Flipkart introduces a sale every year during the festive season in India. This sale is known as the Big Billion Days sale. The focus of the sale is on electronics of every kind, including flagship smartphones. So, if you want to analyze the products and offers on a sale of an e-commerce platform, this article is for you. In this article, I will take you through the task of Flipkart Sale Analysis using Python.
Flipkart Sale Analysis
To analyze the Flipkart Big Billion Days sale, I researched and found an ideal data set on Kaggle. The dataset contains all the information about all the products in the sale. As this sale started on September 23, we can analyze the first day of the sale as the most attractive offers were offered on the first day by Flipkart.
For this task of Flipkart Sale Analysis, I will only choose the data about the sale on smartphones on the first day of the sale by Flipkart. You can download the dataset I am using for this task here. You can also use the complete dataset of Flipkart sale here.
In the section below, I will take you through Flipkart’s sale analysis by analyzing the offers on the sale of smartphones.
Flipkart Sale Analysis using Python
Let’s start the task of Flipkart sale analysis by importing the necessary Python libraries and the dataset:
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
data = pd.read_csv("23_09_2022.csv")
print(data.head()) name offer_price original_price off_now \
0 APPLE iPhone 13 (Blue, 128 GB) 57990 69900 17% off
1 APPLE iPhone 11 (White, 128 GB) 41990 48900 14% off
2 APPLE iPhone 13 (Midnight, 128 GB) 57990 69900 17% off
3 IAIR D25 1098 1699 35% off
4 APPLE iPhone 13 (Starlight, 128 GB) 58990 69900 15% off
total_ratings total_reviews rating \
0 13052 1036 4.6
1 96244 7044 4.6
2 13052 1036 4.6
3 11 8 4.1
4 13052 1036 4.6
description \
0 ['128 GB ROM', '15.49 cm (6.1 inch) Super Reti...
1 ['128 GB ROM', '15.49 cm (6.1 inch) Liquid Ret...
2 ['128 GB ROM', '15.49 cm (6.1 inch) Super Reti...
3 ['32 MB RAM | 32 MB ROM', '4.32 cm (1.7 inch) ...
4 ['128 GB ROM', '15.49 cm (6.1 inch) Super Reti...
created_at
0 2022-09-23 22:37:42.702432+05:30
1 2022-09-23 22:37:42.703432+05:30
2 2022-09-23 22:37:42.703432+05:30
3 2022-09-23 22:37:42.703432+05:30
4 2022-09-23 22:37:42.704431+05:30
The discount column mentioned in the dataset contains string values. So I will create a new discount column by calculating the discount offered by Flipkart on every smartphone:
data["Discount"] = (data['original_price'] - data['offer_price']) / data['original_price'] * 100
Now let’s have a look at the top deals on smartphones offered by Flipkart on the sale:
top_deals = data.sort_values(by="Discount", ascending=False)
deals = top_deals["name"][:15].value_counts()
label = deals.index
counts = top_deals["Discount"][:15].values
colors = ['gold','lightgreen']
fig = go.Figure(data=[go.Pie(labels=label, values=counts)])
fig.update_layout(title_text='Highest Discount Deals in the Flipkart Big Billion Days Sale')
fig.update_traces(hoverinfo='label+percent', textinfo='value', textfont_size=30,
marker=dict(colors=colors, line=dict(color='black', width=3)))
fig.show()
So the top deals on smartphones had discounts of around 60%. Some popular smartphones from Samsung were offered with a discount of around 50%. Now let’s have a look at some of the highest-rated smartphones on Flipkart on this sale:
highest_rated = data.sort_values(by="rating", ascending=False)
deals = highest_rated["name"][:10].value_counts()
label = deals.index
counts = highest_rated["rating"][:10].values
colors = ['gold','lightgreen']
fig = go.Figure(data=[go.Pie(labels=label, values=counts)])
fig.update_layout(title_text='Highest Rated Discount Deals in the Flipkart Big Billion Days Sale')
fig.update_traces(hoverinfo='label+percent', textinfo='value', textfont_size=30,
marker=dict(colors=colors, line=dict(color='black', width=3)))
fig.show()
So we can see the highest-rated products from all price segments in this sale. Now let’s have a look at the most expensive smartphone deals in the sale:
most_expensive = data.sort_values(by="offer_price", ascending=False)
deals = most_expensive["name"][:10].value_counts()
label = deals.index
counts = most_expensive["offer_price"][:10].values
colors = ['gold','lightgreen']
fig = go.Figure(data=[go.Pie(labels=label, values=counts)])
fig.update_layout(title_text='Most Expensive Offers in the Flipkart Big Billion Days Sale')
fig.update_traces(hoverinfo='label+percent', textinfo='value', textfont_size=30,
marker=dict(colors=colors, line=dict(color='black', width=3)))
fig.show()
All the expensive offers on smartphones in the sale were on Apple iPhones.
The Daily Cost of Sale on Smartphones to Flipkart
The discounts offered are expenses for a business. A business reduces the price of a product to increase its sale of the products. Discount fall under the category of promotional costs.
We are using the Flipkart sale data on smartphones on the first day of the sale. So let’s calculate the cost of this sale to Flipkart on just smartphones on the first day of the sale:
label = ["Total of Offer Prices in Sales", "Total of Original Prices (MRP)"]
counts = [sum(data["offer_price"]), sum(data["original_price"])]
colors = ['gold','lightgreen']
fig = go.Figure(data=[go.Pie(labels=label, values=counts)])
fig.update_layout(title_text='Total Discounts Offered Vs. MRP')
fig.update_traces(hoverinfo='label+percent', textinfo='value', textfont_size=30,
marker=dict(colors=colors, line=dict(color='black', width=3)))
fig.show()
print("Cost of big billion days sale to flipkart on smartphones = ", 12876594 - 10522822)Cost of big billion days sale to flipkart on smartphones = 2353772
So the cost of a discount to Flipkart on just smartphones will be ₹23,53,772 for just one quantity of all the smartphones offered in the sale.
Summary
So this is how you can analyze the Flipkart Big Billion Days Sale. Feel free to take inspiration from this article to analyze more of the Flipkart sale on the complete dataset here. I hope you liked this article on Flipkart sale analysis using Python. Feel free to ask valuable questions in the comments section below.
