5 Best Ways to Create a New DataFrame from an Existing One in Pandas

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💡 Problem Formulation: When working with data in Python, you might encounter a scenario where you need to generate a new DataFrame based on an existing DataFrame using pandas. For instance, this can include creating a subset with specific rows or columns, copying it entirely, or transforming the data in some way. Let’s look at how we can start with a DataFrame named original_df and produce a new DataFrame called new_df that reflects the desired changes.

Method 1: Direct Copy

This method involves creating an exact copy of the original DataFrame. The copy() function is important when you need to ensure that modifications to the new DataFrame do not affect the original one.

Here’s an example:

import pandas as pd

original_df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
new_df = original_df.copy()

print(new_df)

Output:

   A  B
0  1  3
1  2  4

The code snippet above shows the use of the copy() method to create a new DataFrame that is a copy of the original DataFrame. Any changes to new_df will not affect original_df.

Method 2: Selecting Specific Columns

Selecting specific columns to create a new DataFrame is common when you want to focus on a subset of the data. You can select columns using their names.

Here’s an example:

import pandas as pd

original_df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 'C': [5, 6]})
new_df = original_df[['A', 'C']]

print(new_df)

Output:

   A  C
0  1  5
1  2  6

In this code snippet, we selected columns ‘A’ and ‘C’ from the original DataFrame to create a new DataFrame consisting only of these columns.

Method 3: Filtering Rows Using Conditions

Creating a new DataFrame from another based on a condition allows you to filter the rows that meet certain criteria. The new DataFrame will only contain rows for which the condition is True.

Here’s an example:

import pandas as pd

original_df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
new_df = original_df[original_df['A'] > 1]

print(new_df)

Output:

   A  B
1  2  4

The code snippet filters the original DataFrame to include rows where the values in column ‘A’ are greater than 1, thus creating a new DataFrame with the filtered rows.

Method 4: Using the assign() Method for Transformation

The assign() method is useful for adding new columns to a DataFrame or transforming existing ones, without altering the original DataFrame, resulting in a new DataFrame.

Here’s an example:

import pandas as pd

original_df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
new_df = original_df.assign(C=lambda x: x['A'] + x['B'])

print(new_df)

Output:

   A  B  C
0  1  3  4
1  2  4  6

This example demonstrates adding a new column ‘C’ to a new DataFrame by summing up the ‘A’ and ‘B’ columns from the original DataFrame using the assign() method.

Bonus One-Liner Method 5: Slicing Rows

For quickly creating a new DataFrame by selecting a range of rows from an existing DataFrame, slicing using the .iloc[] or .loc[] methods can be very efficient.

Here’s an example:

import pandas as pd

original_df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
new_df = original_df.iloc[0:2]

print(new_df)

Output:

   A  B
0  1  4
1  2  5

This compact code snippet slices the first two rows of the DataFrame to form a new DataFrame using the .iloc[] method.

Summary/Discussion

  • Method 1: Direct Copy. This method creates an independent copy of the DataFrame, which is useful for preserving the original data. However, it might not be necessary if you do not plan to modify the new DataFrame.
  • Method 2: Selecting Specific Columns. This method is efficient for working with a subset of columns. It is straightforward but limited to creating DataFrames with the same row length as the original.
  • Method 3: Filtering Rows Using Conditions. Filtering allows for a focused analysis and is highly flexible based on conditions, but it requires logical operations that might not always be straightforward.
  • Method 4: Using assign() for Transformation. The assign() method makes it easy to create derived columns or transform existing columns, but it might involve lambda functions, which can be less readable for beginners.
  • Method 5: Slicing Rows. Slicing is a simple one-liner method for creating a DataFrame based on row indices; however, it is limited to index-based selections and may not cover more complex data extraction needs.