Sales Data Analysis With Python | Solving Real World Data Science Problems | Python Case Study | EDS
In this video we use Python Pandas & Python Matplotlib to analyze and answer business questions about 3 years worth of sales data. The data contains thousands of store purchases broken down by month, product type, cost, purchase address, etc.
Setup!
Github source code & data: https://github.com/EDSOfficial/Sales-...
Installing Jupyter Notebook: https://jupyter.readthedocs.io/en/lat...
Installing Pandas library: https://pandas.pydata.org/pandas-docs...
Detailed video description! (timeline can be found in comments)
We have answered these 5 questions through our data analysis mainly using pandas and matplotlib library.
Q1. What is the overall sales trend?
Q2. Which are the Top 10 products by sales?
Q3. Which are the Most Selling Products?
Q4. Which is the most preferred Ship Mode?
Q5. Which are the Most Profitable Category and Sub-Category?
To answer these questions we walk through many different pandas & matplotlib and seaborn library methods. They include:
Adding columns
Parsing cells as strings to make new columns (.str)
Using the .apply() method
Using groupby to perform aggregate analysis
Plotting bar charts and lines graphs to visualize our results
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Video Timeline!
0:00 - Intro
0:20 - Downloading the Data
1:30 - Opening Jupyter Notebok
2:05 - Objective
3:30 - Importing Libraries
4:23 - Importing Dataset
5:03 - Data Audit
11:10 - Q1. What is the overall sales trend?
16:35 - Q2. Which are the Top 10 products by sales?
20:22 - Q3. Which are the Most Selling Products?
23:40 - Q4. Which is the most preferred Ship Mode?
25:30 - Q5. Which are the Most Profitable Category and Sub-Category?
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