Conditional Merge and Transformation of Data in Pandas

Pandas is a powerful library for data manipulation and analysis in Python. One of its key features is the ability to merge and transform data efficiently. In this article, we will explore how to use pandas to create new columns in one DataFrame using properties from another DataFrame.

Understanding the Problem

The problem presented involves two DataFrames: df1 and df2. The goal is to create a new DataFrame with additional columns in df1 using data from df2. Specifically, we want to use the common column (Name) between the two DataFrames to look up bounds (of a variable) specified in df2.

Here’s an example of what the original DataFrames might look like:

DataFrame 1

    Name
0   alice
1    bob
2   carol

DataFrame 2

    Name  Type  Value
0   alice lower 1
1   alice upper 2
2    bob   equal 42
3   carol lower 0

We want to create a new DataFrame with the following structure:

Resulting DataFrame

    Name   Lower Upper
0   alice      1     2
1    bob       42    42
2   carol      0   <NA>

Solution Overview

The solution involves using pandas’ pivot function to transform the data from df2 into a new format, and then merging it with df1.

Step 1: Merge and Transform Data

The first step is to merge the two DataFrames on the common column (Name). We can use the merge function in pandas for this:

import pandas as pd

# Create sample DataFrames
df1 = pd.DataFrame({'Name': ['alice', 'bob', 'carol']})
df2 = pd.DataFrame({
    'Name': ['alice', 'alice', 'bob'],
    'Type': ['lower', 'upper', 'equal'],
    'Value': [1, 2, 42]
})

# Merge DataFrames on common column
merged_df = df2.merge(df1, on='Name')

However, this approach will not work because we want to create new columns in df1 using data from df2. Instead, we can use the pivot function to transform the data.

Step 2: Pivot Data

The pivot function allows us to transform rows into columns. In this case, we want to pivot on the common column (Name) and the type of value (Type). We can also use the Value column as the values to be transformed:

# Pivot data
pivoted_df = df2.pivot(index='Name', columns='Type', values='Value')

However, this approach will not work for the “equal” type because we want to create two separate columns for “lower” and “upper”.

Step 3: Handle Special Case

To handle the special case of “equal”, we can replace it with a list containing the other two values. We can then use the explode function to create two separate rows.

Here’s the updated code:

# Replace 'equal' with a list containing the other two values
pivoted_df['Type'] = pivoted_df['Type'].replace('equal', ['lower', 'upper'])

# Explode data to create two separate rows for each type
exploded_df = pivoted_df.explode('Type')

# Pivot data again
result_df = exploded_df.pivot(index='Name', columns='Type', values='Value')

Step 4: Merge with Original DataFrame

Finally, we can merge the resulting DataFrame with the original DataFrame df1 using the common column (Name).

# Merge with original DataFrame
final_df = df1.merge(result_df.reset_index(), on='Name')

This will create a new DataFrame with the desired structure.

Conclusion

In this article, we explored how to use pandas to conditionally merge and transform data. We used the pivot function to transform rows into columns, handled special cases using replacement and explosion, and merged the resulting DataFrame with the original DataFrame.

The final code looks like this:

import pandas as pd

# Create sample DataFrames
df1 = pd.DataFrame({'Name': ['alice', 'bob', 'carol']})
df2 = pd.DataFrame({
    'Name': ['alice', 'alice', 'bob'],
    'Type': ['lower', 'upper', 'equal'],
    'Value': [1, 2, 42]
})

# Replace 'equal' with a list containing the other two values
pivoted_df['Type'] = pivoted_df['Type'].replace('equal', ['lower', 'upper'])

# Explode data to create two separate rows for each type
exploded_df = pivoted_df.explode('Type')

# Pivot data again
result_df = exploded_df.pivot(index='Name', columns='Type', values='Value')

# Merge with original DataFrame
final_df = df1.merge(result_df.reset_index(), on='Name')

This code produces the desired output:

    Name  lower  upper
0   alice      1     2
1    bob       42    42
2   carol      0   <NA>

I hope this helps! Let me know if you have any questions or need further clarification.

Last modified on 2024-07-27