Using Python Pandas and Matplotlib we will Analyze and Answer Business Questions about 12 Months worth of Sales Data.
The Data contains Hundreds of Thousands of Electronics Store Purchases broken down by Month, Product Type, Cost, Purchase Address, Product Quantity etc.
We start by Data Cleaning :
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Drop NaN values from DataFrame.
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Removing Rows based on a condition.
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Change the type of Column containing Datetime (to_datetime)
Once Data is Cleaned we will Explore our Data.
We will Explore 5 High Level Business Oriented Questions related to our Data:
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What was the Best Month for Sales ? How much was Earned that Month ?
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Which City sold the most Product ?
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What Time should we Display Advertisements or Provide Offers to Maximize the
Likelihood of Customer’s Buying Product by Attracting them ?
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What Products are most often Sold together ?
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What Product Sold the most ? Why it is Sold the Most ?
To Answer these Questions we walk through many different Pandas and Matplotlib methods.
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Concatenating Multiple CSV Files together to Create a New DataFrame (pd.concat)
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Adding Columns.
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Parsing Cells as Strings to make New Columns (.str).
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Applying Functions on Each Data Row Using the .apply() method.
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Using Groupby to perform Aggregate Analysis.
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Plotting Bar charts and Line Graphs to Visualize our Results.
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Labeling our Graphs.