Applying Function to Multiple Dataframes and Columns

As a data analyst or scientist, working with multiple dataframes can be a challenging task. When you need to apply a custom function to different columns or dataframes, it’s essential to understand the underlying concepts and techniques to avoid common pitfalls.

In this article, we’ll delve into the details of applying functions to multiple dataframes and columns using Python’s Pandas library. We’ll explore the issues with the original code, discuss alternative approaches, and provide a step-by-step guide on how to achieve the desired outcome.

Understanding the Original Code

The original code attempts to apply a custom function rec_date to a dataframe df. The function checks if a specific condition is met based on the values in another dataframe df_workable. If the condition is true, it returns “this”; otherwise, it returns “that”.

def rec_date(row):
    if row['flag'] == '2.1':
        # Check if the condition is met
        if (df_workable[(df_workable['workable_day'] == int(row['wday_a1'])) & (df_workable['month'] == 1)]['day'] <= dt.datetime.today().day).all():
            val = "this"
        else:
            val = "that"
    else:
        val = "Still missing"
    return val

The issue with the original code lies in the way it attempts to combine multiple conditions using the & operator. Additionally, the result of this check has the form of a Series, which can lead to confusion when trying to convert it into a single boolean value.

Alternative Approach

To resolve these issues, we need to wrap each condition in parentheses and clarify how to aggregate the resulting Series. One way to do this is by using the series.all() or series.any() method to ensure that at least one of the values meets the condition.

def rec_date(row):
    if row['flag'] == '2.1':
        # Check if the condition is met
        series = (df_workable[(df_workable['workable_day'] == int(row['wday_a1'])) & (df_workable['month'] == 1)]['day'] <= dt.datetime.today().day)
        val = "this" if series.any() else "that"
    else:
        val = "Still missing"
    return val

Step-by-Step Guide

To apply the rec_date function to the dataframe df, follow these steps:

1. Import necessary libraries: Make sure you have Pandas installed and imported.
2. Define the function: Create a function that takes a row as input and applies the desired logic. In this case, we’ve modified the original code to use series.any() instead of all().
3. Apply the function: Use the apply() method to apply the function to each row in the dataframe. Specify axis=1 to apply the function to each column.
4. Store the results: Assign the resulting values to a new column in the dataframe.

import pandas as pd

# Create sample dataframes
df = pd.DataFrame({
    'day_a1': [24, 31, 27, 25, 31],
    'wday_a1': [4, 22, 18, 19, 20],
    'iwday_a1': [6, 1, 5, 7, 10],
    'flag': ['2.1', '2.2', '3.3.2.1.3', '1', '3.2']
})

df_workable = pd.DataFrame({
    'workable_day': [4, 22, 18, 19, 20],
    'month': [1, 1, 1, 1, 1]
})

# Define the function
def rec_date(row):
    if row['flag'] == '2.1':
        # Check if the condition is met
        series = (df_workable[(df_workable['workable_day'] == int(row['wday_a1'])) & (df_workable['month'] == 1)]['day'] <= dt.datetime.today().day)
        val = "this" if series.any() else "that"
    else:
        val = "Still missing"
    return val

# Apply the function
df['rec_date'] = df.apply(rec_date, axis=1)

print(df)

By following these steps and understanding the underlying concepts, you can effectively apply functions to multiple dataframes and columns using Pandas.

Last modified on 2024-05-31