我认为,这之所以有意义,是因为你将<代码>numpy间接从严进口进入范围。

>>> import numpy as np
>>> [0.0,0.0]/0
Traceback (most recent call last):
  File "<ipython-input-3-aae9e30b3430>", line 1, in <module>
    [0.0,0.0]/0
TypeError: unsupported operand type(s) for /:  list  and  int 

>>> [0.0,0.0]/np.float64(0)
array([ nan,  nan])

When you did

from matplotlib.pylab import *

它用<代码>numpy.sum:

>>> from matplotlib.pylab import *
>>> sum is np.sum
True
>>> [0.0,0.0]/sum([0.0, 0.0])
array([ nan,  nan])

您可以检测到,this nan Object(nan isn t un in general)正在通过身份被列入清单,但如果你在array上尝试,似乎通过平等测试,nan ! nan :

>>> nan == nan
False
>>> nan == nan, nan is nan
(False, True)
>>> nan in [nan]
True
>>> nan in np.array([nan])
False

您可使用<代码>np.isnan:

>>> np.isnan([nan, nan])
array([ True,  True], dtype=bool)
>>> np.isnan([nan, nan]).any()
True

您应使用math。

>>> import math
>>> math.isnan(item)

愿是你们所期待的......

a = [2,3,np.nan]
b = True if True in np.isnan(np.array(a)) else False
print(b)

TLDR: best performance

(scroll down for graphs/charts of performance)

has_nan = any(each!=each for each in your_list)

Python NaN is more Weird than you think

import math
my_nan = float("NaN")

list1 = [ math.nan ]
list2 = [ float("NaN") ]
list3 = [ my_nan ]

math.nan     in list1       # True
math.nan     in list2       # False
math.nan     in list3       # False

float("NaN") in list1       # False
float("NaN") in list2       # False
float("NaN") in list3       # False

my_nan       in list1       # False
my_nan       in list2       # False
my_nan       in list3       # True

# also makes sets really annoying:

set1 = set([math.nan    , math.nan    , math.nan     ])
set2 = set([my_nan      , float("nan"), my_nan       ])
set3 = set([float("nan"), float("nan"), float("nan") ])

len(set1)   # >>> 1
len(set2)   # >>> 2
len(set3)   # >>> 3

无论出于何种原因,python 处理NaN的类似

Optimal Performance Answers

has_nan = any(each!=each for each in your_list)
# from math import isnan #<- is slow

# NOTE: can *average* 4x slower for small lists
has_nan = numpy.isnan(numpy.array(your_list)).any()

然而,如果数据已经存在于一个绝食阵列中,那么这是最快的方式:

# when len() == 20,000 this is literally 100 times faster than the pure-python approach
has_nan = numpy.isnan(your_array).any()

这里的表现/标准(假设1.23,python 3.11):

import timeit
from math import isnan
import numpy
import random
from statistics import mean as average
import json


values = []
sample_size = 200
for index in range(1,15):
    list_size = 2**index
    source = list(range(0,list_size))
    cases = {
        "pure python: !=":[],
        "pure python: isnan":[],
        "numpy convert to array":[],
        "numpy (data already in array)":[],
    }
    pure_times = []
    numpy_times = []
    numpy_prebuilts = []
    for _ in range(0,sample_size):
        index = random.randint(-(list_size-1),list_size-1)
        local_source = list(source)
        if index >= 0:
            local_source[index] = float("NaN")
        local_source = tuple(local_source)
        prebuilt = numpy.array(local_source)
        
        cases["pure python: !="].append(timeit.timeit(lambda: any(each!=each for each in local_source  ), number=1_000))
        cases["pure python: isnan"].append(timeit.timeit(lambda: any(isnan(each) for each in local_source  ), number=1_000))
        cases["numpy convert to array"].append(timeit.timeit(lambda: numpy.isnan(numpy.array(local_source)).any(), number=1_000))
        cases["numpy (data already in array)"].append(timeit.timeit(lambda: numpy.isnan(prebuilt).any(), number=1_000))
    
    for each_key, each_value in cases.items():
        cases[each_key] = average(each_value)
    
    print(json.dumps({ "list_size":list_size, **cases,}))
    values.append({ "number of elements":list_size, **cases },)
    
# draw chart
import pandas
import plotly.express as px
df = pandas.DataFrame(values)
df = pandas.melt(df, value_vars=list(cases.keys()), id_vars=[ number of elements ])
df["computation time"] = df["value"]
df.sort_values(by=["variable","number of elements"], inplace=True)
fig = px.line(df, color="variable",x="number of elements",y="computation time")
fig.update_layout(xaxis_type="log",yaxis_type="log")
fig.show()