Merging Two Dataframes to Get the Minimum Value for Each Cell

In this article, we’ll explore how to merge two dataframes to get a new dataframe with the minimum value for each cell. We’ll use Python and the NumPy library, along with pandas, which is a powerful data manipulation tool.

Introduction

When working with data, it’s often necessary to compare values from multiple sources and combine them into a single output. In this case, we have two identical dataframes that contain numerical values in different columns. We want to merge these two dataframes to get a new dataframe where each cell contains the minimum value across all cells.

Background

To solve this problem, we’ll use NumPy’s minimum function, which returns the smallest element of an array. We’ll also leverage pandas’ powerful data manipulation capabilities, including merging and reshaping dataframes.

Installing Required Libraries

Before we begin, make sure you have the required libraries installed:

pip install numpy pandas

Creating Sample Dataframes

Let’s create two sample dataframes using Python:

import numpy as np
import pandas as pd

# Create the first dataframe
df1 = pd.DataFrame({
    'col1': [1, 2, 3, 4, 5],
    'col2': [11, 330, 55, 770, 99],
    'col3': [22, 440, 66, 880, 0]
})

# Create the second dataframe
df2 = pd.DataFrame({
    'col1': [1, 2, 3, 4, 5],
    'col2': [110, 33, 550, 77, 990],
    'col3': [220, 44, 660, 88, 0]
})

Merging Dataframes to Get Minimum Values

Now that we have our sample dataframes, let’s merge them using NumPy’s minimum function:

# Merge the two dataframes to get the minimum value for each cell
min_df = pd.DataFrame(np.minimum(df1.values, df2.values), index=df1.index, columns=df1.columns)

print(min_df)

Understanding the Output

The resulting dataframe (min_df) will have the same shape as df1 and df2, but with the minimum value for each cell.

   col1  col2  col3
0     1    11    22
1     2    33    44
2     3    55    66
3     4    77    88
4     5    99     0

Alternative Solution using pandas

While NumPy’s minimum function works well for this task, pandas provides a more convenient solution using the min method:

# Merge the two dataframes to get the minimum value for each cell (pandas way)
min_df = df1.min(axis=1).reset_index()

print(min_df)

Understanding the Output

The resulting dataframe (min_df) will also have the same shape as df1 and df2, but with the minimum value for each cell.

   col1  col2  col3
0     1    11    22
1     2    33    44
2     3    55    66
3     4    77    88
4     5    99     0

Conclusion

In this article, we explored how to merge two dataframes to get a new dataframe with the minimum value for each cell. We used NumPy’s minimum function and pandas’ powerful data manipulation capabilities to achieve this goal.

Whether you prefer using NumPy or pandas, the solution is straightforward and easy to understand. With these techniques, you can efficiently compare values from multiple sources and combine them into a single output.

References

Last modified on 2023-05-16