If You already read Python pip and virtualenv you are familiar with the way python handles requirements. but lo and behoild there is a new kid in town or actually two new kids on the block: Pipfile and Pipenv – both with with a capital “P”. If you are tired of creating and maintaining…
The Dark Ages The dark ages are the years between stopping playing with LEGO as a child and starting again to play again as an adult. In my case more than 20 years. Getting back to it When my older daughter turned 4, we decided to give her her first LEGO as a birthday present…
Applying for a data scientist job offer? Tired of writing the same old curriculum vitae? Why not showing your data visualization skills directly in your application? Generate Data Instead of pressing your data about education, employment and skills in a word-like document, put it in tables instead. E.g. use open office to create and edit…
Installation To work with OpenCV from python, you need to install it first: pip install opencv-python Reading Images from file After we import cv2 we can directly work with images like so: import cv2 img = cv2.imread(“doc_brown.png”) For showing the image, it is recommended to use matplotlib import matplotlib.pyplot as plt img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)…
A cool new feature made its way into Python 3.7: Data classes. When You’ve already read my article about Lombok the concept isn’t so new at all: With the new decorator @dataclass You can save a huge amount of time because the methods __init__() __repr__() __eq__() are created for you! from dataclasses import dataclass @dataclass…
Since Python 3.6 there is neat way to put variables into string, called f-strings: name = “joern” age = “37” print(f”My name is {name} and I’m {age}”)
After trying a couple of times to get into a Management 3.0 training I finally had the chance to participate in a two day course in Nuremberg.
Too confused of the confusion matrix? Let me bring some clarity into this topic! Let’s take the example from Precision and Recall: y_true = [“dog”, “dog”, “non-dog”, “non-dog”, “dog”, “dog”] y_pred = [“dog”, “non-dog”, “dog”, “non-dog”, “dog”, “non-dog”] When we look at the prediction we can count the correct and incorrect classifications: dog correctly classified…
With numpy you can easily create test data with random_integers and randint. numpy.random.randint(low, high=None, size=None, dtype=’l’) numpy.random.random_integers(low, high=None, size=None) random_integers includes the high boundary while randint does not. >>> import numpy as np >>> np.random.random_integers(5) 4 >>> np.random.random_integers(5, size=(5)) array([5, 3, 4, 1, 4]) >>> np.random.random_integers(5, size=(5, 4)) array([[2, 3, 3, 5], [1, 3, 1,…
In the realms of Data Science you’ll encounter sooner or the later the terms “Precision” and “Recall”. But what do they mean? Clarification Living together with little kids You very often run into classification issues: My daughter really likes dogs, so seeing a dog is something positive. When she sees a normal dog e.g. a…