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Learning NumPy Array

Learning NumPy Array

By : Ivan Idris
4 (9)
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Learning NumPy Array

Learning NumPy Array

4 (9)
By: Ivan Idris

Overview of this book

Table of Contents (14 chapters)
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Learning NumPy Array
Learning NumPy Array
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Getting Started with NumPy
Getting Started with NumPy
Python
Installing NumPy, Matplotlib, SciPy, and IPython on Windows
Installing NumPy, Matplotlib, SciPy, and IPython on Linux
Installing NumPy, Matplotlib, and SciPy on Mac OS X
Building from source
NumPy arrays
Online resources and help
Summary
2
NumPy Basics
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NumPy Basics
The NumPy array object
Creating a multidimensional array
Selecting array elements
NumPy numerical types
Creating a record data type
One-dimensional slicing and indexing
Manipulating array shapes
Creating views and copies
Fancy indexing
Indexing with a list of locations
Indexing arrays with Booleans
Stride tricks for Sudoku
Broadcasting arrays
Summary
3
Basic Data Analysis with NumPy
Basic Data Analysis with NumPy
Introducing the dataset
Determining the daily temperature range
Looking for evidence of global warming
Comparing solar radiation versus temperature
Analyzing wind direction
Analyzing wind speed
Analyzing precipitation and sunshine duration
Analyzing monthly precipitation in De Bilt
Analyzing atmospheric pressure in De Bilt
Analyzing atmospheric humidity in De Bilt
Summary
4
Simple Predictive Analytics with NumPy
Simple Predictive Analytics with NumPy
Examining autocorrelation of average temperature with pandas
Describing data with pandas DataFrames
Correlating weather and stocks with pandas
Predicting temperature
Analyzing intra-year daily average temperatures
Introducing the day-of-the-year temperature model
Modeling temperature with the SciPy leastsq function
Day-of-year temperature take two
Moving-average temperature model with lag 1
The Autoregressive Moving Average temperature model
The time-dependent temperature mean adjusted autoregressive model
Outliers analysis of average De Bilt temperature
Using more robust statistics
Summary
5
Signal Processing Techniques
Signal Processing Techniques
Introducing the Sunspot data
Moving averages
Smoothing functions
Forecasting with an ARMA model
Filtering a signal
Demonstrating cointegration
Summary
6
Profiling, Debugging, and Testing
Profiling, Debugging, and Testing
Assert functions
Profiling a program with IPython
Debugging with IPython
Performing Unit tests
Nose tests decorators
Summary
7
The Scientific Python Ecosystem
The Scientific Python Ecosystem
Numerical integration
Interpolation
Using Cython with NumPy
Clustering stocks with scikit-learn
Detecting corners
Comparing NumPy to Blaze
Summary
Index
Index

Summary

We learned a lot in this chapter about the NumPy fundamentals: data types and arrays. Arrays have several attributes describing them. We learned that one of these attributes is the data type which, in NumPy, is represented by a full-fledged object.

NumPy arrays can be sliced and indexed in an efficient manner, just as in the case of Python lists. NumPy arrays have the added ability of working with multiple dimensions.

The shape of an array can be manipulated in many ways, such as stacking, resizing, reshaping, and splitting. A great number of convenience functions for shape manipulation were demonstrated in this chapter.

Having learned about the basics, it's time to move on to data analysis with commonly used functions in Chapter 3, Basic Data Analysis with NumPy. This includes the usage of basic statistical and mathematical functions.

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