Pandas，Jupyter Notebook 中的规范化错误

Question

我是 Python 和 Tensorflow 的新手。我创建了一个使用 Pandas 和现有 CSV 文件进行简单分类的示例。但是为了规范化 CSV 中的列，我收到以下错误。

调试 IDE：Jupyter Notebook

import pandas as pd
patients = pd.read_csv("../npushpakaran/TENSORFLOW/Tensorflow-Bootcamp-master/02-TensorFlow-Basics/pima-indians-diabetes.csv")
patients.columns
Index(['Number_pregnant', 'Glucose_concentration', 'Blood_pressure', 'Triceps',
       'Insulin', 'BMI', 'Pedigree', 'Age', 'Class', 'Group'],
      dtype='object')
cols_to_norm =['Number_pregnant', 'Glucose_concentration', 'Blood_pressure', 'Triceps',
       'Insulin', 'BMI', 'Pedigree', 'Age', 'Class', 'Group'] 


patients[cols_to_norm] = patients[cols_to_norm].apply(lambda x: (x- x.min())/(x.max()-x.min()))

在最后一行，我收到以下错误。

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~/anaconda3/lib/python3.6/site-packages/pandas/core/ops.py in na_op(x, y)
   1008         try:
-> 1009             result = expressions.evaluate(op, str_rep, x, y, **eval_kwargs)
   1010         except TypeError:

~/anaconda3/lib/python3.6/site-packages/pandas/core/computation/expressions.py in evaluate(op, op_str, a, b, use_numexpr, **eval_kwargs)
    204     if use_numexpr:
--> 205         return _evaluate(op, op_str, a, b, **eval_kwargs)
    206     return _evaluate_standard(op, op_str, a, b)

~/anaconda3/lib/python3.6/site-packages/pandas/core/computation/expressions.py in _evaluate_numexpr(op, op_str, a, b, truediv, reversed, **eval_kwargs)
    119     if result is None:
--> 120         result = _evaluate_standard(op, op_str, a, b)
    121 

~/anaconda3/lib/python3.6/site-packages/pandas/core/computation/expressions.py in _evaluate_standard(op, op_str, a, b, **eval_kwargs)
     64     with np.errstate(all='ignore'):
---> 65         return op(a, b)
     66 

TypeError: unsupported operand type(s) for -: 'str' and 'str'

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
~/anaconda3/lib/python3.6/site-packages/pandas/core/ops.py in safe_na_op(lvalues, rvalues)
   1029             with np.errstate(all='ignore'):
-> 1030                 return na_op(lvalues, rvalues)
   1031         except Exception:

~/anaconda3/lib/python3.6/site-packages/pandas/core/ops.py in na_op(x, y)
   1019                 mask = notna(x)
-> 1020                 result[mask] = op(x[mask], y)
   1021 

TypeError: unsupported operand type(s) for -: 'str' and 'str'

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
<ipython-input-22-44ad2490d2ae> in <module>()
----> 1 patients[cols_to_norm] = patients[cols_to_norm].apply(lambda x: (x- x.min())/(x.max()-x.min()))

~/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py in apply(self, func, axis, broadcast, raw, reduce, result_type, args, **kwds)
   6002                          args=args,
   6003                          kwds=kwds)
-> 6004         return op.get_result()
   6005 
   6006     def applymap(self, func):

~/anaconda3/lib/python3.6/site-packages/pandas/core/apply.py in get_result(self)
    316                                       *self.args, **self.kwds)
    317 
--> 318         return super(FrameRowApply, self).get_result()
    319 
    320     def apply_broadcast(self):

~/anaconda3/lib/python3.6/site-packages/pandas/core/apply.py in get_result(self)
    140             return self.apply_raw()
    141 
--> 142         return self.apply_standard()
    143 
    144     def apply_empty_result(self):

~/anaconda3/lib/python3.6/site-packages/pandas/core/apply.py in apply_standard(self)
    246 
    247         # compute the result using the series generator
--> 248         self.apply_series_generator()
    249 
    250         # wrap results

~/anaconda3/lib/python3.6/site-packages/pandas/core/apply.py in apply_series_generator(self)
    275             try:
    276                 for i, v in enumerate(series_gen):
--> 277                     results[i] = self.f(v)
    278                     keys.append(v.name)
    279             except Exception as e:

<ipython-input-22-44ad2490d2ae> in <lambda>(x)
----> 1 patients[cols_to_norm] = patients[cols_to_norm].apply(lambda x: (x- x.min())/(x.max()-x.min()))

~/anaconda3/lib/python3.6/site-packages/pandas/core/ops.py in wrapper(left, right)
   1064             rvalues = rvalues.values
   1065 
-> 1066         result = safe_na_op(lvalues, rvalues)
   1067         return construct_result(left, result,
   1068                                 index=left.index, name=res_name, dtype=None)

~/anaconda3/lib/python3.6/site-packages/pandas/core/ops.py in safe_na_op(lvalues, rvalues)
   1032             if is_object_dtype(lvalues):
   1033                 return libalgos.arrmap_object(lvalues,
-> 1034                                               lambda x: op(x, rvalues))
   1035             raise
   1036 

pandas/_libs/algos_common_helper.pxi in pandas._libs.algos.arrmap_object()

~/anaconda3/lib/python3.6/site-packages/pandas/core/ops.py in <lambda>(x)
   1032             if is_object_dtype(lvalues):
   1033                 return libalgos.arrmap_object(lvalues,
-> 1034                                               lambda x: op(x, rvalues))
   1035             raise
   1036 

TypeError: ("unsupported operand type(s) for -: 'str' and 'str'", 'occurred at index Group')

大家有什么想法，请帮忙

Answer 1

您不需要将pd.DataFrame.apply 与自定义函数一起使用。相反，使用 Pandas 中可用的矢量化方法：

cols = cols_to_norm
df_sub = df.loc[:, cols]

df.loc[:, cols] = (df_sub - df_sub.min()) / (df_sub.max() - df_sub.min())