Copying values of a numpy array with floats

Question

I have the following numpy arrays:

X = np.array([[1,2,3], [4,5,6], [7,8,9]])
y = np.array([[0.1,0.2,0.3], [0.4,0.5,0.743], [0.834,0.96,0.1]])

I was trying to copy some columns in y into X using the following:

X[:, [0,1]] = y[:, [0,1]]

However after I print X I get:

In[20]: X
Out[20]: 
array([[0, 0, 3],
       [0, 0, 6],
       [0, 0, 9]])

As you can see, as if the floats are rounded. I want the floats as is without rounding, how can I fix that ?

Sociopath · Accepted Answer · 2019-11-13 10:55:20Z

1

The dtype of x is int as it has all integer values,

so just change the dtype while creating x as below:

x = np.array([[1,2,3], [4,5,6], [7,8,9]], dtype=float)
y = np.array([[0.1,0.2,0.3], [0.4,0.5,0.743], [0.834,0.96,0.1]])

x[:, [0,1]] = y[:, [0,1]]

print(x)

Output:

[[0.1   0.2   3.   ]
 [0.4   0.5   6.   ]
 [0.834 0.96  9.   ]]

Edit

Or if you dont create x manually then as @Arda Keskiner suggested in comments use:

x = x.astype(float)
x[:, [0,1]] = y[:, [0,1]]

edited Nov 13, 2019 at 10:55

answered Nov 13, 2019 at 10:46

Sociopath

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2 Comments

Arda Keskiner Over a year ago

Correct answer. What you can also do is define X and then change it's type to float by using .astype(float) . X = np.array([[1,2,3], [4,5,6], [7,8,9]]).astype(float)

Sociopath Over a year ago

That's useful when you are getting value from some source and don't have control over it. But if you are creating array then using dtype parameter is effcient as there is overhead for calling a methd astype.

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