Data Manipulation & Cleaning with Python & R -Part 1

Part 1 — Python Basic related to Data Science

1.What is Python?

Python is a very popular interpreted, high-level, general-purpose programming language. Python’s design philosophy emphasizes code readability with its notable use of significant whitespace

Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.

2. Python IDEs

There are popular IDEs that can use with python related to the Data science field. An IDE (Integrated Development Environment) understands your code much better than a text editor. It usually provides features such as build automation, code linting, testing, and debugging.

E.g: Jupyter, Spyder, Pycharm, Anaconda, IDLE

3.Variables in Python

A Python variable is a reserved memory location to store values In other words, a variable in a python program gives data to the computer for processing

4. Comments in Python

In computer programming, a comment is a programmer readable explanation or annotation in the source code of a computer program, They are added with the purpose of making the source code easier for humans to understand, and are generally ignored by compilers and interpreters

5.Primitive Data Types of Python

Data types are pre-defined and supported by the programming language. Python has 4 primitive data types

  • Integers

6. Compound Data Types

Compound data types in Python are quite similar to collections in Java. We have lists, tuples, sets, and dictionaries.

a. Lists

  • Lists are used to store multiple items in a single variable

b. Tuples

  • Tuples are used to store multiple items in a single variable

c. Sets

  • Sets are used to store multiple items in a single variable

d. Dictionary

  • Dictionaries have keys and values

7. What are Python functions

  • Python functions are repeated blocks that will be used in several places in a program

8. What is Python Strings

  • A primitive data type of Python

In this article, we are discussed Python basics which are related to Data Science. Data wrangling and preprocessing parts are discussed in Part 2 of the next article.

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