How to Efficiently Add a Mapped Column to a Pandas DataFrame?

Adding a Mapped Column to a Pandas DataFrame

When working with pandas, adding a new column with a mapped value based on an existing column can be a straightforward task. However, certain approaches may result in errors or difficulties.

One common attempt is to directly assign the mapped value to the new column:

<code class="python">df["B"] = equiv(df["A"])</code>

However, this will fail as equiv, representing a dictionary, is not a callable function.

Another approach that may not yield the desired result is using map with a lambda function:

<code class="python">df["B"] = df["A"].map(lambda x: equiv[x])</code>

This expression will likely raise a KeyError unless the dictionary keys exactly match the column values.

The Correct Solution

The proper method to add a mapped column is to use map directly with the dictionary:

<code class="python">df["B"] = df["A"].map(equiv)</code>

This approach will create a new column, B, with the mapped values from the equiv dictionary. If a key does not exist in the dictionary, the corresponding row will be assigned NaN.

Example

Consider the following DataFrame:

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

Applying the correct mapping will produce the desired result:

<code class="python">df["B"] = df["A"].map(equiv)

print(df)

      A  B
0  7001  1
1  8001  2
2  9001  3</code>

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