Python Pandas DataFrame Exercise on groupby cut bins with solutions

Part I : Creating and grouping data

Create one student mark list with two subjects for 10 ( variable n ) number of students. Marks are given against two subjects and it can vary from 0 to 100. Use random numbers for generating marks. Your DataFrame should have two subject columns Math and Eng. One more column student ID should start from 1 and continue for all students.
Add one more column my_result based on mark in the subject Math. Result ( my_result ) to be arranged in the range 0– 39 Fail, 40 – 50 Third, 50 – 75 Second, 75 to 100 First.

Note that if a student gets 40 then he is passed, If he gets 75 then he is to be placed at First division. ( same is to be followed for other groups )

Part II ( Data Visualization)

Tutorials for Reference

Pandas cut() to arrange data in Bins

Pandas groupby: data in groups

Based on the marks in Math subject plot a scatter graph to show distribution of marks of students.
Create one Pie chart showing the result of total class distributed in bins. Similarly create on Bar chart showing the result.

Solution

import numpy as np
import pandas as pd
n=5 # Number of students , increase this number 
my_id=np.arange(1,n+1) # student id from 1 to n 

my_math=np.random.randint(0,100,size=n) # 0 to 100 random mark
my_english=np.random.randint(0,100,size=n)

my_pd=pd.DataFrame(data=[my_id,my_math,my_english]).T # transpose the matrix 

my_pd.columns=['ID','MATH','ENG'] # adding columns to DataFrame
#print(my_pd.to_string(index=None))

my_labels=['Fail','Third','Second','First'] # labels  
my_pd['my_result'] = pd.cut(x=my_pd['MATH'],
                            bins=[0,40, 50, 75, 100],
                            labels=my_labels,right=False) 
print(my_pd)

Output ( Output will change as we are using random numbers as data (mark))

   ID  MATH  ENG my_result
0   1    98   54     First
1   2    25   29      Fail
2   3    72   25    Second
3   4    26    7      Fail
4   5    78   79     First

Increase the number of students by changing n=25 to get better distribution of data.

Data visualization

To the above code we will add for plotting of graphs.

my_pd.plot.scatter(title='Math  Vs ID ',x='ID',y='MATH')

my_data=my_pd.groupby(['my_result'])[['ID']].count()
print(my_data)
my_data.plot.pie(title="Result ",y='ID',figsize=(4,4))
my_data.plot.bar(title="Result ",y='ID',figsize=(4,4))

loc mask where query

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Subhendu Mohapatra

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Pandas DataFrame Exercise 1-1

Part I : Creating and grouping data

Part II ( Data Visualization)

Solution

Data visualization

Subhendu Mohapatra