Python Things To Remember

Table of Contents

• Exception Handling
• Stack
• Class 11 Python
• Python Keywords
• Python Data Types
• Mutable (Changeable) and Immutable (Not Changeable) Data Types
• Operators
• Explicit Conversion (Type Casting)
• Implicit Conversion (Coercion)
• Argument (Functions)
• Parameter (Functions)
• Scope of a Variable
• Built in Python Functions
• Built in Modules
• String Functions
• List Functions
• Tuple Functions
• Dictionary Functions
• File Handling Things To Remember
• My SQL TTR
• Definitions
• MySQL Commands
• Connectivity
• Results
• Built in Modules: Math, Random, Statistics
• Arithmetic & Other Functions Tables
• String Functions
• List Functions
• Tuple Functions
• Dictionary Functions
• MySQL Changes
• Data Definition Language (DDL) vs Data Manipulation Language (DML) Statements
• MySQL Commands (continued)
• File Handling Image

---

Exception Handling

S. No | Name of the Built-in Exception | Explanation

---|---|---

1. | SyntaxError | It is raised when there is an error in the syntax of the Python code.

2. | ValueError | It is raised when a built-in method or operation receives an argument that has the right data type but an inappropriate value.

3. | IOError | It is raised when the file specified in a program statement cannot be opened.

4. | KeyboardInterrupt | It is raised when the user accidentally hits the Delete or Esc key while executing a program due to which the normal flow of the program is interrupted.

5. | ImportError | It is raised when the requested module definition is not found.

6. | EOFError | It is raised when the end of file condition is reached without reading any data by input().

7. | ZeroDivisionError | It is raised when the denominator in a division operation is zero.

8. | IndexError | It is raised when the index or subscript in a sequence is out of range.

9. | NameError | It is raised when a local or global variable name is not defined.

10. | IndentationError | It is raised due to incorrect indentation in the program code.

11. | TypeError | It is raised when an operator is supplied with a value of incorrect data type.

12. | OverflowError | It is raised when the result of a calculation exceeds the maximum limit for numeric data type.

Syntax

raise Exception("Text")
assert(10>100), "Not possible"

try:
 a = int("abc")
 print(10/0)
except ZeroDivisionError:
 print("Division by zero not possible")
except:
 print("Some error")
else:
 print("No exception has been raised if this\nprints")
finally:
 print("Always prints regardless of exception or\nnot")

print("Always prints regardless of exception or\nnot")

Stack

A stack is a linear data structure in which all the insertion and deletion of data / values are done at one end only.

It is type of linear data structure.

It follows LIFO(Last In First Out) property.

Insertion / Deletion in stack can only be done from top.

Insertion in stack is also known as a PUSH operation.

Deletion from stack is also known as POP operation in stack.

Class 11 Python

Python Keywords

Python Data Types

Numbers → Integers, Floating Point, Complex

Sequences → Strings, Lists, Tuples

Sets → Mutable and Immutable Data Types

Dictionaries

Mutable (Changeable) and Immutable (Not Changeable) Data Types

• Mutable Data Types: Lists, Sets, Dictionaries
• Immutable Data Types: Integers, Floats, Booleans, Strings, Tuples

Operators:

1. Arithmetic Operators:

a. +

b. -

c. *

d. /

e. %

f. //

g. **

2. Relational Operators:

a. ==

b. !=

c. >

d. <

e. >=

f. <=

3. Assignment Operators:

a. =

b. +=

c. -=

d. /=

e. %=

f. //=

g. **=

4. Logical Operators:

a. and

b. or

c. not

5. Identity Operators:

a. is

b. is not

6. Membership Operators:

a. in

b. not in

Order of Precedence

Explicit Conversion (Type Casting)

When data type conversion takes place because the programmer forced it in the program.

Implicit Conversion (Coercion)

When data type conversion is done automatically by Python.

Argument (Functions)

An argument is a value passed to the function during the function call.

Parameter (Functions)

Parameter is defined in the function header.

Scope of a Variable

The part of the program where a variable is accessible can be defined as the scope of that variable.

1. Global Variable:

A variable that is defined outside any function or any block is known as a global variable.

2. Local Variable:

A variable that is defined inside any function or a block is known as a local variable.

Built in Python Functions

Built in Modules

1. Math Module

2. Random Module

3. Statistics Module

String Functions

List Functions

Tuple Functions

Dictionary Functions

📁 File Handling TTR

File Handling Things To Remember

Definitions:

File: A file is a collection of related data stored in a particular area on the disk.

Stream: It refers to a sequence of bytes. File handling is an important part of any web application.

Data Files: The data files that store data pertaining to specific applications, for later use.

Data files can be stored in two ways:

Text Files: Text files are structured as a sequence of lines, where each line includes a sequence of characters.

Binary Files : A binary file is any type of file that is not a text file.

Text Files:

1. file.read(n): Reads n bytes, if no n is given reads the entire file.

2. file.readline(n): Reads n bytes till it reaches EOF character, if no n is given reads till end of newline char.

3. file.readlines(): Reads all lines and returns it as a list.

Note: All the read function has ‘\\n’ at the end so to access the last character we need to use [-2]

4. file.seek(offset, from_where): Moves cursor of the file.

From_where:

a. 0: sets the cursor at the beginning of the file.

b. 1: sets the cursor at the current position (only binary).

c. 2: sets the cursor at the end of the file (only binary).

Note: when using seek in binary mode in “2” mode, there are two characters at the end of a line ‘\\r\\n’

5. file.tell(): Gives the cursor's current position as an integer.

6. file.write(text): Writes the text to the file.

7. file.writelines(list): Write each sentence in a list.

Note: writelines does not add EOF character so each string in list must have EOF character at the end.

Binary Files:

import pickle

Creating A Binary File

def create_file():

file = open("stud.DAT", "wb")

n = int(input("Enter number of entries: "))

for i in range(n):

roll = int(input("Roll No: "))

name = int(input("Name: "))

mark = int(input("Marks: "))

rec = [roll, name, mark]

pickle.dump(rec, file)

file.close()

Loading A Binary File

def show_file():

file = open("stud.DAT", "rb")

try:

while True:

print(pickle.load(file))

except:

print("End of File")

file.close()

CSV Files:

import csv

Note: We use text mode for csv files, also ensure to define

newline as empty

file = open('file.csv', 'w', newline='')

writer = csv.writer(file, delimiter=',', quotechar='"')

writer.writerow(['Abinav', 12, 'C'])

data = [['John', 12, 'A'], ['Doe', 11, 'B']]

writer.writerows(data)

file.close()

file = open('file.csv', 'r', newline='')

Note: Rows doesn't give data as a list, for loop is must or

you need to convert to list

rows = csv.reader(file)

for row in rows:

print(row)

file.close()

Note: Data given by csv is always string, so for numbers we

need to use int() to convert to integer.

My SQL TTR

Definitions

Relational Database: A database that has a collection of tables of data items, which are well described and organised according to the relational model.

Degree: Number of columns / attributes in a table.

Cardinality: Number of rows / tuples in a table.

Relation/Table: Data placed / organised in rows and columns.

Domain: All possible unique values of a particular column.

Row/Tuple: Horizontal subset / information in a table is called tuple.

Column/Attribute: Set of data values of a particular simple type, one for each row of the table.

Primary Key: A column that uniquely identifies a row within a table.

Candidate Key: Columns that are eligible to act as a primary key.

Alternate Key: Candidate keys other than the primary key.

Foreign Key: Columns that are used to establish a link between two tables.

MySQL Commands

1.Show:

a.SHOW DATABASES;

b.SHOW TABLES;

2.Create:

a.CREATE DATABASE CBSE2024;

b.CREATE TABLE PLAYERS(GCODE INT, GNAME

VARCHAR(20), PRIZE_MONEY INT, SDATE DATE);

c.CREATE TABLE PLAYERS(GCODE INT PRIMARY KEY,

GNAME VARCHAR(20), PRIZE_MONEY INT, SDATE

DATE);

Note: Constraints can be defined with each

value

• Not Null
• Primary Key
• Unique

3.Use:

a.USE CBSE2024;

4.Describe:

a.DESC PLAYERS;

5.Insert:

a.INSERT INTO PLAYERS VALUES(101, ‘CARROM

BOARD’, 2, 5000, ‘2004-01-23’);

6.Select:

a.SELECT * FROM PLAYERS;

b.SELECT GCODE FROM PLAYERS;

c.SELECT GCODE, GNAME FROM PLAYERS

d.SELECT GCODE AS “GAME CODE” FROM PLAYERS;

e.SELECT DISTINCT GNAME FROM PLAYERS;

7.Mathematical Operations (Aggregate Functions):

a.Sum: SELECT SUM(PRIZE_MONEY) FROM PLAYERS;

b.Count:

i. SELECT COUNT(*) FROM PLAYERS;

ii. SELECT COUNT(GCODE) FROM PLAYERS;

c.Max: SELECT MAX(PRIZE_MONEY) FROM PLAYERS;

d.Min: SELECT MIN(PRIZE_MONEY) FROM PLAYERS;

e.Avg: SELECT AVG(PRIZE_MONEY) FROM PLAYERS;

Note: Always gives a float number.

8.Where:

a.Comparison:

i. SELECT * FROM PLAYERS WHERE GCODE = 101;

ii. SELECT * FROM PLAYERS WHERE GCODE !=

101;

iii. SELECT * FROM PLAYERS WHERE PRIZE_MONEY

10000;

iv. SELECT * FROM PLAYERS WHERE PRIZE_MONEY

< 10000;

v. SELECT * FROM PLAYERS WHERE PRIZE_MONEY

<= 9000;

vi. SELECT * FROM PLAYERS WHERE PRIZE_MONEY

>= 12000;

vii. SELECT * FROM PLAYERS WHERE SDATE

BETWEEN ‘2003-12-30‘ AND ‘2004-12-30’;

b.Like (Wildcard):

Note: % sign is used to ignore all

characters before or after.

_ sign ignore the character before or

after it

i. SELECT * FROM PLAYERS WHERE GNAME LIKE

‘%TENNIS’;

ii. SELECT * FROM PLAYERS WHERE GNAME LIKE

‘BALL%’;

iii. SELECT * FROM PLAYERS WHERE GNAME LIKE

‘% %’; # Gives all elements with space

in between.

c.Mathematical Operations:

i. SELECT * FROM PLAYERS WHERE

PRIZE_MONEY%3 = 0; # Gives all elements

that are divisible by 3

d.Miscellaneous Operations:

i. SELECT * FROM PLAYERS WHERE GCODE IN

(101, 102, 103);

ii. SELECT * FROM PLAYERS WHERE GCODE NOT IN

(104, 105, 106);

iii. SELECT * FROM PLAYER WHERE GCODE IS NOT

NULL;

9.Order By:

a.Ascending Order:

SELECT * FROM PLAYERS ORDER BY GNAME ASC;

b.Descending Order:

SELECT * FROM PLAYERS ORDER BY SDATE DESC;

10. Delete:

DELETE FROM PLAYERS WHERE GCODE = 101;

Note: If where clause is not there, the whole

table will be deleted

11. Group By:

a.SELECT COUNT(GCODE) FROM PLAYERS GROUP BY

NUM;

b.SELECT GNAME FROM PLAYERS GROUP BY NUM HAVING

COUNT(*) > 2;

c.SELECT GNAME FROM PLAYERS GROUP BY GNAME

ORDER BY ROLL;

12. Alter Table:

a.ALTER TABLE PLAYERS ADD PRIMARY KEY(GCODE);

b.ALTER TABLE PLAYERS ADD FOREIGN KEY(ROLL)

REFERENCES SPORTS(ROLL);

c.ALTER TABLE PLAYERS ADD UNIQUE(GNAME);

d.ALTER TABLE PLAYERS ADD AGE INT;

e.ALTER TABLE PLAYERS MODIFY AGE VARCHAR(255);

f.ALTER TABLE PLAYERS MODIFY GNAME VARCHAR(255)

NOT NULL;

g.ALTER TABLE PLAYERS MODIFY AGE INT DEFAULT

18;

h.ALTER TABLE PLAYERS DROP COLUMN AGE;

i.ALTER TABLE PLAYERS DROP PRIMARY KEY;

j.ALTER TABLE PLAYERS RENAME COLUMN GNAME TO

NAME;

13. Update:

a.UPDATE PLAYERS SET PRIZE_MONEY = 10000 WHERE

GCODE=104;

14. Drop:

a.DROP TABLE PLAYERS;

b.DROP DATABASE CBSE2024;

15. Joining:

a.Equi-Join:

SELECT * FROM TABLE1, TABLE2 WHERE TABLE1.COL

= TABLE2.COL;

SELECT * FROM TABLE1 JOIN TABLE2 ON

TABLE1.COL = TABLE2.COL;

b.Natural-Join:

SELECT * FROM TABLE1 NATURAL JOIN TABLE2;

Connectivity:

import mysql.connector as sql

mycon = mysql.connector.connect(host='localhost', user='root',

password='tiger')

mycursor = mycon.cursor()

roll = input('Enter Roll Number: ')

name = input('Enter Name: ')

class_ = input('Enter Class: ')

marks = input('Enter Marks: ')

mycursor.execute('use cbse2024')

mycursor.execute(f"insert into student values({roll}, '{name}',

'{class_}', {marks})")

mycursor.close()

mycon.commit()

mycon.close()

import mysql.connector as sql

con = sql.connect(host='localhost', user='root', password='tiger')

cur = con.cursor()

cur.execute('use cbse2024')

cur.execute('select * from student')

print(cur.fetchall())

print(cur.fetchone())

Result When using Fetchall:

[(102, 'Badminton', 2, 12000, datetime.date(2003, 12, 12)), (103,

'Table Tennis', 4, 8000, datetime.date(2004, 2, 14)), (104, 'Chess', 2,

9000, datetime.date(2004, 1, 1)), (108, 'Lawn Tennis', 4, 25000,

datetime.date(2004, 3, 19)), (101, 'Carrom Board', 2, 5000,

datetime.date(2004, 1, 23))]

Result When using Fetchone:

(1, 'Abinav', '12', 50)

📊 My SQL TTR

### My SQL Things To Remember

Definitions

Relational Database: A database that has a collection of tables of data items, which are well described and organised according to the relational model.
Degree: Number of columns / attributes in a table.
Cardinality: Number of rows / tuples in a table.
Relation/Table: Data placed / organised in rows and columns.
Domain: All possible unique values of a particular column.
Row/Tuple: Horizontal subset / information in a table is called tuple.
Column/Attribute: Set of data values of a particular simple type, one for each row of the table.
Primary Key: A column that uniquely identifies a row within a table.
Candidate Key: Columns that are eligible to act as a primary key.
Alternate Key: Candidate keys other than the primary key.
Foreign Key: Columns that are used to establish a link between two tables.

### MySQL Commands
1.Show:
 a.SHOW DATABASES;
 b.SHOW TABLES;
2.Create:
 a.CREATE DATABASE CBSE2024;
 b.CREATE TABLE PLAYERS(GCODE INT, GNAME
VARCHAR(20), PRIZE_MONEY INT, SDATE DATE);
 c.CREATE TABLE PLAYERS(GCODE INT PRIMARY KEY,
GNAME VARCHAR(20), PRIZE_MONEY INT, SDATE
DATE);
Note: Constraints can be defined with each value
- Not Null
- Primary Key
- Unique
3.Use:
 a.USE CBSE2024;
4.Describe:
 a.DESC PLAYERS;
5.Insert:
 a.INSERT INTO PLAYERS VALUES(101, ‘CARROM BOARD’, 2, 5000, ‘2004-01-23’);
6.Select:
 a.SELECT * FROM PLAYERS;
 b.SELECT GCODE FROM PLAYERS;
 c.SELECT GCODE, GNAME FROM PLAYERS
 d.SELECT GCODE AS “GAME CODE” FROM PLAYERS;
 e.SELECT DISTINCT GNAME FROM PLAYERS;
7.Mathematical Operations (Aggregate Functions):
 a.Sum: SELECT SUM(PRIZE_MONEY) FROM PLAYERS;
 b.Count:
 i. SELECT COUNT(*) FROM PLAYERS;
ii. SELECT COUNT(GCODE) FROM PLAYERS;
c.Max: SELECT MAX(PRIZE_MONEY) FROM PLAYERS;
d.Min: SELECT MIN(PRIZE_MONEY) FROM PLAYERS;
e.Avg: SELECT AVG(PRIZE_MONEY) FROM PLAYERS;
Note: Always gives a float number.
8.Where:
 a.Comparison:
 i. SELECT * FROM PLAYERS WHERE GCODE = 101;
 ii. SELECT * FROM PLAYERS WHERE GCODE !=
101;
 iii. SELECT * FROM PLAYERS WHERE PRIZE_MONEY
> 10000;
 iv. SELECT * FROM PLAYERS WHERE PRIZE_MONEY
< 10000;
 v. SELECT * FROM PLAYERS WHERE PRIZE_MONEY
<= 9000;
 vi. SELECT * FROM PLAYERS WHERE PRIZE_MONEY
>= 12000;
 vii. SELECT * FROM PLAYERS WHERE SDATE
BETWEEN ‘2003-12-30‘ AND ‘2004-12-30’; 

 b.Like (Wildcard):
 Note: % sign is used to ignore all
 characters before or after.
 _ sign ignore the character before or
 after it
 i. SELECT * FROM PLAYERS WHERE GNAME LIKE
 ‘%TENNIS’;
 ii. SELECT * FROM PLAYERS WHERE GNAME LIKE
 ‘BALL%’;
 iii. SELECT * FROM PLAYERS WHERE GNAME LIKE
 ‘% %’; # Gives all elements with space
 in between.
 c.Mathematical Operations:
 i. SELECT * FROM PLAYERS WHERE
 PRIZE_MONEY%3 = 0; # Gives all elements
 that are divisible by 3
 d.Miscellaneous Operations:
 i. SELECT * FROM PLAYERS WHERE GCODE IN
 (101, 102, 103);
 ii. SELECT * FROM PLAYERS WHERE GCODE NOT IN
 (104, 105, 106);
 iii. SELECT * FROM PLAYER WHERE GCODE IS NOT
 NULL;
9.Order By:
 a.Ascending Order:
 SELECT * FROM PLAYERS ORDER BY GNAME ASC;
 b.Descending Order:
 SELECT * FROM PLAYERS ORDER BY SDATE DESC;
10. Delete:
 DELETE FROM PLAYERS WHERE GCODE = 101;
 Note: If where clause is not there, the whole
 table will be deleted
11. Group By:
 a.SELECT COUNT(GCODE) FROM PLAYERS GROUP BY
 NUM;
 b.SELECT GNAME FROM PLAYERS GROUP BY NUM HAVING
 COUNT(*) > 2;
 c.SELECT GNAME FROM PLAYERS GROUP BY GNAME
 ORDER BY ROLL;
12. Alter Table:
 a.ALTER TABLE PLAYERS ADD PRIMARY KEY(GCODE);
 b.ALTER TABLE PLAYERS ADD FOREIGN KEY(ROLL)
 REFERENCES SPORTS(ROLL);
 c.ALTER TABLE PLAYERS ADD UNIQUE(GNAME);
 d.ALTER TABLE PLAYERS ADD AGE INT;
 e.ALTER TABLE PLAYERS MODIFY AGE VARCHAR(255);
 f.ALTER TABLE PLAYERS MODIFY GNAME VARCHAR(255)
NOT NULL;
 g.ALTER TABLE PLAYERS MODIFY AGE INT DEFAULT
18;
 h.ALTER TABLE PLAYERS DROP COLUMN AGE;
 i.ALTER TABLE PLAYERS DROP PRIMARY KEY;
 j.ALTER TABLE PLAYERS RENAME COLUMN GNAME TO
 NAME;
13. Update:
 a.UPDATE PLAYERS SET PRIZE_MONEY = 10000 WHERE
GCODE=104;
14. Drop:
 a.DROP TABLE PLAYERS;
 b.DROP DATABASE CBSE2024;
15. Joining:
 a.Equi-Join:
SELECT * FROM TABLE1, TABLE2 WHERE TABLE1.COL
= TABLE2.COL;
SELECT * FROM TABLE1 JOIN TABLE2 ON
TABLE1.COL = TABLE2.COL;
b.Natural-Join:
SELECT * FROM TABLE1 NATURAL JOIN TABLE2;
Connectivity:
import mysql.connector as sql
mycon = mysql.connector.connect(host='localhost', user='root',
password='tiger')
mycursor = mycon.cursor()
roll = input('Enter Roll Number: ')
name = input('Enter Name: ')
class_ = input('Enter Class: ')
marks = input('Enter Marks: ')
mycursor.execute('use cbse2024')
mycursor.execute(f"insert into student values({roll}, '{name}',
'{class_}', {marks})")
mycursor.close()
mycon.commit()
mycon.close()

import mysql.connector as sql
con = sql.connect(host='localhost', user='root', password='tiger')
cur = con.cursor()
cur.execute('use cbse2024')
cur.execute('select * from student')
print(cur.fetchall())
print(cur.fetchone())

Result When using Fetchall:

[(102, 'Badminton', 2, 12000, datetime.date(2003, 12, 12)), (103,
'Table Tennis', 4, 8000, datetime.date(2004, 2, 14)), (104, 'Chess', 2,
9000, datetime.date(2004, 1, 1)), (108, 'Lawn Tennis', 4, 25000,
datetime.date(2004, 3, 19)), (101, 'Carrom Board', 2, 5000,
datetime.date(2004, 1, 23))]

Result When using Fetchone:

(1, 'Abinav', '12', 50)

Statistics Module

Function Syntax

String Functions

| len() | Returns the length of the given string | >>> str1 = 'Hello World!'

>>> len(str1)

12 |

| title() | Returns the string with first letter of every word in the string in uppercase and rest in lowercase | >>> str1 = 'hello world!'

>>> str1.title()

'Hello World!' |

Built in Modules

1. Math Module

2. Random Module

3. Statistics Module

File Handling Image

Data Definition Language (DDL) Statements

Data Definition Language (DDL) or Data Description Language (DDL) is a standard for commands that defines the different structures in a database. DDL statements are used to create structure of a table, modify the existing structure of the table and remove the existing table.

Some of the DDL statements are CREATE TABLE, ALTER TABLE and DROP TABLE.

Data Manipulation Language (DML) Statements

Data Manipulation Language (DML) statements are used to access and manipulate data in existing tables.

The manipulation includes inserting data into tables, deleting data from the tables, retrieving data and modifying the existing data.

The common DML statements are SELECT, UPDATE, DELETE and INSERT.

MySQL Commands (continued)

1. Show:

a. SHOW DATABASES;

b. SHOW TABLES;

2. Create:

File Handling Image (continued)

Data Definition Language (DDL) vs Data Manipulation Language (DML) Statements (Image)

CSV & Text Handling (Image)

Text Functions (Image)

List & Tuple & Dictionary Functions (Images)

Additional Notes

• The document includes several slide-style images with diagrams and tables that convey core concepts such as Python keywords, data types, stack operations, and SQL commands. The examples illustrate exception handling, file operations (text, binary, CSV), MySQL command usage (SHOW, CREATE, USE, DESCRIBE, INSERT, SELECT, UPDATE, DELETE, JOIN, GROUP BY, ALTER), and connectivity with Python's MySQL connector.
• Wherever there are code blocks in the text, they are shown here with proper formatting to preserve syntax highlighting and readability.
• Images referenced include Python keyword diagrams, data type classifications, stack diagrams, Python built-in functions and modules, CSV and binary file handling examples, MySQL connectivity and data manipulation examples, and various table-based command references.

File Handling TTR

Definitions (Image)

The file handling section defines and explains File, Stream, Data Files, Text Files, and Binary Files, along with characteristics and usage in web applications. It emphasizes the difference between text and binary files and introduces the concept of data files used for persistent storage.

Text Files (Image)

The text file section includes operations such as reading a fixed number of bytes, reading lines, and seeking within the file, with notes about newline characters and end-of-file handling.

Binary Files (Image)

Binary file handling is described with an example using the pickle module for serialization and deserialization of data records.

CSV Files (Image)

CSV file handling demonstrates writing rows and reading rows with Python's csv module, including the handling of strings and numeric types.

Final Notes

The document comprises a mixture of textual notes and referenced images, all intended to provide a compact, slide-friendly reference for Python, File Handling, and MySQL topics with practical code examples and SQL commands. While some detailed visuals are embedded as images, the textual sections reproduce the core concepts, syntax, and typical usage patterns common to introductory CS material.