After working for a couple of years with Python and external dependencies I’ve ran again and again into the same kind of problems. Bad habits Say you have a global python installation under e.g. C:\Python36 on Windows. When you start working on your first python project you want to use external packages and you encounter…
Python is a dynamically typed language which makes it easy and fun to program. But sometimes -especially in bigger projects- it can become quite cumbersome when you just receive errors at run time. Given the hypothetical example where we define a function which multiplies integer: def multiply(a, b): return a * b print(multiply(“I”, “You”)) It…
Diving deeper into data science I started to brush up my knowledge about math especially statistics. The Mother of all Distributions The normal distribution was formulated by Carl Friedrich Gauß in 1809 and can be implemented in Python like the following : def normal_distribution_pdf(x, mu=0, sigma=1): sqrt_two_pi = math.sqrt(2*math.pi) return (1 / (sqrt_two_pi * sigma))…
Motivation Cross-validation is a technique to validate the quality of your machine learning model. For validating your model you split your training data into a training and a test data set. ———————————————– | | | | training data | test data | | | | ———————————————– More training data means a better model, more test…
JuPyteR Do You know the feeling of being already late to a party when encountering something new? But when you actually start telling others about it, you realize that it is not too common knowledge at all, e.g. Jupyter Notebooks. What is a Jupyter notebook? In my own words: a browser-based document-oriented command line style…
Motivation When you are going to learn some data science the aquisition of data is often the first step. To get you started scikit-learn comes with a bunch of so called “toy datasets”. One of them is the Iris dataset. Prerequisites & Imports Besides scikit-learn we will use pandas for data handling and matplotlib with…
HTML <my-component params=’from: “foo”‘></my-component> JS class MyViewModel { constructor(params) { console.log(params[“from”]); } } ko.components.register(‘table-edit’, { template: “, viewModel: { createViewModel(params) { return new TableViewModel(params); }, } });
Sometimes you want to make your python package usable for different situations, e.g. flask or bottle or django. If You want to minimize dependencies You can use an optional dependency in setup.py: extras_require={ ‘flask’: [‘Flask>=0.8’, ‘blinker>=1.1’] } Now you can install the library with: pip install raven[flask]
On Windows You can add the Maven archetype for Vaadin to IntelliJ IDEA the following way: Open: c:\Users\username\.IdeaIC2017.3\system\Maven\Indices\UserArchetypes.xml and add the line: <archetypes> <archetype groupId=”com.vaadin” artifactId=”vaadin-archetype-application” version=”8.3.2″ /> </archetypes Now you can create a new Vaadin project via Maven
To create e.g. x-axis indices you can use the linspace function from numpy. You give it a range (e.g. 0 to 23) and the number of divisions and it will distribute the values evenly across that range. The stop values is included in the resulting value array by default. Example: import numpy as np np.linspace(0,…