Practice Exercises for Merging Data

Overview

In this section, you'll practice techniques for merging and reshaping data from multiple sources using pandas. Go through the core concepts first, and then work on the exercises.

Core Concepts

Merging DataFrames with Pandas

Merging is a crucial operation in data manipulation. The primary function you will use in pandas for merging is .merge(). The merge function allows for a variety of join operations similar to those in SQL.

Examples:

left = pd.DataFrame({'key': ['A', 'B'], 'value': [1, 2]})
right = pd.DataFrame({'key': ['A', 'B'], 'value': [3, 4]})
merged = left.merge(right, on='key')

Validating Merges

You can validate merges using the validate parameter in the .merge() function. This can be set to:

• '1:1': Check if merge keys are unique in both left and right datasets.
• '1:m': Check if merge keys are unique in the left dataset.
• 'm:1': Check if merge keys are unique in the right dataset.
• 'm:m': No validation.

Debugging and Data Cleaning

Utilizing functions like .pipe() to streamline your operations can also allow you to insert debugging functions to better understand the flow of your data.

Example:

def debug(df):
    print(df.shape)
    return df

df.pipe(debug)

Exporting Data to Excel

Pandas provides functionalities to write to Excel files. This can be achieved using the .to_excel() function and the pd.ExcelWriter context.

Exercises

1. Create two small DataFrames with 3 columns each and perform an inner join using the .merge() function.
2. Try different types of joins (left, right, outer, inner) on the above DataFrames and observe the results.
3. Take two DataFrames with non-unique keys and try to validate the merge using '1:1'. Note down the error and understand why it's happening.
4. Create a debugging function that prints out the shape of the DataFrame and integrate it using the .pipe() method in a sequence of operations.