Pandas Library Introduction

Pandas is a powerful Python library providing high-performance, easy-to-use data structures and data analysis tools. It is widely used in data science, finance, and many other fields. This library makes data manipulation and analysis more efficient and easier to understand.

Key Features

• DataFrame: A two-dimensional tabular data structure with labeled axes (rows and columns).
• Series: One-dimensional labeled array capable of holding any data type.
• Data Loading and Cleaning: Functions to load data from various file formats like CSV, Excel, JSON, and more.
• Data Aggregation and Transformation: Functions for grouping, summarizing, and reshaping data.

Quick Start

To get started with Pandas, you can install it using pip:

pip install pandas

After installation, you can import Pandas and create a DataFrame:

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'Los Angeles', 'Chicago']
}

df = pd.DataFrame(data)
print(df)

Example

Here is a simple example of how to filter and sort data using Pandas:

filtered_df = df[df['Age'] > 28]
sorted_df = filtered_df.sort_values(by='City')
print(sorted_df)

Pandas DataFrame Example

For more information and tutorials, please visit our Pandas Documentation.

If you're looking to expand your knowledge on Pandas, we recommend checking out our comprehensive Pandas Tutorial.