Merging DataFrames Based on Two Conditions

In this article, we’ll explore how to merge two Pandas DataFrames based on multiple conditions. The goal is to join the DataFrames while performing calculations on one of them and then reassigning the results back into the resulting DataFrame.

Introduction

When working with data in Python, it’s common to encounter situations where you need to merge or combine multiple DataFrames. These DataFrames can come from various sources, such as different databases, CSV files, or even other libraries like NumPy or Matplotlib.

In this article, we’ll focus on merging DataFrames using Pandas. Specifically, we’ll explore how to join two DataFrames based on a common column while performing calculations on one of the DataFrames and then reassigning the results back into the resulting DataFrame.

Background

To understand the concept of merging DataFrames, let’s first look at what each component represents:

• DataFrames: A 2-dimensional labeled data structure with columns of potentially different types. You can think of it as an Excel spreadsheet or a SQL table.
• Columns: The vertical segments of the DataFrame that contain individual values. Each column has a unique name, and you can access its values using square bracket notation (e.g., df['column_name']).
• Rows: The horizontal segments of the DataFrame that contain multiple values. You can think of rows as individual records or observations.
• Indexing: A way to address specific elements within a DataFrame by their position or label.

When merging DataFrames, you’re essentially combining two or more DataFrames into one new DataFrame while preserving the relationships between columns and rows.

The Problem

Given two DataFrames, df and df1, we want to merge them based on the ‘date’ column. However, instead of directly joining the DataFrames, we need to perform some calculations on df1 (specifically, summing its ‘counts’ values) and then reassign these results back into the resulting DataFrame.

The goal is to achieve the following output:

Solution Overview

To solve this problem, we’ll employ two techniques:

1. Using pivot_table and combine_first: We’ll use the pivot_table function from Pandas to reshape df1 into a more suitable format for joining. Then, we’ll combine the resulting DataFrame with df using combine_first.
2. Using pivot and combine_first: This approach is similar to the first one but uses the pivot function instead of pivot_table.

Using pivot_table and combine_first

Here’s how you can achieve this:

import pandas as pd

# Create DataFrames
df = pd.DataFrame(columns=['lt1', 'lt2','lt3','lt4','lt5','lt6'])
df['date'] = pd.date_range('2016-1-1', periods=5, freq='D')
df

df1 = pd.DataFrame({'location': ['lt1','lt3', 'lt6', 'lt1','lt2', 'lt3'], \
                   'date': ['2016-01-1', '2016-01-02','2016-01-1','2016-01-03','2016-01-5','2016-01-4'], \
                   'counts': ['2', '1','1','1', '3','1']})

df1.date = pd.to_datetime(df1.date)
df1

# Pivot and combine
m=df1.pivot_table(index='date',columns='location',values='counts',aggfunc='sum')
final=df.set_index('date').combine_first(m).fillna(0).reset_index()

The pivot_table function creates a new DataFrame with the ‘date’ column as the index, the ’location’ column as the columns, and the ‘counts’ values summed up for each location on each date.

Then, we use df.set_index('date') to set the ‘date’ column as the index of the original DataFrame. We combine the resulting DataFrame with m using combine_first, which replaces NaN values in the original DataFrame with the corresponding values from m.

Finally, we reset the index of the final DataFrame using reset_index().

Using pivot and combine_first

Alternatively, you can use the pivot function instead of pivot_table. The only difference is that pivot_table returns a DataFrame with an integer MultiIndex for the columns, whereas pivot returns a DataFrame with separate indices for each column.

Here’s how to achieve this:

import pandas as pd

# Create DataFrames
df = pd.DataFrame(columns=['lt1', 'lt2','lt3','lt4','lt5','lt6'])
df['date'] = pd.date_range('2016-1-1', periods=5, freq='D')
df

df1 = pd.DataFrame({'location': ['lt1','lt3', 'lt6', 'lt1','lt2', 'lt3'], \
                   'date': ['2016-01-1', '2016-01-02','2016-01-1','2016-01-03','2016-01-5','2016-01-4'], \
                   'counts': ['2', '1','1','1', '3','1']})

df1.date = pd.to_datetime(df1.date)
df1

# Pivot and combine
(final_df=pd.concat([df.set_index('date').combine_first(df1.pivot('date','location','counts'))])) .fillna(0).reset_index()

This approach uses pd.concat to concatenate the original DataFrame (df) with the result of combining it with the pivoted DataFrame from df1. Finally, we reset the index using reset_index().

Conclusion

Merging DataFrames can be a powerful tool in data analysis. By understanding how to use Pandas’ built-in functions like pivot_table and combine_first, you can efficiently join DataFrames while performing calculations on one of them and reassigning the results back into the resulting DataFrame.

These techniques are particularly useful when working with large datasets or when you need to perform complex data transformations. Remember that practice makes perfect, so be sure to experiment with different approaches and scenarios to become more proficient in using Pandas for data analysis tasks!

Last modified on 2025-04-02