TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced
我正在尝试将 csv 转换为 numpy 数组。在 numpy 数组中，我用 NaN 替换了几个元素。然后，我想在 numpy 数组中找到 NaN 元素的索引。代码是：
import pandas as pd
import matplotlib.pyplot as plyt
import numpy as np
filename = 'wether.csv'
df = pd.read_csv(filename,header = None )
list = df.values.tolist()
labels = list[0]
wether_list = list[1:]
year = []
month = []
day = []
max_temp = []
for i in wether_list:
year.append(i[1])
month.append(i[2])
day.append(i[3])
max_temp.append(i[5])
mid = len(max_temp) // 2
temps = np.array(max_temp[mid:])
temps[np.where(np.array(temps) == -99.9)] = np.nan
plyt.plot(temps,marker = '.',color = 'black',linestyle = 'none')
# plyt.show()
print(np.where(np.isnan(temps))[0])
# print(len(pd.isnull(np.array(temps))))
当我执行此操作时，我收到警告和错误。警告是：
wether.py:26: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
temps[np.where(np.array(temps) == -99.9)] = np.nan
错误是：
Traceback (most recent call last):
File "wether.py", line 30, in <module>
print(np.where(np.isnan(temps))[0])
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
这是我正在使用的数据集的一部分：
83168,2014,9,7,0.00000,89.00000,78.00000, 83.50000
83168,2014,9,22,1.62000,90.00000,72.00000, 81.00000
83168,2014,9,23,0.50000,87.00000,74.00000, 80.50000
83168,2014,9,24,0.35000,82.00000,73.00000, 77.50000
83168,2014,9,25,0.60000,85.00000,75.00000, 80.00000
83168,2014,9,26,0.76000,89.00000,77.00000, 83.00000
83168,2014,9,27,0.00000,89.00000,79.00000, 84.00000
83168,2014,9,28,0.00000,90.00000,81.00000, 85.50000
83168,2014,9,29,0.00000,90.00000,79.00000, 84.50000
83168,2014,9,30,0.50000,89.00000,75.00000, 82.00000
83168,2014,10,1,0.02000,91.00000,75.00000, 83.00000
83168,2014,10,2,0.03000,93.00000,77.00000, 85.00000
83168,2014,10,3,1.40000,93.00000,75.00000, 84.00000
83168,2014,10,4,0.06000,89.00000,75.00000, 82.00000
83168,2014,10,5,0.22000,91.00000,68.00000, 79.50000
83168,2014,10,6,0.00000,84.00000,68.00000, 76.00000
83168,2014,10,7,0.17000,85.00000,73.00000, 79.00000
83168,2014,10,8,0.06000,84.00000,73.00000, 78.50000
83168,2014,10,9,0.00000,87.00000,73.00000, 80.00000
83168,2014,10,10,0.00000,88.00000,80.00000, 84.00000
83168,2014,10,11,0.00000,87.00000,80.00000, 83.50000
83168,2014,10,12,0.00000,88.00000,80.00000, 84.00000
83168,2014,10,13,0.00000,88.00000,81.00000, 84.50000
83168,2014,10,14,0.04000,88.00000,77.00000, 82.50000
83168,2014,10,15,0.00000,88.00000,77.00000, 82.50000
83168,2014,10,16,0.09000,89.00000,72.00000, 80.50000
83168,2014,10,17,0.00000,85.00000,67.00000, 76.00000
83168,2014,10,18,0.00000,84.00000,65.00000, 74.50000
83168,2014,10,19,0.00000,84.00000,65.00000, 74.50000
83168,2014,10,20,0.00000,85.00000,69.00000, 77.00000
83168,2014,10,21,0.77000,87.00000,76.00000, 81.50000
83168,2014,10,22,0.69000,81.00000,71.00000, 76.00000
83168,2014,10,23,0.31000,82.00000,72.00000, 77.00000
83168,2014,10,24,0.71000,79.00000,73.00000, 76.00000
83168,2014,10,25,0.00000,81.00000,68.00000, 74.50000
83168,2014,10,26,0.00000,82.00000,67.00000, 74.50000
83168,2014,10,27,0.00000,83.00000,64.00000, 73.50000
83168,2014,10,28,0.00000,83.00000,66.00000, 74.50000
83168,2014,10,29,0.03000,86.00000,76.00000, 81.00000
83168,2014,10,30,0.00000,85.00000,69.00000, 77.00000
83168,2014,10,31,0.00000,85.00000,69.00000, 77.00000
83168,2014,11,1,0.00000,86.00000,59.00000, 72.50000
83168,2014,11,2,0.00000,77.00000,52.00000, 64.50000
83168,2014,11,3,0.00000,70.00000,52.00000, 61.00000
83168,2014,11,4,0.00000,77.00000,59.00000, 68.00000
83168,2014,11,5,0.02000,79.00000,73.00000, 76.00000
83168,2014,11,6,0.02000,82.00000,75.00000, 78.50000
83168,2014,11,7,0.00000,83.00000,66.00000, 74.50000
83168,2014,11,8,0.00000,84.00000,65.00000, 74.50000
83168,2014,11,9,0.00000,84.00000,65.00000, 74.50000
83168,2014,11,10,1.20000,72.00000,65.00000, 68.50000
83168,2014,11,11,0.08000,77.00000,61.00000, 69.00000
83168,2014,11,12,0.00000,80.00000,61.00000, 70.50000
83168,2014,11,13,0.00000,83.00000,63.00000, 73.00000
83168,2014,11,14,0.00000,83.00000,65.00000, 74.00000
83168,2014,11,15,0.00000,82.00000,64.00000, 73.00000
83168,2014,11,16,0.00000,83.00000,64.00000, 73.50000
83168,2014,11,17,0.07000,84.00000,64.00000, 74.00000
83168,2014,11,18,0.00000,86.00000,71.00000, 78.50000
83168,2014,11,19,0.57000,78.00000,55.00000, 66.50000
83168,2014,11,20,0.05000,72.00000,56.00000, 64.00000
83168,2014,11,21,0.05000,77.00000,63.00000, 70.00000
83168,2014,11,22,0.22000,77.00000,69.00000, 73.00000
83168,2014,11,23,0.06000,79.00000,76.00000, 77.50000
83168,2014,11,24,0.02000,84.00000,78.00000, 81.00000
83168,2014,11,25,0.00000,86.00000,78.00000, 82.00000
83168,2014,11,26,0.07000,85.00000,77.00000, 81.00000
83168,2014,11,27,0.21000,82.00000,55.00000, 68.50000
83168,2014,11,28,0.00000,73.00000,53.00000, 63.00000
83168,2015,1,8,0.00000,80.00000,57.00000,
83168,2015,1,9,0.05000,72.00000,56.00000,
83168,2015,1,10,0.00000,72.00000,57.00000,
83168,2015,1,11,0.00000,80.00000,57.00000,
83168,2015,1,12,0.05000,80.00000,59.00000,
83168,2015,1,13,0.85000,81.00000,69.00000,
83168,2015,1,14,0.05000,81.00000,68.00000,
83168,2015,1,15,0.00000,81.00000,64.00000,
83168,2015,1,16,0.00000,78.00000,63.00000,
83168,2015,1,17,0.00000,73.00000,55.00000,
83168,2015,1,18,0.00000,76.00000,55.00000,
83168,2015,1,19,0.00000,78.00000,55.00000,
83168,2015,1,20,0.00000,75.00000,56.00000,
83168,2015,1,21,0.02000,73.00000,65.00000,
83168,2015,1,22,0.00000,80.00000,64.00000,
83168,2015,1,23,0.00000,80.00000,71.00000,
83168,2015,1,24,0.00000,79.00000,72.00000,
83168,2015,1,25,0.00000,79.00000,49.00000,
83168,2015,1,26,0.00000,79.00000,49.00000,
83168,2015,1,27,0.10000,75.00000,53.00000,
83168,2015,1,28,0.00000,68.00000,53.00000,
83168,2015,1,29,0.00000,69.00000,53.00000,
83168,2015,1,30,0.00000,72.00000,60.00000,
83168,2015,1,31,0.00000,76.00000,58.00000,
83168,2015,2,1,0.00000,76.00000,58.00000,
83168,2015,2,2,0.05000,77.00000,58.00000,
83168,2015,2,3,0.00000,84.00000,56.00000,
83168,2015,2,4,0.00000,76.00000,56.00000,
我无法纠正错误。如何克服第26行的警告？如何解决错误？请帮忙！提前致谢！
更新：
当我以不同的方式尝试相同的事情时，比如从文件中读取数据集而不是转换为数据帧，我没有收到错误。那是什么原因呢？代码是：
weather_filename = 'wether.csv'
weather_file = open(weather_filename)
weather_data = weather_file.read()
weather_file.close()
# Break the weather records into lines
lines = weather_data.split('\n')
labels = lines[0]
values = lines[1:]
n_values = len(values)
# Break the list of comma-separated value strings
# into lists of values.
year = []
month = []
day = []
max_temp = []
j_year = 1
j_month = 2
j_day = 3
j_max_temp = 5
for i_row in range(n_values):
split_values = values[i_row].split(',')
if len(split_values) >= j_max_temp:
year.append(int(split_values[j_year]))
month.append(int(split_values[j_month]))
day.append(int(split_values[j_day]))
max_temp.append(float(split_values[j_max_temp]))
# Isolate the recent data.
i_mid = len(max_temp) // 2
temps = np.array(max_temp[i_mid:])
year = year[i_mid:]
month = month[i_mid:]
day = day[i_mid:]
temps[np.where(temps == -99.9)] = np.nan
# Remove all the nans.
# Trim both ends and fill nans in the middle.
# Find the first non-nan.
i_start = np.where(np.logical_not(np.isnan(temps)))[0][0]
temps = temps[i_start:]
year = year[i_start:]
month = month[i_start:]
day = day[i_start:]
i_nans = np.where(np.isnan(temps))[0]
print(i_nans)
第一个代码有什么问题，为什么第二个代码甚至不发出警告？请帮忙！
temps 的 dtype 是什么。我可以用字符串 dtype:
重现你的警告和错误
In [26]: temps = np.array([1,2,'string',0])
In [27]: temps
Out[27]: array(['1', '2', 'string', '0'], dtype='<U21')
In [28]: temps==-99.9
/usr/local/bin/ipython3:1: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
#!/usr/bin/python3
Out[28]: False
In [29]: np.isnan(temps)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-29-2ff7754ed926> in <module>()
----> 1 np.isnan(temps)
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
首先，将字符串与数字进行比较会给出此未来警告。
其次，测试 nan 会产生错误。
请注意，给定 dtype，nan 赋值分配的是字符串值，而不是浮点数（np.nan 是浮点数）。
In [30]: temps[-1] = np.nan
In [31]: temps
Out[31]: array(['1', '2', 'string', 'nan'], dtype='<U21')
Note: This answer is somewhat related to the title of the question because this error prompts when working with Decimal types.
我在考虑 Decimal 类型值时遇到了同样的错误。出于某种原因，我正在考虑的 dataframe 的一列是十进制的。例如，当在此列上调用 .unique() 时，我得到
[Decimal('0'), Decimal('95'), Decimal('38'), Decimal('25'),
Decimal('42'), Decimal('11'), Decimal('18'), Decimal('22'),
.....Decimal('220'), Decimal('724')]
因为错误的回溯告诉我它在调用某些 numpy 函数时失败了。我设法通过考虑上述数组 min 和 max 的值来重现错误
from decimal import Decimal
xmin, xmax = Decimal('0'), Decimal('724')
np.isnan([xmin, xmax])
会提示错误
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
这种情况下的解决方案是将所有这些值转换为 int。
df.astype({col:int for col in desired_columns_to_convert})
isnan(ndarray) 在“object”的 ndarray dtype 上失败
isnan(ndarray.astype(np.float))，但不能强制字符串浮动。
张贴可能对未来的用户有帮助。
正如其他人正确指出的那样，np.isnan 不适用于 object 或 string dtypes。如果您使用 pandas，如前所述 here，您可以直接使用 pd.isnull，这应该适用于您的情况。
import pandas as pd
import numpy as np
var1 = ''
var2 = np.nan
>>> type(var1)
<class 'str'>
>>> type(var2)
<class 'float'>
>>> pd.isnull(var1)
False
>>> pd.isnull(var2)
True
尝试用 pd.isna 替换 np.isnan。 Pandas' isna 支持类别数据类型
我正在尝试将 csv 转换为 numpy 数组。在 numpy 数组中，我用 NaN 替换了几个元素。然后，我想在 numpy 数组中找到 NaN 元素的索引。代码是：
import pandas as pd
import matplotlib.pyplot as plyt
import numpy as np
filename = 'wether.csv'
df = pd.read_csv(filename,header = None )
list = df.values.tolist()
labels = list[0]
wether_list = list[1:]
year = []
month = []
day = []
max_temp = []
for i in wether_list:
year.append(i[1])
month.append(i[2])
day.append(i[3])
max_temp.append(i[5])
mid = len(max_temp) // 2
temps = np.array(max_temp[mid:])
temps[np.where(np.array(temps) == -99.9)] = np.nan
plyt.plot(temps,marker = '.',color = 'black',linestyle = 'none')
# plyt.show()
print(np.where(np.isnan(temps))[0])
# print(len(pd.isnull(np.array(temps))))
当我执行此操作时，我收到警告和错误。警告是：
wether.py:26: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
temps[np.where(np.array(temps) == -99.9)] = np.nan
错误是：
Traceback (most recent call last):
File "wether.py", line 30, in <module>
print(np.where(np.isnan(temps))[0])
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
这是我正在使用的数据集的一部分：
83168,2014,9,7,0.00000,89.00000,78.00000, 83.50000
83168,2014,9,22,1.62000,90.00000,72.00000, 81.00000
83168,2014,9,23,0.50000,87.00000,74.00000, 80.50000
83168,2014,9,24,0.35000,82.00000,73.00000, 77.50000
83168,2014,9,25,0.60000,85.00000,75.00000, 80.00000
83168,2014,9,26,0.76000,89.00000,77.00000, 83.00000
83168,2014,9,27,0.00000,89.00000,79.00000, 84.00000
83168,2014,9,28,0.00000,90.00000,81.00000, 85.50000
83168,2014,9,29,0.00000,90.00000,79.00000, 84.50000
83168,2014,9,30,0.50000,89.00000,75.00000, 82.00000
83168,2014,10,1,0.02000,91.00000,75.00000, 83.00000
83168,2014,10,2,0.03000,93.00000,77.00000, 85.00000
83168,2014,10,3,1.40000,93.00000,75.00000, 84.00000
83168,2014,10,4,0.06000,89.00000,75.00000, 82.00000
83168,2014,10,5,0.22000,91.00000,68.00000, 79.50000
83168,2014,10,6,0.00000,84.00000,68.00000, 76.00000
83168,2014,10,7,0.17000,85.00000,73.00000, 79.00000
83168,2014,10,8,0.06000,84.00000,73.00000, 78.50000
83168,2014,10,9,0.00000,87.00000,73.00000, 80.00000
83168,2014,10,10,0.00000,88.00000,80.00000, 84.00000
83168,2014,10,11,0.00000,87.00000,80.00000, 83.50000
83168,2014,10,12,0.00000,88.00000,80.00000, 84.00000
83168,2014,10,13,0.00000,88.00000,81.00000, 84.50000
83168,2014,10,14,0.04000,88.00000,77.00000, 82.50000
83168,2014,10,15,0.00000,88.00000,77.00000, 82.50000
83168,2014,10,16,0.09000,89.00000,72.00000, 80.50000
83168,2014,10,17,0.00000,85.00000,67.00000, 76.00000
83168,2014,10,18,0.00000,84.00000,65.00000, 74.50000
83168,2014,10,19,0.00000,84.00000,65.00000, 74.50000
83168,2014,10,20,0.00000,85.00000,69.00000, 77.00000
83168,2014,10,21,0.77000,87.00000,76.00000, 81.50000
83168,2014,10,22,0.69000,81.00000,71.00000, 76.00000
83168,2014,10,23,0.31000,82.00000,72.00000, 77.00000
83168,2014,10,24,0.71000,79.00000,73.00000, 76.00000
83168,2014,10,25,0.00000,81.00000,68.00000, 74.50000
83168,2014,10,26,0.00000,82.00000,67.00000, 74.50000
83168,2014,10,27,0.00000,83.00000,64.00000, 73.50000
83168,2014,10,28,0.00000,83.00000,66.00000, 74.50000
83168,2014,10,29,0.03000,86.00000,76.00000, 81.00000
83168,2014,10,30,0.00000,85.00000,69.00000, 77.00000
83168,2014,10,31,0.00000,85.00000,69.00000, 77.00000
83168,2014,11,1,0.00000,86.00000,59.00000, 72.50000
83168,2014,11,2,0.00000,77.00000,52.00000, 64.50000
83168,2014,11,3,0.00000,70.00000,52.00000, 61.00000
83168,2014,11,4,0.00000,77.00000,59.00000, 68.00000
83168,2014,11,5,0.02000,79.00000,73.00000, 76.00000
83168,2014,11,6,0.02000,82.00000,75.00000, 78.50000
83168,2014,11,7,0.00000,83.00000,66.00000, 74.50000
83168,2014,11,8,0.00000,84.00000,65.00000, 74.50000
83168,2014,11,9,0.00000,84.00000,65.00000, 74.50000
83168,2014,11,10,1.20000,72.00000,65.00000, 68.50000
83168,2014,11,11,0.08000,77.00000,61.00000, 69.00000
83168,2014,11,12,0.00000,80.00000,61.00000, 70.50000
83168,2014,11,13,0.00000,83.00000,63.00000, 73.00000
83168,2014,11,14,0.00000,83.00000,65.00000, 74.00000
83168,2014,11,15,0.00000,82.00000,64.00000, 73.00000
83168,2014,11,16,0.00000,83.00000,64.00000, 73.50000
83168,2014,11,17,0.07000,84.00000,64.00000, 74.00000
83168,2014,11,18,0.00000,86.00000,71.00000, 78.50000
83168,2014,11,19,0.57000,78.00000,55.00000, 66.50000
83168,2014,11,20,0.05000,72.00000,56.00000, 64.00000
83168,2014,11,21,0.05000,77.00000,63.00000, 70.00000
83168,2014,11,22,0.22000,77.00000,69.00000, 73.00000
83168,2014,11,23,0.06000,79.00000,76.00000, 77.50000
83168,2014,11,24,0.02000,84.00000,78.00000, 81.00000
83168,2014,11,25,0.00000,86.00000,78.00000, 82.00000
83168,2014,11,26,0.07000,85.00000,77.00000, 81.00000
83168,2014,11,27,0.21000,82.00000,55.00000, 68.50000
83168,2014,11,28,0.00000,73.00000,53.00000, 63.00000
83168,2015,1,8,0.00000,80.00000,57.00000,
83168,2015,1,9,0.05000,72.00000,56.00000,
83168,2015,1,10,0.00000,72.00000,57.00000,
83168,2015,1,11,0.00000,80.00000,57.00000,
83168,2015,1,12,0.05000,80.00000,59.00000,
83168,2015,1,13,0.85000,81.00000,69.00000,
83168,2015,1,14,0.05000,81.00000,68.00000,
83168,2015,1,15,0.00000,81.00000,64.00000,
83168,2015,1,16,0.00000,78.00000,63.00000,
83168,2015,1,17,0.00000,73.00000,55.00000,
83168,2015,1,18,0.00000,76.00000,55.00000,
83168,2015,1,19,0.00000,78.00000,55.00000,
83168,2015,1,20,0.00000,75.00000,56.00000,
83168,2015,1,21,0.02000,73.00000,65.00000,
83168,2015,1,22,0.00000,80.00000,64.00000,
83168,2015,1,23,0.00000,80.00000,71.00000,
83168,2015,1,24,0.00000,79.00000,72.00000,
83168,2015,1,25,0.00000,79.00000,49.00000,
83168,2015,1,26,0.00000,79.00000,49.00000,
83168,2015,1,27,0.10000,75.00000,53.00000,
83168,2015,1,28,0.00000,68.00000,53.00000,
83168,2015,1,29,0.00000,69.00000,53.00000,
83168,2015,1,30,0.00000,72.00000,60.00000,
83168,2015,1,31,0.00000,76.00000,58.00000,
83168,2015,2,1,0.00000,76.00000,58.00000,
83168,2015,2,2,0.05000,77.00000,58.00000,
83168,2015,2,3,0.00000,84.00000,56.00000,
83168,2015,2,4,0.00000,76.00000,56.00000,
我无法纠正错误。如何克服第26行的警告？如何解决错误？请帮忙！提前致谢！
更新： 当我以不同的方式尝试相同的事情时，比如从文件中读取数据集而不是转换为数据帧，我没有收到错误。那是什么原因呢？代码是：
weather_filename = 'wether.csv'
weather_file = open(weather_filename)
weather_data = weather_file.read()
weather_file.close()
# Break the weather records into lines
lines = weather_data.split('\n')
labels = lines[0]
values = lines[1:]
n_values = len(values)
# Break the list of comma-separated value strings
# into lists of values.
year = []
month = []
day = []
max_temp = []
j_year = 1
j_month = 2
j_day = 3
j_max_temp = 5
for i_row in range(n_values):
split_values = values[i_row].split(',')
if len(split_values) >= j_max_temp:
year.append(int(split_values[j_year]))
month.append(int(split_values[j_month]))
day.append(int(split_values[j_day]))
max_temp.append(float(split_values[j_max_temp]))
# Isolate the recent data.
i_mid = len(max_temp) // 2
temps = np.array(max_temp[i_mid:])
year = year[i_mid:]
month = month[i_mid:]
day = day[i_mid:]
temps[np.where(temps == -99.9)] = np.nan
# Remove all the nans.
# Trim both ends and fill nans in the middle.
# Find the first non-nan.
i_start = np.where(np.logical_not(np.isnan(temps)))[0][0]
temps = temps[i_start:]
year = year[i_start:]
month = month[i_start:]
day = day[i_start:]
i_nans = np.where(np.isnan(temps))[0]
print(i_nans)
第一个代码有什么问题，为什么第二个代码甚至不发出警告？请帮忙！
temps 的 dtype 是什么。我可以用字符串 dtype:
In [26]: temps = np.array([1,2,'string',0])
In [27]: temps
Out[27]: array(['1', '2', 'string', '0'], dtype='<U21')
In [28]: temps==-99.9
/usr/local/bin/ipython3:1: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
#!/usr/bin/python3
Out[28]: False
In [29]: np.isnan(temps)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-29-2ff7754ed926> in <module>()
----> 1 np.isnan(temps)
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
首先，将字符串与数字进行比较会给出此未来警告。
其次，测试 nan 会产生错误。
请注意，给定 dtype，nan 赋值分配的是字符串值，而不是浮点数（np.nan 是浮点数）。
In [30]: temps[-1] = np.nan
In [31]: temps
Out[31]: array(['1', '2', 'string', 'nan'], dtype='<U21')
Note: This answer is somewhat related to the title of the question because this error prompts when working with Decimal types.
我在考虑 Decimal 类型值时遇到了同样的错误。出于某种原因，我正在考虑的 dataframe 的一列是十进制的。例如，当在此列上调用 .unique() 时，我得到
[Decimal('0'), Decimal('95'), Decimal('38'), Decimal('25'),
Decimal('42'), Decimal('11'), Decimal('18'), Decimal('22'),
.....Decimal('220'), Decimal('724')]
因为错误的回溯告诉我它在调用某些 numpy 函数时失败了。我设法通过考虑上述数组 min 和 max 的值来重现错误
from decimal import Decimal
xmin, xmax = Decimal('0'), Decimal('724')
np.isnan([xmin, xmax])
会提示错误
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
这种情况下的解决方案是将所有这些值转换为 int。
df.astype({col:int for col in desired_columns_to_convert})
isnan(ndarray) 在“object”的 ndarray dtype 上失败
isnan(ndarray.astype(np.float))，但不能强制字符串浮动。
张贴可能对未来的用户有帮助。
正如其他人正确指出的那样，np.isnan 不适用于 object 或 string dtypes。如果您使用 pandas，如前所述 here，您可以直接使用 pd.isnull，这应该适用于您的情况。
import pandas as pd
import numpy as np
var1 = ''
var2 = np.nan
>>> type(var1)
<class 'str'>
>>> type(var2)
<class 'float'>
>>> pd.isnull(var1)
False
>>> pd.isnull(var2)
True
尝试用 pd.isna 替换 np.isnan。 Pandas' isna 支持类别数据类型