python2 python3 TypeError: unsupported operand type(s) for /: 'dict_values' and 'int'

最新推荐文章于 2024-10-02 16:12:24 发布

gdengden

最新推荐文章于 2024-10-02 16:12:24 发布

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分类专栏： GAN tensorflow 基本

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本文介绍了一种在从Python 2.x迁移至Python 3.x过程中常见的错误：尝试对字典的values方法返回的对象进行数学运算导致的TypeError。通过将dict_values对象转换为列表，可以解决此问题。此外，文章还提供了如何使用np.mean等函数处理转换后的列表的具体示例。

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plot绘制各种曲线的程序，在python27下运行正常，到python35下提示出现这个错误：

TypeError: unsupported operand type(s) for /: 'dict_values' and 'int'

In python3, dict.values returns a dict_values object, which is not a list or tuple. Try coercing that into a list. total_minutes = list(total_minutes_by_account.values()). – Abdou Apr 27 '17 at 16:40
It's a good idea to check the results of a np.array(...) statement. During testing look at total_minutes, or at least check its shape and dtype. Don't just assume, check. In this case the dtype is probably object, not floats or ints. – hpaulj Apr 27 '17 at 17:29

Find below a summary of his answer. It helped me a lot.

In [1618]: dd = {'a':[1,2,3], 'b':[4,5,6]}
In [1619]: dd
Out[1619]: {'a': [1, 2, 3], 'b': [4, 5, 6]}
In [1620]: dd.values()
Out[1620]: dict_values([[1, 2, 3], [4, 5, 6]])
In [1621]: np.mean(dd.values())
... 
TypeError: unsupported operand type(s) for /: 'dict_values' and 'int'

Solution: convert the dict_values to list:

In [1623]: list(dd.values())
Out[1623]: [[1, 2, 3], [4, 5, 6]]
In [1624]: np.mean(list(dd.values()))
Out[1624]: 3.5

In Py3, range and dict.keys() require the same extra touch.

========

np.mean first tries to convert the input to an array, but with values() that isn't what we want. It makes a single item object array containing this whole object.

In [1626]: np.array(dd.values())
Out[1626]: array(dict_values([[1, 2, 3], [4, 5, 6]]), dtype=object)
In [1627]: _.shape
Out[1627]: ()
In [1628]: np.array(list(dd.values()))
Out[1628]: 
array([[1, 2, 3],
       [4, 5, 6]])

25行修改后 28行也要加list函数，否则提示如下错误（下面代码黑色两行，原来python2中无list函数）

numpy.core._internal.AxisError: axis -1 is out of bounds for array of dimension 0
import numpy as np

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

import collections
import time
import pickle as pickle

_since_beginning = collections.defaultdict(lambda: {})
_since_last_flush = collections.defaultdict(lambda: {})

_iter = [0]
def tick():
   _iter[0] += 1

def plot(name, value):
   _since_last_flush[name][_iter[0]] = value

def flush():
   prints = []

   for name, vals in _since_last_flush.items():
       prints.append("{}\t{}".format(name, np.mean(list(vals.values()))))
       _since_beginning[name].update(vals)

       x_vals = np.sort(list(_since_beginning[name].keys()))
       y_vals = [_since_beginning[name][x] for x in x_vals]

       plt.clf()
       plt.plot(x_vals, y_vals)
       plt.xlabel('iteration')
       plt.ylabel(name)
       plt.savefig(name.replace(' ', '_')+'.jpg')

   print ("iter {}\t{}".format(_iter[0], "\t".join(prints)))
   _since_last_flush.clear()

   with open('log.pkl', 'wb') as f:
       pickle.dump(dict(_since_beginning), f, pickle.HIGHEST_PROTOCOL)