Book Image

Data Analysis with Python

By : David Taieb
Book Image

Data Analysis with Python

By: David Taieb

Overview of this book

Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects. Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you’re likely to meet in today. The first of these is an image recognition application with TensorFlow – embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis - pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You'll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence.
Table of Contents (16 chapters)
Data Analysis with Python
Contributors
Preface
Other Books You May Enjoy
3
Accelerate your Data Analysis with Python Libraries
Index

Getting started with NumPy


The NumPy library is one of the main reasons why Python has gained so much traction in the data scientist community. It is a foundational library upon which a lot of the most popular libraries, such as pandas (https://pandas.pydata.org), Matplotlib (https://matplotlib.org), SciPy (https://www.scipy.org), and scikit-learn (http://scikit-learn.org) are built.

The key capabilities provided by NumPy are:

  • A very powerful multidimensional NumPy array called ndarray with very high-performance mathematical operations (at least compared to regular Python lists and arrays)

  • Universal functions also called ufunc for short, for providing very efficient and easy-to-use element by element operations on one or more ndarray

  • Powerful ndarray slicing and selection capabilities

  • Broadcasting functions that make it possible to apply arithmetic operations on ndarray of different shapes provided that some rules are respected

Before we start exploring the NumPy APIs, there is one API that is...