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  • Book Overview & Buying Python Data Analysis
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Python Data Analysis

Python Data Analysis

By : Ivan Idris
3.9 (16)
Buy this Book
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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 (17 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Getting Started with Python Libraries
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1. 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
2. NumPy Arrays
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2. 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
3. Statistics and Linear Algebra
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3. 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
4. pandas Primer
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4. 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
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5. Retrieving, Processing, and Storing Data
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5. 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
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6. Data Visualization
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6. 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
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7. Signal Processing and Time Series
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7. 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
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8. Working with Databases
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8. 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
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9. Analyzing Textual Data and Social Media
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9. 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
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10. Predictive Analytics and Machine Learning
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10. 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
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11. Environments Outside the Python Ecosystem and Cloud Computing
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11. 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
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12. Performance Tuning, Profiling, and Concurrency
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12. 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
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A. Key Concepts
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A. Key Concepts
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B. Useful Functions
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B. 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
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C. Online Resources
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C. Online Resources
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Index
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Index

Chapter 6. Data Visualization

One of the first steps in data analysis is visualization. Even when looking at a table of values, we can form a mental image of what the data might look like when graphed. Data visualization calls for the conception and analysis of the visual representation of information, signifying data that has been abstracted in some formal pattern, including properties or quantities for units of measurements of the data. Data visualization is tightly associated with scientific visualization and statistical graphics. The Python matplotlib (all lowercase) library is a well-known plotting library based on NumPy, which we will be using in this chapter. It has an object-oriented and a procedural MATLAB-like API, which can be used in parallel. A gallery with matplotlib examples can be found at http://matplotlib.org/gallery.html. The following is a list of topics that will be covered in this chapter:

  • Basic matplotlib plots
  • Logarithmic plots
  • Scatter plots
  • Legends and annotations...

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