'list' object has no attribute 'add_subplot' in matplotlib-CodePudding

I have followed the suggestions from another threat, but this code still gives me the list object has no no attribute error - what is the correction?

import numpy as np 
import seaborn as sns
import matplotlib.pyplot as plt


#create data
x = np.array([4.1,7.1,5.2,7.1,0.3, 8.3, 9.7, 8.2])
y = np.array([0.7,0.72,0.5,0.73, 0.65, 0.57, 0.7, 0.8])

#create basic scatterplot
plt.plot(x, y, 'o',markersize=10,c='black')

#obtain m (slope) and b(intercept) of linear regression line
m, b = np.polyfit(x, y, 1)

#add linear regression line to scatterplot 
fig = plt.plot(x, m*x b)
ax = fig.add_subplot(111)
ax.set_ylim([0.45,0.8])

sns.despine(top=True, right=True, left=False, bottom=False)
plt.xlabel('Accuracy', fontsize=22)
plt.ylabel('Score', fontsize=22)

CodePudding user response：

On the line

fig = plt.plot(x, m*x b)

the plt.plot function returns a list of Line2d objects rather than a Figure object. I'd suggest doing the following:

fig, ax = plt.subplots()  # create figure and axes

# plot on ax (Axes) object
ax.plot(x, y, 'o',markersize=10,c='black')

#obtain m (slope) and b(intercept) of linear regression line
m, b = np.polyfit(x, y, 1)

#add linear regression line to scatterplot 
ax.plot(x, m*x b)
ax.set_ylim([0.45,0.8])

sns.despine(top=True, right=True, left=False, bottom=False)
ax.set_xlabel('Accuracy', fontsize=22)
ax.set_ylabel('Score', fontsize=22)

CodePudding user response：

The error is because fig.add_subplot(111) is not returning an axis object, but instead is returning the Line2D object from plt.plot(x, m*x b). Replace the line with fig, ax = plt.subplots() to create a figure and axis object, then set the y-limit using ax.set_ylim([0.45,0.8]). Also, sns.despine() should be called after plt.xlabel() and plt.ylabel()

#create data
x = np.array([4.1,7.1,5.2,7.1,0.3, 8.3, 9.7, 8.2])
y = np.array([0.7,0.72,0.5,0.73, 0.65, 0.57, 0.7, 0.8])

#create basic scatterplot
plt.plot(x, y, 'o',markersize=10,c='black')

#obtain m (slope) and b(intercept) of linear regression line
m, b = np.polyfit(x, y, 1)

#add linear regression line to scatterplot 
fig, ax = plt.subplots()
plt.plot(x, m*x b)
ax.set_ylim([0.45,0.8])

plt.xlabel('Accuracy', fontsize=22)
plt.ylabel('Score', fontsize=22)
sns.despine(top=True, right=True, left=False, bottom=False)
plt.xlabel('Accuracy', fontsize=22)
plt.ylabel('Score', fontsize=22)