Create stacked histogram from unequal length arrays

I'd like to create a stacked histogram. If I have a single 2-D array, made of three equal length data sets, this is simple. Code and image below:

import numpy as np
from matplotlib import pyplot as plt

# create 3 data sets with 1,000 samples
mu, sigma = 200, 25
x = mu + sigma*np.random.randn(1000,3)

#Stack the data
plt.figure()
n, bins, patches = plt.hist(x, 30, stacked=True, density = True)
plt.show()

However, if I try similar code with three data sets of a different length the results are that one histogram covers up another. Is there any way I can do the stacked histogram with mixed length data sets?

##Continued from above
###Now as three separate arrays
x1 = mu + sigma*np.random.randn(990,1)
x2 = mu + sigma*np.random.randn(980,1)
x3 = mu + sigma*np.random.randn(1000,1)

#Stack the data
plt.figure()
plt.hist(x1, bins, stacked=True, density = True)
plt.hist(x2, bins, stacked=True, density = True)
plt.hist(x3, bins, stacked=True, density = True)
plt.show()

Well, this is simple. I just need to put the three arrays in a list.

##Continued from above
###Now as three separate arrays
x1 = mu + sigma*np.random.randn(990,1)
x2 = mu + sigma*np.random.randn(980,1)
x3 = mu + sigma*np.random.randn(1000,1)

#Stack the data
plt.figure()
plt.hist([x1,x2,x3], bins, stacked=True, density=True)
plt.show()

• If pandas is an option, the arrays can be loaded into a dataframe and plotted.
• The benefit of using pandas, is the data is now in a useful format for additional analysis and other plots.
• The following code will create a list of DataFrames with pandas.DataFrame, for each array, and then concat the arrays together in a list-comprehension.
  • This is a correct way to create a dataframe of arrays that are not equal in length.
    • SO: Creating dataframe from a dictionary where entries have different lengths has more ways to create dataframes from arrays of unequal length.
  • For equal length arrays, use df = pd.DataFrame({'x1': x1, 'x2': x2, 'x3': x3})
• Use pandas.DataFrame.plot, which uses matplotlib as the default plot engine.
  • normed has been replaced with density in matplotlib
  • See the density parameter in matplotlib.pyplot.hist for an explanation of the y-axis values.
• For additional information:
  1. Plot a histogram such that bar heights sum to 1 (probability)
  2. Plot a histogram such that the total area of the histogram equals 1 (density)

import pandas as pd
import numpy as np

# create the uneven arrays
mu, sigma = 200, 25
np.random.seed(365)
x1 = mu + sigma*np.random.randn(990, 1)
x2 = mu + sigma*np.random.randn(980, 1)
x3 = mu + sigma*np.random.randn(1000, 1)

# create the dataframe; enumerate is used to make column names
df = pd.concat([pd.DataFrame(a, columns=[f'x{i}']) for i, a in enumerate([x1, x2, x3], 1)], axis=1)

# plot the data
df.plot.hist(stacked=True, bins=30, density=True, figsize=(10, 6), grid=True)