Unlocking the Power of Indexing & Slicing in Pandas Series

Welcome! to the ultimate guide on “Unlocking the Power of Indexing & Slicing in Pandas Series”. In this session, we will learn how to access and manipulate data using indexing & slicing techniques.

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Now, Let’s dive into the topic!

Indexing in Pandas Series

Indexing is used to select a specific row or column from a DataFrame. You can use various indexing techniques such as loc, iloc to select rows or columns. We use square bracket notation to perform indexing.

Using Integer Indexing:

import pandas as pd

# Create a Pandas Series
data = pd.Series([10, 20, 38, 40, 50])

# Access the element at index position 2
print(data[2])  # Output: 38

Using Custom Index Label:

import pandas as pd

# Create a Pandas Series with custom index labels
data = pd.Series([10, 20, 28, 40, 50], index=['A', 'B', 'C', 'D', 'E'])

# Access the element with index label 'C'
print(data['C'])  # Output: 28

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Slicing

Slicing is used to select a subset of rows or columns from a Series or a DataFrame.

Slicing with Integer Positions:

import pandas as pd

# Create a Pandas Series
data = pd.Series([10, 20, 32, 40, 50])

# Slice elements from index position 1 to 3 (exclusive)
print(data[1:3])  # Output: 20, 32

Slicing with Custom Index Lables:

import pandas as pd

# Create a Pandas Series with custom index labels
data = pd.Series([10, 20, 30, 45, 50], index=['A', 'B', 'C', 'D', 'E'])

# Slice elements from index label 'B' to 'D' (inclusive)
print(data['B':'E'])  # Output: 20, 30, 45

Slicing with Step Size:

import pandas as pd

# Create a Pandas Series
data = pd.Series([11, 22, 33, 44, 55])

# Slice elements with a step size of 2
print(data[::2])  # Output: 11, 33, 55

NOTE:

Indexing & Slicing in Pandas Series are zero-based, meaning the first element has index 0, the second has index 1, and so on. Understanding these concepts are important while working with data analysis or data manipulation.

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