Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Advanced Search Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Learning NumPy Array
  • Table Of Contents Toc
  • Feedback & Rating feedback
Learning NumPy Array

Learning NumPy Array

By : Ivan Idris
4 (9)
Buy this Book
close
close
Learning NumPy Array

Learning NumPy Array

4 (9)
By: Ivan Idris
Buy this Book

Overview of this book

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

Chapter 5. Signal Processing Techniques

We will learn about some signal-processing techniques in this chapter, and we will analyze time-series data with these. As example data, we will use the sunspot data provided by a Belgian scientific institute. We can download this data from several places on the Internet, and it is also provided as sample data by the statsmodels library. There are a number of things we can do with the data, such as:

  • Trying to determine periodic cycles within the data. This can be done, but this is a bit advanced, so we will just get you started.

  • Smoothing the data to filter out noise.

  • Forecasting.

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech
Start a 7-day FREE trial
End of Section 1
Next Section

Create a Note

Modal Close icon
You need to login to use this feature.
notes
bookmark search playlist font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Delete Note

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Edit Note

Modal Close icon
Write a note (max 255 characters)
Cancel
Update Note

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY