Aligning and Adding Columns in Multiple Pandas Dataframes Based on Date Column

In this article, we’ll explore how to align and add columns from multiple Pandas dataframes based on a common date column. This problem arises when you have different numbers of rows in each dataframe and want to aggregate the numerical data in the ‘Cost’ columns across all dataframes.

Background and Prerequisites

Before diving into the solution, let’s cover some background information and prerequisites.

Pandas is a powerful Python library for data manipulation and analysis.
DataFrames are the primary data structure used in Pandas to store and manipulate tabular data.
set_index method is used to set a column as the index of a DataFrame, allowing for efficient date-based operations.
The add method adds two DataFrames element-wise, filling missing values with a specified value.

Problem Statement

The problem at hand involves adding the ‘Total Cost’ columns from multiple dataframes based on their common ‘Date’ column. However, there are different numbers of rows in each dataframe, which causes issues when using the add method directly.

Solution Overview

To solve this problem, we can use the following approach:

Set the ‘Date’ column as the index for each DataFrame.
Use the add method to add the dataframes element-wise, filling missing values with 0.
Select the desired columns (in this case, the ‘Total Cost’ column) from the resulting DataFrame.

Code Solution

Here’s a Python code snippet that demonstrates the solution:

import pandas as pd

# Create sample DataFrames
df1 = pd.DataFrame({
    'Date': ['2015-09-30', '2015-10-31', '2015-11-15', '2015-11-30'],
    'Total Cost': [724824, 757605, 788051, 809368]
})

df2 = pd.DataFrame({
    'Date': ['2015-11-30', '2016-01-15'],
    'Total Cost': [3022, 3051]
})

# Set Date as index for each DataFrame
df1 = df1.set_index('Date')
df2 = df2.set_index('Date')

# Add DataFrames element-wise, filling missing values with 0
result_df = df1.add(df2, fill_value=0)

# Select desired column ('Total Cost') from the resulting DataFrame
total_cost_column = result_df['Total Cost']

print(total_cost_column)

Explanation

This code snippet creates two sample DataFrames df1 and df2, sets the ‘Date’ column as the index for each dataframe using set_index, and then adds the dataframes element-wise using the add method. The resulting DataFrame is stored in the result_df variable, and the desired column (‘Total Cost’) is selected from it.

Recursion Solution

If you need to apply this solution recursively to multiple DataFrames, you can modify the code as follows:

import pandas as pd

def add_dataframes(f_arg, *argv):
    df_total = f_arg
    for arg in argv:
        df_total = df_total.add(arg, fill_value=0)
    return df_total

# Create sample DataFrames
df1 = pd.DataFrame({
    'Date': ['2015-09-30', '2015-10-31', '2015-11-15', '2015-11-30'],
    'Total Cost': [724824, 757605, 788051, 809368]
})

df2 = pd.DataFrame({
    'Date': ['2015-11-30', '2016-01-15'],
    'Total Cost': [3022, 3051]
})

# Set Date as index for each DataFrame
df1 = df1.set_index('Date')
df2 = df2.set_index('Date')

# Add DataFrames recursively
result_df = add_dataframes(df1, df2)

# Select desired column ('Total Cost') from the resulting DataFrame
total_cost_column = result_df['Total Cost']

print(total_cost_column)

Conclusion

In this article, we’ve explored how to align and add columns from multiple Pandas dataframes based on a common date column. We provided a code solution that uses the add method to perform element-wise addition of DataFrames, filling missing values with 0. Additionally, we showed a recursive solution for applying this operation to multiple DataFrames.

Last modified on 2023-11-05