def cal_properties(pressure):
if pressure>=0 and pressure<=1000:
density=1/pressure #myfunction(pressure)
else:
density=pressure*10
return density
print(df)
WELL_NKNME 10A74 10A75 10A77 10A78 11A74 11A75 11A77 11A78
Date
2022-06-05 0.0 0.0 0.0 0.0 0.0 122.8 56.3 96.3
2022-06-06 0.0 0.0 0.0 0.0 0.0 118.3 52.0 85.3
2022-06-07 0.0 0.0 0.0 0.0 0.0 119.5 52.9 87.4
df=df.apply(lambda row: cal_properties(row),axis=1)
然后我得到了一个与if语句有关的错误
----> 7 df=df.apply(lambda row: cal_properties(row),axis=1)
8 df
C:Anacondaenvsdash_tflibsite-packagespandascoreframe.py in apply(
self,
func,
axis,
raw,
result_type,
args,
**kwargs
)
8738 kwargs=kwargs,
8739 )
-> 8740 return op.apply()
8741
8742 def applymap(
C:Anacondaenvsdash_tflibsite-packagespandascoreapply.py in apply(self)
686 return self.apply_raw()
687
--> 688 return self.apply_standard()
689
690 def agg(self):
C:Anacondaenvsdash_tflibsite-packagespandascoreapply.py in apply_standard(self)
810
811 def apply_standard(self):
--> 812 results, res_index = self.apply_series_generator()
813
814 # wrap results
C:Anacondaenvsdash_tflibsite-packagespandascoreapply.py in apply_series_generator(self)
826 for i, v in enumerate(series_gen):
827 # ignore SettingWithCopy here in case the user mutates
--> 828 results[i] = self.f(v)
829 if isinstance(results[i], ABCSeries):
830 # If we have a view on v, we need to make a copy because
C:Temp1ipykernel_8403896317687.py in <lambda>(row)
5 # print(df.iloc[0:1,:])
6 # print(df.to_dict())
----> 7 df=df.apply(lambda row: cal_properties(row),axis=1)
8 df
C:Temp1ipykernel_8402054338456.py in cal_properties(pressure)
1 def cal_properties(pressure):
2
----> 3 if pressure>=0 and pressure<=1000:
4 density=1/pressure #myfunction(pressure)
5 else:
C:Anacondaenvsdash_tflibsite-packagespandascoregeneric.py in __nonzero__(self)
1535 @final
1536 def __nonzero__(self):
-> 1537 raise ValueError(
1538 f"The truth value of a {type(self).__name__} is ambiguous. "
1539 "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
这里是数据帧字典数据，因此您可以执行代码。如果没有if语句，代码也没问题。我不知道怎么解决它。谢谢你的帮助。
print(df.to_dict())
{'10A74': {Timestamp('2022-06-05 00:00:00'): 0.0, Timestamp('2022-06-06 00:00:00'): 0.0, Timestamp('2022-06-07 00:00:00'): 0.0}, '10A75': {Timestamp('2022-06-05 00:00:00'): 0.0, Timestamp('2022-06-06 00:00:00'): 0.0, Timestamp('2022-06-07 00:00:00'): 0.0}, '10A77': {Timestamp('2022-06-05 00:00:00'): 0.0, Timestamp('2022-06-06 00:00:00'): 0.0, Timestamp('2022-06-07 00:00:00'): 0.0}, '10A78': {Timestamp('2022-06-05 00:00:00'): 0.0, Timestamp('2022-06-06 00:00:00'): 0.0, Timestamp('2022-06-07 00:00:00'): 0.0}, '11A74': {Timestamp('2022-06-05 00:00:00'): 0.0, Timestamp('2022-06-06 00:00:00'): 0.0, Timestamp('2022-06-07 00:00:00'): 0.0}, '11A75': {Timestamp('2022-06-05 00:00:00'): 122.8, Timestamp('2022-06-06 00:00:00'): 118.3, Timestamp('2022-06-07 00:00:00'): 119.5}, '11A77': {Timestamp('2022-06-05 00:00:00'): 56.3, Timestamp('2022-06-06 00:00:00'): 52.0, Timestamp('2022-06-07 00:00:00'): 52.9}, '11A78': {Timestamp('2022-06-05 00:00:00'): 96.3, Timestamp('2022-06-06 00:00:00'): 85.3, Timestamp('2022-06-07 00:00:00'): 87.4}}
这似乎是np.where的工作:
df.loc[:, :] = np.where((df >= 0) & (df <= 1000), 1/df, df*10)
可以按行应用相同的逻辑:
def cal_properties(pressure_row):
return pd.Series(
np.where(pressure_row.between(0, 1000), 1/pressure_row, pressure_row*10),
index=pressure_row.index
)
df = df.apply(cal_properties,axis=1)
我们知道你不能除以0
将您的函数更改为严格的>0，然后您可以执行applymap，因为您正在按单元计算而不是按行/列计算。因此:
def cal_properties(pressure):
if pressure>0 and pressure<=1000:
density=1/pressure #myfunction(pressure)
else:
density=pressure*10
return density
df.applymap(cal_properties)
10A74 10A75 10A77 10A78 11A74 11A75 11A77 11A78
2022-06-05 0.0 0.0 0.0 0.0 0.0 0.008143 0.017762 0.010384
2022-06-06 0.0 0.0 0.0 0.0 0.0 0.008453 0.019231 0.011723
2022-06-07 0.0 0.0 0.0 0.0 0.0 0.008368 0.018904 0.011442