numpy版本1.9.0
1 & (2**63)
0
np.bitwise_and(1, 2**63)
TypeError: ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
np.bitwise_and(1, 2**63 + 100)
TypeError: ufunc
我有一个错误
**'ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'**
当我尝试通过我的代码进行过滤时:
(xls[xls['DisabilityFriendly'] == 'приспособлен для всех групп инвалидов'
比较一下:
>>> import numpy; numpy.int32(-1) & 0xFFFFFFFF00000000
TypeError: ufunc 'bitwise_and' not supported for the input types, and the inputs
could not be safely coerced to any supported types according to the casting rule ''safe''
有了这个:
>>> import nump
我正在尝试这样做: X = U[(U > lims[0] & U < lims[1])] #U is numpy array 输出: Traceback (most recent call last):
File "Ha.py", line 19, in <module>
X = U[(U > lims[0] & U < lims[1])]
TypeError: ufunc 'bitwise_and' not supported for the input types, and the inpu
为什么开发人员不允许在.ix上进行按位操作?奇怪的是,这是一个技术约束的问题,还是一个逻辑问题,我忽略了。
df.ix[df["ptdelta"]<=0 & df["ptdelta"]>5]
追踪结果是:
TypeError: ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule &
我有两个变量,我想对它们执行元素逻辑操作。但是,我得到了以下错误:
tp = sum(actual & predicted)
TypeError: ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
下面是我的代码:
import pandas as pd
impor
下面有下面的for循环,但是我想把它变成一个计算效率更高的变体。我以为我可以理解列表,但这给了我以下错误:TypeError: ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
编辑I:我试图比较input1和input2,如果input1大于input2,那么差额应该由定标器进行
给定一个NaN数组,我希望确定哪些行包含numpy值和对象。例如,一行将同时包含浮点值和列表。
对于输入数组arr,我尝试执行arr[~np.isnan(arr).any(axis=1)],但随后得到错误消息
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''
如果我使用下面的代码选择空白元素,我会得到错误,如果我使用行‘’col‘== np.nan /'NaN’/ 'nan‘等,值也不会改变。 col是一个对象类型,我想指定val = str('...')这些空白元素 if np.isnan(row['col']) == True:
val = str('somewords') TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safel
当triyng对带有numpy的数组进行right_shift时,我会得到一个错误:
以下是代码:
import numpy as np
a = np.ones((10, 10)) * 64
a.astype("int16")
b = a >> 2
我得到了
TypeError: ufunc 'right_shift' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the
nat = np.datetime64('NaT')
nat == nat
>> FutureWarning: In the future, 'NAT == x' and 'x == NAT' will always be False.
np.isnan(nat)
>> TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported
在Vaex中,什么表达式可以用作筛选器来选择所有行?我希望创建一个过滤器作为一个变量,并将其传递给一个函数。
filter = True
if x > 5:
filter = y > 20
df['new_col'] = filter & z < 10
我的愿望是,如果x <= 5,它将忽略过滤器(因此,我试图使用True作为一个值)。这样做会给出错误的'bitwise_and' not supported for the input types, and the inputs could not be safely coerc
我是tensorflow的新手,我想把一个正伽玛函数应用到一个现有的张量上。当我试着这个
from scipy.special import gamma
gamma_t = K.map_fn(lambda x:gamma(1.0 + 1.0 / x) ,b)
其中b是我得到的现有张量。
TypeError: ufunc 'gamma' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casti
我有一个计算值的列表,我得到的一个值是'nan‘
countries= [nan, 'USA', 'UK', 'France']
我试着删除它,但每次都得到一个错误
cleanedList = [x for x in countries if (math.isnan(x) == True)]
TypeError: a float is required
当我尝试这个的时候:
cleanedList = cities[np.logical_not(np.isnan(countries))]
cleanedList = cities[~np.
我需要计算python中具有大浮点数的变量的scipy.special三伽马和迪伽马函数,但我得到了以下错误消息:
TypeError: ufunc 'psi' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule 'safe'
为了精确起见,我需要将我的变量保持为大浮动。有没有人知道如何计算这些变量的迪伽玛函数和三伽马函数?谢谢。
TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'' 我正在绘制一张体育(足球)热图。在艰难地打开包含事件数据的csv文件之后,我将编码更改为utf-16,并成功打开了该文件。现在,当我在最后绘制热图时,我得到了这个错误。我得到的不是显示球员热图的红色球场,而是一个空
我是NumPy新手,我试图计算一些简单的统计数据,比如median或stddev。
我想探讨的“列”之一是时间差(它的类型为timedelta64 NumPy类型),但我不能直接应用这些统计ufuncs:
----> 1 age_request.std()
TypeError: ufunc 'divide' not supported for the input types, and the inputs could not be
safely coerced to any supported types according to the casting rule &
我正在试图计算定义为的。如果我用
from scipy.special import gamma,gammainc
from numpy import linspace
a = 0
z = (2+3j)*np.linspace(0,10)
gamma(a)*(1-gammainc(a,z))
当z是一个复向量时,我得到一个错误
TypeError: ufunc 'gammainc' not supported for the input types, and the inputs could not be safely coerced to any supported typ
下午好,
当我尝试转换这个函数时,我面临一个问题:
from typing import Optional
import numpy as np
from numba import njit
from numba.typed import List
@njit
def check_something(list_arrays: List[Optional[np.ndarray]], mask_array: np.ndarray):
for array in list_arrays:
if (
array is not None and
(
我编写这个函数是为了用LabelEncoder转换分类特性。
#convert columns to dummies with LabelEncoder
cols = ['ToolType', 'TestType', 'BatteryType']
#apply ene hot encoder
le = LabelEncoder()
for col in cols:
data[col] = data[col].astype('|S') #convert object to str type before apply labe
我正在按百分比建立ARIMA/Sarima模型,但得到的误差为1- model = SARIMAX(np.asarray(train), order = (0, 1, 1), seasonal_order =(1, 1, 1, 12))
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
试图为牛顿法优化计算矩阵的特征值。
在PyDev for Eclipse中使用Python2.7.6。
这是从PyDev返回的变量(Hessian):
ndarray: [[ 0.01 0. ]
[ 0. 1. ]]
以下命令:
np.linalg.eig(Hessian)
返回异常:
ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting
我正在尝试一个TF模型,其中输入是字符串张量作为输入,我的模型包含一个用于文本处理的TextVectorization层,该层在TF2.2中可用。 W&B回调训练失败,错误如下 TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'' 调试时,我发现问题是在计算
我有一个数组,如下所示:
array([[ 1., 2., None],
[ nan, 4., 5.]])
我正在尝试以下操作:
np.equal(A, None) #works and finds index of None correctly
np.equal(A, np.nan) #doesn't work
np.isnan(A) #errors out
错误是:
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not
gen=(G1.subgraph(c) for c in nx.connected_components(G1))
G1_LCC=max((G1.subgraph(c) for c in nx.connected_components(G1)),key=len)
G2_LCC=max((G2.subgraph(c) for c in nx.connected_components(G2)),key=len)
plt.figure()
nx.draw(G1_LCC,node_color="red",edge_color="grey",node_size=
当我试图运行以下代码时:
import math
from scipy import special as spec
import numpy as np
from sympy import *
y = Symbol('y')
x = spec.hyp2f1(1.5, 2.5, 1, y**2)
ans = x.diff(y)
print ans
我知道错误:
Traceback (most recent call last):
File "calc.py", line 74, in
我想初始化一个大小为(n,m)的numpy数组,它只能包含0或1。此外,我想稍后使用数组进行np.bitwise_or。
例如,如果我尝试:
import numpy as np
myArray = np.zeros([4,4])
myRow = myArray[1,]
myCol = myArray[,1]
np.bitwise_or(myRow, myCol)
它失败了:
TypeError: ufunc 'bitwise_or' not supported for the input types, and the inputs could not be safely co
我是第一次接触numpy,但是我似乎不能让这段代码工作。
item3.apply(lambda x : (x[np.isneginf(x)] = 0))
item3是一个由numpy数组组成的向量,每个数组有300个维度。
抛出的错误是无效语法。如何实现此功能。
然而,假设它是float64数值向量的向量。数据类型为object。它抛出一个异常
TypeError: ufunc 'isinf' not supported for the input types, and the inputs could not be safely coerced to any supporte
我在使用pandas分析数据时面对的是TypeError,如下所示: 汇总数据集:2%1/59 00:09<09:20,9.66s/it,描述变量:项目-->> ~\Anaconda3\lib\site-packages\pandas\core\algorithms.py in isin(comps, values)
441 # If the the values include nan we need to check for nan explicitly
442 # since np.nan it not equal to
基本上,我想数的最频繁的项目分组为2个变量。我使用以下代码:
dfgrouped = data[COLUMNS.copy()].groupby(['Var1','Var2']).agg(lambda x: stats.mode(x)[1])
此代码工作,但不工作的列有南值,因为Nan值是浮动的,而其他是str。因此,显示了此错误:
'<' not supported between instances of 'float' and 'str'
我想省略NaN值和其余的计数模式。因此str(x)不是一个解决方案
我在一个Databricks笔记本上运行网格搜索优化,同样的代码在我的本地机器上运行,但是当我尝试在Databricks上运行时,我得到了如下的TypeError: 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'' 我正在运行的拟合过程是这样的(请注意,这里定义了p,d
我正在尝试使用pymc3来拟合一个涉及voigt函数的模型(来自scipy.special)。voigt函数的输入应该是数组,而a,b是pymc3类。如何让scipy.special函数将pymc3 RV作为输入?运行下面附加的代码会产生错误: import pymc3 as pm
from scipy.special import voigt_profile
import numpy as np
with pm.Model() as linear_model:
a = pm.Lognormal('a',mu=0, sigma=2.)
b = pm.Logn