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I'm fairly new to this, so there might be a very obvious answer to this. My apologies!

I'm plotting two histograms via a groubpy. I'd like my subplots to each have the same x and y labels and a common title. I understood that sharex=True would do the trick, but apparently not if I set the axis only after the df.hist. I've tried various versions of setting the xlabels and am lost now.

import pylab as pl from pandas import * histo_survived = df.groupby('Survived').hist(column='Age', sharex=True, sharey=True) pl.title("Histogram of Ages") pl.xlabel("Age") pl.ylabel("Individuals")

So what I end up with is labels only for the subplot. 

Out: <matplotlib.text.Text at 0x11a27ead0>

Screenshot of my histograms

Any idea on how to solve this? (Have to use pandas/python.)

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40

Labels are properties of axes objects, that needs to be set on each of them. Here's an example that worked for me:

frame = pd.DataFrame([np.random.rand(20), np.sign(np.random.rand(20) - 0.5)]).T frame.columns = ['Age', 'Survived'] # Note that you can let the hist function do the groupby # the function hist returns the list of axes created axarr = frame.hist(column='Age', by = 'Survived', sharex=True, sharey=True, layout = (2, 1)) for ax in axarr.flatten(): ax.set_xlabel("Age") ax.set_ylabel("Individuals")
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Hi, thanks! After a bit of trying I think I now got it! (with leaving the frame.columns line out).
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I came here looking for the same thing, but found it easier to do in plotly

import plotly.express as px px.histogram(df, x="t_depth", nbins=150).update_layout( xaxis = dict(dtick=10), bargap=0.2 )

enter image description here

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For those looking for a simple one line solution in matplotlib, just tack on .set_xlabel("your_label") to the end.

df.col_to_graph.plot.hist(bins=10, alpha=0.5, figsize=(16, 12)).set_xlabel("col_to_graph label")
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