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 processing with NumPy

Having introduced the essential pandas commands to upload and preprocess your data in memory completely, or even in smaller batches (or in single data rows), you'll have to work on it in order to prepare a suitable data matrix for your supervised and unsupervised learning procedures.

As a best practice, we suggest that you divide the task between a phase of your work when your data is still heterogeneous (a mix of numerical and symbolic values) and another phase when it is turned into a numeric table of data arranged in rows that represent your examples, and columns that contain the characteristic observed values of your examples, which are your variables.

In doing so, you'll have to wrangle between two key Python packages for scientific analysis, pandas and NumPy, and their two pivotal data structures, DataFrame and ndarray.

Since the target data structure is a NumPy ndarray object, let's start from the result we want to achieve.

NumPy's n-dimensional array