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NumPy Python Package for Data Science Fresco Play Handson Solution Hacker Rank

NumPy Python Package for Data Science Fresco Play Solution, Learn Array manipulation, slicing, dimensions, Indexing, matrices, dot Products, vectors.
D Shwari
NumPy Python Package for Data Science Fresco Play Handson Solution Hacker Rank - www.pdfcup.com

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Fresco Play Numpy Hacker Rank Hands On 

Lab 1 : Try it Out - Array Creation [Welcome to Array Creation - Hands-on]

Python "ndarray Array Creation" Hands-on

Solution: Python "ndarray Array Creation" Hands-on

  
import numpy as np

# Enter your code here. Read input from STDIN. Print output to STDOUT

def array_operations(l):
    #Write your code below
    x = np.array(l, dtype='float64')
    print( type(x) )
    print( x.ndim )
    print( x.shape)
    print(x.size)
    
if __name__ == "__main__":
    l = list(map(int,input().split()))
    array_operations(l)

Lab 2 : Try it Out - Determine Attributes [Welcome to Python Array - Determine Attributes - Hands-on]

Python Array "Determine Attributes" Hands-on

Solution : Python Array "Determine Attributes" Hands-on

  
import numpy as np
# Enter your code here. Read input from STDIN.

def array_attributes(l):
    y = np.array(l)
    x=y
    # Type of the Array
    print( type(x) )    
    # Dimensions of X
    print( x.ndim )    
    #Shape of X
    print( x.shape)    
    # Size of Y
    print(x.size)    
    # Type of each data element of X
    print(x.dtype)    
    #Number of Bytes occupied by each data element of X
    print(x.itemsize)

if __name__=="__main__":
    r=int(input())
    l=[]
    for i in range(r):
        n = list(map(int,input().split()))
        l.append(n)
    array_attributes(l)

Lab 3: Try it Out - ndarray [Welcome to Python - ndarray - hands-on]

Python "ndarray" Hands-on

Solution: Python "ndarray" Hands-on

  
import numpy as np
# Enter your code here. Read input from STDIN. Print output to STDOUT
  
def ndarray(array_input):
  #Write your code here
  x1=np.array(array_input)
   
  print(x1.ndim)
  print(x1.shape)
   
  print(x1.size)

Lab 4: Try it Out - ndarray with Shapes [Welcome to Python - ndarray shape - Hands-on]

Try it Out - Python "ndarray shape" Hands-on

Solution: Python "ndarray shape" Hands-on

  
# ndarray shape 
import numpy as np

# Enter your code here. Read input from STDIN. Print output to STDOUT
def ndshape(d, shape1):
    #Write your code here
    x = np.eye(d)
    print(x)
    print(np.ones(shape1))

if __name__ == "__main__":
    d=int(input())
    shape1=list(map(int,input().split()))    
    ndshape(d, shape1)

LAB 5. Try it Out - 3D array [Welcome to Python - 3D Array - Hands-on]

LAB 5. Python - 3D Array

Solution: 3D array 

  
import numpy as np

# Enter your code here. Read input from STDIN. Print output to STDOUT
def array_3d(n, x, y, z):
    #Write your code below
    np.random.seed(100)
    x1 = 5 + 2.5*np.random.randn(n) # normal distribution with mean=5 and sd=2.5
    A=x1.nbytes
    
    m = np.random.uniform(0,1,x*y*z)
    x2 = m.reshape(x,y,z)
    print(A)
    print(x2)

if __name__ ==  "__main__":
    n=int(input())
    x, y, z=list(map(int,input().split()))
    array_3d(n, x, y, z)

Lab 6 : Try it Out - More on ndarray [Welcome to Python - More on ndarray - Hands-on]

Lab 6 : More on ndarray

Solution: More on ndarray

  
import numpy as np

# Enter your code here. Read input from STDIN. Print output to STDOUT
def arr(a, x, y, z):
    #Write your code here
    x1=np.arange(2,a,2)
    x2=np.linspace(x, y, z)
    print(x1)
    print(x2)
    

if __name__ == "__main__":
    a=int(input())
    x, y, z=list(map(int,input().split()))
    arr(a, x, y, z)

LAB 7 : Try it Out - Array Manipulation 1 [Welcome to Python - Array Manipulation 1 - Hands-on]

LAB 7: Python - Array Manipulation 1 - Hands-on

Solution: Array Manipulation 1 - Hands-on

  
import numpy as np

# Enter your code here. Read input from STDIN. Print output to STDOUT
def array_split(i,r,c):
    #Write your code below
    
    x=np.arange(i)
    y = np.array(x).reshape(r,c)    
    a,b = np.hsplit(y,2)
    print(a)
    print(b)
    

if __name__== "__main__":
    i=int(input())
    r,c = list(map(int,input().split()))
    array_split(i,r,c)

Lab 8 : : Try it Out - Array Manipulation 2 [Welcome to Array Manipulation2]

Lab 8 : : Array Manipulation 2 - Hands-on

Solution: Array Manipulation 2 - Hands-on

  
import ast
import numpy as np

# Enter your code here. Read input from STDIN. Print output to STDOUT
def array_split(n,n_row,n_col):
    
    x =np.arange(n)
    z= np.array(x, ).reshape(n_row,n_col)
    a,b=np.vsplit(z,2)
    print(a)
    print(b)
  
if __name__ == "__main__":
    n = ast.literal_eval(input())
    n_row = ast.literal_eval(input())
    n_col = ast.literal_eval(input())
    array_split(n,n_row,n_col)

Lab 9 : : Try it Out - Join Arrays [Welcome to Join Arrays]

Lab 9 : : Welcome to Join Arrays - Hands-on

Solution: Welcome to Join Arrays - Hands-on

  
import numpy as np
import ast

# Enter your code here. Read input from STDIN. Print output to STDOUT
def join_array(list1,list2):
    p= np.array(list1).reshape(2,2)
    q= np.array(list2).reshape(2,3)

    print(np.hstack((p,q))) 

if __name__ == "__main__":
    l1 = ast.literal_eval(input())
    l2 = ast.literal_eval(input())
    join_array(l1,l2)

LAB 10 : : Welcome to Operations on Arrays 1 - Hands-on

LAB 10 : : Welcome to Operations on Arrays 1 - Hands-on

Solution: Welcome to Operations on Arrays 1

  
import numpy as np
 
# Enter your code here. Read input from STDIN. Print output to STDOUT
def array_oper(num1,num2):    
    y= np.arange(num1,num2+1)
    y=y.reshape(2,3)
    y=y**2
    y=y+5
    print(y)
        
if __name__ == "__main__":
    num1 = int(input())
    num2 = int(input())
    array_oper(num1,num2)

LAB 11: Welcome to Operation on Arrays 2 - Hands-on

LAB 11: Welcome to Operation on Arrays 2 - Hands-on

Solution: Welcome to Operation on Arrays 2 - Hands-on

  
import numpy as np

#Enter your code here. Read input from STDIN. Print output to STDOUT 
def array_oper(num1,num2):
    np.random.seed(100)
    r=np.random.randint(num1,num2,  30).reshape(5,6)
    x= np.sum(r,axis=0)
    y=np.sum(r,axis=1)
    print(x[-1])
    print(y[-1])    

if __name__ == "__main__":
    num1 = int(input())
    num2 = int(input())
    array_oper(num1,num2)

LAB 12: Welcome to Operation on Arrays 3 - Hands-on

LAB 12 : : Welcome to Operation on Arrays 3 - Hands-on

Solution: Welcome to Operation on Arrays 3

  
import numpy as np
import ast


# Enter your code here. Read input from STDIN. Print output to STDOUT
def array_oper(n,m,sd):
    np.random.seed(100)
    x=m + sd*np.random.randn(n)
    print(np.mean(x))
    print(np.std(x))
    print(np.var(x))

if __name__ == '__main__':
    num = ast.literal_eval(input())
    mean = ast.literal_eval(input())
    sd = ast.literal_eval(input())
    array_oper(num,mean,sd)

LAB 13: : Try it Out - Array Indexing [Welcome to Array Indexing]

LAB 13: : Welcome to Array Indexing - Hands-on

Solution: Welcome to Array Indexing

  
import numpy as np

# Enter your code here. Read input from STDIN. Print output to STDOUT
def array_index(n,n_row,n_col):
    x=  np.arange(n).reshape(n_row,n_col)
    
    print(x[-1])
    print(x[:,2])
    print(x[ :2, 2:])
    

if __name__ == '__main__':
    n = int(input())
    n_row = int(input())
    n_col = int(input())
    array_index(n,n_row,n_col)

LAB 14: Try it Out - Slicing [Welcome to Slicing]

LAB 14: Welcome to Slicing - Hands-on

Solution: Welcome to Slicing

  
import numpy as np
# Enter your code here. Read input from STDIN. Print output to STDOUT
def array_slice(n,n_dim,n_row,n_col):
    x= np.arange(n).reshape(n_dim,n_row,n_col)    
    b=np.array([True, False])
    print(x[b])
    print(x[b,:,1:3])

if __name__ == '__main__':
    n = int(input())
    n_dim = int(input())
    n_row = int(input())
    n_col = int(input())
    array_slice(n,n_dim,n_row,n_col)

Toipc Covered:

Numpy python package for data fresco play hands-on github

Python Packages and Data Access Fresco Play Hands-on

Numpy Python package for data Hands-on

Python Packages and Data Access Fresco Play Hands-on

numpy python package for data fresco play hands-on array creation

Python Packages and Data Access Fresco Play handson solutions

About the author

D Shwari
I'm a professor at National University's Department of Computer Science. My main streams are data science and data analysis. Project management for many computer science-related sectors. Next working project on Al with deep Learning.....

5 comments

  1. Anonymous
    could you share some key points of DS mini project.
    1. Anonymous
      Course ID?
  2. Anonymous
    second and fourth codes are geettind failed
    1. Anonymous
      I also faced that problem in my handson. I have noticed that there was some extra space in code, after removing that it runs successfully.
  3. Anonymous

    import numpy as np
    # Enter your code here. Read input from STDIN. Print output to STDOUT

    def ndarray(array_input):
      #Write your code here
      x1=np.array(array_input)
       
    print(x1.ndim)
      print(x1.shape)
       
    print(x1.size)


    this is getting failed