Numpy is a Python package which is well known for its data-crunching functions. It is one of the most used Python packages for Data Science. We will look into the package in brief below:-

To start with, import the numpy package

*import numpy as np*

**Few striking points when you use numpy functions/objects:-**

1. When you use traditional list, then you can include elements with different data types. But, numpy

**array can’t have elements of different data types**

2. You can perform operation on each element of Numpy array, but you can’t do the same with a list. Example below:-

*x = [1,2,3]*

y = numpy.array([1,2,3])

x*3

y = numpy.array([1,2,3])

x*3

**will throw an error**

y*3will give you output as 3,6,9y*3

3. How to store a list in a numpy array?

*x = [4,6,7]*

*z =*numpy

*.array(x)*

4. How to create a 2 Dimensional numpy array?

x = numpy.array([[1,2,3],[9,8,7]])

Output[[1 2 3] [9 8 7]]

5. The first cell in a two-dimensional array will have the address [0,0]. Thus to access the 2nd row and 3 rd column of a 2D array, you should give the address as x[1][2]

6. You can directly convert a list of list into 2D array by passing the list in numpy.array() function

7. To access all the columns of 14th row, use the below command

*x[13,:]*

8. If you want to add two 2D array, then you can do it by ‘+’ operator. Example below:-

x = numpy.array([[1,2,3],[9,8,7]])y = numpy.array([[4,5,6],[3,4,5]])

print(x+y)

Output

[[ 5 7 9] [12 12 12]]9. Different built-in functions in Numpy package

a. sum()b. sort()

c. mean()

d. median()

e. corrcoef()

f. std()

10. Generate sample data from

x = numpy.random.normal((3,0.5,100),2)

The above code will get you 100 sample with distribution mean of 3 and standard deviation of 0.5.

Numpy contains a very very diverse set of mathematical function. I could have copied it from somewhere !! But here is a website which seems to have all the built-in mathematical functions of numpy. Do check out.