Glowingpython.blogspot.com Website Review

A collection of sloppy snippets for scientific computing and data visualization in Python. And here's a function to plot a compact calendar with matplotlib: import numpy as np from matplotlib.patches import Rectangle     # non days are grayed     ax = plt.gca().axes     plt.xticks(np.arange(1,32)+.5, np.arange(1,32))     plt.xlim(1, 32)     # remove borders and ticks     for spine in plt.gca().spines.values(): You can use it to highlight the days with full moon in 2018: Have you ever wanted to check carbon emissions in the UK and never had an easy way to do it? Now you can use the Official Carbon the emissions in the month of May. First, we do*nload the data with a request to the API: url = url % period response = urllib.request.urlopen(url) We organize the result in a DataFrame indexed by timestamps: From the clas*ification provided we extract the thresholds to label emissions in low, high and moderate: Now we group the data by hour of the day and create a boxplot that shows some interesting facts about carbon emissions in May: plt.figure(figsize=(12, 6)) plt.title('UK Carbon Intensity in May 2018') plt.xlabel('hour of the day') plt.legend(loc='upper left', ncol=3,            shadow=True, fancybox=True) plt.show() We notice that the medians almost always falls in the moderate emissions region and in two cases it even falls in the low region. In the early afternoon the medians reach their minimum while the maximum is reached in the evening. It's nice to see that most of the hours present outliers in the low emissions region and only few outliers are in the high region. Isolation Forest is an algorithm to detect outliers. It partitions the data using a set of trees and provides an anomaly scores looking at how isolated is the point in the structure found, the anomaly score is then used to tell apart outliers from normal observations. In this post we will see an example of how IsolationForest behaves in simple case. First, we will generate 1-dimensional data from bimodal