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

Now that Pandas supports the powerful .dt namespace on every series, it is possible to identify the start and end of each weekend without any explicit Python loops. Simply filter your time values with t.dt.dayofweek >= 5 to select only times falling on the weekend, and then group by a made-up value that is different every week — here I use year * 100 + weekofyear because the result looks like 201603 which is fairly pleasant to read for debugging.

The resulting function is:

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)

Simply pass it the axis and the time series that is your x axis, and it will highlight the weekends for you!

I tried a lot and for now these hacks work. Await a more Pythonic and consistent solutions. Solution to labeling problems:

def correct_labels(ax):
    labels = [item.get_text() for item in ax.get_xticklabels()]
    days=[label.split(" ")[0] for label in labels]
    months=["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"]
    final_labels=[]
    for i in range(len(days)):
        a=days[i].split("-")
        final_labels.append(a[2]+"\n"+months[int(a[1])-1])
    ax.set_xticklabels(final_labels)

Also while plotting i make the following change

ax=df.plot(kind='bar',rot=0)

This makes the labels at 0 rotation.

For finding weekends and highlighting them, i wrote the following two functions:

def find_weekend_indices(datetime_array):
    indices=[]
    for i in range(len(datetime_array)):
        if datetime_array[i].weekday()>=5:
            indices.append(i)
    return indices

def highlight_weekend(weekend_indices,ax):
    i=0
    while i<len(weekend_indices):
         ax.axvspan(weekend_indices[i], weekend_indices[i]+2, facecolor='green', edgecolor='none', alpha=.2)
         i+=2

Now, the plot looks much more useful and covers these use cases.