Pandas Boolean Condition from Nested List of Dictionaries

When working with pandas dataframes, it’s common to encounter nested lists of dictionaries. In this post, we’ll explore how to perform a boolean condition on the ’type’ field within these nested structures.

Background

A pandas dataframe is a two-dimensional table of data with rows and columns. Each column represents a variable, while each row represents an observation. Dataframes can be created from various sources, such as CSV files, SQL databases, or even other dataframes.

In this specific case, we’re dealing with a nested list of dictionaries, where each dictionary has several key-value pairs, including ‘id’, ’type’, ’tx’, and ‘sector’. The ’type’ field is particularly interesting because it’s a string value that can take on the values ‘salary’, ‘other’, or even None.

Problem Statement

Given a pandas column containing information stored as a nested list of dictionaries, we want to perform a boolean condition to determine whether:

1. There are more than one ’type’ values equal to ‘salary’.
2. The value in the ’tx’ field differs between any two ’type’ values equal to ‘salary’.

Flattening and Filtering

One approach is to flatten the nested list of dictionaries using list comprehension or the pd.json_normalize function.

import pandas as pd

# Create a sample dataframe with nested lists of dictionaries
data = [{'id': 123, 'type': 'salary', 'tx': 'house', 'sector': 'EU'},
        {'userid': 123, 'type': 'salary', 'tx': 'office', 'transition': [{'id': 'hash', 'id': 123, 'type': 'salary', 'tx': 'house'}]},
        {'id': 456, 'type': 'other', 'tx': 'home', 'sector': 'EU'}]

df = pd.DataFrame(data)

# Flatten the nested list of dictionaries
a = df.iloc[0].info

values = [item for sublist in [[list(i.values()) for i in a]][0]for item in sublist]

However, this approach does not solve our problem because we need to consider the ’type’ field and its corresponding value in the ’tx’ field.

Solution

The solution involves defining a function that takes an individual dictionary as input and returns a boolean value based on the conditions specified. We then apply this function to each element of the nested list using the apply method.

import pandas as pd

def check(x):
    total_salary = sum([1 if i['type']=='salary' else 0 for i in x]) # get count of "type": "salary" matches
    tx_list = [i['tx'] for i in x if i['type']=='salary'] # get tx values when type==salary
    tx_check = tx_list.count(tx_list[0]) != len(tx_list) # check all values are same in tx_list
    return total_salary > 1 and tx_check

df['check'] = df['info'].apply(check)

This solution works as follows:

• The check function takes an individual dictionary x as input.
• It counts the number of ’type’ values equal to ‘salary’ by summing up 1 for each matching value and 0 otherwise.
• It extracts the tx values corresponding to the ’type’ values equal to ‘salary’.
• It checks whether all tx values are the same by comparing the count of unique tx values with the total number of tx values.
• The function returns True if both conditions are met (i.e., there’s more than one ’type’ value equal to ‘salary’, and the tx values differ between any two such ’type’ values) and False otherwise.

By applying this function to each element of the nested list using the apply method, we can perform the desired boolean condition on the dataframe.

Example Use Cases

This solution is useful in various scenarios where you need to analyze data with complex structures. Here are a few examples:

• Salary Analysis: Imagine you have a dataset containing employee information, including their salary and work location. You might want to identify employees with salaries above a certain threshold or those working in specific locations.
• Customer Segmentation: Suppose you have customer data stored as a nested list of dictionaries. You could use this solution to categorize customers based on their demographic characteristics, such as age, income, or occupation.
• Inventory Management: In an e-commerce setting, you might have product information stored in a nested structure. This solution can help you analyze inventory levels, track stock movements, and optimize storage spaces.

Conclusion

In conclusion, this post demonstrated how to perform a boolean condition on the ’type’ field within a pandas column containing nested lists of dictionaries. By defining a custom function that takes an individual dictionary as input, we can efficiently apply this logic to the dataframe using the apply method. This approach is applicable in various real-world scenarios where data analysis and manipulation are crucial.

Last modified on 2023-09-09