overlay histogram distributions using seaborn or matplotlib

In this example, we show two methods to overlay histogram distributions using seaborn and matplotlib separately.

import library

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

generate some data for histgram

list1 = np.random.randint(100,size=1000)
list2 = np.random.randint(200,size=1000)

# also getthem into a dataframe into one column, but add a second column to label source
list = np.concatenate([list1,list2])
lables = ['l1']*len(list1)+['l2']*len(list2)
d = {'x':list,'label':lables}
df = pd.DataFrame(d)
display(df)

	x	label
0	49	l1
1	93	l1
2	38	l1
3	96	l1
4	32	l1
...	...	...
1995	18	l2
1996	102	l2
1997	1	l2
1998	175	l2
1999	77	l2

2000 rows × 2 columns

method 1, overlay dataframe columns with different labels

sns.displot(df, x="x", hue="label", stat="density")

<seaborn.axisgrid.FacetGrid at 0x24b5c685988>

method 2, overlay two seperate lists into histograms using matplotlib

plt.figure(figsize=(8,6))
plt.hist(list1,bins=20, alpha=0.5, label="data1")
plt.hist(list2, bins=20, alpha=0.5, label="data2")

plt.xlabel("Data", size=14)
plt.ylabel("Count", size=14)

plt.title("Multiple Histograms with Matplotlib")
plt.legend(loc='upper right')

<matplotlib.legend.Legend at 0x24b5face788>