TSE-Data Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('Dataset/train.csv')\n",
    "df_test = pd.read_csv('Dataset/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 27481 entries, 0 to 27480\n",
      "Data columns (total 4 columns):\n",
      "textID           27481 non-null object\n",
      "text             27480 non-null object\n",
      "selected_text    27480 non-null object\n",
      "sentiment        27481 non-null object\n",
      "dtypes: object(4)\n",
      "memory usage: 858.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3534 entries, 0 to 3533\n",
      "Data columns (total 3 columns):\n",
      "textID       3534 non-null object\n",
      "text         3534 non-null object\n",
      "sentiment    3534 non-null object\n",
      "dtypes: object(3)\n",
      "memory usage: 82.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_train.append(df_test,'sort=False')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 31014 entries, 0 to 31013\n",
      "Data columns (total 4 columns):\n",
      "selected_text    27480 non-null object\n",
      "sentiment        31014 non-null object\n",
      "text             31014 non-null object\n",
      "textID           31014 non-null object\n",
      "dtypes: object(4)\n",
      "memory usage: 969.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info() #the column 'selected text' is only in training set  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>selected_text</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>text</th>\n",
       "      <th>textID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I`d have responded, if I were going</td>\n",
       "      <td>neutral</td>\n",
       "      <td>I`d have responded, if I were going</td>\n",
       "      <td>cb774db0d1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sooo SAD</td>\n",
       "      <td>negative</td>\n",
       "      <td>Sooo SAD I will miss you here in San Diego!!!</td>\n",
       "      <td>549e992a42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bullying me</td>\n",
       "      <td>negative</td>\n",
       "      <td>my boss is bullying me...</td>\n",
       "      <td>088c60f138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>leave me alone</td>\n",
       "      <td>negative</td>\n",
       "      <td>what interview! leave me alone</td>\n",
       "      <td>9642c003ef</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sons of ****,</td>\n",
       "      <td>negative</td>\n",
       "      <td>Sons of ****, why couldn`t they put them on t...</td>\n",
       "      <td>358bd9e861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         selected_text sentiment  \\\n",
       "0  I`d have responded, if I were going   neutral   \n",
       "1                             Sooo SAD  negative   \n",
       "2                          bullying me  negative   \n",
       "3                       leave me alone  negative   \n",
       "4                        Sons of ****,  negative   \n",
       "\n",
       "                                                text      textID  \n",
       "0                I`d have responded, if I were going  cb774db0d1  \n",
       "1      Sooo SAD I will miss you here in San Diego!!!  549e992a42  \n",
       "2                          my boss is bullying me...  088c60f138  \n",
       "3                     what interview! leave me alone  9642c003ef  \n",
       "4   Sons of ****, why couldn`t they put them on t...  358bd9e861  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "selected_text    3534\n",
       "sentiment           0\n",
       "text                0\n",
       "textID              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21430e60dd8>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RhlcCZ1fnMmBRkmcAhwHrq2pzVd0DrAdWzOGmSJK2MtZrKkmWAi8FLgeeXlV3Qhc8wNNatcXA7QOzbWxl2yqXJI3J2EIlyROBvwPeWlXf217VIWW1nfJh61qTZEOSDZs2bZp+YyVJIxlLqCR5NF2gfLyqPtWKv9NOa9He72rlG4F9BmZfAtyxnfKHqaozqmp5VS2fmJjob0MkSQ8xjru/ApwJ3FhVfzEw6UJg6g6u1cAFA+XHtLvADgTua6fHLgYOTbJnu0B/aCuTJI3JwjGs8yDgN4Frk1zdyv4zcBpwXpLjgduAo9q0i4BXA5PAA8BxAFW1OckpwJWt3juravPcbIIkaZg5D5Wq+t8Mvx4CcMiQ+gWcsI1lrQXW9tc6SdIj4RP1kqTeGCqSpN4YKpKk3hgqkqTeGCqSpN4YKpKk3hgqkqTeGCqSpN4YKpKk3hgqkqTeGCqSpN4YKpKk3hgqkqTeGCqSpN4YKpKk3hgqkqTeGCqSpN4YKpKk3hgqkqTeGCqSpN4YKpKk3hgqkqTeGCqSpN4YKpKk3sz7UEmyIslNSSaTnDju9kjS7mxeh0qSBcAHgMOB/YCjk+w33lZJ0u5rXocKcAAwWVW3VNWPgXOAlWNukyTttuZ7qCwGbh8Y39jKJEljsHDcDXiEMqSsHlYpWQOsaaP3J7lpVls1XnsDd4+7EaPKe1aPuwk7k3m17wA4ediv4G5rXu2//O60993PjVJpvofKRmCfgfElwB1bV6qqM4Az5qpR45RkQ1UtH3c7NH3uu/nN/deZ76e/rgSWJdk3yR7AKuDCMbdJknZb8/pIpaq2JHkzcDGwAFhbVdePuVmStNua16ECUFUXAReNux07kd3iNN8uyn03v7n/gFQ97Lq2JEkzMt+vqUiSdiKGyi4mydIk/36G897fd3s0M0kWJfkPA+PPTHL+ONuk4ZL8dpJj2vCxSZ45MO0ju1svH57+2sUkORj4/ap6zZBpC6tqy3bmvb+qnjib7dNokiwFPltVLxhzUzQNSS6l+/3bMO62jItHKjuJdoRxY5K/SXJ9ki8keVySZyf5fJKrkvxDkue1+mclOXJg/qmjjNOAX0pydZK3tf+cPpnkM8AXkjwxySVJvpbk2iR2azMDM9hfz05yWZIrk7xzan9tZ3+cBjy77cd3t/Vd1+a5PMnzB9pyaZL9kzwhydq2jq+7b3esfa7fSLIuyTVJzk/y+CSHtM/w2vaZPqbVPy3JDa3ue1rZO5L8fvt9XA58vO23x7V9szzJ7yT5rwPrPTbJ+9vwG5Nc0eb569an4fxVVb52ghewFNgCvKSNnwe8EbgEWNbKXg58sQ2fBRw5MP/97f1guv9wp8qPpXtIdK82vhB4chveG5jkZ0es94/7c5gvrxnsr88CR7fh3x7YX0P3R1v+dVut77o2/DbgT9rwM4BvtuE/A97YhhcB3wSeMO7Pamd+tc+1gIPa+Frgv9B1//QLrexs4K3AXsBNA78vi9r7O+iOTgAuBZYPLP9SuqCZoOuncKr8c8C/Bn4R+Azw6Fb+QeCYcX8uj+TlkcrO5daquroNX0X3A/9K4JNJrgb+mu6PyHStr6rNbTjAnyW5BvhfdH2lPf0RtXr3NZ399Qrgk234vw8sYyb74zzgqDb8+oHlHgqc2NZ9KfBY4FnT3qrdz+1V9ZU2/LfAIXT79putbB3wy8D3gB8CH0nyOuCBUVdQVZuAW5IcmOSpwHOBr7R17Q9c2fbbIcDP97BNYzPvn1PZxfxoYPhBuj8u91bVS4bU3UI7fZkkwB7bWe4PBobfQPdf0/5V9S9JvkX3x0fTN539tS3T3h9V9e0k303yIuA3gN9qkwL8elXtyn3bzYaRLixX97D1AXR/+FcBbwZeNY31nEv3T8A3gE9XVbXf3XVVddI027zT8khl5/Y94NYkR0EXHkle3KZ9i+4/HOi6+390G/4+8KTtLPMpwF3tD9ivMGIncRrJ9vbXZcCvt+FVA/Nsa3/saD+eA/wB8JSquraVXQy8pf2hIslLH+kG7SaeleQVbfhouiPGpUme08p+E/hykifSfd4X0Z0OG/bPw/b226eAI9o6zm1llwBHJnkaQJK9kszr30lDZef3BuD4JP8IXM/Pvi/mb4B/k+QKunP3U0cj1wBbkvxjkrcNWd7HgeVJNrRlf2NWW7/72db+eivw9ra/ngHc18qH7o+q+i7wlSTXJXn3kPWcTxdO5w2UnUL3z8U17aL+Kb1u2a7rRmB1OwW5F3A6cBzdacxrgZ8AH6YLi8+2el+mu7a1tbOAD09dqB+cUFX3ADcAP1dVV7SyG+iu4XyhLXc9MzvFvdPwlmJpDiR5PPDP7ZTHKrqL9t6dNWbx1u3eeU1Fmhv7A3/VTk3dC7xpzO2RZoVHKpKk3nhNRZLUG0NFktQbQ0WS1BtDRZojSV6S5NUD469NcuIsr/PgJK+czXVIgwwVae68BPhpqFTVhVV12iyv82C6rmOkOeHdX9IIkjyB7kHDJcACugcLJ4G/AJ4I3A0cW1V3puv+/HLgV+g6djy+jU8CjwO+Dfx5G15eVW9Ochbwz8Dz6J6qPw5YTddn2OVVdWxrx6HAnwCPAf4vcFxV3d+6d1kH/BrdA5BH0fVTdRldFzKbgLdU1T/MxucjTfFIRRrNCuCOqnpxe1Du88D76XqK3p+ud9tTB+ovrKoD6J6kP7mqfgz8MXBuVb2kqs7l4fak60vqbXQ9154OPB94YTt1tjfd09e/WlUvAzYAbx+Y/+5W/iG6XnO/Rfck+OltnQaKZp0PP0qjuRZ4T5J30XVjfw/wAmB962prAXDnQP1Ptfep3otH8Zn2xP21wHem+vRKcn1bxhJgP7ruW6DrRPSr21jn66axbVJvDBVpBFX1zST7010T+XO6Ppqur6pXbGOWqR6MH2T037OpeX7CQ3tA/klbxoN0X2NwdI/rlHrl6S9pBOm+d/yBqvpb4D10nXhOTPVum+TRg9/GuA076nl4Ry4DDprqPbd9Q+EvzPI6pWkxVKTRvBC4on2R0h/RXR85EnhX65H4anZ8l9WXgP1aD7a/Md0GtC96Ohb4ROvR9jK6C/vb8xng37V1/tJ01ylNl3d/SZJ645GKJKk3hookqTeGiiSpN4aKJKk3hookqTeGiiSpN4aKJKk3hookqTf/H2YzMCyW0onuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('sentiment',data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['text_length'] = df['text'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21432bbe6a0>"
      ]
     },
     "execution_count": 102,
     "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": [
    "sns.stripplot(x='sentiment', y='text_length', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000214341A6A58>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000021419DE8B38>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000021419DDE0F0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000021419DD2668>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column='text_length',by='sentiment', bins=50,figsize=(20,10))"
   ]
  },
  {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}