Use a list with the fmt parameter to specify the formatting for each column:

fmt=['%d', '%1.1f', '%1.1f', '%1.1f']

Complete example:

import numpy as np
prob_rf = [[1, 0.4, 0.4, 0.4],
           [2, 0.5, 0.5, 0.5],
           [3, 0.6, 0.6, 0.6]]
np.savetxt("foo.csv", prob_rf, delimiter=",", fmt=['%d', '%1.1f', '%1.1f', '%1.1f'])

The resulting file:

1,0.4,0.4,0.4
2,0.5,0.5,0.5
3,0.6,0.6,0.6

Use np.insert

import numpy as np

np.insert(array, index, number)

Use np.where to get the indices where a given condition is True.

Examples:

For a 2D np.ndarray called a:

i, j = np.where(a == value) # when comparing arrays of integers

i, j = np.where(np.isclose(a, value)) # when comparing floating-point arrays

For a 1D array:

i, = np.where(a == value) # integers

i, = np.where(np.isclose(a, value)) # floating-point

Note that this also works for conditions like >=, <=, != and so forth...

You can also create a subclass of np.ndarray with an index() method:

class myarray(np.ndarray):
    def __new__(cls, *args, **kwargs):
        return np.array(*args, **kwargs).view(myarray)
    def index(self, value):
        return np.where(self == value)

Testing:

a = myarray([1,2,3,4,4,4,5,6,4,4,4])
a.index(4)
#(array([ 3,  4,  5,  8,  9, 10]),)

You can just do df.index.values:

df = pd.DataFrame(index=['a', 'b', 'c'])

df.index.values
# array(['a', 'b', 'c'], dtype=object)

Numpy arrays have a fixed data type that you must specify. It looks like a data type of int for your item and float for you value would work best. Something like:

import numpy as np
dtype = [("item", int), ("value", float)]
a = np.array([(0, 0.), (1, .1), (2, .2)], dtype=dtype)

The string part of the dtype is the name of each field. The names allow you to access the fields more easily like this:

print a['value']
# [ 0.,  0.1,  0.2]

a['value'] = [7, 8, 9]
print a
# [(0, 7.0) (1, 8.0) (2, 9.0)]

If you need to copy another array into the array I describe above, you can do it just by using the filed name:

new = np.empty(len(a), dtype)
new['index'] = [3, 4, 5]
new['value'] = a['value']
print new
# [(3, 7.0) (4, 8.0) (5, 9.0)]