This project is based on Library Management. Python and MySQL are the programming platforms which are used in making of this project.
Subject-Informatics Practices
Class-11/12
This document contains solutions to questions from a computer science examination. It includes questions on topics like Python, Pandas, SQL, data visualization, and computer networks. The solutions demonstrate how to write Python code to create and manipulate dataframes, plot charts, and perform SQL queries. Examples of network topologies and devices like switches, modems, and gateways are also provided. The document aims to test students' understanding of key concepts in informatics practices.
Programming Fundamentals Arrays and Strings imtiazalijoono
This document provides an overview of arrays and strings in C programming. It discusses initializing and declaring arrays of different types, including multidimensional arrays. It also covers passing arrays as arguments to functions. For strings, it explains that strings are arrays of characters that are null-terminated. It provides examples of declaring and initializing string variables, and using string input/output functions like scanf() and printf().
This document contains SQL queries to create tables, insert records, update records, and perform aggregation functions on sample employee and student data. The key steps are:
1) Create tables to store student and employee data with various fields
2) Insert sample records into the tables
3) Update records by calculating totals, averages, and increasing/decreasing field values
4) Use aggregation functions like count, sum, avg to analyze the data
5) Display records by filtering on field values
The document provides an introduction to SQL (Structured Query Language). It discusses the history and evolution of SQL standards. SQL is introduced as the most widely used and accepted language for managing data in relational database management systems. The key benefits of SQL and its role in creating, querying, updating and managing relational databases are described. Common SQL commands like CREATE, ALTER, DROP, INSERT, SELECT, UPDATE, DELETE are explained. Additional topics covered include functions, joins, subqueries and other advanced SQL features.
The document discusses various operations that can be performed on sets in Python like adding, removing, and checking for elements. It provides examples of using common set methods such as add(), remove(), pop(), clear(), len(), in, issubset(), issuperset(), copy(), isdisjoint(), all(), any(), enumerate(), max(), min(), sum(), sorted() and comparing sets using == and != operators. It emphasizes that sets are unordered and do not allow indexing or slicing as they are immutable.
A matrix is a two-dimensional rectangular data structure that can be created in R using a vector as input to the matrix function. The matrix function arranges the vector elements into rows and columns based on the number of rows and columns specified. Basic matrix operations include accessing individual elements and submatrices, computing transposes, products, and inverses. Matrices allow efficient storage and manipulation of multi-dimensional data.
This document provides an overview of a machine learning course that teaches Pandas basics. The course aims to teach students how to handle and visualize data, apply basic learning algorithms, develop supervised and unsupervised learning techniques, and build machine learning models. The document outlines the course objectives, outcomes, syllabus including data preprocessing, feature extraction, and data visualization techniques. It also provides references for further reading.
This document contains solutions to questions from a computer science examination. It includes questions on topics like Python, Pandas, SQL, data visualization, and computer networks. The solutions demonstrate how to write Python code to create and manipulate dataframes, plot charts, and perform SQL queries. Examples of network topologies and devices like switches, modems, and gateways are also provided. The document aims to test students' understanding of key concepts in informatics practices.
Programming Fundamentals Arrays and Strings imtiazalijoono
This document provides an overview of arrays and strings in C programming. It discusses initializing and declaring arrays of different types, including multidimensional arrays. It also covers passing arrays as arguments to functions. For strings, it explains that strings are arrays of characters that are null-terminated. It provides examples of declaring and initializing string variables, and using string input/output functions like scanf() and printf().
This document contains SQL queries to create tables, insert records, update records, and perform aggregation functions on sample employee and student data. The key steps are:
1) Create tables to store student and employee data with various fields
2) Insert sample records into the tables
3) Update records by calculating totals, averages, and increasing/decreasing field values
4) Use aggregation functions like count, sum, avg to analyze the data
5) Display records by filtering on field values
The document provides an introduction to SQL (Structured Query Language). It discusses the history and evolution of SQL standards. SQL is introduced as the most widely used and accepted language for managing data in relational database management systems. The key benefits of SQL and its role in creating, querying, updating and managing relational databases are described. Common SQL commands like CREATE, ALTER, DROP, INSERT, SELECT, UPDATE, DELETE are explained. Additional topics covered include functions, joins, subqueries and other advanced SQL features.
The document discusses various operations that can be performed on sets in Python like adding, removing, and checking for elements. It provides examples of using common set methods such as add(), remove(), pop(), clear(), len(), in, issubset(), issuperset(), copy(), isdisjoint(), all(), any(), enumerate(), max(), min(), sum(), sorted() and comparing sets using == and != operators. It emphasizes that sets are unordered and do not allow indexing or slicing as they are immutable.
A matrix is a two-dimensional rectangular data structure that can be created in R using a vector as input to the matrix function. The matrix function arranges the vector elements into rows and columns based on the number of rows and columns specified. Basic matrix operations include accessing individual elements and submatrices, computing transposes, products, and inverses. Matrices allow efficient storage and manipulation of multi-dimensional data.
This document provides an overview of a machine learning course that teaches Pandas basics. The course aims to teach students how to handle and visualize data, apply basic learning algorithms, develop supervised and unsupervised learning techniques, and build machine learning models. The document outlines the course objectives, outcomes, syllabus including data preprocessing, feature extraction, and data visualization techniques. It also provides references for further reading.
This document provides a summary of a seminar presentation on robotic process automation and virtual internships. It introduces popular Python libraries for data science like NumPy, SciPy, Pandas, matplotlib and Seaborn. It covers reading, exploring and manipulating data frames; filtering and selecting data; grouping; descriptive statistics. It also discusses missing value handling and aggregation functions. The goal is to provide an overview of key Python tools and techniques for data analysis.
The document is a template for a computer science practical record file submitted by a student for their class 12 board exams. It includes sections for an acknowledgement, certificate signed by teachers certifying the completion of 10 practical exercises, and records of the completed practical exercises which include Python programs and SQL queries related to data structures, stacks, strings, databases, and more.
This document provides an overview of arrays in the C programming language. It defines arrays as collections of variables of the same data type stored in contiguous memory locations. The document discusses declaring, initializing, and accessing array elements using indices. It provides examples of inserting elements into arrays, deleting elements from arrays, and printing the contents of 1D and 2D arrays. The document is intended to teach the fundamentals of array programming in C.
The document discusses arrays and character strings in C programming. It defines arrays as a collection of similar data items accessed using an index. Arrays must be declared before use with the syntax type array_name[size]. Individual elements can be accessed using the index, such as array_name[i]. The document also discusses declaring, initializing, accessing, and manipulating character strings in C, which are arrays of characters terminated by a null character. Common string functions like strcpy(), strlen(), strcmp() are also introduced.
This document provides an overview of learning the R programming language. It covers topics like simple arithmetic and logical operations, vectors, built-in functions, reading/writing files, and exercises for practice. The exercises involve creating random vectors, calculating statistics, investing/compound interest scenarios, and solving a quadratic equation.
The document provides instructions and sample code for Python and MySQL practical programs. It includes:
1. Instructions for 8 Python programs covering topics like factorials, strings, lists, sets and classes.
2. Instructions for 2 MySQL programs to create tables, insert/update data, and perform queries on employee and student tables.
3. Sample code and outputs for each program are provided for reference in the practical exams.
The document provides instructions and sample code for Python and MySQL practical programs. It includes:
1. Instructions for 8 Python programs covering topics like factorials, strings, lists, sets and classes.
2. Instructions for 2 MySQL programs to create tables, insert/update data, and perform queries on employee and student tables.
3. Sample code and outputs for each program are provided for reference in the practical exams.
The document contains lecture notes on one-dimensional and two-dimensional arrays in C programming. It discusses the syntax, declaration, initialization, and accessing of array elements. Examples are provided to demonstrate reading input from users, traversing arrays using for loops, and performing operations like addition and multiplication on two-dimensional arrays. Class exercises described include programs to read and display arrays, find the highest number in an array, and perform matrix addition and multiplication using two-dimensional arrays.
The document discusses various data manipulation techniques in pandas such as creating, filtering, joining and merging DataFrames. Some key points:
- Pandas DataFrames can be created from lists, dictionaries or other DataFrames and allow storing and manipulating tabular data.
- Common operations include filtering rows based on conditions, aggregating using functions like mean(), sorting values, and joining/merging DataFrames on indexes.
- DataFrames support different types of joins like inner, outer, left and right joins to combine data from multiple tables.
Best Data Science Ppt using Python
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
This document contains an activity on introductory R commands and operations using basic statistical functions. It includes examples of adding variables, performing calculations, creating graphs and loading built-in datasets. For one activity, commute times are entered and organized using R commands. Standard deviations, means and medians are calculated for price data. Probabilities are found for standard normal distributions.
This document describes a micro project to create a simple Oracle database to store course enrollment data for a university. It involves designing tables to model student, class, enrollment and other data; populating the tables with sample data; writing SQL queries to retrieve and analyze the data; and creating PL/SQL functions and procedures to generate reports on departments, students, and faculty. Key tasks include creating tables with primary keys and foreign keys, inserting records, writing queries to retrieve aggregated data on departments and their faculty/students, creating views to display related data, and procedures to output formatted reports.
data_selectionOctober 19, 2022[1] # Data Selection.docxrichardnorman90310
data_selection
October 19, 2022
[1]: # Data Selection
[2]: import numpy as np
[3]: # This is weather data recorded in Memphis during summer (June to September).
# Column 0: month
# Column 1: temperature in Farenheit
# Column 2: precipitation in inches
data = np.array([
[6, 70, 3],
[7, 75, 3],
[6, 85, 4],
[7, 90, 4],
[7, 91, 5],
[8, 85, 2],
[8, 87, 4],
[6, 83, 5],
[8, 77, 3],
[6, 69, 6],
[9, 68, 1],
[6, 80, 6],
[9, 65, 3],
[9, 75, 4],
[9, 80, 5]])
[4]: data.shape
[4]: (15, 3)
[5]: # Select the data for the row 0:
data[0, :]
# row_selection: 0
# column_selection: all
[5]: array([ 6, 70, 3])
1
[6]: # Select the data of column 2:
data[:, 2]
# row_selection: all
# column_selection: 2
[6]: array([3, 3, 4, 4, 5, 2, 4, 5, 3, 6, 1, 6, 3, 4, 5])
[7]: # Get the data for the first five rows.
data[0:5, :]
[7]: array([[ 6, 70, 3],
[ 7, 75, 3],
[ 6, 85, 4],
[ 7, 90, 4],
[ 7, 91, 5]])
[8]: # Get the data for the first five rows,
# and the first two columns.
data[0:5, 0:2]
[8]: array([[ 6, 70],
[ 7, 75],
[ 6, 85],
[ 7, 90],
[ 7, 91]])
[9]: # Get the data for the last two columns,
# and the first five rows.
data[0:5, 1:3]
[9]: array([[70, 3],
[75, 3],
[85, 4],
[90, 4],
[91, 5]])
[10]: # or can be written as
data[:5, 1:]
[10]: array([[70, 3],
[75, 3],
[85, 4],
[90, 4],
[91, 5]])
[11]: # or can be written as
data[:5, -2:]
2
[11]: array([[70, 3],
[75, 3],
[85, 4],
[90, 4],
[91, 5]])
[12]: # Get the last 4 rows
data[-4:, :]
[12]: array([[ 6, 80, 6],
[ 9, 65, 3],
[ 9, 75, 4],
[ 9, 80, 5]])
[13]: # Find the temperature values, and store them in a variable
temp = data[:, 1]
[14]: temp
[14]: array([70, 75, 85, 90, 91, 85, 87, 83, 77, 69, 68, 80, 65, 75, 80])
[15]: # Find the month values, and store them in a variable
month = data[:, 0]
[16]: month
[16]: array([6, 7, 6, 7, 7, 8, 8, 6, 8, 6, 9, 6, 9, 9, 9])
[17]: # Find the maximum temperature
np.max(temp)
[17]: 91
[18]: # Find the index (or position) of the maximum temperature
np.argmax(temp)
[18]: 4
[19]: # Find the month that corresponds to the maximum temperature
data[np.argmax(temp), 0]
[19]: 7
[20]: m = np.argmax(temp)
data[m, 0]
[20]: 7
3
[21]: # boolean selection
[22]: # Find all the temperatures below 70 degrees
data[temp < 70, 1]
[22]: array([69, 68, 65])
[23]: # Find the months with temperatures below 70 degrees
data[temp < 70, 0]
[23]: array([6, 9, 9])
[24]: np.unique(data[temp < 70, 0])
[24]: array([6, 9])
[25]: # Find all the temperatures for the month of August
data[month == 8, 1]
[25]: array([85, 87, 77])
[26]: # Find the average temperature for August
np.average(data[month == 8, 1])
[26]: 83.0
[27]: # Find the temperatures above 80 for June
data[(month == 6) & (temp > 80), 1]
# & means and
[27]: array([85, 83])
[28]: # Find the temperatures for the months of June, July, and August
data[month != 9, 1]
[28]: array([70, 75, 85, 90, 91, 85, 87, 83, 77, 69, 80])
[29]: data[(month == 6) | (month == 7) | (month == 8), 1]
[29]: array([70, 75, 85, 90.
This document provides examples of Power BI DAX queries and functions for common reporting tasks like counting, filtering, aggregating, ranking, calculating differences and ratios, and dynamic date selections. It also includes examples of using CTEs, TOPN filtering, and bucketing/aging to group and segment data.
INFORMATIVE ESSAYThe purpose of the Informative Essay assignme.docxcarliotwaycave
INFORMATIVE ESSAY
The purpose of the Informative Essay assignment is to choose a job or task that you know how to do and then write a minimum of 2 full pages, maximum of 3 full pages, Informative Essay teaching the reader how to do that job or task. You will follow the organization techniques explained in Unit 6.
Here are the details:
1. Read the Lecture Notes in Unit 6. You may also find the information in Chapter 10.5 in our text on Process Analysis helpful. The lecture notes will really be the most important to read in writing this assignment. However, here is a link to that chapter that you may look at in addition to the lecture notes:
https://open.lib.umn.edu/writingforsuccess/chapter/10-5-process-analysis/ (Links to an external site.)
2. Choose your topic, that is, the job or task you want to teach. As the notes explain, this should be a job or task that you already know how to do, and it should be something you can do well. At this point, think about your audience (reader). Will your reader need any knowledge or experience to do this job or task, or will you write these instructions for a general reader where no experience is required to perform the job?
3. Plan your outline to organize this essay. Unit 6 notes offer advice on this organization process. Be sure to include an introductory paragraph that has the four main points presented in the lecture notes.
4. Write the essay. It will need to be at least 2 FULL pages long, maximum of 3 full pages long. You will use the MLA formatting that you used in previous essays from Units 3, 4, and 5.
5. Be sure to include a title for your essay.
6. After writing the essay, be sure to take time to read it several times for revision and editing. It would be helpful to have at least one other person proofread it as well before submitting the assignment.
Quiz2
# comments start with #
# to quit q()
# two steps to install any library
#install.packages("rattle")
#library(rattle)
setwd("D:/AJITH/CUMBERLANDS/Ph.D/SEMESTER 3/Data Science & Big Data Analy (ITS-836-51)/RStudio/Week2")
getwd()
x <- 3 # x is a vector of length 1
print(x)
v1 <- c(2,4,6,8,10)
print(v1)
print(v1[3])
v <- c(1:10) #creates a vector of 10 elements numbered 1 through 10. More complicated data
print(v)
print(v[6])
# Import test data
test<-read.csv("CVEs.csv")
test1<-read.csv("CVEs.csv", sep=",")
test2<-read.table("CVEs.csv", sep=",")
write.csv(test2, file="out.csv")
# Write CSV in R
write.table(test1, file = "out1.csv",row.names=TRUE, na="",col.names=TRUE, sep=",")
head(test)
tail(test)
summary(test)
head <- head(test)
tail <- tail(test)
cor(test$X, test$index)
sd(test$index)
var(test$index)
plot(test$index)
hist(test$index)
str(test$index)
quit()
Quiz3
setwd("C:/Users/ialsmadi/Desktop/University_of_Cumberlands/Lectures/Week2/RScripts")
getwd()
# Import test data
data<-read.csv("yearly_sales.csv")
#A 5-number summary is a set of 5 descriptive statistics for summarizing a continuous univariate data set.
#It consists o ...
The document discusses arrays in C programming. It begins by defining an array as a structure that contains a group of related data items of the same type. It notes that arrays allow accessing elements via an index, with the first element having an index of 0. The document then provides examples of declaring, initializing, accessing, and printing single-dimensional and multi-dimensional arrays. It also demonstrates how to store user input into arrays and perform operations like addition and multiplication on 2D arrays representing matrices.
The document contains summaries of 12 programs implementing various operating system concepts like memory management algorithms, CPU scheduling algorithms, and page replacement algorithms. It includes programs for first fit, best fit, worst fit, priority scheduling, producer consumer problem, FCFS, SJF, SRTF, round robin, and page replacement algorithms like FIFO, LRU, and optimal page replacement. For each program, it lists the code, inputs/outputs and provides a brief 1-2 line description.
Chapter 16-spreadsheet1 questions and answerRaajTech
This document discusses spreadsheets and Excel. It defines key spreadsheet concepts like workbooks, cells, cell addresses, and formulas. It describes built-in Excel functions for date/time, arithmetic, statistical, logical, and financial calculations. The document also covers charts, macros, and databases in Excel. Spreadsheets allow users to enter, manipulate, and analyze numerical data using formulas and functions in a tabular format.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
This document provides a summary of a seminar presentation on robotic process automation and virtual internships. It introduces popular Python libraries for data science like NumPy, SciPy, Pandas, matplotlib and Seaborn. It covers reading, exploring and manipulating data frames; filtering and selecting data; grouping; descriptive statistics. It also discusses missing value handling and aggregation functions. The goal is to provide an overview of key Python tools and techniques for data analysis.
The document is a template for a computer science practical record file submitted by a student for their class 12 board exams. It includes sections for an acknowledgement, certificate signed by teachers certifying the completion of 10 practical exercises, and records of the completed practical exercises which include Python programs and SQL queries related to data structures, stacks, strings, databases, and more.
This document provides an overview of arrays in the C programming language. It defines arrays as collections of variables of the same data type stored in contiguous memory locations. The document discusses declaring, initializing, and accessing array elements using indices. It provides examples of inserting elements into arrays, deleting elements from arrays, and printing the contents of 1D and 2D arrays. The document is intended to teach the fundamentals of array programming in C.
The document discusses arrays and character strings in C programming. It defines arrays as a collection of similar data items accessed using an index. Arrays must be declared before use with the syntax type array_name[size]. Individual elements can be accessed using the index, such as array_name[i]. The document also discusses declaring, initializing, accessing, and manipulating character strings in C, which are arrays of characters terminated by a null character. Common string functions like strcpy(), strlen(), strcmp() are also introduced.
This document provides an overview of learning the R programming language. It covers topics like simple arithmetic and logical operations, vectors, built-in functions, reading/writing files, and exercises for practice. The exercises involve creating random vectors, calculating statistics, investing/compound interest scenarios, and solving a quadratic equation.
The document provides instructions and sample code for Python and MySQL practical programs. It includes:
1. Instructions for 8 Python programs covering topics like factorials, strings, lists, sets and classes.
2. Instructions for 2 MySQL programs to create tables, insert/update data, and perform queries on employee and student tables.
3. Sample code and outputs for each program are provided for reference in the practical exams.
The document provides instructions and sample code for Python and MySQL practical programs. It includes:
1. Instructions for 8 Python programs covering topics like factorials, strings, lists, sets and classes.
2. Instructions for 2 MySQL programs to create tables, insert/update data, and perform queries on employee and student tables.
3. Sample code and outputs for each program are provided for reference in the practical exams.
The document contains lecture notes on one-dimensional and two-dimensional arrays in C programming. It discusses the syntax, declaration, initialization, and accessing of array elements. Examples are provided to demonstrate reading input from users, traversing arrays using for loops, and performing operations like addition and multiplication on two-dimensional arrays. Class exercises described include programs to read and display arrays, find the highest number in an array, and perform matrix addition and multiplication using two-dimensional arrays.
The document discusses various data manipulation techniques in pandas such as creating, filtering, joining and merging DataFrames. Some key points:
- Pandas DataFrames can be created from lists, dictionaries or other DataFrames and allow storing and manipulating tabular data.
- Common operations include filtering rows based on conditions, aggregating using functions like mean(), sorting values, and joining/merging DataFrames on indexes.
- DataFrames support different types of joins like inner, outer, left and right joins to combine data from multiple tables.
Best Data Science Ppt using Python
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
This document contains an activity on introductory R commands and operations using basic statistical functions. It includes examples of adding variables, performing calculations, creating graphs and loading built-in datasets. For one activity, commute times are entered and organized using R commands. Standard deviations, means and medians are calculated for price data. Probabilities are found for standard normal distributions.
This document describes a micro project to create a simple Oracle database to store course enrollment data for a university. It involves designing tables to model student, class, enrollment and other data; populating the tables with sample data; writing SQL queries to retrieve and analyze the data; and creating PL/SQL functions and procedures to generate reports on departments, students, and faculty. Key tasks include creating tables with primary keys and foreign keys, inserting records, writing queries to retrieve aggregated data on departments and their faculty/students, creating views to display related data, and procedures to output formatted reports.
data_selectionOctober 19, 2022[1] # Data Selection.docxrichardnorman90310
data_selection
October 19, 2022
[1]: # Data Selection
[2]: import numpy as np
[3]: # This is weather data recorded in Memphis during summer (June to September).
# Column 0: month
# Column 1: temperature in Farenheit
# Column 2: precipitation in inches
data = np.array([
[6, 70, 3],
[7, 75, 3],
[6, 85, 4],
[7, 90, 4],
[7, 91, 5],
[8, 85, 2],
[8, 87, 4],
[6, 83, 5],
[8, 77, 3],
[6, 69, 6],
[9, 68, 1],
[6, 80, 6],
[9, 65, 3],
[9, 75, 4],
[9, 80, 5]])
[4]: data.shape
[4]: (15, 3)
[5]: # Select the data for the row 0:
data[0, :]
# row_selection: 0
# column_selection: all
[5]: array([ 6, 70, 3])
1
[6]: # Select the data of column 2:
data[:, 2]
# row_selection: all
# column_selection: 2
[6]: array([3, 3, 4, 4, 5, 2, 4, 5, 3, 6, 1, 6, 3, 4, 5])
[7]: # Get the data for the first five rows.
data[0:5, :]
[7]: array([[ 6, 70, 3],
[ 7, 75, 3],
[ 6, 85, 4],
[ 7, 90, 4],
[ 7, 91, 5]])
[8]: # Get the data for the first five rows,
# and the first two columns.
data[0:5, 0:2]
[8]: array([[ 6, 70],
[ 7, 75],
[ 6, 85],
[ 7, 90],
[ 7, 91]])
[9]: # Get the data for the last two columns,
# and the first five rows.
data[0:5, 1:3]
[9]: array([[70, 3],
[75, 3],
[85, 4],
[90, 4],
[91, 5]])
[10]: # or can be written as
data[:5, 1:]
[10]: array([[70, 3],
[75, 3],
[85, 4],
[90, 4],
[91, 5]])
[11]: # or can be written as
data[:5, -2:]
2
[11]: array([[70, 3],
[75, 3],
[85, 4],
[90, 4],
[91, 5]])
[12]: # Get the last 4 rows
data[-4:, :]
[12]: array([[ 6, 80, 6],
[ 9, 65, 3],
[ 9, 75, 4],
[ 9, 80, 5]])
[13]: # Find the temperature values, and store them in a variable
temp = data[:, 1]
[14]: temp
[14]: array([70, 75, 85, 90, 91, 85, 87, 83, 77, 69, 68, 80, 65, 75, 80])
[15]: # Find the month values, and store them in a variable
month = data[:, 0]
[16]: month
[16]: array([6, 7, 6, 7, 7, 8, 8, 6, 8, 6, 9, 6, 9, 9, 9])
[17]: # Find the maximum temperature
np.max(temp)
[17]: 91
[18]: # Find the index (or position) of the maximum temperature
np.argmax(temp)
[18]: 4
[19]: # Find the month that corresponds to the maximum temperature
data[np.argmax(temp), 0]
[19]: 7
[20]: m = np.argmax(temp)
data[m, 0]
[20]: 7
3
[21]: # boolean selection
[22]: # Find all the temperatures below 70 degrees
data[temp < 70, 1]
[22]: array([69, 68, 65])
[23]: # Find the months with temperatures below 70 degrees
data[temp < 70, 0]
[23]: array([6, 9, 9])
[24]: np.unique(data[temp < 70, 0])
[24]: array([6, 9])
[25]: # Find all the temperatures for the month of August
data[month == 8, 1]
[25]: array([85, 87, 77])
[26]: # Find the average temperature for August
np.average(data[month == 8, 1])
[26]: 83.0
[27]: # Find the temperatures above 80 for June
data[(month == 6) & (temp > 80), 1]
# & means and
[27]: array([85, 83])
[28]: # Find the temperatures for the months of June, July, and August
data[month != 9, 1]
[28]: array([70, 75, 85, 90, 91, 85, 87, 83, 77, 69, 80])
[29]: data[(month == 6) | (month == 7) | (month == 8), 1]
[29]: array([70, 75, 85, 90.
This document provides examples of Power BI DAX queries and functions for common reporting tasks like counting, filtering, aggregating, ranking, calculating differences and ratios, and dynamic date selections. It also includes examples of using CTEs, TOPN filtering, and bucketing/aging to group and segment data.
INFORMATIVE ESSAYThe purpose of the Informative Essay assignme.docxcarliotwaycave
INFORMATIVE ESSAY
The purpose of the Informative Essay assignment is to choose a job or task that you know how to do and then write a minimum of 2 full pages, maximum of 3 full pages, Informative Essay teaching the reader how to do that job or task. You will follow the organization techniques explained in Unit 6.
Here are the details:
1. Read the Lecture Notes in Unit 6. You may also find the information in Chapter 10.5 in our text on Process Analysis helpful. The lecture notes will really be the most important to read in writing this assignment. However, here is a link to that chapter that you may look at in addition to the lecture notes:
https://open.lib.umn.edu/writingforsuccess/chapter/10-5-process-analysis/ (Links to an external site.)
2. Choose your topic, that is, the job or task you want to teach. As the notes explain, this should be a job or task that you already know how to do, and it should be something you can do well. At this point, think about your audience (reader). Will your reader need any knowledge or experience to do this job or task, or will you write these instructions for a general reader where no experience is required to perform the job?
3. Plan your outline to organize this essay. Unit 6 notes offer advice on this organization process. Be sure to include an introductory paragraph that has the four main points presented in the lecture notes.
4. Write the essay. It will need to be at least 2 FULL pages long, maximum of 3 full pages long. You will use the MLA formatting that you used in previous essays from Units 3, 4, and 5.
5. Be sure to include a title for your essay.
6. After writing the essay, be sure to take time to read it several times for revision and editing. It would be helpful to have at least one other person proofread it as well before submitting the assignment.
Quiz2
# comments start with #
# to quit q()
# two steps to install any library
#install.packages("rattle")
#library(rattle)
setwd("D:/AJITH/CUMBERLANDS/Ph.D/SEMESTER 3/Data Science & Big Data Analy (ITS-836-51)/RStudio/Week2")
getwd()
x <- 3 # x is a vector of length 1
print(x)
v1 <- c(2,4,6,8,10)
print(v1)
print(v1[3])
v <- c(1:10) #creates a vector of 10 elements numbered 1 through 10. More complicated data
print(v)
print(v[6])
# Import test data
test<-read.csv("CVEs.csv")
test1<-read.csv("CVEs.csv", sep=",")
test2<-read.table("CVEs.csv", sep=",")
write.csv(test2, file="out.csv")
# Write CSV in R
write.table(test1, file = "out1.csv",row.names=TRUE, na="",col.names=TRUE, sep=",")
head(test)
tail(test)
summary(test)
head <- head(test)
tail <- tail(test)
cor(test$X, test$index)
sd(test$index)
var(test$index)
plot(test$index)
hist(test$index)
str(test$index)
quit()
Quiz3
setwd("C:/Users/ialsmadi/Desktop/University_of_Cumberlands/Lectures/Week2/RScripts")
getwd()
# Import test data
data<-read.csv("yearly_sales.csv")
#A 5-number summary is a set of 5 descriptive statistics for summarizing a continuous univariate data set.
#It consists o ...
The document discusses arrays in C programming. It begins by defining an array as a structure that contains a group of related data items of the same type. It notes that arrays allow accessing elements via an index, with the first element having an index of 0. The document then provides examples of declaring, initializing, accessing, and printing single-dimensional and multi-dimensional arrays. It also demonstrates how to store user input into arrays and perform operations like addition and multiplication on 2D arrays representing matrices.
The document contains summaries of 12 programs implementing various operating system concepts like memory management algorithms, CPU scheduling algorithms, and page replacement algorithms. It includes programs for first fit, best fit, worst fit, priority scheduling, producer consumer problem, FCFS, SJF, SRTF, round robin, and page replacement algorithms like FIFO, LRU, and optimal page replacement. For each program, it lists the code, inputs/outputs and provides a brief 1-2 line description.
Chapter 16-spreadsheet1 questions and answerRaajTech
This document discusses spreadsheets and Excel. It defines key spreadsheet concepts like workbooks, cells, cell addresses, and formulas. It describes built-in Excel functions for date/time, arithmetic, statistical, logical, and financial calculations. The document also covers charts, macros, and databases in Excel. Spreadsheets allow users to enter, manipulate, and analyze numerical data using formulas and functions in a tabular format.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
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𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
How Barcodes Can Be Leveraged Within Odoo 17Celine George
In this presentation, we will explore how barcodes can be leveraged within Odoo 17 to streamline our manufacturing processes. We will cover the configuration steps, how to utilize barcodes in different manufacturing scenarios, and the overall benefits of implementing this technology.
How to Download & Install Module From the Odoo App Store in Odoo 17Celine George
Custom modules offer the flexibility to extend Odoo's capabilities, address unique requirements, and optimize workflows to align seamlessly with your organization's processes. By leveraging custom modules, businesses can unlock greater efficiency, productivity, and innovation, empowering them to stay competitive in today's dynamic market landscape. In this tutorial, we'll guide you step by step on how to easily download and install modules from the Odoo App Store.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
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These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
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(A Free eBook comprising 3 Sets of Presentation of a selection of Puzzles, Brain Teasers and Thinking Problems to exercise both the mind and the Right and Left Brain. To help keep the mind and brain fit and healthy. Good for both the young and old alike.
Answers are given for all the puzzles and problems.)
With Metta,
Bro. Oh Teik Bin 🙏🤓🤔🥰
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
🔥🔥🔥🔥🔥🔥🔥🔥🔥
إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
💀💀💀💀💀💀💀💀💀💀
تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
#فهم_ماكو_درخ
3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊
🔥🔥🔥🔥🔥🔥🔥🔥🔥
1. AISSCE 2021(Sample Practical Paper)
SET-A
Subject- Informatics Practices (065)
M.Marks-30 Time-3 Hours
1. Write a program to create a dataframe a list containing dictionaries of the exam performances of five
students- 8
Name Eng Acct Eco Bst IP
Pankaj 67 78 89 76 90
Rekha 98 87 84 89 93
Ajay 98 56 49 87 76
Raju 34 51 76 54 67
Suraj 78 54 45 63 61
Perform the following-
• Display the DataFrame
• Add the another column PE:[56,78,98,45,78]
• Display from 1st
to 3rd
rows
• Display the columns from Name to Eco.
• D display another column ‘Total ‘which will show the total marks of all subjects
• Display the rows in order of total
2. Table: Employee 7
No Name Salary Zone Age Grade Dept
1 Mukul 30000 West 28 A 10
2 Kritika 35000 Centre 30 A 10
3 Naveen 32000 West 40 20
4 Uday 38000 North 38 C 30
5 Nupur 32000 East 26 20
6 Mokesh 37000 South 28 B 10
7 Shelly 36000 North 26 A 30
Create the above table-Employee and insert all the rows. Based on this tables write SQL statements for
the following queries: -
• To display 2nd
to 4th
letters from all names.
• Display the highest and lowest salaries of the employees .
• Display lowest salary all employees where names contain 5 characters.
• Display zone wise highest salary having highest salary above 35000.
• To display no of employees in each department where minimum salary is 30000.
3. Practical File 5
4. Project Work 5
5.Viva-Voce 5
2. Solution
1.
import pandas as pd
result={'Name':['Pankaj','Rekha','Ajay','Raju','Suraj'],'Eng':[67,98,98,34,78], 'Acct':[78,87,56,51,54],
'Eco':[89,84,49,76,45],
'Bst':[76,89,87,54,63],
'IP':[90,93,76,67,61]}
df=pd.DataFrame(result)
print(df)
df['PE']=[56,78,98,45,78]
print(df)
print(df[0:3])
print(df.rename(columns={"Bst":"BStudies"}))
print(df.iloc[:,0:4])
df['Total']=df['Eng']+df['Acct']+df['Eco']+df['Bst']+df['IP']+df['PE']
print(df)
print(df.sort_values(by=['Total']))
2.
create table Employee(No integer(4),Name varchar(28),Salary decimal(8,2),Zone varchar(15),Age
integer(4),Grade char(1),Dept integer(4));
insert into Employee values(1,'mukul',30000,'west',28,'A',30);
insert into Employee values(2,'kritika',35000,'centre',30,'A',10);
insert into Employee values(3,'naveen',32000,'west',40,null,10);
insert into Employee values(4,'uday',38000,'north',38,'A',30);
insert into Employee values(5,'nupur',32000,'east',26,null,20);
insert into Employee values(6,'mokesh',37000,'south',28,'B',10);
insert into Employee values(7,'shelly',36000,'north',26,'A',30);
Select substr(Name,2,3) from Emplyee;
Select Max(salary),Min(salary) from Employee;
Select min(salary) from Employee where length(name)=5;
Select zone,max(salary) from Employee group by zone having max(salary)>35000;
Select dept,count(*),min(salary) from Employee group by dept having min(salary)=30000;
3. AISSCE 2021(Sample Practical Paper)
SET-B
Subject- Informatics Practices (065)
Marks-30 Time-3 Hours
1. Write command to create a datafarme with following list of values
A=[[23,56,87,98],[78],[32,54],[43,65,90]]
Write command for the following-
• Display the DataFrame
• To count the number of non-NA values present each column and rows in the dataframe
• Replace all NaN values of 2nd
column with 50 and 4th
column with 70.
• To transpose the dataframe
• To fill missing values by copying value from above adjacent cell 8
2. Table: Employee 7
No Name Salary Zone Age Grade Dept
1 Mukul 30000 West 28 A 10
2 Kritika 35000 Centre 30 A 10
3 Naveen 32000 West 40 20
4 Uday 38000 North 38 C 30
5 Nupur 32000 East 26 20
6 Mokesh 37000 South 28 B 10
7 Shelly 36000 North 26 A 30
Create the above table-Employee and insert all the rows. Based on this tables write SQL statements for
the following queries: -
• To display first three letters from all names.
• To display the total salary for all the employees who are from West zone.
• To count no of employees without any grade.
• To display zone wise highest salary and lowest salary.
• To display minimum age of employees in each zone whose name start with ‘N’.
3. Practical File 5
4. Project Work 5
5.Viva-Voce 5
4. Solution:
1. import pandas as pd
A=[[23,56,87,98],[78],[32,54],[43,65,90]]
df=pd.DataFrame(A)
print(df)
print(df.count())
print(df.count(1))
print(df.fillna({1:50,3:70}))
print(df.T)
print(df.fillna(method='ffill'))
2.
create table Employee(No integer(4),Name varchar(28),Salary decimal(8,2),Zone
varchar(15),Age integer(4),Grade char(1),Dept integer(4));
insert into Employee values(1,'mukul',30000,'west',28,'A',30);
insert into Employee values(2,'kritika',35000,'centre',30,'A',10);
insert into Employee values(3,'naveen',32000,'west',40,null,10);
insert into Employee values(4,'uday',38000,'north',38,'A',30);
insert into Employee values(5,'nupur',32000,'east',26,null,20);
insert into Employee values(6,'mokesh',37000,'south',28,'B',10);
insert into Employee values(7,'shelly',36000,'north',26,'A',30);
Select left(Name,3) from Emplyee;
Select sum(Salary) from Employee where dept=10 and zone=”west”;
Select count(*)from Employee where grade is NULL;
Select zone,max(salary),min(salary) from Employee group by zone;
Select zone,min(age) from Employee where zone like”N%” group by zone ;
5. AISSCE 2021(Sample Practical Paper)
SET-C
Subject- Informatics Practices (065)
M.Marks-30 Time-3 Hours
1. Write a program in Python Pandas to create the following DataFrame batsman from a Dictionary:
B_NO Name Score1 Score2
1 Sunil Pillai 90 80
2 Gaurav Sharma 65 45
3 Piyush Goel 70 90
4 Kartik Thakur 80 76
Perform the following operations on the DataFrame :
1)Display the DataFrame.
2)Add both the scores of a batsman and assign to column “Total”
3)Display the highest score in both Score1 and Score2 of the DataFrame.
4)Display the lowest score in Score2.
5)Display no of records present in each rows and columns.
6)Display sum of Score1 . 8
2. Table: Employee 7
No Name Salary Zone Age Grade Dept
1 Mukul 30000 West 28 A 10
2 Kritika 35000 Centre 30 A 10
3 Naveen 32000 West 40 20
4 Uday 38000 North 38 C 30
5 Nupur 32000 East 26 20
6 Mokesh 37000 South 28 B 10
7 Shelly 36000 North 26 A 30
Create the above table-Employee and insert all the rows. Based on this tables write SQL statements for
the following queries: -
• To display employee name and zone together.
• To display the average salary of all the employees whose names start with ‘N’.
• To count zone wise no of employees where no of employees less than 2.
• To display department wise highest, lowest ,total and average salary.
• To display zone wise average salary where average salary is above 35000 in descending
order of zone name.
3. Practical File 5
4. Project Work 5
5. Viva-Voce 5
6. Solution:
1.
import pandas as pd
d1={'B_NO':[1,2,3,4],'Name':["Sunil Pillai","Gaurav Sharma","Piyush Goel","Kartik Thakur"],
'Score1':[90,65,70,80],'Score2':[80,45,95,76]}
df=pd.DataFrame(d1)
print(df)
df['Total'] = df['Score1']+ df['Score2']
print(df)
print("Maximum scores are : " ,max(df['Score1']), max(df['Score2']))
print("Minimum score of Score2:",min(df['Score2']))
print("The number records present in each rows in the dataframe is",(df.count(axis=1)))
print("The number records present in each columns in the dataframe is",(df.count(axis=0)))
print("The sum of Score1 is",(df['Score1'].sum()))
2.
create table Employee(No integer(4),Name varchar(28),Salary decimal(8,2),Zone varchar(15),Age
integer(4),Grade char(1),Dept integer(4));
insert into Employee values(1,'mukul',30000,'west',28,'A',30);
insert into Employee values(2,'kritika',35000,'centre',30,'A',10);
insert into Employee values(3,'naveen',32000,'west',40,null,10);
insert into Employee values(4,'uday',38000,'north',38,'A',30);
insert into Employee values(5,'nupur',32000,'east',26,null,20);
insert into Employee values(6,'mokesh',37000,'south',28,'B',10);
insert into Employee values(7,'shelly',36000,'north',26,'A',30);
Select concat(Name,Zone) from Employee;
Select Avg(Salary) from Employee where Name like”N%”;
Select zone,count(*) from Employee group by zone having count(*)>2;
Select dept,max(salary),min(salary),sum(salary),avg(salary)from Employee group by dept;
Select zone,avg(salary)from Employee group by zone having avg(salary)>35000 order by zone;