# NumPy ceil()

The `ceil()` function rounds up floating point element(s) in an array to the nearest integer greater than or equal to the array element.

### Example

``````import numpy as np

array1 = np.array([1.2, 2.7, 3.5, 4.8, 5.1])

# round up each element in array1 using ceil()
result = np.ceil(array1)

print(result)

# Output: [2. 3. 4. 5. 6.]``````

## ceil() Syntax

The syntax of `ceil()` is:

``numpy.ceil(array, out = None)``

## ceil() Arguments

The `ceil()` function takes following arguments:

• `array` - the input array
• `out` (optional) - the output array where the result is stored

## ceil() Return Value

The `ceil()` function returns a new array with the rounded-up values.

## Example 1: Use ceil() with 2D Array

``````import numpy as np

# create a 2D array
array1 = np.array([[1.2, 2.7, 3.5],
[4.8, 5.1, 6.3],
[7.2, 8.5, 9.9]])

# round up the elements in  a 2D array with numpy.ceil()
result = np.ceil(array1)

print("Rounded-up values:")
print(result)``````

Output

```Rounded-up values:
[[ 2.  3.  4.]
[ 5.  6.  7.]
[ 8.  9. 10.]]```

Here, we have used the `ceil()` function to round up each element in array1.

The value 1.2 is rounded up to 2, the value 2.7 is rounded up to 3, and so on.

Note: The `ceil()` function returns an array with the same data type as the input array, and the resulting values are floating-point numbers representing the rounded up values.

## Example 2: Create Different Output Array to Store Result

``````import numpy as np

# create an array
array1 = np.array([1.2, 2.7, 3.5, 4.9])

# create an empty array with the same shape as array1
result = np.zeros_like(array1)

# store the result of ceil() in out_array
np.ceil(array1, out=result)

print(result)``````

Output

`[2. 3. 4. 5.]`

Here, the `ceil()` function is used with the `out` parameter set to result. This ensures that the result of applying `ceil()` function is stored in result.