**Python Pandas Fresco Play Hands-on Solutions**

**Course Path: Data Science/DATA SCIENTIST'S TOOLBOX/Python Pandas**

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**1.Welcome to Python Pandas | 1 | Data Structures in Pandas(60 Min)**

**Pandas Data Structures - Hands-on**

**File Name: prog.py**

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)

**2.Welcome to Python Pandas | 3 | Working with CSV files(90 min)**

**Working with CSVs**

**File Name: prog.py**

#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)

**3.Welcome to Python Pandas | 4 | Indexing Dataframes(75 Min)**

**Hands-on with Indexes**

**File Name: prog.py**

#Write your code here

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)

**4.Welcome to Python Pandas | 5 | Data Cleaning**

**Data Cleaning - Handson**

**File Name: prog.py**

#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)

**5.Welcome to Python Pandas | 6 | Data Aggregation(75 Min)**

**Data Aggregation - Handson**

**File Name: prog.py**

#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())

**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)

**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)

**8.Welcome to Python Pandas | 2 | Accessing Pandas Data Structures(90 Min)**

**Access Elements in Data Structures**

**File Name: prog.py**

#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)

**If you have any queries, please feel free to ask on the comment section.**

**If you want MCQs and Hands-On solutions for any courses, Please feel free to ask on the comment section too.**

Python Pandas MCQ Solution --> https://www.mncanswers.site/2022/03/python-pandas-mcq-solutions-tcs-fresco.html

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