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)