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Python Data Analysis, Second Edition

Python Data Analysis, Second Edition

By : Ivan Idris
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Python Data Analysis, Second Edition

Python Data Analysis, Second Edition

4 (4)
By: Ivan Idris
Buy this Book

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.
Table of Contents (22 chapters)
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Python Data Analysis - Second Edition
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Python Data Analysis - Second Edition
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
Customer Feedback
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Customer Feedback
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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Installing Python 3
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Using IPython as a shell
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Reading manual pages
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Jupyter Notebook
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NumPy arrays
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A simple application
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Where to find help and references
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Listing modules inside the Python libraries
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Visualizing data using Matplotlib
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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
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References
3
The Pandas Primer
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The Pandas Primer
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Installing and exploring Pandas
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The Pandas DataFrames
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The 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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Summary
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References
4
Statistics and Linear Algebra
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Statistics and Linear Algebra
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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
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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The binary .npy and pickle formats
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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
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Reference
6
Data Visualization
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Data Visualization
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The 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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The statsmodels modules
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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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Storing data in memcache
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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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About 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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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 package
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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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PythonAnywhere Cloud
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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

Summary

We stored annual sunspots cycles data in different relational and NoSQL databases.

The term relational here does not just pertain to relationships between tables; firstly, it has to do with the relationship between columns inside a table; secondly, it relates to connections between tables.

The sqlite3 module in the standard Python distribution can be used to work with a SQLite database. We can give Pandas an SQLite database connection or an SQLAlchemy connection.

SQLAlchemy is renowned for its ORM, based on a design pattern, where Python classes are mapped to database tables. The ORM pattern is a general architectural pattern applicable to other object-oriented programming languages. SQLAlchemy abstracts away the technical details of working with databases, including writing SQL.

MongoDB is a document-based store, which can hold a huge amount of data.

In the in-memory mode, Redis is extremely fast, with writing and reading being almost equally fast. Redis is a key-value store that functions...

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