pandas.tar.gz

pandas

jupyter

jupyter-display

Python

pandas-with-sqlite3

jupyter-live

The course is a comprehensive guide to using pandas for data analysis. It covers a wide range of topics related to data manipulation, including filtering, merging, grouping, pivoting, and reshaping data.

In this course, you’ll learn about the best practices and efficient techniques for working with data. You’ll be provided with numerous examples of real-world data analysis problems and gain hands-on experience with the pandas library to solve them effectively. This course will also cover performance considerations and provide insights on common pitfalls to avoid when using pandas.

The course is suitable for both beginners and experienced users of pandas. It includes detailed explanations of pandas concepts and functions, as well as tips and tricks for optimizing your code. By the time you’re done with this course, you’ll be able to effectively handle data using pandas.

Effective Data Manipulation with pandas

## Feather

Here's an option that is a relative newcomer. Feather is a binary file format for persisting columnar data that is found in DataFrames. This is not a surprise because the creator of pandas works on it. Feather tends to be fast, and it keeps type information (for the most part). It's also supposed to be supported by other languages in case we need to deal with processing data in R, Julia, or others.

> **Note:** We'll need to install the Feather format library to leverage this functionality.

Let’s try exporting our data:

# Feather

Here's an option that is a relative newcomer. Feather is a binary file format for persisting columnar data that is found in DataFrames. This is not a surprise because the creator of pandas works on it. Feather tends to be fast, and it keeps type information (for the most part). It's also supposed to be supported by other languages in case we need to deal with processing data in R, Julia, or others.

> **Note:** We'll need to install the Feather format library to leverage this functionality.

Let’s try exporting our data:

Explore how to export data to Feather,  JSON, and SQL formats.

Introduction

Series Deep Dive

DataFrames

Manipulating Data

Wrapping Up

Appendix

Exporting Data to Feather, SQL, and JSON

Feather