How to Map Dictionary Values to Create a New Column in a Pandas DataFrame?

Addressing Pandas Mapping Frustrations: Adding a Column with Mapped Dictionary Values

Initial Dilemma:

Encountering seemingly insurmountable challenges while attempting to incorporate a new column into a Pandas DataFrame, with values mapped based on existing column data. Employing both a lambda function and a dictionary results in errors or unexpected outcomes.

Resolution:

The correct approach lies in utilizing the map() function in conjunction with the target dictionary. The syntax is: df["B"] = df["A"].map(equiv).

Explanation:

The map() function applies an operation to each element in a specified column. In this case, the operation is retrieving the mapped value from the dictionary equiv based on the key provided by the column data.

Example:

Consider the following code:

<code class="python">import pandas as pd
equiv = {7001:1, 8001:2, 9001:3}
df = pd.DataFrame( {"A": [7001, 8001, 9001]} )
df["B"] = df["A"].map(equiv)
print(df)</code>

Output:

      A  B
0  7001  1
1  8001  2
2  9001  3

This approach effectively adds the mapped values to the new column B. Moreover, it handles missing keys gracefully by returning NaN.

Additional Considerations:

• Key Handling: If a key in the dictionary does not correspond to any element in the column, the mapped value becomes NaN.
• Performance: The mapping operation can be computationally intensive for large datasets. Consider optimization techniques if necessary.

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