rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Migrating from R to Python for data analysis
  name:
    name part: Jatana, Rajat
    role:
      role term: author,
    role:
      role term: speaker.
  name:
    name part: Packt Publishing
    role:
      role term: production company.
  type of resource: moving image
  genre: videorecording
  genre: Instructional films.
  genre: Films de formation.
  origin info:
    place:
      place term: enk
    date issued: 2019
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (178 minutes)
  abstract: Learn: Writing and running Python in IPython. Using Python lists and dictionaries. Creating NumPy arrays. Indexing and slicing in NumPy. Downloading and parsing data files into NumPy and pandas. Using multilevel series in pandas. Aggregating data in pandas. About: R has been the go-to language in data science for the last decade. However, given the latest developments and enhancements in Python's capabilities and its numerous supportive libraries, Python has dethroned R and is the first choice for data science enthusiasts, especially those working on descriptive and prescriptive analytics. In this course, you will be performing data analysis on some popular datasets from Kaggle such as the Red Wine and White Wine analysis datasets. You will see how the coding structure for Python analyses on Jupyter notebooks is drastically simplified using fewer lines of code, with far fewer dependencies. While working with powerful libraries (NumPy and pandas), you will slice and dice data for relevant insights. Moving on, you will also learn about great visualization features which are greatly enhanced, faster, and easier to use in Python 3. By the end of this course, you will be familiar with Python functions and will be able to transform your R code into Python with far fewer lines of code, better performance, and increased speed. All the related code files are placed on GitHub repository.
  target audience: adult
  note: Rajat Jatana.
  note: Title from resource description page (viewed April 23, 2020).
  note: Presenter, Rajat Jatana.
  note: In English.
  subject:
    topic: Python (Computer program language)
  subject:
    topic: R (Computer program language)
  subject:
    topic: Data mining
  subject:
    topic: Information visualization
  subject:
    topic: Python (Langage de programmation)
  subject:
    topic: R (Langage de programmation)
  subject:
    topic: Exploration de données (Informatique)
  subject:
    topic: Visualisation de l'information
  subject:
    topic: Data mining
  subject:
    topic: Information visualization
  subject:
    topic: Python (Computer program language)
  subject:
    topic: R (Computer program language)
  related item:
    title info:
      title: Academic Video Online
  identifier: 9781838643690
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: ALSTP
    record creation date: 200423
    record change date: 20241201232331.9
    record identifier: in00005576493
    language of cataloging:
      language term: eng