Aryan_NLP_Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas For NLP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Pandas and reading CSV Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"Tweets.csv\") #reads and stores the contents of Tweets.csv in data variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>tweet_id</th>\n",
       "      <th>airline_sentiment</th>\n",
       "      <th>airline_sentiment_confidence</th>\n",
       "      <th>negativereason</th>\n",
       "      <th>negativereason_confidence</th>\n",
       "      <th>airline</th>\n",
       "      <th>airline_sentiment_gold</th>\n",
       "      <th>name</th>\n",
       "      <th>negativereason_gold</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>text</th>\n",
       "      <th>tweet_coord</th>\n",
       "      <th>tweet_created</th>\n",
       "      <th>tweet_location</th>\n",
       "      <th>user_timezone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>570306133677760513</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cairdin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:35:52 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>570301130888122368</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.3486</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:59 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>570301083672813571</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yvonnalynn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:48 -0800</td>\n",
       "      <td>Lets Play</td>\n",
       "      <td>Central Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>570301031407624196</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Bad Flight</td>\n",
       "      <td>0.7033</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:36 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>570300817074462722</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Can't Tell</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:14:45 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>570300767074181121</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Can't Tell</td>\n",
       "      <td>0.6842</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica seriously would pay $30 a fligh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:14:33 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>570300616901320704</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.6745</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cjmcginnis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica yes, nearly every time I fly VX...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:13:57 -0800</td>\n",
       "      <td>San Francisco CA</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>570300248553349120</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6340</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pilot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica Really missed a prime opportuni...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:12:29 -0800</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>570299953286942721</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.6559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dhepburn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@virginamerica Well, I didn't…but NOW I DO! :-D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:11:19 -0800</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>570295459631263746</td>\n",
       "      <td>positive</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YupitsTate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica it was amazing, and arrived an ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 10:53:27 -0800</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id airline_sentiment  airline_sentiment_confidence  \\\n",
       "0  570306133677760513           neutral                        1.0000   \n",
       "1  570301130888122368          positive                        0.3486   \n",
       "2  570301083672813571           neutral                        0.6837   \n",
       "3  570301031407624196          negative                        1.0000   \n",
       "4  570300817074462722          negative                        1.0000   \n",
       "5  570300767074181121          negative                        1.0000   \n",
       "6  570300616901320704          positive                        0.6745   \n",
       "7  570300248553349120           neutral                        0.6340   \n",
       "8  570299953286942721          positive                        0.6559   \n",
       "9  570295459631263746          positive                        1.0000   \n",
       "\n",
       "  negativereason  negativereason_confidence         airline  \\\n",
       "0            NaN                        NaN  Virgin America   \n",
       "1            NaN                     0.0000  Virgin America   \n",
       "2            NaN                        NaN  Virgin America   \n",
       "3     Bad Flight                     0.7033  Virgin America   \n",
       "4     Can't Tell                     1.0000  Virgin America   \n",
       "5     Can't Tell                     0.6842  Virgin America   \n",
       "6            NaN                     0.0000  Virgin America   \n",
       "7            NaN                        NaN  Virgin America   \n",
       "8            NaN                        NaN  Virgin America   \n",
       "9            NaN                        NaN  Virgin America   \n",
       "\n",
       "  airline_sentiment_gold        name negativereason_gold  retweet_count  \\\n",
       "0                    NaN     cairdin                 NaN              0   \n",
       "1                    NaN    jnardino                 NaN              0   \n",
       "2                    NaN  yvonnalynn                 NaN              0   \n",
       "3                    NaN    jnardino                 NaN              0   \n",
       "4                    NaN    jnardino                 NaN              0   \n",
       "5                    NaN    jnardino                 NaN              0   \n",
       "6                    NaN  cjmcginnis                 NaN              0   \n",
       "7                    NaN       pilot                 NaN              0   \n",
       "8                    NaN    dhepburn                 NaN              0   \n",
       "9                    NaN  YupitsTate                 NaN              0   \n",
       "\n",
       "                                                text tweet_coord  \\\n",
       "0                @VirginAmerica What @dhepburn said.         NaN   \n",
       "1  @VirginAmerica plus you've added commercials t...         NaN   \n",
       "2  @VirginAmerica I didn't today... Must mean I n...         NaN   \n",
       "3  @VirginAmerica it's really aggressive to blast...         NaN   \n",
       "4  @VirginAmerica and it's a really big bad thing...         NaN   \n",
       "5  @VirginAmerica seriously would pay $30 a fligh...         NaN   \n",
       "6  @VirginAmerica yes, nearly every time I fly VX...         NaN   \n",
       "7  @VirginAmerica Really missed a prime opportuni...         NaN   \n",
       "8    @virginamerica Well, I didn't…but NOW I DO! :-D         NaN   \n",
       "9  @VirginAmerica it was amazing, and arrived an ...         NaN   \n",
       "\n",
       "               tweet_created    tweet_location               user_timezone  \n",
       "0  2015-02-24 11:35:52 -0800               NaN  Eastern Time (US & Canada)  \n",
       "1  2015-02-24 11:15:59 -0800               NaN  Pacific Time (US & Canada)  \n",
       "2  2015-02-24 11:15:48 -0800         Lets Play  Central Time (US & Canada)  \n",
       "3  2015-02-24 11:15:36 -0800               NaN  Pacific Time (US & Canada)  \n",
       "4  2015-02-24 11:14:45 -0800               NaN  Pacific Time (US & Canada)  \n",
       "5  2015-02-24 11:14:33 -0800               NaN  Pacific Time (US & Canada)  \n",
       "6  2015-02-24 11:13:57 -0800  San Francisco CA  Pacific Time (US & Canada)  \n",
       "7  2015-02-24 11:12:29 -0800       Los Angeles  Pacific Time (US & Canada)  \n",
       "8  2015-02-24 11:11:19 -0800         San Diego  Pacific Time (US & Canada)  \n",
       "9  2015-02-24 10:53:27 -0800       Los Angeles  Eastern Time (US & Canada)  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10) #lets you see the different columns of the data file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14640, 15)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape #the shape of the table indicates that there are 14640 tweets stored in the CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14640 entries, 0 to 14639\n",
      "Data columns (total 15 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   tweet_id                      14640 non-null  int64  \n",
      " 1   airline_sentiment             14640 non-null  object \n",
      " 2   airline_sentiment_confidence  14640 non-null  float64\n",
      " 3   negativereason                9178 non-null   object \n",
      " 4   negativereason_confidence     10522 non-null  float64\n",
      " 5   airline                       14640 non-null  object \n",
      " 6   airline_sentiment_gold        40 non-null     object \n",
      " 7   name                          14640 non-null  object \n",
      " 8   negativereason_gold           32 non-null     object \n",
      " 9   retweet_count                 14640 non-null  int64  \n",
      " 10  text                          14640 non-null  object \n",
      " 11  tweet_coord                   1019 non-null   object \n",
      " 12  tweet_created                 14640 non-null  object \n",
      " 13  tweet_location                9907 non-null   object \n",
      " 14  user_timezone                 9820 non-null   object \n",
      "dtypes: float64(2), int64(2), object(11)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info() #used to produce a concise summary of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id                          int64\n",
       "airline_sentiment                object\n",
       "airline_sentiment_confidence    float64\n",
       "negativereason                   object\n",
       "negativereason_confidence       float64\n",
       "airline                          object\n",
       "airline_sentiment_gold           object\n",
       "name                             object\n",
       "negativereason_gold              object\n",
       "retweet_count                     int64\n",
       "text                             object\n",
       "tweet_coord                      object\n",
       "tweet_created                    object\n",
       "tweet_location                   object\n",
       "user_timezone                    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes #A method to specifically get the datatype of individual column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Specific Rows and Columns can be extracted from the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           NaN\n",
       "1           NaN\n",
       "2           NaN\n",
       "3    Bad Flight\n",
       "4    Can't Tell\n",
       "5    Can't Tell\n",
       "6           NaN\n",
       "7           NaN\n",
       "8           NaN\n",
       "9           NaN\n",
       "Name: negativereason, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "negr = data['negativereason'] \n",
    "#extracts the data from the 'negativereason' column\n",
    "negr.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specific rows that conform to certain restraints can also be extracted in the following way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>airline_sentiment</th>\n",
       "      <th>airline_sentiment_confidence</th>\n",
       "      <th>negativereason</th>\n",
       "      <th>negativereason_confidence</th>\n",
       "      <th>airline</th>\n",
       "      <th>airline_sentiment_gold</th>\n",
       "      <th>name</th>\n",
       "      <th>negativereason_gold</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>text</th>\n",
       "      <th>tweet_coord</th>\n",
       "      <th>tweet_created</th>\n",
       "      <th>tweet_location</th>\n",
       "      <th>user_timezone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>570301130888122368</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.3486</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:59 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>570300616901320704</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.6745</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cjmcginnis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica yes, nearly every time I fly VX...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:13:57 -0800</td>\n",
       "      <td>San Francisco CA</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>570299953286942721</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.6559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dhepburn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@virginamerica Well, I didn't…but NOW I DO! :-D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:11:19 -0800</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>570295459631263746</td>\n",
       "      <td>positive</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YupitsTate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica it was amazing, and arrived an ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 10:53:27 -0800</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>570289724453216256</td>\n",
       "      <td>positive</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HyperCamiLax</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica I &amp;lt;3 pretty graphics. so muc...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 10:30:40 -0800</td>\n",
       "      <td>NYC</td>\n",
       "      <td>America/New_York</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              tweet_id airline_sentiment  airline_sentiment_confidence  \\\n",
       "1   570301130888122368          positive                        0.3486   \n",
       "6   570300616901320704          positive                        0.6745   \n",
       "8   570299953286942721          positive                        0.6559   \n",
       "9   570295459631263746          positive                        1.0000   \n",
       "11  570289724453216256          positive                        1.0000   \n",
       "\n",
       "   negativereason  negativereason_confidence         airline  \\\n",
       "1             NaN                        0.0  Virgin America   \n",
       "6             NaN                        0.0  Virgin America   \n",
       "8             NaN                        NaN  Virgin America   \n",
       "9             NaN                        NaN  Virgin America   \n",
       "11            NaN                        NaN  Virgin America   \n",
       "\n",
       "   airline_sentiment_gold          name negativereason_gold  retweet_count  \\\n",
       "1                     NaN      jnardino                 NaN              0   \n",
       "6                     NaN    cjmcginnis                 NaN              0   \n",
       "8                     NaN      dhepburn                 NaN              0   \n",
       "9                     NaN    YupitsTate                 NaN              0   \n",
       "11                    NaN  HyperCamiLax                 NaN              0   \n",
       "\n",
       "                                                 text tweet_coord  \\\n",
       "1   @VirginAmerica plus you've added commercials t...         NaN   \n",
       "6   @VirginAmerica yes, nearly every time I fly VX...         NaN   \n",
       "8     @virginamerica Well, I didn't…but NOW I DO! :-D         NaN   \n",
       "9   @VirginAmerica it was amazing, and arrived an ...         NaN   \n",
       "11  @VirginAmerica I &lt;3 pretty graphics. so muc...         NaN   \n",
       "\n",
       "                tweet_created    tweet_location               user_timezone  \n",
       "1   2015-02-24 11:15:59 -0800               NaN  Pacific Time (US & Canada)  \n",
       "6   2015-02-24 11:13:57 -0800  San Francisco CA  Pacific Time (US & Canada)  \n",
       "8   2015-02-24 11:11:19 -0800         San Diego  Pacific Time (US & Canada)  \n",
       "9   2015-02-24 10:53:27 -0800       Los Angeles  Eastern Time (US & Canada)  \n",
       "11  2015-02-24 10:30:40 -0800               NYC            America/New_York  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "posrec = data[data['airline_sentiment'] == 'positive'] \n",
    "#extracts all the colums from the rows with 'positive' airline sentiment\n",
    "posrec.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Manipulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The datatype of individial columns of slices can be changed according to the user's needs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of 0         neutral\n",
       "1        positive\n",
       "2         neutral\n",
       "3        negative\n",
       "4        negative\n",
       "           ...   \n",
       "14635    positive\n",
       "14636    negative\n",
       "14637     neutral\n",
       "14638    negative\n",
       "14639     neutral\n",
       "Name: airline_sentiment, Length: 14640, dtype: object>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new = data['airline_sentiment']\n",
    "new.describe \n",
    "#the describe function is used to get information about pandas series/Dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An important distinction needs to made between pandas series and pandas dataframe. Series are nothing but single column of data, when there are multiple columns it is known as a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         neutral\n",
       "1        positive\n",
       "2         neutral\n",
       "3        negative\n",
       "4        negative\n",
       "           ...   \n",
       "14635    positive\n",
       "14636    negative\n",
       "14637     neutral\n",
       "14638    negative\n",
       "14639     neutral\n",
       "Name: airline_sentiment, Length: 14640, dtype: string"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.astype('string') #function to convert datatype of any give series or dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding and eliminating rows that have NaN values is one of the most important finctions that pandas serves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['airline_sentiment'].isnull().values.any()\n",
    "# False means this particular column does not have any NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['negativereason_confidence'].isnull().values.any()\n",
    "# Returns true if there are any NaN values in specified series/dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4118"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['negativereason_confidence'].isnull().sum()# returns the number of NaN values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fillna method replaces all the NaN values with the value provided within the brackets, which in this case is the mean of the entire column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0.638298\n",
       "1        0.000000\n",
       "2        0.638298\n",
       "3        0.703300\n",
       "4        1.000000\n",
       "           ...   \n",
       "14635    0.000000\n",
       "14636    1.000000\n",
       "14637    0.638298\n",
       "14638    0.665900\n",
       "14639    0.000000\n",
       "Name: negativereason_confidence, Length: 14640, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['negativereason_confidence'].fillna(data['negativereason_confidence'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['airline_sentiment'].isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drawing Inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms and Patterns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas can be used to extract and clean data in such a way that lets you draw inferences about the data and the patterns hidden in it. One of the most useful way of finding these hidden patterns in data is through Histograms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos = data[data['airline_sentiment'] == 'positive'].shape[0]#number of passengers which find the airlines good\n",
    "neg = data[data['airline_sentiment'] == 'negative'].shape[0]#number of passengers which find the airlines good\n",
    "neu = data[data['airline_sentiment'] == 'neutral'].shape[0]#number of passengers which find the airlines good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt #importing matplotlib for plotting the histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Histogram below shows that a vast majority of the passengers found their journey inadequate. This needs to be further investigated to find out which of the airlines are doing bad. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(10,pos,3, label=\"Positive\")\n",
    "plt.bar(15,neg,3, label=\"Negative\")\n",
    "plt.bar(20,neu,3, label=\"Neutral\")\n",
    "plt.legend()\n",
    "plt.ylabel('Number of examples')\n",
    "plt.title('Proportion of examples')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting with Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas has inbuilt plotting functions that can help us draw inferences and maybe replace the matplotlib library.\n",
    "The Pandas and the Matplotlib library are built upon the numpy library in Pythone. This means that a lot of functions used in matplotlib can be used in pandas as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (20,20)) #defining the size of the histogram\n",
    "ax = fig.gca() #the current axes of the histogram\n",
    "negr.hist(ax = ax)# the pandas function for plotting histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Breaking the Data Down"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Breaking down the data on the basis of different features helps us draw inferences and gain deep insights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Virgin America', 'United', 'Southwest', 'Delta', 'US Airways',\n",
       "       'American'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.airline.unique() #Finding the number of airlines that have been mentioned in the tweets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the code below, we are chaining together operations by first finding the rows of a particular airline and then taking out the 'airline_sentimen' row from it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "va = data[data['airline'] == 'Virgin America']['airline_sentiment']\n",
    "#finding the rection of customers per airline and plotting the results\n",
    "va.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "va = data[data['airline'] == 'United']['airline_sentiment']\n",
    "va.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "va = data[data['airline'] == 'Southwest']['airline_sentiment']\n",
    "va.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "va = data[data['airline'] == 'Delta']['airline_sentiment']\n",
    "va.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "va = data[data['airline'] == 'US Airways']['airline_sentiment']\n",
    "va.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "va = data[data['airline'] == 'American']['airline_sentiment']\n",
    "va.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, Pandas sorts the data into different categories, regardless of the datatype. This allows for easy handling of the data. The graphs mentioned above indicate that except for Virgin America, all the airlines are doing very poorly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}