How to Efficiently Split a Pandas Column of Dictionaries into Separate Columns?

How to Split a Pandas Column of Dictionaries into Separate Columns

In a Pandas DataFrame, it's possible to encounter a column containing dictionaries. To extract the values from these dictionaries into individual columns, the json_normalize function is an efficient solution.

The following code demonstrates the process:

import pandas as pd

df = pd.DataFrame()  # Your existing DataFrame with the dictionary column
df2 = pd.json_normalize(df['Pollutant Levels'])

This will create a new DataFrame df2 with the values from the 'Pollutant Levels' dictionary column split into separate columns.

Handling Different Length Lists:

The provided requirement specifies that all lists in the dictionaries contain the same three keys ('a', 'b', 'c') but not necessarily the same length. To accommodate this, the code uses json_normalize, which automatically handles the variable list lengths by padding missing values with NaN.

Unicode Issue Resolution:

If the dictionary values are in Unicode format (u{'a': '1', 'b': '2', 'c': '3'} instead of {u'a': '1', u'b': '2', u'c': '3'}), the code will still function correctly. json_normalize can handle both types of Unicode strings.

Example with Unicode:

For data imported from a PostgreSQL database in Unicode format:

import pandas as pd

# Convert the Unicode strings to strings
df['Pollutant Levels'] = df['Pollutant Levels'].astype('unicode')

# Use json_normalize to split the dictionary column
df2 = pd.json_normalize(df['Pollutant Levels'])

This will convert the Unicode strings to regular strings and then split the dictionary column into separate columns.

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