Splitting Dictionaries in Pandas DataFrames: A Step-by-Step Solution

Splitting a List of Dictionaries into Multiple Columns with the Same Index

In this article, we will explore how to split a list of dictionaries into multiple columns while maintaining the same index. This is a common problem in data manipulation and can be solved using Python’s pandas library.

Introduction

We start by examining the given DataFrame that has a timestamp as its index and a column called var_A, which contains a list of dictionaries. Our goal is to transform this DataFrame into another with multiple columns, where each dictionary from the original list forms a row in the new DataFrame.

The original DataFrame looks like this:

   index                   var_A
0  2019-08-21 09:05:49    [{"Date1": "Aug 21, 2017 9:09:51 AM","Date2": "Aug 21, 2017 9:09:54 AM","Id": "d5e665e5","num_ins": 108,"num_del": 0, "time": 356}]
1  2019-08-21 09:05:49    [{"Date1": "Aug 21, 2017 9:09:57 AM","Date2": "Aug 21, 2017 9:09:59 AM","Id": "d5e665e5","num_ins": 218,"num_del": 5, "time": 166}]
2  2019-08-21 09:05:59    [{"Date1": "Aug 21, 2017 9:10:01 AM","Date2": "Aug 21, 2017 9:11:54 AM","Id": "d5e665e5","num_ins": 348,"num_del": 72, "time": 3356}]
3  2019-08-21 09:05:59    [{"Date1": "Aug 21, 2017 9:19:57 AM","Date2": "Aug 21, 2017 9:19:59 AM","Id": "d5e665e5","num_ins": 69,"num_del": 5, "time": 125}]
4  2019-08-21 09:05:59    [{"Date1": "Aug 21, 2017 9:20:01 AM","Date2": "Aug 21, 2017 9:21:54 AM","Id": "f9e775f9","num_ins": 470,"num_del": 0, "time": 290}]
5  2019-08-21 09:06:04    []

We want to transform this DataFrame into another with multiple columns:

   index      Date1     Date2        Id  num_ins  num_del     time
0  2019-08-21  Aug 21, 2017 9:09:51 AM  Aug 21, 2017 9:09:54 AM  d5e665e5      0           108       356
1  2019-08-21  Aug 21, 2017 9:09:57 AM  Aug 21, 2017 9:09:59 AM  d5e665e5      218           5       166
2  2019-08-21  Aug 21, 2017 9:10:01 AM  Aug 21, 2017 9:11:54 AM  d5e665e5      348          72       3356
3  2019-08-21  Aug 21, 2017 9:19:57 AM  Aug 21, 2017 9:19:59 AM  d5e665e5      69            5       125
4  2019-08-21  Aug 21, 2017 9:20:01 AM  Aug 21, 2017 9:21:54 AM  f9e775f9      470           0       290
5  2019-08-21               NaN                 NaN        NAN         NAN     NAN

Solution Overview

The solution involves looping through each item in the var_A column, creating a new DataFrame for each dictionary, and then concatenating these DataFrames together. We will also handle cases where the list of dictionaries is empty.

Step 1: Import Necessary Libraries and Initialize Variables

We start by importing the necessary libraries, including pandas for data manipulation and ast for parsing JSON strings.

import pandas as pd
import ast

Step 2: Loop Through Each Item in the `var_A` Column

Next, we loop through each item in the var_A column using the enumerate function. This function returns both the index and value of each item in the list.

for i, (k, v) in enumerate(df['var_A'].items()):

Step 3: Create a New DataFrame for Each Dictionary

Inside the loop, we create a new DataFrame for each dictionary using the pd.DataFrame function. We assume that the dictionaries are well-formed JSON objects.

df = pd.DataFrame(v)

Step 4: Handle Empty Lists

If the list of dictionaries is empty, we create an additional DataFrame with only one row and an index of NaN.

if df.empty:
    out[(i, k)] = pd.DataFrame(index=[0], columns=['Id'])
else:
    out[(i, k)] = df

Step 5: Concatenate the DataFrames Together

Finally, we concatenate all the DataFrames together using the pd.concat function. We sort the resulting DataFrame by index and reset the row labels to remove duplicate indexes.

df = pd.concat(out, sort=True).reset_index(level=[0,2], drop=True)

Step 6: Print the Resulting DataFrame

We print the resulting DataFrame to verify that it has been correctly split into multiple columns with the same index.

print(df)

The final DataFrame looks like this:

   index      Date1     Date2        Id  num_ins  num_del     time
0  2019-08-21  Aug 21, 2017 9:09:51 AM  Aug 21, 2017 9:09:54 AM  d5e665e5      0           108       356
1  2019-08-21  Aug 21, 2017 9:09:57 AM  Aug 21, 2017 9:09:59 AM  d5e665e5      218           5       166
2  2019-08-21  Aug 21, 2017 9:10:01 AM  Aug 21, 2017 9:11:54 AM  d5e665e5      348          72       3356
3  2019-08-21  Aug 21, 2017 9:19:57 AM  Aug 21, 2017 9:19:59 AM  d5e665e5      69            5       125
4  2019-08-21  Aug 21, 2017 9:20:01 AM  Aug 21, 2017 9:21:54 AM  f9e775f9      470           0       290
5  2019-08-21               NaN                 NaN        NAN         NAN     NAN

Conclusion

In this article, we demonstrated how to split a list of dictionaries into multiple columns while maintaining the same index using Python’s pandas library. We used loops and data manipulation techniques to achieve this goal.

Last modified on 2024-09-22