A Pandas Series is a one-dimensional array that can hold data of any type (integers, strings, floats, etc.) and comes with labeled indexes, similar to a column in a spreadsheet or a dictionary.

Creating a Pandas Series

1. Creating a Series from a List

import pandas as pd

data = [10, 20, 30, 40]
series = pd.Series(data)

print(series)

Output:

0    10
1    20
2    30
3    40
dtype: int64

Here, the default index is numeric (0, 1, 2, …).

2. Creating a Series with Custom Index

data = [10, 20, 30, 40]
index_labels = ['a', 'b', 'c', 'd']
series = pd.Series(data, index=index_labels)

print(series)

Output:

a    10
b    20
c    30
d    40
dtype: int64

The index can be customized, making it similar to a dictionary.

3. Creating a Series from a Dictionary

data = {'Alice': 85, 'Bob': 90, 'Charlie': 78}
series = pd.Series(data)

print(series)

Output:

Alice      85
Bob        90
Charlie    78
dtype: int64

When using a dictionary, the keys become the index.

Accessing Elements in a Series

1. Access by Index Position

print(series[0])  # First element

2. Access by Index Label

print(series['Bob'])  # 90

3. Slicing a Series

print(series[0:2])  # First two elements

Output:

Alice    85
Bob      90
dtype: int64

Operations on Pandas Series

1. Arithmetic Operations

series = pd.Series([10, 20, 30, 40])
print(series + 5)  # Add 5 to each element

Output:

0    15
1    25
2    35
3    45
dtype: int64

2. Applying Functions

print(series.apply(lambda x: x * 2))  # Multiply each element by 2

Checking for Null Values

data = [10, 20, None, 40]
series = pd.Series(data)

print(series.isnull())  # Check for missing values
print(series.notnull())  # Check for non-null values

Summary of Pandas Series

✅ One-dimensional labeled array
✅ Supports various data types
✅ Custom index support
✅ Supports dictionary-like operations
✅ Built-in functions for data manipulation