Issue 1:
Bug report

Bug summary

I'm fairly new to matplotlib and I was trying to create a confusion matrix following the tutorial here: https://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html. I downloaded the example code and when I ran it, the plots looked slightly cropped on the top and bottom edges.

Code for reproduction

print(__doc__)

import numpy as np
import matplotlib.pyplot as plt

from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels

# import some data to play with
iris = datasets.load_iris()
X = iris.data
y = iris.target
class_names = iris.target_names

# Split the data into a training set and a test set
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

# Run classifier, using a model that is too regularized (C too low) to see
# the impact on the results
classifier = svm.SVC(kernel='linear', C=0.01)
y_pred = classifier.fit(X_train, y_train).predict(X_test)

def plot_confusion_matrix(y_true, y_pred, classes,
normalize=False,
title=None,
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if not title:
if normalize:
title = 'Normalized confusion matrix'
else:
title = 'Confusion matrix, without normalization'

# Compute confusion matrix
cm = confusion_matrix(y_true, y_pred)
# Only use the labels that appear in the data
classes = classes[unique_labels(y_true, y_pred)]
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')

print(cm)

fig, ax = plt.subplots()
im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
ax.figure.colorbar(im, ax=ax)
# We want to show all ticks...
ax.set(xticks=np.arange(cm.shape[1]),
yticks=np.arange(cm.shape[0]),
# ... and label them with the respective list entries
xticklabels=classes, yticklabels=classes,
title=title,
ylabel='True label',
xlabel='Predicted label')

# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")

# Loop over data dimensions and create text annotations.
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
ax.text(j, i, format(cm[i, j], fmt),
ha="center", va="center",
color="white" if cm[i, j] > thresh else "black")
fig.tight_layout()
return ax

np.set_printoptions(precision=2)

# Plot non-normalized confusion matrix
plot_confusion_matrix(y_test, y_pred, classes=class_names,
title='Confusion matrix, without normalization')

# Plot normalized confusion matrix
plot_confusion_matrix(y_test, y_pred, classes=class_names, normalize=True,
title='Normalized confusion matrix')

plt.show()

Actual outcome

Expected outcome

The plots were expected to not look cropped.

Matplotlib version

Operating system: Ubunto 18.04

Matplotlib version: 3.1.1

Matplotlib backend (print(matplotlib.get_backend())):

Python version: 3.7

Jupyter version (if applicable):

Other libraries:

matplotlib installed via pip.

Issue 2:
Bug report

Bug summary

The very top and bottom of the heatmaps are getting truncated to 1/2 height in version 3.1.1. This does not happen for version 3.0.3.

This is the code from a Jupyter Notebook

import matplotlib
import pandas as pd
import numpy as np
import seaborn as sb
%pylab inline

print(matplotlib.__version__)
print(sb.__version__)

grid = pd.DataFrame(np.arange(9).reshape((3,3))/10)
fig, ax = subplots(1, 1, figsize=(5, 5))

sb.heatmap(grid, annot=True, fmt=".0f", ax=ax, cmap='RdBu', vmin=0, vmax=1, cbar=True);

Actual outcome

Populating the interactive namespace from numpy and matplotlib
3.1.1
0.9.0

Matplotlib version

Operating system: Ubuntu Linux

Matplotlib version: 3.1.1

Matplotlib backend: module://ipykernel.pylab.backend_inline

Python version: 3.6

Jupyter version (if applicable): 4.4.0

Other libraries: