NBA-Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jake Gluck - Capital News Service - jagluck.github.io\n",
    "\n",
    "NBA Twitter Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "from io import StringIO\n",
    "xrange = range\n",
    "from tokenize import generate_tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load twitter conv data gathered by the scraper\n",
    "df = pd.read_csv('/Users/jagluck/Documents/GitHub/nba-twitter/nba-convs.csv')\n",
    "\n",
    "NBACAPS = ['CAVS','OKCTHUNDER','CELTICS','NYKNICKS','BROOKLYNNETS','PELICANSNBA', 'PACERS', 'ORLANDOMAGIC','TIMBERWOLVES','MIAMIHEAT',\n",
    "'HORNETS', 'DETROITPISTONS', 'DALLASMAVS', 'LACLIPPERS', 'LAKERS', 'UTAHJAZZ', 'NUGGETS', 'WASHWIZARDS', 'CHICAGOBULLS',\n",
    "'SPURS', 'SUNS', 'HOUSTONROCKETS', 'WARRIORS', 'ATLHAWKS', 'MEMGRIZZ', 'BUCKS', 'RAPTORS', 'SACRAMENTOKINGS', 'SIXERS', 'TRAILBLAZERS']\n",
    "\n",
    "def parts(a):\n",
    "    \"\"\"Split a python-tokenizable expression on comma operators\"\"\"\n",
    "    compos = [-1] # compos stores the positions of the relevant commas in the argument string\n",
    "    compos.extend(t[2][1] for t in generate_tokens(StringIO(a).readline) if t[1] == ',')\n",
    "    compos.append(len(a))\n",
    "    return [ a[compos[i]+1:compos[i+1]] for i in xrange(len(compos)-1)]\n",
    "\n",
    "#coverts to list of strings\n",
    "def convertType(toConv, makeUpper):\n",
    "    \n",
    "    tcs = []\n",
    "    for tc in toConv:\n",
    "        chain = parts(tc)\n",
    "        \n",
    "        chain2 = []\n",
    "        for c in chain:\n",
    "            c = c.strip('[')\n",
    "            c = c.strip(']')\n",
    "            c = c.strip(' \\'')\n",
    "            c = c.strip('\\'')\n",
    "            c = c.strip('@')\n",
    "            chain2.append(c) \n",
    "        \n",
    "        #make uppercase if specified\n",
    "        chain3 = []\n",
    "        if makeUpper:\n",
    "            for c in chain2:\n",
    "                chain3.append(c.upper())\n",
    "            tcs.append(chain3)\n",
    "        else:\n",
    "            tcs.append(chain2)\n",
    "        \n",
    "    return np.array(tcs)\n",
    "\n",
    "\n",
    "# Helper: counts time value is in list of lists\n",
    "def countContains(toCount, data):\n",
    "    count = 0\n",
    "    for x in data:\n",
    "        if toCount in x:\n",
    "            count = count + 1\n",
    "    return count\n",
    "\n",
    "# Helper: stores value when value in list of list is found\n",
    "def countLengths(toCount, data1, data2):\n",
    "    count = []\n",
    "    for x, y in zip(data1, data2):\n",
    "        if toCount in x:\n",
    "            count.append(y)\n",
    "    return np.mean(count)\n",
    "    \n",
    "# convert types  \n",
    "df = df.drop(['Unnamed: 0'], axis=1)\n",
    "df = df.drop(['to'], axis=1)\n",
    "df = df.drop(['from'], axis=1)\n",
    "df['names'] = convertType(df['names'], False)\n",
    "df['usernames'] = convertType(df['usernames'], True)\n",
    "df['dates'] = convertType(df['dates'], True)\n",
    "df['tweets'] = convertType(df['tweets'], True)\n",
    "\n",
    "lens = []\n",
    "for ob in df[\"names\"]:\n",
    "    lens.append(len(ob))\n",
    "df[\"num_parties\"] = lens\n",
    "\n",
    "\n",
    "two_parties = df.loc[df[\"num_parties\"] == 2]\n",
    "sort = df.sort_values(by='num_parties', ascending=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1918.000000\n",
       "mean        2.839416\n",
       "std         2.154025\n",
       "min         2.000000\n",
       "25%         2.000000\n",
       "50%         2.000000\n",
       "75%         3.000000\n",
       "max        52.000000\n",
       "Name: size, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## What is the typical converation size? ##########\n",
    "df['size'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Participation 133.56666666666666\n",
      "5.710344827586207\n",
      "     team_1  team_2  convo_count\n",
      "355      23      20           50\n",
      "343      23       8           47\n",
      "243      13      29           37\n",
      "434      29      22           36\n",
      "429      28      20           36\n",
      "363      23      29           33\n",
      "359      23      25           32\n",
      "407      25      29           31\n",
      "362      23      28           29\n",
      "378      24      27           26\n",
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      "432      29      15           25\n",
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      "352      23      17           24\n",
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      "380      24      29           23\n",
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      "347      23      12           23\n",
      "348      23      13           23\n",
      "241      13      27           22\n",
      "372      24      16           22\n",
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      "105       4      29           22\n",
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      "346      23      11           16\n",
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      "273      16      20           13\n",
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      "201      11      29           12\n",
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      "23        0      27           12\n",
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      "38        1      29            7\n",
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      "375      24      20            6\n",
      "126       6      29            6\n",
      "100       4      24            6\n",
      "387      25       7            6\n",
      "6         0       8            6\n",
      "236      13      19            6\n",
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      "329      21      24            6\n",
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      "97        4      20            4\n",
      "8         0      10            4\n",
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      "10        0      12            4\n",
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      "298      18      24            4\n",
      "131       7      17            4\n",
      "215      12      17            3\n",
      "3         0       5            3\n",
      "210      12      11            3\n",
      "321      21      13            3\n",
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      "24        0      28            3\n",
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      "77        3      29            3\n",
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      "17        0      19            3\n",
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      "14        0      16            2\n",
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      "58        2      22            2\n",
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      "9         0      11            2\n",
      "82        4       5            2\n",
      "428      28      19            2\n",
      "69        3      15            2\n",
      "5         0       7            2\n",
      "190      11      15            2\n",
      "208      12       9            2\n",
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      "55        2      19            2\n",
      "52        2      16            2\n",
      "1         0       2            2\n",
      "216      12      19            2\n",
      "203      12       3            2\n",
      "229      13       7            2\n",
      "195      11      21            2\n",
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      "253      14      17            1\n",
      "344      23       9            1\n",
      "316      21       5            1\n",
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      "30        1      15            1\n",
      "96        4      19            1\n",
      "326      21      19            1\n",
      "394      25      14            1\n",
      "384      25       3            1\n",
      "92        4      15            1\n",
      "89        4      12            1\n",
      "87        4      10            1\n",
      "186      11       9            1\n",
      "365      24       3            1\n",
      "79        4       1            1\n",
      "76        3      28            1\n",
      "349      23      14            1\n",
      "247      14       6            1\n",
      "73        3      22            1\n",
      "4         0       6            1\n",
      "72        3      20            1\n",
      "70        3      17            1\n",
      "19        0      21            1\n",
      "86        4       9            1\n",
      "315      21       3            1\n",
      "54        2      18            1\n",
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      "181      11       3            1\n",
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      "46        2      10            1\n",
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      "2         0       3            0\n",
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      "50        2      14            0\n",
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      "0         0       1            0\n"
     ]
    }
   ],
   "source": [
    "### What teams Tweet the most #######\n",
    "names = []\n",
    "participations = []\n",
    "lengths = []\n",
    "for name in NBACAPS:\n",
    "    names.append(name)\n",
    "    participations.append(countContains(name,df['usernames']))\n",
    "    lengths.append(countLengths(name,df['usernames'],df['size']))\n",
    "\n",
    "teams = pd.DataFrame(\n",
    "        {'name': names,\n",
    "         'participation': participations,\n",
    "         'avg_lengths': lengths\n",
    "        })\n",
    "    \n",
    "teams = teams.sort_values(by='participation', ascending=False)\n",
    "#print(teams)\n",
    "print(\"Participation \" + str(np.mean(teams['participation'])))\n",
    "\n",
    "#What teams have the longest and shortest conversations\n",
    "teams = teams.sort_values(by='avg_lengths', ascending=False)\n",
    "#print(teams)\n",
    "\n",
    "#What teams talk to other teams the most/least? \n",
    "\n",
    "def countDuoConvos(name, name2):\n",
    "    count = 0\n",
    "    for x in df['usernames']:\n",
    "        if name in x:\n",
    "            if name2 in x:\n",
    "                count = count + 1\n",
    "    return count\n",
    " \n",
    "tos = []\n",
    "froms = []\n",
    "convo_counts = []\n",
    "\n",
    "for name in NBACAPS:\n",
    "    for name2 in NBACAPS:\n",
    "        if name != name2:\n",
    "            if name < name2:\n",
    "                tos.append(NBACAPS.index(name))\n",
    "                froms.append(NBACAPS.index(name2))\n",
    "                convo_counts.append(countDuoConvos(name, name2))\n",
    "                \n",
    "convos = pd.DataFrame(\n",
    "        {'team_1': tos,\n",
    "         'team_2': froms,\n",
    "         'convo_count': convo_counts\n",
    "        })\n",
    " \n",
    "convos = convos.sort_values(by='convo_count', ascending=False)       \n",
    "print(np.mean(convos['convo_count']))\n",
    "with pd.option_context('display.max_rows', None, 'display.max_columns', 3):\n",
    "    print(convos)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\jagluck\\appdata\\local\\programs\\python\\python36-32\\lib\\site-packages\\pandas\\core\\ops.py:1164: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  result = method(y)\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "invalid type comparison",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-040f53b8b476>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[0mconvo_percent\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconvo_percent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mby\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'percent'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mascending\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m \u001b[0mhawks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconvo_percent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mconvo_percent\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"from\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"ATLHAWKS\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     27\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moption_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'display.max_rows'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'display.max_columns'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconvo_percent\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\jagluck\\appdata\\local\\programs\\python\\python36-32\\lib\\site-packages\\pandas\\core\\ops.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, other, axis)\u001b[0m\n\u001b[0;32m   1251\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1252\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'ignore'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1253\u001b[1;33m                 \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mna_op\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1254\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1255\u001b[0m                 raise TypeError('Could not compare {typ} type with Series'\n",
      "\u001b[1;32mc:\\users\\jagluck\\appdata\\local\\programs\\python\\python36-32\\lib\\site-packages\\pandas\\core\\ops.py\u001b[0m in \u001b[0;36mna_op\u001b[1;34m(x, y)\u001b[0m\n\u001b[0;32m   1164\u001b[0m                     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1165\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1166\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"invalid type comparison\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1167\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1168\u001b[0m                 \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: invalid type comparison"
     ]
    }
   ],
   "source": [
    "# What percent of each team is sent to other teams\n",
    "\n",
    "team_name = []\n",
    "to_name = []\n",
    "percent = []\n",
    "for t_index, t_row in teams.iterrows():\n",
    "    total = t_row['participation']\n",
    "    name = t_row['name']\n",
    "    for c_index, c_row in convos.iterrows():\n",
    "        if (name == c_row['team_1']):\n",
    "            team_name.append(name)\n",
    "            to_name.append(c_row['team_2'])\n",
    "            count = c_row['convo_count']\n",
    "            if (count == 0):\n",
    "                percent.append(0)\n",
    "            else:\n",
    "                percent.append(c_row['convo_count']/total)\n",
    "            \n",
    "convo_percent = pd.DataFrame(\n",
    "        {'from': team_name,\n",
    "         'to': to_name,\n",
    "         'percent': percent\n",
    "        })\n",
    "        \n",
    "convo_percent = convo_percent.sort_values(by='percent', ascending=False)  \n",
    "hawks = convo_percent.loc[convo_percent[\"from\"] == \"ATLHAWKS\"]\n",
    "with pd.option_context('display.max_rows', None, 'display.max_columns', 3):\n",
    "    print(convo_percent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# How has the number of conversations changed over time?\n",
    "\n",
    "years = []\n",
    "for index, row in df.iterrows():\n",
    "    year = row['dates'][0][-4:]\n",
    "    years.append(year)\n",
    "\n",
    "output = set()\n",
    "for x in years:\n",
    "    output.add(x)\n",
    "output = sorted(list(output))\n",
    "f_years = []\n",
    "counts = []\n",
    "\n",
    "for x in output:\n",
    "    f_years.append(x)\n",
    "    counts.append(years.count(x))\n",
    "    \n",
    "year_count = pd.DataFrame(\n",
    "        {'year': f_years,\n",
    "         'count': counts\n",
    "        }) \n",
    "        \n",
    "print(year_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# What was the language of the tweets like/ \\sentiment analysis\n",
    "\n",
    "tweets = df['tweets']\n",
    "names = df['usernames']\n",
    "text = []\n",
    "team = []\n",
    "\n",
    "for tweet, name in zip(tweets,names):\n",
    "\n",
    "    for c in tweet:\n",
    "        text.append(c)\n",
    "        \n",
    "    for n in name:\n",
    "        team.append(n)\n",
    "\n",
    "neg = []\n",
    "pos = []\n",
    "neu = []\n",
    "comp = []\n",
    "\n",
    "analyzer = SentimentIntensityAnalyzer()\n",
    "\n",
    "for t in text:\n",
    "   vs = analyzer.polarity_scores(t)\n",
    "   neg.append(vs['neg'])\n",
    "   pos.append(vs['pos'])\n",
    "   neu.append(vs['neu'])\n",
    "   comp.append(vs['compound'])\n",
    "\n",
    "all_tweets = pd.DataFrame(\n",
    "        {'text': text,\n",
    "         'team': team,\n",
    "         'neg' : neg,\n",
    "         'pos' : pos,\n",
    "         'neu' : neu,\n",
    "         'comp' : comp\n",
    "        }) \n",
    "\n",
    "all_tweets=all_tweets[all_tweets['team'].isin(NBACAPS)]\n",
    "\n",
    "print(\"Pos Avg: \" + str(all_tweets['pos'].mean()))\n",
    "print(\"Pos Max: \" + str(all_tweets['pos'].max()))\n",
    "\n",
    "print(\"Neg Avg: \" + str(all_tweets['neg'].mean()))\n",
    "print(\"Neg Max: \" + str(all_tweets['neg'].max()))\n",
    "\n",
    "print(\"Neu Avg: \" + str(all_tweets['neu'].mean()))\n",
    "\n",
    "print(\"Comp Avg: \" + str(all_tweets['comp'].mean()))\n",
    "\n",
    "negative = all_tweets[all_tweets['neg'] == 1]\n",
    "\n",
    "print(\"\\nMost Negative Tweets\")\n",
    "print(negative)\n",
    "\n",
    "positive = all_tweets[all_tweets['pos'] == 1]\n",
    "print(\"\\nMost Positive Tweets\")\n",
    "print(positive)\n",
    "\n",
    "pos_groups = all_tweets.groupby('team')['pos'].mean()\n",
    "pos_groups = pos_groups.sort_values(ascending=False)\n",
    "print(\"\\nTeam Pos Avg\")\n",
    "print(pos_groups)\n",
    "\n",
    "neg_groups = all_tweets.groupby('team')['neg'].mean()\n",
    "neg_groups = neg_groups.sort_values(ascending=False)\n",
    "print(\"\\nTeam Neg Avg\")\n",
    "print(neg_groups)\n",
    "\n",
    "comp_groups = all_tweets.groupby('team')['comp'].mean()\n",
    "comp_groups = comp_groups.sort_values(ascending=False)\n",
    "print(\"\\nTeam Comp Avg\")\n",
    "print(comp_groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
   },
   "outputs": [],
   "source": []
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