Today, I will share 10 very practical python learning materials on Github.
If you are learning python, try these projects.
1. Python
- url: github.com/TheAlgorithms/Python
- star: 150000
- fork: 38000
- watch: 5900
The Python implementation of all algorithms is gathered here, which is a collection of algorithms in python language, which can be used for teaching work. This includes search algorithms, sorting algorithms, data structures, machine learning, encryption algorithms, neural networks, and more.
2. Python-100-Days
- url: github.com/jackfrued/Python-100-Days
- star: 126000
- fork: 47000
- watch: 6200
The e-book "Python 100 Days From Novice to Master" is an introductory learning material for Python, and the learning difficulty is relatively low. Novices can also learn quickly. The knowledge points in the first 15 days may be difficult for friends with zero foundation to learn. There is also a video explanation version, Portal->
3. PythonDataScienceHandbook
- url: github.com/jakevdp/PythonDataScienceHandbook
- star: 37000
- fork: 16000
- watch: 1800
As the name suggests, this is the original English online book "Python Data Science Handbook".
This book is very helpful for those who want to work in data science, and it is an important learning manual for Python engineers.
4. 500lines
- url: github.com/aosabook/500lines
- star: 28000
- fork: 5900
- watch: 1800
What can be written in less than 500 lines of Python code? This project will inspire you a lot. Each project is developed by a master in the industry.
The project itself is an English version, and the Chinese translation version is still in progress. Interested partners can join the project.
5. python-guide
- url: github.com/realpython/python-guide
- star: 25000
- fork: 5800
- watch: 1400
The author of this project, kennethreitz, is also the author of the Requests library. The project is an introductory Python tutorial. From the grammatical level, to project structure, code style, advanced, tools and other aspects are introduced. Although the English version of the project book is not difficult, it is easy to understand, and the Chinese translation version is also being produced, go and have a look, it is beneficial to open the book.
6. 30-Days-Of-Python
- url: github.com/Asabeneh/30-Days-Of-Python
- star: 16000
- fork: 3600
- watch: 598
This is a free zero-based Python tutorial. Although the project itself is in English, the requirements for English are not high, and the reading barrier is relatively small, so you can use it with confidence.
7. learn-python
- url: github.com/trekhleb/learn-python
- star: 13000
- fork: 2200
- watch: 724
This free tutorial is a learning resource for Python explained in code and comments. All the grammar and knowledge points of Python are explained by using actual code as an example, together with notes and reference materials, so that you can quickly master the basic knowledge of Python.
This project is not only a tutorial for beginners to learn Python, but also a cheat sheet for reviewing knowledge points in the future.
"""WHILE statement
@see: https://docs.python.org/3/tutorial/controlflow.html
@see: https://docs.python.org/3/reference/compound_stmts.html#the-while-statement
The while loop executes as long as the condition remains true. In Python, like in C, any
non-zero integer value is true; zero is false. The condition may also be a string or list
value, in fact any sequence; anything with a non-zero length is true, empty sequences are
false.
The test used in the example is a simple comparison. The standard comparison operators are
written the same as in C: < (less than), > (greater than), == (equal to), <= (less than or
equal to), >= (greater than or equal to) and != (not equal to).
"""
def test_while_statement():
"""WHILE statement"""
# Let's raise the number to certain power using while loop.
number = 2
power = 5
result = 1
while power > 0:
result *= number
power -= 1
# 2^5 = 32
assert result == 32
8. ds-cheatsheets
- url: github.com/FavioVazquez/ds-cheatsheets
- star: 12000
- fork: 3400
- watch: 509
This is a cheat sheet for data science in Python, including the following common libraries: Pandas, Jupyter, SQL, Dask, etc.
Although they are all basic API calls, if you can master all the codes in the project proficiently, it is enough to deal with 90% of the data science Python application scenarios.
9. MLAlgorithms
- url: github.com/rushter/MLAlgorithms
- star: 9100
- fork: 1600
- watch: 415
Here are common machine learning algorithms, implemented in Python, including:
- Linear regression, logistic regression
- K-Means
- Support vector machine (SVM) with kernels (Linear, Poly, RBF)
- Random Forests
- Deep learning (MLP, CNN, RNN, LSTM)
- etc.
10. practical-python
- url: github.com/dabeaz-course/practical-python
- star: 8200
- fork: 4900
- watch: 344
Author David Beazley is the author of "Python Cookbook Third Edition" and "Python Reference Manual". This open source project is a free, introductory level tutorial on Python that is taught and includes exercises.
The tutorial directory is as follows:
I hope the above 10 items will help you.
Recommended reading:
If you want to know how to learn python systematically, what preparations you need to do, and what content to learn, you can click the link below to get information: