**NumPy Library **

NumPy is a library in python that is created to work efficiently with arrays in python. It is fast, easy to learn, and provides efficient storage. It also provides a better way of handling data for the process. We can create an n-dimensional array in NumPy. To use NumPy simply have to import it into our program and then we can easily use the functionality of NumPy in our program.

NumPy is a Python library that is frequently used for scientific and statistical analysis. NumPy arrays are grids of the same datatype’s values.

** Numpy matrix.partition() Function:**

Using the matrix.partition() method of the Numpy module, we can partition the matrix in such a manner that the index value that we pass sorts the matrix in such a way that all the values smaller than that value are moved to the left, and others are moved to the right.

**Syntax:**

matrix.partition(index)

**Return Value:**

The partitioned matrix is returned by the partition() function.

## Numpy matrix.partition() Function in Python

**For 1-Dimensional (1D) Matrix(array)**

**Approach:**

- Import numpy module using the import keyword
- Create a matrix(1-Dimensional) using the matrix() function of numpy module by passing some random 1D matrix as an argument to it and store it in a variable
- Print the given matrix
- Apply partition() function on the given matrix by passing some random index value as an argument to it to partition the given matrix.
- Here index value = 3, and the value at that 3rd index is 4. The values less than 4 (i.e, 1,2) are moved to the left, and others (6) are moved to the right.
- Print the given matrix after partition.
- The Exit of the Program.

**Below is the implementation:**

# Import numpy module using the import keyword import numpy as np # Create a matrix(1-Dimensional) using the matrix() function of numpy module by passing # some random 1D matrix as an argument to it and store it in a variable gvn_matrx = np.matrix('[2, 1, 6, 4]') # Print the given matrix print("The given matrix is:") print(gvn_matrx) # Apply partition() function on the given matrix by passing some random index value as an argument to it # to partition the given matrix. # Here index value = 3, the value at the 3rd index is 4. The values less than 4 (i.e, 1,2) are # moved to the left and others (6) are moved to the right gvn_matrx.partition(3) # Print the given matrix after partition. print("The given matrix after partition is:") print(gvn_matrx)

**Output:**

The given matrix is: [[2 1 6 4]] The given matrix after partition is: [[1 2 4 6]]

**Explanation:**

Here index value = 3, the value at the 3rd index is 4. The values less than 4 (i.e, 1 and 2) are moved to the left and other number 6 is moved to the right

**For 2-Dimensional (2D) Matrix**

**Approach:**

- Import numpy module using the import keyword
- Create a matrix(2-Dimensional) using the matrix() function of numpy module by passing some random 2D matrix as an argument to it and store it in a variable
- Print the given matrix
- Apply partition() function on the given matrix by passing some random index value as an argument to it to partition the given matrix.
- Print the given matrix after partition.
- The Exit of the Program.

**Below is the implementation:**

# Import numpy module using the import keyword import numpy as np # Create a matrix(2-Dimensional) using the matrix() function of numpy module by passing # some random 2D matrix as an argument to it and store it in a variable gvn_matrx = np.matrix('[10, 1; 2, 7]') # Print the given matrix print("The given matrix is:") print(gvn_matrx) # Apply partition() function on the given matrix by passing some random index value as an argument to it # to partition the given matrix. gvn_matrx.partition(1) # Print the given matrix after partition. print("The given matrix after partition is:") print(gvn_matrx)

**Output:**

The given matrix is: [[10 1] [ 2 7]] The given matrix after partition is: [[ 1 10] [ 2 7]]