Learning pandas : get to grips with pandas--a versatile and high-performance Python library for data manipulation, analysis, and discovery / Michael Heydt.

Online resource; title from cover (Safari, viewed May 8, 2015).

Includes index.

Annotation 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.

Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 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; Chapter 2: Installing pandas; Getting Anaconda; Installing Anaconda; Installing Anaconda on Linux; Installing Anaconda on Mac OS X

Installing Anaconda on WindowsEnsuring 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; Chapter 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; Chapter 4: The pandas Series Object

The Series objectImporting 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; Chapter 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 operatorSelecting 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 DataFrameRemoving 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; Chapter 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

English.

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