Comprehensive-market-data-analysis.ipynb

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    "<h1 align=center style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "پیش‌پردازش\n",
    "</font>\n",
    "</h1>"
   ]
  },
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    "<p  style=\"text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "This project has 5 steps:\n",
    "</font>\n",
    "</p>\n",
    "<ol>\n",
    "  <li><code><b>Data preprocessing</b></code>: A series of preprocessing steps are performed on the entire data, such as handling the missing data</li>\n",
    "  <li><code><b>Exploration</b></code>: I answer a series of high-level questions and obtain an intuitive view of the company's financial information.</li>\n",
    "  <li><code><b>Study of target markets</b></code>: I will analyze different locations of sale and supply and I will check which countries, despite having many customers, experience little sales.</li>\n",
    "  <li><code><b>Customer value</b></code>: Using the RFM practical criteria, I divide the company's customers into 7 categories, each of which has its meaning and behavior in terms of marketing.</li>\n",
    "  <li><code><b>Customer retention rate analysis</b></code>: What percentage of customers buy from this company in the following months after their first purchase.?</li>\n",
    "</ol>\n",
    "\n",
    "<br><em><b>I only used Numpy, Pandas, Matplotlib, and Seaborn in this project</b></em>\n"
   ]
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    "<h2 style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "The Dataset\n",
    "</font>\n",
    "</h2>\n",
    "\n",
    "\n",
    "\n",
    "<p  style=\"text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "The data of this project is available in the Excel file <code>sales.xlsx</code> and the descriptions of the columns of the dataset are given in the table below.\n",
    "</font>\n",
    "</p>\n",
    "\n",
    "<center>\n",
    "<div  style=\"line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "    \n",
    "|Column|Description|\n",
    "|:------:|:---:|\n",
    "|<code>InvoiceNumber</code>|A 6-digit number uniquely assigned to each invoice. If the beginning of this number starts with the letter C, it means that the invoice has been cancelled.|\n",
    "|<code>ProductCode</code>|A 5-digit number that is uniquely assigned to each type of product.|\n",
    "|<code>ProductName</code>|Product's name|\n",
    "|<code>Quantity</code>|The number of orders of a product type in the invoice|\n",
    "|<code>InvoiceDate</code>|Invoice creation date|\n",
    "|<code>UnitPrice</code>|The price of a product type per unit|\n",
    "|<code>CusotmerId</code>|A 5-digit number that is uniquely assigned to each customer.|\n",
    "|<code>Country</code>|The name of the customer's country of residence|\n",
    "\n",
    "</font>\n",
    "</div>\n",
    "</center>"
   ]
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from datetime import datetime as dt\n",
    "import datetime\n",
    "from datetime import timedelta "
   ]
  },
  {
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   "execution_count": null,
   "id": "aba0df78-5677-4fd2-8f18-da4bff0c544b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# in order to use read_excel in the next cell\n",
    "!pip install openpyxl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dcb58d50",
   "metadata": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNumber</th>\n",
       "      <th>ProductCode</th>\n",
       "      <th>ProductName</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489434</td>\n",
       "      <td>85048</td>\n",
       "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.95</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323P</td>\n",
       "      <td>PINK CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323W</td>\n",
       "      <td>WHITE CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>489434</td>\n",
       "      <td>22041</td>\n",
       "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
       "      <td>48</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>489434</td>\n",
       "      <td>21232</td>\n",
       "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
       "      <td>24</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "  InvoiceNumber ProductCode                          ProductName  Quantity  \\\n",
       "0        489434       85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
       "1        489434      79323P                   PINK CHERRY LIGHTS        12   \n",
       "2        489434      79323W                  WHITE CHERRY LIGHTS        12   \n",
       "3        489434       22041         RECORD FRAME 7\" SINGLE SIZE         48   \n",
       "4        489434       21232       STRAWBERRY CERAMIC TRINKET BOX        24   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerId         Country  \n",
       "0 2009-12-01 07:45:00       6.95     13085.0  United Kingdom  \n",
       "1 2009-12-01 07:45:00       6.75     13085.0  United Kingdom  \n",
       "2 2009-12-01 07:45:00       6.75     13085.0  United Kingdom  \n",
       "3 2009-12-01 07:45:00       2.10     13085.0  United Kingdom  \n",
       "4 2009-12-01 07:45:00       1.25     13085.0  United Kingdom  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\"sales.xlsx\")\n",
    "df.head()"
   ]
  },
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       "(525461, 8)"
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    "df.shape"
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   "source": [
    "<h1 style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "Pre-processing   \n",
    "</font>\n",
    "</h1>"
   ]
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    "<p  style=\"text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "In the first step of this project, I apply a series of pre-processing steps on the data in order to have better quality data for the next steps of the project. In addition to these steps, I also answered some analytical questions.\n",
    "</font>\n",
    "</p>"
   ]
  },
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    "<h2 style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "Pre-processing: Part 1    \n",
    "</font>\n",
    "</h2>\n",
    "\n",
    "<p style=\"text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>    \n",
    "First, I drop the invoices whose customer ID (<code>CustomerId</code>) is not available.\r",
    "<br>\n",
    "I do this on a copy of the data frame named <code>df_no_missing_CustomerId</code>\n",
    "    \n",
    "</font>\n",
    "</p>\n"
   ]
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   "execution_count": 4,
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNumber</th>\n",
       "      <th>ProductCode</th>\n",
       "      <th>ProductName</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Country</th>\n",
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       "      <th>0</th>\n",
       "      <td>489434</td>\n",
       "      <td>85048</td>\n",
       "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.95</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323P</td>\n",
       "      <td>PINK CHERRY LIGHTS</td>\n",
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       "      <td>6.75</td>\n",
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       "      <th>2</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323W</td>\n",
       "      <td>WHITE CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
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       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>489434</td>\n",
       "      <td>22041</td>\n",
       "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
       "      <td>48</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>489434</td>\n",
       "      <td>21232</td>\n",
       "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
       "      <td>24</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  InvoiceNumber ProductCode                          ProductName  Quantity  \\\n",
       "0        489434       85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
       "1        489434      79323P                   PINK CHERRY LIGHTS        12   \n",
       "2        489434      79323W                  WHITE CHERRY LIGHTS        12   \n",
       "3        489434       22041         RECORD FRAME 7\" SINGLE SIZE         48   \n",
       "4        489434       21232       STRAWBERRY CERAMIC TRINKET BOX        24   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerId         Country  \n",
       "0 2009-12-01 07:45:00       6.95     13085.0  United Kingdom  \n",
       "1 2009-12-01 07:45:00       6.75     13085.0  United Kingdom  \n",
       "2 2009-12-01 07:45:00       6.75     13085.0  United Kingdom  \n",
       "3 2009-12-01 07:45:00       2.10     13085.0  United Kingdom  \n",
       "4 2009-12-01 07:45:00       1.25     13085.0  United Kingdom  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_no_missing_CustomerId = df.copy()\n",
    "df_no_missing_CustomerId.dropna(subset=['CustomerId'], inplace=True)\n",
    "df_no_missing_CustomerId.head()"
   ]
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    "<p style=\"text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>    \n",
    "Then, I keep only one row out of all the duplicated rows. I do this on a copy of the previous dataframe named <code>df_no_duplicate</code>.\n",
    "</font>\n",
    "</p>\n"
   ]
  },
  {
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   "execution_count": 5,
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       "      <th>ProductName</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
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       "      <td>489434</td>\n",
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       "      <td>6.95</td>\n",
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       "      <td>United Kingdom</td>\n",
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       "      <th>1</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323P</td>\n",
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       "      <th>2</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323W</td>\n",
       "      <td>WHITE CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
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       "      <td>489434</td>\n",
       "      <td>22041</td>\n",
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       "      <td>48</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>489434</td>\n",
       "      <td>21232</td>\n",
       "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
       "      <td>24</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
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      "text/plain": [
       "  InvoiceNumber ProductCode                          ProductName  Quantity  \\\n",
       "0        489434       85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
       "1        489434      79323P                   PINK CHERRY LIGHTS        12   \n",
       "2        489434      79323W                  WHITE CHERRY LIGHTS        12   \n",
       "3        489434       22041         RECORD FRAME 7\" SINGLE SIZE         48   \n",
       "4        489434       21232       STRAWBERRY CERAMIC TRINKET BOX        24   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerId         Country  \n",
       "0 2009-12-01 07:45:00       6.95     13085.0  United Kingdom  \n",
       "1 2009-12-01 07:45:00       6.75     13085.0  United Kingdom  \n",
       "2 2009-12-01 07:45:00       6.75     13085.0  United Kingdom  \n",
       "3 2009-12-01 07:45:00       2.10     13085.0  United Kingdom  \n",
       "4 2009-12-01 07:45:00       1.25     13085.0  United Kingdom  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_no_duplicate = df_no_missing_CustomerId.copy()\n",
    "df_no_duplicate = df_no_duplicate.drop_duplicates()\n",
    "df_no_duplicate.head()"
   ]
  },
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   },
   "source": [
    "<p style=\"text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>        \n",
    "I also drop the rows where (<code>UnitPrice</code>) is zero or a negative number. I do this on a copy of the previous dataframe named <code>df_no_missing_price</code>.\n",
    "</font>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b2d944eb",
   "metadata": {
    "id": "97e5386d"
   },
   "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>InvoiceNumber</th>\n",
       "      <th>ProductCode</th>\n",
       "      <th>ProductName</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489434</td>\n",
       "      <td>85048</td>\n",
       "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.95</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323P</td>\n",
       "      <td>PINK CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323W</td>\n",
       "      <td>WHITE CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>489434</td>\n",
       "      <td>22041</td>\n",
       "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
       "      <td>48</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>489434</td>\n",
       "      <td>21232</td>\n",
       "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
       "      <td>24</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNumber ProductCode                          ProductName  Quantity  \\\n",
       "0        489434       85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
       "1        489434      79323P                   PINK CHERRY LIGHTS        12   \n",
       "2        489434      79323W                  WHITE CHERRY LIGHTS        12   \n",
       "3        489434       22041         RECORD FRAME 7\" SINGLE SIZE         48   \n",
       "4        489434       21232       STRAWBERRY CERAMIC TRINKET BOX        24   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerId         Country  \n",
       "0 2009-12-01 07:45:00       6.95     13085.0  United Kingdom  \n",
       "1 2009-12-01 07:45:00       6.75     13085.0  United Kingdom  \n",
       "2 2009-12-01 07:45:00       6.75     13085.0  United Kingdom  \n",
       "3 2009-12-01 07:45:00       2.10     13085.0  United Kingdom  \n",
       "4 2009-12-01 07:45:00       1.25     13085.0  United Kingdom  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_no_missing_price = df_no_duplicate.copy()\n",
    "\n",
    "df_no_missing_price = df_no_missing_price[df_no_missing_price['UnitPrice'] > 0]\n",
    "\n",
    "df_no_missing_price.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a115b39-b811-470d-936b-36ebca8f6a58",
   "metadata": {},
   "source": [
    "<h2 style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "Pre-processing: Part 2    \n",
    "</font>\n",
    "</h2>\n",
    "\n",
    "<p style=\"text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>    \n",
    "Now I calculate what percentage of all orders are canceled and store the rounded number in a variable named <code>percentage_cancelled_orders</code>.\n",
    "</font>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "68e8836c-f589-4880-9b6e-54df99f0b6bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4372"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_no_missing_price[df_no_missing_price['InvoiceNumber'].str.startswith('C', na = False)].nunique().iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ddf9f6d6",
   "metadata": {
    "id": "d54f4178",
    "outputId": "fa029c4a-a313-4726-a4d3-33f15cc1b59f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage_cancelled_orders =  19\n"
     ]
    }
   ],
   "source": [
    "len_unique_all = df_no_missing_price.InvoiceNumber.nunique()\n",
    "len_unique_cancelled = len(df_no_missing_price[df_no_missing_price['InvoiceNumber'].str.startswith('C', na = False)]['InvoiceNumber'].unique())\n",
    "\n",
    "percentage_cancelled_orders = round((len_unique_cancelled / len_unique_all) * 100)\n",
    "\n",
    "print(\"percentage_cancelled_orders = \", percentage_cancelled_orders)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a9a04d3c",
   "metadata": {
    "id": "386781c4"
   },
   "source": [
    "<p  style=\"text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>  \n",
    "In this part, I need to find the five customers with the highest number of order cancellations using the <code>df_no_missing_price</code> dataframe and store their IDs in the form of a list (in descending order) in <code>top_cancelling_customers</code> .\n",
    "</font>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "934f33da-2277-4d20-ba06-ec9c18f0d632",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['14911.0', '14527.0', '15311.0', '14156.0', '17841.0']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_list = df_no_missing_price[df_no_missing_price['InvoiceNumber'].str.startswith('C', na = False)].drop_duplicates(subset=['InvoiceNumber', 'CustomerId'])['CustomerId'].value_counts().index[:5].to_list()\n",
    "top_cancelling_customers = [str(element) for element in f_list]\n",
    "top_cancelling_customers"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cd269684",
   "metadata": {
    "id": "aeb7766e"
   },
   "source": [
    "<p  style=\"text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>  \n",
    "Now, as the last stage of preprocessing, I delete the rows related to canceled orders from the dataframe. For this, I use a copy of the <code>df_no_missing_price</code> dataframe called <code>df_no_cancelled_invoice</code> and apply the changes to this dataframe.\n",
    "</font>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e4d2ac62",
   "metadata": {
    "id": "1888ca41"
   },
   "outputs": [],
   "source": [
    "df_no_canceled_invoice = df_no_missing_price.copy()\n",
    "\n",
    "index_del = df_no_canceled_invoice[df_no_canceled_invoice['InvoiceNumber'].str.startswith('C', na = False)].index\n",
    "\n",
    "df_no_canceled_invoice = df_no_canceled_invoice.drop(index_del)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5538655f-a8ee-45d9-a638-1c1e43dd75b3",
   "metadata": {},
   "source": [
    "<h1 style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "EDA & Questions   \n",
    "</font>\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f385e0c-0f7d-41ef-b397-b1676a75b168",
   "metadata": {},
   "source": [
    "<p  style=\"text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "In this step of the project, I will analyze the orders more and by answering some questions and drawing some graphs, I will get a better view of the company's financial information.\n",
    "</font>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "962748f0-1c65-4a36-bd6b-7ecf0037bb0e",
   "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>InvoiceNumber</th>\n",
       "      <th>ProductCode</th>\n",
       "      <th>ProductName</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489434</td>\n",
       "      <td>85048</td>\n",
       "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.95</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323P</td>\n",
       "      <td>PINK CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323W</td>\n",
       "      <td>WHITE CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>489434</td>\n",
       "      <td>22041</td>\n",
       "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
       "      <td>48</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>489434</td>\n",
       "      <td>21232</td>\n",
       "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
       "      <td>24</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNumber ProductCode                          ProductName  Quantity  \\\n",
       "0        489434       85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
       "1        489434      79323P                   PINK CHERRY LIGHTS        12   \n",
       "2        489434      79323W                  WHITE CHERRY LIGHTS        12   \n",
       "3        489434       22041         RECORD FRAME 7\" SINGLE SIZE         48   \n",
       "4        489434       21232       STRAWBERRY CERAMIC TRINKET BOX        24   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerId         Country  \n",
       "0 2009-12-01 07:45:00       6.95     13085.0  United Kingdom  \n",
       "1 2009-12-01 07:45:00       6.75     13085.0  United Kingdom  \n",
       "2 2009-12-01 07:45:00       6.75     13085.0  United Kingdom  \n",
       "3 2009-12-01 07:45:00       2.10     13085.0  United Kingdom  \n",
       "4 2009-12-01 07:45:00       1.25     13085.0  United Kingdom  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_no_canceled_invoice.copy()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8902f464-a945-4467-9c3e-627f7c2cad71",
   "metadata": {},
   "source": [
    "<h2  style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "Question 1\n",
    "</font>\n",
    "</h2>\n",
    "\n",
    "<p  style=\"line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "How many are the remaining unique invoices?        \n",
    "</font>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "83359e3e-f1bf-4fdf-b4f4-9e24ebf251c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19213"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_of_orders = len(df['InvoiceNumber'].unique())\n",
    "number_of_orders"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46a468df-5ffc-4f56-a7ff-c3163aa52ba8",
   "metadata": {},
   "source": [
    "<h2  style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "Question 2\n",
    "</font>\n",
    "</h2>\n",
    "\n",
    "<p  style=\"line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "Over what period of time was this data collected? I store the answer as a tuple in the <code>window_period</code> variable, which contains the first time and the last time, respectively.\n",
    "</font>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6bef5b33-8bf4-4243-bda6-c87af3d01fb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2009-12-01 07:45:00'), Timestamp('2010-12-09 20:01:00'))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "window_period = (min(df.InvoiceDate), max(df.InvoiceDate))\n",
    "window_period"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb0e8586-dcef-4051-be5d-a6491369bed3",
   "metadata": {},
   "source": [
    "<h2  style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "Question 3\n",
    "</font>\n",
    "</h2>\n",
    "\n",
    "<p  style=\"line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "Using a bar chart, I show how many unique invoices have been issued on each day of the week (Monday, Tuesday, etc.)?\n",
    "</font>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "25154e7c-93f0-4b88-a019-0f327a76f89a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of orders for different Days')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax1 = plt.subplots(figsize=(15, 6))\n",
    "\n",
    "# extract the day of the week from the InvoiceDate column\n",
    "df['day_of_week'] = pd.to_datetime(df[\"InvoiceDate\"]).dt.day_of_week\n",
    "\n",
    "# group by day of the week and count the number of unique orders\n",
    "df.groupby('day_of_week')['InvoiceNumber'].nunique().sort_index().plot(ax=ax1, kind=\"bar\", color=\"lime\")\n",
    "\n",
    "# set the xticklabels to the names of the days\n",
    "days = ('Mon', 'Tue', 'Wed', 'Thur', 'Fri', 'Sat', 'Sun')\n",
    "ax1.set_xticklabels(days, rotation=0, size=15)\n",
    "\n",
    "# set the labels and title\n",
    "ax1.set_xlabel('Day', color='lightseagreen', size=15)\n",
    "ax1.set_ylabel('Number of Orders', color='lightseagreen', size=15)\n",
    "ax1.set_title('Number of orders for different Days', color='green', size=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afeba1f2-304c-4889-836c-0511fe692f50",
   "metadata": {},
   "source": [
    "<h2  style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "Question 4\n",
    "</font>\n",
    "</h2>\n",
    "\n",
    "<p  style=\"line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "Now I draw a bar graph of the total sales for each month of each year. The total sales of each row of the dataframe is obtained by multiplying (column <code>UnitPrice</code>) by (column <code>Quantity</code>).\n",
    "</font>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f178d08e-ca22-40fb-9b45-be0bad138fd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of orders for different Months')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6WwgmlU928vuB/RUVGqqvVq/xOWOmr+d9uPc5oltXT7AmScEBARrRvbskaXXawYNUN2IjI9WhVSst3LRZRSWlkuQJjwftG5PNlx83bVLu3gJ1ionxCtYkaUT37kpq2VLZe/Zo4ebNVW4bGhioywcO8HqN9D2mndo2b66s/Hxl7HY3ecLYgf292jwmIsLzHFZXev3vLiiUJHVtE1elN2ZkSEiVLxgAAKiMcA0AAB/O7Jms4IAAfbl6jXLqsMdC9/j4KssqBllv1SxccfuNT+Tn56eWzZqptKxMeQUFVW7bLrpFlTGNJGlgh/aSpDWVPowv375dkjxjie2vc2z5pVsbMjOrrGseGlol2DmYNfs+yJ7QPkkBflVPQU7s2LFKjW6t2dd7a2CHqkFAoL+/Z8w6X+FE9/g2Vca/qnyfAzokVVnXLCREPXzMZFrRxse3a+fz8sljWrZUSECAzzZ2pGovNU1q1VL5RUV64dvvtG3nTp/bHI6svDxl5ecrIiREPXz0kOyd2FZhQUFK273bZ4+yPu18132wx4sMCVHXNlXbsWL/rWubs7K0u7BQnVrH+Lz0OCggQEkty9u+Yuy8ynw978O9zx4JVY8Rcft6W+XUsDefG4M6dtDe4mIt2bJFJaWl+mnTJh0THa2E5s2rvU3Fa3xgNQFcRajl6zWe1LKlzzEuY6P2Pde9B7+0eH/+fn7q6iPk9bRfpWN7UsvyHr2f7utluLe4+JAfDwBw9OKyUAAAfIgMCdFpXbrok19/1ce/LNNlNRio3439B9WXpOCAwGrXSfKMO1ZcWrXnWqtw35dNtto3m+KuPb99QM3c1xPk2a+/0bNff1Ntjb5CvOhmVXtTHUzFDI2tfMzs6FVjDcbnOuhj7fsgHlNNnRWPtXNP1ceK9tFTrPJ9+pqZsvJ9VpaZV97G7yz+We8s/rnaeotLS6ssiwgNqTLgeoXLBw7QP+fM1Tdr1+mbtesUGRqiLrFx6ntMO52QdEyVXneHaue+0KZVNe3nOI5aNgvXnuwi7dqzx6tnkCRFV7MfHuzxWlbzeGFBQQoLCtKeoqJDut9DlZmXL0lavn2H10D5vuQVFEr75di+nvfh3qevnosVM6AWl1Xdb2rLgA7tNePHnzR/wwb5+znKLyrS6AP0WpNq/hr39bprEe77eOd5rj6OdwcTFRrq87Xw233+1n7d4ttoRPdumr18hZ756mv5O46OadlS3ePbaEjnY9WamWUBAAdAuObD119/rUceeUSLFi3Sjh079N577+ncc889pPswMz322GOaPn26Nm/erFatWumPf/yj7rzzzropGgBQ60Yld9eXq1Zp7uo1OtPHoP41YQcZhb26sYsOtq42lO2rLTkhQZGh1Y8rF7FvEP3Kqgt9DkcdP13vxzrAutp8bhWX+XWObe3pkVhTB6ojMTpafzvvXC3btk2/bN2qValp+nHTJv24aZM+XxGj20eNUEAd/I0qcw7QinWxf9SHir9XbESEOh1g0H5JCq/h6+Jw7/NA7VyXIkNC1D0+Xsu2blNBcbH8HOewexAe6JnUxfHuUO/y0v4n6NTjOuvnlC1avn271qZnaENmpj75dbmuP+VknVBpcgwAACojXPMhPz9fvXr10lVXXaXzzz/f1X3cfPPNmj17th599FElJycrOztb2dnZtVwpAKAuRYSEaHi3rvrol2X66Jdlal5NT7KKSxwLS6peRpSdn1+nNe4vM9/3uERZ+3pQVX4O0WHlPWJO7nxsvXxorOiJV1HL/ip60jWvpgfLIT1WaJhSc3KVmZevBB9jJVX0KKuud6AvzffdZ1Zens9L47Lyqv6to/fdf5927TSqR/caP1ZNBAX4eyZ9kKRtO3fqma++0bqMDH21Zq1O22/Mq0PRYl9PtEwfz6mCr33KrYqeb77aUJL2FhXVea81SYret+/FNT/wQPkNfZ/1ZVDHDvpl2zat3JGq7vFtDvq3Puhr3MXrrr61iYpSm+QonZncQ0UlpZqzcqXeWLhIr8xfQLgGAKgWY675MGrUKD3wwAM677zzfK4vLCzUX/7yFyUkJCg8PFwDBgzQvHnzPOtXrlypp59+Wu+//77OOecctW/fXn379tXpp59eT88AAFBbRnbvppDAQM1bs8bnpUxSeegiSak5VcdLWr5te53Wt7+U7J0+6/hh4yZJ0rGVes503zdG2OLNKfVSW+d9j/3Tps0q8zEZw/cbNuzbLvbwHyuu/D4WbNxYZV35+FHlA6ofdwiPVVH/j/vasrK8wkL9ur3q37r7vvGy6qONE1q08ARq23btOqz7atmsmVqGh2t3QYFWbN9RZf2SLVuVX1Sk2IiIKpeEutGqWTNFh4crt6BAq1JTq6z/wUeb14X2rVopNChQq1PTlFdY2GjvszoVQX+ZHfollL4cf0w7RYeFqVlwsE7q1Omg21e8Rn7w8bqTpPm19BoP8N/3PMsO3DP4cAUF+GtUcg81Dw3VbmYMBQAcAOGaCxMmTND8+fM1Y8YM/fLLL7rooos0cuRIrV1bPpvahx9+qA4dOuijjz5S+/btlZSUpGuuuYaeawDQBDULCdHpXbuopLRM36zxPWvmcfuCnFnLl6uwpMSzfMX2HZpdzWyKdcXM9L8ffvCqY1Nmlr5YuUqO5DUzab+kYxTfPErzN2zQ+0uWVhn3y8y0Ni3d54yabnSJi1PbFi2UmZend39eIqs0M+KizZu1aHOKQgICdPKxB/8QfzAnH9tJQf7++nHDRi3ZstWzvMxMby9arJ179iipZUuvsPFghhzbSQH+fpq/YaNnogJJKikr0+s//OTV5hU6xsSoe3wbrU1P1yvzF2ivj95XKdnlM4rWVGFJiT5fsVL5hd73VWamZfvup7px4w5FxWyPr//4k3Irjbu3a89evfnTQknS6d26HvbjVKjYN2f8uNArhErfvVvvL1laa49zIIH+/jqzRw8VFBfrX1/OVfru3VW22Zmfr+/WrW/Q+6xORc+yHT4CdjeCAwI07ZKL9ORlvzvgLKEV+iclKTI0RGvS0jVv9RqvdZ+vWKmNmVlqERamfsccXg+wiOBg+fv5KX33bp9BvRuLNqdoXXpGleWbMrOUU1CgkIAAhQXVfHZkAMDRhctCD1FKSopeeuklpaSkKH7fDG9/+ctfNGvWLL300kt68MEHtWHDBm3evFlvvfWWXnnlFZWWluqWW27RhRdeqC+//LKBnwEA4FCN7NFdX6xcVe3scQM7tNcnv/6qdekZuuPdmWrfqpWy9+RrY2aWRnTvplm/Lq+3WnslttWW7J36v7ff1XGxsdpTXKSVO1JVWlam0b16es3w6e/npz8NG6bHZn+u935eojkrVykxuoUiQkKUV1ColOxs5RYU6NL+JxxSCFUdx3H0h5OH6OFZn+mjX5Zp8eYUtWsZray8fK1NT5e/4+iqkwbXymWGLZs10xUnDtLz336nJ76Yo2NjWys6PFybsrKUmpOryNAQXXfykEO6z5iICP3uhBP0vwU/6NHZX+i42FhFhYZofUam8osKNahDB0/PnMr+cPIQPTb7C325arUWbNiodtHRah4Wqr1Fxdqyc6ey8/N1ereu6tm26qycvpSUlunVH37UjJ8WKqllS7VqFq6SsjJtzMxSdn6+WjVrplM7H3tIz82XEd27aeWOVP2ybZtue+dddYtrI5NpxY5UFRQX6/h2iRp2GJee7m9k925aumWr1qan67a331XXNm1UXFaqldt3qGt8G/k5jrLq4TLrs3oma0dOjr5fv0F3vDtT7VpGK6ZZM5WUlSk1J1fbd+1S2+gWGtypY4Pepy99EhP1wZKleuOnhVq+fbsigsvHUry43/E+Z+KsbcGBgbr+5JP1+Bdz9PL38zVv9RrFRUVqx64cbc7OVkhAgK4/5WQFBRzemHwB/v5KTojXki1bdff7H+qYltEK8PPXsbExGnKsu31/VWqqPl+xUi3CwtSuZbRCAwO1a89erUlLk5np3D6963wcQwBA00W4doiWLVum0tJSde7c2Wt5YWGhWu6bwrusrEyFhYV65ZVXPNu98MIL6tu3r1avXq3jjjuuyv0CABqv8OBgndG9W7W9Z4ICAnTbiDP0xsJFWra1fID5hBbN9cdTT1ZSy1b1Gq41Cw7W3WefqTcXLtKv27Zrb3GR4ptH6Yxu3TTER4+wuKhI3TtmtL5YuUqLNm/W+vQMlZopKjRU7VpGq09iovq3T6q1+hKjW+jec87WB0t/0bJt2/XTps0KCwzU8e0SdXbPZHWIiam1xxrcqaNaR0To42XLtC49Q+szMtU8NFTDuhyn0T2Tfc7CeDDDu3ZRi7AwfbxsmTZkZCjQ31+d42J1Ud++1V4KFxkaqjvPOlNfrVmjHzZu1ObsLK1LL1VkaKhiIprp9K5dNOAQBooPCQzQ5QMHaMWOHdqSvVNbdu5UgJ+fWoaH65TOx+q0rl3UzMfA+IfKz89PNw8fpi9Xrda3a9dp2b7eegnNo3RSp04aelxn+dXiIPQB/v76y4jT9eHSXzR//QYt2bJFzcPCdEb3bjq3dy/d9u57tfZYB+LnOLru5CE6IekYzVu9VhszM7U5K1vhQUGKDg/XqB49Dvk1URf36UtSq5a67uQhmrV8uZZv266ifb1RR/fqqWZ1n61JKp91c8ros/ThL8vK99GdOxURHKxBHTvonF491SYq6uB3UgNXDT5RM35aqOXbd2jBho0qM1OplbkO107q1En+jqPVaWnamJGpPcVFigoNVc+2CTqjWzd123cZPQAAvjhW+ZoMVOE4jtdsoW+88YbGjh2r5cuXy3+/b6+aNWumuLg4TZkyRQ8++KCKK/Vw2Lt3r8LCwjR79mzGXgMAAAAAADhC0HPtEPXp00elpaVKT0/XkCG+LycZPHiwSkpKtH79enXsWN69f82a8nEnjjnMMSYAAAAAAADQeNBzzYe8vDytW7dOUnmYNm3aNA0dOlTR0dFq166dfv/73+u7777TY489pj59+igjI0Nz5sx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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2, ax2 = plt.subplots(figsize=(15, 6))\n",
    "\n",
    "# create a new column for the total price of each row\n",
    "df['TotalPrice'] = df['Quantity'] * df['UnitPrice']\n",
    "\n",
    "# extract the month and year from the InvoiceDate column\n",
    "df['month_year'] = pd.to_datetime(df[\"InvoiceDate\"].sort_values()).dt.strftime(\"%b_%Y\")\n",
    "\n",
    "# group by month and year and sum the total price\n",
    "df.groupby('month_year', sort=False)['TotalPrice'].sum().plot(ax=ax2, kind=\"bar\", color=\"darkkhaki\")\n",
    "\n",
    "# set the xticklabels to the month and year and change the jul_2010 label\n",
    "labels = ax2.get_xticklabels()\n",
    "labels[7] = \"July_2010\"\n",
    "ax2.set_xticklabels(labels, rotation=45, size=13)\n",
    "\n",
    "# set the labels and title\n",
    "ax2.set_xlabel(\"Month\", size=15, color=\"orange\")\n",
    "ax2.set_ylabel(\"Sales amount\", size=15, color=\"orange\")\n",
    "ax2.set_title(\"Number of orders for different Months\", color=\"cadetblue\", size=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a0e69e8-81b1-465c-b637-9ec7ae87b52a",
   "metadata": {},
   "source": [
    "<h1 style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "Market Research\n",
    "</font>\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa87d92b-067b-4b25-ac7c-e51870410dd3",
   "metadata": {},
   "source": [
    "<p  style=\"line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "In this step of the project, I will group the countries in terms of the amount of sales and the number of customers, and I will analyze the sales and supply location by drawing a functional diagram.\n",
    "</font>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7013a85-6b2e-4105-85b3-f317517ed4ea",
   "metadata": {},
   "source": [
    "<h2  style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "1. Scatter diagram of the amount of sales and the number of customers of the countries\n",
    "</font>\n",
    "</h2>\n",
    "\n",
    "<p  style=\"line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "In this step, on a scatter chart, I show different countries based on their sales and number of customers. In addition, I divide the countries into 4 groups according to the amount of sales and the number of customers, and I determine the color of the points related to the countries based on their group.\n",
    "</font>\n",
    "</p>\n",
    "\n",
    "<p  style=\"line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "If the amount of sales is <code>SA</code> and its third quartile is <code>qSA</code>, the number of customers is <code>CC</code> and its third quartile is <code>qCC</code>, 4 groups of countries are defined as below:\n",
    "</font>\n",
    "</p>\n",
    "\n",
    "<center>\n",
    "<div  style=\"line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "\n",
    "| <b>The Group</b> | <b>Group's Name</b> | <b>Condition</b> | <b>Color</b> |\n",
    "| :---: | :---: | :---: | :---: |\n",
    "| ۱ | Highest customer & revenue | <code dir=ltr>SA > qSA</code> و <code dir=ltr>CC > qCC</code> | <code>red</code> |\n",
    "| ۲ | High customer & low revenue | <code dir=ltr>SA < qSA</code> و <code dir=ltr>CC > qCC</code> | <code>blue</code> |\n",
    "| ۳ | High revenue & low customer | <code dir=ltr>SA > qSA</code> و <code dir=ltr>CC < qCC</code> | <code>green</code> |\n",
    "| ۴ | Low customer & revenue | <code dir=ltr>SA < qSA</code> و <code dir=ltr>CC < qCC</code> | <code>cyan</code> |\n",
    "\n",
    "</font>\n",
    "</div>\n",
    "</center>\n",
    "\n",
    "<p  style=\"line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "To draw the diagram, I went through the following steps:\n",
    "\n",
    "<ol >\n",
    "  <li>First, I deleted the orders related to the United Kingdom.</li>\n",
    "  <li>For each country, I calculated the number of unique customers and the amount of sales.</li>\n",
    "  <li>I calculated the third quarter for the amount of sales and the number of customers.</li>\n",
    "  <li>Using the above explanation, I divided the countries into four groups.</li>\n",
    "</ol>\n",
    "</font>\n",
    "</p>\n",
    "\n",
    "<p style=\"text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "In the end, I showed the countries of France, Spain and the Netherlands on the map.\n",
    "</font>\n",
    "</p>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5b821f5e-8cb0-49bd-a575-ef88b532beef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare the dataframe\n",
    "index_del = df[df.Country == \"United Kingdom\"].index\n",
    "dff = df.drop(index_del).copy()\n",
    "dff[\"Sales_amount\"] = dff['UnitPrice'] * dff['Quantity']\n",
    "\n",
    "dff_grouped = dff.groupby('Country')[['CustomerId', 'Sales_amount']].agg({'CustomerId': 'nunique', 'Sales_amount': 'sum'})\n",
    "quan_3 = dff_grouped.quantile(.75)\n",
    "qCC = quan_3.iloc[0]\n",
    "qSA = quan_3.iloc[1]\n",
    "\n",
    "g1 = dff_grouped[(dff_grouped['CustomerId'] > qCC) & (dff_grouped['Sales_amount'] > qSA)]\n",
    "g2 = dff_grouped[(dff_grouped['CustomerId'] > qCC) & (dff_grouped['Sales_amount'] < qSA)]\n",
    "g3 = dff_grouped[(dff_grouped['CustomerId'] < qCC) & (dff_grouped['Sales_amount'] > qSA)]\n",
    "g4 = dff_grouped[(dff_grouped['CustomerId'] < qCC) & (dff_grouped['Sales_amount'] < qSA)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "24cbac92-403c-47d9-8f15-6d97ecfab971",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "fig , ax = plt.subplots(figsize=(10, 10))\n",
    "\n",
    "#ax.scatter(markersize = 20, c=, label=)\n",
    "ax.scatter(x = g1['Sales_amount'], y = g1['CustomerId'], s = 20, c='red', label='Highest customer & revenue')\n",
    "ax.scatter(x = g2['Sales_amount'], y = g2['CustomerId'], s = 20, c='blue', label='High customer & low revenue')\n",
    "ax.scatter(x = g3['Sales_amount'], y = g3['CustomerId'], s = 20, c='green', label='High revenue & low customer')\n",
    "ax.scatter(x = g4['Sales_amount'], y = g4['CustomerId'], s = 20, c='cyan', label='Low customer & revenue')\n",
    "\n",
    "ax.text(dff_grouped.loc['France'].iloc[1], dff_grouped.loc['France'].iloc[0] + 0.5, \"France\",fontsize=15)\n",
    "ax.text(dff_grouped.loc['Spain'].iloc[1], dff_grouped.loc['Spain'].iloc[0] + 0.5, \"Spain\",fontsize=15)\n",
    "ax.text(dff_grouped.loc['Netherlands'].iloc[1], dff_grouped.loc['Netherlands'].iloc[0] + 0.5, \"Netherlands\",fontsize=15)\n",
    "\n",
    "ax.set_title(\"Market Study\", fontsize=18)\n",
    "ax.set_xlabel(\"Sales amount\", fontsize=16)\n",
    "ax.set_ylabel(\"Number of customers\", fontsize=16)\n",
    "ax.legend(title=\"Group\", title_fontsize=16, frameon=True, facecolor='silver', fontsize=14)\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66e14b58-e0fc-46ee-bb09-488644f6a949",
   "metadata": {},
   "source": [
    "<h2  style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "2. RFM\n",
    "</font>\n",
    "</h2>\n",
    "\n",
    "<p  style=\"line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font>\n",
    "To continue the marketing related matters, in this step, I grouped customers based on their value.\n",
    "To do this, I used a well-known method in the field of customer behavior analysis called <a href=\"https://en.wikipedia.org/wiki/RFM_(market_research)\"><i>RFM</i></a>.\n",
    "In this method, three criteria are calculated for each customer:\n",
    "</font>\n",
    "</p>\n",
    "\n",
    "\n",
    "<div  style=\"line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font>\n",
    "    <ol>\n",
    "        <li>\n",
    "            <b>Recency</i>:</b> The number of days that have passed since the customer's last purchase.\n",
    "        </li>\n",
    "        <li>\n",
    "            <b><i>Frequency</i>:</b> Number of times the customer has made a purchase.\n",
    "        </li>\n",
    "        <li>\n",
    "            <b><i>Monetary Value</i>:</b> Total revenue that the company has received from a customer.\n",
    "        </li>\n",
    "    </ol>\n",
    "</font>\n",
    "</div>\n",
    "\n",
    "\n",
    "<p style=\"text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font> \n",
    "These criteria indicate important indicators in customer behavior. For example, the more recent a customer has made a purchase, the more likely they will buy from us again. The more times a customer buys, the more satisfied the customer is, and the more revenue a customer generates for the company can differentiate them from other buyers.\n",
    "</font>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4173885e-f466-4b23-9829-23cdfb085ecd",
   "metadata": {},
   "source": [
    "<h2  style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "<i>2.1</i> Recency\n",
    "</font>\n",
    "</h2>\n",
    "\n",
    "<p style=\"text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "First, I need to calculate the <i>Recency</i> criterion for each (<code>CustomerId</code>).\r\n",
    "So I need to calculate the number of days since the customer's last purchase compared to one day after the last date in the dataset (column <code>InvoiceDate</code>).\n",
    "</font>\n",
    "</p>\n",
    "\n",
    "<p style=\"text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>So first, I find the last date in the dataset, and after summing it with <code>1</code>, I store the result in the <code>last_day</code> variable..\n",
    "</font>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ee2560b3-f69a-46dd-9688-6e731e778035",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2010-12-10 20:01:00')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_day = max(df.InvoiceDate) + timedelta(days=1)\n",
    "last_day"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39da29a3-cd11-41f9-b2df-278919fcdf97",
   "metadata": {},
   "source": [
    "<p  style=text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "Now I can find for each customer the date of their last purchase and calculate the number of days past <code>last_day</code>.\r\n",
    "     <br>\r\n",
    "I saved the result in a dataframe named <code>df_customer_segments</code> and with two columns <code>CustomerId</code> and <code>Recency</code>.\n",
    "</font>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6aaa6e17-b2da-4b64-b3c6-43355c91e56e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Recency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>12347.0</td>\n",
       "      <td>3</td>\n",
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       "      <td>12348.0</td>\n",
       "      <td>74</td>\n",
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       "      <th>3</th>\n",
       "      <td>12349.0</td>\n",
       "      <td>43</td>\n",
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       "      <th>4</th>\n",
       "      <td>12351.0</td>\n",
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      "text/plain": [
       "   CustomerId  Recency\n",
       "0     12346.0      165\n",
       "1     12347.0        3\n",
       "2     12348.0       74\n",
       "3     12349.0       43\n",
       "4     12351.0       11"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_customer_segments = pd.DataFrame({'Recency': (last_day - df.groupby('CustomerId')['InvoiceDate'].agg('max')).dt.days}).reset_index()\n",
    "df_customer_segments.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "570f005f-4f16-4058-bae3-7f90e061b8b9",
   "metadata": {},
   "source": [
    "<h2  style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "<i>2.2</i> Frequency\n",
    "</font>\n",
    "</h2>\n",
    "\n",
    "<p style=\"text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "Now I need to calculate how many <b>unique</b> purchases each customer has made so far. I add the result in a column named <code>Frequency</code> to the <code>df_customer_segments</code> data frame.\n",
    "</font>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "de00c920-5221-4af2-ae56-1c2f877a3542",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
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      "text/plain": [
       "   CustomerId  Recency  Frequency\n",
       "0     12346.0      165         11\n",
       "1     12347.0        3          2\n",
       "2     12348.0       74          1\n",
       "3     12349.0       43          3\n",
       "4     12351.0       11          1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_customer_segments['Frequency'] = df.groupby('CustomerId').InvoiceNumber.nunique().values\n",
    "df_customer_segments.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6817294-e4ea-43ae-a688-1156b210adbb",
   "metadata": {},
   "source": [
    "<h2  style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "<i>2.3</i> Monetary Value\n",
    "</font>\n",
    "</h2>\n",
    "\n",
    "<p style=\"text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "Here, I calculated how much was the total purchase of each customer? To do this, I multiplied (<code>UnitPrice</code>) by (<code>Quantity</code>) and used the result to calculate the total purchase of each customer.\r\n",
    "I added the result in a column named <code>MonetaryValue</code> to the <code>df_customer_segments</code> dataframe.\n",
    "</font>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "47f47bc3-5194-4161-9226-eac1e596a98c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"sales_amount\"] = df.UnitPrice * df.Quantity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "89852f97-c6ea-49de-9d9c-ed6351fbff6f",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
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       "      <td>12348.0</td>\n",
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       "    </tr>\n",
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       "</table>\n",
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      "text/plain": [
       "   CustomerId  Recency  Frequency  MonetaryValue\n",
       "0     12346.0      165         11         372.86\n",
       "1     12347.0        3          2        1323.32\n",
       "2     12348.0       74          1         222.16\n",
       "3     12349.0       43          3        2671.14\n",
       "4     12351.0       11          1         300.93"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_customer_segments['MonetaryValue'] = df.groupby('CustomerId').sales_amount.agg('sum').values\n",
    "df_customer_segments.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1c45bdd-a225-4db8-8c46-8defd6eb5785",
   "metadata": {},
   "source": [
    "<h2   style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "<i>2.4</i> Grouping of each criterion\n",
    "</font>\n",
    "</h2>\n",
    "\n",
    "<p   style=\" text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "Now I need to divide each of these three criteria into 4 different groups based on the first, second and third quartiles as shown below:\n",
    "</font>\n",
    "</p>\n",
    "\n",
    "<center>\n",
    "<div   style=\" line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "\n",
    "| <b>The Group</b> | <b>Condition</b> |\n",
    "| :---: | :---: | \n",
    "| 1 | <code dir=ltr>value <= Q1</code> | \n",
    "| 2 | <code dir=ltr>Q1 < value <= Q2</code> | \n",
    "| 3 | <code dir=ltr>Q2 < value <= Q3</code> | \n",
    "| 4 | <code dir=ltr>Q3 < value</code> | \n",
    "\n",
    "</font>\n",
    "</div>\n",
    "</center>\n",
    "\n",
    "<p style=\" text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "I did this for each of these three criterias and stored the results in three other columns named <code>F_quartile</code>, <code>R_quartile</code> and <code>M_quartile</code>.\n",
    "</font>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "91529444-e592-4987-904b-db8ab5232672",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_quartile(arr):\n",
    "    qu = np.quantile(arr, [0.25, 0.5, 0.75])\n",
    "    output = []\n",
    "    for value in arr:\n",
    "        if value <= qu[0]:\n",
    "            output.append(\"1\")\n",
    "        elif value > qu[0] and value <= qu[1]:\n",
    "            output.append(\"2\")\n",
    "        elif value > qu[1] and value <= qu[2]:\n",
    "            output.append(\"3\")\n",
    "        elif value > qu[2]:\n",
    "            output.append(\"4\")\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "46c3f857-19e9-400d-845b-0915f765ad22",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "      <th>R_quartile</th>\n",
       "      <th>F_quartile</th>\n",
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       "      <th>RFM</th>\n",
       "      <th>Segment</th>\n",
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       "      <th>0</th>\n",
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       "      <td>2</td>\n",
       "      <td>0       4\\n1       1\\n2       3\\n3       2\\n4 ...</td>\n",
       "      <td>Normal</td>\n",
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       "      <th>1</th>\n",
       "      <td>12347.0</td>\n",
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       "      <td>Normal</td>\n",
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       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
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       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0       4\\n1       1\\n2       3\\n3       2\\n4 ...</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12351.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>300.93</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0       4\\n1       1\\n2       3\\n3       2\\n4 ...</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerId  Recency  Frequency  MonetaryValue R_quartile F_quartile  \\\n",
       "0     12346.0      165         11         372.86          4          4   \n",
       "1     12347.0        3          2        1323.32          1          2   \n",
       "2     12348.0       74          1         222.16          3          1   \n",
       "3     12349.0       43          3        2671.14          2          3   \n",
       "4     12351.0       11          1         300.93          1          1   \n",
       "\n",
       "  M_quartile                                                RFM Segment  \n",
       "0          2  0       4\\n1       1\\n2       3\\n3       2\\n4 ...  Normal  \n",
       "1          3  0       4\\n1       1\\n2       3\\n3       2\\n4 ...  Normal  \n",
       "2          1  0       4\\n1       1\\n2       3\\n3       2\\n4 ...  Normal  \n",
       "3          4  0       4\\n1       1\\n2       3\\n3       2\\n4 ...  Normal  \n",
       "4          1  0       4\\n1       1\\n2       3\\n3       2\\n4 ...  Normal  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_customer_segments['R_quartile'] = get_quartile(df_customer_segments['Recency'])\n",
    "df_customer_segments['F_quartile'] = get_quartile(df_customer_segments['Frequency'])\n",
    "df_customer_segments['M_quartile'] = get_quartile(df_customer_segments['MonetaryValue'])\n",
    "df_customer_segments.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0693257-5610-4d87-918a-bc8d859f8364",
   "metadata": {},
   "source": [
    "<h2   style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "<i>2.5</i> R + F + M\n",
    "</font>\n",
    "</h2>\n",
    "\n",
    "<p  style=\"text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "Now by putting together the values of the columns\n",
    "    <code dir=ltr>[R_quartile, F_quartile, M_quartile]</code>\n",
    "I calculated the final <i>RFM</i> value and stored it in a column with the same name.\n",
    "</font>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f0862feb-6ec7-4f0c-9839-01ea6defb18e",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349.0</td>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>2671.14</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>234</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12351.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>300.93</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerId  Recency  Frequency  MonetaryValue R_quartile F_quartile  \\\n",
       "0     12346.0      165         11         372.86          4          4   \n",
       "1     12347.0        3          2        1323.32          1          2   \n",
       "2     12348.0       74          1         222.16          3          1   \n",
       "3     12349.0       43          3        2671.14          2          3   \n",
       "4     12351.0       11          1         300.93          1          1   \n",
       "\n",
       "  M_quartile  RFM Segment  \n",
       "0          2  442  Normal  \n",
       "1          3  123  Normal  \n",
       "2          1  311  Normal  \n",
       "3          4  234  Normal  \n",
       "4          1  111  Normal  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_customer_segments['RFM'] = df_customer_segments.R_quartile + df_customer_segments.F_quartile + df_customer_segments.M_quartile\n",
    "df_customer_segments.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "099354ae-ca30-40b1-a345-84a29b54bc27",
   "metadata": {},
   "source": [
    "<h2  style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "<i>2.6</i> Grouping of customers\n",
    "</font>\n",
    "</h2>\n",
    "\n",
    "<p  style=\"text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "Now, based on the values of the <code>RFM</code> column and according to the table below, I grouped the customers and stored their group names in a column called <code>Segment</code>.\n",
    "</font>\n",
    "</p>\n",
    "\n",
    "<center>\n",
    "<div  style=\"line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "\n",
    "| <b>Segment</b> | <b>RFM</b> |\n",
    "| :---: | :---: | \n",
    "| Best | <code dir=ltr>144</code> | \n",
    "| AlmostLost | <code dir=ltr>344</code> | \n",
    "| LostBigSpenders | <code dir=ltr>444</code> | \n",
    "| LostCheap | <code dir=ltr>441</code> | \n",
    "| Loyal | <code dir=ltr>X4X</code> | \n",
    "| BigSpenders | <code dir=ltr>XX4</code> | \n",
    "\n",
    "</font>\n",
    "</div>\n",
    "</center>\n",
    "\n",
    "<p style=\"text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3\n",
    "    <span style=\"color:purple\"><b>*</b></span>\n",
    "    If a customer was not included in any of the groups in the table, I considered her group to be <i>Normal</i>.\n",
    "    <br>\n",
    "    <span style=\"color:purple\"><b>*</b></span>\n",
    "    Also, if an RFM can be placed in two groups of the table, priority is given to the group that is <b>higher</b> in the table.\n",
    "</font>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d38705e0-c1f0-475b-94cd-8a886ff98a45",
   "metadata": {},
   "outputs": [],
   "source": [
    "def group_rfm(input):\n",
    "    output = []\n",
    "    for i in input:\n",
    "        if i == 144:\n",
    "            output.append('Best')\n",
    "        elif i == 344:\n",
    "            output.append('AlmostLost')\n",
    "        elif i == 444:\n",
    "            output.append('LostBigSpenders')\n",
    "        elif i == 441:\n",
    "            output.append('LostCheap')\n",
    "        elif str(i)[1] == '4':\n",
    "            output.append('Loyal')\n",
    "        elif str(i)[2] == '4':\n",
    "            output.append('BigSpenders')\n",
    "        else:\n",
    "            output.append('Normal')\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "314c1607-9fcb-4475-a893-bfe2a1ea1c8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>MonetaryValue</th>\n",
       "      <th>R_quartile</th>\n",
       "      <th>F_quartile</th>\n",
       "      <th>M_quartile</th>\n",
       "      <th>RFM</th>\n",
       "      <th>Segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346.0</td>\n",
       "      <td>165</td>\n",
       "      <td>11</td>\n",
       "      <td>372.86</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>442</td>\n",
       "      <td>Loyal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1323.32</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>123</td>\n",
       "      <td>Normal</td>\n",
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       "      <th>2</th>\n",
       "      <td>12348.0</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>222.16</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>311</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349.0</td>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>2671.14</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>234</td>\n",
       "      <td>BigSpenders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12351.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>300.93</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerId  Recency  Frequency  MonetaryValue R_quartile F_quartile  \\\n",
       "0     12346.0      165         11         372.86          4          4   \n",
       "1     12347.0        3          2        1323.32          1          2   \n",
       "2     12348.0       74          1         222.16          3          1   \n",
       "3     12349.0       43          3        2671.14          2          3   \n",
       "4     12351.0       11          1         300.93          1          1   \n",
       "\n",
       "  M_quartile  RFM      Segment  \n",
       "0          2  442        Loyal  \n",
       "1          3  123       Normal  \n",
       "2          1  311       Normal  \n",
       "3          4  234  BigSpenders  \n",
       "4          1  111       Normal  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_customer_segments['Segment'] = group_rfm(df_customer_segments['RFM'])\n",
    "df_customer_segments.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1903ef9-10df-4244-b403-70ec9af02bb0",
   "metadata": {},
   "source": [
    "<h1 style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "Customer retention analysis\n",
    "</font>\n",
    "</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82edb22b-d623-484d-a3bb-dc2a9178f338",
   "metadata": {},
   "source": [
    "<p  style=\"line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "Usually, it is difficult and expensive for a company to attract new customers because for this the marketing team needs to use different methods of advertising and communicating with customers.\n",
    "Therefore, attention should be paid as much as possible to maintaining current customers and increasing their retention.\n",
    "<br>To facilitate such work, in the last step of the project, I will draw a diagram that represents the retention rate of the company's customers to help the marketing team.\n",
    "</font>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a5070ae-a6c3-4335-a232-4e2053d7f07a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = pd.read_csv(\"preprocessed_sales.csv\", parse_dates=[\"InvoiceDate\"])\n",
    "# df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cdc1f0f-c0cc-4d0d-a057-3cbf599fcb39",
   "metadata": {},
   "source": [
    "<h2  style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "Retention chart\n",
    "</font>\n",
    "</h2>\n",
    "\n",
    "<p style=\"text-align: justify;line-height:200%;font-family:vazir;font-size:medium\">\n",
    "<font face=\"vazir\" size=3>\n",
    "I drew the following graph, the vertical axis of which shows the month when a customer had a transaction with the company for the first time, and the horizontal axis shows how many percent of new customers remain customers of the company in the months after the first transaction and register at least one invoice per month.\n",
    "</font>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e7ee080b-c509-4e5c-90b1-f45586418db4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\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>InvoiceNumber</th>\n",
       "      <th>ProductCode</th>\n",
       "      <th>ProductName</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Country</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>TotalPrice</th>\n",
       "      <th>month_year</th>\n",
       "      <th>sales_amount</th>\n",
       "      <th>InvoiceMonth</th>\n",
       "      <th>CohortMonth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489434</td>\n",
       "      <td>85048</td>\n",
       "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.95</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1</td>\n",
       "      <td>83.4</td>\n",
       "      <td>Dec_2009</td>\n",
       "      <td>83.4</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>2009-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323P</td>\n",
       "      <td>PINK CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1</td>\n",
       "      <td>81.0</td>\n",
       "      <td>Dec_2009</td>\n",
       "      <td>81.0</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>2009-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>489434</td>\n",
       "      <td>79323W</td>\n",
       "      <td>WHITE CHERRY LIGHTS</td>\n",
       "      <td>12</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>6.75</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1</td>\n",
       "      <td>81.0</td>\n",
       "      <td>Dec_2009</td>\n",
       "      <td>81.0</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>2009-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>489434</td>\n",
       "      <td>22041</td>\n",
       "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
       "      <td>48</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1</td>\n",
       "      <td>100.8</td>\n",
       "      <td>Dec_2009</td>\n",
       "      <td>100.8</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>2009-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>489434</td>\n",
       "      <td>21232</td>\n",
       "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
       "      <td>24</td>\n",
       "      <td>2009-12-01 07:45:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Dec_2009</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>2009-12-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNumber ProductCode                          ProductName  Quantity  \\\n",
       "0        489434       85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS        12   \n",
       "1        489434      79323P                   PINK CHERRY LIGHTS        12   \n",
       "2        489434      79323W                  WHITE CHERRY LIGHTS        12   \n",
       "3        489434       22041         RECORD FRAME 7\" SINGLE SIZE         48   \n",
       "4        489434       21232       STRAWBERRY CERAMIC TRINKET BOX        24   \n",
       "\n",
       "          InvoiceDate  UnitPrice  CustomerId         Country  day_of_week  \\\n",
       "0 2009-12-01 07:45:00       6.95     13085.0  United Kingdom            1   \n",
       "1 2009-12-01 07:45:00       6.75     13085.0  United Kingdom            1   \n",
       "2 2009-12-01 07:45:00       6.75     13085.0  United Kingdom            1   \n",
       "3 2009-12-01 07:45:00       2.10     13085.0  United Kingdom            1   \n",
       "4 2009-12-01 07:45:00       1.25     13085.0  United Kingdom            1   \n",
       "\n",
       "   TotalPrice month_year  sales_amount InvoiceMonth CohortMonth  \n",
       "0        83.4   Dec_2009          83.4   2009-12-01  2009-12-01  \n",
       "1        81.0   Dec_2009          81.0   2009-12-01  2009-12-01  \n",
       "2        81.0   Dec_2009          81.0   2009-12-01  2009-12-01  \n",
       "3       100.8   Dec_2009         100.8   2009-12-01  2009-12-01  \n",
       "4        30.0   Dec_2009          30.0   2009-12-01  2009-12-01  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new column \"InvoiceMonth\" with a datetime object representing the first day of the month of each transaction\n",
    "df['InvoiceMonth'] = df['InvoiceDate'].apply(lambda x: datetime.datetime(x.year, x.month, 1))\n",
    "\n",
    "# Create a new column \"CohortMonth\" with a datetime object representing the first month that a customer made a purchase\n",
    "df['CohortMonth'] = df.groupby('CustomerId')['InvoiceMonth'].transform('min')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1fe7d261-2546-4983-b243-d545b47b67d7",
   "metadata": {},
   "outputs": [
    {
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       "      <th>525460</th>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400916 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CohortMonth  CohortIndex\n",
       "0       2009-12-01            1\n",
       "1       2009-12-01            1\n",
       "2       2009-12-01            1\n",
       "3       2009-12-01            1\n",
       "4       2009-12-01            1\n",
       "...            ...          ...\n",
       "525456  2009-12-01           13\n",
       "525457  2009-12-01           13\n",
       "525458  2009-12-01           13\n",
       "525459  2009-12-01           13\n",
       "525460  2009-12-01           13\n",
       "\n",
       "[400916 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define a function to extract the year and month from a datetime column in a Pandas DataFrame\n",
    "def get_date_int(df, column):\n",
    "    year = df[column].dt.year\n",
    "    month = df[column].dt.month\n",
    "    return year, month\n",
    "\n",
    "# Extract the year and month from the \"InvoiceMonth\" and \"CohortMonth\" columns using the get_date_int function\n",
    "invoice_year, invoice_month = get_date_int(df, 'InvoiceMonth')\n",
    "cohort_year, cohort_month  = get_date_int(df, 'CohortMonth')\n",
    "\n",
    "# Calculate the difference in years and months between the \"InvoiceMonth\" and \"CohortMonth\" columns\n",
    "years_diff = invoice_year - cohort_year\n",
    "months_diff = invoice_month - cohort_month\n",
    "\n",
    "# Calculate the \"CohortIndex\" column by converting the difference in years and months to months, and adding one\n",
    "df['CohortIndex'] = years_diff * 12 + months_diff + 1\n",
    "\n",
    "# Select only the \"CohortMonth\" and \"CohortIndex\" columns of the DataFrame\n",
    "df[['CohortMonth', 'CohortIndex']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "58cf0888-38bb-4be0-8a4f-973d45f9f3b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>CohortIndex</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CohortMonth</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",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-12-01</th>\n",
       "      <td>955.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>319.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>327.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>346.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>473.0</td>\n",
       "      <td>237.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>383.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-01</th>\n",
       "      <td>374.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2010-03-01</th>\n",
       "      <td>443.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-01</th>\n",
       "      <td>294.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-01</th>\n",
       "      <td>254.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>20.0</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",
       "      <th>2010-06-01</th>\n",
       "      <td>270.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>18.0</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",
       "      <th>2010-07-01</th>\n",
       "      <td>186.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>19.0</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",
       "      <th>2010-08-01</th>\n",
       "      <td>162.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>19.0</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",
       "      <th>2010-09-01</th>\n",
       "      <td>243.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>24.0</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",
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       "    <tr>\n",
       "      <th>2010-10-01</th>\n",
       "      <td>377.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>35.0</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",
       "      <th>2010-11-01</th>\n",
       "      <td>325.0</td>\n",
       "      <td>35.0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-01</th>\n",
       "      <td>46.0</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "CohortIndex     1      2      3      4      5      6      7      8      9   \\\n",
       "CohortMonth                                                                  \n",
       "2009-12-01   955.0  337.0  319.0  406.0  363.0  343.0  360.0  327.0  321.0   \n",
       "2010-01-01   383.0   79.0  119.0  117.0  101.0  115.0   99.0   88.0  107.0   \n",
       "2010-02-01   374.0   89.0   84.0  109.0   92.0   75.0   72.0  107.0   95.0   \n",
       "2010-03-01   443.0   84.0  102.0  107.0  103.0   90.0  109.0  134.0  122.0   \n",
       "2010-04-01   294.0   57.0   57.0   48.0   54.0   66.0   81.0   77.0   20.0   \n",
       "2010-05-01   254.0   40.0   43.0   44.0   45.0   65.0   54.0   20.0    NaN   \n",
       "2010-06-01   270.0   47.0   51.0   55.0   62.0   77.0   18.0    NaN    NaN   \n",
       "2010-07-01   186.0   29.0   34.0   55.0   54.0   19.0    NaN    NaN    NaN   \n",
       "2010-08-01   162.0   33.0   48.0   52.0   19.0    NaN    NaN    NaN    NaN   \n",
       "2010-09-01   243.0   55.0   57.0   24.0    NaN    NaN    NaN    NaN    NaN   \n",
       "2010-10-01   377.0   97.0   35.0    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "2010-11-01   325.0   35.0    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "2010-12-01    46.0    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "CohortIndex     10     11     12     13  \n",
       "CohortMonth                              \n",
       "2009-12-01   346.0  403.0  473.0  237.0  \n",
       "2010-01-01   122.0  116.0   38.0    NaN  \n",
       "2010-02-01   103.0   27.0    NaN    NaN  \n",
       "2010-03-01    35.0    NaN    NaN    NaN  \n",
       "2010-04-01     NaN    NaN    NaN    NaN  \n",
       "2010-05-01     NaN    NaN    NaN    NaN  \n",
       "2010-06-01     NaN    NaN    NaN    NaN  \n",
       "2010-07-01     NaN    NaN    NaN    NaN  \n",
       "2010-08-01     NaN    NaN    NaN    NaN  \n",
       "2010-09-01     NaN    NaN    NaN    NaN  \n",
       "2010-10-01     NaN    NaN    NaN    NaN  \n",
       "2010-11-01     NaN    NaN    NaN    NaN  \n",
       "2010-12-01     NaN    NaN    NaN    NaN  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the DataFrame by \"CohortMonth\" and \"CohortIndex\"\n",
    "grouping = df.groupby(['CohortMonth', 'CohortIndex'])\n",
    "\n",
    "# Count the number of unique customer IDs in each (cohort month, cohort index) group\n",
    "cohort_data = grouping['CustomerId'].apply(pd.Series.nunique)\n",
    "\n",
    "# Reset the index of the DataFrame to create a flat table with columns for cohort month, cohort index, and customer count\n",
    "cohort_data = cohort_data.reset_index()\n",
    "\n",
    "# Reshape the DataFrame so that the columns represent the \"CohortIndex\" values,\n",
    "# the rows represent the \"CohortMonth\" values, and the cell values represent the count of unique customer IDs\n",
    "cohort_counts = cohort_data.pivot(index='CohortMonth', columns='CohortIndex', values='CustomerId')\n",
    "\n",
    "cohort_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "117185e1-03a2-4a47-9fff-9b5d62eb805b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2009-12-01</th>\n",
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       "      <td>43.0</td>\n",
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       "      <td>36.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>50.0</td>\n",
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       "      <th>2010-01-01</th>\n",
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       "      <td>31.0</td>\n",
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       "      <td>26.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>32.0</td>\n",
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       "      <td>10.0</td>\n",
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       "      <th>2010-02-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>29.0</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2010-03-01</th>\n",
       "      <td>100.0</td>\n",
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       "      <td>23.0</td>\n",
       "      <td>24.0</td>\n",
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       "      <td>25.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2010-04-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2010-05-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
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       "      <td>26.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2010-06-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7.0</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",
       "      <th>2010-07-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>10.0</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",
       "      <th>2010-08-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>12.0</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",
       "      <th>2010-09-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>10.0</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",
       "      <th>2010-10-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>9.0</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",
       "      <th>2010-11-01</th>\n",
       "      <td>100.0</td>\n",
       "      <td>11.0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-01</th>\n",
       "      <td>100.0</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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "CohortIndex     1     2     3     4     5     6     7     8     9     10  \\\n",
       "CohortMonth                                                                \n",
       "2009-12-01   100.0  35.0  33.0  43.0  38.0  36.0  38.0  34.0  34.0  36.0   \n",
       "2010-01-01   100.0  21.0  31.0  31.0  26.0  30.0  26.0  23.0  28.0  32.0   \n",
       "2010-02-01   100.0  24.0  22.0  29.0  25.0  20.0  19.0  29.0  25.0  28.0   \n",
       "2010-03-01   100.0  19.0  23.0  24.0  23.0  20.0  25.0  30.0  28.0   8.0   \n",
       "2010-04-01   100.0  19.0  19.0  16.0  18.0  22.0  28.0  26.0   7.0   NaN   \n",
       "2010-05-01   100.0  16.0  17.0  17.0  18.0  26.0  21.0   8.0   NaN   NaN   \n",
       "2010-06-01   100.0  17.0  19.0  20.0  23.0  29.0   7.0   NaN   NaN   NaN   \n",
       "2010-07-01   100.0  16.0  18.0  30.0  29.0  10.0   NaN   NaN   NaN   NaN   \n",
       "2010-08-01   100.0  20.0  30.0  32.0  12.0   NaN   NaN   NaN   NaN   NaN   \n",
       "2010-09-01   100.0  23.0  23.0  10.0   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2010-10-01   100.0  26.0   9.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2010-11-01   100.0  11.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2010-12-01   100.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "\n",
       "CohortIndex    11    12    13  \n",
       "CohortMonth                    \n",
       "2009-12-01   42.0  50.0  25.0  \n",
       "2010-01-01   30.0  10.0   NaN  \n",
       "2010-02-01    7.0   NaN   NaN  \n",
       "2010-03-01    NaN   NaN   NaN  \n",
       "2010-04-01    NaN   NaN   NaN  \n",
       "2010-05-01    NaN   NaN   NaN  \n",
       "2010-06-01    NaN   NaN   NaN  \n",
       "2010-07-01    NaN   NaN   NaN  \n",
       "2010-08-01    NaN   NaN   NaN  \n",
       "2010-09-01    NaN   NaN   NaN  \n",
       "2010-10-01    NaN   NaN   NaN  \n",
       "2010-11-01    NaN   NaN   NaN  \n",
       "2010-12-01    NaN   NaN   NaN  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the first column of the \"cohort_counts\" DataFrame, which represents the count of customers in each cohort group\n",
    "cohort_sizes = cohort_counts.iloc[:,0]\n",
    "\n",
    "# Divide the entire \"cohort_counts\" DataFrame by the \"cohort_sizes\" series, row-wise, to create a matrix of customer retention rates\n",
    "retention = cohort_counts.divide(cohort_sizes, axis=0)\n",
    "\n",
    "# Multiply the entire \"retention\" DataFrame by 100 and round to two decimal places to create human-readable percentages\n",
    "retention = retention.round(2) * 100\n",
    "\n",
    "retention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "51338633-5588-47ce-a178-e5a47e4af726",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert the index of the \"retention\" DataFrame to a date-based index for better labeling on the heatmap\n",
    "retention.index = retention.index.date\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 8))\n",
    "\n",
    "# Create a heatmap using Seaborn's `sns.heatmap` function\n",
    "ax = sns.heatmap(data = retention, annot = True,\n",
    "                 fmt=\".0f\",\n",
    "                 vmin = 0,\n",
    "                 vmax = 50,\n",
    "                 cmap = 'BuGn'\n",
    "                    )\n",
    "ax.set_title(\"Retention rates\")\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f14a5061-a8eb-4128-8762-f7c4728811ad",
   "metadata": {},
   "source": [
    "<center>\n",
    "<h1 style=\"line-height:200%;font-family:vazir;color:#0099cc\">\n",
    "<font face=\"vazir\" color=\"#0099cc\">\n",
    "THE END!\n",
    "</font>\n",
    "</h1>\n",
    "</center>"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "preprocessing_answers.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  },
  "vscode": {
   "interpreter": {
    "hash": "6a29b8f2e1e8b11f80c3820468461bcdbea6564508a235f61a46eeb59d48b308"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}