Understanding pandas.DataFrame.combine_first() method (5 examples)

Overview

The pandas.DataFrame.combine_first() method is a powerful tool for handling missing data and combining two DataFrame objects. It’s particularly useful when you want to update a DataFrame with values from another DataFrame, but only where the original DataFrame has missing values (NaNs). In this tutorial, we will explore the combine_first() method through five progressive examples, ranging from basic usage to more advanced applications.

Example 1: Basic Usage

To begin, we’ll look at the most straightforward use case of combine_first(). Consider two DataFrames, DF1 and DF2:

import pandas as pd

# Create DataFrame DF1
df1 = pd.DataFrame({'A': [None, 2, None], 'B': [4, None, 6]})

# Create DataFrame DF2
df2 = pd.DataFrame({'A': [1, None, 3], 'B': [None, 5, None]})

Now, we use combine_first() to fill in missing values in DF1 with values from DF2:

# Combine DF1 and DF2
df_combined = df1.combine_first(df2)
print(df_combined)

Output:

     A    B
0  1.0  4.0
1  2.0  5.0
2  3.0  6.0

In this example, combine_first() updated DF1’s NaNs with values from DF2, resulting in a complete DataFrame without missing values.

Example 2: Overlapping Data

What happens when both DataFrames have valid, but different, values in the same positions? Let’s find out:

import pandas as pd

df1 = pd.DataFrame({'A': [None, 2, 3], 'B': [4, None, 6]})
df2 = pd.DataFrame({'A': [1, 10, 3], 'B': [None, 5, 7]})

df_combined = df1.combine_first(df2)
print(df_combined)

Output:

     A    B
0  1.0  4.0
1  2.0  5.0
2  3.0  6.0

In this case, combine_first() only updates missing values in DF1 without modifying existing non-NaN values, even if DF2 has different non-NaN values in the same positions.

Example 3: Index Alignment

The combine_first() method also aligns on indexes, a crucial feature when working with non-identically indexed DataFrames. Let’s illustrate this:

import pandas as pd

df1 = pd.DataFrame({'A': [None, 2]}, index=[1, 2])
df2 = pd.DataFrame({'A': [1, 3]}, index=[0, 2])

df_combined = df1.combine_first(df2)
print(df_combined)

Output:

     A
0  1.0
1  NaN
2  2.0

In this example, combine_first() fills the value for index 2 from DF2 into DF1, but leaves the value at index 1 as NaN because DF2 doesn’t provide a value.

Example 4: Adding New Columns and Rows

combine_first() can also add new columns and rows from the second DataFrame if they’re not present in the first. See the following:

import pandas as pd

df1 = pd.DataFrame({'A': [None, 2, 3]})
df2 = pd.DataFrame({'B': [1, 2, 3]}, index=[3, 4, 5])

df_combined = df1.combine_first(df2)
print(df_combined)

Output:

     A    B
0  NaN  NaN
1  2.0  NaN
2  3.0  NaN
3  NaN  1.0
4  NaN  2.0
5  NaN  3.0

This demonstrates how combine_first() brings in the entirely new ‘B’ column and indices 3, 4, and 5 from DF2 into the combined DataFrame.

Example 5: Handling DataFrames with Different Columns

Lastly, we’ll see how combine_first() deals with DataFrames having different sets of columns:

import pandas as pd

df1 = pd.DataFrame({'A': [None, 2, 3], 'C': [None, None, 'C3']})
df2 = pd.DataFrame({'A': [1, None, 3], 'B': [None, 5, None]})

df_combined = df1.combine_first(df2)
print(df_combined)

Output:

     A    B     C
0  1.0  NaN  None
1  2.0  5.0  None
2  3.0  NaN    C3

The result is a DataFrame that combines non-NaN values across DataFrames, with combine_first() filling in NaNs in DF1 with values from DF2 where possible and retaining DF1’s unique columns.

Conclusion

The pandas.DataFrame.combine_first() method offers a nuanced approach for dealing with missing data across multiple DataFrames. Through these examples, we observed its ability to handle NaNs elegantly, align indices, and merge differing structures, making it a crucial tool for real-world data manipulation and cleaning tasks.