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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（例如从文件中读取数据集而不是转换为数据框）时，我没有收到错误消息。那会是什么原因呢？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没有发出警告？请帮忙！

4

4 回答 4

15

是什么dtype。temps我可以使用字符串 dtype 重现您的警告和错误：

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未来的警告。

其次，测试nan产生错误。

请注意，给定dtype，nan赋值分配一个字符串值，而不是一个浮点数（np.nan是一个浮点数）。

In [30]: temps[-1] = np.nan
In [31]: temps
Out[31]: array(['1', '2', 'string', 'nan'], dtype='<U21')
于 2018-10-05T02:00:50.243 回答
3

发布，因为它可能会帮助未来的用户。

正如其他人正确指出的那样， np.isnan 不适用于objector stringdtypes。如果您使用的是熊猫，如此处所述，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
于 2022-02-22T18:54:33.803 回答
1

isnan(ndarray)“对象”的ndarray dtype失败

isnan(ndarray.astype(np.float))，但不能强制字符串浮动。

于 2021-05-20T23:41:18.463 回答
0

注意：此答案与问题的标题有些相关，因为在使用 Decimal 类型时会提示此错误。

在考虑Decimal类型值时我得到了同样的错误。dataframe出于某种原因，我正在考虑的一列是十进制的。例如，当.unique()我在这个专栏上打电话时，我得到了

[Decimal('0'), Decimal('95'), Decimal('38'), Decimal('25'),
 Decimal('42'), Decimal('11'), Decimal('18'), Decimal('22'), 
.....Decimal('220'), Decimal('724')]

由于错误的回溯向我显示它在调用某些numpy函数时失败。我设法通过考虑上述数组的min和值来重现错误max

from decimal import Decimal

xmin, xmax = Decimal('0'), Decimal('724')
np.isnan([xmin, xmax])

它会提示错误

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''

在这种情况下，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})
于 2020-09-11T15:43:23.440 回答