Learn the best practices and efficient techniques to manipulate and reshape data using pandas.

Machine Learning Essentials

This module 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 module, we’ll learn about the best practices and efficient techniques for working with data. We’ll encounter numerous examples of real-world data analysis problems and will gain hands-on experience with the pandas library to solve them effectively. This module will also cover performance considerations and provide insights on common pitfalls to avoid when using pandas. The module 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 our code. At the end of this module, we’ll be able to effectively handle data using pandas.

Effective Data Manipulation with pandas

## Conversion methods

There are various methods and functions for changing the types of a Series in pandas. We can call `astype` to update column types. Or we can use the `assign` method to return a new DataFrame with the updated type.


The `astype` method allows us to specify the types of each column with a dictionary.

The `assign` method is a key method to master. We specify the name of a column with a keyword argument. If the argument name is an existing column, it will change the values of the column. If the argument name is a new column, it creates a new column. One caveat is that this method returns a new DataFrame and doesn't mutate the existing DataFrame.

We should also know that we can pass in a scalar value, a Series, or a callable as the value for the keyword argument. The callable (a function or lambda) should accept the current state of the DataFrame (this is important when chaining because each step returns a new DataFrame) and should return a scalar or Series.

We saw some examples of these methods in the `tweak_siena_pres` and `int64_to_uint8` functions. 

Here are the relevant snippets:

# Conversion methods

There are various methods and functions for changing the types of a Series in pandas. We can call `astype` to update column types. Or we can use the `assign` method to return a new DataFrame with the updated type.


The `astype` method allows us to specify the types of each column with a dictionary.

The `assign` method is a key method to master. We specify the name of a column with a keyword argument. If the argument name is an existing column, it will change the values of the column. If the argument name is a new column, it creates a new column. One caveat is that this method returns a new DataFrame and doesn't mutate the existing DataFrame.

We should also know that we can pass in a scalar value, a Series, or a callable as the value for the keyword argument. The callable (a function or lambda) should accept the current state of the DataFrame (this is important when chaining because each step returns a new DataFrame) and should return a scalar or Series.

We saw some examples of these methods in the `tweak_siena_pres` and `int64_to_uint8` functions. 

Here are the relevant snippets:

Explore updating columns, creating columns, and changing the types of columns.

Introduction

Series Deep Dive

DataFrames

Manipulating Data

Conclusion

Columns Types, .assign, and Memory Usage

Conversion methods