将值添加到未堆积的条形图-Python

Question

这是我的堆积条形图代码。我可以为第一个细分（AA）添加百分比值，但是如何为所有4个细分添加值？

df = pd.read_csv("123.csv")

df1 = df.groupby(['Country', 'ClassWeight']) 
['Count'].sum().unstack('ClassWeight').fillna(0)

#sort on the 'total' column, and then drop it to avoid double plotting

ax = df1.sort_values(['total']).iloc[:,:-1].plot(kind='barh', width=0.8, 
stacked=True, figsize=(15, 10),colormap=ListedColormap(sns.color_palette("Blues_d")))

#plot barchart
ax.set_xlabel('No.of Shipments',fontsize=15)

ax.set_ylabel('Country',fontsize=15)

plt.xticks(fontsize=15)

plt.yticks(fontsize=15)

plt.title('Total Shipments by Country and Customer Class',fontsize=15)

df2=df1.sort_values(['total'],ascending=True)

df2['AA'] = 100*df2['AA']/df2['total']

df2['A'] = 100*df2['A']/df2['total']

df2['B'] = 100*df2['B']/df2['total']

df2['C'] = 100*df2['C']/df2['total']

df3 = df2.iloc[:,:-1]

#Can only enumerate on the AA column. How could we do all 4 columns?

for i, v in enumerate(df3['AA']):
    ax.text(v + -1, i + -0.2, str("{0:.1f}%".format(v)), color='white', 
fontweight='bold', fontsize=15)

示例数据：

ClassWeight            AA          A          B          C
Country                                                   
Romania         17.142857  32.268908  28.235294  22.352941
Finland         60.325203  13.495935  12.682927  13.495935

{'Country'：{0：'France'，1：'Poland'，2：'Lithuania'，3：'United Kingdom'，4：'Denmark'}，'Count'：{0：233， 1：232，2：286，3：236，4：223}，'SumWeight'：{0：8072469.5，1：6689511.05，2：5158305.25，3：4675914.53，4：3536684.52}，'AvgWeight'：{0： 34645.79，1：28834.1，2：18036.03，3：19813.2，4：15859.57}，'ClassWeight'：{0：'AA'，1：'AA'，2：'AA'，3：'AA'，4： 'AA'}}

Answer 1

我已经尝试过对代码进行一些重新排列-您不需要每次都创建新的数据框，并且只需要按总数对一次即可。

import pandas as pd

import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import seaborn as sns

df = pd.DataFrame(
    {'Country': {0: 'France', 1: 'France', 2: 'France', 3: 'France', 4: 'France'},
     'Count': {0: 100, 1: 232, 2: 286, 3: 236, 4: 854},
     'ClassWeight': {0: 'AA', 1: 'A', 2: 'B', 3: 'C', 4: 'total'}}
)

# Track which value columns we want to plot
VALUE_COLS =['AA', 'A', 'B', 'C']

# We only need to sort_values once, so we might as well do it as we generate df1
df1 = df.groupby(['Country', 'ClassWeight'])['Count']\
        .sum()\
        .unstack('ClassWeight')\
        .fillna(0)\
        .sort_values(by='total', ascending=False)

# Get percentage values 
for col in VALUE_COLS:
    df1[col + '_%'] = 100*df1[col]/df1['total']

ax = df1[VALUE_COLS].plot(kind='barh', width=0.8,stacked=True,
                          figsize=(15, 10),
                          colormap=ListedColormap(sns.color_palette("Blues_d")))

# Set up labels and ticks
ax.set_xlabel('No.of Shipments',fontsize=15)
ax.set_ylabel('Country',fontsize=15)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.title('Total Shipments by Country and Customer Class',fontsize=15)

# Add in text labels
df1['label_tot'] = 0
for col in VALUE_COLS:
    df1['label_tot'] += df1[col]
    for i, (val, pos) in enumerate(df1[[col + '_%', 'label_tot']].itertuples(index=False, name=None)):
        ax.text(pos + -1, i, str("{0:.1f}%".format(val)),
                color='white',fontweight='bold', fontsize=15, ha='right')

使用您输入数据的略微修改版本，结果如​​下：