module0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3e36966c",
   "metadata": {},
   "source": [
    "% Introduction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "abdaa43b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%precision 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c954aa9",
   "metadata": {},
   "source": [
    "# Explore the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "22a17087",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>customer_id</th>\n",
       "      <th>purchase_amount</th>\n",
       "      <th>date_of_purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>760</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2009-11-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>860</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2012-09-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1200</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2005-10-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1420</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2009-07-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1940</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2013-01-25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id  purchase_amount date_of_purchase\n",
       "0          760             25.0       2009-11-06\n",
       "1          860             50.0       2012-09-28\n",
       "2         1200            100.0       2005-10-25\n",
       "3         1420             50.0       2009-07-09\n",
       "4         1940             70.0       2013-01-25"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add headers to the data and load text as a dataframe\n",
    "headers = ['customer_id', 'purchase_amount', 'date_of_purchase']\n",
    "df = pd.read_csv('purchases.txt', header=None, \n",
    "                 names=headers, sep='\\t')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "e4df3d49",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Interpret the last column as a date\n",
    "df['date_of_purchase'] = pd.to_datetime(df['date_of_purchase'], \n",
    "                                        format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "50066d87",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract year of purchase and save it as a column\n",
    "df['year_of_purchase'] = df['date_of_purchase'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "bf34bb45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>customer_id</th>\n",
       "      <th>purchase_amount</th>\n",
       "      <th>date_of_purchase</th>\n",
       "      <th>year_of_purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1597</th>\n",
       "      <td>16730</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2006-11-08</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>99890</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2008-06-18</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38205</th>\n",
       "      <td>165760</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2011-11-25</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6400</th>\n",
       "      <td>47500</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2005-12-28</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18334</th>\n",
       "      <td>9790</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2012-03-03</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id  purchase_amount date_of_purchase  year_of_purchase\n",
       "1597         16730             40.0       2006-11-08              2006\n",
       "501          99890             20.0       2008-06-18              2008\n",
       "38205       165760             60.0       2011-11-25              2011\n",
       "6400         47500             15.0       2005-12-28              2005\n",
       "18334         9790             30.0       2012-03-03              2012"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display 5 randomly selected rows\n",
    "df.sample(n=5, random_state=26)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "aac88fd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 51243 entries, 0 to 51242\n",
      "Data columns (total 4 columns):\n",
      " #   Column            Non-Null Count  Dtype         \n",
      "---  ------            --------------  -----         \n",
      " 0   customer_id       51243 non-null  int64         \n",
      " 1   purchase_amount   51243 non-null  float64       \n",
      " 2   date_of_purchase  51243 non-null  datetime64[ns]\n",
      " 3   year_of_purchase  51243 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(1), int64(2)\n",
      "memory usage: 1.6 MB\n"
     ]
    }
   ],
   "source": [
    "# Show data information after data transformation\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "a0a4ed0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    51243.000000\n",
       "mean        62.337195\n",
       "std        156.606801\n",
       "min          5.000000\n",
       "25%         25.000000\n",
       "50%         30.000000\n",
       "75%         60.000000\n",
       "max       4500.000000\n",
       "Name: purchase_amount, dtype: float64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['purchase_amount'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "967d7397",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    51243.000000\n",
       "mean      2010.869699\n",
       "std          2.883072\n",
       "min       2005.000000\n",
       "25%       2009.000000\n",
       "50%       2011.000000\n",
       "75%       2013.000000\n",
       "max       2015.000000\n",
       "Name: year_of_purchase, dtype: float64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['year_of_purchase'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "44a10b5e",
   "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>purchase_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_of_purchase</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>1470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>2182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>4674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>4331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>5054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>4939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>4785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>5960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>5912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>5739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>6197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  purchase_amount\n",
       "year_of_purchase                 \n",
       "2005                         1470\n",
       "2006                         2182\n",
       "2007                         4674\n",
       "2008                         4331\n",
       "2009                         5054\n",
       "2010                         4939\n",
       "2011                         4785\n",
       "2012                         5960\n",
       "2013                         5912\n",
       "2014                         5739\n",
       "2015                         6197"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df[['year_of_purchase', 'purchase_amount']].groupby('year_of_purchase').count()\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "bc4a612a",
   "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>Number of Purchases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year_of_purchase</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>1470</td>\n",
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       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>2182</td>\n",
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       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>4674</td>\n",
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       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>4331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>5054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>4939</td>\n",
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       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>4785</td>\n",
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       "    <tr>\n",
       "      <th>2012</th>\n",
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       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>5912</td>\n",
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       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>5739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>6197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Number of Purchases\n",
       "year_of_purchase                     \n",
       "2005                             1470\n",
       "2006                             2182\n",
       "2007                             4674\n",
       "2008                             4331\n",
       "2009                             5054\n",
       "2010                             4939\n",
       "2011                             4785\n",
       "2012                             5960\n",
       "2013                             5912\n",
       "2014                             5739\n",
       "2015                             6197"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.rename(columns={'purchase_amount': 'Number of Purchases'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "512c3e20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Number of Purchases per year\n",
    "\n",
    "ax = x.plot.bar(ylabel='Number of Purchases per Year', xlabel='Year')\n",
    "plt.gca().get_legend().remove()\n",
    "#plt.legend(['Number of Purchases per Year'])\n",
    "plt.title('Number of Purchases per Year')\n",
    "for spine in ax.spines:\n",
    "    ax.spines[spine].set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "14850350",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average Purchases per Year\n",
    "\n",
    "x = df[['year_of_purchase', 'purchase_amount']].groupby('year_of_purchase').mean()\n",
    "ax = x.plot.bar(xlabel='Year', ylabel='in \\$')\n",
    "plt.gca().get_legend().remove()\n",
    "#plt.legend(['Number of Purchases per Year'])\n",
    "plt.title('Average Purchases per Year')\n",
    "for spine in ax.spines:\n",
    "    ax.spines[spine].set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "d1eef5fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total purchase amounts per year\n",
    "\n",
    "x = df[['year_of_purchase', 'purchase_amount']].groupby('year_of_purchase').sum()\n",
    "ax = x.plot.bar(xlabel='Year', ylabel='in \\$')\n",
    "plt.gca().get_legend().remove()\n",
    "#plt.legend(['Number of Purchases per Year'])\n",
    "plt.title('Total Amount Purchases per Year')\n",
    "for spine in ax.spines:\n",
    "    ax.spines[spine].set_visible(False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78e695e0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e56e8fbe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbc13ba1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}