Seaborn workaround for hue barplot
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()
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)