{ "cells": [ { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "np=pd.np\n", "from sdd_api.api import Api\n", "from credentials import *\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "api = Api(username=username, password=password, client_id=client_id, client_secret=client_secret)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get the data, enter in the table name from our metadata sheet. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "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", "
seasonweek_numplayer_nameteam_namepositionopp_namefd_pointsfd_salarydk_pointsdk_salaryyh_pointsyh_salaryplayer_id
5345201713Jaydon MickensJACWRIND0.04500.00.03000.00.010.0
5499201713DeVante ParkerMIAWRDEN1.06100.01.54500.01.013.017007
256520176Robby AndersonNYJWRNE9.65600.011.64400.09.613.0479
74620172Rex BurkheadNERBNO11.95000.013.43900.011.913.02996
\n", "
" ], "text/plain": [ " season week_num player_name team_name position opp_name fd_points \\\n", "5345 2017 13 Jaydon Mickens JAC WR IND 0.0 \n", "5499 2017 13 DeVante Parker MIA WR DEN 1.0 \n", "2565 2017 6 Robby Anderson NYJ WR NE 9.6 \n", "746 2017 2 Rex Burkhead NE RB NO 11.9 \n", "\n", " fd_salary dk_points dk_salary yh_points yh_salary player_id \n", "5345 4500.0 0.0 3000.0 0.0 10.0 \n", "5499 6100.0 1.5 4500.0 1.0 13.0 17007 \n", "2565 5600.0 11.6 4400.0 9.6 13.0 479 \n", "746 5000.0 13.4 3900.0 11.9 13.0 2996 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfs=api.get_dataframe('dfs_salaries')\n", "dfs.sample(4)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "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", "
seasonweek_numfd_pointsfd_salarydk_pointsdk_salaryyh_pointsyh_salary
count7723.07723.0000006996.0000007679.0000006453.0000007115.0000006464.0000006412.000000
mean2017.09.1319446.1712045322.8024487.0480153936.5565716.05206414.956956
std0.04.9267966.9275411139.7319727.9557571456.7169967.0403577.093512
min2017.01.000000-4.0000000.000000-4.0000000.000000-4.00000010.000000
25%2017.05.0000000.3000004500.0000000.0000003000.0000000.00000010.000000
50%2017.09.0000004.0000004800.0000004.4000003400.0000003.60000011.000000
75%2017.013.0000009.8450005900.00000011.0000004700.0000009.60000018.000000
max2017.017.00000044.8000009800.00000055.60000010000.00000044.80000041.000000
\n", "
" ], "text/plain": [ " season week_num fd_points fd_salary dk_points \\\n", "count 7723.0 7723.000000 6996.000000 7679.000000 6453.000000 \n", "mean 2017.0 9.131944 6.171204 5322.802448 7.048015 \n", "std 0.0 4.926796 6.927541 1139.731972 7.955757 \n", "min 2017.0 1.000000 -4.000000 0.000000 -4.000000 \n", "25% 2017.0 5.000000 0.300000 4500.000000 0.000000 \n", "50% 2017.0 9.000000 4.000000 4800.000000 4.400000 \n", "75% 2017.0 13.000000 9.845000 5900.000000 11.000000 \n", "max 2017.0 17.000000 44.800000 9800.000000 55.600000 \n", "\n", " dk_salary yh_points yh_salary \n", "count 7115.000000 6464.000000 6412.000000 \n", "mean 3936.556571 6.052064 14.956956 \n", "std 1456.716996 7.040357 7.093512 \n", "min 0.000000 -4.000000 10.000000 \n", "25% 3000.000000 0.000000 10.000000 \n", "50% 3400.000000 3.600000 11.000000 \n", "75% 4700.000000 9.600000 18.000000 \n", "max 10000.000000 44.800000 41.000000 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfs.describe()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "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", "
seasonweek_numplayer_nameteam_nameopp_namefd_pointsfd_salarydk_pointsdk_salaryyh_pointsyh_salaryplayer_id
maxmaxmaxmaxmaxmaxmaxmaxmaxmaxmaxmax
position
DST201717Washington RedskinsWASWAS33.005900.032.004500.032.0022.0
K201717Zane GonzalezWASWAS27.005400.0NaNNaNNaNNaN9338
QB201717Tyrod TaylorWASWAS37.649600.040.648300.037.6441.09979
RB201717Zach ZennerWASWAS44.609800.055.6010000.044.6041.09817
TE201717Zach MillerWASWAS26.208500.033.107400.026.2032.09824
WR201717Zay JonesWASWAS44.809600.053.809800.044.8041.09977
\n", "
" ], "text/plain": [ " season week_num player_name team_name opp_name fd_points \\\n", " max max max max max max \n", "position \n", "DST 2017 17 Washington Redskins WAS WAS 33.00 \n", "K 2017 17 Zane Gonzalez WAS WAS 27.00 \n", "QB 2017 17 Tyrod Taylor WAS WAS 37.64 \n", "RB 2017 17 Zach Zenner WAS WAS 44.60 \n", "TE 2017 17 Zach Miller WAS WAS 26.20 \n", "WR 2017 17 Zay Jones WAS WAS 44.80 \n", "\n", " fd_salary dk_points dk_salary yh_points yh_salary player_id \n", " max max max max max max \n", "position \n", "DST 5900.0 32.00 4500.0 32.00 22.0 \n", "K 5400.0 NaN NaN NaN NaN 9338 \n", "QB 9600.0 40.64 8300.0 37.64 41.0 9979 \n", "RB 9800.0 55.60 10000.0 44.60 41.0 9817 \n", "TE 8500.0 33.10 7400.0 26.20 32.0 9824 \n", "WR 9600.0 53.80 9800.0 44.80 41.0 9977 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfs.groupby([\"position\"]).agg([max])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEUCAYAAAA7l80JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngfj/AA8Ca7EVZ+gAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dfs.groupby(\"position\")[['position','dk_points','fd_points','yh_points']].agg(max).plot(kind=\"bar\")" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Defaultly, we give you results from 2016-present. To get more data, include a season_start parameter" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "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", "
seasonweek_numplayer_nameteam_namepositionopp_namefd_pointsfd_salarydk_pointsdk_salaryyh_pointsyh_salaryplayer_id
59320162Brock OsweilerHOUQBKC12.427200.012.426100.012.422716829
281820157Marques ColstonNOWRIND2.505100.03.003400.0NoneNone4331
6796201416Alex SmithKCQBPIT13.847400.016.845600.0NoneNone20414
4796201612Paul PerkinsNYGRBCLE5.304500.06.303900.05.31417305
49120152Oakland RaidersOAKDSTBAL3.004200.03.002400.0NoneNone
43420141Andrew HawkinsCLEWRPIT12.705200.016.704500.0NoneNone9354
6793201517Kamar AikenBALWRCIN10.106500.012.605400.0NoneNone149
284920147Andre RobertsWASWRTEN2.805000.03.804100.0NoneNone18694
4728201711Jay CutlerMIAQB-4.426600.04.425400.04.42314971
5666201713Brice ButlerDALWRWAS0.004700.00.003100.00103128
\n", "
" ], "text/plain": [ " season week_num player_name team_name position opp_name \\\n", "593 2016 2 Brock Osweiler HOU QB KC \n", "2818 2015 7 Marques Colston NO WR IND \n", "6796 2014 16 Alex Smith KC QB PIT \n", "4796 2016 12 Paul Perkins NYG RB CLE \n", "491 2015 2 Oakland Raiders OAK DST BAL \n", "434 2014 1 Andrew Hawkins CLE WR PIT \n", "6793 2015 17 Kamar Aiken BAL WR CIN \n", "2849 2014 7 Andre Roberts WAS WR TEN \n", "4728 2017 11 Jay Cutler MIA QB - \n", "5666 2017 13 Brice Butler DAL WR WAS \n", "\n", " fd_points fd_salary dk_points dk_salary yh_points yh_salary player_id \n", "593 12.42 7200.0 12.42 6100.0 12.42 27 16829 \n", "2818 2.50 5100.0 3.00 3400.0 None None 4331 \n", "6796 13.84 7400.0 16.84 5600.0 None None 20414 \n", "4796 5.30 4500.0 6.30 3900.0 5.3 14 17305 \n", "491 3.00 4200.0 3.00 2400.0 None None \n", "434 12.70 5200.0 16.70 4500.0 None None 9354 \n", "6793 10.10 6500.0 12.60 5400.0 None None 149 \n", "2849 2.80 5000.0 3.80 4100.0 None None 18694 \n", "4728 4.42 6600.0 4.42 5400.0 4.42 31 4971 \n", "5666 0.00 4700.0 0.00 3100.0 0 10 3128 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfs2014_present=api.get_dataframe('dfs_salaries',season_start=2014)\n", "dfs2014_present.sample(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Advanced Features\n", "We also have a few more features" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loading season: 2017: 100%|██████████| 7/7 [00:03<00:00, 1.70it/s]\n" ] }, { "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", "
seasonweek_numplayer_nameteam_namepositionopp_namefd_pointsfd_salarydk_pointsdk_salaryyh_pointsyh_salaryplayer_id
17720131Philip RiversLACQBHOU24.66900.0NoneNoneNoneNone18658
191220135Brian QuickSTLWRJAC5.54500.0NoneNoneNoneNone17996
6199201714Mason CrosbyGBKCLE3.04800.0NaNNaNNaNNaN4803
4037201210Anthony McCoySEATENYJ0.04600.0NoneNoneNoneNone14516
7171201317Vernon DavisSFTEARI12.06400.0NoneNoneNoneNone5301
5119201412Matt ForteCHIRBTB25.79300.028.29500NoneNone7137
3863201510Darren SprolesPHIRBMIA5.95500.08.43600NoneNone20938
5384201613Terrance WilliamsDALWRMIN2.34800.03.332002.31024151
4385201611Theo RiddickDETRBJAC12.36700.016.3510012.32218567
5014201313Oakland RaidersOAKDSTDAL9.05000.0NoneNoneNoneNone
\n", "
" ], "text/plain": [ " season week_num player_name team_name position opp_name \\\n", "177 2013 1 Philip Rivers LAC QB HOU \n", "1912 2013 5 Brian Quick STL WR JAC \n", "6199 2017 14 Mason Crosby GB K CLE \n", "4037 2012 10 Anthony McCoy SEA TE NYJ \n", "7171 2013 17 Vernon Davis SF TE ARI \n", "5119 2014 12 Matt Forte CHI RB TB \n", "3863 2015 10 Darren Sproles PHI RB MIA \n", "5384 2016 13 Terrance Williams DAL WR MIN \n", "4385 2016 11 Theo Riddick DET RB JAC \n", "5014 2013 13 Oakland Raiders OAK DST DAL \n", "\n", " fd_points fd_salary dk_points dk_salary yh_points yh_salary player_id \n", "177 24.6 6900.0 None None None None 18658 \n", "1912 5.5 4500.0 None None None None 17996 \n", "6199 3.0 4800.0 NaN NaN NaN NaN 4803 \n", "4037 0.0 4600.0 None None None None 14516 \n", "7171 12.0 6400.0 None None None None 5301 \n", "5119 25.7 9300.0 28.2 9500 None None 7137 \n", "3863 5.9 5500.0 8.4 3600 None None 20938 \n", "5384 2.3 4800.0 3.3 3200 2.3 10 24151 \n", "4385 12.3 6700.0 16.3 5100 12.3 22 18567 \n", "5014 9.0 5000.0 None None None None " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#progress bar\n", "dfs2014_present=api.get_dataframe('dfs_salaries',season_start=2011, progress_bar=True)\n", "dfs2014_present.sample(10)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loading season: 2002: 100%|██████████| 3/3 [00:18<00:00, 6.04s/it]\n" ] } ], "source": [ "#season_end\n", "player_game_logs=api.get_dataframe(\"player_game_logs\", season_start=2000, season_stop=2002, progress_bar=True)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "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", "
all_purpose_ydsall_tddef_intdef_int_longdef_int_tddef_int_ydsfgafgmfumblesfumbles_forced...team_nametwo_pt_mduniform_numberxpaxpmyds_from_scrimmageseasondraft_kings_pointsplayer_idstarted_game
17061None0.02.0None0.017.0NaNNaNNoneNone...PHINoneNoneNaNNaNNone20020.021551True
9333NoneNaNNaNNoneNaNNaNNaNNaNNoneNone...INDNoneNoneNaNNaNNone20020.020584True
13763None0.0NaNNoneNaNNaNNaNNaNNoneNone...NONoneNoneNaNNaNNone20011.54355True
\n", "

3 rows × 62 columns

\n", "
" ], "text/plain": [ " all_purpose_yds all_td def_int def_int_long def_int_td def_int_yds \\\n", "17061 None 0.0 2.0 None 0.0 17.0 \n", "9333 None NaN NaN None NaN NaN \n", "13763 None 0.0 NaN None NaN NaN \n", "\n", " fga fgm fumbles fumbles_forced ... team_name two_pt_md \\\n", "17061 NaN NaN None None ... PHI None \n", "9333 NaN NaN None None ... IND None \n", "13763 NaN NaN None None ... NO None \n", "\n", " uniform_number xpa xpm yds_from_scrimmage season draft_kings_points \\\n", "17061 None NaN NaN None 2002 0.0 \n", "9333 None NaN NaN None 2002 0.0 \n", "13763 None NaN NaN None 2001 1.5 \n", "\n", " player_id started_game \n", "17061 21551 True \n", "9333 20584 True \n", "13763 4355 True \n", "\n", "[3 rows x 62 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "player_game_logs.sample(3)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "That's it for this guide! Check out some of our other examples like our daily fantasy lineup optimizer or our power rankings example." ] } ], "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": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }