Test Task_NIket.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('Data Analyst - Test Data - US.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "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>Review</th>\n",
       "      <th>date</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I was very impressed with the resort.\\n Great ...</td>\n",
       "      <td>2019/08/20</td>\n",
       "      <td>Sebastian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The rooms were nice the outside needs work als...</td>\n",
       "      <td>2019/08/20</td>\n",
       "      <td>Los Angeles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Great location! I have stayed at this hotel on...</td>\n",
       "      <td>2019/08/20</td>\n",
       "      <td>Georgia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The hotel was adequate for my stay. The strips...</td>\n",
       "      <td>2019/08/20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Great location, room was large and spacious. P...</td>\n",
       "      <td>2019/08/19</td>\n",
       "      <td>Palm Harbor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>All the options for fun were great! Do wish th...</td>\n",
       "      <td>2019/08/19</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Old. Musty. Motel. \\n Bath need an update asap...</td>\n",
       "      <td>2019/08/19</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Loved the layout of the hotel and the relaxing...</td>\n",
       "      <td>2019/08/19</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>My wife and I loved everything about it. It’s ...</td>\n",
       "      <td>2019/08/19</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>rooms were very small, was told i had a compli...</td>\n",
       "      <td>2019/08/19</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Review        date     Location\n",
       "0  I was very impressed with the resort.\\n Great ...  2019/08/20    Sebastian\n",
       "1  The rooms were nice the outside needs work als...  2019/08/20  Los Angeles\n",
       "2  Great location! I have stayed at this hotel on...  2019/08/20      Georgia\n",
       "3  The hotel was adequate for my stay. The strips...  2019/08/20          NaN\n",
       "4  Great location, room was large and spacious. P...  2019/08/19  Palm Harbor\n",
       "5  All the options for fun were great! Do wish th...  2019/08/19          NaN\n",
       "6  Old. Musty. Motel. \\n Bath need an update asap...  2019/08/19          NaN\n",
       "7  Loved the layout of the hotel and the relaxing...  2019/08/19          NaN\n",
       "8  My wife and I loved everything about it. It’s ...  2019/08/19          NaN\n",
       "9  rooms were very small, was told i had a compli...  2019/08/19          NaN"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "loc=data['Location'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1082,)"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Review        55\n",
       "date           0\n",
       "Location    4737\n",
       "dtype: int64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# totall null vaues \n",
    "\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       I was very impressed with the resort.\\n Great ...\n",
       "1       The rooms were nice the outside needs work als...\n",
       "2       Great location! I have stayed at this hotel on...\n",
       "3       The hotel was adequate for my stay. The strips...\n",
       "4       Great location, room was large and spacious. P...\n",
       "                              ...                        \n",
       "6443    ·the staff were all so friendly. frank, the ch...\n",
       "6444           ·the internet barely worked or didn't work\n",
       "6445    ·the location was great.\\n \\n \\n \\n \\n ·the be...\n",
       "6446    ·we were early for check in and the staff was ...\n",
       "6447    ·staff, free coffee, location and activities.\\...\n",
       "Name: Review, Length: 6448, dtype: object"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Review']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Keywords to find out Reviewers' Emotions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos=['loved','love','nice','friendly','great','super','helpful','easy','awesome','beautiful','excellent',\n",
    "     'fine','well','good','like','liked','amazing','comfortable']\n",
    "neg=['old','not',\"didn't\",'small','tiny','hard','need','limited','uncomfortable','rude','dirty','no','no','no','no','no','no'\n",
    "     ,'no']\n",
    "# mid=['but','else']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "# obj to str converting \n",
    "data['Review']=data['Review'].astype(str)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nkdhe\\anaconda3_2020\\lib\\site-packages\\ipykernel_launcher.py:9: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6448 4366\n"
     ]
    }
   ],
   "source": [
    "# Appending positive review in \"emos\" col.\n",
    "\n",
    "count=0\n",
    "c=0\n",
    "for i in range(data.shape[0]):\n",
    "    st=data['Review'][i].lower()\n",
    "    count=count+1\n",
    "    for j in pos:\n",
    "       # st=data['Review'][i]\n",
    "        if(st.find(j)>=0):\n",
    "            data['emos'][i]=1\n",
    "            c=c+1\n",
    "            break\n",
    "\n",
    "print(count,c)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    4366\n",
       "0    2082\n",
       "Name: emos, dtype: int64"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['emos'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6448, 3)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['emos']=[0 for i in range(data.shape[0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "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>Review</th>\n",
       "      <th>date</th>\n",
       "      <th>Location</th>\n",
       "      <th>emos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I was very impressed with the resort.\\n Great ...</td>\n",
       "      <td>2019/08/20</td>\n",
       "      <td>Sebastian</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The rooms were nice the outside needs work als...</td>\n",
       "      <td>2019/08/20</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Great location! I have stayed at this hotel on...</td>\n",
       "      <td>2019/08/20</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The hotel was adequate for my stay. The strips...</td>\n",
       "      <td>2019/08/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Great location, room was large and spacious. P...</td>\n",
       "      <td>2019/08/19</td>\n",
       "      <td>Palm Harbor</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Review        date     Location  \\\n",
       "0  I was very impressed with the resort.\\n Great ...  2019/08/20    Sebastian   \n",
       "1  The rooms were nice the outside needs work als...  2019/08/20  Los Angeles   \n",
       "2  Great location! I have stayed at this hotel on...  2019/08/20      Georgia   \n",
       "3  The hotel was adequate for my stay. The strips...  2019/08/20          NaN   \n",
       "4  Great location, room was large and spacious. P...  2019/08/19  Palm Harbor   \n",
       "\n",
       "   emos  \n",
       "0     0  \n",
       "1     0  \n",
       "2     0  \n",
       "3     0  \n",
       "4     0  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New dataset after adding emostion column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "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>Review</th>\n",
       "      <th>date</th>\n",
       "      <th>Location</th>\n",
       "      <th>emos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I was very impressed with the resort.\\n Great ...</td>\n",
       "      <td>2019/08/20</td>\n",
       "      <td>Sebastian</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The rooms were nice the outside needs work als...</td>\n",
       "      <td>2019/08/20</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Great location! I have stayed at this hotel on...</td>\n",
       "      <td>2019/08/20</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The hotel was adequate for my stay. The strips...</td>\n",
       "      <td>2019/08/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Great location, room was large and spacious. P...</td>\n",
       "      <td>2019/08/19</td>\n",
       "      <td>Palm Harbor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>All the options for fun were great! Do wish th...</td>\n",
       "      <td>2019/08/19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Old. Musty. Motel. \\n Bath need an update asap...</td>\n",
       "      <td>2019/08/19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Loved the layout of the hotel and the relaxing...</td>\n",
       "      <td>2019/08/19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>My wife and I loved everything about it. It’s ...</td>\n",
       "      <td>2019/08/19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>rooms were very small, was told i had a compli...</td>\n",
       "      <td>2019/08/19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Review        date     Location  \\\n",
       "0  I was very impressed with the resort.\\n Great ...  2019/08/20    Sebastian   \n",
       "1  The rooms were nice the outside needs work als...  2019/08/20  Los Angeles   \n",
       "2  Great location! I have stayed at this hotel on...  2019/08/20      Georgia   \n",
       "3  The hotel was adequate for my stay. The strips...  2019/08/20          NaN   \n",
       "4  Great location, room was large and spacious. P...  2019/08/19  Palm Harbor   \n",
       "5  All the options for fun were great! Do wish th...  2019/08/19          NaN   \n",
       "6  Old. Musty. Motel. \\n Bath need an update asap...  2019/08/19          NaN   \n",
       "7  Loved the layout of the hotel and the relaxing...  2019/08/19          NaN   \n",
       "8  My wife and I loved everything about it. It’s ...  2019/08/19          NaN   \n",
       "9  rooms were very small, was told i had a compli...  2019/08/19          NaN   \n",
       "\n",
       "   emos  \n",
       "0     1  \n",
       "1     1  \n",
       "2     1  \n",
       "3     1  \n",
       "4     1  \n",
       "5     1  \n",
       "6     0  \n",
       "7     1  \n",
       "8     1  \n",
       "9     0  "
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handling data and time\n",
    "\n",
    "data[\"date\"] = pd.to_datetime(data['date'])\n",
    "\n",
    "data[\"year\"] = data['date'].dt.year\n",
    "\n",
    "data[\"month\"] = data['date'].dt.month\n",
    "data['day']= data['date'].dt.day\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "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>Review</th>\n",
       "      <th>date</th>\n",
       "      <th>Location</th>\n",
       "      <th>emos</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I was very impressed with the resort.\\n Great ...</td>\n",
       "      <td>2019-08-20</td>\n",
       "      <td>Sebastian</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The rooms were nice the outside needs work als...</td>\n",
       "      <td>2019-08-20</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Great location! I have stayed at this hotel on...</td>\n",
       "      <td>2019-08-20</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The hotel was adequate for my stay. The strips...</td>\n",
       "      <td>2019-08-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Great location, room was large and spacious. P...</td>\n",
       "      <td>2019-08-19</td>\n",
       "      <td>Palm Harbor</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>All the options for fun were great! Do wish th...</td>\n",
       "      <td>2019-08-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Old. Musty. Motel. \\n Bath need an update asap...</td>\n",
       "      <td>2019-08-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Loved the layout of the hotel and the relaxing...</td>\n",
       "      <td>2019-08-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>My wife and I loved everything about it. It’s ...</td>\n",
       "      <td>2019-08-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>rooms were very small, was told i had a compli...</td>\n",
       "      <td>2019-08-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Very clean and friendly and I love the locatio...</td>\n",
       "      <td>2019-08-19</td>\n",
       "      <td>California</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Nice and comfortable good position near the st...</td>\n",
       "      <td>2019-08-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Location was great, lobby area was nice but ro...</td>\n",
       "      <td>2019-08-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Quiet and at a great location. \\n Good control...</td>\n",
       "      <td>2019-08-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>El servicio super amables no todos en el buffe...</td>\n",
       "      <td>2019-08-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Very nice and quiet and clean rooms. \\n We wil...</td>\n",
       "      <td>2019-08-19</td>\n",
       "      <td>Redding</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Rooms were clean, updated, and beds were comfo...</td>\n",
       "      <td>2019-08-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>The staff was exceptionally helpful and the pr...</td>\n",
       "      <td>2019-08-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>It was great for what we needed, a place to sl...</td>\n",
       "      <td>2019-08-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>rooms were clean. we had two room and were abl...</td>\n",
       "      <td>2019-08-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Review       date     Location  \\\n",
       "0   I was very impressed with the resort.\\n Great ... 2019-08-20    Sebastian   \n",
       "1   The rooms were nice the outside needs work als... 2019-08-20  Los Angeles   \n",
       "2   Great location! I have stayed at this hotel on... 2019-08-20      Georgia   \n",
       "3   The hotel was adequate for my stay. The strips... 2019-08-20          NaN   \n",
       "4   Great location, room was large and spacious. P... 2019-08-19  Palm Harbor   \n",
       "5   All the options for fun were great! Do wish th... 2019-08-19          NaN   \n",
       "6   Old. Musty. Motel. \\n Bath need an update asap... 2019-08-19          NaN   \n",
       "7   Loved the layout of the hotel and the relaxing... 2019-08-19          NaN   \n",
       "8   My wife and I loved everything about it. It’s ... 2019-08-19          NaN   \n",
       "9   rooms were very small, was told i had a compli... 2019-08-19          NaN   \n",
       "10  Very clean and friendly and I love the locatio... 2019-08-19   California   \n",
       "11  Nice and comfortable good position near the st... 2019-08-19          NaN   \n",
       "12  Location was great, lobby area was nice but ro... 2019-08-19          NaN   \n",
       "13  Quiet and at a great location. \\n Good control... 2019-08-19          NaN   \n",
       "14  El servicio super amables no todos en el buffe... 2019-08-19          NaN   \n",
       "15  Very nice and quiet and clean rooms. \\n We wil... 2019-08-19      Redding   \n",
       "16  Rooms were clean, updated, and beds were comfo... 2019-08-19          NaN   \n",
       "17  The staff was exceptionally helpful and the pr... 2019-08-19          NaN   \n",
       "18  It was great for what we needed, a place to sl... 2019-08-19          NaN   \n",
       "19  rooms were clean. we had two room and were abl... 2019-08-19          NaN   \n",
       "\n",
       "    emos  year  month  day  \n",
       "0      1  2019      8   20  \n",
       "1      1  2019      8   20  \n",
       "2      1  2019      8   20  \n",
       "3      1  2019      8   20  \n",
       "4      1  2019      8   19  \n",
       "5      1  2019      8   19  \n",
       "6      0  2019      8   19  \n",
       "7      1  2019      8   19  \n",
       "8      1  2019      8   19  \n",
       "9      0  2019      8   19  \n",
       "10     1  2019      8   19  \n",
       "11     1  2019      8   19  \n",
       "12     1  2019      8   19  \n",
       "13     1  2019      8   19  \n",
       "14     1  2019      8   19  \n",
       "15     1  2019      8   19  \n",
       "16     1  2019      8   19  \n",
       "17     1  2019      8   19  \n",
       "18     1  2019      8   19  \n",
       "19     0  2019      8   19  "
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Different insights from data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019    3856\n",
       "2018    2592\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking reviews based on dates\n",
    "data['year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22215412648>"
      ]
     },
     "execution_count": 268,
     "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.distplot(data['month'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Months vs Years')"
      ]
     },
     "execution_count": 317,
     "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": [
    "plt.scatter(data['day'],data['year'],)\n",
    "plt.title('Months vs Years')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8     728\n",
       "12    630\n",
       "3     605\n",
       "4     593\n",
       "7     549\n",
       "5     526\n",
       "10    524\n",
       "11    519\n",
       "1     513\n",
       "9     481\n",
       "2     418\n",
       "6     362\n",
       "Name: month, dtype: int64"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['month'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     1083\n",
       "26     211\n",
       "4      209\n",
       "17     197\n",
       "13     196\n",
       "14     196\n",
       "2      196\n",
       "18     195\n",
       "15     194\n",
       "5      193\n",
       "12     190\n",
       "9      187\n",
       "20     186\n",
       "10     184\n",
       "25     182\n",
       "11     182\n",
       "21     180\n",
       "3      178\n",
       "27     174\n",
       "22     173\n",
       "8      173\n",
       "6      172\n",
       "23     172\n",
       "7      171\n",
       "30     168\n",
       "19     168\n",
       "24     163\n",
       "28     159\n",
       "29     155\n",
       "16     145\n",
       "31     116\n",
       "Name: day, dtype: int64"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['day'].value_counts()\n",
    "# count=0\n",
    "\n",
    "# for i in range (data.shape[0]):\n",
    "#     if(data['month'][i]==8 and data['year'][i]==2018 and data['emos'][i]==1):\n",
    "        \n",
    "#         count=count+1\n",
    "        \n",
    "# print('max customers visited ',count)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States of America    116\n",
       "New York                     34\n",
       "California                   19\n",
       "San Jose                     19\n",
       "Canada                       16\n",
       "                           ... \n",
       "Auburn maine                  1\n",
       "Dorado, PR                    1\n",
       "sagamore beach ma             1\n",
       "Louisville Kentucky           1\n",
       "Loves Park, IL                1\n",
       "Name: Location, Length: 1082, dtype: int64"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Location'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000002220EECEE08>"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# groupby usa and emos\n",
    "data.groupby(['Location','emos'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "happy customer in USA  71\n",
      "unhappy customer in USA  45\n"
     ]
    }
   ],
   "source": [
    "count=0\n",
    "neg_count=0\n",
    "for i in range (data.shape[0]):\n",
    "    if(data['Location'][i]=='United States of America' and data['emos'][i]==1):\n",
    "        count=count+1\n",
    "for i in range (data.shape[0]):\n",
    "    if(data['Location'][i]=='United States of America' and data['emos'][i]==0):\n",
    "        neg_count=neg_count+1        \n",
    "print(\"happy customer in USA \",count) \n",
    "print(\"unhappy customer in USA \",neg_count)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7894736842105263"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "15/19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "happy customer in USA  22\n",
      "unhappy customer in USA  12\n"
     ]
    }
   ],
   "source": [
    "count=0\n",
    "neg_count=0\n",
    "for i in range (data.shape[0]):\n",
    "    if(data['Location'][i]=='New York' and data['emos'][i]==1):\n",
    "        count=count+1\n",
    "for i in range (data.shape[0]):\n",
    "    if(data['Location'][i]=='New York' and data['emos'][i]==0):\n",
    "        neg_count=neg_count+1        \n",
    "print(\"happy customer in USA \",count) \n",
    "print(\"unhappy customer in USA \",neg_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "happy customer in USA  15\n",
      "unhappy customer in USA  4\n"
     ]
    }
   ],
   "source": [
    "count=0\n",
    "neg_count=0\n",
    "for i in range (data.shape[0]):\n",
    "    if(data['Location'][i]=='California' and data['emos'][i]==1):\n",
    "        count=count+1\n",
    "for i in range (data.shape[0]):\n",
    "    if(data['Location'][i]=='California' and data['emos'][i]==0):\n",
    "        neg_count=neg_count+1        \n",
    "print(\"happy customer in USA \",count) \n",
    "print(\"unhappy customer in USA \",neg_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "happy customer in USA  17\n",
      "unhappy customer in USA  2\n"
     ]
    }
   ],
   "source": [
    "count=0\n",
    "neg_count=0\n",
    "for i in range (data.shape[0]):\n",
    "    if(data['Location'][i]=='San Jose' and data['emos'][i]==1):\n",
    "        count=count+1\n",
    "for i in range (data.shape[0]):\n",
    "    if(data['Location'][i]=='San Jose' and data['emos'][i]==0):\n",
    "        neg_count=neg_count+1        \n",
    "print(\"happy customer in USA \",count) \n",
    "print(\"unhappy customer in USA \",neg_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ems(name):\n",
    "    count=0\n",
    "    neg_count=0\n",
    "    for i in range (data.shape[0]):\n",
    "        if(data['Location'][i]==name and data['emos'][i]==1):\n",
    "            count=count+1\n",
    "    for i in range (data.shape[0]):\n",
    "        if(data['Location'][i]==name and data['emos'][i]==0):\n",
    "            neg_count=neg_count+1        \n",
    "    print(\"happy customer in USA \",count) \n",
    "    print(\"unhappy customer in USA \",neg_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States of America    116\n",
       "New York                     34\n",
       "California                   19\n",
       "San Jose                     19\n",
       "Canada                       16\n",
       "Florida                      16\n",
       "United Kingdom               14\n",
       "New York, NY                 13\n",
       "Orlando                      12\n",
       "Seattle                      12\n",
       "Name: Location, dtype: int64"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loc.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in range(100):\n",
    "#     ems(data['Location'][i])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in 2019 happy customer in USA  2621\n",
      "in 2019 unhappy customer in USA  1235\n"
     ]
    }
   ],
   "source": [
    "count=0\n",
    "neg_count=0\n",
    "for i in range (data.shape[0]):\n",
    "    if(data['year'][i]==2019 and data['emos'][i]==1):\n",
    "        count=count+1\n",
    "for i in range (data.shape[0]):\n",
    "    if(data['year'][i]==2019 and data['emos'][i]==0):\n",
    "        neg_count=neg_count+1        \n",
    "print(\"in 2019 happy customer in USA \",count) \n",
    "print(\"in 2019 unhappy customer in USA \",neg_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6797199170124482"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2621 /(2621+1235)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in 2018 happy customer in USA  1745\n",
      "in 2018 unhappy customer in USA  847\n"
     ]
    }
   ],
   "source": [
    "count=0\n",
    "neg_count=0\n",
    "for i in range (data.shape[0]):\n",
    "    if(data['year'][i]==2018 and data['emos'][i]==1):\n",
    "        count=count+1\n",
    "for i in range (data.shape[0]):\n",
    "    if(data['year'][i]==2018 and data['emos'][i]==0):\n",
    "        neg_count=neg_count+1        \n",
    "print(\"in 2018 happy customer in USA \",count) \n",
    "print(\"in 2018 unhappy customer in USA \",neg_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67.32253086419753"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1745/(1745+847) *100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2220e9ca688>"
      ]
     },
     "execution_count": 234,
     "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.countplot(x=data['emos'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6448, 7)"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67.71091811414392"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4366/6448 *100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2082"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "6448-4366\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3228908188585608"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2082/6448"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "438-296"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Switzerland                  1\n",
       "Long Island NY               1\n",
       "San Jose ca                  1\n",
       "Des Moines, IA               1\n",
       "san lorenzo                  1\n",
       "Falkirk,GB.                  1\n",
       "Catania, it                  1\n",
       "BOSTON                       1\n",
       "Coatbridge Scotland          1\n",
       "Guanajuato, México.          1\n",
       "Hoboken                      1\n",
       "Bellevue, WA                 1\n",
       "Prescott Valley, AZ          1\n",
       "Sacramento, California       1\n",
       "Howard Beach                 1\n",
       "Los Angeles, ca              1\n",
       "Staffordshire                1\n",
       "Nashville, TN                1\n",
       "Stl, mo                      1\n",
       "Avon, us                     1\n",
       "Plano TX                     1\n",
       "Bronx,new york               1\n",
       "Lake alfred, us              1\n",
       "Port St. Lucie               1\n",
       "Monterrey                    1\n",
       "Lockport NY                  1\n",
       "Champaign,IL                 1\n",
       "Antioch ca                   1\n",
       "Richmond Va                  1\n",
       "Carluke South lanarkshire    1\n",
       "Livingston                   1\n",
       "Rochester NY                 1\n",
       "Petaluma, Ca.                1\n",
       "Scotts Valley                1\n",
       "St Barth                     1\n",
       "Atlanta Georgis              1\n",
       "Cambridge, England           1\n",
       "Idaho                        1\n",
       "Terre Haute, IN USA          1\n",
       "savasse France               1\n",
       "Biella IT                    1\n",
       "atlantic city                1\n",
       "Lee's Summit                 1\n",
       "San Juan, PR                 1\n",
       "La quinta                    1\n",
       "Auburn maine                 1\n",
       "Dorado, PR                   1\n",
       "sagamore beach ma            1\n",
       "Louisville Kentucky          1\n",
       "Loves Park, IL               1\n",
       "Name: Location, dtype: int64"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loc.tail(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x222166b0c08>]"
      ]
     },
     "execution_count": 319,
     "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": [
    "plt.plot(data['day'],data['emos'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22216842148>"
      ]
     },
     "execution_count": 321,
     "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.countplot(x='emos',hue='month',data=data)"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}