Color Outliers in LineChart - python-3.x

Question

I am generating line charts with the following syntax:
df2 = df2[['runtime','per','dev','var']]
op = f"/tmp/image.png"
fig, ax = plt.subplots(facecolor='darkslategrey')
df2.plot(x='runtime',xlabel="Date", kind='line', marker='o',linewidth=2,alpha=.7,subplots=True,color=['khaki', 'lightcyan','thistle'])
plt.style.use('dark_background')
plt.suptitle(f'Historical Data:', fontsize=12,fontname = 'monospace')
#file output
plt.savefig(op, transparent=False,bbox_inches="tight")
plt.close('all')
Where df2 dataframe sample:
runtime per dev var
1 2021-05-28 50.85 2.11 2.13
1 2021-05-30 50.85 2.11 2.13
1 2021-06-02 51.13 2.16 2.11
1 2021-06-04 51.13 2.16 2.11
1 2021-06-07 51.13 2.16 2.11
1 2021-06-09 51.11 2.13 2.10
1 2021-06-10 51.11 2.13 2.10
1 2021-06-14 51.11 2.13 2.10
1 2021-06-16 51.34 2.12 2.10
1 2021-06-18 51.34 2.12 2.10
1 2021-06-21 51.34 2.12 2.10
1 2021-06-23 51.69 1.97 2.17
1 2021-06-25 51.69 1.97 2.17
1 2021-06-28 51.69 1.97 2.17
1 2021-06-30 56.46 1.74 2.14
1 2021-07-02 56.46 1.74 2.14
1 2021-07-05 56.46 1.74 2.14
1 2021-07-07 55.10 1.84 2.08
1 2021-07-09 55.10 1.84 2.08
1 2021-07-12 55.10 1.84 2.08
1 2021-07-14 54.58 1.85 2.07
1 2021-07-16 54.58 1.85 2.07
1 2021-07-19 54.58 1.85 2.07
1 2021-07-21 54.33 1.87 2.06
1 2021-07-23 54.33 1.87 2.06
1 2021-07-26 54.33 1.87 2.06
1 2021-07-28 54.98 1.91 2.19
1 2021-07-30 54.98 1.91 2.19
This works great.
Now, I would like to change the color of points if their values are "abnormal", specifically if per < 90.00 or per > 10.00, or if dev < 10.00 or if var < 10.00 to color the point RED.
Is this possible?

Answer 1

Instead of drawing the 3 subplots in one call, they could be drawn one-by-one. First draw the subplot as before, and on top of it a scatter plot, only with the "abnormal" points. zorder=3 makes sure that the scatter dots appear on top of the existing dots.
Here is some example code:
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
df2 = pd.DataFrame({'runtime': pd.date_range('20210101', freq='D', periods=100),
'per': np.random.uniform(1, 99, 100),
'dev': np.random.uniform(1, 11, 100),
'var': np.random.uniform(2, 11, 100)})
fig, axs = plt.subplots(nrows=3, figsize=(6, 10), facecolor='darkslategrey', sharex=True)
for ax, column, color, (min_normal, max_normal) in zip(axs,
['per', 'dev', 'var'],
['khaki', 'lightcyan', 'thistle'],
[(10, 90), (-np.inf, 10), (-np.inf, 10)]):
df2.plot(x='runtime', xlabel="Date", y=column, ylabel=column,
kind='line', marker='o', linewidth=2, alpha=.7, color=color, legend=False, ax=ax)
df_abnormal = df2[(df2[column] < min_normal) | (df2[column] > max_normal)]
df_abnormal.plot(x='runtime', xlabel="Date", y=column, ylabel=column,
kind='scatter', marker='o', color='red', legend=False, zorder=3, ax=ax)
plt.style.use('dark_background')
plt.suptitle(f'Historical Data:', fontsize=12, fontname='monospace')
plt.tight_layout()
plt.show()