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

## Clipping data

If we have outliers in our data, we might want to use the `clip` method. In the example below, we’ll see the original first 447 entries in the "city" range from 9 to 31:


# Clipping data

If we have outliers in our data, we might want to use the `clip` method. In the example below, we’ll see the original first 447 entries in the "city" range from 9 to 31:


Review other data manipulation techniques provided by pandas.

Introduction

Series Deep Dive

DataFrames

Manipulating Data

Wrapping Up

Appendix

Other Manipulation Methods

Clipping data