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

Mostrar otras versiones (1)

Annotation

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Heydt, Michael (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing, 2015.
Colección:Community experience distilled.
Temas:
Python (Computer program language)
Electronic data processing.
Python (Langage de programmation)
COMPUTER SCIENCE.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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

Ejemplares similares