Matplotlib饼图与'所有其他类别'(Matplotlib Pie Graph with 'All Other Categories")

我创建了一个matplotlib饼图：

df.plot(kind='pie', subplots=True, figsize=(6, 4))

我的数据框由两列组成 - 国家和价值（％分布），列出了大约25个国家/地区。我想只按价值（按最高百分比）绘制前10个国家，并在图中计算剩余国家的百分比值，并将其命名为“所有其他国家”。如何使用matplotlib使用.plot函数执行此操作？

Country Value Albania 4% Brazil 3% Denmark 5% France 10% Mexico 3% Nigeria 15% Spain 4% U.S. 5%

I have created a matplotlib pie chart:

df.plot(kind='pie', subplots=True, figsize=(6, 4))

My dataframe consists of two columns - Country and Value (% distribution) and has about 25 countries listed. I would like to only plot the top 10 countries by values (by highest %) and within the plot, calculate the remaining countries % value and give it the title of 'All Other Countries'. How do I do this using matplotlib using the .plot function?

Country Value Albania 4% Brazil 3% Denmark 5% France 10% Mexico 3% Nigeria 15% Spain 4% U.S. 5%

最满意答案

正如评论中已经说明的那样，最好的方法是在绘图之前进行操作。这是一种如何做到这一点的方法：

import pandas as pd from matplotlib import pyplot as plt import numpy as np countries = [ 'Albania', 'Brazil', 'Denmark', 'France', 'Mexico', 'Nigeria', 'Spain', 'Germany', 'Finland', ] #the full dataframe df = pd.DataFrame( data = {'country': countries, 'value' :np.random.rand(len(countries))}, ).sort_values('value', ascending = False) #the top 5 df2 = df[:5].copy() #others new_row = pd.DataFrame(data = { 'country' : ['others'], 'value' : [df['value'][5:].sum()] }) #combining top 5 with others df2 = pd.concat([df2, new_row]) #plotting -- for comparison left all countries and right #the others combined fig, axes = plt.subplots(nrows = 1, ncols = 2, figsize = (9,4)) df.plot(kind = 'pie', y = 'value', labels = df['country'], ax = axes[0]) df2.plot(kind = 'pie', y = 'value', labels = df2['country'], ax = axes[1]) axes[0].set_title('all countries') axes[1].set_title('top 5') plt.show()

结果看起来像这样。

上述代码的结果

希望这可以帮助。

As already stated in the comments, the best way to do this is probably to do the manipulations before plotting. Here's a way how to do it:

import pandas as pd from matplotlib import pyplot as plt import numpy as np countries = [ 'Albania', 'Brazil', 'Denmark', 'France', 'Mexico', 'Nigeria', 'Spain', 'Germany', 'Finland', ] #the full dataframe df = pd.DataFrame( data = {'country': countries, 'value' :np.random.rand(len(countries))}, ).sort_values('value', ascending = False) #the top 5 df2 = df[:5].copy() #others new_row = pd.DataFrame(data = { 'country' : ['others'], 'value' : [df['value'][5:].sum()] }) #combining top 5 with others df2 = pd.concat([df2, new_row]) #plotting -- for comparison left all countries and right #the others combined fig, axes = plt.subplots(nrows = 1, ncols = 2, figsize = (9,4)) df.plot(kind = 'pie', y = 'value', labels = df['country'], ax = axes[0]) df2.plot(kind = 'pie', y = 'value', labels = df2['country'], ax = axes[1]) axes[0].set_title('all countries') axes[1].set_title('top 5') plt.show()

The result looks like this.

result of above code

Hope this helps.

更多推荐