Learning Pandas by Heydt, Michael -- Read -- Imperial Library of Trantor

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Learning pandas

Table of Contents Learning pandas Credits About the Author About the Reviewers www.PacktPub.com

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Preface

What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support

Downloading the example code Downloading the color images of this book Errata Piracy Questions

1. A Tour of pandas

pandas and why it is important pandas and IPython Notebooks Referencing pandas in the application Primary pandas objects

The pandas Series object The pandas DataFrame object

Loading data from files and the Web

Loading CSV data from files Loading data from the Web

Simplicity of visualization of pandas data Summary

2. Installing pandas

Getting Anaconda Installing Anaconda

Installing Anaconda on Linux Installing Anaconda on Mac OS X Installing Anaconda on Windows

Ensuring pandas is up to date Running a small pandas sample in IPython Starting the IPython Notebook server Installing and running IPython Notebooks Using Wakari for pandas Summary

3. NumPy for pandas

Installing and importing NumPy Benefits and characteristics of NumPy arrays Creating NumPy arrays and performing basic array operations Selecting array elements Logical operations on arrays Slicing arrays Reshaping arrays Combining arrays Splitting arrays Useful numerical methods of NumPy arrays Summary

4. The pandas Series Object

The Series object Importing pandas Creating Series Size, shape, uniqueness, and counts of values Peeking at data with heads, tails, and take Looking up values in Series

Alignment via index labels

Arithmetic operations The special case of Not-A-Number (NaN) Boolean selection Reindexing a Series

Modifying a Series in-place

Slicing a Series Summary

5. The pandas DataFrame Object

Creating DataFrame from scratch Example data

S&P 500 Monthly stock historical prices

Selecting columns of a DataFrame Selecting rows and values of a DataFrame using the index

Slicing using the [] operator Selecting rows by index label and location: .loc[] and .iloc[] Selecting rows by index label and/or location: .ix[] Scalar lookup by label or location using .at[] and .iat[]

Selecting rows of a DataFrame by Boolean selection Modifying the structure and content of DataFrame

Renaming columns Adding and inserting columns Replacing the contents of a column Deleting columns in a DataFrame Adding rows to a DataFrame

Appending rows with .append() Concatenating DataFrame objects with pd.concat() Adding rows (and columns) via setting with enlargement

Removing rows from a DataFrame

Removing rows using .drop() Removing rows using Boolean selection Removing rows using a slice

Changing scalar values in a DataFrame

Arithmetic on a DataFrame Resetting and reindexing Hierarchical indexing Summarized data and descriptive statistics Summary

6. Accessing Data

Setting up the IPython notebook

CSV and Text/Tabular format

The sample CSV data set Reading a CSV file into a DataFrame Specifying the index column when reading a CSV file Data type inference and specification Specifying column names Specifying specific columns to load Saving DataFrame to a CSV file

General field-delimited data

Handling noise rows in field-delimited data

Reading and writing data in an Excel format

Reading and writing JSON files

Reading HTML data from the Web Reading and writing HDF5 format files

Accessing data on the web and in the cloud Reading and writing from/to SQL databases Reading data from remote data services

Reading stock data from Yahoo! and Google Finance

Retrieving data from Yahoo! Finance Options Reading economic data from the Federal Reserve Bank of St. Louis Accessing Kenneth French's data Reading from the World Bank

Summary

7. Tidying Up Your Data

What is tidying your data? Setting up the IPython notebook Working with missing data

Determining NaN values in Series and DataFrame objects Selecting out or dropping missing data How pandas handles NaN values in mathematical operations Filling in missing data Forward and backward filling of missing values Filling using index labels Interpolation of missing values

Handling duplicate data Transforming Data

Mapping Replacing values Applying functions to transform data

Summary

8. Combining and Reshaping Data

Setting up the IPython notebook Concatenating data Merging and joining data

An overview of merges Specifying the join semantics of a merge operation Pivoting

Stacking and unstacking

Stacking using nonhierarchical indexes Unstacking using hierarchical indexes Melting

Performance benefits of stacked data Summary

9. Grouping and Aggregating Data

Setting up the IPython notebook The split, apply, and combine (SAC) pattern Split

Data for the examples Grouping by a single column's values Accessing the results of grouping Grouping using index levels

Apply

Applying aggregation functions to groups The transformation of group data

An overview of transformation Practical examples of transformation

Filtering groups

Discretization and Binning Summary

10. Time-series Data

Setting up the IPython notebook Representation of dates, time, and intervals

The datetime, day, and time objects Timestamp objects Timedelta

Introducing time-series data

DatetimeIndex Creating time-series data with specific frequencies

Calculating new dates using offsets

Date offsets Anchored offsets Representing durations of time using Period objects The Period object PeriodIndex

Handling holidays using calendars Normalizing timestamps using time zones Manipulating time-series data

Shifting and lagging Frequency conversion Up and down resampling Time-series moving-window operations

Summary

11. Visualization

Setting up the IPython notebook Plotting basics with pandas

Creating time-series charts with .plot() Adorning and styling your time-series plot

Adding a title and changing axes labels Specifying the legend content and position Specifying line colors, styles, thickness, and markers Specifying tick mark locations and tick labels Formatting axes tick date labels using formatters

Common plots used in statistical analyses

Bar plots Histograms Box and whisker charts Area plots Scatter plots Density plot The scatter plot matrix Heatmaps

Multiple plots in a single chart Summary

12. Applications to Finance

Setting up the IPython notebook Obtaining and organizing stock data from Yahoo! Plotting time-series prices

Plotting volume-series data Calculating the simple daily percentage change Calculating simple daily cumulative returns Resampling data from daily to monthly returns Analyzing distribution of returns

Performing a moving-average calculation

The comparison of average daily returns across stocks The correlation of stocks based on the daily percentage change of the closing price

Volatility calculation Determining risk relative to expected returns Summary

Index

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