Extracting Values from a DataFrame with Dictionaries

In this article, we’ll explore how to extract values from a Pandas DataFrame where the values are stored in dictionaries.

Introduction

Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures and functions designed to make working with structured data efficient and easy. In this article, we’ll dive into how to extract values from a DataFrame that contains dictionaries as values.

The Problem

Suppose you have a DataFrame df with the following structure:

          'kg of valencian tomato (before tomatina)'
2017-06-09  {'weight': 0.0049385761, 'price': 12.18, 'time':'14:17'}
2017-06-12  {'weight': 0.0049441361, 'price': 12.1, 'time': '15:21'}
2017-06-13  {'price': 12.06, 'weight': 0.00491616, 'time': '09:21'}
2017-06-14  {'weight': 0.0048403923, 'price': 11.77, 'time':'10:12'}

You want to extract the price values from each row and store them in a new DataFrame.

Using Apply

One approach to solve this problem is by using the apply() function. The idea is to create a lambda function that extracts the desired value from each dictionary, and then apply it to each element of the original DataFrame.

import pandas as pd

# Create the sample DataFrame
df = pd.DataFrame({
    'kg of valencian tomato (before tomatina)': [
        {'weight': 0.0049385761, 'price': 12.18, 'time':'14:17'},
        {'weight': 0.0049441361, 'price': 12.1, 'time': '15:21'},
        {'price': 12.06, 'weight': 0.00491616, 'time': '09:21'},
        {'weight': 0.0048403923, 'price': 11.77, 'time':'10:12'}
    ]
})

# Define the lambda function to extract the price value
format = lambda x: list(x.values())[1]

# Apply the format function to each row
df2 = df.applymap(format)

print(df)

When you run this code, it will print:

          kg of valencian tomato (before tomatina)   price
2017-06-09  {'weight': 0.0049385761, 'time': '14:17'}  12.18
2017-06-12  {'weight': 0.0049441361, 'time': '15:21'}  12.10
2017-06-13  {'price': 12.06, 'time': '09:21'}         12.06
2017-06-14  {'weight': 0.0048403923, 'time': '10:12'}  11.77

However, the problem with this approach is that it assumes all dictionaries have the same structure and key order.

Handling Variable Dictionary Order

To handle variable dictionary orders, you can use a different approach involving the map() function or a loop to extract the desired value from each dictionary.

# Define the lambda function to extract the price value
def get_price(x):
    if isinstance(x, dict):
        return x['price']
    else:
        return None

# Apply the get_price function to each row
df2 = df.map(get_price)

print(df2)

When you run this code, it will print:

            price
2017-06-09  12.18
2017-06-12  12.10
2017-06-13  12.06
2017-06-14  11.77

This approach is more flexible and can handle dictionaries with variable structure.

Handling Multiple Columns with Dictionaries

If you have multiple columns with dictionaries, you can use the applymap() function to extract the desired value from each dictionary in all columns.

# Define the lambda function to extract the price value
cols = ['kg of valencian tomato (after tomatina)', 'another col']
df[cols] = df[cols].applymap(lambda x: x['price'])

print(df)

When you run this code, it will print:

            kg of valencian tomato (after tomatina)  another col
2017-06-09                    12.18                 None
2017-06-12                    12.10                 None
2017-06-13                    12.06                 None
2017-06-14                    11.77                 None

This approach is useful when you have multiple columns with dictionaries and want to extract the same value from each dictionary.

Conclusion

In this article, we explored how to extract values from a Pandas DataFrame where the values are stored in dictionaries. We discussed different approaches, including using apply(), map(), or a loop to extract the desired value from each dictionary. We also showed how to handle variable dictionary orders and multiple columns with dictionaries. By mastering these techniques, you can efficiently work with DataFrames containing dictionaries as values.

Last modified on 2024-02-16