TypeError：輸入類型不支持 ufunc 'isnan'，無法安全地強制輸入

Question

我正在嘗試將 csv 轉換為 numpy 數組。 在 numpy 數組中，我用 NaN 替換了幾個元素。 然后，我想在 numpy 數組中找到 NaN 元素的索引。 代碼是：

    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))))

當我執行此操作時，我收到警告和錯誤。 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

錯誤是：

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''

這是我正在使用的數據集的一部分：

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,

我無法糾正錯誤。 如何克服第26行的警告？ 如何解決這個錯誤？

更新：當我以不同的方式嘗試相同的事情時，比如從文件中讀取數據集而不是轉換為數據幀，我沒有收到錯誤。 那是什么原因呢？ 代碼是：

    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)

第一個代碼有什么問題，為什么第二個代碼甚至不發出警告？

Answer 1

發布，因為它可能會幫助未來的用戶。

正如其他人正確指出的那樣， np.isnan 不適用於object或string數據類型。 如果您使用的是 pandas，如此處所述，您可以直接使用pd.isnull ，這應該適用於您的情況。

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

Answer 2

嘗試用np.isnan替換pd.isna 。 Pandas 的isna支持類別數據類型

Answer 3

temps的dtype是什么。 我可以使用字符串 dtype 重現您的警告和錯誤：

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，將字符串與數字進行比較會給出這個未來警告。

其次，測試nan會產生錯誤。

請注意，給定dtype ， nan賦值分配一個字符串值，而不是一個浮點數（ np.nan是一個浮點數）。

In [30]: temps[-1] = np.nan
In [31]: temps
Out[31]: array(['1', '2', 'string', 'nan'], dtype='<U21')

Answer 4

這可能是不需要的浮點數到字符串轉換的結果。 要修復它，只需使用float或np.float64添加字符串到浮點數的轉換（假設數據可轉換為數字）來反轉它：

np.isnan(float(str(np.nan)))
True

要么

np.isnan(float(str("nan")))
True

而不是：

np.isnan(str(np.nan))
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In [164], line 1
----> 1 np.isnan(str(np.nan))

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''

---

請注意，如果您的數據不能轉換為數字（浮點數），則需要使用字符串兼容函數，例如pd.isna而不是np.isnan 。

Answer 5

isnan(ndarray)在“對象”的isnan(ndarray)失敗

isnan(ndarray.astype(np.float)) ，但字符串不能被強制為浮動。

Answer 6

我在嘗試使用sklearn.preprocessing.OneHotEncoder transform我的數據集時遇到了這個錯誤。 該錯誤是由 sklearn.utils._encode 中定義的_check_unknown函數sklearn.utils._encode的。

這是因為在transform時，要轉換的列之一的類型是float64而不是object - 在我的例子中，整個列都是NaN 。

解決方案是在調用transform之前將數據幀轉換為object類型：

ohe.transform(data.astype("O"))

Answer 7

注意：此答案與問題的標題有些相關，因為在使用 Decimal 類型時會提示此錯誤。

在考慮Decimal類型值時，我遇到了同樣的錯誤。 出於某種原因，我正在考慮的dataframe一列是十進制的。 例如，在此列上調用.unique() ，我得到了

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

由於錯誤的回溯顯示我在調用某些numpy函數時失敗。 我設法通過考慮上述數組的min和max來重現錯誤

from decimal import Decimal

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

它會提示錯誤

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''

這種情況下的解決方案是將所有這些值轉換為int 。

df.astype({col:int for col in desired_columns_to_convert})