Seaborn:带有频率的 countplot() [英] Seaborn: countplot() with frequencies

问题描述

我有一个 Pandas DataFrame，其中有一列名为AXLES"，它可以采用 3-12 之间的整数值.我正在尝试使用 Seaborn 的 countplot() 选项来实现以下图:

1. 左 y 轴显示这些值在数据中出现的频率.轴延伸为 [0%-100%]，每 10% 处有刻度线.
2. 右 y 轴显示实际计数，值对应于由左 y 轴确定的刻度线(每 10% 标记.)
3. x 轴显示条形图的类别 [3、4、5、6、7、8、9、10、11、12].
4. 条形顶部的注释显示该类别的实际百分比.

以下代码为我提供了下面的图，带有实际计数，但我找不到将它们转换为频率的方法.我可以使用 df.AXLES.value_counts()/len(df.index) 获取频率，但我不确定如何将此信息插入 Seaborn 的 countplot().

我还找到了注释的解决方法，但我不确定这是否是最佳实现.

任何帮助将不胜感激！

谢谢

plt.figure(figsize=(12,8))ax = sns.countplot(x="AXLES", data=dfWIM, order=[3,4,5,6,7,8,9,10,11,12])plt.title('卡车配置分布')plt.xlabel('轴数')plt.ylabel('频率[%]')对于 ax.patches 中的 p:ax.annotate('%{:.1f}'.format(p.get_height()), (p.get_x()+0.1, p.get_height()+50))

我使用以下代码更接近我需要的东西，使用 Pandas 的条形图，抛弃 Seaborn.感觉就像我使用了很多变通方法，并且必须有一种更简单的方法来做到这一点.这种方法的问题:

• Pandas 的条形图函数中没有 order 关键字，因为 Seaborn 的 countplot() 具有，因此我无法像在 countplot() 中那样绘制 3-12 的所有类别.即使该类别中没有数据，我也需要显示它们.

• 由于某种原因，辅助 y 轴弄乱了条形和注释(请参阅在文本和条形上绘制的白色网格线).

  plt.figure(figsize=(12,8))plt.title('卡车配置分布')plt.xlabel('轴数')plt.ylabel('频率[%]')ax = (dfWIM.AXLES.value_counts()/len(df)*100).sort_index().plot(kind="bar", rot=0)ax.set_yticks(np.arange(0, 110, 10))ax2 = ax.twinx()ax2.set_yticks(np.arange(0, 110, 10)*len(df)/100)对于 ax.patches 中的 p:ax.annotate('{:.2f}%'.format(p.get_height()), (p.get_x()+0.15, p.get_height()+1))

解决方案

您可以通过制作

I have a Pandas DataFrame with a column called "AXLES", which can take an integer value between 3-12. I am trying to use Seaborn's countplot() option to achieve the following plot:

1. left y axis shows the frequencies of these values occurring in the data. The axis extends are [0%-100%], tick marks at every 10%.
2. right y axis shows the actual counts, values correspond to tick marks determined by the left y axis (marked at every 10%.)
3. x axis shows the categories for the bar plots [3, 4, 5, 6, 7, 8, 9, 10, 11, 12].
4. Annotation on top of the bars show the actual percentage of that category.

The following code gives me the plot below, with actual counts, but I could not find a way to convert them into frequencies. I can get the frequencies using df.AXLES.value_counts()/len(df.index) but I am not sure about how to plug this information into Seaborn's countplot().

I also found a workaround for the annotations, but I am not sure if that is the best implementation.

Any help would be appreciated!

Thanks

plt.figure(figsize=(12,8))
ax = sns.countplot(x="AXLES", data=dfWIM, order=[3,4,5,6,7,8,9,10,11,12])
plt.title('Distribution of Truck Configurations')
plt.xlabel('Number of Axles')
plt.ylabel('Frequency [%]')

for p in ax.patches:
        ax.annotate('%{:.1f}'.format(p.get_height()), (p.get_x()+0.1, p.get_height()+50))

EDIT:

I got closer to what I need with the following code, using Pandas' bar plot, ditching Seaborn. Feels like I'm using so many workarounds, and there has to be an easier way to do it. The issues with this approach:

• There is no order keyword in Pandas' bar plot function as Seaborn's countplot() has, so I cannot plot all categories from 3-12 as I did in the countplot(). I need to have them shown even if there is no data in that category.

• The secondary y-axis messes up the bars and the annotation for some reason (see the white gridlines drawn over the text and bars).

  plt.figure(figsize=(12,8))
  plt.title('Distribution of Truck Configurations')
  plt.xlabel('Number of Axles')
  plt.ylabel('Frequency [%]')
  
  ax = (dfWIM.AXLES.value_counts()/len(df)*100).sort_index().plot(kind="bar", rot=0)
  ax.set_yticks(np.arange(0, 110, 10))
  
  ax2 = ax.twinx()
  ax2.set_yticks(np.arange(0, 110, 10)*len(df)/100)
  
  for p in ax.patches:
      ax.annotate('{:.2f}%'.format(p.get_height()), (p.get_x()+0.15, p.get_height()+1))
  

解决方案

You can do this by making a twinx axes for the frequencies. You can switch the two y axes around so the frequencies stay on the left and the counts on the right, but without having to recalculate the counts axis (here we use tick_left() and tick_right() to move the ticks and set_label_position to move the axis labels

You can then set the ticks using the matplotlib.ticker module, specifically ticker.MultipleLocator and ticker.LinearLocator.

As for your annotations, you can get the x and y locations for all 4 corners of the bar with patch.get_bbox().get_points(). This, along with setting the horizontal and vertical alignment correctly, means you don't need to add any arbitrary offsets to the annotation location.

Finally, you need to turn the grid off for the twinned axis, to prevent grid lines showing up on top of the bars (ax2.grid(None))

Here is a working script:

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

# Some random data
dfWIM = pd.DataFrame({'AXLES': np.random.normal(8, 2, 5000).astype(int)})
ncount = len(dfWIM)

plt.figure(figsize=(12,8))
ax = sns.countplot(x="AXLES", data=dfWIM, order=[3,4,5,6,7,8,9,10,11,12])
plt.title('Distribution of Truck Configurations')
plt.xlabel('Number of Axles')

# Make twin axis
ax2=ax.twinx()

# Switch so count axis is on right, frequency on left
ax2.yaxis.tick_left()
ax.yaxis.tick_right()

# Also switch the labels over
ax.yaxis.set_label_position('right')
ax2.yaxis.set_label_position('left')

ax2.set_ylabel('Frequency [%]')

for p in ax.patches:
    x=p.get_bbox().get_points()[:,0]
    y=p.get_bbox().get_points()[1,1]
    ax.annotate('{:.1f}%'.format(100.*y/ncount), (x.mean(), y), 
            ha='center', va='bottom') # set the alignment of the text

# Use a LinearLocator to ensure the correct number of ticks
ax.yaxis.set_major_locator(ticker.LinearLocator(11))

# Fix the frequency range to 0-100
ax2.set_ylim(0,100)
ax.set_ylim(0,ncount)

# And use a MultipleLocator to ensure a tick spacing of 10
ax2.yaxis.set_major_locator(ticker.MultipleLocator(10))

# Need to turn the grid on ax2 off, otherwise the gridlines end up on top of the bars
ax2.grid(None)

plt.savefig('snscounter.pdf')

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