After laying the foundation in Introduction to matplotlib and Introduction to matplotlib – Part 2 I want to show you another important chart Bar Charts A bar chart is useful to show total values over time e.g. the revenue of a company. years = (2017, 2018, 2019) revenue = (5000, 7000, 9000) plt.bar(years, revenue, width=0.35)…
# import and instantiate model from sklearn.linear_model import LinearRegression model = LinearRegression() #prepare test data features_train = df_train.loc[:, ‘feature_name’] target_train = df_train.loc[:, ‘target_name’] #fit (train) model and print coefficient and intercept model.fit(features_train , target_train ) print(model.coef_) print(model.intercept_) # calculate model quality from sklearn.metrics import mean_squared_error from sklearn.metrics import r2_score target_prediction = model.predict(features_train) print(mean_squared_error(target_train , target_prediction))…
This article is part of my SQL-Tutorial Motivation Sometimes you need to retrieve data from a database without knowing the exact string to look up so wildcards for the rescue Underscore Se_en represents a pattern with a wildcard character. The _ means you can substitute any individual character here without breaking the pattern. It matches…
As the Junior Data Scientist of Knight Industries we created a table to keep track of all our operatives: SQL-Basics: Create, Read, Update, Delete Devon asks us to keep track of our operations aka missions as well. For the first implementation let us assume that a mission has one operative and an operative can participate…
In SQL-Basics we learned the standard SQL statements to create a table, insert data into it, retrieving data from tables and altering data. Now we want to do even Devon is a data driven man and to evaluate our mission, he likes to know from us: how many missions we finished the total cost of…
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…
<=> is the NULL-safe equals operator SELECT * FROM reports WHERE NOT driver <=> user_id
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…
Overview matplotlib is the workhorse of data science visualization. The module pyplot gives us MATLAB like plots. You can install it via pip install matplotlib The most basic plot is done with the “plot”-function. It looks like this: import matplotlib.pyplot as plt plt.plot([0, 1, 2, 3], [0, 1, 2, 3]) plt.show() The plot function takes…
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…