Introduction to Python libraries- Pandas, Matplotlib.

Data structures in Pandas - Series and Data Frames.

Series: Creation of Series from – ndarray, dictionary, scalar value; mathematical operations; Head and Tail functions; Selection, Indexing and Slicing.

Data Frames: creation - from dictionary of Series, list of dictionaries.

Text/CSV files; display; iteration; Operations on rows and columns: add, select, delete, rename.

Head and Tail functions; Indexing using Labels, Boolean Indexing.

Importing/Exporting Data between CSV files and Data Frames.

Customizing plots: adding label, title, and legend in plots.

Purpose of plotting; drawing and saving following types of plots using Matplotlib – line plot, bar graph, histogram.

Math functions: POWER (), ROUND (), MOD ()

Text functions: UCASE ()/UPPER (), LCASE ()/LOWER (), MID ()/SUBSTRING ()/SUBSTR (), LENGTH (), LEFT (), RIGHT (), INSTR (), LTRIM (), RTRIM (), TRIM ().

Date Functions: NOW (), DATE (), MONTH (), MONTHNAME (), YEAR (), DAY (), DAYNAME ().

Aggregate Functions: MAX (), MIN (), AVG (), SUM (), COUNT (); using COUNT (*)

Querying and manipulating data using Group by, Having, Order by.

Digital footprint, Net and communication Etiquettes, Data protection.

Intellectual Property Rights (IPR), Plagiarism, Licensing and Copyright.

Free and Open source software (FOSS), Cybercrime and Cyber laws.

Hacking, Phishing, Cyber Bullying, Overview of Indian IT Act

E-waste: hazards and management.

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

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

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 for examination result and display row labels, column labels data types of each column and the dimensions

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

Importing and exporting data between pandas and CSV file

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

For the Data frames created above, analyze, and plot appropriate charts with title and legend.

Take data of your interest from an open source (e.g. data.gov.in), aggregate and summarize it. Then plot it using different plotting functions of the Matplotlib library.

Create a student table with the student id, name, and marks as attributes where the student id is the primary key.

Insert the details of a new student in the above table.

Delete the details of a student in the above table.

Use the select command to get the details of the students with marks more than 80.

Find the min, max, sum, and average of the marks in a student marks table.

Find the total number of customers from each country in the table (customer ID, customer Name, country) using group by.

Write a SQL query to order the (student ID, marks) table in descending order of the marks