پایتون ( Machine Learning | Data Science )

چیت شیت شماره ۲۲ از مجموعه ی هر روز یک Cheat Sheet :

🔖 ده چیت شیت پایتون و کتاب‌خانه‌های پایتون

👉Python Cheatsheet

Based on the book Automate the Boring Stuff with Python and many other sources
https://lnkd.in/grD8XUS6

👉Pandas cheat sheet

https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf

👉NumPy cheat sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Numpy_Python_Cheat_Sheet.pdf

👉Matplotlib cheat sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Matplotlib_Cheat_Sheet.pdf

👉Season cheat sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Seaborn_Cheat_Sheet.pdf

👉Scikit-learn cheat sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf

👉Tensorflow cheat sheet

https://www.altoros.com/tensorflow-cheat-sheet.html

👉Keras cheat sheet

https://www.datacamp.com/cheat-sheet/keras-cheat-sheet-neural-networks-in-python

👉PyTorch cheat sheet

https://www.sznajdman.com/wp-content/uploads/2018/02/pytorch-cheat.jpg

👉Scipy cheat sheet

https://www.datacamp.com/cheat-sheet/scipy-cheat-sheet-linear-algebra-in-python

#cheat_sheet #Python #library

🆔 @Python4all_pro

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مجموعه چیت شیت های کاربردی

1. Python: https://t.co/piKNCap4OM
2. Pandas : https://t.co/khYH8blvbp
3. NumPy: https://t.co/bLQJ7QwpLJ
4. Matplotlib: https://t.co/FetNAcfbNs
5. Seaborn: https://t.co/M5ATDFl74d
6. Scikit-learn? https://t.co/3G7xEehIWC
7. TensorFlow: https://t.co/YEpQ9XO8le
8. Keras: https://t.co/3f4oKzUkkz
9. PyTorch: https://t.co/EwvqWF0gtN
10. SQL: https://t.co/qkePCfAgdD
11. R: https://t.co/BHhM7z6YE5
12. Git: https://t.co/tn4BdsdyJP
13. AWS https://t.co/ZQ1JckpXtM
14. Azure https://t.co/CM3ORVWR9s
15. Google Cloud Platform: https://t.co/q1eRcWJ5kH
16. Dockr : https://t.co/2ncSV6K2gl
17. Kubernetes https://t.co/mjo3mwcR5F
18. Linux Command Line: https://t.co/vKmygIJ68B
19. Jupyter Notebook https://t.co/9Bet0esetC
20. Data Wrangling:https://t.co/0sexNfthZG
21. Data Visualization: https://t.co/hKWpqsTvvt

#cheat_sheet #Python #library

🆔 @Python4all_pro

www.pythoncheatsheet.org

Python Cheatsheet

The Python Cheatsheet

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3.09K views13:40

پایتون ( Machine Learning | Data Science )

Python Pandas Cheat Sheet

#cheat_sheet #Python
🆔 @Python4all_pro

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3.27K views10:05

پایتون ( Machine Learning | Data Science )

2.58K views06:37

پایتون ( Machine Learning | Data Science )

#NumPy cheat sheet for #datascience :

*Array Creation*

1. numpy.array() - Create an array from a list or other iterable.
2. numpy.zeros() - Create an array filled with zeros.
3. numpy.ones() - Create an array filled with ones.
4. numpy.empty() - Create an empty array.
5. numpy.arange() - Create an array with evenly spaced values.
6. numpy.linspace() - Create an array with evenly spaced values.

*Array Operations*

1. + - Element-wise addition.
2. - - Element-wise subtraction.
3. * - Element-wise multiplication.
4. / - Element-wise division.
5. ** - Element-wise exponentiation.
6. numpy.sum() - Sum of all elements.
7. numpy.mean() - Mean of all elements.
8. numpy.median() - Median of all elements.
9. numpy.std() - Standard deviation.
10. numpy.var() - Variance.

*Array Indexing*

1. arr[i] - Access ith element.
2. arr[i:j] - Access slice from ith to jth element.
3. arr[i:j:k] - Access slice with step k.

*Array Reshaping*

1. arr.reshape() - Reshape array.
2. arr.flatten() - Flatten array.
3. arr.ravel() - Flatten array.

*Array Manipulation*

1. numpy.concatenate() - Concatenate arrays.
2. numpy.split() - Split array.
3. numpy.transpose() - Transpose array.
4. numpy.flip() - Flip array.

*Mathematical Functions*

1. numpy.sin() - Sine.
2. numpy.cos() - Cosine.
3. numpy.tan() - Tangent.
4. numpy.exp() - Exponential.
5. numpy.log() - Natural logarithm.

*Statistical Functions*

1. numpy.min() - Minimum value.
2. numpy.max() - Maximum value.
3. numpy.percentile() - Percentile.
4. numpy.quantile() - Quantile.

*Random Number Generation*

1. numpy.random.rand() - Random numbers.
2. numpy.random.normal() - Normal distribution.
3. numpy.random.uniform() - Uniform distribution.

*Linear Algebra*

1. numpy.dot() - Dot product.
2. numpy.matmul() - Matrix multiplication.
3. numpy.linalg.inv() - Matrix inverse.

#cheat_sheet #Python
🆔 @Python4all_pro

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پایتون ( Machine Learning | Data Science )

Python Cheatsheet

#cheat_sheet #Python

🆔 @Python4all_pro

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3.76K views08:12

پایتون ( Machine Learning | Data Science )

Python Cheat-Sheet - A Quick Reference Guide for Programmers

#cheat_sheet #Python

🆔 @Python4all_pro

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3.66K views10:02

پایتون ( Machine Learning | Data Science )

۱۵ چیت شیت machine learning و Data Science

https://blog.dailydoseofds.com/p/15-dsml-cheat-sheets

#cheat_sheet #Python #DataScience #MachineLearning

🆔 @Python4all_pro

Dailydoseofds

15 DS/ML Cheat Sheets

Single frame summaries of must-know DS/ML concepts and techniques.

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2.56K views09:50

پایتون ( Machine Learning | Data Science )

Python cheatsheet

📚 Guide

#پایتون #cheat_sheet

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@Python4all_pro

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پایتون ( Machine Learning | Data Science )

New to Pandas?

Here's a cheat sheet you can use

#پایتون #cheat_sheet #pandas

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@Python4all_pro

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پایتون ( Machine Learning | Data Science )

Quick Python cheatsheet

#پایتون #cheat_sheet #library

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پایتون ( Machine Learning | Data Science )

image_2025-08-17_09-32-25.png

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👈چت شیت برای مصاحبه پایتون

1. Swap variables without a temporary one

a, b = 5, 10
a, b = b, a

2. One-line if-else (ternary)

result = "Even" if x % 2 == 0 else "Odd"

3. List Comprehension

squares = [x**2 for x in range(10)]
evens = [x for x in range(10) if x % 2 == 0]

4. Set and Dict Comprehension

unique = {x for x in [1,2,2,3]}        # remove duplicates
squares = {x: x**2 for x in range(5)}  # dict comprehension

5. Most common element in a list

from collections import Counter
most_common = Counter(['a','b','a','c']).most_common(1)[0][0]

6. Merging dictionaries (Python 3.9+)

a = {'x': 1}
b = {'y': 2}
merged = a | b

7. Returning multiple values

def stats(x):
    return max(x), min(x), sum(x)

high, low, total = stats([1, 2, 3])

8. Using zip to iterate over two lists

names = ['a', 'b']
scores = [90, 85]

for n, s in zip(names, scores):
    print(f"{n}: {s}")

9. Flattening nested lists

nested = [[1,2], [3,4]]
flat = [item for sublist in nested for item in sublist]

10. Default values in a dictionary

from collections import defaultdict
d = defaultdict(int)
d['apple'] += 1   # no KeyError

11. Lambda in one line

square = lambda x: x**2
print(square(4))

12. enumerate with index

for i, v in enumerate(['a', 'b', 'c']):
    print(i, v)

13. Sorting by key or value

d = {'a': 3, 'b': 1, 'c': 2}
sorted_by_val = sorted(d.items(), key=lambda x: x[1])

14. Reading file lines into a list

with open('file.txt') as f:
    lines = f.read().splitlines()

15. Type Hints

def add(x: int, y: int) -> int:
    return x + y

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#پایتون #Python

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