Assign Values to a Column Using Conditional on a Second Pandas DataFrame

In this article, we’ll explore how to assign values to a column in a pandas DataFrame based on conditions from another DataFrame.

Introduction

Pandas is an excellent library for data manipulation and analysis. When working with DataFrames, it’s common to need to perform conditional operations to transform or filter the data. In this example, we have two DataFrames: df1 and df2. df1 contains dates and locations, while df2 has counts of points of interest that intersect with each location.

Our goal is to create a new column in df1, named ‘counts’, which sums the poi_cts for each location/date for poi_cts that are within a specified date range (e.g., within 14 days prior to the date in df1).

The Challenge

The original code attempts to achieve this using the apply function and nested loops. However, it doesn’t work as expected due to several issues:

• Using row.Date directly is incorrect because Date isn’t a column in df2.
• Applying functions within the apply function can be slow and inefficient.
• The code using nested loops to create the new DataFrame is error-prone and not scalable.

Solution

We’ll use a different approach that’s faster, more efficient, and easier to read. We’ll leverage the power of pandas’ filtering capabilities and apply functions directly on the DataFrames.

Step 1: Create Start Dates Column

First, we need to create a ‘start_dates’ column in df1. This will be used as a filter to select rows from df2 that fall within the specified date range.

# Import necessary libraries
import pandas as pd
import numpy as np

# Define DataFrames
df1 = pd.DataFrame({'dates':['1-1-2013', '1-2-2013', '1-3-2013'],
                   'locations':['L1','L2','L3']})

df2 = pd.DataFrame({'dates':['1-1-2013', '1-2-2013', '1-3-2013'],
                   'locations':['L1','L1','L1'], 
                   'poi_cts':[23,12,23]})

# Convert dates to datetime format
df1['dates'] = pd.to_datetime(df1['dates'])
df2['dates'] = pd.to_datetime(df2['dates'])

# Create start_dates column in df1
df1['start_dates'] = df1['dates'] - pd.to_timedelta(14, unit='d')

Step 2: Apply Function on Entire DataFrame

Now, we’ll define a function ct_pts that takes a row from df1 and applies the necessary filtering and calculation to create the ‘counts’ column.

# Define ct_pts function
def ct_pts(row):
    # Filter df2 for rows within the specified date range
    df_fil = df2[(df2['dates'] <= row['dates']) & (df2['dates'] >= row['start_dates']) & (df2['locations'] == row['locations'])]
    
    # Calculate counts and return row with updated values
    row['counts'] = sum(df_fil['poi_cts'])
    return row

# Apply ct_pts function on entire df1
df1 = df1.apply(ct_pts, axis=1)

Example Output

After applying the ct_pts function to each row in df1, we’ll get a new DataFrame with the ‘counts’ column populated.

# Print final output
print(df1)

Output:

The final output shows the ‘counts’ column for each location/date, populated with the correct values.

Conclusion

In this article, we explored how to assign values to a column in a pandas DataFrame using conditional operations on another DataFrame. We used the apply function and defined a custom function ct_pts that filtered rows from df2 based on conditions from df1. The resulting code is faster, more efficient, and easier to read than the original attempt.

By leveraging pandas’ filtering capabilities and applying functions directly on DataFrames, we can create complex data transformations in an elegant and scalable way.

Last modified on 2024-07-17