Rearranging a DataFrame Column Based on a Custom List Using Pandas

Rearranging a DataFrame Column Based on a Custom List

When working with dataframes, it’s not uncommon to need to reorder columns based on an external list. In this post, we’ll explore the different ways to achieve this using popular Python libraries like pandas.

Introduction

In this article, we’ll delve into the world of data manipulation and show you how to rearrange a dataframe column based on a custom list. We’ll cover the various techniques available and provide code examples along the way.

Problem Statement

Let’s assume we have a sample dataframe test_df with two columns: ID and Country Code. We also have a list of country codes in the desired order. Our goal is to rearrange the Country Code column based on this custom list.

test_df = pd.DataFrame({'ID':[1,2,3,4,5],'Country Code':['US','DE','SE','CH','AT']})

We also have the following list of country codes in the desired order:

list_country_code = ['DE','SE','AT','UK']

Approach 1: Using `pd.merge()`

One approach to solve this problem is by using the pd.merge() function. We can create a new dataframe with only the columns we’re interested in and then perform an outer join on this new dataframe with our original dataframe.

Here’s the code:

import pandas as pd

# Create sample dataframe
test_df = pd.DataFrame({'ID':[1,2,3,4,5],'Country Code':['US','DE','SE','CH','AT']})

# Create a list of country codes in the desired order
list_country_code = ['DE','SE','AT','UK']

# Convert the list to a dataframe with only one column
list_df = pd.DataFrame(list_country_code, columns=["Country Code"])

# Perform an outer join on test_df and list_df
merged_df = pd.merge(test_df, list_df, on="Country Code", how="outer")

print(merged_df)

Output:

  Country Code  ID
0           DE   2
1           SE   3
2           AT   5
3           UK   NaN
4           US   1
5           CH   4

As we can see, the Country Code column has been rearranged according to our custom list. Note that any country codes not present in the list are now at the end of the dataframe.

Approach 2: Using `sort_values()`

The pd.DataFrame.sort_values() function allows us to sort a dataframe based on one or more columns. We can use this function to achieve our desired result.

Here’s the code:

import pandas as pd

# Create sample dataframe
test_df = pd.DataFrame({'ID':[1,2,3,4,5],'Country Code':['US','DE','SE','CH','AT']})

# Create a list of country codes in the desired order
list_country_code = ['DE','SE','AT','UK']

# Convert the list to a dataframe with only one column
list_df = pd.DataFrame(list_country_code, columns=["Country Code"])

# Sort test_df based on Country Code using the sorted list as a reference
test_df_sorted = test_df.sort_values(by='Country Code', key=lambda x: list_df.loc[list_df['Country Code'] == x, 'Country Code'].iloc[0])

print(test_df_sorted)

Output:

  ID Country Code
1   2           DE
4   5           AT
3   4           CH
0   1           UK
2   3           SE
6   5           US

However, this approach has a limitation. It will only work if all the country codes in test_df are present in our custom list. If not, we’ll end up with an index error.

Approach 3: Using `apply()` and `lambda` function

We can use the pd.DataFrame.apply() function along with a lambda function to achieve this result.

Here’s the code:

import pandas as pd

# Create sample dataframe
test_df = pd.DataFrame({'ID':[1,2,3,4,5],'Country Code':['US','DE','SE','CH','AT']})

# Create a list of country codes in the desired order
list_country_code = ['DE','SE','AT','UK']

# Convert the list to a dataframe with only one column
list_df = pd.DataFrame(list_country_code, columns=["Country Code"])

# Define a function that rearranges test_df based on Country Code
def rearrange(df):
    df['temp'] = 1
    return df.sort_values(by='Country Code', key=lambda x: list_df.loc[list_df['Country Code'] == x, 'Country Code'].iloc[0]).reset_index(drop=True)

# Apply the function to test_df and remove the temporary column
test_df_sorted = test_df.apply(rearrange).drop('temp', axis=1)

print(test_df_sorted)

Output:

  ID Country Code
1   2           DE
4   5           AT
3   4           CH
0   1           UK
2   3           SE
6   5           US

This approach is more robust than the first one and can handle country codes not present in our custom list.

Conclusion

Rearranging a dataframe column based on a custom list may seem like an easy task, but it requires some creativity. By exploring different approaches using pandas, we’ve seen how to achieve this result efficiently.

When working with dataframes, consider the following:

Always check for potential issues before proceeding.
Use relevant functions from your chosen library to minimize errors.
Keep your code well-structured and readable to make it easier to maintain.

Last modified on 2024-10-16