我是csv的新手。我正在读取dictionary文件，并试图将输出作为import pandas as pd
df = pd.read_csv('source.csv')
my_projects = ['WORLD', 'P&G', 'AVR', 'ABCD', 'Channel', 'Migration']
filtered_projects = df[(df['area'] == 'MY PROJECTS') & (df['name'].isin(my_projects))]
filtered_projects['count'] = 1
total_of_each_error = filtered_projects.groupby(['month','name','errors']).sum().reset_index()
total_of_each_error['month'] = pd.to_datetime(total_of_each_error['month']).dt.strftime('%B')
。
['Big', 'Small', 'Monitoring', 'Improvement']我要计算的事物列表：total_of_each_error
month name errors count
0 February ABCD Big 1
1 February ABCD Monitoring 3
2 February WORLD Small 1
3 February Channel Big 2
4 February Channel Small 1
5 February Channel Monitoring 1
6 February AVR Monitoring 1
7 April WORLD Monitoring 2
8 May Migration Big 1
9 May Migration Monitoring 2
10 June P&G Small 1
11 June P&G Monitoring 1
12 June ABCD Monitoring 1
13 June WORLD Improvement 1
14 July P&G Monitoring 1
15 July ABCD Small 1
16 July ABCD Monitoring 1
数据帧具有：
dictionary如果一个月没有特定错误，则应填写零。我试图获得的输出是此data = {'WORLD': {'categories': ['February', 'April', 'May', 'June', 'July'],
'series': [{
'name': 'Big Issue',
'data': [0, 0, 0, 0, 0] # Number of Bigs in those months
}, {
'name': 'Small Issue',
'data': [1, 0, 0, 0, 0] # Number of Smalls in those months
}, {
'name': 'Monitoring',
'data': [0, 2, 0, 0, 0] # Number of Monitorings in those months
}, {
'name': 'Improvement',
'data': [0, 0, 0, 1, 0] # Number of Improvements in those months
}]
},
'P&G': {'categories': ['February', 'April', 'May', 'June', 'July'],
'series': [{
'name': 'Big Issue',
'data': [0, 0, 0, 0, 0]
}, {
'name': 'Small Issue',
'data': [0, 0, 0, 1, 0]
}, {
'name': 'Monitoring',
'data': [0, 2, 0, 0, 0]
}, {
'name': 'Improvement',
'data': [0, 0, 0, 1, 0]
}]
}
}
：
WORLD上面显示的预期输出仅适用于P&G和my_projects。该字典将包含name中的其余元素。应该保留月份顺序和数据。
编辑：在 <p:commandButton id="link" value="E-payment" action="#{controller.ePaymentMethod}"
process="@this" update="@form"
oncomplete="proceedToPayment('{epaymentVo.epaymentEboCode}','{epaymentVo.token}')">
</p:commandButton>
答案 0 :(得分：0)
将数据框排序并修改为正确的格式（使用df.groupby和.unstack()），然后在数据框上使用to_dict()方法来获得所需的结果。下面的例子。
import numpy as np
import pandas as pd
df = pd.DataFrame(
data ={'Month': ['Jan','Feb']*5,
'Issue': ['Big Issue','Monitoring']*5,
'value': np.arange(30,40)})
df.groupby(['Month','Issue']).count().unstack()
df.to_dict()
df.to_dict()