Matplotlib/Seaborn/Visualization | Basics | Priority

Question 1

Scatterplot matrix.

Machine Learning with PyTorch and Scikit-Learn Chapter 9 p274

Answer 1

import matplotlib.pyplot as plt
from mlxtend.plotting import scatterplotmatrix
scatterplotmatrix(df.values, figsize=(12, 10), names=df.columns, alpha=0.5)
plt.tight_layout()
plt.show()

Machine Learning with PyTorch and Scikit-Learn Chapter 9 p274

Question 2

Heatmap.

Machine Learning with PyTorch and Scikit-Learn Chapter 9 p277

Answer 2

from mlxtend.plotting import heatmap
cm = np.corrcoef(df.values.T)
hm = heatmap(cm, row_names=df.columns, column_names=df.columns)
plt.tight_layout()
plt.show()

Machine Learning with PyTorch and Scikit-Learn Chapter 9 p277

Question 3

Plot on a log scale.

Machine Learning with PyTorch and Scikit-Learn Chapter 3 p75

Answer 3

plt.xscale(‘log’)

Question 4

Plot a legend, choosing location automatically.

Answer 4

plt.legend(loc=’best’)

Question 5

Function: Adjust the padding between and around subplots.

Machine Learning with PyTorch and Scikit-Learn Chapter 3 p61

Answer 5

plt.tight_layout()

Question 6

Function: Configure the grid lines.

Machine Learning with PyTorch and Scikit-Learn Chapter 3 p61

Answer 6

ax.yaxis.grid(True)

Question 7

Function: Get the current Axes.

Machine Learning with PyTorch and Scikit-Learn Chapter 3 p61

Answer 7

ax = plt.gca()

Question 8

Function: Add a vertical line across the Axes.

Machine Learning with PyTorch and Scikit-Learn Chapter 3 p61

Answer 8

import matplotlib.pyplot as plt; plt.axvline(0.0, color=’k’)