Pandas Series

What Is a Pandas Series?

A Pandas Series is a one-dimensional labeled array capable of holding data of any type such as integers, floats, strings, or Python objects. Each value in a Series is associated with an index, which allows for fast and intuitive data access.

You can think of a Series as a single column of a table with row labels.

Why Use Pandas Series?

A Pandas Series is useful because it:

• Stores labeled data

• Supports fast indexing and slicing

• Handles missing values (NaN) gracefully

• Performs vectorized operations

• Integrates seamlessly with NumPy

Creating a Pandas Series

1. Create Series from a List

import pandas as pd s = pd.Series([10, 20, 30, 40]) print(s) # Output: # 0 10 # 1 20 # 2 30 # 3 40 # dtype: int64

If no index is provided, Pandas assigns a default integer index starting from 0.

2. Create Series with Custom Index

import pandas as pd

s = pd.Series([100, 200, 300], index=["a", "b", "c"]) print(s) # Output: # a 100 # b 200 # c 300 # dtype: int64

Custom indices make data more meaningful and readable.

3. Create Series from a Dictionary

import pandas as pd data = {"Math": 85, "Science": 90, "English": 88} s = pd.Series(data) print(s) # Output: # Math 85 # Science 90 # English 88 # dtype: int64

Dictionary keys become index labels, and values become Series values.

4. Create Series with a Scalar Value

import pandas as pd s = pd.Series(5, index=["a", "b", "c"]) print(s) # Output: # a 5 # b 5 # c 5 # dtype: int64

The scalar value is repeated for each index label.

Accessing Elements in a Series

Access by Index Position

import pandas as pd s = pd.Series([10, 20, 30, 40]) print(s[0]) print(s[2]) # Output: # 10 # 30

Access by Index Label

import pandas as pd s = pd.Series([100, 200, 300], index=["a", "b", "c"]) print(s["b"]) # Output: # 200

Series Operations

Pandas Series supports vectorized operations, meaning operations are applied element-wise.

import pandas as pd s = pd.Series([1, 2, 3, 4]) print(s + 10) # Output: # 0 11 # 1 12 # 2 13 # 3 14 # dtype: int64

Handling Missing Values

Pandas automatically handles missing data using NaN.

import pandas as pd s = pd.Series([10, 20, None, 40]) print(s) # Output: # 0 10.0 # 1 20.0 # 2 NaN # 3 40.0 # dtype: float64

Useful Series Attributes

Difference Between Pandas Series and NumPy Array

Key Points to Remember

• A Series is a one-dimensional labeled array

• Index labels make data access intuitive

• Supports vectorized operations

• Handles missing values efficiently

• Forms the foundation of Pandas DataFrames