Week3 Python libraries for data analysis-Chinook Music Store.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this assignment, I'll be using what I've learnt about numpy, pandas and matplotlib to analyze and visualize the Chinook database.\n",
    "The Chinook database contains information about the media in their store, their customers and employees, and their invoice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Which city has the best customers?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### This is the city with the highest sum of invoice totals. It'll be an ideal place for a promotional music festival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     InvoiceId  CustomerId  InvoiceDate  \\\n",
      "0            1           2          NaN   \n",
      "1            2           4          NaN   \n",
      "2            3           8          NaN   \n",
      "3            4          14          NaN   \n",
      "4            5          23          NaN   \n",
      "..         ...         ...          ...   \n",
      "407        408          25          NaN   \n",
      "408        409          29          NaN   \n",
      "409        410          35          NaN   \n",
      "410        411          44          NaN   \n",
      "411        412          58          NaN   \n",
      "\n",
      "                               BillingAddress BillingCity BillingState  \\\n",
      "0                     Theodor-Heuss-Straße 34   Stuttgart          NaN   \n",
      "1                            Ullevålsveien 14        Oslo          NaN   \n",
      "2                             Grétrystraat 63    Brussels          NaN   \n",
      "3                              8210 111 ST NW    Edmonton           AB   \n",
      "4                             69 Salem Street      Boston           MA   \n",
      "..                                        ...         ...          ...   \n",
      "407                     319 N. Frances Street     Madison           WI   \n",
      "408                    796 Dundas Street West     Toronto           ON   \n",
      "409  Rua dos Campeões Europeus de Viena, 4350       Porto          NaN   \n",
      "410                           Porthaninkatu 9    Helsinki          NaN   \n",
      "411                       12,Community Centre       Delhi          NaN   \n",
      "\n",
      "    BillingCountry BillingPostalCode  Total  \n",
      "0          Germany             70174   1.98  \n",
      "1           Norway              0171   3.96  \n",
      "2          Belgium              1000   5.94  \n",
      "3           Canada           T6G 2C7   8.91  \n",
      "4              USA              2113  13.86  \n",
      "..             ...               ...    ...  \n",
      "407            USA             53703   3.96  \n",
      "408         Canada           M6J 1V1   5.94  \n",
      "409       Portugal               NaN   8.91  \n",
      "410        Finland             00530  13.86  \n",
      "411          India            110017   1.99  \n",
      "\n",
      "[412 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "#loading invoice data\n",
    "\n",
    "invoice = pd.read_csv(\"Invoice.csv\")\n",
    "print(invoice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             Total\n",
      "BillingCity       \n",
      "Prague       90.24\n"
     ]
    }
   ],
   "source": [
    "# This returns the city name and sum total of the city with the highest sum of invoice totals.\n",
    " \n",
    "best_city = invoice[['BillingCity', 'Total']].groupby('BillingCity').sum().sort_values(by = 'Total', ascending = False)\n",
    "\n",
    "print(best_city.head(1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a bar chart showing total invoice from each city\n",
    "\n",
    "plt.figure(figsize=(20,30))\n",
    "plt.barh('BillingCity', 'Total', data=invoice)\n",
    "plt.xlabel('Invoice Totals')\n",
    "plt.ylabel('City')\n",
    "plt.title('Invoice Total by City')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### From the bar plot, we see that Prague is the city with the best customers, hence the music festival should be held there."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Which countries have the most Invoices?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USA               91\n",
      "Canada            56\n",
      "France            35\n",
      "Brazil            35\n",
      "Germany           28\n",
      "United Kingdom    21\n",
      "Czech Republic    14\n",
      "Portugal          14\n",
      "India             13\n",
      "Italy              7\n",
      "Denmark            7\n",
      "Finland            7\n",
      "Argentina          7\n",
      "Belgium            7\n",
      "Poland             7\n",
      "Australia          7\n",
      "Austria            7\n",
      "Netherlands        7\n",
      "Spain              7\n",
      "Ireland            7\n",
      "Chile              7\n",
      "Sweden             7\n",
      "Hungary            7\n",
      "Norway             7\n",
      "Name: BillingCountry, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# This return a table of BillingCountry and Invoices ordered by the number of invoices for each country.\n",
    "# The country with the most invoices will appear first. \n",
    "\n",
    "best_country = invoice['BillingCountry'].value_counts()\n",
    "print(best_country)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### The top 5 countries with the most invoices are USA, Canada, France, Brazil and Germany."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Who is the best customer?\n",
    "\n",
    "##### The best customer is the customer who has spent the most money."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "scrolled": true
   },
   "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>CustomerId</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>LastName</th>\n",
       "      <th>Company</th>\n",
       "      <th>Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>Phone</th>\n",
       "      <th>Fax</th>\n",
       "      <th>Email</th>\n",
       "      <th>SupportRepId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Luís</td>\n",
       "      <td>Gonçalves</td>\n",
       "      <td>Embraer - Empresa Brasileira de Aeronáutica S.A.</td>\n",
       "      <td>Av. Brigadeiro Faria Lima, 2170</td>\n",
       "      <td>São José dos Campos</td>\n",
       "      <td>SP</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>12227-000</td>\n",
       "      <td>+55 (12) 3923-5555</td>\n",
       "      <td>+55 (12) 3923-5566</td>\n",
       "      <td>luisg@embraer.com.br</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Leonie</td>\n",
       "      <td>Köhler</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Theodor-Heuss-Straße 34</td>\n",
       "      <td>Stuttgart</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Germany</td>\n",
       "      <td>70174</td>\n",
       "      <td>+49 0711 2842222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>leonekohler@surfeu.de</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>François</td>\n",
       "      <td>Tremblay</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1498 rue Bélanger</td>\n",
       "      <td>Montréal</td>\n",
       "      <td>QC</td>\n",
       "      <td>Canada</td>\n",
       "      <td>H2G 1A7</td>\n",
       "      <td>+1 (514) 721-4711</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ftremblay@gmail.com</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Bjørn</td>\n",
       "      <td>Hansen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ullevålsveien 14</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Norway</td>\n",
       "      <td>0171</td>\n",
       "      <td>+47 22 44 22 22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bjorn.hansen@yahoo.no</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>František</td>\n",
       "      <td>Wichterlová</td>\n",
       "      <td>JetBrains s.r.o.</td>\n",
       "      <td>Klanova 9/506</td>\n",
       "      <td>Prague</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>14700</td>\n",
       "      <td>+420 2 4172 5555</td>\n",
       "      <td>+420 2 4172 5555</td>\n",
       "      <td>frantisekw@jetbrains.com</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerId  FirstName     LastName  \\\n",
       "0           1       Luís    Gonçalves   \n",
       "1           2     Leonie       Köhler   \n",
       "2           3   François     Tremblay   \n",
       "3           4      Bjørn       Hansen   \n",
       "4           5  František  Wichterlová   \n",
       "\n",
       "                                            Company  \\\n",
       "0  Embraer - Empresa Brasileira de Aeronáutica S.A.   \n",
       "1                                               NaN   \n",
       "2                                               NaN   \n",
       "3                                               NaN   \n",
       "4                                  JetBrains s.r.o.   \n",
       "\n",
       "                           Address                 City State         Country  \\\n",
       "0  Av. Brigadeiro Faria Lima, 2170  São José dos Campos    SP          Brazil   \n",
       "1          Theodor-Heuss-Straße 34            Stuttgart   NaN         Germany   \n",
       "2                1498 rue Bélanger             Montréal    QC          Canada   \n",
       "3                 Ullevålsveien 14                 Oslo   NaN          Norway   \n",
       "4                    Klanova 9/506               Prague   NaN  Czech Republic   \n",
       "\n",
       "  PostalCode               Phone                 Fax  \\\n",
       "0  12227-000  +55 (12) 3923-5555  +55 (12) 3923-5566   \n",
       "1      70174    +49 0711 2842222                 NaN   \n",
       "2    H2G 1A7   +1 (514) 721-4711                 NaN   \n",
       "3       0171     +47 22 44 22 22                 NaN   \n",
       "4      14700    +420 2 4172 5555    +420 2 4172 5555   \n",
       "\n",
       "                      Email  SupportRepId  \n",
       "0      luisg@embraer.com.br             3  \n",
       "1     leonekohler@surfeu.de             5  \n",
       "2       ftremblay@gmail.com             3  \n",
       "3     bjorn.hansen@yahoo.no             4  \n",
       "4  frantisekw@jetbrains.com             4  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading customer data\n",
    "\n",
    "customers = pd.read_csv('Customer.csv')\n",
    "customers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CustomerId\n",
      "6    49.62\n",
      "Name: Total, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# The customer who has spent the most money\n",
    "\n",
    "best_customer = invoice[['CustomerId', 'Total']].groupby('CustomerId')['Total'].sum().sort_values(ascending = False).head(1)\n",
    "print(best_customer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>LastName</th>\n",
       "      <th>Company</th>\n",
       "      <th>Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>Phone</th>\n",
       "      <th>Fax</th>\n",
       "      <th>Email</th>\n",
       "      <th>SupportRepId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Helena</td>\n",
       "      <td>Holý</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rilská 3174/6</td>\n",
       "      <td>Prague</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>14300</td>\n",
       "      <td>+420 2 4177 0449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hholy@gmail.com</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerId FirstName LastName Company        Address    City State  \\\n",
       "5           6    Helena     Holý     NaN  Rilská 3174/6  Prague   NaN   \n",
       "\n",
       "          Country PostalCode             Phone  Fax            Email  \\\n",
       "5  Czech Republic      14300  +420 2 4177 0449  NaN  hholy@gmail.com   \n",
       "\n",
       "   SupportRepId  \n",
       "5             5  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Details of customer with ID no 6\n",
    "\n",
    "customers[customers['CustomerId'] == 6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The best customer is from Prague, her name is Helena Holy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Details of customers who listen to rock music"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GenreId</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jazz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Metal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Alternative &amp; Punk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Rock And Roll</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GenreId                Name\n",
       "0        1                Rock\n",
       "1        2                Jazz\n",
       "2        3               Metal\n",
       "3        4  Alternative & Punk\n",
       "4        5       Rock And Roll"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading the Genre data\n",
    "\n",
    "genre = pd.read_csv('Genre.csv')\n",
    "genre.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TrackId</th>\n",
       "      <th>Name</th>\n",
       "      <th>AlbumId</th>\n",
       "      <th>MediaTypeId</th>\n",
       "      <th>GenreId</th>\n",
       "      <th>Composer</th>\n",
       "      <th>Milliseconds</th>\n",
       "      <th>Bytes</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>For Those About To Rock (We Salute You)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Angus Young, Malcolm Young, Brian Johnson</td>\n",
       "      <td>343719</td>\n",
       "      <td>11170334</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Balls to the Wall</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>342562</td>\n",
       "      <td>5510424</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Fast As a Shark</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>F. Baltes, S. Kaufman, U. Dirkscneider &amp; W. Ho...</td>\n",
       "      <td>230619</td>\n",
       "      <td>3990994</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Restless and Wild</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>F. Baltes, R.A. Smith-Diesel, S. Kaufman, U. D...</td>\n",
       "      <td>252051</td>\n",
       "      <td>4331779</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Princess of the Dawn</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Deaffy &amp; R.A. Smith-Diesel</td>\n",
       "      <td>375418</td>\n",
       "      <td>6290521</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TrackId                                     Name  AlbumId  MediaTypeId  \\\n",
       "0        1  For Those About To Rock (We Salute You)        1            1   \n",
       "1        2                        Balls to the Wall        2            2   \n",
       "2        3                          Fast As a Shark        3            2   \n",
       "3        4                        Restless and Wild        3            2   \n",
       "4        5                     Princess of the Dawn        3            2   \n",
       "\n",
       "   GenreId                                           Composer  Milliseconds  \\\n",
       "0        1          Angus Young, Malcolm Young, Brian Johnson        343719   \n",
       "1        1                                                NaN        342562   \n",
       "2        1  F. Baltes, S. Kaufman, U. Dirkscneider & W. Ho...        230619   \n",
       "3        1  F. Baltes, R.A. Smith-Diesel, S. Kaufman, U. D...        252051   \n",
       "4        1                         Deaffy & R.A. Smith-Diesel        375418   \n",
       "\n",
       "      Bytes  UnitPrice  \n",
       "0  11170334       0.99  \n",
       "1   5510424       0.99  \n",
       "2   3990994       0.99  \n",
       "3   4331779       0.99  \n",
       "4   6290521       0.99  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading Track data\n",
    "\n",
    "tracks = pd.read_csv('Track.csv')\n",
    "tracks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceLineId</th>\n",
       "      <th>InvoiceId</th>\n",
       "      <th>TrackId</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>Quantity</th>\n",
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       "      <td>0.99</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   InvoiceLineId  InvoiceId  TrackId  UnitPrice  Quantity\n",
       "0              1          1        2       0.99         1\n",
       "1              2          1        4       0.99         1\n",
       "2              3          2        6       0.99         1\n",
       "3              4          2        8       0.99         1\n",
       "4              5          2       10       0.99         1"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading Invoiceline data\n",
    "\n",
    "invoice_line = pd.read_csv('InvoiceLine.csv')\n",
    "invoice_line.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "      <td>Rock</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
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       "</div>"
      ],
      "text/plain": [
       "   TrackId  GenreId  Name\n",
       "0        1        1  Rock\n",
       "1        2        1  Rock\n",
       "2        3        1  Rock\n",
       "3        4        1  Rock\n",
       "4        5        1  Rock"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merging genre and track dataframes \n",
    "\n",
    "genre_tracks = pd.merge(tracks[['TrackId', 'GenreId']], genre, on = 'GenreId', how = 'right')\n",
    "genre_tracks.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  We can now use data on customer ID to get the kind of track they purchased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "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",
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       "    }\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>InvoiceId</th>\n",
       "      <th>TrackId</th>\n",
       "      <th>CustomerId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   InvoiceId  TrackId  CustomerId\n",
       "0          1        2           2\n",
       "1          1        4           2\n",
       "2          2        6           4\n",
       "3          2        8           4\n",
       "4          2       10           4"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merging invoice line data and invoive data to get each customer ID with invoice details\n",
    "\n",
    "purchases = pd.merge(invoice_line[['InvoiceId', 'TrackId']], invoice[['InvoiceId', 'CustomerId']], on = 'InvoiceId', how = 'left')\n",
    "purchases.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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>InvoiceId</th>\n",
       "      <th>TrackId</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>GenreId</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   InvoiceId  TrackId  CustomerId  GenreId  Name\n",
       "0          1        2           2        1  Rock\n",
       "1          1        4           2        1  Rock\n",
       "2          2        6           4        1  Rock\n",
       "3          2        8           4        1  Rock\n",
       "4          2       10           4        1  Rock"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merging purchases with genre_tracks to get the name of track corresponding to each customer's purchase \n",
    "\n",
    "genre_category = pd.merge(purchases, genre_tracks, on = 'TrackId', how = 'left')\n",
    "genre_category.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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>CustomerId</th>\n",
       "      <th>Email</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>LastName</th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>luisg@embraer.com.br</td>\n",
       "      <td>Luís</td>\n",
       "      <td>Gonçalves</td>\n",
       "      <td>São José dos Campos</td>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>leonekohler@surfeu.de</td>\n",
       "      <td>Leonie</td>\n",
       "      <td>Köhler</td>\n",
       "      <td>Stuttgart</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>ftremblay@gmail.com</td>\n",
       "      <td>François</td>\n",
       "      <td>Tremblay</td>\n",
       "      <td>Montréal</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>bjorn.hansen@yahoo.no</td>\n",
       "      <td>Bjørn</td>\n",
       "      <td>Hansen</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Norway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>frantisekw@jetbrains.com</td>\n",
       "      <td>František</td>\n",
       "      <td>Wichterlová</td>\n",
       "      <td>Prague</td>\n",
       "      <td>Czech Republic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerId                     Email  FirstName     LastName  \\\n",
       "0           1      luisg@embraer.com.br       Luís    Gonçalves   \n",
       "1           2     leonekohler@surfeu.de     Leonie       Köhler   \n",
       "2           3       ftremblay@gmail.com   François     Tremblay   \n",
       "3           4     bjorn.hansen@yahoo.no      Bjørn       Hansen   \n",
       "4           5  frantisekw@jetbrains.com  František  Wichterlová   \n",
       "\n",
       "                  City         Country  \n",
       "0  São José dos Campos          Brazil  \n",
       "1            Stuttgart         Germany  \n",
       "2             Montréal          Canada  \n",
       "3                 Oslo          Norway  \n",
       "4               Prague  Czech Republic  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting customers details, we can then use their ID to get the genre of tracks each customer purchased.\n",
    "\n",
    "customer_details = customers[['CustomerId', 'Email', 'FirstName', 'LastName', 'City', 'Country']]\n",
    "customer_details.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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>CustomerId</th>\n",
       "      <th>Name</th>\n",
       "      <th>Email</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>LastName</th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Rock</td>\n",
       "      <td>leonekohler@surfeu.de</td>\n",
       "      <td>Leonie</td>\n",
       "      <td>Köhler</td>\n",
       "      <td>Stuttgart</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Rock</td>\n",
       "      <td>leonekohler@surfeu.de</td>\n",
       "      <td>Leonie</td>\n",
       "      <td>Köhler</td>\n",
       "      <td>Stuttgart</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Rock</td>\n",
       "      <td>bjorn.hansen@yahoo.no</td>\n",
       "      <td>Bjørn</td>\n",
       "      <td>Hansen</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Norway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Rock</td>\n",
       "      <td>bjorn.hansen@yahoo.no</td>\n",
       "      <td>Bjørn</td>\n",
       "      <td>Hansen</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Norway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Rock</td>\n",
       "      <td>bjorn.hansen@yahoo.no</td>\n",
       "      <td>Bjørn</td>\n",
       "      <td>Hansen</td>\n",
       "      <td>Oslo</td>\n",
       "      <td>Norway</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerId  Name                  Email FirstName LastName       City  \\\n",
       "0           2  Rock  leonekohler@surfeu.de    Leonie   Köhler  Stuttgart   \n",
       "1           2  Rock  leonekohler@surfeu.de    Leonie   Köhler  Stuttgart   \n",
       "2           4  Rock  bjorn.hansen@yahoo.no     Bjørn   Hansen       Oslo   \n",
       "3           4  Rock  bjorn.hansen@yahoo.no     Bjørn   Hansen       Oslo   \n",
       "4           4  Rock  bjorn.hansen@yahoo.no     Bjørn   Hansen       Oslo   \n",
       "\n",
       "   Country  \n",
       "0  Germany  \n",
       "1  Germany  \n",
       "2   Norway  \n",
       "3   Norway  \n",
       "4   Norway  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# category of genre purchased by eash customer\n",
    "\n",
    "customer_genre = pd.merge(genre_category[['CustomerId', 'Name']], customer_details, on = 'CustomerId', how = 'left')\n",
    "customer_genre.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Name</th>\n",
       "      <th>Email</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>LastName</th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>32</td>\n",
       "      <td>Rock</td>\n",
       "      <td>aaronmitchell@yahoo.ca</td>\n",
       "      <td>Aaron</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>Winnipeg</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>32</td>\n",
       "      <td>Rock</td>\n",
       "      <td>aaronmitchell@yahoo.ca</td>\n",
       "      <td>Aaron</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>Winnipeg</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1569</th>\n",
       "      <td>32</td>\n",
       "      <td>Rock</td>\n",
       "      <td>aaronmitchell@yahoo.ca</td>\n",
       "      <td>Aaron</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>Winnipeg</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>32</td>\n",
       "      <td>Rock</td>\n",
       "      <td>aaronmitchell@yahoo.ca</td>\n",
       "      <td>Aaron</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>Winnipeg</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1564</th>\n",
       "      <td>32</td>\n",
       "      <td>Rock</td>\n",
       "      <td>aaronmitchell@yahoo.ca</td>\n",
       "      <td>Aaron</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>Winnipeg</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1162</th>\n",
       "      <td>42</td>\n",
       "      <td>Rock</td>\n",
       "      <td>wyatt.girard@yahoo.fr</td>\n",
       "      <td>Wyatt</td>\n",
       "      <td>Girard</td>\n",
       "      <td>Bordeaux</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1161</th>\n",
       "      <td>42</td>\n",
       "      <td>Rock</td>\n",
       "      <td>wyatt.girard@yahoo.fr</td>\n",
       "      <td>Wyatt</td>\n",
       "      <td>Girard</td>\n",
       "      <td>Bordeaux</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>42</td>\n",
       "      <td>Rock</td>\n",
       "      <td>wyatt.girard@yahoo.fr</td>\n",
       "      <td>Wyatt</td>\n",
       "      <td>Girard</td>\n",
       "      <td>Bordeaux</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>42</td>\n",
       "      <td>Rock</td>\n",
       "      <td>wyatt.girard@yahoo.fr</td>\n",
       "      <td>Wyatt</td>\n",
       "      <td>Girard</td>\n",
       "      <td>Bordeaux</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1166</th>\n",
       "      <td>42</td>\n",
       "      <td>Rock</td>\n",
       "      <td>wyatt.girard@yahoo.fr</td>\n",
       "      <td>Wyatt</td>\n",
       "      <td>Girard</td>\n",
       "      <td>Bordeaux</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>835 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CustomerId  Name                   Email FirstName  LastName      City  \\\n",
       "266           32  Rock  aaronmitchell@yahoo.ca     Aaron  Mitchell  Winnipeg   \n",
       "267           32  Rock  aaronmitchell@yahoo.ca     Aaron  Mitchell  Winnipeg   \n",
       "1569          32  Rock  aaronmitchell@yahoo.ca     Aaron  Mitchell  Winnipeg   \n",
       "333           32  Rock  aaronmitchell@yahoo.ca     Aaron  Mitchell  Winnipeg   \n",
       "1564          32  Rock  aaronmitchell@yahoo.ca     Aaron  Mitchell  Winnipeg   \n",
       "...          ...   ...                     ...       ...       ...       ...   \n",
       "1162          42  Rock   wyatt.girard@yahoo.fr     Wyatt    Girard  Bordeaux   \n",
       "1161          42  Rock   wyatt.girard@yahoo.fr     Wyatt    Girard  Bordeaux   \n",
       "160           42  Rock   wyatt.girard@yahoo.fr     Wyatt    Girard  Bordeaux   \n",
       "162           42  Rock   wyatt.girard@yahoo.fr     Wyatt    Girard  Bordeaux   \n",
       "1166          42  Rock   wyatt.girard@yahoo.fr     Wyatt    Girard  Bordeaux   \n",
       "\n",
       "     Country  \n",
       "266   Canada  \n",
       "267   Canada  \n",
       "1569  Canada  \n",
       "333   Canada  \n",
       "1564  Canada  \n",
       "...      ...  \n",
       "1162  France  \n",
       "1161  France  \n",
       "160   France  \n",
       "162   France  \n",
       "1166  France  \n",
       "\n",
       "[835 rows x 7 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This returns contact details of all Rock Music listeners in alphabetical order(Email)\n",
    "# This is done by selecting rows with logic \n",
    "\n",
    "customer_genre[customer_genre['Name'] == 'Rock'].sort_values(by = 'Email', ascending = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Most popular music genre for each country\n",
    "\n",
    "#### The most popular genre is the genre with the highest amount of purchases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceLineId</th>\n",
       "      <th>InvoiceId</th>\n",
       "      <th>TrackId</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>BillingCountry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Norway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Norway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Norway</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   InvoiceLineId  InvoiceId  TrackId  UnitPrice  Quantity  CustomerId  \\\n",
       "0              1          1        2       0.99         1           2   \n",
       "1              2          1        4       0.99         1           2   \n",
       "2              3          2        6       0.99         1           4   \n",
       "3              4          2        8       0.99         1           4   \n",
       "4              5          2       10       0.99         1           4   \n",
       "\n",
       "  BillingCountry  \n",
       "0        Germany  \n",
       "1        Germany  \n",
       "2         Norway  \n",
       "3         Norway  \n",
       "4         Norway  "
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merging invoice lines data with invoice data so we can match invoice line items to customers\n",
    "\n",
    "customer_purchase = pd.merge(invoice_line, invoice[['InvoiceId', 'CustomerId', 'BillingCountry']], on = 'InvoiceId', how = 'left')\n",
    "customer_purchase.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The dataframe can now be completed by adding the genre for each track purchased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceLineId</th>\n",
       "      <th>InvoiceId</th>\n",
       "      <th>TrackId</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>BillingCountry</th>\n",
       "      <th>GenreId</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Germany</td>\n",
       "      <td>1</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Germany</td>\n",
       "      <td>1</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Norway</td>\n",
       "      <td>1</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Norway</td>\n",
       "      <td>1</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Norway</td>\n",
       "      <td>1</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   InvoiceLineId  InvoiceId  TrackId  UnitPrice  Quantity  CustomerId  \\\n",
       "0              1          1        2       0.99         1           2   \n",
       "1              2          1        4       0.99         1           2   \n",
       "2              3          2        6       0.99         1           4   \n",
       "3              4          2        8       0.99         1           4   \n",
       "4              5          2       10       0.99         1           4   \n",
       "\n",
       "  BillingCountry  GenreId  Name  \n",
       "0        Germany        1  Rock  \n",
       "1        Germany        1  Rock  \n",
       "2         Norway        1  Rock  \n",
       "3         Norway        1  Rock  \n",
       "4         Norway        1  Rock  "
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding genre of track purchased to customer_purchase data\n",
    "\n",
    "customer_purchase = pd.merge(customer_purchase, genre_tracks, on = 'TrackId', how = 'left')\n",
    "customer_purchase.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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>BillingCountry</th>\n",
       "      <th>Name</th>\n",
       "      <th>UnitPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Alternative &amp; Punk</td>\n",
       "      <td>8.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Easy Listening</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Jazz</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Latin</td>\n",
       "      <td>7.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Metal</td>\n",
       "      <td>6.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BillingCountry                Name  UnitPrice\n",
       "0      Argentina  Alternative & Punk       8.91\n",
       "1      Argentina      Easy Listening       1.98\n",
       "2      Argentina                Jazz       1.98\n",
       "3      Argentina               Latin       7.92\n",
       "4      Argentina               Metal       6.93"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the amount of purchases made in each country based on genre of track purchased.\n",
    "\n",
    "genre_totals = pd.DataFrame(customer_purchase.groupby(['BillingCountry', 'Name'], as_index=False)['UnitPrice'].sum())\n",
    "genre_totals.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### We can now find the highest revenue from a single genre for each country."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>UnitPrice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BillingCountry</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>8.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>21.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>14.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>20.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>80.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                UnitPrice\n",
       "BillingCountry           \n",
       "Argentina            8.91\n",
       "Australia           21.78\n",
       "Austria             14.85\n",
       "Belgium             20.79\n",
       "Brazil              80.19"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  This returns the genre with the highest amount of purchases for each country.\n",
    "\n",
    "max_purchase = pd.DataFrame(genre_totals.groupby('BillingCountry')['UnitPrice'].max())\n",
    "max_purchase.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>BillingCountry</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.91</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Alternative &amp; Punk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.91</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.78</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.85</td>\n",
       "      <td>Austria</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.79</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>80.19</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>105.93</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.91</td>\n",
       "      <td>Chile</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>24.75</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20.79</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>17.82</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>64.35</td>\n",
       "      <td>France</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>61.38</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10.89</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>24.75</td>\n",
       "      <td>India</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>13.93</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>TV Shows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17.82</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17.82</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>16.83</td>\n",
       "      <td>Norway</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>21.78</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>30.69</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21.78</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11.88</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Latin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>155.43</td>\n",
       "      <td>USA</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>36.63</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    UnitPrice  BillingCountry                Name\n",
       "0        8.91       Argentina  Alternative & Punk\n",
       "1        8.91       Argentina                Rock\n",
       "2       21.78       Australia                Rock\n",
       "3       14.85         Austria                Rock\n",
       "4       20.79         Belgium                Rock\n",
       "5       80.19          Brazil                Rock\n",
       "6      105.93          Canada                Rock\n",
       "7        8.91           Chile                Rock\n",
       "8       24.75  Czech Republic                Rock\n",
       "9       20.79         Denmark                Rock\n",
       "10      17.82         Finland                Rock\n",
       "11      64.35          France                Rock\n",
       "12      61.38         Germany                Rock\n",
       "13      10.89         Hungary                Rock\n",
       "14      24.75           India                Rock\n",
       "15      13.93         Ireland            TV Shows\n",
       "16      17.82           Italy                Rock\n",
       "17      17.82     Netherlands                Rock\n",
       "18      16.83          Norway                Rock\n",
       "19      21.78          Poland                Rock\n",
       "20      30.69        Portugal                Rock\n",
       "21      21.78           Spain                Rock\n",
       "22      11.88          Sweden               Latin\n",
       "23     155.43             USA                Rock\n",
       "24      36.63  United Kingdom                Rock"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most popular genre by country\n",
    "\n",
    "pd.merge(max_purchase, genre_totals, on = ['UnitPrice', 'BillingCountry'], how = 'left')"
   ]
  }
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