top of page

# List of Programs for Informatics Practs. Practical file - XII

## CODING:

Create a pandas series from a dictionary of values and an ndarray.
1. # Create a pandas series from a dictionary of values and an ndarray.

# Create a panda’s series from a dictionary of values and a ndarray

import pandas as pd
import numpy as np
s=pd.Series(np.array([1,3,4,7,8,8,9]))
print(s)

#  create a dictionary
dictionary = {'X' : 10, 'Y' : 20, 'Z' : 30} #  create a series
series = pd.Series(dictionary)
print(series)

# 2. Write a Pandas program to perform arithmetic operations on two Pandas Series.

Write a Pandas program to perform arithmetic operations on two Pandas Series.

# Write a Pandas program to perform arithmetic operations on two Pandas Series.
import pandas as pd
ds1 = pd.Series([3, 6, 9, 12, 15])
ds2 = pd.Series([2, 4, 6, 8, 10])
ds = ds1 + ds2
print(ds)
print("Subtract two Series:")
ds = ds1 - ds2
print(ds)
print("Multiply two Series:")
ds = ds1 * ds2
print(ds)
print("Divide Series1 by Series2:")
ds = ds1 / ds2
print(ds)

Write a Pandas program to add some data to an existing Series.

# 3. Write a Pandas program to add some data to an existing Series.

# Write a Pandas program to add some data to an existing Series.

import pandas as pd
s = pd.Series(['S101', 'Amjad', 'C.Sc.', 'XII – A1', '450'])
print("Original Data Series:")
print(s)
print("\nData Series after adding some data:")
new_s = s.append(pd.Series(['90.0', 'PASS']))
print(new_s)

# 4. Write a Pandas program to select the rows where the percentage greater than 70.

Write a Pandas program to select the rows where the percentage greater than 70.

# Write a Pandas program to select the rows where the percentage greater than 70.
import pandas as pd
import numpy as np

exam_data  = {'name': ['Aman', 'Kamal', 'Amjad', 'Rohan', 'Amit', 'Sumit', 'Matthew', 'Kartik', 'Kavita', 'Pooja'],
'perc': [79.5, 29, 90.5, np.nan, 32, 65, 56, np.nan, 29, 89],
'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}
labels = ['A', 'B', 'C', 'B', 'E', 'F', 'G', 'H', 'I', 'J']

df = pd.DataFrame(exam_data , index=labels)
print("Number of student whoes percentage more than 70:")
print(df[df['perc'] > 70])

Write a Pandas program to select the rows the percentage is between 70 and 90 (inclusive)

# 5. Write a Pandas program to select the rows the percentage is between 70 and 90 (inclusive)

# Write a Pandas program to select the rows the percentage is between 70 and 90 (inclusive)
import pandas as pd
import numpy as np

exam_data  = {'name': ['Aman', 'Kamal', 'Amjad', 'Rohan', 'Amit', 'Sumit', 'Matthew', 'Kartik', 'Kavita', 'Pooja'],
'perc': [79.5, 29, 90.5, np.nan, 32, 65, 56, np.nan, 29, 89],
'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}
labels = ['A', 'B', 'C', 'B', 'E', 'F', 'G', 'H', 'I', 'J']

df = pd.DataFrame(exam_data , index=labels)
print("Number of student whoes percentage more than 70:")
print(df[df['perc'].between(70,90)])

# 6. Write a Pandas program to change the percentage in a given row by the user.

Write a Pandas program to change the percentage in a given row by the user.

# Write a Pandas program to change the percentage in given row by user.
import pandas as pd
import numpy as np

exam_dic  = {'name': ['Aman', 'Kamal', 'Amjad', 'Rohan', 'Amit', 'Sumit', 'Matthew', 'Kartik', 'Kavita', 'Pooja'],
'perc': [79.5, 29, 90.5, np.nan, 32, 65, 56, np.nan, 29, 89],
'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}
labels = ['A', 'B', 'C', 'B', 'E', 'F', 'G', 'H', 'I', 'J']

df = pd.DataFrame(exam_dic , index=labels)
print("\nOriginal data frame:")
print(df)
ch = input("Enter the index of row : ")
per = float(input("Enter percentage to be changed: "))
print('\nChange the percentage in row '+ch+ ' to',per)
df.loc[ch, 'perc'] = per
print(df)

Write a Pandas program to join the two given dataframes along rows and assign all data.

# 7. Write a Pandas program to join the two given dataframes along rows and assign all data.

# Write a Pandas program to join the two given dataframes along rows and assign all data.
import pandas as pd
import numpy as np

exam_dic1  = {'name': ['Aman', 'Kamal', 'Amjad', 'Rohan', 'Amit', 'Sumit', 'Matthew', 'Kartik', 'Kavita', 'Pooja'],
'perc': [79.5, 29, 90.5, np.nan, 32, 65, 56, np.nan, 29, 89],
'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}

exam_data1 = pd.DataFrame(exam_dic1)

exam_dic2  = {'name': ['Parveen', 'Ahil', 'Ashaz', 'Shifin', 'Hanash'],
'perc': [89.5, 92, 90.5, 91.5, 90],
'qualify': ['yes', 'yes', 'yes', 'yes', 'yes']}

exam_data2 = pd.DataFrame(exam_dic2)

print("Original DataFrames:")
print(exam_data1)
print("-------------------------------------")
print(exam_data2)
print("\nJoin the said two dataframes along rows:")
result_data = pd.concat([exam_data1, exam_data2])
print(result_data)

# 8. Write a Pandas program to join the two given dataframes along columns and assign all data.

Write a Pandas program to join the two given dataframes along columns and assign all data.

# Write a Pandas program to join the two given dataframes along columns and assign all data.​

import pandas as pd
import numpy as np

exam_dic1  = {'name': ['Aman', 'Kamal', 'Amjad', 'Rohan', 'Amit', 'Sumit', 'Matthew', 'Kartik', 'Kavita', 'Pooja'],
'perc': [79.5, 29, 90.5, np.nan, 32, 65, 56, np.nan, 29, 89],
'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}

exam_data1 = pd.DataFrame(exam_dic1)

exam_dic2  = {'name': ['Parveen', 'Ahil', 'Ashaz', 'Shifin', 'Hanash'],
'perc': [89.5, 92, 90.5, 91.5, 90],
'qualify': ['yes', 'yes', 'yes', 'yes', 'yes']}

exam_data2 = pd.DataFrame(exam_dic2)

print("Original DataFrames:")
print(exam_data1)
print("-------------------------------------")
print(exam_data2)
print("\nJoin the said two dataframes along rows:")
result_data = pd.concat([exam_data1, exam_data2],axis=1)
print(result_data)

# 9. Write a Pandas program to append a list of dictionaries or series to an existing DataFrame and display the combined data.

Write a Pandas program to append a list of dictionaries or series to an existing DataFrame and display the combined data.

# Write a Pandas program to append a list of dictioneries or series to a existing # DataFrame and display the combined data.
import pandas as pd
import numpy as np

exam_dic1  = {'name': ['Aman', 'Kamal', 'Amjad', 'Rohan', 'Amit', 'Sumit', 'Matthew', 'Kartik', 'Kavita', 'Pooja'],
'perc': [79.5, 29, 90.5, np.nan, 32, 65, 56, np.nan, 29, 89],
'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}

exam_data1 = pd.DataFrame(exam_dic1)

s = pd.Series(['Sukhvir', 54,'yes'], index=['name', 'perc','qualify'])

dicts = [{'name': 'Krish', 'perc': 45,'qualify':'yes'},
{'name': 'Kumar', 'perc': 67,'qualify':'yes'}]

print("Original DataFrames:")
print(exam_data1)
print("\nDictionary:")
print(s)
combined_data =  exam_data1.append(s, ignore_index=True, sort=False)
combined_info =  combined_data.append(dicts, ignore_index=True, sort=False)
print("\nCombined Data:")
# Print Combined Data/info
print(combined_info)

# 10. Program to select or filter rows from a DataFrame based on values in columns in pandas.( Use of Relational and Logical Operators)

Program to select or filter rows from a DataFrame based on values in columns in pandas.( Use of Relational and Logical Operators)

# Program to select or filter rows from a DataFrame based on values in columns in pandas.( Use of Relational and Logical Operators)
import pandas as pd
import numpy as np

exam_dic1  = {'name': ['Aman', 'Kamal', 'Amjad', 'Rohan', 'Amit', 'Sumit', 'Matthew', 'Kartik', 'Kavita', 'Pooja'],
'perc': [79.5, 29, 90.5, np.nan, 32, 65, 56, np.nan, 29, 89],
'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}

exam_data1 = pd.DataFrame(exam_dic1)

print("Original DataFrames:")
print(exam_data1)
print("\nUse == operator\n")
print(exam_data1.loc[exam_data1['name'] == 'Rohan'])

print("\nUse < operator\n")
print(exam_data1.loc[exam_data1['perc'] < 40])

print("\n Use != operator\n")
print(exam_data1.loc[exam_data1['qualify'] != 'no'])

print("\n Multiple Conditions\n")
print(exam_data1.loc[(exam_data1['qualify'] != 'yes') & (exam_data1['perc'] <40)])

# 11. Filter out rows based on different criteria such as duplicate rows

Filter out rows based on different criteria such as duplicate rows

# Filter out rows based on different criteria such as duplicate rows

import pandas as pd
data={'Name':['Aman','Rohit','Deepika','Aman','Deepika','Sohit','Geeta'],
'Sales':[8500,4500,9200,8500,9200,9600,8400]}
sales=pd.DataFrame(data)
# Find duplicate rows
duplicated = sales[sales.duplicated(keep=False)]
print("duplicate Row:\n",duplicated)

# 12. Importing and exporting data between pandas and CSV file. # To create and open a data frame using ‘Student_result.csv’ file using Pandas. # To display row labels, column labels data types of each  column and the dimensions # To display the shape (number of rows and columns) of the CSV file.

Importing and exporting data between pandas and CSV file.

# Importing and exporting data between pandas and CSV file.
# To create and open a data frame using ‘Student_result.csv’ file using Pandas.
# To display row labels, column labels data types of each  column and the dimensions
# To display the shape (number of rows and columns) of the CSV file.

import pandas as pd
import csv

# Display Name of Columns
print(df.columns)

# Display no of rows and column
print(df.shape)

# Display Column Names and their types
print(df.info())

# 13. Read the ‘Student_result.csv’ to create a data frame and do the following  operation: # To display Adm_No, Gender and Percentage from ‘student_result.csv’ file. # To display the first 5 and last 5 records from ‘student_result.csv’ file.

Read the ‘Student_result.csv’ to create a data frame and do the following  operation: # To display Adm_No, Gender and Percentage from ‘student_result.csv’ file. # To display the first 5 and last 5 records from ‘student_result.csv’ file.

# Read the ‘Student_result.csv’ to create a data frame and do the following  operation:
# To display Adm_No, Gender and Percentage from ‘student_result.csv’ file.
# To display the first 5 and last 5 records from ‘student_result.csv’ file.

import pandas as pd
import csv

#To display Adm_No, Gender and Percentage from ‘student_result.csv’ file.

print("To display Adm_No, Gender and Percentage from ‘student_result.csv’ file.")
print(df)

#To display first 5 and last 5 records from ‘student_result.csv’ file.
print(df1.tail())

# 14. Read the ‘Student_result.csv’ to create a data frame and do the following  operation: # To display Student_result file with new column names. # To modify the Percentage of student below 40 with NaN value in dataframe.

Read the ‘Student_result.csv’ to create a data frame and do the following  operation: # To display Student_result file with new column names. # To modify the Percentage of student below 40 with NaN value in dataframe.

# Read the ‘Student_result.csv’ to create a data frame and do the following  operation:
# To display Student_result file with new column names.
# To modify the Percentage of student below 40 with NaN value in dataframe.

import pandas as pd
import numpy as np
import csv

print(df)

#To display Student_result file with new column names.

'Maths','Sc.','SSt','San','IT','Perc'])

print("To display Student_result file with new column names")
print(df1)

# To modify the Percentage of student below 40 with NaN value.
print(df2)

print("To modify the Percentage of student below 40 with NaN value.")
df2.loc[(df2['PERCENTAGE'] <40, 'PERCENTAGE')] = np.nan
print(df2)

# 15. Read the ‘Student_result.csv’ to create a data frame and do the following  operation: # To create a duplicate file for ‘student_result.csv’ containing Adm_No, Name and Percentage. # Write the statement in Pandas to find the highest percentage and also print the student’s name and percentage.

15. Read the ‘Student_result.csv’ to create a data frame and do the following  operation: # To create a duplicate file for ‘student_result.csv’ containing Adm_No, Name and Percentage. # Write the statement in Pandas to find the highest percentage and also print the student’s name and percentage.

# Read the ‘Student_result.csv’ to create a data frame and do the following  operation:
# To create a duplicate file for ‘student_result.csv’ containing Adm_No, Name and Percentage.
# Write the statement in Pandas to find the highest percentage and also print the student’s name and percentage.

import pandas as pd
import numpy as np
import csv

# To create a duplicate file for ‘student_result.csv’ containing Adm_No, Name and Percentage.
# Display Copied Dataframe
print(df2)

# find the highest percentage and also print the student’s name and percentage.
df1 = df1[["STUDENT'S_NAME",'PERCENTAGE']]
[df1.PERCENTAGE== df1['PERCENTAGE'].max()]
print(df1)

# 16. Importing and exporting data between pandas and MySQL database

Importing and exporting data between pandas and MySQL database

# Importing and exporting data between pandas and MySQL database

import pymysql
import pandas as pd
import mysql.connector
from sqlalchemy import types, create_engine

# Create dataframe
dic={
'EMPNO':[7369,7499,7566,7654,7698,7782,7788,7839,7844,7900,7902,7934],

'BLAKE','MARTIN','TURNER'],
'JOB':['CLERK','CLERK','ANALYST','MANAGER','MANAGER','PRESIDENT','ANALYST',

'CLERK','MANAGER','ANALYST','SALESMAN','CLERK'],
'MGR':[7876,7876,7782,7900,7900 ,7900,7782,7876,7900,7782,7900,7876],
'HIREDATE':['2005/02/18','2005/01/04','2001/05/18','2003/04/19','2001/07/02',
'2006/09/21','2007/03/13','2005/03/06', '2007/01/12','2009/07/19','2009/01/05',

'2004/11/30'],
'SAL':[11400,19200,29400,60000,15000,95700,13200,36000,36000,34200,15000,18000],
'COMM':[4000,5000,5000,4000,2500,4000,2500,3000 ,3000,2500,2000 ,6000],
'DEPTT':[20,30,20,30,30,10,20,10,30,30,20,10]
}

data = pd.DataFrame(dic)
print('Our DataFrame is:\n',data)

tableName="employeedata"

# create sqlalchemy engine
sqlEngine = create_engine("mysql+pymysql://root:@localhost/Company")
dbConnection = sqlEngine.connect()

try:
# Exporting dataframe to SQl
frame = data.to_sql(tableName, dbConnection, if_exists='fail');

except ValueError as vx:

print(vx)

except Exception as ex:

print(ex)

else:

print("Table %s created successfully.\n"%tableName);

finally:

dbConnection.close()

# – Read a MySQL Database Table and write into a Pandas DataFrame:

sqlEngine   = create_engine('mysql+pymysql://root:@127.0.0.1')

dbConnection= sqlEngine.connect()

dframe       = pd.read_sql("select * from Company.employeedata", dbConnection);

print("After importing data from MySql:\n")
print(dframe)

dbConnection.close()

Find the sum of each column, or find the column with the lowest mean

# 17. Find the sum of each column, or find the column with the lowest mean

# Find the sum of each column, or find the column with the lowest mean
import pandas as pd
Pass_Perc ={'Phy': {'2017':95.4,'2018':96.4,'2019':99.2,'2020':97.4},
'Che': {'2017':96.5,'2018':97.4,'2019':100,'2020':99.2},
'Maths': {'2017':90.2,'2018':92.6,'2019':97.4,'2020':98.0},
'Eng': {'2017':99.2,'2018':100,'2019':100,'2020':100},
'IP': {'2017':95.6,'2018':100,'2019':100,'2020':100}}

df=pd.DataFrame(Pass_Perc)
print(df)
print()
print('Column wise sum in datframe is :')
print(df.sum(axis=0))
#  Print mean vaLue of each coLumn
print()
print('Column wise mean value are:')
print(df.mean(axis=0).round(1))
#  Returns CoLumn with minimum mean vaLue
print()
print('Column with minimum mean value is:')
print(df.mean(axis=0).idxmin())

Locate the 3 largest values in a data frame.

# 18. Locate the 3 largest values in a data frame.

# Locate the 3 largest values in a data frame.
import pandas as pd
'Sales':[8500,4500,9300,8600,9200,9600,8400]}
sales=pd.DataFrame(data)
# Find  3 Largest Value for MarksinlP Column
print(sales.nlargest(3,['Sales']))

Subtract the mean of a row from each element of the row in a Data Frame

# 19. Subtract the mean of a row from each element of the row in a Data Frame

# Subtract the mean of a row from each element of the row in a Data Frame
import pandas as pd
Pass_Perc ={'Phy': {'2017':95.4,'2018':96.4,'2019':99.2,'2020':97.4},
'Che': {'2017':96.5,'2018':97.4,'2019':100,'2020':99.2},
'Maths': {'2017':90.2,'2018':92.6,'2019':97.4,'2020':98.0},
'Eng': {'2017':99.2,'2018':100,'2019':100,'2020':100},
'IP': {'2017':95.6,'2018':100,'2019':100,'2020':100}}

df=pd.DataFrame(Pass_Perc)
print(df)
print()

print('Mean of each row is:')
print(df.mean(axis=1))
print()
print('Datafranie after Subtracting mean value of\
each row from each element of that Row is:')
print(df.sub(df.mean(axis=1), axis=0))

# 20. Replace all negative values in a data frame with a 0.

Replace all negative values in a data frame with a 0.

# Replace all negative values in a data frame with a 0.

import pandas as pd

data = {'sales1':[10,20,-4,5,-1,15],
'sales2':[20,15,10,-1,12,-2]}

df = pd.DataFrame(data)

print("Data Frame")
print(df)

print('Display DataFrame after replacing every negative value with 0')

df[df<0]=0
print(df)

# 21. Replace all missing values in a data frame with a 999

Replace all missing values in a data frame with a 999

# Replace all missing values in a data frame with a 999
import pandas as pd
import numpy as np
Srec={'sid':[101,102,103,104,np.nan,106,107,108,109,110],
'smarks':[98,67,np.nan,56,38,98,67,np.nan,56,np.nan],
'remark':['P','P','P','F',np.nan,'P','P','F','P','P'],
'mobile':[9990009991,9990009992,9990009993,np.nan,9990009995,np.nan,
9990009997,

9990009998, np.nan,9999010000]}
# Convert the dictionary into DataFrame
df=pd.DataFrame(Srec)
print("\n- Dataframe Before Replacing NaN with 999-\n")
print(df)

#Replace missing value with zeros
print("\n-After Replacing missing value with 999-\n")
df=df.fillna(999)
print(df)

# 22. Given a Series, print all the elements that are above the 75th percentile.

Given a Series, print all the elements that are above the 75th percentile.

# Given a Series, print all the elements that are above the 75th percentile.

import pandas as pd
import numpy as np
s=pd.Series(np.array([2,4,5,10,18,20,25]))
print(s)
res=s.quantile(q=0.75)
print()
print('75th Percentile of the series is::')
print(res)
print()
print('The elements that above the 75th percentile:')
print(s[s>res])

# 23. Create a Data Frame quarterly sales where each row contains the item category, item name, and expenditure. Group the rows by the category and print the total expenditure per category.

Create a Data Frame quarterly sales where each row contains the item category, item name, and expenditure. Group the rows by the category and print the total expenditure per category.

# Create a Data Frame quarterly sales where each row contains the item category,
#item name, and expenditure. Group the rows by the category and print the total
#expenditure per category.

import pandas as pd

# initialize list of lists
data = [['CAR','Maruti',1000000],['AC','Hitachi',55000],['AIRCOLLER','Bajaj',12000],
['WASHING MACHINE','LG',15000],['CAR','Ford',7000000],['AC','SAMSUNG',45000],['AIRCOLLER','Symphony',20000],['WASHING MACHINE','Wirlpool',25000]]

Col=['itemcat','itemname','expenditure']
# Create the pandas DataFrame

qrtsales = pd.DataFrame(data,columns=Col)

# print dataframe.
print (qrtsales)

qs=qrtsales.groupby('itemcat')
print('Result after Filtering Dataframe')
print(qs['itemcat','expenditure'].sum())

# 24. Create a data frame based on e-commerce data and generate descriptive statistics (mean, median, mode, quartile, and variance)

Create a data frame based on e-commerce data and generate descriptive statistics (mean, median, mode, quartile, and variance)

# Create a data frame based on ecommerce data and generate descriptive statistics # (mean, median,mode, quartile, and variance)

import pandas as pd
sales = {'InvoiceNo': [1001,1002,1903,1004,1085,1006,1007],
'ProductName': ['LCD','AC','Deodrant','leans','Books','Shoes','Jacket'],
'Quantity': [2,1,2,1,2,1,1],
'Price':[65000,55000,500,3000,958,3000,2200]}
df=pd.DataFrame(sales)
print(df)
print("Mean price of Item:", df['Price']. mean ().round (2))
print("Median price of Item:", df['Price']. median ().round (2))
print("Mode of price:\n", df[['Price']]. mode ())
print("Quartile of price:\n",df[['Price']].quantile([.1,.25,.5,.75],axis=0))
print("Variance of Price:\n",df[['Price']].var())

# 25. Given the school result data, analyses the performance of the students on different parameters, e.g subject wise or class wise.

Given the school result data, analyses the performance of the students on different parameters, e.g subject wise or class wise.

# Given the school result data, analyses the performance of the students on #different parameters, e.g subject wise  or class wise.
# x-axis is shows the subject and y -axis
# shows the markers in each subject

# import pandas and matplotlib
import pandas as pd
import matplotlib.pyplot as plt

# Simple Line Chart with setting of Label of X and Y axis,
# title for chart line and color of line
subject = ['Physic','Chemistry','Mathematics', 'Biology','Computer']
marks =[80,75,70,78,82]
# To draw line in red colour

plt.plot(subject,marks,'r',marker ='*')
# To Write Title of the Line Chart

plt.title('Marks Scored')
# To Put Label At Y Axis

plt.xlabel('SUBJECT')
# To Put Label At X Axis

plt.ylabel('MARKS')
plt.show()

# 26. Write a program to plot a bar chart in python to display the result of a school for five consecutive years.

Write a program to plot a bar chart in python to display the result of a school for five consecutive years.

#Write a program to plot a bar chart in python to display the result of a school for five consecutive years.

import matplotlib.pyplot as pl

year=['2015','2016','2017','2018','2019'] # list of years
p=[98.50,70.25,55.20,90.5,61.50] #list of pass percentage
j=['b','g','r','m','c'] # color code of bar charts
pl.bar(year, p, width=0.2, color=j) # bar( ) function to create the bar chart
pl.xlabel("year") # label for x-axis
pl.ylabel("Pass%") # label for y-axis
pl.show( ) # function to display bar chart

# •  Number of Students against Scores in all the 7 subjects •  Show the Highest score of each subject

For the Data frames created above, analyze, and plot appropriate charts with title and legend. •  Number of Students against Scores in all the 7 subjects •  Show the Highest score of each subject

# #• Number of Students against Scores in all the 7 subjects #• Show the Highest score of each subject

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import csv

#Number of Students against Scores in all the 7 subjects

plt.hist([df['ENG'],df['HINDI'],df['MATHS'],df['SCIENCE'],df['SSC'],df['SANSK'],df['CA']],color=['red', 'yellow', 'blue','green','orange','black','pink'])
plt.title('Number of Students against Scores')
plt.xlabel('Score')
plt.ylabel('Number of Students')
plt.legend(['English', 'Hindi', 'Maths','Science','S.Sc.','Sanskrit','CA'])
plt.show()

# Show the Highest score of each subject.
y = ['ENGG','HINNDI','MATHS','SCIENCE','SSC','SANSK','CA']
width = [df['ENG'].max(),df['HINDI'].max(),df['MATHS'].max(),df['SCIENCE'].max(),df['SSC'].max(),df['SANSK'].max(),df['CA'].max()]

plt.figure(figsize = (12,2))
plt.barh(y = y, width = width)
plt.title('Average Scores')
plt.xlabel('Average Score')
plt.ylabel('Subjects')
for i,v in enumerate(width):
plt.text(v, i, " "+str(round(v,2)), color='blue', va='center', fontweight='bold')
plt.show()

# 28. For the Data frames created above, analyze, and plot appropriate charts with title and legend. • Show the Average score of each subject

For the Data frames created above, analyze, and plot appropriate charts with title and legend. • Show the Average score of each subject

# For the Data frames created above, analyze, and plot appropriate charts with title and legend.
# • Show the Average score of each subject

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import csv

# Show the Average score of each subject
y = ['ENGG','HINNDI','MATHS','SCIENCE','SSC','SANSK','CA']
width = [df['ENG'].mean(),df['HINDI'].mean(),df['MATHS'].mean(),df['SCIENCE'].mean(),
df['SSC'].mean(),df['SANSK'].mean(),df['CA'].mean()]

plt.figure(figsize = (12,2))
plt.barh(y = y, width = width)
plt.title('Average Scores')
plt.xlabel('Average Score')
plt.ylabel('Subjects')
for i,v in enumerate(width):
plt.text(v, i, " "+str(round(v,2)), color='blue', va='center', fontweight='bold')
plt.show()

# 29. For the Data frames created above, analyze, and plot appropriate charts with title and legend. • Number of Females and Males • Average Percentage of Females and Males

For the Data frames created above, analyze, and plot appropriate charts with title and legend. • Number of Females and Males • Average Percentage of Females and Males

# For the Data frames created above, analyze, and plot appropriate charts
# with title and legend.
# • Number of Females and Males
# • Average Percentage of Females and Males

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import csv

# Analyzing Scores based on Gender

df_gender = df.groupby('GENDER')

#Number of Females and Males
y = df_gender['GENDER'].count().keys()
width = df_gender['GENDER'].count()
plt.figure(figsize = (12,2))
plt.barh(y = y, width = width)
plt.title('No. of Females and Males')
plt.xlabel('Count')
plt.ylabel('Gender')
for i,v in enumerate(width):
plt.text(v, i, " "+str(v), color='blue', va='center', fontweight='bold')
plt.show()

#Average Percentage of Females and Males
y = df_gender['PERCENTAGE'].mean().keys()
width = df_gender['PERCENTAGE'].mean()
plt.figure(figsize = (12,2))
plt.barh(y = y,
width = width)
plt.title('Av Percentage of Female and Males')
plt.xlabel('Av. total Percentage ')
plt.ylabel('Gender')
for i,v in enumerate(width):
plt.text(v, i, " "+str(round(v,2)), color='blue', va='center', fontweight='bold')
plt.show()

bottom of page