Time_Series_Data_EDA.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align='center'> Time Series Exploratory Data Analysis(EDA) </h1>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, I will be showing some common but effective ways to explore the time series data. In the first section we will explore how can we use `pandas` and other libraries in python to deal with data time columns, and in later section we will try to summaries the data at hand using several modules and functions present in those tools.\n",
    "\n",
    "We will be using following tools and tech stacks:\n",
    "\n",
    "    - Python\n",
    "    - Pandas\n",
    "    - Numpy\n",
    "    - Matplotlib\n",
    "    - statsmodels\n",
    "\n",
    "The only pre-requisit is having some introductory familiarity with `Python`.\n",
    "\n",
    "Let's start!\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contents:\n",
    "\n",
    "* About Data\n",
    "* Introduction to Time Series\n",
    "* Dealing with datetime in Pandas\n",
    "* Time Resampling\n",
    "* Time upsampling\n",
    "* Rolling window\n",
    "* Expanding window\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About Data\n",
    "\n",
    "We are using Superstore sales data that can be downloaded from <a href = 'https://community.tableau.com/docs/DOC-1236'>here</a>. This data has `21` columns and `9995` rows. \n",
    "\n",
    "The data shows sales of items of differrent categories from a supestore of USA for a certain period of time. \n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction to time series\n",
    "\n",
    "Time series can be think as an ordered sequence of values of a variable at equally spaced time intervals. \n",
    "\n",
    "Time series analysis involves studying **patterns**, **trends** and dependencies in the data points to make predictions, understand underlying processes and make informed decisions.\n",
    "\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "#imports\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales = pd.read_excel(\"data/Superstore_Sales_Records.xls\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>...</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CA-2016-152156</td>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>...</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.96</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.9136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>CA-2016-152156</td>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>...</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.94</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>219.5820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CA-2016-138688</td>\n",
       "      <td>2016-06-12</td>\n",
       "      <td>2016-06-16</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>...</td>\n",
       "      <td>90036</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.62</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.8714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Row ID        Order ID Order Date  Ship Date     Ship Mode Customer ID  \\\n",
       "0       1  CA-2016-152156 2016-11-08 2016-11-11  Second Class    CG-12520   \n",
       "1       2  CA-2016-152156 2016-11-08 2016-11-11  Second Class    CG-12520   \n",
       "2       3  CA-2016-138688 2016-06-12 2016-06-16  Second Class    DV-13045   \n",
       "\n",
       "     Customer Name    Segment        Country         City  ... Postal Code  \\\n",
       "0      Claire Gute   Consumer  United States    Henderson  ...       42420   \n",
       "1      Claire Gute   Consumer  United States    Henderson  ...       42420   \n",
       "2  Darrin Van Huff  Corporate  United States  Los Angeles  ...       90036   \n",
       "\n",
       "   Region       Product ID         Category Sub-Category  \\\n",
       "0   South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1   South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2    West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "\n",
       "                                        Product Name   Sales  Quantity  \\\n",
       "0                  Bush Somerset Collection Bookcase  261.96         2   \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.94         3   \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.62         2   \n",
       "\n",
       "   Discount    Profit  \n",
       "0       0.0   41.9136  \n",
       "1       0.0  219.5820  \n",
       "2       0.0    6.8714  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.head(3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic EDA"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how many rows and columns we have in the sales data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of rows : 9994\n",
      "Total number of columns : 21\n"
     ]
    }
   ],
   "source": [
    "print(f\"Total number of rows : {sales.shape[0]}\")\n",
    "print(f\"Total number of columns : {sales.shape[1]}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the data type of columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row ID                    int64\n",
       "Order ID                 object\n",
       "Order Date       datetime64[ns]\n",
       "Ship Date        datetime64[ns]\n",
       "Ship Mode                object\n",
       "Customer ID              object\n",
       "Customer Name            object\n",
       "Segment                  object\n",
       "Country                  object\n",
       "City                     object\n",
       "State                    object\n",
       "Postal Code               int64\n",
       "Region                   object\n",
       "Product ID               object\n",
       "Category                 object\n",
       "Sub-Category             object\n",
       "Product Name             object\n",
       "Sales                   float64\n",
       "Quantity                  int64\n",
       "Discount                float64\n",
       "Profit                  float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this analysis we are only concerned about sales columns and the dates of those transactions. So, the columns of interests are `OrderDate`, `Category` and `Sales`.\n",
    "\n",
    "Here we want to analyze how the sales of a particular category varies with time. For example suppose in the data we have a category for furnitures and it might be possible people are more prone to buy furnitures in summer. We want to do these kind of analysis to gain more sight in sales and the we can take more mature decisions about buying buffer stocks or not doing so.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "#select the relevant columns\n",
    "\n",
    "sales = sales[['Order Date', 'Category', 'Sales']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>261.9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>731.9400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-06-12</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>14.6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-10-11</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>957.5775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-10-11</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>22.3680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  Order Date         Category     Sales\n",
       "0 2016-11-08        Furniture  261.9600\n",
       "1 2016-11-08        Furniture  731.9400\n",
       "2 2016-06-12  Office Supplies   14.6200\n",
       "3 2015-10-11        Furniture  957.5775\n",
       "4 2015-10-11  Office Supplies   22.3680"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Get an overall summary**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>9994.0</td>\n",
       "      <td>229.858001</td>\n",
       "      <td>623.245101</td>\n",
       "      <td>0.444</td>\n",
       "      <td>17.28</td>\n",
       "      <td>54.49</td>\n",
       "      <td>209.94</td>\n",
       "      <td>22638.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count        mean         std    min    25%    50%     75%       max\n",
       "Sales  9994.0  229.858001  623.245101  0.444  17.28  54.49  209.94  22638.48"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9994 entries, 0 to 9993\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   Order Date  9994 non-null   datetime64[ns]\n",
      " 1   Category    9994 non-null   object        \n",
      " 2   Sales       9994 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(1), object(1)\n",
      "memory usage: 234.4+ KB\n"
     ]
    }
   ],
   "source": [
    "sales.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't have any null values in these three tables so we don't need to deal with null values in preprocessing step before modeling."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check for distinct values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order Date    1237\n",
       "Category         3\n",
       "Sales         6144\n",
       "dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.nunique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the whole data set we have only three categories"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Display the categories and their frequencies**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Office Supplies    6026\n",
       "Furniture          2121\n",
       "Technology         1847\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales['Category'].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Set Order Date as Index**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-11-08</th>\n",
       "      <td>Furniture</td>\n",
       "      <td>261.9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-08</th>\n",
       "      <td>Furniture</td>\n",
       "      <td>731.9400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-12</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>14.6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-11</th>\n",
       "      <td>Furniture</td>\n",
       "      <td>957.5775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-11</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>22.3680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Category     Sales\n",
       "Order Date                           \n",
       "2016-11-08        Furniture  261.9600\n",
       "2016-11-08        Furniture  731.9400\n",
       "2016-06-12  Office Supplies   14.6200\n",
       "2015-10-11        Furniture  957.5775\n",
       "2015-10-11  Office Supplies   22.3680"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales['Order Date'] = pd.to_datetime(sales['Order Date'])\n",
    "\n",
    "sales.set_index('Order Date', inplace=True)\n",
    "\n",
    "sales.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Range of Order Date**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum order date is 2017-12-30 00:00:00\n",
      "Minimum order date is 2014-01-03 00:00:00\n",
      "Total time span is around 3 years\n"
     ]
    }
   ],
   "source": [
    "max_date = sales.index.max()\n",
    "min_date = sales.index.min()\n",
    "\n",
    "print(f\"Maximum order date is {max_date}\")\n",
    "print(f\"Minimum order date is {min_date}\")\n",
    "print(f\"Total time span is around {(max_date.year - min_date.year)} years\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time resampling\n",
    "\n",
    "Resampling is the for frequency conversion and resampling of time series. So if you need to change the data from daily frequency to monthly or yearly we can resample the data accordingly. \n",
    "\n",
    "In `pandas` we have resampling functionm `resample()`. When calling `.resample()` we need to pass in a rule parameter, then we need to call some aggregate functions.\n",
    "\n",
    "The **rule** parameter describes the frequency with which to apply the agg function(daily, monthly yearly etc).\n",
    "\n",
    "**Note** that we need to apply some aggregate function after resampling because after resampling we need to join the rows (mean, sum, count etc.)\n",
    "\n",
    "**Example:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "#imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales = pd.read_excel(\"data/Superstore_Sales_Records.xls\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Downsampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>...</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CA-2016-152156</td>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>...</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.9136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>CA-2016-152156</td>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>...</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>219.5820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>US-2015-108966</td>\n",
       "      <td>2015-10-11</td>\n",
       "      <td>2015-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>...</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>5</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-383.0310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>CA-2014-115812</td>\n",
       "      <td>2014-06-09</td>\n",
       "      <td>2014-06-14</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>BH-11710</td>\n",
       "      <td>Brosina Hoffman</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>...</td>\n",
       "      <td>90032</td>\n",
       "      <td>West</td>\n",
       "      <td>FUR-FU-10001487</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Furnishings</td>\n",
       "      <td>Eldon Expressions Wood and Plastic Desk Access...</td>\n",
       "      <td>48.8600</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.1694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>CA-2014-115812</td>\n",
       "      <td>2014-06-09</td>\n",
       "      <td>2014-06-14</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>BH-11710</td>\n",
       "      <td>Brosina Hoffman</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>...</td>\n",
       "      <td>90032</td>\n",
       "      <td>West</td>\n",
       "      <td>FUR-TA-10001539</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Chromcraft Rectangular Conference Tables</td>\n",
       "      <td>1706.1840</td>\n",
       "      <td>9</td>\n",
       "      <td>0.20</td>\n",
       "      <td>85.3092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Row ID        Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "0        1  CA-2016-152156 2016-11-08 2016-11-11    Second Class    CG-12520   \n",
       "1        2  CA-2016-152156 2016-11-08 2016-11-11    Second Class    CG-12520   \n",
       "3        4  US-2015-108966 2015-10-11 2015-10-18  Standard Class    SO-20335   \n",
       "5        6  CA-2014-115812 2014-06-09 2014-06-14  Standard Class    BH-11710   \n",
       "10      11  CA-2014-115812 2014-06-09 2014-06-14  Standard Class    BH-11710   \n",
       "\n",
       "      Customer Name   Segment        Country             City  ...  \\\n",
       "0       Claire Gute  Consumer  United States        Henderson  ...   \n",
       "1       Claire Gute  Consumer  United States        Henderson  ...   \n",
       "3    Sean O'Donnell  Consumer  United States  Fort Lauderdale  ...   \n",
       "5   Brosina Hoffman  Consumer  United States      Los Angeles  ...   \n",
       "10  Brosina Hoffman  Consumer  United States      Los Angeles  ...   \n",
       "\n",
       "   Postal Code  Region       Product ID   Category Sub-Category  \\\n",
       "0        42420   South  FUR-BO-10001798  Furniture    Bookcases   \n",
       "1        42420   South  FUR-CH-10000454  Furniture       Chairs   \n",
       "3        33311   South  FUR-TA-10000577  Furniture       Tables   \n",
       "5        90032    West  FUR-FU-10001487  Furniture  Furnishings   \n",
       "10       90032    West  FUR-TA-10001539  Furniture       Tables   \n",
       "\n",
       "                                         Product Name      Sales  Quantity  \\\n",
       "0                   Bush Somerset Collection Bookcase   261.9600         2   \n",
       "1   Hon Deluxe Fabric Upholstered Stacking Chairs,...   731.9400         3   \n",
       "3       Bretford CR4500 Series Slim Rectangular Table   957.5775         5   \n",
       "5   Eldon Expressions Wood and Plastic Desk Access...    48.8600         7   \n",
       "10           Chromcraft Rectangular Conference Tables  1706.1840         9   \n",
       "\n",
       "    Discount    Profit  \n",
       "0       0.00   41.9136  \n",
       "1       0.00  219.5820  \n",
       "3       0.45 -383.0310  \n",
       "5       0.00   14.1694  \n",
       "10      0.20   85.3092  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filter out for furniture category\n",
    "sales_sample = sales[sales['Category']=='Furniture']\n",
    "\n",
    "sales_sample.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For same date we have multiple sales values. We need to do group by over `Order Date` so that we can have one to one mapping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_sample = sales_sample.reset_index().groupby(by=['Order Date']).agg({'Sales':sum})\n",
    "\n",
    "#order by Order Date\n",
    "sales_sample.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>2573.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>76.728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>51.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-11</th>\n",
       "      <td>9.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-13</th>\n",
       "      <td>879.939</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sales\n",
       "Order Date          \n",
       "2014-01-06  2573.820\n",
       "2014-01-07    76.728\n",
       "2014-01-10    51.940\n",
       "2014-01-11     9.940\n",
       "2014-01-13   879.939"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_sample.head(5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above data does not seem to have daily or weekly records. There seems some discontinuties. \n",
    "\n",
    "We can use resampling technique to make it on monthly level like start date of each month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>480.194231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-01</th>\n",
       "      <td>367.931600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-01</th>\n",
       "      <td>857.291529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-01</th>\n",
       "      <td>567.488357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-01</th>\n",
       "      <td>432.049188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Sales\n",
       "Order Date            \n",
       "2014-01-01  480.194231\n",
       "2014-02-01  367.931600\n",
       "2014-03-01  857.291529\n",
       "2014-04-01  567.488357\n",
       "2014-05-01  432.049188"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_sample_monthly = sales_sample.resample(rule = 'MS').mean() # MS - Month Start is rule\n",
    "\n",
    "sales_sample_monthly.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now this seems nice! You have sales values for start of each month between given data range in the data.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot\n",
    "\n",
    "Let's visualize raw data and data after resampling over `MS` frequency.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc942266ac0>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 5))\n",
    "# Plot the raw data\n",
    "sales_sample['Sales'].plot(ax=ax, label='Per day sales data')\n",
    "\n",
    "# Plot the resampled data \n",
    "sales_sample_monthly['Sales'].plot(ax=ax, label='Resampled data', color='orange')\n",
    "\n",
    "# Set labels and title\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Sales')\n",
    "ax.set_title('Daily Sales vs. Monthly Sales')\n",
    "lines, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(lines, labels, loc='upper left')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resampled data seems to have some nice pattern wrt the raw sales data."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Pandas, we have many inbuilt rules like I used `MS` rule in above cell. We can also make it on yealy level or even daily level using inbuilt rules. For aggregate functions(min/max/sum etc) we can also define custom aggregators by our own.\n",
    "\n",
    "Let's see a custom rule below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "def first_day(entry):\n",
    "    \"\"\"\n",
    "        Returns the first isnatnce of the period, regardless of sampling rate\n",
    "    \"\"\"\n",
    "    if(len(entry)):\n",
    "\n",
    "        return entry[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>2573.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>452.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>173.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-31</th>\n",
       "      <td>975.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Sales\n",
       "Order Date         \n",
       "2014-12-31  2573.82\n",
       "2015-12-31   452.45\n",
       "2016-12-31   173.94\n",
       "2017-12-31   975.49"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_sample.resample(rule = 'A').apply(first_day)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time upsampling\n",
    "\n",
    "In this we resample to shorter time frame, for example monthly data to weekly/biweekly/daily etc. Because of this, many bins are created with `NaN` values and to fill these there are different methods that can be used as pad method and bfill method. \n",
    "\n",
    "Let's try to upsample monthly sales data to weekly and then visualize the resulatnt plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-05</th>\n",
       "      <td>367.931600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-12</th>\n",
       "      <td>367.931600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-19</th>\n",
       "      <td>367.931600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-26</th>\n",
       "      <td>367.931600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-02</th>\n",
       "      <td>857.291529</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Sales\n",
       "Order Date            \n",
       "2014-01-05  367.931600\n",
       "2014-01-12  367.931600\n",
       "2014-01-19  367.931600\n",
       "2014-01-26  367.931600\n",
       "2014-02-02  857.291529"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_sample_monthly = sales_sample.resample(rule = 'MS').mean() # MS - Month Start is rule\n",
    "\n",
    "sales_sample_weekly = sales_sample_monthly.resample('W').bfill()\n",
    "\n",
    "sales_sample_weekly.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data is now on weekly level. Let's plot the downsampled and upsampled data in same view."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc958cdc700>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 5))\n",
    "# Plot the down sampled\n",
    "sales_sample_monthly['Sales'].plot(ax=ax, label='Monthly sales')\n",
    "\n",
    "# Plot the upsampled data \n",
    "sales_sample_weekly['Sales'].plot(ax=ax, label='Weekly sales', color='orange')\n",
    "\n",
    "# Set labels and title\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Sales')\n",
    "ax.set_title('Monthly Sales vs. Weekly Sales')\n",
    "lines, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(lines, labels, loc='upper left')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Shifting\n",
    "\n",
    "Sometimes we may need to shift all the data up or down along the time series index. We can use pandas in built method `shift()` to accomplished that.\n",
    "\n",
    "**Example:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "#imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### shift forward\n",
    "\n",
    "This methods shifts the entire data index a given number of rows, without regard for time periods (months & years). It returns a modified copy of the original DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>2573.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>76.728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>51.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-11</th>\n",
       "      <td>9.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-13</th>\n",
       "      <td>879.939</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sales\n",
       "Order Date          \n",
       "2014-01-06  2573.820\n",
       "2014-01-07    76.728\n",
       "2014-01-10    51.940\n",
       "2014-01-11     9.940\n",
       "2014-01-13   879.939"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales = pd.read_excel(\"data/Superstore_Sales_Records.xls\")\n",
    "\n",
    "sales_furniture = sales[sales['Category']=='Furniture']\n",
    "\n",
    "#sort the values and group by over \n",
    "sales_furniture = sales_furniture.groupby(by='Order Date').agg({'Sales':sum})\n",
    "sales_furniture.sort_index(inplace=True)\n",
    "\n",
    "sales_furniture.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>480.194231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-01</th>\n",
       "      <td>367.931600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-01</th>\n",
       "      <td>857.291529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-01</th>\n",
       "      <td>567.488357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-01</th>\n",
       "      <td>432.049188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Sales\n",
       "Order Date            \n",
       "2014-01-01  480.194231\n",
       "2014-02-01  367.931600\n",
       "2014-03-01  857.291529\n",
       "2014-04-01  567.488357\n",
       "2014-05-01  432.049188"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#resample it on monthly level\n",
    "sales_furniture = sales_furniture.resample('MS').mean()\n",
    "sales_furniture.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>Sales</th>\n",
       "      <th>lag_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>480.194231</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-01</th>\n",
       "      <td>367.931600</td>\n",
       "      <td>480.194231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-01</th>\n",
       "      <td>857.291529</td>\n",
       "      <td>367.931600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-01</th>\n",
       "      <td>567.488357</td>\n",
       "      <td>857.291529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-01</th>\n",
       "      <td>432.049188</td>\n",
       "      <td>567.488357</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Sales       lag_1\n",
       "Order Date                        \n",
       "2014-01-01  480.194231         NaN\n",
       "2014-02-01  367.931600  480.194231\n",
       "2014-03-01  857.291529  367.931600\n",
       "2014-04-01  567.488357  857.291529\n",
       "2014-05-01  432.049188  567.488357"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shit the time by perioid 1\n",
    "\n",
    "sales_furniture['lag_1'] = sales_furniture.shift(periods=1)\n",
    "sales_furniture.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you compare the above two tables you obeserve that we shifted the `Sales` values in forward fashion so after shifting the first entry got `nan` value."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc941b68f40>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 5))\n",
    "# Plot orig ts\n",
    "sales_furniture['Sales'].plot(ax=ax, label='Original TIme Series')\n",
    "\n",
    "# Plot the shifted ts\n",
    "sales_furniture['lag_1'].plot(ax=ax, label='Shifted Time Series', color='orange')\n",
    "\n",
    "# Set labels and title\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Sales')\n",
    "ax.set_title('Original vs Shifted TS')\n",
    "lines, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(lines, labels, loc='upper left')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Shift backward\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-24</th>\n",
       "      <td>1393.4940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-25</th>\n",
       "      <td>832.4540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-28</th>\n",
       "      <td>551.2568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-29</th>\n",
       "      <td>2330.7180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-30</th>\n",
       "      <td>323.1360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Sales\n",
       "Order Date           \n",
       "2017-12-24  1393.4940\n",
       "2017-12-25   832.4540\n",
       "2017-12-28   551.2568\n",
       "2017-12-29  2330.7180\n",
       "2017-12-30   323.1360"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_sample.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-24</th>\n",
       "      <td>832.4540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-25</th>\n",
       "      <td>551.2568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-28</th>\n",
       "      <td>2330.7180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-29</th>\n",
       "      <td>323.1360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-30</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Sales\n",
       "Order Date           \n",
       "2017-12-24   832.4540\n",
       "2017-12-25   551.2568\n",
       "2017-12-28  2330.7180\n",
       "2017-12-29   323.1360\n",
       "2017-12-30        NaN"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_sample.shift(periods=-1).tail()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This also works as shift forward but just in opposite direction.\n",
    "\n",
    "**Note** that the shift also takes a parameter `freq` and when it is not passed, it shift the index without realigning the data. If freq is passed the index will be increased using the periods the freq."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rolling window mean\n",
    "\n",
    "The rolling average or moving average is simple mean of of last `n` values. It can help us in finding trends that would be otherwise hard to detect.\n",
    "\n",
    "    df.rolling(n).mean(n)  where n - window size\n",
    "\n",
    "The main idea is to divide the data into windows to time, and then calculate an aggregate function for each window. In this way we obtain a simple moving avearge.\n",
    "\n",
    "Let's look at sales data of furniture and try to calculate the rolling mean for it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "sales_data = pd.read_excel('data/Superstore_Sales_Records.xls', index_col='Order Date', parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Row ID', 'Order ID', 'Ship Date', 'Ship Mode', 'Customer ID',\n",
       "       'Customer Name', 'Segment', 'Country', 'City', 'State', 'Postal Code',\n",
       "       'Region', 'Product ID', 'Category', 'Sub-Category', 'Product Name',\n",
       "       'Sales', 'Quantity', 'Discount', 'Profit'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
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       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-11-08</th>\n",
       "      <td>1</td>\n",
       "      <td>CA-2016-152156</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.9136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-08</th>\n",
       "      <td>2</td>\n",
       "      <td>CA-2016-152156</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>219.5820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-11</th>\n",
       "      <td>4</td>\n",
       "      <td>US-2015-108966</td>\n",
       "      <td>2015-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>5</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-383.0310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-09</th>\n",
       "      <td>6</td>\n",
       "      <td>CA-2014-115812</td>\n",
       "      <td>2014-06-14</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>BH-11710</td>\n",
       "      <td>Brosina Hoffman</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90032</td>\n",
       "      <td>West</td>\n",
       "      <td>FUR-FU-10001487</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Furnishings</td>\n",
       "      <td>Eldon Expressions Wood and Plastic Desk Access...</td>\n",
       "      <td>48.8600</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.1694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-06-09</th>\n",
       "      <td>11</td>\n",
       "      <td>CA-2014-115812</td>\n",
       "      <td>2014-06-14</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>BH-11710</td>\n",
       "      <td>Brosina Hoffman</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90032</td>\n",
       "      <td>West</td>\n",
       "      <td>FUR-TA-10001539</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Chromcraft Rectangular Conference Tables</td>\n",
       "      <td>1706.1840</td>\n",
       "      <td>9</td>\n",
       "      <td>0.20</td>\n",
       "      <td>85.3092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Row ID        Order ID  Ship Date       Ship Mode Customer ID  \\\n",
       "Order Date                                                                  \n",
       "2016-11-08       1  CA-2016-152156 2016-11-11    Second Class    CG-12520   \n",
       "2016-11-08       2  CA-2016-152156 2016-11-11    Second Class    CG-12520   \n",
       "2015-10-11       4  US-2015-108966 2015-10-18  Standard Class    SO-20335   \n",
       "2014-06-09       6  CA-2014-115812 2014-06-14  Standard Class    BH-11710   \n",
       "2014-06-09      11  CA-2014-115812 2014-06-14  Standard Class    BH-11710   \n",
       "\n",
       "              Customer Name   Segment        Country             City  \\\n",
       "Order Date                                                              \n",
       "2016-11-08      Claire Gute  Consumer  United States        Henderson   \n",
       "2016-11-08      Claire Gute  Consumer  United States        Henderson   \n",
       "2015-10-11   Sean O'Donnell  Consumer  United States  Fort Lauderdale   \n",
       "2014-06-09  Brosina Hoffman  Consumer  United States      Los Angeles   \n",
       "2014-06-09  Brosina Hoffman  Consumer  United States      Los Angeles   \n",
       "\n",
       "                 State  Postal Code Region       Product ID   Category  \\\n",
       "Order Date                                                               \n",
       "2016-11-08    Kentucky        42420  South  FUR-BO-10001798  Furniture   \n",
       "2016-11-08    Kentucky        42420  South  FUR-CH-10000454  Furniture   \n",
       "2015-10-11     Florida        33311  South  FUR-TA-10000577  Furniture   \n",
       "2014-06-09  California        90032   West  FUR-FU-10001487  Furniture   \n",
       "2014-06-09  California        90032   West  FUR-TA-10001539  Furniture   \n",
       "\n",
       "           Sub-Category                                       Product Name  \\\n",
       "Order Date                                                                   \n",
       "2016-11-08    Bookcases                  Bush Somerset Collection Bookcase   \n",
       "2016-11-08       Chairs  Hon Deluxe Fabric Upholstered Stacking Chairs,...   \n",
       "2015-10-11       Tables      Bretford CR4500 Series Slim Rectangular Table   \n",
       "2014-06-09  Furnishings  Eldon Expressions Wood and Plastic Desk Access...   \n",
       "2014-06-09       Tables           Chromcraft Rectangular Conference Tables   \n",
       "\n",
       "                Sales  Quantity  Discount    Profit  \n",
       "Order Date                                           \n",
       "2016-11-08   261.9600         2      0.00   41.9136  \n",
       "2016-11-08   731.9400         3      0.00  219.5820  \n",
       "2015-10-11   957.5775         5      0.45 -383.0310  \n",
       "2014-06-09    48.8600         7      0.00   14.1694  \n",
       "2014-06-09  1706.1840         9      0.20   85.3092  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_furniture = sales_data.query(\"Category=='Furniture'\", )\n",
    "\n",
    "sales_furniture.head(5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sort the data over Order Date and filter out all the extra columns other than `Sales` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
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       "      <th>2014-01-06</th>\n",
       "      <td>2573.820</td>\n",
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       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>76.728</td>\n",
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       "      <th>2014-01-10</th>\n",
       "      <td>51.940</td>\n",
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       "      <th>2014-01-11</th>\n",
       "      <td>9.940</td>\n",
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       "      <th>2014-01-13</th>\n",
       "      <td>545.940</td>\n",
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      "text/plain": [
       "               Sales\n",
       "Order Date          \n",
       "2014-01-06  2573.820\n",
       "2014-01-07    76.728\n",
       "2014-01-10    51.940\n",
       "2014-01-11     9.940\n",
       "2014-01-13   545.940"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sort the data over Order Date\n",
    "sales_furniture = sales_furniture.sort_index()\n",
    "sales_furniture = sales_furniture[['Sales']]\n",
    "\n",
    "sales_furniture.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "#aggregate values over Order Date\n",
    "sales_furniture = sales_furniture.groupby(by='Order Date').agg({'Sales':sum})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2014-01-06</th>\n",
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       "      <th>2014-01-11</th>\n",
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      "text/plain": [
       "               Sales\n",
       "Order Date          \n",
       "2014-01-06  2573.820\n",
       "2014-01-07    76.728\n",
       "2014-01-10    51.940\n",
       "2014-01-11     9.940\n",
       "2014-01-13   879.939"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_furniture.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the above sales data per Order Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_furniture.plot(figsize=(12, 5)).autoscale(axis='x', tight=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This seems messy. Let's try to resample data over monthly basis and then try to visualize the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>6242.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-01</th>\n",
       "      <td>1839.658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-01</th>\n",
       "      <td>14573.956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-01</th>\n",
       "      <td>7944.837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-01</th>\n",
       "      <td>6912.787</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Sales\n",
       "Order Date           \n",
       "2014-01-01   6242.525\n",
       "2014-02-01   1839.658\n",
       "2014-03-01  14573.956\n",
       "2014-04-01   7944.837\n",
       "2014-05-01   6912.787"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_furniture = sales_furniture.resample('MS').agg(sum)\n",
    "\n",
    "sales_furniture.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also do resampling on Weekly level and try to visualize the resultant data but for time being I am doing on Monthly level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_furniture.plot(figsize=(12, 5)).autoscale(axis='x', tight=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now this seems to  have some pattern. Here we have plotted the Sales of Furnitures on monthly frequency like for at start of each month what is the Sales.\n",
    "\n",
    "Now let's see the rolling mean of Sales data and I will be ploitting the data in the same view since it will be much easier to visualiza and compare. I will be taking the window size as `3` which basically is per quarter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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>Sales</th>\n",
       "      <th>Quaterly rolling mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>6242.525</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-01</th>\n",
       "      <td>1839.658</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-01</th>\n",
       "      <td>14573.956</td>\n",
       "      <td>7552.046333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-04-01</th>\n",
       "      <td>7944.837</td>\n",
       "      <td>8119.483667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-01</th>\n",
       "      <td>6912.787</td>\n",
       "      <td>9810.526667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Sales  Quaterly rolling mean\n",
       "Order Date                                  \n",
       "2014-01-01   6242.525                    NaN\n",
       "2014-02-01   1839.658                    NaN\n",
       "2014-03-01  14573.956            7552.046333\n",
       "2014-04-01   7944.837            8119.483667\n",
       "2014-05-01   6912.787            9810.526667"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_furniture['Quaterly rolling mean'] = sales_furniture['Sales'].rolling(window=3).mean()\n",
    "\n",
    "sales_furniture.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In above step we got some `NaN` values and it will be always the case in calculating rolling window aggregation. If we choose the window size as `n` the first `n-1` values will be `NaN`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 5))\n",
    "# Plot the sales_furniture data\n",
    "sales_furniture['Sales'].plot(ax=ax, label='Monthly Sales')\n",
    "\n",
    "# Plot the sales_furniture_rolling data\n",
    "sales_furniture['Quaterly rolling mean'].plot(ax=ax, label='Rolling Mean', color='orange')\n",
    "\n",
    "# Set labels and title\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Sales')\n",
    "ax.set_title('Sales Furniture vs. Rolling Mean')\n",
    "lines, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(lines, labels, loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rolling mean seems more stable here."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Expanding window\n",
    "\n",
    "Expanding window, also known as a cumulative moving window, is a concept used in time series analysis to calculate various statistics over a growing portion of the data as the window expands. \n",
    "\n",
    "**This can be particularly useful for understanding trends and patterns in a time series over time.**\n",
    "\n",
    "*Plot the cumulative average sales of each quater.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_furniture['Exapanding mean'] = sales_furniture['Sales'].expanding(min_periods=3).mean()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 5))\n",
    "\n",
    "# Plot the sales_furniture data\n",
    "sales_furniture['Sales'].plot(ax=ax, label='Monthly Sales')\n",
    "\n",
    "# Plot the sales_furniture rolling mean\n",
    "sales_furniture['Quaterly rolling mean'].plot(ax=ax, label='Rolling Mean', color='orange')\n",
    "\n",
    "# Plot the sales_furniture cumulative mean\n",
    "sales_furniture['Exapanding mean'].plot(ax=ax, label='Cumulative Mean', color='green')\n",
    "\n",
    "# Set labels and title\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Sales')\n",
    "ax.set_title('Sales Furniture vs. Rolling Mean vs. Cumulative Mean')\n",
    "lines, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(lines, labels, loc='upper left')\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cumulative means seems somewhat stationary over time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}