Create a Pandas Series from Array (original) (raw)

Last Updated : 17 Mar, 2025

A **Pandas Series is a one-dimensional labeled array that stores various data types, including numbers (integers or floats), strings, and Python objects. It is a fundamental data structure in the Pandas library used for efficient data manipulation and analysis. In this guide we will explore two simple methods to create a Pandas **Series from a NumPy array.

Creating a Pandas Series Without an Index

By default when you create a Series from a NumPy array Pandas automatically assigns a numeric index starting from **0. Here pd.Series(data) converts the array into a **Pandas Series automatically assigning an index.

Python `

import pandas as pd import numpy as np

data = np.array(['a', 'b', 'c', 'd', 'e'])

s = pd.Series(data) print(s)

`

**Output:

**Explanation:

• The default index starts from **0 and increments by **1.
• The data type (dtype: object) means it stores text values

Creating a Pandas Series With a Custom Index

In this method we specify custom indexes instead of using Pandas’ default numerical indexing. This is useful when working with structured data, such as **employee IDs, timestamps, or product codes where meaningful indexes enhance data retrieval and analysis.

Python `

import pandas as pd import numpy as np

data = np.array(['a', 'b', 'c', 'd', 'e'])

s = pd.Series(data, index=[1000, 1001, 1002, 1003, 1004])

print(s)

`

**Output:

**Explanation:

• Custom indexes (**1000, 1001, 1002…) replace the default ones and allow meaningful data representation.
• Custom indexing enhances **data retrieval, and make easier to access specific values directly using meaningful labels (e.g., s[1002] instead of s[2]).

Creating a Pandas Series from a NumPy array is simple and efficient. You can use the default index for quick access or assign a custom index for better data organization.

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