{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# NBA Data Extraction & Visualization\n", "#### Eugene Han" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "This tutorial will introduce some basic methods from scraping data from http://stats.nba.com/ while also focusing on demonstrating methods of visualizing data through [seaborn](http://seaborn.pydata.org/), a \"Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics\". Our method of scraping will primarily consist of locating the API endpoints instead of extracting the actual data on the webpage. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tutorial Content\n", "\n", "We will be utilizing these packages to demonstrate scraping, analyzing, and visualizing NBA data.\n", "\n", "[pandas](http://pandas.pydata.org/),\n", "[json](https://docs.python.org/2.7/library/json.html),\n", "[requests](http://docs.python-requests.org/en/master/),\n", "[numpy](http://www.numpy.org/),\n", "[seaborn](http://seaborn.pydata.org/)\n", "\n", "We will cover the following topics in this tutorial:\n", "- [Installing seaborn](#Installing-seaborn)\n", "- [Determining how to retrieve the data](#Retrieving-data)\n", "- [Cleaning and formatting team data](#Data-cleaning)\n", "- [Visualizing and analyzing the data](#Data-analysis-and-visualization)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Instaling seaborn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before getting started, you will need to install seaborn. We can do so using a quick `pip` install:\n", "\n", " $ pip install --upgrade seaborn" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import json\n", "import requests\n", "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Retrieving data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the first part, we will determine how to scrape the data from [here](http://stats.nba.com/league/team/#!/?Season=2015-16&SeasonType=Regular%20Season&sort=W&dir=1).\n", "\n", "If we take a look at the webpage in which we will be scraping for, we observe that there is table for the 2015-2016 NBA season with lots of averages for us to analyze and visualize. \n", "\n", "- Inspect the page\n", "- Head to the Network tab\n", "- Click on the XHR filter and refresh the tab\n", "\n", "XHR is short for XMLHttpRequest. This type of request is used to fetch XML or JSON data. If you look closely, we can find the API request that returns the data we are looking for. With most client-side apps these days, our browser does most of the work. Since the browser renders the HTML, we can view where the data comes from." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[u'TEAM_ID', u'TEAM_NAME', u'GP', u'W', u'L', u'W_PCT', u'MIN', u'FGM', u'FGA', u'FG_PCT', u'FG3M', u'FG3A', u'FG3_PCT', u'FTM', u'FTA', u'FT_PCT', u'OREB', u'DREB', u'REB', u'AST', u'TOV', u'STL', u'BLK', u'BLKA', u'PF', u'PFD', u'PTS', u'PLUS_MINUS', u'CFID', u'CFPARAMS']\n", "\n", "[1610612737, u'Atlanta Hawks', 82, 48, 34, 0.585, 48.4, 38.6, 84.4, 0.458, 9.9, 28.4, 0.35, 15.6, 20.0, 0.783, 8.3, 33.8, 42.1, 25.6, 15.0, 9.1, 5.9, 5.0, 19.1, 18.3, 102.8, 3.6, 10, u'Atlanta Hawks']\n" ] } ], "source": [ "#The XHR, for those familiar with HTTP you can see the parameters the request has\n", "\n", "url = \"http://stats.nba.com/stats/leaguedashteamstats?Conference=&DateFrom=&DateTo=&Division=&GameScope=&GameSegment=&LastNGames=0&LeagueID=00&Location=&MeasureType=Base&Month=0&OpponentTeamID=0&Outcome=&PORound=0&PaceAdjust=N&PerMode=PerGame&Period=0&PlayerExperience=&PlayerPosition=&PlusMinus=N&Rank=N&Season=2015-16&SeasonSegment=&SeasonType=Regular+Season&ShotClockRange=&StarterBench=&TeamID=0&VsConference=&VsDivision=\"\n", "response = requests.get(url)\n", "\n", "#if we run the below line, we observe that the response we get is json encoded\n", "#print(response.text)\n", "\n", "scrape = response.json()\n", "\n", "print(scrape[\"resultSets\"][0]['headers']) #the column headers\n", "print #to space out the two print statements\n", "print(scrape[\"resultSets\"][0]['rowSet'][0]) #first team data \"Atlanta Hawks\"" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1610612737, u'Atlanta Hawks', 82, 44, 38, 0.537, 48.1, 36.2, 78.4, 0.462, 6.1, 17.4, 0.352, 16.4, 21.1, 0.779, 9.3, 30.0, 39.3, 22.0, 13.6, 6.1, 4.2, 4.2, 19.0, 18.5, 95.0, -0.8, 10, u'Atlanta Hawks']\n" ] } ], "source": [ "# Given a season of type int returns the JSON-formatted team data for that given season\n", "# Assumes if Season = 2015, then referring to the 2014-2015 Season\n", "# REQUIRES: Season should be an integer between 1997 and 2017\n", "# ENSURES: returns a dictionary of team statistics for given season\n", "\n", "def getTeamData(Season):\n", " start = Season - 1\n", " arg = str(start) + \"-\" + str(Season)[2:]\n", " url = \"http://stats.nba.com/stats/leaguedashteamstats?Conference=&DateFrom=&DateTo=&Division=&GameScope=&GameSegment=&LastNGames=0&LeagueID=00&Location=&MeasureType=Base&Month=0&OpponentTeamID=0&Outcome=&PORound=0&PaceAdjust=N&PerMode=PerGame&Period=0&PlayerExperience=&PlayerPosition=&PlusMinus=N&Rank=N&Season=\" + arg + \"&SeasonSegment=&SeasonType=Regular+Season&ShotClockRange=&StarterBench=&TeamID=0&VsConference=&VsDivision=\"\n", " try:\n", " r = requests.get(url).json()\n", " return r\n", " except:\n", " print(str(Season) + ' failed')\n", " print(url)\n", " return None\n", "\n", "for i in range(1997, 2017):\n", " getTeamData(i)\n", "\n", "print(getTeamData(2011)[\"resultSets\"][0]['rowSet'][0]) #Atlanta Hawks data from 2010" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perhaps we seek more than just a season's team data.\n", "\n", "If we traverse to http://stats.nba.com/players/, we see a list of currently active and historic players. If we inspect the page, look at the network tab, turn on the XHR filter and refresh the page, we get the below image.\n", "\n", "![alt text](https://i.imgur.com/D3DAkSC.png)\n", "\n", "Under 'name' we see the link of the API request. In the preview tab, we can see the response of the API request. Interestingly enough, even when the season parameter is set to 2015-2016 in the API request, we still receive all the IDs of every single basketball player who played post 1995-1996 when the NBA started keeping track of player statistics. I'm not entirely sure why this is, but we will find a way to work around it." ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[u'PERSON_ID', u'DISPLAY_LAST_COMMA_FIRST', u'DISPLAY_FIRST_LAST', u'ROSTERSTATUS', u'FROM_YEAR', u'TO_YEAR', u'PLAYERCODE', u'TEAM_ID', u'TEAM_CITY', u'TEAM_NAME', u'TEAM_ABBREVIATION', u'TEAM_CODE', u'GAMES_PLAYED_FLAG']\n", "\n", "Abdelnaby, Alaa\n", "Abdul-Aziz, Zaid\n", "Abdul-Jabbar, Kareem\n", "Abdul-Rauf, Mahmoud\n", "Abdul-Wahad, Tariq\n", "Abdur-Rahim, Shareef\n", "Abernethy, Tom\n", "Able, Forest\n", "Abramovic, John\n", "Abrines, Alex\n" ] } ], "source": [ "individual_url = \"http://stats.nba.com/stats/commonallplayers?IsOnlyCurrentSeason=0&LeagueID=00&Season=2016-17\"\n", "individual_ids = requests.get(individual_url).json()\n", "print(individual_ids['resultSets'][0]['headers'])\n", "print\n", "for name in range(10):\n", " print(individual_ids[\"resultSets\"][0]['rowSet'][name][1])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "## Data cleaning" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Now that we have learned how to scrape the page, it is time for us to load the data into something more \"friendly\" for analysis and visualization. We will add the data into a pandas dataframe." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " TEAM_NAME W L W_PCT MIN FGM FGA FG_PCT FG3M FG3A \\\n", "0 Atlanta Hawks 48 34 0.585 48.4 38.6 84.4 0.458 9.9 28.4 \n", "1 Boston Celtics 48 34 0.585 48.2 39.2 89.2 0.439 8.7 26.1 \n", "2 Brooklyn Nets 21 61 0.256 48.2 38.2 84.4 0.453 6.5 18.4 \n", "3 Charlotte Hornets 48 34 0.585 48.4 37.0 84.4 0.439 10.6 29.4 \n", "4 Chicago Bulls 42 40 0.512 48.5 38.6 87.4 0.441 7.9 21.4 \n", "\n", " ... REB AST TOV STL BLK BLKA PF PFD PTS PLUS_MINUS \n", "0 ... 42.1 25.6 15.0 9.1 5.9 5.0 19.1 18.3 102.8 3.6 \n", "1 ... 44.9 24.2 13.7 9.2 4.2 5.5 21.9 21.0 105.7 3.2 \n", "2 ... 42.4 22.3 14.8 7.6 4.0 5.2 18.0 18.4 98.6 -7.4 \n", "3 ... 43.9 21.7 12.5 7.3 5.3 5.5 18.1 20.4 103.4 2.7 \n", "4 ... 46.3 22.8 13.9 6.0 5.7 5.7 18.8 18.7 101.6 -1.5 \n", "\n", "[5 rows x 26 columns]\n" ] } ], "source": [ "columns = scrape[\"resultSets\"][0]['headers'] \n", "nba_data = pd.DataFrame(scrape[\"resultSets\"][0]['rowSet'], columns = columns)\n", "\n", "#we choose to drop the below columns because they aren't very useful\n", "nba_data = nba_data.drop([\"TEAM_ID\", \"GP\", \"CFID\", \"CFPARAMS\"], axis = 1)\n", "\n", "print(nba_data.head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once again, perhaps we wish to extract individual player data instead of team data. Below, we look through the list of players and determine which have played for a particular season so we can extract their data for the specific season. Below is how we would theoretically access the data, unfortunately we can't anymore due to the NBA restricting access to certain API endpoints while in the middle of the creation of this notebook." ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "permission denied\n" ] } ], "source": [ "player_id = None\n", "count = 0\n", "\n", "while player_id == None:\n", " if int(individual_ids[\"resultSets\"][0]['rowSet'][count][5]) >= 2015: #year played until\n", " player_id = individual_ids[\"resultSets\"][0]['rowSet'][count][0]\n", " count += 1\n", "\n", "# print(player_id)\n", "player_stats_url = \"http://stats.nba.com/stats/playerdashboardbygeneralsplits?DateFrom=&DateTo=&GameSegment=&LastNGames=0&LeagueID=00&Location=&MeasureType=Base&Month=0&OpponentTeamID=0&Outcome=&PORound=0&PaceAdjust=N&PerMode=PerGame&Period=0&PlayerID=\" + str(player_id) + \"&PlusMinus=N&Rank=N&Season=2016-17&SeasonSegment=&SeasonType=Pre+Season&ShotClockRange=&VsConference=&VsDivision=\"\n", "try:\n", " player_stats_response = requests.get(player_stats_url).json()\n", " print(player_stats_response['resultSets'][0]['headers'])\n", " print\n", " print(player_stats_response['resultSets'][0]['rowSet'])\n", "except:\n", " print(\"permission denied\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data analysis and visualization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For our visualizations we will be using the [seaborn](http://seaborn.pydata.org/api.html) package. I have attached a link to the API reference page.\n", "\n", "Suppose we wanted to determine if there is a correlation between the average number of 3-pointers made per game and the number of total wins in a season. We first plot the average number of 3-pointers made per game vs. the number of total wins in a season to make a visual observation." ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "sns.set_style(\"white\") #sets our background white\n", "sns.lmplot(x = \"FG3M\", y = \"W\", data=nba_data, fit_reg = False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hmm, the above graph seems quite bland and we can't really inference anything. Perhaps, we should try adding more data points to see if we can conclude anything. Using our getTeamData function we built earlier, we will do exactly this. We will also be adding a \"YEAR\" column to our dataset because the response for the API request to nba.stats does not include the season in the rowSet." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " AST BLK BLKA CFID CFPARAMS DREB FG3A FG3M FG3_PCT FGA \\\n", "0 19.0 5.2 4.0 10.0 Atlanta Hawks 28.7 22.4 8.0 0.360 76.9 \n", "0 19.1 6.0 4.5 10.0 Atlanta Hawks 29.5 12.4 4.1 0.332 77.5 \n", "0 15.6 5.2 4.9 10.0 Atlanta Hawks 30.0 12.9 3.9 0.306 75.2 \n", "0 18.9 5.6 4.9 10.0 Atlanta Hawks 31.3 9.9 3.1 0.317 83.0 \n", "0 19.0 4.7 6.3 10.0 Atlanta Hawks 30.4 11.4 4.1 0.357 81.3 \n", "\n", " ... PLUS_MINUS PTS REB STL TEAM_ID TEAM_NAME TOV \\\n", "0 ... 5.4 94.8 41.1 8.5 1.610613e+09 Atlanta Hawks 15.0 \n", "0 ... 3.5 95.9 43.0 7.9 1.610613e+09 Atlanta Hawks 14.8 \n", "0 ... 2.9 86.3 43.5 6.9 1.610613e+09 Atlanta Hawks 14.9 \n", "0 ... -5.4 94.3 45.3 6.1 1.610613e+09 Atlanta Hawks 15.4 \n", "0 ... -5.2 91.0 42.9 7.7 1.610613e+09 Atlanta Hawks 16.7 \n", "\n", " W W_PCT YEAR \n", "0 56.0 0.683 1997.0 \n", "0 50.0 0.610 1998.0 \n", "0 31.0 0.620 1999.0 \n", "0 28.0 0.341 2000.0 \n", "0 25.0 0.305 2001.0 \n", "\n", "[5 rows x 31 columns]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:10: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n" ] } ], "source": [ "all_seasons = pd.DataFrame(None, columns = columns)\n", "\n", "for season in range(1997, 2017):\n", " data = getTeamData(season)\n", " if data == None: continue\n", " temp = pd.DataFrame(data[\"resultSets\"][0]['rowSet'], columns = columns)\n", " temp['YEAR'] = int(season)\n", " all_seasons = pd.concat([all_seasons, temp])\n", "\n", "all_seasons = all_seasons.sort(['TEAM_NAME', 'YEAR'])\n", "print all_seasons.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For some unknown reason, the requests call to the NBA API occasionally fails to return a non-empty response. So for this reason, since we have actually been able to successfully grab all the data on this attempt, we will save a copy of this data locally." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "all_seasons.to_csv('data.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To retrieve the dataset we saved locally, we can easily get it back via" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index([u'AST', u'BLK', u'BLKA', u'CFID', u'CFPARAMS', u'DREB', u'FG3A',\n", " u'FG3M', u'FG3_PCT', u'FGA', u'FGM', u'FG_PCT', u'FTA', u'FTM',\n", " u'FT_PCT', u'GP', u'L', u'MIN', u'OREB', u'PF', u'PFD', u'PLUS_MINUS',\n", " u'PTS', u'REB', u'STL', u'TEAM_ID', u'TEAM_NAME', u'TOV', u'W',\n", " u'W_PCT', u'YEAR'],\n", " dtype='object')\n", " AST BLK BLKA CFID CFPARAMS DREB FG3A FG3M FG3_PCT FGA \\\n", "0 19.0 5.2 4.0 10.0 Atlanta Hawks 28.7 22.4 8.0 0.360 76.9 \n", "0 19.1 6.0 4.5 10.0 Atlanta Hawks 29.5 12.4 4.1 0.332 77.5 \n", "0 15.6 5.2 4.9 10.0 Atlanta Hawks 30.0 12.9 3.9 0.306 75.2 \n", "0 18.9 5.6 4.9 10.0 Atlanta Hawks 31.3 9.9 3.1 0.317 83.0 \n", "0 19.0 4.7 6.3 10.0 Atlanta Hawks 30.4 11.4 4.1 0.357 81.3 \n", "\n", " ... PLUS_MINUS PTS REB STL TEAM_ID TEAM_NAME TOV \\\n", "0 ... 5.4 94.8 41.1 8.5 1.610613e+09 Atlanta Hawks 15.0 \n", "0 ... 3.5 95.9 43.0 7.9 1.610613e+09 Atlanta Hawks 14.8 \n", "0 ... 2.9 86.3 43.5 6.9 1.610613e+09 Atlanta Hawks 14.9 \n", "0 ... -5.4 94.3 45.3 6.1 1.610613e+09 Atlanta Hawks 15.4 \n", "0 ... -5.2 91.0 42.9 7.7 1.610613e+09 Atlanta Hawks 16.7 \n", "\n", " W W_PCT YEAR \n", "0 56.0 0.683 1997.0 \n", "0 50.0 0.610 1998.0 \n", "0 31.0 0.620 1999.0 \n", "0 28.0 0.341 2000.0 \n", "0 25.0 0.305 2001.0 \n", "\n", "[5 rows x 31 columns]\n" ] } ], "source": [ "all_seasons = pd.read_csv('data.csv', index_col = 0)\n", "print(all_seasons.columns)\n", "print(all_seasons.head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we will clean the dataframe by removing the years in which a NBA lockout occurred (i.e. the years in which 82 regular seasons were not played). Then we will remove the columns that are not of importance." ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 121, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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4WKYQMXlYmFSOUeoGHAyOZxxTun9zZ/rwV0tPQc+JMMbG4+jsD2HnwR5NnZXN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\nSJawZBhGHGsilkBZiZskByI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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "all_seasons = all_seasons[all_seasons['GP'] == 82]\n", "all_seasons = all_seasons.drop([\"TEAM_ID\", \"GP\", \"CFID\", \"CFPARAMS\"], axis = 1)\n", "\n", "sns.set(color_codes=True)\n", "sns.lmplot(x=\"FG3M\", y=\"W\", data=all_seasons, fit_reg = False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hmm, this graph still seems to not tell us too much other than that wins and average 3-points per made per season have very little correlation. Perhaps we should examine the prominence of the 3-pointer over the years. `lm.plot` is a seaborn function that plots data and a regression model fit. Use the paramater `fit_reg = bool` to include or not include the regression model fit." ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 122, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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x+CZsPH4JGmsjUz5nuRagrqZqPqZLlFeGYWAoZcHxJWih/EQpoiGBmurKvIxF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\nYjXgReHq8P/+n+1wKQo6wq4Vs3VxPtM0YRkq/G47PJ6lDx4gqtX8hGB70Jn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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set(color_codes=True)\n", "sns.lmplot(x=\"FG3M\", y=\"W\", col=\"YEAR\", data=all_seasons, fit_reg = True, col_wrap=6, size=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the graphs above, we can visually determine that over the years the 3-point shot has become more correlated with totals wins in a season. Intuitively, any avid watcher of the NBA can tell you that this is indeed true due to the recent emergence of shooters and the emphasis on \"big men\" being more well-rounded, which includes shooting from the perimeter. We will take a look at one more graph to see if we can find anything interesting. We will be looking at the average of 3-pointers made per season vs the averages wins per season for teams that have played more than 5 seasons. Though typically one would merge the statistics that are essentially the same but under different branding (i.e. Seattle SuperSonics to the Oklahoma City Thunder), we won't be." ] }, { "cell_type": "code", "execution_count": 159, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " TEAM_NAME W FG3M\n", "0 Atlanta Hawks 38.611111 6.111111\n", "1 Boston Celtics 41.176471 6.617647\n", "3 Charlotte Bobcats 31.777778 5.511111\n", "4 Charlotte Hornets 46.428571 5.785714\n", "5 Chicago Bulls 40.888889 5.561111\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 159, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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JeWeaJlLpNNLpNDQd0E0TxvAaBLWiGkk902eVFGC6rpViCoF9R6Jo7QyhrbMf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\nK7hHGRHlF0N3FMMwoEDDrCD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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = all_seasons[['TEAM_NAME', 'W', 'FG3M']]\n", "\n", "teams = all_seasons['TEAM_NAME'].unique()\n", "\n", "averages = pd.DataFrame(columns = ['TEAM_NAME', 'W', 'FG3M'])\n", "\n", "for i, team in enumerate(teams):\n", " temp_df = all_seasons[all_seasons['TEAM_NAME'] == team]\n", " if len(temp_df) > 5:\n", " avg_wins = temp_df['W'].mean()\n", " avg_FG3M = temp_df['FG3M'].mean()\n", " averages.loc[i] = [team, avg_wins, avg_FG3M]\n", "\n", "\n", "print(averages.head())\n", "sns.lmplot(x = \"FG3M\", y = \"W\", data=averages, fit_reg = True)\n", "#observe that the graph does not really tell us anything new that we haven't already determined from the other graphs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Suppose we wish to observe the correlation between average points scored per game and total number of season wins. Normally a scatter plot would suffice. However, for the sake of learning seaborn, we will turn the points into a categorical feature. \n", "\n", "Let x = the mean of average points scored across all seasons with 82 games played and y = the standard deviation. We create a new column labeled 'label' and assign it -1 if the 'PTS' column is less than x-y, 0 if the 'PTS' column is greater than or equal to x-y and less than x+y, 1 otherwise." ] }, { "cell_type": "code", "execution_count": 193, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " TEAM_NAME W PTS YEAR label <2006\n", "0 Atlanta Hawks 56.0 94.8 1997.0 0 1\n", "0 Atlanta Hawks 50.0 95.9 1998.0 0 1\n", "0 Atlanta Hawks 28.0 94.3 2000.0 0 1\n", "0 Atlanta Hawks 25.0 91.0 2001.0 -1 1\n", "0 Atlanta Hawks 33.0 94.0 2002.0 0 1\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:13: 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/indexing.html#indexing-view-versus-copy\n", "/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:14: 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/indexing.html#indexing-view-versus-copy\n" ] } ], "source": [ "mean = all_seasons['PTS'].mean()\n", "std = all_seasons['PTS'].std()\n", "\n", "def label(x):\n", " if x < mean-std:\n", " return -1\n", " elif x < mean+std:\n", " return 0\n", " else:\n", " return 1\n", " \n", "cat_example = all_seasons[['TEAM_NAME', 'W', 'PTS', 'YEAR']]\n", "cat_example['label'] = cat_example['PTS'].apply(label)\n", "cat_example['<2006'] = cat_example['YEAR'].apply(lambda x: 1 if x <= 2005 else 0)\n", "print(cat_example.head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A simple way to show the values of some quantitative variable across the levels of a categorical variable is by using `stripplot()`, which generalizes a scatterplot to the case where one of the variables is categorical" ] }, { "cell_type": "code", "execution_count": 189, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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HOPlhkKi/YHFLbt1HRWi7GVhtoxd7jgRfoyS6WHW++qC5ryq/FpCEiDrC4pZc\neXVT0FzRST7HTb3r4xN/Fx2BiM5jcUtuypjgU5hZVyQLSEJGds/EFaIjkOT8dbVw79+HgNstOor0\n+DiY5KaPTUDerpLWscWsYOggl8BEZEQuF3+nqOfqt+ej4vcvAYEAFJsNyT+6F66Jk0THkhaPuCX3\n4p8P6Mb+gIa3P/9GUBoyqjcPvis6AklKU1VUvfnH1mVONa8XZ/73TcGp5MbillxzS/CavxUdXPcm\nCsWhmiOiI5CsVBWBxkbdlL+2TlAYY2BxS272xKSgudvmjxGQhIws98oHRUcgSSkWC6JmzNTNRV95\npaA0xsBr3JJbft04eP0qdh48DavFhKXXjkVCbIToWCQxq2KBT+PWntR7rElD2o2HCkpiDCxuA1ix\ncAJWLJwgOgYZREel/f7RD3HTmOsFpCHZaaqK2o//ppur+fAviM26SlAi+fFUORF1y+NvER2BZKVp\n0FRVP8X9uEPC4jaA4vJ6bPj4MN7dcgx1bq/oOCQ5E5SgudvG3SIgCRmBYjYjdn62bi5uwbWC0hgD\nT5VL7puyOjy1fjcC59c9zT9Qgcf/NRNWCz+TUc/E2eNQ3XJWdAwykMHfuRWO1DS0HC9GxGXj+Ax3\niPjuLrmtX5e3ljYAVNV6cOA433Sp5zoq7V0VewQkIaNQFAVR06Zj8KLFLO1eENYjblVVsWbNGhQX\nF8NkMuEXv/gFbDYbVq9eDZPJhPT0dOTm5oYzguFF2IP/Cp0dzBGFItbOPZSJ+ouwHnFv2rQJiqLg\nj3/8I+677z7853/+J9auXYuVK1di/fr1UFUVeXl54YxgeNnTh8NuvfDXODIxEmNHxApMRLIbH5eu\nG5tgwpi4VEFpiKi9sBZ3dnY2HnvsMQBAWVkZYmJiUFhYiIyMDABAVlYW8vPzwxnB8D7fU4YW34U7\nNk+ebkR1nUdgIpLdwXarpKlQO3klEYkQ9mvcJpMJq1evxuOPP44bb7wRmnbheqzL5UJDQ0O4Ixja\n9oOVQXOfFpwSkISM7JX9r4uOQETn9cnF0KeeegrV1dVYvHgxWlouPA/qdrsRHd31tbO4OCcsFnO4\nI0pryCAXTtc06+amXJaEhIQoQYnIiLLHzuHvFFE/Edbifu+991BZWYkf/vCHsNvtMJlMmDRpEnbs\n2IGZM2di8+bNyMzM7PJ71NRww4yuLMtOx0PHqlvvLE+MdeCyYdGoquKZDOqZeGsczvpqdHMjbKP4\nO0U95q3/GqtUAAARI0lEQVQ6jdPrXoXn/ONgScvvgKWbg7aBrqsPymEt7muvvRYPPfQQli1bBr/f\njzVr1iA1NRVr1qyBz+dDWloacnJywhnB8A6erNE9DlZd34KahhbERdkFpiKZtS9tADjbXIf4iBgB\nacgIKn73PDzHjgEA3F/txmkoSP7RjwWnkldYizsiIgK//vWvg+bXrVsXzh87oBSdrNWNA6qGo6V1\nmDEuUVAiMqINB9/Cj6f9QHQMkpDq87aW9j80FR0SlMYYuACL5FKG6E+nKAowagivRVLvWjT6BtER\nSFImqw22YcN1c46UFDFhDILFLbkZlyXA1mZ50+TBLiRyW08KwXBnctDcsLhhApKQUQxZ8YPW8nak\njUHi8u8LTiQ3LrEluVf+VgSv/8JztqVVbnz9zRlMThssMBXJrKSpLGiuvLECQyOHdPBqou45Ro5C\nyi8eh+rzwWS1io4jPR5xS6682h00d/B48M1FRKHYWrpddAQyAJZ272BxS+7y1EFBc/OmBJ/qJArF\nzaOvFx2BJOdvqEfTwUIEmviIb6h4qlxys8YnYdPu0taxxawgebBLYCKS3WUxY1BUd7R1bIIJNptN\nYCKSXcOO7aj4/e+g+f1Q7A4Mu+deOMdPEB1LWjziltwL7x/Qjf0BDe9uOdbJq4m617a0gXNrlR+r\nPS4mDElP0zScfvN1aH7/uXGLB1VvvSE4ldxY3JJravEHzZVUNQpIQkZ2rO6E6Agkq0AAgXZ7Uvhr\nazt5MV0MFrfkZo4PXmjl1qvHCEhCRmEzBd9AlD3qKgFJyAgUiwVR0zN0c1GZswWlMQYWt+RmjU/S\nja0WEwbFOgSlISMYZIsTHYEMxpasX4DF3m5BFro0LG7JvfWZ/nqkz6/i010lgtKQEZR7TgfN7Tm9\nT0ASMgJNVVHz8Ye6ubN//UBQGmNgcUvOH9CC5ry+gIAkZGRuPx/hoR7StNYb01qn/D5BYYyBxS25\nb81J0Y1NCrBgxggxYcgQYu3B2y3OSZ4lIAkZgWI2I/bq+bq52GsWCEpjDHyOW3JlZ/Qrp6ka4A+o\nnbyaqHv+AM/YUO8afNsSONLS4CkuhnPceLgunyw6ktR4xC25vA6uZ2/4+LCAJGQUjf7gZXS3lHDJ\nU+o5RVEQlTETCbd+l6XdC1jckrOYlaC5KCdXuaLeFdfB6XMiEoPFLbnl143TjU0K8N35aYLSkBGM\ncukf1VEATEoYLyYMEQVhcUuu/SppqgbUuXnHJvXcqXbbegY/t0BEIrG4JZd/oCJoLm/XKQFJyChU\nLfjmxneO/kVAEiLqCItbcvFR9qC5kUlRApKQkU2M56lyov6CxS257103VjeOsFuQOXGIoDRkBKOi\ngpejHBufKiAJEXWExS25P/ytSDdubvHjQHG1oDRkBCcagh8xrG9p6OCVRCQCi1tyJyqC31A/4Vrl\n1MveOcJr3ET9BYtbcgmxEUFzM8cFb/VJFIobRl4jOgIRncfiltz/XToVduuFv8aUIVG48vKhAhOR\n7FKjRunGChQkRCcISkNE7XGtcslFRtjwP/9+Nc7WNyPCYUGEzSo6EknuWMMJ3ViDhvLGCgyN5E2P\nRP0Bj7gNIj46gqVNYbOl5EvREYjoPBa3AfhVFVu+LuPd5BQ2t6TeIDoCEZ3HU+WSqzzbhJ+/vAO+\n81t5Dh3kxBM/yBScimQ2MnI4Tjbqn0yw2bhxDVF/wSNuyb3y4cHW0gaA8uombNtXLjARya59aQPA\noeojApIQUUfCdsTt9/vx8MMPo7S0FD6fD3feeSfGjBmD1atXw2QyIT09Hbm5ueH68QNGXaM3aK68\nOng/ZaJQFNUcxbhB6aJjEBHCeMT9/vvvIy4uDhs2bMBLL72Exx57DGvXrsXKlSuxfv16qKqKvLy8\ncP34AWPu5OSgueyMEQKSkFGYlOC3hZvHXC8gCRF1JGzFff311+O+++4DAAQCAZjNZhQWFiIjIwMA\nkJWVhfz8/HD9+AEjdVi0bmy3mhDt5N3l1HM5I+frxvH2WEFJiKgjYSvuiIgIOJ1ONDY24r777sP9\n998PTbuws6/L5UJDA9c/DtVbn+qvPbb4VORxyVMKwWclW3Xjsy21qGo6IygNEbUX1rvKy8vLcc89\n92DZsmVYuHAhnn766davud1uREdHd/FfnxMX54TFYg5nTKlpUILmTFYzEhK4tSf1jIrg/bgjY2xI\niOXvFFF/ELbiPnPmDFasWIGf//znyMw893jS+PHjsXPnTsyYMQObN29une9KTU1TuCIaQs7MEXjx\nz4WtY5MCzJ2YhKoqns2gnslImoptZdtbx5EWFyJ80fydIupDXR18ha24X3jhBdTX1+O5557Ds88+\nC0VR8NOf/hSPP/44fD4f0tLSkJOTE64fP2CobS4/AICiBB+BE12Kgso9unGjn08pEPUniqa1e+fv\nZ/gpv2trXtqOsjP6N9ab56bg5rmpghKR7H606f8Gzd2cuhDXplwlIA3RwNTVETcXYJGczRL8V+i0\nc0E86l3x9u7vRyGivsHiltyS+WN0Y7vVhGumDxeUhowg0TEoaC5j6FQBSYioIyxuyR0uqdWNW3wq\n6tzBq6kRXaxZyTN049HRozp5JRGJwOKW3NZ9FUFzfI6bQrH79F7duLj+BGo8tZ28moj6GotbcrGR\nwbs2DUtwCUhCRhHXbqU0u9kGp9UpKA0Rtcfiltw/3zBeN7aaFVw5aaigNGQEVwadKk+B3cxtPYn6\nCxa35J7asFs39gU0fL6bp8qp535/4HXd+FDNYfj9fkFpiKg9FrfkOtrW8y/bTwpIQkbhV4NLekvZ\nlwKSEFFHWNySc9iC13GfNjZBQBIyClMHbwtTEy8XkISIOsLillzuHfrrkRE2M5Zcky4oDRnBDSkL\ndOMERzxiHTGC0hBRe1xiS3JJ8U78fvV8HCmpRVyUDYNjePcvhSa/YqdufMZzFh6/Bw6LQ1AiImqL\nR9wGkT48lqVNvaKupU431gCcaigTE4aIgrC4DSAQCOCDbcXYXhi8GAvRpUpyJerGZsWEtJgUMWGI\nKAhPlUuuuLwej7+6C//Y4m39x4fx3z/JEpqJ5DZl8ESUNpa3juPscTCZ+BmfqL/gv0bJPffufrTd\nl9Xt8eODbcXC8pD8PjrxmW58xlONM81nBaUhovZY3JJzN/uC5ooruIc59VxACwTNlTSWCkhCRB1h\ncUtu+tjBQXOLr04TkISMYnC7bT0VKLgigc9xE/UXLG7J3Tw3FRaz0jpOio/AkHjeXU4999NZ9yPZ\nNQQmxQSX1YmfTL1TdCQiaoM3p0nu090l8AcuXOWuPNuMw6dqcdnIOIGpSGY2sw0/nbVSdAwi6gSP\nuCXX4lM7mAu+RklERMbA4pZc1pShMJsunCpPjIvAhJR4gYmIiCicWNySU6BAUdqOiYjIyFjcktu8\nt0x/jbumGYXHawQmIiKicGJxS85qCf4rtHUwR0RExsB3eMnNnz5ctyf36KFRuGxkrMBEREQUTixu\nyVXVNMPjvXAX+Zk6D+8qJyIyMBa35LYfrNSNG5p8vMZNRGRgLG7JxUfZg+biOpgjIiJjYHFLLjtj\nBGJcttbx5LRBGD00WmAiIiIKJxa35PYdq0ad29s6PniiBvVtxkREZCwsbskdKNbvk+zzqzh8qlZQ\nGiIiCrewF/fevXuxfPlyAMDJkyexdOlSLFu2DL/4xS/C/aMHhBGJkbqxAmB4uzminvAGvNA0rfsX\nElGfCmtxv/TSS1izZg18Ph8AYO3atVi5ciXWr18PVVWRl5cXzh8/IMyfNgwZ4xKhKIDDZsZ3r0nn\ntp4UknpvA36z+wXc//ka5OY/hUNnj4iORERthLW4R40ahWeffbZ1fODAAWRkZAAAsrKykJ+fH84f\nPyBYLWbcfcsk/Pd9WfjNvXNx7YwRoiOR5N49+lccrv0GAFDtqcEfCv+IgMq1AYj6i7Dux71gwQKU\nlpa2jtuednO5XGhoaOj2e8TFOWGxmLt9HRH1jtKmMt24wdsIk8uPhEiuyEfUH4S1uNszmS4c4Lvd\nbkRHd//YUk1NUzgjEVE7adGjUVJf3joe7IiH1mRFVXP3H7SJqHckJER1+rU+vat8woQJ2LlzJwBg\n8+bNmD59el/+eCK6CDelXo/MoRmItLqQHpuKH07+PkwKH0Ah6i/69Ih71apV+NnPfgafz4e0tDTk\n5OT05Y8noovgsNixfPxtomMQUScUrZ8/71FVxdNzREQ0sPSbU+VEREQUGhY3ERGRRFjcREREEmFx\nExERSYTFTUREJBEWNxERkURY3ERERBJhcRMREUmExU1ERCQRFjcREZFEWNxEREQSYXETERFJhMVN\nREQkERY3ERGRRFjcREREEmFxExERSYTFTUREJBEWNxERkURY3ERERBJhcRMREUmExU1ERCQRFjcR\nEZFEWNxEREQSYXETERFJhMVNREQkERY3ERGRRFjcREREEmFxExERSYTFTUREJBEWNxERkUQsff0D\nNU3DI488gqKiIthsNjzxxBMYMWJEX8cgIiKSUp8fcefl5cHr9eKNN97Av//7v2Pt2rV9HYGIiEha\nfV7cBQUFmDdvHgBgypQp2L9/f19HICIiklafF3djYyOioqJaxxaLBaqq9nUMIiIiKfX5Ne7IyEi4\n3e7WsaqqMJk6//yQkBDV6deIiIgGmj4/4p42bRo+//xzAMCePXswduzYvo5AREQkLUXTNK0vf2Db\nu8oBYO3atRg9enRfRiAiIpJWnxc3ERER9RwXYCEiIpIIi5uIiEgiLG4iIiKJsLiJiIgkwuI2kObm\nZtx+++0oLi4WHYUkpmkacnNzsWTJEnzve9/DqVOnREcig9i7dy+WL18uOob0WNwGsX//fixbtoxv\nshQy7idA4fDSSy9hzZo18Pl8oqNIj8VtED6fD8899xxSU1NFRyHJcT8BCodRo0bh2WefFR3DEFjc\nBjF16lQkJSWBj+VTqLifAIXDggULYDabRccwhD5fq5x6z69//WsUFBRAURS8+uqrUBRFdCQygEvd\nT4CI+haLW2I/+clPREcgA5o2bRo+++wz5OTkcD8B6nU8Kxg6FrfB8KibQrVgwQJs27YNS5YsAQDe\nnEa9iu9RoeNa5URERBLhhSsiIiKJsLiJiIgkwuImIiKSCIubiIhIIixuIiIiibC4iYiIJMLiJhpg\nduzY0eUOTQ899BDefffdXvt+RNS7WNxEA1BvL4LBRTWI+g6Lm2iA2rlzJ5YuXYpFixYhOzsbH330\nUevXNm3ahEWLFuGmm27Chx9+CODcmuVPPfUUFi1ahFtuuQWvvvqqqOhEAxqXPCUagDRNw/r16/HE\nE09g9OjR+PLLL/Hkk0/iuuuuAwC0tLTg7bffRlVVFRYtWoSZM2fik08+gaIo2LhxI7xeL1asWIFJ\nkyYJ/pMQDTwsbqIBSFEUPP300/jss8/w4YcfYu/evWhqamr9+i233AJFUZCYmIhp06Zhz549+OKL\nL1BUVIT8/HwAQHNzMw4fPoy0tDRRfwyiAYnFTTQAaZqGpUuXIjMzEzNnzsTs2bPxwAMPtH7dYrnw\n1qCqKqxWKzRNw4MPPojs7GwAQE1NDVwuF/bs2dPn+YkGMl7jJhqA6urqcOLECdx7773IysrC1q1b\noapq69c/+OADAEBpaSn279+PyZMnY9asWXjzzTfh9/vhdruxdOlS7N27V9QfgWjA4hE30QAUGxuL\nOXPmYOHChYiKisIVV1wBj8cDj8cDAHA6nVi0aBH8fj8effRRxMbGYsmSJThx4gS+/e1vIxAIYPHi\nxZgxYwZ27Ngh+E9DNLBwW08iIiKJ8FQ5ERGRRFjcREREEmFxExERSYTFTUREJBEWNxERkURY3ERE\nRBJhcRMREUnk/wPixJIPOtnnsAAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.stripplot(x=\"label\", y=\"W\", data=cat_example);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In a strip plot, the scatterplot points will usually overlap. This makes it difficult to see the full distribution of data. One easy solution is to adjust the positions (only along the categorical axis) using some random “jitter”" ] }, { "cell_type": "code", "execution_count": 187, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 187, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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OQGf0XHn6WG6eeD0Pb3hMF4yXlF5Bg6eRrU07Yl7jolEVrD/2oa7spgnXsaBw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\nmIfKhg5320O3j8dtWWNGO0wiIroJXKs8gESHGXDrrCSPpA0ANU2dsqQ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.stripplot(x=\"label\", y=\"W\", data=cat_example, jitter=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A different approach would be to use the function `swarmplot()`, which positions each scatterplot point on the categorical axis with an algorithm that avoids overlapping points" ] }, { "cell_type": "code", "execution_count": 186, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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siiXrk+j3DZz0OrvexqBvSBW7JG8lt5So88y/L3+cip5DqtjXl9xNkV1s45jI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GJWXAGOXxxqHzshLAZqUY22/qFtlpsnFmP1lNKxnzRRiOIetwJFmXSyoA0ApxotsqANDDYgGc7l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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.swarmplot(x=\"label\", y=\"W\", data=cat_example);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It’s also possible to add a nested categorical variable with the hue parameter. From the graph below, it is quite clear to see that either the pace of the game has increased over time or teams have become more efficient in scoring." ] }, { "cell_type": "code", "execution_count": 194, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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4vEw1w9+OE70tD8+ol/heFEYj3rgngKvyV7O342BsAftyZwklydPGuVbC5WxR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\nwi8DS2dkc+/yKXz72U10OvoS+tx0RSErlsmSsUIMBenjHuN6vc6woA1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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.stripplot(x=\"label\", y=\"W\", hue=\"<2006\", data=cat_example, jitter=True);" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "That concludes this brief tutorial on extracting and visualizing NBA data extraction and visualization." ] } ], "metadata": { "anaconda-cloud": {}, "celltoolbar": "Raw Cell Format", "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }