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  • Practical Data Wrangling
  • Allan Visochek
  • 280字
  • 2021-07-02 15:16:04

Python

Python is a generalized programming language used for everything from web development (Django and Flask) to game development, and for scientific and numerical computation. See Python.org/about/apps/.

Python is really useful for data wrangling and scientific computing in general because it emphasizes simplicity, readability, and modularity.

To see this, take a look at a Python implementation of the hello world program, which prints the words Hello World!:

Print("Hello World!")

To do the same thing in Java, another popular programming language, we need something a bit more verbose:

System.out.println("Hello World!");

While this may not seem like a huge difference, extra research and consultation of documentation can add up, adding time to the data wrangling process.

Python also has built-in data structures that are relatively flexible in the way that they handle data.

Data structures are abstractions that help organize the data in a program for easy manipulation. We will explore the various data structures in Python and R in Chapter 2, Introduction to Programming in Python.

This contributes to Python's relative ease of use, particularly when working with data on a low level.

Finally, because of Python's modularity and popularity within the scientific community, there are a number of packages built around Python that can be quite useful to us in data wrangling.

Packages/ modules/ libraries are extensions of a language, or prewritten code in that language--typically built by individual users and the open source community--that add on functionality that is not built into the language. They can be imported in a program to include new tools. We will be leveraging packages throughout the book, both in R and Python, to extract, read, clean, shape, and store data.
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