# 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
Reviewed by TECH UPDATE
on
March 08, 2021
Rating:

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

ReplyDeleteBro data merging 1 executed for u now

DeleteNumpy hands-on answers kooda pettu bro :)

ReplyDeletecan anyone put hands on numpy also for fresco play!! plz

ReplyDeletemachine learning handson also upload bro

ReplyDeleteHi , 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

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

ReplyDeleteCan you please tell me how to solve , I'm also facing same issue

DeleteEven i am facing similar issue. can someone help how to solve this issue

Deleteeven i got that issue. can someone help me in this issue

DeleteAnyone fixed this am also getting the same

DeleteCan you please provide hands on for node.js essential fron fresco

ReplyDeleteThis comment has been removed by a blog administrator.

ReplyDeletenot useful

ReplyDeleteData merging hands-on-1 is not passing preliminary validations can u help out??

ReplyDelete#Write your code here

Deleteimport 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)

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(:

ReplyDeleteJayme Silvestri

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

ReplyDeleteData 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)

Not getting passed in Fresco..could you please help

Delete6.Welcome to Python Pandas | 7 | Data Merging 1(75 Min)

ReplyDeleteData 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)

Please upload PySpark hands on answers.I need🥺

ReplyDeleteAccessing pandas Data Structures

ReplyDeleteimport errors are coming. what can I do now?

import pandas as pd

ReplyDeleteimport numpy as np

Here import is error occurred how to resolve it

I try it my level best but i didn't clear