Python Data Analysis Cookbook

Python Data Analysis Cookbook

By : Ivan Idris

Buy this Book

Python Data Analysis Cookbook

By: Ivan Idris

Buy this Book

Overview of this book

Data analysis is a rapidly evolving field and Python is a multi-paradigm programming language suitable for object-oriented application development and functional design patterns. As Python offers a range of tools and libraries for all purposes, it has slowly evolved as the primary language for data science, including topics on: data analysis, visualization, and machine learning. Python Data Analysis Cookbook focuses on reproducibility and creating production-ready systems. You will start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas. You will learn to create visualizations by choosing color maps and palettes then dive into statistical data analysis using distribution algorithms and correlations. You’ll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining. In this book, you will dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods. Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. You will achieve parallelism to improve system performance by using multiple threads and speeding up your code. By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios.

Python Data Analysis Cookbook

Credits

About the Author

About the Reviewers

www.PacktPub.com

Preface

Free Chapter

Laying the Foundation for Reproducible Data Analysis

Introduction

Setting up Anaconda

Installing the Data Science Toolbox

Creating a virtual environment with virtualenv and virtualenvwrapper

Sandboxing Python applications with Docker images

Keeping track of package versions and history in IPython Notebook

Configuring IPython

Learning to log for robust error checking

Unit testing your code

Configuring pandas

Configuring matplotlib

Seeding random number generators and NumPy print options

Standardizing reports, code style, and data access

Creating Attractive Data Visualizations

Introduction

Graphing Anscombe's quartet

Choosing seaborn color palettes

Choosing matplotlib color maps

Interacting with IPython Notebook widgets

Viewing a matrix of scatterplots

Visualizing with d3.js via mpld3

Creating heatmaps

Combining box plots and kernel density plots with violin plots

Visualizing network graphs with hive plots

Displaying geographical maps

Using ggplot2-like plots

Highlighting data points with influence plots

Statistical Data Analysis and Probability

Introduction

Fitting data to the exponential distribution

Fitting aggregated data to the gamma distribution

Fitting aggregated counts to the Poisson distribution

Determining bias

Estimating kernel density

Determining confidence intervals for mean, variance, and standard deviation

Sampling with probability weights

Exploring extreme values

Correlating variables with Pearson's correlation

Correlating variables with the Spearman rank correlation

Correlating a binary and a continuous variable with the point biserial correlation

Evaluating relations between variables with ANOVA

Dealing with Data and Numerical Issues

Introduction

Clipping and filtering outliers

Winsorizing data

Measuring central tendency of noisy data

Normalizing with the Box-Cox transformation

Transforming data with the power ladder

Transforming data with logarithms

Rebinning data

Applying logit() to transform proportions

Fitting a robust linear model

Taking variance into account with weighted least squares

Using arbitrary precision for optimization

Using arbitrary precision for linear algebra

Web Mining, Databases, and Big Data

Introduction

Simulating web browsing

Scraping the Web

Dealing with non-ASCII text and HTML entities

Implementing association tables

Setting up database migration scripts

Adding a table column to an existing table

Adding indices after table creation

Setting up a test web server

Implementing a star schema with fact and dimension tables

Using HDFS

Setting up Spark

Clustering data with Spark

Signal Processing and Timeseries

Introduction

Spectral analysis with periodograms

Estimating power spectral density with the Welch method

Analyzing peaks

Measuring phase synchronization

Exponential smoothing

Evaluating smoothing

Using the Lomb-Scargle periodogram

Analyzing the frequency spectrum of audio

Analyzing signals with the discrete cosine transform

Block bootstrapping time series data

Moving block bootstrapping time series data

Applying the discrete wavelet transform

Selecting Stocks with Financial Data Analysis

Introduction

Computing simple and log returns

Ranking stocks with the Sharpe ratio and liquidity

Ranking stocks with the Calmar and Sortino ratios

Analyzing returns statistics

Correlating individual stocks with the broader market

Exploring risk and return

Examining the market with the non-parametric runs test

Testing for random walks

Determining market efficiency with autoregressive models

Creating tables for a stock prices database

Populating the stock prices database

Optimizing an equal weights two-asset portfolio

Text Mining and Social Network Analysis

Introduction

Creating a categorized corpus

Tokenizing news articles in sentences and words

Stemming, lemmatizing, filtering, and TF-IDF scores

Recognizing named entities

Extracting topics with non-negative matrix factorization

Implementing a basic terms database

Computing social network density

Calculating social network closeness centrality

Determining the betweenness centrality

Estimating the average clustering coefficient

Calculating the assortativity coefficient of a graph

Getting the clique number of a graph

Creating a document graph with cosine similarity

Ensemble Learning and Dimensionality Reduction

Introduction

Recursively eliminating features

Applying principal component analysis for dimension reduction

Applying linear discriminant analysis for dimension reduction

Stacking and majority voting for multiple models

Learning with random forests

Fitting noisy data with the RANSAC algorithm

Bagging to improve results

Boosting for better learning

Nesting cross-validation

Reusing models with joblib

Hierarchically clustering data

Taking a Theano tour

Evaluating Classifiers, Regressors, and Clusters

Introduction

Getting classification straight with the confusion matrix

Computing precision, recall, and F1-score

Examining a receiver operating characteristic and the area under a curve

Visualizing the goodness of fit

Computing MSE and median absolute error

Evaluating clusters with the mean silhouette coefficient

Comparing results with a dummy classifier

Determining MAPE and MPE

Comparing with a dummy regressor

Calculating the mean absolute error and the residual sum of squares

Examining the kappa of classification

Taking a look at the Matthews correlation coefficient

Analyzing Images

Introduction

Setting up OpenCV

Applying Scale-Invariant Feature Transform (SIFT)

Detecting features with SURF

Quantizing colors

Denoising images

Extracting patches from an image

Detecting faces with Haar cascades

Searching for bright stars

Extracting metadata from images

Extracting texture features from images

Applying hierarchical clustering on images

Segmenting images with spectral clustering

Parallelism and Performance

Introduction

Just-in-time compiling with Numba

Speeding up numerical expressions with Numexpr

Running multiple threads with the threading module

Launching multiple tasks with the concurrent.futures module

Accessing resources asynchronously with the asyncio module

Distributed processing with execnet

Profiling memory usage

Calculating the mean, variance, skewness, and kurtosis on the fly

Caching with a least recently used cache

Caching HTTP requests

Streaming counting with the Count-min sketch

Harnessing the power of the GPU with OpenCL

Glossary

Function Reference

IPython

Matplotlib

NumPy

pandas

Scikit-learn

SciPy

Seaborn

Statsmodels

Online Resources

IPython notebooks and open data

Mathematics and statistics

Tips and Tricks for Command-Line and Miscellaneous Tools

Reproducible sessions

Docker tips

Index

Customer Reviews

5 star

4 star

3 star

2 star

1 star

Applying linear discriminant analysis for dimension reduction

Linear discriminant analysis (LDA) is an algorithm that looks for a linear combination of features in order to distinguish between classes. It can be used for classification or dimensionality reduction by projecting to a lower dimensional subspace. LDA requires a target attribute both for classification and dimensionality reduction.

If we represent class densities as multivariate Gaussians, then LDA assumes that the classes have the same covariance matrix. We can use training data to estimate the parameters of the class distributions.

In scikit-learn, lda.LDA has been deprecated in 0.17 and renamed discriminant_analysis.LinearDiscriminantAnalysis. The default solver of this class uses singular value decomposition, does not need to calculate the covariance matrix, and is therefore fast.

How to do it...

The code is in the applying_lda.ipynb file in this book's code bundle:

The imports are as follows:
```
import dautil as dl
from sklearn...
```

Python Data Analysis Cookbook

By : Ivan Idris

Python Data Analysis Cookbook

By: Ivan Idris

Overview of this book

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Current Title:

Python Data Analysis Cookbook

Applying linear discriminant analysis for dimension reduction

How to do it...