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Home > Data > Data Analysis > Python Data Analysis
Python Data Analysis
Python Data Analysis

Python Data Analysis: Learn how to apply powerful data analysis techniques with popular open source Python modules

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Python Data Analysis

Chapter 2. NumPy Arrays

After installing NumPy and other key Python-programming libraries and getting some code to work, it's time to pass over NumPy arrays. This chapter acquaints you with the fundamentals of NumPy and arrays. At the end of this chapter, you will have a basic understanding of NumPy arrays and their related functions.

The topics we will address in this chapter are as follows:

  • Data types
  • Array types
  • Type conversions
  • Creating arrays
  • Indexing
  • Fancy indexing
  • Slicing
  • Manipulating shapes

The NumPy array object

NumPy has a multidimensional array object called ndarray. It consists of two parts, which are as follows:

  • The actual data
  • Some metadata describing the data

The bulk of array procedures leaves the raw information unaffected; the sole facet that varies is the metadata.

We have already discovered in the preceding chapter how to produce an array by applying the arange() function. Actually, we made a one-dimensional array that held a set of numbers. The ndarray can have more than a single dimension.

The advantages of NumPy arrays

The NumPy array is, in general, homogeneous (there is a particular record array type that is heterogeneous)—the items in the array have to be of the same type. The advantage is that if we know that the items in an array are of the same type, it is easy to ascertain the storage size needed for the array. NumPy arrays can execute vectorized operations, processing a complete array, in contrast to Python lists, where you usually have to loop through...

Creating a multidimensional array

Now that we know how to create a vector, we are set to create a multidimensional NumPy array. After we produce the matrix, we will again need to show its shape (check arrayattributes.py from this book's code bundle), as demonstrated in the following code snippets:

  1. Create a multidimensional array as follows:
    In: m = array([arange(2), arange(2)])
In: m
Out:
array([[0, 1],
       [0, 1]])
  2. Show the array shape as follows:
    In: m.shape
Out: (2, 2)

We made a 2 x 2 array with the arange() subroutine. The array() function creates an array from an object that you pass to it. The object has to be an array, for example, a Python list. In the previous example, we passed a list of arrays. The object is the only required parameter of the array() function. NumPy functions tend to have a heap of optional arguments with predefined default options.

Selecting NumPy array elements

From time to time, we will wish to select a specific constituent of an array. We will take a look at how to do this, but to kick off, let's make a 2 x 2 matrix again (see the elementselection.py file in this book's code bundle):

In: a = array([[1,2],[3,4]])
In: a
Out:
array([[1, 2],
       [3, 4]])

The matrix was made this time by giving the array() function a list of lists. We will now choose each item of the matrix one at a time, as shown in the following code snippet. Recall that the index numbers begin from 0:

In: a[0,0]
Out: 1
In: a[0,1]
Out: 2
In: a[1,0]
Out: 3
In: a[1,1]
Out: 4

As you can see, choosing elements of an array is fairly simple. For the array a, we just employ the notation a[m,n], where m and n are the indices of the item in the array. Have a look at the following figure for your reference:

Selecting NumPy array elements

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...

One-dimensional slicing and indexing

Slicing of one-dimensional NumPy arrays works just like the slicing of standard Python lists. Let's define an array containing the numbers 0, 1, 2, and so on up to and including 8. We can select a part of the array from indexes 3 to 7, which extracts the elements of the arrays 3 through 6 (have a look at slicing1d.py in this book's code bundle):

In: a = arange(9)
In: a[3:7]
Out: array([3, 4, 5, 6])

We can choose elements from indexes the 0 to 7 with an increment of 2:

In: a[:7:2]
Out: array([0, 2, 4, 6])

Just as in Python, we can use negative indices and reverse the array:

In: a[::-1]
Out: array([8, 7, 6, 5, 4, 3, 2, 1, 0])

The NumPy array object

NumPy has a multidimensional array object called ndarray. It consists of two parts, which are as follows:

  • The actual data

  • Some metadata describing the data

The bulk of array procedures leaves the raw information unaffected; the sole facet that varies is the metadata.

We have already discovered in the preceding chapter how to produce an array by applying the arange() function. Actually, we made a one-dimensional array that held a set of numbers. The ndarray can have more than a single dimension.

The advantages of NumPy arrays

The NumPy array is, in general, homogeneous (there is a particular record array type that is heterogeneous)—the items in the array have to be of the same type. The advantage is that if we know that the items in an array are of the same type, it is easy to ascertain the storage size needed for the array. NumPy arrays can execute vectorized operations, processing a complete array, in contrast to Python lists, where you usually have to loop through the...

Creating a multidimensional array

Now that we know how to create a vector, we are set to create a multidimensional NumPy array. After we produce the matrix, we will again need to show its shape (check arrayattributes.py from this book's code bundle), as demonstrated in the following code snippets:

  1. Create a multidimensional array as follows:

    In: m = array([arange(2), arange(2)])
In: m
Out:
array([[0, 1],
       [0, 1]])

  2. Show the array shape as follows:

    In: m.shape
Out: (2, 2)

We made a 2 x 2 array with the arange() subroutine. The array() function creates an array from an object that you pass to it. The object has to be an array, for example, a Python list. In the previous example, we passed a list of arrays. The object is the only required parameter of the array() function. NumPy functions tend to have a heap of optional arguments with predefined default options.

Selecting NumPy array elements

From time to time, we will wish to select a specific constituent of an array. We will take a look at how to do this, but to kick off, let's make a 2 x 2 matrix again (see the elementselection.py file in this book's code bundle):

In: a = array([[1,2],[3,4]])
In: a
Out:
array([[1, 2],
       [3, 4]])

The matrix was made this time by giving the array() function a list of lists. We will now choose each item of the matrix one at a time, as shown in the following code snippet. Recall that the index numbers begin from 0:

In: a[0,0]
Out: 1
In: a[0,1]
Out: 2
In: a[1,0]
Out: 3
In: a[1,1]
Out: 4

As you can see, choosing elements of an array is fairly simple. For the array a, we just employ the notation a[m,n], where m and n are the indices of the item in the array. Have a look at the following figure for your reference:

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...

One-dimensional slicing and indexing

Slicing of one-dimensional NumPy arrays works just like the slicing of standard Python lists. Let's define an array containing the numbers 0, 1, 2, and so on up to and including 8. We can select a part of the array from indexes 3 to 7, which extracts the elements of the arrays 3 through 6 (have a look at slicing1d.py in this book's code bundle):

In: a = arange(9)
In: a[3:7]
Out: array([3, 4, 5, 6])

We can choose elements from indexes the 0 to 7 with an increment of 2:

In: a[:7:2]
Out: array([0, 2, 4, 6])

Just as in Python, we can use negative indices and reverse the array:

In: a[::-1]
Out: array([8, 7, 6, 5, 4, 3, 2, 1, 0])

Manipulating array shapes

We have already learned about the reshape() function. Another repeating chore is the flattening of arrays. Flattening in this setting entails transforming a multidimensional array into a one-dimensional array. The code for this example is in the shapemanipulation.py file in this book's code bundle.

import numpy as np

# Demonstrates multi dimensional arrays slicing.
#
# Run from the commandline with
#
#  python shapemanipulation.py
print "In: b = arange(24).reshape(2,3,4)"
b = np.arange(24).reshape(2,3,4)

print "In: b"
print b
#Out: 
#array([[[ 0,  1,  2,  3],
#        [ 4,  5,  6,  7],
#        [ 8,  9, 10, 11]],
#
#       [[12, 13, 14, 15],
#        [16, 17, 18, 19],
#        [20, 21, 22, 23]]])

print "In: b.ravel()"
print b.ravel()
#Out: 
#array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
#       17, 18, 19, 20, 21, 22, 23])

print "In: b.flatten()"
print b.flatten()
#Out: 
#array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,...

Creating array views and copies

In the example about ravel(), views were brought up. Views should not be confused with the construct of database views. Views in the NumPy universe are not read only and you don't have the possibility to protect the underlying information. It is crucial to know when we are handling a shared array view and when we have a replica of the array data. A slice of an array, for example, will produce a view. This entails that if you assign the slice to a variable and then alter the underlying array, the value of this variable will change. We will create an array from the famed Lena picture, and then create a view and alter it at the final stage. The Lena image array comes from a SciPy function.

  1. Create a copy of the Lena array:

    acopy = lena.copy()

  2. Create a view of the array:

    aview = lena.view()

  3. Set all the values in the view to 0 with a flat iterator:

    aview.flat = 0

The final outcome is that only one of the pictures depicts the model. The other ones are censored altogether...

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Description

This book is for programmers, scientists, and engineers who have knowledge of the Python language and know the basics of data science. It is for those who wish to learn different data analysis methods using Python and its libraries. This book contains all the basic ingredients you need to become an expert data analyst.

What you will learn

  • Install open source Python modules on various platforms
  • Get to know about the fundamentals of NumPy including arrays
  • Manipulate data with pandas
  • Retrieve, process, store, and visualize data
  • Understand signal processing and timeseries data analysis
  • Work with relational and NoSQL databases
  • Discover more about data modeling and machine learning
  • Get to grips with interoperability and cloud computing
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Publication date : Oct 28, 2014
Length: 348 pages
Edition : 1st
Language : English
ISBN-13 : 9781783553358
Category :
Data
Languages :
Python
Concepts :
Data Analysis
Tools :
Pandas

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Language : English
ISBN-13 : 9781783553358
Category :
Data
Languages :
Python
Concepts :
Data Analysis
Tools :
Pandas

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Table of Contents

16 Chapters
1. Getting Started with Python Libraries Chevron down icon Chevron up icon
1. Getting Started with Python Libraries
Software used in this book
Building NumPy, SciPy, matplotlib, and IPython from source
Installing with setuptools
NumPy arrays
A simple application
Using IPython as a shell
Reading manual pages
IPython notebooks
Where to find help and references
Summary
2. NumPy Arrays Chevron down icon Chevron up icon
2. NumPy Arrays
The NumPy array object
Creating a multidimensional array
Selecting NumPy array elements
NumPy numerical types
One-dimensional slicing and indexing
Manipulating array shapes
Creating array views and copies
Fancy indexing
Indexing with a list of locations
Indexing NumPy arrays with Booleans
Broadcasting NumPy arrays
Summary
3. Statistics and Linear Algebra Chevron down icon Chevron up icon
3. Statistics and Linear Algebra
NumPy and SciPy modules
Basic descriptive statistics with NumPy
Linear algebra with NumPy
Finding eigenvalues and eigenvectors with NumPy
NumPy random numbers
Creating a NumPy-masked array
Summary
4. pandas Primer Chevron down icon Chevron up icon
4. pandas Primer
Installing and exploring pandas
pandas DataFrames
pandas Series
Querying data in pandas
Statistics with pandas DataFrames
Data aggregation with pandas DataFrames
Concatenating and appending DataFrames
Joining DataFrames
Handling missing values
Dealing with dates
Pivot tables
Remote data access
Summary
5. Retrieving, Processing, and Storing Data Chevron down icon Chevron up icon
5. Retrieving, Processing, and Storing Data
Writing CSV files with NumPy and pandas
Comparing the NumPy .npy binary format and pickling pandas DataFrames
Storing data with PyTables
Reading and writing pandas DataFrames to HDF5 stores
Reading and writing to Excel with pandas
Using REST web services and JSON
Reading and writing JSON with pandas
Parsing RSS and Atom feeds
Parsing HTML with Beautiful Soup
Summary
6. Data Visualization Chevron down icon Chevron up icon
6. Data Visualization
matplotlib subpackages
Basic matplotlib plots
Logarithmic plots
Scatter plots
Legends and annotations
Three-dimensional plots
Plotting in pandas
Lag plots
Autocorrelation plots
Plot.ly
Summary
7. Signal Processing and Time Series Chevron down icon Chevron up icon
7. Signal Processing and Time Series
statsmodels subpackages
Moving averages
Window functions
Defining cointegration
Autocorrelation
Autoregressive models
ARMA models
Generating periodic signals
Fourier analysis
Spectral analysis
Filtering
Summary
8. Working with Databases Chevron down icon Chevron up icon
8. Working with Databases
Lightweight access with sqlite3
Accessing databases from pandas
SQLAlchemy
Pony ORM
Dataset – databases for lazy people
PyMongo and MongoDB
Storing data in Redis
Apache Cassandra
Summary
9. Analyzing Textual Data and Social Media Chevron down icon Chevron up icon
9. Analyzing Textual Data and Social Media
Installing NLTK
Filtering out stopwords, names, and numbers
The bag-of-words model
Analyzing word frequencies
Naive Bayes classification
Sentiment analysis
Creating word clouds
Social network analysis
Summary
10. Predictive Analytics and Machine Learning Chevron down icon Chevron up icon
10. Predictive Analytics and Machine Learning
A tour of scikit-learn
Preprocessing
Classification with logistic regression
Classification with support vector machines
Regression with ElasticNetCV
Support vector regression
Clustering with affinity propagation
Mean Shift
Genetic algorithms
Neural networks
Decision trees
Summary
11. Environments Outside the Python Ecosystem and Cloud Computing Chevron down icon Chevron up icon
11. Environments Outside the Python Ecosystem and Cloud Computing
Exchanging information with MATLAB/Octave
Installing rpy2
Interfacing with R
Sending NumPy arrays to Java
Integrating SWIG and NumPy
Integrating Boost and Python
Using Fortran code through f2py
Setting up Google App Engine
Running programs on PythonAnywhere
Working with Wakari
Summary
12. Performance Tuning, Profiling, and Concurrency Chevron down icon Chevron up icon
12. Performance Tuning, Profiling, and Concurrency
Profiling the code
Installing Cython
Calling C code
Creating a process pool with multiprocessing
Speeding up embarrassingly parallel for loops with Joblib
Comparing Bottleneck to NumPy functions
Performing MapReduce with Jug
Installing MPI for Python
IPython Parallel
Summary
A. Key Concepts Chevron down icon Chevron up icon
A. Key Concepts
B. Useful Functions Chevron down icon Chevron up icon
B. Useful Functions
matplotlib
NumPy
pandas
Scikit-learn
SciPy
C. Online Resources Chevron down icon Chevron up icon
C. Online Resources
Index Chevron down icon Chevron up icon
Index

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Miss Fatty Nov 23, 2014
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A really a good source for analysts/scientists using the python language. The author does a great job introducing the reader to core Python libraries, I found the information to be digestible even with my limited knowledge of the subject matter. I must say very well written: covers concrete practical examples to show you the capabilities of a suite of tools. I am very happy with the purchase. This book is helping me move my skill set beyond R and into Python.
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Mikel Viera Nov 25, 2014
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If you're looking for a book that discusses Python data analysis in a broadpractical sense, this is the book. The author conveys his data analysisexperience into the text really well. The chapters are genuinely helpfulwith well written tutorials on Pythons excellent libraries: NumPy, SciPy,Pandas, IPython, Matplotlib etc.One of the better books I've read on the subject . Highly recommended!
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Harris Nov 23, 2014
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Really happy with it so far. This is very nicely written and a must have for data analysts using Python. Author covers examples with all the main libraries including NumPY, SciPY, Ipython in a clear concise manner. Well structured and focused throughout. Nice to see Sci-kit coverage too! (Looking forward to the Machine learning chapters).
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Daniel Lee Feb 22, 2015
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As an experienced Python developer, I really enjoyed the book. That being said, it has a targeted audience and if you aren't in it, you'll probably be happier not picking it up. The book is aimed at people who:1. Already know Python2. Already know data analysis3. Want a broad overview rather than in-depth explanationsThe book got interesting for me in chapter 2, where NumPy arrays were explained, as well as a lot of the operations that can be done on them. I have limited experience with NumPy, and I found the section just in-depth enough to be interesting and just shallow enough to allow me to use it as a springboard to find what I needed to in other areas. As NumPy is a dependency for just about anything that does any number crunching in Python, it was good to have these basics.What really opened my eye, though, was chapter 4 - the pandas primer. I'm a heavy R user for research and have often wished that I could do more data analysis in Python. pandas allows you to do this as comfortably, if not more comfortably, than in Python, and because the package is in Python it's a lot faster and a lot easier to embed in other software. I'm in the middle of a project at the moment and had been struggling with R - the implementation of an analysis milestone was just too slow. After reading this chapter, I implemented it in Python and was able to notice amaing speed improvements, not to mention the maintainability advantages. Data analysis is fun again!The rest of the book is more of a quick blowthrough of what's possible in Python. Although the author doesn't discuss any topic in depth, he does refer you to other books and websites. The purpose was to show readers what's possible. In my opinion, this is basically a longer preview of several data analysis possibilities. If you know roughly what you want to do in the following areas:* Processing common formats like CSV, HDF or XML* Interfacing with databases* Interfacing with other languages* Visualizing data* Signal processing and machine learning* Text analysis* Profiling, performance optimization and parallelizationthen you'll find some good recommendations here with short descriptions and examples that will help you decide which of the packages introduced here are interesting. If you want more, you'll have to dig on your own, but the author gives you good starting points and with such a variety of topics I don't know how one could realistically expect anything else.All in all, a great read, especially for data analysts whose main question is "Can I do this in Python, and if yes, how?"
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Michael Bright Feb 17, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book provides a very broad overview of toolkits and methods available forperforming Data Analysis with Python - and it does an excellent job at that.Whilst breadth of coverage will always be a trade off against depth, the bookprovides good balance by providing runnable code and data examplesacross the broad spectrum of tools and techniques referred to.It also provides many internet references to allow to dig deeper into particular subjects.Even in chapters where reference is made to interfacing to non-Pythonenvironments (Matlab/Octave, R, Java, SWIG, Boost, Fortran), external cloudenvironments (GAE, PythonAnywhere, Wakari) or Performance tools (Profiling,Cython, JobLib, JUG, MPI) or many Databases/tools code examples are given.Each chapter finishes with a summary as well as a preface of the followingchapter - there are many useful forward and backward references in the bookmaking it easy to digest and find information.The book starts with setting up the environment (either building from sourceor installing on various operating systems), followed by a brief introduction to Numpybefore demonstrating how much faster Numpy is than Python native arrays whilstbenefiting from array abstractions.Chapters 2,3,4 cover Numpy in detail (data types, slicing and shaping arrays,boolean indexing to select subsets of data), some Statistics and LinearAlgebra (probability distributions, SciPy, removing extreme values, plottingimages and graphs), Pandas (DataFrames and Series, concatenating frames, basicanalysis and aggregation of data).Chapter 5 covers retrieving of data from a wide variety of sources/formats(CSV, .npy Pickled files, HDF5, REST/json, RSS, HTML/BeautifulSoup, Excel)complete with working examples.Chapter 6 delves into Data Visualization using matplotlib, or Pandas.plots,for a variety of types of plots (histogram, scatter, bubble, lag, log, autocorrelation plots).Chapter 7 covers Signal Processing and Time series. It shows the statsmodelsubpackage and dicusses moving averages, windows, (boxcar, triangle, hamming),co-integration/autocorrelation, auto-regression, Fourier transforms.Chapter 8 provides information on interfacing with Databases and brieflycovers many ways to do this using Sqlite, SQLAlchemy, Pony, Dataset, MongoDB,Redis or Cassandra, again with usable examples.Chapters 9 and 10 go deeper into analysis covering analysis of textual data,social media data through natural language processing (NLTK), Bayesclassification, sentiment analysis, followed by predictive analysis usingscikit-learn, support vector machines, ElasticNetCV, neural nets, decisiontrees and clustering.Chapter 11 covers interfacing to external environments through the integrationwith other toolkits such as Matlab/Octave, languages such as R, Fortran, Java,C and use of some Python supporting cloud platforms (Googles' GAE,PythonAnywhere, Wakari).The final chapter 12 addresses performance profiling and concurrency -covering tools such as ****PROFILER****, Cython, several process pool andparallel processing tools and JUG for MapReduce.Overall this book, and indeed each chapter, provide an excellent coverageof its' stated subject with many examples and links to external information.It is well worth buying for someone relatively new to Data Analysis in Pythonwho wants an introduction to the broad panoply of tools and techniquescurrently available.A few minor points follow which I feel could improve the book.Just a few of the examples given were not particularly interesting (e.g.grouping of foods/pricing/weather) but given the diversity of examples in thebook this is understandable.Although there are many diverse examples - using diverse toolkits, but alsodata scenarios I felt that these exercises often lacked the final step ofproviding some analysis. Examples would finish with a set of figures, or aplot which may be considered to be self evident but I felt lacked a conclusionof the form "notice how there is a cluster of values around ...","in this graph we see that there is a correlation ...", or"and so this result demonstrates the validity of our assumption that ...".The first Appendix is called "Key Concepts" - I'd have called this a Glossaryincluding an entry for all the tools touched on in the book and I've had madereference to this glossary early on in the book.I'd also have liked to see a section comparing the main tools so that it isclear in which situation to use different tools.That said the appendix is useful and it is followed by other useful appendices"Useful Functions" summarizing the most useful functions of Numpy, Pandas etc,and "Online Resources" which provides an extensive list of resources (tools,datasources, informative sites) referred to throughout the book and finally avery complete index.At various chapters there we see module 'subpackage lists' whilst it'sinteresting to be able to produce such lists, they're not actually veryreadable in the book.Once again, a very excellent introduction.
Amazon Verified review Amazon

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About the author

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Idris
Ivan Idris has an MSc in experimental physics. His graduation thesis had a strong emphasis on applied computer science. After graduating, he worked for several companies as a Java developer, data warehouse developer, and QA analyst. His main professional interests are business intelligence, big data, and cloud computing. Ivan Idris enjoys writing clean, testable code and interesting technical articles. Ivan Idris is the author of NumPy 1.5. Beginner's Guide and NumPy Cookbook by Packt Publishing.
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