Class 12 IP 065 Ch 1 Python Pandas 1 Type C Long Answer Questions

Programming Problems (Type – C)

6. Write a 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 stores sales of these 5 items in 1^st month, S2 stores sales of these 5 items in 2^nd month, and so on.

The program should display the summary sales report like this

Total Yearly Sales, item-wise (should display sum of items’ sales over the months)

Maximum sales of item made : <name of item that was sold the maximum in whole year> Maximum sales for individual items

Maximum sales of item 1 made : <month in which that item sold the maximum>

Maximum sales of item 2 made : <month in which that item sold the maximum>

Maximum sales of item 3 made : <month in which that item sold the maximum>

Maximum sales of item 4 made : <month in which that item sold the maximum>

Maximum sales of item 5 made : <rzonth in which that item sold the maximum>

Ans:

import pandas as pd

import numpy as np

items = [‘SOAP’, ‘TEA’, ‘RICE’, ‘DAL’, ‘TOYS’]

jan = pd.Series([25,35,12,65,98], index=items)

feb = pd.Series([36,48,95,12,18], index=items)

mar = pd.Series([12,50,25,35,58], index=items)

apr = pd.Series([45,36,45,98,12], index=items)

may = pd.Series([68,36,45,98,12], index=items)

jun = pd.Series([140,36,45,98,12], index=items)

jul = pd.Series([154,99,87,78,47], index=items)

aug = pd.Series([75,36,45,98,65], index=items)

sep = pd.Series([60,36,45,98,105], index=items)

octo =pd.Series([70,68,54,15,25], index=items)

nov = pd.Series([80,56,80,8,62], index=items)

dec = pd.Series([105,15,40,98,22], index=items)

df = pd.DataFrame({‘JAN’:jan, ‘FEB’:feb, ‘MAR’:mar,

                   ‘APR’:apr, ‘MAY’:may, ‘JUN’:jun,

                   ‘JUL’:jul, ‘AUG’:aug, ‘SEP’:sep,

                   ‘OCT’:octo,’NOV’:nov,’DEC’:dec},

                  index=items)

print(“-“*30)

print(“Total Yearly Sales Item Wise”)

maxitem=[0,”]

for item in items:

    total = df.loc[item].sum()

    if total > maxitem[0]:

        maxitem[0]=total

        maxitem[1]=item

    print(item, “==>”, total)

print(“-“*30)

print(“-“*30)

print(“Maximum sales of item made : “, maxitem[1] ,”is”, maxitem[0])

print(“-“*30)

print(“-“*30)

print(“Name of Month (Maximum Sale) Item Wise”)

print(“-“*30)

t = 0

for i in items:

    monthname = df.columns[(df == df.loc[i].max()).iloc[t]][0]

    print(i,’sold maximun’, df.loc[i].max(), end=’ ‘)

    print(‘in month of’, monthname)

    t = t +1

print(“-“*30)

---

7. Three Series objects store the marks of 10 students in three terms. Roll numbers of students form the index of these Series objects. 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% Term 2 + 50% Term 3

Store the Final marks of students in another Series object.

Ans:

import pandas as pd

import numpy as np

term1=[20,15,25,22,24,21,12,20,30,28]

term2=[27,17,23,19,28,29,30,16,25,19]

term3=[25,15,22,26,26,27,30,20,28,28]

s1= pd.Series(term1, index=range(1,11))

s2= pd.Series(term2, index=range(1,11))

s3= pd.Series(term3, index=range(1,11))

finalMarks = 0.25 * s1 + 0.25 * s2 + 0.5 * s3

print(finalMarks)

---

8. Write code to print all the information about a Series object.

Ans:

import pandas as pd

import numpy as np

days = [‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’]

tempArr = np.random.randint(10,30,7)

series = pd.Series(tempArr, index=days)

print(“Series Values : “, series.values)

print(“Series Index : “, series.index)

print(“Series Size : “, series.size)

print(“Series Shape : “, series.shape)

print(“Series Empty : “, series.empty)

print(“Series has NaN :”, series.hasnans)

print(“Series Dimension : “, series.ndim)

print(“Series Data Types: “, series.dtypes)

print(“Series nbytes : “, series.nbytes)

---

9. Write a program to create three different Series objects from the three columns of a DataFrame df.

Ans:

import pandas as pd

import numpy as np

dic = {‘Maths’:[25,35,69,78],

       ‘Science’: [35,45,25,90],

       ‘SSt’: [65,25,85,35]}

dfScore = pd.DataFrame(dic)

print(“=”*30)

print(“DataFrame is”)

print(“-“*20)

print(dfScore)

serMath = dfScore[‘Maths’]

serSc = dfScore[‘Science’]

serSSt = dfScore[‘SSt’]

print(“=”*30)

print(“Series 1 : Math”)

print(“-“*20)

print(serMath)

print(“=”*30)

print(“Series 2 : Science”)

print(“-“*20)

print(serSc)

print(“=”*30)

print(“Series 3 : SSt”)

print(“-“*20)

print(serSSt)

print(“=”*30)

---