NumPy.py

# -*- coding: utf-8 -*-
"""SFDS_0002_Exp1.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1EVps-M6p5pL2mPU7V7EYkQymR5GK9pyY
"""

import numpy as np
a1=np.array([2,4,6,8,10])
a2=np.array([3,5,7,9,11])
sum=a1+a2
print("The addition of two numpy arrays:",sum)

import numpy as np      #1D array
a1=np.array([2,4,6,8,10])
print(a1)

import numpy as np     #2D array
a1=np.array([[1,2,3],[4,5,6]])
print(a1)

import numpy as np
a1=np.array([2,4,6,8,10])
print(a1)
a2=np.zeros((2,3))
print(a2)
a3=np.ones((2,3))
print(a3)
a4=np.arange(0,10,2)
print(a4)
a5=np.linspace(0,1,5)
print(a5)
a6=np.random.rand(2,2)
print(a6)

"""# Array Multiplication"""

import numpy as np
a1=np.array([2,4,6,8,10])
a2=np.array([3,5,7,9,11])
m1=np.dot(a1,a2)
print(m1)
m2=np.matmul(a1,a2)
print(m2)
m3=np.multiply(a1,a2)
print(m3)
m4=a1*a2
print(m4)

"""# MATHEMATICAL FUNCTIONS

---

**bold text**
"""

import numpy as np
a=np.pi/4
sinval=np.sin(a)
print(sinval)
cosval=np.cos(a)
print(cosval)
tanval=np.tan(a)
print(tanval)

"""# **Statistical Measures**"""

import numpy as np
a1=np.array([2,4,6,8,10])
me=np.mean(a1)
med=np.median(a1)
mini=np.min(a1)
maxi=np.max(a1)
stdev=np.std(a1)
vari=np.var(a1)
s=np.sum(a1)
p=np.prod(a1)
print(me)
print(med)
print(mini)
print(maxi)
print(s)
print(p)
print(stdev)
print(vari)

"""# **Broadcast**"""

import numpy as np
a1=np.array([1,2,3,4,5])
a1+=5
print(a1)

"""# **Indexing and Slicing**"""

import numpy as np
a1=np.array([1,2,3,4,5])
x=a1[4]
print(x)
y=a1[2:4]
print(y)
print(a1[-4:-2])

"""# **Linear Algebra**"""

import numpy as np     #2D array
a1=np.array([[1,2],[3,4]])
a2=np.array([[2,4],[6,8]])
r=np.linalg.inv(a1)@a2
print(r)

"""OPERATION:ADDITION"""

import numpy as np  #1D
a1=np.array([1,2,3,4,5,6,7,8,9,10])
print("Original Array:",a1)
a2=np.copy(a1)
print("Copied Array:",a2)

import numpy as np  #2D
a1=np.array([[1,2,3,4],[4,3,2,1]])
print("Original Array:",a1)
a2=np.copy(a1)
print("Copied Array:",a2)

import numpy as np  #1D
a1=np.array([1,2,3,4,5,6,7,8,9,10])
print("Original Array:",a1)
a2=a1
print("Assigned:",a2)

"""Resizing a numpy array"""

import numpy as np
a=np.array([2,4,6,8,10,12])
print("Resize:",a)
r=np.resize(a,(3,4))
print("Resized:",r)

import numpy as np
a=np.array([2,4,6,8,10,12])
print("Resize:",a)
a.resize(3,4)
print(a)

"""Numpy Flatten"""

import numpy as np
l1 = [1, 2, 3]
l2 = [5, 6, 7]
a = np.array([l1, l2])
print(a.flatten())

"""Dealing with missing values and fill in with with missing values
Data missing is an common issue that data analyist and scientist face when working with dataset.These missing values can occur due to various reasons such as data collection errors,sensor malfunctions or because the data was not available at the time of measurement

In numpy missing values a represented using a special value "np.nan(NotANumber)this value represents invalid or missing data and can be easily identified within an array . np.nanis a floating point value so any array containing it will be coerced to floating point data type

Identifying missing values
np.isnan() is a method that returns a boolean array highlighting the positions of missing values
"""

import numpy as np
a=np.array([1,2,np.nan,4,np.nan,5])
miss=np.isnan(a)
print(miss)
#removing missing values
a2=a[~np.isnan(a)]
print(a2)

"""To handle missing values is to remove from the data set.np.isnan function along with boolean indexing is used to remove missing values
~ represents to create a boolean array that selects the elements that are not NaN

Replacing missing values
"""

import numpy as np
a=np.array([1,2,np.nan,4,np.nan,5])
replace=np.nan_to_num(a,nan=-1)
print(replace)

"""to replace nan values with other specified values we can use np.nan_to_num() function.In this case we replace the missing values with -1 which can be any value of choice"""

import numpy as np
a=np.array([1,2,np.nan,4,np.nan,5])
s=np.sum(a)
means=np.nanmean(a)
print(s)
print(means)

import numpy as np
a=np.array([[1,2,np.nan,5],[4,np.nan,6,np.nan]])
miss=np.isnan(a)
print(miss)
#removing missing values
a2=a[~np.isnan(a)]
print(a2)
#replacing
replace=np.nan_to_num(a,nan=-1)
print(replace)
#sum and mean
s=np.sum(a)
means=np.nanmean(a)
print(s)
print(means)