Pandas DataFrame combine_first() Method – Explained with examples

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Pandas is a powerful and versatile library in Python for data manipulation and analysis. Among its many features, the combine_first() method stands out for its ability to merge data from two DataFrames, filling in missing values from one DataFrame with values from another. This method is particularly useful in data cleaning and preparation stages. In this blog, we’ll explore the combine_first() method, its syntax, and practical applications with examples.

What is the combine_first() Method?

The combine_first() method is used to combine two DataFrames. It updates missing values (NaNs) in the calling DataFrame with the corresponding values from another DataFrame. This method is essentially a specialized form of the combine method that uses DataFrame.combine with a function that prioritizes the first non-NA value.

Syntax

The syntax for the combine_first() method is straightforward:

Python
DataFrame.combine_first(other)
  • other: The DataFrame to use to fill holes (NaNs) in the calling DataFrame.
How Does it Work?

When you call the combine_first() method on a DataFrame, it looks at each element in the DataFrame and fills in any missing values (NaNs) with the corresponding value from the other DataFrame. If the value is not missing, it remains unchanged. The resulting DataFrame is a combination of the original DataFrame and the other DataFrame, with missing values filled.

Practical Examples

Let’s dive into some practical examples to understand how the combine_first() method works.

Example 1: Basic Usage

Consider two DataFrames, df1 and df2:

Python
import pandas as pd
import numpy as np

# Create first DataFrame
data1 = {'A': [1, 2, np.nan, 4], 'B': [np.nan, 2, 3, 4]}
df1 = pd.DataFrame(data1)

# Create second DataFrame
data2 = {'A': [5, 6, 7, 8], 'B': [1, np.nan, 3, np.nan]}
df2 = pd.DataFrame(data2)

print("DataFrame 1:")
print(df1)
print("\nDataFrame 2:")
print(df2)

Output:

Markdown
DataFrame 1:
     A    B
0  1.0  NaN
1  2.0  2.0
2  NaN  3.0
3  4.0  4.0

DataFrame 2:
     A    B
0  5.0  1.0
1  6.0  NaN
2  7.0  3.0
3  8.0  NaN

We can use the combine_first() method to fill the missing values in df1 with the values from df2:

Python
combined_df = df1.combine_first(df2)
print("\nCombined DataFrame:")
print(combined_df)

Output:

Markdown
Combined DataFrame:
     A    B
0  1.0  1.0
1  2.0  2.0
2  7.0  3.0
3  4.0  4.0

In the combined DataFrame, the NaN values in df1 are filled with the corresponding values from df2.

Example 2: Real-World Scenario

Imagine you have two DataFrames representing sales data from two different sources. Each source may have some missing data, and you want to combine them to get a complete dataset.

Python
# Sales data from source 1
data1 = {'Product': ['A', 'B', 'C', 'D'], 'Sales': [100, np.nan, 150, np.nan]}
sales1 = pd.DataFrame(data1)

# Sales data from source 2
data2 = {'Product': ['A', 'B', 'C', 'D'], 'Sales': [np.nan, 200, np.nan, 400]}
sales2 = pd.DataFrame(data2)

print("Sales Data 1:")
print(sales1)
print("\nSales Data 2:")
print(sales2)

Output:

Markdown
Sales Data 1:
  Product  Sales
0       A  100.0
1       B    NaN
2       C  150.0
3       D    NaN

Sales Data 2:
  Product  Sales
0       A    NaN
1       B  200.0
2       C    NaN
3       D  400.0

We can use combine_first() to merge these DataFrames:

Python
complete_sales = sales1.combine_first(sales2)
print("\nComplete Sales Data:")
print(complete_sales)

Output:

Markdown
Complete Sales Data:
  Product  Sales
0       A  100.0
1       B  200.0
2       C  150.0
3       D  400.0

In the combined DataFrame, the missing values in sales1 are filled with the corresponding values from sales2. As a result, we obtain a complete sales dataset without any missing values, ensuring data integrity for further analysis. Now, we have a complete sales dataset with no missing values.

Key Points to Remember
  • combine_first() is used to fill missing values in a DataFrame with values from another DataFrame.
  • It prioritizes the calling DataFrame, using values from the other DataFrame only when the calling DataFrame has NaNs.
  • The resulting DataFrame retains the structure and index of the calling DataFrame.
Conclusion

The combine_first() method is a powerful tool in Pandas for data cleaning and preparation. It allows you to merge two DataFrames, filling in missing values efficiently. Whether you’re dealing with sales data, survey results, or any other dataset, understanding and utilizing combine_first() can streamline your data manipulation tasks.

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