How to display custom values on a bar plot

I hope this helps for item #2:

a) You can sort by total bill and then reset the index to this column
b) Use palette="Blue" to use this color to scale your chart from light blue to dark blue (if dark blue to light blue then use palette="Blues_d")

import pandas as pd
import seaborn as sns
%matplotlib inline

df = pd.read_csv("https://raw.githubusercontent.com/wesm/pydata-book/master/ch08/tips.csv", sep=',')
groupedvalues = df.groupby('day').sum().reset_index()
groupedvalues = groupedvalues.sort_values('total_bill').reset_index()
g = sns.barplot(x='day', y='tip', data=groupedvalues, palette="Blues")

This works with a single ax or with a matrix of ax (subplots):

from matplotlib import pyplot as plt
import numpy as np

def show_values_on_bars(axs):
    def _show_on_single_plot(ax):
        for p in ax.patches:
            _x = p.get_x() + p.get_width() / 2
            _y = p.get_y() + p.get_height()
            value = '{:.2f}'.format(p.get_height())
            ax.text(_x, _y, value, ha="center")

    if isinstance(axs, np.ndarray):
        for idx, ax in np.ndenumerate(axs):
            _show_on_single_plot(ax)
    else:
        _show_on_single_plot(axs)

fig, ax = plt.subplots(1, 2)
show_values_on_bars(ax)

Just in case if anyone is interested in labeling horizontal barplot graph, I modified Sharon’s answer as below:

def show_values_on_bars(axs, h_v="v", space=0.4):
    def _show_on_single_plot(ax):
        if h_v == "v":
            for p in ax.patches:
                _x = p.get_x() + p.get_width() / 2
                _y = p.get_y() + p.get_height()
                value = int(p.get_height())
                ax.text(_x, _y, value, ha="center") 
        elif h_v == "h":
            for p in ax.patches:
                _x = p.get_x() + p.get_width() + float(space)
                _y = p.get_y() + p.get_height()
                value = int(p.get_width())
                ax.text(_x, _y, value, ha="left")

    if isinstance(axs, np.ndarray):
        for idx, ax in np.ndenumerate(axs):
            _show_on_single_plot(ax)
    else:
        _show_on_single_plot(axs)

Two parameters explained:

h_v – Whether the barplot is horizontal or vertical. "h" represents the horizontal barplot, "v" represents the vertical barplot.

space – The space between value text and the top edge of the bar. Only works for horizontal mode.

Example:

show_values_on_bars(sns_t, "h", 0.3)

plt.figure(figsize=(15, 10))
graph = sns.barplot(x='name_column_x_axis', y="name_column_x_axis", data=dataframe_name, color="salmon")
for p in graph.patches:
    graph.annotate('{:.0f}'.format(p.get_height()),
                   (p.get_x() + 0.3, p.get_height()),
                   ha='center',
                   va='bottom',
                   color='black')

A simple way to do so is to add the below code (for Seaborn):

for p in splot.patches:
    splot.annotate(format(p.get_height(), '.1f'),
                   (p.get_x() + p.get_width() / 2., p.get_height()),
                   ha = 'center', va = 'center',
                   xytext = (0, 9),
                   textcoords = 'offset points')

Example:

splot = sns.barplot(df['X'], df['Y'])
# Annotate the bars in plot
for p in splot.patches:
    splot.annotate(format(p.get_height(), '.1f'),
                   (p.get_x() + p.get_width() / 2., p.get_height()),
                   ha = 'center', va = 'center',
                   xytext = (0, 9),
                   textcoords = 'offset points')
plt.show()

import seaborn as sns

fig = plt.figure(figsize = (12, 8))
ax = plt.subplot(111)

ax = sns.barplot(x="Knowledge_type", y="Percentage", hue="Distance", data=knowledge)

for p in ax.patches:
    ax.annotate(format(p.get_height(), '.2f'), (p.get_x() + p.get_width() / 2., p.get_height()), 
       ha = 'center', va = 'center', xytext = (0, 10), textcoords = 'offset points')

New in matplotlib 3.4.0

There is now a built-in Axes.bar_label to automatically label bar containers:

• For single-group bar plots, pass the single bar container:

  ax = sns.barplot(x='day', y='tip', data=groupedvalues)
  ax.bar_label(ax.containers[0])
  

  

• For multi-group bar plots (with hue), iterate the multiple bar containers:

  ax = sns.barplot(x='day', y='tip', hue='sex', data=df)
  for container in ax.containers:
      ax.bar_label(container)
  

  

More details:

• How to label count plots (sns.countplot and sns.catplot)
• How to label percentage counts (fmt param)
• How to label with commas as thousands separators (labels param)
• How to label thresholded bar plots
• How to label horizontal bar plots

Color-ranked version

Is there a way to scale the colors of the bars, with the lowest value of total_bill having the lightest color (in this case Friday) and the highest value of total_bill having the darkest?

1. Find the rank of each total_bill value:

   • Either use Series.sort_values:

     ranks = groupedvalues.total_bill.sort_values().index
     # Int64Index([1, 0, 3, 2], dtype='int64')
     

   • Or condense Ernest’s Series.rank version by chaining Series.sub:

     ranks = groupedvalues.total_bill.rank().sub(1).astype(int).array
     # [1, 0, 3, 2]
     

2. Then reindex the color palette using ranks:

   palette = sns.color_palette('Blues_d', len(ranks))
   ax = sns.barplot(x='day', y='tip', palette=np.array(palette)[ranks], data=groupedvalues)