5 Best Ways to Copy a Pandas Series in Python

💡 Problem Formulation: When working with Pandas in Python, a common need is to create a copy of a Series object. This could be necessary for a variety of reasons, such as needing to preserve the original data before applying transformations, or to avoid altering data shared across multiple DataFrame columns. For example, our input may be a Series object ser, and the goal is to create a new Series object new_ser that is a copy of ser, but independent from the original data.

Method 1: Using the copy method

Pandas Series objects have a copy() method designed specifically to copy the Series. The method creates a deep copy of the Series by default, meaning that the data is fully copied and changes made to the new Series do not affect the original. It’s part of the pandas.core.series.Series API and is the most straightforward and explicit way to copy a Series.

Here’s an example:

import pandas as pd
ser = pd.Series([1, 2, 3])
new_ser = ser.copy()

Output:

0    1
1    2
2    3
dtype: int64

This code snippet demonstrates how to create a copy of a Pandas Series. We first import Pandas and create a Series ser. Then, we use the copy() method to make a new Series new_ser that is a copy of the original. Making changes to new_ser will not affect ser.

Method 2: Using the deep=False Argument

If you only need a shallow copy of the Series, you can use the copy() method with the deep=False argument. A shallow copy does not create a new object for the data itself; instead, it creates a new Series with a reference to the original data. This method should be used with caution, as changes to the data in the new Series will reflect in the original Series.

Here’s an example:

shallow_copy_ser = ser.copy(deep=False)

Output:

0    1
1    2
2    3
dtype: int64

The code above demonstrates using a shallow copy in Pandas. By passing deep=False to the copy() method, the Series shallow_copy_ser is created with a reference to the data of the original Series ser. Modifying the copied Series would alter the original Series as well.

Method 3: Copy by Assignment

Simply assigning the Series to a new variable will create a shallow copy similar to using the copy() method with deep=False. This method does not inherently indicate intention and can lead to unintentional manipulation of the original Series if not used carefully.

Here’s an example:

assigned_ser = ser

Output:

0    1
1    2
2    3
dtype: int64

This method assigns the original Series ser to a new variable assigned_ser. While this is the simplest form of copying, it does not create an independent copy. Any changes to assigned_ser will directly affect the original ser.

Method 4: Using Pandas’s Series() Constructor

A new Series object can be created using the Pandas Series() constructor, passing in the original Series. This creates a deep copy of the Series by default, similar to using the copy() method without any arguments. The new Series will be independent of the original.

Here’s an example:

constructed_ser = pd.Series(ser)

Output:

0    1
1    2
2    3
dtype: int64

By passing the original Series ser into the pd.Series() constructor, we create a new, independent Series constructed_ser. Any modifications to constructed_ser will not impact ser.

Bonus One-Liner Method 5: Copying with List Comprehension

While not common, a Series can technically be copied using list comprehension to create a new list of data, which is then fed into the Series constructor. However, this method is less explicit and clear in terms of data copying semantics.

Here’s an example:

comprehended_ser = pd.Series([x for x in ser])

Output:

0    1
1    2
2    3
dtype: int64

This quirky one-liner creates a copy of the Series using list comprehension to iterate over the original Series ser, and construct a new list that is then passed to the pd.Series() constructor. The new Series comprehended_ser is a deep copy of the original.

Summary/Discussion

• Method 1: Using the copy method. Most explicit and clear. Creates a deep copy by default. Recommended for ensuring no unintended side effects.
• Method 2: Using the deep=False Argument. Creates a shallow copy. Changes to the copy affect the original. Use with caution.
• Method 3: Copy by Assignment. Simplest but potentially misleading. Changes to the new variable affect the original Series.
• Method 4: Using Pandas’s Series() Constructor. Explicit, creates a deep copy. It is redundant when compared to Method 1.
• Bonus One-Liner Method 5: Copying with List Comprehension. Non-traditional, less clear. Creates a deep copy but not recommended for readability and maintenance.