Contents:
- Practice Programs with solution
- Assignment Programs with solution
Practice Program
1.Write python code to create a Series object using the python sequence [4,6,8,10]. Assume that pandas is imported as alias name pd.
import pandas as pd
sr=pd.Series(data=[4,6,8,10])
print("The series object created is: ")
print(sr)
2.Write code to create a Series object using the python sequence(11,21,31,41). Assume that pandas is imported as alias name pd
import pandas as pd
sr=pd.Series(data=(11,21,31,41))
print("The series object created is: ")
print(sr)
3.Write a python program to create a Series object using a dictionary that stores the item name and its price of your grocery list.
maggie 10
salt 25
mustard oil 150
turmeric 25
import pandas as pd
gr={"maggie":10, "salt":25, "mustard oil":150, "turmeric":25}
sr=pd.Series(data=gr)
print(sr)
4.Write a program to create a series object using an ndarray that has 10 number in the range of 20 to 25
import pandas as pd
import numpy as np
arr=np.linspace(20,25,10)
sr=pd.Series(data=arr)
print(sr)
- Consider the series object listed below, Write python code to the amount section 'A' as 7500 and for section 'c' and 'd' as 6000. print the changed object.
A 6700 B 5000 C 4800 D 8700
import pandas as pd
dc={"A":6700,"B":5000,"C":4800,"D":8700}
sr=pd.Series(dc)
print("Original \n",sr)
sr[1]=7500
sr[2:]=6000
print("Modified \n",sr)
6.A series object data consists of around 2500 rows of data. write a program to print the following details.
- First 100 rows of the data
- Last 5 rows of data
import pandas as pd
sr=pd.Series(range(100,250))
print(sr.head(100))
print(sr.tail(5))
7.Number of students in class 11 and 12 in 2 streams ("Science","Humanities") are stored in a series object, write code to find total number of students in each stream combining both the class.Take any data you want in series object.
import pandas as pd
sr11=pd.Series(data={"science":20,"humanities":15})
sr12=pd.Series(data={"science":25,"humanities":35})
print("total number of students in each stream is:")
print(sr11+sr12)
8.srPop store the population details of four states in india and srIncome stores the total income reported in previous year in each city. Calculate income per capita for each of these cities.
population data:
delhi=10927986
mumbai=12691836
kolkata=46132392
chennai=4328956
Total income data:
delhi=75467963145698
mumbai=78423694528478
kolkata=4789657893214
chennai=9845637852496
import pandas as pd
srPop=pd.Series(data=[10927986,12691836,46132392,4328956], index=["delhi","mumbai","kolkata","chennai"])
srIncome=pd.Series(data=[75467963145698,78423694528478,4789657893214,9845637852496], index=["delhi","mumbai","kolkata","chennai"])
print("Percapita income in each city: ")
print(srIncome/srPop)
9.Series objects temp1, temp2, temp3, temp4 store the temperature of days of week1, week2, week3, week4 respectively. Write a script to
a. print average temperature per week
b. print average temperature of entire month
import pandas as pd
temp1=pd.Series(data={'mon':24,"tue":26,"wed":24,"thus":23,"fri":26,"sat":25,"sun":25})
temp2=pd.Series(data={'mon':25,"tue":26,"wed":25,"thus":24,"fri":25,"sat":26,"sun":23})
temp3=pd.Series(data={'mon':24,"tue":25,"wed":23,"thus":24,"fri":27,"sat":27,"sun":24})
temp4=pd.Series(data={'mon':22,"tue":23,"wed":26,"thus":25,"fri":26,"sat":25,"sun":25})
print("Average temperature per week: ")
print(pd.Series(data={"week1":temp1.mean(),"week2":temp2.mean(),"week3":temp3.mean(),"week4":temp4.mean()}))
print()
print()
print("Average temperature of entire month: ")
print((pd.Series(data=(temp1.mean(),temp2.mean(),temp3.mean(),temp4.mean())).mean()))
10.Write a python program that stores the sales of 5 fast moving items of a store for each month in 12 Series objects, i.e. s1 Series object store sales of these 5 items in 1st month, s2 stores the sales of these 5 items in the 2nd month and so on, consider the following table of data where each row is a series object:
| Series Object | item1 | item2 | item3 | item4 | item5 |
| s1 | 4578 | 658 | 4753 | 452 | 1256 |
| s2 | 4568 | 6158 | 753 | 4252 | 156 |
| s3 | 3578 | 6581 | 4713 | 4152 | 5256 |
| s4 | 1578 | 258 | 753 | 4152 | 1856 |
| s5 | 1278 | 6158 | 4153 | 432 | 156 |
| s6 | 1278 | 6158 | 4153 | 432 | 156 |
| s7 | 1278 | 6158 | 4153 | 432 | 156 |
| s8 | 1278 | 6158 | 4153 | 432 | 156 |
| s9 | 1278 | 6158 | 4153 | 432 | 156 |
| s10 | 1278 | 6158 | 4153 | 432 | 156 |
| s11 | 1278 | 6158 | 4153 | 432 | 156 |
| s12 | 1278 | 6158 | 4153 | 432 | 156 |
The program should display the summary sales report like this:
- Total yearly sales, item-wise(should display sum of items' sales over the month)
- Maximum sales of items made: (name of items that was sold the maximum in whole year)
- Maximum sales of individual items
- Maximum sales of item 1 made: (month in which that items sold the maximum)
- Maximum sales of item 2 made: (month in which that items sold the maximum)
- Maximum sales of item 3 made: (month in which that items sold the maximum)
- Maximum sales of item 4 made: (month in which that items sold the maximum)
- Maximum sales of item 5 made: (month in which that items sold the maximum)
import pandas as pd
idnx=["item1","item2","item3","item4","item5"]
s1=pd.Series(data=[4578,658,4753,452,1256],index=idnx)
s2=pd.Series(data=[4568,6158,753,4252,156],index=idnx)
s3=pd.Series(data=[3578,6581,4713,4152,5256],index=idnx)
s4=pd.Series(data=[1578,258,753,4152,1856],index=idnx)
s5=pd.Series(data=[1278,6158,4153,432,156],index=idnx)
s6=pd.Series(data=[1278,6158,4153,432,156],index=idnx)
s7=pd.Series(data=[1278,6158,4153,432,156],index=idnx)
s8=pd.Series(data=[1278,6158,4153,432,156],index=idnx)
s9=pd.Series(data=[1278,6158,4153,432,156],index=idnx)
s10=pd.Series(data=[1278,6158,4153,432,156],index=idnx)
s11=pd.Series(data=[1278,6158,4153,432,156],index=idnx)
s12=pd.Series(data=[1278,6158,4153,432,156],index=idnx)
print("Yearly Sales Itemwise:")
print(pd.Series(s1+s2+s3+s4+s5+s6+s7+s8+s9+s10+s11+s12))
print()
print()
print("Items that was sold the maximum in whole year:")
print((pd.Series(s1+s2+s3+s4+s5+s6+s7+s8+s9+s10+s11+s12)).idxmax())
print()
print()
print("Maximum sales of individual items:")
def monNm(a):
if a==0:
return "January"
elif a==1:
return "February"
elif a==2:
return "March"
elif a==3:
return "April"
elif a==4:
return "May"
elif a==5:
return "June"
elif a==6:
return "July"
elif a==7:
return "August"
elif a==8:
return "September"
elif a==9:
return "october"
elif a==10:
return "November"
elif a==11:
return "December"
srtemp=pd.Series((s1,s2,s3,s4,s5,s6,s7,s8,s9,s10,s11,s12))
max=0
mnam=""
for items in srtemp.iteritems():
if max<items[1]['item1']:
max=items[1]['item1']
mnam=monNm(items[0])
print("maximum of item1",mnam)
mnam=""
max=0
for items in srtemp.iteritems():
if max<items[1]['item2']:
max=items[1]['item2']
mnam=monNm(items[0])
print("maximum of item2",mnam)
mnam=""
max=0
for items in srtemp.iteritems():
if max<items[1]['item3']:
max=items[1]['item3']
mnam=monNm(items[0])
print("maximum of item3",mnam)
mnam=""
max=0
for items in srtemp.iteritems():
if max<items[1]['item4']:
max=items[1]['item4']
mnam=monNm(items[0])
print("maximum of item4",mnam)
mnam=""
max=0
for items in srtemp.iteritems():
if max<items[1]['item5']:
max=items[1]['item5']
mnam=monNm(items[0])
print("maximum of item5",mnam)
mnam=""
max=0
11.Three series object store the marksof 10 students in three terms. Roll numbers of students from the index of these Series object. The Three Series objects have the same indexes.
Calculate the total weighted marks obtained by students as per following formula:
Final Marks=25% Term 1 + 25% Terms 2 + 50% Terms 3
import pandas as pd
roll=[1,2,3,4,5,6,7,8,9,10]
trm1=pd.Series(data=[452,356,489,476,463,432,395,398,452,436],index=roll)
trm2=pd.Series(data=[469,356,499,476,463,392,395,418,252,336],index=roll)
trm3=pd.Series(data=[472,358,489,496,333,332,495,354,352,498],index=roll)
finalMarks=pd.Series(trm1*0.25+trm2*0.25+trm3*0.5)
print("Final marks for each student from all the terms combined")
print()
print(finalMarks)