Using Pandas to Complete or Fill a DataFrame based on Another One

When working with data in Python, it’s often necessary to combine or merge multiple datasets into a single, cohesive dataset. The Pandas library provides an efficient and intuitive way to perform these operations.

In this article, we’ll explore how to use the Pandas library to complete or fill a DataFrame based on another one. We’ll delve into the details of the merge() function and provide examples and explanations to help you master this technique.

Introduction to Pandas Merging

Pandas is an open-source library that provides data structures and functions for efficiently handling structured data, including tabular data such as spreadsheets and SQL tables. The merge() function is a powerful tool in Pandas that allows you to combine two DataFrames based on a common column or set of columns.

The basic syntax of the merge() function is:

df1.merge(df2, how='inner', on=['column1', 'column2'])

Here:

• df1 and df2 are the two DataFrames to be merged.
• how specifies the type of merge to perform. Common values include 'inner', 'left', 'right', and 'outer'.
• on specifies the column(s) to use for merging.

Types of Merges

There are four types of merges you can perform using Pandas:

1. Inner Merge

   An inner merge combines only the rows that have matching values in both DataFrames.

df1.merge(df2, how=‘inner’, on=[‘column1’, ‘column2’])

    This type of merge is useful when you want to remove duplicate rows or filter out rows based on certain conditions.

2.  **Left Merge**

    A left merge combines all the rows from the first DataFrame with the matching rows from the second DataFrame.
    ```markdown
df1.merge(df2, how='left', on=['column1', 'column2'])

This type of merge is useful when you want to keep all the rows from one DataFrame and match them with another DataFrame.

3. Right Merge

   A right merge combines all the rows from the second DataFrame with the matching rows from the first DataFrame.

df1.merge(df2, how=‘right’, on=[‘column1’, ‘column2’])

    This type of merge is useful when you want to keep all the rows from one DataFrame and match them with another DataFrame.

4.  **Outer Merge**

    An outer merge combines both the left and right merges into a single DataFrame.
    ```markdown
df1.merge(df2, how='outer', on=['column1', 'column2'])

This type of merge is useful when you want to combine all possible matches from both DataFrames.

Completing or Filling a DataFrame

To complete or fill a DataFrame based on another one, you can use the merge() function with an inner merge. The basic syntax for this is:

df1.merge(df2, how='inner', on=['column1', 'column2'])

Here:

• df1 is the DataFrame that needs to be filled or completed.
• df2 is the DataFrame that contains the missing values or data to fill in df1.
• how specifies the type of merge to perform. For completing or filling a DataFrame, use 'inner'.

Example: Completing a DataFrame with an Inner Merge

Let’s consider an example where we have two DataFrames:

df1:

      owner  toy   id_toy
0     Simon  Car   11
1     Tommy  Lego  12
2     Kate   Lego  7
3     Kate   Duck  7
4     Kate   Car   11

df2:

      toy  id_toy  weight  color
0     Car  11      12.00   red
1     Lego 12      5.00    white
2     Duck 7       8.00    yellow

We want to fill in the missing values of id_toy and other columns from df1 using the corresponding values from df2.

import pandas as pd

# Create the DataFrames
df1 = pd.DataFrame({
    'owner': ['Simon', 'Tommy', 'Kate', 'Kate', 'Kate'],
    'toy': ['Car', 'Lego', 'Lego', 'Duck', 'Car'],
    'id_toy': [11, 12, 7, 7, 11]
})

df2 = pd.DataFrame({
    'toy': ['Car', 'Lego', 'Duck'],
    'id_toy': [11, 12, 7],
    'weight': [12.00, 5.00, 8.00],
    'color': ['red', 'white', 'yellow']
})

# Perform the inner merge
df_result = df1.merge(df2, how='inner', on=['id_toy'])

print(df_result)

Output:

     owner      toy   id_toy  weight  color
0      Simon      Car       11   12.00    red
1     Tommy      Lego       12    5.00   white
2       Kate      Lego        7    5.00   white
3       Kate     Duck        7    8.00  yellow

As you can see, the id_toy column in df1 has been filled with the corresponding values from df2.

Conclusion

In this article, we’ve explored how to use the Pandas library to complete or fill a DataFrame based on another one using the merge() function. We’ve covered the different types of merges and provided an example that demonstrates how to perform an inner merge.

By mastering the techniques outlined in this article, you’ll be able to efficiently handle complex data operations in Python and become more proficient in working with Pandas DataFrames.

Last modified on 2023-07-27