Delve into effective data manipulation with pandas. Discover filtering, merging, grouping, and reshaping techniques through hands-on examples, best practices, and performance tips for real-world analysis.

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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

We have multiple syntaxes to creat pivot tables in pandas. One of these uses the `pivot_table` method, another common one leverages the `groupby` method, and we can also represent some of these operations with the `pd.crosstab` function.

# A basic example
When our boss asks us to get numbers "by X column," that should be a hint to pivot (or group) our data. Assume the boss asked, "What is the average age by country for each employment status?" This is like one of those word 
problems that we had to learn to do in math class, where we needed to translate the words into math operations. In this case, we need to pick a pandas operation and map the problem into it.

The problem can be resolved into:

* Put the country in the index.
* Have a column for each employment status.
* Put the average age in each cell.

## Using `pivot_table`
These map cleanly to the parameters of the `pivot_table` method. One solution would look like this:

Explore one of the most powerful options for data manipulations: pivot tables.

Introduction

Series Deep Dive

DataFrames

Manipulating Data

Wrapping Up

Appendix

Reshaping by Pivoting and Grouping

A basic example