I want to plot the best fit line to every Iris class per feature histogram plot. I have tried the solutions from these examples: 1 and 2, but dont get the result i want.
This is how the histogram looks like now, and how I want them to look, but with an best fit line per class.
Here is the code that I have used to achive this.
def load_data(path):
data = pd.read_csv(path, sep=',')
return data
#the reason I have imported it like this is because I needed it on this form for something else.
tot_data = load_data(Iris.csv)
setosa = load_data(path_setosa)
versicolor = load_data(path_versicolour,)
virginica = load_data(path_virginica)
split_data_array = [setosa,versicolor,virginica]
fig, axes = plt.subplots(nrows= 2, ncols=2, sharex='col', sharey='row')#basis for subplots
colors= ['blue', 'red', 'green', 'black'] #colors for histogram
for i, ax in enumerate(axes.flat):#loop through every feature
for label, color in zip(range(len(iris_names)), colors): #loop through every class
_,bins,_ = ax.hist(data[label][features[i]], label=iris_names[label], color=color, stacked=True,alpha=0.5)
b = np.arange(50)
ax.set(xlabel='Measured [cm]', ylabel='Number of samples') #sets label name
ax.label_outer() #makes the label only be on the outer part of the plots
ax.legend(prop={'size': 7}) #change size of legend
ax.set_title(f'Feature {i+1}: {features[i]}') #set title for each plot
#ax.grid('on') #grid on or off
#plt.savefig('histogram_rap.png',dpi=200)
plt.show()
With seaborn you can add a kde curve via sns.histplot(..., kde=True)
. Here is an example:
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import norm
import pandas as pd
sns.set()
iris = sns.load_dataset('iris')
# make the 'species' column categorical to fix the order
iris['species'] = pd.Categorical(iris['species'])
fig, axs = plt.subplots(2, 2, figsize=(12, 6))
for col, ax in zip(iris.columns[:4], axs.flat):
sns.histplot(data=iris, x=col, kde=True, hue='species', common_norm=False, legend=ax==axs[0,0], ax=ax)
plt.tight_layout()
plt.show()
Some parameters of sns.histplot()
:
common_norm=
: when True
(default) scaled down each curve (or histogram) depending on the number of rows belonging to each hue valuestat=
: one of “count”,
“frequency”,
“density”,
“probability”`; determines how the y-axis gets scaledmultiple=
: “layer”
: default, all on the same spot;“dodge”
: bars next to each other; “stack”
: bars and/or curves stacked; “fill”: for each x-value the bars (and/or curves) are stacked to sum to
1`.