eda_hits_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import numpy as np\n",
    "import operator\n",
    "df = pd.read_csv(\"data/hits_data.csv\",delimiter=\";\", \n",
    "                 #index_col=\"row_num\"\n",
    "                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(988681, 10)\n"
     ]
    },
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     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row_num</th>\n",
       "      <th>locale</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>hour_of_day</th>\n",
       "      <th>agent_id</th>\n",
       "      <th>entry_page</th>\n",
       "      <th>path_id_set</th>\n",
       "      <th>traffic_type</th>\n",
       "      <th>session_durantion</th>\n",
       "      <th>hits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>988681</td>\n",
       "      <td>L6</td>\n",
       "      <td>Monday</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>2111</td>\n",
       "      <td>31672;0</td>\n",
       "      <td>6</td>\n",
       "      <td>7037</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>988680</td>\n",
       "      <td>L2</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>2113</td>\n",
       "      <td>31965;0</td>\n",
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       "      <td>14</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
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       "      <td>988678</td>\n",
       "      <td>L3</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>2113</td>\n",
       "      <td>51462</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>988677</td>\n",
       "      <td>L2</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>2116</td>\n",
       "      <td>31931;0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row_num locale day_of_week  hour_of_day  agent_id  entry_page path_id_set  \\\n",
       "0   988681     L6      Monday           17         1        2111     31672;0   \n",
       "1   988680     L2    Thursday           22        10        2113     31965;0   \n",
       "2   988679     L4    Saturday           21         2        2100     0;78464   \n",
       "3   988678     L3    Saturday           19         8        2113       51462   \n",
       "4   988677     L2     Tuesday            6        10        2116     31931;0   \n",
       "\n",
       "   traffic_type session_durantion hits  \n",
       "0             6              7037   \\N  \n",
       "1             2                49   14  \n",
       "2             1              1892   14  \n",
       "3             6                 0    1  \n",
       "4             1                 2    3  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "row_num                 0\n",
       "locale                  0\n",
       "day_of_week             0\n",
       "hour_of_day             0\n",
       "agent_id                0\n",
       "entry_page              0\n",
       "path_id_set          4889\n",
       "traffic_type            0\n",
       "session_durantion       0\n",
       "hits                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df[df['hits']!=\"\\\\N\"]\n",
    "test = df[df['hits']==\"\\\\N\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(619235, 10)\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row_num</th>\n",
       "      <th>locale</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>hour_of_day</th>\n",
       "      <th>agent_id</th>\n",
       "      <th>entry_page</th>\n",
       "      <th>path_id_set</th>\n",
       "      <th>traffic_type</th>\n",
       "      <th>session_durantion</th>\n",
       "      <th>hits</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>988680</td>\n",
       "      <td>L2</td>\n",
       "      <td>Thursday</td>\n",
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       "      <td>L4</td>\n",
       "      <td>Saturday</td>\n",
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       "      <td>988678</td>\n",
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       "      <td>Saturday</td>\n",
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       "      <th>4</th>\n",
       "      <td>988677</td>\n",
       "      <td>L2</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>2116</td>\n",
       "      <td>31931;0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>988676</td>\n",
       "      <td>L3</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row_num locale day_of_week  hour_of_day  agent_id  entry_page path_id_set  \\\n",
       "1   988680     L2    Thursday           22        10        2113     31965;0   \n",
       "2   988679     L4    Saturday           21         2        2100     0;78464   \n",
       "3   988678     L3    Saturday           19         8        2113       51462   \n",
       "4   988677     L2     Tuesday            6        10        2116     31931;0   \n",
       "5   988676     L3      Monday            1         8        2100           0   \n",
       "\n",
       "   traffic_type session_durantion hits  \n",
       "1             2                49   14  \n",
       "2             1              1892   14  \n",
       "3             6                 0    1  \n",
       "4             1                 2    3  \n",
       "5             1                 0    2  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(train.shape)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(369446, 10)\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>row_num</th>\n",
       "      <th>locale</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>hour_of_day</th>\n",
       "      <th>agent_id</th>\n",
       "      <th>entry_page</th>\n",
       "      <th>path_id_set</th>\n",
       "      <th>traffic_type</th>\n",
       "      <th>session_durantion</th>\n",
       "      <th>hits</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>988681</td>\n",
       "      <td>L6</td>\n",
       "      <td>Monday</td>\n",
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       "      <td>1</td>\n",
       "      <td>2111</td>\n",
       "      <td>31672;0</td>\n",
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       "      <td>\\N</td>\n",
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       "      <td>2113</td>\n",
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       "      <td>\\N</td>\n",
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       "      <th>17</th>\n",
       "      <td>988664</td>\n",
       "      <td>L3</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>2100</td>\n",
       "      <td>0;34602;34604</td>\n",
       "      <td>1</td>\n",
       "      <td>8041</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>988663</td>\n",
       "      <td>L3</td>\n",
       "      <td>Friday</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>2111</td>\n",
       "      <td>34287;0;60579</td>\n",
       "      <td>2</td>\n",
       "      <td>117</td>\n",
       "      <td>\\N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    row_num locale day_of_week  hour_of_day  agent_id  entry_page  \\\n",
       "0    988681     L6      Monday           17         1        2111   \n",
       "15   988666     L3    Thursday           16        10        2700   \n",
       "16   988665     L4      Sunday           16        10        2113   \n",
       "17   988664     L3     Tuesday           19         6        2100   \n",
       "18   988663     L3      Friday           20        10        2111   \n",
       "\n",
       "      path_id_set  traffic_type session_durantion hits  \n",
       "0         31672;0             6              7037   \\N  \n",
       "15  0;34387;84765             1              5189   \\N  \n",
       "16        79148;0             4                 5   \\N  \n",
       "17  0;34602;34604             1              8041   \\N  \n",
       "18  34287;0;60579             2               117   \\N  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(test.shape)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Keshav\\Anaconda3\\envs\\tf_py36\\lib\\site-packages\\pandas\\core\\frame.py:3140: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self[k1] = value[k2]\n"
     ]
    }
   ],
   "source": [
    "train[['hits']]  = train[['hits']].astype(int, copy=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(619235, 10)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are totally 147 different categories in total, of which only 137 are present in train set. Which again means that for the new 10 values that occur in test set would be hard to analyze.\n",
    "From the data and above plot it is clear that 98% of the data fall into 10 of the categories. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2113    205968\n",
       "2116    125338\n",
       "2100    110762\n",
       "2111     51932\n",
       "2114     44589\n",
       "2700     38823\n",
       "8001     10867\n",
       "2115     10655\n",
       "2226      6400\n",
       "2316      2012\n",
       "2227      1821\n",
       "2142      1275\n",
       "2391       996\n",
       "2704       808\n",
       "2317       652\n",
       "2370       605\n",
       "2707       488\n",
       "2365       455\n",
       "8101       419\n",
       "2166       322\n",
       "2358       307\n",
       "2146       284\n",
       "2306       243\n",
       "2775       223\n",
       "2170       187\n",
       "2371       146\n",
       "2119       144\n",
       "2283       144\n",
       "2257       143\n",
       "2181       132\n",
       "         ...  \n",
       "2844         2\n",
       "2353         2\n",
       "2135         2\n",
       "2713         2\n",
       "2130         1\n",
       "2443         1\n",
       "2136         1\n",
       "2449         1\n",
       "2311         1\n",
       "2892         1\n",
       "2848         1\n",
       "2812         1\n",
       "2794         1\n",
       "2778         1\n",
       "2503         1\n",
       "2840         1\n",
       "2843         1\n",
       "2761         1\n",
       "2753         1\n",
       "2851         1\n",
       "2168         1\n",
       "2733         1\n",
       "2728         1\n",
       "2727         1\n",
       "2850         1\n",
       "2721         1\n",
       "2712         1\n",
       "2356         1\n",
       "2172         1\n",
       "2202         1\n",
       "Name: entry_page, Length: 137, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['entry_page'].value_counts()#[:10].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.se"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "#ax.bar(first_ten['locale'], width=0.2, height = 5)\n",
    "ax.hist(train['agent_id'],15, (0,15))\n",
    "ax.set_xlabel(\"Agent_IDs\")\n",
    "ax.set_ylabel(\"Counts\")\n",
    "ax.set_title('Agent ID counts Train')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 16 agent ids, where the id “6” is not present in the test set. Which would cause difficult to get accurate predictions for those results. From the table it can also be seen that around 5 of the ids can be considered as outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "#ax.bar(first_ten['locale'], width=0.2, height = 5)\n",
    "ax.hist(test['agent_id'],15, (0,15))\n",
    "ax.set_xlabel(\"Agent_IDs\")\n",
    "ax.set_ylabel(\"Counts\")\n",
    "ax.set_title('Agent ID counts Test')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The entry page data was encoded with dummy variables for easier visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 2, ..., 2, 2, 5], dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder = LabelEncoder()\n",
    "X_train_cat = train['entry_page']\n",
    "X_train_cat_encoded = encoder.fit_transform(X_train_cat)\n",
    "X_train_cat_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(147,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder = LabelEncoder()\n",
    "X_test_cat = df['entry_page']\n",
    "X_test_cat_encoded = encoder.fit_transform(X_test_cat)\n",
    "np.unique(X_test_cat_encoded).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "ax.hist(X_train_cat_encoded,137, (0,137))\n",
    "ax.set_xlabel(\"Entry Page dummy values\")\n",
    "ax.set_ylabel(\"Counts\")\n",
    "ax.set_title('Entry Page counts Train')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "ax.hist(X_test_cat_encoded,147, (0,147))\n",
    "ax.set_xlabel(\"Entry Page dummy values\")\n",
    "ax.set_ylabel(\"Counts\")\n",
    "ax.set_title('Entry Page counts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "ax.hist(train['traffic_type'],10, (1,10))\n",
    "ax.set_xlabel(\"Traffic Type\")\n",
    "ax.set_ylabel(\"Counts\")\n",
    "ax.set_title('Traffic Type counts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "ax.hist(test['traffic_type'],10, (1,10))\n",
    "ax.set_xlabel(\"Traffic Type\")\n",
    "ax.set_ylabel(\"Counts\")\n",
    "ax.set_title('Traffic Type counts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique Ids with train data: 28216\n"
     ]
    }
   ],
   "source": [
    "path_id_train= train.loc[train[\"path_id_set\"].notnull(),\"path_id_set\"].str.split(';').tolist()\n",
    "ids = {}\n",
    "ids_rep=[]\n",
    "for path_split in path_id_train:\n",
    "    for split in path_split:\n",
    "        ids_rep.append(split)\n",
    "        if split in ids:\n",
    "            ids[split]+=1\n",
    "        else:\n",
    "            ids[split]=1\n",
    "print(\"Unique Ids with train data:\",len(ids))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique Ids with train and test data: 33215\n"
     ]
    }
   ],
   "source": [
    "path_id_test= test.loc[test[\"path_id_set\"].notnull(),\"path_id_set\"].str.split(';').tolist()\n",
    "for path_split in path_id_test:\n",
    "    for split in path_split:\n",
    "        if split in ids:\n",
    "            ids[split]+=1\n",
    "        else:\n",
    "            ids[split]=1\n",
    "print(\"Unique Ids with train and test data:\",len(ids))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The path id data has multiple values in each column. These values are not continuous data, in total there are 33215 separate values or categories, of which only 28216 ever come in the train data (There are 3153 null values, which are not considered here for now). The data was found from above code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_by_value = sorted(ids.items(), key=lambda kv: kv[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=[]\n",
    "b=[]\n",
    "for k,v in enumerate(sorted_by_value):\n",
    "    a.append(int(k))\n",
    "    b.append(v[1])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(15,5))\n",
    "ax.bar(a[:20],b[:20])\n",
    "ax.set_xlabel(\"Set id Path\")\n",
    "ax.set_ylabel(\"Counts\")\n",
    "ax.set_title('Total Set ID Paths')\n",
    "plt.show()"
   ]
  },
  {
   "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>path_1</th>\n",
       "      <th>path_2</th>\n",
       "      <th>path_3</th>\n",
       "      <th>path_4</th>\n",
       "      <th>path_5</th>\n",
       "      <th>no_of_ids</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31965</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>78464</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51462</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31931</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   path_1  path_2  path_3  path_4  path_5  no_of_ids\n",
       "1   31965       0      -1      -1      -1        2.0\n",
       "2       0   78464      -1      -1      -1        2.0\n",
       "3   51462      -1      -1      -1      -1        1.0\n",
       "4   31931       0      -1      -1      -1        2.0\n",
       "5       0      -1      -1      -1      -1        1.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = train[\"path_id_set\"].str.split(';', n=5,expand=True)\n",
    "path['no_of_ids']=train['path_id_set'].str.split(';')\n",
    "path['no_of_ids']=path['no_of_ids'].str.len()\n",
    "path.drop(axis=1, columns=5,inplace=True)\n",
    "path.columns = ['path_1','path_2','path_3','path_4','path_5','no_of_ids'] \n",
    "#path.no_of_ids.fillna(0,inplace=True)\n",
    "path.fillna(-1,inplace=True)\n",
    "path[['path_1','path_2','path_3','path_4','path_5']]=path[['path_1','path_2','path_3','path_4','path_5']].astype(int)\n",
    "path.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0         133690\n",
       " 38715       9899\n",
       " 34741       6829\n",
       " 34812       6297\n",
       " 78506       4838\n",
       " 34227       4647\n",
       " 78509       4269\n",
       " 31490       3777\n",
       " 8514        3751\n",
       " 34390       3713\n",
       " 32027       3545\n",
       " 78622       3479\n",
       " 32042       3343\n",
       " 44310       3294\n",
       " 31965       3285\n",
       " 34961       3217\n",
       " 46814       3179\n",
       "-1           3153\n",
       " 36103       2871\n",
       " 9644        2545\n",
       " 31672       2466\n",
       " 79454       2378\n",
       " 51462       2226\n",
       " 44337       1948\n",
       " 78598       1764\n",
       " 79447       1749\n",
       " 655         1747\n",
       " 44314       1667\n",
       " 35094       1514\n",
       " 31909       1497\n",
       "            ...  \n",
       " 82671          1\n",
       " 15790          1\n",
       " 88812          1\n",
       " 4008           1\n",
       " 45777          1\n",
       " 585908         1\n",
       " 10946          1\n",
       " 172691         1\n",
       " 348781         1\n",
       " 355946         1\n",
       " 81294          1\n",
       " 510800         1\n",
       " 43964          1\n",
       " 443249         1\n",
       " 74476          1\n",
       " 5035           1\n",
       " 37819          1\n",
       " 119026         1\n",
       " 43229          1\n",
       " 39131          1\n",
       " 200            1\n",
       " 73098          1\n",
       " 135306         1\n",
       " 352356         1\n",
       " 638960         1\n",
       " 128761         1\n",
       " 69350          1\n",
       " 484677         1\n",
       " 441726         1\n",
       " 4094           1\n",
       "Name: path_1, Length: 21879, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path['path_1'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(15,4))\n",
    "ax.hist(path['path_1'],200, (0,700000))\n",
    "ax.set_xlabel(\"Path 1\")\n",
    "ax.set_ylabel(\"Counts\")\n",
    "ax.set_title('Path_1 counts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we plot the values in Path_1, it can be seen majority values are 0’s. The next high point we see is a combination of many categories, which are shown as one as it’s a Histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "session_durantion_N = train[train['session_durantion']=='\\\\N']\n",
    "session_durantion = train[train['session_durantion']!='\\\\N']\n",
    "session_durantion[['session_durantion']]  = session_durantion[['session_durantion']].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "row_num               int64\n",
       "locale               object\n",
       "day_of_week          object\n",
       "hour_of_day           int64\n",
       "agent_id              int64\n",
       "entry_page            int64\n",
       "path_id_set          object\n",
       "traffic_type          int64\n",
       "session_durantion     int32\n",
       "hits                  int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session_durantion.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The session duration is a continuous data. There is some missing data in the session duration column. On analyzing the above scatter plot we can see a pattern that none of the sessions crossed the 24 hour from hour of day. So when this happened it could have got a null value. (\"session_duration\"<(24-\"hour_of_day\")x60x60).\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(7,7))\n",
    "ax.scatter(session_durantion['session_durantion'],session_durantion['hour_of_day'])\n",
    "ax.set_xlabel(\"Session Duration in Hours\")\n",
    "ax.set_ylabel(\"Hour of Day\")\n",
    "ax.set_xticklabels([-1,0,5,10,15,20,24])\n",
    "ax.set_yticklabels([-1,0,5,10,15,20,24])\n",
    "#ax.set_xlim(-1,24)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:tf_py36]",
   "language": "python",
   "name": "conda-env-tf_py36-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}