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Data Analysis with Python

You're reading from Data Analysis with Python A Modern Approach

Product type Paperback

Published in Dec 2018

Publisher Packt

ISBN-13 9781789950069

Length 490 pages

Edition 1st Edition

Languages

Python

Tools

Jupyter

Concepts

Data Analysis

Author (1):

David Taieb

View More author details

Table of Contents (14) Chapters

Preface

1. Programming and Data Science – A New Toolset FREE CHAPTER

2. Python and Jupyter Notebooks to Power your Data Analysis

3. Accelerate your Data Analysis with Python Libraries

4. Publish your Data Analysis to the Web - the PixieApp Tool

5. Python and PixieDust Best Practices and Advanced Concepts

6. Analytics Study: AI and Image Recognition with TensorFlow

7. Analytics Study: NLP and Big Data with Twitter Sentiment Analysis

8. Analytics Study: Prediction - Financial Time Series Analysis and Forecasting

9. Analytics Study: Graph Algorithms - US Domestic Flight Data Analysis

10. The Future of Data Analysis and Where to Develop your Skills

A. PixieApp Quick-Reference

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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...

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Authors (1)

David Taieb

David Taieb

David Taieb is the Distinguished Engineer for the Watson and Cloud Platform Developer Advocacy team at IBM, leading a team of avid technologists on a mission to educate developers on the art of the possible with data science, AI and cloud technologies. He's passionate about building open source tools, such as the PixieDust Python Library for Jupyter Notebooks, which help improve developer productivity and democratize data science. David enjoys sharing his experience by speaking at conferences and meetups, where he likes to meet as many people as possible.

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