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

## Similar to NumPy
The Series object behaves similarly to a NumPy array. As shown below, both types respond to index operations:

import numpy as np
import pandas as pd

songs3 = pd.Series([145, 142, 38, 13], name='counts', index=['Paul', 'John', 'George', 'Ringo'])
numpy_ser = np.array([145, 142, 38, 13])

print(songs3[1])
print(numpy_ser[1])

print(songs3.mean())
print(numpy_ser.mean())

They also both have a notion of a boolean array. A **boolean array** is a Series with the same index as the Series we're working with and has boolean values. It can be used as a mask to filter out items. Normal Python lists do not support such fancy index operations, like sticking a list into an index operation.

# Similar to NumPy
The Series object behaves similarly to a NumPy array. As shown below, both types respond to index operations:

Learn about the similarities between pandas and NumPy.

Introduction

Series Deep Dive

DataFrames

Manipulating Data

Conclusion

Similarities in pandas and NumPy

Similar to NumPy