Exercise: Exploring E-Commerce

Explore e-commerce data analysis by learning to write Python functions that identify top customers by order count and spending, find leading countries by orders, track monthly sales in 2011, and list most ordered products. Gain hands-on experience applying groupby and sorting techniques on transactional datasets for effective exploratory data analysis.

We'll cover the following...

- E-Commerce data
- 1. Top $5$ customers with the highest number of orders
  - Input
  - Output
- 2. Top $5$ customers with most amount of money spent
  - Input
  - Output
- 3. Top $5$ countries with the highest number of orders
  - Input
  - Output
- 4. Number of orders for every month in 2011.
  - Input
  - Output
- 5. Top 10 most ordered products
  - Input
  - Output
- - Some useful tips

E-Commerce data

In this lesson, you are going to be tested on exploring E-commerce data. The dataset was made available on the UCI Machine Learning Repository. This is a transnational data set that contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. We will be using a sample of it.

In the below exercise, you will be writing functions for every task. The functions will receive a dataframe df as an input argument. Your task will be to perform the required operations to answer a question and return the answer to that.

Python 3.5

# E-commerce Data Attributes
# InvoiceNo: Invoice number. Nominal, a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c', it indicates a cancellation. 
# StockCode: Product (item) code. Nominal, a 5-digit integral number uniquely assigned to each distinct product. 
# Description: Product (item) name. Nominal. 
# Quantity: The quantities of each product (item) per transaction. Numeric. 
# InvoiceDate: Invoice Date and time. Numeric, the day and time when each transaction was generated. 
# UnitPrice: Unit price. Numeric, Product price per unit in sterling. 
# CustomerID: Customer number. Nominal, a 5-digit integral number uniquely assigned to each customer. 
# Country: Country name. Nominal, the name of the country where each customer resides.
# AmountSpent: Total amount of order i.e. UnitPrice * Quantity
# PurchaseYear: year of purchase
# PurchaseMonth: Month of purchase (1-12)
# PurcahseHour: Hour of purchase (0-23)

1.What is Data Science

2.Python Basics

3.Handling Tabular Data in Python

4.Data Cleaning

5.Exploratory Data Analysis

6.Statistical Inference

7.Predictive Models

8.Machine Learning

Project

Mock Interview

Exercise: Exploring E-Commerce

E-Commerce data

1. Top $5$ customers with the highest number of orders

Exercise: Exploring E-Commerce

E-Commerce data

1. Top 555 customers with the highest number of orders

1. Top $5$ customers with the highest number of orders