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