data_df = pandas.read_csv('details.csv')
data_df = data_df.replace('Null', np.nan)
df = data_df.groupby(['country', 'branch']).count()
df = df.drop('sales', axis=1)
df = df.reset_index()
print df
我想将数据帧(df)的结果转换为我下面提到的用户定义的 json 格式。打印结果(df)后，我将得到表单中的结果
country branch no_of_employee total_salary count_DOB count_email
x a 30 2500000 20 25
x b 20 350000 15 20
y c 30 4500000 30 30
z d 40 5500000 40 40
z e 10 1000000 10 10
z f 15 1500000 15 15
我想将其转换为 Json。我想要的格式是
x
{
a
{
no.of employees:30
total salary:2500000
count_email:25
}
b
{
no.of employees:20
total salary:350000
count_email:25
}
}
y
{
c
{
no.of employees:30
total salary:4500000
count_email:30
}
}
z
{
d
{
no.of employees:40
total salary:550000
count_email:40
}
e
{
no.of employees:10
total salary:100000
count_email:15
}
f
{
no.of employees:15
total salary:1500000
count_email:15
}
}
请注意，我不希望数据帧结果中的所有字段都为 Json(例如:count_DOB)
最佳答案
您可以使用groupby与 apply to_dict最后to_json :
country branch no_of_employee total_salary count_DOB count_email
0 x a 30 2500000 20 25
1 x b 20 350000 15 20
2 y c 30 4500000 30 30
3 z d 40 5500000 40 40
4 z e 10 1000000 10 10
5 z f 15 1500000 15 15
g = df.groupby('country')[["branch", "no_of_employee","total_salary", "count_email"]]
.apply(lambda x: x.set_index('branch').to_dict(orient='index'))
print g.to_json()
{
"x": {
"a": {
"total_salary": 2500000,
"no_of_employee": 30,
"count_email": 25
},
"b": {
"total_salary": 350000,
"no_of_employee": 20,
"count_email": 20
}
},
"y": {
"c": {
"total_salary": 4500000,
"no_of_employee": 30,
"count_email": 30
}
},
"z": {
"e": {
"total_salary": 1000000,
"no_of_employee": 10,
"count_email": 10
},
"d": {
"total_salary": 5500000,
"no_of_employee": 40,
"count_email": 40
},
"f": {
"total_salary": 1500000,
"no_of_employee": 15,
"count_email": 15
}
}
}
我尝试print g.to_dict()，但 JSON 无效(检查 here )。
关于python - 如何将 pandas DataFrame 结果转换为用户定义的 json 格式，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/35473711/