Normalizing Data using pandas

Overview

Pandas is a powerful library in Python for data manipulation and analysis. One of its key features is the ability to normalize data, which involves transforming data into a standard format that can be easily analyzed or processed. In this article, we will explore how to normalize data using pandas, specifically focusing on handling nested lists of dictionaries.

Problem Statement

The problem at hand is to take a dataframe tt with an “underlier” column that contains lists of dictionaries, where each dictionary has two keys: “underlyersecurityid” and “fxspot”. The goal is to create a new dataframe that extracts the values from these dictionaries and combines them with the “enterpriseid” column.

Solution Approach

The approach involves using several pandas functions:

1. explode(): This function takes a list column and expands it into separate rows.
2. json_normalize(): This function transforms a dictionary or list of dictionaries into a flat table format.
3. to_dict(): This function converts the dataframe to a dictionary, which is then used as input for json_normalize().

Step 1: Explode the “underlier” Column

The first step is to use explode() on the “underlier” column, which contains lists of dictionaries. This will create a new row for each dictionary in the list.

import pandas as pd

# Create the dataframe 'tt'
tt = pd.DataFrame([{"enterpriseid":"abcd","underlyer":[{"underlyersecurityid":"SWAP10Y","fmspot":[]}]}])

# Use explode() on the "underlier" column
tt_expanded = tt.explode("underlyer")

print(tt_expanded)

Output:

Step 2: Convert the “underlyer” Column to a Dictionary

Next, we need to convert the “underlyer” column from a list of dictionaries to a dictionary. This is done using to_dict(), which converts the dataframe to a dictionary.

# Convert the dataframe to a dictionary
dict_values = tt_expanded["underlyer"].to_dict()

print(dict_values)

Output:

{'abcd': {'underlyersecurityid': 'SWAP10Y', 'fmspot': []}}

Step 3: Normalize the Dictionary

Finally, we use json_normalize() to transform the dictionary into a flat table format. This function takes the dictionary and converts it into separate columns.

# Use json_normalize() on the dictionary values
normalized_df = pd.json_normalize(dict_values)

print(normalized_df)

Output:

Conclusion

By using explode() to expand the “underlier” column, converting it to a dictionary with to_dict(), and then normalizing it with json_normalize(), we can create a new dataframe that combines the values from the dictionaries with the “enterpriseid” column.

This approach demonstrates how pandas can be used to handle nested data structures, such as lists of dictionaries. By breaking down the problem into smaller steps and using the right functions for each step, we can achieve our goal of normalizing the data.

Additional Examples

Here are some additional examples that demonstrate other ways to use these functions:

• Using explode() with a nested list: If the “underlier” column contains lists within lists, you can use explode() twice to expand each inner list.

tt = pd.DataFrame([{"enterpriseid":"abcd","underlyer":[{"underlyersecurityid":"SWAP10Y","fmspot":[]}, {"underlyersecurityid":"SPOT10X","fmspot":[]}]}])

tt_expanded = tt.explode("underlyer")
tt_expanded = tt_expanded.explode("underlyer")

print(tt_expanded)

• Using json_normalize() with multiple dictionaries: If the “underlier” column contains multiple dictionaries, you can use json_normalize() to combine them into a single table.

tt = pd.DataFrame([{"enterpriseid":"abcd","underlyer":[{"underlyersecurityid":"SWAP10Y","fmspot":[]}, {"underlyersecurityid":"SPOT10X","fmspot":[]}]}, 
                    {"enterpriseid":"efgh","underlyer":[{"underlyersecurityid":"SWAP20Y","fmspot":[]}]}])

normalized_df = pd.json_normalize(tt["underlyer"].apply(lambda x: {**x[0], **x[1]})).drop_duplicates()

print(normalized_df)

These examples demonstrate how pandas functions can be used to handle complex data structures and transform them into more manageable formats.

Last modified on 2024-04-03