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Python Data Analysis

Python Data Analysis

By : Ivan Idris
3.9 (16)
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Python Data Analysis

Python Data Analysis

3.9 (16)
By: Ivan Idris
Buy this Book

Overview of this book

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.
Table of Contents (22 chapters)
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Python Data Analysis
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Python Data Analysis
Credits
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Credits
About the Author
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About the Author
About the Reviewers
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About the Reviewers
www.PacktPub.com
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www.PacktPub.com
Preface
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Preface
Free Chapter
1
Getting Started with Python Libraries
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Getting Started with Python Libraries
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Software used in this book
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Building NumPy, SciPy, matplotlib, and IPython from source
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Installing with setuptools
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NumPy arrays
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A simple application
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Using IPython as a shell
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Reading manual pages
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IPython notebooks
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Where to find help and references
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Summary
2
NumPy Arrays
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NumPy Arrays
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The NumPy array object
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Creating a multidimensional array
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Selecting NumPy array elements
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NumPy numerical types
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One-dimensional slicing and indexing
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Manipulating array shapes
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Creating array views and copies
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Fancy indexing
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Indexing with a list of locations
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Indexing NumPy arrays with Booleans
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Broadcasting NumPy arrays
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Summary
3
Statistics and Linear Algebra
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Statistics and Linear Algebra
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NumPy and SciPy modules
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Basic descriptive statistics with NumPy
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Linear algebra with NumPy
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Finding eigenvalues and eigenvectors with NumPy
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NumPy random numbers
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Creating a NumPy-masked array
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Summary
4
pandas Primer
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pandas Primer
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Installing and exploring pandas
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pandas DataFrames
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pandas Series
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Querying data in pandas
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Statistics with pandas DataFrames
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Data aggregation with pandas DataFrames
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Concatenating and appending DataFrames
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Joining DataFrames
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Handling missing values
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Dealing with dates
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Pivot tables
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Remote data access
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Summary
5
Retrieving, Processing, and Storing Data
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Retrieving, Processing, and Storing Data
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Writing CSV files with NumPy and pandas
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Comparing the NumPy .npy binary format and pickling pandas DataFrames
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Storing data with PyTables
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Reading and writing pandas DataFrames to HDF5 stores
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Reading and writing to Excel with pandas
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Using REST web services and JSON
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Reading and writing JSON with pandas
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Parsing RSS and Atom feeds
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Parsing HTML with Beautiful Soup
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Summary
6
Data Visualization
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Data Visualization
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matplotlib subpackages
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Basic matplotlib plots
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Logarithmic plots
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Scatter plots
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Legends and annotations
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Three-dimensional plots
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Plotting in pandas
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Lag plots
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Autocorrelation plots
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Plot.ly
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Summary
7
Signal Processing and Time Series
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Signal Processing and Time Series
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statsmodels subpackages
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Moving averages
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Window functions
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Defining cointegration
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Autocorrelation
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Autoregressive models
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ARMA models
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Generating periodic signals
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Fourier analysis
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Spectral analysis
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Filtering
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Summary
8
Working with Databases
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Working with Databases
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Lightweight access with sqlite3
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Accessing databases from pandas
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SQLAlchemy
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Pony ORM
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Dataset – databases for lazy people
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PyMongo and MongoDB
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Storing data in Redis
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Apache Cassandra
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Summary
9
Analyzing Textual Data and Social Media
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Analyzing Textual Data and Social Media
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Installing NLTK
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Filtering out stopwords, names, and numbers
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The bag-of-words model
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Analyzing word frequencies
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Naive Bayes classification
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Sentiment analysis
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Creating word clouds
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Social network analysis
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Summary
10
Predictive Analytics and Machine Learning
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Predictive Analytics and Machine Learning
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A tour of scikit-learn
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Preprocessing
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Classification with logistic regression
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Classification with support vector machines
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Regression with ElasticNetCV
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Support vector regression
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Clustering with affinity propagation
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Mean Shift
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Genetic algorithms
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Neural networks
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Decision trees
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Summary
11
Environments Outside the Python Ecosystem and Cloud Computing
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Environments Outside the Python Ecosystem and Cloud Computing
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Exchanging information with MATLAB/Octave
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Installing rpy2
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Interfacing with R
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Sending NumPy arrays to Java
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Integrating SWIG and NumPy
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Integrating Boost and Python
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Using Fortran code through f2py
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Setting up Google App Engine
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Running programs on PythonAnywhere
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Working with Wakari
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Summary
12
Performance Tuning, Profiling, and Concurrency
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Performance Tuning, Profiling, and Concurrency
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Profiling the code
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Installing Cython
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Calling C code
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Creating a process pool with multiprocessing
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Speeding up embarrassingly parallel for loops with Joblib
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Comparing Bottleneck to NumPy functions
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Performing MapReduce with Jug
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Installing MPI for Python
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IPython Parallel
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Summary
Key Concepts
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Key Concepts
Useful Functions
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Useful Functions
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matplotlib
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NumPy
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pandas
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Scikit-learn
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SciPy
Online Resources
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Online Resources
Index
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Index

Support vector regression

As mentioned before, support vector machines can be used for regression. In the case of regression, we are using a hyperplane not to separate points, but for a fit. A learning curve is a way to visualize the behavior of a learning algorithm. It is a plot of training and test scores for a range of train data sizes. Creating a learning curve forces us to train the estimator multiple times and is therefore on aggregate slow. We can compensate for this by creating multiple concurrent estimator jobs. Support vector regression is one of the algorithms that may require scaling. We get the following top scores:

Max test score Rain 0.0161004084576
Max test score Boston 0.662188537037

This is similar to the results obtained with the ElasticNetCV class. Many scikit-learn classes have an n_jobs parameter for that purpose. As a rule of thumb, we often create as many jobs as there are CPUs in our system. The jobs are created using the standard Python multiprocessing API. Call...

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