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:
enter image description here

Asked By: Rohit

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Source

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:
enter image description here

Answered By: Paul H

Answer 2

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)

Answered By: blalterman

Answer 3

Here is the snippet, In my case I have explicitly specified bins and range as I didn’t handle outlier removal as the author of the book.

fig, ax = plt.subplots()
ax.hist([first.prglngth, others.prglngth], 10, (27, 50), histtype="bar", label=("First", "Other"))
ax.set_title("Histogram")
ax.legend()

Refer Matplotlib multihist plot with different sizes example.

Answered By: sathyz

Answer 4

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)

Answered By: lin_bug

Answer 5

You make two dataframes and one matplotlib axis

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

df1 = pd.DataFrame({
    'data1': np.random.randn(10),
    'data2': np.random.randn(10)
})

df2 = df1.copy()

fig, ax = plt.subplots()
df1.hist(column=['data1'], ax=ax)
df2.hist(column=['data2'], ax=ax)

Answered By: Joshua Zastrow

Answer 6

this could be done with brevity

plt.hist([First, Other], bins = 40, color =('teal','blue'), label=("First", "Other"))
plt.legend(loc='best')

Note that as the number of bins increase, it may become a visual burden.

Answered By: Rose Chuang

Answer 7

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():

Answered By: Christian Bueno

Answer 8

You could also try to check out the pandas.DataFrame.plot.hist() function which will plot the histogram of each column of the dataframe in the same figure.
Visibility is limited though but you can check out if it helps!
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.hist.html

Answered By: Andrea Bragantini

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