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  • Book Overview & Buying Learning Pandas
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Learning Pandas

Learning Pandas

By : Michael Heydt
4.2 (10)
Buy this Book
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Learning Pandas

Learning Pandas

4.2 (10)
By: Michael Heydt
Buy this Book

Overview of this book

If you are a Python programmer who wants to get started with performing data analysis using pandas and Python, this is the book for you. Some experience with statistical analysis would be helpful but is not mandatory.
Table of Contents (14 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. A Tour of pandas
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1. A Tour of pandas
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pandas and why it is important
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pandas and IPython Notebooks
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Referencing pandas in the application
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Primary pandas objects
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Loading data from files and the Web
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Simplicity of visualization of pandas data
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Summary
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2. Installing pandas
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2. Installing pandas
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Getting Anaconda
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Installing Anaconda
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Ensuring pandas is up to date
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Running a small pandas sample in IPython
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Starting the IPython Notebook server
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Installing and running IPython Notebooks
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Using Wakari for pandas
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Summary
3
3. NumPy for pandas
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3. NumPy for pandas
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Installing and importing NumPy
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Benefits and characteristics of NumPy arrays
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Creating NumPy arrays and performing basic array operations
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Selecting array elements
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Logical operations on arrays
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Slicing arrays
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Reshaping arrays
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Combining arrays
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Splitting arrays
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Useful numerical methods of NumPy arrays
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Summary
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4. The pandas Series Object
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4. The pandas Series Object
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The Series object
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Importing pandas
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Creating Series
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Size, shape, uniqueness, and counts of values
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Peeking at data with heads, tails, and take
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Looking up values in Series
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Arithmetic operations
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The special case of Not-A-Number (NaN)
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Boolean selection
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Reindexing a Series
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Slicing a Series
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Summary
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5. The pandas DataFrame Object
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5. The pandas DataFrame Object
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Creating DataFrame from scratch
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Example data
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Selecting columns of a DataFrame
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Selecting rows and values of a DataFrame using the index
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Selecting rows of a DataFrame by Boolean selection
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Modifying the structure and content of DataFrame
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Arithmetic on a DataFrame
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Resetting and reindexing
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Hierarchical indexing
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Summarized data and descriptive statistics
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Summary
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6. Accessing Data
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6. Accessing Data
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Setting up the IPython notebook
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Reading and writing JSON files
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Accessing data on the web and in the cloud
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Reading and writing from/to SQL databases
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Reading data from remote data services
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Summary
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7. Tidying Up Your Data
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7. Tidying Up Your Data
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What is tidying your data?
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Setting up the IPython notebook
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Working with missing data
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Handling duplicate data
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Transforming Data
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Summary
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8. Combining and Reshaping Data
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8. Combining and Reshaping Data
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Setting up the IPython notebook
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Concatenating data
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Merging and joining data
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Stacking and unstacking
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Performance benefits of stacked data
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Summary
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9. Grouping and Aggregating Data
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9. Grouping and Aggregating Data
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Setting up the IPython notebook
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The split, apply, and combine (SAC) pattern
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Split
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Apply
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Discretization and Binning
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Summary
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10. Time-series Data
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10. Time-series Data
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Setting up the IPython notebook
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Representation of dates, time, and intervals
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Introducing time-series data
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Calculating new dates using offsets
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Handling holidays using calendars
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Normalizing timestamps using time zones
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Manipulating time-series data
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Summary
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11. Visualization
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11. Visualization
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Setting up the IPython notebook
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Plotting basics with pandas
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Common plots used in statistical analyses
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Multiple plots in a single chart
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Summary
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12. Applications to Finance
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12. Applications to Finance
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Setting up the IPython notebook
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Obtaining and organizing stock data from Yahoo!
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Plotting time-series prices
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Performing a moving-average calculation
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Volatility calculation
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Determining risk relative to expected returns
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Summary
13
Index
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Index

Setting up the IPython notebook

We will, as usual, start with some imports and set up options for pandas that facilitate the examples:

In [1]:
   # import pandas and numpy
   import numpy as np
   import pandas as pd

   # Set some pandas options for controlling output
   pd.set_option('display.notebook_repr_html', False)
   pd.set_option('display.max_columns', 10)
   pd.set_option('display.max_rows', 10)

CSV and Text/Tabular format

It is a pretty safe bet to say that Comma Separated Values (CSV) is likely to be the most common format of data that you will deal with in pandas. Many web-based services provide data in a CSV format, as well as many information systems within an enterprise. It is an easy format to use and is commonly used as an export format for spreadsheet applications, such as Excel.

CSV is a file consisting of multiple lines of text-based data with values separated by commas. It can be thought of as a table of data similar to a single sheet in...

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