How to make a normal distribution graph from data frame in Python?

Question

my question is how to make a normal distribution graph from data frame in Python. I can find many information to make such a graph from random numbers, but I don't know how to make it from data frame.

First, I generated random numbers and made a data frame.

import numpy as np
import pandas 
from pandas import DataFrame

cv1 = np.random.normal(50, 3, 1000)

source = {"Genotype": ["CV1"]*1000, "AGW": cv1}
Cultivar_1=DataFrame(source)

Then, I tried to make a normal distribution graph.

sns.kdeplot(data = Cultivar_1['AGW'])
plt.xlim([30,70])  
plt.xlabel("Grain weight (mg)", size=12)    
plt.ylabel("Frequency", size=12)                
plt.grid(True, alpha=0.3, linestyle="--")     
plt.show()

However, this is a density graph, not a normal distribution graph which is calculated using mean and standard deviation.

Could you let me know which codes I need to use to make a normal distribution graph?

Thanks!!

Answer 1

I found one solution to make a normal distribution graph from data frame.

#Library
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.stats as stats

#Generating data frame
x = np.random.normal(50, 3, 1000)
source = {"Genotype": ["CV1"]*1000, "AGW": x}
df = pd.DataFrame(source)

# Calculating mean and Stdev of AGW
df_mean = np.mean(df["AGW"])
df_std = np.std(df["AGW"])
 
# Calculating probability density function (PDF)
pdf = stats.norm.pdf(df["AGW"].sort_values(), df_mean, df_std)

# Drawing a graph
plt.plot(df["AGW"].sort_values(), pdf)
plt.xlim([30,70])  
plt.xlabel("Grain weight (mg)", size=12)    
plt.ylabel("Frequency", size=12)                
plt.grid(True, alpha=0.3, linestyle="--")
plt.show()