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

Aggregate methods collapse the values of a Series down to a scalar. It allows us to take detailed data and collapse it to a single value. 

## Aggregations

If we want to calculate the mean value of a Series, we can use the aggregation method `mean`:


Aggregate methods collapse the values of a Series down to a scalar. It allows us to take detailed data and collapse it to a single value. 

# Aggregations

If we want to calculate the mean value of a Series, we can use the aggregation method `mean`:


Introduction

Series Deep Dive

DataFrames

Manipulating Data

Wrapping Up

Appendix

Aggregate Methods

Aggregations