如何在条形图上显示自定义值

我正在研究如何在 Seaborn 使用条形图来显示数据框中的值，而不是图表中的值。

  1. 我希望在数据框中显示一个字段的值，同时绘制另一个字段的图形。例如，在下面，我正在绘制‘ tip’，但是我想把 'total_bill'的值放在每个条形图的中心(比如，周五上方325.88，周六上方1778.40，等等)
  2. 是否有一种方法来缩放条的颜色，最低值的 'total_bill'有最浅的颜色(在这种情况下星期五)和最高值的 'total_bill'有最深的颜色？显然，在缩放时，我会坚持使用一种颜色(即 蓝色)。

虽然我看到其他人认为这是另一个问题(或两个)的重复，但我忽略了如何使用一个不在图中的值作为标签或阴影的基础。怎么说呢，使用 total _ bill 作为基础。对不起，但我不能根据这些答案来判断。

从以下代码开始,

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)

我得到了以下结果:

Enter image description here

临时解决方案:

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

在 阴影上，使用下面的示例，我尝试了以下操作:

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")

但这给了我以下错误:

AttributeError: ‘ DataFrame’对象没有属性‘ argsort’

所以我尝试了一个修改:

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])

这样我就只剩下

IndexError: 索引4超出了大小为4的坐标轴0的界限

210028 次浏览
小开

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")
小开
最佳答案

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 that 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

小开

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)
小开

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

小开 
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')
小开

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()
小开 
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')
小开

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])

    seaborn bar plot labeled

  • 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)

    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


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