Split / Explode a column of dictionaries into separate columns with pandas

Splitting a column of dictionaries into separate columns with pandas

Are you struggling to split a column of dictionaries into separate columns in a pandas DataFrame? Look no further! In this guide, I'll walk you through a step-by-step solution to this common problem. Let's dive in! 💪

Understanding the problem

You have a pandas DataFrame, df, with a column called Pollutants that contains dictionaries. Your goal is to split this column into separate columns, a, b, and c, with the corresponding values from each dictionary.

However, there are a few challenges you need to address. First, the lists within the dictionaries have different lengths. Second, the order of the keys in the dictionaries is consistent, with 'a' coming first, then 'b', and finally 'c'.

The code that USED to work

Previously, you were using the following code to split the column and create a new DataFrame, df2:

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

However, recently, you encountered an IndexError: out-of-bounds on slice (end) error, indicating that the code is no longer working as expected.

Finding a robust solution

To overcome the issues and find a more robust solution, we'll take a different approach. Let's start by converting the Unicode strings into proper dictionaries.

import ast

# Convert Unicode strings to dictionaries
df['Pollutants'] = df['Pollutants'].apply(ast.literal_eval)

Now that the column values are proper dictionaries, we can easily split them into separate columns.

# Split the Pollutants column into separate columns
df2 = pd.concat([df.drop('Pollutants', axis=1), df['Pollutants'].apply(pd.Series)], axis=1)

Let's break down the code:

• We drop the original Pollutants column from df using df.drop('Pollutants', axis=1) since we will replace it with the new columns.

• We create new columns by applying pd.Series to each value in the Pollutants column using df['Pollutants'].apply(pd.Series).

• Finally, we concatenate the two DataFrames, df.drop('Pollutants', axis=1) and the newly created columns, using pd.concat.

Handling missing values

By default, missing values will be filled with NaN in the new columns. If desired, you can replace these NaN values with another value, such as 0, using the fillna() method:

df2.fillna(0, inplace=True)

Summary

To recap, here are the steps to split a column of dictionaries into separate columns in a pandas DataFrame:

1. Convert Unicode strings to dictionaries using ast.literal_eval.

2. Use pd.concat and pd.Series to split the column into separate columns.

3. Optionally, handle missing values using fillna().

Your turn to try it out!

Now it's your turn to give it a shot! Apply these steps to your code and see if it solves your issue. Make sure to let me know in the comments if you encounter any difficulties or have any questions. 🤔

So go ahead and give it a try! Happy coding! 💻✨