This is the second of the series on the python libraries. I do not know how much I will write but remember, these are only learning notes. The inspiration came this time from the ever-present stackoverflow.com, with an article on making matplotlib graphs look like R by deafult. I am learning here, so for the moment I do not exactly know what a R graph should look like, I will know when I will tackle R. However, I am tacking Python, and therefore here we are. The seaborn library looks very interesting. On the web site there are plenty of examples in the gallery, if you click on one of them you will get to the source code used to generate the graphs. My examples are slightly modified versions of the ones on the original page, because I am exploring. Let’s start with the original question of the stackoverflow article:
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%matplotlib inline import numpy as np import matplotlib.pyplot as plt import seaborn seaborn.set(style='ticks') # Data to be represented X = np.random.randn(256) # Actual plotting fig = plt.figure(figsize=(8,6), dpi=72, facecolor="white") axes = plt.subplot(111) heights, positions, patches = axes.hist(X, color='grey') seaborn.despine(ax=axes, offset=10, trim=True) fig.tight_layout() plt.show() |
This originates the following plot:
To have just an idea of how powerful this can be, here another example:
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%matplotlib inline import seaborn as sns import numpy as np sns.set_style("whitegrid") data = np.random.normal(size=(20, 20)) + np.arange(20) / 2 sns.boxplot(data); |
And this is the resulting boxplot:
