Posts tagged with seaborn
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Plotting post hoc tests with Python
When three or more groups of samples are compared (e.g. Using ANOVA/Tukey HSD or Kruskal-Wallis/Dunn), you’ll often see results shown as a boxplot, with lines highlighting which groups are significantly different. In Python there is no single package to do this quickly, though by combining scikit-posthocs with statannotations similar plots...
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Fighting Batch Effects with pyComBat
Even high-end scientific equipment can have a bad day! Machine performance can be affected by ambient temperature, humidity, … so when samples are measured in different batches, this needs to be corrected for. In this post we’ll have a look at the Python package pyComBat which does this elegantly and...
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PCA Plots with Loadings in Python
Like the previous Code Nugget, this bit of code will add some often needed features to PCA plots done with Python. Here the loadings and variance explained will be added to the plot, this is something that is included by default in R’s biplot(), but in Python there is more...
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Correlation Heatmaps with Significance in Python
While Pandas and Seaborn offer very quick ways to calculate correlations and show them in a heatmap. Whether those correlations are statistically significant or not is omitted from those plots. Over the years I’ve collected bits and pieces of code, like this, that turn out to be quite useful. Though...
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An Agent Based Model to look at Gwent Pro Ladder (results)
With the Agent Based Model (ABM) from the previous post we can simulate any number of Gwent players, with known skill and play rate, playing throughout a season. Using this model we’ll try to get a firmer grasp on the ladder system and test whether this rewards skill or grinding...