A library system is essential for a college library. The library system has a database that can store and tracks patrons, books, and other resources. It is used to keep track of which patrons have which books, when books are due, and what fines are owed. The library system is capable to track what materials are requested and if they are available. The system should also allow patrons to order books and materials, and allow librarians to search the database for items and patrons. Additionally, the system should be able to generate reports and statistics, such as which books are most popular or which patrons have the most overdue books.
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Books with loan frequency in reverse order (the least frequent first)
In this task, books.csv and bookloans.csv datasets are used in the case study. A simple but very effective cataloging and loan system is made. Book borrowing is allowed for 14 days. For example, if a book is borrowed for 20 days, the late will be 6 days. When the report is made, in this list, the unique book code, the title of the books, the author's details, and the days for borrowing the books are also included (Pilnenskiy and Smetannikov, 2020). In this way, the books are categorized and loaded in the Jupyter Notebook application. This list must contain a valid list of books which is excluding false details. In this task, popularity is measured.
Figure 1: The Code for Reading of the books.CSV file into a Nested list
The above picture in this section shows the proper method of importing or reading CSV files using the python compiler. In this section of task 1, the frequency of the loaned books from the library is calculated in reverse order. CSV files are contained data about the details of the books like the name of the author, and book Title along with the records of the borrowed books from the library. The frequency factor can tell how many times the book was borrowed from the library.
Figure 2: The Code for Reading of the books.CSV file into a Nested dictionary
The figure in this section of Task 1 is to read the books dataset into the nested list and nested dictionary format. From the images in the above section, the python coding for creating the nested list and the nested dictionary of the books CSV file was implemented (Singh, 2019).
The number of times these books were loaned in 2019 along with titles and author
Figure 3: The Code for Reading of the bookloan.CSV file into a Nested list
The image in this section is about determining the number of how many times the books were loaned from the library in 2019 according to the book’s titles and authors’ names The two CSV files books.CSV and bookloan.CSV contained all the details of how many times each book was borrowed from the library in year 2019.
Figure 4: The Code for Reading of the bookloan.CSV file into a Nested dictionary
The images in the above section are also the coding portion for reading the file in the Jupyter Notebook for the book loans CSV dataset file. The nested list and nested dictionary are also generated for this book loans CSV file. Popularity measurement is done by counting the total number of books borrowed (Graesser and Keng, 2019). The main purpose of this task is to sort the books according to the most popular books to the least popular books.
So the listing of the books is completed by writing some codes in the Jupyter Notebook application. All books are sorted in reverse order of frequency according to the borrowing counting’s. The popularity is measured without counting the total days of borrowing the books. For example, if one book is borrowed 9 times in a year while another one is borrowed 2 times in a year then the last book is kept on the top of the list of the books showing the list of least demanded books in the year.
Task 2
The most popular genres
In this task, an analysis is performed of the interests of the readers. The library needs to know about the interest of the readers. This will help for future improvement of the library. The books which would be bought for the library are one of the main key factors to increase the interest of the borrowers and increase the popularity and reputation of the library. That's why some coding work is performed.
Figure 5: The code for calculating the number of books genre-wise
The above image in this section is the coding portion to fulfil the objective of calculating the total number of books, which were available in the library according to the genre wise category of the book. This output section will allow the user to enter genre of the book and the total number book of that particular genre will be shown to the user.
Figure 6: The code for counting the number of popular books borrowed genre-wise
The images in the above section are the coding portion for calculating the total number of books in the library according to the genre of the book along with the popularity report of the total number of books borrowed according to the genre of the book (Tomlinson et al. 2020).
The most popular sub-genres
Figure 7: Code for calculating the total number of books in the library subgenre-wise
The picture here is the coding portion to fulfil the objective of the calculating the total number of books which were available in the library according to the category of sub-genre of books. This output of this coding section can calculate the total number of books available in the library according to the sub-genre category.
Figure 8: Code for counting the number of popular books borrowed subgenre-wise
The images in the above section are the coding portion for calculating the total number of books in the library according to the sub-genre of the book along with the popularity report of the total number of books borrowed according to the sub-genre of the book. For this reason, popularity measurement is very important (Dalwadi et al. 2021). The measurement is done in the previous task.
Based on the number of borrowing of books, the sorting process is performed. In this process, the parameter which is indicating the number of days for book borrowing is taken in the process of the measurement. After that, the popularity is measured based on the genres and sub-genres of the books which are borrowed in the year 2019. In the process of coding, the number of books is calculated which are genres and sub-genres.
Task 3
The average number of days that a book was borrowed
In this task, the duration is calculated. Borrowers borrowed the books from the library but the calculation of the total days for each book is very important. The average is calculated for this purpose. It is recommended to calculate the average by taking up to 2 decimal values for easy calculation (Johnston et al. 2019).
Figure 9: Coding for calculating the average number of days that a book was borrowed
In the above section, the image is the coding for calculating the average number of days for a book being borrowed by the members. The output portion of this coding will show the calculation of the average number of days the book was loaned from the library. The user needs to enter the number of borrowed days of each book for calculating the average number of days of borrowed books.
The percentage proportion of books returned late
Figure 10: Coding for calculating the percentage proportion of books returned late
The picture here is the coding that can help to calculate the proportion of the percentage of late returned books. The output of this coding can determine the percentage proportion of the books, which were returned late to the library. The user needs to enter the return period details of the book then the coding will help the user to calculate the percentage proportion.
The average late period of books returned late
Figure 11: Coding for calculating the average late period
The picture in the above section is the coding portion of task 3 to calculate the average of the late period. For calculation, len () and sum () functions are used in the Jupyter Notebook application. In this task, three points are kept in mind. At the first, the average no of days is calculated which takes to return the books after borrowing.
Then the percentage calculated of the late returned books (Maulik et al. 2022). For example, in the year 2019, a book includes a total of 15 days late was returned for borrowing the book for 300 days, so it is calculated by using the program. In the last point, the average of late book submissions is calculated. All the codes are also shown for this purpose.
Conclusion
In this article, a library data analysis is performed for a small college. The analysis helps to manage books and improve the services of the library. For this purpose, two datasets are analyzed where a checking and cleaning task is performed. Database analysis for library systems deals with the design and development of database structures, data models, and database applications capable of efficiently managing library resources. These database applications provide an efficient and secure way to store, organize, and access library information. The main motive of this article is to know the most and least popular books, know the readers' interest, and calculate the average duration of the borrowing books. The analysis is done by using the Jupyter Notebook application without using the Pandas library for developing the algorithm.
References
Dalwadi, D., Mehta, Y. and Macwan, N., 2021. Face recognition-based attendance system using real-time computer vision algorithms. In International Conference on Advanced Machine Learning Technologies and Applications (pp. 39-49). Springer, Singapore.
Graesser, L. and Keng, W.L., 2019. Foundations of deep reinforcement learning: theory and practice in Python. Addison-Wesley Professional.
Johnston, B., Jones, A. and Kruger, C., 2019. Applied Unsupervised Learning with Python: Discover hidden patterns and relationships in unstructured data with Python. Packt Publishing Ltd.
Maulik, R., Fytanidis, D.K., Lusch, B., Vishwanath, V. and Patel, S., 2022. Pythonfoam: In-situ data analyses with openfoam and python. Journal of Computational Science, 62, p.101750.
Pilnenskiy, N. and Smetannikov, I., 2020. Feature selection algorithms as one of the python data analytical tools. Future Internet, 12(3), p.54.
Singh, H., 2019. Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python. Apress.
Tomlinson, J.E., Arnott, J.H. and Harou, J.J., 2020. A water resource simulator in Python. Environmental Modelling & Software, 126, p.104635.
