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Fresco Play Python Pandas Hands- on Solution || T Factor

 Fresco Play Python Pandas Hands-on Solution - T Factor (Course ID:- 55937)

In Python Pandas(Course Id:- 55937), There are 8 Hands-On Questions Available. The Solutions are 




1. Data Structures in Pandas Solution.





  Code:- 

   #Write your code here
import pandas as pd
import numpy as np
heights_A = pd.Series([176.2,158.4,167.6,156.2,161.4])
heights_A.index = ['s1','s2','s3','s4','s5']
print(heights_A.shape)

# TASK 2
weights_A = pd.Series([85.1,90.2,76.8,80.4,78.9])
weights_A.index = ['s1','s2','s3','s4','s5']
print(weights_A.dtype)

#TASK 3
df_A = pd.DataFrame()
df_A['Student_height'] = heights_A
df_A['Student_weight'] = weights_A
print(df_A.shape)

#TASK 4
my_mean = 170.0
my_std = 25.0
np.random.seed(100)
heights_B = pd.Series(np.random.normal(loc = my_mean, scale = my_std, size = 5))
heights_B.index = ['s1','s2','s3','s4','s5']

my_mean1 = 75.0
my_std1 = 12.0
weights_B = pd.Series(np.random.normal(loc = my_mean1,scale = my_std1,size = 5))
weights_B.index = ['s1','s2','s3','s4','s5']
print(heights_B.mean())

#TASK 5
df_B = pd.DataFrame()
df_B['Student_height'] = heights_B
df_B['Student_weight'] = weights_B
print(df_B.columns)

#TASK 6
data = {'ClassA' : df_A,'ClassB':df_B}
p = pd.Panel.from_dict(data)
print(p.shape)

2. Data Cleaning Solutions - Python Pandas 


Code:-
#Write your code here
import pandas as pd
import numpy as np
height_A = pd.Series([176.2,158.4,167.6,156.2,161.4])
height_A.index = ['s1','s2','s3','s4','s5']
weight_A = pd.Series([85.1,90.2,76.8,80.4,78.9])
weight_A.index = ['s1','s2','s3','s4','s5']
df_A = pd.DataFrame()
df_A['Student_height'] = height_A
df_A['Student_weight'] = weight_A

df_A.loc['s3'] = np.nan
df_A.loc['s5'][1] = np.nan

df_A2 = df_A.dropna(how = 'any')
print(df_A2)


3. Data Merging Hands-On(2) Solution: Python Pandas 





Code:- 

#Write your code here
import pandas as pd
import numpy as np

height_A = pd.Series([176.2,158.4,167.6,156.2,161.4])
height_A.index = ['s1','s2','s3','s4','s5']

weights_A = pd.Series([85.1,90.2,76.8,80.4,78.9])
weights_A.index = ['s1','s2','s3','s4','s5']

df_A = pd.DataFrame()
df_A['Student_height'] = height_A
df_A['Student_weight'] = weights_A

df_A['Gender'] = ['M','F','M','M','F']

s = pd.Series([165.4,82.7,'F'],index = ['Student_height','Student_weight','Gender'],name='s6')

df_AA = df_A.append(s)
print(df_AA)

#TASK - 2
my_mean = 170.0
my_std = 25.0
np.random.seed(100)
heights_B = pd.Series(np.random.normal(loc = my_mean,scale=my_std,size = 5))
heights_B.index = ['s1','s2','s3','s4','s5']

my_mean1 = 75.0
my_std1 = 12.0
np.random.seed(100)
weights_B = pd.Series(np.random.normal(loc = my_mean1,scale=my_std1,size = 5))
weights_B.index = ['s1','s2','s3','s4','s5']

df_B = pd.DataFrame()
df_B['Student_height'] = heights_B
df_B['Student_weight'] = weights_B

df_B.index=['s7','s8','s9','s10','s11']
df_B['Gender'] = ['F','M','F','F','M']

df = pd.concat([df_AA,df_B])
print(df)

4. Data Merging Hands-On(1) Solutions:- Python Pandas

Code:- 

#Write your code here
import pandas as pd
import numpy as np
nameid = pd.Series(range(101,111))
name = pd.Series(['person' + str(i) for i in range(1,11)])
master = pd.DataFrame()
master['nameid'] = nameid
master['name'] = name
transaction = pd.DataFrame({'nameid':[108,108,108,103],'product':['iPhone','Nokia','Micromax','Vivo']})
mdf = pd.merge(master,transaction,on='nameid')
print(mdf)

5. Indexing Dataframe Hands-On Solutions - Python Pandas

Code:- 
import pandas as pd
import numpy as np

#TASK- 1
DatetimeIndex = pd.date_range(start = '09/01/2017',end='09/15/2017')
print(DatetimeIndex[2])

#TASK - 2
datelist = ['14-Sep-2017','09-Sep-2017']
date_to_be_searched = pd.to_datetime(datelist)
print(date_to_be_searched)

#TASK - 3
print(date_to_be_searched.isin(datelist))

#TASK - 4
arraylist = [['classA']*5 + ['classB']*5,['s1','s2','s3','s4','s5']* 2]
mi_index = pd.MultiIndex.from_product(arraylist,names=['First Level','Second Level'])
print(mi_index.levels)

6.Data Aggression:- Python Pandas



Code:- 

#Write your code here
import pandas as pd
import numpy as np
heights_A = pd.Series([176.2,158.4,167.6,156.2,161.4])
heights_A.index = ['s1','s2','s3','s4','s5']
weights_A = pd.Series([85.1,90.2,76.8,80.4,78.9])
weights_A.index = ['s1','s2','s3','s4','s5']

df_A = pd.DataFrame()

df_A['Student_height'] = heights_A
df_A['Student_weight'] = weights_A

df_A_filter1 = df_A[(df_A.Student_weight < 80.0) & (df_A.Student_height > 160.0)]
print(df_A_filter1)

#TASK - 2
df_A_filter2 = df_A[df_A.index.isin(['s5'])]
print(df_A_filter2)

#TASK - 3
df_A['Gender'] = ['M','F','M','M','F']
df_groups = df_A.groupby('Gender')
print(df_groups.mean())

7. Accessing Pandas Data Structures - Python Pandas



 
Code:- 

#Write your code here
import pandas as pd
import numpy as np

heights_A = pd.Series([176.2,158.4,167.6,156.2,161.4])
heights_A.index = ['s1','s2','s3','s4','s5']
print(heights_A[1])

# TASK 2
print(heights_A[1:4])

# TASK 3
weights_A = pd.Series([85.1,90.2,76.8,80.4,78.9])
weights_A.index = ['s1','s2','s3','s4','s5']

df_A = pd.DataFrame()
df_A['Student_height'] = heights_A
df_A['Student_weight'] = weights_A

height = df_A['Student_height']
print(type(height))

# TASK 4
df_s1s2 = df_A[df_A.index.isin(['s1','s2'])]
print(df_s1s2)

# TASK 5
df_s2s5s1 = df_A[df_A.index.isin(['s1','s2','s5'])]
df_s2s5s1 = df_s2s5s1.reindex(['s2','s5','s1'])
print(df_s2s5s1)

#TASK 6
df_s1s4 = df_A[df_A.index.isin(['s1','s4'])]
print(df_s1s4)


8. Working With CSV Files 



Code:- 


#Write your code here
import pandas as pd
import numpy as np
heights_A = pd.Series([176.2,158.4,167.6,156.2,161.4])
heights_A.index = ['s1','s2','s3','s4','s5']
weights_A = pd.Series([85.1,90.2,76.8,80.4,78.9])
weights_A.index = ['s1','s2','s3','s4','s5']
df_A = pd.DataFrame()
df_A['Student_height'] = heights_A
df_A['Student_weight'] = weights_A
df_A.to_csv('classA.csv')

# TASK 2
df_A2 = pd.read_csv('classA.csv')
print(df_A2)

#TASK 3
df_A3 = pd.read_csv('classA.csv',index_col = 0)
print(df_A3)

#TASK 4
my_mean = 170.0
my_std = 25.0
np.random.seed(100)
heights_B = pd.Series(np.random.normal(loc = my_mean, scale = my_std, size = 5))
heights_B.index = ['s1','s2','s3','s4','s5']
my_mean1 = 75.0
my_std1 = 12.0
np.random.seed(100)
weights_B = pd.Series(np.random.normal(loc = my_mean1,scale = my_std1,size = 5))
weights_B.index = ['s1','s2','s3','s4','s5']
df_B = pd.DataFrame()
df_B['Student_height'] = heights_B
df_B['Student_weight'] = weights_B

df_B.to_csv('classB.csv',index = False)
print('classB.csv')

#TASK 5
df_B2 = pd.read_csv('classB.csv')
print(df_B2)

#TASK 6
df_B3 = pd.read_csv('classB.csv',header = None)
print(df_B3)

#TASK 7

df_B4 = pd.read_csv('classB.csv',header = None, skiprows = 2)
print(df_B4)



                                                Thank you 

Fresco Play Python Pandas Hands- on Solution || T Factor Fresco Play Python Pandas Hands- on Solution || T Factor Reviewed by TECH UPDATE on March 08, 2021 Rating: 5

12 comments:

  1. Working with csv and Data Merging are not working for me can you please help me

    ReplyDelete
  2. Numpy hands-on answers kooda pettu bro :)

    ReplyDelete
  3. can anyone put hands on numpy also for fresco play!! plz

    ReplyDelete
  4. machine learning handson also upload bro

    ReplyDelete
  5. Hi , I am unable to clear Accessing Pandas Data Structures - Python Pandas, it's just saying not passed validation in tasks,5,6 it has asked to use .loc or.iloc as well. I have used that too.can you help

    ReplyDelete
  6. Data Merging Handson-2 is getting executed correctly in Hackerank but in fresco results are not updated.

    ReplyDelete
    Replies
    1. Can you please tell me how to solve , I'm also facing same issue

      Delete
  7. Can you please provide hands on for node.js essential fron fresco

    ReplyDelete
  8. This comment has been removed by a blog administrator.

    ReplyDelete
  9. Data merging hands-on-1 is not passing preliminary validations can u help out??

    ReplyDelete

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