How to Remove Frame from Matplotlib (Pyplot.Figure VS Matplotlib.Figure ) (Frameon=False Problematic in Matplotlib)

How to remove frame from matplotlib (pyplot.figure vs matplotlib.figure ) (frameon=False Problematic in matplotlib)

First off, if you're using savefig, be aware that it will override the figure's background color when saving unless you specify otherwise (e.g. fig.savefig('blah.png', transparent=True)).

However, to remove the axes' and figure's background on-screen, you'll need to set both ax.patch and fig.patch to be invisible.

E.g.

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot(range(10))

for item in [fig, ax]:
    item.patch.set_visible(False)

with open('test.png', 'w') as outfile:
    fig.canvas.print_png(outfile)

(Of course, you can't tell the difference on SO's white background, but everything is transparent...)

If you don't want to show anything other than the line, turn the axis off as well using ax.axis('off'):

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot(range(10))

fig.patch.set_visible(False)
ax.axis('off')

with open('test.png', 'w') as outfile:
    fig.canvas.print_png(outfile)

In that case, though, you may want to make the axes take up the full figure. If you manually specify the location of the axes, you can tell it to take up the full figure (alternately, you can use subplots_adjust, but this is simpler for the case of a single axes).

import matplotlib.pyplot as plt

fig = plt.figure(frameon=False)
ax = fig.add_axes([0, 0, 1, 1])
ax.axis('off')

ax.plot(range(10))

with open('test.png', 'w') as outfile:
    fig.canvas.print_png(outfile)

Matplotlib : fill subarea of a Graph

As mentioned, you can use axvspan() to add a suitable background colour.

For example:

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.dates import date2num
import random
from datetime import datetime, timedelta

# Create some random data
data = []
x = datetime(2021, 1, 1)
y = 0
day = timedelta(days=1)

for _ in range(100):
    x += day
    y += random.random()
    data.append([x, y])

df = pd.DataFrame(data, columns=['Date', 'Value'])
df.plot('Date', 'Value')
ax = plt.gca()
march = date2num(datetime(2021, 3, 1))
latest = date2num(data[-1][0])
ax.axvspan(xmin=march, xmax=latest, facecolor='silver')
plt.show()

Giving you:

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