Series in Pandas

A Pandas Series is a one-dimensional labeled array that can hold data of any type, including integers, floats, strings, and even Python objects. It is similar to a column in a spreadsheet or a list in Python, but with additional functionalities like indexing and vectorized operations.

Creating a Pandas Series

To create a Series, first import Pandas:

import pandas as pd

1. Creating a Series from a List

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

print(series)

Output:

0    10
1    20
2    30
3    40
dtype: int64

• The left column (0, 1, 2, 3) is the index.
• The right column (10, 20, 30, 40) is the data.

2. Creating a Series with Custom Index By default, Pandas assigns numeric indices (0, 1, 2, …), but you can define custom index labels:

data = [100, 200, 300]
index_labels = ["A", "B", "C"]

series = pd.Series(data, index=index_labels)
print(series)

Output:

A    100
B    200
C    300
dtype: int64

Now, instead of numeric indices, we have labels A, B, and C.

3. Creating a Series from a Dictionary You can also create a Series using a dictionary:

data = {"Apple": 50, "Banana": 30, "Cherry": 40}
series = pd.Series(data)

print(series)

Output:

Apple     50
Banana    30
Cherry    40
dtype: int64

• The dictionary keys become index labels, and values become the Series data.

Accessing Elements in a Series

1. Accessing Data Using Index

print(series["Apple"])  # Output: 50
print(series[0])  # Output: 50 (if default integer index is used)

2. Slicing a Series

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

3. Filtering Data

print(series[series > 30])  # Filter elements greater than 30

Performing Operations on a Series

Pandas Series supports vectorized operations, meaning you can perform operations on all elements without using loops.

1. Mathematical Operations

print(series * 2)  # Multiply all values by 2
print(series + 10)  # Add 10 to all values

2. Checking for Missing Values

print(series.isnull())  # Returns True for NaN values

3. Applying Functions

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

When to Use a Series?

• When working with single-column data.
• When performing mathematical operations on a single dataset.
• When handling one-dimensional labeled data, like time series or stock prices.