Panda's Task

 Consider,

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

d={'names':['tom','sam','ram','kumar'],'email':['tom21@gmail.com','samdr@yahoo.com','ram@gmail.com','k2@gmail.com'],'first_weekscore':[90,90,90,96],'second_weekscore':[92,89,78,87]}

data=pd.DataFrame(d)

print(data)

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

    data['average']=(data['first_weekscore']+data['second_weekscore'])/2

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

week3=[i+3 for i in data['second_weekscore']] data['week3']=week3 print(data)

3.'gmail.com' -->regular expressions in pandas

print(data[data['email'].str.contains('\w+@gmail.com')==True]) v=data[data['email'].str.contains('\w+@gmail.com')==True] for i in v['email']: print(i.split('@')[0]) print()

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

d=data[data['email'].str.contains('\w+@gmail.com')==True] v=d[d['second_weekscore']>90] for i in v['email']: print(i.split('@')[0])

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

from numpy import random l=random.randint(1,4, size=(4))

data['group']=l

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

table = pd.pivot_table(data=data,index=['group'],aggfunc=np.mean,values="first_weekscore") print(table)