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I'm using Seaborn's FacetGrid to plot some histograms, and I think the automatic bin sizing uses just the data of each category (rather than each subplot), which leads to some weird results (see skinny green bins in y = 2):
g = sns.FacetGrid(df, row='y', hue='category', size=3, aspect=2, sharex='none')
_ = g.map(plt.hist, 'x', alpha=0.6)
Is there a way (using Seaborn, not falling back to matplotlib) to make the histogram bin sizes equal for each plot?
I know I can specify all the bin widths manually, but that forces all the histograms to be the same x range (see notebook).
Notebook: https://gist.github.com/alexlouden/42b5983f5106ec92c092f8a2697847e6
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bins. The bins parameter enables you to control the bins of the histogram (i.e., the number of bars). The most common way to do this is to set the number of bins by providing an integer as the argument to the parameter. For example, if you set bins = 30 , the function will create a histogram with 30 bars (i.e., bins).
Histograms represent the data distribution by forming bins along the range of the data and then drawing bars to show the number of observations that fall in each bin. Seaborn comes with some datasets and we have used few datasets in our previous chapters.
Seaborn enables us to plot both the histogram bars as well as a density curve obtained the same way than kdeplots. With Seaborn, histograms are made using the histplot function. You can call the function with default values, what already gives a nice chart.
You'll need to define a wrapper function for plt.hist that does the hue grouping itself, something like
%matplotlib inline
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
tips = sns.load_dataset("tips")
tips.loc[tips.time == "Lunch", "total_bill"] *= 2
def multihist(x, hue, n_bins=10, color=None, **kws):
bins = np.linspace(x.min(), x.max(), n_bins)
for _, x_i in x.groupby(hue):
plt.hist(x_i, bins, **kws)
g = sns.FacetGrid(tips, row="time", sharex=False)
g.map(multihist, "total_bill", "smoker", alpha=.5, edgecolor="w")
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