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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

23 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
    2. Even i am facing similar issue. can someone help how to solve this issue

      Delete
    3. even i got that issue. can someone help me in this issue

      Delete
    4. Anyone fixed this am also getting the same

      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
    Replies
    1. #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)

      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)

      Delete
  10. Thank you ! Finally a vid that shows what you are doing and doesnt click on the icons and buttons rlly fast so you cant see what they're doin, as if they're trying to impress the viewers with how fast they can type och click... annoying. But I loved this(:
    Jayme Silvestri

    ReplyDelete
  11. 7.Welcome to Python Pandas | 8 | Data Merging 2(75 Min)

    Data Merge - Hands-on 2


    File Name: prog.py


    #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)

    ReplyDelete
    Replies
    1. Not getting passed in Fresco..could you please help

      Delete
  12. 6.Welcome to Python Pandas | 7 | Data Merging 1(75 Min)

    Data Merge - Hands-on 1


    File Name: prog.py


    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)

    ReplyDelete
  13. Please upload PySpark hands on answers.I need🥺

    ReplyDelete
  14. Accessing pandas Data Structures
    import errors are coming. what can I do now?

    ReplyDelete
  15. import pandas as pd
    import numpy as np

    Here import is error occurred how to resolve it
    I try it my level best but i didn't clear

    ReplyDelete

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