NumPy round()
The numpy.round() function rounds elements of an array to a specified number of decimal places. It supports rounding both floating-point and complex numbers.
Syntax
numpy.round(a, decimals=0, out=None)Parameters
| Parameter | Type | Description |
|---|---|---|
a | array_like | Input array containing values to be rounded. |
decimals | int, optional | Number of decimal places to round to (default: 0). A negative value rounds to the left of the decimal point. |
out | ndarray, optional | Optional output array to store the result. Must match the shape of the expected output. |
Return Value
Returns an array with rounded values of the same type as the input. If out is provided, the result is stored in it; otherwise, a new array is created.
Examples
1. Rounding Floating-Point Numbers
Rounding an array of floating-point numbers to 2 decimal places.
import numpy as np
# Define an array of floating-point numbers
arr = np.array([1.234, 2.678, 3.987])
# Round each element to 2 decimal places
rounded_arr = np.round(arr, decimals=2)
# Print the results
print("Original array:", arr)
print("Rounded array:", rounded_arr)Output:
Original array: [1.234 2.678 3.987]
Rounded array: [1.23 2.68 3.99]
2. Rounding to the Nearest Integer
By default, numpy.round() rounds numbers to the nearest integer.
import numpy as np
# Define an array with fractional values
arr = np.array([1.5, 2.3, 3.8, 4.2])
# Round to the nearest integer (default decimals=0)
rounded_arr = np.round(arr)
# Print the results
print("Original array:", arr)
print("Rounded array:", rounded_arr)Output:
Original array: [1.5 2.3 3.8 4.2]
Rounded array: [2. 2. 4. 4.]
3. Rounding to a Negative Decimal Place
Using a negative decimals value rounds numbers to positions to the left of the decimal point.
import numpy as np
# Define an array with larger values
arr = np.array([1234, 5678, 91011])
# Round to the nearest hundred (-2 decimal places)
rounded_arr = np.round(arr, decimals=-2)
# Print the results
print("Original array:", arr)
print("Rounded array:", rounded_arr)Output:
Original array: [ 1234 5678 91011]
Rounded array: [ 1200 5700 91000]
4. Using the out Parameter
Using an output array to store the results instead of creating a new array.
import numpy as np
# Define an array of floating-point numbers
arr = np.array([2.345, 5.678, 8.912])
# Create an output array with the same shape
output_array = np.empty_like(arr)
# Round and store the result in output_array
np.round(arr, decimals=1, out=output_array)
# Print the results
print("Computed rounded values:", output_array)Output:
Computed rounded values: [2.3 5.7 8.9]
5. Rounding Complex Numbers
For complex numbers, both real and imaginary parts are rounded separately.
import numpy as np
# Define an array of complex numbers
arr = np.array([1.23 + 4.56j, 3.78 + 9.87j])
# Round each component to 1 decimal place
rounded_arr = np.round(arr, decimals=1)
# Print the results
print("Original array:", arr)
print("Rounded array:", rounded_arr)Output:
Original array: [1.23+4.56j 3.78+9.87j]
Rounded array: [1.2+4.6j 3.8+9.9j]
The real and imaginary parts are rounded separately, preserving the overall complex structure.