caucasianjames.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of @CaucasianJames's Tweets\n",
    "\n",
    "Medium article written using below code: https://medium.com/@bryan.pfalzgraf/utilizing-twint-and-textblob-for-scraping-and-sentimenet-analysis-of-caucasianjamess-tweets-c1c548f52bbb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import datetime as dt\n",
    "from textblob import TextBlob\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "plt.style.use('fivethirtyeight')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"caucasianjames_tweets.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>conversation_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
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       "      <th>tweet</th>\n",
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       "      <th>urls</th>\n",
       "      <th>photos</th>\n",
       "      <th>replies_count</th>\n",
       "      <th>retweets_count</th>\n",
       "      <th>likes_count</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>cashtags</th>\n",
       "      <th>link</th>\n",
       "      <th>retweet</th>\n",
       "      <th>quote_url</th>\n",
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       "      <td>James</td>\n",
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       "      <td>working on it 🙄😒</td>\n",
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       "      <td>glad zoboomafoo isn’t alive to see all this</td>\n",
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       "<p>12994 rows × 34 columns</p>\n",
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      "text/plain": [
       "                        id      conversation_id     created_at        date  \\\n",
       "0      1242640549108219911  1242640549108219911  1585103568000  2020-03-24   \n",
       "1      1242635082113978368  1242632030334136321  1585102265000  2020-03-24   \n",
       "2      1242622804744421381  1242622804744421381  1585099338000  2020-03-24   \n",
       "3      1242591295639519242  1242546715263205378  1585091825000  2020-03-24   \n",
       "4      1242553830031740928  1242546715263205378  1585082893000  2020-03-24   \n",
       "...                    ...                  ...            ...         ...   \n",
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       "\n",
       "           time timezone    user_id        username   name place  \\\n",
       "0      22:32:48      EDT  272985460  caucasianjames  James   NaN   \n",
       "1      22:11:05      EDT  272985460  caucasianjames  James   NaN   \n",
       "2      21:22:18      EDT  272985460  caucasianjames  James   NaN   \n",
       "3      19:17:05      EDT  272985460  caucasianjames  James   NaN   \n",
       "4      16:48:13      EDT  272985460  caucasianjames  James   NaN   \n",
       "...         ...      ...        ...             ...    ...   ...   \n",
       "12989  22:46:01      EDT  272985460  caucasianjames  James   NaN   \n",
       "12990  22:31:45      EDT  272985460  caucasianjames  James   NaN   \n",
       "12991  13:50:21      EDT  272985460  caucasianjames  James   NaN   \n",
       "12992  23:29:38      EDT  272985460  caucasianjames  James   NaN   \n",
       "12993  12:04:44      EDT  272985460  caucasianjames  James   NaN   \n",
       "\n",
       "                                                   tweet            mentions  \\\n",
       "0      if i have an open casket funeral u can take se...                  []   \n",
       "1                                       working on it 🙄😒         ['pammon_']   \n",
       "2            glad zoboomafoo isn’t alive to see all this                  []   \n",
       "3                                            facetime me    ['youngfishgod']   \n",
       "4                                                    lol     ['themopepope']   \n",
       "...                                                  ...                 ...   \n",
       "12989  @samkrabbenhoft it was on my iPod cause I thin...  ['samkrabbenhoft']   \n",
       "12990  Perfect twit pic for @KillaQwaybin   http://yf...                  []   \n",
       "12991           Chuckin down krispy creams in study hall                  []   \n",
       "12992  Shout out to my only 2 followers. Me and Andre...                  []   \n",
       "12993  Throwin up my first twitter bomb. To bad I don...                  []   \n",
       "\n",
       "                                urls photos  replies_count  retweets_count  \\\n",
       "0                                 []     []             23             917   \n",
       "1                                 []     []              0               1   \n",
       "2                                 []     []             87            7609   \n",
       "3                                 []     []              1               1   \n",
       "4                                 []     []              1               0   \n",
       "...                              ...    ...            ...             ...   \n",
       "12989                             []     []              0               0   \n",
       "12990  ['http://yfrog.com/h8n2hnkj']     []              0               1   \n",
       "12991                             []     []              0               0   \n",
       "12992                             []     []              0               0   \n",
       "12993                             []     []             10              17   \n",
       "\n",
       "       likes_count hashtags cashtags  \\\n",
       "0             8558       []       []   \n",
       "1                4       []       []   \n",
       "2            34066       []       []   \n",
       "3               98       []       []   \n",
       "4               87       []       []   \n",
       "...            ...      ...      ...   \n",
       "12989            0       []       []   \n",
       "12990            1       []       []   \n",
       "12991            3       []       []   \n",
       "12992            0       []       []   \n",
       "12993          256       []       []   \n",
       "\n",
       "                                                    link  retweet quote_url  \\\n",
       "0      https://twitter.com/CaucasianJames/status/1242...    False       NaN   \n",
       "1      https://twitter.com/CaucasianJames/status/1242...    False       NaN   \n",
       "2      https://twitter.com/CaucasianJames/status/1242...    False       NaN   \n",
       "3      https://twitter.com/CaucasianJames/status/1242...    False       NaN   \n",
       "4      https://twitter.com/CaucasianJames/status/1242...    False       NaN   \n",
       "...                                                  ...      ...       ...   \n",
       "12989  https://twitter.com/CaucasianJames/status/5292...    False       NaN   \n",
       "12990  https://twitter.com/CaucasianJames/status/5292...    False       NaN   \n",
       "12991  https://twitter.com/CaucasianJames/status/5278...    False       NaN   \n",
       "12992  https://twitter.com/CaucasianJames/status/5221...    False       NaN   \n",
       "12993  https://twitter.com/CaucasianJames/status/5203...    False       NaN   \n",
       "\n",
       "       video  near  geo  source  user_rt_id  user_rt  retweet_id  \\\n",
       "0          0   NaN  NaN     NaN         NaN      NaN         NaN   \n",
       "1          0   NaN  NaN     NaN         NaN      NaN         NaN   \n",
       "2          0   NaN  NaN     NaN         NaN      NaN         NaN   \n",
       "3          0   NaN  NaN     NaN         NaN      NaN         NaN   \n",
       "4          0   NaN  NaN     NaN         NaN      NaN         NaN   \n",
       "...      ...   ...  ...     ...         ...      ...         ...   \n",
       "12989      0   NaN  NaN     NaN         NaN      NaN         NaN   \n",
       "12990      0   NaN  NaN     NaN         NaN      NaN         NaN   \n",
       "12991      0   NaN  NaN     NaN         NaN      NaN         NaN   \n",
       "12992      0   NaN  NaN     NaN         NaN      NaN         NaN   \n",
       "12993      0   NaN  NaN     NaN         NaN      NaN         NaN   \n",
       "\n",
       "                                                reply_to  retweet_date  \\\n",
       "0      [{'user_id': '272985460', 'username': 'Caucasi...           NaN   \n",
       "1      [{'user_id': '272985460', 'username': 'Caucasi...           NaN   \n",
       "2      [{'user_id': '272985460', 'username': 'Caucasi...           NaN   \n",
       "3      [{'user_id': '272985460', 'username': 'Caucasi...           NaN   \n",
       "4      [{'user_id': '272985460', 'username': 'Caucasi...           NaN   \n",
       "...                                                  ...           ...   \n",
       "12989  [{'user_id': '272985460', 'username': 'Caucasi...           NaN   \n",
       "12990  [{'user_id': '272985460', 'username': 'Caucasi...           NaN   \n",
       "12991  [{'user_id': '272985460', 'username': 'Caucasi...           NaN   \n",
       "12992  [{'user_id': '272985460', 'username': 'Caucasi...           NaN   \n",
       "12993  [{'user_id': '272985460', 'username': 'Caucasi...           NaN   \n",
       "\n",
       "       translate  trans_src  trans_dest  \n",
       "0            NaN        NaN         NaN  \n",
       "1            NaN        NaN         NaN  \n",
       "2            NaN        NaN         NaN  \n",
       "3            NaN        NaN         NaN  \n",
       "4            NaN        NaN         NaN  \n",
       "...          ...        ...         ...  \n",
       "12989        NaN        NaN         NaN  \n",
       "12990        NaN        NaN         NaN  \n",
       "12991        NaN        NaN         NaN  \n",
       "12992        NaN        NaN         NaN  \n",
       "12993        NaN        NaN         NaN  \n",
       "\n",
       "[12994 rows x 34 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_colwidth', -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# acquires both tweet polarity and subjectivity\n",
    "df['sentiment'] = df['tweet'].apply(lambda tweet: TextBlob(tweet).sentiment) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only polarity\n",
    "df['polarity_score'] = df['tweet'].apply(lambda tweet: TextBlob(tweet).sentiment.polarity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['polarity'] = df['polarity_score'].apply(lambda x: 'positive' if x > 0 else ('negative' if x < 0 else 'neutral'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neutral     6837\n",
       "positive    4319\n",
       "negative    1838\n",
       "Name: polarity, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['polarity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08351060261269887"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['polarity_score'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only subjectivity\n",
    "df['subjectivity_score'] = df['tweet'].apply(lambda tweet: TextBlob(tweet).sentiment.subjectivity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['subjectivity'] = df['subjectivity_score'].apply(lambda x: 'subjective' if x>0.5 else ('objective' if x < 0.5 else 'neutral'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "objective     8633\n",
       "subjective    3832\n",
       "neutral       529 \n",
       "Name: subjectivity, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['subjectivity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>likes_count</th>\n",
       "      <th>polarity_score</th>\n",
       "      <th>tweet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2019-09-11</td>\n",
       "      <td>316116</td>\n",
       "      <td>-0.038571</td>\n",
       "      <td>high school girls are in biology right now caffeinated up from their iced coffee saying shit like “we stan the cytoplasm” these poor teachers man</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-06-15</td>\n",
       "      <td>273848</td>\n",
       "      <td>0.211111</td>\n",
       "      <td>black eyed peas: i gotta feeling that tonight’s gonna be a good night \\n\\n13 year old me: 😎😎😎</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-05-05</td>\n",
       "      <td>273779</td>\n",
       "      <td>0.175000</td>\n",
       "      <td>not having to bring my backpack to the last day of elementary school was the last time i truly felt free</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-11-25</td>\n",
       "      <td>268877</td>\n",
       "      <td>0.080804</td>\n",
       "      <td>if i vote on ur instagram poll it doesn’t mean we’re cool. i’m simply just exercising my right to vote</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-04-04</td>\n",
       "      <td>254625</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>white girls will literally boomerang their grandma being lowered into her grave</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-08-06</td>\n",
       "      <td>252636</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>coffee: chugged \\n\\nhomies: hugged</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>248375</td>\n",
       "      <td>-0.400000</td>\n",
       "      <td>fuck it. i’m going out tonight pic.twitter.com/2AWvckqz47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>236328</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>this is the photo that pops up when u click student life on a college website https://twitter.com/euphoriaHBO/status/1237772164994121729 …</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>234210</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>hey i noticed ur bf didn’t make a national girlfriend day post...u guys good? u can come over later and talk about it if u want aha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-03-03</td>\n",
       "      <td>225389</td>\n",
       "      <td>-0.080000</td>\n",
       "      <td>wiz khalifa: so what we get drunk so what we smoke weed \\n\\ncompletely sober high school me that had a 10:30 pm curfew: 😎😎😎</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            likes_count  polarity_score  \\\n",
       "date                                      \n",
       "2019-09-11  316116      -0.038571         \n",
       "2018-06-15  273848       0.211111         \n",
       "2019-05-05  273779       0.175000         \n",
       "2019-11-25  268877       0.080804         \n",
       "2018-04-04  254625       0.000000         \n",
       "2018-08-06  252636       0.000000         \n",
       "2019-12-31  248375      -0.400000         \n",
       "2020-03-11  236328       0.000000         \n",
       "2019-08-01  234210       0.350000         \n",
       "2019-03-03  225389      -0.080000         \n",
       "\n",
       "                                                                                                                                                        tweet  \n",
       "date                                                                                                                                                           \n",
       "2019-09-11  high school girls are in biology right now caffeinated up from their iced coffee saying shit like “we stan the cytoplasm” these poor teachers man  \n",
       "2018-06-15  black eyed peas: i gotta feeling that tonight’s gonna be a good night \\n\\n13 year old me: 😎😎😎                                                      \n",
       "2019-05-05  not having to bring my backpack to the last day of elementary school was the last time i truly felt free                                           \n",
       "2019-11-25  if i vote on ur instagram poll it doesn’t mean we’re cool. i’m simply just exercising my right to vote                                             \n",
       "2018-04-04  white girls will literally boomerang their grandma being lowered into her grave                                                                    \n",
       "2018-08-06  coffee: chugged \\n\\nhomies: hugged                                                                                                                 \n",
       "2019-12-31  fuck it. i’m going out tonight pic.twitter.com/2AWvckqz47                                                                                          \n",
       "2020-03-11  this is the photo that pops up when u click student life on a college website https://twitter.com/euphoriaHBO/status/1237772164994121729 …         \n",
       "2019-08-01  hey i noticed ur bf didn’t make a national girlfriend day post...u guys good? u can come over later and talk about it if u want aha                \n",
       "2019-03-03  wiz khalifa: so what we get drunk so what we smoke weed \\n\\ncompletely sober high school me that had a 10:30 pm curfew: 😎😎😎                        "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort the likes in order\n",
    "likesorder=df[['date','likes_count','polarity_score','tweet']].sort_values(by=['likes_count'],ascending=False)\n",
    "likesorder.set_index('date').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to split up words of each tweet\n",
    "def clean_text(text):\n",
    "    tweet = re.sub(\"(@_?[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)\",\" \",text).split() # removes special characters\n",
    "    filtered_words = [word for word in tweet if word not in stopwords.words('english')] # acquires non-stopwords words\n",
    "    return filtered_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['tweet_clean'] = df['tweet'].map(lambda x: clean_text(str(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# acquires word counts\n",
    "word_freq=df['tweet_clean'].apply(lambda x: pd.value_counts(x)).sum(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rc('xtick', labelsize=12) \n",
    "plt.rc('ytick', labelsize=16) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# graph top words, skips past top 3 because those are pic twitter and com from pic.twitter.com being part\n",
    "# of every tweet that includes a photo\n",
    "imp_word=word_freq.sort_values(ascending=False)[3:28].sort_values(ascending=True)\n",
    "\n",
    "imp_word.plot(kind = \"barh\",figsize=(10,8))\n",
    "plt.title(\"Top 25 Words Frequency\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# acquires mention counts\n",
    "mentions_freq=df['mentions'].apply(lambda x: pd.value_counts(x)).sum(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]                   15990.0\n",
       "['michaelaokla']     1616.0 \n",
       "['caucasianjame']    1394.0 \n",
       "['pissboymcgee']     1376.0 \n",
       "['captainkalvis']    1122.0 \n",
       "                      ...   \n",
       "['jvn']              7.0    \n",
       "['nba']              7.0    \n",
       "['god']              7.0    \n",
       "['fbi']              7.0    \n",
       "['fcc']              7.0    \n",
       "Length: 2129, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mentions_freq.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imp_mentions=mentions_freq.sort_values(ascending=False)[1:26].sort_values(ascending=True)\n",
    "\n",
    "imp_mentions.plot(kind = \"barh\",figsize=(10,8))\n",
    "plt.title(\"Top 25 User Mentions\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EDT    12994\n",
       "Name: timezone, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# establish timezone of tweets is all EDT\n",
    "df.timezone.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    12994\n",
       "Name: retweet, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.retweet.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Datetime'] = pd.to_datetime(df['date'] + ' ' + df['time'])\n",
    "df = df.set_index('Datetime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>conversation_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>timezone</th>\n",
       "      <th>user_id</th>\n",
       "      <th>username</th>\n",
       "      <th>name</th>\n",
       "      <th>place</th>\n",
       "      <th>tweet</th>\n",
       "      <th>mentions</th>\n",
       "      <th>urls</th>\n",
       "      <th>photos</th>\n",
       "      <th>replies_count</th>\n",
       "      <th>retweets_count</th>\n",
       "      <th>likes_count</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>cashtags</th>\n",
       "      <th>link</th>\n",
       "      <th>retweet</th>\n",
       "      <th>quote_url</th>\n",
       "      <th>video</th>\n",
       "      <th>near</th>\n",
       "      <th>geo</th>\n",
       "      <th>source</th>\n",
       "      <th>user_rt_id</th>\n",
       "      <th>user_rt</th>\n",
       "      <th>retweet_id</th>\n",
       "      <th>reply_to</th>\n",
       "      <th>retweet_date</th>\n",
       "      <th>translate</th>\n",
       "      <th>trans_src</th>\n",
       "      <th>trans_dest</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>polarity_score</th>\n",
       "      <th>polarity</th>\n",
       "      <th>subjectivity_score</th>\n",
       "      <th>subjectivity</th>\n",
       "      <th>tweet_clean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2011-03-27 12:04:44</td>\n",
       "      <td>52038352874127361</td>\n",
       "      <td>52038352874127361</td>\n",
       "      <td>1301241884000</td>\n",
       "      <td>2011-03-27</td>\n",
       "      <td>12:04:44</td>\n",
       "      <td>EDT</td>\n",
       "      <td>272985460</td>\n",
       "      <td>caucasianjames</td>\n",
       "      <td>James</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Throwin up my first twitter bomb. To bad I don't have any followers. Help me out wheat thins.</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>256</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>https://twitter.com/CaucasianJames/status/52038352874127361</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'user_id': '272985460', 'username': 'CaucasianJames'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(-0.22499999999999992, 0.5)</td>\n",
       "      <td>-0.225000</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>neutral</td>\n",
       "      <td>[Throwin, first, twitter, bomb, To, bad, I, followers, Help, wheat, thins]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-03-27 23:29:38</td>\n",
       "      <td>52210714185699329</td>\n",
       "      <td>52210714185699329</td>\n",
       "      <td>1301282978000</td>\n",
       "      <td>2011-03-27</td>\n",
       "      <td>23:29:38</td>\n",
       "      <td>EDT</td>\n",
       "      <td>272985460</td>\n",
       "      <td>caucasianjames</td>\n",
       "      <td>James</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Shout out to my only 2 followers. Me and Andrew are going to bed while you guys soak up the spring break life at Mary joes or cindys.</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>https://twitter.com/CaucasianJames/status/52210714185699329</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'user_id': '272985460', 'username': 'CaucasianJames'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(0.0, 1.0)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>subjective</td>\n",
       "      <td>[Shout, 2, followers, Me, Andrew, going, bed, guys, soak, spring, break, life, Mary, joes, cindys]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-03-29 13:50:21</td>\n",
       "      <td>52789706978635776</td>\n",
       "      <td>52789706978635776</td>\n",
       "      <td>1301421021000</td>\n",
       "      <td>2011-03-29</td>\n",
       "      <td>13:50:21</td>\n",
       "      <td>EDT</td>\n",
       "      <td>272985460</td>\n",
       "      <td>caucasianjames</td>\n",
       "      <td>James</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chuckin down krispy creams in study hall</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>https://twitter.com/CaucasianJames/status/52789706978635776</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'user_id': '272985460', 'username': 'CaucasianJames'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(-0.15555555555555559, 0.2888888888888889)</td>\n",
       "      <td>-0.155556</td>\n",
       "      <td>negative</td>\n",
       "      <td>0.288889</td>\n",
       "      <td>objective</td>\n",
       "      <td>[Chuckin, krispy, creams, study, hall]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-03-29 22:31:45</td>\n",
       "      <td>52920919366176768</td>\n",
       "      <td>52920919366176768</td>\n",
       "      <td>1301452305000</td>\n",
       "      <td>2011-03-29</td>\n",
       "      <td>22:31:45</td>\n",
       "      <td>EDT</td>\n",
       "      <td>272985460</td>\n",
       "      <td>caucasianjames</td>\n",
       "      <td>James</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Perfect twit pic for @KillaQwaybin   http://yfrog.com/h8n2hnkj</td>\n",
       "      <td>[]</td>\n",
       "      <td>['http://yfrog.com/h8n2hnkj']</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>https://twitter.com/CaucasianJames/status/52920919366176768</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'user_id': '272985460', 'username': 'CaucasianJames'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(1.0, 1.0)</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>positive</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>subjective</td>\n",
       "      <td>[Perfect, twit, pic]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-03-29 22:46:01</td>\n",
       "      <td>52924510390845440</td>\n",
       "      <td>52921200032231424</td>\n",
       "      <td>1301453161000</td>\n",
       "      <td>2011-03-29</td>\n",
       "      <td>22:46:01</td>\n",
       "      <td>EDT</td>\n",
       "      <td>272985460</td>\n",
       "      <td>caucasianjames</td>\n",
       "      <td>James</td>\n",
       "      <td>NaN</td>\n",
       "      <td>@samkrabbenhoft it was on my iPod cause I think I put it on fbook at one point. Pic will definitely get him followers</td>\n",
       "      <td>['samkrabbenhoft']</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>https://twitter.com/CaucasianJames/status/52924510390845440</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'user_id': '272985460', 'username': 'CaucasianJames'}, {'user_id': '273707765', 'username': 'samkrabbenhoft'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(0.0, 0.5)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>neutral</td>\n",
       "      <td>[iPod, cause, I, think, I, put, fbook, one, point, Pic, definitely, get, followers]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    id    conversation_id     created_at  \\\n",
       "Datetime                                                                   \n",
       "2011-03-27 12:04:44  52038352874127361  52038352874127361  1301241884000   \n",
       "2011-03-27 23:29:38  52210714185699329  52210714185699329  1301282978000   \n",
       "2011-03-29 13:50:21  52789706978635776  52789706978635776  1301421021000   \n",
       "2011-03-29 22:31:45  52920919366176768  52920919366176768  1301452305000   \n",
       "2011-03-29 22:46:01  52924510390845440  52921200032231424  1301453161000   \n",
       "\n",
       "                           date      time timezone    user_id        username  \\\n",
       "Datetime                                                                        \n",
       "2011-03-27 12:04:44  2011-03-27  12:04:44  EDT      272985460  caucasianjames   \n",
       "2011-03-27 23:29:38  2011-03-27  23:29:38  EDT      272985460  caucasianjames   \n",
       "2011-03-29 13:50:21  2011-03-29  13:50:21  EDT      272985460  caucasianjames   \n",
       "2011-03-29 22:31:45  2011-03-29  22:31:45  EDT      272985460  caucasianjames   \n",
       "2011-03-29 22:46:01  2011-03-29  22:46:01  EDT      272985460  caucasianjames   \n",
       "\n",
       "                      name place  \\\n",
       "Datetime                           \n",
       "2011-03-27 12:04:44  James  NaN    \n",
       "2011-03-27 23:29:38  James  NaN    \n",
       "2011-03-29 13:50:21  James  NaN    \n",
       "2011-03-29 22:31:45  James  NaN    \n",
       "2011-03-29 22:46:01  James  NaN    \n",
       "\n",
       "                                                                                                                                                     tweet  \\\n",
       "Datetime                                                                                                                                                     \n",
       "2011-03-27 12:04:44  Throwin up my first twitter bomb. To bad I don't have any followers. Help me out wheat thins.                                           \n",
       "2011-03-27 23:29:38  Shout out to my only 2 followers. Me and Andrew are going to bed while you guys soak up the spring break life at Mary joes or cindys.   \n",
       "2011-03-29 13:50:21  Chuckin down krispy creams in study hall                                                                                                \n",
       "2011-03-29 22:31:45  Perfect twit pic for @KillaQwaybin   http://yfrog.com/h8n2hnkj                                                                          \n",
       "2011-03-29 22:46:01  @samkrabbenhoft it was on my iPod cause I think I put it on fbook at one point. Pic will definitely get him followers                   \n",
       "\n",
       "                               mentions                           urls photos  \\\n",
       "Datetime                                                                        \n",
       "2011-03-27 12:04:44  []                  []                             []      \n",
       "2011-03-27 23:29:38  []                  []                             []      \n",
       "2011-03-29 13:50:21  []                  []                             []      \n",
       "2011-03-29 22:31:45  []                  ['http://yfrog.com/h8n2hnkj']  []      \n",
       "2011-03-29 22:46:01  ['samkrabbenhoft']  []                             []      \n",
       "\n",
       "                     replies_count  retweets_count  likes_count hashtags  \\\n",
       "Datetime                                                                   \n",
       "2011-03-27 12:04:44  10             17              256          []        \n",
       "2011-03-27 23:29:38  0              0               0            []        \n",
       "2011-03-29 13:50:21  0              0               3            []        \n",
       "2011-03-29 22:31:45  0              1               1            []        \n",
       "2011-03-29 22:46:01  0              0               0            []        \n",
       "\n",
       "                    cashtags  \\\n",
       "Datetime                       \n",
       "2011-03-27 12:04:44  []        \n",
       "2011-03-27 23:29:38  []        \n",
       "2011-03-29 13:50:21  []        \n",
       "2011-03-29 22:31:45  []        \n",
       "2011-03-29 22:46:01  []        \n",
       "\n",
       "                                                                            link  \\\n",
       "Datetime                                                                           \n",
       "2011-03-27 12:04:44  https://twitter.com/CaucasianJames/status/52038352874127361   \n",
       "2011-03-27 23:29:38  https://twitter.com/CaucasianJames/status/52210714185699329   \n",
       "2011-03-29 13:50:21  https://twitter.com/CaucasianJames/status/52789706978635776   \n",
       "2011-03-29 22:31:45  https://twitter.com/CaucasianJames/status/52920919366176768   \n",
       "2011-03-29 22:46:01  https://twitter.com/CaucasianJames/status/52924510390845440   \n",
       "\n",
       "                     retweet quote_url  video  near  geo  source  user_rt_id  \\\n",
       "Datetime                                                                       \n",
       "2011-03-27 12:04:44  False    NaN       0     NaN   NaN  NaN     NaN           \n",
       "2011-03-27 23:29:38  False    NaN       0     NaN   NaN  NaN     NaN           \n",
       "2011-03-29 13:50:21  False    NaN       0     NaN   NaN  NaN     NaN           \n",
       "2011-03-29 22:31:45  False    NaN       0     NaN   NaN  NaN     NaN           \n",
       "2011-03-29 22:46:01  False    NaN       0     NaN   NaN  NaN     NaN           \n",
       "\n",
       "                     user_rt  retweet_id  \\\n",
       "Datetime                                   \n",
       "2011-03-27 12:04:44 NaN      NaN           \n",
       "2011-03-27 23:29:38 NaN      NaN           \n",
       "2011-03-29 13:50:21 NaN      NaN           \n",
       "2011-03-29 22:31:45 NaN      NaN           \n",
       "2011-03-29 22:46:01 NaN      NaN           \n",
       "\n",
       "                                                                                                                             reply_to  \\\n",
       "Datetime                                                                                                                                \n",
       "2011-03-27 12:04:44  [{'user_id': '272985460', 'username': 'CaucasianJames'}]                                                           \n",
       "2011-03-27 23:29:38  [{'user_id': '272985460', 'username': 'CaucasianJames'}]                                                           \n",
       "2011-03-29 13:50:21  [{'user_id': '272985460', 'username': 'CaucasianJames'}]                                                           \n",
       "2011-03-29 22:31:45  [{'user_id': '272985460', 'username': 'CaucasianJames'}]                                                           \n",
       "2011-03-29 22:46:01  [{'user_id': '272985460', 'username': 'CaucasianJames'}, {'user_id': '273707765', 'username': 'samkrabbenhoft'}]   \n",
       "\n",
       "                     retweet_date  translate  trans_src  trans_dest  \\\n",
       "Datetime                                                              \n",
       "2011-03-27 12:04:44 NaN           NaN        NaN        NaN           \n",
       "2011-03-27 23:29:38 NaN           NaN        NaN        NaN           \n",
       "2011-03-29 13:50:21 NaN           NaN        NaN        NaN           \n",
       "2011-03-29 22:31:45 NaN           NaN        NaN        NaN           \n",
       "2011-03-29 22:46:01 NaN           NaN        NaN        NaN           \n",
       "\n",
       "                                                      sentiment  \\\n",
       "Datetime                                                          \n",
       "2011-03-27 12:04:44  (-0.22499999999999992, 0.5)                  \n",
       "2011-03-27 23:29:38  (0.0, 1.0)                                   \n",
       "2011-03-29 13:50:21  (-0.15555555555555559, 0.2888888888888889)   \n",
       "2011-03-29 22:31:45  (1.0, 1.0)                                   \n",
       "2011-03-29 22:46:01  (0.0, 0.5)                                   \n",
       "\n",
       "                     polarity_score  polarity  subjectivity_score  \\\n",
       "Datetime                                                            \n",
       "2011-03-27 12:04:44 -0.225000        negative  0.500000             \n",
       "2011-03-27 23:29:38  0.000000        neutral   1.000000             \n",
       "2011-03-29 13:50:21 -0.155556        negative  0.288889             \n",
       "2011-03-29 22:31:45  1.000000        positive  1.000000             \n",
       "2011-03-29 22:46:01  0.000000        neutral   0.500000             \n",
       "\n",
       "                    subjectivity  \\\n",
       "Datetime                           \n",
       "2011-03-27 12:04:44  neutral       \n",
       "2011-03-27 23:29:38  subjective    \n",
       "2011-03-29 13:50:21  objective     \n",
       "2011-03-29 22:31:45  subjective    \n",
       "2011-03-29 22:46:01  neutral       \n",
       "\n",
       "                                                                                                            tweet_clean  \n",
       "Datetime                                                                                                                 \n",
       "2011-03-27 12:04:44  [Throwin, first, twitter, bomb, To, bad, I, followers, Help, wheat, thins]                          \n",
       "2011-03-27 23:29:38  [Shout, 2, followers, Me, Andrew, going, bed, guys, soak, spring, break, life, Mary, joes, cindys]  \n",
       "2011-03-29 13:50:21  [Chuckin, krispy, creams, study, hall]                                                              \n",
       "2011-03-29 22:31:45  [Perfect, twit, pic]                                                                                \n",
       "2011-03-29 22:46:01  [iPod, cause, I, think, I, put, fbook, one, point, Pic, definitely, get, followers]                 "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>replies_count</th>\n",
       "      <th>retweets_count</th>\n",
       "      <th>likes_count</th>\n",
       "      <th>polarity_score</th>\n",
       "      <th>subjectivity_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>12.044349</td>\n",
       "      <td>648.137339</td>\n",
       "      <td>5675.443491</td>\n",
       "      <td>0.100492</td>\n",
       "      <td>0.255648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6.934932</td>\n",
       "      <td>362.078767</td>\n",
       "      <td>2883.143836</td>\n",
       "      <td>0.089879</td>\n",
       "      <td>0.278338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.716049</td>\n",
       "      <td>33.185185</td>\n",
       "      <td>433.555556</td>\n",
       "      <td>0.082899</td>\n",
       "      <td>0.267629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.730769</td>\n",
       "      <td>18.307692</td>\n",
       "      <td>146.038462</td>\n",
       "      <td>0.073436</td>\n",
       "      <td>0.305080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.045536</td>\n",
       "      <td>0.511786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.183333</td>\n",
       "      <td>0.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>0.101815</td>\n",
       "      <td>0.182817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.543478</td>\n",
       "      <td>2.847826</td>\n",
       "      <td>102.500000</td>\n",
       "      <td>0.101169</td>\n",
       "      <td>0.218659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>3.548387</td>\n",
       "      <td>254.198925</td>\n",
       "      <td>2009.629032</td>\n",
       "      <td>0.138773</td>\n",
       "      <td>0.323359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>16.618357</td>\n",
       "      <td>1571.508857</td>\n",
       "      <td>9001.969404</td>\n",
       "      <td>0.118904</td>\n",
       "      <td>0.328735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>15.848997</td>\n",
       "      <td>994.379092</td>\n",
       "      <td>9691.560718</td>\n",
       "      <td>0.096370</td>\n",
       "      <td>0.323416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>22.622040</td>\n",
       "      <td>999.511840</td>\n",
       "      <td>9087.638434</td>\n",
       "      <td>0.081877</td>\n",
       "      <td>0.313400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>11.792857</td>\n",
       "      <td>800.837500</td>\n",
       "      <td>7481.005357</td>\n",
       "      <td>0.076643</td>\n",
       "      <td>0.283053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>8.425044</td>\n",
       "      <td>532.954145</td>\n",
       "      <td>5137.409171</td>\n",
       "      <td>0.077795</td>\n",
       "      <td>0.270248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>12.769417</td>\n",
       "      <td>773.600728</td>\n",
       "      <td>7309.111650</td>\n",
       "      <td>0.080638</td>\n",
       "      <td>0.310112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>10.209804</td>\n",
       "      <td>588.596078</td>\n",
       "      <td>5800.250980</td>\n",
       "      <td>0.094615</td>\n",
       "      <td>0.323318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>8.825581</td>\n",
       "      <td>611.282392</td>\n",
       "      <td>5411.877076</td>\n",
       "      <td>0.079594</td>\n",
       "      <td>0.296569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>14.202353</td>\n",
       "      <td>834.521176</td>\n",
       "      <td>6803.132941</td>\n",
       "      <td>0.063753</td>\n",
       "      <td>0.309383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>13.575937</td>\n",
       "      <td>671.035503</td>\n",
       "      <td>5646.673570</td>\n",
       "      <td>0.071636</td>\n",
       "      <td>0.309775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>15.741961</td>\n",
       "      <td>771.521704</td>\n",
       "      <td>6857.353698</td>\n",
       "      <td>0.083714</td>\n",
       "      <td>0.309064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>14.428571</td>\n",
       "      <td>698.611960</td>\n",
       "      <td>6208.150166</td>\n",
       "      <td>0.072062</td>\n",
       "      <td>0.291988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>13.759907</td>\n",
       "      <td>757.644911</td>\n",
       "      <td>6672.452214</td>\n",
       "      <td>0.080670</td>\n",
       "      <td>0.276590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          replies_count  retweets_count  likes_count  polarity_score  \\\n",
       "Datetime                                                               \n",
       "0         12.044349      648.137339      5675.443491  0.100492         \n",
       "1         6.934932       362.078767      2883.143836  0.089879         \n",
       "2         1.716049       33.185185       433.555556   0.082899         \n",
       "3         0.730769       18.307692       146.038462   0.073436         \n",
       "4         0.375000       0.375000        6.500000     0.000000         \n",
       "5         0.000000       0.250000        4.000000     0.045536         \n",
       "6         0.000000       0.000000        1.000000    -0.183333         \n",
       "8         0.450000       0.600000        21.300000    0.101815         \n",
       "9         0.543478       2.847826        102.500000   0.101169         \n",
       "10        3.548387       254.198925      2009.629032  0.138773         \n",
       "11        16.618357      1571.508857     9001.969404  0.118904         \n",
       "12        15.848997      994.379092      9691.560718  0.096370         \n",
       "13        22.622040      999.511840      9087.638434  0.081877         \n",
       "14        11.792857      800.837500      7481.005357  0.076643         \n",
       "15        8.425044       532.954145      5137.409171  0.077795         \n",
       "16        12.769417      773.600728      7309.111650  0.080638         \n",
       "17        10.209804      588.596078      5800.250980  0.094615         \n",
       "18        8.825581       611.282392      5411.877076  0.079594         \n",
       "19        14.202353      834.521176      6803.132941  0.063753         \n",
       "20        13.575937      671.035503      5646.673570  0.071636         \n",
       "21        15.741961      771.521704      6857.353698  0.083714         \n",
       "22        14.428571      698.611960      6208.150166  0.072062         \n",
       "23        13.759907      757.644911      6672.452214  0.080670         \n",
       "\n",
       "          subjectivity_score  \n",
       "Datetime                      \n",
       "0         0.255648            \n",
       "1         0.278338            \n",
       "2         0.267629            \n",
       "3         0.305080            \n",
       "4         0.000000            \n",
       "5         0.511786            \n",
       "6         0.733333            \n",
       "8         0.182817            \n",
       "9         0.218659            \n",
       "10        0.323359            \n",
       "11        0.328735            \n",
       "12        0.323416            \n",
       "13        0.313400            \n",
       "14        0.283053            \n",
       "15        0.270248            \n",
       "16        0.310112            \n",
       "17        0.323318            \n",
       "18        0.296569            \n",
       "19        0.309383            \n",
       "20        0.309775            \n",
       "21        0.309064            \n",
       "22        0.291988            \n",
       "23        0.276590            "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hours_avg=df[['replies_count','retweets_count','likes_count','polarity_score','subjectivity_score']].groupby(df.index.hour).mean()\n",
    "hours_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "hours_avg.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Datetime</th>\n",
       "      <th>replies_count</th>\n",
       "      <th>retweets_count</th>\n",
       "      <th>likes_count</th>\n",
       "      <th>polarity_score</th>\n",
       "      <th>subjectivity_score</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.044349</td>\n",
       "      <td>648.137339</td>\n",
       "      <td>5675.443491</td>\n",
       "      <td>0.100492</td>\n",
       "      <td>0.255648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.934932</td>\n",
       "      <td>362.078767</td>\n",
       "      <td>2883.143836</td>\n",
       "      <td>0.089879</td>\n",
       "      <td>0.278338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.716049</td>\n",
       "      <td>33.185185</td>\n",
       "      <td>433.555556</td>\n",
       "      <td>0.082899</td>\n",
       "      <td>0.267629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.730769</td>\n",
       "      <td>18.307692</td>\n",
       "      <td>146.038462</td>\n",
       "      <td>0.073436</td>\n",
       "      <td>0.305080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.045536</td>\n",
       "      <td>0.511786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.183333</td>\n",
       "      <td>0.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>0.101815</td>\n",
       "      <td>0.182817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>0.543478</td>\n",
       "      <td>2.847826</td>\n",
       "      <td>102.500000</td>\n",
       "      <td>0.101169</td>\n",
       "      <td>0.218659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>3.548387</td>\n",
       "      <td>254.198925</td>\n",
       "      <td>2009.629032</td>\n",
       "      <td>0.138773</td>\n",
       "      <td>0.323359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>16.618357</td>\n",
       "      <td>1571.508857</td>\n",
       "      <td>9001.969404</td>\n",
       "      <td>0.118904</td>\n",
       "      <td>0.328735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>15.848997</td>\n",
       "      <td>994.379092</td>\n",
       "      <td>9691.560718</td>\n",
       "      <td>0.096370</td>\n",
       "      <td>0.323416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>22.622040</td>\n",
       "      <td>999.511840</td>\n",
       "      <td>9087.638434</td>\n",
       "      <td>0.081877</td>\n",
       "      <td>0.313400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>11.792857</td>\n",
       "      <td>800.837500</td>\n",
       "      <td>7481.005357</td>\n",
       "      <td>0.076643</td>\n",
       "      <td>0.283053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>8.425044</td>\n",
       "      <td>532.954145</td>\n",
       "      <td>5137.409171</td>\n",
       "      <td>0.077795</td>\n",
       "      <td>0.270248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>12.769417</td>\n",
       "      <td>773.600728</td>\n",
       "      <td>7309.111650</td>\n",
       "      <td>0.080638</td>\n",
       "      <td>0.310112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>10.209804</td>\n",
       "      <td>588.596078</td>\n",
       "      <td>5800.250980</td>\n",
       "      <td>0.094615</td>\n",
       "      <td>0.323318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>8.825581</td>\n",
       "      <td>611.282392</td>\n",
       "      <td>5411.877076</td>\n",
       "      <td>0.079594</td>\n",
       "      <td>0.296569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>14.202353</td>\n",
       "      <td>834.521176</td>\n",
       "      <td>6803.132941</td>\n",
       "      <td>0.063753</td>\n",
       "      <td>0.309383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>13.575937</td>\n",
       "      <td>671.035503</td>\n",
       "      <td>5646.673570</td>\n",
       "      <td>0.071636</td>\n",
       "      <td>0.309775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>15.741961</td>\n",
       "      <td>771.521704</td>\n",
       "      <td>6857.353698</td>\n",
       "      <td>0.083714</td>\n",
       "      <td>0.309064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>14.428571</td>\n",
       "      <td>698.611960</td>\n",
       "      <td>6208.150166</td>\n",
       "      <td>0.072062</td>\n",
       "      <td>0.291988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>13.759907</td>\n",
       "      <td>757.644911</td>\n",
       "      <td>6672.452214</td>\n",
       "      <td>0.080670</td>\n",
       "      <td>0.276590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Datetime  replies_count  retweets_count  likes_count  polarity_score  \\\n",
       "0   0         12.044349      648.137339      5675.443491  0.100492         \n",
       "1   1         6.934932       362.078767      2883.143836  0.089879         \n",
       "2   2         1.716049       33.185185       433.555556   0.082899         \n",
       "3   3         0.730769       18.307692       146.038462   0.073436         \n",
       "4   4         0.375000       0.375000        6.500000     0.000000         \n",
       "5   5         0.000000       0.250000        4.000000     0.045536         \n",
       "6   6         0.000000       0.000000        1.000000    -0.183333         \n",
       "7   8         0.450000       0.600000        21.300000    0.101815         \n",
       "8   9         0.543478       2.847826        102.500000   0.101169         \n",
       "9   10        3.548387       254.198925      2009.629032  0.138773         \n",
       "10  11        16.618357      1571.508857     9001.969404  0.118904         \n",
       "11  12        15.848997      994.379092      9691.560718  0.096370         \n",
       "12  13        22.622040      999.511840      9087.638434  0.081877         \n",
       "13  14        11.792857      800.837500      7481.005357  0.076643         \n",
       "14  15        8.425044       532.954145      5137.409171  0.077795         \n",
       "15  16        12.769417      773.600728      7309.111650  0.080638         \n",
       "16  17        10.209804      588.596078      5800.250980  0.094615         \n",
       "17  18        8.825581       611.282392      5411.877076  0.079594         \n",
       "18  19        14.202353      834.521176      6803.132941  0.063753         \n",
       "19  20        13.575937      671.035503      5646.673570  0.071636         \n",
       "20  21        15.741961      771.521704      6857.353698  0.083714         \n",
       "21  22        14.428571      698.611960      6208.150166  0.072062         \n",
       "22  23        13.759907      757.644911      6672.452214  0.080670         \n",
       "\n",
       "    subjectivity_score  \n",
       "0   0.255648            \n",
       "1   0.278338            \n",
       "2   0.267629            \n",
       "3   0.305080            \n",
       "4   0.000000            \n",
       "5   0.511786            \n",
       "6   0.733333            \n",
       "7   0.182817            \n",
       "8   0.218659            \n",
       "9   0.323359            \n",
       "10  0.328735            \n",
       "11  0.323416            \n",
       "12  0.313400            \n",
       "13  0.283053            \n",
       "14  0.270248            \n",
       "15  0.310112            \n",
       "16  0.323318            \n",
       "17  0.296569            \n",
       "18  0.309383            \n",
       "19  0.309775            \n",
       "20  0.309064            \n",
       "21  0.291988            \n",
       "22  0.276590            "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hours_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# @CaucasianJames is from Chicago so changing hours from EDT to CDT\n",
    "hours_avg['Datetime'] = hours_avg['Datetime'] -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "hours_avg['Datetime'][0]=23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>replies_count</th>\n",
       "      <th>retweets_count</th>\n",
       "      <th>likes_count</th>\n",
       "      <th>polarity_score</th>\n",
       "      <th>subjectivity_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>6.934932</td>\n",
       "      <td>362.078767</td>\n",
       "      <td>2883.143836</td>\n",
       "      <td>0.089879</td>\n",
       "      <td>0.278338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.716049</td>\n",
       "      <td>33.185185</td>\n",
       "      <td>433.555556</td>\n",
       "      <td>0.082899</td>\n",
       "      <td>0.267629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.730769</td>\n",
       "      <td>18.307692</td>\n",
       "      <td>146.038462</td>\n",
       "      <td>0.073436</td>\n",
       "      <td>0.305080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.045536</td>\n",
       "      <td>0.511786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.183333</td>\n",
       "      <td>0.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>0.101815</td>\n",
       "      <td>0.182817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.543478</td>\n",
       "      <td>2.847826</td>\n",
       "      <td>102.500000</td>\n",
       "      <td>0.101169</td>\n",
       "      <td>0.218659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>3.548387</td>\n",
       "      <td>254.198925</td>\n",
       "      <td>2009.629032</td>\n",
       "      <td>0.138773</td>\n",
       "      <td>0.323359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>16.618357</td>\n",
       "      <td>1571.508857</td>\n",
       "      <td>9001.969404</td>\n",
       "      <td>0.118904</td>\n",
       "      <td>0.328735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>15.848997</td>\n",
       "      <td>994.379092</td>\n",
       "      <td>9691.560718</td>\n",
       "      <td>0.096370</td>\n",
       "      <td>0.323416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>22.622040</td>\n",
       "      <td>999.511840</td>\n",
       "      <td>9087.638434</td>\n",
       "      <td>0.081877</td>\n",
       "      <td>0.313400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>11.792857</td>\n",
       "      <td>800.837500</td>\n",
       "      <td>7481.005357</td>\n",
       "      <td>0.076643</td>\n",
       "      <td>0.283053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>8.425044</td>\n",
       "      <td>532.954145</td>\n",
       "      <td>5137.409171</td>\n",
       "      <td>0.077795</td>\n",
       "      <td>0.270248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>12.769417</td>\n",
       "      <td>773.600728</td>\n",
       "      <td>7309.111650</td>\n",
       "      <td>0.080638</td>\n",
       "      <td>0.310112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>10.209804</td>\n",
       "      <td>588.596078</td>\n",
       "      <td>5800.250980</td>\n",
       "      <td>0.094615</td>\n",
       "      <td>0.323318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>8.825581</td>\n",
       "      <td>611.282392</td>\n",
       "      <td>5411.877076</td>\n",
       "      <td>0.079594</td>\n",
       "      <td>0.296569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>14.202353</td>\n",
       "      <td>834.521176</td>\n",
       "      <td>6803.132941</td>\n",
       "      <td>0.063753</td>\n",
       "      <td>0.309383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>13.575937</td>\n",
       "      <td>671.035503</td>\n",
       "      <td>5646.673570</td>\n",
       "      <td>0.071636</td>\n",
       "      <td>0.309775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>15.741961</td>\n",
       "      <td>771.521704</td>\n",
       "      <td>6857.353698</td>\n",
       "      <td>0.083714</td>\n",
       "      <td>0.309064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>14.428571</td>\n",
       "      <td>698.611960</td>\n",
       "      <td>6208.150166</td>\n",
       "      <td>0.072062</td>\n",
       "      <td>0.291988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>13.759907</td>\n",
       "      <td>757.644911</td>\n",
       "      <td>6672.452214</td>\n",
       "      <td>0.080670</td>\n",
       "      <td>0.276590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>12.044349</td>\n",
       "      <td>648.137339</td>\n",
       "      <td>5675.443491</td>\n",
       "      <td>0.100492</td>\n",
       "      <td>0.255648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          replies_count  retweets_count  likes_count  polarity_score  \\\n",
       "Datetime                                                               \n",
       "0         6.934932       362.078767      2883.143836  0.089879         \n",
       "1         1.716049       33.185185       433.555556   0.082899         \n",
       "2         0.730769       18.307692       146.038462   0.073436         \n",
       "3         0.375000       0.375000        6.500000     0.000000         \n",
       "4         0.000000       0.250000        4.000000     0.045536         \n",
       "5         0.000000       0.000000        1.000000    -0.183333         \n",
       "7         0.450000       0.600000        21.300000    0.101815         \n",
       "8         0.543478       2.847826        102.500000   0.101169         \n",
       "9         3.548387       254.198925      2009.629032  0.138773         \n",
       "10        16.618357      1571.508857     9001.969404  0.118904         \n",
       "11        15.848997      994.379092      9691.560718  0.096370         \n",
       "12        22.622040      999.511840      9087.638434  0.081877         \n",
       "13        11.792857      800.837500      7481.005357  0.076643         \n",
       "14        8.425044       532.954145      5137.409171  0.077795         \n",
       "15        12.769417      773.600728      7309.111650  0.080638         \n",
       "16        10.209804      588.596078      5800.250980  0.094615         \n",
       "17        8.825581       611.282392      5411.877076  0.079594         \n",
       "18        14.202353      834.521176      6803.132941  0.063753         \n",
       "19        13.575937      671.035503      5646.673570  0.071636         \n",
       "20        15.741961      771.521704      6857.353698  0.083714         \n",
       "21        14.428571      698.611960      6208.150166  0.072062         \n",
       "22        13.759907      757.644911      6672.452214  0.080670         \n",
       "23        12.044349      648.137339      5675.443491  0.100492         \n",
       "\n",
       "          subjectivity_score  \n",
       "Datetime                      \n",
       "0         0.278338            \n",
       "1         0.267629            \n",
       "2         0.305080            \n",
       "3         0.000000            \n",
       "4         0.511786            \n",
       "5         0.733333            \n",
       "7         0.182817            \n",
       "8         0.218659            \n",
       "9         0.323359            \n",
       "10        0.328735            \n",
       "11        0.323416            \n",
       "12        0.313400            \n",
       "13        0.283053            \n",
       "14        0.270248            \n",
       "15        0.310112            \n",
       "16        0.323318            \n",
       "17        0.296569            \n",
       "18        0.309383            \n",
       "19        0.309775            \n",
       "20        0.309064            \n",
       "21        0.291988            \n",
       "22        0.276590            \n",
       "23        0.255648            "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hours_avg = hours_avg.set_index('Datetime')\n",
    "hours_avg.sort_index(inplace=True)\n",
    "hours_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,9))\n",
    "plt.plot(hours_avg['likes_count'],color='blue',marker='o')\n",
    "plt.xlabel('Hour of the Day')\n",
    "plt.ylabel('average likes count') \n",
    "plt.title('Average Tweet Likes by Hour of the Day');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,9))\n",
    "plt.plot(hours_avg['polarity_score'],color='green',marker='o')\n",
    "plt.xlabel('Hour of the Day') \n",
    "plt.title('Average Tweet Sentiment Score by Hour of the Day');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>conversation_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>timezone</th>\n",
       "      <th>user_id</th>\n",
       "      <th>username</th>\n",
       "      <th>name</th>\n",
       "      <th>place</th>\n",
       "      <th>tweet</th>\n",
       "      <th>mentions</th>\n",
       "      <th>urls</th>\n",
       "      <th>photos</th>\n",
       "      <th>replies_count</th>\n",
       "      <th>retweets_count</th>\n",
       "      <th>likes_count</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>cashtags</th>\n",
       "      <th>link</th>\n",
       "      <th>retweet</th>\n",
       "      <th>quote_url</th>\n",
       "      <th>video</th>\n",
       "      <th>near</th>\n",
       "      <th>geo</th>\n",
       "      <th>source</th>\n",
       "      <th>user_rt_id</th>\n",
       "      <th>user_rt</th>\n",
       "      <th>retweet_id</th>\n",
       "      <th>reply_to</th>\n",
       "      <th>retweet_date</th>\n",
       "      <th>translate</th>\n",
       "      <th>trans_src</th>\n",
       "      <th>trans_dest</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>polarity_score</th>\n",
       "      <th>polarity</th>\n",
       "      <th>subjectivity_score</th>\n",
       "      <th>subjectivity</th>\n",
       "      <th>tweet_clean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2011-03-31</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-04-30</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-05-31</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-06-30</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-07-31</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  conversation_id  created_at  date  time  timezone  user_id  \\\n",
       "Datetime                                                                     \n",
       "2011-03-31  9   9                9           9     9     9         9         \n",
       "2011-04-30  7   7                7           7     7     7         7         \n",
       "2011-05-31  26  26               26          26    26    26        26        \n",
       "2011-06-30  28  28               28          28    28    28        28        \n",
       "2011-07-31  22  22               22          22    22    22        22        \n",
       "\n",
       "            username  name  place  tweet  mentions  urls  photos  \\\n",
       "Datetime                                                           \n",
       "2011-03-31  9         9     0      9      9         9     9        \n",
       "2011-04-30  7         7     0      7      7         7     7        \n",
       "2011-05-31  26        26    0      26     26        26    26       \n",
       "2011-06-30  28        28    0      28     28        28    28       \n",
       "2011-07-31  22        22    0      22     22        22    22       \n",
       "\n",
       "            replies_count  retweets_count  likes_count  hashtags  cashtags  \\\n",
       "Datetime                                                                     \n",
       "2011-03-31  9              9               9            9         9          \n",
       "2011-04-30  7              7               7            7         7          \n",
       "2011-05-31  26             26              26           26        26         \n",
       "2011-06-30  28             28              28           28        28         \n",
       "2011-07-31  22             22              22           22        22         \n",
       "\n",
       "            link  retweet  quote_url  video  near  geo  source  user_rt_id  \\\n",
       "Datetime                                                                     \n",
       "2011-03-31  9     9        0          9      0     0    0       0            \n",
       "2011-04-30  7     7        0          7      0     0    0       0            \n",
       "2011-05-31  26    26       0          26     0     0    0       0            \n",
       "2011-06-30  28    28       0          28     0     0    0       0            \n",
       "2011-07-31  22    22       0          22     0     0    0       0            \n",
       "\n",
       "            user_rt  retweet_id  reply_to  retweet_date  translate  trans_src  \\\n",
       "Datetime                                                                        \n",
       "2011-03-31  0        0           9         0             0          0           \n",
       "2011-04-30  0        0           7         0             0          0           \n",
       "2011-05-31  0        0           26        0             0          0           \n",
       "2011-06-30  0        0           28        0             0          0           \n",
       "2011-07-31  0        0           22        0             0          0           \n",
       "\n",
       "            trans_dest  sentiment  polarity_score  polarity  \\\n",
       "Datetime                                                      \n",
       "2011-03-31  0           9          9               9          \n",
       "2011-04-30  0           7          7               7          \n",
       "2011-05-31  0           26         26              26         \n",
       "2011-06-30  0           28         28              28         \n",
       "2011-07-31  0           22         22              22         \n",
       "\n",
       "            subjectivity_score  subjectivity  tweet_clean  \n",
       "Datetime                                                   \n",
       "2011-03-31  9                   9             9            \n",
       "2011-04-30  7                   7             7            \n",
       "2011-05-31  26                  26            26           \n",
       "2011-06-30  28                  28            28           \n",
       "2011-07-31  22                  22            22           "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months_count=df.resample('M').count()\n",
    "months_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,9))\n",
    "plt.plot(months_count['username'],color='blue',marker='o')\n",
    "plt.title('Tweets by Month');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>replies_count</th>\n",
       "      <th>retweets_count</th>\n",
       "      <th>likes_count</th>\n",
       "      <th>polarity_score</th>\n",
       "      <th>subjectivity_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2011-03-31</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>28.888889</td>\n",
       "      <td>0.211684</td>\n",
       "      <td>0.541623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-04-30</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.123160</td>\n",
       "      <td>0.408333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-05-31</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.053972</td>\n",
       "      <td>0.290963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-06-30</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.084413</td>\n",
       "      <td>0.318887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-07-31</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>4.954545</td>\n",
       "      <td>0.054951</td>\n",
       "      <td>0.360444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-11-30</td>\n",
       "      <td>49.814016</td>\n",
       "      <td>1614.237197</td>\n",
       "      <td>15570.574124</td>\n",
       "      <td>0.112929</td>\n",
       "      <td>0.323651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>38.286096</td>\n",
       "      <td>1905.454545</td>\n",
       "      <td>20424.441176</td>\n",
       "      <td>0.114232</td>\n",
       "      <td>0.312158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>33.882353</td>\n",
       "      <td>1436.827451</td>\n",
       "      <td>16405.180392</td>\n",
       "      <td>0.100173</td>\n",
       "      <td>0.280119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>31.523132</td>\n",
       "      <td>1643.921708</td>\n",
       "      <td>17517.135231</td>\n",
       "      <td>0.098909</td>\n",
       "      <td>0.315109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>50.049020</td>\n",
       "      <td>1563.950980</td>\n",
       "      <td>19746.681373</td>\n",
       "      <td>0.120643</td>\n",
       "      <td>0.287022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            replies_count  retweets_count   likes_count  polarity_score  \\\n",
       "Datetime                                                                  \n",
       "2011-03-31  1.111111       2.000000        28.888889     0.211684         \n",
       "2011-04-30  0.000000       0.142857        0.000000      0.123160         \n",
       "2011-05-31  0.000000       0.076923        0.038462      0.053972         \n",
       "2011-06-30  0.214286       0.071429        0.035714      0.084413         \n",
       "2011-07-31  0.409091       0.863636        4.954545      0.054951         \n",
       "...              ...            ...             ...           ...         \n",
       "2019-11-30  49.814016      1614.237197     15570.574124  0.112929         \n",
       "2019-12-31  38.286096      1905.454545     20424.441176  0.114232         \n",
       "2020-01-31  33.882353      1436.827451     16405.180392  0.100173         \n",
       "2020-02-29  31.523132      1643.921708     17517.135231  0.098909         \n",
       "2020-03-31  50.049020      1563.950980     19746.681373  0.120643         \n",
       "\n",
       "            subjectivity_score  \n",
       "Datetime                        \n",
       "2011-03-31  0.541623            \n",
       "2011-04-30  0.408333            \n",
       "2011-05-31  0.290963            \n",
       "2011-06-30  0.318887            \n",
       "2011-07-31  0.360444            \n",
       "...              ...            \n",
       "2019-11-30  0.323651            \n",
       "2019-12-31  0.312158            \n",
       "2020-01-31  0.280119            \n",
       "2020-02-29  0.315109            \n",
       "2020-03-31  0.287022            \n",
       "\n",
       "[109 rows x 5 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months_avg=df[['replies_count','retweets_count','likes_count','polarity_score','subjectivity_score']].resample('M').mean()\n",
    "months_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,9))\n",
    "plt.plot(months_avg['likes_count'],color='blue',marker='o')\n",
    "plt.title('Average Tweet Likes by Month');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,9))\n",
    "plt.plot(months_avg['polarity_score'],color='green',marker='o')\n",
    "plt.title('Average Tweet Sentiment Score by Month');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "week_df = df[['replies_count','retweets_count','likes_count','polarity_score','subjectivity_score']].groupby(df.index.weekday_name).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# label each day of week in order so it will be sorted this way rather than alphabetically\n",
    "week_df.at['Sunday', 'sort'] = 1\n",
    "week_df.at['Monday', 'sort'] = 2\n",
    "week_df.at['Tuesday', 'sort'] = 3\n",
    "week_df.at['Wednesday', 'sort'] = 4\n",
    "week_df.at['Thursday', 'sort'] = 5\n",
    "week_df.at['Friday', 'sort'] = 6\n",
    "week_df.at['Saturday', 'sort'] = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "week_df=week_df.sort_values(by=['sort'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,9))\n",
    "plt.plot(week_df['likes_count'],color='blue',marker='o')\n",
    "plt.title('Average Tweet Likes by Day of the Week');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,9))\n",
    "plt.plot(week_df['polarity_score'],color='green',marker='o')\n",
    "plt.title('Average Tweet Sentiment Score by Day of the Week');"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}