How can I efficiently split a Pandas DataFrame column of dictionaries into separate columns?

Splitting a Column of Dictionaries into Separate Columns with Pandas

When working with dataframes in Pandas, it is common to encounter columns that contain dictionary values. Splitting these columns into individual columns can improve data organization and accessibility.

Consider the following DataFrame:

Station ID     Pollutants
8809           {"a": "46", "b": "3", "c": "12"}
8810           {"a": "36", "b": "5", "c": "8"}
8811           {"b": "2", "c": "7"}
8812           {"c": "11"}
8813           {"a": "82", "c": "15"}

To split the "Pollutants" column into separate "a", "b", and "c" columns, you can use the json_normalize function introduced in Pandas version 0.23.0:

import pandas as pd

df2 = pd.json_normalize(df['Pollutants'])

This approach is efficient and avoids the use of potentially costly apply functions. The resulting DataFrame df2 will look like this:

Station ID     a      b       c
8809           46     3       12
8810           36     5       8
8811           NaN    2       7
8812           NaN    NaN     11
8813           82     NaN     15

Note that the resulting DataFrame contains null values (NaN) for missing dictionary keys. To handle these cases, you can use the fillna method to replace missing values with default values or apply custom logic.

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