Project 1 - Customer Database.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project 1: Customer Database\n",
    "**This is the first of three mandatory projects to be handed in as part of the assessment for the course 02807 Computational Tools for Data Science at Technical University of Denmark, autumn 2019.**\n",
    "\n",
    "#### Practical info\n",
    "- **The project is to be done in groups of at most 3 students**\n",
    "- **Each group has to hand in _one_ Jupyter notebook (this notebook) with their solution**\n",
    "- **The hand-in of the notebook is due 2019-10-13, 23:59 on DTU Inside**\n",
    "\n",
    "#### Your solution\n",
    "- **Your solution should be in Python**\n",
    "- **For each question you may use as many cells for your solution as you like**\n",
    "- **You should document your solution and explain the choices you've made (for example by using multiple cells and use Markdown to assist the reader of the notebook)**\n",
    "- **You should not remove the problem statements, and you should not modify the structure of the notebook**\n",
    "- **Your notebook should be runnable, i.e., clicking [>>] in Jupyter should generate the result that you want to be assessed**\n",
    "- **You are not expected to use machine learning to solve any of the exercises**\n",
    "- **You will be assessed according to correctness and readability of your code, choice of solution, choice of tools and libraries, and documentation of your solution**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "Your team has been hired by the company X as data scientists. X makes gadgets for a wide range of industrial and commercial clients.\n",
    "\n",
    "As in-house data scientists, your teams first task, as per request from your new boss, is to optimize business operations. You have decided that a good first step would be to analyze the companys historical sales data to gain a better understanding of where profit is coming from. It may also reveal some low hanging fruit in terms of business opportunities.\n",
    "\n",
    "To get started, you have called the IT department to get access to the customer and sales transactions database. To your horror you've been told that such a database doens't exist, and the only record of sales transactions is kept by John from finance in an Excel spreadsheet. So you've emailed John asking for a CSV dump of the spreadsheet...\n",
    "\n",
    "In this project you need to clean the data you got from John, enrich it with further data, prepare a database for the data, and do some data analysis. The project is comprised of five parts. They are intended to be solved in the order they appear, but it is highly recommended that you read through all of them and devise an overall strategy before you start implementing anything."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: Cleaning the data\n",
    "John has emailed you the following link to the CSV dump you requested.\n",
    "\n",
    "- [transactions.csv](https://raw.githubusercontent.com/patrickcording/02807-comp-tools/master/docker/work/data/transactions.csv)\n",
    "\n",
    "It seems as though he has been a bit sloppy when keeping the records. \n",
    "\n",
    "In this part you should:\n",
    "- Explain what the data is\n",
    "- Clean it to prepare it for inserting into a database and doing data analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\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>part</th>\n",
       "      <th>company</th>\n",
       "      <th>country</th>\n",
       "      <th>city</th>\n",
       "      <th>price</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>54868-5165</td>\n",
       "      <td>Chatterbridge</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>784.79€</td>\n",
       "      <td>2016-01-02 00:01:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>60505-2867</td>\n",
       "      <td>Lajo</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Thessaloniki</td>\n",
       "      <td>187.99€</td>\n",
       "      <td>2016-01-02 00:05:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>24385-268</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Athens</td>\n",
       "      <td>221.73€</td>\n",
       "      <td>2016-01-02 00:18:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>76117-001</td>\n",
       "      <td>Twitterbeat</td>\n",
       "      <td>France</td>\n",
       "      <td>Annecy</td>\n",
       "      <td>1075.82€</td>\n",
       "      <td>2016-01-02 02:32:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>44946-1046</td>\n",
       "      <td>Chatterbridge</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>412.55€</td>\n",
       "      <td>2016-01-02 04:51:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>16729-167</td>\n",
       "      <td>Chatterbridge</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>359.52€</td>\n",
       "      <td>2016-01-02 07:20:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>52125-444</td>\n",
       "      <td>Voomm</td>\n",
       "      <td>France</td>\n",
       "      <td>Paris</td>\n",
       "      <td>266.62€</td>\n",
       "      <td>2016-01-02 07:40:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>43419-018</td>\n",
       "      <td>Buzzbean</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Düsseldorf</td>\n",
       "      <td>103.45€</td>\n",
       "      <td>2016-01-02 08:57:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>54092-515</td>\n",
       "      <td>Zooxo</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>London</td>\n",
       "      <td>£704.94</td>\n",
       "      <td>2016-01-02 09:09:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>24286-1562</td>\n",
       "      <td>Lajo</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Thessaloniki</td>\n",
       "      <td>317.65€</td>\n",
       "      <td>2016-01-02 11:01:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>49035-530</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Athens</td>\n",
       "      <td>841.97€</td>\n",
       "      <td>2016-01-02 12:51:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>36987-1697</td>\n",
       "      <td>Brainsphere</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Braga</td>\n",
       "      <td>830.75€</td>\n",
       "      <td>2016-01-02 13:16:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>44946-1046</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>France</td>\n",
       "      <td>Nanterre</td>\n",
       "      <td>406.4€</td>\n",
       "      <td>2016-01-02 16:34:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>51346-145</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Athens</td>\n",
       "      <td>978.51€</td>\n",
       "      <td>2016-01-02 17:45:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>51138-049</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>France</td>\n",
       "      <td>Nanterre</td>\n",
       "      <td>504.69€</td>\n",
       "      <td>2016-01-02 17:46:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0268-6107</td>\n",
       "      <td>Thoughtmix</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Amadora\\t</td>\n",
       "      <td>477.71€</td>\n",
       "      <td>2016-01-02 18:52:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>51523-002</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Athens</td>\n",
       "      <td>1041.56€</td>\n",
       "      <td>2016-01-02 20:28:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>62670-4404</td>\n",
       "      <td>Wordify</td>\n",
       "      <td>United States</td>\n",
       "      <td>New York</td>\n",
       "      <td>$444.15</td>\n",
       "      <td>2016-01-02 21:10:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>49288-0285</td>\n",
       "      <td>Brainsphere</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Braga</td>\n",
       "      <td>598.52€</td>\n",
       "      <td>2016-01-02 22:18:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>68233-011</td>\n",
       "      <td>Chatterbridge</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>304.59€</td>\n",
       "      <td>2016-01-03 00:19:44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          part        company         country          city     price  \\\n",
       "0   54868-5165  Chatterbridge           Spain     Barcelona   784.79€   \n",
       "1   60505-2867           Lajo          Greece  Thessaloniki   187.99€   \n",
       "2    24385-268      Flipstorm          Greece        Athens   221.73€   \n",
       "3    76117-001    Twitterbeat          France        Annecy  1075.82€   \n",
       "4   44946-1046  Chatterbridge           Spain     Barcelona   412.55€   \n",
       "5    16729-167  Chatterbridge           Spain     Barcelona   359.52€   \n",
       "6    52125-444          Voomm          France         Paris   266.62€   \n",
       "7    43419-018       Buzzbean         Germany    Düsseldorf   103.45€   \n",
       "8    54092-515          Zooxo  United Kingdom        London   £704.94   \n",
       "9   24286-1562           Lajo          Greece  Thessaloniki   317.65€   \n",
       "10   49035-530      Flipstorm          Greece        Athens   841.97€   \n",
       "11  36987-1697    Brainsphere        Portugal         Braga   830.75€   \n",
       "12  44946-1046      Flipstorm          France      Nanterre    406.4€   \n",
       "13   51346-145      Flipstorm          Greece        Athens   978.51€   \n",
       "14   51138-049      Flipstorm          France      Nanterre   504.69€   \n",
       "15   0268-6107     Thoughtmix        Portugal     Amadora\\t   477.71€   \n",
       "16   51523-002      Flipstorm          Greece        Athens  1041.56€   \n",
       "17  62670-4404        Wordify   United States      New York   $444.15   \n",
       "18  49288-0285    Brainsphere        Portugal         Braga   598.52€   \n",
       "19   68233-011  Chatterbridge           Spain     Barcelona   304.59€   \n",
       "\n",
       "                   date  \n",
       "0   2016-01-02 00:01:05  \n",
       "1   2016-01-02 00:05:26  \n",
       "2   2016-01-02 00:18:30  \n",
       "3   2016-01-02 02:32:30  \n",
       "4   2016-01-02 04:51:55  \n",
       "5   2016-01-02 07:20:59  \n",
       "6   2016-01-02 07:40:37  \n",
       "7   2016-01-02 08:57:57  \n",
       "8   2016-01-02 09:09:01  \n",
       "9   2016-01-02 11:01:32  \n",
       "10  2016-01-02 12:51:30  \n",
       "11  2016-01-02 13:16:45  \n",
       "12  2016-01-02 16:34:14  \n",
       "13  2016-01-02 17:45:54  \n",
       "14  2016-01-02 17:46:50  \n",
       "15  2016-01-02 18:52:45  \n",
       "16  2016-01-02 20:28:56  \n",
       "17  2016-01-02 21:10:19  \n",
       "18  2016-01-02 22:18:14  \n",
       "19  2016-01-03 00:19:44  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import requests\n",
    "import sqlite3\n",
    "\n",
    "file = \"data/transactions.csv\"\n",
    "df = pd.read_csv(file)\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is a CSV-file containing sales information from 2016-2019. The data has six columns of attributes (part, company, country, city, price, date). There might be errors within the data that we are not aware of, and so we will begin to clean the data.\n",
    "\n",
    "First, the city attribute was checked for errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Barcelona', 'Thessaloniki', 'Athens', 'Annecy', 'Paris',\n",
       "       'Düsseldorf', 'London', 'Braga', 'Nanterre', 'Amadora\\t',\n",
       "       'New York', 'Arnhem', 'Nice', 'Lisbon', 'Amsterdam', 'Porto',\n",
       "       'Boston', 'Niihama', 'Almada', 'Aranhas', 'Heraklion', 'Amiens',\n",
       "       'Patras', 'Arcueil', 'Lyon', 'Asaka', 'Champagnole', 'Zürich', nan,\n",
       "       'Monção', 'Vila Fria'], dtype=object)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['city'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the city column Amadora is consistently written as \"Amadora\\t\". This is corrected to \"Amadora\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.replace(to_replace = 'Amadora\\t', value = 'Amadora')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, the countries were investigated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Spain', 'Greece', 'France', 'Germany', 'United Kingdom',\n",
       "       'Portugal', 'United States', 'Netherlands', 'Japan', 'Switzerland',\n",
       "       nan, 'US', 'Tyskland', 'Portuga'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['country'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It was seen that there were several errors in the countries. These were mainly either danish names for the countries, non-values, or abbreviations. The observed errors were: nan, 'US', 'Tyskland', 'Portuga'. nan was corrected later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Spain', 'Greece', 'France', 'Germany', 'United Kingdom',\n",
       "       'Portugal', 'United States', 'Netherlands', 'Japan', 'Switzerland',\n",
       "       nan], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_replace = {'US': 'United States', 'Tyskland': 'Germany', 'Portuga': 'Portugal'}\n",
    "df['country'].replace(to_replace, inplace=True)\n",
    "df['country'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then the presence of missing values are checked in all columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "part         10\n",
       "company       0\n",
       "country    2171\n",
       "city         33\n",
       "price         1\n",
       "date          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It was decided that the missing values for the different parts and prices would not possible to calculate, since the price of the same parts seemed to differ. Therefore, these entries were excluded from the data. 11 entries were removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "part          0\n",
       "company       0\n",
       "country    2171\n",
       "city         33\n",
       "price         0\n",
       "date          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[~df.isna()['price']]\n",
    "df = df[~df.isna()['part']]\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step was to fix the missing country and cities values. The incorrect values were initially printed.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Chatterbridge', 'Lajo', 'Flipstorm', 'Twitterbeat', 'Voomm',\n",
       "       'Buzzbean', 'Zooxo', 'Brainsphere', 'Thoughtmix', 'Wordify',\n",
       "       'Teklist', 'Avaveo', 'Ntags', 'Innojam', 'Shufflebeat', 'Zoonder',\n",
       "       'Kanoodle', 'Gabcube', 'Roodel', 'Riffpath', 'Eimbee', 'Yozio',\n",
       "       'Rhycero', 'Realpoint', 'Gabtune', 'Bubblemix', 'Gevee', 'Tagtune',\n",
       "       'Zooxo.', 'Laj0', 'Ntagz', ' -', ' a', 'aa', 'Thoughtmixz'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['company'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The list contains some mispellings, which are easily corrected for, but the company entries \"aa\", \" a\" and \" -\" had to be more thoroughly investigated. To gain more knowledge about these, their corresponding rows were printed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            part company        country      city     price  \\\n",
      "13841  51346-145      aa  United States  New York  $1059.38   \n",
      "\n",
      "                      date  \n",
      "13841  2017-12-16 20:02:15  \n",
      "            part company        country      city   price                 date\n",
      "13840  37205-992       a  United States  New York  $395.3  2017-12-16 18:59:53\n",
      "            part company        country    city     price                 date\n",
      "13839  17156-617       -  United States  Boston  $1168.82  2017-12-16 17:55:55\n"
     ]
    }
   ],
   "source": [
    "print(df[df['company']== 'aa'])\n",
    "print(df[df['company']== ' a'])\n",
    "print(df[df['company']== ' -'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It was observed that for the companies \"aa\", \" a\" and \" -\", they all only had one single sale. Therefore, it was concluded that the naming of the company was an error that needed correction. In order to do this, the cities where the wrongly-named companies made orders from was investigated to see which companies were from these cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Wordify' ' a' 'aa']\n",
      "['Zoonder' ' -']\n"
     ]
    }
   ],
   "source": [
    "print(df[df['city']=='New York']['company'].unique())\n",
    "print(df[df['city']=='Boston']['company'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It was observed that there was only one major customer in each city. It was therefore concluded that the sale to \"aa\" and \" a\" were to the company \"Wordify\" and the sale to \" -\" was to \"Zoonder\". The incorrect names were thus replaced with the correct ones, along with the misspelled ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Chatterbridge', 'Lajo', 'Flipstorm', 'Twitterbeat', 'Voomm',\n",
       "       'Buzzbean', 'Zooxo', 'Brainsphere', 'Thoughtmix', 'Wordify',\n",
       "       'Teklist', 'Avaveo', 'Ntags', 'Innojam', 'Shufflebeat', 'Zoonder',\n",
       "       'Kanoodle', 'Gabcube', 'Roodel', 'Riffpath', 'Eimbee', 'Yozio',\n",
       "       'Rhycero', 'Realpoint', 'Gabtune', 'Bubblemix', 'Gevee', 'Tagtune'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_replace = {'Zooxo.':'Zooxo', 'Laj0':'Lajo', 'Ntagz':'Ntags', ' -': 'Zoonder', ' a':'Wordify', 'aa':'Wordify', 'Thoughtmixz':'Thoughtmix'}\n",
    "df['company'].replace(to_replace, inplace=True)\n",
    "df['company'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then the dataset was separated based on the presence of missing data. This was done to lower the processing time. The two datasets were to be joined afterwards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_na = df[df.isnull().any(1)]\n",
    "df_drop = df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step was to identify which companies had missing values in the country and city columns.\n",
    "A list of unique companies in the df_na dataset were defined and looped through.\n",
    "For each company, its occurances in df_drop were found and their most common country and city was stored in a dictionary. The cities and their occurances were printed to illustrate why we thought this was a valid approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arnhem    387\n",
      "Name: city, dtype: int64\n",
      "Braga     1114\n",
      "Monção       1\n",
      "Name: city, dtype: int64\n",
      "Düsseldorf    1125\n",
      "Name: city, dtype: int64\n",
      "Porto    1382\n",
      "Name: city, dtype: int64\n",
      "Annecy    2037\n",
      "Name: city, dtype: int64\n",
      "Heraklion    130\n",
      "Name: city, dtype: int64\n",
      "Barcelona    1425\n",
      "Name: city, dtype: int64\n",
      "Lisbon    1353\n",
      "Name: city, dtype: int64\n",
      "Athens      713\n",
      "Nanterre    332\n",
      "Name: city, dtype: int64\n",
      "Amadora      2511\n",
      "Vila Fria       1\n",
      "Name: city, dtype: int64\n",
      "Aranhas    552\n",
      "Name: city, dtype: int64\n",
      "New York    858\n",
      "Name: city, dtype: int64\n",
      "Patras    583\n",
      "Name: city, dtype: int64\n",
      "Amiens    452\n",
      "Name: city, dtype: int64\n",
      "Almada    323\n",
      "Name: city, dtype: int64\n",
      "Boston    398\n",
      "Name: city, dtype: int64\n",
      "Paris    220\n",
      "Name: city, dtype: int64\n",
      "Nice    182\n",
      "Name: city, dtype: int64\n",
      "London    1511\n",
      "Name: city, dtype: int64\n",
      "Niihama    112\n",
      "Name: city, dtype: int64\n",
      "Asaka    47\n",
      "Name: city, dtype: int64\n",
      "Arcueil    180\n",
      "Name: city, dtype: int64\n",
      "Lyon    24\n",
      "Name: city, dtype: int64\n",
      "Thessaloniki    201\n",
      "Name: city, dtype: int64\n",
      "Lisbon    143\n",
      "Name: city, dtype: int64\n",
      "Amsterdam    40\n",
      "Name: city, dtype: int64\n",
      "Champagnole    35\n",
      "Name: city, dtype: int64\n",
      "Zürich    11\n",
      "Name: city, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "companies = df_na['company'].unique()\n",
    "\n",
    "comp_dict = {}\n",
    "for company in companies:\n",
    "    comp_dict[company] = dict()\n",
    "    comp_dict[company]['city'] = df_drop[df_drop['company']==company]['city'].value_counts().index[0]\n",
    "    comp_dict[company]['country'] = df_drop[df_drop['company']==company]['country'].value_counts().index[0]\n",
    "    print(df_drop[df_drop['company']==company]['city'].value_counts())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All companies, except for 3, was found to have only one location from which they placed orders. Two of the three only had one occurance of the next most common city, while the third has around two thirds from its primary city. We therefore believe we can assume the missing cities are from the most common in the list above. The country is connected to the city, so the same approach can be used to set those. With the cases where a company ordered once from another city, it is not really possible to say if that is correct, or if either the company name or the location is incorrect, so it was decided to not do any correction there.\n",
    "\n",
    "Next, these found values were added to lists in order and inserted into the dataset. In order to do this in a simple way, the df_na indices were reset.\n",
    "To keep the same positions as the dataset, the current values were stored if they were not missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "countries = list()\n",
    "cities = list()\n",
    "\n",
    "for comp, country, city in zip(df_na['company'], df_na['country'], df_na['city']):\n",
    "    if str(city) == 'nan':\n",
    "        cities.append(comp_dict[comp]['city'])\n",
    "    else:\n",
    "        cities.append(city)\n",
    "    if str(country) == 'nan':\n",
    "        countries.append(comp_dict[comp]['country'])\n",
    "    else:\n",
    "        countries.append(country)\n",
    "\n",
    "df_na = df_na.reset_index(drop=True)\n",
    "df_na['country'] = countries\n",
    "df_na['city'] = cities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two datasets were merged and the number of missing values were printed to see if something had been missed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "part       0\n",
       "company    0\n",
       "country    0\n",
       "city       0\n",
       "price      0\n",
       "date       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_na\n",
    "df = pd.concat([df_drop, df_na])\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset now contains no missing information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Part 2: Enriching the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A common task for a data scientists is to combine or enrich data from internal sources with data available from external sources. The purpose of this can be either to fix issues with the data or to make it easier to derive insights from the data.\n",
    "\n",
    "In this part you should enrich your data with data from at least one external source. You may look to part 4 for some  inspiration as to what is required. Your solution should be automated, i.e., you can not ask the reader of your notebook to download any data manually. You should argue why and what you expect to achieve by the enrichments you are doing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make the prices comparable, they were all converted to Euros and the € signs removed.\n",
    "\n",
    "Exchange rates were collected automatically from: \"https://api.exchangeratesapi.io/latest\".\n",
    "\n",
    "For some values the currency was missing. To solve this it was assumed that the 4 used currencies were USD, GBP, JPY, and EUR (This assumption was made based on the countries previously found). Thus, we checked the origin country and assigned the currency based on that information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'base': 'EUR',\n",
       " 'date': '2019-10-09',\n",
       " 'rates': {'AUD': 1.6282,\n",
       "  'BGN': 1.9558,\n",
       "  'BRL': 4.4783,\n",
       "  'CAD': 1.4607,\n",
       "  'CHF': 1.0927,\n",
       "  'CNY': 7.8265,\n",
       "  'CZK': 25.823,\n",
       "  'DKK': 7.4691,\n",
       "  'GBP': 0.8985,\n",
       "  'HKD': 8.6143,\n",
       "  'HRK': 7.427,\n",
       "  'HUF': 334.04,\n",
       "  'IDR': 15560.08,\n",
       "  'ILS': 3.8465,\n",
       "  'INR': 78.034,\n",
       "  'ISK': 137.3,\n",
       "  'JPY': 117.91,\n",
       "  'KRW': 1314.77,\n",
       "  'MXN': 21.452,\n",
       "  'MYR': 4.6087,\n",
       "  'NOK': 10.0528,\n",
       "  'NZD': 1.7381,\n",
       "  'PHP': 56.816,\n",
       "  'PLN': 4.3236,\n",
       "  'RON': 4.7486,\n",
       "  'RUB': 71.1918,\n",
       "  'SEK': 10.9173,\n",
       "  'SGD': 1.5153,\n",
       "  'THB': 33.311,\n",
       "  'TRY': 6.4053,\n",
       "  'USD': 1.0981,\n",
       "  'ZAR': 16.6248}}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract currency information and print result\n",
    "r = requests.get('https://api.exchangeratesapi.io/latest')\n",
    "json_resp = r.json()\n",
    "json_resp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The exchange rates for USD, GBP, and JPY were stored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "JPY = json_resp['rates']['JPY']\n",
    "USD = json_resp['rates']['USD']\n",
    "GBP = json_resp['rates']['GBP']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These exchange rates were used to convert all prices to Euros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>part</th>\n",
       "      <th>company</th>\n",
       "      <th>country</th>\n",
       "      <th>city</th>\n",
       "      <th>price</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>54868-5165</td>\n",
       "      <td>Chatterbridge</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>784.79</td>\n",
       "      <td>2016-01-02 00:01:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>60505-2867</td>\n",
       "      <td>Lajo</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Thessaloniki</td>\n",
       "      <td>187.99</td>\n",
       "      <td>2016-01-02 00:05:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>24385-268</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Athens</td>\n",
       "      <td>221.73</td>\n",
       "      <td>2016-01-02 00:18:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>76117-001</td>\n",
       "      <td>Twitterbeat</td>\n",
       "      <td>France</td>\n",
       "      <td>Annecy</td>\n",
       "      <td>1075.82</td>\n",
       "      <td>2016-01-02 02:32:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>44946-1046</td>\n",
       "      <td>Chatterbridge</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>412.55</td>\n",
       "      <td>2016-01-02 04:51:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>16729-167</td>\n",
       "      <td>Chatterbridge</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>359.52</td>\n",
       "      <td>2016-01-02 07:20:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>52125-444</td>\n",
       "      <td>Voomm</td>\n",
       "      <td>France</td>\n",
       "      <td>Paris</td>\n",
       "      <td>266.62</td>\n",
       "      <td>2016-01-02 07:40:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>43419-018</td>\n",
       "      <td>Buzzbean</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Düsseldorf</td>\n",
       "      <td>103.45</td>\n",
       "      <td>2016-01-02 08:57:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>54092-515</td>\n",
       "      <td>Zooxo</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>London</td>\n",
       "      <td>784.574</td>\n",
       "      <td>2016-01-02 09:09:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>24286-1562</td>\n",
       "      <td>Lajo</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Thessaloniki</td>\n",
       "      <td>317.65</td>\n",
       "      <td>2016-01-02 11:01:32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         part        company         country          city    price  \\\n",
       "0  54868-5165  Chatterbridge           Spain     Barcelona   784.79   \n",
       "1  60505-2867           Lajo          Greece  Thessaloniki   187.99   \n",
       "2   24385-268      Flipstorm          Greece        Athens   221.73   \n",
       "3   76117-001    Twitterbeat          France        Annecy  1075.82   \n",
       "4  44946-1046  Chatterbridge           Spain     Barcelona   412.55   \n",
       "5   16729-167  Chatterbridge           Spain     Barcelona   359.52   \n",
       "6   52125-444          Voomm          France         Paris   266.62   \n",
       "7   43419-018       Buzzbean         Germany    Düsseldorf   103.45   \n",
       "8   54092-515          Zooxo  United Kingdom        London  784.574   \n",
       "9  24286-1562           Lajo          Greece  Thessaloniki   317.65   \n",
       "\n",
       "                  date  \n",
       "0  2016-01-02 00:01:05  \n",
       "1  2016-01-02 00:05:26  \n",
       "2  2016-01-02 00:18:30  \n",
       "3  2016-01-02 02:32:30  \n",
       "4  2016-01-02 04:51:55  \n",
       "5  2016-01-02 07:20:59  \n",
       "6  2016-01-02 07:40:37  \n",
       "7  2016-01-02 08:57:57  \n",
       "8  2016-01-02 09:09:01  \n",
       "9  2016-01-02 11:01:32  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price = df['price']\n",
    "country = df['country']\n",
    "new_price = list()\n",
    "\n",
    "for val, cur in zip(price, country):\n",
    "    if type(val) == float: #In case currency sign (€,$,£,¥) was missing from the price-value.\n",
    "        if cur == 'Japan':\n",
    "            val = val / JPY\n",
    "        elif cur == 'United States':\n",
    "            val = val / USD\n",
    "        elif cur == 'United Kingdom':\n",
    "            val = val / GBP\n",
    "    elif val[0] == '$':\n",
    "        val = float(val[1:]) / USD\n",
    "    elif val[0] == '£':\n",
    "        val = float(val[1:]) / GBP\n",
    "    elif val[0] == '¥':\n",
    "        val = float(val[1:]) / JPY\n",
    "    elif val[-1] == '€':\n",
    "        val = float(val[:-1])\n",
    "    \n",
    "    new_price.append(val)\n",
    "df['price'] = new_price\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The currencies were thus all converted to Euros.\n",
    "\n",
    "Some of the dates had another format than others (dd/mm/yyyy and yyyy-mm-dd), and so they were all converted to the same format. In the cases of missing time values the time was set to 12:00:00. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>part</th>\n",
       "      <th>company</th>\n",
       "      <th>country</th>\n",
       "      <th>city</th>\n",
       "      <th>price</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>54868-5165</td>\n",
       "      <td>Chatterbridge</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>784.79</td>\n",
       "      <td>2016-01-02 00:01:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>60505-2867</td>\n",
       "      <td>Lajo</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Thessaloniki</td>\n",
       "      <td>187.99</td>\n",
       "      <td>2016-01-02 00:05:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>24385-268</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Athens</td>\n",
       "      <td>221.73</td>\n",
       "      <td>2016-01-02 00:18:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>76117-001</td>\n",
       "      <td>Twitterbeat</td>\n",
       "      <td>France</td>\n",
       "      <td>Annecy</td>\n",
       "      <td>1075.82</td>\n",
       "      <td>2016-01-02 02:32:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>44946-1046</td>\n",
       "      <td>Chatterbridge</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>412.55</td>\n",
       "      <td>2016-01-02 04:51:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>16729-167</td>\n",
       "      <td>Chatterbridge</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>359.52</td>\n",
       "      <td>2016-01-02 07:20:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>52125-444</td>\n",
       "      <td>Voomm</td>\n",
       "      <td>France</td>\n",
       "      <td>Paris</td>\n",
       "      <td>266.62</td>\n",
       "      <td>2016-01-02 07:40:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>43419-018</td>\n",
       "      <td>Buzzbean</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Düsseldorf</td>\n",
       "      <td>103.45</td>\n",
       "      <td>2016-01-02 08:57:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>54092-515</td>\n",
       "      <td>Zooxo</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>London</td>\n",
       "      <td>784.574</td>\n",
       "      <td>2016-01-02 09:09:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>24286-1562</td>\n",
       "      <td>Lajo</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Thessaloniki</td>\n",
       "      <td>317.65</td>\n",
       "      <td>2016-01-02 11:01:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>49035-530</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Athens</td>\n",
       "      <td>841.97</td>\n",
       "      <td>2016-01-02 12:51:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>36987-1697</td>\n",
       "      <td>Brainsphere</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Braga</td>\n",
       "      <td>830.75</td>\n",
       "      <td>2016-01-02 13:16:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>44946-1046</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>France</td>\n",
       "      <td>Nanterre</td>\n",
       "      <td>406.4</td>\n",
       "      <td>2016-01-02 16:34:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>51346-145</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Athens</td>\n",
       "      <td>978.51</td>\n",
       "      <td>2016-01-02 17:45:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>51138-049</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>France</td>\n",
       "      <td>Nanterre</td>\n",
       "      <td>504.69</td>\n",
       "      <td>2016-01-02 17:46:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0268-6107</td>\n",
       "      <td>Thoughtmix</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Amadora</td>\n",
       "      <td>477.71</td>\n",
       "      <td>2016-01-02 18:52:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>51523-002</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Athens</td>\n",
       "      <td>1041.56</td>\n",
       "      <td>2016-01-02 20:28:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>62670-4404</td>\n",
       "      <td>Wordify</td>\n",
       "      <td>United States</td>\n",
       "      <td>New York</td>\n",
       "      <td>404.471</td>\n",
       "      <td>2016-01-02 21:10:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>49288-0285</td>\n",
       "      <td>Brainsphere</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Braga</td>\n",
       "      <td>598.52</td>\n",
       "      <td>2016-01-02 22:18:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>68233-011</td>\n",
       "      <td>Chatterbridge</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>304.59</td>\n",
       "      <td>2016-01-03 00:19:44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          part        company         country          city    price  \\\n",
       "0   54868-5165  Chatterbridge           Spain     Barcelona   784.79   \n",
       "1   60505-2867           Lajo          Greece  Thessaloniki   187.99   \n",
       "2    24385-268      Flipstorm          Greece        Athens   221.73   \n",
       "3    76117-001    Twitterbeat          France        Annecy  1075.82   \n",
       "4   44946-1046  Chatterbridge           Spain     Barcelona   412.55   \n",
       "5    16729-167  Chatterbridge           Spain     Barcelona   359.52   \n",
       "6    52125-444          Voomm          France         Paris   266.62   \n",
       "7    43419-018       Buzzbean         Germany    Düsseldorf   103.45   \n",
       "8    54092-515          Zooxo  United Kingdom        London  784.574   \n",
       "9   24286-1562           Lajo          Greece  Thessaloniki   317.65   \n",
       "10   49035-530      Flipstorm          Greece        Athens   841.97   \n",
       "11  36987-1697    Brainsphere        Portugal         Braga   830.75   \n",
       "12  44946-1046      Flipstorm          France      Nanterre    406.4   \n",
       "13   51346-145      Flipstorm          Greece        Athens   978.51   \n",
       "14   51138-049      Flipstorm          France      Nanterre   504.69   \n",
       "15   0268-6107     Thoughtmix        Portugal       Amadora   477.71   \n",
       "16   51523-002      Flipstorm          Greece        Athens  1041.56   \n",
       "17  62670-4404        Wordify   United States      New York  404.471   \n",
       "18  49288-0285    Brainsphere        Portugal         Braga   598.52   \n",
       "19   68233-011  Chatterbridge           Spain     Barcelona   304.59   \n",
       "\n",
       "                   date  \n",
       "0   2016-01-02 00:01:05  \n",
       "1   2016-01-02 00:05:26  \n",
       "2   2016-01-02 00:18:30  \n",
       "3   2016-01-02 02:32:30  \n",
       "4   2016-01-02 04:51:55  \n",
       "5   2016-01-02 07:20:59  \n",
       "6   2016-01-02 07:40:37  \n",
       "7   2016-01-02 08:57:57  \n",
       "8   2016-01-02 09:09:01  \n",
       "9   2016-01-02 11:01:32  \n",
       "10  2016-01-02 12:51:30  \n",
       "11  2016-01-02 13:16:45  \n",
       "12  2016-01-02 16:34:14  \n",
       "13  2016-01-02 17:45:54  \n",
       "14  2016-01-02 17:46:50  \n",
       "15  2016-01-02 18:52:45  \n",
       "16  2016-01-02 20:28:56  \n",
       "17  2016-01-02 21:10:19  \n",
       "18  2016-01-02 22:18:14  \n",
       "19  2016-01-03 00:19:44  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years = list()\n",
    "dates = list()\n",
    "times = list()\n",
    "\n",
    "for date in df['date']:\n",
    "    if date[4]=='-':   # If format is yyyy-mm-dd\n",
    "        years.append(date[:4])\n",
    "        dates.append(date[5:10])\n",
    "        times.append(date[11:])\n",
    "    else:              # Else format is dd/mm/yyyy\n",
    "        years.append(date[-4:])\n",
    "        new_date = \"{}-{}\".format(date[3:5],date[:2])\n",
    "        dates.append(new_date)\n",
    "        times.append('12:00:00')\n",
    "df['date'] = [\"{}-{} {}\".format(years[x], dates[x], times[x]) for x in range(len(years))]\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Part 3: Creating a database\n",
    "Storing data in a relational database has the advantages that it is persistent, fast to query, and it will be easier access for other employees at Weyland-Yutani.\n",
    "\n",
    "In this part you should:\n",
    "- Create a database and table(s) for the data\n",
    "- Insert data into the tables\n",
    "\n",
    "You may use SQLite locally to do this. You should argue why you choose to store your data the way you do. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Database was created and a connection established. In case the code should be re-run, a \"drop table\" command was implemented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = sqlite3.connect('data/transactions.db')\n",
    "c = conn.cursor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.execute('DROP TABLE IF EXISTS sales')\n",
    "conn.commit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to maintain the correct dtypes, a SQL table names \"sales\" was created and the dataframe was appended to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.execute(\"\"\"CREATE TABLE IF NOT EXISTS sales(id INTEGER PRIMARY KEY, part TEXT,\n",
    "             company TEXT, country TEXT, city TEXT, price REAL, date TEXT)\"\"\")\n",
    "conn.commit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_sql('sales', con = conn, if_exists = 'append', index = False)\n",
    "conn.commit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The database is now ready for analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Part 4: Analyzing the data\n",
    "You are now ready to analyze the data. Your goal is to gain some actionable business insights to present to your boss. \n",
    "\n",
    "In this part, you should ask some questions and try to answer them based on the data. You should write SQL queries to retrieve the data. For each question, you should state why it is relevant and what you expect to find.\n",
    "\n",
    "To get you started, you should prepare answers to the following questions. You should add more questions.\n",
    "#### Who are the most profitable clients?\n",
    "Knowing which clients that generate the most revenue for the company will assist your boss in distributing customer service ressources.\n",
    "\n",
    "#### Are there any clients for which profit is declining?\n",
    "Declining profit from a specific client may indicate that the client is disatisfied with the product. Gaining a new client is often much more work than retaining one. Early warnings about declining profit may help your boss fighting customer churn.\n",
    "\n",
    "\n",
    "Remember, you are taking this to your new boss, so think about how you present the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following plot lists the total sales for each of the company's clients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe28e91e5c0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c.execute('SELECT company, SUM(price) FROM sales GROUP BY company ORDER BY SUM(price) DESC')\n",
    "\n",
    "plot = pd.DataFrame(c.fetchall(), columns = ['Company','price']).plot.bar(x='Company',y='price')\n",
    "plot.set_ylabel('Total sales (€)')\n",
    "plot.set_title('Total sales to customers')\n",
    "plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that Thoughtmix is most profitable client.\n",
    "\n",
    "The following plot illustrates which clients that have had declining business with our company."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe28e97b128>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "command = '''\n",
    "    SELECT company, SUM(price), SUBSTR(date,1,4) FROM sales\n",
    "    GROUP BY company, SUBSTR(date,1,4)\n",
    "'''\n",
    "c.execute(command)\n",
    "data = pd.DataFrame(c.fetchall(), columns = ['Company','price','Year'])\n",
    "data_piv = data.pivot(index='Year', columns='Company', values='price')\n",
    "\n",
    "for company in data['Company'].unique():\n",
    "    if data_piv[company][0] < data_piv[company].dropna()[-1]:\n",
    "        data_piv.drop(columns=company, inplace=True)\n",
    "plot = data_piv.plot()\n",
    "plot.set_ylabel('Sales per year (€)')\n",
    "plot.set_title('Clients with declining business')\n",
    "plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that especially Flipstorm and Voomm have made fewer purchases in 2017 and 2018 than in 2016. Efforts to reclaim these clients will therefore be essential.\n",
    "\n",
    "The next table and plot shows which parts that are sold in greatest quantity. We thought this is relevant since it could provide great info for supply chain management and show which other parts maybe needs more careful marketing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Part</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>17156-617</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>37205-992</td>\n",
       "      <td>295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0268-6107</td>\n",
       "      <td>293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>52959-433</td>\n",
       "      <td>293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>54868-0823</td>\n",
       "      <td>292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>62011-0219</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>51346-145</td>\n",
       "      <td>289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>13537-259</td>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>58118-5060</td>\n",
       "      <td>286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10237-832</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Part  Count\n",
       "0   17156-617    300\n",
       "1   37205-992    295\n",
       "2   0268-6107    293\n",
       "3   52959-433    293\n",
       "4  54868-0823    292\n",
       "5  62011-0219    291\n",
       "6   51346-145    289\n",
       "7   13537-259    287\n",
       "8  58118-5060    286\n",
       "9   10237-832    285"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "command = '''\n",
    "    SELECT part, COUNT(*) FROM sales\n",
    "    GROUP BY part\n",
    "    ORDER BY COUNT(part) DESC\n",
    "'''\n",
    "c.execute(command)\n",
    "data = pd.DataFrame(c.fetchall(), columns = ['Part','Count'])\n",
    "plot = data.plot.bar(x='Part',y='Count')\n",
    "plot.set_ylabel('Units sold')\n",
    "plot.set_title('Display of number of units sold per part')\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the best selling part \"only\" outsells the lowest selling part by a factor of 3. The table could be expanded (by modifying the header input) if the ID of all the parts is to be showen (since it cannot be read from the graph).\n",
    "\n",
    "The following table and plot was made to determine the sales distribution based on the countries. We thought this could provide interesting knowledge into which emerging markets that the company could expand into."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Price (€)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>4.763407e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>France</td>\n",
       "      <td>2.235683e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Greece</td>\n",
       "      <td>1.062999e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>9.522897e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Spain</td>\n",
       "      <td>9.193497e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>United States</td>\n",
       "      <td>8.249672e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Germany</td>\n",
       "      <td>7.139126e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2.672517e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1.053832e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>6.579640e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country     Price (€)\n",
       "0        Portugal  4.763407e+06\n",
       "1          France  2.235683e+06\n",
       "2          Greece  1.062999e+06\n",
       "3  United Kingdom  9.522897e+05\n",
       "4           Spain  9.193497e+05\n",
       "5   United States  8.249672e+05\n",
       "6         Germany  7.139126e+05\n",
       "7     Netherlands  2.672517e+05\n",
       "8           Japan  1.053832e+05\n",
       "9     Switzerland  6.579640e+03"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "command = '''\n",
    "    SELECT country, SUM(price) FROM sales\n",
    "    GROUP BY country\n",
    "    ORDER BY SUM(price) DESC\n",
    "'''\n",
    "c.execute(command)\n",
    "data = pd.DataFrame(c.fetchall(), columns = ['Country','Price (€)'])\n",
    "plot = data.plot.bar(x='Country',y='Price (€)')\n",
    "plot.set_ylabel('Total sales (€)')\n",
    "plot.set_title('Sales per country')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it can be seen, Portugal is by far the largest market. Smaller markets, such as Switzerland and Japan are also very interesting, since they could potentially be markets that the company could tap into. With this knowledge, we thought it could be interesting to see which markets that has had the largest percentage-wise growth over the past 3 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Country</th>\n",
       "      <th>Germany</th>\n",
       "      <th>Netherlands</th>\n",
       "      <th>Spain</th>\n",
       "      <th>Switzerland</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2016</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2017</td>\n",
       "      <td>1.720827</td>\n",
       "      <td>-5.351542</td>\n",
       "      <td>-4.339380</td>\n",
       "      <td>253.001456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018</td>\n",
       "      <td>11.429726</td>\n",
       "      <td>12.471480</td>\n",
       "      <td>5.240812</td>\n",
       "      <td>98.736902</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Country    Germany  Netherlands     Spain  Switzerland\n",
       "Year                                                  \n",
       "2016      0.000000     0.000000  0.000000     0.000000\n",
       "2017      1.720827    -5.351542 -4.339380   253.001456\n",
       "2018     11.429726    12.471480  5.240812    98.736902"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "command = '''\n",
    "    SELECT country, SUM(price), SUBSTR(date,1,4) FROM sales\n",
    "    GROUP BY country, SUBSTR(date,1,4)\n",
    "'''\n",
    "c.execute(command)\n",
    "\n",
    "data = pd.DataFrame(c.fetchall(), columns = ['Country','price','Year'])\n",
    "data_piv = data.pivot(index='Year', columns='Country', values='price').dropna()\n",
    "\n",
    "for country in data['Country'].unique():\n",
    "    dat = [(data_piv[country][x]/data_piv[country][x-1]) for x in range(1,len(data_piv[country]))]\n",
    "    dat = (np.array([1] + dat)-1)*100\n",
    "    data_piv[country] = dat\n",
    "    if dat[0] > dat[-1]:\n",
    "        data_piv.drop(columns=country, inplace=True)\n",
    "    \n",
    "plot = data_piv.plot()\n",
    "plot.set_ylabel('% increase from the previous year')\n",
    "plot.set_title('Country-wise market growth in %')\n",
    "\n",
    "data_piv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see above that there are 4 countries in which the company has seen growth from 2016-2019. Especially Switzerland is interesting, because it was also a market in which the company does not have a lot of business. Therefore our company should look into future oppertunities in this country aswell as perhaps Germany, The Netherlands, and Spain."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Part 5: Performance\n",
    "Your boss is very impressed with what you have achieved in less than two weeks, and he would like to take your idea of storing the customer and sales data in a relational database to production. However, John is concerned that the solution will not scale. His experience is telling him that you will see many occurrences of the following queries.\n",
    "\n",
    "- Show all sales to company X between time $t_1$ and time $t_2$\n",
    "- Show the latest X sales in the database\n",
    "- Show total sales per company per day\n",
    "\n",
    "Show that Johns concern is not justified."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the occurences of queries that John has given us, we thought the best solution would be to index the database based on the date. This would allow for quick results when searching for resent sales, and it would also retain good performance when searching for sales by a specific company per day."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# An index is created\n",
    "c.execute('CREATE INDEX ind ON sales(date)')\n",
    "conn.commit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = ['index','part','company','country','city','price','date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>part</th>\n",
       "      <th>company</th>\n",
       "      <th>country</th>\n",
       "      <th>city</th>\n",
       "      <th>price</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10241</td>\n",
       "      <td>58118-5060</td>\n",
       "      <td>Buzzbean</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Düsseldorf</td>\n",
       "      <td>251.810000</td>\n",
       "      <td>2017-09-24 16:10:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10242</td>\n",
       "      <td>52380-1102</td>\n",
       "      <td>Zooxo</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>London</td>\n",
       "      <td>414.858097</td>\n",
       "      <td>2017-09-24 16:54:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>10243</td>\n",
       "      <td>55566-2100</td>\n",
       "      <td>Zoonder</td>\n",
       "      <td>United States</td>\n",
       "      <td>Boston</td>\n",
       "      <td>252.099080</td>\n",
       "      <td>2017-09-24 17:08:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>10244</td>\n",
       "      <td>54868-0823</td>\n",
       "      <td>Chatterbridge</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>163.520000</td>\n",
       "      <td>2017-09-24 17:10:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>10245</td>\n",
       "      <td>59779-028</td>\n",
       "      <td>Ntags</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Lisbon</td>\n",
       "      <td>562.040000</td>\n",
       "      <td>2017-09-24 19:02:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\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",
       "      <td>6404</td>\n",
       "      <td>16610</td>\n",
       "      <td>0185-0373</td>\n",
       "      <td>Zooxo</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>London</td>\n",
       "      <td>965.008347</td>\n",
       "      <td>2018-09-23 22:20:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6405</td>\n",
       "      <td>16611</td>\n",
       "      <td>0268-1401</td>\n",
       "      <td>Ntags</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Lisbon</td>\n",
       "      <td>1225.770000</td>\n",
       "      <td>2018-09-24 07:50:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6406</td>\n",
       "      <td>16612</td>\n",
       "      <td>49349-842</td>\n",
       "      <td>Shufflebeat</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Porto</td>\n",
       "      <td>295.830000</td>\n",
       "      <td>2018-09-24 10:43:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6407</td>\n",
       "      <td>16613</td>\n",
       "      <td>58118-5060</td>\n",
       "      <td>Chatterbridge</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>132.230000</td>\n",
       "      <td>2018-09-24 11:08:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6408</td>\n",
       "      <td>16614</td>\n",
       "      <td>70253-307</td>\n",
       "      <td>Shufflebeat</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Porto</td>\n",
       "      <td>855.430000</td>\n",
       "      <td>2018-09-24 12:03:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6409 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index        part        company         country        city  \\\n",
       "0     10241  58118-5060       Buzzbean         Germany  Düsseldorf   \n",
       "1     10242  52380-1102          Zooxo  United Kingdom      London   \n",
       "2     10243  55566-2100        Zoonder   United States      Boston   \n",
       "3     10244  54868-0823  Chatterbridge           Spain   Barcelona   \n",
       "4     10245   59779-028          Ntags        Portugal      Lisbon   \n",
       "...     ...         ...            ...             ...         ...   \n",
       "6404  16610   0185-0373          Zooxo  United Kingdom      London   \n",
       "6405  16611   0268-1401          Ntags        Portugal      Lisbon   \n",
       "6406  16612   49349-842    Shufflebeat        Portugal       Porto   \n",
       "6407  16613  58118-5060  Chatterbridge           Spain   Barcelona   \n",
       "6408  16614   70253-307    Shufflebeat        Portugal       Porto   \n",
       "\n",
       "            price                 date  \n",
       "0      251.810000  2017-09-24 16:10:12  \n",
       "1      414.858097  2017-09-24 16:54:30  \n",
       "2      252.099080  2017-09-24 17:08:15  \n",
       "3      163.520000  2017-09-24 17:10:36  \n",
       "4      562.040000  2017-09-24 19:02:48  \n",
       "...           ...                  ...  \n",
       "6404   965.008347  2018-09-23 22:20:26  \n",
       "6405  1225.770000  2018-09-24 07:50:04  \n",
       "6406   295.830000  2018-09-24 10:43:46  \n",
       "6407   132.230000  2018-09-24 11:08:23  \n",
       "6408   855.430000  2018-09-24 12:03:21  \n",
       "\n",
       "[6409 rows x 7 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1 = \"2017-09-24 13:28:13\"   # Insert starting day of interest\n",
    "t2 = \"2018-09-24 13:28:13\"   # Insert ending day of interest\n",
    "c.execute('SELECT * FROM sales WHERE date > ? AND date < ? ORDER BY date', (t1, t2))\n",
    "pd.DataFrame(c.fetchall(), columns = column_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output lists all sales to company x in a DataFrame format to make it more readable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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",
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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>index</th>\n",
       "      <th>part</th>\n",
       "      <th>company</th>\n",
       "      <th>country</th>\n",
       "      <th>city</th>\n",
       "      <th>price</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>18383</td>\n",
       "      <td>43419-018</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Athens</td>\n",
       "      <td>139.56</td>\n",
       "      <td>2019-05-14 22:48:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>18382</td>\n",
       "      <td>49288-0285</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Athens</td>\n",
       "      <td>1051.08</td>\n",
       "      <td>2019-02-21 20:05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>18381</td>\n",
       "      <td>52343-025</td>\n",
       "      <td>Brainsphere</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Braga</td>\n",
       "      <td>686.1</td>\n",
       "      <td>2018-12-31 21:38:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>18380</td>\n",
       "      <td>49999-737</td>\n",
       "      <td>Thoughtmix</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Amadora</td>\n",
       "      <td>828.37</td>\n",
       "      <td>2018-12-31 21:24:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>18379</td>\n",
       "      <td>21695-267</td>\n",
       "      <td>Roodel</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Aranhas</td>\n",
       "      <td>606.37</td>\n",
       "      <td>2018-12-31 20:48:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>18378</td>\n",
       "      <td>68462-565</td>\n",
       "      <td>Shufflebeat</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Porto</td>\n",
       "      <td>882.52</td>\n",
       "      <td>2018-12-31 19:56:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>18377</td>\n",
       "      <td>98132-889</td>\n",
       "      <td>Yozio</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Patras</td>\n",
       "      <td>179.88</td>\n",
       "      <td>2018-12-31 18:24:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>18376</td>\n",
       "      <td>36987-1697</td>\n",
       "      <td>Chatterbridge</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>1017.44</td>\n",
       "      <td>2018-12-31 16:46:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>18375</td>\n",
       "      <td>36800-952</td>\n",
       "      <td>Gabcube</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Almada</td>\n",
       "      <td>286.23</td>\n",
       "      <td>2018-12-31 16:27:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>18374</td>\n",
       "      <td>13537-259</td>\n",
       "      <td>Thoughtmix</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Amadora</td>\n",
       "      <td>762.29</td>\n",
       "      <td>2018-12-31 14:17:24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index        part        company   country       city    price  \\\n",
       "0  18383   43419-018      Flipstorm    Greece     Athens   139.56   \n",
       "1  18382  49288-0285      Flipstorm    Greece     Athens  1051.08   \n",
       "2  18381   52343-025    Brainsphere  Portugal      Braga    686.1   \n",
       "3  18380   49999-737     Thoughtmix  Portugal    Amadora   828.37   \n",
       "4  18379   21695-267         Roodel  Portugal    Aranhas   606.37   \n",
       "5  18378   68462-565    Shufflebeat  Portugal      Porto   882.52   \n",
       "6  18377   98132-889          Yozio    Greece     Patras   179.88   \n",
       "7  18376  36987-1697  Chatterbridge     Spain  Barcelona  1017.44   \n",
       "8  18375   36800-952        Gabcube  Portugal     Almada   286.23   \n",
       "9  18374   13537-259     Thoughtmix  Portugal    Amadora   762.29   \n",
       "\n",
       "                  date  \n",
       "0  2019-05-14 22:48:39  \n",
       "1  2019-02-21 20:05:00  \n",
       "2  2018-12-31 21:38:02  \n",
       "3  2018-12-31 21:24:17  \n",
       "4  2018-12-31 20:48:14  \n",
       "5  2018-12-31 19:56:53  \n",
       "6  2018-12-31 18:24:51  \n",
       "7  2018-12-31 16:46:06  \n",
       "8  2018-12-31 16:27:34  \n",
       "9  2018-12-31 14:17:24  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = 10   # Latest X sales, adjust X as necessary\n",
    "c.execute('SELECT * FROM sales ORDER BY date DESC')\n",
    "pd.DataFrame(c.fetchall(), columns = column_names).head(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above are the latest 10 sales in the database.\n",
    "\n",
    "Next, we wanted to illustrate the total sales per company per day. We were unsure which way of displaying this would be most useful, so first all sale days and companies were listed. Each column is a company and each row represents a day with a sale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\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>company</th>\n",
       "      <th>Avaveo</th>\n",
       "      <th>Brainsphere</th>\n",
       "      <th>Bubblemix</th>\n",
       "      <th>Buzzbean</th>\n",
       "      <th>Chatterbridge</th>\n",
       "      <th>Eimbee</th>\n",
       "      <th>Flipstorm</th>\n",
       "      <th>Gabcube</th>\n",
       "      <th>Gabtune</th>\n",
       "      <th>Gevee</th>\n",
       "      <th>...</th>\n",
       "      <th>Shufflebeat</th>\n",
       "      <th>Tagtune</th>\n",
       "      <th>Teklist</th>\n",
       "      <th>Thoughtmix</th>\n",
       "      <th>Twitterbeat</th>\n",
       "      <th>Voomm</th>\n",
       "      <th>Wordify</th>\n",
       "      <th>Yozio</th>\n",
       "      <th>Zoonder</th>\n",
       "      <th>Zooxo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2019-05-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-02-21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1051.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>686.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>378.98</td>\n",
       "      <td>1017.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>286.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>882.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2068.25</td>\n",
       "      <td>1302.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>179.88</td>\n",
       "      <td>434.077042</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-12-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>664.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1177.83</td>\n",
       "      <td>3539.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1044.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>994.38</td>\n",
       "      <td>3570.47</td>\n",
       "      <td>643.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>399.435388</td>\n",
       "      <td>840.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1040.011130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>794.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>810.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>447.43</td>\n",
       "      <td>1413.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175.894727</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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",
       "      <td>2016-01-06</td>\n",
       "      <td>687.63</td>\n",
       "      <td>946.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2233.76</td>\n",
       "      <td>522.49</td>\n",
       "      <td>789.66</td>\n",
       "      <td>7164.02</td>\n",
       "      <td>479.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>313.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>138.48</td>\n",
       "      <td>3069.76</td>\n",
       "      <td>675.32</td>\n",
       "      <td>859.97</td>\n",
       "      <td>1744.431290</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2262.604341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>528.52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>481.74</td>\n",
       "      <td>1218.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2215.04</td>\n",
       "      <td>316.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>895.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>924.25</td>\n",
       "      <td>1145.94</td>\n",
       "      <td>549.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>557.608597</td>\n",
       "      <td>499.977741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>948.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>356.11</td>\n",
       "      <td>1380.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5125.94</td>\n",
       "      <td>671.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>778.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>600.24</td>\n",
       "      <td>2217.50</td>\n",
       "      <td>458.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>1029.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>272.06</td>\n",
       "      <td>304.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5010.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1396.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1146.66</td>\n",
       "      <td>1431.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>842.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>761.943357</td>\n",
       "      <td>670.417362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1429.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>103.45</td>\n",
       "      <td>1556.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3994.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>477.71</td>\n",
       "      <td>1075.82</td>\n",
       "      <td>266.62</td>\n",
       "      <td>404.471360</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>784.574290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1098 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "company      Avaveo  Brainsphere  Bubblemix  Buzzbean  Chatterbridge  Eimbee  \\\n",
       "day                                                                            \n",
       "2019-05-14      NaN          NaN        NaN       NaN            NaN     NaN   \n",
       "2019-02-21      NaN          NaN        NaN       NaN            NaN     NaN   \n",
       "2018-12-31      NaN       686.10        NaN    378.98        1017.44     NaN   \n",
       "2018-12-30      NaN       664.61        NaN   1177.83        3539.62     NaN   \n",
       "2018-12-29      NaN          NaN        NaN       NaN         794.20     NaN   \n",
       "...             ...          ...        ...       ...            ...     ...   \n",
       "2016-01-06   687.63       946.63        NaN   2233.76         522.49  789.66   \n",
       "2016-01-05      NaN       528.52        NaN    481.74        1218.88     NaN   \n",
       "2016-01-04      NaN       948.34        NaN    356.11        1380.53     NaN   \n",
       "2016-01-03  1029.92          NaN        NaN    272.06         304.59     NaN   \n",
       "2016-01-02      NaN      1429.27        NaN    103.45        1556.86     NaN   \n",
       "\n",
       "company     Flipstorm  Gabcube  Gabtune  Gevee  ...  Shufflebeat  Tagtune  \\\n",
       "day                                             ...                         \n",
       "2019-05-14     139.56      NaN      NaN    NaN  ...          NaN      NaN   \n",
       "2019-02-21    1051.08      NaN      NaN    NaN  ...          NaN      NaN   \n",
       "2018-12-31        NaN   286.23      NaN    NaN  ...       882.52      NaN   \n",
       "2018-12-30        NaN      NaN      NaN    NaN  ...      1044.63      NaN   \n",
       "2018-12-29        NaN      NaN      NaN    NaN  ...       810.69      NaN   \n",
       "...               ...      ...      ...    ...  ...          ...      ...   \n",
       "2016-01-06    7164.02   479.94      NaN    NaN  ...       313.73      NaN   \n",
       "2016-01-05    2215.04   316.01      NaN    NaN  ...       895.28      NaN   \n",
       "2016-01-04    5125.94   671.22      NaN    NaN  ...       778.08      NaN   \n",
       "2016-01-03    5010.31      NaN      NaN    NaN  ...      1396.19      NaN   \n",
       "2016-01-02    3994.86      NaN      NaN    NaN  ...          NaN      NaN   \n",
       "\n",
       "company     Teklist  Thoughtmix  Twitterbeat   Voomm      Wordify   Yozio  \\\n",
       "day                                                                         \n",
       "2019-05-14      NaN         NaN          NaN     NaN          NaN     NaN   \n",
       "2019-02-21      NaN         NaN          NaN     NaN          NaN     NaN   \n",
       "2018-12-31      NaN     2068.25      1302.80     NaN          NaN  179.88   \n",
       "2018-12-30   994.38     3570.47       643.86     NaN   399.435388  840.84   \n",
       "2018-12-29   447.43     1413.38          NaN     NaN          NaN     NaN   \n",
       "...             ...         ...          ...     ...          ...     ...   \n",
       "2016-01-06   138.48     3069.76       675.32  859.97  1744.431290     NaN   \n",
       "2016-01-05      NaN      924.25      1145.94  549.77          NaN     NaN   \n",
       "2016-01-04      NaN      600.24      2217.50  458.77          NaN     NaN   \n",
       "2016-01-03  1146.66     1431.09          NaN  842.93          NaN     NaN   \n",
       "2016-01-02      NaN      477.71      1075.82  266.62   404.471360     NaN   \n",
       "\n",
       "company        Zoonder        Zooxo  \n",
       "day                                  \n",
       "2019-05-14         NaN          NaN  \n",
       "2019-02-21         NaN          NaN  \n",
       "2018-12-31  434.077042          NaN  \n",
       "2018-12-30         NaN  1040.011130  \n",
       "2018-12-29  175.894727          NaN  \n",
       "...                ...          ...  \n",
       "2016-01-06         NaN  2262.604341  \n",
       "2016-01-05  557.608597   499.977741  \n",
       "2016-01-04         NaN          NaN  \n",
       "2016-01-03  761.943357   670.417362  \n",
       "2016-01-02         NaN   784.574290  \n",
       "\n",
       "[1098 rows x 28 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "command = '''\n",
    "    SELECT company, SUM(price), SUBSTR(date,1,10) FROM sales\n",
    "    GROUP BY company, SUBSTR(date,1,10)\n",
    "'''\n",
    "c.execute(command)\n",
    "data = pd.DataFrame(c.fetchall(), columns = ['company','price','day'])\n",
    "data_piv = data.pivot(index='day', columns='company', values='price')\n",
    "data_piv.sort_index(inplace=True, ascending = False)\n",
    "data_piv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output is not incredebly easy to get an overview over, so it was decided to show the same data, but company specific. So one should specify which company is of interest. Flipstorm is used as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>part</th>\n",
       "      <th>company</th>\n",
       "      <th>country</th>\n",
       "      <th>city</th>\n",
       "      <th>price</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>18383</td>\n",
       "      <td>43419-018</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Athens</td>\n",
       "      <td>139.56</td>\n",
       "      <td>2019-05-14 22:48:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>18382</td>\n",
       "      <td>49288-0285</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Athens</td>\n",
       "      <td>1051.08</td>\n",
       "      <td>2019-02-21 20:05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>17629</td>\n",
       "      <td>52959-433</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>France</td>\n",
       "      <td>Nanterre</td>\n",
       "      <td>340.64</td>\n",
       "      <td>2018-11-19 04:51:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>16457</td>\n",
       "      <td>52125-136</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Athens</td>\n",
       "      <td>702.70</td>\n",
       "      <td>2018-09-15 01:10:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>16117</td>\n",
       "      <td>67544-356</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Athens</td>\n",
       "      <td>791.44</td>\n",
       "      <td>2018-08-25 20:57:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>15967</td>\n",
       "      <td>0268-1128</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Athens</td>\n",
       "      <td>1254.21</td>\n",
       "      <td>2018-08-17 23:37:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>15800</td>\n",
       "      <td>62742-4030</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Athens</td>\n",
       "      <td>539.41</td>\n",
       "      <td>2018-08-08 02:29:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>15135</td>\n",
       "      <td>16590-177</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Athens</td>\n",
       "      <td>1210.92</td>\n",
       "      <td>2018-06-30 20:35:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>13697</td>\n",
       "      <td>52380-1102</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Athens</td>\n",
       "      <td>505.58</td>\n",
       "      <td>2018-04-11 02:28:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>13428</td>\n",
       "      <td>49349-842</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Athens</td>\n",
       "      <td>340.52</td>\n",
       "      <td>2018-03-27 05:01:16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index        part    company country      city    price  \\\n",
       "0  18383   43419-018  Flipstorm  Greece    Athens   139.56   \n",
       "1  18382  49288-0285  Flipstorm  Greece    Athens  1051.08   \n",
       "2  17629   52959-433  Flipstorm  France  Nanterre   340.64   \n",
       "3  16457   52125-136  Flipstorm  Greece    Athens   702.70   \n",
       "4  16117   67544-356  Flipstorm  Greece    Athens   791.44   \n",
       "5  15967   0268-1128  Flipstorm  Greece    Athens  1254.21   \n",
       "6  15800  62742-4030  Flipstorm  Greece    Athens   539.41   \n",
       "7  15135   16590-177  Flipstorm  Greece    Athens  1210.92   \n",
       "8  13697  52380-1102  Flipstorm  Greece    Athens   505.58   \n",
       "9  13428   49349-842  Flipstorm  Greece    Athens   340.52   \n",
       "\n",
       "                  date  \n",
       "0  2019-05-14 22:48:39  \n",
       "1  2019-02-21 20:05:00  \n",
       "2  2018-11-19 04:51:50  \n",
       "3  2018-09-15 01:10:24  \n",
       "4  2018-08-25 20:57:43  \n",
       "5  2018-08-17 23:37:10  \n",
       "6  2018-08-08 02:29:06  \n",
       "7  2018-06-30 20:35:45  \n",
       "8  2018-04-11 02:28:46  \n",
       "9  2018-03-27 05:01:16  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.execute('SELECT * FROM sales WHERE company = \"Flipstorm\" ORDER BY date DESC')\n",
    "pd.DataFrame(c.fetchall(), columns = column_names).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above are the last 10 individual sales for a given company (Flipstorm) shown.\n",
    "\n",
    "Perhaps an equally as interesting thing to find is all sales occuring on a specific day. This is achieved by specifying a day of interest on the second line in the command. It should be noted that not all days give a result, as company x does not have a sale every single day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>part</th>\n",
       "      <th>company</th>\n",
       "      <th>country</th>\n",
       "      <th>city</th>\n",
       "      <th>price</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>61398-0828</td>\n",
       "      <td>Flipstorm</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Athens</td>\n",
       "      <td>5010.310000</td>\n",
       "      <td>2016-01-03 22:11:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>54868-0823</td>\n",
       "      <td>Thoughtmix</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Amadora</td>\n",
       "      <td>1431.090000</td>\n",
       "      <td>2016-01-03 19:48:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>37205-992</td>\n",
       "      <td>Shufflebeat</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Porto</td>\n",
       "      <td>1396.190000</td>\n",
       "      <td>2016-01-03 23:41:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>54868-5165</td>\n",
       "      <td>Teklist</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Arnhem</td>\n",
       "      <td>1146.660000</td>\n",
       "      <td>2016-01-03 10:48:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>70253-307</td>\n",
       "      <td>Avaveo</td>\n",
       "      <td>France</td>\n",
       "      <td>Nice</td>\n",
       "      <td>1029.920000</td>\n",
       "      <td>2016-01-03 11:53:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>49349-471</td>\n",
       "      <td>Voomm</td>\n",
       "      <td>France</td>\n",
       "      <td>Paris</td>\n",
       "      <td>842.930000</td>\n",
       "      <td>2016-01-03 00:40:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>51079-947</td>\n",
       "      <td>Zoonder</td>\n",
       "      <td>United States</td>\n",
       "      <td>Boston</td>\n",
       "      <td>761.943357</td>\n",
       "      <td>2016-01-03 22:43:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>67544-356</td>\n",
       "      <td>Zooxo</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>London</td>\n",
       "      <td>670.417362</td>\n",
       "      <td>2016-01-03 00:49:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>62756-707</td>\n",
       "      <td>Ntags</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Lisbon</td>\n",
       "      <td>322.450000</td>\n",
       "      <td>2016-01-03 13:12:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>68233-011</td>\n",
       "      <td>Chatterbridge</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>304.590000</td>\n",
       "      <td>2016-01-03 00:19:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>98132-889</td>\n",
       "      <td>Buzzbean</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Düsseldorf</td>\n",
       "      <td>272.060000</td>\n",
       "      <td>2016-01-03 06:16:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0268-1604</td>\n",
       "      <td>Innojam</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>157.220000</td>\n",
       "      <td>2016-01-03 13:48:28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          part        company         country        city        price  \\\n",
       "0   61398-0828      Flipstorm          Greece      Athens  5010.310000   \n",
       "1   54868-0823     Thoughtmix        Portugal     Amadora  1431.090000   \n",
       "2    37205-992    Shufflebeat        Portugal       Porto  1396.190000   \n",
       "3   54868-5165        Teklist     Netherlands      Arnhem  1146.660000   \n",
       "4    70253-307         Avaveo          France        Nice  1029.920000   \n",
       "5    49349-471          Voomm          France       Paris   842.930000   \n",
       "6    51079-947        Zoonder   United States      Boston   761.943357   \n",
       "7    67544-356          Zooxo  United Kingdom      London   670.417362   \n",
       "8    62756-707          Ntags        Portugal      Lisbon   322.450000   \n",
       "9    68233-011  Chatterbridge           Spain   Barcelona   304.590000   \n",
       "10   98132-889       Buzzbean         Germany  Düsseldorf   272.060000   \n",
       "11   0268-1604        Innojam     Netherlands   Amsterdam   157.220000   \n",
       "\n",
       "                   date  \n",
       "0   2016-01-03 22:11:05  \n",
       "1   2016-01-03 19:48:44  \n",
       "2   2016-01-03 23:41:02  \n",
       "3   2016-01-03 10:48:12  \n",
       "4   2016-01-03 11:53:31  \n",
       "5   2016-01-03 00:40:03  \n",
       "6   2016-01-03 22:43:31  \n",
       "7   2016-01-03 00:49:00  \n",
       "8   2016-01-03 13:12:09  \n",
       "9   2016-01-03 00:19:44  \n",
       "10  2016-01-03 06:16:54  \n",
       "11  2016-01-03 13:48:28  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "command = '''\n",
    "    SELECT part, company, country, city, SUM(price), date FROM sales\n",
    "    WHERE SUBSTR(date,1,10) = \"2016-01-03\"\n",
    "    GROUP BY company\n",
    "    ORDER BY SUM(price) DESC\n",
    "'''\n",
    "c.execute(command)\n",
    "pd.DataFrame(c.fetchall(), columns = column_names[1:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus the total sales per client on a given day can be displayed.\n",
    "\n",
    "Storing the data in a relational database indexed on date allows for incredibly rapid searches and sortings based on date. Therefore, the major valid concern John should have is related to storing of the data, as indexing does increase the required storing capacity. In this particular case, it was increased from 1.5 MB to 2.0 MB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  }
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