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Instant Data Intensive Apps with pandas How-to

Book Description

Manipulate, visualize, and analyze your data with Pandas

  • Learn something new in an Instant! A short, fast, focused guide delivering immediate results

  • Follow simple recipes that will teach common tasks when performing data analysis with Pandas

  • Build a data product for displaying information over the web

  • Create visualizations of the data including displaying tables and line graphs

In Detail

Pandas helps to alleviate a genuinely complex situation in data analytics libraries. Many incumbent languages aren't approachable or are fairly unproductive in general computing tasks in comparison to Python. However with Pandas it's easy to begin working with tabular datasets in a language that's easier to learn and use.

Instant Data Intensive Apps with Pandas How-to starts with Pandas’ functionalities such as joining datasets, cleaning data, and other data munging tasks. It quickly moves onto building a data reporting tool, which consists of analysis in Pandas to determine what’s relevant and present that relevant data in an easy-to-consume manner.

Instant Data Intensive Apps with Pandas How-to starts with data manipulation and other practical tasks for a fundamental understanding, and through successive recipes you will gain a more profitable understanding of Pandas.

Throughout this book the recipes are presented in a structured way. It starts with data transformation techniques, but builds up to more complex examples such as performing statistical analysis and integrating Pandas objects with web applications. The other recipes cover visualization and machine learning, among other things.

Instant Data Intensive Apps with Pandas How-to will get the reader up and running quickly with Pandas and put the user in a position to move up the learning curve faster.

Table of Contents

  1. Instant Data Intensive Apps with pandas How-to
    1. Instant Data Intensive Apps with pandas How-to
    2. Credits
    3. About the Author
    4. About the Reviewer
    5. www.PacktPub.com
      1. Support files, eBooks, discount offers and more
        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. Errata
        3. Piracy
        4. Questions
    7. 1. Instant Data-intensive Apps with pandas How-to
      1. Working with files (Simple)
        1. Getting ready
        2. How to do it...
        3. How it works...
        4. There's more...
          1. Parsing dates at file read time
          2. Accessing data from a public source
      2. Slicing pandas objects (Simple)
        1. Getting ready
        2. How to do it...
        3. How it works...
        4. There's more...
          1. Direct index access
          2. Resetting the index
      3. Subsetting data (Simple)
        1. Getting ready
        2. How to do it...
        3. How it works...
        4. There's more...
          1. The where and mask commands
          2. Substituting with the where command
      4. Working with dates (Medium)
        1. Getting ready
        2. How to do it...
        3. How it works...
        4. There's more...
          1. Alternative date range specification
          2. Upsampling and downsampling Series
      5. Modifying data with functions (Simple)
        1. Getting ready
        2. How to do it...
        3. How it works...
        4. There's more...
          1. Other apply options
          2. Alternative solutions
      6. Combining datasets (Medium)
        1. Getting ready
        2. How to do it...
        3. How it works...
        4. There's more...
          1. Merge and join details
          2. Specifying outputs in join
          3. Concatenation
      7. Using indexes to manipulate objects (Medium)
        1. Getting ready
        2. How to do it...
        3. How it works...
        4. There's more...
          1. Advanced header indexes
          2. Performing aggregate operations with indexes
      8. Getting data from the Web (Simple)
        1. Getting ready
        2. How to do it...
        3. How it works...
        4. There's more...
          1. The Next stage
      9. Combining pandas with scikit-learn (Advanced)
        1. Getting ready
        2. How to do it...
        3. How it works...
        4. There's more
          1. The NumPy object
          2. Other tools
      10. Integrating pandas with statistics packages (Advanced)
        1. Getting ready
        2. How to do it...
        3. There's more
      11. Using Flask for the backend (Advanced)
        1. Getting ready
        2. How to do it...
        3. There's more
      12. Visualizing pandas objects (Advanced)
        1. Getting ready
        2. How to do it...
        3. There's more
          1. Additional options for scatter_matrix
          2. Other options for producing plots
      13. Reporting with pandas objects (Medium)
        1. Getting ready
        2. How to do it...
        3. How it works…
        4. There's more
          1. Next steps in Python visualization
          2. The future of pandas