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

Splitting a Column of Dictionaries into Separate Columns with Pandas

Problem Description

A DataFrame contains a column of dictionaries that needs to be broken down into separate columns. While the dictionaries consist of the same three keys ('a', 'b', and 'c') appearing in the same order, they may vary in length. A previous working solution using pandas.concat() now fails with an IndexError.

Solution

The issue arises from the data being encoded as Unicode strings instead of dictionary objects. To resolve this:

1. Use ast.literal_eval() to Convert Unicode Strings:
   Before applying json_normalize, convert the Unicode strings to dictionaries using ast.literal_eval():

   import ast
   df['Pollutant Levels'] = df['Pollutant Levels'].apply(ast.literal_eval)

2. Normalize JSON using json_normalize:
   Use pandas.json_normalize() to split the column of dictionaries into separate columns:

   df2 = pd.json_normalize(df['Pollutant Levels'])

This solution avoids the use of costly apply functions and provides a more efficient and robust approach.

The above is the detailed content of How Can I Efficiently Split a Pandas Column of Dictionaries into Separate Columns?. For more information, please follow other related articles on the PHP Chinese website!