how to highlight weekends in plots?

Question

For a simple time series:

import pandas as pd
df = pd.DataFrame({'dt':['2020-01-01', '2020-01-02', '2020-01-04', '2020-01-05', '2020-01-06'], 'foo':[1,2, 4,5,6]})
df['dt'] = pd.to_datetime(df.dt)
df['dt_label']= df['dt'].dt.strftime('%Y-%m-%d %a')
df = df.set_index('dt')
#display(df)
df['foo'].plot()
x =plt.xticks(ticks=df.reset_index().dt.values, labels=df.dt_label, rotation=90, horizontalalignment='right')

How can I highlight the x-axis labels for weekends?

edit

Pandas Plots: Separate color for weekends, pretty printing times on x axis

suggests:

def highlight_weekends(ax, timeseries):
    d = timeseries.dt
    ranges = timeseries[d.dayofweek >= 5].groupby(d.year * 100 + d.weekofyear).agg(['min', 'max'])
    for i, tmin, tmax in ranges.itertuples():
        ax.axvspan(tmin, tmax, facecolor='orange', edgecolor='none', alpha=0.1)

but applying it with

highlight_weekends(ax, df.reset_index().dt)

will not change the plot

Jacqui

Answer 1

I've extended your sample data a little so we can can make sure that we can highlight more than a single weekend instance.

In this solution I create a column 'weekend', which is a column of bools indicating whether the corresponding date was at a weekend.

We then loop over these values and make a call to ax.axvspan

import pandas as pd
import matplotlib.pyplot as plt

# Add a couple of extra dates to sample data
df = pd.DataFrame({'dt': ['2020-01-01',
                          '2020-01-02',
                          '2020-01-04',
                          '2020-01-05',
                          '2020-01-06',
                          '2020-01-07',
                          '2020-01-09',
                          '2020-01-10',
                          '2020-01-11',
                          '2020-01-12']})
# Fill in corresponding observations
df['foo'] = range(df.shape[0])

df['dt'] = pd.to_datetime(df.dt)

df['dt_label']= df['dt'].dt.strftime('%Y-%m-%d %a')

df = df.set_index('dt')

ax = df['foo'].plot()
plt.xticks(ticks=df.reset_index().dt.values, 
           labels=df.dt_label,
           rotation=90,
           horizontalalignment='right')

# Create an extra column which highlights whether or not a date occurs at the weekend
df['weekend'] = df['dt_label'].apply(lambda x: x.endswith(('Sat', 'Sun')))

# Loop over weekend pairs (Saturdays and Sundays), and highlight
for i in range(df['weekend'].sum() // 2):
    ax.axvspan(df[df['weekend']].index[2*i],
               df[df['weekend']].index[2*i+1],
               alpha=0.5)

Irredentist

Answer 2

Here is a solution that uses the fill_between plotting function and the x-axis units so that weekends can be highlighted independently from the DatetimeIndex and the frequency of the data.

The x-axis limits are used to compute the range of time covered by the plot in terms of days, which is the unit used for matplotlib dates. Then a weekends mask is computed and passed to the where argument of the fill_between function. The masks are processed as right-exclusive so in this case, they must contain Mondays for the highlights to be drawn up to Mondays 00:00. Because plotting these highlights can alter the x-axis limits when weekends occur near the limits, the x-axis limits are set back to the original values after plotting.

Note that contrary to axvspan, the fill_between function needs the y1 and y2 arguments. For some reason, using the default y-axis limits leaves a small gap between the plot frame and the tops and bottoms of the weekend highlights. This issue is solved by running ax.set_ylim(*ax.get_ylim()) just after creating the plot.

Here is a complete example based on the provided sample code and using an extended dataset similar to the answer provided by jwalton:

import numpy as np                   # v 1.19.2
import pandas as pd                  # v 1.1.3
import matplotlib.pyplot as plt      # v 3.3.2
import matplotlib.dates as mdates

# Create sample dataset
dt = pd.to_datetime(['2020-01-01', '2020-01-02', '2020-01-04', '2020-01-05',
                     '2020-01-06', '2020-01-07', '2020-01-09', '2020-01-10',
                     '2020-01-11', '2020-01-14'])
df = pd.DataFrame(dict(foo=range(len(dt))), index=dt)

# Draw pandas plot: setting x_compat=True converts the pandas x-axis units to
# matplotlib date units. This is not necessary for this particular example but
# it is necessary for all cases where the dataframe contains a continuous
# DatetimeIndex (for example ones created with pd.date_range) that uses a
# frequency other than daily
ax = df['foo'].plot(x_compat=True, figsize=(6,4), ylabel='foo')
ax.set_ylim(*ax.get_ylim()) # reset y limits to display highlights without gaps

# Highlight weekends based on the x-axis units
xmin, xmax = ax.get_xlim()
days = np.arange(np.floor(xmin), np.ceil(xmax)+2) # range of days in date units
weekends = [(dt.weekday()>=5)|(dt.weekday()==0) for dt in mdates.num2date(days)]
ax.fill_between(days, *ax.get_ylim(), where=weekends, facecolor='k', alpha=.1)
ax.set_xlim(xmin, xmax) # set limits back to default values

# Create and format x tick for each data point
plt.xticks(df.index.values, df.index.strftime('%d\n%a'), rotation=0, ha='center')

plt.title('Weekends are highlighted from SAT 00:00 to MON 00:00', pad=15, size=12);

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You can find more examples of this solution in the answers I have posted here and here.

Schaub