Basics of Numpy | (Data Science ss : 7.1)

Basics of Numpy

As we are now quite familiar with the numpy. Now let us explore it with some code & trick's which will help you to be a great Data Scientist.

Here we will see multiple examples and i'll explain it to you.

    

(1) np.zeros :

code :

import numpy as np

a=np.zeros((2,4))        #syntax = np.zeros((num_of_rows,num_of_columns))

print(a)

output :

>> [[ 0.  0.  0.  0.
     0.  0.   0.  0.]]

here you notice that .zeros is a function which is present in the numpy which we can use to create an array with number of row's(2) & column's(4) that we had specified.

(2) print numbers with specific interval : 

code :

import pandas as pd

a=np.array((10,25,5))     #syntax = np.array((start_num,end_num,interval))

print(a)

output :

>>[10   15   20]

Now you must be thinking why i had not written 25...because the end_number is not included.


(3)  np.linspace :

code :

import numpy as np

a=np.linspace(0,10,5)       #syntax = np.linspace(start_num,end_num,num_of_split)

print(a)

output :

>>[0    2.5   5.   7.5   10]

At this in the last variable you will give the number of splits you want might give you float value.


(4) np.full :

code :

import numpy as np

a=np.full((3,4),5)

print(a)

output :

>>[[5   5   5]
      [5   5   5]
      [5   5   5]]

Remember np.zero we did exactly same thing but this time we printed the number which we want.


(5) random :

code :

import numpy as np

a=np.random.random(2,2)

print(a)

output :

>>[[0.856134546       0.97946144]
      [1.884646544        2.66547456]]

Here we just given the number of row's & column's in which the random values will get filled automatically.


(6) shape :

code :

import numpy as np

a=np.array([[1,2,3],[4,5,6]])

print(a.shape)

output :

>>(2,3)

Now notice here we print the shape not the matrix/array.In which the first element it is number of column i.e (2) & second element it is number of element it have i.e (3).


(7) range & .size :

code :

import numpy as np

a=np.array(5)

print(a.size)

print(a)

output :

>>5

     [0   1   2   3   4]

In this we only gave a value i.e range & it created an 1D array of that range & .size helps us to show the size of that array.


(8) ndim :

code :

x=np.array([[1,2,3],[4,5,6]])

print(x.ndim)

output :

>>2

This will give us the dimension of that array.


(9) .dtype :

code :

x=np.array([[1,2,3],[4,5,6]])

print(x.dtype)

output :

>>int32

It basically provides the data type of each elemnt in numpy array.