Hello and welcome to this tutorial on Numpy true_divide. In this tutorial, we will be learning about the NumPy true_divide() method and also seeing a lot of examples regarding the same. So let us begin!
Also read: NumPy floor_divide – A Complete Guide
What is NumPy true_divide?
true_divide() is a function in NumPy that divides elements from one array by elements in another array, element-wise and returns an array that contains the answer, i.e. quotient of each element-wise division.
If x1 and x2 are two arrays then true_divide(x1, x2) will perform element-wise division such that each element in x1 is divided by the corresponding element in x2 and store the result in a new array.
Syntax of NumPy true_divide
Let us have a look at the syntax of the true_divide() function.
numpy.true_divide(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])
| Parameter | Description | Required/Optional |
| x1 (array_like) | Dividend array. | Required |
| x2 (array_like) | Divisor array. | Required |
| out | An alternative output array in which to place the result. It must have the same shape as the expected output. | Optional |
| where | Takes an array-like object. At locations where it is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. | Optional |
Returns:
An array containing the quotients of the element-wise division of x1 by x2.
If both the inputs are scalars, then the result is also scalar.
Difference between floor_divide and true_divide
In Python, Numpy floor_divide() function performs the floor division of two arrays, element-wise. It is equivalent to using the / operator.
Whereas, the NumPy true_divide() performs division element-wise and is equivalent to using the // operator.
Examples of NumPy true_divide
Lets now get right into the examples and understand how the true_divide method actually works.
When both inputs are scalars
import numpy as np
a = 18
b = 5
c = 6
# using the true_divide function to perform element-wise division
ans_1 = np.true_divide(a, b)
ans_2 = np.true_divide(a, c)
print("a =", a, "\nb =", b)
print("Result 1 =", ans_1)
print("Result 2 =", ans_2)
Output:
a = 18
b = 5
Result 1 = 3.6
Result 2 = 3.0
Simple example where
ans_1 = 18/5 = 3.6
ans_2 = 18/6 = 3
When one input is a scalar and the other a 1-dimensional array
import numpy as np
a = [2, 36, 10, 4, 20]
b = 5
# using the true_divide function to perform element-wise division
ans = np.true_divide(a, b)
print("a =", a, "\nb =", b)
print("Result =", ans)
Output:
a = [2, 36, 10, 4, 20]
b = 5
Result = [0.4 7.2 2. 0.8 4. ]
Here, the scalar value is the divisor which divides each element in the dividend array as follows:
ans[0] = 2/5 = 0.4
ans[1] = 36/5 = 7.2
ans[2] = 10/5 = 2
ans[3] = 4/5 = 0.8
ans[4] = 20/5 = 4
When both the input arrays are 1-dimensional
import numpy as np
a = [5, 30, 12, 36, 11]
b = [2, 5, 6, 7, 10]
# using the true_divide function to perform element-wise division
ans = np.true_divide(a, b)
print("a =", a, "\nb =", b)
print("Result =", ans)
Output:
a = [5, 30, 12, 36, 11]
b = [2, 5, 6, 7, 10]
Result = [2.5 6. 2. 5.14285714 1.1 ]
Here, each element in a is divided by the corresponding element in b and the output is calculated as:
ans[0] = a[0]/b[0] = 5/2 = 2.5
ans[1] = a[1]/b[1] = 30/5 = 6
ans[2] = a[2]/b[2] = 12/6 = 2
ans[3] = a[3]/b[3] = 36/7 = 5.14285714
ans[4] = a[4]/b[4] = 11/10 = 1.1
When both the input arrays are 2-dimensional
import numpy as np
a = [[25, 23], [12, 18]]
b = [[5, 6], [4, 5]]
# using the true_divide function to perform element-wise division
ans = np.true_divide(a, b)
print("a =", a, "\nb =", b)
print("Result =\n", ans)
Output:
a = [[25, 23], [12, 18]]
b = [[5, 6], [4, 5]]
Result =
[[5. 3.83333333]
[3. 3.6 ]]
Similar to the above example,
ans[0][0] = a[0][0]/b[0][0] = 25/5 = 5
ans[0][1] = a[0][1]/b[0][1] = 23/6 = 3.83333333
ans[1][0] = a[1][0]/b[1][0] = 12/4 = 3
ans[1][1] = a[1][1]/b[1][1] = 18/5 = 3.6
Comparison of true_divide (//) and floor_divide (/)
import numpy as np
a = [5, 30, 12, 36, 11]
b = [2, 5, 6, 7, 10]
# using the true_divide and floor_divide functions to perform element-wise division
ans_true_divide = np.true_divide(a, b)
ans_floor_divide = np.floor_divide(a, b)
print("a =", a, "\nb =", b)
print("Result of true divide =", ans_true_divide)
print("Result of floor divide =", ans_floor_divide)
Output:
a = [5, 30, 12, 36, 11]
b = [2, 5, 6, 7, 10]
Result of true divide = [2.5 6. 2. 5.14285714 1.1 ]
Result of floor divide = [2 6 2 5 1]
The difference in the outputs here is that, in the output of floor division, the floor of the actual quotient is presented as the output as opposed to the true divide method where the actual quotients are included in the output.
Conclusion
That’s all! In this tutorial, we learned about the Numpy true_divide method and practiced different types of examples using the same.
If you want to learn more about NumPy, feel free to go through our NumPy tutorials.