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…
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…
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, 3],…
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…
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,…
I got confused by all the buzzwords: data science, machine learning, deep learning, neural nets, artificial intelligence, big data, and so on and so on. As an engineer I like to put some structure to the chaos. Inspired by Roadmap: How to Learn Machine Learning in 6 Months and Tetiana Ivanova – How to become…