16%37 of 4042 of 522 of 20 of 0100%0%1030004 of 50 of 74 of 60 of 20 of 02 of 35 of 82 of 122 of 20 of 01 of 10 of 0KO/TKO20:265 Rnd (5-5-5-5-5)Robert MadrigalJune 08, 2019Chicago, Illinois, USAUFC Women's Flyweight Title BoutValentina Shevchenko
2Tony FergusonDonald Cerrone00104 of 20068 of 18552%36%104 of 20068 of 1850 of 01 of 10%100%00000065 of 14443 of 15225 of 3715 of 2314 of 1910 of 10103 of 19868 of 1841 of 20 of 10 of 00 of 0TKO - Doctor's Stoppage25:003 Rnd (5-5-5)Dan MiragliottaJune 08, 2019Chicago, Illinois, USALightweight BoutTony Ferguson
3Jimmie RiveraPetr Yan0273 of 19256 of 18938%29%76 of 19558 of 1920 of 31 of 30%33%00010042 of 14540 of 16615 of 2413 of 1916 of 233 of 460 of 17342 of 1679 of 1510 of 124 of 44 of 10Decision - Unanimous35:003 Rnd (5-5-5)Kevin MacDonaldJune 08, 2019Chicago, Illinois, USABantamweight BoutPetr Yan
4Tai TuivasaBlagoy Ivanov0164 of 14473 of 12344%59%66 of 14681 of 1310 of 02 of 20%100%00000039 of 11465 of 1146 of 77 of 819 of 231 of 150 of 12662 of 11114 of 185 of 60 of 06 of 6Decision - Unanimous35:003 Rnd (5-5-5)Dan MiragliottaJune 08, 2019Chicago, Illinois, USAHeavyweight BoutBlagoy Ivanov
..............................................................................................................................
5139Gerard GordeauKevin Rosier1011 of 170 of 364%0%11 of 170 of 30 of 00 of 00%0%0000007 of 130 of 11 of 10 of 13 of 30 of 15 of 80 of 30 of 00 of 06 of 90 of 0KO/TKO10:59No Time LimitJoao Alberto BarretoNovember 12, 1993Denver, Colorado, USAOpen Weight BoutGerard Gordeau
5140Ken ShamrockPatrick Smith001 of 14 of 8100%50%4 of 416 of 201 of 20 of 050%0%2000001 of 11 of 40 of 01 of 10 of 02 of 30 of 01 of 10 of 01 of 11 of 12 of 6Submission11:49No Time LimitJoao Alberto BarretoNovember 12, 1993Denver, Colorado, USAOpen Weight BoutKen Shamrock
5141Royce GracieArt Jimmerson000 of 30 of 00%0%4 of 70 of 01 of 10 of 0100%0%0020000 of 10 of 00 of 00 of 00 of 20 of 00 of 30 of 00 of 00 of 00 of 00 of 0Submission12:18No Time LimitJoao Alberto BarretoNovember 12, 1993Denver, Colorado, USAOpen Weight BoutRoyce Gracie
5142Kevin RosierZane Frazier2015 of 2712 of 2855%42%38 of 5313 of 290 of 00 of 00%0%00000012 of 237 of 193 of 43 of 60 of 02 of 34 of 100 of 74 of 910 of 197 of 82 of 2KO/TKO14:20No Time LimitJoao Alberto BarretoNovember 12, 1993Denver, Colorado, USAOpen Weight BoutKevin Rosier
5143Gerard GordeauTeila Tuli003 of 50 of 160%0%3 of 50 of 10 of 00 of 10%0%0000003 of 50 of 10 of 00 of 00 of 00 of 01 of 30 of 10 of 00 of 02 of 20 of 0KO/TKO10:26No Time LimitJoao Alberto BarretoNovember 12, 1993Denver, Colorado, USAOpen Weight BoutGerard Gordeau
\n", "

5144 rows × 41 columns

\n", "
" ], "text/plain": [ " R_fighter B_fighter R_KD B_KD R_SIG_STR. B_SIG_STR. \\\n", "0 Henry Cejudo Marlon Moraes 0 0 90 of 171 57 of 119 \n", "1 Valentina Shevchenko Jessica Eye 1 0 8 of 11 2 of 12 \n", "2 Tony Ferguson Donald Cerrone 0 0 104 of 200 68 of 185 \n", "3 Jimmie Rivera Petr Yan 0 2 73 of 192 56 of 189 \n", "4 Tai Tuivasa Blagoy Ivanov 0 1 64 of 144 73 of 123 \n", "... ... ... ... ... ... ... \n", "5139 Gerard Gordeau Kevin Rosier 1 0 11 of 17 0 of 3 \n", "5140 Ken Shamrock Patrick Smith 0 0 1 of 1 4 of 8 \n", "5141 Royce Gracie Art Jimmerson 0 0 0 of 3 0 of 0 \n", "5142 Kevin Rosier Zane Frazier 2 0 15 of 27 12 of 28 \n", "5143 Gerard Gordeau Teila Tuli 0 0 3 of 5 0 of 1 \n", "\n", " R_SIG_STR_pct B_SIG_STR_pct R_TOTAL_STR. B_TOTAL_STR. R_TD B_TD \\\n", "0 52% 47% 99 of 182 59 of 121 1 of 4 0 of 2 \n", "1 72% 16% 37 of 40 42 of 52 2 of 2 0 of 0 \n", "2 52% 36% 104 of 200 68 of 185 0 of 0 1 of 1 \n", "3 38% 29% 76 of 195 58 of 192 0 of 3 1 of 3 \n", "4 44% 59% 66 of 146 81 of 131 0 of 0 2 of 2 \n", "... ... ... ... ... ... ... \n", "5139 64% 0% 11 of 17 0 of 3 0 of 0 0 of 0 \n", "5140 100% 50% 4 of 4 16 of 20 1 of 2 0 of 0 \n", "5141 0% 0% 4 of 7 0 of 0 1 of 1 0 of 0 \n", "5142 55% 42% 38 of 53 13 of 29 0 of 0 0 of 0 \n", "5143 60% 0% 3 of 5 0 of 1 0 of 0 0 of 1 \n", "\n", " R_TD_pct B_TD_pct R_SUB_ATT B_SUB_ATT R_PASS B_PASS R_REV B_REV \\\n", "0 25% 0% 1 0 1 0 0 0 \n", "1 100% 0% 1 0 3 0 0 0 \n", "2 0% 100% 0 0 0 0 0 0 \n", "3 0% 33% 0 0 0 1 0 0 \n", "4 0% 100% 0 0 0 0 0 0 \n", "... ... ... ... ... ... ... ... ... \n", "5139 0% 0% 0 0 0 0 0 0 \n", "5140 50% 0% 2 0 0 0 0 0 \n", "5141 100% 0% 0 0 2 0 0 0 \n", "5142 0% 0% 0 0 0 0 0 0 \n", "5143 0% 0% 0 0 0 0 0 0 \n", "\n", " R_HEAD B_HEAD R_BODY B_BODY R_LEG B_LEG \\\n", "0 73 of 150 35 of 89 13 of 16 7 of 8 4 of 5 15 of 22 \n", "1 4 of 5 0 of 7 4 of 6 0 of 2 0 of 0 2 of 3 \n", "2 65 of 144 43 of 152 25 of 37 15 of 23 14 of 19 10 of 10 \n", "3 42 of 145 40 of 166 15 of 24 13 of 19 16 of 23 3 of 4 \n", "4 39 of 114 65 of 114 6 of 7 7 of 8 19 of 23 1 of 1 \n", "... ... ... ... ... ... ... \n", "5139 7 of 13 0 of 1 1 of 1 0 of 1 3 of 3 0 of 1 \n", "5140 1 of 1 1 of 4 0 of 0 1 of 1 0 of 0 2 of 3 \n", "5141 0 of 1 0 of 0 0 of 0 0 of 0 0 of 2 0 of 0 \n", "5142 12 of 23 7 of 19 3 of 4 3 of 6 0 of 0 2 of 3 \n", "5143 3 of 5 0 of 1 0 of 0 0 of 0 0 of 0 0 of 0 \n", "\n", " R_DISTANCE B_DISTANCE R_CLINCH B_CLINCH R_GROUND B_GROUND \\\n", "0 45 of 118 54 of 116 19 of 23 2 of 2 26 of 30 1 of 1 \n", "1 5 of 8 2 of 12 2 of 2 0 of 0 1 of 1 0 of 0 \n", "2 103 of 198 68 of 184 1 of 2 0 of 1 0 of 0 0 of 0 \n", "3 60 of 173 42 of 167 9 of 15 10 of 12 4 of 4 4 of 10 \n", "4 50 of 126 62 of 111 14 of 18 5 of 6 0 of 0 6 of 6 \n", "... ... ... ... ... ... ... \n", "5139 5 of 8 0 of 3 0 of 0 0 of 0 6 of 9 0 of 0 \n", "5140 0 of 0 1 of 1 0 of 0 1 of 1 1 of 1 2 of 6 \n", "5141 0 of 3 0 of 0 0 of 0 0 of 0 0 of 0 0 of 0 \n", "5142 4 of 10 0 of 7 4 of 9 10 of 19 7 of 8 2 of 2 \n", "5143 1 of 3 0 of 1 0 of 0 0 of 0 2 of 2 0 of 0 \n", "\n", " win_by last_round last_round_time Format \\\n", "0 KO/TKO 3 4:51 5 Rnd (5-5-5-5-5) \n", "1 KO/TKO 2 0:26 5 Rnd (5-5-5-5-5) \n", "2 TKO - Doctor's Stoppage 2 5:00 3 Rnd (5-5-5) \n", "3 Decision - Unanimous 3 5:00 3 Rnd (5-5-5) \n", "4 Decision - Unanimous 3 5:00 3 Rnd (5-5-5) \n", "... ... ... ... ... \n", "5139 KO/TKO 1 0:59 No Time Limit \n", "5140 Submission 1 1:49 No Time Limit \n", "5141 Submission 1 2:18 No Time Limit \n", "5142 KO/TKO 1 4:20 No Time Limit \n", "5143 KO/TKO 1 0:26 No Time Limit \n", "\n", " Referee date location \\\n", "0 Marc Goddard June 08, 2019 Chicago, Illinois, USA \n", "1 Robert Madrigal June 08, 2019 Chicago, Illinois, USA \n", "2 Dan Miragliotta June 08, 2019 Chicago, Illinois, USA \n", "3 Kevin MacDonald June 08, 2019 Chicago, Illinois, USA \n", "4 Dan Miragliotta June 08, 2019 Chicago, Illinois, USA \n", "... ... ... ... \n", "5139 Joao Alberto Barreto November 12, 1993 Denver, Colorado, USA \n", "5140 Joao Alberto Barreto November 12, 1993 Denver, Colorado, USA \n", "5141 Joao Alberto Barreto November 12, 1993 Denver, Colorado, USA \n", "5142 Joao Alberto Barreto November 12, 1993 Denver, Colorado, USA \n", "5143 Joao Alberto Barreto November 12, 1993 Denver, Colorado, USA \n", "\n", " Fight_type Winner \n", "0 UFC Bantamweight Title Bout Henry Cejudo \n", "1 UFC Women's Flyweight Title Bout Valentina Shevchenko \n", "2 Lightweight Bout Tony Ferguson \n", "3 Bantamweight Bout Petr Yan \n", "4 Heavyweight Bout Blagoy Ivanov \n", "... ... ... \n", "5139 Open Weight Bout Gerard Gordeau \n", "5140 Open Weight Bout Ken Shamrock \n", "5141 Open Weight Bout Royce Gracie \n", "5142 Open Weight Bout Kevin Rosier \n", "5143 Open Weight Bout Gerard Gordeau \n", "\n", "[5144 rows x 41 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 142 } ] }, { "cell_type": "code", "metadata": { "id": "0NJte1T5kS2W", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 194 }, "outputId": "f3a52dc3-e069-4ac5-d6da-41b128f08840" }, "source": [ "# retrieved from https://www.kaggle.com/rajeevw/ufcdata\n", "# on 11/15/2019\n", "fighter_df = pd.read_csv(\"https://raw.githubusercontent.com/ekoly/DS-Unit-1-Build/master/csv/raw_fighter_details.csv\")\n", "fighter_df.head()" ], "execution_count": 143, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
fighter_nameHeightWeightReachStanceDOB
0AJ Fonseca5' 4\"145 lbs.NaNNaNNaN
1AJ Matthews5' 11\"185 lbs.NaNNaNNaN
2AJ McKee5' 10\"145 lbs.NaNNaNNaN
3AJ Siscoe5' 7\"135 lbs.NaNNaNNaN
4Aalon Cruz6' 0\"145 lbs.NaNNaNNaN
\n", "
" ], "text/plain": [ " fighter_name Height Weight Reach Stance DOB\n", "0 AJ Fonseca 5' 4\" 145 lbs. NaN NaN NaN\n", "1 AJ Matthews 5' 11\" 185 lbs. NaN NaN NaN\n", "2 AJ McKee 5' 10\" 145 lbs. NaN NaN NaN\n", "3 AJ Siscoe 5' 7\" 135 lbs. NaN NaN NaN\n", "4 Aalon Cruz 6' 0\" 145 lbs. NaN NaN NaN" ] }, "metadata": { "tags": [] }, "execution_count": 143 } ] }, { "cell_type": "code", "metadata": { "id": "CPPYzySxkS2Y", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 141 }, "outputId": "d3ee718f-0a08-417b-d200-39061c7625fa" }, "source": [ "fighter_df.isnull().sum()" ], "execution_count": 144, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "fighter_name 0\n", "Height 263\n", "Weight 75\n", "Reach 1744\n", "Stance 874\n", "DOB 740\n", "dtype: int64" ] }, "metadata": { "tags": [] }, "execution_count": 144 } ] }, { "cell_type": "code", "metadata": { "id": "IanJYGUgkS2a", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 428 }, "outputId": "5c69b586-99dc-43f1-f6f3-1360705240d6" }, "source": [ "dat_df.pivot_table(values=\"R_avg_LEG_att\", index=\"R_fighter\")" ], "execution_count": 145, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
R_avg_LEG_att
R_fighter
Aaron Phillips3.000000
Aaron Riley12.937500
Aaron Rosa5.000000
Aaron Simpson7.515760
Abdul Razak Alhassan1.625000
......
Zach Makovsky7.333333
Zak Cummings5.597143
Zak Ottow7.333333
Zhang Lipeng7.722222
Zubaira Tukhugov1.000000
\n", "

1146 rows × 1 columns

\n", "
" ], "text/plain": [ " R_avg_LEG_att\n", "R_fighter \n", "Aaron Phillips 3.000000\n", "Aaron Riley 12.937500\n", "Aaron Rosa 5.000000\n", "Aaron Simpson 7.515760\n", "Abdul Razak Alhassan 1.625000\n", "... ...\n", "Zach Makovsky 7.333333\n", "Zak Cummings 5.597143\n", "Zak Ottow 7.333333\n", "Zhang Lipeng 7.722222\n", "Zubaira Tukhugov 1.000000\n", "\n", "[1146 rows x 1 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 145 } ] }, { "cell_type": "code", "metadata": { "id": "u3nXKeQdkS2c", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 399 }, "outputId": "1db2b363-932d-486d-bf72-0cf9e34e2658" }, "source": [ "# retrieved from https://docs.google.com/spreadsheets/d/1z3QX0uWXv-XHX2Nfuj6zZHrfEeXI3A9CKWkrGaBzB8s/edit#gid=0\n", "# on 11/15/2019\n", "sheet_df = pd.read_csv(\"https://raw.githubusercontent.com/ekoly/DS-Unit-1-Build/master/csv/ALL%20UFC%20FIGHTERS%202_23_2016%20SHERDOG.COM%20-%20Sheet1.csv\")\n", "sheet_df" ], "execution_count": 146, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
urlfidnamenickbirth_dateheightweightassociationclasslocalitycountry
0/fighter/Conor-McGregor-2968829688Conor McGregorNotorious7/14/198868.0145.0SBG IrelandFeatherweightDublinIreland
1/fighter/Jon-Jones-2794427944Jon JonesBones7/19/198776.0205.0Jackson-Wink MMALight HeavyweightRochester, New YorkUnited States
2/fighter/Holly-Holm-7512575125Holly HolmThe Preacher's Daughter10/17/198168.0135.0Jackson-Wink MMABantamweightAlbuquerque, New MexicoUnited States
3/fighter/Dominick-Cruz-1210712107Dominick CruzThe Dominator9/3/198568.0134.0Alliance MMABantamweightSan Diego, CaliforniaUnited States
4/fighter/Demetrious-Johnson-4545245452Demetrious JohnsonMighty Mouse8/13/198663.0125.0AMC PankrationFlyweightKirkland, WashingtonUnited States
....................................
1556/fighter/Thaddeus-Luster-3030Thaddeus LusterNaNNaN75.0200.0NaNLight HeavyweightVan Nuys, CaliforniaUnited States
1557/fighter/Frank-Hamaker-2929Frank HamakerNaNNaN74.0245.0NaNHeavyweightAmsterdamNetherlands
1558/fighter/Ryan-Parker-119119Ryan ParkerNaNNaN75.0235.0NaNHeavyweightMoorehead, MinnesotaUnited States
1559/fighter/Marcus-Davis-85928592Marcus DavisThe Irish Hand Grenade8/24/197368.0170.0Team IrishWelterweightBangor, MaineUnited States
1560/fighter/John-Alessio-259259John AlessioThe Natural7/5/197970.0155.0Xtreme CoutureLightweightVancouver, British ColumbiaCanada
\n", "

1561 rows × 11 columns

\n", "
" ], "text/plain": [ " url fid name \\\n", "0 /fighter/Conor-McGregor-29688 29688 Conor McGregor \n", "1 /fighter/Jon-Jones-27944 27944 Jon Jones \n", "2 /fighter/Holly-Holm-75125 75125 Holly Holm \n", "3 /fighter/Dominick-Cruz-12107 12107 Dominick Cruz \n", "4 /fighter/Demetrious-Johnson-45452 45452 Demetrious Johnson \n", "... ... ... ... \n", "1556 /fighter/Thaddeus-Luster-30 30 Thaddeus Luster \n", "1557 /fighter/Frank-Hamaker-29 29 Frank Hamaker \n", "1558 /fighter/Ryan-Parker-119 119 Ryan Parker \n", "1559 /fighter/Marcus-Davis-8592 8592 Marcus Davis \n", "1560 /fighter/John-Alessio-259 259 John Alessio \n", "\n", " nick birth_date height weight association \\\n", "0 Notorious 7/14/1988 68.0 145.0 SBG Ireland \n", "1 Bones 7/19/1987 76.0 205.0 Jackson-Wink MMA \n", "2 The Preacher's Daughter 10/17/1981 68.0 135.0 Jackson-Wink MMA \n", "3 The Dominator 9/3/1985 68.0 134.0 Alliance MMA \n", "4 Mighty Mouse 8/13/1986 63.0 125.0 AMC Pankration \n", "... ... ... ... ... ... \n", "1556 NaN NaN 75.0 200.0 NaN \n", "1557 NaN NaN 74.0 245.0 NaN \n", "1558 NaN NaN 75.0 235.0 NaN \n", "1559 The Irish Hand Grenade 8/24/1973 68.0 170.0 Team Irish \n", "1560 The Natural 7/5/1979 70.0 155.0 Xtreme Couture \n", "\n", " class locality country \n", "0 Featherweight Dublin Ireland \n", "1 Light Heavyweight Rochester, New York United States \n", "2 Bantamweight Albuquerque, New Mexico United States \n", "3 Bantamweight San Diego, California United States \n", "4 Flyweight Kirkland, Washington United States \n", "... ... ... ... \n", "1556 Light Heavyweight Van Nuys, California United States \n", "1557 Heavyweight Amsterdam Netherlands \n", "1558 Heavyweight Moorehead, Minnesota United States \n", "1559 Welterweight Bangor, Maine United States \n", "1560 Lightweight Vancouver, British Columbia Canada \n", "\n", "[1561 rows x 11 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 146 } ] }, { "cell_type": "code", "metadata": { "id": "e6zirqnGkS2d", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 230 }, "outputId": "45eb379e-654a-47e7-c045-c885429761b5" }, "source": [ "sheet_df.dtypes" ], "execution_count": 147, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "url object\n", "fid int64\n", "name object\n", "nick object\n", "birth_date object\n", "height float64\n", "weight float64\n", "association object\n", "class object\n", "locality object\n", "country object\n", "dtype: object" ] }, "metadata": { "tags": [] }, "execution_count": 147 } ] }, { "cell_type": "code", "metadata": { "id": "tN4kT3AwkS2f", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "outputId": "ecd9fb44-c8e6-4761-de10-96c50927d31c" }, "source": [ "import re\n", "\n", "year_re = re.compile(r\"\\d+\\/\\d+\\/(?P\\d+)\")\n", "year_re.search(\"7/30/1977\").group(\"year\")" ], "execution_count": 148, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'1977'" ] }, "metadata": { "tags": [] }, "execution_count": 148 } ] }, { "cell_type": "code", "metadata": { "id": "gaXPU7rbkS2h", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 194 }, "outputId": "433aea97-49a1-4a24-9c8d-e971dcfcc41b" }, "source": [ "def getYear(s):\n", " \n", " if not s or not isinstance(s, str):\n", " return np.NaN\n", " \n", " m = year_re.search(s)\n", " \n", " if not m:\n", " return np.NaN\n", " \n", " return np.int64(m.group(\"year\"))\n", "\n", "\n", "sheet_df[\"birth_year\"] = sheet_df[\"birth_date\"].apply(getYear)\n", "sheet_df.head()" ], "execution_count": 149, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
urlfidnamenickbirth_dateheightweightassociationclasslocalitycountrybirth_year
0/fighter/Conor-McGregor-2968829688Conor McGregorNotorious7/14/198868.0145.0SBG IrelandFeatherweightDublinIreland1988.0
1/fighter/Jon-Jones-2794427944Jon JonesBones7/19/198776.0205.0Jackson-Wink MMALight HeavyweightRochester, New YorkUnited States1987.0
2/fighter/Holly-Holm-7512575125Holly HolmThe Preacher's Daughter10/17/198168.0135.0Jackson-Wink MMABantamweightAlbuquerque, New MexicoUnited States1981.0
3/fighter/Dominick-Cruz-1210712107Dominick CruzThe Dominator9/3/198568.0134.0Alliance MMABantamweightSan Diego, CaliforniaUnited States1985.0
4/fighter/Demetrious-Johnson-4545245452Demetrious JohnsonMighty Mouse8/13/198663.0125.0AMC PankrationFlyweightKirkland, WashingtonUnited States1986.0
\n", "
" ], "text/plain": [ " url fid name \\\n", "0 /fighter/Conor-McGregor-29688 29688 Conor McGregor \n", "1 /fighter/Jon-Jones-27944 27944 Jon Jones \n", "2 /fighter/Holly-Holm-75125 75125 Holly Holm \n", "3 /fighter/Dominick-Cruz-12107 12107 Dominick Cruz \n", "4 /fighter/Demetrious-Johnson-45452 45452 Demetrious Johnson \n", "\n", " nick birth_date height weight association \\\n", "0 Notorious 7/14/1988 68.0 145.0 SBG Ireland \n", "1 Bones 7/19/1987 76.0 205.0 Jackson-Wink MMA \n", "2 The Preacher's Daughter 10/17/1981 68.0 135.0 Jackson-Wink MMA \n", "3 The Dominator 9/3/1985 68.0 134.0 Alliance MMA \n", "4 Mighty Mouse 8/13/1986 63.0 125.0 AMC Pankration \n", "\n", " class locality country birth_year \n", "0 Featherweight Dublin Ireland 1988.0 \n", "1 Light Heavyweight Rochester, New York United States 1987.0 \n", "2 Bantamweight Albuquerque, New Mexico United States 1981.0 \n", "3 Bantamweight San Diego, California United States 1985.0 \n", "4 Flyweight Kirkland, Washington United States 1986.0 " ] }, "metadata": { "tags": [] }, "execution_count": 149 } ] }, { "cell_type": "code", "metadata": { "id": "hXQNj3Lx48ph", "colab_type": "code", "colab": {} }, "source": [ "sheet_df = sheet_df.replace(\"USA\", \"United States\")\n", "sheet_df = sheet_df.replace(\"Holland\", \"Netherlands\")" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "KyxIPkOakS2i", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 248 }, "outputId": "ad3d92cc-d8b0-4690-f9a2-3102d32181b1" }, "source": [ "sheet_df.isnull().sum()" ], "execution_count": 151, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "url 0\n", "fid 0\n", "name 1\n", "nick 509\n", "birth_date 209\n", "height 55\n", "weight 28\n", "association 195\n", "class 35\n", "locality 63\n", "country 28\n", "birth_year 209\n", "dtype: int64" ] }, "metadata": { "tags": [] }, "execution_count": 151 } ] }, { "cell_type": "code", "metadata": { "id": "ujDRdwjIkS2k", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 194 }, "outputId": "7e07a44d-4b9d-4ab4-fcf0-94da61311c97" }, "source": [ "sheet_df[\"age_group\"] = pd.cut(sheet_df[\"birth_year\"], range(1965, 1995, 5), labels=range(1965, 1990, 5))\n", "sheet_df.head()" ], "execution_count": 152, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
urlfidnamenickbirth_dateheightweightassociationclasslocalitycountrybirth_yearage_group
0/fighter/Conor-McGregor-2968829688Conor McGregorNotorious7/14/198868.0145.0SBG IrelandFeatherweightDublinIreland1988.01985
1/fighter/Jon-Jones-2794427944Jon JonesBones7/19/198776.0205.0Jackson-Wink MMALight HeavyweightRochester, New YorkUnited States1987.01985
2/fighter/Holly-Holm-7512575125Holly HolmThe Preacher's Daughter10/17/198168.0135.0Jackson-Wink MMABantamweightAlbuquerque, New MexicoUnited States1981.01980
3/fighter/Dominick-Cruz-1210712107Dominick CruzThe Dominator9/3/198568.0134.0Alliance MMABantamweightSan Diego, CaliforniaUnited States1985.01980
4/fighter/Demetrious-Johnson-4545245452Demetrious JohnsonMighty Mouse8/13/198663.0125.0AMC PankrationFlyweightKirkland, WashingtonUnited States1986.01985
\n", "
" ], "text/plain": [ " url fid name \\\n", "0 /fighter/Conor-McGregor-29688 29688 Conor McGregor \n", "1 /fighter/Jon-Jones-27944 27944 Jon Jones \n", "2 /fighter/Holly-Holm-75125 75125 Holly Holm \n", "3 /fighter/Dominick-Cruz-12107 12107 Dominick Cruz \n", "4 /fighter/Demetrious-Johnson-45452 45452 Demetrious Johnson \n", "\n", " nick birth_date height weight association \\\n", "0 Notorious 7/14/1988 68.0 145.0 SBG Ireland \n", "1 Bones 7/19/1987 76.0 205.0 Jackson-Wink MMA \n", "2 The Preacher's Daughter 10/17/1981 68.0 135.0 Jackson-Wink MMA \n", "3 The Dominator 9/3/1985 68.0 134.0 Alliance MMA \n", "4 Mighty Mouse 8/13/1986 63.0 125.0 AMC Pankration \n", "\n", " class locality country birth_year \\\n", "0 Featherweight Dublin Ireland 1988.0 \n", "1 Light Heavyweight Rochester, New York United States 1987.0 \n", "2 Bantamweight Albuquerque, New Mexico United States 1981.0 \n", "3 Bantamweight San Diego, California United States 1985.0 \n", "4 Flyweight Kirkland, Washington United States 1986.0 \n", "\n", " age_group \n", "0 1985 \n", "1 1985 \n", "2 1980 \n", "3 1980 \n", "4 1985 " ] }, "metadata": { "tags": [] }, "execution_count": 152 } ] }, { "cell_type": "code", "metadata": { "id": "N3o_4_dh6qoX", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "outputId": "4634de01-7f67-45f5-809a-fde2d993495a" }, "source": [ "num_fighters_by_country = sheet_df.pivot_table(values=\"name\", index=[\"country\"], aggfunc=len)\n", "top_countries = num_fighters_by_country.sort_values(by=\"name\").tail(5).index\n", "secondary_countries = num_fighters_by_country.sort_values(by=\"name\").tail(15).head(10).index\n", "top_countries" ], "execution_count": 153, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Index(['England', 'Japan', 'Canada', 'Brazil', 'United States'], dtype='object', name='country')" ] }, "metadata": { "tags": [] }, "execution_count": 153 } ] }, { "cell_type": "code", "metadata": { "id": "vKbsBK838qQe", "colab_type": "code", "colab": {} }, "source": [ "top = sheet_df[sheet_df[\"country\"].isin(top_countries)]\n", "secondary = sheet_df[sheet_df[\"country\"].isin(secondary_countries)]" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "0ehzqP25kS2m", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 810 }, "outputId": "0d717745-3c08-4d6e-b6ec-a7a06aeabaaf" }, "source": [ "num_fighters_by_year = top.pivot_table(values=\"name\", index=[\"age_group\", \"country\"], aggfunc=len)\n", "num_fighters_by_year" ], "execution_count": 155, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
name
age_groupcountry
1965Brazil11
Canada4
England2
Japan6
United States40
1970Brazil9
Canada5
England2
Japan9
United States82
1975Brazil41
Canada19
England12
Japan14
United States173
1980Brazil68
Canada30
England13
Japan14
United States278
1985Brazil44
Canada11
England12
Japan7
United States159
\n", "
" ], "text/plain": [ " name\n", "age_group country \n", "1965 Brazil 11\n", " Canada 4\n", " England 2\n", " Japan 6\n", " United States 40\n", "1970 Brazil 9\n", " Canada 5\n", " England 2\n", " Japan 9\n", " United States 82\n", "1975 Brazil 41\n", " Canada 19\n", " England 12\n", " Japan 14\n", " United States 173\n", "1980 Brazil 68\n", " Canada 30\n", " England 13\n", " Japan 14\n", " United States 278\n", "1985 Brazil 44\n", " Canada 11\n", " England 12\n", " Japan 7\n", " United States 159" ] }, "metadata": { "tags": [] }, "execution_count": 155 } ] }, { "cell_type": "code", "metadata": { "id": "kedRfw_EkS2p", "colab_type": "code", "colab": {} }, "source": [ "num_fighters_by_year.columns = [\"num_fighters\"]" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "LbdRspzWlDtu", "colab_type": "code", "colab": {} }, "source": [ "num_fighters_by_year = num_fighters_by_year.reset_index(level=[\"age_group\", \"country\"])" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "uKnUO2rgkS2s", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 732 }, "outputId": "1bb8b300-31a5-49a3-c987-94fca543aae3" }, "source": [ "fig, ax = plt.subplots(figsize=(20, 12))\n", "\n", "\n", "sns.lineplot(x=num_fighters_by_year[\"age_group\"], y=num_fighters_by_year[\"num_fighters\"], hue=num_fighters_by_year[\"country\"], ax=ax)\n", "#ax.set_ylim([0, 50])" ], "execution_count": 158, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": { "tags": [] }, "execution_count": 158 }, { "output_type": "display_data", "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABJMAAAK5CAYAAAALlG/uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJEmSJEmSJPXNYpIkSZIkSZL69l/VdFoTK4iU3QAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "id": "PvJRMue5kS2u", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 803 }, "outputId": "54975803-6dae-4602-8f06-d3afd69c1c5b" }, "source": [ "num_fighters_by_year_sec = secondary.pivot_table(values=\"name\", index=[\"age_group\", \"country\"], aggfunc=len)\n", "num_fighters_by_year_sec.columns = [\"num_fighters\"]\n", "num_fighters_by_year_sec = num_fighters_by_year_sec.reset_index(level=[\"age_group\", \"country\"])\n", "\n", "fig, ax = plt.subplots(figsize=(20, 12))\n", "\n", "\n", "sns.lineplot(x=num_fighters_by_year_sec[\"age_group\"], y=num_fighters_by_year_sec[\"num_fighters\"], hue=num_fighters_by_year_sec[\"country\"], ax=ax)\n", "#ax.set_ylim([0, 50])" ], "execution_count": 159, "outputs": [ { "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/seaborn/algorithms.py:70: RuntimeWarning: Mean of empty slice.\n", " return getattr(x, func)()\n", "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n", " ret = ret.dtype.type(ret / rcount)\n" ], "name": "stderr" }, { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": { "tags": [] }, "execution_count": 159 }, { "output_type": "display_data", "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABIwAAAK5CAYAAADdMbWLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3RVVeL28e9JgQChSFEGkCI/gZBO\nQicQ6SKEHmoKTdBRcCwIiiPjwIwzWFHeYUBaIIYeiohSIx0moUgJGNFQIh0TCSSknfePaJahS8pJ\nbp7PWi5y9z1n3+fkyh8+7rOPYZomIiIiIiIiIiIiv7GzOoCIiIiIiIiIiBQtKoxERERERERERCQX\nFUYiIiIiIiIiIpKLCiMREREREREREclFhZGIiIiIiIiIiOTiYHWAB1W1alWzbt26VscQERERERER\nEbEZMTExl03TrHbreLEpjOrWrUt0dLTVMUREREREREREbIZhGKfuNK5b0kREREREREREJBcVRiIi\nIiIiIiIikosKIxERERERERERyaXY7GF0J+np6Zw9e5bU1FSro5RITk5O1KpVC0dHR6ujiIiIiIiI\niEg+KtaF0dmzZylfvjx169bFMAyr45Qopmly5coVzp49S7169ayOIyIiIiIiIiL5qFjfkpaamkqV\nKlVUFlnAMAyqVKmi1V0iIiIiIiIiNqhYF0aAyiIL6XcvIiIiIiIiYpuKfWEkIiIiIiIiIiL5S4VR\nEfbRRx9x48YNq2OIiIiIiIiISAmjwqgIu1dhlJmZWchpRERERERERKSkUGGUR2FhYXh4eODp6UlQ\nUBDx8fG0b98eDw8POnTowOnTpwEIDQ1l+fLlOec5OzsDEBUVhb+/P/369aNRo0YMGTIE0zSZPn06\nP/30E0899RRPPfVUzjmvvPIKnp6eTJ06lV69euXMt3HjRnr37l2IVy4iIiIiIiIitsrB6gDF2dGj\nR5kyZQq7du2iatWqXL16lZCQkJx/5s6dy9ixY1m1atU95zlw4ABHjx6lRo0atG7dmp07dzJ27Fg+\n+OADtm7dStWqVQG4fv06zZs35/3338c0TVxcXLh06RLVqlVj3rx5DB8+vDAuW0RERERERERsnFYY\n5cGWLVvo379/TqFTuXJldu/ezeDBgwEICgpix44d952nWbNm1KpVCzs7O7y8vIiPj7/jcfb29vTt\n2xfIfkJZUFAQixYtIjExkd27d/P000/nz4WJiIiIiIiISImmFUaFxMHBgaysLACysrJIS0vLea90\n6dI5P9vb25ORkXHHOZycnLC3t895PWzYMHr06IGTkxP9+/fHwUFfp4iIiIiIiIjknVYY5UH79u1Z\ntmwZV65cAeDq1au0atWKxYsXAxAeHo6fnx8AdevWJSYmBoA1a9aQnp5+3/nLly/PtWvX7vp+jRo1\nqFGjBlOmTGHYsGF5vRwREREREREREUArjPLE1dWVN998k3bt2mFvb4+3tzeffPIJw4YNY9q0aTl7\nCwGMGjWKnj174unpSdeuXSlXrtx953/22Wfp2rUrNWrUYOvWrXc8ZsiQIVy6dAkXF5d8vTYRERER\nERERKbkM0zStzvBAfH19zejo6FxjsbGxJb4oeeGFF/D29mbEiBGWfL6+AxEREREREZHiyzCMGNM0\nfW8d1wqjYszHx4dy5crx/vvvWx1FRERERERERGyICqNi7Lc9kURERERERERE8pM2vRYRERERERER\nkVxUGImIiIiIiIiISC4qjEREREREREREJBcVRiIiIiIiIiIikosKo3ywatUqDMPg+PHjD33+sWPH\n/vB58+fP54UXXgBg5syZhIWFPdTni4iIiIiIiIj8ngqjfBAREUGbNm2IiIh4qPPvVRhlZGQ80Bxj\nxowhODj4oT5fREREREREROT3CrQwMgxjrmEYFw3DOPK7scqGYWw0DCPu1z8fKcgMBS05OZkdO3Yw\nZ84cFi9eDEBUVBTdu3fPOeaFF15g/vz5AEyYMIHGjRvj4eHBq6++yq5du1izZg2vvfYaXl5enDx5\nEn9/f1566SV8fX35+OOPWbt2Lc2bN8fb25uOHTty4cKF23JMnjyZ9957D4DZs2fTtGlTPD096du3\nLzdu3Cj4X4SIiIiIiIiI2AyHAp5/PvAp8Pt7pSYAm03TfNcwjAm/vn49rx/0t7VHOfbTL3mdJpfG\nNSrwdg/Xex6zevVqunbtSoMGDahSpQoxMTF3PfbKlStERkZy/PhxDMMgMTGRSpUqERAQQPfu3enX\nr1/OsWlpaURHRwPw888/s2fPHgzD4LPPPuPf//4377///l0/p0+fPowaNQqASZMmMWfOHF588cU/\ncukiIiIiIiIiUoIV6Aoj0zS3AVdvGe4JLPj15wVAr4LMUNAiIiIYOHAgAAMHDrznbWkVK1bEycmJ\nESNGsHLlSsqWLXvXYwcMGJDz89mzZ+nSpQvu7u5MmzaNo0eP3jPTkSNH8PPzw93dnfDw8PseLyIi\nIiIiIiIPwDRhz39g16dWJylwBb3C6E4eM03z3K8/nwceu9uBhmE8CzwLULt27XtOer+VQAXh6tWr\nbNmyhcOHD2MYBpmZmRiGQc+ePcnKyso5LjU1FQAHBwf27dvH5s2bWb58OZ9++ilbtmy549zlypXL\n+fnFF1/k5ZdfJiAggKioKCZPnnzPXKGhoaxatQpPT0/mz59PVFRUnq9VREREREREpES7mQxrx8KR\nFeDSA8w/g2FYnarAWLrptWmaJmDe4/1Zpmn6mqbpW61atUJM9mCWL19OUFAQp06dIj4+njNnzlCv\nXj2ysrI4duwYN2/eJDExkc2bNwPZ+x0lJSXRrVs3PvzwQw4dOgRA+fLluXbt2l0/JykpiZo1awKw\nYMGCux73m2vXrvGnP/2J9PR0wsPD8+FKRUREREREREqwy3HwWQc4Ggkd/gr9w2y6LAJrCqMLhmH8\nCeDXPy9akCFfRERE0Lt371xjffv2ZfHixQQGBuLm5kZgYCDe3t5AdpHTvXt3PDw8aNOmDR988AGQ\nfSvbtGnT8Pb25uTJk7d9zuTJk+nfvz8+Pj5UrVr1vrn+/ve/07x5c1q3bk2jRo3y4UpFRERERERE\nSqhja2DWU3D9EgxdCX6vgJ3tP3TeyF7kU4AfYBh1gS9M03T79fU04MrvNr2ubJrm+PvN4+vra/62\nCfRvYmNjcXFxyf/Q8sD0HYiIiIiIiIhNysyALe/Azo+hpg8EhkHFWlanyneGYcSYpul763iB7mFk\nGEYE4A9UNQzjLPA28C6w1DCMEcApILAgM4iIiIiIiIiI/CHJF2H5cIjfDr7Doeu74FDa6lSFqkAL\nI9M0B93lrQ4F+bkiIiIiIiIiIg/lzD5YGgIpV6HXf8BrsNWJLGHFU9JERERERERERIoW04R9s+Hr\nN6BiTRixEf7kYXUqy6gwEhEREREREZGSLe06rH0JDi+FJ7tAn/9CmUesTmUpFUYiIiIiIiIiUnJd\nOQlLguDiMXhqUol5Ctr9qDASERERERERkZLp+DqIHAN29jB0OfxfR6sTFRmqzPLI3t4eLy8v3Nzc\n6NGjB4mJifk2d3R0NGPHjs23+UREREREREQEyMqETX+DxYOh8hPw7Dcqi26hwiiPypQpw8GDBzly\n5AiVK1dmxowZ+Ta3r68v06dPz7f5REREREREREq865dhUR/Y8QE0CYHhX8MjdaxOVeSoMMpHLVu2\nJCEhAYCoqCi6d++e894LL7zA/PnzAZgwYQKNGzfGw8ODV199FYBly5bh5uaGp6cnbdu2vW2Offv2\n0bJlS7y9vWnVqhUnTpwoxCsTERERERERsQFnY+C/7eDUbgj4FAKmg6OT1amKJNvZw2j9BDh/OH/n\nrO4OT7/7QIdmZmayefNmRowYcc/jrly5QmRkJMePH8cwjJxb2N555x2+/vpratasecfb2ho1asT2\n7dtxcHBg06ZNvPHGG6xYseKPX5OIiIiIiIhISWOaED0XvpoA5avDiA1Qw8vqVEWa7RRGFklJScHL\ny4uEhARcXFzo1KnTPY+vWLEiTk5OjBgxgu7du+esIGrdujWhoaEEBgbSp0+f285LSkoiJCSEuLg4\nDMMgPT29QK5HRERERERExKakp8AXL8Ohz7P3KeozG8pWtjpVkWc7hdEDrgTKb7/tYXTjxg26dOnC\njBkzGDt2LA4ODmRlZeUcl5qaCoCDgwP79u1j8+bNLF++nE8//ZQtW7Ywc+ZM9u7dy7p16/Dx8SEm\nJibX57z11ls89dRTREZGEh8fj7+/f2FepoiIiIiIiEjxc/VHWBoE549AuwnQ7nWw0+48D8J2CiOL\nlS1blunTp9OrVy+ef/556tSpw7Fjx7h58yYpKSls3ryZNm3akJyczI0bN+jWrRutW7fmiSeeAODk\nyZM0b96c5s2bs379es6cOZNr/qSkJGrWrAmQsxeSiIiIiIiIiNzFd1/DylGAAYOXQoPOVicqVlSr\n5SNvb288PDyIiIjg8ccfJzAwEDc3NwIDA/H29gbg2rVrdO/eHQ8PD9q0acMHH3wAwGuvvYa7uztu\nbm60atUKT0/PXHOPHz+eiRMn4u3tTUZGRqFfm4iIiIiIiEixkJUJW6bC54FQqQ6M/kZl0UMwTNO0\nOsMD8fX1NaOjo3ONxcbG4uLiYlEiAX0HIiIiIiIiUoTcuAorRsLJzeA1FJ55DxzLWJ2qSDMMI8Y0\nTd9bx3VLmoiIiIiIiIgUfwn7YWkIJJ+HHh9DkxAwDKtTFVsqjERERERERESkeItZAF++Cs6PwfCv\noKaP1YmKPRVGIiIiIiIiIlI8padkF0UHFsETT0HfOVCuitWpbIIKIxEREREREREpfn4+BUuD4Nwh\naPsa+E8EO3urU9kMFUYiIiIiIiIiUrzEbYKVIyErCwYthoZPW53I5qgwEhEREREREZHiISsLtk2D\nqH/CY64QGAZV6ludyiapMMoje3t73N3dc16vWrWKunXrWhdIRERERERExBbduAqRoyFuA3gMhO4f\nQqmyVqeyWSqM8qhMmTIcPHjwru9nZGTg4KBfs4iIiIiIiMhDO3cIlgTBLz/BM++D7wgwDKtT2TQ7\nqwPYovnz5xMQEED79u3p0KEDycnJdOjQgSZNmuDu7s7q1asBiI+Px8XFhVGjRuHq6krnzp1JSUkB\n4Pvvv6djx454enrSpEkTTp48CcC0adNo2rQpHh4evP3225Zdo4iIiIiIiEihOLAI5nSGrAwYth6a\njlRZVAhsZunLv/b9i+NXj+frnI0qN+L1Zq/f85iUlBS8vLwAqFevHpGRkQDs37+fb7/9lsqVK5OR\nkUFkZCQVKlTg8uXLtGjRgoCAAADi4uKIiIhg9uzZBAYGsmLFCoYOHcqQIUOYMGECvXv3JjU1lays\nLDZs2EBcXBz79u3DNE0CAgLYtm0bbdu2zdfrFhEREREREbFcxk1YPx5i5kO9ttB3LjhXszpViWEz\nhZFV7nZLWqdOnahcuTIApmnyxhtvsG3bNuzs7EhISODChQtAdsn0W+Hk4+NDfHw8165dIyEhgd69\newPg5OQEwIYNG9iwYQPe3t4AJCcnExcXp8JIREREREREbEviGVgaDD/thzZ/gacmgb0qjMJkM7/t\n+60EKmzlypXL+Tk8PJxLly4RExODo6MjdevWJTU1FYDSpUvnHGdvb59zS9qdmKbJxIkTGT16dMEF\nFxEREREREbHSyS2wfET2LWgDwsGlu9WJSiTtYVQIkpKSePTRR3F0dGTr1q2cOnXqnseXL1+eWrVq\nsWrVKgBu3rzJjRs36NKlC3PnziU5ORmAhIQELl68WOD5RURERERERApcVhZsmwYL+0D56jBqq8oi\nC9nMCqOibMiQIfTo0QN3d3d8fX1p1KjRfc9ZuHAho0eP5q9//SuOjo4sW7aMzp07ExsbS8uWLQFw\ndnZm0aJFPProowV9CSIiIiIiIiIFJyURIsfAd+vBvT/0+BhKlbv/eVJgDNM0rc7wQHx9fc3o6Ohc\nY7Gxsbi4uFiUSEDfgYiIiIiIiOTR+cOwJAiSzkCXf0CzZ/UUtEJkGEaMaZq+t45rhZGIiIiIiIiI\nWOPQYlj7EpSpBKFfQu3mVieSX6kwEhEREREREZHClZEGX0+E/30GddpA/3ngrO1WihIVRiIiIiIi\nIiJSeJISYGkwJERDqxehw2SwVz1R1OgbEREREREREZHC8cM3sHw4ZKRC/wXg2svqRHIXKoxERERE\nREREpGCZJuz8CDa/A1WehAGLoFoDq1PJPagwEhEREREREZGCk5oEq56H41+Aa28I+BRKO1udSu7D\nzuoAxZ1hGAwdOjTndUZGBtWqVaN79+4PNd+aNWt499138yueiIiIiIiIiHUuHINZT8GJ9dDln9Bv\nnsqiYkIrjPKoXLlyHDlyhJSUFMqUKcPGjRupWbPmQ88XEBBAQEBAPiYUERERERERscDh5bDmRShd\nHkK/gDqtrE4kf4BWGOWDbt26sW7dOgAiIiIYNGhQznvXr19n+PDhNGvWDG9vb1avXg3Ahx9+yPDh\nwwE4fPgwbm5u3Lhxg/nz5/PCCy8AcOHCBXr37o2npyeenp7s2rULgA8++AA3Nzfc3Nz46KOPCvNS\nRURERERERO4tIw2+HA8rRsCfPGH0NpVFxZDNrDA6/49/cDP2eL7OWdqlEdXfeOO+xw0cOJB33nmH\n7t278+233zJ8+HC2b98OwNSpU2nfvj1z584lMTGRZs2a0bFjR8aNG4e/vz+RkZFMnTqV//73v5Qt\nWzbXvGPHjqVdu3ZERkaSmZlJcnIyMTExzJs3j71792KaJs2bN6ddu3Z4e3vn67WLiIiIiIiI/GG/\nnINlIXBmL7T4M3T6G9g7Wp1KHoLNFEZW8vDwID4+noiICLp165brvQ0bNrBmzRree+89AFJTUzl9\n+jQuLi7Mnz8fDw8PRo8eTevWrW+bd8uWLYSFhQFgb29PxYoV2bFjB71796ZcuXIA9OnTh+3bt6sw\nEhEREREREWvF74BlwyDtevZeRW59rE4keWAzhdGDrAQqSAEBAbz66qtERUVx5cqVnHHTNFmxYgUN\nGza87Zy4uDicnZ356aefCjOqiIiIiIiISP4xTdj9KWx8Gyo/ASFr4dFGVqeSPNIeRvlk+PDhvP32\n27i7u+ca79KlC5988gmmaQJw4MABAJKSkhg7dizbtm3jypUrLF++/LY5O3TowH/+8x8AMjMzSUpK\nws/Pj1WrVnHjxg2uX79OZGQkfn5+BXx1IiIiIiIiIndw81r2LWgbJkGjZ2DUFpVFNkKFUT6pVasW\nY8eOvW38rbfeIj09HQ8PD1xdXXnrrbcA+Mtf/sKf//xnGjRowJw5c5gwYQIXL17Mde7HH3/M1q1b\ncXd3x8fHh2PHjtGkSRNCQ0Np1qwZzZs3Z+TIkbodTURERERERArfpRMwuz3EroVOf4fAMHCqYHUq\nySfGbytfijpfX18zOjo611hsbCwuLi4WJRLQdyAiIiIiIlIiHVkJq1+AUmWz9yuqpztfiivDMGJM\n0/S9ddxm9jASERERERERkQKWmZ69V9GeGVCrGQQugAo1rE4lBUCFkYiIiIiIiIjc37Xz2U9BO70L\nmo2GzlPAoZTVqaSAqDASERERERERkXs7tTt7c+ub16DPZ+DR3+pEUsBUGImIiIiIiIjInZkm7PkP\nbHwLKtWGoEh4zNXqVFIIVBiJiIiIiIiIyO1uJsOaF+HoSmj4DPT+DzhVtDqVFBIVRiIiIiIiIiKS\n26XvYGkQXP4OOrwNrV8COzurU0kh0redR4Zh8Morr+S8fu+995g8efI9z4mKimLXrl05r0NDQ1m+\nfHmectStW5fLly/naY7fODs758s8IiIiIiIiUgwdWw2zn4Lrl7JvQfN7WWVRCaRvPI9Kly7NypUr\n/1BZc2thlBemaZKVlZUvc4mIiIiIiEgJlpkBG96CpcFQrRGM3gZP+FudSiyiwiiPHBwcePbZZ/nw\nww9ve+/SpUv07duXpk2b0rRpU3bu3El8fDwzZ87kww8/xMvLi+3btwOwbds2WrVqxRNPPJFrtdG0\nadNo2rQpHh4evP322wDEx8fTsGFDgoODcXNz48yZM7k+t1evXvj4+ODq6sqsWbNyxp2dnXnzzTfx\n9PSkRYsWXLhwAYAff/yRli1b4u7uzqRJk3KOP3fuHG3btsXLyws3N7ecrCIiIiIiImJjki/Cwl6w\nazo0HQnDvoSKtaxOJRaymT2Mti/9jstnkvN1zqqPO+MX2OC+x/35z3/Gw8OD8ePH5xofN24cf/nL\nX2jTpg2nT5+mS5cuxMbGMmbMGJydnXn11VcBmDNnDufOnWPHjh0cP36cgIAA+vXrx4YNG4iLi2Pf\nvn2YpklAQADbtm2jdu3axMXFsWDBAlq0aHFbnrlz51K5cmVSUlJo2rQpffv2pUqVKly/fp0WLVow\ndepUxo8fz+zZs5k0aRLjxo3jueeeIzg4mBkzZuTM8/nnn9OlSxfefPNNMjMzuXHjRh5/oyIiIiIi\nIlLknNmXvaooJRF6/xc8B1qdSIoAmymMrFShQgWCg4OZPn06ZcqUyRnftGkTx44dy3n9yy+/kJx8\n51KrV69e2NnZ0bhx45yVPxs2bGDDhg14e3sDkJycTFxcHLVr16ZOnTp3LIsApk+fTmRkJABnzpwh\nLi6OKlWqUKpUKbp37w6Aj48PGzduBGDnzp2sWLECgKCgIF5//XUAmjZtyvDhw0lPT6dXr154eXk9\n9O9IREREREREihjThH2z4es3oGJNGLkRqrtbnUqKCJspjB5kJVBBeumll2jSpAnDhg3LGcvKymLP\nnj04OTnd9/zSpUvn/GyaZs6fEydOZPTo0bmOjY+Pp1y5cnecJyoqik2bNrF7927Kli2Lv78/qamp\nADg6OmIYBgD29vZkZGTknPfb+O+1bduWbdu2sW7dOkJDQ3n55ZcJDg6+77WIiIiIiIhIEZd2Hda+\nBIeXQoOu0HsmlHnE6lRShGgPo3xSuXJlAgMDmTNnTs5Y586d+eSTT3JeHzx4EIDy5ctz7dq1+87Z\npUsX5s6dm7MqKSEhgYsXL97znKSkJB555BHKli3L8ePH2bNnz30/p3Xr1ixevBiA8PDwnPFTp07x\n2GOPMWrUKEaOHMn+/fvvO5eIiIiIiIgUcVdOwmcd4fAyaD8JBkaoLJLbqDDKR6+88kqup6VNnz6d\n6OhoPDw8aNy4MTNnzgSgR48eREZG5tr0+k46d+7M4MGDczak7tev332Lpq5du5KRkYGLiwsTJky4\n621rv/fxxx8zY8YM3N3dSUhIyBmPiorC09MTb29vlixZwrhx4+47l4iIiIiIiBRhx9fBLH+4dh6G\nroC2r4GdqgG5nfHb7U9Fna+vrxkdHZ1rLDY2FhcXF4sSCeg7EBERERERKRYyM2DrFNjxIdTwhsAw\nqFTb6lRSBBiGEWOapu+t4zazh5GIiIiIiIiI3MH1y7B8OPz4DfiEQtd/geP999qVO/tt4c2d9gK2\nJSqMRERERERERGzV2WhYGpxdGvWcAd5DrU5UrJmmmfPE8U6dOtl0aaQbFUVERERERERsjWnC/+bA\n3K5gZw8jNqgsygdRUVHs2rWL9PR0q6MUOK0wEhEREREREbElaTdg3ctwKAL+rxP0mQVlK1udqtjb\nsWMH33zzDd7e3jz99NM2vboIVBiJiIiIiIiI2I6rP8CSILhwFPwnQtvxegpaPtizZw+bNm3C3d2d\nHj16YFcCfqcqjERERERERERswYmvYOWzYBgwZBk82cnqRDYhJiaGr776ChcXF3r16lUiyiLQHkZ5\ndv78eQYOHEj9+vXx8fGhW7duzJo1i+7du9/x+JEjR3Ls2LFCTikiIiIiIiI2KysTtkyBiAHwSB0Y\n/Y3Konxy6NAh1q5dy5NPPknfvn2xt7e3OlKh0QqjPDBNk969exMSEsLixYuB7H+Z1qxZc9dzPvvs\ns8KKJyIiIiIiIrbu+hVYORJObsne1Lrbe+BYxupUNuHo0aOsWrWKevXqERgYiINDyapQtMIoD7Zu\n3YqjoyNjxozJGfP09MTPz4/k5GT69etHo0aNGDJkCKZpAuDv7090dDQAzs7OvPnmm3h6etKiRQsu\nXLgAwNq1a2nevDne3t507NgxZ1xEREREREQkR8J+mNUO4ndAj+nQc4bKonxy4sQJVqxYweOPP86g\nQYNwdHS0OlKhs5l6bOv8WVw89UO+zvlonSd4KvTZu75/5MgRfHx87vjegQMHOHr0KDVq1KB169bs\n3LmTNm3a5Drm+vXrtGjRgqlTpzJ+/Hhmz57NpEmTaNOmDXv27MEwDD777DP+/e9/8/777+frtYmI\niIiIiEgxZZqwfwF8+Ro4PwbDv4aaTaxOZTNOnjzJ0qVLqV69OoMHD6ZUqVJWR7KEzRRGRU2zZs2o\nVasWAF5eXsTHx99WGJUqVSpnryMfHx82btwIwNmzZxkwYADnzp0jLS2NevXqFW54ERERERERKZrS\nU+DLV+HAIqjfHvp8BuWqWJ3KZsTHxxMREUHVqlUZOnQoTk5OVkeyjM0URvdaCVRQXF1dWb58+R3f\nK126dM7P9vb2ZGRk3HaMo6MjhmHcdsyLL77Iyy+/TEBAAFFRUUyePDn/w4uIiIiIiEjx8nM8LAmC\n899C29fAfyLYlZxNmAva2bNn+fzzz6lUqRJBQUGULVvW6kiW0h5GedC+fXtu3rzJrFmzcsa+/fZb\ntm/fnqd5k5KSqFmzJgALFizI01wiIiIiIiJiA+I2wn/bwc+nYNASaD9JZVE+OnfuHIsWLaJcuXIE\nBwfj7OxsdSTLqTDKA8MwiIyMZNOmTdSvXx9XV1cmTpxI9erV8zTv5MmT6d+/Pz4+PlStWjWf0oqI\niIiIiEixk5UFUe9CeH+o+DiMjoKGXa1OZVMuXrxIWFgYpUuXJiQkhAoVKlgdqUgwfnt6V1Hn6+tr\n/vZ0sd/Exsbi4uJiUSIBfQciIiIiIiIF5sZVWPksfL8RPAfBMx9AqZJ9m1R+u3z5MvPmzcMwDIYP\nH07lypWtjlToDMOIMU3T99Zxm9nDSERERERERMRmnDsES4bCL+eyiyLf4fDrHriSP37++WfCwsIw\nTZPQ0NASWRbdiwojEREREX+t3pQAACAASURBVBERkaLkwCL44mUoVxWGfwW1blv8IXmUlJTEggUL\nSEtLIzQ0lGrVqlkdqchRYSQiIiIiIiJSFKSnwvrxsH8B1GsH/eZml0aSr5KTkwkLCyMlJYXg4OA8\n70Nsq1QYiYiIiIiIiFgt8TQsDYafDkCbl/UUtAJy/fp1wsLC+OWXXwgKCsp5QrncToWRiIiIiIiI\niJW+3wwrRkBWJgz8HBo9Y3Uim5SSksKiRYu4evUqgwcPpnbt2lZHKtJUGImIiIiIiIhYISsLtr8P\nW6fCoy4wYBFUqW91Kpt08+ZNwsPDuXDhAoMGDeKJJ56wOlKRZ2d1gOLO2dn5D59Tt25dLl++bNnn\ni4iIiIiIiMVSEmHxINg6Bdz7w8hNKosKSFpaGp9//jkJCQn079+fJ5980upIxYJWGBWAjIwMHBz0\nqxUREREREZE7OH8YlgyFpLPQ7T1oOhIMw+pUNikjI4MlS5Zw6tQp+vbti4uLi9WRig2tMMonUVFR\n+Pn5ERAQQOPGjQFYtGgRzZo1w8vLi9GjR5OZmXnbeb169cLHxwdXV1dmzZqVM+7s7Mybb76Jp6cn\nLVq04MKFCwD8+OOPtGzZEnd3dyZNmlQ4FyciIiIiIiL542AEfNYRMm7CsPXQbJTKogKSmZnJsmXL\nOHnyJD179sTd3d3qSMWKzSyDSVx7krSfrufrnKVqlKNSjwdfErh//36OHDlCvXr1iI2NZcmSJezc\nuRNHR0eef/55wsPDCQ4OznXO3LlzqVy5MikpKTRt2pS+fftSpUoVrl+/TosWLZg6dSrjx49n9uzZ\nTJo0iXHjxvHcc88RHBzMjBkz8vV6RUREREREpIBk3ISvJkL0HKjrB/3mgvOjVqeyWZmZmaxcuZIT\nJ07QrVs3vL29rY5U7GiFUT5q1qwZ9erVA2Dz5s3ExMTQtGlTvLy82Lx5Mz/88MNt50yfPj1nFdGZ\nM2eIi4sDoFSpUnTv3h0AHx8f4uPjAdi5cyeDBg0CICgoqBCuSkRERERERPIk6SzMezq7LGo1FoJW\nqSwqQFlZWaxZs4ajR4/SuXNnmjVrZnWkYslmVhj9kZVABaVcuXI5P5umSUhICP/85z/venxUVBSb\nNm1i9+7dlC1bFn9/f1JTUwFwdHTE+HVZor29PRkZGTnnGVquKCIiIiIiUjz8EAXLh0NGGgSGQeOe\nVieyaaZpsm7dOg4dOsRTTz1Fq1atrI5UbGmFUQHp0KEDy5cv5+LFiwBcvXqVU6dO5TomKSmJRx55\nhLJly3L8+HH27Nlz33lbt27N4sWLAQgPD8//4CIiIiIiIpJ3pgnbP4CFvaFcNXh2q8qiAmaaJl9/\n/TUxMTG0adOGtm3bWh2pWFNhVEAaN27MlClT6Ny5Mx4eHnTq1Ilz587lOqZr165kZGTg4uLChAkT\naNGixX3n/fjjj5kxYwbu7u4kJCQUVHwRERERERF5WKlJsHgIbP4bNO4FIzdDVT3KvaBt2bKFPXv2\n0KJFCzp06KC7c/LIME3T6gwPxNfX14yOjs41Fhsbq0fiWUzfgYiIiIiIyO9cOApLhkLiaeg8BZqP\n0VPQCsE333zD1q1b8fHxoXv37iqL/gDDMGJM0/S9ddxm9jASERERERERsdS3S2HtOChdHkK+gDot\nrU5UIuzatYutW7fi6enJM888o7Ion6gwEhEREREREcmLjDTY8CbsmwW1W0H/eVC+utWpSoR9+/ax\nYcMGXF1dCQgIwM5OO+/kFxVGIiIiIiIiIg/rl59gaQic3Qct/gyd/gb2jlanKhEOHDjAl19+ScOG\nDenTpw/29vZWR7IpKoxEREREREREHsaP22H5MEi7Af3mgVsfqxOVGIcPH2b16tXUr1+f/v37qywq\nACqMRERERERERP4I04Rd02HT36BK/ez9ih5tZHWqEiM2NpaVK1dSp04dBgwYgIODqo2CoN+qiIiI\niIiIyINK/QVWPw+xa6FxT+g5I3uTaykUcXFxLFu2jJo1azJ48GBKlSpldSSbpd2g8sje3h4vLy/c\n3Nzo378/N27cuOfxzs7O+fK58fHxuLm55ctcIiIiIiIi8gAuHofZ7eH4l9B5CvRfoLKoEP3www8s\nWbKExx57jCFDhlC6dGmrI9k0FUZ5VKZMGQ4ePMiRI0coVaoUM2fOtDqSiIiIiIiI5LcjK7LLotRE\nCFkDrV4EPb690Jw+fZqIiAgqV65MUFAQZcqUsTqSzVNhlI/8/Pz4/vvvAfjggw9wc3PDzc2Njz76\n6LZjk5OT6dChA02aNMHd3Z3Vq1cD2SuHXFxcGDVqFK6urnTu3JmUlBQAYmJi8PT0xNPTkxkzZhTe\nhYmIiIiIiJRUmenw1URYPhyqu8Ho7VC3jdWpSpSEhATCw8OpUKECwcHBlC1b1upIJYLN7GG0fv16\nzp8/n69zVq9enaeffvqBjs3IyGD9+vV07dqVmJgY5s2bx969ezFNk+bNm9OuXTu8vb1zjndyciIy\nMpIKFSpw+fJlWrRoQUBAAJB9T2ZERASzZ88mMDCQFStWMHToUIYNG8ann35K27Ztee211/L1WkVE\nREREROQW187DslA4vRuaj4FOfwcH7ZlTmM6fP8/ChQspU6YMwcHB+bbNi9yfVhjlUUpKCl5eXvj6\n+lK7dm1GjBjBjh076N27N+XKlcPZ2Zk+ffqwffv2XOeZpskbb7yBh4cHHTt2JCEhgQsXLgBQr149\nvLy8APDx8SE+Pp7ExEQSExNp27YtAEFBQYV7oSIiIiIiIiXJqV3w37Zw7hD0nQNP/0tlUSG7dOkS\nYWFhlCpVipCQECpWrGh1pBLFZlYYPehKoPz22x5Gf1R4eDiXLl0iJiYGR0dH6tatS2pqKkCujbvs\n7e1zbkkTERERERGRAmaasOf/wYa34JG6ELQKHmtsdaoS5+rVq4SFhWFnZ0dwcDCPPPKI1ZFKHK0w\nKgB+fn6sWrWKGzducP36dSIjI/Hz88t1TFJSEo8++iiOjo5s3bqVU6dO3XPOSpUqUalSJXbs2AFk\nF04iIiIiIiKSj24mw/Jh8PUb0PBpeHaryiILJCYmsmDBAjIyMggODqZq1apWRyqRbGaFUVHSpEkT\nQkNDadasGQAjR47MtX8RwJAhQ+jRowfu7u74+vrSqFGj+847b948hg8fjmEYdO7cuUCyi4iIiIiI\nlEiXvoMlQ+FKHHT8G7Qep6egWeCXX35hwYIFpKamEhoayqOPPmp1pBLLME3T6gwPxNfX14yOjs41\nFhsbi4uLi0WJBPQdiIiIiIiIDTi2GlY9Dw5O0G8uPNHO6kQlUnJyMvPnz+eXX34hKCiIxx9/3OpI\nJYJhGDGmafreOq4VRiIiIiIiIlIyZWbA5smw6xOo6QuBYVCxptWpSqQbN26wcOFCEhMTGTp0qMqi\nIkCFkYiIiIiIiJQ8yRdh2TA4tQOajoQu/wCH0vc/T/JdamoqixYt4vLlywwePJi6detaHUlQYSQi\nIiIiIiIlzem9sCwEUhKh93/Bc6DViUqstLQ0wsPDOX/+PAMGDKB+/fpWR5JfqTASERERERGRksE0\nYd+s7KegVXwcRm6C6m5Wpyqx0tPTiYiI4OzZs/Tr14+GDRtaHUl+R4WRiIiIiIiI2L6067BmLBxZ\nDg2eht4zoUwlq1OVWBkZGSxdupQff/yR3r174+rqanUkuYUKIxEREREREbFtl7+HpUFwMRbavwVt\nXgY7O6tTlViZmZksX76cuLg4evTogaenp9WR5A70NySPpk6diqurKx4eHnh5ebF37958m9vZ2Tnf\n5hIRERERESmRYtfC7Kfg2nkIWgltX1VZZKGsrCxWrVrF8ePH6dq1Kz4+PlZHkrvQCqM82L17N198\n8QX79++ndOnSXL58mbS0NKtjiYiIiIiISGYGbPk77PwIanhD4EKopEe1WykrK4u1a9dy+PBhOnbs\nSIsWLayOJPegWjUPzp07R9WqVSldOvvRi1WrViUhIYE+ffoAsHr1asqUKUNaWhqpqak88cQTAJw8\neTKnSfXz8+P48eMA/Pjjj7Rs2RJ3d3cmTZqU67OmTZtG06ZN8fDw4O233wYgPj4eFxcXRo0ahaur\nK507dyYlJaWwLl9ERERERKRoSr4Ei3pnl0U+w2D41yqLLGaaJuvXr+fAgQO0a9eONm3aWB1J7sNm\nVhh9993fuZYcm69zlnd2oUGDt+76fufOnXnnnXdo0KABHTt2ZMCAAbRu3ZqDBw8CsH37dtzc3Pjf\n//5HRkYGzZs3B+DZZ59l5syZPPnkk+zdu5fnn3+eLVu2MG7cOJ577jmCg4OZMWNGzuds2LCBuLg4\n9u3bh2maBAQEsG3bNmrXrk1cXBwRERHMnj2bwMBAVqxYwdChQ/P19yAiIiIiIlJsnPkfLA2GlKvQ\n8/+B9xCrE5V4pmmyceNG/ve//9GqVSv8/f2tjiQPwGYKIys4OzsTExPD9u3b2bp1KwMGDODdd9+l\nfv36xMbGsm/fPl5++WW2bdtGZmYmfn5+JCcns2vXLvr3758zz82bNwHYuXMnK1asACAoKIjXX38d\nyC6MNmzYgLe3NwDJycnExcVRu3Zt6tWrh5eXFwA+Pj7Ex8cX4m9ARERERESkiDBN+N9n8NVEqFAD\nRmyAP2kz5aIgKiqKXbt20axZMzp16oRhGFZHkgdgM4XRvVYCFSR7e3v8/f3x9/fH3d2dBQsW0LZt\nW9avX4+joyMdO3YkNDSUzMxMpk2bRlZWFpUqVcpZhXSrO/3FMU2TiRMnMnr06Fzj8fHxObfD/ZZF\nt6SJiIiIiEiJk3YDvvgLfLsYnuwMfWZBmUesTiVk33nzzTff4O3tTdeuXVUWFSPawygPTpw4QVxc\nXM7rgwcPUqdOHfz8/Pjoo49o2bIl1apV48qVK5w4cQI3NzcqVKhAvXr1WLZsGZBdBh06dAiA1q1b\ns3jxYgDCw8Nz5u3SpQtz584lOTkZgISEBC5evFhYlykiIiIiIlJ0Xf0B5nSCb5eA/xswaInKoiJi\nz549bN68GXd3d3r06IGdnk5XrNjMCiMrJCcn8+KLL5KYmIiDgwP/93//x6xZsyhXrhwXLlygbdu2\nAHh4eHD+/PmcJjU8PJznnnuOKVOmkJ6ezsCBA/H09OTjjz9m8ODB/Otf/6Jnz545n9O5c2diY2Np\n2bIlkH0r3KJFi7C3ty/8ixYRERERESkqTqyHlaPBMGDIcniyo9WJ5FfR0dF89dVXuLi40KtXL5VF\nxZBhmqbVGR6Ir6+vGR0dnWssNjYWFxcXixIJ6DsQERERERELZGXC1n/A9vey9ykKXAiP1LE6lfzq\n0KFDREZG8uSTTzJgwAAcHLRWpSgzDCPGNE3fW8f1rYmIiIiIiEjxcf0KrBgBP2wF7yDo9h44Olmd\nSn519OhRVq1aRb169QgMDFRZVIzpmxMREREREZHiISEGloZA8kXoMR18QqxOJL9z4sQJVqxYweOP\nP86gQYNwdHS0OpLkgQojERERERERKdpME2Lmw/rx4FwdRnwNNbytTiW/c/LkSZYuXUr16tUZPHgw\npUqVsjqS5JEKIxERERERESm60lNg3atwcBHU7wB9P4Oyla1OJb8THx9PREQEVatWZejQoTg56RZB\nW6DCSERERERERIqmqz/C0mA4/y20ez37Hzs9LbooOXPmDJ9//jmVKlUiODiYsmXLWh1J8ollhZFh\nGH8BRgImcBgYZppmqlV5REREREREpAj5bgOsHAWYMHgpNOhidSK5xU8//cSiRYtwdnYmJCSEcuXK\nWR1J8pGdFR9qGEZNYCzga5qmG2APDLQiS15NnToVV1dXPDw88PLyYu/evQ81T1RUFLt27cp5HRoa\nyvLly+97nrOzc87PX375JQ0aNODUqVMPlUFERERERMRyWZmw9R/weSBUfBye/UZlURF04cIFFi5c\niJOTE8HBwZQvX97qSJLPrLwlzQEoYxhGOlAW+MnCLA9l9+7dfPHFF+zfv5/SpUtz+fJl0tLSHmqu\nqKgonJ2dadWq1UOdv3nzZsaOHcvXX39NnTp1HuicjIwMPeJQRERERESKjhtXs1cVfb8JPAdD9w/A\nsYzVqeQWly9fJiwsDAcHB0JCQqhUqZLVkaQAWLLCyDTNBOA94DRwDkgyTXPDrccZhvGsYRjRhmFE\nX7p0qbBj3te5c+eoWrUqpUuXBqBq1arUqFEDyC5wvL29cXd3Z/jw4dy8eROAunXrcvnyZQCio6Px\n9/cnPj6emTNn8uGHH+Ll5cX27dsB2LZtG61ateKJJ56452qjbdu2MWrUKL744gvq168PZG861r59\nezw8POjQoQOnT58GslcujRkzhubNmzN+/HiuX7/O8OHDadasGd7e3qxevTrnfD8/P5o0aUKTJk1y\nrX4SERERERHJdz8dhFnt4Mdt0P1D6PX/VBYVQT///DNhYWGYpklwcDCVK2sDcltlyfISwzAeAXoC\n9YBEYJlhGENN01z0++NM05wFzALw9fU17zXnW3FnOZKckq853ZzL8Pcna931/c6dO/POO+/QoEED\nOnbsyIABA2jXrh2pqamEhoayefNmGjRoQHBwMP/5z3946aWX7jhP3bp1GTNmDM7Ozrz66qsAzJkz\nh3PnzrFjxw6OHz9OQEAA/fr1u+3cmzdv0qtXL6KiomjUqFHO+IsvvkhISAghISHMnTuXsWPHsmrV\nKgDOnj3Lrl27sLe354033qB9+/bMnTuXxMREmjVrRseOHXn00UfZuHEjTk5OxMXFMWjQIKKjo/Py\n6xQREREREbmz/Qth3StQrhoM+wpq+VidSO4gKSmJBQsWkJ6eTkhICNWqVbM6khQgS1YYAR2BH03T\nvGSaZjqwEni4e7Es5OzsTExMDLNmzaJatWoMGDCA+fPnc+LECerVq0eDBg0ACAkJYdu2bX94/l69\nemFnZ0fjxo25cOHCHY9xdHSkVatWzJkzJ9f47t27GTx4MABBQUHs2LEj573+/ftjb5/9ZIENGzbw\n7rvv4uXlhb+/P6mpqZw+fZr09HRGjRqFu7s7/fv359ixY384v4iIiIiIyD2lp8KaF2HNC1CnJYze\nprKoiLp27RphYWGkpKQwdOhQqlevbnUkKWBWbWBzGmhhGEZZIAXoAORp+cq9VgIVJHt7e/z9/fH3\n98fd3Z0FCxbg7e191+MdHBzIysoCIDX13g+F++1WNwDTvPMCKzs7O5YuXUqHDh34xz/+wRtvvHHf\nzL/fud40TVasWEHDhg1zHTN58mQee+wxDh06RFZWFk5OTvedV0RERERE5IElnoYlQXDuIPi9Ak+9\nCXb2VqeSO7h+/TphYWH88ssvBAUFUbNmTasjSSGwag+jvcByYD9w+Nccs6zIkhcnTpwgLi4u5/XB\ngwepU6cODRs2JD4+nu+//x6AhQsX0q5dOyD79rOYmBgAVqxYkXNu+fLluXbt2kPlKFu2LOvWrSM8\nPDxnpVGrVq1YvHgxAOHh4fj5+d3x3C5duvDJJ5/kFFIHDhwAspca/ulPf8LOzo6FCxeSmZn5UNlE\nRERERERu8/0m+G9buPoDDIyADn9VWVREpaSksHDhQn7++WcGDx5M7dq1rY4khcSqW9IwTfNt0zQb\nmabpZppmkGmaN63K8rCSk5MJCQmhcePGeHh4cOzYMSZPnoyTkxPz5s2jf//+uLu7Y2dnx5gxYwB4\n++23GTduHL6+vjm3hQH06NGDyMjIXJte/xGVK1fmq6++YsqUKaxZs4ZPPvmEefPm4eHhwcKFC/n4\n44/veN5bb71Feno6Hh4euLq68tZbbwHw/PPPs2DBAjw9PTl+/HiuVUkiIiIiIiIPJSsLvvk3LOoH\n5WvAs1HQqJvVqeQubt68SXh4OBcvXmTAgAHUq1fP6khSiIy73epU1Pj6+pq3brocGxuLi4uLRYkE\n9B2IiIiIiMgDSvkZVo6GuK/BY0D2k9BK6X9MF1VpaWmEh4dz+vRpAgMD9d99NswwjBjTNH1vHbdq\nDyMREREREREpKc59C0uDICkBur0HTUeCYVidSu4iPT2dxYsXc/r0afr06aOyqIRSYSQiIiIiIiIF\n5+Dn8MVfoExlGPYlPN7M6kRyD5mZmSxbtowffviBnj174u7ubnUksUixL4xM08RQM22J4nI7o4iI\niIiIWCDjJnw1AaLnQl0/6DcPnKtZnUruITMzkxUrVvDdd9/xzDPP3PMJ4GL7inVh5OTkxJUrV6hS\npYpKo0JmmiZXrlzBycnJ6igiIiIiIlLUJJ2FpcGQEAOtx0H7v4J9sf7PT5uXlZXF6tWrOXbsGF26\ndKFp06ZWRxKLFeu/sbVq1eLs2bNcunTJ6iglkpOTE7Vq1bI6hoiIiIiIFCUnt8KKEZCRBoELoXGA\n1YnkPkzTZN26dXz77be0b9+eli1bWh1JioBiXRg5OjrqsX4iIiIiIiJFQVYW7PwQtkyBqg1hwEKo\n+qTVqeQ+TNPkq6++IiYmBj8/P9q2bWt1JCkiinVhJCIiIiIiIkVASiKseg5OfAlufaHHdCjtbHUq\nuQ/TNNm8eTN79+6lRYsWtG/f3upIUoSoMBIREREREZGHd+EoLBkKiaeh67+g+WjQHrPFwrZt29ix\nYwe+vr506dJFewNLLiqMRERERERE5OF8uxTWjAWnihC6Dmq3sDqRPKCdO3eydetWPD096datm8oi\nuY0KIxEREREREfljMtJgw5uwbxbUaQ395kH5x6xOJQ9o3759bNy4EVdXV3r27ImdnZ3VkaQIUmEk\nIiIiIiIiDy4pAZaFwtl90PIF6DgZ7B0tDiUPav/+/Xz55Zc0bNiQPn36qCySu1JhJCIiIiIiIg/m\nx22wfDikp0D/+eDa2+pE8gccPnyY/8/efYdHXeVvH39Pek8mobcUepEuVRQbIqIiSCcJ2FB37br9\nt8Vdt7jqWlexLKTQm2JBEEVRCEjvPSEEQiAkk95nzvNHWB931wJkkm/K/bourgsmZe60Id97zvmc\nVatW0bFjRyZOnIinp6fVkaQeU2EkIiIiIiIiP8wY2PgSfPoHiOhUPa+oeVerU8klOHjwICtWrCAq\nKorJkyfj5aU6QH6YvkNERERERETk+5UVwHsPwsH3occ4uP1V8A22OpVcgiNHjrB06VLatm3L1KlT\n8fHxsTqSNAAqjEREREREROS7nTsIi2dAbhqMegaG/gR0mlaDkpqayuLFi2nZsiXTp0/H19fX6kjS\nQKgwEhERERERkf+1dxmsegh8giD+fYgabnUiuUQnT55k4cKFREREEBsbi7+/v9WRpAFRYSQiIiIi\nIiL/n7MS1v4fbHkd2g+pHm4d0trqVHKJTp8+TXJyMiEhIcTFxREQEGB1JGlgVBiJiIiIiIhItcIs\nWBIPGZth8AMw6o/g6W11KrlEWVlZJCUlERgYSHx8PEFBQVZHkgZIhZGIiIiIiIjAiY2wdCZUFMGE\nd+CKO61OJJchOzubxMREfHx8iIuLIyQkxOpI0kCpMBIREREREWnKjIGU1+CT30J4NMSvghbdrU4l\nlyEnJ4eEhAQ8PDyIj4/HbrdbHUkaMBVGIiIiIiIiTVV5Ibz3UzjwLnQbC+NeBz+tSGmI8vLySExM\nxOVyMXPmTCIiIqyOJA2cCiMREREREZGmKPswLJ4BOcfgxqdh2MNgs1mdSi5DQUEBCQkJlJeXEx8f\nT4sWLayOJI2ACiMREREREZGmZv+78N5PwMsPYt+FmGusTiSXqaioiMTERIqLi4mLi6N1a51oJ+6h\nwkhERERERKSpcFbBut9ByqvQ7kqYmAChba1OJZeppKSEpKQk8vLyiI2NpV27dlZHkkZEhZGIiIiI\niEhTUHgWlt0F6V/BlffCTX8GLx+rU8llKisrIzk5mfPnzzNt2jQiIyOtjiSNjAojERERERGRxu7k\nZlgSD2X5cMeb0Gey1YmkBsrLy5k/fz5ZWVlMnjyZjh07Wh1JGiEVRiIiIiIiIo2VMbBlDqz9NYR1\ngBnLoVUvq1NJDVRWVrJo0SJOnTrFxIkT6dq1q9WRpJFSYSQiIiIiItIYlRfB+4/AvmXQdQyMex38\nw6xOJTVQVVXF4sWLSUtL44477qBHjx5WR5JGTIWRiIiIiIhIY3P+GCyeAecPw/W/heGPgYeH1amk\nBpxOJ8uWLePYsWPceuut9OnTx+pI0sipMBIREREREWlMDr4PKx+oHmg9YwV0vNbqRFJDLpeLlStX\ncujQIW6++WYGDBhgdSRpAlQYiYiIiIiINAbOKvjsj7DxRWjTHyYlQlh7q1NJDblcLlatWsW+ffu4\n4YYbGDx4sNWRpIlQYSQiIiIiItLQFWXDsllw4ksYeBeM/it4+VqdSmrIGMPq1avZtWsXI0eO5Kqr\nrrI6kjQhKoxEREREREQasoytsCQOSnOrB1v3nWZ1InEDYwxr165l69atDB8+nGuuucbqSNLEqDAS\nERERERFpiIyBrW/Dx7+EkDZw9yfQurfVqcRN1q9fT0pKCoMGDeKGG27AZrNZHUmaGBVGIiIiIiIi\nDU1FCXzwKOxZDJ1vgvFzwN9udSpxky+//JINGzbQv39/Ro8erbJILKHCSEREREREpCHJOQ6LY+Hc\nAbj2NzDiCfDwsDqVuElKSgqffvopV1xxBWPHjsVDX1uxiAojERERERGRhuLQR7Dy/uqCaMYy6HSD\n1YnEjbZt28aaNWvo3r0748aNU1kkllJhJCIiIiIiUt+5nLD+GfjyeWjdFyYlgj3S6lTiRrt27eKD\nDz6gc+fOTJgwAU9PT6sjSROnwkhERERERKQ+K86B5XdB6ufQPw5u/jt4+1mdStxo3759vPfee8TE\nxDBp0iS8vHSpLtbTd6GIiIiIiEh9dWo7LImD4my47ZXqwkgalUOHDrFixQrat2/PlClT8Pb2tjqS\nCKDCSEREREREpP4xBrbPhdU/h+BWcPcaaNPP6lTiZseOHWPp0qW0bt2aadOm4ePjY3UkkW+oMBIR\nEREREalPKkvhg8dh4aK35AAAIABJREFU94Lqodbj34KAcKtTiZudOHGCRYsW0bx5c2bMmIGfn7YZ\nSv2iwkhERERERKS+yE2DJbGQtQ+u+QVc8zPw0PDjxiYjI4P58+djt9uJjY3F39/f6kgi/0OFkYiI\niIiISH1wZA2suLf679OWQJdR1uaRWpGZmUlycjLBwcHExcURGBhodSSR76TCSERERERExEouJ3zx\nt+o/ra6ASUkQHm11KqkFZ8+eJSkpCT8/P+Li4ggODrY6ksj3UmEkIiIiIiJilZJcWH4PHP8U+k6H\nW54Hb21PaozOnz9PYmIiXl5exMfHExYWZnUkkR+kwkhERERERMQKmTthcRwUZcHYF2HATLDZrE4l\ntSA3N5eEhAQA4uLiCA/XEHOp/1QYiYiIiIiI1LUdifDhkxDYHO76GNoOsDqR1JL8/HwSExOpqqpi\n5syZNG/e3OpIIhdFhZGIiIiIiEhdqSyDj56EnUkQcy1MeAcCI6xOJbWksLCQhIQESktLiY+Pp2XL\nllZHErloKoxERERERETqgiMdlsTBmV0w4km49lfg4Wl1KqklxcXFJCYmUlhYSGxsLG3atLE6ksgl\nUWEkIiIiIiJS246ugxX3gMsFUxdB15utTiS1qLS0lKSkJBwOB9OnT6dDhw5WRxK5ZCqMRERERERE\naovLBV8+B+v/DC17wqREiOhodSqpReXl5SQnJ5Odnc3UqVOJjo62OpLIZVFhJCIiIiIiUhtKHbBi\nNhxdA70nV5+E5hNgdSqpRRUVFSxYsIDMzEwmT55Mp06drI4kctlUGImIiIiIiLjbmd2wOBYKMmHM\nc3DlPWCzWZ1KalFlZSWLFi3i5MmTTJgwgW7dulkdSaRGVBiJiIiIiIi408758OHj4B8Os1ZD+yut\nTiS1rKqqiqVLl5Kamsq4cePo1auX1ZFEakyFkYiIiIiIiDtUlcPqn8P2uRB9NUz4FwQ1tzqV1DKn\n08mKFSs4cuQIt9xyC3379rU6kohbqDASERERERGpqbwMWBIHmTvgqsfg2t+Apy63GjuXy8V7773H\ngQMHuOmmm7jySq0mk8ZDj2AiIiIiIiI1cXw9LLsLXFUweT50H2t1IqkDxhg++OAD9uzZw3XXXcfQ\noUOtjiR1pMrpoqzKRZBv465UPKwOICIiIiIi0mDtWw7J4yGoJdy7XmVRE2GM4eOPP2bHjh2MGDGC\nq6++2upIUkeyC8uZ8c4WfrpgB8YYq+PUKhVGIiIiIiIil+PcIXjvp9B+MNz7KTTTEepNgTGGdevW\nsWXLFoYOHcp1111ndSSpI9vTcxn7ypfsysjjtj5tsDXykw8b9/opERERERGR2lBeCEtiwScI7pwL\nPoFWJ5I68sUXX7Bx40YGDhzIqFGjGn1pINUlYWJKOn/84ABtwvxZ8cAgerQJsTpWrVNhJCIiIiIi\ncimMgVUPQ84xiFsFIa2tTiR1ZOPGjXz++ef07duXMWPGqCxqAkoqqvjlir28tyuT67u14IVJfQkN\n8LY6Vp1QYSQiIiIiInIpvn4T9q+AG34P0SOsTiN15Ouvv+aTTz6hZ8+e3HbbbXh4aMJLY5d2vpj7\nk7Zz5FwhT47qwoMjO+Hh0XRKQhVGIiIiIiIiFyvja1jza+g6BoY/anUaqSM7duzgo48+omvXrowf\nP15lUROwdn8WTyzZjZenjYRZg7i6S3OrI9U5FUYiIiIiIiIXo/g8LJ0JoW1h3Oug7UhNwp49e1i1\nahWdOnVi4sSJeHp6Wh1JalGV08Xznxzh9c+P07tdKP+c3p929gCrY1lChZGIiIiIiMiPcTlh+d3V\npdE9n4B/mNWJpA4cOHCAlStXEhUVxeTJk/Hy0iV0Y5ZTVM7Di3ay8VgOUwd14He39sDPu+kWhPpu\nFxERERER+TGf/xVSP4fbXoHWfaxOI3XgyJEjLFu2jLZt2zJ16lS8vZvGoOOmaudJBw/O30FOcQXP\n3tmbSQPbWx3JciqMREREREREfsjRT2DDs9B3BvSPszqN1IHU1FQWL15My5YtmTFjBr6+vlZHklpi\njGH+lpP84f39tAzxY8UDw+jVNtTqWPWCCiMREREREZHvk3cSVtwLLa+AW56zOo3UgfT0dBYuXEhE\nRASxsbH4+flZHUlqSWmFk1+/u5cVO04zsmtzXpzcl7AAH6tj1RsqjERERERERL5LVTksiaueXzQp\nAbz9rU4ktezUqVPMnz+fkJAQ4uLiCAhomsOOm4L0nGJmJ23n8NlCHr2hMw9f1xkPDw2y/zYVRiIi\nIiIiIt/l419C5k6YPB8iOlqdRmrZmTNnSE5OJjAwkPj4eIKCgqyOJLXk04NneXTxLjxsNv4180qu\n7drC6kj1kgojERERERGR/7ZnCWx7B4Y9DN3HWp1Gatm5c+dISkrCx8eHuLg4QkJCrI4ktcDpMry4\n7givfHaMXm1DeH36ANqHaxXZ91FhJCIiIiIi8m3nDsL7j0DkcLj+d1ankVqWk5NDYmIiHh4exMfH\nY7fbrY4ktSC3uIJHFu3ky6PnmTSwHU/f3gs/b0+rY9VrKoxERERERET+rbwQFseCbzDc+S/w1CVT\nY5aXl0dCQgIul4uZM2cSERFhdSSpBXtO5fFA8g6yC8v56/grmDKog9WRGgQ9+omIiIiIiAAYA+/9\nFHJTIf59CG5ldSKpRQUFBSQkJFBRUUF8fDwtWmiOTWNjjGHR1gx+995+mgf7suyBofRuF2Z1rAZD\nhZGIiIiIiAjAljfgwLtw49MQNdzqNFKLioqKSEhIoLi4mLi4OFq3bm11JHGzskonv31vH0u2nWJE\n52a8NKUf4YE+VsdqUFQYiYiIiIiInNwMa38D3cZWD7qWRqukpITExEQKCgqYMWMG7dq1szqSuFlG\nbgn3J29nf2YBD13XiUdv6IKnh83qWA2OCiMREREREWnairJh6UwIbQ+3vwY2XVg2VmVlZSQlJZGT\nk8O0adOIjIy0OpK42frD53h00S5cxvBO/ECu797S6kgNlgojERERERFpulxOWH43lDrgnnXgr/km\njVV5eTnz58/n7NmzTJkyhY4dO1odSdzI5TK89OlRXv7sKN1ahfDGjP5ERgRaHatBU2EkIiIiIiJN\n1/o/Q9oX1SuLWl1hdRqpJZWVlSxcuJBTp04xceJEunTpYnUkcaO8kgoeXbyLzw9nM75/W54ZdwX+\nPp5Wx2rwVBiJiIiIiEjTdGQNfPkc9IuFfjOsTiO1pKqqisWLF3PixAnGjx9Pjx49rI4kbrTvdD73\nJ2/nbEEZfxrXi+mDO2DTtlK3UGEkIiIiIiJNjyMdVtxXvapozN+tTiO1xOl0smzZMo4dO8Ztt91G\n7969rY4kbrRkWwa/eXcfEYE+LJk9lH4d7FZHalRUGImIiIiISNNSWQZL4sAYmJQE3v5WJ5Ja4HK5\nWLlyJYcOHeLmm2+mf//+VkcSNymrdPKH9/ez8OsMhneK4OUp/YgI8rU6VqOjwkhERERERJqWj38B\nZ3bBlIUQHm11GqkFLpeLVatWsW/fPm688UYGDx5sdSRxk1OOEh6cv4M9p/J5cGRHnhjVFU8PbUGr\nDSqMRERERESk6di9CLbPhaseg25jrE4jtcAYw0cffcSuXbsYOXIkw4cPtzqSuMmGI9k8vGgnTqfh\nzdgBjOrZyupIjZoKIxERERERaRrO7of3H4WoEXDtb6xOI7XAGMPatWvZtm0bw4cP55prrrE6kriB\ny2V4bf0xXlh3hC4tgnkjdgDRzQKtjtXoqTASEREREZHGr6wAFseCXyjc+S/w1KVQY7R+/XpSUlIY\nNGgQN9xwg07LagTySyt5fPEuPj10jnF92/Dn8VcQ4KOf37qgz7KIiIiIiDRuxsB7D4LjBMz8AIJa\nWJ1IasGGDRvYsGED/fv3Z/To0SqLGoEDmQXcn7ydzLxSnr69J7FDIvV1rUMqjEREREREpHFLeQ0O\nvg+j/gSRw6xOI7UgJSWFzz77jN69ezN27Fg8PDysjiQ1tHz7KX61ci9hAd4snj2UAZF2qyM1OSqM\nRERERESk8UpPgU9+C91vhaE/tTqN1IKtW7eyZs0aevTowe23366yqIErr3Lyxw8OkLz5JENiwnll\nan+aB/taHatJUmEkIiIiIiKNU9E5WDoT7JFw+2ugrSyNzq5du/jwww/p0qUL48ePx9PT0+pIUgOZ\neaU8MH8HuzPymH11DE/d1BUvTxWAVlFhJCIiIiIijY+zCpbdBWX5MGN59bBraVT27dvHe++9R0xM\nDBMnTsTLS5e3DdnGY+d5aOFOKqpcvD69Pzdf0drqSE2efqJERERERKTxWf8MnPgSxr0OrXpZnUbc\n7NChQ6xYsYL27dszZcoUvL29rY4kl8kYw+tfHOe5NYfp2DyIN2IH0LF5kNWxBBVGIiIiIiLS2Bxe\nDV+9AP3joe80q9OImx07doylS5fSunVrpk+fjo+Pj9WR5DIVlFXyxJLdfHLgLGN7t+ZvE3oT6Kua\nor7QV0JERERERBqP3DRYORta94Gbn7U6jbhZWloaixYtonnz5syYMQNfXw1DbqgOZRVwf9J2TjlK\n+e3YHswaHoVNc8bqFRVGIiIiIiLSOFSWwdL46r9PSgRvP2vziFtlZGSwYMEC7HY7sbGx+Pv7Wx1J\nLtO7O0/zyxV7CfLzYuF9Q7gyKtzqSPIdVBiJiIiIiEjjsPpncGY3TF0M9iir04gbZWZmkpycTHBw\nMHFxcQQGBlodSS5DRZWLZz48QEJKOoOiwnl1Wj9ahKjYra9UGImIiIiISMO3awHsSIART0DX0Van\nETc6e/YsSUlJ+Pn5ER8fT3BwsNWR5DJk5Zfx4Pzt7DiZx91XRfOLm7vh7elhdSz5ASqMRERERESk\nYcvaCx88BtFXw7W/tjqNuNH58+dJTEzEy8uL+Ph4QkNDrY4klyHleA4PLdxBSYWTV6f1Y2zvNlZH\nkougwkhERERERBqusnxYEgf+dpjwL/DwtDqRuElubi4JCQkAxMfHEx6uOTcNjTGGt75M5W8fHyYq\nIoCF9w6hc0utEGsoVBiJiIiIiEjDZAy8+yDknYSZH0JQc6sTiZvk5+eTmJhIVVUVM2fOpFmzZlZH\nkktUWFbJz5btYfW+LMZc0Ypn7+xDkK8qiIZEXy0REREREWmYNr0Chz6Am/4MHYZYnUbcpLCwkISE\nBEpLS4mPj6dly5ZWR5JLdPRsIbOTt5OeU8Kvx3TnnhHR2Gw2q2PJJVJhJCIiIiIiDc+JjbDu99Dj\ndhjyoNVpxE2Ki4tJTEyksLCQuLg42rTRrJuG5v3dmfx8+R4CfDyZf89ghsREWB1JLpMKIxERERER\naVgKz8KyWRAeDbe9Clq50CiUlpaSlJSEw+Fg+vTptG/f3upIcgkqnS7+8tEh/rUxjQGRdv45vT8t\nQ/ysjiU1oMJIREREREQaDmcVLLsLygogdiX4hVidSNygrKyM5ORksrOzmTp1KtHR0VZHkktwrqCM\nnyzYwdYTDmYOi+JXY7rj4+VhdSypIRVGIiIiIiLScHz2R0j/Cu6YAy17Wp1G3KCiooIFCxZw5swZ\nJk2aRKdOnayOJJfg67RcfrJgB0VlVbw0pS+3921rdSRxExVGIiIiIiLSMBz6CDa+CANmQZ8pVqcR\nN6isrGTRokVkZGQwYcIEunXrZnUkuUjGGN75Ko2/rD5Eh/AAku8eTNdWwVbHEjdSYSQiIiIiIvVf\nbiqsvB/a9IPRf7U6jbhBVVUVS5YsITU1lXHjxtGrVy+rI8lFKi6v4mfL9/DhnjPc1LMlf5/YhxA/\nb6tjiZupMBIRERERkfqtshSWxFUPt56YAN4apNvQOZ1Oli9fztGjRxk7dix9+/a1OpJcpGPnirg/\neTup2UX84uZuzL46BpsGzzdKKoxERERERKR+++gpyNoL05aCPdLqNFJDLpeLd999l4MHD3LTTTcx\ncOBAqyPJRVq99wxPLt2Nn7cnyXcPZlinZlZHklqkwkhEREREROqvHUmwMwmufgq6jLI6jdSQMYYP\nPviAvXv3cv311zN06FCrI8lFqHK6eHbNYd7ckErf9mG8PqM/rUP9rY4ltUyFkYiIiIiI1E9n9sBH\nT0LMSBj5S6vTSA0ZY1i9ejU7duzg6quvZsSIEVZHkouQXVjOTxfsYEtaLrFDIvnN2O74enlaHUvq\ngAojERERERGpf0rzYEks+IfDhHfAQxeoDZkxhnXr1vH1118zdOhQrr32WqsjyUXYnp7Lg/N3kF9a\nyQuT+jC+fzurI0kdUmEkIiIiIiL1izHw7oOQfwpmrYZAzUlp6L744gs2btzIwIEDGTVqlIYk13PG\nGBI2neBPHx6krd2febMG0b11iNWxpI6pMBIRERERkfpl40tw+EMY/VdoP8jqNFJDX331FZ9//jl9\n+/ZlzJgxKovquZKKKn6xfC+rdmdyQ/cWPD+pL6H+3lbHEguoMBIRERERkfrjxFfw6R+g5x0w+H6r\n00gNbdmyhXXr1tGrVy9uu+02PDw8rI4kPyA1u4gHkndw5FwhT93UlQeu6YiHhwq+pkqFkYiIiIiI\n1A+FWbB0FoR3hNteAa1EadC2b9/O6tWr6datG3fccYfKonru431ZPLV0N16eNhLvGsSIzs2tjiQW\nU2EkIiIiIiLWc1bBsrugogjiV4FvsNWJpAb27NnD+++/T6dOnbjzzjvx9NTQ8vqqyuniubVHeOOL\n4/RuF8rrMwbQNszf6lhSD6gwEhERERER6336B0jfCOPfghbdrU4jNXDgwAFWrlxJVFQUkydPxstL\nl5311fmich5euJNNx3OYNrgDv7u1B75eKvekmn5yRURERETEWgc/gE0vw5X3QO9JVqeRGjhy5AjL\nli2jXbt2TJ06FW9vDUuur3aedPDg/B3kFlfw9zt7M3Fge6sjST2jwkhERERERKyTcxzefQDaDoCb\n/mx1GqmB48ePs3jxYlq1asX06dPx9fW1OpJ8B2MMyVtO8vT7+2kV6sfyB4bRq22o1bGkHrKsMLLZ\nbGHA20AvwAB3GWNSrMojIiIiIiJ1rKIElsSBhydMnAdeKhgaqvT0dBYtWkSzZs2YMWMGfn5+VkeS\n71Ba4eTXK/eyYudpru3anBcn9yM0QKvA5LtZucLoJeBjY8ydNpvNBwiwMIuIiIiIiNQlY+CjJ+Hs\nfpi+DMI6WJ1ILtOpU6eYP38+oaGhxMbGEhCgS7v6KD2nmNlJ2zl8tpDHbujCQ9d1wsNDJxHK97Ok\nMLLZbKHA1cBMAGNMBVBhRRYRERERd3K6DJ76BVzkx+1IhF3z4ZqfQ+cbrE5TI8a4sNma5pHxZ86c\nITk5mcDAQOLi4ggKCrI6knyHdQfO8tiSXXjYbMydeSUju7awOlKDVuVwYPPwwDO0cW/ls2qFUTSQ\nDcy12Wx9gO3AI8aYYovyiIiIiNTYvtP5xL6zhc4tg3lyVFcGRYdbHUmkfsrcBR89BR2vqy6MGqjC\nwv0cT/0HubkbCA7qiT18GHb7UMJCB+Dp2fiPJT937hxJSUn4+PgQHx9PSEiI1ZHkvzhdhn98coRX\n1x+jV9sQXp8+gPbhWgFWE87CQjLuuRebtzeRCxdgszXeJ4lsxpi6v1ObbSCwGRhujNlis9leAgqM\nMf/3X693H3AfQIcOHQakp6fXeVYRERGRi3HkbCGT56Tg6+WJ0xiyC8u5uktznhrVlSvaNe5nIEUu\nSakD5lwDriqY/SUERlid6JIVFx8jNfVFzmWvxssrlJYtx1JUdJiCgl0YU4XN5kNoaD/C7UOx24cS\nEtIHD4/GNScmJyeHuXPnAjBr1iwiIhre17Gxyy2u4JFFO/ny6HkmD2zPH27viZ+3p9WxGjRXcTEn\n77mX0n37aPfKywSPHGl1JLew2WzbjTED/+d2iwqjVsBmY0zUhX+PAH5hjLnl+95m4MCBZtu2bXWU\nUEREROTipWYXMWnOZjxssPT+obQI9iMx5QSvf3GcvJJKRvdsxeOjutClZbDVUUWs5XLBomlwbB3M\nWg3tr7Q60SUpLc0gLe1lzmS9i6enP+3bz6JD+7vx9q5eWVNVVUx+/jZyHZtwOFIoLDwAGDw9AwgL\nuxK7fRjh9qEEBXVv0FvYHA4Hc+fOpaqqipkzZ9KihbY31Te7M/J4cP4OsovKefq2nkwZpBlhNeUq\nKyPjvtmUbN9O2xdeIOSmUVZHcpvvK4wuekuazWbrCJwyxpTbbLaRQG8g0RiTd6lhjDFZNpstw2az\ndTXGHAauBw5c6vsRERERsVpGbgnT396CMYYF9w0hMiIQgNnXdGTa4A6881Uab3+ZxpoDWYzr25ZH\nru9MVLNAi1OLWGTji3BkNdz8bIMqi8rKszhx4jUyM5dgs3nSof0sIiNn4+Pzn6tqvLwCiYi4hoiI\nawCorHTgcHz9TYGUk/OXC68Xht0+hHB79Ra2gIDoBrOtpaCggMTERCoqKlQW1UPGGBZ+ncHvV+2n\nebAvy+8fplWubuCqqODUQw9TsnUrbZ79W6Mqi37IRa8wstlsu4CBQBTwEfAe0NMYM+ay7thm6wu8\nDfgAqcAsY4zj+15fK4xERESkvjmTX8qkOSkUlFax6L4hdG/93fM7HMUVvLHhOAmbTlDpNEwa2I6H\nrutMm7DGP+NE5BtpGyDxduh5B0x4BxpAQVJRkUN6+hxOnU7GGCdt2kwmKupB/HxbXdb7KyvPwuHY\njCN3E7mOTZSXnwHA17fVfxRIfn5t3PlhuE1RURFz586lsLCQuLg42rVrZ3Uk+ZaySif/9+4+lm4/\nxdVdmvPS5L7YA32sjtXgmcpKTj36GEWffkrrP/2RsDvvtDqS29V4S5rNZtthjOlvs9meAsqMMa/Y\nbLadxph+7g77XVQYiYiISH2SXVjO5DkpZBeWM//ewfRuF/ajb3OuoIzX1h9jwdcnsdlszBgcyYPX\ndqRZkG8dJBaxUMEZmDMC/O1w73rwrd8naVVVFZJ+8m0yMubidJbSutU4oqMfxt+/vdvuwxhDaWk6\nDkfKhRVIm6mszAXA3z+qev5R+DDsYYP/ZyWTFUpKSpg3bx4Oh4MZM2YQGRlpdST5lpM5JTwwfzv7\nMwt4+PrOPHJ9Z53Y6QbG6STzqaco+Gg1LX/zG8JnTLc6Uq1wR2G0BXgR+DVwqzEmzWaz7TPG9HJv\n1O+mwkhERETqi9ziCqa+uZkMRwmJdw1iYNSlnYZ2ylHCy58eZdn2U/h5ezJreBT3jehIaEDjGoor\nAoCzEhJuhTN74N7PoEU3qxN9L6ezhIyMRNJPvklVVT4tWowhJvoRAgM71fp9G+OiqPgIDkcKjtxN\nOPK+xuksAiAoqDt2+1DC7cMIC7sSL6+6LdxKS0tJTEzk3LlzTJ8+nZiYmDq9f/lh6w+d49HFuzDG\n8OKUvlzXraXVkRoF43Jx5te/IX/lSlo89SQRd99tdaRa447CqAdwP5BijFlos9migUnGmL+5N+p3\nU2EkIiIi9UF+aSXT397M0bNFzJ15JcM6Nbvs95WaXcQ/1h3l/d2ZBPt5cd+IGGZdFU2Q70WPmRSp\n/9b8GlJerd6GdkX93MrhdJZzOnMBJ068TmVlDhER19Ix5jGCg3talsnlqqKwcO83K5Dy87fjclVg\ns3kSEtwb+4UVSKEh/fH0rL1ViuXl5SQlJZGZmcmUKVPo0qVLrd2XXBqny/DSp0d55bOjdGsVwpwZ\nA+gQEWB1rEbBGEPW00+Tt3ARzR76Kc1/8hOrI9WqGhVGNpvNk+oB15atv1JhJCIiIlYrKq8i9p0t\n7Dudz5txA7m2q3uGvR7ILOCFTw6z7uA5IgJ9eGBkR2YMidTxx9LwHVgFS2Jh0H0w5u9Wp/kfLlcl\nZ7JWkJb2CuXlZ7CHDSGm4+OEhQ6wOtr/cDrLyc/ffqFASqGwcA/GOPHw8CU0dED1Fjb7MIKDe+Hh\n4Z7SubKykvnz55Oens7EiRPp0aOHW96v1FxeSQWPLNrFF0eymdC/Hc/c0Uv/Z7iJMYZzf/0buQkJ\nRNx7D80ff7zBDKW/XO5YYfQVcJ0xpsLd4S6GCiMRERGxUmmFk5lzv2ZbuoPXpvVndK/LG3r7Q3ae\ndPD82iN8dew8rUL8eOj6Tkwa2B5vz4Z7/LY0YeePwZsjoXlXmLUavOrP8F1jXJw9+wGpaS9SWppO\nSEhfOsY8Tnj4cKujXbSqqkLy8raS60jB4dhEUdEhADw9g7DbB3+zhS0wsDM226U/hlRVVbFw4UKO\nHz/O+PHj6d27t7s/BLlM+07nc3/yds4VlPO723owbVCHRl9o1KVzL75IzhtzsMfG0vJXv2wSn1t3\nFEaJQHdgFVD879uNMS+4K+QPUWEkIiIiVimvcnJPwja+Onael6b047Y+tXuCUcrxHJ5be5jt6Q46\nhAfw6A2dub1vWw0wlYajogTevgEKz8DsDRDmvmHRNWGM4fz5Tzie+g+Ki48QFNSNmJjHaRZxXYO/\nKKyoyKk+ge3CFrbS0nQAvL3DL5RH1SuQ/P1/vFxwOp0sWbKEw4cPc9ttt9G/f/+6+BDkIizZmsFv\n3ttHs0Af/jljAH3b//iBC3Lxzr/xBtkvvkTYxIm0evoPDf5x4WK5ozD63Xfdboz5Qw2zXRQVRiIi\nImKFSqeLB5J3sO7gWZ69szeTBtbNha8xhs8PZ/P3NYc5cKaAzi2CePzGLozu1arJ/AIrDZQx8O4D\nsHsRzFgOna63OhHGGHJzv+R46gsUFu4lICCamOhHadFizGWtvmkIysoyL5y+loIjN4XyirMA+Pm2\nqT597UKJ5Ov7nwOSXS4Xy5cvZ//+/YwZM4ZBgwZZEV/+S1mlkz+8v5+FX2dwVadmvDy1H+GB9WfV\nXmOQM28e5/76N0Juu5U2f/kLNs+ms8WvxoXRt95RgDGmxG3JLpIKIxEREalrVU4XjyzexYd7zvDH\n23sSOzSqzjO4XIbV+7J44ZPDHM8uplfbEJ4Y1ZWRXZqrOJL6adtc+OBRGPlLGPkLq9OQl7eN46nP\nk5f3NX5+bYmRsB9CAAAgAElEQVSOephWrca5bc5PQ2CMoaQkDYdj04UtbJupqsoDICCgI+H26gIp\nNPRKPvroC3bv3s2NN97I8OENZ4teY3bKUcIDyTvYezqfn1zbkcdv7KoVp27mWLSIrN//geCbbqLt\n889h82o6jw/gnhVGQ4F3gCBjTAebzdYHmG2MedC9Ub+bCiMRERGpSy6X4cllu1mx4zS/uaU794yw\n9hhpp8vw7s7TvPjpETJySxkYaefJm7oyJCbC0lwi/yFzJ7wzCqJGwPRl4GHd6p2Cgr2kpr5ATu4G\nfHyaExX1E9q2mYSHR+2dKNZQGOOiqOjgNyuQ8vK24nSWYIyNoiI7IcGD6NNnEmFhV+LpqVO3rPTF\nkWweWbQTp9Pw/KQ+jOrp/vl5TV3eync588tfEjRyJO1efgmbT9NbueWOwmgLcCewyhjT78Jt+4wx\nvdya9HuoMBIREZG6Yozh1+/uY8GWkzxxYxceur6z1ZG+UVHlYsm2DF757ChnC8oZ0bkZT4zqqjkW\nYr2SXHjzGnC5qucWBVpTZhYVHSE17UWys9fg5RVGVORs2rWLxdPT35I8DYHTWc66dW9x+vRndIgs\nwcPjBMZUYrN5ExLS51srkPri4dH0Lqat4HIZXl1/jH+sO0LXlsG8PmMA0c0CrY7V6BR89BGnn3yK\nwCGDaff663j4Ns1C+fsKo0taZ2WMyfivpc/OmgYTERERqU+MMTz9wQEWbDnJgyM78tPrOlkd6T/4\neHkwY0gkdw5oR1JKOq9/cZxxr23kxh4teWJUF7q1CrE6ojRFLhesvB8KzsBdaywpi0pK0klLe5ms\ns+/h6RlIdPQjdGg/Cy+v4DrP0tB8/vlXpKScZ/Dge7n+utG4XGXk5W37ZoB22olXSTvxMh4e/oSF\nDbwwQHsowcE9sdmazpyXupJfUsljS3bx2aFz3NGvLc/c0YsAn6a1RaouFH76Kaef+hn+/fvR7tVX\nm2xZ9EMu5bsuw2azDQOMzWbzBh4BDtZOLBERERFrPLf2MHM3nuCu4dE8dVPXejsnyM/bk3uvjmHq\n4A7866s03tqQys0vfcmtvdvw2I1d9Ey01K2vXoCja2DMc9BuQJ3edVnZGdJOvMqZM8uw2byI7HAv\nkZH34e1tr9McDdWGDRv48ssv6d+/P6NHj8Zms+Hp6U9ExAgiIkYAUFmZT17elgvzj1I4dvxZALy8\nQrCHDf5miHZgQKd6+5jZUOzPzOeB5B2cyS/l6dt7EjskUp/TWlD05ZecfvQx/Hr1pP0bc/AI0NbL\n73IpW9KaAS8BNwA2YC3wsDEmt/bi/X/akiYiIiK17ZVPj/L8J0eYNrgDz4zr1aB+Sc8rqeDNDanM\n3XiCCqeLO/u34+EbOtM2TNtwpJalfg5Jd0DP8TDhbaijn5uKivOcSH+D06fnY4yhbdspREU+iK9v\nizq5/8YgJSWFNWvW0Lt3b8aNG4fHRc6cKi/Prj597cIKpLKyUwD4+DS/cPraMOz2Yfj7t63N+I3O\n8u2n+NXKvdgDfHhten8GRKr0rA3FW74m47778ImJIXLeXDxDQ62OZDl3zDAabozZ+GO31RYVRiIi\nIlKb3tqQyjMfHWR8/7Y8d2cfPBroCTTZheW8tv4YC7acBGDa4A48eG1HWgT7WZxMGqWCTHhjBAQ2\ng3s+Bd+gWr/Lysp8Tp58i4xTCTidZbRuPYHoqIdUTlyirVu38uGHH9KjRw8mTJiAZw2OEC8tzfhm\ngLbDkUJFxXkA/P06YA+v3r5mtw/F16eZu+I3KuVVTp5+/wDzt5xkaEwEr0zrR7MgbY+qDSU7dnLy\nnnvwbtOayMREvMLDrY5UL7ijMNphjOn/Y7fVFhVGIiIiUluSUk7wf+/t55berXlpcl+8PK072cld\nTueV8sqnR1m6/RQ+nh7ED4vi/mtiCAvQwFpxE2clzBsLZ/fBveuheZdavbuqqmIyTs3j5Mm3qKoq\npGWLscTEPEpAQHSt3m9jtGvXLt599126dOnCpEmT8HLjEeLGGIqLj+JwbCLXkUJe3haqqgoBCAzs\n8s0Abbt9sOZLAZl5pTwwfwe7M/KYfU0MT43q2ij+D6qPSvft5+TMmXhFRNAhKRHvFlqN+G+XXRjZ\nbLahwDDgUeAf33pRCHCHMaaPO4N+HxVGIiIiUhuWbMvgZ8v2cEP3lrw+oz/ejewX9bTzxby47gir\ndmcS5OPFPSNiuOuqKIL9vK2OJg3dx7+Cza/BnXOh1/hauxuns5zTp+dzIv11KitzadbsBmJiHiM4\nqFut3Wdjtm/fPpYvX050dDRTp07F27t2HwtcriqKig6Qm1u9AikvfxsuVxngQUjIFdjtwwi3DyU0\ndACenk1rJeRXR8/z8KKdVFS5eG5ib0b3am11pEar7PARTsbF4REURGRyEt6t9bn+tpoURtcAI4H7\ngTe+9aJC4H1jzFE35vxeKoxERETE3d7bdZpHF+/iqk7NeDt+IL5ejfe0n8NZhTy/9jBrD5zFHuDN\nAyM7Ejc0Cj/vxvsxSy3avxKWzoTB98PNf6uVu3C5Ksk8s5QTJ16jvDyLcPtwYmIeJzS0b63cX1Nw\n8OBBlixZQocOHZg+fTo+PnW/4tDlKic/f9c3W9gKCnZjTBU2mw+hof2qVyCFDyUkuDceHo2z2Ha5\nDK9/cZzn1x6mU4sgXp8xgI7Na387Z1NVnppKemwcNi8vIpOT8Gnf3upI9Y47tqRFGmPS3Z7sIqkw\nEhEREXf6eF8WP1mwg4GRdubNGoS/T9MoTnZn5PHc2sN8efQ8LYJ9eei6Tky+sgM+Xo1rZZXUovNH\n4c2R0KIHzPwQvNxbOhjjJCtrFWlpL1NadpLQ0P50jHkCu32IW++nqTl69CiLFi2iVatWxMXF4VtP\njhCvqioiL38bjtxNOBybKSw6ABg8PQMJC7vymy1sQUHdsNka/uNUfmklTyzZzbqDZ7m1Txv+Ov4K\nAn3dtyVQ/lPFyZOkz4jFOJ1EJiXhG6MtrN/FHYVRF+BJIAr45jvaGHOdmzL+IBVGIiIi4i7rD5/j\nvsRtXNE2lMS7BxPUBH9Z35Kaw3NrD7P1hIN2dn8eub4zd/Rrq9kZ8sMqiuGt66H4HMz+EkLdN2ja\nGEN29hpS016kuPgoQUE96BjzOBERIxvUiYX1UVpaGvPnz6dZs2bEx8fj719/T0+srHTgcGz5ZgVS\nSUkqAN7eduxhQ7CHV29h8/ePanDfF4eyCrg/aTunHKX8+pbuzBzW8D6GhqQyM5P0GbG4iovpkJiI\nX9fanbPWkLmjMNpN9Za07YDz37cbY7a7K+QPUWEkIiIi7rDp2HlmzdtK55ZBzL9nCKH+jXPLw8Uw\nxvDFkWyeX3uEvafziWkeyOM3dmFMr9YN9pQ4qUXGwMrZsGcJxK6Aju553tgYQ07uF6SmvkBh4X4C\nAjoSE/MoLZqPbhQrSqx28uRJkpKSCAsLY+bMmQQGBlod6ZKUlZ3B4dh8YYj2JsrLswDw9W2F3T70\nmxVIfn71eybNuztP84sVewjx8+af0/szMEqnc9WmynPnSI+NxZnroMO8ufj37Gl1pHrNHYXRdmPM\nALcnu0gqjERERKSmtp3IJe5fX9PeHsCi+4ZgD9SJYVB9wb5mfxbPrz3C0XNFdG8dwpOjunBdtxZ6\n9lv+v63vwIePw7W/hmt+5pZ36XBs4Xjq8+Tnb8fPrz0x0Q/TqtXt2GxNY4tobcvMzCQhIYHAwEBm\nzZpFcHDDPpXMGENp6QlyHSk4HCk4HJuprMwFwN8/ivDwYdjtw7CHDcbHp34UMhVVLp758AAJKekM\nig7n1Wn9aBHctIZ717Wq3FzS4+KozDxDh3feJqBfP6sj1Xs1GXr975+0h4FzwEqg/N8vN8bkujHn\n91JhJCIiIjWx51Qe09/aQvNgXxbPHkrz4Poxv6M+cboMq3af5h+fHOVkbgn9O4Tx5KiuDOvUzOpo\nYrXTO+BfN0H0NTBtCXjUbOVPQcEejh9/nlzHV/j6tCQq+qe0aX0nHh4qcd0lKyuLhIQEfH19mTVr\nFqGhoVZHcjtjXBQVHcbhSCHXsYm8vK9xOosBCArqQbh9KHb7UMLCrsTLq+6HSp/JL+Un83ew42Qe\n946I5mejuzW6kzjrG2d+PukzZ1GRmkr7N98kcPAgqyM1CDUpjNIAA3zX00vGGBPjnog/TIWRiIiI\nXK6DZwqY8uZmQvy9WDJ7KK1D6+/8jvqg0uli6bZTvPLZUc7klzGsYwRP3tSV/h3sVkcTK5Tkwpxr\nAAOzN0DA5a/cKCo6zPHUFzh/fh3e3uFERs6mXdsZTe449dqWnZ3NvHnz8PDw4K677sJubxo/uy5X\nJYWFe6tXIOVuIr9gBy5XBTabJyHBvS/MPxpGSEg/PD1r90mDTcfP8/DCnZRWOHn2zj7c0rt+b5lr\nDJxFRZy8627KDx6k3T//SdCIq6yO1GDUeEua1VQYiYiIyOU4dq6QyXM24+PlwZLZQ2kfHmB1pAaj\nrNLJ/C0n+ef6Y+QUV3B9txY8MaorPdqEWB1N6orLBQsmQdoXcNfH0PbyJlSUlKSRmvYSZ89+gKdn\nIJEd7qF9+1mWrPpo7HJzc5k7dy4ul4tZs2bRrFnTXSHodJaRn7/9wgqkFAoK9gAuPDx8CQsdiN0+\nFHv4MIKDeuLh4Z7DD4wxzNmQyrMfHyK6WSBzYgfQqUXD3grYELhKSjh5332U7tpNu5dfIvi6Ojmb\nq9Fwxwyj8d9xcz6w1xhzrob5fpQKIxEREblUJ84XM2lOCgZYfN8QYprr4vRyFJdXMW/TCeZ8cZyC\nsipu6d2ax2/sQkd9Phu/L/4O6/8EtzwPV95zyW9eVpZJWtornMlajs3mQ/v2M4nscA/e3mG1EFby\n8vKYO3cuFRUVzJw5k5YtW1odqV6pqirEkfd19fyj3E0UFR8GwNMzCLt9CHb7EMLtwwgM7HJZ89sK\nyyp5aukePt6fxS1XtOZvd/Zukqdw1jVXeTmnHniA4s1baPv8c4TcfLPVkRocdxRGHwJDgfUXbhpJ\n9Ylp0cDTxpgk90T9biqMRERE5FKczitl0hsplFRUsXj2ULq01DO8NZVfWslbG1L518Y0yiqdjO/f\njkeu76xVW43V8fWQdAf0ngR3zIFLuIAuL8/mRPo/OX16EQBt204lKupBfH2a7mqX2lZYWMjcuXMp\nLi4mPj6eNm3aWB2p3quoOI/DsfnCEO1NlJaeBMDbO+Kb+Ufh4cPw82v/owXSkbOF3J+0nfTcEn55\nczfuvipahwbUAVNRwamHHqboiy9o/de/EDZunNWRGiR3FEZrgDhjzNkL/24JJAJTgQ3GmF5uzPs/\nVBiJiIjIxTpbUMakOSnkFlew8N4h9Grb+Ia9Wul8UTmvf36cpM3pGGOYcmUHHrquEy1CNIem0cg/\nDXNGQGALuPdT8Lm4o9grK/NIT3+TjFOJGFNB61YTiI5+CD8/lRe1qbi4mHnz5pGfn09sbCzt27e3\nOlKDVFp6+sLpa9VDtCsqqjfS+Pm1xW4f9k2J5Ovb4j/e7v3dmfx8+R4CfLx4bVo/BsdEWBG/yTFV\nVZx+/AkK166l1e9/j33KZKsjNVjuKIwOGGN6fOvfNmC/MaaHzWbbaYyp1bPqVBiJiIjIxThfVM7k\nOSlk5ZeRdM9gDWquRWfyS3nls2Ms2ZqBp4eN+GFR3H9NR8IDddJVg1ZVAfPGwLlDcN96aNb5x9+k\nqpCTGfM4efJtnM5iWra8lZjohwkIiK6DwE1baWkp8+bNIycnhxkzZhAVFWV1pEbBGENJSSq5jk0X\nSqTNVFXlAxAQ0Inw8KGEhA7hX1+H89bGbAZG2nlten9aqjivE8bpJPMXv6Tg/fdp+ctfEB4fb3Wk\nBs0dhdE/gQ7A0gs3TQBOAU8BHxhjrnVT1u+kwkhERER+TF5JBVPf2kLa+SISZg3Ss7x1JD2nmJfW\nHWXlrtME+nhx11XR3DMimhA/b6ujyeVY/XPY8gZMTICeP7y9w+ks49TpJNLT51BZ6aB5sxuJiXmM\noKCudRS2aSsrKyMpKYmsrCymTp1Kp06drI7UaBnjpLDoII7c6gIpN28rxlWKy9godnWke9S1NAsf\nRljYQDw9tU23NhmXi6zf/Y68pcto/thjNJt9n9WRGjx3FEY2qkui4Rdu2ggsN3V0zJoKIxEREfkh\nBWWVxL69hYNZhbwTP5ARnZtbHanJOXq2kBc+OcLqfVmEBXgz++qOxA+LJMBHQ18bjH3LYdldMORB\nGP2X7301l6uCzMwlpJ14jYqKc4SHj6BjzOOEhPSuw7BNW0VFBcnJyZw6dYrJkyfTtatKurqyJTWH\nhxdupZnvMX4yPI+WvvvJz9+JMZXYbN6EhvTFHj4Mu30ooSF98PDQqkt3McZw9pk/40hOJuKB+2nx\nyCNWR2oUalwYWU2FkYiIiHyfkooq4t75ml0ZecyJHcD13XUykJX2nc7nubWH+fxwNs2CfPnptR2Z\nOrgDvl6eVkeTH5J9BN66Flr2hJkfguf/rhBzuarIOvsuaWmvUFZ2itDQgXSMeQK7fZAFgZuuyspK\nFixYwIkTJ5gwYQK9etXqOFm5wBjDO1+l8ZfVh4gMD+CN2AHfHKjgdJaQl7cdh2MTuY5NFBbuBwwe\nHv6EhQ0k3F5dIAUH98Bm02Ph5TDGkP388+S8/Q7hs2bR4mdPabC4m1x2YWSz2b4yxlxls9kKgW+/\nsg0wxpgQ90b9biqMRERE5LuUVTq5a95WNqfm8Oq0/oy5orXVkeSCbSdy+fuaw2xJy6VtmD8PX9+J\nCf3b4eXpYXU0+W/lRfD29VB8Hu7/EkL+c0i1MS7OnVtNatqLlJSkEhzci44xjxMefrUu2OpYVVUV\nixcv5ujRo4wbN46+fftaHalJKCqv4ufL9vDh3jPc1LMlz03sQ/APbLutrMwnL2/LhRlImykuPgqA\nl1codvvgb4ZoBwR01M/QRcp+9TXOv/oqYVOn0Oq3v9XnzY1qUhjFGGNSay3ZRVJhJCIiIv+tosrF\n7KRtfH4kmxcm9eGOfu2sjiT/xRjDV8fO89yaw+w+lU90s0AevaEzt/Zug4eHftmvF4yBFfdWb0eL\nXQkxI7/1IkNOznqOp75AUdFBAgM7ExP9GM2bj9LFmgWcTifLli3j4MGDjB07loED/+f6TmrBsXOF\n3J+8g9TsIn4+uhv3XR1zyd//5eXncDg2XyiQNlFWdhoAH58WF05fq16B5O/ftjY+hAYv5+23Offc\n84SOH0/rP/0Rm4eeeHCnmhRG240xA2w226fGmOtrLeGPUGEkIiIi31bldPGTBTtYs/8sfxl/BVMH\ndbA6kvwAYwyfHDjLC58c4VBWId1aBfPEqK7c0L2Figerff0WfPQkXPcbuPqpb27Ozd1EauoL5Bfs\nxN+vA9Exj9Cq5a3aTmMRl8vFypUr2bt3L6NHj2bIkCFWR2oSPtp7hqeW7sbP25NXpvZjWKdmNX6f\nxhjKyjLIdaTgyN1EriOFysocAPz9O2C3D72whW0IPj41v7+GLjcpmbPPPEPImDG0+fuz2Dz1GORu\nNSmMdlJ9MtoDwD/+++XGmBfcFfKHqDASERGRf3O6DI8v2cV7uzL53a09mDVcR3c3FC6X4f09mby4\n7ihp54vp8//Yu8/wqKq1jeP/9N57bxB67wjSsaCAIAGlo1QFKXaPil2PCoioFKWDEhBEUZEiItJN\nEOkB0kN6Jj2Zut8PeHhPQaUk2ZnJ87uu8+EAmX0nJpPZ96z1rDBPnhrYhDsa+UhxpIbMBFh5F8T0\nhYe+AGtrSkpOcDn5fTSawzg4BBIV+ThBQQ9ibS2n3qnFZDKxY8cOEhMT6devHz179lQ7ksUzGE28\ns/M8Kw6k0C7ck49HtyfIw6lWrqUoChUVSVdPX9McRqM5gtFYDoCrSxO8vLpdHaLt2RlbW7dayVBf\naTZvJufFl3Dt34/QhQuxspPnodpwO4VRE2AoMBtY+t9/ryjKKzUV8q9IYSSEEEIIuFo4PLv1d+J/\nzeSZu5syvXeM2pHELTAYTXyZmMkHey5ypaSaLlHePHVXEzpGeqsdreGoLIJld4KVFUzZT5kxl+Tk\nBRQU/oidnTeRkTMICX4YGxsHtZM2aIqi8P3333Ps2DHuvPNO+vbtq3Yki5dXVs3MjSc4mlLEuG4R\n/GNQc+xt624LlMlkoKz8DJqiw2g0hygu+RWTSYuVlQ1ubq3+WIHUDQ+PDtjYONZZrrpW8vXXXHnm\nWVx69CD0oyVY28tpc7Xltk9Js7KyukdRlO//4u/HK4qy5jYy/iUpjIQQQgihKAovf32GtYfTeKJf\nY+YMiFU7krhNWoORz4+ms2TfZQrKtfRp4se8gU1oGeKhdjTLZjLBxhGQ8jMVY1aSXLGLvLzvsLV1\nJyJ8MqGh47G1dVE7ZYOnKAq7d+/m0KFDdOvWjYEDZXZUbfs1tYgZGxIprdbz1rBW9WI2nsmkpaTk\nxB+rjw5RWnoSRTFibW2Ph3v7qwWSd3fc3FpZzErA0p0/kDV3Ls6dOhG2bCnWjpZbjNUHt10Y3cAF\nEhVFaV8jD3YdUhgJIYQQDZuiKLz1/XmW/5zM1DujefaepnLjZEEqdQbWHEpj6f7LlFTpubdVIHMH\nxNLIv2Ftv6gzP71D1eG3SenRm2z9aWxsHAkLnUB4+GTs7OrkEGRxA/bt28f+/fvp1KkT9957rzzn\n1SJFUVh9KJU3vj1HqJcTn4zpQLOg+vmzYDCUU1x8/NoWtvLyswDY2Lji6dnp2hBtV9cmWFmZ33Do\nsp9+IvPxmTi1akX4pyuwdpHyurbVRWF0QlGUdjXyYNchhZEQQgjRsC3YncTivRcZ3y2C+YNbyI2T\nhSqt1vPpgRQ+O5BMld7I0HYhzO4XS7iPs9rRLIb2/Jek/DqLK0FOWNnYERIyhsiIqTJct5755Zdf\n2LNnD23btmXw4MFYy6lQtaZCa+C5raf4+uQV+jcL4P24Nng4mc9KHZ2uCE3xUTSaQ2g0h6msTAHA\nzs4bL6+u17awOTlF1vvfnRWHDpExbToOsbGEr1qJjZu8aVAXZIWREEIIIczWxz9d4p87LzCyYxhv\nDWslx7E3AEUVOpbuv8yaQ6kYTQpxncKY1bcxgR6yLeFW6XRFpCW9R2bOFyhWVgQHjSAy+gkcHYPU\njib+y9GjR/n+++9p2bIlw4YNk7KoFiXnlzNtfQKX8sqZN7AJ03vFmP3vmOrq7D9WH10tkLTaHAAc\nHIL+WH10dYi2o0Ogykn/U+Xx46RPnoJ9RAQRa1Zj4+mpdqQGQ1YYCSGEEMIsrfwlhVd3nGVI22AW\nxLXFxsxfyIubk1tazZIfL/HF8XSsrKwY1zWC6b1j8HGVQcw3ymAoIz39M9IzVmI0VBBYYCSqx1qc\nQ+5UO5q4joSEBL755huaNm3KiBEjsJEjxGvNztM5PLn5JPa21iwe1Y4ejS1vlZ2iKFRVpV6df1R0\nCE3xEfR6DQDOzlF4eXX/YwVSV+zsvFTLWXXyJOkTJ2EbGEjEurXY+violqUhqovCaImiKI/XyINd\nhxRGQgghRMOz8Wg6z287xd0tAlnycDtsbeRd9oYqo6iSD/ZeZGtiJk52NkzqEcWjPaPNattIXTMa\nK8nIXEda2nIMhmL8jMFEnziD632roflgteOJ6zh58iTbtm2jUaNGjBo1CltbW7UjWSSD0cR7u5JY\nuv8ybUI9+HhMB0I8ndSOVScUxUR5+flrK5CKi49jNFYAVri6Nru2AsnTs3OdDb6vPnuWtAkTsfHw\nIGL9OuwCAurkuuL/1cQpaZ7AOCASuPbMpSjKrBrK+JekMBJCCCEalq2JmczbfJLesX4sG9uxTo80\nFvXXpbxyFu5J4tvfs3F3tGVqrxgmdI/ExUFurP/FZNKSlfUFqWkfo9MV4OPTi2hjG9y/fgm6PQ53\nvaF2RHEdZ86cYcuWLURGRvLwww9jZydlaG0oKNcyc+MJDicXMrpLOC/d3xwH24a7istk0lNa9jua\noqsFUknJCRRFh5WVLe7urfHy6o63Vzc8PNphbV3zKzu1Fy+SNm48Vo6ORK5fh11ISI1fQ/y9miiM\nDgFHgFOA6V9/rijKmpoK+VekMBJCCCEajm9/z2bm54l0i/Hhs/GdcLRruC/mxfWduVLCgl1J7D2f\nh6+rPTN6N+LhLuEN+nvFZDKQk7OVlJQPqdZewdOzCzHRc/HUucKKvhDUGsZ/AzZSRNQ3Fy5cYNOm\nTYSEhDBmzBgcHGTLZW1ITNcwY30imkodbzzQigc7hKodqd4xGqspKUm4uoVNc4jS0qu3/9bWDnh6\ndLy6hc27G+5uLbGyur3nW11qKqljx2KFFRHr12EfEVEzn4S4aTVRGNXqUOu/I4WREEII0TDsOZvL\ntPUJtAv3ZM2kzjjby8oR8ecS0jS8v+sChy4XEuThyKx+jXmwQyh2DWj7oqKYyM3dQXLKB1RVpeLu\n1promHl4e92Bla7iallUVQRTD4C7DLiuby5fvszGjRsJCAhg3LhxODrKYPeapigK64+k8eqOswR6\nOLJ0TAdaBHuoHcssGAxlaDRHr21hq6hIAsDW1g1Pzy7XtrC5uMTe1Alsusws0saORdFqiVi3FoeY\nmNr6FMQNqInCaA5QDuwAtP/6c0VRimoq5F+RwkgIIYSwfAcu5vPI6l9pFuTG+ke74OYoKyHEjTl0\nqYB3d13gRHoxET7OzOkfy/1tgi16SLqiKBQU7CE5eSHlFRdwdWlCdPQcfH37X71xUxT48hE4sw3G\nbYcoGXJd36SmprJ+/Xp8fHwYP348zs7OakeyOFU6I89vO8W2E1n0berPwri2eDjL75ZbpdUVoNEc\nvvq/osNUVacDYGfnc7U88r66hc3JKfxPH0Ofm0va6DEYS0uJWLsGx6ZN6yq++BM1URg9BrwBFAP/\n+iBFUZToGkv5F6QwEkIIISzbkeRCJqw6RpSvK59P7oKns73akYSZURSFH8/n8d6uJM5llxIb4Mrc\nAU24q9BeNx4AACAASURBVEXATb3zXd8pikKR5iDJyQsoLT2Jk1Mk0VFPEBBwH1ZW/7ay6uhy+P4p\n6PcS9JynXmBxXZmZmaxduxZ3d3cmTJiAq6ur2pEsTmpBBdPWJ3Aht4y5/WN5rE8jrC24RFZDVVXm\ntQKpSHMInS4fAEfH0D9OX+uOl1dXHBz8ATAUFJA2dhyGvDzCV6/CqVUrNeOLP9REYZQMdFYUpaCm\nw90IKYyEEEIIy5WYrmHsp0cJ8nRi05SucmS6uC0mk8J3p7NZsDuJ5PwKWod6MG9gE+5s7Gv2xVFx\n8a9cTl5AcfFRHByCiI6aRWDgMKyt/2vrZsZxWHUPNOoHoz4H64azRc8cZGdns2bNGpycnJg4cSLu\n7u5qR7I4u8/mMjf+N2ysrfhgVDt6xfqpHcniKYpCZeVlijSH/iiRjmAwlALg4tIYD6d2GFYexuZY\nCZEffopzhw4qJxb/UhOF0S5gqKIolTUd7kZIYSSEEEJYptNZJTy04gg+LvbET+2Gv7vM7xA1w2A0\nse1EFov2XCSruIrOkd48eVcTOkd5qx3tppWWnSY5eQGFhfuxt/clMmIGISGjrn9qUUUhLLsTrG1g\n6n5w8qr7wOJP5eXlsXr1auzs7Jg4cSKenp5qR7IoRpPCwt1JLNl3iVYhHnw8uj1h3rLVTw2KYqSs\n7CwazSEK83+huPAIip0JsMLNreUf84+64+nZARsb+W+kppoojLYBLYB9/OcMo1k1FfKvSGEkhBBC\nWJ4LOWWMWn4YZ3tb4qd1I8TTSe1IwgLpDCY2HU/nwx8vkVem5c5YP54cGEvr0Pp/o15ecZHk5EXk\n5+/E1taDiIiphIWO/fObK5MRNjwIqQfh0d0Q1KZuA4u/VFhYyKpVqwCYOHEiPj4+KieyLEUVOp74\n4gQHLhYwqlMY8we3aNAnJ9YXxvIKMh59lMrzp/FcNIvq8Eo0msOUlP6GouixsrLDw6PdtS1s7u6t\nsbaWbel1qSYKo/HX+3NFUdbcZrYbIoWREEIIYVmS88uJW3YEG2uIn9qNCB8XtSMJC1elM7LuSCqf\n/HQZTaWeu1oEMG9gE2ID3NSO9j+qqtJJTllMTs52bGycCA+bRHj4I9ja/k3WfW/C/nfg/sXQ4bov\n34VKNBoNq1atwmAwMHHiRPz8ZItUTfoto5gZ6xMoqNDx2pAWjOz050OXRd0xVVWRMXUalQkJhCxc\ngPvAgdf+zmispLj412tb2MrKzgAKNjbOeHp0xOuPIdpurs2wspLirzbddmGkNimMhBBCCMuRUVRJ\n3LLDV1d+TO1GI38Z9irqTlm1npW/pPLpgWTKdQaGtAlmdv9YIn3VLy2rtTmkpizhSvZmrKxsCA0d\nS0T4VOztb2Ab3cU9V1cXtX0YhnwEZj6vyZKUlJSwatUqqqurmTBhAoGBgWpHshiKorDxWDqvfH0W\nPzcHlo7pQKtQD7VjCcCk05E54zEqDh4k+J//xOP++/7y3+v1xWiKj6IpOkyR5jCVlZcAsLX1wMur\n67UVSM7O0WY/j66+qYkVRin8/+lo18gpaUIIIYS4GdklVYxYephyrYHPJ3elWZAMexXq0FToWPZz\nMqsPpaA3KsR1DGVm38YEq7A1UqcrJDVtKVlZ61EUheDgkURFzsDBIeDGHqA4/ercIvcQeGQ32Ms8\nkPqivLycVatWUVZWxvjx4wkJCVE7ksWo1hv5x1en2ZKQSa9YPxaNbIuXi2xlqg8UvZ7M2XMo37uX\noDdex3P48Jt+DK02F43myNUVSEWHqNZeAcDBPuDq6iOvbnh7d8fRMbim4zc4NVEY/fsGW0dgBOCt\nKMpLNRPxr0lhJIQQQpi/vLJqRi07Qn6Zlg2Tu5jFDBlh+fLKqvl432U2Hk0HYHTXcGb0boSfW+2f\n1qfXl5Ke8SkZGaswGqsJCnyAqKhZODmF3viDGLSw8m4ovARTfgKfmNqKK25SZWUlq1evRqPRMGbM\nGCIiItSOZDHSCyuZtj6Bs9mlPNGvMbP6NcbGWlad1AeK0UjWk09S9v1OAl78B96jR9/+YyoKVVXp\naDSHr21h0+uLAHByCsfLq/sfQ7S7YW8vs8FuVq1sSfvjQevkLDwpjIQQQgjzVlShY9Tyw2Rqqlj3\nSGc6RJjfSVXCsmVqKvlw7yW2JGZib2PNxDsimXpnDB7OdjV+LYOhgszMtaSlL8dgKMXf/16io2bj\n4nILZc+38+D4pzByPTS7v8aziltTVVXF2rVrycvLY/To0URH18nGjAZh3/k8nvjiBAAfjGpHn6b+\nKicS/6KYTGQ//wIlX32F/1NP4fPIpNq5jqJQUZF0rTzSaI5iNJYD4Ora9NoKJC/Pzn8/+03UyAqj\n9v/2f62BjsB0RVHq5OgFKYyEEEII81VSpefhFUe4lFfOqomd6B7jq3YkIf5Ucn45i/Zc5OuTV3Bz\ntGVKz2gm9ojC1cH2th/baNSSdWUjqamfoNcX4uvTl+joObi5Nb+1B/x9M2x9FLrPhIGv33Y+UTO0\nWi3r1q3jypUrjBo1itjYWLUjWQSjSeGDvRdZvPcizYPcWTqmA+E+sv2yvlAUhZxXXqH4i034znwc\nv8ceq7Nrm0wGyspOX1uBVFKSgMmkxcrKBje3VtdWH3l4dMDGxrHOcpmLmiiM9vH/M4wMQCrwnqIo\nSTUV8q9IYSSEEEKYp3KtgbGfHeV0VgkrxnWkdxN5J1iYh3PZpby/K4k953LxdrFnRu8YxnSNuKVj\nuk0mPdnZX5KS+iFabQ5eXt2IiZ6Lh0f7v//gP5N3Dlb0haC2MP4bsLn9QkvcPp1Ox8aNG0lLSyMu\nLo5mzZqpHckiaCp0PLHpN35OyufBDqG8PrTlLf0sitqhKAp5b79N0Zq1+EyejN/cOaoOpjYatZSU\nJl5dfVR0iNKy31EUI9bW9nh4dLg2QNvNrRXW1vLcWROFkSMwHIgE/vUVVRRFebWmQv4VKYyEEEII\n81OlMzJ+1TES0jR8PLo9d7WQk4GE+fkto5j3d13gwMUCAtwdmNm3MXEdw7C3tf7bj1UUI7m5O0hO\nWURVVTru7u2IiZ6Lt3f32wulLYPlfaC6BKYdADf52aoPDAYDn3/+OZcvX2b48OG0atVK7UgW4VRm\nCdPWJ5BfpmX+4BY81DlMTsmqZ/IWLaJw6TK8xo4l4Pnn6t1/H4OhnOLi49dWIJWXnwPAxsYVL8/O\nV7eveXfH1SUWK6u/f263NDVRGO0EioFEwPivP1cU5f2aCvlXpDASQgghzEu13sjktb9y8FIBH4xq\nx/1t5BQTYd6OJBfy3g8X+DVNQ5i3E7P7xTK0Xch1B+0qikJ+wS6SkxdSUXERV9dmxETPxcenz+3f\nSCkKbJkIZ7fDuK8hquftPZ6oEUajkfj4eC5cuMDgwYNp3/42Vo+JazYdT+fF7WfwdbHnkzEdaBMm\nhyXUNwVLl5K/6AM8R4wg8NVX6l1ZdD06XRGa4iNoig5RpDlMVVUqAHZ23nh5db22AsnJKcIsPp/b\nVROF0WlFUVrWeLIbJIWREEIIYT70RhPT1yew51we7z7YmhEdw9SOJESNUBSFn5Lyee+HC5y5Ukoj\nf1fmDojl7haBWFtboSgKRUU/czl5AWVlp3F2jiY6ajb+/vfU3LvWR5bCzmeg/3zoMadmHlPcFpPJ\nxJdffsmZM2e499576dy5s9qRzF613sjL28+w6dcMejb25YNR7fB2sVc7lvgvhatWk/fOO3gMGUzQ\nW29hZW2eq3Oqq6/8sfroMBrNYbTaHAAcHILw9ur+xwqkbjg6WOZqzpoojJYDHyqKcqqmw90IKYyE\nEEII82Awmnjii9/49lQ2rw1tydiucoy0sDwmk8LOMzks2J3EpbxyWgS7M69XFe6GlZSUHMfRMYSo\nqFkEBgyt2fkYGcdg1T3Q+C4YtQEawDvf9V2lwcCK735Ak3icgQMH0r37bW43FGQUVTJjQyKnskp4\nvE8j5gyIve5KPqEuzeefk/PKq7jdfTch772Lla1lzAJSFIWqqlSKig79cQrbEQyGYgCcnaPx8uqO\no21z3F064B3YSOW0NaMmCqOzQCMgBdACVlydYdS6JoP+GSmMhBBCiPrPZFJ4cvNJtp7I4h+DmvFo\nTzlGWlg2o0lhx697yclaTCOPM1QYPPH0n0KP1hOxtq7h1RAVBbC0J9g6wJSfwEm25qitqqqKubv2\ns809kC7o+WfnVjRxkROYbsf+pHye+OIERpPCwri29G8eoHYkcR3FW7eR/fzzuPbpQ+gHi7Cyt9zV\nX4piorz83LXVRxrNUUymKnQlHtw9NMEitqz9WWF0MxXgPTWYRwghhBAWRlEUXvjqNFtPZPHkwFgp\ni4TFKy+/QHLKIlzLd9HUx4si66ksONaSjGKFHr+e4Mm7mtC2puatmIywZRJUFcEju6Usqgdyc3PZ\ntGkT3mXljLxzIDsUB3ofO8/wAC+ejAok0slB7YhmxWRSWLLvEgv3JNEkwI2lYzoQ6euidixxHSXf\nfkv2P/6BS/fuhCxaaNFlEYCVlTVubi1wc2uBn8eDbFrzNAbrDHqPG2cRZdFfueEVRmqTFUZCCCFE\n/aUoCq/uOMuqg6k81ieGp+5qqnYkIWpNZWUqKSmLycn9GhsbF8LDHyU8bAK2tm5U642sP5LGxz9d\npqhCR/9mAcwbGEuzIPfbu+iPr8PP78LgJdB+bM18IuKW/f7773z99dc4OjoSFxdHeHg4hToDS9Jz\nWZVVgEFReDjIh9kRAQQ7WvbNdE0oqdQzJ/43fjyfxwPtQnjzgVY42duoHUtcR9mePWQ+MRvndu0I\nW7EcaycntSPVmaryMja/8hyanGyGP/cKoc1VG/Fc4257S5rapDASQggh6idFUfjnDxf45KfLPNIj\nin8Mambx77iJhqm6+gopqUvIzt6ClZUdYaHjiIiYgp2d1//823KtgVW/pLD8QDLlWgP3tQ5mTv/G\nRPu53vyFk3bBxhHQbgwM+agGPhNxqwwGA7t27eLYsWNERETw4IMP4ubm9h//Jker54O0XNZfKcTa\nCiYE+/J4hD9+9nYqpa7fzlwpYfr6RLJLqnjpvuaM6dowTqUyR+UHDpAx4zEcmzcj/LOV2Lg2nBVg\n2soKNr/2DwrSUxj6zMtEtm6ndqQaJYWREEIIIWrF4r0XWbA7idFdwnl9aEt5oS8sjlZXQFrqJ2Rm\nbQQgJGQUkREzcHDw+9uPLanUs/zAZVYdTEVrMDG8fQiz+jUm1Mv5xi6uSYNld4Jn2NWtaHYN5938\n+qakpITNmzeTmZlJt27d6N+/PzY2f74KJr1Ky4LUXOJzinC0sWZKqB/Tw/zwsLOMwcA1YUtCJi9s\nO4WXsz0fj2lP+/D/LV9F/VBx5CgZU6diHxNNxOrV2Ljf5qpJM6KrruLLN18m59IFBs97npgOXdSO\nVOOkMBJCCCFEjVv+82Xe/O48w9uH8u6DrbGWU2yEBdHrS0hLX0FGxmoURUdQ4HAiIx/HySnkph8r\nv0zLxz9dYsORdAAe6hzGY30b4e/2FwOSDVpYeRcUJsPUn8Bb5oKpJSUlhS1btqDX6xkyZAgtWrS4\n4Y+9VFnNuyk5bM8rxsPWhhlh/jwa6ouLbcPdcqU1GHnlm7NsPJpO9xgfFj/UDl9XmflUX1UmJpL+\n6GTsQ4IJX7sWW6+GU+zpdVq+eucVMs6c5r7ZTxPbtYfakWqFFEZCCCGEqFHrDqfy4vYz3Nc6iA9G\ntZMjj4XFMBjKychYTXrGpxgMZQQE3E901BM4O0fd9mNfKa7iwx8vEv9rJnY2VozvHsm0O2PwcrnO\nnJsdc+DXlTBqIzQddNvXFjdPURQOHjzI3r178fHxYeTIkfj5/f3Ksus5XVbJOyk57C4sxcfOlici\n/BkX7IujjXUNp67fsoqrmLE+gZOZJUzvHcO8AbHYNrCvgTmpOnWa9IkTsfXxIWL9Omxv8fvfHBn0\ner5+73VSTiZyz2Nzad6zj9qRao0URkIIIYSoMfHHM3j6y98Z0DyAj0e3x05e7AsLYDRWk5W1gdS0\npej1Rfj69ic6eg5urjU/xD21oIJFe5LYfvIKrva2PNIzikd6ROHm+Mecm5ObYNsUuOMJGPBqjV9f\n/L3q6mq2b9/OuXPnaN68OUOGDMHB4fZXwSSUVPB2SjYHNOUEO9gxJzKAUYE+2DWA0v2XiwXM/DwR\ng1Hhvbg23NUiUO1I4i9UX7hA2rjx2Li6ErF+HXZBQWpHqjNGg4Edi97h0vHDDJjyOK373a12pFol\nhZEQQgghasT237KYvek3ejb2Y8W4Djg04G0VwjKYTDquZG8hNWUJWl0u3l49iI6Zi4d7m1q/dlJu\nGQt2JbHzTA5eznZM6xXD+JhKHFcPgJAOMG472MjMm7qWl5fHpk2bKCoqYsCAAXTr1q3G57P9oinj\nreRsEkoriXSy58nIQB4I8MLGAufAmUwKn+y/zPu7LtDI35WlYzrc2gB4UWe0ycmkjRmLlZ0dERvW\nYx8aqnakOmMyGfl+yQLOH9xPnwlTaH/PYLUj1TopjIQQQghx23aezuGxjYl0ivRi1YTOcuyxMGuK\nYiQnZzvJKYuprs7Aw6M9MdHz8PLqWudZfs8s5r1dSSQmpfGt40v42euwnfEL9p4N5x39+uLUqVN8\n/fXX2NvbM2LECCIjI2vtWoqisLuwlHdSsjlTXk2ssyPPRAdyr6+HxRwgUFKlZ178Sfacy2Vwm2De\nHt4KZ3spQeszXXo6aWPGophMRKxbi0PU7W/HNReKycQPyxZz5qc99Hx4Ap2HPKh2pDohhZEQQggh\nbsu+83lMWfcrrUI8WPtIF1wd5AW/ME+KYiIv/weSkxdRWXkJN9cWREfPwcent7o36YpC0eqH8Ej7\ngYe0L5Dl0Z4n+jdmWLsQmfFSB4xGI7t27eLo0aOEhYUxYsQI3OvoJCiTovBNfjHvpuRwqVJLazcn\nno0Koo+3m1kXR+eyS5m2PoEsTRX/GNSM8d0jzfrzaQj0V66QNmYspspKwteuwTE2Vu1IdUZRFPau\nXMrJXd/S7cGH6D5itNqR6owURkIIIYS4ZQcvFTBx9XFiA1zZ8GhXPJzs1I4kxE1TFIXCwp9ITl5I\nWfkZnJ0bER09G3+/u7CyqgeFzOGP4YfnUAa8xs9+D/H+rgv8nllCtJ8Lc/rHMqhVkJxEWEvKysqI\nj48nIyODLl26MHDgQGxs6n4FpcGksCW3iPdTc8mo1tHFw4Vno4Po5ml+27e2ncjkua2ncHe04+PR\n7ekY6a12JPE39Ll5pI0bi7FIQ/jqVTjdxGmA5k5RFPavX0nCjm10vH8Yd46e2KDKTSmMhBBCCHFL\nfk0tYuxnxwj3duaLKV2vf5qTEPWcRnOEy8nvU1KSiKNjGNFRswgMHIKVVT3ZVpl+BFYPgti7YeR6\nsLJCURR+OJPLgt0XSMotp1mQO08OjKVvU/8GdSNT21JTU9m8eTM6nY7BgwfTqlUrtSOhM5nYkF3E\notQccnUGenu58Ux0EO3cndWO9rd0BhOvf3uWtYfT6BLlzYcPt8PfzVHtWOJvGIqKSBs7DkN2NuEr\nP8OpbVu1I9Wpg/HrOfLlF7S96z76Tpza4J5jpTASQgghxE07mVHM6E+P4u/uwKYp3fBzu/0TgoSo\nSyWlJ0m+/D5FmoM42AcQGfU4wUEPYm1dj4rP8nxY1hPsnGDKT+Do8R9/bTQpfHPyCgv3JJFWWEm7\ncE+eGtiE7o18VYlrKRRF4fDhw+zevRtvb29GjhyJv7+/2rH+Q6XRxOqsApak51KkN3KPrwdPRwXS\nzNVJ7WjXlV1SxYwNiZxIL2bKndE8fVcT2U5pBozFxaRNmIguNZWw5ctw6dxZ7Uh16ui2eH75Yi0t\n+wxk4JTHsbJueN+zUhgJIYQQ4qacvVLKQyuO4OFkR/zUbgR6yDvEwnyUlZ8nOXkhBQV7sLPzJjJi\nGiEho7GxqWffxyYjrBsKGcfg0T0Q+OerW/RGE1sSMlm89yLZJdV0j/Fh3sAmdIjwqsPAlkGr1bJ9\n+3bOnj1L06ZNGTp0KI6O9ex749+UGYwsz8hnaUYe5UYTDwR48VRkIFHO9afEP3S5gJkbT1CtN/Lu\niDbc20oGtpsDY3k56RMnoT1/ntBPPsG1xx1qR6pTid9tZ9+aFTS9oxf3PD4Xa+t6suq0jklhJIQQ\nQogbdimvjJHLjmBva0381G6Eedf/bRBCAFRWppCcvIjcvG+xtXUlPOxRwsImYGtbT2fA7H0VDrwP\nQz6Gdjc2YLVab2Tj0XQ+/ukSBeU6+jb1Z97AWFoEe/z9Bwvy8/PZtGkThYWF9O/fn+7du5vN9hON\n3sDH6Xl8mlmATjExKtCbOZGBhDqqt2JOURSW/ZzMP3eeJ9rPlaVjOtDIv57+vIn/YKqsJH3yFKpO\nniR08WLc+vZRO1Kd+n3PTnavWELjzt25b/YzWKswt6y+kMJICCGEEDcktaCCuGWHUYD4qd2I8nVR\nO5IQf6uqKouU1A/JydmKlZU9YWETiAifjJ1dPS5RLuyEz0dC+3Ew+MOb/vBKnYFVB1NZtv8ypdUG\nBrUOYk7/WLlZ/wtnzpxh+/bt2NraMmLECKLM9LjwPK2eD9JyWXelEIBxIT48ERGAn33dHkhQVq3n\nyc0n+eFMLoNaBfHOg63lBE0zYaquJmP6dCqPHiNkwfu433232pHq1Jn9e9n5ySKi2nZgyJMvYGPb\nsA/zkMJICCGEEH8rU1PJyGVHqNIb2TSlK40D3NSOJMRf0mrzSU37iKysTQCEhjxMROR0HOzr+Xwf\nTSosuxM8I+CR3WB369uhSqr0fHogmZW/pFClNzKsfShP9GssKwP/jdFoZM+ePRw+fJjQ0FBGjBiB\nh0c9LhNvUGa1jgWpOWzKKcLeyppHQ32ZEe6Pl13tlzZJuWVMW5dAWlElz93TlEd6RJnNSq2GTtHp\nyJg5k4qfDxD89lt4DBmidqQ6deHwL3z7wT8Ja9GKB555GVv7ejTTTiVSGAkhhBDiL+WUVDNy+WE0\nFTo2Tu5KyxDzv5kSlkuv15CWtpyMzLUoip6goAeJinwcR8dgtaP9PX01rBx4tTSash+8a2aVS2G5\nlk9+uszaI2koisKoTuE83rcRAe71dzZPXSgrK2PLli2kpaXRuXNnBg4ciK2tZa2CSa7U8m5KNl/l\nFeNqY830cH+mhPrhals7W2y2/5bFs1+ewtXRliUPtaNLtE+tXEfUPMVgIGvOXMp27ybwlVfwGhmn\ndqQ6denXo3yz4E2CGjdh+HOvYlePZ5fVJSmMhBBCCPGnCsq1jFx2mNxSLese6Uy7cBmiK+ong6GM\n9IxVpKd/htFYQWDAYKKiZuHsHKl2tBv3zROQsBoe+gKa3FPjD59TUs2HP15k0/EMbKytGN89kmm9\nYvB2aXjvoqenpxMfH091dTWDBw+mdevWakeqVefKq3gnJZudBaV429kwMzyACSG+ONXQSWV6o4k3\nvzvHqoOpdIr04qOH2+PfwAtJc6IYjVx55llKd+wg4Pnn8B43Tu1IdSr1ZCJf/fNV/CKiePAfb+Dg\nLKsw/0UKIyGEEEJcV3GljlHLj5BaWMHaSV3oHOWtdiQh/ofRWEVm5jrS0pej12vw8xtIdNRsXF2b\nqB3t5vz2OXw1DXrMgf7za/VS6YWVLNqbxFcnsnC2t2VSjyge7RmFu6Plz+pQFIWjR4+ya9cuPD09\niYuLIzAwUO1YdSaxtIJ3knPYrykj0N6O2ZEBPBzkjf1tHBeeW1rNYxsS+TVNw6Q7onju3qbY1VAR\nJWqfYjKR/dJLlGz5Er+5c/GdMlntSHUq4+wptr41H6+gYEa89CZOrrLl/t9JYSSEEEKI/1FarWfM\np0c5n1PGyvGd6NG4ns99EQ2OyaQj68omUlM/RqfLw9u7JzHRc3F3N8OVIrlnYEU/CO0IY78Cm7rZ\nFnUpr4wFu5P47lQOHk52TOsVw/juETjbW9a2rH/RarV88803nD59miZNmjB06FCcnJzUjqWKQ5py\n3k7J5lhJBeGO9syLDOTBQC9sbnLW0NHkQh7beIJKnYF3hrfm/jZmsPVTXKMoCrmvv4FmwwZ8Z0zH\nb9YstSPVqStJ59jyxku4+fgycv7bOLvLlvv/JoWREEIIIf5DhdbAuJXHOJlRzLKxHejXLEDtSEJc\nYzIZyMn5ipTUxVRXZ+Hp0YnomHl4eXZSO9qtqS6B5X1AVwHTDoCrf51HOJ1Vwvu7LrDvQj6+rg48\n3ieGh7qE41BLc27UUFBQwKZNmygoKKBv377ccccdWN/GqhpLoCgKPxaV8U5yNr+XV9HY2YGnooK4\nz88D678pjhRF4dMDKby98zwRPs4sHdOBWDkMwawoikLee+9R9NlKvCdOxP/ppxrUcPLc5Etsfu0F\nnNzdGTn/HVy9ZBX19UhhJIQQQohrqvVGJq0+zpHkQj56uD33tApSO5IQACiKiby870hO+YDKymTc\n3FoSEz0Pb++e5nuToygQPxbOfwcTvoWIbqrGSUgr4t0fLnAkuYgQTydm9WvE8Pah2Jr59qJz586x\nbds2bG1tGT58ODExMWpHqlcUReHb/BLeScnmYqWWlq5OPBMVSH8f9+v+bJVrDTy95STfncrh7haB\nvDuiNW4NYDujpcn/cAkFH32E18MPEfDii+b7PHoLCtJT2fTq89g7OjJy/tu4+9Z9UW8upDASQggh\nBABag5Gp6xLYn5TPgrg2PNAuVO1IQqAoCgWFP5KcvJDy8nO4uDQmOnoOfr4Dzf8G59CHsOsfMPAN\n6P642mmAq1/vg5cKeXfXBU5mFBPl68Ls/o25v3Uw1tbm9fU2Go38+OOPHDx4kJCQEOLi4vDwkC0n\nf8aoKGzN1fBeSg5p1To6ujvzbHQQPbz+f+XQpbwypq5LIKWggmfubsqUO6PN/+ewASpYsYL89xfg\nMWwYQa+/hlUDWm1XdCWTTfOfxdrampHz38EzUN4Y+ytSGAkhhBACvdHE4xsT+eFMLm8Pa8WozuFq\nx2fChwAAIABJREFURxKCoqKDXE5eSGnpCZycwomOmk1AwH1YWVnAVqm0Q7D6Pmg6COLWQj276VYU\nhT3n8nh/1wXO55TRNNCNuQNiGdA8wCwKgvLycrZs2UJqaiodO3bk7rvvxtbWMmcz1TS9SeHz7EIW\npuWSrdXT08uV56KCyE4r5ektJ3Gyt2HxQ+3oHiOz7cxR0dp15L75Ju6DBhH8z3ewsrGA59MbVJKX\nwxcvP4PRYGDk/LfxCQlTO1K9J4WREEII0cAZTQpzNv3G1yevMP/+5ky4I0rtSKKBKylJ5PLl99EU\nH8HBIZCoyJkEBQ3H2tpCtr2U5cKyO8HeBab8BI7uaif6UyaTwo5T2SzcnURKQQVtwjx5cmAsPRr5\n1tviKCMjg/j4eKqqqrjvvvto27at2pHMUrXRxJorBSxOy6VQb8Q6r4pW5bDmwXYEejiqHU/cAk18\nPDkvvYzbgP6ELFiAlZ2FPKfegNKCfDbNfxZdVSVxL72JX4S81rkRUhgJIYQQDZjJpPDMl7+zOSGT\nZ+9pyrReMttDqKes7CyXkxdQWLgPOzsfIiOnExL8MDY2DmpHqzlGA6wbCpm/wuS9ENBC7UQ3xGA0\nsTUxiw/2XiSruIouUd48dVcTOkbWn0GxiqJw/Phxdu7cibu7OyNHjiQoSLab3I68smqmf36Co+iw\nbuSBzgqG+HvyVFQgjZylNDInJV9/zZVnnsWlZw9ClyzB2t5e7Uh1pqJYw6b5z1BRXMyIF98gMKax\n2pHMhhRGQgghRAOlKAovbT/DuiNpzO7fmNn9Y9WOJBqoiorLJKcsIi/vO2xt3YkIn0Jo6DhsbV3U\njlbz9syHXxbC0KXQ9iG109w0rcHIF8cyWLLvEvllWno38ePJgU1oGaLubCCdTseOHTv4/fffady4\nMcOGDcPJyUnVTObueGoRj21IpKzawFvDWtG7ZQCfZOSzIjOfaqOJuEBv5kUFEubYcIoHc1W6cydZ\nc+fh3LkzYUs/wdqx4ZR9laUlxL/yHKX5eQx/4TVCmjRTO5JZkcJICCGEaIAUReHN786x4kAKU3tF\n8+zdTevt9hJhuaqqMkhJWUx2zlfY2DgRFjaB8LBHsbOrv1u0bsv57+CLh6DDBLj/A7XT3JYqnZE1\nh1NZuv8yxZV67mkZyNwBsTRW4Wj1wsJCNm3aRF5eHn369KFnz55YN6AhvjVNURRWHUzlze/OEerl\nxNKxHWga+P8/k/k6PR+m5bHmSgEmBcYE+zA7IoAAh4azvcmclO3bR+bMWTi1bk34iuVYu1hgEf8n\nqsvL2fzaCxRlZfDAs/MJb9la7UhmRwojIYQQogFasOsCi3+8xITukbx8f3Mpi0Sd0mpzSUn9iCtX\n4rGysiI0ZCwREVOxt/dRO1rtKUqBZb3AOxIm7QI7y3iHv7Raz2cHUvjslxQqdQaGtg1hdv9Ywn2c\n6+T658+fZ9u2bVhbWzN8+HAaNWpUJ9e1VBVaA89uPcU3J68woHkA78e1wd3x+kXQlWodC9Ny+Ty7\nEDsrKyaG+PF4hD/edjJcvL4oP3iQzGnTcWjShPBVK7Fxq/tCVy26qkq2vP4iuSmXGfr0i0S17aB2\nJLMkhZEQQgjRwHy07xLv/nCBUZ3CePOBVmZ3VLYwXzpdEWlpS8nMWo+iGAkOjiMy8jEcHQLVjla7\n9NXw2QAoToep+8ErUu1ENa6oQsey/ZdZczgVg1EhrlMYM/s2IsijdraFmUwm9u3bx4EDBwgKCiIu\nLg4vL69auVZDcTm/nGnrEricX86TdzVh2p0xN/T7IbVKy3spOXyZq8HFxpqpYX5MC/PHzbbhnL5V\nH1UeP0765CnYR0QQsWY1Np6eakeqM3ptNVvfmk/WhbPcP/c5GnfqpnYksyWFkRBCCNGAfPZLCq/t\nOMvQtsG8H9cWGymLRB0wGMpIS/+UjIxVGI1VBAYOITpqFk5O4WpHqxtfz4TEtfBwPMTepXaaWpVX\nWs2SfZf4/Fg6VlZWjO0awYzeMfi41tzg8oqKCr788kuSk5Np374999xzD3YN6LSn2rDzdDZPbv4d\ne1trPnyoHXc08r3pxzhfUcW7KTl8m1+Cl60Nj4X7MynUD2cb2R5Y16pOniR94iRsAwOJWLcWWx8L\nXr35Xww6HV+9+xrpp05y78x5NL2jl9qRzJoURkIIIUQDseFoGi9sO809LQP58KF22MqLeFHLjMZK\nMjLWkpa+HIOhBH+/e4iOno2LSwPaNnRiA2yfAT3nQb+X1E5TZzKKKlm89yJfJmbiaGfDpDuimHxn\nNB5Ot1fsZGZmEh8fT0VFBYMGDaJ9+/Y1lLhhMhhNvLvrAsv2J9MmzJNPRrcn2PP2VoWdLKvk7eRs\n9hWV4W9vyxMRAYwJ9sFB5krVieqzZ0mbMBEbT08i1q3DLsBf7Uh1xmjQ8/X7b5KceJy7ps+mZe/+\nakcye1IYCSGEEA3AlwmZPLnlJH2a+LN0TAfsbeWFu6g9JpOWrKzPSU37BJ2uAB+f3kRHz8HdraXa\n0epWzin4tD+EdYGx28C64W3RuZxfzsLdSez4PRt3R1um9ophQvdIXBxubs6NoigkJCTw/fff4+bm\nRlxcHMHBwbWUumHIL9My6/MTHE4uZEzXcF68rzkONbiN7EhxOW8nZ3OkpIIQBzvmRQUSF+CNraxs\nrTXaixdJGzsOK2cnItetwy4kRO1IdcZkNPLt4ndJOvIL/R6ZQduB96odySJIYSSEEEJYuB2/X2HW\n5yfoHuPLp+M74mjX8G5aRd0wmQxk53xJSsqHaLXZeHp2ISZ6Lp6e//Na0/JVFcPy3mDQwtSfwdVP\n7USqOnullAW7L7DnXB6+rvZM792I0V3Cb+j5SK/Xs2PHDk6ePEmjRo0YNmwYzs51M1TbUiWkaXhs\nQyKaSh1vPtCK4R1Ca+U6iqKwX1PGW8nZnCyrIsbJgaeiAhns74m1HLZQo7QpKVfLIisrItavwz4i\nQu1IdUYxmdj58ULOHthH73GP0mHQULUjWQwpjIQQQggLtvtsLtPXJ9A+3IvVkzrhbC+n14iapygm\ncnN3kJyyiKqqNNzd2xATPQ8vr+4N8wQ+RYFNYyBpJ0z4DsK7qJ2o3khM1/D+rgscvFRIkIcjM/s2\nZkTHUOz+ZItsUVER8fHx5OTk0KtXL3r16oW1bG26ZYqisPZwGq9/e5YgDyc+GdOeFsEedXLdnQUl\nvJOSw/mKapq7OPJMdBADfdwb5nNEDdNlZpI2ZiyKTkfEurU4xMSoHanOKIrC7hVLOLX3B+4YOZau\nw0aqHcmiSGEkhBBCWKifk/J5dM2vNAt2Z/0jnXH7k6ORhbhViqJQULCHy8kLqKhIwtWlCdHRc/H1\n7dewbwIPfgC7X4K73oJuM9ROUy8dulzAez9cIDG9mAgfZ2b3b8zgNiH/MYg/KSmJrVu3AjBs2DBi\nY2PVimsRKnUGnt96iq9+u0K/pv4siGuLh3Pd/l4wKgrb84p5NyWblCod7d2deTYqiJ5erg37OeM2\n6HNySBszFmNZGRFrVuPYtKnakeqMoijsW7OcE99/Q5cH4ugxapzakSyOFEZCCCGEBTqSXMiEVceI\n9nXl88ld6/ymQFg2RVEoKvqF5OQFlJb9jpNTJNHRswnwH4SVVQNf/ZF6ENbcD83uhxGrQW6C/5Si\nKOy7kMd7PyRxNruU2ABX5g6IZUAzf37++Wf2799PYGAgcXFxeHt7qx3XrKUWVDBtfQIXcsuY2z+W\nx/o0wlrFWUJ6k0J8ThELUnPI0urp7unKc9FBdPJwUS2TOTIUFJA2ZiyG/HzCV6/CqVUrtSPVGUVR\nOPD5Go5v30KHQUPoNfZRKR1rgRRGQgghhIVJSNMw9rOjhHg68cWUrjV6nLUQxcW/cjn5fYqLj+Ho\nEExU1CwCAx/A2lq2O1KWA8vuBAc3mLwPHN3VTmQWTCaF70/nsGD3BTLzS7jXLR0PfSFt27Zl0KBB\n2NlJ4X07dp/NZW78b9hYW/HBqHb0iq0/87SqjSbWZxeyKDWXAr2Bft7uPBsdSCs3mVH1dwwaDenj\nxqPLzCT80xU4d+igdqQ6dfjLzzkUv4E2A+6h3yMzpCyqJX9WGMlvfCGEEMIMnc4qYcKqY/i7ObDh\n0S5SFokaU1p6iuTkBRQW/Yy9vS+xsS8TEjwSa2v5HgPAaIAtk0BbBmO/krLoJlhbWzGodRCtvY2s\n3fAF2qoKDukjOJvtT1BGKV2ifdSOaJaMJoUFuy/w0b7LtA714OPR7Qn1ql9FjKONNY+G+vFQkDcr\nMwv4KD2PAb8mcZ+fB09HBRHr4qh2xHrJWFpKxiOPoktLI2z5sgZXFh3/ZiuH4jfQolc/+k2aLmWR\nCqQwEkIIIczMhZwyxn52FHdHOzZM7oq/u7zQFrevvOIiyckLyc//AVtbTxrFPE1o6DhsbJzUjla/\n/PgqpB2EB5ZDQHO105idxMREvv32W1xcXBj90CQaXzHx4d6LjFx+hJ6NfXlyYBPahHmqHdNsFJZr\neeKL3/jlUgEPdQ7j5ftb1OsTMl1sbJgZEcC4YB+WZuSzPDOf7/JLGB7oxZORgUQ4STH9L8byCjIm\nT6H64kXCPlqCS9euakeqUyd+2MHP61fSpFtPBk6bhZUMwVeFbEkTQgghzMjl/HJGLjuCjTVsntqd\ncJ/69S6yMD+VlWmkpC4mJ2c7NjYuhIdNIjx8Era2bmpHq3/OfwtfPAwdJ8F9C9VOY1b0ej3fffcd\nJ06cIDo6muHDh+PicnWOTbXeyLrDaXz80yU0lXoGNg9g3sAmNAmU78G/8ltGMTPWJ1BQoeP1IS2J\n6xSmdqSbVqAzsCQ9l9VZBRgUhYeDfJgTGUCQg73a0VRlqqoiY8pUKhMTCVm0EPcBA9SOVKdO7dvF\nrqWLienYlfvnPIuNraxzqW0yw0gIIYQwcxlFlYxYehiDycQXU7rRyN9V7UjCjFVXZ5OSuoTs7C1Y\nWdkSGjqWiPAp2NvL0OHrKkqGZb3BJxom/QC2shLiRmk0GuLj48nOzqZnz5706dMH6+usFijXGlj5\nSworfk6mXGdgcJtg5vSPJdJXBiT/O0VR2HA0nVe/OYu/uwNLx3SgZYiH2rFuS7ZWx6LUXDZkF2Jr\nZcX4EF9mhgfga9/wigKTTkfm9BlUHDpE8Lvv4nHfILUj1alzv/zEd0veJ7J1O4Y89SK2MtusTkhh\nJIQQQpixK8VVxC07TLnWwBdTutI0UOamiFuj0xWQmraMrKz1KIpCSPAoIiNn4ODgr3a0+ktfBZ8O\ngJIMmPozeEWonchsXLx4ka1bt2IymRg2bBhNmjT5248prtSx7OdkVh9MRWc0MaJDKLP6NSbYU7ZH\nVuuNvLDtNF8mZtIr1o8PRrXF09lyVuOkVWl5PzWHLTkanGysmRLqx7QwPzzsGkZxpOj1ZD4xm/If\nfyTojTfwHD5M7Uh16uLRQ3yz6G1Cm7bggWdfxs5BttzXFSmMhBBCCDOVV1bNyGVHKCjTsnFyV1qF\nmvc7yUIden0p6ekryMhcjdFYTVDQMKIiZ+LkFKp2tPpv+2NwYgOM3gyNG9bWkFtlMpk4cOAA+/bt\nIyAggLi4OHx8bm6odV5ZNR/vu8zGo+kAPNwlnMf6NMLPrWGu7kovrGTa+gTO5ZQyq29jnujXGGtr\nyxwCnFRRzbspOXyTX4ynrQ0zwv15JNQXF5v6O5/pdikGA1lPPkXZzp0EvPQi3g8/rHakOpV84jjb\n332DgJhGPPjCa9g7SkFcl6QwEkIIIcxQUYWOUcsPk6mpYt0jnekQIduFxM0xGCrIzFxDWvoKDIZS\n/P0HER01GxeXaLWjmYfEtfD1TLjzaej7gtppzEJVVRVbt27l4sWLtG7dmvvuuw97+1tfBZNVXMWH\ney+yOSETextrJtwRydQ7oy1qZc3f+fF8LrO/+A0rKysWjWpLnyYNY0XgqbJK3knJYU9hKb52tjwR\nEcDYYB8cbSxrALJiMpH93POUbN+O/9NP4zNpotqR6lTaqd/Y9s4r+IZFMOLFN3Bwlm2odU0KIyGE\nEMLMlFTqeWjFES7nl7N6Yme6xciR0+LGGY1asrI2kJr2CXp9Eb6+/YiOmoObWzO1o5mP7JNXt6JF\ndIcxX4K15a5uqCnZ2dls2rSJ0tJS7r77bjp16lRjR2GnFFSwaE8SX5+8gquDLZN7RjOpRxSuDpa7\nXcloUvhgTxKLf7xEi2B3lo7pQJh3wzvs4HhJBW8nZ3OwuJwQBzvmRAYyMtAbOwtYYaUoCjnzX6F4\n0yZ8Z83Eb8YMtSPVqczzZ/jyzZfw9A8k7uW3cHKTLfdqkMJICCGEMCPlWgNjPj3K2SulrBjfkV6x\nfmpHEmbCZNKTnb2FlNQlaLU5eHl1JyZ6Lh4e7dSOZl6qimF5LzDqr84tcvFVO1G9d+LECb799luc\nnJyIi4sjLKx2Tu06n1PKgl1J7Dqbi7eLPdN7xTC2W0S9Pk7+VmgqdDyx6Td+TspnRIdQXhva0uI+\nx5t1oKiMt1KySSytJMrJnicjAxka4IVNDZWSdU1RFHLfegvN2nX4TJmC35zZNVawmoOcS0lsfv0F\nXLx8GPnyW7h4eqkdqcGSwkgIIYQwE1U6I+NXHSMhTcMno9szsEWg2pGEGVAUIzm535CS8gFVVel4\nuLcjOmYe3l7d1I5mfkwm2DQaLu6Cid9DWGe1E9VrBoOB77//noSEBKKiohg+fDiurrV/iuPJjGLe\n23WBAxcLCHB3YGbfxsR1DMPe1vy3K/2eWcz09Ynkl2l5ZUgLRnUKa1BFwl9RFIXdhaW8nZzN2Ypq\nmrg48kxUIPf4epjd1yhv4SIKly3Da9xYAp57zuzy34681GQ2v/o8Dq6ujJz/Nm7eUsqrSQojIYQQ\nwgxU641MXvsrBy8V8MGodtzfJljtSKKeUxSF/PxdJKcspKLiIq6uzYmJnouPT+8GdfNRo35ZCHvm\nw93vQNdpaqep14qLi4mPj+fKlSv06NGDPn36YFPHg4mPJhfy3q4LHE/VEObtxOx+sQxtF4KNmW5X\n+uJYOi9tP4OfmwMfj25PmzBPtSPVSyZF4eu8Yt5NyeFylZY2bk48GxVEb283s3juK/jkE/I/WIxn\nXByBr8w3i8w1pTAznU3zn8XG3p5R89/Bwz9A7UgNnhRGQgghRD2nM5iYvj6BvefzeG9EGx7sIKdX\niT+nKApFRT9zOXkBZWWncXaOITp6Nv5+d2NlZf4rLFSTcgDWDobmQ+DB/2PvPsOjqtY2jv9n0nvv\nPYTeW0AEFCkiVVqiUqQJVhTRYwE7Kip2j0pvgiahI4ogIAhCQhCQlgDpvfeemf1+wMN7PDaQJHuS\neX5fznVFT+YOONl77r3Ws9aAEX2Iu1EJCQls3rwZvV7P3XffTfv26s3HUhSFQ5fyWLo3nnMZpbRy\ns+HJoW25q5NnszlJrLpOx4s7zhEZm86A1q58eE93nG2MZ7D3P1WvV4jKKeTd5GzSq+vo62DDs8Fe\n9HVs/FVu/1TB6jXkvv02DmPH4PXmm2i0xvM7uyg7k4iXnwVFIfzlJTh5+agdSSCFkRBCCGHQ6nV6\n5n11im/OZrP47k5M6RugdiRhwIqKYkhIfJeSklgsLX0JCnoMT4+70Wpb7vDfJlGWDZ8PACtHeOAA\nWNipncgg6fV6jhw5wsGDB3F1dSU8PBxXV8PYTqIoCnvOZfPevktczi2no7c9Tw1ry+1t3Qx6BUda\nYSUPbTzJuYxSHrsjhCeGtGm2K6TUUqPXszGzgA9ScsitrWeQsx3PBnvR1c6whoQXbtpEzquvYTd8\nOD5L30Fjajy/t0vzcvnqpWeor60h/OUluPj6qx1J/EoKIyGEEMJA6fUKC6LOsO1UBotGtmf2ADnu\nXPyx0tJfSEh8j8LCHzE3dyco8FG8vSeh1coqhJumq4N1YyDr9NWyyF1Ok/sjVVVVbNu2jUuXLtGp\nUyfGjBmDubnh/fen0yvsPJPB+/suk1pYSc8AJ54a1tYgT5v8IT6XJyJOo9MrvB/WjSEdZHvOzajU\n6VmTkc8nKTkU1esY6ebA00GetLOxUjsaxVu2krVwIbaDBuH70YdozMzUjtRkygrziXj5WarLy5j0\nwht4BLVSO5L4L1IYCSGEEAZIURSe33aWL2PSePrOtjwyKETtSMIAlZfHk5j4Pnn5+zAzcyIg4EF8\nfaZgYmKpdrSWY+8i+OljGL8SukxSO41Bys7OJiIigpKSEu68805CQ0MNetUOQJ1OT1RsOh/tv0x2\naTX9Q1xZMKwN3f3VP41Jr1f4+MAVPth/ibYednw+pSeBrjZqx2oxyup1LEvL4/O0XCp0eiZ4OLEg\n0JMgawtV8pR8vZvMp5/Gpl8/fD/9N1oLdXKoobKkmIiXn6W8qICJCxfj1bqt2pHE/5DCSAghhDAw\niqLwyq4LrP0pmUcHhfDUnXIDJX6rsjKZxKQPycnZhYmJDf7+s/H3m46pqWyValAXd0HEFOg9G0a+\nq3Yag3TmzBl27dqFlZUVkyZNwt+/eW0lqa7TsTE6lU8PXqGgopYh7d1ZMKwt7b3sVclTUlnHExGn\nOBifx/juPrw+rjNW5k07LNxYFNbV8+/UXFan51GrKNzr6cL8QA98LJtuZVzpvn1kPDEf6x498Fu+\nDK2V+qudmkpVeRmRrzxHcXYWE55/Bd/2ndSOJP6AFEZCCCGEAVEUhbf2xPP5oQRm9Q9i0cj2Bv+k\nXjSd6upMkpI+Jit7CxqNOX5+9xPg/wBmZnJaUoMrSIDlt4Nra5jxLZgaz1P/61FfX893333HiRMn\nCAgIYOLEidjZNd/CsqKmnjVHk1h2OJGy6npGd/Vm/pDWBLs13YDkcxklPLTxJNkl1bw4uiNT+vjL\n7/8mkFNTx4cpOWzILECrgfu9XXkswB0388bdFlZ++DBpjzyKVYcO+K1ahYmt8awiq6msIOq1ReSn\nJTPuXy8R0KWb2pHEn5DCSAghhDAgH+2/zHv7LjG5jz+L7+4kHxYEADW1+SQnf0pGxpcA+PjcS2DA\nQ1hYuKmcrIWqrYRVQ6E0E+YeBkc/tRMZlJKSEqKiokhPT6dfv34MHjwYE5OWsQqmpLKOFT8msvpo\nEjX1eib08GHe4Nb4OjXugOSo2DQWbT+Hs405n07uYRBb44xNWnUt7yVnE5FViKWJlgd83XjIzw1H\ns4YfPl1x/Dhpcx/EvFUwAWvXYmKvzoo2NdRWV7Hl9RfJTrjEmAULadUzVO1I4i9IYSSEEEIYiOWH\nE3jjmzgm9vTl7Qldms2Rz6Lx1NUVk5K6grS0dShKLV6eEwgKegxLS2+1o7VcigLbH4YzX8KUzRAy\nRO1EBiUpKYmoqCjq6+sZO3YsHTt2VDtSo8gvr+GzHxLYcDwFRVG4L9SfRwaF4G7fsPPBaup1vLzz\nAl/GpNKvlQsf39sdF1tZzaamhMpq3knKZntuMfamWh7yc+cBXzdsTRumFK38+WdSZ83G3NcX//Xr\nMHUynnKwrraGbUteIf3COUbNf4Y2fW5VO5L4G1IYCSGEEAZg/bFkXtxxnlFdvPjwnu5ybLKRq68v\nJy1tDSmpK9HpKvDwGEVw0ONYWwepHa3lO7kWdj0Otz0Lg55TO43BUBSFo0ePsn//flxcXAgPD8fN\nreWvcMsqqeKj/VeIik3D1ETD/f0CeXBgK5xsbn7OTUZxFQ9/cZIz6SU8dHsrFgxtg6mJtgFSi4Zw\nvryKtxKz2FtQirOZCfP8PbjfxxWrm/g7qjp7ltTpMzB1cyNgw3pMjeA99B/1dXXsWLqY5DM/M+KR\nJ2k/YJDakcR1kMJICCGEUFnEiVSe2XKWoR08+HRyD8zkA4PR0umqSc/4gpSUZdTVFeLmOpTg4PnY\n2srg8yaReRpWDYPAW2HyZtC2jG1WN6u6uprt27cTFxdHx44dGTNmDBZGdJITQEpBBR9+f5ltpzOw\nNTdl1oAgZvUPws7yn825+fFyHvO+PEW9TmFpWFfu7OjZwIlFQ/m5pIIlSVkcLirHy8KM+QEe3Ovl\ngtkNPtipjo8nZdr9mNjZEfDFBsw8jefvXFdfz9cfLOHKieMMmzuPzncMUzuSuE5SGAkhhBAq2nE6\ngyciTjOwtRvLp/XEooGWvIvmRa+vJTMziuTkf1NTm4OzU3+CWz2Jg31XtaMZj6oiWDYQ9Pqrc4ts\nXNROZBByc3OJiIigsLCQYcOG0bdvX6OerXY5p4z39l3i23PZOFmb8eBtrZh2S+B1n2Sm1yt8diiB\npXvjaeNux+dTexLkajzDjpuzo0VlLEnM5kRpBQGW5jwV5Ml4DydMruP9UJOQQMrUaWjMzQn4YgPm\nvr5NkNgw6PU6vvn4XeJ/Osyg6XPpcddotSOJGyCFkRBCCKGSPeeyeGTTKUIDnVkzozeWZlIWGRtF\n0ZGdvZ3EpI+ork7HwaEnrYIX4OTUR+1oxkWvh6/uhSv7YeYe8P3dvbFROnv2LDt37sTCwoKJEycS\nGBiodiSDcTa9hKV74zl0KQ83OwseuyOEe3r7Y2765ytES6rqWBB5mu8v5jK2mzdvju+MtXnDD1QW\njUdRFPYXlrEkMYtz5VW0trbgX0FejHRzQPsnxVFtSgopU6aiKAoBG9ZjEWQ8W4sVvZ7vPv+I84e+\nZ8B90wkdO1HtSOIGGWRhpNFoTIBYIENRlFF/9e9KYSSEEKI5OhiXy5wNsXT2cWDDrD7YWMiHBmOi\nKHpy8/aQmPgBlZUJ2Nl1JDj4SVycbzPq1Ruq+fFd2P8q3PUO9JmjdhrV1dfXs2/fPqKjo/H392fS\npEnY2dmpHcsgnUgu5J3v4olJKsTH0YrHh7RmfHef380iuphVyoNfnCSjqIoXRnVg2i0B8l5vxvSK\nwtd5JbyTlMXlyhq62FrxTLAXdzjb/ebvtS4zk+QpU1Aqq/Bfvw7LNm1UTN20FEVh/6rPOLPvG26Z\neB/9Jt2ndiTxDxhqYfQk0Auwl8JICCFES3P0Sj4z1p6grYcdGx/og/0/nIEhmh9FUSgo+IGzxHZC\nAAAgAElEQVSExPcoL7+AtXUIrYLn4+Z2p3x4VEvSYVg/FjqOgwmrwMj/HkpLS4mKiiItLY2+ffsy\ndOhQTExk9eNfURSFHy/n8+7eeM6klxDsZsP8IW0Y2dkLrVbD1p/TeX7bWRyszPh0cg96BjirHVk0\nEJ2isCWniKVJ2aRW1xLqYMOzQV70c7KlLieXlKlT0RUV4b92DVYt9ETBP6IoCoc2rOLk7u30HjOB\nAfdNl2tcM2VwhZFGo/EF1gGvA09KYSSEEKIlOZFcyLRVMQS4WPPlA30b5KQd0TwUFh0jMeFdSkpP\nYWXpT1DQPDw9x3B1YbVQRWnm1blFVs7wwAGwsFU7kaqSk5OJioqitraWsWPH0qlTJ7UjNSuKorD3\nQg7v7b1EfE4Z7b3sae9px9ZTGfQJcuaT+3rgZmdcw8KNRa1ez5dZhbyfnEN2bR0DbS2Y+tkHtDkd\ni//qVVh166Z2xCZ1NGIDx7dG0H34aAZNnyNlUTNmiIXRZuBNwA546o8KI41GMweYA+Dv798zJSWl\naUMKIYQQ/8DptGKmrIzG3d6CiDm3yAcHI1JTk8vRnwZibu5CYOAjeHtNQquVlWWq0tXB2lGQfRbm\nHAQ34z2JTlEUjh07xr59+3B2diY8PBx3d3e1YzVbOr3C179k8v6+SyQXVDJ3YDBP39n2d9vURMtT\npdOzJiGNjy6nU2xtwxATPc/3aE8HWyu1ozWZ6G2RHPlqPZ3vGMbQBx5Fo5X/7pszgyqMNBrNKGCE\noigPazSa2/mTwui/yQojIYQQzcH5zBLuXX4cR2tzIufegqeDpdqRRBMrLDyKg0MvTEykKDQI3y2E\nY59c3YbW2XgHsdbU1LBjxw4uXLhA+/btGTt2LJaW8vupIdTr9KQXVREop6AZDV1ZGakzZ1GUlMze\ndz9hlcaScp2eu90deTrIi2Drlv37/+TuHfywfgXt+9/O8Efmo9XKCtrm7s8KI7Umb94KjNFoNCMA\nS8Beo9F8oSjKFJXyCCGEEDftck4ZU1fFYGthysbZfaQsMlLOzreqHUH8x4UdV8ui0DlGXRbl5eUR\nERFBQUEBQ4cOpV+/frJ1pAGZmmilLDIi+spK0uY+SPXFi7T+6CN63BbK3Lp6PkvNZUV6Pjvzign3\ndObJQE98LVvedvQz+77lh/UraN2nH8MflrKopVN16DWArDASQgjREiTnVxC27BgKEDn3FoLkw4MQ\n6sq/Astvv7oFbca3YNryPrhdj/Pnz7Njxw7MzMyYOHEiQUZ01LcQDU1fXU3agw9RGRODz3vvYj98\n+G/+eV5tHR+m5LA+owCAqd4uPB7ggbtFy9iafP7QfvZ8+j7BPXozZsHzmJi2jJ9LGN4KIyGEEKLF\nSC+q5L4Vx6nXK0TM6StlkRBqq62AyKlXS6KwdUZZFul0Or7//nuOHTuGr68vYWFh2Nvbqx1LiGZL\nqa0l/fHHqYyOxvutJb8riwDczM1Y3NqXB/3ceT85m7WZ+WzKKmCWrxuP+LvjZNZ8P37HH/uR7z77\nEP/O3Rg9/zkpi4yE6iuMrpesMBJCCGGIskuqCVt2jOLKWr6c05eO3g5qRxLCuCkKbHsQfomAqVuh\n1R1qJ2pyZWVlREVFkZqaSmhoKMOGDcPUtPl+UBVCbUp9PRnz51O273s8X3kFp/Cw6/r/JVbWsDQ5\nm205RdiaaHnQz505fm7YmTavbVxXThxn1/tv4tW6HROeewUzmX/W4vzZCiMZZS6EEEL8Q/nlNUxe\neZzCilrWz+ojZZEQhuDkGvjlK7j9OaMsi1JSUli2bBlZWVmMHz+eESNGSFkkxE1QdDoyn3mWsn3f\n4/H889ddFgEEW1vwaYcADvRuywAnO95JzqbP8Qt8mppLlU7fiKkbTvLpk3z9wRLcg1ox7pmXpCwy\nMlIYCSGEEP9AcWUtU1ZGk1lczZoZvenm56h2JCFExs/w7TMQMgQGPq12mialKArHjx9n3bp1mJub\nM3v2bLp06aJ2LCGaNUWvJ+uFFyndvRu3BU/iPG3qP/o+7W2tWN05iG97tqGLrTWvJmTS9/gF1mTk\nU6s33OIo7fwv7Fj6Os6+/kx47lUsrK3VjiSamGxJE0IIIW5QaXUdk1dEE59Txur7e9O/tavakYQQ\nlYWw7DZAgbmHwdpZ7URNpqamhp07d3L+/Hnatm3LuHHjsJRVAELcFEVRyHltMUWbNuH68MO4zXus\nwb73seJyliRmEV1SgZ+lOQsCPZjo4Yyp1nBOL8y8dJHNi1/A3s2dsJfexNpeVlG3ZLIlTQghhGgA\nFTX1zFhzgrjsUj6f0kPKIiEMgV4P2+ZCWRZMWmdUZVF+fj4rV67kwoULDB48mPDwcCmLhLhJiqKQ\n+85SijZtwnnmTFwfe7RBv/8tjrZs7x7Cpi7BOJmZ8ERcGrefiGNHbhF6A1jQkZN4hS1vvISNkxMT\nFy2WssiISWEkhBBCXKfqOh2z18VyOq2Yj+7pzh3tPNSOJIQAOPIuXN4Lw98E355qp2kyFy5cYPny\n5VRUVDB16lQGDBiAViu390LcrPyPP6Fw9Wqc7rsP96efQqNp+JU/Go2GO1zs+a5nG1Z1CkSLhrnn\nUxgaG8/e/BLU2gmUl5rM5tdfwNLWlkkvvI6tk/EU8OL3ZAKeEEIIcR1q6nXM3XCS40kFvB/Wjbs6\ne6kdSQgBkPgDHHwDOk+C3rPVTtMkdDodBw4c4OjRo/j4+BAWFoaDg6wAEKIh5C9fQf6nn+IwYTwe\nixY2Sln03zQaDSPdHBnu6sC2nCLeScpm2tkketpb81ywF/2d7Br19f9bYWY6mxcvwtTMjEkvvIG9\nq3uTvbYwTFIYCSGEEH+jTqfnsU2nOHQpj7cmdObu7j5qRxJCAJRmwuZZ4NoGRn8IjfzBzhCUl5ez\nefNmkpOT6dWrF8OHD5dT0IRoIIXr15P33nvYjxqF16uvomnCFXsmGg0TPZ0Z6+7EV9kFvJ+cw8TT\nCfR3tOW5YC96Otg06usX52QT9dpCACa+8DqOHp6N+nqieZCrixBCCPEXdHqFJyPPsPdCDq+M6Uh4\nb3+1IwkhAHR1EDUd6qshbAOYN+6HKUOQlpZGZGQkVVVV3H333XTr1k3tSEK0GEURkeS88SZ2Q4fi\nveRNNCYmquQw02qY6u3KJA9n1mfm82FKLiN/vsxQF3ueCfKkk13Dn1RWmp9H1GsLqa+tJeylN3Hx\n8Wvw1xDNk2xyFkIIIf6EXq/wzJZf2HUmk+fuasf9/QLVjiSE+I99L0JaNIz5GNzaqJ2mUSmKQnR0\nNGvWrMHU1JRZs2ZJWSREAyrZsYPsl1/G5raB+Ly7FI0BrNqzNNEyx8+dmL7teS7Ii5iSCobEXmLO\n+WQuV1Q32OuUFxUS9drzVJeXMXHha7j5BzbY9xbNn/rvBCGEEMIAKYrCizvPsflkOk8Mac3c21qp\nHUkI8R/ntsLxT6HPQ9BpvNppGlVtbS27du3i7NmztGnThnHjxmFlZaV2LCFajNI9e8h87nms+/TB\n98MP0Zibqx3pN2xMTXg80IPpPi58lpbH8vQ8vs4tZpKnMwsCPfC3svjH37uytITNixdRUVTEhIWv\n4REc0oDJRUugUWv6+o3q1auXEhsbq3YMIYQQRkBRFF7ffZGVR5J48LZWPDO8baMPvRRCXKe8S7Bi\nELh3gOm7wdSwPtw1pIKCAiIiIsjNzWXQoEFyCpoQDazswEHS583DqmtX/FcsR2vd8Nu9GlpebR2f\npOSyNjMfvQKTvV14IsADTwuzG/o+1eXlRL72PEUZ6Yx/7mX8OnZppMSiOdBoNCcVRen1u69LYSSE\nEEL81rt74/n4wBWm9wvkpdEdpCwSwlDUVsCKwVCRC3N/BIeWO4A+Li6Obdu2odVqmTBhAiEh8uRf\niIZUfvQo6Q8+hEW7dvivWY2Jra3akW5IZnUtH6TksCmrAFONhhk+rjzq74GL+d9vIqqprGTz64vI\nS07k7qdfILBbzyZILAzZnxVGsiVNCCGE+C//PniFjw9c4Z7efrw4SsoiIQyGosCuJyAvDqZua7Fl\nkV6v58CBAxw5cgRvb2/CwsJwdHRUO5YQLUpFTAzpjzyKeatW+K9Y3uzKIgBvS3PebuvHw/7uLE3K\n5vO0PDZkFjDHz40H/dyxN/3jod11NdVse+sVcpMSGD3/OSmLxF+SNa1CCCHEr1YdSeKd7+IZ192H\n18d1RquVskgIgxG7Cs5GwqCF0GqQ2mkaRUVFBV988QVHjhyhR48ezJgxQ8oiIRpY1enTpD/4EGY+\nPvivWolJM3+PBVpZ8EmHAH4Ibcdtzna8l5xD6LELfJySQ4VO95t/t762lu3vLCYz/iJ3PbqAkN59\nVUotmgvZkiaEEEIAG6NTWLjtHCM6e/LRPd0xNZFnKkIYjIyTsHo4BN8O90ZAC5zjk56eTmRkJBUV\nFYwcOZIePXqoHUmIFqfq/HlSp8/AxMmJgA0bMPNwVztSg/ulrJIliVkcKCzDzdyUxwM8mOrtgqle\nx8533yDx5xMMf3g+HW8brHZUYUBkhpEQQgjxJzafTOepqDMMbufOZ1N6Ym7a8j6MCtFsVRbCsoGA\nBuYeAmtntRM1KEVRiI2NZc+ePdjZ2REWFoa3t7fasYRocaovXSJ12v1orK0I/OILzFr4+yymuJw3\nk7I4VlyBj4UZgy+dxG3PZobNepCuQ0eoHU8YGJlhJIQQQvyBXWcy+dfmM/QPceXfk3tIWSSEIdHr\nYescKM+Bmd+1uLKotraW3bt3c+bMGUJCQhg/fjzWzeCUJiGam5qkJFJnzkJjZkbA2rUtviwCCHW0\nZWu3EH4oKOH56DOsD+iC9wPtCekUQmdFQSszGsV1kLtiIYQQRmvv+WzmR5ymV4Azy6f1xNLsjwdE\nCiFU8uNSuLIPhi8Bn5a1RauwsJBVq1Zx5swZbrvtNu677z4pi4RoBLXp6aTOmAl6Pf5r12Du7692\npCZVt2U9E9a/w/PlGTjY2fHQhRQGn4hnT14JzWW3kVCPrDASQghhlA5dyuPRTafo5OPA6hm9sb6O\nY2iFEE0o4QAcfAO6hEOvmWqnaVDx8fFs27YNgMmTJ9O6dWuVEwnRMtVlZ5N6/3T0VVUErF+HRatW\nakdqMoqicHDtcs4e2EvfceH0Hz2SRxWFHbnFvJOUzfRzSXS3s+bZYC8GOtnKqbDiD8ndsRBCCKNz\nLKGAOetjCXG3Zd2MUGwt5HIohEEpSYcts8GtHYx6H1rIBxm9Xs8PP/zA4cOH8fT0JDw8HCcnJ7Vj\nCdEi1eflkTp9BrqSEvzXrMGybVu1IzUZRVH4cdNaTu3ZRc+Rd3Nr+BQAtBoN4zycGO3mSGR2Ie8m\nZxN+JoFbHG14LsiLUEdblZMLQyN3yEIIIYzKyZQiZq07gb+zNRtmheJgbaZ2JCHEf6uvhcj7r/5v\n+AYwt1E7UYOorKxky5YtJCQk0K1bN0aOHImZmfz+EaIx1BcVkTpzJnW5ufivXIFV505qR2pSx7d8\nxYmdW+g69C5umzrrd6uHTLUa7vN2YYKnExsyC/gwJYcxp65wh7MdzwZ70cVOtseKq6QwEkIIYTTO\nppcwfXUMHvaWbJzdBxdbC7UjCSH+195FkBELk9aBa8vYqpWRkUFkZCTl5eWMHj2aHj16yPYPIRqJ\nrrSU1FmzqE1Nw2/Z51j3aFnzz/7OiZ1b+ClqIx1vG8LgmQ/95e8aC62W2b5u3OvlzOr0fP6dmsuw\n2EuMdHPgX0FetLWxbMLkwhBJYSSEEMIoxGWXMnV1NPZWZmyc3Qd3e7kJEsLgnN0MMcug7yPQ8W61\n0zSIkydP8s0332Bra8vMmTPx8fFRO5IQLZauvIK0B+ZQc/kKfv/+BJu+fdWO1KRO7dnF4Y1raHvL\nAIY9+Bga7fWdcWVjYsJjAR7c7+PK52m5LEvL49u8EsZ7OPF0kCcBVvKAzVhJYSSEEKLFS8grZ8rK\naCxNTfjygb54O1qpHUkI8b/y4mHnPPDrC0NfUTvNTaurq+Obb77h1KlTBAcHM2HCBGxsWsb2OiEM\nkb6qivQHH6Tq3Dl8Pngf24ED1Y7UpM4e2MuBNcto1asvdz26AK32xk9+tTc14V9BXszyceOT1BzW\nZOSzPbeI+7xcmB/ogZeFeSMkF4ZMCiMhhBAtWmpBJZNXRAOw8YE++LvIvnwhDE5NOURMBXNrmLQG\nTJr3bJ+ioiIiIyPJyspi4MCB3H777Wiv80m/EOLG6WtqSH/kUSpPnsR76TvYDx2qdqQmdfHHg+xd\n/jGB3Xoy6olnMDG9uY/5LuamvBTiw1w/dz5IyWFjZgER2YVM93blsQAPXOVkWaMhf9NCCCFarMzi\nKu5beZzqeh1fzelLKzc5/UMIg6MosOtxKLgMU7eDvbfaiW7K5cuX2bp1K3q9nnvvvZe2RnQykxBq\nUOrqyHhiPhU//YTXG2/gMHKk2pGa1KXoo3z76fv4dejMmAXPY9qAw/Q9LcxY0saXh/3ceDc5hxXp\neWzIKmCOrxsP+bnhYCZ1QksnjzqEEEK0SLml1dy34jglVXVsmNmHdp72akcSQvyREyvh3GYYtBCC\nb1M7zT+m1+v54Ycf2LhxI/b29syZM0fKIiEamVJfT8bT/6L84EE8X3oRx/Hj1I7UpBJ/PsHuD9/B\nK6Qtd//rBczMG2fWkL+VBR+29+dQaDuGuNjzQUoOoccv8mFyDhX1ukZ5TWEYNIqiqJ3huvTq1UuJ\njY1VO4YQQohmoKC8hnuWHyejuIoNs/rQM8BJ7UhCiD+SHgurh0PIYLjnS2im27YqKyvZtm0bly9f\npkuXLowaNQpzc5n1IURjUvR6sp57jpIdO3F/5hlcZkxXO1KTSvnlNNvefgVXvwAmvfA6FtZNNyPt\nXFklbyVls6+gFFczU+YFuDPN2xVLk+b5O1yARqM5qShKr999XQojIYQQLUlJZR33rjhOQl45a2eE\ncksrF7UjCSH+SEUBLBt4tSSaexismmexm5WVRUREBKWlpdx111306tXrL4+xFkLcPEVRyH7pZYoj\nI3F7fB6uDz2kdqQmlX7xHFvefAlHDy/CXnwDKzt1VlHHllSwJDGLI8XleFuYMT/Qg3s8XTDTyu/A\n5ubPCiOpAIUQQrQYZdV1TFsTw5XccpZP6yVlkRCGSq+DrQ9ARS6ErW+2ZdGpU6dYtWoVer2emTNn\n0rt3bymLhGhkiqKQ88abFEdG4jJ3rtGVRVlX4tn21ivYu7gxceFrqpVFAL0cbNjcPYTN3VrhZWHG\n0/HpDIi5yObsQnTNZGGK+GtSGAkhhGgRKmvrmbU2lvMZJfx7cg9ua+OmdiQhxJ85/A4k7Ie73gbv\n7mqnuWH19fXs2rWLHTt24Ofnx9y5c/H19VU7lhAtnqIo5L33PkUbNuB8/zTcnnhc7UhNKjc5kS1v\nvIiVvQMTX1iMjaNhlO39nez4ukdr1ncOwsZEy6MXU7njRDy784ppLjuaxB+TseZCCCGaveo6HXPW\nnyQ2pZCP7u3O0A4eakcSQvyZK9/DD0ug633Qc7raaW5YcXExkZGRZGZm0r9/fwYNGoSJiYnasYQw\nCvmffUbBihU4hofj/uyzRrWiryA9lc2LF2Fuac2kRa9j5+yqdqTf0Gg0DHN1YIiLPbvyinknKZtZ\n55LpYmfFs0FeDHK2M6q/r5ZCCiMhhBDNWm29noc3/syRK/m8O6kro7o07yO5hWjRitNgy2zw6Agj\n34Vm9uEhISGBzZs3o9frCQ8Pp3379mpHEsJoFKxaTf5HH+MwdiyeL71oVOVDUVYGUa8tRGtqyqQX\nFuPgbrgPxrQaDWPdnRjp6sjmnELeTc7hvl8S6etgwzPBXtziaKt2RHEDpDASQgjRbNXr9Dz+1SkO\nxOXy+rhOTOgpW0KEMFj1NRB1/9X5RWHrwdxa7UTXTa/Xc+TIEQ4cOIC7uzvh4eG4uMiMNCGaSuGm\nTeS+8w52dw3H6/XFaJrpiYr/REluDlGvLUKv0xH+8hKcvHzUjnRdTLUa7vFyYbyHExuzCvkgOZtx\np65wu5MdzwR70d2++VwDjJkURkIIIZolnV7hqagzfHsumxdGdWBynwC1Iwkh/sp3CyHjJIRtAJdW\naqe5blVVVWzbto1Lly7RuXNnRo8ejbm5udqxhDAaxVu2kPPqa9jecQc+b7+NxtR4PsKWFeYTtXgh\ntdWVhL34Ji6+/mpHumHmWi0zfFwJ93RmbUY+n6TmcNfJS9zl6sC/gjxpb2uldkTxF4zn3SaEEKLF\nUBSFhdvOsv10Jk/f2ZZZ/YPUjiSE+Cu/RMGJFXDLo9BhjNpprlt2djYRERGUlJRw1113ERoaalTb\nYIRQW8nXu8la9AI2t96KzwfvozEzUztSk6koLiLqtUVUlZYwcdFi3AOD1Y50U6xNtDzs785UbxeW\np+XxeVoue/JLGOfhxNOBngRZW6gdUfwBTXOZWt6rVy8lNjZW7RhCCCFUpigKr+y6wNqfknnsjhAW\nDGurdiQhxF/JjYMVg8CrK9y/C0yaxwe+M2fOsGvXLqysrJg0aRL+/s3vyb4QzVnpvn1kPDEf6x49\n8Fu+DK2V8axEqSorJfLV5ynOyWLC86/i266j2pEaXGFdPZ+m5rIqPY9aReEeT2eeDPTEx1JWcKpB\no9GcVBSl1/9+XVYYCSGEaDYURWHJnjjW/pTM7P5BPDm0jdqRhBB/paYMIqeCuS1MXNMsyqL6+nr2\n7NlDbGwsAQEBTJo0CVtbGdIqRFMqP3SIjCcXYNW5M76ffWZUZVFNZQVb3niRoqwMxj3zUossiwCc\nzUxZ1MqbOb5ufJiSw4bMAqKyi5jm48LjAR64mRv+9cIYSGEkhBCi2fho/xWWHUpkSl9/Fo5sL1tD\nhDBkigI750HBFZi2E+y91E70t0pKSoiMjCQjI4N+/foxePBgTExM1I4lhFGpOHaM9MfmYdm6NX7L\nl2Fia6N2pCZTW13F1jdfJi8lmbFPLySgcze1IzU6dwszXm/jy4P+7ryfnM2ajHw2ZhYy29eVh/3d\ncTKTykJNxjNeXgghRLO27FAC739/iYk9fXl1TCcpi4QwdDHL4fxWuOMFCBqgdpq/lZiYyLJly8jL\ny2PSpEkMGzZMyiIhmljlzz+T9vAjmAcE4LdqJSb29mpHajJ1tTVsf+tVsq7EM/Lxpwnu3lvtSE3K\nz9Kc99r5czi0HcNd7fkkNZc+xy/wXnI25fU6teMZLZlhJIQQwuCt+ymZl3aeZ3RXbz4I74aJVsoi\nIQxa2glYcxeEDIF7NoEBH4GtKApHjx5l//79uLi4EB4ejpubm9qxhDA6VWfPkjp9BqZubgR8sQFT\nV1e1IzWZ+ro6dixdTPKZnxnx6ALa979d7Uiqu1hexVtJWezJL8XZzITH/D2Y7uOKlYnhXk+asz+b\nYSSFkRBCCIMWcSKVZ7acZVgHD/49uQdmcqMghGGryIdlA0FrCnMPgZWT2on+VHV1Ndu3bycuLo6O\nHTsyZswYLCzkpB4hmlp1XBwp90/HxM6OgC82YObpqXakJqOrr2fX+0tIiD3OsAfn0XnQMLUjGZSf\nSyt4KzGbQ0VleJqbMT/Qg3u9nDE34AcRzdGfFUbypyyEEMJgbT+VwbNbz3JbGzc+vq+7lEVCGDq9\nDrbMvloahW8w6LIoJyeH5cuXEx8fz5133snEiROlLBJCBTVXrpA6cxZaKyv81641qrJIr9fx7Sfv\nkhB7nDtmzJWy6A/0sLcholsrtnYLwd/KnGcupdM/Oo7I7EJ0zWTxS3Mmd95CCCEM0rdns1gQdYa+\nQS4sm9oTC1OZJSKEwfthCSQehJFLwaur2mn+1NmzZ1m5ciW1tbVMnz6dW265ReaiCaGC2pQUUmfM\nBBMtAWvXYO7ro3akJqPo9ez9/CPij/3IwMkz6D58tNqRDFo/J1t2dA9hY5dgHE1NmHcxldtj4tiZ\nW4xeiqNGIyPHhRBCGJwDcTnM++oU3fwcWXl/LyzNpCwSwuBd3geH34ZuU6DHNLXT/KH6+nr27t1L\nTEwM/v7+TJo0CTs7O7VjCWGU6jIySJkxA6WujoAN6zEPDFQ7UpNRFIX9qz/j/KH99Js0md5jJqgd\nqVnQaDQMdrHnDmc7dueV8FZSFnPOJ9PZ1op/BXkyxMVeyv8GJoWREEIIg3Lkcj4PfvEz7b3sWTOj\nNzYWcqkSwuAVp8LWB8Cj89XVRQaotLSUqKgo0tLS6Nu3L0OHDpVT0IRQSV1ODikzZqIvryBg7Ros\nWrdWO1KTURSFQxtWcmbft/QeO5G+E+5RO1Kzo9FoGOXuyF1uDmzNKWJpUjZTzybR296GZ4M9udVJ\nHgQ0FLkLF0IIYTBikgp5YH0swa42rJ8Zir2lmdqRhBB/p74GIqddnV8Utg7MrNRO9DvJyclERUVR\nW1vLxIkT6dSpk9qRhDBa9QUFpM6YiS4/H/81q7Hs0EHtSE3qaMQXnNy9g+53jWbAvffLipibYKLR\nMMnTmbvdnfgyq4D3U3KYcDqBgU62PBvkRQ8HG7UjNntSGAkhhDAIp9OKmbn2BN6Olnwxuw+O1uZq\nRxJCXI89z0HmKQjfCC6t1E7zG4qicOzYMfbt24ezszP3338/7u7uascSwmjpiotJnTmLusxM/Fcs\nx6qr4c46awzHt0YQvS2CzoPvZND9c6QsaiBmWg3TfFyZ5OnM+sx8PkzJYcTPlxnmYs+zwV50sDW8\nBxnNhRRGQgghVHc+s4Rpq6JxtjFn4+y+uNrKSUVCNAu/RELsKug3D9qPUjvNb9TU1LBjxw4uXLhA\n+/btGTt2LJaWlmrHEsJo6crKSJ39ALVJSfh+9inWvXurHalJndy9naMRG2g/YBBDZj8sZVEjsDLR\nMtfPncleLqxIz+OztFzuOBHP3e6OPB3kSStruQbcKCmMhBBCqOpyThlTV8Vga2HKpgf64OkgF3Mh\nmoXci7DrcQi4FQa/pHaa38jLyyMiIoKCggKGDh1Kv3795MOZECrSV1SQNmcu1XFx+AXgzAUAACAA\nSURBVH78Eba33qp2pCZ1Zt83/LB+JW363Mrwh55Aq5X5aY3J1tSE+YGezPBx5bO0PFak57Err5gw\nT2eeDPTEz1JWsV8vKYyEEEKoJim/gvtWRmOq1bDpgb74OlmrHUkIcT1qyiBiKpjbwsTVYGI4t5Tn\nzp1jx44dmJubM23aNIKCgtSOJIRR01dXk/bIo1SdOYPPe+9hN2iQ2pGa1PlD+/l+5acE9+jNiHlP\noZVh+03G0cyU54K9mO3ryscpuazLzGdzdhFTvV14PMADDwuZlfl3DOfqLoQQwqikFVYyecVxdHqF\niDl9CXSVwYRCNAuKAjsehcJEuH8n2HmqnQgAnU7Hvn37OH78OH5+fkyaNAl7e3u1Ywlh1PS1taTP\nm0dldDTeby3BfvidakdqUnE/Hea7zz4koEt3Rs9/DhNTKSjU4GZuxqutfZjr58YHKTmsy8zny6wC\nZvi48WiAO85mUov8GfmTEUII0eSyS6qZvDKa8pp6vppzC6095PhTIZqN6M/hwnYY8goE9lc7DQBl\nZWVERUWRmppKaGgow4YNw9RUbnOFUJNSV0fmggVUHP4Rz1dfwWHMGLUjNakrJ47zzcdL8WnXgbFP\nLcTUXLZBqc3H0px32vrxsJ87S5Oz+Swtl/WZ+Tzo585cPzfsTGX11//SKIqidobr0qtXLyU2Nlbt\nGEIIIW5SXlkN4cuPkVtawxez+9DNz1HtSEKI65UaDWtHQOs74Z6NYABzgVJSUoiKiqKmpobRo0fT\npUsXtSMJYfQUnY7Mfz1D6e7deCxciPPUKWpHalJJp0+y453XcA9sxcRFr2FuJVvuDVFcRRXvJGWz\nO68EZzMTHvH3YIaPK9YmWrWjNTmNRnNSUZRe//t14/uTEEIIoZqiilqmroomq7iaNTN6S1kkRHNS\nngdR08HBD+7+VPWySFEUjh07xrp16zA3N2f27NlSFglhABS9nqwXXqR0927cn1pgdGVR6rlf2Ln0\ndZx9/Rn/3CtSFhmwdjZWrOoUxJ6ebehqZ81rCZn0PX6B1el51Oj1asczCLJWVwghRJMoqapj2uoY\nEvMrWDO9N70DndWOJIS4XnodbJkFVYUwax9YqVv21tTUsHPnTs6fP0/btm0ZN24clpZywqIQalMU\nhZzFiynZuhXXRx7BZfZstSM1qYz4i2x/+1UcPDyZuPA1LG1t1Y4krkM3e2u+7NqK48XlLEnM4vnL\nGXyalsuTgZ6EeThjqlV/Na1aZIWREEKIRldRU8+MNTHEZZeybEpPbg1xVTuSEOJGHHwDkg7ByHfB\nS91VPHl5eaxcuZILFy4wePBgwsPDpSwSwgAoikLu2+9QtOlLnGfNxPXRR9SO1KRyEq+w9c2XsHV2\nZuKixVjbO6gdSdygvo62bOsewpddgnExM+XJuDRui4lje04R+mYyyqehSWEkhBCiUVXX6Zi17gRn\n0kv4+N7uDGrnrnYkIcSNuPQd/LgUuk+F7upuLblw4QIrVqygoqKCqVOnMmDAALRauZ0VwhDkf/wx\nhWvW4DR5Mu5PPYXGAGacNZW8lCQ2v/4ClrZ2TFz0OrZOsoq6udJoNAxysWdPzzas6RSImVbDgxdS\nGBobz978EprLDOiGIlvShBBCNJqaeh1zNpwkOqmQD8K7MbyTl9qRhBA3oigFts4Bz84w4h3VYuh0\nOvbv389PP/2Ej48PYWFhODjI03shDEX+suXkf/oZDhMn4LHweaMqiwoy0tj8+guYmpsz6YXXsXd1\nUzuSaAAajYa73BwZ5urAjtxi3k7KYtrZJHrYW/NckBcDnI3jhF8pjIQQQjSKOp2eRzed4vClPN6e\n0IWx3XzUjiREo9PrFdIuFmJpbYabvy3a5nzSSl01RE4DRYGwDWBmpUqM8vJyNm/eTHJyMr179+bO\nO+/E1FRuYYXhSC9LJzYnlj6effCyNb4HI4Xr1pH3/vvYjxqF1yuvoDGiVX/F2Vlsfm0hAJNeeB1H\nD0+VE4mGZqLRMN7DidFujkRkF/JecjaTziTQ08yCuVb2jOnZsu9v5WorhBCiwen0CvMjTrPvQg6v\nju1IWG8/tSMJ0agURSHxdB7RO5MoyqoAwNzKFO/Wjvi2c8K3rRPO3jbN66n7nmch6zTc8yU4B6kS\nIS0tjcjISKqqqhg3bhxdu3ZVJYcQf+VQ+iGWxCwBwN/On1CvUPp49SHUMxRny5a9Nanoqwhy3lyC\n3bBheC95E42JidqRmkxpfi5RixdSX1dH2Etv4uztq3Yk0Uh09XrykkppE1fCM5eq+UZbw5F2et4o\nrGJ0D+/mdW2/QZrmsgevV69eSmxsrNoxhBBC/A29XuHpzb+w5ed0nh/RjjkDW6kdSYhGoygKqRcK\nid6RSF5qGU6e1vQeGQQaSI8rIj2+iNK8KgCs7MzwbeuEbztnfNo64eCmzoqd63LmK9g2F259Aoa+\n0uQvrygKMTExfPfddzg4OBAeHo6npzy5F4ZJr+i5XHSZmOwYorOiic2JpaLuanHcxqkNoZ6h9PXq\nS0+Pntiat5xTs4q3byfrueexGTgAv48/RmNurnakJlNeVEjEy89QVVrKpBdexyM4RO1IogHp9Qr5\naWXXruNZV4qpr9WDBtz97fBt54RzGwdM/GwJsTfga/kN0Gg0JxVF6fW7r0thJIQQoqEoisKi7efY\nGJ3K/CFteHxIa7UjCdFoMi8XcXxHIllXSrB3taT3qCDahHqi/Z/jd0sLqsiIL7p241lZUguAnYvl\nrwWSEz5tnbBxsFDjx/i9nPOwYjD49oKp28GkaRek19bWsmvXLs6ePUubNm0YN24cVlYt44ZcGId6\nfT3nC84TkxVDdHY0p3JOUauvxURjQkfXjvTx7EMfrz50c++GhYmBvO9vUOm335Kx4Cms+4Ti9/nn\naC2a58/xT1SWlhD5ynOU5uUycdFreLdpr3YkcZMURaEou5L0uCIy4ovIuFRETWU9AE5eNtdWCnu3\ndsTSxkzltI1DCiMhhBCNSlEUFu++yKojSTx0eyv+dWfbFr1EVxivnORSoncmknahEBsHc3qNDKJ9\nPy9MTP9+bsd/bkr/UyD97qb01wJJtZvS6lJYfjvUVsDcw2Dn0aQvX1BQQEREBLm5udxxxx30799f\nTkETzV6NrobTuaeJzoomOjua8/nn0Sk6zLXmdHfvfm0LW0eXjphqDX9iSNmBA6TPexyrrl3xX7Ec\nrbW12pGaTHV5OZGvPU9RRjrjn38Fvw6d1Y4k/qHSgqqrD3J+LYkqS//rYc6vBZFBPcxpZFIYCSGE\naFRLv4vnk4NXmN4vkJdGd5CySLQ4BRnlRO9MJOlMPpa2ZvQcHkCngT6Ymv/zmR3Xlr3HF5ERV0Tm\nr8veNRpw87fD59cCyauVI2YWjTwbRFGuDrmO2w3Tv4aAfo37ev8jLi6Obdu2odVqmTBhAiEhssVD\ntEzlteWczDlJdHY0MVkxxBfFA2BjZkMvj16Eel4tkFo7tUarMazCtPzIUdIfegiLdu3wX7MaE9uW\ns8Xu79RUVrL59UXkJSdy99MvENitp9qRxA2oLK29+rAmvoj0uEJK86sBsLI3v/qw5tfrrb2rca5o\nlcJICCFEo/nkwGWW7r3EvaF+vDGus5RFokUpzq0kZlcSl2NzMLc0pftQP7rc4Ye5ZcOvBNDV68lJ\nKr12Q5uTVIpep6A10eAZ7HCtQPIItL+uFU035Ni/4bvnYehrcOu8hv3ef0Gn03Hw4EGOHDmCt7c3\nYWFhODo6NtnrC6G2wupCYrJjrm5hy4omtSwVAGdLZ3p79r42A8nPzk/V62tFTAxpc+ZiHhhIwNo1\nmBjR+7Suupotb75E1uU4Rj/5PCG9+qgdSfyNmqp6Mi/9pyAqojDz/w+k8GnjeO166uzVzA6kaCRS\nGAkhhGgUK39MZPHui4zv7sPSSV1/N79FiOaqrLCa2N1JXDyWjYmphi53+NF9qH+TbhWrq9GRdaX4\n2g1vXloZKGBqYYJ3yNUCya+dMy6+tjf33ks5BmtHQrsRELYBmujmuaKigs2bN5OUlETPnj0ZPnw4\nZmYtcz6EENcrqzzr2uqj6KxocqtyAfCy8bq2+qiPVx/crd2bLFPlqVOkzpqNmZcXARvWY+rcsk9/\n+2/1tbVse/tV0s79wsjHn6btLQPUjiT+QH2tjqyEkv+/XqaUoihgaqbFK+Q/D1yccfOzRWtiWCv3\nDIEURkIIIRrcF8dTWLT9HCM6e/LRPd0xlQuwaAEqS2s5+W0y537MAKDTQB96Dg/E2l79E4CqK+rI\nvFR8bQVSUXYlABbWpldvhn99YuroYX39T0zLc+HzAWBuDXN+AEuHRsv/39LT04mMjKSiooJRo0bR\nvXv3JnldIZoTRVFILk0mOiv66iqk7BhKakoACHIIulYghXqG4mDROO/dqvPnSZ0+AxNnJwI2bMDM\nvemKKrXp6uvY+e4bJP58guEPz6fjbYPVjiR+pdPpyU0uIyO+kPS4IrISS9DXK2i1GtwD7a/OIWrn\nhGeQAyZmcn/6d6QwEkII0aA2n0znqagzDGnvzqeTe2Le0NtjhGhi1RV1nNqbyi8H09DVK7Tv50Wv\nEYHYOVuqHe1PVZTUXBvYmR5XRFnh1ZkMNg7m+LT7T4Hk/Oc/g64eNtwN6bEw+3vw7NTomRVFITY2\nlm+//RZ7e3vCwsLw9vZu9NcVoiXQK3riC+OvDdA+mXOSqvoqNGho59zu2uqjHu49sDa7+WHU1Zcu\nkTrtfrTW1gR8sQEzI3qv6nU6vv7wLS5H/8SQ2Y/QdehdakcyaopeIT+j/Nr1LvNyMXU1OtCAq6/t\nteudV4hDo2wZb+mkMBJCCNFgdp3J5PGvTnFriCsrpvXC0qyRh/EK0Yhqq+s5sz+N0/tSqa3R0bqX\nB6GjgnD0aF4n/yiKQml+NelxhdcGe1aV1QFg72b1/6e+tHH6/9VS378CR96Duz+Dbvc1esba2lp2\n797NmTNnCAkJYfz48Vgb0QlLQjS0On0d5/LPcTzrODFZMZzJO0Odvg5TjSld3LpcPYHNsw9d3Lpg\nbnJjqyRrEpNImTYNjVZLwBcbMPf3b6SfwvDo9Tq+/eQ94o4e4vZpD9Bz5Fi1IxkdRVEoya26tqI2\nI76Y6oqr1zRHD+trK2p92jhhaStbmW+WFEZCCCEaxHfns3l448/0DHBi3YxQrG7ihCgh1FRfq+Ps\noQx+/i6F6vI6gru5ETo6CBeflnHqj6IoFGZWXD02OL6IzEtF1FbrAHDxscHXvQSfpLfw6d0V8wnv\nNXqewsJCIiIiyMnJ4fbbb2fgwIFotbIyUYiGVFVfxancU1e3sGXFcKHwAnpFj6WJJT08elwboN3O\nuR0m2j+/ftempZEyZSpKfT0BG9ZjERzchD+FuhS9nn0rPuHsgb30v2cafcaFqR3JaJQXVV+bQZQR\nX0R5UQ0Atk4WVx94/Prgw9bJcFf+NldSGAkhhLhpP8TnMmf9STp42/PF7D7YWsiSX9H86Or1XDya\nSew3yVSU1OLXwZk+Y4LxCLRXO1qj0uv05KWWkx5fSPq5TLISStEp5mi04B5gf+1m3CvYAdMGLoLj\n4+PZunUrGo2GCRMm0Lp16wb9/kKIP1ZaW0psduy1GUhXiq8AYGduR2+P3oR6XS2Qgh2Cr809q8vK\nImXKVPTl5fivX4dl27Zq/ghNSlEUDqxZxunvvqbv+HBuDZ+qdqQWraq8loz4q3P5MuKLKM65OpfP\n0sbs2ilmvm2dcHC3kpPMGpkURkIIIW7KTwn5zFhzghB3WzY90BcHK1n+K5oXvV7hUnQ2MV8nUVZQ\njVeIA33HBuPd2kntaE2rrhpWD6O+IIOcO78mPcOC9LgicpJLUfQKJqZaPFvZ49vWGd92TrgH2P3j\nE2X0ej0//PADhw8fxtPTk/DwcJycjOzPWwgDkl+Vf/X0texoorOiySi/Otzf1cqVUM9Q+ll2oN2i\nDVBYgv+aNVh1bvy5ZoZCURQOb1xD7K6t9Bw1jtumzJSSooHVVteTefn/C6L8tHIAzCxM8G7jeG2b\nmYu3LRo5dbdJSWEkhBDiHzuZUsjUVTH4Olnx1ZxbcLZR/7QoIa6XoldIOJVHzK5EirIrcfO3o8/Y\nYPw7OBvnh4Gd8+DndXBvBLQdfu3Lf3ojb2mCd+sbv5GvqKhg69atJCQk0L17d0aMGIGZmRTNQhiS\n9LJ0YrJjOJ51nPNXjjNvdR7uJbBshgeefW8n1DOUUK9QXK1c1Y7a6H6K2sSxzZvoOnQEg2c9ZJzX\nhwZWX6cjJ7H02jaz3ORS9L95MPHrUfcBdpjISbuqksJICCHEP3I2vYT7VhzH1c6CiLl9cbeTfeOi\neVAUhZRzBUTvTCQ/rRxnbxv6jA4mqJur8X4QOL0Jtj8E/Z+EIS/95b/6p1sFbM2ubl9r++dbBTIy\nMoiMjKS8vJwRI0bQs2fPRvuRhBA3T1dSQsr06dQkJHJpURj7XXOIzY6lrK4MgBDHEPp49SHUM5Re\nnr2wN29ZW3hjdmzmx01r6Xj7EO6cOw+NzFf7R36z9TmuiKyEEnR1ejQacA+0v7bNrDG2PoubI4WR\nEEKIG3Yxq5R7VxzH1sKUyLm34O1opXYkIa5LenwR0TsSyE4sxd7VktDRwbTu7YHWmJe4Z5+DlYPB\ntzdM3Q4mNzaDrKywmoxLV58Sp8cVUVH822Gkvu2c8G7jxKWk83zzzTfY2toSFhaGj49PY/w0QogG\noiuvIHXWTKovXMTv039jO2DA1a/rdVwsvEh01tXta6dyT1Gtq0ar0dLBucPVAskrlO7u3bEybb73\nBz9/u4uDa5fRtt9ARjy2AO1fDAMXv/V3hyv4/LqCyLu14/+xd5/xbdZX/8c/Gt5Tkvfedvb0SMKG\nEFYSRgZtoZRNW3p3t5SwV2lLe3N3/AsUWkYHSVgZ7A0ZtrO3k3gvyUOWLQ/JGtf1fyCjQAuUJI7l\ncd6vV580snyMLVvX9zq/cwgJk7mXo5kERkIIIY5LdXsfVz6xFb1Wy9pb5pFulNXXYvSz1PVQsa6W\n5iobkYYQ5l6URdH8ZGl1d/bAE2eBawBu+RgiE07q6fzrjqu6hjqQunH0O+mLrsYZ3oYhPInzTr+Q\n3Gkpsu5YiFFMcThouvEmBnbtIu3/HiXqvPO+8LEur4s9HXuotFRSYa5gX8c+PKqHIG0QM+JnUJpc\nSmlyKVPjphKkHRuv+73vvsnbT/yBvOJ5XPKDn6PTS6jxZVRVxd7pHFpz7wuJHL2+VffR8WGfWXUf\nHi3jC8YSCYyEEEJ8ZQ3WflY8vhWvAmtuLiMnfnysGRfjV2dzLxXr66jf20lYVBBzLshiyhkp6IPk\nTjGqCquvgsOvw7dehcx5w/4puqxd/POfz9NpbSc5rBBak/EMKqCBuLRI/5yK5LwYgkPlgkyI0UAZ\nHKT529+hv7yc1Ed+Q/RFFx3Xxw+4B9jRtsMfIFV1VaGiEqYPY07iHMqSyyhJKqHQWIhWM/pC+4Mf\nv8/rf/od2TNms+Qnd6CXGWufq79n0L/mvrnKRm+XE4DwmOBjAVGhgWjT2O0yExIYCSGE+Ipauh2s\neGwrAy4Pz980j8KkqECXJMQXsln6qdxYR/X2dkLC9cxcmMH0s9MklPi0zb+Ht++ERQ/BvO8O+9Mf\nPXqUF198EVVVufzyyyksLMTrVWiv7/XfhTbX9qB4VLRaDYnZ0aQOrUpOyo5BFzT6LiSFGO9Ul4vm\n//k+fR98QPJDDxF7+WUn/Zzdzm62tW3zH2Grt9cDEBMSQ0lSCaVJviNsWdFZAZ8jd6R8Exsf/TVp\nk6dy2W13ExQcEtB6RhNnv5vWI91Dg6q7sFl88+tCwvWkFhwLiAxJ4QH/PorhI4GREEKI/6rd7mTF\n41ux9rv4141lTE2NCXRJQnwuu9XBtlfrObzVjC5Yx4xz0pi1MIOQcLlD/Bn1m+GZxTDpElj+DAzj\nm3tFUfjoo4/44IMPSExMZOXKlRiNxs99rNvlxVLT49+U09FgR1VBH6QlOS+GtCIjqYUG4jOiJvac\nKSFGgOrx0PLjn9D75psk3X0Xhq997ZR8nrb+Nn/3UYWlAku/BYCE8ARKk0r9R9iSIpJOyef/IrU7\nt7HukQdIyivkitvvJTh0YnfGuAe9mKu7j/1+buoFFfTBWlLyYv0Bf1y6/H4ezyQwEkII8aWsfYNc\n+UQ5rd0OnruhlNkZhkCXJMR/6O8ZZMdr9RzY1IpGo2HqmanMXpQpsxI+T28bPH46hETBje9D6PBt\nNRoYGOCll16iurqaGTNmcPHFFxMc/NW/B4MOD62fDNA+bKOrtR+A4DA9qQWx/jvYxuQIuYMtxDBS\nFYXW227Dvn4DCbf9HNO3vjUyn1dVaeptotxcTqWlkkpzJbZBGwCZ0Zm+DqTkUoqTijGGfn7wPBwa\n9u7m5V/fS1x6FsvvfICQ8IhT9rlGK69Hoa3O7u8gaquzo3hVtDpfB2hakZG0QgOJ2dHo9NIBOlFI\nYCSEEOIL9Qy4+dpfyqnp6OOZ60ooyzEFuiQhPsPZ52bnmw3s+6AZxasyaUEycy/KItIQGujSRiev\nB55dCi074MZ3IXHKsD212Wxm9erV2O12LrzwQubOnXvSoc6A3TU0H8M3RNve6ZuRERZ9bEZGWqGB\n6LiJ3QkgxMlQVRXLXXfTvXYt8T/4PnG33BKwWhRV4ajtKBXmCiotlWxv206/2xccFxoKKUkuoSy5\njDmJc4gIGp5Qp/ngfl785d0YkpJZfvcvCYucGEfuFUWls6nXt6CgykZrdTcel2/GXHx6lP/3a3Je\nLEEhMvdvopLASAghxOfqdbq56qlKDrXaefKauZxREB/okoTwczk87H6nkd3vNuEe9FJYkkTxJdnE\nxEtw8KXevhs2PwqXPQ4zrhy2p921axcbN24kIiKCFStWkJaWNmzP/Wn2Tof/eETLYRsDdhcA0XGh\nQ2uafVt4ImJk7ogQX4WqqrQ99Etszz2H6ZabSfjBDwJd0md4FA8HrAd8AZK5kl3tu3ApLnQaHVPj\nplKS5AuQZiTMIER3/K9789HDrH3gDqKMJlbe8zDhMbGn4KsYHVRVxWYZ8A+pbjliY3DAA4AhKdzf\nQZRSEEtohBzjFj4SGAkhhPgPAy4P1/y1kl2N3Tx21RzOm5wY6JKEAHwzb/a938zOtxoY7PeQOyue\nksU5GFMm3vGB41b1Gjz/NZhzLSx+dFie0u128/rrr7Nz506ys7NZtmwZEREj871QVRWbecB/fKL1\naLf/4seYEuELkAoNpBbEygwrIT6Hqqp0/O53WP/yJMZrriHhtp+P+qOeTo+TPR17/POPDnQewKt6\nCdGFMDNhpn8G0mTTZPTaL19y0FZXw9r7bycsMpqV9zxMpHH8dVHbrQ5/QNR82MZAjy9kjzKG+o/4\nphVJyC6+mARGQgghPsPp9nL9M9vYWmPl91+bxSXTUwJdkhB43QoHNrWy4/V6BuwuMqaYKFuaQ3zG\nxDg6cNK6auHxs8CYDde9CUEnf2Svu7ubNWvW0NraymmnncY555yDVhu4uRb+4xVDF0bmo9143Aoa\nDcRnfHK8wkhSXgxBwXK8QoiOP/2Jzj/8kdgrV5J0992jPiz6PH2uPna07fDPQDpiOwJAZFAkcxPn\nUprs28CWH5v/ma+vs6mBNff+An1wCFfe+yui4xMC9SUMqwG7i5ZPzYGzdzgACIsK8gXohQbSioxE\nx4WOye+3GHkSGAkhhPBzeRRu+fsO3j/cziPLZnDFnFNzrESIr0rxKlSVW9j2ah19XYOk5MdStjSH\n5Lzxe2xg2Lkd8NRC6G6Cmz8CQ+ZJP2V1dTUvvvgiiqJw2WWXUVRUNAyFDi+vW6Gtvsd/4dRWa0dR\nVLR6DUnZMf75HAnZ0eh0MsBVTCzWp56i/TePEHPppSQ/9CCaAIa9w8nqsLKtbZv/CFtjbyMAxlCj\nf4D2ZE02Hz3yKGg0rLznYQxJY/fG2KDDQ+vRbpqrumg5bMPaMrQoIFRHSoHBP+vNmCKLAsSJkcBI\nCCEEAB6vwvf+tYvX91t46LJpfL00I9AliQlMVVSqd7RTubGO7rYBEjKjKFuaS9okg7zpPV7rboVd\nz8HX10DBopN6KkVR+Pjjj3n//fdJSEhg5cqVmExj4xiHy+nBXNNDy1CA5F8RHaIjJS/Wf2EVlxaJ\nRlZEi3Gs6x//oO3+B4i+6EJSfvMbNLrx23Fn7jNTYanwHWEzV+CwdnFheRJ6Vcfg8skUTzmTkqQS\nEsLHRoeRx+XFXNvjn+PW3tCLqqjogrQk58b4OynjMyLRShAuhoEERkIIIfAqKj9as5t1u1u585LJ\nXH9adqBLEhOUqqrU7+2kYn0d1pY+TKkRlCzOIXtGnARFJ2LX32Hdd+H0n8C5d57UUzkcDl5++WWO\nHDnCtGnTWLx4McHBwcNU6Mhz9rtpOWLzB0g2ywAAIRF60gqOzfaITQyXnz0xbnS/8ALmO+4k8txz\nSXv0f9EETZz5XnZrJ/+468c4+nuxXJzEZs8e7C47ADkxOf4B2nOT5hITEhPgan0Ur0J7wydHbbuw\n1NjxehQ0Wg2JWdH+TsnEnGj0QeM3+BOBI4GREEJMcIqicvvL+3h+WxM/XVTId8/OC3RJYgJSVZXm\nKhsV62tpq7MTkxBGyeJs8uckSrfHiTLv9R1FSy+Fq18G7YlfTFgsFlavXk1PTw8XXHABxcXF4y5E\n6e8e9A3QHhqi3dc1CEBEbMinZn8YiDKe/PwnIQKhZ8NGWn/2MyIWLCDt//0J7RgOfI9Xf7eN1ff+\ngn6bleV3PEhSXgGKqlDVVUWluZJySzk723bi8DjQoKHIWERZchmlyaXMSphFeFD4iNSpKirW1j7/\nUdrWo924nV4A4tIj/cP8U/JjCQ798qHeQgwHCYyEEGICU1WVe9Yf4JmtDfzPOXn86PzCQJckJiBz\nTQ8V62poOdJNpCGE4kuyKSpLknb6k+HohifOBI/LN7coMv6En2r37t1s3LiRPTDWDwAAIABJREFU\nsLAwli9fTkbG+D+uqqoq9k6H/6Kt5bANR68bgJj4sGPbhQoNhEVNnItuMXbZ33qLlh/+iPA5c0h/\n/DG0YWGBLmnEOHrtrLn3F3S3W7ji9vtIK5ryuY9ze93s69znP8K2p2MPHsWDXqtnetx0SpN9G9im\nx00nSDc8nVmqqtLT7hgKqn2r7p19vt81sYnhx7Y9FsYSFim/a8TIk8BICCEmKFVVefj1Kh7/qJYb\nT8/m9osmjbuOATG6dTT2UrG+lob9VsKig5l7YSZTTktFFyRB0UlRVXj+G3D0TfjWa5BRekJP4/F4\neOONN9i+fTtZWVksW7aMyMjIYS52bFAVlS5z/7EA6YjNf9fflBrpPxaSkh9LcJjc9RejS9+HH9J0\n6/cImzKFjKeeRBsREeiSRoyzv4+196/C2tzIZT+/m8xpM7/yxzo8Dna17fIHSAetB1FRCdOHMTth\nNiXJviHaRYYidMfRwdlnG6TlcJf/90mf7VPdjEW+TsbUAulmFKODBEZCCDFBPfrOER595yhXl2Vy\n39IpEhaJEdNl7qdyQy01OzsICdcze1Em085KIyhE5i8Mi02Pwjt3wwUPQ9m3T+gpenp6WLNmDS0t\nLcyfP59zzz0X3TgejHu8FK9Ce2Ovf/CsuaYHr9s3VyQhM8ofICXlxshcERFQ/Vu30nTzLYTk55Px\n9N/QRUUFuqQR43IM8MJDd9FWU83Sn64iZ1bxST1fz2AP29u2U2mupMJcQU1PDQDRwdEUJxX7ZyBl\nx2R/5j2Vs8/9mVX33W2+eWmhEUGkFsaSVmQkrdBATEKYvBcTo44ERkIIMQE99mEND79exfI5afzq\niuloZUaMGAE9HQ62vVrHkQoL+mAdM85LZ+Z5GYRIR8bwqd8EzyyGyUth2d/gBC4+amtreeGFF/B4\nPFx66aVMnjz5FBQ6vnjcXiy1dv9q67b6oc1Fei1J/s1FBhIyo+SopRgxAzt20HjDjQSnpZHx7DPo\nDYZAlzRi3INOXn74XpqrDrD4B7eRXzp/2D9Hp6OTCnMFlRZfgNTS1wJAUlAKC7QLyeqdgt4STU/r\nIKgQFKIjJT/Wf6Q1LlU2MorRTwIjIYSYYJ7eXMc9Gw6yZEYK/7tyJjp5syJOsT7bINtfr+fQplY0\nOg3Tzkpj9qIMmccw3Hot8NjpEBoDN70PIcfXSaCqKps2beK9994jLi6OlStXEhcXd4qKHd9cDg+t\n1d3+jgJrcx8AQaE6UvN9HQWphQZMKRFywShOCcfevTReex36hAQyn3sW/QR6LXvcbl759X007NvN\nRbf+mEmnnXVKP5/XrWCp6+Hg3gbqDrbjMmvRqFq8Gg+WqDp64y0k5kcxe+okSlNLMIWZTmk9Qgyn\nLwqM5FafEEKMQ89XNnLPhoMsmpLIb1fMkLBInFKOXhc73mxg/4ctqIrK5NNTmHthFhGxIYEubfzx\neuCF68DVB9esP+6wyOl08sorr1BVVcWUKVNYsmQJISHyfTpRwWF6sqbFkTXNd5Hu6HXRcqSb5qou\nmg/bqN9nBSAsKojUAoO/4yAmXo6kiJPnPHSIxhtuRGc0kvH03yZUWOT1eNj46MM07N3F+bf8zykJ\nixRFpaOx199RaK7uweNW0GggPtNI2iIDqQWxOOKsbLd2UGG28prlTVZv7gUgLzaPsuQySpJKmJs0\nl6jgiXNMUIwf0mEkhBDjzMu7mvnRmj2cWRDP41fPIUQvczXEqTE44Gb3O03sebcJj8tLYVkSxRdn\nEx03cbbyjLi37oQtv4fL/wLTVxzXh7a1tbF69Wq6u7s5//zzKS0tldDiFOvtctIytBWpuaqL/h4X\nAJHGENIKDf6ZJhKuiuM1WF1Nw9XfRBMaSuZzzxGclhrokkaMonh59fePcGTrx5xz3S3MWnTJsDyv\nqh4bet9y2EbLkW5cDg8AxpSIY0PvCwxfeMTao3io6qqi3FxOpbmSXe27cHqdaDVappimUJpcSklS\nCbMSZhGql2HXYvSQI2lCCDEBvLbPzK3/3ElZjom/fquYUBnCKk4B96CXve83seutRgYHPOTNSaBk\ncTaGpImzkScgDm2E1d+AudfDJb87rg/du3cvGzZsICQkhOXLl5OZmXmKihRfRFVVutsGjgVIR2wM\n9vsuRg1J4b6V2kNbk0IjhmeVtxifXA0NNFx1NSoqWc89R3BWVqBLGjGqovDGnx/l4EfvccZV11G8\n+PKTej57p8N/pLT5sA2H3RfqRseF+gPd1EID4dEndrTa5XWxp2OPfwbSvo59eFQPQdogZibMpDSp\nlNLkUqbETSFIK697ETgSGAkhxDj37qE2bn5uBzPTY3nmuhIiQuTUsRheHreXAx+1suONehy9brKm\nmShZkkN8urTZn3LWGnjiLDDlwXVvgP6rdaR4PB7eeustKisrycjIYPny5URNoO1Jo5mqqHQ29/ku\nVKtstFZ34xn0ggbi06NILfQdYUvJi5XNgsLP3dJC/VVXozqdZD73LCF5eYEuacSoqso7T/6Jve+8\nwfwV32DeFV877ufo7xn0hbZDr7teqxOA8Ohg/5HRtELDKeuU7Xf3s7Ntpz9AquqqQkUlXB/OnMQ5\nlCb7AqQCQwFajQzOFyNHAiMhhBjHPj7awfVPb6coOYq/31BKdKjcpRLDx+tVqNpiZvtr9fTZBkkt\nNFC2NIeknJhAlzYxuB3w5EKwN8PNH0Fsxlf6MLvdzpo1a2hubqasrIyFCxei00nwMFp5vQrtdXb/\nhaylrgfFo6LVaUjMjh46wmYgMSsGXZBcSE5E7rY2Gq66Gm9PD5nPPE3opEmBLmnEqKrKB88+yc7X\n1lGydBmnfe2ar3SkdnDA7ZsrNvS6spn7AQgJ15NaYPAHs4ak8IAc0e12drOtbRsV5goqzBXU2+sB\niA2JpTip2D8DKTM6U44Qi1NKAiMhhBinKmqtXPO3SrJMETx/Uxmx4bKRSgwPRVE5uq2NbRvr6Olw\nkJgdTdnSHNKKjIEubWJ55buw+x/wjbWQv/ArfUhdXR0vvPACLpeLpUuXMnXq1FNcpBhubpcXS3UP\nzYe7aK6y0dHYi6qCPkhLcn6sP0CKS49CK4sNxj1PZycNV38TT3s7GX/7K2HTpwe6pBG16flnqXh5\nDbMuXMzZ19z0heGJ2+XFXN3tP/rpf90Ea0nJi/UHRKP1dWPpt7DNso1yczkV5graBtoASAxP9Hcf\nlSSVkBSRFOBKxXgjgZEQQoxDuxptXPVkBUkxoay+eR5xkTI4VZw8VVWp291JxYZaulr7MaVFUrYk\nh8xpJrnDOdJ2Pgvrvwdn/AzOWfVfH66qKlu2bOGdd97BZDKxYsUKEhISRqBQcap9ulOi5bCNrtZj\nnRIp+bH+eSuG5MB0SohTx9vdTcM3r8HV2EjGk38hfO5/XNONa+UvrWbz6ueYfu4FnHfjdz/z8+31\nKLTV2/0BkaW2B8X77515RhKzo9Hpx1ZnnqqqNPY2+ruPtlm2YRu0AZAVnUVJUgmlyaUUJxVjCDUE\nuFox1klgJIQQ48z+lh6+/pdyDBHBrLl5HonRsm1DnBxVVWk62EXF+lraG3qJTQynZHE2ebMT0IzC\nO7HjXutueOp8yJwPV70I2i8/TuZ0Olm3bh2HDh1i8uTJLF26lJAQCZHHq/6eQVqO2Pwbneydx2ax\nfNJFcSpnsYiR4e3tpfFb1zJ49Cjpj/2ZiPnzA13SiNq+8WU+fO4pJp9+Nhd854eAxjf7q8pG8+Eu\nWqt7PjP765POu+RxOPtLURWO2o76AiRLBdst2xnwDABQZCzyB0hzEucQESRLKMTxkcBICCHGkSNt\nvVz5RDlhQTpW31xGmiE80CWJMa71aDfl62owV/cQZQql+OJsCksT0erG1h3ZccNhg8fPBMUDN38M\nEaYvfXh7ezurV6+mq6uLhQsXMm/ePOkymWDsnQ7/nJb/2PY01GVxMtuexMhT+vtpvOFGHPv3k/aH\n3xN11lmBLmlE7X7rNd596v+RNbOMvNKraT1qp+VztgumFRlJKYidcNsF3YqbA50HqLRUUmGuYHf7\nblyKC71Gz9S4qZQkl1CWXMb0+OmE6OTmgfhyEhgJIcQ4UdfZz4rHt6IB1tw8j6w4uYskTlx7g52K\ndbU0HuwiPCaYuRdmMfm0lDHXuj+uKAo8/3WofgeufR3Si7/04fv372fdunUEBwezfPlysibQim3x\n+VRVpcvc7z+m03KkG5fDd5FtTInwd2Gk5McSEj6xLrLHCsXppOnmWxjYto3U//1fohedH+iSRkxv\nl5Pylzay9+2nCQrLQxtyMRqNjkhjiP/oZVqhgYhYCUE+zelxsrtjN5VmX4C037ofRVUI0YUwK2GW\nbwZSUimTTJPQa2WTrvgsCYyEEGIcaOoaYMXjW3F5FFbfXEZegqzHFifG2tpH5fo6and3EBoRxOxF\nmUw9K5Wg4PHVwj8mffw7ePdeuPDXUHrzFz7M6/Xy9ttvU15eTnp6OsuXLyc6OnoECxVjhaKodDT2\nDgVIXZire/C4FTQaiM88toEtKTdGfgeMAorLRfN3b6V/0yZSfv0rYhYvDnRJp5Sj1+Xrjjtso6XK\nRlfLLtz9r6MPzaRg/vVkTE4grch3vFI6J7+6XlcvO9p2+I+wHbUdBSAqKIo5SXP8G9jyYvPkv6uQ\nwEgIIcY6S4+T5Y9vwe7w8K8by5icIheG4vh1tw+wbWMdR7a1ERyiY+bCDGack05wmNxtHBXqPoJn\nl8KUy+CKp+AL3sT39vaydu1aGhsbKS0tZeHChej18j0UX43XrWCp6/FfoLfV2VEUFa1eQ3JOzNAM\nJCMJWVHo5FjqiFLdbpp/+EP63nmX5AfuJ3bZskCXNOxcDg+tR7v9xyetLX0ABIfqiDKaaT34TxKy\nC1lx930Eh8oMruFidVjZZtlGhcU3RLuptwkAY6iR0qShDWzJJaRHpQe4UhEIEhgJIcQY1tE7yMon\nttJuH+QfN5QyIz020CWJMaa3y8n21+o5tMWMTqdh+jlpzFqYSWikHEcZNexmePx0CDPAje9DSOTn\nPqyhoYG1a9cyODjIkiVLmDZt2ggXKsYbl9ODubpnaAZSF53NfaBCUIiOlPxPrSJPjZQB+KeQ6vXS\n+tOfYX/tNRJXrcJ49VWBLmlYeNxeLDU9/oCovaEXVVHRBWlJzo3x/3wN2I6w7pEHSczOZdkd9xMc\nJvMZT6XWvlYqzBX+GUgdjg4AUiNT/QO0S5JKiA+PD3ClYiRIYCSEEGOUrd/FlU+U09g1wLPXl1Cc\nZQx0SWIMGbC72PFGPQc+akVFZcrpqcy5IJOIGJn9MKp43fDMYjDvhRvfg4Si/3iIqqqUl5fz1ltv\nYTAYWLlyJYmJiQEoVox3zj63bwPb0Ayk7jbfJqbQiCBSC2P9g4ZjEuSI0HBRFQXzqjvoefllEn7y\nY0w33BDokk6Y4lVob+j1B0SWmh68HgWNVkNiVpS/gy0pJxp9kO8IZOP+Pbz88L0YU9NZfteDhEZ8\nfmAuTg1VVamz1/kCJHMllZZK7C47ALkxuZQk+wKkuYlziQmJCXC14lSQwEgIIcagHoebbzxZzpG2\nPp7+VjHz8+ICXZIYI5z9bna93cje95rwelSK5iUx96Isok3S3j8qvbkKtv7Rdwxt2n8eQRkcHGT9\n+vUcOHCAoqIiLr30UkJDQwNQqJiI+myDtBzu8gdIfbZBACINIb6L/6EOkUiD/EyeCFVVsdx3H93/\nep64W28l/tbvBrqk46IqKtbWfpqrumg5bKPlaDdupxcAU1okaUW+n5GUvNjPPf7cUnWQFx+6i+j4\nBFbc/UvCoyWQCDSv4uWw7bB//tHOtp04PA60Gi2TjJN8G9iSypiZMJPwIOkEGw8kMBJCiDGmb9DD\nN5+qYF9LD09cPZezixICXZIYA1xOD3vfa2LX2024nB7y5yZSckk2sYnyhm7UOrge1lwNxTfCxY/8\nxz93dHSwevVqrFYr5557LgsWLJCuDhEwqqrS0+HwbV8bGlTs7HMDEJMQ5t9ilVoYS1hkcICrHf1U\nVaX9V7+m6+mnMd1wPfE//vGof33/+89AyxEbjt5P/QwMdRB9lZ8BS81R1t6/iojYWFbe8ysiYg0j\n8SWI4+T2utnXuc8fIO3p2INH8aDX6pkRP8M/A2la3DSCdHLUfSySwEgIIcYQh8vLtU9Xsq3exp++\nPpsLpiYFuiQxynlcXvZ/1MKONxpw9rnJnhFH6ZIcTKnS1j+qWWvgibMgLh+ufR30nz0qePDgQV55\n5RX0ej3Lli0jJycnMHUK8QW+cndJfizBoTKY/d+1/9//Yf3zYxiuuorEVbeP2rCov3uQ5qou/zaz\nvi5fl1lEbIj/e5xaaCDK+NW7zDoa6lhz7y8IDo9g5T0PEx0ns3LGigH3ALvbd1NuKafSXMlB60FU\nVML0YcxOnO0PkAoNhei0snlxLJDASAghxohBj5cbntnOpupOHl05k6UzUwNdkhjFvB6FQ1vMbH+t\nnv7uQdInGShdkktitmzRG/VcA/DkedBrhps/gthjm2m8Xi/vvvsuW7ZsITU1lRUrVhATI8c0xOjn\n9Sp0fMn8Gl/nieEz82smqs7HHqfj0UeJXb6MpHvvRaMdPRvpnP1ufwdZy2EbNotvjlVIhJ60At8R\nxJOZY2VtaWL1Pbeh0+u58t5fEZMgN8bGsp7BHra3bffPQKrpqQEgOjiakqQS/wyk7OjsURuKTnQS\nGAkhxBjg9ip8++87eedQG7++YjorimW1qfh8iqJypNLCto112DudJOfGULo0h9QCaecfE1QVXvk2\n7HkernoB8s7z/1NfXx9r166loaGB4uJiFi1ahF4vnRlibPK4vFhqv3hDVlqRrzMlISMKrW70BCan\nWtczz9D2y4eJXryYlId/iUYX2PDM5fRg/mST2ac25elDdKR+simv0EBc2slvyuu2mFl9z89RFIWV\n9/wKY4rcGBtvOgY6/NvXKswVtPa3ApAQlkBJcgklSSWUJZeRHJkc4ErFJyQwEkKIUc6rqPzP87t4\nda+Z+5ZO4ZvzsgJdkhiFVEWlZlcHlRtqsVkGiEuPpGxpLhlTjHLXbizZ/jfY+AM46xdw1m3+/7ux\nsZG1a9ficDhYvHgxM2bMCGCRQgy/QYcH89Fuf4BkbekDIDhUR0rBsQHaxpSIcfs7zfb881juuZeo\n888n9Xe/RROAQNjrVmir76FpaA5RW60dRVHR6jUkZcf4j5klZEejG8Ygz97ZzvN3/xz34CAr73qI\nuIysYXtuMTqpqkpzXzOV5kr/DKQuZxcA6VHplCaXUppUSnFSMaYwU4CrnbgkMBJCiFFMUVR+8sIe\nXtrZwqqLJnHjGTKnRHyWqqo07LdSsb6WzqY+DEnhlCzOIXdW/Enf7RUjrHUXPHU+ZJ0O33gBtFpU\nVaWyspI333yTmJgYVq5cSVKSHNEQ49+A3UXLkaGjT1U2ejocAIRFBX1mA1t03IkdfRptul9+BfMv\nfkHkmWeS9offowkemcHgiqLS2XTsqKD5aDcet4JGA/EZUf5h5Ul5MQQFn5pup74uK6vvuQ1Hr53l\ndz5IYk7eKfk8YnRTVZXq7moqLZWUm8vZbtlOn9sXHOcb8v3zj+YmziUyWOYwjhQJjIQQYpRSVZVV\nr+znnxWN/GhhAf9zbn6gSxKjTMthGxXrazHX9BAdF0rJJdnklyShlaBo7BnogifOBEXxzS2KMOFy\nudiwYQP79u2joKCAyy67jLCwsEBXKkRA2K0OWg5303y4i+YqGwM9LgCijKGkFh0LkCJiQv7LM40+\n9tdeo+UnPyWirJS0P/8Zbcip+xpUVcVmHvD/d2w92s3ggAcAY0qEf0h1akEsIeGnfqvVgL2H1ffc\nRq+1k2Wr7ieloOiUf04xNngUD4esh6iw+I6v7WrfxaB3EJ1GxxTTFEqTSylJLmFm/ExC9V99qLo4\nPhIYCSHEKKSqKvdvPMRfN9fx7bNy+dmiwnFxB1UMj7Y6O+XramiushERG8Lci7KYtCB5WI8HiBGk\nKPCvK6HmPbjuTUibQ2dnJ2vWrKG9vZ1zzjmH0047De0oGnwrRCCpqkp324C/K6blsM0fehiSwv3r\n21MKYgmNGN2rvHvffZfm7/+AsJkzyHjiCbTh4cP+OeydDt8Ws6FjZgN2X9gWHRfqC4iKDKQVGgmP\nHpmupk84+npZe9/t2MytXP6Le0ifPG1EP78YW1xeF3s69vjnH+3r3IdX9RKsDWZmwkxfgJRUwtS4\nqei1Mt9vuEhgJIQQo9Bv3qziT+/XcO2CLO66ZLKERQKAzuY+KjfUUrenk9DIIOZckMnUM1LRn6Jj\nAmKEfPQIvHc/XPQIlNzIoUOHeOWVV9BqtSxbtozc3NxAVyjEqKYoKtbmvqEAqYvWo914XApoID49\nyj93JzkvlqCQ0fP7su/jTTR/5zuETJpExl+fQhc5PMdsBuwumg930TIUqNk7nQCERwf7jvMN/feI\njgtcx+LgwAAvPLCKjoY6Lv3ZXWTNmB2wWsTY1O/uZ0fbDt8MJEsFVV1VAEQERTAncY7/CFu+IR+t\nRm64nCgJjIQQYpT543tHeeStI3ytJIOHLpsqYZGgu22Ayg21HN3RTnConlkL05l+TjrBoXIHbcyr\n/QCeuwymXI73sid477332Lx5MykpKaxYsYLY2NhAVyjEmOP1KLTV233r36tsWGp7ULwqWp2GxOxo\n/1yexOxodPrAXEj2V1TSdNNNBOfkkPn039DFxJzwcw0OuGk50u1fd9/V2g9ASLielPzYoYDIiCE5\nfFS8p3A7nbz4y7swHz3Mkh/fTu6c0kCXJMYBm9PGNss2KswVVFoqqbfXA2AIMVCcVOwbop1cSkZU\nxqh4HYwVEhgJIcQo8uTHtTzw6iEun5XKI8tnyCyaCc5udbD91Xqqyi3ogrTMODuNmQszRv0RC/EV\n2VvhsdMh3ETf1zfw4oY3qKurY86cOVx44YXoA7AhSYjxyO3yYq7u9gdI7Y29vtXwwVpS8mL9M5Di\n0qNG5O/uwK5dNF5/A0EpyWQ++yx6o/G4Pt7t8mKp7hk6ZtZFR2Mvqgr6IC3J+bH+eU4j9fUcD7dr\nkFd+dR9NB/Zx8fd/RuG80wJdkhinLP0WKi2V/iNsbQNtACRFJFGSVEJZchklSSUkRiQGuNLRTQIj\nIYQYJZ4rb+DOV/Zz8bRk/u/KmehlHs2E1d8zyI7XGziwqQWAqWekMueCrBGfLyFOIa8bnr4ELPto\nXrKWNW9XMDAwwMUXX8ysWbMCXZ0Q45qz303r0W7/DCSb+VhHTmqBL2xJLTRgSBr+jhzH/gM0futb\n6ExGMp97jqCEhP/6MV6vQnud3T+HyFLXg+JR0Wo1JOZEk1poIL3IQGJWDLqg0fvewetxs+6RB6nb\nvYMLv/NDJp9xTqBLEhOEqqo09jZSYa6g3FzONss2uge7AciKzvJ3HxUnFhMbKp29nyaBkRBCjAJr\ntzfx0xf2ct6kBP581RyCJCyakJx9bna93cDe95rxelUmzU9m7kVZRBll+8e488btqOV/Yvus3/D6\nHgvR0dGsXLmS5OTkQFcmxITT3zPo7z5qrrLR2zU08ycm2N+tk1poINp0cjN/nIeP0PjNb6KNiCDz\n788RlJLyuY9TFZXO5j5/QNRa3Y1n0OufyfTJHKLk3JgxczRZ8XrZ+OivOFq5hYU33sr08y4IdEli\nAlNUhSO2I/7ja9st2xnwDKBBQ5GxiJKkEkqTS5mTOIfwoOEfRD+WSGAkhBABtn5PKz94fhcL8uL4\nyzfnEho0egZyipHhcnjY/W4Te95pxDXopaA4keJLsolNmNhvUsatA6/gWns9G03fZq9VT35+Ppdf\nfjlhYYEbQCuEOKanwzEUIHXRfNiGo9cNQHR82LEAqcBwXF2fg7V1NFx9NRqdjsx//J3g9HT/v32y\n9c0fWh2xMdh/bOvbJwFRar6B0MixdyRZUby8/sffUbX5Q86+5kZmX7Q00CUJ8Rluxc2BzgO+42uW\nCna378atuNFr9EyLn+YPkGbEzyBYN7G6vUdVYKTRaNKBZ4FEQAWeUFX1/77sYyQwEkKMZW8esPCd\nf+xkTqaBZ64tIUy2XU0obpeXfR80s+vNRpz9bnJmxlOyOBtT6vBsyhGjUOdRuh5fwmouoc0dwVln\nncUZZ5yBVitdhUKMRqqq0tXaf6zb54gNl9MLgCk1YijMMZKSH0tI2Od3+7iammi46mpUj4fM554l\nJCeH3i7nsYDosI3+7kEAIg0hviHVRUZSCwxEGkJG7Gs9FVRF4a0n/sj+99/itK9dQ+mlywNdkhD/\nldPjZHfHbl8HkrmS/db9KKpCqC6UWQmzKEn2zUCaZJyETju+37uPtsAoGUhWVXWnRqOJAnYAl6qq\nevCLPkYCIyHEWPXB4XZufHY7U1NjeO76UiJDxkZbuTh5Xo/CwU2tbH+9noEeFxmTjZQuzSEhMzrQ\npYlTydXP4T9eyUv2aWhCorhi2TLy8/MDXZUQ4jgoXoWOxj6aD3fRXGXDXNOD162g0UBCVvSx42I5\nMeiDdbjNZhquuhqnU0V3229ps4fSfNhGT7sDgNDIoM8ce4uJDxs3G5xUVeW9vz3G7jdfpeyKK1mw\n4qpAlyTECel19bKjbYd/BlJ1dzUAUUFRzE2a65uBlFRKbmzuuHn9fmJUBUb/UYRGsw74o6qqb3/R\nYyQwEkKMNZYeJ+9VtXPvhgPkJUTyzxvLiAkbey3m4vgpXoXDFRa2baynt8tJcl4MZUtzScmXAYvj\nneL18v7jP+Pj9iiSjZGsuPp6DAZDoMsSQpwkr1vBUtvj70Bqr7ejKCo6vZbE9DB0ezZh0yfSF+6b\nVxQUqiM1P9bXQVRowJQSgWaUbTIbDqqq8tE//sb2DS8xd/HlnPGNa8fdhbSYuDodnWy3bKfcXE6l\npZKm3iYATKEmSpJLOCPtDC7JuSTAVQ6PURsYaTSaLOAjYKqqqvZ/+7fe4ZUHAAAgAElEQVSbgJsA\nMjIy5jQ0NIx4fUII8VXZ+l2U11rZXNPJlhortR2+bSyTk6P5+w2lGCMm1lnoiUhVVKp3tlO5oY7u\ntgESMqMoXZJD+mSjvIGeIHpbjvL//vIURckRXHTdbQQFSUgsxHjkcnpoPdpNy2EbDeV19PQoJKaH\nk1GcQVqhgYTMKLQTYLHFlrX/YOsL/2Lmoos559pb5G+dGNda+1r9848qzBUUGgt57LzHAl3WsBiV\ngZFGo4kEPgQeVFX1pS97rHQYCSFGm/5BD5X1XWyp9gVEB812VBUignWUZBtZkBfHvFwTk5Ki0Y7D\nu4riGFVVadhnpXx9LdbmPowpEZQuziF7Zpy8eZ6A7OZaohOzQOYVCTFhuNvaCEpMDHQZI6py3Qt8\n/M+nmXr2Qs6/6Xto5HeemEBUVaXX3Ut08PgYM/BFgVHABmloNJog4EXgH/8tLBJCiNFg0ONlV2O3\nPyDa3dSNR1EJ1mmZnRnLj84rYH6eielpsQRNgLuKwqe5qovydbW01dmJjg/jvGsnk1+cKCHhBBad\nnBPoEoQQI2yihUU7X1/Px/98mqIFZ7LwplslLBITjkajGTdh0ZcJSGCk8d1ufQo4pKrq7wJRgxBC\n/DdeRWV/Sw+bazrZWmNlW30XTreCVgPT0mK56Ywc5ufGMTfLQGjQ+N6cIP6TpbaH8nW1tBy2EWkI\n4axvFFI0PxmdhIVCCCHGsb3vvsH7Tz9BXvE8LvjOD9GO8+1RQkxkgeowWgBcDezTaDS7h/6/21VV\nfS1A9QghBKqqcrS9j81DHUTltVZ6nR4AChOj+FpJBvNz4yjNMRIdKnNJJqqOpl4q1tfSsM9KWFQQ\npy3PZ8oZKeglNBRCCDHOHfz4fd7+y5/InjWXS37wM3R62fwqxHgWkFe4qqqbAOnVF0IEXFPXgD8g\n2lJjpbNvEIAMYziXTE9mXm4c83JMxEeFBLhSEWg2Sz+VG+qo3tFOSLiesktzmHZWGsGh8mZZCCHE\n+Hd46ybe+NP/kjFlGot/9At0erl5JsR4J+9yhRATSnuvk601VrZU+7aZNdscAMRHhXBanon5ub5B\n1enG8ABXKkYLe6eDba/Wcbjcgj5Yx9yLsph5Xjoh4fJGWQghxMRQs6OC1/7wG5ILirj0p3cRFCw3\n0oSYCCQwEkKMaz0ON+W1VrbWWNlc3cnR9j4AokP1zMs1cePpOSzIM5EbHynbrMRn9HcPsv31eg5u\nakWj0TD93HTmLMokLCo40KUJIYQQI6Z+7y42/O6XJGTlcPlt9xAUGhrokoQQI0QCIyHEuOJwedlW\n3zV0xKyT/S09KCqEBekozjZyxZw0FuTGMTklGp1ssRKfw9HnYucbDez7sAXVqzLptBTmXphFpEHu\npgohhJhYmg/uZ91vHsCYksblt99HSLh0YAsxkUhgJIQY01wehT3N3f4jZrsabbi9KkE6DbPSDXzv\nnHwW5MUxMz2WYL1srxJfbNDhYfc7jex5pwmPy0tBaRLFF2cTEx8W6NKEEEKIEdd6pIqXfnUv0fEJ\nLLvjAcIiowJdkhBihElgJIQYUxRF5aDZzpaaTjZX+1bdD7i8aDQwJSWa6xZkMz8vjuIsA+HB8itO\n/HfuQS/7Pmhm55sNDA54yJ0dT8niHIzJEYEuTQghhAiIttpqXvrl3UTExLL8jgcIj4kNdElCiACQ\nqykhxKimqio1Hf1sHQqIyuusdA+4AchLiGTZnDTm55ooyzERGy6zZcRX53UrHNjUwvbXG3DYXWRO\nNVG6JIf4DLmDKoQQYuLqbGrghYfuIjg8nOV3Pkik0RTokoQQASKBkRBi1GnpdrDFv+q+kza7b9V9\namwYCyclMn9om1litAxdFMdP8SpUlVvY9modfV2DpBbEUnrTVJLz5O6pEEKIia2rtYW1969Cp9ez\n4s6HiI5PCHRJQogAksBICBFw1r5BttZa2VxtZWtNJ/XWAQBMEcHMy/WFQwvyTGQYw2WTmThhqqJy\ndEcblRvq6Gl3kJAVzTlXTyKtyCA/V0IIISa8nnYLax9YhaqqrLjjQWKTkgNdkhAiwCQwEkKMuF6n\nm8o63yazzdWdVFl6AYgK0VOaY+TqeVksyDNRmBglF/LipKmqSt2eTio31GJt6ceUGslF355G1vQ4\n+fkSQgghgF5rJ2vvX4XH6WT5XQ9hSksPdElCiFFAAiMhxCnndHvZ2WBjc43vmNne5h68ikqIXsvc\nLAM/XVTI/FwT01Jj0Otkk5kYHqqq0nSoi4p1tbQ39BKTEMb5108hb04CGq0ERUIIIQRAf7eNtfev\nwtHby/I7HyQhKyfQJQkhRgkJjIQQw87jVdjb0uOfQ7S9wYbLo6DTapiRFsO3z8xlfp6J2RkGQoN0\ngS5XjEPm6m7K19XSerSbSGMIZ19dRFFZEloJJIUQQgi/AXsPLzxwB71dnSy7/X6ScvMDXZIQYhSR\nwEgIcdIUReVwWy+bqzvZWmOloq6LvkEPAJOSo/lmWSbz80wUZxmJCg0KcLViPOto7KV8XS2NB6yE\nRwdz+soCppyWgi5IgiIhhBDi05z9fbz40F10W8xcdtvdpBZNDnRJQohRRgIjIcRxU1WVBuuA/4hZ\neY0Va78LgOy4CJbMTGFBbhzzck0YI2TVvTj1ulr7qdxQS82uDkIi9My7LJdpZ6cRFCwdbEIIIcS/\nczkGeOmXd9PZ2MClP72DjKkzAl2SEGIUksBICPGVtNmdbB46Yra1xkpLtwOApOhQziyMZ35uHPNz\nTaTEhgW4UjGR9HQMsG1jPYcrLQSF6Ci+OIsZ52UQEiZ/3oQQQojP4x508vKv78NSc5TFP7yN7Flz\nA12SEGKUknfUQojP1T3gonxo1f2Wmk5qOvoBiA0PYl6OiVvOymVBronsuAjZNCVGXJ/NyfbX6jm0\n2YxWp2HWeRnMWpRBWKR0tAkhhBBfxONyse6RB2k+dICLvvcT8kvmB7okIcQoJoGREAKA/kEP2+p9\nq+631HRyoNWOqkJ4sI6SbCNXFmcwL9fE5ORotLJhSgTIgN3Fzjcb2P9hC6qqMuX0FOZclEVETEig\nSxNCCCFGNa/Hw4ZHH6Zh7y4W3fJ9Ji04M9AlCSFGOQmMhJigBj1edjd2s7nGytaaTnY1duNRVIJ1\nWmZlxPLD8wqYn2tiRnosQbJZSgTY4ICbXW83sue9ZrwuL4Xzkim+KIvoODkCKYQQQvw3itfLa394\nhNodlZx73beZevbCQJckhBgDJDASYoLwKioHWnv8R8y21XfhdCtoNTAtNYYbz8hhfq6JuZlGwmRQ\nsBglXE4Pe99vZvfbjQwOeMibm0DJJdkYkiICXZoQQggxJqiKwpt/fpQj5Zs486rrmLno4kCXJIQY\nIyQwEmKcUlWV6vY+/6Dq8lordqdv1X1BYiRXFmcwP9dEaY6JmDBZdS9GF4/by4GPWtnxRj2OXjdZ\n0+MoXZJNXFpUoEsTQgghxgxVVXnnyf/HwY/fZ8GKq5i7+PJAlySEGEMkMBJiHGnqGmDL0Kr7LTVW\nOnoHAUg3hnHRtGTm5ZqYnxtHfJTMexGjk9erULXFzPbX6umzDZJWZKB0SQ5JOTGBLk0IIYQYU1RV\n5YNn/sLed9+g5NLllF6+MtAlCSHGGAmMhBjDOnoH2VLTydYaK5trOmnq8q26j48KYX6uaeh/caQb\nwwNcqRBfTlFUjm5ro3JjHfYOB0k50Zz7rcmkFRoCXZoQQggx5qiqyqbnn2Xn6+uZfdFSTrvym7LV\nVghx3CQwEmIM6XG4qai1+jeZHWnrAyA6VE9ZjonrF2SzIC+OvIRIeVMgxgRVVand3UHF+jps5n7i\n0iO5+LvTyZxqkp9hIYQQ4gRVvLSaylfWMv28CzjrmzfI31QhxAmRwEiIUczh8rK9YWjVfXUn+1p6\nUFQIDdJSnGXksllpLMgzMSUlBp2suhdjiKqqNB7somJdLR2NvRiSwll041RyZ8WjkZ9lIYQQ4oRt\n3/ASm9f8nclnnMN5139HwiIhxAmTwEiIUcTtVdjT1M2WGiubq32r7l1eBb1Ww6yMWG49J58FuSZm\nZsQSopdNZmJsaj1qo3xdLebqHqJMoZx7zSQKShLR6rSBLk0IIYQY03a/+Sof/v2vFMw7nUW3fB+N\nVv62CiFOnARGQgSQoqgcNNv9M4gq67oYcHnRaGBKSjTfWpDF/FwTxVlGIkLk5SrGtrZ6OxXra2k6\n2EV4TDBnfq2ASQtS0OnlzawQQghxsva//zbv/vXP5M4t5aJbf4xWJzcXhRAnR65AhRhBqqpS29nv\nP2K2tdZK94AbgNz4CK6Y7TtiVpptwhARHOBqhRge1pY+KtbXUrenk9CIIOZfkce0M1PRB8sbWSGE\nEGI4HNr8IW8+/nsyp8/iku//HJ1eLvOEECdPfpMIcYq1djv8AdGWGisWuxOAlJhQzpuUyII8E/Ny\n4kiKCQ1wpUIMr+72ASo31HF0exvBITpKFmcz49x0gkPlT48QQggxXI5WbuH1P/6WtElTWPqTVeiD\n5aajEGJ4yLt2IYaZtW+Q8touNg+tu6/r7AfAGBHMvFwTC3LjmJ9rItMULkMIxbjU2+Vk+6t1HNpq\nQafXMPv8TGadn0FoRFCgSxNCCCHGlbpd29n46K9Jys3nsp/dRVCI3IAUQgwfCYyEOEl9gx4q66xs\nrvatuz9ktgMQGaKnNNvIVWWZzM81UZgYhVa2P4lxbMDuYsfr9ez/uAWAaWemMvuCTCJiQgJcmRBC\nCDH+NO7fw/rfPkRcRiaX/+JegsPCA12SEGKckcBIiOPkdHvZ2WhjS7WVLTWd7GnuwauoBOu1zM00\n8NNFhczLNTE9NQa9bH0SE4Cz382utxrZ+34TXo/KpHlJzL04myij3OUUQgghToWWqoO8/Ov7iE1K\nZtmq+wmNiAx0SUKIcUgCIyH+C49XYV9Lj28OUU0n2+ttDHoUdFoN09Ni+PaZuczPNTE700BokAzx\nFROHy+lhz7tN7H67Edegl/y5iZRckk1sotzhFEIIIU4VS/URXnr4bqKMcSy74wHCoqIDXZIQYpyS\nwEiIf6MoKkfae9lcbWVrTScVtV30DnoAKEqK8h8xK8k2EhUqM1nExONxedn3YQs732zA2ecme0Yc\npUtyMKXK3U0hhBDiVGqvr+XFh+4iLCqa5Xc+SESsIdAlCSHGMQmMxISnqiqNXQNDM4h8g6qt/S4A\nskzhLJ6ZwvxcE/NyTJgiZRaLmLi8HoVDm1vZ/lo9/T0u0icbKV2SQ2KW3NkUQgghTjVrcxMvPHgn\n+tBQlt/5IFGmuECXJIQY5yQwEhNSm93JlprOoTlEVlq6HQAkRodwZkE883JNzM+LIzU2LMCVChF4\niqJypMLCtlfrsHc6Sc6NYeH1U0gtkLuaQgghxEiwWVpZ+8AqtFoty+94kJiEpECXJISYACQwEhNC\n94CL8lpfOLS5upOaDt+q+9jwIOblmLjlzBzm58WRExchq+6FGKIqKjW7OqjcUIvNMkB8RhSX3FpI\nxhSjvE6EEEKIEWLvaGft/avwejysvPuXGFNSA12SEGKCkMBIjEsDLg/b6m1sqe5kc00nB1rtqCqE\nB+soyTaysjid+blxTE6OllX3QvwbVVVp2G+lYn0tnU19GJIjuOCmqeTMipegSAghhBhBfV1W1t6/\nCtfAAMvveoi49MxAlySEmEAkMBLjgsujsLupm83VvhlEu5psuL0qwTotszJi+cG5BSzIMzE9LZZg\nvay6F+KLNB+2UbGuBkutnei4UM67djL5xYkSrAohhBAjbKCnm7X3r6K/p5tlq+4nMTs30CUJISYY\nCYzEmORVVA622tlc08nmat+qe4fbi1YD01JjuP60HBbkmZibaSQsWFbdC/HfWOp6qFhXS3OVjYjY\nEM76RiFF85PR6SRgFUIIIUaao6+XFx64A3tnB1f84l5SCooCXZIQYgKSwEiMCaqqUtPR599kVl7b\nRY/DDUBBYuTQETMTpdkmYsJl1b0QX1Vncy8V6+uo39tJWFQQC5blMfXMVPRBErQKIYQQgTA40M+L\nD95FV2szl/78btImTw10SUKICUoCIzFqNdsG2FJtZXNNJ1tqrHT0DgKQZgjjgilJzM8zMS/XREJU\naIArFWLssVn6qdxYR/X2doLD9JQuyWH6OWkEh8qfBSGEECJQXE4HLz18Lx0NtSz58e1kTZ8V6JKE\nEBOYXBmIUaOjd5CttVa21nSyudpKY9cAAHGRIczPNbEgz8T83DjSjeEBrlSIsctudbDt1XoObzWj\nC9Yx54JMZi7MIDRCOvOEEEKIQHK7Bln3m/sxH6nikh/8jNw5pYEuSQgxwUlgJALG7nRTUdvFlppO\ntlRbOdzWC0BUqJ6yHBPXLchifl4c+QmRsplJiJPU3zPIjtfqObCpFY1Gw/Sz05l9QSbh0cGBLk0I\nIYSY8DxuNxt++xCNB/Zx4Xd/REHZaYEuSQghJDASI8fp9rK93saWmk4211jZ19yNokJokJbiLCOX\nzkplfq6Jqakx/P/27jzKiupO4Pj3RzcgNHs3RJG1WxQ17ogCLlETjcY1Ucclq3PGcWIm5mQmmSQT\nk0wm60xO9jgmMetMNIuJkbjHxCUBRJYAglu6WUTFhQZBBKGXO3+8gvRru5FuHv368b6fc+pQXcut\nW/z6vlv9e1W3Knwjk1QQr25qYuHdq3jk/qdpbUlMnrEfx541gUHDfZRTkqTeoKW5mdu/8V+sWLSA\nt1z5AQ458ZRiV0mSABNG2oOaWlpZ8vRLO8YhWrjqJba1tFLZJzhy7DA+cOokptdVc9S4YfSvdIBd\nqZC2bWlm0b1PsegPq2na2sKBU9/A1LMnMnSkj3RKktRbtLa2cNd1X6N+3hxOee+VHH7aW4tdJUna\nwYSRCqa1NfHYcxuZ09DIrPq1PLxiHa9sayECDtlvCO+dMYFpddVMnTCCqv7+6kl7QtO2Fh6572kW\n3rOKra80U3vUSKaeM5Hq0YOKXTVJktRGam3lnu9+i8dnPcCJl72Xo888t9hVkqQ8/tWubkspsWLt\nK8xuyL3qfk5DI+s35151XzuyircfPYbpddUcX1vN8CrHSZH2pJamVpb9+VkW3LmSzRu3Me7QERx3\nbi2jxg8pdtUkSVI7KSX+8KPvsuz+e5l24aVMPe/CYldJkl7DhJG6ZM2GLTseMZvT0MiaDa8CMHro\nPpx28BuYXpd7k9m+Qx0fReoJrS2tPP7Qc8y7fQWb1m1l9KRhnHHlGxl9wLBiV02SJHUgpcQD//dD\nFt9zO1POeTvTLrys2FWSpA6ZMNJOrXtlGw8tzz1iNqehkeVrXwFgRFU/ptVV5153X1fD+OqBvslM\n6kGpNVG/4AUevm0FLz2/mVHjB3PKOycz9uARtkVJknqx2b+6kQW33cKRZ5zNSZe/z35bUq9lwkh5\nNm1tZt6KdcyqX8vshkYeXbMRgKp+FRxXW81lx41jel0Nk/cdTB/fZCb1uJQSK5esZe7MFTQ+s4kR\no6s486rDmHhEjReckiT1cnN/+yse+vVNvPGU0zn1vVfad0vq1UwYlblXm1r4y1MvMbshlyBavPol\nmlsT/Sr7cMy44fzr6Qcyra6Gw8cMpW9Fn2JXVypbKSWefnw9c2cu5/kVGxk6cgBvueIQDpjyBpO3\nkiSVgIV33Mqfb/oJk2eczFuuvJro47W1pN7NhFGZaW5pZemzG3c8YjZv5Tq2NrfSJ+DwMcP4x5Nr\nmV5XwzHjh7NPX191L/UGaxo2MPfWBp558iUGDe/PKe+czEHT9qXCJK4kSSVhyb13cd9Pvs+kqdM5\n8+oP06eP19mSej8TRnu5lBJPPr9pxyNmc5c38vLWZgAm7zuYy48bz/S6aqbWjmDIPn2LXFtJbb34\n1MvMnbmcVUsbGTC4LydcPIlDTxxNpclcSZJKxqMP/pHf3/AdJh41hbdd8xH6VNiPSyoNJoz2Mikl\nVq/bwqzsEbM5DWtZu2kbAOOrB3L2EaOZXlfNtLpqagb1L3JtJXVk3ZpXePh3y2lY+CL9B1Zy/Pm1\nHH7KWPr29wJTkqRS8sScP3PXdV9n3KGHc+6HP0FFpV/QSiodJoz2Ai9sfJXZDY3MbljLrPpGnnlp\nCwCjBvfnxEkjd7zNbMzwgUWuqaSd2fDiFubdvoIn5z5HZb8Kppw1gSPfPJb+A724lCSp1NTPn8sd\n3/pvRh80mfM/ci2V/foVu0qS1CUmjErQhs1NzFmeu3toVkMj9S9sAmDogL5Mq63eMQ5R3cgq37wg\nlYBN67cy/86VPPbnZ4mK4IjTxnL0GeMZMNgLS0mSStHKxQu57WtfZNSEWi74t8/Qd599il0lSeoy\nE0YlYPO2ZuatXJ97k1l9I0uf3UBKMKBvBVMnjuCiY8Yw44AaDt5vCBW+LUkqGVte3saCu1ex9IFn\nSC2JQ04YzTFnTmDQcB8XlSSpVK1+9BFu/crnGbH/WN7+ic/Sf6B3+UsqTSaMeqFtza0sWv3SjgTR\nX1avp6kl0bciOGrccK45bRIzDqjhiDHD6FfpW5KkUrN1cxOL7l3N4j+spnlbCwcdty/Hnj2RITUD\nil01SZK0G5598jFu+fJnGTJyFBd+8nMMGDS42FWSpG4zYdQLtLQmHn12Y24MooZG5q1Yx5amFiLg\nsP2HcsUJE5lRV8OUCcMZ2M+QSaWqaWsLS+5bzV/ueYqtm5upO3oUU8+ZyIj9qopdNUmStJueX17P\nb774GaqGDeOiaz/PwCFDi10lSdotZh+KIKVEw4ubmN3QyKz6tTy0fB0btjQBMGnUIC6eMobpB9Rw\n/MRqhjrYrVTymptaWPbgsyy4ayVbXm5i/GHVHHdOLSPH+a2jJEl7g7VPreTmL3yK/lVVXHTt5xk0\nfESxqyRJu82EUQ9a9uwGbvjTCmY3rOX5jVsB2H/YAM449A3MOKCGabXVjBrigHjS3uTxOWuYO3M5\nm9ZvZf+DhnHcuXXsV+c3jtLeqrWlhXt/cF2xqyGphzXMn0tlZSUXffLzDKkZVezqSFJBmDDqQVu2\ntfCnv77ItLoaZtRVM72uhnHVDoIn7c1eWPUyVcP6c+p7DmbsZL9tlPZ2KSWWL5xX7GpI6mFVw4bz\ntms+yrB99yt2VSSpYCKlVOw67JIpU6ak+fPnF7sau6W1NRGBr7qXykhzUwsVlX1s95IkSZJ6pYhY\nkFKa0n65dxj1oD6+8l4qO5V9K4pdBUmSJEnqMt/JLkmSJEmSpDwmjCRJkiRJkpTHhJEkSZIkSZLy\nmDCSJEmSJElSHhNGkiRJkiRJymPCSJIkSZIkSXlMGEmSJEmSJCmPCSNJkiRJkiTlMWEkSZIkSZKk\nPCaMJEmSJEmSlMeEkSRJkiRJkvKYMJIkSZIkSVIeE0aSJEmSJEnKY8JIkiRJkiRJeUwYSZIkSZIk\nKY8JI0mSJEmSJOUxYSRJkiRJkqQ8JowkSZIkSZKUx4SRJEmSJEmS8pgwkiRJkiRJUh4TRpIkSZIk\nScpjwkiSJEmSJEl5TBhJkiRJkiQpjwkjSZIkSZIk5TFhJEmSJEmSpDwmjCRJkiRJkpTHhJEkSZIk\nSZLyREqp2HXYJRHxIrCq2PUogBpgbbEroaIw9uXL2JcvY1++jH35MvblybiXL2Nfvvam2I9PKY1s\nv7BkEkZ7i4iYn1KaUux6qOcZ+/Jl7MuXsS9fxr58GfvyZNzLl7EvX+UQex9JkyRJkiRJUh4TRpIk\nSZIkScpjwqjnfa/YFVDRGPvyZezLl7EvX8a+fBn78mTcy5exL197fewdw0iSJEmSJEl5vMNIkiRJ\nkiRJeUwYSZIkSZIkKY8Joy6KiB9GxAsRsbTNsiMiYk5EPBIRv4uIIW3WHZ6tW5at3ydbfn9EPBER\ni7JpVCfH+3hE1GfbnrHnz1CdKUTsI2Jwm5gvioi1EfH1Do41ISK2tNnu+p46T71WV2IfEZe3i3Fr\nRByZrTsm274+Ir4ZEdHBsSJbVx8RSyLi6J47U7VViLhHxMCIuD0iHs8+C77UybFs871IAdu8fX2J\nKVC7t68vQV2Mfd+I+Em2/LGI+Hibfd6ateX6iPhYJ8fqHxG/yLaZGxET9vT5qXOFiH1EjI2I+yLi\n0ay/v6aTY70pIja0afef6pmzVEcK2O5XZssXRcT8To4VUYrX+Cklpy5MwEnA0cDSNsvmASdn81cA\n/5nNVwJLgCOyn6uBimz+fmDK6xzrEGAx0B+YCDRs39+pdGPfrswFwEkdLJ/Q9jhOpRP7dvsdBjS0\n+flh4HgggDuBMzvY56xsXWTbzi32+ZfrVIi4AwOBU7L5fsCfOom7bb4XTQVs8/b1JTYVKvbt1tnX\nl8DUxeu8y4CfZ/MDgZVZPCuyNlybfeYvBg7p4FjvB67P5i8BflHs8y/nqUCx3w84Ols+GHiyk9i/\nCbit2OfsVLjYZz+vBGpe51gleY3vHUZdlFJ6EFjXbvGBwIPZ/O+Bd2TzpwNLUkqLs30bU0otXTjc\neeR+KbemlFYA9cDUbldeu6XQsY+IA4FR5P6AVC/Wxdi3dSnwc4CI2A8YklJ6KOV6jZ8C53ewz3nA\nT1POQ8CwbF/1sELEPaW0OaV0Xza/DVgIjNkjFVbBFCL2XWBf34sUOvb29aWji7FPQFVEVAIDgG3A\nRnJttz6ltDz7zP85uTbe3nnAT7L5m4HTIl5717F6RiFin1Jak1JamJX3MvAYsP+errt2T4Ha/a4q\nyWt8E0aFsYy/dQYXAWOz+QOBFBF3R8TCiPhou/1+lN22dm0nncT+wOo2Pz+NHzy9TXdjD3/7Rqmz\nVxVOjIi/RMQDEXFiYautAugs9m39HXBTNr8/uTa8XWft2Xbfu3U17jtExDDgHOAPnZRtm+/duht7\n+/rS1+12j319qess9jcDrwBrgKeAr6SU1rHr7XnHdimlZmADubvR1Xt0NfY7ZI8YHgXM7aTsaRGx\nOCLujIhDC1xv7b7uxD4B90TEgoi4spNyS7K/N2FUGFcA74+IBXkWMrsAAAbLSURBVORuQdyWLa8E\nTgAuz/69ICJOy9ZdnlI6DDgxm97Vs1VWgXQn9ttdQscXl5D7IBqXUjoK+DBwY7QZH0m9QmexByAi\njgM2p5SWdrSzSla34p59G3UT8M2U0vIOyrXN937dib19/d5hdz7v7etLW2exnwq0AKPJPUr6LxFR\nW5wqag/pVuwjYhDwa+BDKaWO7j5ZCIxPKR0BfAv47Z47BXVTd2J/QkrpaOBM4OqIOKmH67zHmDAq\ngJTS4yml01NKx5C7KGjIVj0NPJhSWptS2gzcQe4ZSVJKz2T/vgzcSMe3nz9D/rdYY7Jl6iW6E3vI\nDaYGVKaUFnRS7taUUmM2vyAr98A9eCrqop3Efrv2fyQ8Q/6jSJ21Z9t9L9aNuG/3PeCvKaXXDHyb\nlWub7+W6E3v7+r1Dd9u9fX3p20nsLwPuSik1pZReAGYBU9j19rxju+wLhaFA4545C3VHN2JPRPQl\nlyz6WUrpN52UuzGltCmbvwPoGxE1e/h01AXdiX2b/v4F4Bb2ov7ehFEBRPbWk4joA3wS2P6Wi7uB\nwyL3lpxK4GTg0Yio3P7BkH2wnA109K3UTOCSyL1JYSIwidygueoluhr7NrteSuffOBIRIyOiIpuv\nJRf7ju5KUJHsJPbbl11Mm/EsUkprgI0RcXz2WMq7gVs7KHom8O7sTQrHAxuyfdULdDXu2fLPkftj\n4EM7Kdc238t1Nfb29XuP7rT7jH19idtJ7J8CTs3WVZEbwPZxcoPlToqIiRHRj1wycWYHRc8E3pPN\nXwj8cSePLaoIuhr77NruB8BjKaWv7qTcfbc/nhwRU8n9PW6ysBfpRuyrImJwm+Wn03l/X3LX+JXF\nrkCpiYibyI1uXxMRTwOfBgZFxNXZJr8BfgSQUlofEV8l13kk4I6U0u3ZL9Ld2QVkBXAv8P2s/HPJ\nvVHlUymlZRHxS3KJhmbg6i4Omq0CKkTs2xR3MbmR8tuWvyP25Ebs/2xENAGtwFXtn49Wz+lK7DMn\nAas7ePTo/cCPyQ2Ud2c2ERFXAaSUrid3N9pZ5Aa+3Qy8r/BnpF1RiLhHxBjg38n9IbEwu0b8dkrp\nBtt871WgNt8f+/qSU8DPe7CvLyldjP13yI1PtozcG49+lFJakpXzAXJfHFYAP0wpLcuWfxaYn1Ka\nSS6x8L8RUU9uwN1LeuAU1YlCxD4iTiD32PEjEbEo2/YTKaU72l3nXQj8U0Q0A1uAS0wWFk+BYl8L\n3JJd41UCN6aU7srKL/lr/PD3U5IkSZIkSW35SJokSZIkSZLymDCSJEmSJElSHhNGkiRJkiRJymPC\nSJIkSZIkSXlMGEmSJEmSJCmPCSNJkqQeEjlef0mSpF7PCxZJklRWIuK3EbEgIpZFxJXZsr+PiCcj\n4uGI+H5EfDtbPjIifh0R87Jpxk7KHRkRv8/KvSEiVkVETURMiIgnIuKnwFJgbERcGhGPRMTSiPhy\nmzI2tZm/MCJ+nM3/OCKuj4j5WT3P3kP/PZIkSYAJI0mSVH6uSCkdA0wBPhgR+wPXAscDM4DJbbb9\nBvC1lNKxwDuAG3ZS7qeBP6aUDgVuBsa1WTcJuC5b1wR8GTgVOBI4NiLO34V6TwCmAm8Dro+IfXZh\nH0mSpG6pLHYFJEmSetgHI+KCbH4s8C7ggZTSOoCI+BVwYLb+zcAhEbF93yERMSiltInXOgG4ACCl\ndFdErG+zblVK6aFs/ljg/pTSi9nxfgacBPz2der9y5RSK/DXiFhOLrG1aJfOWJIkqYtMGEmSpLIR\nEW8ilwSallLaHBH3A48DB3eySx/g+JTSq7t56Fd2cbvUZr79HUTpdX6WJEkqGB9JkyRJ5WQosD5L\nFk0m9xhaFXByRAyPiEpyj55tdw/wz9t/iIgjd1L2LODibLvTgeGdbPdwdryaiKgALgUeyNY9HxEH\nZwNjX9Buv4siok9E1AG1wBO7cL6SJEndYsJIkiSVk7uAyoh4DPgS8BDwDPAFcomcWcBKYEO2/QeB\nKRGxJCIeBa7aSdn/AZweEUuBi4DngJfbb5RSWgN8DLgPWAwsSCndmq3+GHAbMBtY027Xp7I63glc\nVYC7niRJkjoVKXk3syRJKm/bxyXK7jC6BfhhSumWLpbRH2hJKTVHxDTgf1JKO7sjqStl/xi4LaV0\ncyHKkyRJej2OYSRJkgSfiYg3kxs36B5efwDqjowDfpk9TrYN+IcC1k+SJKlHeYeRJElSF0TE+4Br\n2i2elVK6uhj1kSRJ2hNMGEmSJEmSJCmPg15LkiRJkiQpjwkjSZIkSZIk5TFhJEmSJEmSpDwmjCRJ\nkiRJkpTHhJEkSZIkSZLy/D/yu5twL81rlQAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "id": "h_3FAckHTgU9", "colab_type": "code", "colab": {} }, "source": [ "# now I'd like to do per-capita measurements" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "ZuKN3UsnMNHr", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 194 }, "outputId": "ed5a535a-4fbb-4a36-e90e-6ff4f1177d7f" }, "source": [ "# retrieved from https://stats.oecd.org/Index.aspx?DataSetCode=POP_PROJ#\n", "# on 11/17/2019\n", "pop_df = pd.read_csv(\"https://raw.githubusercontent.com/ekoly/DS-Unit-1-Build/master/csv/POP_PROJ_17112019195558853.csv\")\n", "pop_df.head()" ], "execution_count": 161, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
LOCATIONCountrySEXSexAGEAgeVARVariantTIMETimeUnit CodeUnitPowerCode CodePowerCodeReference Period CodeReference PeriodValueFlag CodesFlags
0AUSAustraliaMAMalesD199G501Population (hist&proj) 15-64VAR1Baseline19501950PERPersons3ThousandsNaNNaN2709.7NaNNaN
1AUSAustraliaMAMalesD199G501Population (hist&proj) 15-64VAR1Baseline19511951PERPersons3ThousandsNaNNaN2775.9NaNNaN
2AUSAustraliaMAMalesD199G501Population (hist&proj) 15-64VAR1Baseline19521952PERPersons3ThousandsNaNNaN2837.0NaNNaN
3AUSAustraliaMAMalesD199G501Population (hist&proj) 15-64VAR1Baseline19531953PERPersons3ThousandsNaNNaN2869.4NaNNaN
4AUSAustraliaMAMalesD199G501Population (hist&proj) 15-64VAR1Baseline19541954PERPersons3ThousandsNaNNaN2897.3NaNNaN
\n", "
" ], "text/plain": [ " LOCATION Country SEX Sex AGE Age \\\n", "0 AUS Australia MA Males D199G501 Population (hist&proj) 15-64 \n", "1 AUS Australia MA Males D199G501 Population (hist&proj) 15-64 \n", "2 AUS Australia MA Males D199G501 Population (hist&proj) 15-64 \n", "3 AUS Australia MA Males D199G501 Population (hist&proj) 15-64 \n", "4 AUS Australia MA Males D199G501 Population (hist&proj) 15-64 \n", "\n", " VAR Variant TIME Time Unit Code Unit PowerCode Code PowerCode \\\n", "0 VAR1 Baseline 1950 1950 PER Persons 3 Thousands \n", "1 VAR1 Baseline 1951 1951 PER Persons 3 Thousands \n", "2 VAR1 Baseline 1952 1952 PER Persons 3 Thousands \n", "3 VAR1 Baseline 1953 1953 PER Persons 3 Thousands \n", "4 VAR1 Baseline 1954 1954 PER Persons 3 Thousands \n", "\n", " Reference Period Code Reference Period Value Flag Codes Flags \n", "0 NaN NaN 2709.7 NaN NaN \n", "1 NaN NaN 2775.9 NaN NaN \n", "2 NaN NaN 2837.0 NaN NaN \n", "3 NaN NaN 2869.4 NaN NaN \n", "4 NaN NaN 2897.3 NaN NaN " ] }, "metadata": { "tags": [] }, "execution_count": 161 } ] }, { "cell_type": "code", "metadata": { "id": "PUpHCgoFMtQf", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 746 }, "outputId": "c252a1ac-e3ef-4185-8b2b-f45900f92df7" }, "source": [ "pop_df[\"Age\"].value_counts()" ], "execution_count": 162, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Population (hist&proj) All ages 14664\n", "Population (hist&proj) 15-19 7362\n", "Population (hist&proj) < 15 7362\n", "Population (hist&proj) < 20 7362\n", "Population (hist&proj) 75-79 7362\n", "Population (hist&proj) 30-34 7362\n", "Population (hist&proj) 05-09 7362\n", "Population (hist&proj) 15-64 7362\n", "Population (hist&proj) 50-54 7362\n", "Population (hist&proj) 20-24 7362\n", "Population (hist&proj) 55-59 7362\n", "Population (hist&proj) 00-04 7362\n", "Population (hist&proj) 65-69 7362\n", "Population (hist&proj) 70-74 7362\n", "Population (hist&proj) 40-44 7362\n", "Population (hist&proj) 45-49 7362\n", "Population (hist&proj) 60-64 7362\n", "Population (hist&proj) 25-29 7362\n", "Population (hist&proj) 35-39 7362\n", "Population (hist&proj) 10-14 7362\n", "Population (hist&proj) 20-64 7362\n", "Population (hist&proj) 65+ 7302\n", "Population (hist&proj) 80+ 7302\n", "Population (hist&proj) 80-84 7254\n", "Population (hist&proj) 85+ 7239\n", "Youth (-15) Dependency Ratio (15-64) 2454\n", "Youth (-20) Dependency Ratio (20-64) 2454\n", "Old (+65) Dependency Ratio (All ages) 2434\n", "Share (80+) in all ages population 2434\n", "Old (+65) Dependency Ratio (20-64) 2434\n", "Old (+65) Dependency Ratio (15-64) 2434\n", "Working Age (20-64) per Pension Age (+65) 2434\n", "Age(-15 & +65) Dependency Ratio (15-64) 2434\n", "Youth (-20) Dependency Ratio (All ages) 2434\n", "Age(-15 & +65) Dependency Ratio (All ages) 2434\n", "Youth (-15) Dependency Ratio (All ages) 2434\n", "Working Age (15-64) per Pension Age (+65) 2434\n", "Age(-20 & +65) Dependency Ratio (20-64) 2434\n", "Age(-20 & +65) Dependency Ratio (All ages) 2434\n", "Population (hist&proj) 75+ 153\n", "Name: Age, dtype: int64" ] }, "metadata": { "tags": [] }, "execution_count": 162 } ] }, { "cell_type": "code", "metadata": { "id": "_IOd6RT3PCSr", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 194 }, "outputId": "e5999435-3d22-47b6-d7b2-fc07da9d1d71" }, "source": [ "pop_df = pop_df[pop_df[\"Age\"] == \"Population (hist&proj) All ages\"]\n", "pop_df.head()" ], "execution_count": 163, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
LOCATIONCountrySEXSexAGEAgeVARVariantTIMETimeUnit CodeUnitPowerCode CodePowerCodeReference Period CodeReference PeriodValueFlag CodesFlags
35745AUSAustraliaMAMalesD199G5TTPopulation (hist&proj) All agesVAR1Baseline19501950PERPersons3ThousandsNaNNaN4122.9NaNNaN
35746AUSAustraliaMAMalesD199G5TTPopulation (hist&proj) All agesVAR1Baseline19511951PERPersons3ThousandsNaNNaN4253.7NaNNaN
35747AUSAustraliaMAMalesD199G5TTPopulation (hist&proj) All agesVAR1Baseline19521952PERPersons3ThousandsNaNNaN4372.6NaNNaN
35748AUSAustraliaMAMalesD199G5TTPopulation (hist&proj) All agesVAR1Baseline19531953PERPersons3ThousandsNaNNaN4462.6NaNNaN
35749AUSAustraliaMAMalesD199G5TTPopulation (hist&proj) All agesVAR1Baseline19541954PERPersons3ThousandsNaNNaN4546.1NaNNaN
\n", "
" ], "text/plain": [ " LOCATION Country SEX Sex AGE \\\n", "35745 AUS Australia MA Males D199G5TT \n", "35746 AUS Australia MA Males D199G5TT \n", "35747 AUS Australia MA Males D199G5TT \n", "35748 AUS Australia MA Males D199G5TT \n", "35749 AUS Australia MA Males D199G5TT \n", "\n", " Age VAR Variant TIME Time Unit Code \\\n", "35745 Population (hist&proj) All ages VAR1 Baseline 1950 1950 PER \n", "35746 Population (hist&proj) All ages VAR1 Baseline 1951 1951 PER \n", "35747 Population (hist&proj) All ages VAR1 Baseline 1952 1952 PER \n", "35748 Population (hist&proj) All ages VAR1 Baseline 1953 1953 PER \n", "35749 Population (hist&proj) All ages VAR1 Baseline 1954 1954 PER \n", "\n", " Unit PowerCode Code PowerCode Reference Period Code \\\n", "35745 Persons 3 Thousands NaN \n", "35746 Persons 3 Thousands NaN \n", "35747 Persons 3 Thousands NaN \n", "35748 Persons 3 Thousands NaN \n", "35749 Persons 3 Thousands NaN \n", "\n", " Reference Period Value Flag Codes Flags \n", "35745 NaN 4122.9 NaN NaN \n", "35746 NaN 4253.7 NaN NaN \n", "35747 NaN 4372.6 NaN NaN \n", "35748 NaN 4462.6 NaN NaN \n", "35749 NaN 4546.1 NaN NaN " ] }, "metadata": { "tags": [] }, "execution_count": 163 } ] }, { "cell_type": "code", "metadata": { "id": "SgQy9K7jPqaM", "colab_type": "code", "colab": {} }, "source": [ "pop_df = pop_df.pivot_table(values=\"Value\", index=[\"Country\", \"Time\"], aggfunc=np.sum).reset_index([\"Country\", \"Time\"])" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "NbtWU6I3Rz9j", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 634 }, "outputId": "331e00f9-360c-40ca-f4ef-adbe8a0689f3" }, "source": [ "fighters_df = pd.merge(pop_df, num_fighters_by_year, left_on=[\"Country\", \"Time\"], right_on=[\"country\", \"age_group\"])\n", "fighters_df" ], "execution_count": 165, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
CountryTimeValueage_groupcountrynum_fighters
0Brazil1965337555.921965Brazil11
1Brazil1970384313.201970Brazil9
2Brazil1975432896.361975Brazil41
3Brazil1980474250.101980Brazil68
4Brazil1985531997.161985Brazil44
5Canada196580137.001965Canada4
6Canada197086984.001970Canada5
7Canada197592576.001975Canada19
8Canada198098068.001980Canada30
9Canada1985103370.001985Canada11
10Japan1965393099.001965Japan6
11Japan1970414880.001970Japan9
12Japan1975447760.001975Japan14
13Japan1980468243.001980Japan14
14Japan1985484194.001985Japan7
15United States1965777211.921965United States40
16United States1970820208.801970United States82
17United States1975863892.801975United States173
18United States1980908898.801980United States278
19United States1985951695.201985United States159
\n", "
" ], "text/plain": [ " Country Time Value age_group country num_fighters\n", "0 Brazil 1965 337555.92 1965 Brazil 11\n", "1 Brazil 1970 384313.20 1970 Brazil 9\n", "2 Brazil 1975 432896.36 1975 Brazil 41\n", "3 Brazil 1980 474250.10 1980 Brazil 68\n", "4 Brazil 1985 531997.16 1985 Brazil 44\n", "5 Canada 1965 80137.00 1965 Canada 4\n", "6 Canada 1970 86984.00 1970 Canada 5\n", "7 Canada 1975 92576.00 1975 Canada 19\n", "8 Canada 1980 98068.00 1980 Canada 30\n", "9 Canada 1985 103370.00 1985 Canada 11\n", "10 Japan 1965 393099.00 1965 Japan 6\n", "11 Japan 1970 414880.00 1970 Japan 9\n", "12 Japan 1975 447760.00 1975 Japan 14\n", "13 Japan 1980 468243.00 1980 Japan 14\n", "14 Japan 1985 484194.00 1985 Japan 7\n", "15 United States 1965 777211.92 1965 United States 40\n", "16 United States 1970 820208.80 1970 United States 82\n", "17 United States 1975 863892.80 1975 United States 173\n", "18 United States 1980 908898.80 1980 United States 278\n", "19 United States 1985 951695.20 1985 United States 159" ] }, "metadata": { "tags": [] }, "execution_count": 165 } ] }, { "cell_type": "code", "metadata": { "id": "yEbJnR4kUbhu", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 634 }, "outputId": "4085a84e-ac51-493a-eb6d-7b6a8a51ecde" }, "source": [ "fighters_df[\"per_capita_fighters\"] = fighters_df[\"num_fighters\"]/fighters_df[\"Value\"]*1000.\n", "fighters_df" ], "execution_count": 166, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
CountryTimeValueage_groupcountrynum_fightersper_capita_fighters
0Brazil1965337555.921965Brazil110.032587
1Brazil1970384313.201970Brazil90.023418
2Brazil1975432896.361975Brazil410.094711
3Brazil1980474250.101980Brazil680.143384
4Brazil1985531997.161985Brazil440.082707
5Canada196580137.001965Canada40.049915
6Canada197086984.001970Canada50.057482
7Canada197592576.001975Canada190.205237
8Canada198098068.001980Canada300.305910
9Canada1985103370.001985Canada110.106414
10Japan1965393099.001965Japan60.015263
11Japan1970414880.001970Japan90.021693
12Japan1975447760.001975Japan140.031267
13Japan1980468243.001980Japan140.029899
14Japan1985484194.001985Japan70.014457
15United States1965777211.921965United States400.051466
16United States1970820208.801970United States820.099975
17United States1975863892.801975United States1730.200256
18United States1980908898.801980United States2780.305865
19United States1985951695.201985United States1590.167070
\n", "
" ], "text/plain": [ " Country Time Value age_group country num_fighters \\\n", "0 Brazil 1965 337555.92 1965 Brazil 11 \n", "1 Brazil 1970 384313.20 1970 Brazil 9 \n", "2 Brazil 1975 432896.36 1975 Brazil 41 \n", "3 Brazil 1980 474250.10 1980 Brazil 68 \n", "4 Brazil 1985 531997.16 1985 Brazil 44 \n", "5 Canada 1965 80137.00 1965 Canada 4 \n", "6 Canada 1970 86984.00 1970 Canada 5 \n", "7 Canada 1975 92576.00 1975 Canada 19 \n", "8 Canada 1980 98068.00 1980 Canada 30 \n", "9 Canada 1985 103370.00 1985 Canada 11 \n", "10 Japan 1965 393099.00 1965 Japan 6 \n", "11 Japan 1970 414880.00 1970 Japan 9 \n", "12 Japan 1975 447760.00 1975 Japan 14 \n", "13 Japan 1980 468243.00 1980 Japan 14 \n", "14 Japan 1985 484194.00 1985 Japan 7 \n", "15 United States 1965 777211.92 1965 United States 40 \n", "16 United States 1970 820208.80 1970 United States 82 \n", "17 United States 1975 863892.80 1975 United States 173 \n", "18 United States 1980 908898.80 1980 United States 278 \n", "19 United States 1985 951695.20 1985 United States 159 \n", "\n", " per_capita_fighters \n", "0 0.032587 \n", "1 0.023418 \n", "2 0.094711 \n", "3 0.143384 \n", "4 0.082707 \n", "5 0.049915 \n", "6 0.057482 \n", "7 0.205237 \n", "8 0.305910 \n", "9 0.106414 \n", "10 0.015263 \n", "11 0.021693 \n", "12 0.031267 \n", "13 0.029899 \n", "14 0.014457 \n", "15 0.051466 \n", "16 0.099975 \n", "17 0.200256 \n", "18 0.305865 \n", "19 0.167070 " ] }, "metadata": { "tags": [] }, "execution_count": 166 } ] }, { "cell_type": "code", "metadata": { "id": "TvaiaVC5SliJ", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 732 }, "outputId": "b64641df-3af9-49de-e09d-0ca94a190bda" }, "source": [ "fig, ax = plt.subplots(figsize=(20, 12))\n", "\n", "\n", "sns.lineplot(x=fighters_df[\"Time\"], y=fighters_df[\"per_capita_fighters\"], hue=fighters_df[\"Country\"], ax=ax)\n", "#ax.set_ylim([0, 50])" ], "execution_count": 167, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": { "tags": [] }, "execution_count": 167 }, { "output_type": "display_data", "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABJYAAAK5CAYAAADtvaSqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "id": "rv9Z-3TsVy61", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 732 }, "outputId": "615e35e4-4748-4c1d-ba5f-7d20b3152c9f" }, "source": [ "fighters_sec_df = pd.merge(pop_df, num_fighters_by_year_sec, left_on=[\"Country\", \"Time\"], right_on=[\"country\", \"age_group\"])\n", "\n", "fighters_sec_df[\"per_capita_fighters\"] = fighters_sec_df[\"num_fighters\"]/fighters_sec_df[\"Value\"]*1000.\n", "\n", "fig, ax = plt.subplots(figsize=(20, 12))\n", "sns.lineplot(x=fighters_sec_df[\"Time\"], y=fighters_sec_df[\"per_capita_fighters\"], hue=fighters_sec_df[\"Country\"], ax=ax)" ], "execution_count": 168, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": { "tags": [] }, "execution_count": 168 }, { "output_type": "display_data", "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAABJAAAAK5CAYAAADgo9TtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCCABAAAAAACg1v8DHBroK57TgcQAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "id": "LCHt8uRV7kY7", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 695 }, "outputId": "e4b8a376-d47d-4562-9821-00afc61dcfc1" }, "source": [ "red_df = dat_df[red]\n", "red_df[\"is_winner\"] = red_df[\"Winner\"] == \"Red\"\n", "red_df.columns = [s.strip(\"R_\") for s in red_df.columns]\n", "red_df" ], "execution_count": 169, "outputs": [ { "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " \n" ], "name": "stderr" }, { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
fightercurrent_lose_streakcurrent_win_streakdrawavg_BODY_attavg_BODY_landedavg_CLINCH_attavg_CLINCH_landedavg_DISTANCE_attavg_DISTANCE_landedavg_GROUND_attavg_GROUND_landedavg_HEAD_attavg_HEAD_landedavg_KDavg_LEG_attavg_LEG_landedavg_PASSavg_REVavg_SIG_STR_attavg_SIG_STR_landedavg_SIG_STR_pctavg_SUB_ATTavg_TD_attavg_TD_landedavg_TD_pctavg_TOTAL_STR_attavg_TOTAL_STR_landedlongest_win_streaklossesavg_opp_BODY_attavg_opp_BODY_landedavg_opp_CLINCH_attavg_opp_CLINCH_landedavg_opp_DISTANCE_attavg_opp_DISTANCE_landedavg_opp_GROUND_attavg_opp_GROUND_landedavg_opp_HEAD_attavg_opp_HEAD_landedavg_opp_KDavg_opp_LEG_attavg_opp_LEG_landedavg_opp_PASSavg_opp_REVavg_opp_SIG_STR_attavg_opp_SIG_STR_landedavg_opp_SIG_STR_pctavg_opp_SUB_ATTavg_opp_TD_attavg_opp_TD_landedavg_opp_TD_pctavg_opp_TOTAL_STR_attavg_opp_TOTAL_STR_landedtotal_rounds_foughttotal_time_fought(seconds)total_title_boutswin_by_Decision_Majoritywin_by_Decision_Splitwin_by_Decision_Unanimouswin_by_KO/TKOwin_by_Submissionwin_by_TKO_Doctor_StoppagewinsStanceHeight_cmseach_cmsWeight_lbsageWinnerdateweight_classis_winner
0Henry Cejudo0.04.00.021.90000016.40000017.00000011.00000075.00000026.5000009.4000006.50000074.20000023.900.4005.3000003.7000001.2000000.000000101.40000044.0000000.4660000.1000005.3000001.9000000.458000129.90000069.1000004.02.013.3000008.8000007.5000005.10000090.50000026.8000000.8000000.30000076.10000017.3000000.1000009.4000006.1000000.0000000.00000098.80000032.2000000.3360000.0000000.9000000.1000000.050000110.50000043.30000027.0742.603.00.02.04.02.00.00.08.0Orthodox162.56162.56135.032.0Red2019-06-08BantamweightTrue
1Valentina Shevchenko0.02.00.012.0000007.7142869.2857146.85714388.14285736.14285718.42857116.42857184.57142937.000.00019.28571414.7142861.7142860.142857115.85714359.4285710.5757140.4285715.1428572.4285710.601429161.571429102.8571432.02.024.57142914.14285710.5714297.85714398.57142932.5714296.4285714.28571461.85714312.4285710.00000029.14285718.1428571.1428570.000000115.57142944.7142860.4371430.2857143.2857140.8571430.147143158.14285782.28571425.01062.002.00.01.02.00.02.00.05.0Southpaw165.10167.64125.031.0Red2019-06-08Women's FlyweightTrue
2Tony Ferguson0.011.00.013.8666678.6666672.8666671.733333116.13333349.4666675.3333334.26666796.73333335.600.20013.73333311.2000000.3333330.133333124.33333355.4666670.4300001.0000000.9333330.4000000.277333133.00000063.40000011.01.014.4666678.1333332.8000000.73333391.06666732.2000004.8666672.80000078.26666723.2000000.2666676.0000004.4000000.3333330.13333398.73333335.7333330.3400000.0666672.8666670.6666670.131333102.13333338.60000033.0604.402.00.01.03.03.06.01.014.0Orthodox180.34193.04155.035.0Red2019-06-08LightweightTrue
3Jimmie Rivera1.00.00.018.25000010.2500005.8750004.125000104.87500041.0000001.0000000.62500080.50000024.000.37513.00000011.5000000.1250000.000000111.75000045.7500000.3662500.0000002.2500000.6250000.103750117.37500050.7500005.02.020.25000013.3750006.8750005.625000103.12500038.5000000.8750000.75000077.37500020.3750000.12500013.25000011.1250000.0000000.000000110.87500044.8750000.4462500.0000002.3750000.0000000.000000115.12500048.87500020.0690.250.00.01.04.01.00.00.06.0Orthodox162.56172.72135.029.0Blue2019-06-08BantamweightFalse
4Tai Tuivasa1.00.00.07.7500006.75000011.0000007.25000050.75000024.7500000.5000000.50000050.75000022.750.5003.7500003.0000000.2500000.00000062.25000032.5000000.5450000.0000000.5000000.0000000.00000063.50000032.7500003.01.06.2500004.7500004.5000003.50000042.75000016.2500007.7500002.75000043.25000014.0000000.2500005.5000003.7500000.7500000.00000055.00000022.5000000.3975000.0000001.0000000.0000000.00000060.50000027.7500007.0440.750.00.00.01.02.00.00.03.0Southpaw187.96190.50264.026.0Blue2019-06-08HeavyweightFalse
..............................................................................................................................................................................................................................
5139Gerard Gordeau0.01.00.00.0000000.0000000.0000000.0000003.0000001.0000002.0000002.0000005.0000003.000.0000.0000000.0000000.0000000.0000005.0000003.0000000.6000000.0000000.0000000.0000000.0000005.0000003.0000001.00.00.0000000.0000000.0000000.0000001.0000000.0000000.0000000.0000001.0000000.0000000.0000000.0000000.0000000.0000000.0000001.0000000.0000000.0000000.0000001.0000000.0000000.0000001.0000000.0000001.026.000.00.00.00.01.00.00.01.0Orthodox195.58NaN216.034.0Red1993-11-12Open WeightTrue
5140Ken Shamrock0.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaN0.00.00.00.00.00.00.00.0Orthodox185.42182.88205.029.0Red1993-11-12Open WeightTrue
5141Royce Gracie0.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaN0.00.00.00.00.00.00.00.0Southpaw185.42NaN175.026.0Red1993-11-12Open WeightTrue
5142Kevin Rosier0.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaN0.00.00.00.00.00.00.00.0Orthodox193.04NaN275.0NaNRed1993-11-12Open WeightTrue
5143Gerard Gordeau0.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaN0.00.00.00.00.00.00.00.0Orthodox195.58NaN216.034.0Red1993-11-12Open WeightTrue
\n", "

5144 rows × 73 columns

\n", "
" ], "text/plain": [ " fighter current_lose_streak current_win_streak draw \\\n", "0 Henry Cejudo 0.0 4.0 0.0 \n", "1 Valentina Shevchenko 0.0 2.0 0.0 \n", "2 Tony Ferguson 0.0 11.0 0.0 \n", "3 Jimmie Rivera 1.0 0.0 0.0 \n", "4 Tai Tuivasa 1.0 0.0 0.0 \n", "... ... ... ... ... \n", "5139 Gerard Gordeau 0.0 1.0 0.0 \n", "5140 Ken Shamrock 0.0 0.0 0.0 \n", "5141 Royce Gracie 0.0 0.0 0.0 \n", "5142 Kevin Rosier 0.0 0.0 0.0 \n", "5143 Gerard Gordeau 0.0 0.0 0.0 \n", "\n", " avg_BODY_att avg_BODY_landed avg_CLINCH_att avg_CLINCH_landed \\\n", "0 21.900000 16.400000 17.000000 11.000000 \n", "1 12.000000 7.714286 9.285714 6.857143 \n", "2 13.866667 8.666667 2.866667 1.733333 \n", "3 18.250000 10.250000 5.875000 4.125000 \n", "4 7.750000 6.750000 11.000000 7.250000 \n", "... ... ... ... ... \n", "5139 0.000000 0.000000 0.000000 0.000000 \n", "5140 NaN NaN NaN NaN \n", "5141 NaN NaN NaN NaN \n", "5142 NaN NaN NaN NaN \n", "5143 NaN NaN NaN NaN \n", "\n", " avg_DISTANCE_att avg_DISTANCE_landed avg_GROUND_att \\\n", "0 75.000000 26.500000 9.400000 \n", "1 88.142857 36.142857 18.428571 \n", "2 116.133333 49.466667 5.333333 \n", "3 104.875000 41.000000 1.000000 \n", "4 50.750000 24.750000 0.500000 \n", "... ... ... ... \n", "5139 3.000000 1.000000 2.000000 \n", "5140 NaN NaN NaN \n", "5141 NaN NaN NaN \n", "5142 NaN NaN NaN \n", "5143 NaN NaN NaN \n", "\n", " avg_GROUND_landed avg_HEAD_att avg_HEAD_landed avg_KD avg_LEG_att \\\n", "0 6.500000 74.200000 23.90 0.400 5.300000 \n", "1 16.428571 84.571429 37.00 0.000 19.285714 \n", "2 4.266667 96.733333 35.60 0.200 13.733333 \n", "3 0.625000 80.500000 24.00 0.375 13.000000 \n", "4 0.500000 50.750000 22.75 0.500 3.750000 \n", "... ... ... ... ... ... \n", "5139 2.000000 5.000000 3.00 0.000 0.000000 \n", "5140 NaN NaN NaN NaN NaN \n", "5141 NaN NaN NaN NaN NaN \n", "5142 NaN NaN NaN NaN NaN \n", "5143 NaN NaN NaN NaN NaN \n", "\n", " avg_LEG_landed avg_PASS avg_REV avg_SIG_STR_att avg_SIG_STR_landed \\\n", "0 3.700000 1.200000 0.000000 101.400000 44.000000 \n", "1 14.714286 1.714286 0.142857 115.857143 59.428571 \n", "2 11.200000 0.333333 0.133333 124.333333 55.466667 \n", "3 11.500000 0.125000 0.000000 111.750000 45.750000 \n", "4 3.000000 0.250000 0.000000 62.250000 32.500000 \n", "... ... ... ... ... ... \n", "5139 0.000000 0.000000 0.000000 5.000000 3.000000 \n", "5140 NaN NaN NaN NaN NaN \n", "5141 NaN NaN NaN NaN NaN \n", "5142 NaN NaN NaN NaN NaN \n", "5143 NaN NaN NaN NaN NaN \n", "\n", " avg_SIG_STR_pct avg_SUB_ATT avg_TD_att avg_TD_landed avg_TD_pct \\\n", "0 0.466000 0.100000 5.300000 1.900000 0.458000 \n", "1 0.575714 0.428571 5.142857 2.428571 0.601429 \n", "2 0.430000 1.000000 0.933333 0.400000 0.277333 \n", "3 0.366250 0.000000 2.250000 0.625000 0.103750 \n", "4 0.545000 0.000000 0.500000 0.000000 0.000000 \n", "... ... ... ... ... ... \n", "5139 0.600000 0.000000 0.000000 0.000000 0.000000 \n", "5140 NaN NaN NaN NaN NaN \n", "5141 NaN NaN NaN NaN NaN \n", "5142 NaN NaN NaN NaN NaN \n", "5143 NaN NaN NaN NaN NaN \n", "\n", " avg_TOTAL_STR_att avg_TOTAL_STR_landed longest_win_streak losses \\\n", "0 129.900000 69.100000 4.0 2.0 \n", "1 161.571429 102.857143 2.0 2.0 \n", "2 133.000000 63.400000 11.0 1.0 \n", "3 117.375000 50.750000 5.0 2.0 \n", "4 63.500000 32.750000 3.0 1.0 \n", "... ... ... ... ... \n", "5139 5.000000 3.000000 1.0 0.0 \n", "5140 NaN NaN 0.0 0.0 \n", "5141 NaN NaN 0.0 0.0 \n", "5142 NaN NaN 0.0 0.0 \n", "5143 NaN NaN 0.0 0.0 \n", "\n", " avg_opp_BODY_att avg_opp_BODY_landed avg_opp_CLINCH_att \\\n", "0 13.300000 8.800000 7.500000 \n", "1 24.571429 14.142857 10.571429 \n", "2 14.466667 8.133333 2.800000 \n", "3 20.250000 13.375000 6.875000 \n", "4 6.250000 4.750000 4.500000 \n", "... ... ... ... \n", "5139 0.000000 0.000000 0.000000 \n", "5140 NaN NaN NaN \n", "5141 NaN NaN NaN \n", "5142 NaN NaN NaN \n", "5143 NaN NaN NaN \n", "\n", " avg_opp_CLINCH_landed avg_opp_DISTANCE_att avg_opp_DISTANCE_landed \\\n", "0 5.100000 90.500000 26.800000 \n", "1 7.857143 98.571429 32.571429 \n", "2 0.733333 91.066667 32.200000 \n", "3 5.625000 103.125000 38.500000 \n", "4 3.500000 42.750000 16.250000 \n", "... ... ... ... \n", "5139 0.000000 1.000000 0.000000 \n", "5140 NaN NaN NaN \n", "5141 NaN NaN NaN \n", "5142 NaN NaN NaN \n", "5143 NaN NaN NaN \n", "\n", " avg_opp_GROUND_att avg_opp_GROUND_landed avg_opp_HEAD_att \\\n", "0 0.800000 0.300000 76.100000 \n", "1 6.428571 4.285714 61.857143 \n", "2 4.866667 2.800000 78.266667 \n", "3 0.875000 0.750000 77.375000 \n", "4 7.750000 2.750000 43.250000 \n", "... ... ... ... \n", "5139 0.000000 0.000000 1.000000 \n", "5140 NaN NaN NaN \n", "5141 NaN NaN NaN \n", "5142 NaN NaN NaN \n", "5143 NaN NaN NaN \n", "\n", " avg_opp_HEAD_landed avg_opp_KD avg_opp_LEG_att avg_opp_LEG_landed \\\n", "0 17.300000 0.100000 9.400000 6.100000 \n", "1 12.428571 0.000000 29.142857 18.142857 \n", "2 23.200000 0.266667 6.000000 4.400000 \n", "3 20.375000 0.125000 13.250000 11.125000 \n", "4 14.000000 0.250000 5.500000 3.750000 \n", "... ... ... ... ... \n", "5139 0.000000 0.000000 0.000000 0.000000 \n", "5140 NaN NaN NaN NaN \n", "5141 NaN NaN NaN NaN \n", "5142 NaN NaN NaN NaN \n", "5143 NaN NaN NaN NaN \n", "\n", " avg_opp_PASS avg_opp_REV avg_opp_SIG_STR_att avg_opp_SIG_STR_landed \\\n", "0 0.000000 0.000000 98.800000 32.200000 \n", "1 1.142857 0.000000 115.571429 44.714286 \n", "2 0.333333 0.133333 98.733333 35.733333 \n", "3 0.000000 0.000000 110.875000 44.875000 \n", "4 0.750000 0.000000 55.000000 22.500000 \n", "... ... ... ... ... \n", "5139 0.000000 0.000000 1.000000 0.000000 \n", "5140 NaN NaN NaN NaN \n", "5141 NaN NaN NaN NaN \n", "5142 NaN NaN NaN NaN \n", "5143 NaN NaN NaN NaN \n", "\n", " avg_opp_SIG_STR_pct avg_opp_SUB_ATT avg_opp_TD_att avg_opp_TD_landed \\\n", "0 0.336000 0.000000 0.900000 0.100000 \n", "1 0.437143 0.285714 3.285714 0.857143 \n", "2 0.340000 0.066667 2.866667 0.666667 \n", "3 0.446250 0.000000 2.375000 0.000000 \n", "4 0.397500 0.000000 1.000000 0.000000 \n", "... ... ... ... ... \n", "5139 0.000000 0.000000 1.000000 0.000000 \n", "5140 NaN NaN NaN NaN \n", "5141 NaN NaN NaN NaN \n", "5142 NaN NaN NaN NaN \n", "5143 NaN NaN NaN NaN \n", "\n", " avg_opp_TD_pct avg_opp_TOTAL_STR_att avg_opp_TOTAL_STR_landed \\\n", "0 0.050000 110.500000 43.300000 \n", "1 0.147143 158.142857 82.285714 \n", "2 0.131333 102.133333 38.600000 \n", "3 0.000000 115.125000 48.875000 \n", "4 0.000000 60.500000 27.750000 \n", "... ... ... ... \n", "5139 0.000000 1.000000 0.000000 \n", "5140 NaN NaN NaN \n", "5141 NaN NaN NaN \n", "5142 NaN NaN NaN \n", "5143 NaN NaN NaN \n", "\n", " total_rounds_fought total_time_fought(seconds) total_title_bouts \\\n", "0 27.0 742.60 3.0 \n", "1 25.0 1062.00 2.0 \n", "2 33.0 604.40 2.0 \n", "3 20.0 690.25 0.0 \n", "4 7.0 440.75 0.0 \n", "... ... ... ... \n", "5139 1.0 26.00 0.0 \n", "5140 0.0 NaN 0.0 \n", "5141 0.0 NaN 0.0 \n", "5142 0.0 NaN 0.0 \n", "5143 0.0 NaN 0.0 \n", "\n", " win_by_Decision_Majority win_by_Decision_Split \\\n", "0 0.0 2.0 \n", "1 0.0 1.0 \n", "2 0.0 1.0 \n", "3 0.0 1.0 \n", "4 0.0 0.0 \n", "... ... ... \n", "5139 0.0 0.0 \n", "5140 0.0 0.0 \n", "5141 0.0 0.0 \n", "5142 0.0 0.0 \n", "5143 0.0 0.0 \n", "\n", " win_by_Decision_Unanimous win_by_KO/TKO win_by_Submission \\\n", "0 4.0 2.0 0.0 \n", "1 2.0 0.0 2.0 \n", "2 3.0 3.0 6.0 \n", "3 4.0 1.0 0.0 \n", "4 1.0 2.0 0.0 \n", "... ... ... ... \n", "5139 0.0 1.0 0.0 \n", "5140 0.0 0.0 0.0 \n", "5141 0.0 0.0 0.0 \n", "5142 0.0 0.0 0.0 \n", "5143 0.0 0.0 0.0 \n", "\n", " win_by_TKO_Doctor_Stoppage wins Stance Height_cms each_cms \\\n", "0 0.0 8.0 Orthodox 162.56 162.56 \n", "1 0.0 5.0 Southpaw 165.10 167.64 \n", "2 1.0 14.0 Orthodox 180.34 193.04 \n", "3 0.0 6.0 Orthodox 162.56 172.72 \n", "4 0.0 3.0 Southpaw 187.96 190.50 \n", "... ... ... ... ... ... \n", "5139 0.0 1.0 Orthodox 195.58 NaN \n", "5140 0.0 0.0 Orthodox 185.42 182.88 \n", "5141 0.0 0.0 Southpaw 185.42 NaN \n", "5142 0.0 0.0 Orthodox 193.04 NaN \n", "5143 0.0 0.0 Orthodox 195.58 NaN \n", "\n", " Weight_lbs age Winner date weight_class is_winner \n", "0 135.0 32.0 Red 2019-06-08 Bantamweight True \n", "1 125.0 31.0 Red 2019-06-08 Women's Flyweight True \n", "2 155.0 35.0 Red 2019-06-08 Lightweight True \n", "3 135.0 29.0 Blue 2019-06-08 Bantamweight False \n", "4 264.0 26.0 Blue 2019-06-08 Heavyweight False \n", "... ... ... ... ... ... ... \n", "5139 216.0 34.0 Red 1993-11-12 Open Weight True \n", "5140 205.0 29.0 Red 1993-11-12 Open Weight True \n", "5141 175.0 26.0 Red 1993-11-12 Open Weight True \n", "5142 275.0 NaN Red 1993-11-12 Open Weight True \n", "5143 216.0 34.0 Red 1993-11-12 Open Weight True \n", "\n", "[5144 rows x 73 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 169 } ] }, { "cell_type": "code", "metadata": { "id": "dXz5-Vid8Bxn", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 695 }, "outputId": "1e05894c-37e9-4687-89f9-82f43bcde0b3" }, "source": [ "blue_df = dat_df[blue]\n", "blue_df[\"is_winner\"] = blue_df[\"Winner\"] == \"Blue\"\n", "blue_df.columns = [s.strip(\"B_\") for s in blue_df.columns]\n", "blue_df" ], "execution_count": 170, "outputs": [ { "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " \n" ], "name": "stderr" }, { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
fightercurrent_lose_streakcurrent_win_streakdrawavg_BODY_attavg_BODY_landedavg_CLINCH_attavg_CLINCH_landedavg_DISTANCE_attavg_DISTANCE_landedavg_GROUND_attavg_GROUND_landedavg_HEAD_attavg_HEAD_landedavg_KDavg_LEG_attavg_LEG_landedavg_PASSavg_REVavg_SIG_STR_attavg_SIG_STR_landedavg_SIG_STR_pctavg_SUB_ATTavg_TD_attavg_TD_landedavg_TD_pctavg_TOTAL_STR_attavg_TOTAL_STR_landedlongest_win_streaklossesavg_opp_BODY_attavg_opp_BODY_landedavg_opp_CLINCH_attavg_opp_CLINCH_landedavg_opp_DISTANCE_attavg_opp_DISTANCE_landedavg_opp_GROUND_attavg_opp_GROUND_landedavg_opp_HEAD_attavg_opp_HEAD_landedavg_opp_KDavg_opp_LEG_attavg_opp_LEG_landedavg_opp_PASSavg_opp_REVavg_opp_SIG_STR_attavg_opp_SIG_STR_landedavg_opp_SIG_STR_pctavg_opp_SUB_ATTavg_opp_TD_attavg_opp_TD_landedavg_opp_TD_pctavg_opp_TOTAL_STR_attavg_opp_TOTAL_STR_landedtotal_rounds_foughttotal_time_fought(seconds)total_title_boutswin_by_Decision_Majoritywin_by_Decision_Splitwin_by_Decision_Unanimouswin_by_KO/TKOwin_by_Submissionwin_by_TKO_Doctor_StoppagewinsStanceHeight_cmsReach_cmsWeight_lbsageWinnerdateweight_classis_winner
0Marlon Moraes0.04.00.09.2000006.0000000.2000000.00000062.60000020.6000002.6000002.00000048.60000011.2000000.8000007.65.4000000.4000000.00000065.4022.6000000.4660000.4000000.800000.2000000.10000066.40000023.6000004.01.06.4000004.0000001.0000000.6000051.20000017.4000000.6000000.20000039.6000009.4000000.2000006.800004.8000000.0000000.00000052.80000018.200000.2360000.0000001.0000000.4000000.10000053.80000019.2000009.0419.4000000.00.01.00.02.01.00.04.0Orthodox167.64170.18135.031.0Red2019-06-08BantamweightFalse
1Jessica Eye0.03.00.014.6000009.10000011.8000007.300000124.70000042.1000002.4000001.900000112.00000032.0000000.00000012.310.2000000.8000000.000000138.9051.3000000.3990000.7000001.000000.5000000.225000158.70000069.6000003.06.013.0000009.30000012.8000009.60000101.70000032.0000008.1000006.90000097.70000030.8000000.10000011.900008.4000001.4000000.000000122.60000048.500000.4080000.7000002.3000000.9000000.231000151.50000075.40000029.0849.0000000.00.02.01.00.00.01.04.0Orthodox167.64167.64125.032.0Red2019-06-08Women's FlyweightFalse
2Donald Cerrone0.03.00.015.35483911.3225816.7419354.38709784.74193538.5806455.5161293.80645267.64516123.2580650.64516114.012.1935480.9354840.09677497.0046.7741940.4961290.3548392.161290.6774190.295484103.70967752.5483878.08.017.90322611.8709688.4193555.8387184.54838738.0645161.7419350.93548467.64516125.4838710.2258069.161297.4838710.0322580.03225894.70967744.838710.4532260.0967742.0967740.2258060.063548100.38709749.77419468.0581.8709681.00.00.07.010.06.00.023.0Orthodox185.42185.42155.036.0Red2019-06-08LightweightFalse
3Petr Yan0.04.00.017.00000014.00000013.75000011.000000109.50000048.75000013.00000010.500000116.25000053.7500000.5000003.02.5000000.5000000.250000136.2570.2500000.5500000.2500002.500001.2500000.287500154.75000086.7500004.00.012.2500006.0000006.0000003.7500094.25000026.7500001.7500001.25000082.50000021.5000000.2500007.250004.2500000.0000000.000000102.00000031.750000.3375000.0000004.5000000.7500000.097500104.75000034.2500009.0652.0000000.00.00.02.02.00.00.04.0Switch170.18170.18135.026.0Blue2019-06-08BantamweightTrue
4Blagoy Ivanov0.01.00.017.00000014.5000002.5000002.000000201.00000059.5000000.0000000.000000184.50000045.0000000.0000002.02.0000000.0000000.000000203.5061.5000000.3100000.0000000.000000.0000000.000000204.00000062.0000001.01.042.50000023.5000000.5000000.50000205.00000089.5000000.0000000.000000152.50000056.5000000.00000010.5000010.0000000.0000000.000000205.50000090.000000.4300000.0000000.5000000.0000000.000000205.50000090.0000008.01200.0000000.00.00.01.00.00.00.01.0Southpaw180.34185.42250.032.0Blue2019-06-08HeavyweightTrue
..............................................................................................................................................................................................................................
5139Kevin Rosier0.01.00.04.0000003.0000009.0000004.00000010.0000004.0000008.0000007.00000023.00000012.0000002.0000000.00.0000000.0000000.00000027.0015.0000000.5500000.0000000.000000.0000000.00000053.00000038.0000001.00.06.0000003.00000019.00000010.000007.0000000.0000002.0000002.00000019.0000007.0000000.0000003.000002.0000000.0000000.00000028.00000012.000000.4200000.0000000.0000000.0000000.00000029.00000013.0000001.0260.0000000.00.00.00.01.00.00.01.0Orthodox193.04NaN275.0NaNRed1993-11-12Open WeightFalse
5140Patrick Smith0.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaN0.00.00.00.00.00.00.00.0Orthodox187.96NaN225.030.0Red1993-11-12Open WeightFalse
5141Art Jimmerson0.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaN0.00.00.00.00.00.00.00.0Orthodox185.42NaN196.030.0Red1993-11-12Open WeightFalse
5142Zane Frazier0.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaN0.00.00.00.00.00.00.00.0Orthodox195.58NaN250.0NaNRed1993-11-12Open WeightFalse
5143Teila Tuli0.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaN0.00.00.00.00.00.00.00.0Orthodox182.88NaN430.024.0Red1993-11-12Open WeightFalse
\n", "

5144 rows × 73 columns

\n", "
" ], "text/plain": [ " fighter current_lose_streak current_win_streak draw \\\n", "0 Marlon Moraes 0.0 4.0 0.0 \n", "1 Jessica Eye 0.0 3.0 0.0 \n", "2 Donald Cerrone 0.0 3.0 0.0 \n", "3 Petr Yan 0.0 4.0 0.0 \n", "4 Blagoy Ivanov 0.0 1.0 0.0 \n", "... ... ... ... ... \n", "5139 Kevin Rosier 0.0 1.0 0.0 \n", "5140 Patrick Smith 0.0 0.0 0.0 \n", "5141 Art Jimmerson 0.0 0.0 0.0 \n", "5142 Zane Frazier 0.0 0.0 0.0 \n", "5143 Teila Tuli 0.0 0.0 0.0 \n", "\n", " avg_BODY_att avg_BODY_landed avg_CLINCH_att avg_CLINCH_landed \\\n", "0 9.200000 6.000000 0.200000 0.000000 \n", "1 14.600000 9.100000 11.800000 7.300000 \n", "2 15.354839 11.322581 6.741935 4.387097 \n", "3 17.000000 14.000000 13.750000 11.000000 \n", "4 17.000000 14.500000 2.500000 2.000000 \n", "... ... ... ... ... \n", "5139 4.000000 3.000000 9.000000 4.000000 \n", "5140 NaN NaN NaN NaN \n", "5141 NaN NaN NaN NaN \n", "5142 NaN NaN NaN NaN \n", "5143 NaN NaN NaN NaN \n", "\n", " avg_DISTANCE_att avg_DISTANCE_landed avg_GROUND_att \\\n", "0 62.600000 20.600000 2.600000 \n", "1 124.700000 42.100000 2.400000 \n", "2 84.741935 38.580645 5.516129 \n", "3 109.500000 48.750000 13.000000 \n", "4 201.000000 59.500000 0.000000 \n", "... ... ... ... \n", "5139 10.000000 4.000000 8.000000 \n", "5140 NaN NaN NaN \n", "5141 NaN NaN NaN \n", "5142 NaN NaN NaN \n", "5143 NaN NaN NaN \n", "\n", " avg_GROUND_landed avg_HEAD_att avg_HEAD_landed avg_KD avg_LEG_att \\\n", "0 2.000000 48.600000 11.200000 0.800000 7.6 \n", "1 1.900000 112.000000 32.000000 0.000000 12.3 \n", "2 3.806452 67.645161 23.258065 0.645161 14.0 \n", "3 10.500000 116.250000 53.750000 0.500000 3.0 \n", "4 0.000000 184.500000 45.000000 0.000000 2.0 \n", "... ... ... ... ... ... \n", "5139 7.000000 23.000000 12.000000 2.000000 0.0 \n", "5140 NaN NaN NaN NaN NaN \n", "5141 NaN NaN NaN NaN NaN \n", "5142 NaN NaN NaN NaN NaN \n", "5143 NaN NaN NaN NaN NaN \n", "\n", " avg_LEG_landed avg_PASS avg_REV avg_SIG_STR_att avg_SIG_STR_landed \\\n", "0 5.400000 0.400000 0.000000 65.40 22.600000 \n", "1 10.200000 0.800000 0.000000 138.90 51.300000 \n", "2 12.193548 0.935484 0.096774 97.00 46.774194 \n", "3 2.500000 0.500000 0.250000 136.25 70.250000 \n", "4 2.000000 0.000000 0.000000 203.50 61.500000 \n", "... ... ... ... ... ... \n", "5139 0.000000 0.000000 0.000000 27.00 15.000000 \n", "5140 NaN NaN NaN NaN NaN \n", "5141 NaN NaN NaN NaN NaN \n", "5142 NaN NaN NaN NaN NaN \n", "5143 NaN NaN NaN NaN NaN \n", "\n", " avg_SIG_STR_pct avg_SUB_ATT avg_TD_att avg_TD_landed avg_TD_pct \\\n", "0 0.466000 0.400000 0.80000 0.200000 0.100000 \n", "1 0.399000 0.700000 1.00000 0.500000 0.225000 \n", "2 0.496129 0.354839 2.16129 0.677419 0.295484 \n", "3 0.550000 0.250000 2.50000 1.250000 0.287500 \n", "4 0.310000 0.000000 0.00000 0.000000 0.000000 \n", "... ... ... ... ... ... \n", "5139 0.550000 0.000000 0.00000 0.000000 0.000000 \n", "5140 NaN NaN NaN NaN NaN \n", "5141 NaN NaN NaN NaN NaN \n", "5142 NaN NaN NaN NaN NaN \n", "5143 NaN NaN NaN NaN NaN \n", "\n", " avg_TOTAL_STR_att avg_TOTAL_STR_landed longest_win_streak losses \\\n", "0 66.400000 23.600000 4.0 1.0 \n", "1 158.700000 69.600000 3.0 6.0 \n", "2 103.709677 52.548387 8.0 8.0 \n", "3 154.750000 86.750000 4.0 0.0 \n", "4 204.000000 62.000000 1.0 1.0 \n", "... ... ... ... ... \n", "5139 53.000000 38.000000 1.0 0.0 \n", "5140 NaN NaN 0.0 0.0 \n", "5141 NaN NaN 0.0 0.0 \n", "5142 NaN NaN 0.0 0.0 \n", "5143 NaN NaN 0.0 0.0 \n", "\n", " avg_opp_BODY_att avg_opp_BODY_landed avg_opp_CLINCH_att \\\n", "0 6.400000 4.000000 1.000000 \n", "1 13.000000 9.300000 12.800000 \n", "2 17.903226 11.870968 8.419355 \n", "3 12.250000 6.000000 6.000000 \n", "4 42.500000 23.500000 0.500000 \n", "... ... ... ... \n", "5139 6.000000 3.000000 19.000000 \n", "5140 NaN NaN NaN \n", "5141 NaN NaN NaN \n", "5142 NaN NaN NaN \n", "5143 NaN NaN NaN \n", "\n", " avg_opp_CLINCH_landed avg_opp_DISTANCE_att avg_opp_DISTANCE_landed \\\n", "0 0.60000 51.200000 17.400000 \n", "1 9.60000 101.700000 32.000000 \n", "2 5.83871 84.548387 38.064516 \n", "3 3.75000 94.250000 26.750000 \n", "4 0.50000 205.000000 89.500000 \n", "... ... ... ... \n", "5139 10.00000 7.000000 0.000000 \n", "5140 NaN NaN NaN \n", "5141 NaN NaN NaN \n", "5142 NaN NaN NaN \n", "5143 NaN NaN NaN \n", "\n", " avg_opp_GROUND_att avg_opp_GROUND_landed avg_opp_HEAD_att \\\n", "0 0.600000 0.200000 39.600000 \n", "1 8.100000 6.900000 97.700000 \n", "2 1.741935 0.935484 67.645161 \n", "3 1.750000 1.250000 82.500000 \n", "4 0.000000 0.000000 152.500000 \n", "... ... ... ... \n", "5139 2.000000 2.000000 19.000000 \n", "5140 NaN NaN NaN \n", "5141 NaN NaN NaN \n", "5142 NaN NaN NaN \n", "5143 NaN NaN NaN \n", "\n", " avg_opp_HEAD_landed avg_opp_KD avg_opp_LEG_att avg_opp_LEG_landed \\\n", "0 9.400000 0.200000 6.80000 4.800000 \n", "1 30.800000 0.100000 11.90000 8.400000 \n", "2 25.483871 0.225806 9.16129 7.483871 \n", "3 21.500000 0.250000 7.25000 4.250000 \n", "4 56.500000 0.000000 10.50000 10.000000 \n", "... ... ... ... ... \n", "5139 7.000000 0.000000 3.00000 2.000000 \n", "5140 NaN NaN NaN NaN \n", "5141 NaN NaN NaN NaN \n", "5142 NaN NaN NaN NaN \n", "5143 NaN NaN NaN NaN \n", "\n", " avg_opp_PASS avg_opp_REV avg_opp_SIG_STR_att avg_opp_SIG_STR_landed \\\n", "0 0.000000 0.000000 52.800000 18.20000 \n", "1 1.400000 0.000000 122.600000 48.50000 \n", "2 0.032258 0.032258 94.709677 44.83871 \n", "3 0.000000 0.000000 102.000000 31.75000 \n", "4 0.000000 0.000000 205.500000 90.00000 \n", "... ... ... ... ... \n", "5139 0.000000 0.000000 28.000000 12.00000 \n", "5140 NaN NaN NaN NaN \n", "5141 NaN NaN NaN NaN \n", "5142 NaN NaN NaN NaN \n", "5143 NaN NaN NaN NaN \n", "\n", " avg_opp_SIG_STR_pct avg_opp_SUB_ATT avg_opp_TD_att avg_opp_TD_landed \\\n", "0 0.236000 0.000000 1.000000 0.400000 \n", "1 0.408000 0.700000 2.300000 0.900000 \n", "2 0.453226 0.096774 2.096774 0.225806 \n", "3 0.337500 0.000000 4.500000 0.750000 \n", "4 0.430000 0.000000 0.500000 0.000000 \n", "... ... ... ... ... \n", "5139 0.420000 0.000000 0.000000 0.000000 \n", "5140 NaN NaN NaN NaN \n", "5141 NaN NaN NaN NaN \n", "5142 NaN NaN NaN NaN \n", "5143 NaN NaN NaN NaN \n", "\n", " avg_opp_TD_pct avg_opp_TOTAL_STR_att avg_opp_TOTAL_STR_landed \\\n", "0 0.100000 53.800000 19.200000 \n", "1 0.231000 151.500000 75.400000 \n", "2 0.063548 100.387097 49.774194 \n", "3 0.097500 104.750000 34.250000 \n", "4 0.000000 205.500000 90.000000 \n", "... ... ... ... \n", "5139 0.000000 29.000000 13.000000 \n", "5140 NaN NaN NaN \n", "5141 NaN NaN NaN \n", "5142 NaN NaN NaN \n", "5143 NaN NaN NaN \n", "\n", " total_rounds_fought total_time_fought(seconds) total_title_bouts \\\n", "0 9.0 419.400000 0.0 \n", "1 29.0 849.000000 0.0 \n", "2 68.0 581.870968 1.0 \n", "3 9.0 652.000000 0.0 \n", "4 8.0 1200.000000 0.0 \n", "... ... ... ... \n", "5139 1.0 260.000000 0.0 \n", "5140 0.0 NaN 0.0 \n", "5141 0.0 NaN 0.0 \n", "5142 0.0 NaN 0.0 \n", "5143 0.0 NaN 0.0 \n", "\n", " win_by_Decision_Majority win_by_Decision_Split \\\n", "0 0.0 1.0 \n", "1 0.0 2.0 \n", "2 0.0 0.0 \n", "3 0.0 0.0 \n", "4 0.0 0.0 \n", "... ... ... \n", "5139 0.0 0.0 \n", "5140 0.0 0.0 \n", "5141 0.0 0.0 \n", "5142 0.0 0.0 \n", "5143 0.0 0.0 \n", "\n", " win_by_Decision_Unanimous win_by_KO/TKO win_by_Submission \\\n", "0 0.0 2.0 1.0 \n", "1 1.0 0.0 0.0 \n", "2 7.0 10.0 6.0 \n", "3 2.0 2.0 0.0 \n", "4 1.0 0.0 0.0 \n", "... ... ... ... \n", "5139 0.0 1.0 0.0 \n", "5140 0.0 0.0 0.0 \n", "5141 0.0 0.0 0.0 \n", "5142 0.0 0.0 0.0 \n", "5143 0.0 0.0 0.0 \n", "\n", " win_by_TKO_Doctor_Stoppage wins Stance Height_cms Reach_cms \\\n", "0 0.0 4.0 Orthodox 167.64 170.18 \n", "1 1.0 4.0 Orthodox 167.64 167.64 \n", "2 0.0 23.0 Orthodox 185.42 185.42 \n", "3 0.0 4.0 Switch 170.18 170.18 \n", "4 0.0 1.0 Southpaw 180.34 185.42 \n", "... ... ... ... ... ... \n", "5139 0.0 1.0 Orthodox 193.04 NaN \n", "5140 0.0 0.0 Orthodox 187.96 NaN \n", "5141 0.0 0.0 Orthodox 185.42 NaN \n", "5142 0.0 0.0 Orthodox 195.58 NaN \n", "5143 0.0 0.0 Orthodox 182.88 NaN \n", "\n", " Weight_lbs age Winner date weight_class is_winner \n", "0 135.0 31.0 Red 2019-06-08 Bantamweight False \n", "1 125.0 32.0 Red 2019-06-08 Women's Flyweight False \n", "2 155.0 36.0 Red 2019-06-08 Lightweight False \n", "3 135.0 26.0 Blue 2019-06-08 Bantamweight True \n", "4 250.0 32.0 Blue 2019-06-08 Heavyweight True \n", "... ... ... ... ... ... ... \n", "5139 275.0 NaN Red 1993-11-12 Open Weight False \n", "5140 225.0 30.0 Red 1993-11-12 Open Weight False \n", "5141 196.0 30.0 Red 1993-11-12 Open Weight False \n", "5142 250.0 NaN Red 1993-11-12 Open Weight False \n", "5143 430.0 24.0 Red 1993-11-12 Open Weight False \n", "\n", "[5144 rows x 73 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 170 } ] }, { "cell_type": "code", "metadata": { "id": "MYKjNP-0907E", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 730 }, "outputId": "b184256c-6886-4051-dc8e-f21718fbcd3d" }, "source": [ "fighters_df = pd.concat([red_df, blue_df])\n", "fighters_df = fighters_df.reset_index()\n", "fighters_df" ], "execution_count": 171, "outputs": [ { "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n", "of pandas will change to not sort by default.\n", "\n", "To accept the future behavior, pass 'sort=False'.\n", "\n", "To retain the current behavior and silence the warning, pass 'sort=True'.\n", "\n", " \"\"\"Entry point for launching an IPython kernel.\n" ], "name": "stderr" }, { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
indexHeight_cmsReach_cmsStanceWeight_lbsWinnerageavg_BODY_attavg_BODY_landedavg_CLINCH_attavg_CLINCH_landedavg_DISTANCE_attavg_DISTANCE_landedavg_GROUND_attavg_GROUND_landedavg_HEAD_attavg_HEAD_landedavg_KDavg_LEG_attavg_LEG_landedavg_PASSavg_REVavg_SIG_STR_attavg_SIG_STR_landedavg_SIG_STR_pctavg_SUB_ATTavg_TD_attavg_TD_landedavg_TD_pctavg_TOTAL_STR_attavg_TOTAL_STR_landedavg_opp_BODY_attavg_opp_BODY_landedavg_opp_CLINCH_attavg_opp_CLINCH_landedavg_opp_DISTANCE_attavg_opp_DISTANCE_landedavg_opp_GROUND_attavg_opp_GROUND_landedavg_opp_HEAD_attavg_opp_HEAD_landedavg_opp_KDavg_opp_LEG_attavg_opp_LEG_landedavg_opp_PASSavg_opp_REVavg_opp_SIG_STR_attavg_opp_SIG_STR_landedavg_opp_SIG_STR_pctavg_opp_SUB_ATTavg_opp_TD_attavg_opp_TD_landedavg_opp_TD_pctavg_opp_TOTAL_STR_attavg_opp_TOTAL_STR_landedcurrent_lose_streakcurrent_win_streakdatedraweach_cmsfighteris_winnerlongest_win_streaklossestotal_rounds_foughttotal_time_fought(seconds)total_title_boutsweight_classwin_by_Decision_Majoritywin_by_Decision_Splitwin_by_Decision_Unanimouswin_by_KO/TKOwin_by_Submissionwin_by_TKO_Doctor_Stoppagewins
00162.56NaNOrthodox135.0Red32.021.90000016.40000017.00000011.00000075.00000026.5000009.4000006.50000074.20000023.900.4005.3000003.7000001.2000000.000000101.40000044.0000000.4660000.1000005.3000001.9000000.458000129.90000069.10000013.3000008.8000007.5000005.10000090.50000026.8000000.8000000.30000076.10000017.3000000.1000009.4000006.1000000.0000000.00000098.80000032.2000000.3360000.0000000.9000000.1000000.050000110.50000043.3000000.04.02019-06-080.0162.56Henry CejudoTrue4.02.027.0742.603.0Bantamweight0.02.04.02.00.00.08.0
11165.10NaNSouthpaw125.0Red31.012.0000007.7142869.2857146.85714388.14285736.14285718.42857116.42857184.57142937.000.00019.28571414.7142861.7142860.142857115.85714359.4285710.5757140.4285715.1428572.4285710.601429161.571429102.85714324.57142914.14285710.5714297.85714398.57142932.5714296.4285714.28571461.85714312.4285710.00000029.14285718.1428571.1428570.000000115.57142944.7142860.4371430.2857143.2857140.8571430.147143158.14285782.2857140.02.02019-06-080.0167.64Valentina ShevchenkoTrue2.02.025.01062.002.0Women's Flyweight0.01.02.00.02.00.05.0
22180.34NaNOrthodox155.0Red35.013.8666678.6666672.8666671.733333116.13333349.4666675.3333334.26666796.73333335.600.20013.73333311.2000000.3333330.133333124.33333355.4666670.4300001.0000000.9333330.4000000.277333133.00000063.40000014.4666678.1333332.8000000.73333391.06666732.2000004.8666672.80000078.26666723.2000000.2666676.0000004.4000000.3333330.13333398.73333335.7333330.3400000.0666672.8666670.6666670.131333102.13333338.6000000.011.02019-06-080.0193.04Tony FergusonTrue11.01.033.0604.402.0Lightweight0.01.03.03.06.01.014.0
33162.56NaNOrthodox135.0Blue29.018.25000010.2500005.8750004.125000104.87500041.0000001.0000000.62500080.50000024.000.37513.00000011.5000000.1250000.000000111.75000045.7500000.3662500.0000002.2500000.6250000.103750117.37500050.75000020.25000013.3750006.8750005.625000103.12500038.5000000.8750000.75000077.37500020.3750000.12500013.25000011.1250000.0000000.000000110.87500044.8750000.4462500.0000002.3750000.0000000.000000115.12500048.8750001.00.02019-06-080.0172.72Jimmie RiveraFalse5.02.020.0690.250.0Bantamweight0.01.04.01.00.00.06.0
44187.96NaNSouthpaw264.0Blue26.07.7500006.75000011.0000007.25000050.75000024.7500000.5000000.50000050.75000022.750.5003.7500003.0000000.2500000.00000062.25000032.5000000.5450000.0000000.5000000.0000000.00000063.50000032.7500006.2500004.7500004.5000003.50000042.75000016.2500007.7500002.75000043.25000014.0000000.2500005.5000003.7500000.7500000.00000055.00000022.5000000.3975000.0000001.0000000.0000000.00000060.50000027.7500001.00.02019-06-080.0190.50Tai TuivasaFalse3.01.07.0440.750.0Heavyweight0.00.01.02.00.00.03.0
....................................................................................................................................................................................................................................
102835139193.04NaNOrthodox275.0RedNaN4.0000003.0000009.0000004.00000010.0000004.0000008.0000007.00000023.00000012.002.0000.0000000.0000000.0000000.00000027.00000015.0000000.5500000.0000000.0000000.0000000.00000053.00000038.0000006.0000003.00000019.00000010.0000007.0000000.0000002.0000002.00000019.0000007.0000000.0000003.0000002.0000000.0000000.00000028.00000012.0000000.4200000.0000000.0000000.0000000.00000029.00000013.0000000.01.01993-11-120.0NaNKevin RosierFalse1.00.01.0260.000.0Open Weight0.00.00.01.00.00.01.0
102845140187.96NaNOrthodox225.0Red30.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.01993-11-120.0NaNPatrick SmithFalse0.00.00.0NaN0.0Open Weight0.00.00.00.00.00.00.0
102855141185.42NaNOrthodox196.0Red30.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.01993-11-120.0NaNArt JimmersonFalse0.00.00.0NaN0.0Open Weight0.00.00.00.00.00.00.0
102865142195.58NaNOrthodox250.0RedNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.01993-11-120.0NaNZane FrazierFalse0.00.00.0NaN0.0Open Weight0.00.00.00.00.00.00.0
102875143182.88NaNOrthodox430.0Red24.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.01993-11-120.0NaNTeila TuliFalse0.00.00.0NaN0.0Open Weight0.00.00.00.00.00.00.0
\n", "

10288 rows × 75 columns

\n", "
" ], "text/plain": [ " index Height_cms Reach_cms Stance Weight_lbs Winner age \\\n", "0 0 162.56 NaN Orthodox 135.0 Red 32.0 \n", "1 1 165.10 NaN Southpaw 125.0 Red 31.0 \n", "2 2 180.34 NaN Orthodox 155.0 Red 35.0 \n", "3 3 162.56 NaN Orthodox 135.0 Blue 29.0 \n", "4 4 187.96 NaN Southpaw 264.0 Blue 26.0 \n", "... ... ... ... ... ... ... ... \n", "10283 5139 193.04 NaN Orthodox 275.0 Red NaN \n", "10284 5140 187.96 NaN Orthodox 225.0 Red 30.0 \n", "10285 5141 185.42 NaN Orthodox 196.0 Red 30.0 \n", "10286 5142 195.58 NaN Orthodox 250.0 Red NaN \n", "10287 5143 182.88 NaN Orthodox 430.0 Red 24.0 \n", "\n", " avg_BODY_att avg_BODY_landed avg_CLINCH_att avg_CLINCH_landed \\\n", "0 21.900000 16.400000 17.000000 11.000000 \n", "1 12.000000 7.714286 9.285714 6.857143 \n", "2 13.866667 8.666667 2.866667 1.733333 \n", "3 18.250000 10.250000 5.875000 4.125000 \n", "4 7.750000 6.750000 11.000000 7.250000 \n", "... ... ... ... ... \n", "10283 4.000000 3.000000 9.000000 4.000000 \n", "10284 NaN NaN NaN NaN \n", "10285 NaN NaN NaN NaN \n", "10286 NaN NaN NaN NaN \n", "10287 NaN NaN NaN NaN \n", "\n", " avg_DISTANCE_att avg_DISTANCE_landed avg_GROUND_att \\\n", "0 75.000000 26.500000 9.400000 \n", "1 88.142857 36.142857 18.428571 \n", "2 116.133333 49.466667 5.333333 \n", "3 104.875000 41.000000 1.000000 \n", "4 50.750000 24.750000 0.500000 \n", "... ... ... ... \n", "10283 10.000000 4.000000 8.000000 \n", "10284 NaN NaN NaN \n", "10285 NaN NaN NaN \n", "10286 NaN NaN NaN \n", "10287 NaN NaN NaN \n", "\n", " avg_GROUND_landed avg_HEAD_att avg_HEAD_landed avg_KD avg_LEG_att \\\n", "0 6.500000 74.200000 23.90 0.400 5.300000 \n", "1 16.428571 84.571429 37.00 0.000 19.285714 \n", "2 4.266667 96.733333 35.60 0.200 13.733333 \n", "3 0.625000 80.500000 24.00 0.375 13.000000 \n", "4 0.500000 50.750000 22.75 0.500 3.750000 \n", "... ... ... ... ... ... \n", "10283 7.000000 23.000000 12.00 2.000 0.000000 \n", "10284 NaN NaN NaN NaN NaN \n", "10285 NaN NaN NaN NaN NaN \n", "10286 NaN NaN NaN NaN NaN \n", "10287 NaN NaN NaN NaN NaN \n", "\n", " avg_LEG_landed avg_PASS avg_REV avg_SIG_STR_att \\\n", "0 3.700000 1.200000 0.000000 101.400000 \n", "1 14.714286 1.714286 0.142857 115.857143 \n", "2 11.200000 0.333333 0.133333 124.333333 \n", "3 11.500000 0.125000 0.000000 111.750000 \n", "4 3.000000 0.250000 0.000000 62.250000 \n", "... ... ... ... ... \n", "10283 0.000000 0.000000 0.000000 27.000000 \n", "10284 NaN NaN NaN NaN \n", "10285 NaN NaN NaN NaN \n", "10286 NaN NaN NaN NaN \n", "10287 NaN NaN NaN NaN \n", "\n", " avg_SIG_STR_landed avg_SIG_STR_pct avg_SUB_ATT avg_TD_att \\\n", "0 44.000000 0.466000 0.100000 5.300000 \n", "1 59.428571 0.575714 0.428571 5.142857 \n", "2 55.466667 0.430000 1.000000 0.933333 \n", "3 45.750000 0.366250 0.000000 2.250000 \n", "4 32.500000 0.545000 0.000000 0.500000 \n", "... ... ... ... ... \n", "10283 15.000000 0.550000 0.000000 0.000000 \n", "10284 NaN NaN NaN NaN \n", "10285 NaN NaN NaN NaN \n", "10286 NaN NaN NaN NaN \n", "10287 NaN NaN NaN NaN \n", "\n", " avg_TD_landed avg_TD_pct avg_TOTAL_STR_att avg_TOTAL_STR_landed \\\n", "0 1.900000 0.458000 129.900000 69.100000 \n", "1 2.428571 0.601429 161.571429 102.857143 \n", "2 0.400000 0.277333 133.000000 63.400000 \n", "3 0.625000 0.103750 117.375000 50.750000 \n", "4 0.000000 0.000000 63.500000 32.750000 \n", "... ... ... ... ... \n", "10283 0.000000 0.000000 53.000000 38.000000 \n", "10284 NaN NaN NaN NaN \n", "10285 NaN NaN NaN NaN \n", "10286 NaN NaN NaN NaN \n", "10287 NaN NaN NaN NaN \n", "\n", " avg_opp_BODY_att avg_opp_BODY_landed avg_opp_CLINCH_att \\\n", "0 13.300000 8.800000 7.500000 \n", "1 24.571429 14.142857 10.571429 \n", "2 14.466667 8.133333 2.800000 \n", "3 20.250000 13.375000 6.875000 \n", "4 6.250000 4.750000 4.500000 \n", "... ... ... ... \n", "10283 6.000000 3.000000 19.000000 \n", "10284 NaN NaN NaN \n", "10285 NaN NaN NaN \n", "10286 NaN NaN NaN \n", "10287 NaN NaN NaN \n", "\n", " avg_opp_CLINCH_landed avg_opp_DISTANCE_att avg_opp_DISTANCE_landed \\\n", "0 5.100000 90.500000 26.800000 \n", "1 7.857143 98.571429 32.571429 \n", "2 0.733333 91.066667 32.200000 \n", "3 5.625000 103.125000 38.500000 \n", "4 3.500000 42.750000 16.250000 \n", "... ... ... ... \n", "10283 10.000000 7.000000 0.000000 \n", "10284 NaN NaN NaN \n", "10285 NaN NaN NaN \n", "10286 NaN NaN NaN \n", "10287 NaN NaN NaN \n", "\n", " avg_opp_GROUND_att avg_opp_GROUND_landed avg_opp_HEAD_att \\\n", "0 0.800000 0.300000 76.100000 \n", "1 6.428571 4.285714 61.857143 \n", "2 4.866667 2.800000 78.266667 \n", "3 0.875000 0.750000 77.375000 \n", "4 7.750000 2.750000 43.250000 \n", "... ... ... ... \n", "10283 2.000000 2.000000 19.000000 \n", "10284 NaN NaN NaN \n", "10285 NaN NaN NaN \n", "10286 NaN NaN NaN \n", "10287 NaN NaN NaN \n", "\n", " avg_opp_HEAD_landed avg_opp_KD avg_opp_LEG_att avg_opp_LEG_landed \\\n", "0 17.300000 0.100000 9.400000 6.100000 \n", "1 12.428571 0.000000 29.142857 18.142857 \n", "2 23.200000 0.266667 6.000000 4.400000 \n", "3 20.375000 0.125000 13.250000 11.125000 \n", "4 14.000000 0.250000 5.500000 3.750000 \n", "... ... ... ... ... \n", "10283 7.000000 0.000000 3.000000 2.000000 \n", "10284 NaN NaN NaN NaN \n", "10285 NaN NaN NaN NaN \n", "10286 NaN NaN NaN NaN \n", "10287 NaN NaN NaN NaN \n", "\n", " avg_opp_PASS avg_opp_REV avg_opp_SIG_STR_att avg_opp_SIG_STR_landed \\\n", "0 0.000000 0.000000 98.800000 32.200000 \n", "1 1.142857 0.000000 115.571429 44.714286 \n", "2 0.333333 0.133333 98.733333 35.733333 \n", "3 0.000000 0.000000 110.875000 44.875000 \n", "4 0.750000 0.000000 55.000000 22.500000 \n", "... ... ... ... ... \n", "10283 0.000000 0.000000 28.000000 12.000000 \n", "10284 NaN NaN NaN NaN \n", "10285 NaN NaN NaN NaN \n", "10286 NaN NaN NaN NaN \n", "10287 NaN NaN NaN NaN \n", "\n", " avg_opp_SIG_STR_pct avg_opp_SUB_ATT avg_opp_TD_att \\\n", "0 0.336000 0.000000 0.900000 \n", "1 0.437143 0.285714 3.285714 \n", "2 0.340000 0.066667 2.866667 \n", "3 0.446250 0.000000 2.375000 \n", "4 0.397500 0.000000 1.000000 \n", "... ... ... ... \n", "10283 0.420000 0.000000 0.000000 \n", "10284 NaN NaN NaN \n", "10285 NaN NaN NaN \n", "10286 NaN NaN NaN \n", "10287 NaN NaN NaN \n", "\n", " avg_opp_TD_landed avg_opp_TD_pct avg_opp_TOTAL_STR_att \\\n", "0 0.100000 0.050000 110.500000 \n", "1 0.857143 0.147143 158.142857 \n", "2 0.666667 0.131333 102.133333 \n", "3 0.000000 0.000000 115.125000 \n", "4 0.000000 0.000000 60.500000 \n", "... ... ... ... \n", "10283 0.000000 0.000000 29.000000 \n", "10284 NaN NaN NaN \n", "10285 NaN NaN NaN \n", "10286 NaN NaN NaN \n", "10287 NaN NaN NaN \n", "\n", " avg_opp_TOTAL_STR_landed current_lose_streak current_win_streak \\\n", "0 43.300000 0.0 4.0 \n", "1 82.285714 0.0 2.0 \n", "2 38.600000 0.0 11.0 \n", "3 48.875000 1.0 0.0 \n", "4 27.750000 1.0 0.0 \n", "... ... ... ... \n", "10283 13.000000 0.0 1.0 \n", "10284 NaN 0.0 0.0 \n", "10285 NaN 0.0 0.0 \n", "10286 NaN 0.0 0.0 \n", "10287 NaN 0.0 0.0 \n", "\n", " date draw each_cms fighter is_winner \\\n", "0 2019-06-08 0.0 162.56 Henry Cejudo True \n", "1 2019-06-08 0.0 167.64 Valentina Shevchenko True \n", "2 2019-06-08 0.0 193.04 Tony Ferguson True \n", "3 2019-06-08 0.0 172.72 Jimmie Rivera False \n", "4 2019-06-08 0.0 190.50 Tai Tuivasa False \n", "... ... ... ... ... ... \n", "10283 1993-11-12 0.0 NaN Kevin Rosier False \n", "10284 1993-11-12 0.0 NaN Patrick Smith False \n", "10285 1993-11-12 0.0 NaN Art Jimmerson False \n", "10286 1993-11-12 0.0 NaN Zane Frazier False \n", "10287 1993-11-12 0.0 NaN Teila Tuli False \n", "\n", " longest_win_streak losses total_rounds_fought \\\n", "0 4.0 2.0 27.0 \n", "1 2.0 2.0 25.0 \n", "2 11.0 1.0 33.0 \n", "3 5.0 2.0 20.0 \n", "4 3.0 1.0 7.0 \n", "... ... ... ... \n", "10283 1.0 0.0 1.0 \n", "10284 0.0 0.0 0.0 \n", "10285 0.0 0.0 0.0 \n", "10286 0.0 0.0 0.0 \n", "10287 0.0 0.0 0.0 \n", "\n", " total_time_fought(seconds) total_title_bouts weight_class \\\n", "0 742.60 3.0 Bantamweight \n", "1 1062.00 2.0 Women's Flyweight \n", "2 604.40 2.0 Lightweight \n", "3 690.25 0.0 Bantamweight \n", "4 440.75 0.0 Heavyweight \n", "... ... ... ... \n", "10283 260.00 0.0 Open Weight \n", "10284 NaN 0.0 Open Weight \n", "10285 NaN 0.0 Open Weight \n", "10286 NaN 0.0 Open Weight \n", "10287 NaN 0.0 Open Weight \n", "\n", " win_by_Decision_Majority win_by_Decision_Split \\\n", "0 0.0 2.0 \n", "1 0.0 1.0 \n", "2 0.0 1.0 \n", "3 0.0 1.0 \n", "4 0.0 0.0 \n", "... ... ... \n", "10283 0.0 0.0 \n", "10284 0.0 0.0 \n", "10285 0.0 0.0 \n", "10286 0.0 0.0 \n", "10287 0.0 0.0 \n", "\n", " win_by_Decision_Unanimous win_by_KO/TKO win_by_Submission \\\n", "0 4.0 2.0 0.0 \n", "1 2.0 0.0 2.0 \n", "2 3.0 3.0 6.0 \n", "3 4.0 1.0 0.0 \n", "4 1.0 2.0 0.0 \n", "... ... ... ... \n", "10283 0.0 1.0 0.0 \n", "10284 0.0 0.0 0.0 \n", "10285 0.0 0.0 0.0 \n", "10286 0.0 0.0 0.0 \n", "10287 0.0 0.0 0.0 \n", "\n", " win_by_TKO_Doctor_Stoppage wins \n", "0 0.0 8.0 \n", "1 0.0 5.0 \n", "2 1.0 14.0 \n", "3 0.0 6.0 \n", "4 0.0 3.0 \n", "... ... ... \n", "10283 0.0 1.0 \n", "10284 0.0 0.0 \n", "10285 0.0 0.0 \n", "10286 0.0 0.0 \n", "10287 0.0 0.0 \n", "\n", "[10288 rows x 75 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 171 } ] }, { "cell_type": "code", "metadata": { "id": "BKxG2DBW-g7e", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 689 }, "outputId": "1e8d4934-e5bf-427c-95be-be8c799a63d7" }, "source": [ "fighters_df = fighters_df.merge(sheet_df, left_on=\"fighter\", right_on=\"name\")\n", "fighters_df" ], "execution_count": 172, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
indexHeight_cmsReach_cmsStanceWeight_lbsWinnerageavg_BODY_attavg_BODY_landedavg_CLINCH_attavg_CLINCH_landedavg_DISTANCE_attavg_DISTANCE_landedavg_GROUND_attavg_GROUND_landedavg_HEAD_attavg_HEAD_landedavg_KDavg_LEG_attavg_LEG_landedavg_PASSavg_REVavg_SIG_STR_attavg_SIG_STR_landedavg_SIG_STR_pctavg_SUB_ATTavg_TD_attavg_TD_landedavg_TD_pctavg_TOTAL_STR_attavg_TOTAL_STR_landedavg_opp_BODY_attavg_opp_BODY_landedavg_opp_CLINCH_attavg_opp_CLINCH_landedavg_opp_DISTANCE_attavg_opp_DISTANCE_landedavg_opp_GROUND_attavg_opp_GROUND_landedavg_opp_HEAD_attavg_opp_HEAD_landedavg_opp_KDavg_opp_LEG_attavg_opp_LEG_landedavg_opp_PASSavg_opp_REVavg_opp_SIG_STR_attavg_opp_SIG_STR_landedavg_opp_SIG_STR_pctavg_opp_SUB_ATTavg_opp_TD_attavg_opp_TD_landedavg_opp_TD_pctavg_opp_TOTAL_STR_attavg_opp_TOTAL_STR_landedcurrent_lose_streakcurrent_win_streakdatedraweach_cmsfighteris_winnerlongest_win_streaklossestotal_rounds_foughttotal_time_fought(seconds)total_title_boutsweight_classwin_by_Decision_Majoritywin_by_Decision_Splitwin_by_Decision_Unanimouswin_by_KO/TKOwin_by_Submissionwin_by_TKO_Doctor_Stoppagewinsurlfidnamenickbirth_dateheightweightassociationclasslocalitycountrybirth_yearage_group
00162.56NaNOrthodox135.0Red32.021.90000016.40000017.00000011.00000075.00000026.5000009.4000006.50000074.20000023.9000000.4000005.3000003.7000001.2000000.0101.40000044.0000000.4660000.1000005.3000001.9000000.458000129.90000069.10000013.3000008.8000007.5000005.10000090.50000026.8000000.8000000.30000076.10000017.3000000.1000009.4000006.1000000.00.098.80000032.2000000.3360000.00.9000000.1000000.050000110.50000043.3000000.04.02019-06-080.0162.56Henry CejudoTrue4.02.027.0742.6000003.0Bantamweight0.02.04.02.00.00.08.0/fighter/Henry-Cejudo-125297125297Henry CejudoThe Messenger2/9/198764.0125.0Fight ReadyFlyweightPhoenix, ArizonaUnited States1987.01985
1210162.56NaNOrthodox135.0Red31.024.22222218.11111118.88888912.22222282.66666729.1111118.5555565.55555680.00000024.6666670.3333335.8888894.1111111.3333330.0110.11111146.8888890.4311110.1111115.8888892.1111110.508889141.77777874.77777814.7777789.7777788.3333335.666667100.22222229.6666670.8888890.33333384.44444419.2222220.11111110.2222226.6666670.00.0109.44444435.6666670.3366670.01.0000000.1111110.055556122.44444448.0000000.03.02019-01-190.0162.56Henry CejudoTrue4.02.026.0821.5555562.0Flyweight0.02.04.01.00.00.07.0/fighter/Henry-Cejudo-125297125297Henry CejudoThe Messenger2/9/198764.0125.0Fight ReadyFlyweightPhoenix, ArizonaUnited States1987.01985
2733162.56NaNOrthodox135.0Red30.025.71428619.28571420.71428612.85714381.14285730.7142866.8571434.28571477.57142925.0000000.4285715.4285713.5714291.0000000.0108.71428647.8571430.4442860.1428575.2857141.8571430.530000135.71428672.57142915.4285719.4285719.4285716.14285797.71428624.4285710.4285710.14285786.71428618.7142860.1428575.4285712.5714290.00.0107.57142930.7142860.3114290.00.8571430.0000000.000000116.57142939.0000000.01.02017-12-020.0162.56Henry CejudoTrue4.02.018.0713.4285711.0Flyweight0.01.03.01.00.00.05.0/fighter/Henry-Cejudo-125297125297Henry CejudoThe Messenger2/9/198764.0125.0Fight ReadyFlyweightPhoenix, ArizonaUnited States1987.01985
3860162.56NaNOrthodox135.0Red30.028.66666721.33333323.66666714.66666787.83333331.8333335.6666673.66666782.66666725.1666670.3333335.8333333.6666671.1666670.0117.16666750.1666670.4216670.1666675.8333331.8333330.451667147.66666778.16666718.00000011.00000011.0000007.166667109.83333327.6666670.5000000.16666797.16666721.1666670.1666676.1666672.8333330.00.0121.33333335.0000000.3300000.00.8333330.0000000.000000131.83333344.6666672.00.02017-09-090.0162.56Henry CejudoTrue4.02.016.0778.1666671.0Flyweight0.01.03.00.00.00.04.0/fighter/Henry-Cejudo-125297125297Henry CejudoThe Messenger2/9/198764.0125.0Fight ReadyFlyweightPhoenix, ArizonaUnited States1987.01985
41902162.56NaNOrthodox135.0Red28.030.00000022.50000024.00000015.50000079.00000034.50000016.00000011.00000085.50000035.5000000.5000003.5000003.0000003.5000000.0119.00000061.0000000.5100000.5000004.5000003.0000000.425000155.50000093.50000017.0000009.0000006.0000003.000000110.00000020.5000000.0000000.00000096.50000013.5000000.0000002.5000001.0000000.00.0116.00000023.5000000.2250000.00.5000000.0000000.000000126.50000033.0000000.02.02015-06-130.0162.56Henry CejudoTrue2.00.06.0900.0000000.0Flyweight0.00.02.00.00.00.02.0/fighter/Henry-Cejudo-125297125297Henry CejudoThe Messenger2/9/198764.0125.0Fight ReadyFlyweightPhoenix, ArizonaUnited States1987.01985
...........................................................................................................................................................................................................................................................................
86665134NaNNaNNaNNaNRedNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.01994-03-110.0NaNRay WizardFalse0.00.00.0NaN0.0Open Weight0.00.00.00.00.00.00.0/fighter/Ray-Wizard-2626Ray WizardNaNNaNNaNNaNNaNNaNLos Angeles, CaliforniaUnited StatesNaNNaN
86675135182.88NaNNaN175.0Red18.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.01994-03-110.0NaNSean DaughertyFalse0.00.00.0NaN0.0Open Weight0.00.00.00.00.00.00.0/fighter/Sean-Daugherty-2525Sean DaughertyNaN12/4/197570.0190.0NaNLight HeavyweightAkron, OhioUnited States1975.01970
86685137187.96NaNNaN185.0RedNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.01993-11-120.0NaNTrent JenkinsFalse0.00.00.0NaN0.0Open Weight0.00.00.00.00.00.00.0/fighter/Trent-Jenkins-2323Trent JenkinsNaNNaN74.0185.0NaNMiddleweightDenver, ColoradoUnited StatesNaNNaN
86695141185.42NaNOrthodox196.0Red30.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.01993-11-120.0NaNArt JimmersonFalse0.00.00.0NaN0.0Open Weight0.00.00.00.00.00.00.0/fighter/Art-Jimmerson-2020Art JimmersonNaN8/4/196373.0196.0NaNLight HeavyweightSt. Louis, MissouriUnited States1963.0NaN
86705143182.88NaNOrthodox430.0Red24.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.01993-11-120.0NaNTeila TuliFalse0.00.00.0NaN0.0Open Weight0.00.00.00.00.00.00.0/fighter/Teila-Tuli-1616Teila TuliNaN6/14/196974.0415.0NaNSuper HeavyweightHonolulu, HawaiiUnited States1969.01965
\n", "

8671 rows × 88 columns

\n", "
" ], "text/plain": [ " index Height_cms Reach_cms Stance Weight_lbs Winner age \\\n", "0 0 162.56 NaN Orthodox 135.0 Red 32.0 \n", "1 210 162.56 NaN Orthodox 135.0 Red 31.0 \n", "2 733 162.56 NaN Orthodox 135.0 Red 30.0 \n", "3 860 162.56 NaN Orthodox 135.0 Red 30.0 \n", "4 1902 162.56 NaN Orthodox 135.0 Red 28.0 \n", "... ... ... ... ... ... ... ... \n", "8666 5134 NaN NaN NaN NaN Red NaN \n", "8667 5135 182.88 NaN NaN 175.0 Red 18.0 \n", "8668 5137 187.96 NaN NaN 185.0 Red NaN \n", "8669 5141 185.42 NaN Orthodox 196.0 Red 30.0 \n", "8670 5143 182.88 NaN Orthodox 430.0 Red 24.0 \n", "\n", " avg_BODY_att avg_BODY_landed avg_CLINCH_att avg_CLINCH_landed \\\n", "0 21.900000 16.400000 17.000000 11.000000 \n", "1 24.222222 18.111111 18.888889 12.222222 \n", "2 25.714286 19.285714 20.714286 12.857143 \n", "3 28.666667 21.333333 23.666667 14.666667 \n", "4 30.000000 22.500000 24.000000 15.500000 \n", "... ... ... ... ... \n", "8666 NaN NaN NaN NaN \n", "8667 NaN NaN NaN NaN \n", "8668 NaN NaN NaN NaN \n", "8669 NaN NaN NaN NaN \n", "8670 NaN NaN NaN NaN \n", "\n", " avg_DISTANCE_att avg_DISTANCE_landed avg_GROUND_att \\\n", "0 75.000000 26.500000 9.400000 \n", "1 82.666667 29.111111 8.555556 \n", "2 81.142857 30.714286 6.857143 \n", "3 87.833333 31.833333 5.666667 \n", "4 79.000000 34.500000 16.000000 \n", "... ... ... ... \n", "8666 NaN NaN NaN \n", "8667 NaN NaN NaN \n", "8668 NaN NaN NaN \n", "8669 NaN NaN NaN \n", "8670 NaN NaN NaN \n", "\n", " avg_GROUND_landed avg_HEAD_att avg_HEAD_landed avg_KD avg_LEG_att \\\n", "0 6.500000 74.200000 23.900000 0.400000 5.300000 \n", "1 5.555556 80.000000 24.666667 0.333333 5.888889 \n", "2 4.285714 77.571429 25.000000 0.428571 5.428571 \n", "3 3.666667 82.666667 25.166667 0.333333 5.833333 \n", "4 11.000000 85.500000 35.500000 0.500000 3.500000 \n", "... ... ... ... ... ... \n", "8666 NaN NaN NaN NaN NaN \n", "8667 NaN NaN NaN NaN NaN \n", "8668 NaN NaN NaN NaN NaN \n", "8669 NaN NaN NaN NaN NaN \n", "8670 NaN NaN NaN NaN NaN \n", "\n", " avg_LEG_landed avg_PASS avg_REV avg_SIG_STR_att avg_SIG_STR_landed \\\n", "0 3.700000 1.200000 0.0 101.400000 44.000000 \n", "1 4.111111 1.333333 0.0 110.111111 46.888889 \n", "2 3.571429 1.000000 0.0 108.714286 47.857143 \n", "3 3.666667 1.166667 0.0 117.166667 50.166667 \n", "4 3.000000 3.500000 0.0 119.000000 61.000000 \n", "... ... ... ... ... ... \n", "8666 NaN NaN NaN NaN NaN \n", "8667 NaN NaN NaN NaN NaN \n", "8668 NaN NaN NaN NaN NaN \n", "8669 NaN NaN NaN NaN NaN \n", "8670 NaN NaN NaN NaN NaN \n", "\n", " avg_SIG_STR_pct avg_SUB_ATT avg_TD_att avg_TD_landed avg_TD_pct \\\n", "0 0.466000 0.100000 5.300000 1.900000 0.458000 \n", "1 0.431111 0.111111 5.888889 2.111111 0.508889 \n", "2 0.444286 0.142857 5.285714 1.857143 0.530000 \n", "3 0.421667 0.166667 5.833333 1.833333 0.451667 \n", "4 0.510000 0.500000 4.500000 3.000000 0.425000 \n", "... ... ... ... ... ... \n", "8666 NaN NaN NaN NaN NaN \n", "8667 NaN NaN NaN NaN NaN \n", "8668 NaN NaN NaN NaN NaN \n", "8669 NaN NaN NaN NaN NaN \n", "8670 NaN NaN NaN NaN NaN \n", "\n", " avg_TOTAL_STR_att avg_TOTAL_STR_landed avg_opp_BODY_att \\\n", "0 129.900000 69.100000 13.300000 \n", "1 141.777778 74.777778 14.777778 \n", "2 135.714286 72.571429 15.428571 \n", "3 147.666667 78.166667 18.000000 \n", "4 155.500000 93.500000 17.000000 \n", "... ... ... ... \n", "8666 NaN NaN NaN \n", "8667 NaN NaN NaN \n", "8668 NaN NaN NaN \n", "8669 NaN NaN NaN \n", "8670 NaN NaN NaN \n", "\n", " avg_opp_BODY_landed avg_opp_CLINCH_att avg_opp_CLINCH_landed \\\n", "0 8.800000 7.500000 5.100000 \n", "1 9.777778 8.333333 5.666667 \n", "2 9.428571 9.428571 6.142857 \n", "3 11.000000 11.000000 7.166667 \n", "4 9.000000 6.000000 3.000000 \n", "... ... ... ... \n", "8666 NaN NaN NaN \n", "8667 NaN NaN NaN \n", "8668 NaN NaN NaN \n", "8669 NaN NaN NaN \n", "8670 NaN NaN NaN \n", "\n", " avg_opp_DISTANCE_att avg_opp_DISTANCE_landed avg_opp_GROUND_att \\\n", "0 90.500000 26.800000 0.800000 \n", "1 100.222222 29.666667 0.888889 \n", "2 97.714286 24.428571 0.428571 \n", "3 109.833333 27.666667 0.500000 \n", "4 110.000000 20.500000 0.000000 \n", "... ... ... ... \n", "8666 NaN NaN NaN \n", "8667 NaN NaN NaN \n", "8668 NaN NaN NaN \n", "8669 NaN NaN NaN \n", "8670 NaN NaN NaN \n", "\n", " avg_opp_GROUND_landed avg_opp_HEAD_att avg_opp_HEAD_landed \\\n", "0 0.300000 76.100000 17.300000 \n", "1 0.333333 84.444444 19.222222 \n", "2 0.142857 86.714286 18.714286 \n", "3 0.166667 97.166667 21.166667 \n", "4 0.000000 96.500000 13.500000 \n", "... ... ... ... \n", "8666 NaN NaN NaN \n", "8667 NaN NaN NaN \n", "8668 NaN NaN NaN \n", "8669 NaN NaN NaN \n", "8670 NaN NaN NaN \n", "\n", " avg_opp_KD avg_opp_LEG_att avg_opp_LEG_landed avg_opp_PASS \\\n", "0 0.100000 9.400000 6.100000 0.0 \n", "1 0.111111 10.222222 6.666667 0.0 \n", "2 0.142857 5.428571 2.571429 0.0 \n", "3 0.166667 6.166667 2.833333 0.0 \n", "4 0.000000 2.500000 1.000000 0.0 \n", "... ... ... ... ... \n", "8666 NaN NaN NaN NaN \n", "8667 NaN NaN NaN NaN \n", "8668 NaN NaN NaN NaN \n", "8669 NaN NaN NaN NaN \n", "8670 NaN NaN NaN NaN \n", "\n", " avg_opp_REV avg_opp_SIG_STR_att avg_opp_SIG_STR_landed \\\n", "0 0.0 98.800000 32.200000 \n", "1 0.0 109.444444 35.666667 \n", "2 0.0 107.571429 30.714286 \n", "3 0.0 121.333333 35.000000 \n", "4 0.0 116.000000 23.500000 \n", "... ... ... ... \n", "8666 NaN NaN NaN \n", "8667 NaN NaN NaN \n", "8668 NaN NaN NaN \n", "8669 NaN NaN NaN \n", "8670 NaN NaN NaN \n", "\n", " avg_opp_SIG_STR_pct avg_opp_SUB_ATT avg_opp_TD_att avg_opp_TD_landed \\\n", "0 0.336000 0.0 0.900000 0.100000 \n", "1 0.336667 0.0 1.000000 0.111111 \n", "2 0.311429 0.0 0.857143 0.000000 \n", "3 0.330000 0.0 0.833333 0.000000 \n", "4 0.225000 0.0 0.500000 0.000000 \n", "... ... ... ... ... \n", "8666 NaN NaN NaN NaN \n", "8667 NaN NaN NaN NaN \n", "8668 NaN NaN NaN NaN \n", "8669 NaN NaN NaN NaN \n", "8670 NaN NaN NaN NaN \n", "\n", " avg_opp_TD_pct avg_opp_TOTAL_STR_att avg_opp_TOTAL_STR_landed \\\n", "0 0.050000 110.500000 43.300000 \n", "1 0.055556 122.444444 48.000000 \n", "2 0.000000 116.571429 39.000000 \n", "3 0.000000 131.833333 44.666667 \n", "4 0.000000 126.500000 33.000000 \n", "... ... ... ... \n", "8666 NaN NaN NaN \n", "8667 NaN NaN NaN \n", "8668 NaN NaN NaN \n", "8669 NaN NaN NaN \n", "8670 NaN NaN NaN \n", "\n", " current_lose_streak current_win_streak date draw each_cms \\\n", "0 0.0 4.0 2019-06-08 0.0 162.56 \n", "1 0.0 3.0 2019-01-19 0.0 162.56 \n", "2 0.0 1.0 2017-12-02 0.0 162.56 \n", "3 2.0 0.0 2017-09-09 0.0 162.56 \n", "4 0.0 2.0 2015-06-13 0.0 162.56 \n", "... ... ... ... ... ... \n", "8666 0.0 0.0 1994-03-11 0.0 NaN \n", "8667 0.0 0.0 1994-03-11 0.0 NaN \n", "8668 0.0 0.0 1993-11-12 0.0 NaN \n", "8669 0.0 0.0 1993-11-12 0.0 NaN \n", "8670 0.0 0.0 1993-11-12 0.0 NaN \n", "\n", " fighter is_winner longest_win_streak losses \\\n", "0 Henry Cejudo True 4.0 2.0 \n", "1 Henry Cejudo True 4.0 2.0 \n", "2 Henry Cejudo True 4.0 2.0 \n", "3 Henry Cejudo True 4.0 2.0 \n", "4 Henry Cejudo True 2.0 0.0 \n", "... ... ... ... ... \n", "8666 Ray Wizard False 0.0 0.0 \n", "8667 Sean Daugherty False 0.0 0.0 \n", "8668 Trent Jenkins False 0.0 0.0 \n", "8669 Art Jimmerson False 0.0 0.0 \n", "8670 Teila Tuli False 0.0 0.0 \n", "\n", " total_rounds_fought total_time_fought(seconds) total_title_bouts \\\n", "0 27.0 742.600000 3.0 \n", "1 26.0 821.555556 2.0 \n", "2 18.0 713.428571 1.0 \n", "3 16.0 778.166667 1.0 \n", "4 6.0 900.000000 0.0 \n", "... ... ... ... \n", "8666 0.0 NaN 0.0 \n", "8667 0.0 NaN 0.0 \n", "8668 0.0 NaN 0.0 \n", "8669 0.0 NaN 0.0 \n", "8670 0.0 NaN 0.0 \n", "\n", " weight_class win_by_Decision_Majority win_by_Decision_Split \\\n", "0 Bantamweight 0.0 2.0 \n", "1 Flyweight 0.0 2.0 \n", "2 Flyweight 0.0 1.0 \n", "3 Flyweight 0.0 1.0 \n", "4 Flyweight 0.0 0.0 \n", "... ... ... ... \n", "8666 Open Weight 0.0 0.0 \n", "8667 Open Weight 0.0 0.0 \n", "8668 Open Weight 0.0 0.0 \n", "8669 Open Weight 0.0 0.0 \n", "8670 Open Weight 0.0 0.0 \n", "\n", " win_by_Decision_Unanimous win_by_KO/TKO win_by_Submission \\\n", "0 4.0 2.0 0.0 \n", "1 4.0 1.0 0.0 \n", "2 3.0 1.0 0.0 \n", "3 3.0 0.0 0.0 \n", "4 2.0 0.0 0.0 \n", "... ... ... ... \n", "8666 0.0 0.0 0.0 \n", "8667 0.0 0.0 0.0 \n", "8668 0.0 0.0 0.0 \n", "8669 0.0 0.0 0.0 \n", "8670 0.0 0.0 0.0 \n", "\n", " win_by_TKO_Doctor_Stoppage wins url fid \\\n", "0 0.0 8.0 /fighter/Henry-Cejudo-125297 125297 \n", "1 0.0 7.0 /fighter/Henry-Cejudo-125297 125297 \n", "2 0.0 5.0 /fighter/Henry-Cejudo-125297 125297 \n", "3 0.0 4.0 /fighter/Henry-Cejudo-125297 125297 \n", "4 0.0 2.0 /fighter/Henry-Cejudo-125297 125297 \n", "... ... ... ... ... \n", "8666 0.0 0.0 /fighter/Ray-Wizard-26 26 \n", "8667 0.0 0.0 /fighter/Sean-Daugherty-25 25 \n", "8668 0.0 0.0 /fighter/Trent-Jenkins-23 23 \n", "8669 0.0 0.0 /fighter/Art-Jimmerson-20 20 \n", "8670 0.0 0.0 /fighter/Teila-Tuli-16 16 \n", "\n", " name nick birth_date height weight association \\\n", "0 Henry Cejudo The Messenger 2/9/1987 64.0 125.0 Fight Ready \n", "1 Henry Cejudo The Messenger 2/9/1987 64.0 125.0 Fight Ready \n", "2 Henry Cejudo The Messenger 2/9/1987 64.0 125.0 Fight Ready \n", "3 Henry Cejudo The Messenger 2/9/1987 64.0 125.0 Fight Ready \n", "4 Henry Cejudo The Messenger 2/9/1987 64.0 125.0 Fight Ready \n", "... ... ... ... ... ... ... \n", "8666 Ray Wizard NaN NaN NaN NaN NaN \n", "8667 Sean Daugherty NaN 12/4/1975 70.0 190.0 NaN \n", "8668 Trent Jenkins NaN NaN 74.0 185.0 NaN \n", "8669 Art Jimmerson NaN 8/4/1963 73.0 196.0 NaN \n", "8670 Teila Tuli NaN 6/14/1969 74.0 415.0 NaN \n", "\n", " class locality country birth_year \\\n", "0 Flyweight Phoenix, Arizona United States 1987.0 \n", "1 Flyweight Phoenix, Arizona United States 1987.0 \n", "2 Flyweight Phoenix, Arizona United States 1987.0 \n", "3 Flyweight Phoenix, Arizona United States 1987.0 \n", "4 Flyweight Phoenix, Arizona United States 1987.0 \n", "... ... ... ... ... \n", "8666 NaN Los Angeles, California United States NaN \n", "8667 Light Heavyweight Akron, Ohio United States 1975.0 \n", "8668 Middleweight Denver, Colorado United States NaN \n", "8669 Light Heavyweight St. Louis, Missouri United States 1963.0 \n", "8670 Super Heavyweight Honolulu, Hawaii United States 1969.0 \n", "\n", " age_group \n", "0 1985 \n", "1 1985 \n", "2 1985 \n", "3 1985 \n", "4 1985 \n", "... ... \n", "8666 NaN \n", "8667 1970 \n", "8668 NaN \n", "8669 NaN \n", "8670 1965 \n", "\n", "[8671 rows x 88 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 172 } ] }, { "cell_type": "code", "metadata": { "id": "93azkKUHAB3R", "colab_type": "code", "colab": {} }, "source": [ "values = [\"win_by_Decision_Majority\", \"win_by_Decision_Split\", \"win_by_Decision_Unanimous\", \"win_by_KO/TKO\", \"win_by_Submission\", \"win_by_TKO_Doctor_Stoppage\"]\n", "win_df = pd.DataFrame()\n", "#for value in values:\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "V_AziSWvHHVW", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 399 }, "outputId": "9e4029dd-8b4f-4d27-97b8-fee14f0ec1a5" }, "source": [ "fighters_df.pivot_table(values=values, index=[\"age_group\", \"country\"]).reset_index([\"age_group\", \"country\"])" ], "execution_count": 185, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
age_groupcountrywin_by_Decision_Majoritywin_by_Decision_Splitwin_by_Decision_Unanimouswin_by_KO/TKOwin_by_Submissionwin_by_TKO_Doctor_Stoppage
01965Australia0.0000000.0000000.0000000.0000000.0000000.000000
11965Brazil0.0000000.0000000.0000000.3421053.8157890.000000
21965Canada0.0000000.0000000.0000001.5294120.0000000.000000
31965England0.0000000.0000000.3750000.5000000.0000000.000000
41965Japan0.0000000.0000000.0000000.1428570.0000000.000000
...........................
981985Singapore0.0000000.3333330.6666670.0000000.0000000.000000
991985South Korea0.0000000.2307690.0000000.3846151.0769230.000000
1001985Sweden0.0000000.0000000.9130431.9130431.1739130.000000
1011985Taiwan0.0000000.0000000.0000000.0000000.0000000.000000
1021985United States0.0088760.2998031.2445760.8313610.6360950.050296
\n", "

103 rows × 8 columns

\n", "
" ], "text/plain": [ " age_group country win_by_Decision_Majority win_by_Decision_Split \\\n", "0 1965 Australia 0.000000 0.000000 \n", "1 1965 Brazil 0.000000 0.000000 \n", "2 1965 Canada 0.000000 0.000000 \n", "3 1965 England 0.000000 0.000000 \n", "4 1965 Japan 0.000000 0.000000 \n", ".. ... ... ... ... \n", "98 1985 Singapore 0.000000 0.333333 \n", "99 1985 South Korea 0.000000 0.230769 \n", "100 1985 Sweden 0.000000 0.000000 \n", "101 1985 Taiwan 0.000000 0.000000 \n", "102 1985 United States 0.008876 0.299803 \n", "\n", " win_by_Decision_Unanimous win_by_KO/TKO win_by_Submission \\\n", "0 0.000000 0.000000 0.000000 \n", "1 0.000000 0.342105 3.815789 \n", "2 0.000000 1.529412 0.000000 \n", "3 0.375000 0.500000 0.000000 \n", "4 0.000000 0.142857 0.000000 \n", ".. ... ... ... \n", "98 0.666667 0.000000 0.000000 \n", "99 0.000000 0.384615 1.076923 \n", "100 0.913043 1.913043 1.173913 \n", "101 0.000000 0.000000 0.000000 \n", "102 1.244576 0.831361 0.636095 \n", "\n", " win_by_TKO_Doctor_Stoppage \n", "0 0.000000 \n", "1 0.000000 \n", "2 0.000000 \n", "3 0.000000 \n", "4 0.000000 \n", ".. ... \n", "98 0.000000 \n", "99 0.000000 \n", "100 0.000000 \n", "101 0.000000 \n", "102 0.050296 \n", "\n", "[103 rows x 8 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 185 } ] }, { "cell_type": "code", "metadata": { "id": "oXcbQ8uvOhJK", "colab_type": "code", "colab": {} }, "source": [ "" ], "execution_count": 0, "outputs": [] } ] }