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  3. Data Manipulation with pandas
  • 1

    Transforming DataFrames

    Free

    Let’s master the pandas basics. Learn how to inspect DataFrames and perform fundamental manipulations, including sorting rows, subsetting, and adding new columns.

  • 2

    Aggregating DataFrames

    In this chapter, you’ll calculate summary statistics on DataFrame columns, and master grouped summary statistics and pivot tables.

  • 3

    Slicing and Indexing DataFrames

    Indexes are supercharged row and column names. Learn how they can be combined with slicing for powerful DataFrame subsetting.

  • 4

    Creating and Visualizing DataFrames

    Learn to visualize the contents of your DataFrames, handle missing data values, and import data from and export data to CSV files.

Initializing

Exercise

What percent of sales occurred at each store type?

While .groupby() is useful, you can calculate grouped summary statistics without it.

Walmart distinguishes three types of stores: "supercenters," "discount stores," and "neighborhood markets," encoded in this dataset as type "A," "B," and "C." In this exercise, you'll calculate the total sales made at each store type, without using .groupby(). You can then use these numbers to see what proportion of Walmart's total sales were made at each type.

sales is available and pandas is imported as pd.

Instructions

100 XP
  • Calculate the total weekly_sales over the whole dataset.
  • Subset for type "A" stores, and calculate their total weekly sales.
  • Do the same for type "B" and type "C" stores.
  • Combine the A/B/C results into a list, and divide by sales_all to get the proportion of sales by type.