Python Standard Library
Python comes with a vast library of modules that are included in any installation of Python, known as the Python Standard Library.
These modules offer a wide range of functionality that can be used for various tasks such as working with data, networking, file handling, and much more.
Here is a brief introduction to some of the commonly used modules in the Python Standard Library:
os
This module provides a way of interacting with the operating system, allowing you to access system files and directories, work with environment variables, and much more.
sys
This module provides access to some variables used or maintained by the interpreter and to functions that interact strongly with the interpreter. It allows you to manipulate the Python runtime environment and perform system-specific operations.
datetime
This module provides classes for working with dates and times. It allows you to create, manipulate, and format dates and times and perform calculations with them.
math
This module provides mathematical functions such as trigonometric functions, logarithmic functions, and many others. It also includes constants such as pi and e.
random
This module provides functions for generating pseudo-random numbers. It can be used for simulating random events, creating games, and much more.
re
This module provides support for regular expressions, a powerful tool for text processing. It allows you to search for patterns in text, extract specific parts of text, and perform various operations on text.
urllib
This module provides a high-level interface for working with URLs and URIs. It allows you to retrieve data from web pages, download files, and much more.
json
This module provides support for working with JSON (JavaScript Object Notation), a lightweight data interchange format. It allows you to encode and decode JSON data, convert JSON data to Python objects, and vice versa.
argparse
This module provides a way of creating command-line interfaces. It allows you to specify arguments and options for your program and provides help messages and error handling.
Years of Experience
For the most part, teams assume analysts can master basic Python syntax in a matter of weeks.
It’s learning and using the vast array of libraries available that can take many years of experience.
Learning how to use this freely available code can be very valuable.
Official Documentation
Python External Libraries
Python has a vast ecosystem of external libraries for data analytics, visualization, and statistical processing. Here are some of the most popular and widely used libraries:
NumPy
NumPy is a powerful library for numerical computing in Python. It provides a high-performance multidimensional array object, along with tools for working with these arrays. NumPy is widely used in scientific computing and data analysis, and is the foundation for many other Python libraries.
pandas
pandas is a library for data manipulation and analysis. It provides a high-performance DataFrame object for working with structured data, and includes tools for data cleaning, merging, and reshaping. pandas is widely used in data science and machine learning, and is a key component of the PyData ecosystem.
Matplotlib
Matplotlib is a library for creating static, animated, and interactive visualizations in Python. It provides a wide range of plotting tools and options, and can create a variety of charts, plots, and graphs. Matplotlib is widely used in scientific computing, data analysis, and machine learning.
Seaborn
Seaborn is a library for creating statistical visualizations in Python. It provides a high-level interface for creating a variety of statistical charts, plots, and graphs, including heatmaps, bar plots, and scatter plots. Seaborn is built on top of Matplotlib and integrates well with pandas data structures.
Scikit-learn
Scikit-learn is a library for machine learning in Python. It provides tools for data preprocessing, feature selection, model selection, and evaluation, and includes a wide range of supervised and unsupervised learning algorithms. Scikit-learn is widely used in data science and machine learning, and is the foundation for many other Python machine learning libraries.
TensorFlow
TensorFlow is a library for machine learning and deep learning in Python. It provides tools for building and training deep neural networks, and includes a wide range of pre-built models for image recognition, natural language processing, and more. TensorFlow is widely used in artificial intelligence, data science, and machine learning.
PyTorch
PyTorch is a library for machine learning and deep learning in Python. It provides tools for building and training deep neural networks, and includes a wide range of pre-built models for image recognition, natural language processing, and more. PyTorch is known for its dynamic computational graph, which enables flexible and efficient model building.
More
These are just a few of the many external libraries available for data analytics, visualization, and statistical processing in Python.
Each library has its own strengths and use cases, so it’s important to know enough about the major options to be able to choose the right tool for the job.