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

Splitting a Column of Dictionaries into Separate Columns with Pandas

Problem Introduction

When working with Pandas DataFrames, it is often encountered that a column contains dictionaries as its values. This can pose challenges in further data analysis, as the dictionaries need to be split into separate columns for better accessibility and manipulation. This issue becomes particularly relevant when the dictionaries have varying lengths and contain shared keys.

Original Approach and Error

The user in the forum post describes a DataFrame where the 'Pollutant Levels' column contains dictionaries. Initially, they attempted to split this column using the following code:

objs = [df, pandas.DataFrame(df['Pollutant Levels'].tolist()).iloc[:, :3]]
df2 = pandas.concat(objs, axis=1).drop('Pollutant Levels', axis=1)

However, this method resulted in an IndexError due to out-of-bounds slicing.

Unicode Issue

The user further suspects that the Unicode format of the dictionaries in the 'Pollutant Levels' column may be causing the issue. They are in the form:

u{'a': '1', 'b': '2', 'c': '3'}

instead of:

{u'a': '1', u'b': '2', u'c': '3'}

Solution

To address these issues, the following approach is recommended:

import pandas as pd

df['Pollutant Levels'] = df['Pollutant Levels'].apply(lambda x: dict(x))
df2 = pd.json_normalize(df['Pollutant Levels'])

Explanation

The first line of code converts the Unicode dictionaries to standard dictionaries. The second line utilizes the json_normalize function from Pandas, which provides a convenient way to convert a column of dictionaries into separate columns. This function avoids the need for costly apply functions and produces the desired DataFrame:

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

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