User advertisement.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# User advertisement click predicton"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### data is of two types- continous(eg. age, name, basically that keeps on changing from person to change) and the other is categorical (eg. survival, gender, pclass basically that only has particular values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### continous data- best visualization is histogram and categorical data- best visualization is bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as mpt\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('advertising.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Daily Time Spent on Site</th>\n",
       "      <th>Age</th>\n",
       "      <th>Area Income</th>\n",
       "      <th>Daily Internet Usage</th>\n",
       "      <th>Ad Topic Line</th>\n",
       "      <th>City</th>\n",
       "      <th>Male</th>\n",
       "      <th>Country</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Clicked on Ad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>68.95</td>\n",
       "      <td>35</td>\n",
       "      <td>61833.90</td>\n",
       "      <td>256.09</td>\n",
       "      <td>Cloned 5thgeneration orchestration</td>\n",
       "      <td>Wrightburgh</td>\n",
       "      <td>0</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>27/03/2016 0:53</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80.23</td>\n",
       "      <td>31</td>\n",
       "      <td>68441.85</td>\n",
       "      <td>193.77</td>\n",
       "      <td>Monitored national standardization</td>\n",
       "      <td>West Jodi</td>\n",
       "      <td>1</td>\n",
       "      <td>Nauru</td>\n",
       "      <td>04/04/2016 1:39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69.47</td>\n",
       "      <td>26</td>\n",
       "      <td>59785.94</td>\n",
       "      <td>236.50</td>\n",
       "      <td>Organic bottom-line service-desk</td>\n",
       "      <td>Davidton</td>\n",
       "      <td>0</td>\n",
       "      <td>San Marino</td>\n",
       "      <td>13/03/2016 20:35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Daily Time Spent on Site  Age  Area Income  Daily Internet Usage  \\\n",
       "0                     68.95   35     61833.90                256.09   \n",
       "1                     80.23   31     68441.85                193.77   \n",
       "2                     69.47   26     59785.94                236.50   \n",
       "\n",
       "                        Ad Topic Line         City  Male     Country  \\\n",
       "0  Cloned 5thgeneration orchestration  Wrightburgh     0     Tunisia   \n",
       "1  Monitored national standardization    West Jodi     1       Nauru   \n",
       "2    Organic bottom-line service-desk     Davidton     0  San Marino   \n",
       "\n",
       "          Timestamp  Clicked on Ad  \n",
       "0   27/03/2016 0:53              0  \n",
       "1   04/04/2016 1:39              0  \n",
       "2  13/03/2016 20:35              0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting a histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sb.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1509b73a1d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpt.hist(data['Age'], label='Total users')\n",
    "mpt.hist(data['Age'][data['Clicked on Ad']==1], label='Clicked on Ad')\n",
    "mpt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 100% stacked bar chart of gender and clicks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = pd.crosstab(data['Male'], data['Clicked on Ad'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Clicked on Ad</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>229</td>\n",
       "      <td>236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>232</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Clicked on Ad    0    1\n",
       "Male                   \n",
       "0              229  236\n",
       "1              232  203"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data['total']=new_data.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Clicked on Ad</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>229</td>\n",
       "      <td>236</td>\n",
       "      <td>465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>232</td>\n",
       "      <td>203</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Clicked on Ad    0    1  total\n",
       "Male                          \n",
       "0              229  236    465\n",
       "1              232  203    435"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data=new_data.apply(lambda x: round(100*x/new_data['total']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Clicked on Ad</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Clicked on Ad     0     1  total\n",
       "Male                            \n",
       "0              49.0  51.0  100.0\n",
       "1              53.0  47.0  100.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data.drop('total', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1509b842588>"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_data.plot(kind='bar', stacked='True')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data = data[['Male', 'Age']]\n",
    "output_data = data['Clicked on Ad']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, x_text, Y, y_test = train_test_split(training_data, output_data, test_size=0.3, random_state=42) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ishit\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,Y)              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(x_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 0, 1, 0, 0, 1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[106,  48],\n",
       "       [ 25,  91]], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(predictions, y_test)               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7296296296296296"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = accuracy_score(predictions, y_test)\n",
    "score                                       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### visualization for country and clicked on ads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Daily Time Spent on Site</th>\n",
       "      <th>Age</th>\n",
       "      <th>Area Income</th>\n",
       "      <th>Daily Internet Usage</th>\n",
       "      <th>Ad Topic Line</th>\n",
       "      <th>City</th>\n",
       "      <th>Male</th>\n",
       "      <th>Country</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Clicked on Ad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>68.95</td>\n",
       "      <td>35</td>\n",
       "      <td>61833.90</td>\n",
       "      <td>256.09</td>\n",
       "      <td>Cloned 5thgeneration orchestration</td>\n",
       "      <td>Wrightburgh</td>\n",
       "      <td>0</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>27/03/2016 0:53</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80.23</td>\n",
       "      <td>31</td>\n",
       "      <td>68441.85</td>\n",
       "      <td>193.77</td>\n",
       "      <td>Monitored national standardization</td>\n",
       "      <td>West Jodi</td>\n",
       "      <td>1</td>\n",
       "      <td>Nauru</td>\n",
       "      <td>04/04/2016 1:39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69.47</td>\n",
       "      <td>26</td>\n",
       "      <td>59785.94</td>\n",
       "      <td>236.50</td>\n",
       "      <td>Organic bottom-line service-desk</td>\n",
       "      <td>Davidton</td>\n",
       "      <td>0</td>\n",
       "      <td>San Marino</td>\n",
       "      <td>13/03/2016 20:35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74.15</td>\n",
       "      <td>29</td>\n",
       "      <td>54806.18</td>\n",
       "      <td>245.89</td>\n",
       "      <td>Triple-buffered reciprocal time-frame</td>\n",
       "      <td>West Terrifurt</td>\n",
       "      <td>1</td>\n",
       "      <td>Italy</td>\n",
       "      <td>10/01/2016 2:31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68.37</td>\n",
       "      <td>35</td>\n",
       "      <td>73889.99</td>\n",
       "      <td>225.58</td>\n",
       "      <td>Robust logistical utilization</td>\n",
       "      <td>South Manuel</td>\n",
       "      <td>0</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>03/06/2016 3:36</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Daily Time Spent on Site  Age  Area Income  Daily Internet Usage  \\\n",
       "0                     68.95   35     61833.90                256.09   \n",
       "1                     80.23   31     68441.85                193.77   \n",
       "2                     69.47   26     59785.94                236.50   \n",
       "3                     74.15   29     54806.18                245.89   \n",
       "4                     68.37   35     73889.99                225.58   \n",
       "\n",
       "                           Ad Topic Line            City  Male     Country  \\\n",
       "0     Cloned 5thgeneration orchestration     Wrightburgh     0     Tunisia   \n",
       "1     Monitored national standardization       West Jodi     1       Nauru   \n",
       "2       Organic bottom-line service-desk        Davidton     0  San Marino   \n",
       "3  Triple-buffered reciprocal time-frame  West Terrifurt     1       Italy   \n",
       "4          Robust logistical utilization    South Manuel     0     Iceland   \n",
       "\n",
       "          Timestamp  Clicked on Ad  \n",
       "0   27/03/2016 0:53              0  \n",
       "1   04/04/2016 1:39              0  \n",
       "2  13/03/2016 20:35              0  \n",
       "3   10/01/2016 2:31              0  \n",
       "4   03/06/2016 3:36              0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Clicked on Ad</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Clicked on Ad   0  1  Total\n",
       "Country                    \n",
       "Afghanistan     2  5      7\n",
       "Albania         3  3      6\n",
       "Algeria         3  3      6\n",
       "American Samoa  2  3      5\n",
       "Andorra         0  2      2"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data2 = pd.crosstab(data['Country'], data['Clicked on Ad'], margins=True, margins_name='Total')\n",
    "new_data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Clicked on Ad</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Clicked on Ad  0  1  Total\n",
       "Country                   \n",
       "Afghanistan    2  5      7\n",
       "Albania        3  3      6"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([0, 1, 'Total'], dtype='object', name='Clicked on Ad')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(235, 3)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(236):\n",
    "    a=(new_data2.iloc[:i, 1:2])\n",
    "    b=(new_data2.iloc[:i, 2:3])\n",
    "new_data2['ratio'] = round(100*(a[1]/b['Total']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Clicked on Ad</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>Total</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kiribati</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guatemala</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sierra Leone</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saint Barthelemy</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Clicked on Ad     0  1  Total  ratio\n",
       "Country                             \n",
       "Kiribati          0  1      1  100.0\n",
       "Guatemala         0  3      3  100.0\n",
       "Sierra Leone      0  2      2  100.0\n",
       "Saint Barthelemy  0  1      1  100.0\n",
       "Spain             0  3      3  100.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data2.sort_values(by=['ratio'], ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization based on daily internet usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Daily Time Spent on Site</th>\n",
       "      <th>Age</th>\n",
       "      <th>Area Income</th>\n",
       "      <th>Daily Internet Usage</th>\n",
       "      <th>Ad Topic Line</th>\n",
       "      <th>City</th>\n",
       "      <th>Male</th>\n",
       "      <th>Country</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Clicked on Ad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>68.95</td>\n",
       "      <td>35</td>\n",
       "      <td>61833.90</td>\n",
       "      <td>256.09</td>\n",
       "      <td>Cloned 5thgeneration orchestration</td>\n",
       "      <td>Wrightburgh</td>\n",
       "      <td>0</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>27/03/2016 0:53</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80.23</td>\n",
       "      <td>31</td>\n",
       "      <td>68441.85</td>\n",
       "      <td>193.77</td>\n",
       "      <td>Monitored national standardization</td>\n",
       "      <td>West Jodi</td>\n",
       "      <td>1</td>\n",
       "      <td>Nauru</td>\n",
       "      <td>04/04/2016 1:39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69.47</td>\n",
       "      <td>26</td>\n",
       "      <td>59785.94</td>\n",
       "      <td>236.50</td>\n",
       "      <td>Organic bottom-line service-desk</td>\n",
       "      <td>Davidton</td>\n",
       "      <td>0</td>\n",
       "      <td>San Marino</td>\n",
       "      <td>13/03/2016 20:35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74.15</td>\n",
       "      <td>29</td>\n",
       "      <td>54806.18</td>\n",
       "      <td>245.89</td>\n",
       "      <td>Triple-buffered reciprocal time-frame</td>\n",
       "      <td>West Terrifurt</td>\n",
       "      <td>1</td>\n",
       "      <td>Italy</td>\n",
       "      <td>10/01/2016 2:31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68.37</td>\n",
       "      <td>35</td>\n",
       "      <td>73889.99</td>\n",
       "      <td>225.58</td>\n",
       "      <td>Robust logistical utilization</td>\n",
       "      <td>South Manuel</td>\n",
       "      <td>0</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>03/06/2016 3:36</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Daily Time Spent on Site  Age  Area Income  Daily Internet Usage  \\\n",
       "0                     68.95   35     61833.90                256.09   \n",
       "1                     80.23   31     68441.85                193.77   \n",
       "2                     69.47   26     59785.94                236.50   \n",
       "3                     74.15   29     54806.18                245.89   \n",
       "4                     68.37   35     73889.99                225.58   \n",
       "\n",
       "                           Ad Topic Line            City  Male     Country  \\\n",
       "0     Cloned 5thgeneration orchestration     Wrightburgh     0     Tunisia   \n",
       "1     Monitored national standardization       West Jodi     1       Nauru   \n",
       "2       Organic bottom-line service-desk        Davidton     0  San Marino   \n",
       "3  Triple-buffered reciprocal time-frame  West Terrifurt     1       Italy   \n",
       "4          Robust logistical utilization    South Manuel     0     Iceland   \n",
       "\n",
       "          Timestamp  Clicked on Ad  \n",
       "0   27/03/2016 0:53              0  \n",
       "1   04/04/2016 1:39              0  \n",
       "2  13/03/2016 20:35              0  \n",
       "3   10/01/2016 2:31              0  \n",
       "4   03/06/2016 3:36              0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making a histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1509c414ba8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpt.hist(data['Daily Internet Usage'], label='Daily Internet Usage')\n",
    "mpt.hist(data['Daily Internet Usage'][data['Clicked on Ad']==1], label='CLicked on Ad')\n",
    "mpt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making a scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Daily Time Spent on Site</th>\n",
       "      <th>Age</th>\n",
       "      <th>Area Income</th>\n",
       "      <th>Daily Internet Usage</th>\n",
       "      <th>Ad Topic Line</th>\n",
       "      <th>City</th>\n",
       "      <th>Male</th>\n",
       "      <th>Country</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Clicked on Ad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>68.95</td>\n",
       "      <td>35</td>\n",
       "      <td>61833.90</td>\n",
       "      <td>256.09</td>\n",
       "      <td>Cloned 5thgeneration orchestration</td>\n",
       "      <td>Wrightburgh</td>\n",
       "      <td>0</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>27/03/2016 0:53</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80.23</td>\n",
       "      <td>31</td>\n",
       "      <td>68441.85</td>\n",
       "      <td>193.77</td>\n",
       "      <td>Monitored national standardization</td>\n",
       "      <td>West Jodi</td>\n",
       "      <td>1</td>\n",
       "      <td>Nauru</td>\n",
       "      <td>04/04/2016 1:39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69.47</td>\n",
       "      <td>26</td>\n",
       "      <td>59785.94</td>\n",
       "      <td>236.50</td>\n",
       "      <td>Organic bottom-line service-desk</td>\n",
       "      <td>Davidton</td>\n",
       "      <td>0</td>\n",
       "      <td>San Marino</td>\n",
       "      <td>13/03/2016 20:35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74.15</td>\n",
       "      <td>29</td>\n",
       "      <td>54806.18</td>\n",
       "      <td>245.89</td>\n",
       "      <td>Triple-buffered reciprocal time-frame</td>\n",
       "      <td>West Terrifurt</td>\n",
       "      <td>1</td>\n",
       "      <td>Italy</td>\n",
       "      <td>10/01/2016 2:31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68.37</td>\n",
       "      <td>35</td>\n",
       "      <td>73889.99</td>\n",
       "      <td>225.58</td>\n",
       "      <td>Robust logistical utilization</td>\n",
       "      <td>South Manuel</td>\n",
       "      <td>0</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>03/06/2016 3:36</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Daily Time Spent on Site  Age  Area Income  Daily Internet Usage  \\\n",
       "0                     68.95   35     61833.90                256.09   \n",
       "1                     80.23   31     68441.85                193.77   \n",
       "2                     69.47   26     59785.94                236.50   \n",
       "3                     74.15   29     54806.18                245.89   \n",
       "4                     68.37   35     73889.99                225.58   \n",
       "\n",
       "                           Ad Topic Line            City  Male     Country  \\\n",
       "0     Cloned 5thgeneration orchestration     Wrightburgh     0     Tunisia   \n",
       "1     Monitored national standardization       West Jodi     1       Nauru   \n",
       "2       Organic bottom-line service-desk        Davidton     0  San Marino   \n",
       "3  Triple-buffered reciprocal time-frame  West Terrifurt     1       Italy   \n",
       "4          Robust logistical utilization    South Manuel     0     Iceland   \n",
       "\n",
       "          Timestamp  Clicked on Ad  \n",
       "0   27/03/2016 0:53              0  \n",
       "1   04/04/2016 1:39              0  \n",
       "2  13/03/2016 20:35              0  \n",
       "3   10/01/2016 2:31              0  \n",
       "4   03/06/2016 3:36              0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mpt.scatter(x=data['Daily Internet Usage'], y=data['Clicked on Ad'])\n",
    "# mpt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data4=pd.crosstab(data['Daily Internet Usage'], data['Clicked on Ad'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Clicked on Ad</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daily Internet Usage</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>104.78</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105.00</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105.15</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105.22</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105.69</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "Clicked on Ad         0  1\n",
       "Daily Internet Usage      \n",
       "104.78                0  1\n",
       "105.00                0  1\n",
       "105.15                0  1\n",
       "105.22                0  1\n",
       "105.69                0  1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([0, 1], dtype='int64', name='Clicked on Ad')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data4.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "2",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2656\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2657\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2658\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 2",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-52-c7e4f861989d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;33m[\u001b[0m\u001b[0mnew_data4\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_data4\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2925\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2926\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2927\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2928\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2929\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2657\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2658\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2659\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2660\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2661\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 2"
     ]
    }
   ],
   "source": [
    "[new_data4[1], new_data4[2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Float64Index([104.78,  105.0, 105.15, 105.22, 105.69, 105.71, 105.86, 105.94,\n",
       "              106.04, 106.86,\n",
       "              ...\n",
       "              255.61, 256.09,  256.4, 258.06, 258.62, 259.76, 261.02, 261.52,\n",
       "              267.01, 269.96],\n",
       "             dtype='float64', name='Daily Internet Usage', length=870)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpt.scatter(x=new_data4.index, y=new_data4[[]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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