Distributing your own package on PyPi – Part 2

In Distributing your own package on PyPi I wrote about my first package on PyPI. Here are some refinements aka lessons learned:

Project Description on PyPI

I wondered why the project description on PyPi was empty. Solution: You need a long_description. If You already have a README.md, you can read it into a string and use this as the description.

But you have to add long_description_content_type=’text/markdown’ as well.

from setuptools import setup

# read the contents of your README file
from os import path
this_directory = path.abspath(path.dirname(__file__))
with open(path.join(this_directory, 'README.md'), encoding='utf-8') as f:
    long_description = f.read()

setup(
    name='flask_url_mapping',
    version='0.6',
    packages=['flask_url_mapping'],
    url='https://github.com/jboegeholz/flaskurls',
    download_url='https://github.com/jboegeholz/flaskurls/archive/0.2.tar.gz',
    license='MIT',
    author='Joern Boegeholz',
    author_email='boegeholz.joern@gmail.com',
    description='Django-style URL handling for Flask',
    long_description=long_description,
    long_description_content_type='text/markdown',
    install_requires=["Flask", "Flask-Login"]
)

 

Dependencies of your Package

If your package relies on the usage of other python packages you should add them to your setup.py as well via install_requires.

setup(
    name='flask_url_mapping',
    version='0.6',
    packages=['flask_url_mapping'],
    url='https://github.com/jboegeholz/flaskurls',
    download_url='https://github.com/jboegeholz/flaskurls/archive/0.2.tar.gz',
    license='MIT',
    author='Joern Boegeholz',
    author_email='boegeholz.joern@gmail.com',
    description='Django-style url handling for Flask',
    install_requires=["Flask", "Flask-Login"]
)

Introduction to matplotlib – Part 2

When you finished reading part 1 of the introduction you might have wondered how to draw more than one line or curve into on plot. I will show you now.

To make it a bit more interesting we generate two functions: sine and cosine. We generate our x-values with numpy’s linspace function

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 2*np.pi)

sin = np.sin(x)
cos = np.cos(x)

plt.plot(x, sin, color='b')
plt.plot(x, cos, color='r')
plt.show()

You can plot two or more curves by repeatedly calling the plot method.

That’s fine as long as the individual plots share the same axis-description and values.

Subplots

fig = plt.figure()
p1 = fig.add_subplot(2, 1, 1)
p2 = fig.add_subplot(2, 1, 2)
p1.plot(x, sin, c='b')
p2.plot(x, cos, c='r'

The add_subplot method allows us to put many plots into one “parent” plot aka figure. The arguments are (number_of_rows, number_of_columns, place in the matrix) So in this example we have 2 rows in 1 column, sine is in first, cosine in second position:

when you have a 2 by 2 matrix it is counted from columns to row

fig = plt.figure()
p1 = fig.add_subplot(221)
p2 = fig.add_subplot(222)
p3 = fig.add_subplot(223)
p4 = fig.add_subplot(224)
p1.plot(x, sin, c='b')
p2.plot(x, cos, c='r')
p3.plot(x, -sin, c='g')
p4.plot(x, -cos, c='y')

The code is available as a Jupyter Notebook on my github

Python datetime and format

One of the things I always forget is date and time in Python.
So message to myself:

the strftime method is used for formatting (string_format_time)

import datetime
start_date = datetime.datetime.now()
DATE_FORMAT = '%d/%m/%Y %H:%M'
print(start_date.strftime(DATE_FORMAT))

Cheatsheet

Checking test coverage with pytest-cov

Test coverage

I wanted to analyze my python package flaskurls for test coverage. this is how you can do it:

pipenv install pytest-cov
py.test --cov=flask_url_mapping tests/
----------- coverage: platform win32, python 3.6.5-final-0 -----------
Name                              Stmts   Miss  Cover
-----------------------------------------------------
flask_url_mapping\__init__.py         1      0   100%
flask_url_mapping\flask_urls.py      73      0   100%
-----------------------------------------------------
TOTAL                                74      0   100%


========================== 10 passed in 0.60 seconds ==========================

10 things I didn’t know about Data Science a year ago

In my article My personal road map for learning data science in 2018 I wrote about how I try to tackle the data science knowledge sphere. Due to the fact that 2018 is slowly coming to an end I think it is time for a little wrap up.

What are the things I learned about Data Science in 2018? Here we go:

1. The difference between Data Science, Machine Learning, Deep Learning and AI

Continue reading “10 things I didn’t know about Data Science a year ago”

The Agile Manifesto

When you are working in an agile team e.g. Scrum you might have heard about the agile manifesto. Formulated in 2001 it influenced a lot of software developers and methodologies like Scrum.

The Agile Manifesto consists of 4 values and 12 principles:

Values

Principles

  1. Our highest priority is to satisfy the customer through early and continuous delivery of valuable software.
  2. Welcome changing requirements, even late in development. Agile processes harness change for the customer’s competitive advantage.
  3. Deliver working software frequently, from a couple of weeks to a couple of months, with a preference to the shorter timescale.
  4. Business people and developers must work together daily throughout the project.
  5. Build projects around motivated individuals. Give them the environment and support they need, and trust them to get the job done.
  6. The most efficient and effective method of conveying information to and within a development team is face-to-face conversation.
  7. Working software is the primary measure of progress.
  8. Agile processes promote sustainable development. The sponsors, developers, and users should be able to maintain a constant pace indefinitely.
  9. Continuous attention to technical excellence and good design enhances agility.
  10. Simplicity–the art of maximizing the amount of work not done–is essential.
  11. The best architectures, requirements, and designs emerge from self-organizing teams.
  12. At regular intervals, the team reflects on how to become more effective, then tunes and adjusts its behavior accordingly.

Distributing your own package on PyPi

In Regular Expressions Demystified I developed a little python package and distributed it via PyPi.

I wanted to publish my second self-written package as well, but coming back after almost a year, some things have changed in the world of PyPi, i.e. the old tutorials aren’t working anymore.

So I wrote this article to bring some clarity into this topic.

Distutils vs Setuptools

Continue reading “Distributing your own package on PyPi”