Learn to structure your data more effectively and do some basic analysis: group, apply functions, and more.

Python Data Analysis and Visualization

Data Analysis & Processing with Pandas

## What is `pandas`?

Pandas is an open-source Python library that provides powerful, flexible and high-performance tools to process data. It was developed in 2008 by Wes McKinney. 

Some of its key features are as follows:

- Provides high-performance DataFrame objects with effective indexing.
- Provides tools for loading data into memory with multiple file formats.
- Provides high performance for operations such as data merges and joins.
- Supports manipulation of time series data.
- Easy to manipulate row and column data.
- Supports SQL-like operations. 
- Supports vectorized operations.
- Provides label-based slicing, indexing, and subsetting of large data sets.

The standard Python installation doesn't contain this library, so you can install it using `pip` as below.

```
pip install pandas
```

# What is `pandas`?

Pandas is an open-source Python library that provides powerful, flexible and high-performance tools to process data. It was developed in 2008 by Wes McKinney. 

Some of its key features are as follows:

- Provides high-performance DataFrame objects with effective indexing.
- Provides tools for loading data into memory with multiple file formats.
- Provides high performance for operations such as data merges and joins.
- Supports manipulation of time series data.
- Easy to manipulate row and column data.
- Supports SQL-like operations. 
- Supports vectorized operations.
- Provides label-based slicing, indexing, and subsetting of large data sets.

The standard Python installation doesn't contain this library, so you can install it using `pip` as below.

```
pip install pandas
```

Learn about the data manipulation library pandas in this lesson.

Introduction

Basic Operations on DataFrames

Other topics

Improve performance

Introduction

What is `pandas`?

Introduction

Basic Operations on DataFrames

Other topics

Improve performance

Introduction

What is pandas?

What is `pandas`?