User Overview 1-1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Clean the data from null values\n",
    "missing_values = [\"n/a\", \"na\", \"undefined\"]\n",
    "dataFrame = pd.read_excel(\"../week2.xlsx\", na_values = missing_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Huawei B528S-23A                19752\n",
       "Apple iPhone 6S (A1688)          9419\n",
       "Apple iPhone 6 (A1586)           9023\n",
       "Apple iPhone 7 (A1778)           6326\n",
       "Apple iPhone Se (A1723)          5187\n",
       "Apple iPhone 8 (A1905)           4993\n",
       "Apple iPhone Xr (A2105)          4568\n",
       "Samsung Galaxy S8 (Sm-G950F)     4520\n",
       "Apple iPhone X (A1901)           3813\n",
       "Samsung Galaxy A5 Sm-A520F       3724\n",
       "Name: Handset Type, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame['Handset Type'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Apple      59565\n",
       "Samsung    40839\n",
       "Huawei     34423\n",
       "Name: Handset Manufacturer, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace undefined with mode\n",
    "mode=dataFrame['Handset Manufacturer'].mode()\n",
    "dataFrame['Handset Manufacturer'].fillna(mode,inplace=True)\n",
    "dataFrame['Handset Manufacturer'].value_counts().head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apple, Samsung and Huawei are the top macufacurers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Handset Type\n",
      "Huawei B528S-23A                  19752\n",
      "Huawei E5180                       2079\n",
      "Huawei P20 Lite Huawei Nova 3E     2021\n",
      "Huawei P20                         1480\n",
      "Huawei Y6 2018                      997\n",
      "Name: Handset Type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(dataFrame.groupby('Handset Manufacturer')['Handset Type'].value_counts()['Huawei'].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Handset Type\n",
      "Apple iPhone 6S (A1688)    9419\n",
      "Apple iPhone 6 (A1586)     9023\n",
      "Apple iPhone 7 (A1778)     6326\n",
      "Apple iPhone Se (A1723)    5187\n",
      "Apple iPhone 8 (A1905)     4993\n",
      "Name: Handset Type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(dataFrame.groupby('Handset Manufacturer')['Handset Type'].value_counts()['Apple'].head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The above two cells are for top 10 handsets manufactured by apple and huawei"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{33626320676.0: 18, 33625779332.0: 17, 33614892860.0: 17, 33659725664.0: 16, 33675877202.0: 15}\n"
     ]
    }
   ],
   "source": [
    "sessionsCountData=dataFrame['MSISDN/Number'].value_counts().head()\n",
    "\n",
    "sessionsCount=sessionsCountData.values.tolist()\n",
    "\n",
    "msisdn=sessionsCountData.index.values\n",
    "\n",
    "sessionPerUserDictionary = dict(zip(msisdn, sessionsCount))\n",
    "\n",
    "print(sessionPerUserDictionary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This above dictionary shows the number of times each user has connected to the network. (Session Frequency )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSISDN/Number\n",
       "3.360100e+10    116720.0\n",
       "3.360100e+10    181230.0\n",
       "3.360100e+10    134969.0\n",
       "3.360101e+10     49878.0\n",
       "3.360101e+10     37104.0\n",
       "                  ...   \n",
       "3.379000e+10      8810.0\n",
       "3.379000e+10    140988.0\n",
       "3.197021e+12    877385.0\n",
       "3.370000e+14    253030.0\n",
       "8.823971e+14    869844.0\n",
       "Name: Dur. (ms), Length: 106856, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame.groupby('MSISDN/Number')['Dur. (ms)'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
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       " 33603698444.0: 645460936.0,\n",
       " ...}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame=dataFrame.rename(columns = {'Total DL (Bytes)' : 'totalDL','Total UL (Bytes)' : 'totalUL'})\n",
    "sum_column = dataFrame[\"totalUL\"] + dataFrame[\"totalDL\"]\n",
    "dataFrame[\"totalData\"] = sum_column\n",
    "totalDataDF=dataFrame.groupby('MSISDN/Number')['totalData'].sum()\n",
    "totalDataValues = totalDataDF.values\n",
    "msisdn=totalDataDF.index.values\n",
    "\n",
    "dataPerUserDictionary = dict(zip(msisdn, totalDataValues))\n",
    "dataPerUserDictionary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The above shows the total data used by each customer ( User )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:21: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "msisdn\n",
       "3.360100e+10    8.786906e+08\n",
       "3.360100e+10    1.568596e+08\n",
       "3.360100e+10    5.959665e+08\n",
       "3.360101e+10    4.223207e+08\n",
       "3.360101e+10    1.457411e+09\n",
       "                    ...     \n",
       "3.379000e+10    7.146416e+08\n",
       "3.379000e+10    4.803073e+08\n",
       "3.197021e+12    2.321240e+08\n",
       "3.370000e+14    5.962878e+08\n",
       "8.823971e+14    1.391536e+08\n",
       "Name: totalData, Length: 106856, dtype: float64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame=dataFrame.rename(columns = {'Total DL (Bytes)' : 'totalDL','Total UL (Bytes)' : 'totalUL','Dur. (ms)' : 'dur','MSISDN/Number':'msisdn','Last Location Name':'location','Handset Manufacturer':'manufacturer','Handset Type':'handset'})\n",
    "\n",
    "sum_column = dataFrame[\"totalUL\"] + dataFrame[\"totalDL\"]\n",
    "\n",
    "\n",
    "google = dataFrame['Google DL (Bytes)']+ dataFrame['Google UL (Bytes)']\n",
    "email = dataFrame['Email DL (Bytes)']+ dataFrame['Email UL (Bytes)']\n",
    "gaming = dataFrame['Gaming DL (Bytes)']+ dataFrame['Gaming UL (Bytes)']\n",
    "youtube = dataFrame['Youtube DL (Bytes)']+ dataFrame['Youtube UL (Bytes)']\n",
    "netflix = dataFrame['Netflix DL (Bytes)']+ dataFrame['Netflix UL (Bytes)']\n",
    "social = dataFrame['Social Media DL (Bytes)']+ dataFrame['Social Media UL (Bytes)']\n",
    "\n",
    "dataFrame['google']=google\n",
    "dataFrame['email']=email\n",
    "dataFrame['gaming']=gaming\n",
    "dataFrame['youtube']=youtube\n",
    "dataFrame['netflix']=netflix\n",
    "dataFrame['social']=social\n",
    "\n",
    "relevantDataFrame=dataFrame[['msisdn', 'google','email','gaming','youtube','netflix','social']]\n",
    "relevantDataFrame[\"totalData\"] = sum_column\n",
    "relevantDataFrame.groupby('msisdn')['totalData'].sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The above result shows the total data used by each customer over multiple sessions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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