Multiple histograms in Pandas

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)

From the pandas website (http://pandas.pydata.org/pandas-docs/stable/visualization.html#visualization-hist):

df4 = pd.DataFrame({'a': np.random.randn(1000) + 1, 'b': np.random.randn(1000),
                    'c': np.random.randn(1000) - 1}, columns=['a', 'b', 'c'])

plt.figure();

df4.plot(kind='hist', alpha=0.5)

A quick solution is to use melt() from pandas and then plot with seaborn.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# make dataframe
df = pd.DataFrame(np.random.normal(size=(200,2)), columns=['A', 'B'])

# plot melted dataframe in a single command
sns.histplot(df.melt(), x='value', hue='variable',
             multiple='dodge', shrink=.75, bins=20);

Setting multiple='dodge' makes it so the bars are side-by-side, and shrink=.75 makes it so the pair of bars take up 3/4 of the whole bin.

To help understand what melt() did, these are the dataframes df and df.melt():

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: