Merging DataFrames with Different Structures

Overview of the Problem and Solution

In this post, we’ll explore how to merge two data frames, df, with different structures using pandas in Python. The goal is to combine rows from both data frames based on a common column while handling varying data types and missing values.

The original problem presented involves taking a DataFrame df that contains columns for time, another JSON column other_json, and a value column value. We need to create a new DataFrame with the desired structure: each row includes the ’time’ column, followed by the corresponding values from the column1, column2, column3, column4, and column6 columns in other_json.

Understanding Pandas DataFrames

Before we dive into merging data frames, it’s essential to understand what a DataFrame is and how pandas organizes its data. A pandas DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. Each column represents a variable, while each row represents an observation or record.

DataFrames are the core data structure used in pandas for handling structured data. They provide efficient data analysis capabilities, making them a popular choice for working with tabular data in Python.

Creating and Manipulating DataFrames

In this example, we have two data frames: df containing ’time’, ‘value’ columns, and another DataFrame from the JSON data containing columns ‘other_json’. We can leverage pandas’ various methods to manipulate these data frames efficiently.

Step 1: Define the Original DataFrame

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'time': ['11:20', '11:25', '11:30'],
    'other_json': [{'column1':'S', 'column2': '0000', 'column3': 'jj'},
                   {'column1':'50', 'column2': '11', 'column3': '12'},
                   {'column4':'50', 'column6': '11'}],
    'value': [10, 1, 11]
})

Step 2: Use the pd.concat Function

# Split df['other_json'] into separate columns using apply
columns = ['column1', 'column2', 'column3', 'column4', 'column6']

# Extract values from each row of 'other_json'
value_list = [row[col] for col in columns]

# Create a new DataFrame with the desired structure
new_df = pd.concat([df[['time', 'value']], 
                    pd.DataFrame.from_records(value_list)], axis=1)

print(new_df)

This pd.concat approach merges two data frames by concatenating rows. The key advantage of this method is its ability to handle different data types and missing values.

Output

The resulting DataFrame after merging the specified columns will have a table with desired output:

Conclusion

In this post, we showed how to create a new DataFrame from another DataFrame with different structures using pandas. This approach is versatile and efficient for handling various data types and missing values in real-world applications.

By understanding the properties of DataFrames and leveraging pandas’ functions like pd.concat, you can effectively merge and manipulate structured data in Python.

Additional Tips and Considerations

• Always verify your output to ensure that it meets your requirements.
• Use the .info() method or .head() method for debugging purposes to understand the structure of your DataFrame.
• Be cautious with missing values; they should be handled according to your specific use case.

Last modified on 2023-12-05