How to shorten the distance of two far apart date points in matplotlib?

I'm using pandas and matplotlib to plot a basketball team's performance in the last 20 seasons.

The code is fairly straightforward: plt.plot(date, performance) date is a list contains all the timestamp of the dates where a team plays, and performance is a corresponding list that contains that team's performance at that date.

The problem is that the team only plays at a certain time span in a year, for example, WNBA only plays from May to October every year. If I connect the data from the past many seasons together, there will be an undesirable long line across seasons that takes too long of a time span.

This is the two season plot, you can see this long line connecting two seasons

This is the plot starting from 1997, you can see the problem here: I can't express how the performance changes in a season since almost half of the timeline is occupied by the idle time between seasons

It'll work to some extent if I use string instead of timestamps for the x-axis. This is what I want But can I do it using date type? I want to shorten the distance of two date points between the end of the season and the start of the next season. Or how can I shorten the intervals of those idle months in a year and possibly lengthen the intervals of the active months?

Using the brokenaxes package (from, e.g., this answer), which is based on the Matplotlib broken axis example, and expanding on the brokenaxes datetime example, you could do something like:

import matplotlib.pyplot as plt
from brokenaxes import brokenaxes
import numpy as np
import datetime

fig = plt.figure(figsize=(10, 5))

xlims = []
xx = []

# create some fake data
for year in range(1997, 2011):
    xx.extend([datetime.datetime(year, x, 1) for x in range(5, 11)])
    
    # set the extents of each part of the axis, i.e., between May and Oct each year
    xlims.append((datetime.datetime(year, 5, 1), datetime.datetime(year, 10, 1)))

yy = np.random.randn(len(xx))

# set the broken axes
bax = brokenaxes(
    xlims=xlims,  # extents of each part of axis
    d=0,  # no diagonal tick
    wspace=0,  # no space between breaks
)

bax.plot(xx, yy)

fig.autofmt_xdate()
[x.remove() for x in bax.diag_handles]

import matplotlib.dates as mdates
for i, ax in enumerate(bax.axs):
    ax.set_xticks([xlims[i][0]])  # just add initial date for each year at the x-tick
    ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))