我尝试模块化我的python代码。
在我的主要代码笔记本中,我插入了这个:
import numpy as np
import pandas as pd
from pandas import DataFrame
import my_math
df["std_line_amount_log_normal"] = df["std_line_amount"].apply(lambda f: my_math.feature_log_normalize(f))
我创建了一个名为“my_math”的模块作为外部.py文件:这里的代码:
def feature_std_normalize(f):
return (f - mu) / std
def feature_log_normalize(f):
# return (f - mu) / std
return np.log(f+1)
当我在笔记本中运行此语句时:
df["std_line_amount_log_normal"] = df["std_line_amount"].apply(lambda f: my_math.feature_log_normalize(f))
我收到此错误:
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-90-32256b03bfa2> in <module>()
----> 1 df["std_line_amount_log_normal"] = df["std_line_amount"].apply(lambda f: my_math.feature_log_normalize(f))
~/anaconda3/envs/python3/lib/python3.6/site-packages/pandas/core/series.py in apply(self, func, convert_dtype, args, **kwds)
2549 else:
2550 values = self.asobject
-> 2551 mapped = lib.map_infer(values, f, convert=convert_dtype)
2552
2553 if len(mapped) and isinstance(mapped[0], Series):
pandas/_libs/src/inference.pyx in pandas._libs.lib.map_infer()
<ipython-input-90-32256b03bfa2> in <lambda>(f)
----> 1 df["std_line_amount_log_normal"] = df["std_line_amount"].apply(lambda f: my_math.feature_log_normalize(f))
~/SageMaker/my_math.py in feature_log_normalize(f)
10
11 def feature_log_normalize(f):
---> 12 # return (f - mu) / std
13 return np.log(f+1)
14
NameError: name 'np' is not defined
解决办法:尝试在外部.py中导入numpy,这样:
import numpy as np
def feature_std_normalize(f):
return (f - mu) / std
def feature_log_normalize(f):
# return (f - mu) / std
return np.log(f+1)