Damien Gonot

Python

Homepage / Notes / Computer Science / Programming Languages / Python

Language Features

Strings

'hello'
hello

Numbers

1 + 1
2

Ranges

for i in range(1, 5):
    print(i)
1
2
3
4

List Comprehensions

[x**2 for x in range(5)]
[0, 1, 4, 9, 16]

[x for x in range(10) if x % 2 == 0]
[0, 2, 4, 6, 8]

Classes

Docs: https://docs.python.org/3/tutorial/classes.html

Most basic example:

class MyClass:
    """A simple example class"""
    i = 12345

    def f(self):
        return 'hello world'

About __init()__:

>>> class Complex:
...     def __init__(self, realpart, imagpart):
...         self.r = realpart
...         self.i = imagpart
...
>>> x = Complex(3.0, -4.5)
>>> x.r, x.i
(3.0, -4.5)

Conventions

Single leading underscore: var

Hint to the programmer that it is for internal use only. Not enforced in any way.

Double leading underscore: _var

"Name mangling".

>>> class Test:
...     def __mangled_name(self):
...         pass
...     def normal_name(self):
...         pass
...
>>> t = Test()
>>> [attr for attr in dir(t) if "name" in attr]
['_Test__mangled_name', 'normal_name']

Single trailing underscore: var_

To avoid naming conflicts, for example class_.

Double leading and trailing underscore: var

Reserved for spcial use like __init__.

Single underscore: _

By convention, used as a temporary variable name.

Type hints

Typing

Docs: https://docs.python.org/3/library/typing.html

Not enforced in any way, only there to help.

Basic example

Takes a string, outputs a string:

def greeting(name: str) -> str:
    return 'Hello ' + name

Pattern Matching

Introduced in Python 3.10

def string_age(age):
    match age:
        case x if x < 6:
            return "User is very young"
        case x if 6 <= x < 18:
            return "User is a minor"
        case x if 18 <= x < 65:
            return "User is an adult"
        case x if 65 <= x:
            return "User is probably retired"

print(string_age(2))
print(string_age(12))
print(string_age(22))
print(string_age(90))
User is very young
User is a minor
User is an adult
User is probably retired

itertools

repeat

from itertools import repeat
return list(repeat('A', 3))
AAA

combinations

from itertools import combinations
return list(combinations('ABC', 2))
AB
AC
BC

permutations

from itertools import permutations
return list(permutations('ABC', 2))
AB
AC
BA
BC
CA
CB

batched

from itertools import batched
return list(batched(['green', 'blue', 'red', 'yellow', 'black', 'pink'], 2))

Libraries

NumPy

https://numpy.org/

Scientific Computing

NumPy's main object if the multidimensional array, called ndarray. Different from standard Python array, which only handles one-dimensional arrays. Dimensions are called axes.

pip3 install numpy

Basics

import numpy as np

a = np.arange(15).reshape(3, 5)
print(a)
[[ 0  1  2  3  4]
 [ 5  6  7  8  9]
 [10 11 12 13 14]]

print(a.shape)
(3, 5)

print(a.ndim)
2

print(a.size)
15

Creating

You can create an array from a regular Python list

b = np.array([1, 2, 3, 4])
print(b)
[1 2 3 4]

Or zeros / ones

print(np.zeros((3, 4)))
[[0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]

print(np.ones((2, 3, 4)))
[[[1. 1. 1. 1.]
  [1. 1. 1. 1.]
  [1. 1. 1. 1.]]

 [[1. 1. 1. 1.]
  [1. 1. 1. 1.]
  [1. 1. 1. 1.]]]

Operations

a = np.array([100, 100, 100])
b = np.array([10, 20, 30])
print(a - b)
[90 80 70]

print(b*2)
[20 40 60]

Pandas

https://pandas.pydata.org/

pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool

pip3 install pandas

Basics

Basic object of pandas is the DataFrame. A DataFrame is made of rows and columns, like a spreadsheet. Each column in a DataFrame is a Series.

import pandas as pd

df = pd.DataFrame(
    {
        "Name": [
            "Braund, Mr. Owen Harris",
            "Allen, Mr. William Henry",
            "Bonnell, Miss. Elizabeth",
        ],
        "Age": [22, 35, 58],
        "Sex": ["male", "male", "female"],
    }
)

print(df)
                       Name  Age     Sex
0   Braund, Mr. Owen Harris   22    male
1  Allen, Mr. William Henry   35    male
2  Bonnell, Miss. Elizabeth   58  female

Series

Selecting a single Series

print(df["Age"])
0    22
1    35
2    58
Name: Age, dtype: int64

print(df["Age"].max())
58

print(df["Age"].describe())
count     3.000000
mean     38.333333
std      18.230012
min      22.000000
25%      28.500000
50%      35.000000
75%      46.500000
max      58.000000
Name: Age, dtype: float64

IceCream

https://github.com/gruns/icecream

Never use print() to debug again.

Arrow

https://arrow.readthedocs.io/en/latest/

Better dates & times for Python

Rich

https://github.com/willmcgugan/rich

Rich is a Python library for rich text and beautiful formatting in the terminal.

Kats

https://facebookresearch.github.io/Kats/

One stop shop for time series analysis in Python

Darts

https://github.com/unit8co/darts

A Python library for easy manipulation and forecasting of time series.

Numba

https://numba.pydata.org/

Numba makes Python code fast

Numba is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code.

Pyscript

https://github.com/pyscript/pyscript

PyScript is a Pythonic alternative to Scratch, JSFiddle, and other "easy to use" programming frameworks, with the goal of making the web a friendly, hackable place where anyone can author interesting and interactive applications.

Textual

https://textual.textualize.io/

Textual is a framework for building applications that run within your terminal. Text User Interfaces (TUIs) have a number of advantages over web and desktop apps.

Ibis

https://ibis-project.org/

An open source dataframe library that works with any data system

Resources

https://learnpythontherightway.com/

https://python-poetry.org/

Layman's Guide to Python Built-in Functions

https://www.mattlayman.com/blog/2024/layman-guide-python-built-in-functions/

Python Tricks: A Buffet of Awesome Python Features

by Dan Bader