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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 3. Statistics and Linear Algebra 4. pandas Primer 5. Retrieving, Processing, and Storing Data 6. Data Visualization 7. Signal Processing and Time Series 8. Working with Databases 9. Analyzing Textual Data and Social Media 10. Predictive Analytics and Machine Learning 11. Environments Outside the Python Ecosystem and Cloud Computing 12. Performance Tuning, Profiling, and Concurrency A. Key Concepts
B. Useful Functions C. Online Resources
Index

NumPy numerical types


Python has an integer type, a float type, and complex type; nonetheless, this is not sufficient for scientific calculations. In practice, we still demand more data types with varying precisions and, consequently, different storage sizes of the type. For this reason, NumPy has many more data types. The bulk of the NumPy mathematical types ends with a number. This number designates the count of bits related to the type. The following table (adapted from the NumPy user guide) presents an overview of NumPy numerical types:

Type

Description

bool

Boolean (True or False) stored as a bit

inti

Platform integer (normally either int32 or int6 4)

int8

Byte (-128 to 127)

int16

Integer (-32768 to 32767)

int32

Integer (-2 ** 31 to 2 ** 31 -1)

int64

Integer (-2 ** 63 to 2 ** 63 -1)

uint8

Unsigned integer (0 to 255)

uint16

Unsigned integer (0 to 65535)

uint32

Unsigned integer (0 to 2 ** 32 - 1)

uint64

Unsigned integer (0 to 2 ** 64 - 1)

float16...

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