Basic python Programs

In this story, we are gonna see some questions on manipulation of some student's data and the solution will be composed of various python libraries such as pandas, regex, and numpy.

Question

Consider,

data=pd.DataFrame({‘names’:[‘tom’,’sam’,…],’email’:[‘tom21@gmail.com’,’samdr@yahoo.com’,’jk21456@abc.com’,..],’Firstweekscore’:[],’secondweekscore:[]})

1.Write a function that will create a new column consisting of an average of two scores

2.List comprehension → create another column which is consisting of scores 93 → 96

3.’gmail.com’ →regular expressions in pandas

4.Select rows which are having gmail address and also secondtestscore greater than 90

5.Create a new column ‘group’ and randomly assign values as 1,2 and 3

6.Create a pivot table having means of first test scores, by group

Solution

Let us see the solution step by step and I will share the whole code at the end of the story.

1. Write a function that will create a new column consisting of an average of two scores

import pandas as pd
import numpy as np
import re
import random
data = pd.DataFrame({‘name’:[‘nandha’,’tom’,’ram’,’jon’,’sam’,’robb’],”FirstWeekscore”:[92,82,91,93,91,95],”SecondWeekscore”:[91,81,95,93,92,96],’emailid’:[‘nandha12@abc.com’,’tom34@gmail.com’,’ram78@gmail.com’,’jon21@yahoo.com’,’sam11@gmail.com’,’robb@abc.com’]})
data[‘average’] = [(data[“FirstWeekscore”][i]+data[“SecondWeekscore”][i])/2 for i in range (len(data[“FirstWeekscore”]))]

Here we are creating a data frame first and adding the average column using list comprehension.

2.List comprehension → create another column which is consisting of scores 93 → 96

import pandas as pd
import numpy as np
import re
import random
data = pd.DataFrame({'name':['nandha','tom','ram','jon','sam','robb'],"FirstWeekscore":[92,82,91,93,91,95],"SecondWeekscore":[91,81,95,93,92,96],'emailid':['nandha12@abc.com','tom34@gmail.com','ram78@gmail.com','jon21@yahoo.com','sam11@gmail.com','robb@abc.com']})
data['average'] = [(data["FirstWeekscore"][i]+data["SecondWeekscore"][i])/2  for i in range (len(data["FirstWeekscore"]))]
data['ThirdWeekscore'] = [j+3 for j in data["SecondWeekscore"] ]
print("Data after adding average and 3rd week score")
print(data)

The output will be like

3.’gmail.com’ →regular expressions in pandas

Here we have to find out and return the username of the student using gmail domains we are gonna use regex for this

import pandas as pd
import numpy as np
import re
import random
data = pd.DataFrame({'name':['nandha','tom','ram','jon','sam','robb'],"FirstWeekscore":[92,82,91,93,91,95],"SecondWeekscore":[91,81,95,93,92,96],'emailid':['nandha12@abc.com','tom34@gmail.com','ram78@gmail.com','jon21@yahoo.com','sam11@gmail.com','robb@abc.com']})
data['average'] = [(data["FirstWeekscore"][i]+data["SecondWeekscore"][i])/2  for i in range (len(data["FirstWeekscore"]))]
data['ThirdWeekscore'] = [j+3 for j in data["SecondWeekscore"] ]
print("Data after adding average and 3rd week score")
print(data)

o=[]
print("Students using Gmail Domain are: ")
for i in range(len(data['emailid'])):
 x = data['emailid']
 y = data['SecondWeekscore']
 a = re.findall('@gmail.com', x[i])
 if a != []:
  c = re.split('@', x[i])
  print(c[0])

The output for the three tasks be like

4.Select rows which are having gmail address and also secondtestscore greater than 90

import pandas as pd
import numpy as np
import re
import random
data = pd.DataFrame({'name':['nandha','tom','ram','jon','sam','robb'],"FirstWeekscore":[92,82,91,93,91,95],"SecondWeekscore":[91,81,95,93,92,96],'emailid':['nandha12@abc.com','tom34@gmail.com','ram78@gmail.com','jon21@yahoo.com','sam11@gmail.com','robb@abc.com']})
data['average'] = [(data["FirstWeekscore"][i]+data["SecondWeekscore"][i])/2  for i in range (len(data["FirstWeekscore"]))]
data['ThirdWeekscore'] = [j+3 for j in data["SecondWeekscore"] ]
print("Data after adding average and 3rd week score")
print(data)
o=[]
print("Students using Gmail Domain are: ")
for i in range(len(data['emailid'])):
 x = data['emailid']
 y = data['SecondWeekscore']
 a = re.findall('@gmail.com', x[i])
 if a != []:
  c = re.split('@', x[i])
  print(c[0])

5.Create a new column ‘group’ and randomly assign values as 1,2 and 3

Here we are going to use random

6.Create a pivot table having means of first test scores, by group

This is done by a panda method called pd.pivot_table()

Full code

import pandas as pd
import numpy as np
import re
import random
data = pd.DataFrame({'name':['nandha','tom','ram','jon','sam','robb'],"FirstWeekscore":[92,82,91,93,91,95],"SecondWeekscore":[91,81,95,93,92,96],'emailid':['nandha12@abc.com','tom34@gmail.com','ram78@gmail.com','jon21@yahoo.com','sam11@gmail.com','robb@abc.com']})
data['average'] = [(data["FirstWeekscore"][i]+data["SecondWeekscore"][i])/2  for i in range (len(data["FirstWeekscore"]))]
data['ThirdWeekscore'] = [j+3 for j in data["SecondWeekscore"] ]
print("Data after adding average and 3rd week score")
print(data)
o=[]
print("Students using Gmail Domain are: ")
for i in range(len(data['emailid'])):
 x = data['emailid']
 y = data['SecondWeekscore']
 a = re.findall('@gmail.com', x[i])
 if a != []:
  c = re.split('@', x[i])
  print(c[0])
r = []
for i in range(6):
 n = random.randint(1,3)
 r.append(n)
data['group'] = r
print("Data after adding Group coloumn: ")
print(data)
table = pd.pivot_table(data=data, values ='FirstWeekscore',index=["group"],aggfunc = np.mean)
print("Mean of 1st week score by group:")
print(table)

The final output is:

you may get different output because we are using random for assigning the values for the group column.

That’s it. Hope you found this article useful in giving you a solid grasp of how pandas, regex, and random are performed in Python. Thank you.