Seaborn workaround for hue barplot

Question

I have the following DataFrame on a Jupyter notebook which plots using seaborn a barplot:

data = {'day_index': [0, 1, 2, 3, 4, 5, 6],
        'avg_duration': [708.852242, 676.7021900000001, 684.572677, 708.92534, 781.767476, 1626.575057, 1729.155673],
        'trips': [114586, 120936, 118882, 117868, 108036, 43740, 37508]}

df = pd.DataFrame(data)

daysOfWeek = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']

plt.figure(figsize=(16,10));
sns.set_style('ticks')
ax = sns.barplot(data=df, \
                 x='day_index', \
                 y='avg_duration', \
                 hue='trips', \
                 palette=sns.color_palette("Reds_d", n_colors=7, desat=1))

ax.set_xlabel("Week Days", fontsize=18, alpha=0.8)
ax.set_ylabel("Duration (seconds)", fontsize=18, alpha=0.8)
ax.set_title("Week's average Trip Duration", fontsize=24)
ax.set_xticklabels(daysOfWeek, fontsize=16)
ax.legend(fontsize=15)
sns.despine()
plt.show()

Plot A:

As it can be seen the bars do not match the x_ticklabels and are very thin.
This is all fixed if I remove the hue='trips' part, it's a known seaborn issue. Although It's very important to show the amount of trips in the visualization so: is there a way around seaborn (maybe with matplotlib directly) to add a hue attribute?

Answer 1

The hue argument probably only makes sense to introduce a new dimension to the plot, not to show another quantity on the same dimension.

It's probably best to plot the bars without the hue argument (it's quite misleading to call it hue actually) and simply colorize the bars according to the values in the "trips" column.

This is shown also in this question: Seaborn Barplot - Displaying Values.

The code here would look like:

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np

di = np.arange(0,7)
avg  = np.array([708.852242,676.702190,684.572677,708.925340,781.767476,
                 1626.575057,1729.155673])
trips = np.array([114586,120936,118882,117868,108036,43740,37508])
df = pd.DataFrame(np.c_[di, avg, trips], columns=["day_index","avg_duration", "trips"])

daysOfWeek = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', \
'Friday', 'Saturday', 'Sunday']

plt.figure(figsize=(10,7));
sns.set_style('ticks')
v  = df.trips.values
colors=plt.cm.viridis((v-v.min())/(v.max()-v.min()))
ax = sns.barplot(data=df, x='day_index',   y='avg_duration', palette=colors)

for index, row in df.iterrows():
    ax.text(row.day_index,row.avg_duration, row.trips, color='black', ha="center")

ax.set_xlabel("Week Days", fontsize=16, alpha=0.8)
ax.set_ylabel("Duration (seconds)", fontsize=16, alpha=0.8)
ax.set_title("Week's average Trip Duration", fontsize=18)
ax.set_xticklabels(daysOfWeek, fontsize=14)
ax.legend(fontsize=15)
sns.despine()
plt.show()

Answer 2

I think you don't need to specify hue parameter in this case:

In [136]: ax = sns.barplot(data=dfGroupedAgg, \
     ...:                  x='day_index', \
     ...:                  y='avg_duration', \
     ...:                  palette=sns.color_palette("Reds_d", n_colors=7, desat=1))
     ...:

you can add amount of trips as annotations:

def autolabel(rects, labels=None, height_factor=1.05):
    for i, rect in enumerate(rects):
        height = rect.get_height()
        if labels is not None:
            try:
                label = labels[i]
            except (TypeError, KeyError):
                label = ' '
        else:
            label = '%d' % int(height)
        ax.text(rect.get_x() + rect.get_width()/2., height_factor*height,
                '{}'.format(label),
                ha='center', va='bottom')

autolabel(ax.patches, labels=df.trips, height_factor=1.02)