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…
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…
When you area already familiar with the basic plot from the introduction to matplotlib here is another type of plot used in data science. A very basic visualization is the scatter plot: import numpy as np import matplotlib.pyplot as plt N = 100 x = np.random.rand(N) y = np.random.rand(N) plt.scatter(x, y) plt.show() Color of the…
Pandas is a data analyzing tool. Together with numpy and matplotlib it is part of the data science stack You can install it via pip install pandas Working with real data The data set we are using is the astronauts data set from kaggle: Download Data Set NASA Astronauts from Kaggle During this introduction we…
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…
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}”)
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,…
Lesson 2 of the Udacity Course UD120 – Intro to Machine Learning deals with Naive Bayes classification.
Numpy is a package for scientific computing in Python. It is blazing fast due to its implementation in C. It is often used together with pandas, matplotlib and Jupyter notebooks. Often these packages are referred to as the datascience stack. Installation You can install numpy via pip pip install numpy Basic Usage In the datascience…