CODEWITHSUNDEEP
pythonpandasmatplotlibseabornbar-chart
Stumbling Through Data Science
Stumbling Through Data Science

Reputation: 1960

How to display custom values on a bar plot

I'm looking to see how to do two things in Seaborn with using a bar chart to display values that are in the dataframe, but not in the graph.

  1. I'm looking to display the values of one field in a dataframe while graphing another. For example, below, I'm graphing 'tip', but I would like to place the value of 'total_bill' centered above each of the bars (i.e.325.88 above Friday, 1778.40 above Saturday, etc.)
  2. 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? Obviously, I'd stick with one color (i.e., blue) when I do the scaling.

While I see that others think that this is a duplicate of another problem (or two), I am missing the part of how I use a value that is not in the graph as the basis for the label or the shading. How do I say, use total_bill as the basis. I'm sorry, but I just can't figure it out based on those answers.

Starting with the following code,

import pandas as pd
import seaborn as sns
%matplotlib inline

df = pd.read_csv("https://raw.githubusercontent.com/wesm/pydata-book/1st-edition/ch08/tips.csv", sep=',')
groupedvalues = df.groupby('day').sum().reset_index()
g = sns.barplot(x='day', y='tip', data=groupedvalues)

I get the following result:

Enter image description here

Interim Solution:

for index, row in groupedvalues.iterrows():
    g.text(row.name, row.tip, round(row.total_bill, 2), color='black', ha="center")

Enter image description here

On the shading, using the example below, I tried the following:

import pandas as pd
import seaborn as sns
%matplotlib inline

df = pd.read_csv("https://raw.githubusercontent.com/wesm/pydata-book/1st-edition/ch08/tips.csv", sep=',')
groupedvalues = df.groupby('day').sum().reset_index()

pal = sns.color_palette("Greens_d", len(data))
rank = groupedvalues.argsort().argsort()
g = sns.barplot(x='day', y='tip', data=groupedvalues)

for index, row in groupedvalues.iterrows():
    g.text(row.name, row.tip, round(row.total_bill, 2), color='black', ha="center")

But that gave me the following error:

AttributeError: 'DataFrame' object has no attribute 'argsort'

So I tried a modification:

import pandas as pd
import seaborn as sns
%matplotlib inline

df = pd.read_csv("https://raw.githubusercontent.com/wesm/pydata-book/1st-edition/ch08/tips.csv", sep=',')
groupedvalues = df.groupby('day').sum().reset_index()

pal = sns.color_palette("Greens_d", len(data))
rank = groupedvalues['total_bill'].rank(ascending=True)
g = sns.barplot(x='day', y='tip', data=groupedvalues, palette=np.array(pal[::-1])[rank])

and that leaves me with

IndexError: index 4 is out of bounds for axis 0 with size 4

Upvotes: 96

Views: 263923

Answers (8)

tdy
tdy

Reputation: 41327

New in matplotlib 3.4.0

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

    ax = sns.barplot(x='day', y='tip', data=groupedvalues)
ax.bar_label(ax.containers[0]) # only 1 container needed unless using `hue`

    seaborn bar plot labeled

  • For custom labels (e.g., tip bars with total_bill values), use the labels parameter:

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

    seaborn bar plot custom values

  • For multi-group bar plots (i.e., with hue), there will be multiple bar containers that need to be iterated:

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

    seaborn grouped bar plot labeled

More details:

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)

    seaborn bar plot color-ranked

Upvotes: 163

ImportanceOfBeingErnest
ImportanceOfBeingErnest

Reputation: 339220

Stick to the solution from Changing color scale in seaborn bar plot, which uses argsort to determine the order of the bar colors. In the linked question, argsort is applied to a Series object, while here you have a DataFrame. Select one column of the DataFrame to apply argsort on.

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

df = sns.load_dataset('tips')
groupedvalues = df.groupby('day').sum().reset_index()

pal = sns.color_palette('Greens_d', len(groupedvalues))
rank = groupedvalues['total_bill'].argsort().argsort() 
g = sns.barplot(x='day', y='tip', data=groupedvalues, palette=np.array(pal[::-1])[rank])

for index, row in groupedvalues.iterrows():
    g.text(row.name, row.tip, round(row.total_bill, 2), color='black', ha='center')
    
plt.show()

enter image description here

The second attempt works fine as well, the only issue is the rank, as returned by rank(), starts at 1 instead of 0. So one has to subtract 1 from the array. For indexing, we need integer values, so cast it to int.

rank = groupedvalues['total_bill'].rank(ascending=True).values
rank = (rank-1).astype(int)
  • From matplotlib 3.4.0, there is .bar_label, which has a label parameter for custom labels.
    • Other answers using .bar_label didn't customize the labels with labels=.
    • See this answer from May 16, 2021, for a thorough explanation of .bar_label with links to documentation and examples.
  1. The day column downloads as a category Dtype, which keeps the days of the week in order. This also ensures the plot order of the bars on the x-axis and the values in tb.
    • .bar_label adds labels from left to right, so the values in tb are in the same order as the bars.
    • If working with a column that isn't categorical, pd.Categorical can be used on the column to set the order.
  • In sns.barplot, estimator=sum is specified to sum tip. The default is mean.
df = sns.load_dataset("tips")

# sum total_bill by day
tb = df.groupby('day').total_bill.sum()

# get the colors in blues as requested
pal = sns.color_palette("Blues_r", len(tb))

# rank the total_bill sums
rank = tb.argsort()

# plot
fig, ax = plt.subplots(figsize=(8, 6))
sns.barplot(x='day', y='tip', data=df, palette=np.array(pal[::-1])[rank], estimator=sum, ci=False, ax=ax)

# 1. add labels using bar_label with custom labels from tb
ax.bar_label(ax.containers[0], labels=tb, padding=3)

# pad the spacing between the number and the edge of the figure
ax.margins(y=0.1)

plt.show()

enter image description here

Upvotes: 61

Sarthak Rana
Sarthak Rana

Reputation: 305

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()

Upvotes: 4

user3663280
user3663280

Reputation: 149

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')

Upvotes: 14

Sharon Soussan
Sharon Soussan

Reputation: 711

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)

Upvotes: 71

jose_bacoy
jose_bacoy

Reputation: 12684

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")

Upvotes: 4

Farid Mammadaliyev
Farid Mammadaliyev

Reputation: 118

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')

Upvotes: 4

Secant Zhang
Secant Zhang

Reputation: 741

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)

enter image description here

Upvotes: 53

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