2022 Data Analysis Python Project
2022 Data Analysis Python Project

Python (programming language)


Python is a high-level,general purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. Its language constructors and object-oriented approach aim to help programmers write clear, logical code for small- and large-scale projects


What are Python Modules?

In Python, Modules are simply files with the “.py” extension containing Python code that can be imported inside another Python Program.

In simple terms, we can consider a module to be the same as a code library or a file that contains a set of functions that you want to include in your application.

With the help of modules, we can organize related functions, classes, or any code block in the same file. So, It is considered a best practice while writing bigger codes for production-level projects in Data Science is to split the large Python code blocks into modules containing up to 300–400 lines of code.

Some of the most popular modules include:

  • NumPy: NumPy is a Python library that provides support for many mathematical tasks on large, multidimensional arrays and matrices.
  • Pandas: The Pandas library is one of the most popular and easy-to-use libraries available. It allows for easy manipulation of tabular data for data cleaning and data analysis.
  • Matplotlib: This library provides simple ways to create static or interactive boxplots, scatterplots, line graphs, and bar charts. It’s useful for simplifying your data visualization tasks.
  • Seaborn: Seaborn is another data visualization library built on top of Matplotlib that allows for visually appealing statistical graphs. It allows you to easily visualize beautiful confidence intervals, distributions, and other graphs.
  • Statsmodels: This statistical modeling library builds all of your statistical models and statistical tests including linear regression, generalized linear models, and time series analysis models.
  • Scipy: Scipy is a library used for scientific computing that helps with linear algebra, optimization, and statistical tasks.
  • Requests: This is a useful library for scraping data from websites. It provides a user-friendly and responsive way to configure HTTP requests.