Mastering Python Data Analysis

Table of contents

1. Mastering Python Data Analysis
   1. Mastering Python Data Analysis
   2. Credits
   3. About the Authors
   4. About the Reviewer
   5. www.PacktPub.com
      1. Why subscribe?
      2. Free access for Packt account holders
   6. Preface
      1. What this book covers
      2. What you need for this book
      3. Who this book is for
      4. Conventions
      5. Reader feedback
      6. Customer support
         1. Downloading the example code
         2. Downloading the color images of this book
         3. Errata
         4. Piracy
         5. Questions
   7. 1. Tools of the Trade
      1. Before you start
      2. Using the notebook interface
      3. Imports
      4. An example using the Pandas library
      5. Summary
   8. 2. Exploring Data
      1. The General Social Survey
         1. Obtaining the data
         2. Reading the data
      2. Univariate data
         1. Histograms
            1. Making things pretty
            2. Characterization
         2. Concept of statistical inference
         3. Numeric summaries and boxplots
      3. Relationships between variables – scatterplots
      4. Summary
   9. 3. Learning About Models
      1. Models and experiments
      2. The cumulative distribution function
      3. Working with distributions
      4. The probability density function
      5. Where do models come from?
      6. Multivariate distributions
      7. Summary
   10. 4. Regression
       1. Introducing linear regression
          1. Getting the dataset
          2. Testing with linear regression
       2. Multivariate regression
          1. Adding economic indicators
          2. Taking a step back
       3. Logistic regression
          1. Some notes
       4. Summary
   11. 5. Clustering
       1. Introduction to cluster finding
          1. Starting out simple – John Snow on cholera
       2. K-means clustering
          1. Suicide rate versus GDP versus absolute latitude
       3. Hierarchical clustering analysis
          1. Reading in and reducing the data
          2. Hierarchical cluster algorithm
       4. Summary
   12. 6. Bayesian Methods
       1. The Bayesian method
          1. Credible versus confidence intervals
          2. Bayes formula
          3. Python packages
       2. U.S. air travel safety record
          1. Getting the NTSB database
          2. Binning the data
          3. Bayesian analysis of the data
             1. Binning by month
          4. Plotting coordinates
             1. Cartopy
             2. Mpl toolkits – basemap
       3. Climate change - CO2 in the atmosphere
          1. Getting the data
          2. Creating and sampling the model
       4. Summary
   13. 7. Supervised and Unsupervised Learning
       1. Introduction to machine learning
       2. Scikit-learn
       3. Linear regression
          1. Climate data
          2. Checking with Bayesian analysis and OLS
       4. Clustering
       5. Seeds classification
          1. Visualizing the data
          2. Feature selection
          3. Classifying the data
             1. The SVC linear kernel
             2. The SVC Radial Basis Function
             3. The SVC polynomial
             4. K-Nearest Neighbour
             5. Random Forest
          4. Choosing your classifier
       6. Summary
   14. 8. Time Series Analysis
       1. Introduction
       2. Pandas and time series data
       3. Indexing and slicing
       4. Resampling, smoothing, and other estimates
       5. Stationarity
       6. Patterns and components
          1. Decomposing components
          2. Differencing
       7. Time series models
          1. Autoregressive – AR
          2. Moving average – MA
          3. Selecting p and q
             1. Automatic function
             2. The (Partial) AutoCorrelation Function
          4. Autoregressive Integrated Moving Average – ARIMA
       8. Summary
   15. A. More on Jupyter Notebook and matplotlib Styles
       1. Jupyter Notebook
          1. Useful keyboard shortcuts
             1. Command mode shortcuts
             2. Edit mode shortcuts
          2. Markdown cells
          3. Notebook Python extensions
             1. Installing the extensions
             2. Codefolding
             3. Collapsible headings
             4. Help panel
             5. Initialization cells
             6. NbExtensions menu item
             7. Ruler
             8. Skip-traceback
             9. Table of contents
          4. Other Jupyter Notebook tips
             1. External connections
             2. Export
             3. Additional file types
       2. Matplotlib styles
       3. Useful resources
          1. General resources
          2. Packages
          3. Data repositories
          4. Visualization of data
       4. Summary