Remove Weekends¶

By default, weekends (Sat–Sun) are collapsed to zero width on the business axis. The price line jumps directly from Friday 24:00 to Monday 00:00, eliminating the flat segments visible on the calendar axis.

Core code:

ax.set_xscale("busday")

import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

import busdayaxis

busdayaxis.register_scale()

# prepare dummy data
num_days = 13
dates = pd.date_range("2025-01-02", periods=num_days * 24, freq="h")
returns = np.random.normal(0, 0.002, len(dates))
returns[~np.is_busday(np.array(dates, dtype="datetime64[D]"))] = 0.0
prices = (1 + pd.Series(returns, index=dates)).cumprod()


fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 7), sharey=True)
fig.suptitle("Comparison of default (linear) scale vs scale='busday'", fontsize=14)

# axis with default linear scale
ax1.plot(dates, prices.values, linewidth=1.3)
ax1.set_title("default linear matplotlib scale")
ax1.xaxis.set_major_locator(mdates.DayLocator())
ax1.xaxis.set_major_formatter(mdates.DateFormatter("%a %d"))

# axis with business scale
ax2.plot(dates, prices.values, linewidth=1.3)
ax2.set_xscale("busday")
ax2.set_title("using `ax.set_xscale('busday')`")
ax2.xaxis.set_major_locator(busdayaxis.DayLocator())
ax2.xaxis.set_major_formatter(mdates.DateFormatter("%a %d"))


# Shade weekends on linear scale
full_days = pd.date_range(dates.min().normalize(), dates.max().normalize(), freq="D")
for d in full_days:
    if d.weekday() == 5:  # Saturday
        ax1.axvspan(d, d + pd.Timedelta(days=2), color="grey", alpha=0.15, linewidth=0)
# Mark weekend boundaries on business scale
for d in full_days:
    if d.weekday() == 5:
        ax2.axvline(d, linestyle="--", linewidth=0.8, alpha=0.6)
        ax2.axvline(d + pd.Timedelta(days=2), linestyle="--", linewidth=0.8, alpha=0.6)


_ = plt.tight_layout(rect=[0, 0, 1, 0.96])

Comparison of default (linear) scale vs scale='busday', default linear matplotlib scale, using ax.set_xscale('busday')

Plot for README

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 3))

# axis with default linear scale
ax1.plot(dates, prices.values, linewidth=1.3)
ax1.set_title("default linear matplotlib scale")
ax1.xaxis.set_major_locator(mdates.DayLocator())
ax1.xaxis.set_major_formatter(mdates.DateFormatter("%a %d"))
ax1.tick_params(axis="x", rotation=45)

# axis with business scale
ax2.plot(dates, prices.values, linewidth=1.3)
ax2.set_xscale("busday")
ax2.set_title("using `ax.set_xscale(scale='busday')`")
ax2.xaxis.set_major_locator(busdayaxis.DayLocator())
ax2.xaxis.set_major_formatter(mdates.DateFormatter("%a %d"))
ax2.tick_params(axis="x", rotation=45)


# Shade weekends on linear scale
full_days = pd.date_range(dates.min().normalize(), dates.max().normalize(), freq="D")
for d in full_days:
    if d.weekday() == 5:  # Saturday
        ax1.axvspan(d, d + pd.Timedelta(days=2), color="grey", alpha=0.15, linewidth=0)
# Mark weekend boundaries on business scale
for d in full_days:
    if d.weekday() == 5:
        ax2.axvline(d, linestyle="--", linewidth=0.8, alpha=0.6)
        ax2.axvline(d + pd.Timedelta(days=2), linestyle="--", linewidth=0.8, alpha=0.6)

plt.tight_layout(rect=[0, 0, 1, 1])
_ = plt.savefig("../docs/assets/remove_weekend.png", dpi=300)