📊 Day 28 of #100DaysOfCode in Python: Advancing with Pandas

On Day 28, we bolster our data analysis prowess by diving into Pandas, a cornerstone library in the Python data analysis ecosystem. It’s revered for its ability to provide high-level data structures and tools that streamline the process of data manipulation and analysis.

1. Pandas: A Brief Overview

Pandas stand for “Python Data Analysis Library”. It is open-source and offers highly performant, easy-to-use data structures. It forms the backbone of many Python-based data science tasks.

2. Core Components of Pandas: Series and DataFrames

• Series: A one-dimensional labeled array capable of holding data of any type.
• DataFrame: A two-dimensional labeled data structure with columns that can be of different types, akin to a spreadsheet or SQL table.

3. DataFrames: The Heart of Pandas

Creating a DataFrame is straightforward:

import pandas as pd

data = {
    'Name': ['John', 'Anna', 'Peter', 'Linda'],
    'Age': [28, 22, 34, 41],
    'City': ['New York', 'Paris', 'Berlin', 'London']
}

df = pd.DataFrame(data)

4. Indexing and Selecting Data