# Convert Float Array to Int Array in Numpy

Often, we have to convert float values to integer values for a variety of use cases. Similar is the case with Python arrays and NumPy arrays.

Using some functions from `numpy`, we can easily convert 2D float NumPy arrays into 2D integer NumPy arrays.

In this article, we will talk about two such methods, `ndarray.astype()` and `numpy.asarray()`.

## Convert a 2D Array From Float to Int Using `ndarray.astype()` in Numpy

NumPy arrays are of type `ndarray`. These objects have in-built functions, and one such function is `astype()`. This function is used to create a copy of a NumPy array of a specific type. This method accepts five arguments, namely, `dtype`, `order`, `casting`, `subok`, and `copy`. `dtype` refers to the data type of the copied array. `order` is an optional argument and it controls the memory layout of the resulting array. All the other options are optional.

To learn more about the other parameters of this function, refer to the official documentation of this function here

Refer to the following code to understand this function better.

``````import numpy as np

myArray = np.array([[1.0, 2.5, 3.234, 5.99, 99.99999], [0.3, -23.543, 32.9999, 33.0000001, -0.000001]])
myArray = myArray.astype(int)
print(myArray)
``````

Output:

``````[[  1   2   3   5  99]
[  0 -23  32  33   0]]
``````

## Convert a 2D Array From Float to Int Using `ndarray.asarray()`

Secondly, we can use the `asarray()` function. This function accepts four arguments, `a`, `dtype`, `order`, and `like`.

• `a` refers to the input array that has to be converted.
• `dtype` refers to the data type to which the array has to be converted. Interestingly, `dtype` is an optional argument, and its default value is inferred from the input itself.
• `order` and `like` are also other optional arguments. `order` refers to the output array’s memory layout.

To learn more about the arguments of this function, refer to the official documentation of this function here

``````import numpy as np

myArray = np.array([[1.923, 2.34, 23.134], [-24.000001, 0.000001, -0.000223]])
myArray = np.asarray(myArray, dtype = int)
print(myArray)
``````

Output:

``````[[  1   2  23]
[-24   0   0]]
``````

In the above code, the data type is mention as `int`, and the output array is also an integer NumPy array.

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