Python 3 Basics # 6 | Numpy Array | Create | Access | Update | Slice | Basic Operation | Functions

Pubblicato il: 18 ottobre 2018
sul canale di: technologyCult

1,540

Python 3 Basics # 6 | Numpy Array | Create | Access | Update | Slice | Basic Operation | Functions

Python Basics - Session 6

Topic to be covered - Numpy in Python

1. What is Numpy
2. Creating Numpy
3. Accessing Numpy elements
4. Updating Numpy
5. Indexing / Slicing in Numpy
6. Basic Operations in Numpy
7. Functions using Numpy
mean, max, min, sort, var, std, argmin, argmax, nonzero, where, extract,
8. Broadcasting in Numpy
9. Numpy String Functions
10. Storage Comparision between List and Numpy
11. Processing time comparision between LiSst and Numpy
12. Matrix / Linear Algebra using Numpy
13. Iterations with Numpy
14. Numpy - converting to hexadecimal
15. I/O with Numpy
16. Matplotlib with Numpy - Various options to be explored

1. What is Numpy ?
a. Numpy is a library for scientific computing.
b. Numpys stands for Numerical Python.
c. Numpy consists of Multidimensional array objects and it has collection of
functions/routines to process those arrays.
d. There are advantages of using Numpy
i. Takes less memory as compared to List
ii. Processing speed of num

2. How do we create numpy array?

Code Starts Here
==============
import numpy as np

x = np.array([1,2,3])
print(x)

print(x.dtype)

x = np.array([1,2,3.0])
print(x.dtype)
print(x)

x = np.array([10,20,30,40,50], ndmin = 3)
print(x)
print(x.size)
print(x.shape)

3. Accessing Numpy Elements

x = np.array([10,20,30,40,50])
print(x[2])
print(x[-1])
print(x[-3])

4. Updating Numpy array

print(x)
x[2] = 80
print
5. Indexing / Slicing in Numpy

Type 1

x = np.arange(10)

s = slice(2,9,2)
print(x[s])
print(x[slice(0,8,2)])
print(x[slice(1,8,3)])

print(x[0:8:2])
print(x[1:8:3])

x = np.arange(20)
y = x[10]
print(y)

y = x[:10]
print(y)

y = x[10:]
print(y)

print(y[2:8])

print(y[2:10:2])
print(y[2:10:3])

x = np.array([[10,20,30], [40,50,60], [70,80,90]])
print(x)
'''
[[10 20 30] ----- 0
[40 50 60] ----- 1
[70 80 90]] ----- 2
'''

print(x[1:])
print(x[2:])
print(x[0:])
print(x[3:])

print(x[:,0])
print(x[:,1])
print(x[:,2])

""" 6. Basic Operations in Numpy"""

x = [10,20,30]
y = [30,60,70]

print(x + y)
print(y / 10)

x = np.array([10,20,30])
y = np.array([30,60,70])

print(x+y)
print( y / 10)

print ( x * 10)

"""7. Functions using Numpy
mean, max, min, sort, var, std, argmin, argmax, nonzero, where, extract """

Sachin_runs = np.array([110,105,155,0,191,174,0])

print(np.mean(Sachin_runs))
print(np.min(Sachin_runs))
print(np.max(Sachin_runs))
print(np.var(Sachin_runs))
print(np.std(Sachin_runs))
print(np.argmax(Sachin_runs))
print(np.argmin(Sachin_runs))
print(np.nonzero(Sachin_runs))

print(np.where(Sachin_runs GT 120))

condition = (Sachin_runs GT 100) & (Sachin_runs LT 160)
print(np.extract(condition, Sachin_runs))

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