5 Best Ways to Create a New Indexed DataFrame from an Original in Pandas

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💡 Problem Formulation: When working with pandas DataFrames in Python, there are situations where you might need to retain the original data but enforce a new index onto the DataFrame. For example, you might have input data indexed by time but require re-indexing based on a unique identifier. This article explores methods to create a new DataFrame using the original data and set a new index.

Method 1: Using the set_index() Method

This method is straightforward and involves replacing the current index of the DataFrame with a new one using the set_index() function. This is beneficial when the new index is already a column within the DataFrame.

Here’s an example:

import pandas as pd

# Original DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

# New index
new_index = [10, 11, 12]

# Set the new index
df_indexed = df.set_index([new_index])

print(df_indexed)

Output:

    A  B
10  1  4
11  2  5
12  3  6

The above code replaces the original index of the DataFrame df with new_index. The set_index() method assigns the list new_index as the new index for the DataFrame.

Method 2: Reassigning the Index Attribute

If you have a predetermined list or array that represents the new index, you can directly assign it to df.index. This method is quick and does not require any additional functions.

Here’s an example:

import pandas as pd

# Original DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

# Assigning a new index
df.index = [101, 102, 103]

print(df)

Output:

      A  B
101  1  4
102  2  5
103  3  6

The code snippet demonstrates assigning a new index to the DataFrame by setting the df.index property directly. This method is less flexible but fast for straightforward reindexing tasks.

Method 3: Using the reindex() Method

The reindex() method is used to create a new DataFrame with the desired index. This approach is flexible as it allows for the insertion of missing values if the new index contains labels not present in the original DataFrame.

Here’s an example:

import pandas as pd

# Original DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

# New index
new_index = [100, 101, 102]

# Using `reindex`
df_reindexed = df.reindex(new_index)

print(df_reindexed)

Output:

       A    B
100  NaN  NaN
101  NaN  NaN
102  NaN  NaN

In this code, the reindex() function creates a new DataFrame with the specified new_index. Because the new index labels do not match the original index, NaN (Not a Number) values are inserted.

Method 4: Creating a DataFrame with a Custom Index During Initialization

This method involves specifying a custom index at the time of DataFrame initialization. If you’re creating a DataFrame from scratch, you can set the index as part of the DataFrame constructor.

Here’s an example:

import pandas as pd

# New index
new_index = [999, 1000, 1001]

# Creating a DataFrame with a custom index
df_with_custom_index = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
}, index=new_index)

print(df_with_custom_index)

Output:

        A  B
999   1  4
1000  2  5
1001  3  6

The DataFrame df_with_custom_index is created with the new_index directly passed to the constructor. This provides a clean and explicit way to define the index.

Bonus One-Liner Method 5: Using the .assign() Method and Chaining

The .assign() method along with method chaining allows you to create a new DataFrame and assign a new index in a single line of code. This approach is elegant and concise for simple operations.

Here’s an example:

import pandas as pd

# Creating and indexing a DataFrame in one line
df_one_liner = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
}).set_index([100, 101, 102])

print(df_one_liner)

Output:

      A  B
100  1  4
101  2  5
102  3  6

This snippet combines DataFrame creation and the set_index() method in a single chain of operations, resulting in efficient and readable code for simple reindexing.

Summary/Discussion

  • Method 1: Using set_index(). Ideal for when the new index is a column in the DataFrame. Straightforward and widely used. Only downside is when the new index is external, it must be added to the DataFrame first.
  • Method 2: Reassigning the Index Attribute. Best for quick, direct index changes without additional functions. However, there’s limited flexibility and it must fit the DataFrame size exactly.
  • Method 3: Using reindex(). Offers flexibility to handle non-matching indices with automatic NaN value insertion. Drawbacks include potential confusion with NaN values and possibly undesired data manipulation.
  • Method 4: Initializing with Custom Index. Clean and explicit, it’s used when creating a DataFrame from scratch with a known index. It’s less useful for existing DataFrames.
  • Method 5: Using .assign() with Chaining. Provides a concise, one-liner option for creating and indexing a DataFrame simultaneously. However, it’s limited in flexibility and can become less readable with complex DataFrame operations.