Book Image

Python Data Science Essentials

By : Alberto Boschetti, Luca Massaron
Book Image

Python Data Science Essentials

By: Alberto Boschetti, Luca Massaron

Overview of this book

The book starts by introducing you to setting up your essential data science toolbox. Then it will guide you across all the data munging and preprocessing phases. This will be done in a manner that explains all the core data science activities related to loading data, transforming and fixing it for analysis, as well as exploring and processing it. Finally, it will complete the overview by presenting you with the main machine learning algorithms, the graph analysis technicalities, and all the visualization instruments that can make your life easier in presenting your results. In this walkthrough, structured as a data science project, you will always be accompanied by clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.
Table of Contents (13 chapters)

Data loading and preprocessing with pandas

In the previous chapter, we discussed where to find useful datasets and examined basic import commands of Python packages. In this section, having kept your toolbox ready, you are about to learn how to structurally load, manipulate, preprocess, and polish data with pandas and NumPy.

Fast and easy data loading

Let's start with a CSV file and pandas. The pandas library offers the most accessible and complete function to load tabular data from a file (or a URL). By default, it will store the data into a specialized pandas data structure, index each row, separate variables by custom delimiters, infer the right data type for each column, convert data (if necessary), as well as parse dates, missing values, and erroneous values.

In: import pandas as pd
iris_filename = 'datasets-uci-iris.csv'
iris = pd.read_csv(iris_filename, sep=',', decimal='.', header=None, names= ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'target'])

You can specify...