您可以使用pyplot.bar绘制条形图。您可以查看documentation以了解更多有关它的细节，但您需要条形图的位置（在本例中，是value_x_mean变量）和条形图的高度（value_x_count）。我建议在字典中重命名你的参数，以使迭代更容易：

data = {
"value-A" : {
    "value-a-mean" : 10.0,
    "value-a-count" : 100 
},
"value-B" : {
    "value-b-mean" : 2.0,
    "value-b-count" : 150 
},
"value-C" : {
    "value-c-mean" : 6.6,
    "value-c-count" : 220 
},
"value-D" : {
    "value-d-mean" : 11.4,
    "value-d-count" : 200 
    }
} 

for key, value in data.items():
    for sub_key, sub_value in value.items():
        # When you do 'value_a_mean'.split('-') you get ['value', 'a', 'mean']
        # We take the last item from this list [-1], so just 'mean' or 'count'
        value[sub_key.split('-')[-1]] = value[sub_key]
        del value[sub_key]

现在data看起来像这样：

{
    "value-A" : {
        "mean" : 10.0,
        "count" : 100 
    },
    "value-B" : {
        "mean" : 2.0,
        "count" : 150 
    },
    "value-C" : {
        "mean" : 6.6,
        "count" : 220 
    },
    "value-D" : {
        "mean" : 11.4,
        "count" : 200 
    }
}

然后您可以执行以下操作：

from matplotlib import pyplot

data = {
    "value-A" : {
        "mean" : 10.0,
        "count" : 100 
    },
    "value-B" : {
        "mean" : 2.0,
        "count" : 150 
    },
    "value-C" : {
        "mean" : 6.6,
        "count" : 220 
    },
    "value-D" : {
        "mean" : 11.4,
        "count" : 200 
    }
} 

means, counts, labels = [], [], []

# Read the dictionary and save values
for key, value in data.items():
    labels.append(key)
    means.append(value['mean'])
    counts.append(value['count'])

# Bar plot
pyplot.bar(means, counts, tick_label=labels)
pyplot.show()

结果：

您可以为任意多个其他字典执行此操作，条形图将一起显示。您可以使用图例来标记它们：

# Random 2nd dictionary
data2 = {
    "value-A" : {
        "mean" : 20.0,
        "count" : 100 
    },
    "value-B" : {
        "mean" : 3.0,
        "count" : 150 
    },
    "value-C" : {
        "mean" : 5.6,
        "count" : 220 
    },
    "value-D" : {
        "mean" : 8.4,
        "count" : 200 
    }
} 

means, counts, labels = [], [], []

for key, value in data.items():
    means.append(value['mean'])
    counts.append(value['count'])

pyplot.bar(means, counts, label="Dataset 1")

means, counts, labels = [], [], []

for key, value in data2.items():
    means.append(value['mean'])
    counts.append(value['count'])

pyplot.bar(means, counts, label="Dataset 2")
pyplot.legend()
pyplot.show()

结果：

• 给定嵌套字典或一般字典，通常使用pandas更容易，然后使用seaborn或pandas.DataFrame.plot进行绘图。
• seaborn是matplotlib的高级API
• pandas.DataFrame.plot使用matplotlib作为默认后端。
• 数据框的形状和所需的绘图应确定要使用哪个绘图API。
• 另请参见How to plot a bar plot from a nested dictionary，它直接使用pandas.DataFrame.plot.绘图

import pandas as pd
import seaborn as sns

# create a dataframe with a unique column, data, to identify which dictionary
df = pd.concat([pd.DataFrame(d).assign(data=i) for i, d in enumerate([data1, data2], start=1)], ignore_index=False)

# reset the index to be a column and rename it
df = df.reset_index().rename({'index': 'category'}, axis=1)

# convert the dataframe to a long for to work more easily with seaborn
dfm = df.melt(id_vars=['category', 'data'])

# plot both mean and count
g = sns.catplot(data=dfm, kind='bar', x='variable', y='value', hue='data', col='category')

# plot just one category by selecting the data value for that column and then plotting
data = dfm[dfm['category'].eq('mean')]
g = sns.catplot(data=data, kind='bar', x='variable', y='value', hue='data', col='category')

df

category  value-A  value-B  value-C  value-D  data
0     mean     10.0      2.0      6.6     11.4     1
1    count    100.0    150.0    220.0    200.0     1
2     mean     20.0      6.0     18.6     30.4     2
3    count     50.0    100.0    150.0    120.0     2

dfm

category  data variable  value
0      mean     1  value-A   10.0
1     count     1  value-A  100.0
2      mean     2  value-A   20.0
3     count     2  value-A   50.0
4      mean     1  value-B    2.0
5     count     1  value-B  150.0
6      mean     2  value-B    6.0
7     count     2  value-B  100.0
8      mean     1  value-C    6.6
9     count     1  value-C  220.0
10     mean     2  value-C   18.6
11    count     2  value-C  150.0
12     mean     1  value-D   11.4
13    count     1  value-D  200.0
14     mean     2  value-D   30.4
15    count     2  value-D  120.0