我有 3 列,即模型(应作为索引)、无归一化的准确度、归一化后的准确度(zscore、minmax、maxabs、robust),这些需要创建为:
------------------------------------------------------------------------------------
| Models | Accuracy without normalization | Accuracy with normalization |
| | |-----------------------------------|
| | | zscore | minmax | maxabs | robust |
------------------------------------------------------------------------------------
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dfmod-> Models column
dfacc-> Accuracy without normalization
dfacc1-> Accuracy with normalization - zscore
dfacc2-> Accuracy with normalization - minmax
dfacc3-> Accuracy with normalization - maxabs
dfacc4-> Accuracy with normalization - robust
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dfout=pd.DataFrame({('Accuracy without Normalization'):{dfacc},
('Accuracy using Normalization','zscore'):{dfacc1},
('Accuracy using Normalization','minmax'):{dfacc2},
('Accuracy using Normalization','maxabs'):{dfacc3},
('Accuracy using Normalization','robust'):{dfacc4},
},index=dfmod
)
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我试图做这样的事情,但我无法进一步弄清楚
测试数据: …
我试图继承str阶级是为了好玩。
我提供了两种方法,1)使用super()和2)str在构造函数中使用类,如下所示:
class Str2(str):
def __init__(self, value):
super().__init__() # I did not use `value` here, but my code works!
def ishello(self):
if self == "Hello":
return True
else:
False
s = Str2("Hello")
print(s.upper()) # a method of str class
print(s.ishello()) # a new method I defined in Str2 class
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class Str2(str):
def __init__(self, value):
str.__init__(value) # It makes sense for me
def ishello(self):
if self == "Hello":
return True
else:
False
s = Str2("Hello") …Run Code Online (Sandbox Code Playgroud) 您好,我想将字典附加到 DataFrame,但在这个字典中我没有任何索引值。我还需要从“KP”值中删除除“1393”之外的所有内容。这看起来像这样:
对此 df:在此处输入图像描述
我想像这样附加字典:
dict = {'KP': [1.0 1393
Name: KP, dtype: int64],
'Wiek': [Timedelta('176 days 12:59:43.042156102')],
'KY1': [113.0],
'OKO': [57.51],
'GS': [10.59],
'T': [654.31],
'MP': [58.9]}
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我试试这个:
dict = {'KP': [1.0 1393
Name: KP, dtype: int64],
'Wiek': [Timedelta('176 days 12:59:43.042156102')],
'KY1': [113.0],
'OKO': [57.51],
'GS': [10.59],
'T': [654.31],
'MP': [58.9]}
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但什么也没发生, df 仍然相同,我没有收到任何错误。我尝试将此字典转换为列表并将列表作为行附加到 df 但我得到了相同的结果。
好的,我试试这个:
df.append(dict,ignore_index=True)
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我得到这个输出: 在此处输入图像描述
这还不够好,我需要清晰的数据。