import this

The Zen of Python, by Tim Peters

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!

• Official docs
  
• Style guide
  
• Official third-party package repository
  
• Pyhon subreddit, Subreddit for beginners
  
• Python Module of the Week dives into the standard library.
  

• Automate the Boring Stuff Dive into small projects
  
• Codeacademy provides interactive exercises.
  
• MIT OpenCourseware
  
• Think Python
  

• Requests - for making web requests with easy syntax
  
• Pytest - unit tests; cleaner than the built-in module.
  
• SQLalchemy - for working with databases.
  
• BeautifulSoup - for parsing HTML and XML. Recommended over a built-in alternative
  
• Websockets – Live communication betweeen browser and server.
  
• PyQt5 Lets you use the C++ GUI library Qt with Python. Steep learning curve, but QT is a powerful, well-documented way to make desktop and mobile GUIs. Used by Matplotlib, Ipython Qtconsole, and Spyder.
  
• Toolz - functional programming tools like currying, accumulate, take, compose, and pipe. Some of this functionality's included in the built-in itertools and functools libraries, or is easily created from them. You should check those libraries out too!
  

• PyCharm Robust and full-featured. Its community edition is free, and works well for Python. Its Profession edition costs ~$60/year, and includes addition features like web-development tools. Free for students and contributors to open-source projects.
  
• Spyder is targeted at scientific computing, and is simpler than PyCharm. Can be installed with pip, and is included with Anaconda.
  
• If you'd prefer a text-editor with language-specific features, try Visual Studio Code.
  

• Django – Batteries-included and popular. Intimidating to start with, and involves many files working together, but includes most of what you need to build a website. Extensive docs, and many people who can help on StackOverflow.
  
• Flask Minimalist, and easy to start with. Customizable. As your projects grow, you’ll likely want to add other modules for things like database management, migrations, admin, and authentication.
  
• Pyramid and Pylons are other popular frameworks.This page shows an overview of what's available.
  

f(x) = 2x^2

f = lambda x: 2 * x**2

• C: Python runs on C, so it's a natural language to write high-speed extensions in. Rust provides a modern alternative.
  
• HTML, CSS, and Javascript for web development.
  
• SQL, if you use Python to manage databases.
  

• PyPy is a Just-in-time (JIT) compiler for Python that allows a subset of the language to run very fast.
  
• Numba is another way to speed up Python to near-C-speeds with a JIT. By applying a decorator, it can make python functions run much faster, but limits which parts of the language you can use in these functions. It usually requires writing loops manually, where otherwise you might used vectorized code.
  
• Micropython allows you to code custom microcontrollers with Python.
  

• Python Data Science Handbook – Introduction to scientific programming in Python.
  
• Kalman and Bayesian Filters in Python – A detailed introduction to Kalman filters, making heavy use of Python.
  
• Python for Scientific Computing
  

Dominoes fucked around with this message at 16:22 on Apr 23, 2020

def try_this(my_dict: dict, key) -> Union[dict, int]:
    DEFAULT = 10
    try:
        return my_dict[key]
    except KeyError:
        return DEFAULT
    

my_dict = {'a_key': 20, 'b_key': 30}

print(try_this(my_dict, 'a_key'))  # 20
print(try_this(my_dict, 'not_a_key'))  # 10

try_my_dict = functools.partial(try_this, my_dict)


try_my_dict('a_key')  # 20
try_my_dict('not_a_key')  # 10

Dominoes fucked around with this message at 21:24 on Mar 8, 2017

class FastDf:
    def __init__(self, df: pd.DataFrame, convert_to_date: bool=False):
        """Used to improve DataFrame index speed by converting to an array. """
        if convert_to_date:
            # Convert from PD timestamps to datetime.date.
            self.row_indexes = {row.date(): i for i, row in enumerate(df.index)}
        else:
            self.row_indexes = {row: i for i, row in enumerate(df.index)}
        self.column_indexes = {col: i for i, col in enumerate(df.columns)}
        self.array = df.values

    def loc(self, row, col):
        return self.array[self.row_indexes[row], self.column_indexes[col]]

    def loc_range(self, row_start, row_end):
        return self.array[self.row_indexes[row_start]: self.row_indexes[row_end]]

Dominoes fucked around with this message at 12:09 on Mar 31, 2017

Dominoes fucked around with this message at 02:35 on Apr 1, 2017

Dominoes fucked around with this message at 18:46 on Apr 18, 2017

Dominoes fucked around with this message at 11:58 on Jun 1, 2017

class Snake
    def __init__(self, age, personality,  adult):
        self.age = age
        self.personality = personality
        self.adult = adult

class Snake
    def __init__(self, age, personality,  adult):
        self.age = age
        self.personality = personality
        self.adult = adult

        self.length = age * 5 if adult else age * 10

    # Another double-underscore function demonstrating how the arguments you give the new class 
    # instance are now available by self. - prefixed methods; see what it's for below.
    def __string__(self): 
        return f'{A {self.personality} snake {self.age} years old'

martha = Snake(5, 'friendly', True)

print(martha)  # A friendly snake 5 years old

if __name__ == "__main__":
    run_all()

Dominoes fucked around with this message at 12:18 on Jun 1, 2017

conda install numpy

# or, 

pip install numpy

pip freeze 

# or

pip list

# or

conda list  # Which you said you tried.

import numpy as np

np.linspace(0, 1, 100)

Dominoes fucked around with this message at 13:38 on Jun 4, 2017

pip : The term 'pip' is not recognized as the name of a cmdlet, function...

Dominoes fucked around with this message at 16:50 on Jun 4, 2017

Dominoes fucked around with this message at 13:24 on Jun 5, 2017

Dominoes fucked around with this message at 12:16 on Jun 6, 2017

Dominoes fucked around with this message at 14:32 on Jun 6, 2017

Dominoes fucked around with this message at 05:07 on Jun 7, 2017

Dominoes fucked around with this message at 19:41 on Jun 17, 2017

# Install the following packages from Chris Gohlke's site:
# numpy-mkl
# scipy
# llvmlite (Numba prereq)

# Some of the packages below require MS VSC++ build tools; link will show
# in the terminal when attempting to install.

# Scipy stack
pandas
matplotlib
jupyter
sympy


# Science extras
pandas-datareader
scikit-learn
numba  
scikit-image
pillow
seaborn


# Misc
PyQt5
django-toolbelt
twine
wheel
requests
requests_oauthlib
pytest
h5py
toolz
cytoolz
beautifulsoup4
lxml
sqlalchemy
saturn
fplot

Dominoes fucked around with this message at 05:17 on Jul 1, 2017

Dominoes fucked around with this message at 21:22 on Sep 12, 2017

Dominoes fucked around with this message at 16:21 on Sep 22, 2017