Useful Functions | Python Data Analysis, Second Edition

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Python Data Analysis, Second Edition

By : Ivan Idris

4 (4)

Python Data Analysis, Second Edition

4 (4)

By: Ivan Idris

Overview of this book

Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.

Python Data Analysis - Second Edition

Python Data Analysis - Second Edition

Credits

Credits

About the Author

About the Author

About the Reviewers

About the Reviewers

www.PacktPub.com

www.PacktPub.com

Customer Feedback

Customer Feedback

Preface

Preface

Free Chapter

Getting Started with Python Libraries

Getting Started with Python Libraries

Installing Python 3

Using IPython as a shell

Reading manual pages

Jupyter Notebook

NumPy arrays

A simple application

Where to find help and references

Listing modules inside the Python libraries

Visualizing data using Matplotlib

Summary

NumPy Arrays

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

References

The Pandas Primer

The Pandas Primer

Installing and exploring Pandas

The Pandas DataFrames

The 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

Summary

References

Statistics and Linear Algebra

Statistics and Linear Algebra

Basic descriptive statistics with NumPy

Linear algebra with NumPy

Finding eigenvalues and eigenvectors with NumPy

NumPy random numbers

Creating a NumPy masked array

Summary

Retrieving, Processing, and Storing Data

Retrieving, Processing, and Storing Data

Writing CSV files with NumPy and Pandas

The binary .npy and pickle formats

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

Reference

Data Visualization

Data Visualization

The 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

Signal Processing and Time Series

Signal Processing and Time Series

The statsmodels modules

Moving averages

Window functions

Defining cointegration

Autocorrelation

Autoregressive models

ARMA models

Generating periodic signals

Fourier analysis

Spectral analysis

Filtering

Summary

Working with Databases

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

Storing data in memcache

Apache Cassandra

Summary

Analyzing Textual Data and Social Media

Analyzing Textual Data and Social Media

Installing NLTK

About 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

Predictive Analytics and Machine Learning

Predictive Analytics and Machine Learning

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

Environments Outside the Python Ecosystem and Cloud Computing

Environments Outside the Python Ecosystem and Cloud Computing

Exchanging information with Matlab/Octave

Installing rpy2 package

Interfacing with R

Sending NumPy arrays to Java

Integrating SWIG and NumPy

Integrating Boost and Python

Using Fortran code through f2py

PythonAnywhere Cloud

Summary

Performance Tuning, Profiling, and Concurrency

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

Key Concepts

Key Concepts

Useful Functions

Useful Functions

Matplotlib

NumPy

Pandas

Scikit-learn

SciPy

Online Resources

Online Resources

Customer Reviews

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Matplotlib

The following are useful matplotlib functions:

matplotlib.pyplot.axis(*v, **kwargs): This is the method to get or set axis properties. For example, axis('off') turns off the axis lines and labels.

matplotlib.pyplot.figure(num=None, figsize=None, dpi=None, facecolor=None, edgecolor=None, frameon=True, FigureClass=<class 'matplotlib.figure.Figure'>, **kwargs): This function creates a new figure.

matplotlib.pyplot.grid(b=None, which='major', axis='both', **kwargs): This function turns the plot grids on or off.

matplotlib.pyplot.hist(x, bins=10, range=None, normed=False, weights=None, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, color=None, label=None, stacked=False, hold=None, **kwargs): This function plots a histogram.

matplotlib.pyplot.imshow(X, cmap=None, norm=None, aspect=None, interpolation=None, alpha=None, vmin=None, vmax=None, origin=None, extent=None, shape=None, filternorm=1, filterrad=4.0, imlim=None, resample=None, url=None, hold=None, **kwargs): This function displays an image for array-like data.

matplotlib.pyplot.legend(*args, **kwargs): This function shows a legend at an optionally specified location (for instance, plt.legend(loc='best')).

matplotlib.pyplot.plot(*args, **kwargs): This function creates a two-dimensional plot with single or multiple (x, y) pairs and a corresponding optional format string.

matplotlib.pyplot.scatter(x, y, s=20, c='b', marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=None, hold=None, **kwargs): This function creates a scatter plot of two arrays.

matplotlib.pyplot.show(*args, **kw): This function displays a plot.

matplotlib.pyplot.subplot(*args, **kwargs): This function creates subplots if the row number, column number, and index number of the plot are given. All these numbers start from one. For instance, plt.subplot(221) creates the first subplot in a two-by-two grid.

matplotlib.pyplot.title(s, *args, **kwargs): This function puts a title on the plot.

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