Let's put the concepts you've learned about grouping and aggregation into practice. We'll work through several examples using a sample dataset to solidify your understanding of the split-apply-combine pattern with Pandas groupby().

Setting Up Our Example Data

First, make sure you have Pandas imported. If you're following along in a Jupyter Notebook or Python script, start with this:

import pandas as pd
import numpy as np # Often useful alongside Pandas

Now, let's create a simple DataFrame representing sales data for different product categories across various regions. This will be our data for the exercises.

# Create the sample data
data = {'Category': ['Electronics', 'Clothing', 'Electronics', 'Groceries', 'Clothing', 'Groceries', 'Electronics', 'Clothing'],
        'Region': ['North', 'South', 'North', 'East', 'West', 'East', 'West', 'South'],
        'Sales': [1200, 300, 1500, 250, 450, 200, 1800, 350],
        'Quantity': [2, 3, 1, 5, 4, 4, 3, 4]}
sales_df = pd.DataFrame(data)

print("Sample Sales Data:")
print(sales_df)

This should output:

Sample Sales Data:
      Category Region  Sales  Quantity
0  Electronics  North   1200         2
1     Clothing  South    300         3
2  Electronics  North   1500         1
3    Groceries   East    250         5
4     Clothing   West    450         4
5    Groceries   East    200         4
6  Electronics   West   1800         3
7     Clothing  South    350         4

Grouping by a Single Column

Our first task is to find the total sales for each product Category. This involves splitting the DataFrame by Category, applying the sum() function to the Sales column within each group, and combining the results.

# Group by 'Category' and calculate the sum of 'Sales' for each category
category_sales_sum = sales_df.groupby('Category')['Sales'].sum()

print("\nTotal Sales per Category:")
print(category_sales_sum)

Output:

Total Sales per Category:
Category
Clothing        1100
Electronics     4500
Groceries        450
Name: Sales, dtype: int64

Notice the result is a Pandas Series, where the index is the Category we grouped by, and the values are the summed Sales.

What if we want the average quantity sold per category?

# Group by 'Category' and calculate the mean of 'Quantity'
category_quantity_avg = sales_df.groupby('Category')['Quantity'].mean()

print("\nAverage Quantity per Category:")
print(category_quantity_avg)

Output:

Average Quantity per Category:
Category
Clothing       3.666667
Electronics    2.000000
Groceries      4.500000
Name: Quantity, dtype: float64

You can apply other aggregation functions similarly, such as count(), size(), min(), max(), std() (standard deviation), etc. Remember count() excludes missing values (NaN), while size() includes them.

Applying Multiple Aggregations

Often, you'll want to calculate several summary statistics for each group at once. The .agg() method is perfect for this.

Let's calculate the total sales (sum) and the average quantity (mean) for each Category.

# Group by 'Category' and apply multiple aggregations
category_summary = sales_df.groupby('Category').agg(
    Total_Sales=('Sales', 'sum'),
    Average_Quantity=('Quantity', 'mean'),
    Number_of_Sales=('Sales', 'count') # Count non-missing sales entries
)

print("\nSummary Statistics per Category:")
print(category_summary)

Output:

Summary Statistics per Category:
             Total_Sales  Average_Quantity  Number_of_Sales
Category
Clothing            1100          3.666667                3
Electronics         4500          2.000000                3
Groceries            450          4.500000                2

Here, we passed a dictionary to .agg(). The keys of the dictionary ('Total_Sales', 'Average_Quantity', 'Number_of_Sales') become the new column names in the resulting DataFrame. The values are tuples where the first element is the original column to aggregate ('Sales' or 'Quantity') and the second element is the aggregation function ('sum', 'mean', 'count'). This provides a clear and organized summary.

Alternatively, you can pass a list of functions if you want to apply the same set of aggregations to all numerical columns (or a selected column):

# Calculate min and max sales for each category
category_sales_min_max = sales_df.groupby('Category')['Sales'].agg(['min', 'max'])

print("\nMin and Max Sales per Category:")
print(category_sales_min_max)

Output:

Min and Max Sales per Category:
             min   max
Category
Clothing     300   450
Electronics 1200  1800
Groceries    200   250

Grouping by Multiple Columns

We can create more granular groups by passing a list of column names to groupby(). Let's find the total sales for each combination of Region and Category.

# Group by both 'Region' and 'Category', then sum 'Sales'
region_category_sales = sales_df.groupby(['Region', 'Category'])['Sales'].sum()

print("\nTotal Sales per Region and Category:")
print(region_category_sales)

Output:

Total Sales per Region and Category:
Region  Category
East    Groceries     450
North   Electronics  2700
South   Clothing      650
West    Clothing      450
        Electronics  1800
Name: Sales, dtype: int64

The result is a Series with a MultiIndex (Region, Category). This hierarchical index represents the combinations of the groups.

You can apply multiple aggregations here as well:

# Group by 'Region' and 'Category', calculate sum of Sales and mean Quantity
region_category_summary = sales_df.groupby(['Region', 'Category']).agg(
    Total_Sales=('Sales', 'sum'),
    Average_Quantity=('Quantity', 'mean')
)

print("\nSummary per Region and Category:")
print(region_category_summary)

Output:

Summary per Region and Category:
                     Total_Sales  Average_Quantity
Region Category
East   Groceries             450               4.5
North  Electronics          2700               1.5
South  Clothing              650               3.5
West   Clothing              450               4.0
       Electronics          1800               3.0

This gives us a DataFrame with the MultiIndex on the rows.

Visualizing Grouped Data

Aggregated data is often best understood visually. Let's create a simple bar chart of the total sales per category using Plotly.

Total sales generated by each product category. Electronics clearly dominates in this small dataset.

Practice Tasks

Now, try these on your own using the sales_df DataFrame:

Find the average Sales for each Region.
Count the number of sales records (size()) for each Region.
Find the max quantity sold within each Region and Category combination.
Calculate the sum of Sales and the sum of Quantity for each Region. Use the .agg() method.

These exercises reinforce the different ways you can apply groupby() and aggregation functions to extract meaningful summaries from your data. Mastering grouping is a significant step in data analysis with Pandas.