# Numpy true_divide(): How to Use np true_divide() in Python

Numpy true_divide() **function is used to divide two arrays of the same size. If we have two arrays arr1 and arr2, then true_divide will divide values of arr2 by values of arr1, but we will get a floor result. **

**Numpy true_divide()**

**Numpy true_divide() function works similar to the Python floor_divide() function, the only difference is, this function uses / operator instead of // operator pair with a remainder operator (%). The simplified equation is: ***b = a % b + b * (a / b) up to round off. *We can divide array elements by any scalar also.

**Syntax**

numpy.true_divide(arr1,arr2,out=None)

**Parameters**

The np true_divide() function takes mainly two parameters:

- arr1: This is the input array that acts like a dividend.
- arr2: This is an input array that acts as a divisor.
- out: This is an optional field. A place the result will be stored in. If given, the shape to which the inputs broadcast must be in. If a freshly-allocated array is returned unless received or None. A tuple (possible as a keyword argument only) must have a length equal to the output number.

**Return Value**

The true_divide() function returns the true division of arr1 and arr2 (arr1/arr2).

**Programming Example of np true_divide()**

**Program to show the working of true_divide() when both inputs are arrays.**

See the following code.

# importing the numpy module import numpy as np # First Parameter arr_A = np.array([8, 18, 28, 34]) # Shape of first array, arr_A is: print("Shape of first array, arr_A is: ", arr_A.shape) # Second Parameter arr_B = np.array([2, 4, 5, 4]) # Shape of second array, arr_B is: print("Shape of second array, arr_B is:", arr_B.shape) out = np.true_divide(arr_A, arr_B) print("Output obtained is: ", out) # Shape of output array, out is: print("Shape of output array, out is: ", out.shape)

**Output**

Shape of first array, arr_A is: (4,) Shape of second array, arr_B is: (4,) Output obtained is: [4. 4.5 5.6 8.5] Shape of output array, out is: (4,)

**Explanation**

In this program, we have taken two numpy arrays named arr_A and arr_B consisting of different array elements, we have passed these two arrays as parameters inside the np.true_divide() method, first parameter arr_A acts as the dividend whose elements needs to be divided and arr_B acts as the divisor.

The elements of arr_A are divided into element-wise fashion by the elements of arr_B. The result of the floor_division is stored in a variable named out, which is a numpy array of the same shape as the input arrays and contains the quotient values obtained after division.

**When divisor is a scalar**

See the following code.

# importing the numpy module import numpy as np # First Parameter arr_A = np.array([8, 18, 28, 34]) print("Elements in arr_A are: ", arr_A) # Second Parameter is a scalar value scal_val = 6 print("Scalar value is: ", scal_val) out = np.true_divide(arr_A, scal_val) print("Output obtained is: ", out)

**Output**

Elements in arr_A are: [ 8 18 28 34] Scalar value is: 6 Output obtained is: [1.33333333 3. 4.66666667 5.66666667]

**Explanation**

In the program, we have taken a numpy input array named arr_A which performs the function of dividend and a scalar value, i.e., 6 which will act as the divisor, we have passed arr_A as the first parameter and the scalar value as the second parameter inside np.true_divide() method, the scalar value will be broadcasted into an array of the same shape as arr_A. Then the division is performed element-wise as usual.

The result of the division is stored in a variable named out, which is a numpy array of the same shape as the input array and contains the quotient values obtained after division.

Finally, Numpy true_divide() function is over.