How to Remap Pandas Column Values Using a Dictionary While Keeping NaN Values?

Remapping Values in Pandas Column Using Dictionary While Preserving NaN

In the context of processing dataframes, it is often necessary to alter values in a specific column based on defined mappings. Consider a scenario where you have a dictionary containing predefined value translations, such as di = {1: "A", 2: "B"}, and you want to apply these mappings to a pandas column named col1. The goal is to modify the values in col1 accordingly, while leaving NaN values untouched.

One highly effective approach to achieve this transformation is by leveraging pandas' .replace method. This method allows for the replacement of specific values or ranges with designated target values. Here's how you can implement it:

import pandas as pd
import numpy as np

# Example DataFrame
df = pd.DataFrame({'col2': {0: 'a', 1: 2, 2: np.nan}, 'col1': {0: 'w', 1: 1, 2: 2}})

# Mapping dictionary
di = {1: "A", 2: "B"}

# Apply value remapping using .replace
df.replace({"col1": di}, inplace=True)

# Output DataFrame with remapped values while preserving NaN
print(df)

In this example, the .replace method takes a dictionary as an argument, where the keys represent the original values in col1, and the values represent the desired remapped values. By setting the inplace parameter to True, the original dataframe is modified directly, saving the need for reassignment.

Alternatively, if you prefer to apply the transformation specifically to the col1 Series, you can use the following syntax:

df["col1"].replace(di, inplace=True)

This approach ensures that NaN values remain unaffected, as NaN is not a key in the mapping dictionary.

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