# 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

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