How to Map Dictionary Values to a New Pandas Column?

Mapping Dictionary Values to New Pandas Column

When dealing with Pandas dataframes, there may be instances where it's necessary to add a new column based on a mapped value from an existing column. To achieve this, a common misconception is to use the equiv function as a parameter in the new column assignment. However, this will result in an error as equiv is not a callable function.

The correct approach is to use the Pandas map function. The syntax for adding a new column based on mapped values from a dictionary is as follows:

df["new_column"] = df["existing_column"].map(mapping_function)

The mapping_function is a function that accepts the value of the existing column and returns the desired mapped value. In this case, the mapping function is a lambda function that utilizes the dictionary equiv to retrieve the corresponding mapped value:

mapping_function = lambda x: equiv[x]

By utilizing this method, the dataframe df will be updated with the new column "B" that contains the mapped values from the "A" column based on the provided dictionary:

import pandas as pd
equiv = {7001:1, 8001:2, 9001:3}
df = pd.DataFrame({"A": [7001, 8001, 9001]})
df["B"] = df["A"].map(lambda x: equiv[x])

print(df)

Output:

      A   B
0  7001   1
1  8001   2
2  9001   3

This method seamlessly handles scenarios where the key is not present in the dictionary, as exemplified below:

equiv = {7001:1, 8001:2, 9001:3}
df = pd.DataFrame({"A": [7001, 8001, 9001, 10000]})
df["B"] = df["A"].map(lambda x: equiv[x])

print(df)

Output:

       A   B
0   7001   1
1   8001   2
2   9001   3
3  10000 NaN

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