Pandas is a powerful Python library that provides high-performance, easy-to-use data structures and data analysis tools. This documentation page is dedicated to providing you with essential information about Pandas, including its features, installation, and basic usage.

Features of Pandas

  • Data Structures: Pandas provides two primary data structures: Series and DataFrame. These structures allow you to handle structured data efficiently.
  • Data Manipulation: With Pandas, you can perform a wide range of data manipulation tasks, such as sorting, filtering, and aggregating data.
  • Data Analysis: Pandas makes it easy to perform complex data analysis tasks, including statistical analysis, time series analysis, and more.

Installation

To install Pandas, you can use pip:

pip install pandas

For more detailed installation instructions, please visit the official Pandas installation guide.

Basic Usage

Here's a simple example to get you started with Pandas:

import pandas as pd

# Create a DataFrame
data = {'Name': ['Tom', 'Nick', 'John'], 'Age': [20, 21, 19]}
df = pd.DataFrame(data)

# Display the DataFrame
print(df)

For more examples and tutorials, check out the Pandas tutorial section.

Images

  • Pandas_Logo
  • DataFrame_Example

By using Pandas, you can effectively handle and analyze your data. Happy coding!