In Python, how to overlap histogram graphs about data frame inside one panel?-CodePudding

I have a question about overlapping graphs inside one panel in Python.

I generated numbers of two groups

import numpy as np
cv1 = np.random.normal(50, 4, 1000)
cv2 = np.random.normal(40, 7, 1000)


import matplotlib.pyplot as plt

plt.hist(cv1, numpy.linspace(0, 70, 100))
plt.hist(cv2, numpy.linspace(0, 70, 100))
plt.xlabel("Grain weight (mg)", size=12)
plt.ylabel("Frequency_Histogram", size=12)
plt.show()

It works perfectly. However, if the data is the format of data frame, how can I make the same graph?

For example, I simulated that I have two data set for different genotypes, cv1, cv2. In each data, grain weight for 1,000 grains was measured.

cv1_data = {"Genotype": ["cv1"]*1000,"AGW": cv1}
cv1_weight = DataFrame(cv1_data)

cv2_data = {"Genotype": ["cv2"]*1000,"AGW": cv2}
cv2_weight = DataFrame(cv2_data)

Then, I'd like to make the same graph above. So I tried below codes

plt.hist(cv1,y="AGW", numpy.linspace(0, 70, 100))
plt.hist(cv2,y="AGW", numpy.linspace(0, 70, 100))
plt.xlabel("Grain weight (mg)", size=12)
plt.ylabel("Frequency_Histogram", size=12)
plt.show()

but it does not work. Could you let me know how to make the same overlapping graph in case of data frame?

Always, many thanks!!!

CodePudding user response：

You can do that using Seaborn library in python

import numpy as np
import pandas as pd
import seaborn as sns


cv1 = np.random.normal(50, 4, 1000)
cv2 = np.random.normal(40, 7, 1000)

cv1_data = {"Genotype": ["cv1"]*1000,"AGW": cv1}
cv1_weight = pd.DataFrame(cv1_data)

cv2_data = {"Genotype": ["cv2"]*1000,"AGW": cv2}
cv2_weight = pd.DataFrame(cv2_data)

df = pd.concat([cv1_weight,cv2_weight],axis = 1)
df.columns = ['Genotype', 'cv1_AGW', 'Genotype', 'cv2_AGW']

sns.histplot(data = df[['cv1_AGW','cv2_AGW']])