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Numpy isnan（）在浮点数组上失败（适用于pandas数据框）

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问题：Numpy isnan（）在浮点数组上失败（适用于pandas数据框）
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回答 1
回答 2
回答 3

问题：Numpy isnan（）在浮点数组上失败（适用于pandas数据框）

我有一个浮点数数组（一些正常数字，一些nans），它们是从对熊猫数据框的应用中得出的。

由于某种原因，numpy.isnan在此数组上失败，但是，如下所示，每个元素都是浮点数，numpy.isnan在每个元素上正确运行，变量的类型肯定是一个numpy数组。

这是怎么回事？！

set([type(x) for x in tester])
Out[59]: {float}

tester
Out[60]: 
array([-0.7000000000000001, nan, nan, nan, nan, nan, nan, nan, nan, nan,
   nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
   nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
   nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
   nan, nan], dtype=object)

set([type(x) for x in tester])
Out[61]: {float}

np.isnan(tester)
Traceback (most recent call last):

File "<ipython-input-62-e3638605b43c>", line 1, in <module>
np.isnan(tester)

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''

set([np.isnan(x) for x in tester])
Out[65]: {False, True}

type(tester)
Out[66]: numpy.ndarray
点击查看英文原文

I have an array of floats (some normal numbers, some nans) that is coming out of an apply on a pandas dataframe.

For some reason, numpy.isnan is failing on this array, however as shown below, each element is a float, numpy.isnan runs correctly on each element, the type of the variable is definitely a numpy array.

What’s going on?!

set([type(x) for x in tester])
Out[59]: {float}

tester
Out[60]: 
array([-0.7000000000000001, nan, nan, nan, nan, nan, nan, nan, nan, nan,
   nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
   nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
   nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
   nan, nan], dtype=object)

set([type(x) for x in tester])
Out[61]: {float}

np.isnan(tester)
Traceback (most recent call last):

File "<ipython-input-62-e3638605b43c>", line 1, in <module>
np.isnan(tester)

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''

set([np.isnan(x) for x in tester])
Out[65]: {False, True}

type(tester)
Out[66]: numpy.ndarray

回答 0

np.isnan 可以应用于本机dtype的NumPy数组（例如np.float64）：

In [99]: np.isnan(np.array([np.nan, 0], dtype=np.float64))
Out[99]: array([ True, False], dtype=bool)

但是在应用于对象数组时引发TypeError：

In [96]: np.isnan(np.array([np.nan, 0], dtype=object))
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''

由于您拥有Pandas，pd.isnull因此可以改用-它可以接受对象或本机dtypes的NumPy数组：

In [97]: pd.isnull(np.array([np.nan, 0], dtype=float))
Out[97]: array([ True, False], dtype=bool)

In [98]: pd.isnull(np.array([np.nan, 0], dtype=object))
Out[98]: array([ True, False], dtype=bool)

请注意，None在对象数组中也将其视为空值。

点击查看英文原文

np.isnan can be applied to NumPy arrays of native dtype (such as np.float64):

In [99]: np.isnan(np.array([np.nan, 0], dtype=np.float64))
Out[99]: array([ True, False], dtype=bool)

but raises TypeError when applied to object arrays:

In [96]: np.isnan(np.array([np.nan, 0], dtype=object))
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''

Since you have Pandas, you could use pd.isnull instead — it can accept NumPy arrays of object or native dtypes:

In [97]: pd.isnull(np.array([np.nan, 0], dtype=float))
Out[97]: array([ True, False], dtype=bool)

In [98]: pd.isnull(np.array([np.nan, 0], dtype=object))
Out[98]: array([ True, False], dtype=bool)

Note that None is also considered a null value in object arrays.

回答 1

np.isnan（）和pd.isnull（）的绝佳替代品是 

for i in range(0,a.shape[0]):
    if(a[i]!=a[i]):
       //do something here
       //a[i] is nan

因为只有nan不等于自己。

点击查看英文原文

A great substitute for np.isnan() and pd.isnull() is 

for i in range(0,a.shape[0]):
    if(a[i]!=a[i]):
       //do something here
       //a[i] is nan

since only nan is not equal to itself.

回答 2

在@unutbu答案的顶部，您可以将pandas numpy对象数组强制转换为本机（float64）类型，沿线进行操作

import pandas as pd
pd.to_numeric(df['tester'], errors='coerce')

指定errors =’coerce’强制将无法解析为数字值的字符串变为NaN。列类型为dtype: float64，然后isnan检查是否可以使用

点击查看英文原文

On top of @unutbu answer, you could coerce pandas numpy object array to native (float64) type, something along the line

import pandas as pd
pd.to_numeric(df['tester'], errors='coerce')

Specify errors=’coerce’ to force strings that can’t be parsed to a numeric value to become NaN. Column type would be dtype: float64, and then isnan check should work

回答 3

确保使用熊猫导入csv文件

import pandas as pd

condition = pd.isnull(data[i][j])
点击查看英文原文

Make sure you import csv file using Pandas

import pandas as pd

condition = pd.isnull(data[i][j])