[英]how to integrate a pandas operation into sklearn pipeline
I have a simple operation on pandas dataframe like this: 我对熊猫数据框有一个简单的操作,如下所示:
# initialization
dct = {1: 'A', 2:'B', 3: 'C'}
df = pd.DataFrame({'id': [1,2,3], 'value':[7,8,9]})
# actual transformation
df['newid'] = df.id.map(dct)
And I would like to put this transformation as a part of a sklearn pipeline. 我想将此转换作为sklearn管道的一部分。 I found a few tutorials here , here , and here .
我在这里 , 这里和这里找到了一些教程。 But I just can't get it work for me.
但是我只是无法让它对我有用。 Here's one version of many versions I have tried:
这是我尝试过的许多版本的一个版本:
# initialization
dct = {1: 'A', 2:'B', 3: 'C'}
df = pd.DataFrame({'id': [1,2,3], 'value':[7,8,9]})
# define a class similar to those in the tutorials
class idMapper(BaseEstimator, TransformerMixin):
def __init__(self, key='id'):
self.key = key
def fit(self, X, y=None):
return self
def transform(self, X):
return X[key].map(dct)
# Apply the transformation
idMapper.fit_transform(df)
The error message is like this: TypeError: fit_transform() missing 1 required positional argument: 'X'
. 错误消息是这样的:
TypeError: fit_transform() missing 1 required positional argument: 'X'
。 Can anyone help me fix this issue and get it working? 谁能帮助我解决此问题并使它正常工作? Thanks!
谢谢!
See below a corrected version of your code. 参见下面的代码更正版本。 Explanation given in the comments.
注释中给出了解释。
dct = {1: 'A', 2:'B', 3: 'C'}
df = pd.DataFrame({'id': [1,2,3], 'value':[7,8,9]})
# define a class similar to those in the tutorials
class idMapper(BaseEstimator, TransformerMixin):
def __init__(self, key='id'):
self.key = key
def fit(self, X, y=None):
return self
def transform(self, X):
return X[self.key].map(dct) # <--- self.key
# Apply the transformation
idMapper().fit_transform(df) # <--- need to instantiate
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