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 笔记本
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()))
在最后一行中，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')
任何人有任何想法，请帮忙。
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())