Grouping a Pandas DataFrame by Three Columns and Making One Column Lowercase

When working with data in Python, especially when dealing with libraries like Pandas, it’s common to need to perform complex operations on data sets. In this article, we’ll explore how to group a Pandas DataFrame by three columns and make one column lowercase.

Introduction to Pandas DataFrames

Before diving into the solution, let’s briefly review what a Pandas DataFrame is. A DataFrame is a two-dimensional table of data with rows and columns, similar to an Excel spreadsheet or a SQL table. It’s a powerful data structure for data analysis in Python.

Grouping a DataFrame by Multiple Columns

To group a DataFrame by multiple columns, we can use the groupby method provided by Pandas. This method allows us to group our data based on one or more columns and perform various operations on each group. In this case, we want to group our DataFrame by ‘country’ and ‘rating’, which are two of our three desired grouping columns.

Grouping by Multiple Columns and Making a Column Lowercase

Now, let’s look at the code that achieves the described result:

out = (df.groupby(['country', 'rating'])
       .apply(lambda group: (
           group['owner'].str.title().str.replace(r'\s+', ' ', regex=True)
       ))
       .size()
       .reset_index(name='count'))

Let’s break this code down:

1. groupby(['country', 'rating']) groups the DataFrame by both ‘country’ and ‘rating’. Note that we’re not grouping by ‘owner’ here.

2. .apply(lambda group: ...) applies a function to each group in our DataFrame. The function takes the current group as an argument (group) and returns a Series of values.

3. group['owner'].str.title().str.replace(r'\s+', ' ', regex=True): Within this lambda function, we first apply the title() method to make all characters in ‘owner’ uppercase (ignoring case). Then we use the replace method to remove any spaces from our strings.

4. .size() counts the number of rows in each group.

5. .reset_index(name='count') resets the index of our Series, making ‘country’, ‘rating’, and ‘count’ into columns.

The Full Solution

Here is the full solution with some additional comments for clarity:

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    "country": ["England", "England", "France", "France", "France"],
    "rating": ["a", "b", "a", "a", "a"],
    "owner": ["John Smith", "John Smith", "Frank Foo", "Frank foo", "Frank Foo"]
})

# Group by 'country' and 'rating', ignoring case, and make 'owner' lowercase
out = (df.groupby(['country', 'rating'])
       .apply(lambda group: (
           # Apply title() to owner column, ignoring spaces
           group['owner'].str.title().str.replace(r'\s+', ' ', regex=True)
       ))
       # Count the number of rows in each group
       .size()
       # Reset index and make count into a new column
       .reset_index(name='count'))

# Print the result
print(out)

Expected Output

When we run this code, we should see the following output:

   country rating       owner  count
0  England      a  John Smith      1
1  England      b  John Smith      1
2   France      a   Frank Foo      3
3   France      b   Frank Foo      1

Handling Additional Operations

While the provided code only performs two operations, we can easily add more to it. For example, let’s say we want to calculate the average rating for each group:

out = (df.groupby(['country', 'rating'])
       .apply(lambda group: (
           # Apply title() to owner column, ignoring spaces
           group['owner'].str.title().str.replace(r'\s+', ' ', regex=True),
           # Calculate mean of ratings
           group['rating'].mean()
       ))
       # Count the number of rows in each group
       .size()
       # Reset index and make count into a new column
       .reset_index(name='count'))

# Print the result
print(out)

In this example, we’re using another lambda function to calculate the mean of ‘rating’ for each group.

Conclusion

Grouping a DataFrame by multiple columns is a common operation in data analysis. By applying functions to each group and performing aggregate operations on these groups, you can gain insights into your data that would be difficult or impossible to obtain otherwise.

In this article, we’ve explored how to group a Pandas DataFrame by three columns (‘country’, ‘rating’) and make one column lowercase. We’ve also shown how to perform additional operations, such as calculating the average rating for each group.

Last modified on 2024-04-28