Multiple histograms in Pandas

Question

I would like to create the following histogram (see image below) taken from the book "Think Stats". However, I cannot get them on the same plot. Each DataFrame takes its own subplot.

I have the following code:

import nsfg import matplotlib.pyplot as plt df = nsfg.ReadFemPreg() preg = nsfg.ReadFemPreg() live = preg[preg.outcome == 1]  first = live[live.birthord == 1] others = live[live.birthord != 1]  #fig = plt.figure() #ax1 = fig.add_subplot(111)  first.hist(column = 'prglngth', bins = 40, color = 'teal', \            alpha = 0.5) others.hist(column = 'prglngth', bins = 40, color = 'blue', \             alpha = 0.5) plt.show() 

The above code does not work when I use ax = ax1 as suggested in: pandas multiple plots not working as hists nor this example does what I need: Overlaying multiple histograms using pandas. When I use the code as it is, it creates two windows with histograms. Any ideas how to combine them?

Here's an example of how I'd like the final figure to look:

Answer 1

As far as I can tell, pandas can't handle this situation. That's ok since all of their plotting methods are for convenience only. You'll need to use matplotlib directly. Here's how I do it:

%matplotlib inline import numpy as np import matplotlib.pyplot as plt import pandas #import seaborn #seaborn.set(style='ticks')  np.random.seed(0) df = pandas.DataFrame(np.random.normal(size=(37,2)), columns=['A', 'B']) fig, ax = plt.subplots()  a_heights, a_bins = np.histogram(df['A']) b_heights, b_bins = np.histogram(df['B'], bins=a_bins)  width = (a_bins[1] - a_bins[0])/3  ax.bar(a_bins[:-1], a_heights, width=width, facecolor='cornflowerblue') ax.bar(b_bins[:-1]+width, b_heights, width=width, facecolor='seagreen') #seaborn.despine(ax=ax, offset=10) 

And that gives me:

Answer 2

In case anyone wants to plot one histogram over another (rather than alternating bars) you can simply call .hist() consecutively on the series you want to plot:

%matplotlib inline import numpy as np import matplotlib.pyplot as plt import pandas   np.random.seed(0) df = pandas.DataFrame(np.random.normal(size=(37,2)), columns=['A', 'B'])  df['A'].hist() df['B'].hist() 

This gives you:

Note that the order you call .hist() matters (the first one will be at the back)