SQL Clauses Operators Functions Reference

Posted by admin on Apr 21, 2025 in Information and Documentation | 0 comments

SQL Clauses

Clauses	How to Use It	Functionality
CREATE TABLE	CREATE TABLE table_name ...	Creates a new table
INSERT	INSERT INTO table_name ...	Inserts new data into the table
SELECT	SELECT col1, col2 ...	Retrieves specified columns
SELECT	SELECT * FROM ...	Retrieves all columns from a table
FROM	FROM table_name	Specifies the table(s) from which to retrieve data
WHERE	WHERE col > 5	Filters rows based on specified conditions
UPDATE, SET	UPDATE table_name SET column1 = value1;	Updates column values for all rows (or specific

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Python Core Concepts: File I/O, Data Structures & Algorithms

Posted by admin on Apr 7, 2025 in Information and Documentation | 0 comments

Python File Handling Basics

Common modes for opening files:

• open(filename, 'w'): Open for writing (truncates file if exists, creates if not).
• open(filename, 'r'): Open for reading (default mode).
• open(filename, 'a'): Open for appending (creates file if not exists).

Common file handle (fh) methods:

• fh.read(): Reads the entire file content.
• fh.readline(): Reads a single line from the file.
• fh.readlines(): Reads all lines into a list of strings.
• fh.write(string): Writes a string to the file.
• fh.writelines(list_

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Using the Exam Cheat Sheet Editor

Posted by admin on Mar 28, 2025 in Information and Documentation | 0 comments

Using the Cheat Sheet Editor

Follow these steps to create your exam cheat sheet:

1. Start writing your exam cheat sheet in the editor below.
2. Pick the font size and the line height. Letter 5 is recommended for exams.
3. Resize the exam cheat sheet by grabbing and moving the bottom right holder of the cheat sheet preview on the right side.
4. Add more cheat sheets by pressing the (+) button.

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Pandas in Python: Essential Techniques

Posted by admin on Feb 17, 2025 in Information and Documentation | 0 comments

Pandas – Part 2

Built-in Functions

• Average of column: df_grades.Column.mean()
  • Average of multiple columns: df_grades[["column1", "column2"]].mean()
• Max/min: df_grades["column name"].max() or .min()
• Statistical summary: df_grades.describe()
  • Can get all key stats for numeric columns at once with the describe() method
    • Count, mean, std, min, 25%, 50%, 75%, max
  • Pick out chunks of the summary by storing the summary in a variable and then use variable[["column name", "column name"]]
• Number of each unique value:

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