011 lesson Retail  analytics/Retail_Customer_Analysis.ipynb

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   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uX9BLrHTcNwH"
   },
   "source": [
    "# Problem\n",
    "Online Retailer Customer Segmentation and Analysis\n",
    "\n",
    "We are going to perform an exploratory data  analysis for an online retail store data set, in order to understand its customers.\n",
    "\n",
    " * How can I scale my current business that is doing really well, in the most effective way?\n",
    " \n",
    "# Data\n",
    "\n",
    "https://raw.githubusercontent.com/anilak1978/ecommerce/master/OnlineRetail.csv\"\n",
    "\n",
    "# Eval\n",
    "* Monthly Revenue Overview\n",
    "* Monthly Items Sold Overview\n",
    "* Monthly Active Customers\n",
    "* Average Revenue per Month\n",
    "* New vs Existing Users\n",
    "\n",
    "\n",
    "\n",
    "# features \n",
    "   InvoiceNo', 'StockCode', 'Description', 'Quantity', 'InvoiceDate',\n",
    "       'UnitPrice', 'CustomerID', 'Country', 'Revenue', 'InvoiceMonth',\n",
    "       'InvoiceYear'\n",
    "\n",
    "# modelling\n",
    "\n",
    "# Evaluation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rqk-1ehycNwI"
   },
   "source": [
    "# Data Collection and Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZMT1AU29cNwK",
    "outputId": "ec09ce22-603d-4a92-8cdb-accb5cc7a263"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 15s\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</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>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "      InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0  12/1/2010 8:26       2.55     17850.0  United Kingdom  \n",
       "1  12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "2  12/1/2010 8:26       2.75     17850.0  United Kingdom  \n",
       "3  12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "4  12/1/2010 8:26       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Import Standard packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the available data and overview\n",
    "df=pd.read_csv(\"https://raw.githubusercontent.com/anilak1978/ecommerce/master/OnlineRetail.csv\", encoding=\"ISO-8859-1\",low_memory=False)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('dataonline.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "egBGeFERcNwR",
    "outputId": "03037b82-4bf8-4e82-e834-b3cf7506d7e8"
   },
   "outputs": [
    {
     "data": {
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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>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</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>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "      InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0  12/1/2010 8:26       2.55     17850.0  United Kingdom  \n",
       "1  12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "2  12/1/2010 8:26       2.75     17850.0  United Kingdom  \n",
       "3  12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "4  12/1/2010 8:26       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "atBr-2mqcNwW",
    "outputId": "455aadcf-e4a4-451c-8170-474f87a23023"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 541909 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype  \n",
      "---  ------       --------------   -----  \n",
      " 0   InvoiceNo    541909 non-null  object \n",
      " 1   StockCode    541909 non-null  object \n",
      " 2   Description  540455 non-null  object \n",
      " 3   Quantity     541909 non-null  int64  \n",
      " 4   InvoiceDate  541909 non-null  object \n",
      " 5   UnitPrice    541909 non-null  float64\n",
      " 6   CustomerID   406829 non-null  float64\n",
      " 7   Country      541909 non-null  object \n",
      "dtypes: float64(2), int64(1), object(5)\n",
      "memory usage: 33.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "s3ktSoP-cNwb",
    "outputId": "c6616a94-c7cf-439c-f4f2-f94584987606"
   },
   "outputs": [],
   "source": [
    "#import pandas_profiling as pp\n",
    "#pp.ProfileReport(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "n-M-f2xUcNwf",
    "outputId": "82ccff3b-c152-4975-c9fb-e822666f8364"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(541909, 8)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "emwMwyeecNwi",
    "outputId": "16a414fe-193e-472b-92d5-437409722184"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo       object\n",
       "StockCode       object\n",
       "Description     object\n",
       "Quantity         int64\n",
       "InvoiceDate     object\n",
       "UnitPrice      float64\n",
       "CustomerID     float64\n",
       "Country         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look for datatypes\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "riqZcMUgcNwl",
    "outputId": "c009e94b-6c6d-4877-f3f5-0bf3b5675330"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo           0\n",
       "StockCode           0\n",
       "Description      1454\n",
       "Quantity            0\n",
       "InvoiceDate         0\n",
       "UnitPrice           0\n",
       "CustomerID     135080\n",
       "Country             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look for missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZlC0i75QcNwo",
    "outputId": "bfa92a28-7640-4b8f-8455-cb953b6c7b5b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\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>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>InvoiceMonth</th>\n",
       "      <th>InvoiceYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>15.30</td>\n",
       "      <td>12</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>12</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>22.00</td>\n",
       "      <td>12</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>12</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "      <td>12</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "  InvoiceDate  UnitPrice  CustomerID         Country  Revenue  InvoiceMonth  \\\n",
       "0  2010-12-01       2.55     17850.0  United Kingdom    15.30            12   \n",
       "1  2010-12-01       3.39     17850.0  United Kingdom    20.34            12   \n",
       "2  2010-12-01       2.75     17850.0  United Kingdom    22.00            12   \n",
       "3  2010-12-01       3.39     17850.0  United Kingdom    20.34            12   \n",
       "4  2010-12-01       3.39     17850.0  United Kingdom    20.34            12   \n",
       "\n",
       "   InvoiceYear  \n",
       "0         2010  \n",
       "1         2010  \n",
       "2         2010  \n",
       "3         2010  \n",
       "4         2010  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add Revenue variable and update InvoiceDate \n",
    "df[\"Revenue\"]=df[\"UnitPrice\"]*df[\"Quantity\"]\n",
    "df[\"InvoiceDate\"]=pd.to_datetime(df[\"InvoiceDate\"])\n",
    "df[\"InvoiceMonth\"]=pd.DatetimeIndex(df[\"InvoiceDate\"]).month\n",
    "df[\"InvoiceYear\"]=pd.DatetimeIndex(df[\"InvoiceDate\"]).year\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "H42NkAVHcNwr",
    "outputId": "d02a5e7c-994b-4f27-e0e6-7c8875b35d8d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(541909, 11)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FYcRY-N0cNwv"
   },
   "source": [
    "Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_ogKzDD1cNww",
    "outputId": "e8db44d8-698e-45c3-aa75-a09f8d5ad3dd"
   },
   "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>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>InvoiceMonth</th>\n",
       "      <th>InvoiceYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>541909.000000</td>\n",
       "      <td>541909.000000</td>\n",
       "      <td>406829.000000</td>\n",
       "      <td>541909.000000</td>\n",
       "      <td>541909.000000</td>\n",
       "      <td>541909.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.552250</td>\n",
       "      <td>4.611114</td>\n",
       "      <td>15287.690570</td>\n",
       "      <td>17.987795</td>\n",
       "      <td>7.553128</td>\n",
       "      <td>2010.921609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>218.081158</td>\n",
       "      <td>96.759853</td>\n",
       "      <td>1713.600303</td>\n",
       "      <td>378.810824</td>\n",
       "      <td>3.509055</td>\n",
       "      <td>0.268787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-80995.000000</td>\n",
       "      <td>-11062.060000</td>\n",
       "      <td>12346.000000</td>\n",
       "      <td>-168469.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>13953.000000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.080000</td>\n",
       "      <td>15152.000000</td>\n",
       "      <td>9.750000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.130000</td>\n",
       "      <td>16791.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80995.000000</td>\n",
       "      <td>38970.000000</td>\n",
       "      <td>18287.000000</td>\n",
       "      <td>168469.600000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quantity      UnitPrice     CustomerID        Revenue  \\\n",
       "count  541909.000000  541909.000000  406829.000000  541909.000000   \n",
       "mean        9.552250       4.611114   15287.690570      17.987795   \n",
       "std       218.081158      96.759853    1713.600303     378.810824   \n",
       "min    -80995.000000  -11062.060000   12346.000000 -168469.600000   \n",
       "25%         1.000000       1.250000   13953.000000       3.400000   \n",
       "50%         3.000000       2.080000   15152.000000       9.750000   \n",
       "75%        10.000000       4.130000   16791.000000      17.400000   \n",
       "max     80995.000000   38970.000000   18287.000000  168469.600000   \n",
       "\n",
       "        InvoiceMonth    InvoiceYear  \n",
       "count  541909.000000  541909.000000  \n",
       "mean        7.553128    2010.921609  \n",
       "std         3.509055       0.268787  \n",
       "min         1.000000    2010.000000  \n",
       "25%         5.000000    2011.000000  \n",
       "50%         8.000000    2011.000000  \n",
       "75%        11.000000    2011.000000  \n",
       "max        12.000000    2011.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "R1gnW6JRcNw1"
   },
   "source": [
    "# Monthly Revenue Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "C6re16yHcNw2",
    "outputId": "e734620c-0fac-47b7-9784-3e3345046363"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Revenue')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "#Monthly Revenue Overview\n",
    "df_revenue=df.groupby([\"InvoiceMonth\", \"InvoiceYear\"])[\"Revenue\"].sum().reset_index()\n",
    "plt.figure(figsize=(15,10))\n",
    "sns.barplot(x=\"InvoiceMonth\", y=\"Revenue\", hue=\"InvoiceYear\", data=df_revenue)\n",
    "plt.title(\"Monthly Revenue\")\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel(\"Revenue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monthly Revenue Overview (lineplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "k6CY4vPAcNw5",
    "outputId": "c5144d93-59fb-44e5-d386-18fa3550ec78"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(8.822523148148143, 0.5, 'Revenue')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1144.62x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure()\n",
    "sns.relplot(x=\"InvoiceMonth\", y=\"Revenue\", hue=\"InvoiceYear\", kind=\"line\", data=df_revenue, height=10, aspect=15/10)\n",
    "plt.title(\"Monthly Revenue\")\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel(\"Revenue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LUPsVJUVcNw8"
   },
   "source": [
    "we realize that our data set starts in December 2010 and goes all the way to December 2011. We notice that the revenue slumps around December 2011. We need to see if this is due to customers not purchasing our merchandise or if it is related to an issue within the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vji-S4GEcNw9",
    "outputId": "7d8f59fa-49bb-4316-d028-15032ec50631"
   },
   "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>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>InvoiceMonth</th>\n",
       "      <th>InvoiceYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>516384</th>\n",
       "      <td>C579889</td>\n",
       "      <td>23245</td>\n",
       "      <td>SET OF 3 REGENCY CAKE TINS</td>\n",
       "      <td>-8</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>4.15</td>\n",
       "      <td>13853.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>-33.20</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516385</th>\n",
       "      <td>C579890</td>\n",
       "      <td>84947</td>\n",
       "      <td>ANTIQUE SILVER TEA GLASS ENGRAVED</td>\n",
       "      <td>-1</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>1.25</td>\n",
       "      <td>15197.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>-1.25</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516386</th>\n",
       "      <td>C579890</td>\n",
       "      <td>23374</td>\n",
       "      <td>RED SPOT PAPER GIFT BAG</td>\n",
       "      <td>-1</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>0.82</td>\n",
       "      <td>15197.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516387</th>\n",
       "      <td>C579890</td>\n",
       "      <td>84945</td>\n",
       "      <td>MULTI COLOUR SILVER T-LIGHT HOLDER</td>\n",
       "      <td>-2</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>0.85</td>\n",
       "      <td>15197.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>-1.70</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516388</th>\n",
       "      <td>C579891</td>\n",
       "      <td>23485</td>\n",
       "      <td>BOTANICAL GARDENS WALL CLOCK</td>\n",
       "      <td>-1</td>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>25.00</td>\n",
       "      <td>13644.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>-25.00</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541904</th>\n",
       "      <td>581587</td>\n",
       "      <td>22613</td>\n",
       "      <td>PACK OF 20 SPACEBOY NAPKINS</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>0.85</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>10.20</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541905</th>\n",
       "      <td>581587</td>\n",
       "      <td>22899</td>\n",
       "      <td>CHILDREN'S APRON DOLLY GIRL</td>\n",
       "      <td>6</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>2.10</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>12.60</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541906</th>\n",
       "      <td>581587</td>\n",
       "      <td>23254</td>\n",
       "      <td>CHILDRENS CUTLERY DOLLY GIRL</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>16.60</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541907</th>\n",
       "      <td>581587</td>\n",
       "      <td>23255</td>\n",
       "      <td>CHILDRENS CUTLERY CIRCUS PARADE</td>\n",
       "      <td>4</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>16.60</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541908</th>\n",
       "      <td>581587</td>\n",
       "      <td>22138</td>\n",
       "      <td>BAKING SET 9 PIECE RETROSPOT</td>\n",
       "      <td>3</td>\n",
       "      <td>2011-12-09</td>\n",
       "      <td>4.95</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "      <td>14.85</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25525 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode                         Description  Quantity  \\\n",
       "516384   C579889     23245          SET OF 3 REGENCY CAKE TINS        -8   \n",
       "516385   C579890     84947   ANTIQUE SILVER TEA GLASS ENGRAVED        -1   \n",
       "516386   C579890     23374             RED SPOT PAPER GIFT BAG        -1   \n",
       "516387   C579890     84945  MULTI COLOUR SILVER T-LIGHT HOLDER        -2   \n",
       "516388   C579891     23485       BOTANICAL GARDENS WALL CLOCK         -1   \n",
       "...          ...       ...                                 ...       ...   \n",
       "541904    581587     22613         PACK OF 20 SPACEBOY NAPKINS        12   \n",
       "541905    581587     22899        CHILDREN'S APRON DOLLY GIRL          6   \n",
       "541906    581587     23254       CHILDRENS CUTLERY DOLLY GIRL          4   \n",
       "541907    581587     23255     CHILDRENS CUTLERY CIRCUS PARADE         4   \n",
       "541908    581587     22138       BAKING SET 9 PIECE RETROSPOT          3   \n",
       "\n",
       "       InvoiceDate  UnitPrice  CustomerID         Country  Revenue  \\\n",
       "516384  2011-12-01       4.15     13853.0  United Kingdom   -33.20   \n",
       "516385  2011-12-01       1.25     15197.0  United Kingdom    -1.25   \n",
       "516386  2011-12-01       0.82     15197.0  United Kingdom    -0.82   \n",
       "516387  2011-12-01       0.85     15197.0  United Kingdom    -1.70   \n",
       "516388  2011-12-01      25.00     13644.0  United Kingdom   -25.00   \n",
       "...            ...        ...         ...             ...      ...   \n",
       "541904  2011-12-09       0.85     12680.0          France    10.20   \n",
       "541905  2011-12-09       2.10     12680.0          France    12.60   \n",
       "541906  2011-12-09       4.15     12680.0          France    16.60   \n",
       "541907  2011-12-09       4.15     12680.0          France    16.60   \n",
       "541908  2011-12-09       4.95     12680.0          France    14.85   \n",
       "\n",
       "        InvoiceMonth  InvoiceYear  \n",
       "516384            12         2011  \n",
       "516385            12         2011  \n",
       "516386            12         2011  \n",
       "516387            12         2011  \n",
       "516388            12         2011  \n",
       "...              ...          ...  \n",
       "541904            12         2011  \n",
       "541905            12         2011  \n",
       "541906            12         2011  \n",
       "541907            12         2011  \n",
       "541908            12         2011  \n",
       "\n",
       "[25525 rows x 11 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at the December 2011 data\n",
    "df_december_2011=df.query(\"InvoiceMonth==12 and InvoiceYear==2011\")\n",
    "df_december_2011"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1P80fVV3cNxB"
   },
   "source": [
    "Based on the december 2011 data, we understand that the data set does not include any purchases after December 9th 2011. We need to make sure, we consider this within our analysis and conclusion.\n",
    "On a separate note; we can see that the revenue grows steadily starting from September 2011 all the way up to December 2011, November being the best month in terms of revenue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HV1ILSzEcNxB"
   },
   "source": [
    "# Monthly Items Sold Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "57tslv0OcNxC",
    "outputId": "7db5530e-b6e1-40f9-b6d0-9e4c109c4730"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Items Sold')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Monthly Items Sold Overview\n",
    "df_quantity=df.groupby([\"InvoiceMonth\", \"InvoiceYear\"])[\"Quantity\"].sum().reset_index()\n",
    "plt.figure(figsize=(15,10))\n",
    "sns.barplot(x=\"InvoiceMonth\", y=\"Quantity\", data=df_quantity)\n",
    "plt.title(\"Monthly Items Sold\")\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel(\"Items Sold\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yjprDtipcNxG",
    "outputId": "302f335b-abc6-4d6b-f3f3-df10cabd2e20"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['InvoiceNo', 'StockCode', 'Description', 'Quantity', 'InvoiceDate',\n",
       "       'UnitPrice', 'CustomerID', 'Country', 'Revenue', 'InvoiceMonth',\n",
       "       'InvoiceYear'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QD8Dj46BcNxJ"
   },
   "source": [
    "# Monthly Active Customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NnsNCl_gcNxK",
    "outputId": "d7dfde2f-f64f-4cf3-d1ed-5668fee07ff9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Active Users')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Monthly Active Customers\n",
    "df_active=df.groupby([\"InvoiceMonth\", \"InvoiceYear\"])[\"CustomerID\"].nunique().reset_index()\n",
    "plt.figure(figsize=(15,10))\n",
    "sns.barplot(x=\"InvoiceMonth\", y=\"CustomerID\", hue=\"InvoiceYear\", data=df_active)\n",
    "plt.title(\"Monthly Active Users\")\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel(\"Active Users\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jy7zd7SFcNxQ"
   },
   "source": [
    "# Average Revenue per Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KP_GMD-ecNxS",
    "outputId": "8ca5dbcb-4962-4bf4-bb1e-0f18439f80cb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Revenue')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average Revenue per Month\n",
    "df_revenue_avg=df.groupby([\"InvoiceMonth\", \"InvoiceYear\"])[\"Revenue\"].mean().reset_index()\n",
    "plt.figure(figsize=(15,10))\n",
    "sns.barplot(x=\"InvoiceMonth\", y=\"Revenue\", data=df_revenue)\n",
    "plt.title(\"Monthly Average Revenue \")\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel(\"Revenue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "putkeinWcNxV"
   },
   "source": [
    "# New vs Existing Users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "R1xVYXE5cNxX",
    "outputId": "87b4efa8-c35f-4037-d52b-83692a0a6281"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(8.44631558641975, 0.5, 'Revenue')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1630.95x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# New vs Existing Users\n",
    "df_first_purchase=df.groupby([\"CustomerID\"])[\"InvoiceDate\"].min().reset_index()\n",
    "df_first_purchase.columns=[\"CustomerID\", \"FirstPurchaseDate\"]\n",
    "df=pd.merge(df, df_first_purchase, on=\"CustomerID\")\n",
    "df[\"UserType\"]=\"New\"\n",
    "df.loc[df[\"InvoiceDate\"]>df[\"FirstPurchaseDate\"], \"UserType\"]=\"Existing\"\n",
    "\n",
    "df.head()\n",
    "# New vs Existing User Revenue Analysis\n",
    "df_new_revenue=df.groupby([\"InvoiceMonth\", \"InvoiceYear\", \"UserType\"])[\"Revenue\"].sum().reset_index()\n",
    "plt.figure()\n",
    "sns.relplot(x=\"InvoiceMonth\", y=\"Revenue\", hue=\"UserType\", data=df_new_revenue, kind=\"line\", height=12, aspect=18/10)\n",
    "plt.title(\"New vs Existing Customer Revenue Overview\")\n",
    "plt.xlabel(\"Month\")\n",
    "plt.ylabel(\"Revenue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4lu_NseMcNyU"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "Retail Customer Analysis.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}