import pandas as pd

data = {
    'city' : ['Toronto', 'Montreal', 'Waterloo'],
    'points' : [80, 70, 90]
}

data

{'city': ['Toronto', 'Montreal', 'Waterloo'], 'points': [80, 70, 90]}

type(data)

dict

df = pd.DataFrame(data)

df

df = df.assign(code = [1, 2, 3])

df

from datetime import datetime

def get_age(d):
    d1 = datetime.now()
    months = (d1.year - d.year) * 12 + d1.month - d.month

    year = int(months / 12)
    return year

age = get_age(datetime(1991, 1, 1))

age

33

a = 5
b = 3

print(a + b)

8

print(a - b)

2

print(a * b)

15

print(a / b)

1.6666666666666667

num = 7
print("Even" if num % 2 == 0 else "Odd")

Odd

num = -10
if num > 0:
    print("Positive")
elif num < 0:
    print("Negative")
else:
    print("Zero")

Negative

a, b …

n = int(input("Enter a number: "))
sum = 0
for i in range(1, n+1):
    sum += i
print("Sum:", sum)

Enter a number:  2


Sum: 3

n = int(input("Enter a number: "))
count = 0
while n > 0:
    count += 1
    n //= 10
print("Digit count:", count)

Enter a number:  4


Digit …

def factorial(n):
    result = 1
    for i in range(2, n+1):
        result *= i
    return result

print(factorial(int(input("Enter number: "))))

Enter number:  62


31469973260387937525653122354950764088012280797258232192163168247821107200000000000000

def fib(n):
    if n <= 1:
        return n
    return fib(n-1) + fib(n-2)

n = int(input("Enter n: "))
for i in range …

def is_anagram(str1, str2):
    return sorted(str1.lower()) == sorted(str2.lower())

print("Anagram" if is_anagram(input("First string: "), input("Second string: ")) else "Not Anagram")

First string:  listen
Second string:  silent


Anagram

import string

def is_pangram(s):
    s = s.lower()
    return all(ch in s for ch in string.ascii_lowercase)

print …

with open("sample.txt", "w") as file:
    file.write("Hello, Stefina!\nThis is a sample file created by Python.\nWelcome to file handling!")
with open("sample.txt", "r") as file:
    content = file.read()
    print("File Content:\n")
    print(content)

File Content:

Hello, Stefina!
This is a sample file …

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import classification_report, confusion_matrix

df = pd.read_csv('spam.csv', encoding='latin-1')[['v1', 'v2']]

df.columns = ['label', 'text']

df['label'] = df['label'].map({'ham': 0, 'spam': 1})
X_train, X_test …

class Person:
    def greet(self):
        print("Hello, I am a person!")

p = Person()
p.greet()

Hello, I am a person!

class Person:
    def __init__(self, name):
        self.name = name

    def greet(self):
        print(f"Hi, I'm {self.name}")

p = Person("Alice")
p.greet()

Hi, I'm Alice

class Dog …

from abc import ABC, abstractmethod

class Shape(ABC):
    @abstractmethod
    def area(self): pass

from abc import ABC, abstractmethod

class Shape(ABC):
    def info(self):
        print("Shape info")

    @abstractmethod
    def area(self): pass

from abc import ABC, abstractmethod

class Appliance(ABC):
    def plug_in(self):
        print("Appliance plugged in")

    @abstractmethod
    def operate …