Class 12 IP Practical - 25

 

25. WAP to calculate median and mean for each subject in DataFrame dfmks.

	A	B	C	D
Acct	99	97	92	97
Eco	94	94	92	97
Eng	95	89	91	89
IP	94	87	99	94
Math	97	87	99	99

 

 

import pandas as pd

import numpy as np

d={'A':{'Acct':99,'Eco':94,'Eng':95,'IP':94,'Math':97}, \

   'B':{'Acct':97,'Eco':94,'Eng':89,'IP':87,'Math':87}, \

   'C':{'Acct':92,'Eco':92,'Eng':91,'IP':99,'Math':99}, \

   'D':{'Acct':97,'Eco':97,'Eng':89,'IP':94,'Math':99}}

 

dfmks=pd.DataFrame(d)

print("DataFrame is: ")

print(dfmks)

print("Mean Value for each subject: ")

print(dfmks.mean(axis=1))

print("Median Value for each Subject: ")

print(dfmks.median(axis=1))

 

>>>

======================= RESTART: C:/Program Files (x86)/Python38-32/IP-25.py ======================

DataFrame is:

       A   B   C   D

Acct  99  97  92  97

Eco   94  94  92  97

Eng   95  89  91  89

IP    94  87  99  94

Math  97  87  99  99

Mean Value for each subject:

Acct    96.25

Eco     94.25

Eng     91.00

IP      93.50

Math    95.50

dtype: float64

Median Value for each Subject:

Acct    97.0

Eco     94.0

Eng     90.0

IP      94.0

Math    98.0

dtype: float64

>>> 

 

 

Class 12 IP Practical - 24

 

24. WAP to calculate mode for each subject and each section in DataFrame dfmks.

	A	B	C	D
Acct	99	97	92	97
Eco	94	94	92	97
Eng	95	89	91	89
IP	94	87	99	94
Math	97	87	99	99

import pandas as pd

import numpy as np

d={'A':{'Acct':99,'Eco':94,'Eng':95,'IP':94,'Math':97}, \

   'B':{'Acct':97,'Eco':94,'Eng':89,'IP':87,'Math':87}, \

   'C':{'Acct':92,'Eco':92,'Eng':91,'IP':99,'Math':99}, \

   'D':{'Acct':97,'Eco':97,'Eng':89,'IP':94,'Math':99}}

 

dfmks=pd.DataFrame(d)

print("DataFrame is: ")

print(dfmks)

print("Mode Value for each subject: ")

print(dfmks.mode(axis=1))

print("Mode Value for each Section: ")

print(dfmks.mode(axis=0))

 

 

RESTART: C:/Program Files (x86)/Python38-32/IP-24.py ======================

DataFrame is:

       A   B   C   D

Acct  99  97  92  97

Eco   94  94  92  97

Eng   95  89  91  89

IP    94  87  99  94

Math  97  87  99  99

Mode Value for each subject:

       0

Acct  97

Eco   94

Eng   89

IP    94

Math  99

Mode Value for each Section:

      A     B   C     D

0  94.0  87.0  92  97.0

1   NaN   NaN  99   NaN

>>> 

 

Class 12 IP Practical - 23

 

23. Consider the DataFrame (dfmks) given below.

	A	B	C	D
Acct	99	94.0	92	97.0
Eco	90	94.0	92	97.0
Eng	95	89.0	91	89.0
IP	94	NaN	99	95.0
Math	97	100.0	99	NaN

WAP to print the maximum marks scored in a section, across all subjects.

import pandas as pd

import numpy as np

d={'A':{'Acct':99,'Eco':90,'Eng':95,'IP':94,'Math':97}, \

   'B':{'Acct':94.0,'Eco':94.0,'Eng':89.0,'IP':np.NaN,'Math':100.0}, \

   'C':{'Acct':92,'Eco':92,'Eng':91,'IP':99,'Math':99}, \

   'D':{'Acct':97.0,'Eco':97.0,'Eng':89.0,'IP':95.0,'Math':np.NaN}}

 

dfmks=pd.DataFrame(d)

print("DataFrame is: ")

print(dfmks)

print("Maximum Marks in a section, across all subjects ")

print(dfmks.max(axis=0))

 

>>>

======================= RESTART: C:/Program Files (x86)/Python38-32/IP-22.py ======================

DataFrame is:

       A      B   C     D

Acct  99   94.0  92  97.0

Eco   90   94.0  92  97.0

Eng   95   89.0  91  89.0

IP    94    NaN  99  95.0

Math  97  100.0  99   NaN

Maximum Marks in a section, across all subjects

A     99.0

B    100.0

C     99.0

D     97.0

dtype: float64

>>> 

 

 

Class 12 IP Practical - 22

 

22. Consider the DataFrame (dfmks) given below.

	A	B	C	D
Acct	99	94.0	92	97.0
Eco	90	94.0	92	97.0
Eng	95	89.0	91	89.0
IP	94	NaN	99	95.0
Math	97	100.0	99	NaN

WAP to print the maximum marks scored in each subject across all sections.

import pandas as pd

import numpy as np

d={'A':{'Acct':99,'Eco':90,'Eng':95,'IP':94,'Math':97}, \

   'B':{'Acct':94.0,'Eco':94.0,'Eng':89.0,'IP':np.NaN,'Math':100.0}, \

   'C':{'Acct':92,'Eco':92,'Eng':91,'IP':99,'Math':99}, \

   'D':{'Acct':97.0,'Eco':97.0,'Eng':89.0,'IP':95.0,'Math':np.NaN}}

 

dfmks=pd.DataFrame(d)

print("DataFrame is: ")

print(dfmks)

print("Maximum Marks in each subject across all sections. ")

print(dfmks.max(axis=1))

 

 

 

>>>

======================= RESTART: C:/Program Files (x86)/Python38-32/IP-22.py ======================

DataFrame is:

       A      B   C     D

Acct  99   94.0  92  97.0

Eco   90   94.0  92  97.0

Eng   95   89.0  91  89.0

IP    94    NaN  99  95.0

Math  97  100.0  99   NaN

Maximum Marks in each subject across all sections.

Acct     99.0

Eco      97.0

Eng      95.0

IP       99.0

Math    100.0

dtype: float64

>>> 

Class 12 IP Practical - 21

 

21. WAP to  create a DataFrame to store weight, age and names of 3 people. Print the DataFrame and its transpose.

import pandas as pd

d={'Weight':[75,72,68],'Age':[25,23,21],'Name':['Ram','Raj','Sam']}

Df=pd.DataFrame(d)

print("DataFrame is : ")

print(Df)

print("Transpose of DataFrame is : ")

print(Df.T)

                     

 

>>>

======================= RESTART: C:/Program Files (x86)/Python38-32/IP-21.py ======================

DataFrame is :

   Weight  Age Name

0      75   25  Ram

1      72   23  Raj

2      68   21  Sam

Transpose of DataFrame is :

          0    1    2

Weight   75   72   68

Age      25   23   21

Name    Ram  Raj  Sam

>>> 

 

Class 12 IP Practical - 20

 

20. Consider the following DataFrame saleDf

	Target	Sales
ZoneA	56000	58000
ZoneB	70000	68000
ZoneC	75000	78000
ZoneD	60000	61000

WAP a program to add a column namely Orders having values 6000, 6700, 6200 and 6000 respectively for the zones A, B, C and D. The program should also add a new row for a new zone ZoneE. Add some dummy values in this row.

 import pandas as pd

d={'Target':{'ZoneA':56000,'ZoneB':70000,'ZoneC':75000,'ZoneD':60000}, \

   'Sales': {'ZoneA':58000,'ZoneB':68000,'ZoneC':78000,'ZoneD':61000}}

saleDf=pd.DataFrame(d)

print("DataFrame: ")

print(saleDf)

saleDf['Orders']=[6000, 6700, 6200 , 6000]

saleDf.at['ZoneE',:]=[70000,58000,6100]

print("Updated DataFrame: ")

print(saleDf)

 

 

>>>

======================= RESTART: C:/Program Files (x86)/Python38-32/IP-20.py ======================

DataFrame:

       Target  Sales

ZoneA   56000  58000

ZoneB   70000  68000

ZoneC   75000  78000

ZoneD   60000  61000

Updated DataFrame:

        Target    Sales  Orders

ZoneA  56000.0  58000.0  6000.0

ZoneB  70000.0  68000.0  6700.0

ZoneC  75000.0  78000.0  6200.0

ZoneD  60000.0  61000.0  6000.0

ZoneE  70000.0  58000.0  6100.0

>>>