**Getting Started With NumPy**

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# Basic Installation Guide (Windows)

Open CMD and type:

pip install numpy

… and done. That’s it.

# Importing NumPy

Just write down the following command:

importnumpyasnp

Here, ‘np’ is just a shorthand convention in place of numpy which is widely adopted in programming community.

# Python List Vs. NumPy Array

Python list can contain different data types within a single list, all of the elements in a NumPy array should be homogeneous. This facilitates for all the mathematical operations meant to be performed using numpy.

- NumPy arrays are
**faster**and more**compact**than Python lists. - NumPy uses much less memory to store data and it provides a mechanism of specifying the data types. This allows the code to be optimized even further.

# 1D Array, 2D Array & ND array

The NumPy `ndarray`

class is used to represent both matrices and vectors.

- A
**vector**is an array with a single dimension (there’s no difference between row and column vectors). - A
**matrix**refers to an array with two dimensions. For**3-D**or higher dimensional arrays, the term**tensor**is also commonly used.

In NumPy, dimensions are called **axes**.

# One dimensional array has 1 axis

arr = np.array([1,2,4,5,2,3,8])

# Two dimensional array has 2 axes

arr2 = np.array([[45,48,45,12],[78,15,6,25]])

# Creating An Array In NumPy

**np.array( )**

`>>> np.array([1,5,9,2])`

array([1,5,9,2]

**np.zeros( )**

`>>> np.zeros(3)`

array([0,0,0])

**np.ones( )**

`>>> np.ones(3)`

array([1,1,1])

**np.empty( )**

`# Here, output is random`

>>> np.empty(2)

array([-1.37196251e+131, -8.51127198e+092])

**np.arange(***start, end, [step]***)**

`>>> np.arange(2,10)`

array([2, 3, 4, 5, 6, 7, 8, 9])

>>> np.arange(2,10,3)

array([2, 5, 8])

**np.linspace( )**

`>>> np.linspace(2,10,num = 3) array([ 2., 6., 10.])`

**Specifying your data type**

While the default data type is floating point (

), you can explicitly specify which data type you want using the **np.float64**

keyword.**dtype**

`>>> a = np.array([1,5,78.2], dtype=np.int64)`

>>> print(type(a[2]))

>>> print(a[2])

# Output : <class 'numpy.int64'>

# Output : 78

# Sorting Arrays

You can quickly sort an array in ascending order using **np.sort( )**

>>> arr = np.array([12,24,5467,5])

>>> np.sort(arr)

array([ 5, 12, 24, 5467])

In addition to np.sort(), you can see all the methods that can be applied here.

# Adding Arrays

You can concatenate arrays with **np.concatenate()**

>>> a = np.array([1, 2, 3, 4, 5])

>>> b = np.array([5, 6, 7, 8])

>>> np.concatenate((a, b))

array([1, 2, 3, 4, 5, 5, 6, 7, 8])

To see more about concatenation, click here,

# Shape and Size of an Array

>>> arr = np.array([[[0, 1, 2, 3], [4, 5, 6, 7]], [[0, 1, 2, 3], [4, 5, 6, 7]], [[0 ,1 ,2, 3], [4, 5, 6, 7]]])

>>> arr.ndim

# Number of axis / dimension

# Output : 3

>>> arr.size

# Total number of elements in array

# Output : 24

>>> arr.shape

# Displays a tuple of integers indicating the number of elements stored along each dimension of array

# Output : (3, 2, 4)

# Reshaping an Array

Using **arr.reshape()**** **will give a new shape to an array without changing the data. Just remember that when you use the reshape method, the array you want to produce needs to have the same number of elements as the original array. If you start with an array with 12 elements, you’ll need to make sure that your new array also has a total of 12 elements.

>>> a = np.array([4,8,5,6,7,2])

array([4, 8, 5, 6, 7, 2])

>>> a.reshape(3,2)

array([[4, 8], [5, 6], [7, 2]])

>>> a.reshape(3,3)

ValueError: cannot reshape array of size 6 into shape (3,3)

For more information on shape manipulation, click here.