Book Image

Become a Python Data Analyst

By : Alvaro Fuentes
Book Image

Become a Python Data Analyst

By: Alvaro Fuentes

Overview of this book

Python is one of the most common and popular languages preferred by leading data analysts and statisticians for working with massive datasets and complex data visualizations. Become a Python Data Analyst introduces Python’s most essential tools and libraries necessary to work with the data analysis process, right from preparing data to performing simple statistical analyses and creating meaningful data visualizations. In this book, we will cover Python libraries such as NumPy, pandas, matplotlib, seaborn, SciPy, and scikit-learn, and apply them in practical data analysis and statistics examples. As you make your way through the chapters, you will learn to efficiently use the Jupyter Notebook to operate and manipulate data using NumPy and the pandas library. In the concluding chapters, you will gain experience in building simple predictive models and carrying out statistical computation and analysis using rich Python tools and proven data analysis techniques. By the end of this book, you will have hands-on experience performing data analysis with Python.
Table of Contents (8 chapters)

NumPy arrays

NumPy's main object is a homogeneous multidimensional array. An array is essentially a table of elements (usually numbers), all of the same type, indexed by a tuple of positive integers. The index in NumPy arrays is zero-based, so the first element is the 0th element; the second element is the 1st element, and so on. In NumPy, dimensions are called axes and the number of axes, or dimensions, is called the rank or dimension of the array. To import NumPy into our Jupyter Notebook, we use the numpy as np convention import.

Creating arrays in NumPy

There are the following two methods to create arrays in Python:

  • Creating arrays from lists
  • Using the built-in functions that NumPy provides