How to highlight line segments of a plot in matplotlib/seaborn?

Question

I have multiple time-series and multiple labels. Whenever there is a label available I would want to highlight a time-series in ie red.

existing plot

I have a line chart where I can highlight certain elements of a plot such as:

for cohort_id in sorted(df.cohort_id.unique()):
    print(cohort_id)
    figsize = (25, 9)
    fig, ax = plt.subplots(figsize=figsize)
    ax = sns.lineplot(x='hour', y='metrik_0', data=df[df.cohort_id == cohort_id], ax=ax)
    ax.xaxis.set_major_locator(aut_locator)
    ax.xaxis.set_major_formatter(aut_formatter)
    
    plt.title(f'cohort_id: {cohort_id}', fontsize=45)
    plt.xlabel('')
    plt.ylabel('metrik_0', fontsize=35)
    
    for index, row in marker_labels.iterrows():
        start = row.start
        end = row.end
        marker_type = row.marker_type
        if marker_type == 'b':
            ax.axvspan(start, end, color='gray', alpha=0.2)
        else:
            ax.axvspan(start, end, color='orange', alpha=0.5)
        
    plt.show()

This plot can be converted into a cycleplot highlighting certain periodicities like:

for cohort_id in sorted(df.cohort_id.unique()):
    print(cohort_id)
    
    figsize = (25, 9)
    fig, ax = plt.subplots(figsize=figsize)
    a1 = sns.lineplot(x=df['hour'].dt.hour, y='metrik_0', hue='device_id', units='dt', style='dt', estimator=None, data=df[(df.cohort_id == cohort_id)], ax=ax)
    handles, labels = a1.get_legend_handles_labels()
    a1.legend(handles=handles[1:], labels=labels[1:], loc='center', bbox_to_anchor=(0.5, -0.25), ncol=6, fontsize=20)

    plt.title(f'cohort_id: {cohort_id}', fontsize=35)
    plt.xlabel('hour of the day', fontsize=35)
    plt.ylabel('metrik_0', fontsize=35)
    plt.show()

But now the labels can not be displayed anymore.

question

How can I re-add the labels to the cycle plot? Any method would be fine. But so far I thought it would be best to highlight the matched time intervals in red

data generation

To generate some example Data:

%pylab inline

import pandas as pd
import numpy as np
import seaborn as sns; sns.set()
import matplotlib.dates as mdates

aut_locator = mdates.AutoDateLocator(minticks=3, maxticks=7)
aut_formatter = mdates.ConciseDateFormatter(aut_locator)

import random
random_seed = 47
np.random.seed(random_seed)

random.seed(random_seed)

def generate_df_for_device(n_observations, n_metrics, device_id, geo_id, topology_id, cohort_id):
        df = pd.DataFrame(np.random.randn(n_observations,n_metrics), index=pd.date_range('2020', freq='H', periods=n_observations))
        df.columns = [f'metrik_{c}' for c in df.columns]
        df['geospatial_id'] = geo_id
        df['topology_id'] = topology_id
        df['cohort_id'] = cohort_id
        df['device_id'] = device_id
        return df
    
def generate_multi_device(n_observations, n_metrics, n_devices, cohort_levels, topo_levels):
    results = []
    for i in range(1, n_devices +1):
        #print(i)
        r = random.randrange(1, n_devices)
        cohort = random.randrange(1, cohort_levels)
        topo = random.randrange(1, topo_levels)
        df_single_dvice = generate_df_for_device(n_observations, n_metrics, i, r, topo, cohort)
        results.append(df_single_dvice)
        #print(r)
    return pd.concat(results)

# hourly data, 1 week of data
n_observations = 7 * 24
n_metrics = 3
n_devices = 20
cohort_levels = 3
topo_levels = 5

df = generate_multi_device(n_observations, n_metrics, n_devices, cohort_levels, topo_levels)
df = df.sort_index()
df = df.reset_index().rename(columns={'index':'hour'})
df['dt'] = df.hour.dt.date

and labels:

marker_labels = pd.DataFrame({'cohort_id':[1,1, 1], 'marker_type':['a', 'b', 'a'], 'start':['2020-01-2', '2020-01-04 05', '2020-01-06'], 'end':[np.nan, '2020-01-05 16', np.nan]})
marker_labels['start'] = pd.to_datetime(marker_labels['start'])
marker_labels['end'] = pd.to_datetime(marker_labels['end'])
marker_labels.loc[marker_labels['end'].isnull(), 'end'] =  marker_labels.start + pd.Timedelta(days=1) - pd.Timedelta(seconds=1)
marker_labels

A detailed Jupyter notebook with example data and current plotting code can be found here: https://github.com/geoHeil/plotting_tricks

edit

Assuming we perform a LEFT join for the labels of the time segments:

merged_res = (df.reset_index()
         .merge(marker_labels, on='cohort_id', how='left')
         .query('start <= hour <= end')
         .set_index('index')
         .reindex(df.index)
      )

merged_res = merged_res.combine_first(df)
merged_res.marker_type = merged_res.marker_type.fillna('no_labels_reported')

with plotting code of:

for cohort_id in sorted(merged_res.cohort_id.unique()):
    print(cohort_id)
    
    figsize = (25, 9)
    fig, ax = plt.subplots(figsize=figsize)
    a1 = sns.lineplot(x=merged_res['hour'].dt.hour, y='metrik_0', hue='marker_type', units='dt', style='dt', estimator=None, data=merged_res[(merged_res.cohort_id == cohort_id)], ax=ax)
    handles, labels = a1.get_legend_handles_labels()
    a1.legend(handles=handles[1:], labels=labels[1:], loc='center', bbox_to_anchor=(0.5, -0.25), ncol=6, fontsize=20)

    plt.title(f'cohort_id: {cohort_id}', fontsize=35)
    plt.xlabel('hour of the day', fontsize=35)
    plt.ylabel('metrik_0', fontsize=35)
    plt.show()

results in:

However:

• this is still rather chaotic
• individual time-series of the devices are aggregated/averaged in the visualization

Answer 1

The best thing so far seems to be hvplot:

merged_res['hour_time'] = merged_res['hour'].dt.hour
merged_res.device_id = merged_res.device_id.astype(str)

for cohort_id in sorted(merged_res.cohort_id.unique()):
    print(cohort_id)
    current_plot = merged_res[merged_res.cohort_id == cohort_id].set_index(['hour_time'])[['metrik_0',  'marker_type', 'device_id', 'dt']].hvplot(by=['marker_type'], 
                                                                                                                                                  hover_cols=['dt', 'device_id'], width=width, height=height).opts(active_tools=['box_zoom'])
    display(current_plot)

resulting in:

As I am still not fully satisfied - I will leave it open (unanswered) to see if someone comes up with a better solution.

In particular, I do not like that this displays lines - probably points would be better. Ie as something is changing from no label to having a label the timeseries is not drawn continously (=changing color) but in fact jumping (=a new different line is created. So using points would also only be a workaround (but probably better than having the jumping lines.