np.nan has type float: arrays containing it must also have this datatype (or the complex or object datatype) so you may need to cast arr before you try to assign this value.

The error arises because the string value 'nan' can't be converted to an integer type to match arr's type.

>>> arr = arr.astype('float')
>>> arr[arr == 0] = 'nan' # or use np.nan
>>> arr
array([[ nan,   1.,   2.],
       [  3.,   4.,   5.]])

A[A==NDV]=numpy.nan

A==NDV will produce a boolean array that can be used as an index for A

Randomly replace values in a numpy array

# The dataset
data = pd.read_csv('iris.data')
mat = data.iloc[:,:4].as_matrix()

Set the number of values to replace. For example 20%:

# Edit: changed len(mat) for mat.size
prop = int(mat.size * 0.2)

Randomly choose indices of the numpy array:

i = [random.choice(range(mat.shape[0])) for _ in range(prop)]
j = [random.choice(range(mat.shape[1])) for _ in range(prop)]

Change values with NaN

mat[i,j] = np.NaN

Dropout for any array dimension

Another way to do that with an array of more than 2 dimensions would be to use the numpy.put() function:

import numpy as np
import random
from sklearn import datasets

data = datasets.load_iris()['data']

def dropout(a, percent):
    # create a copy
    mat = a.copy()
    # number of values to replace
    prop = int(mat.size * percent)
    # indices to mask
    mask = random.sample(range(mat.size), prop)
    # replace with NaN
    np.put(mat, mask, [np.NaN]*len(mask))
    return mat

This function returns a modified array:

modified = dropout(data, 0.2)

We can verify that the correct number of values have been modified:

np.sum(np.isnan(modified))/float(data.size)

[out]:

0.2

np.array([2,1,0,7]).dtype
# => dtype('int64')
np.array([2,1,np.nan,7]).dtype
# => dtype('float64')

Integers don't have a NaN value; np.nan is a float, and can't be placed in an integral array.

roganjosh beat me to the solution though :P (i.e. convert to a float array before trying to put a np.nan in there). With roganjosh's permission:

a = np.array([2,1,0,7])
a = a.astype(np.float64)
a[a == 0] = np.nan
a
# => array([ 2.,  1., nan,  7.])

The reason why converting to list works is the fact that lists, unlike numpy arrays, don't mind mixing element types. When you re-convert a mixed list of integers and floats, numpy will assume you wanted a float array.

You can't use numpy vector boolean indexing or comparisons with a Python list, so your l == 0 is simply giving you 0. It's only a fluke it works even partially, because you happened to have 0 as the first element.

>>> l = [0,2,3,4,0]
>>> l == 0
False
>>> int(l == 0)
0
>>> l[l == 0] = 999
>>> l
[999, 2, 3, 4, 0]

You could use a list comprehension and a ternary:

>>> l = [0,2,3,4,0]
>>> l = [np.nan if x == 0 else x for x in l]
>>> l
[nan, 2, 3, 4, nan]

(If you were working with numpy arrays, vector comparisons and assignments would work, but you can't mix integers and floats (such as np.nan) too nicely.)

Where A is your 2D array:

import numpy as np
A[np.isnan(A)] = 0

The function isnan produces a bool array indicating where the NaN values are. A boolean array can by used to index an array of the same shape. Think of it like a mask.

You can set inplace to True (default is False):

area.replace(0, np.nan, inplace=True)

See examples in docs.