Scientific programming in python cheat sheet

𝐈𝐦𝐩𝐨𝐫𝐭𝐢𝐧𝐠 𝐍𝐞𝐜𝐞𝐬𝐬𝐚𝐫𝐲 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

𝐋𝐨𝐚𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚𝐬𝐞𝐭:

df = pd.read_csv('your_dataset.csv')

𝐈𝐧𝐢𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐈𝐧𝐬𝐩𝐞𝐜𝐭𝐢𝐨𝐧:

1- View the first few rows:
df.head()

2- Summary of the dataset:
df.info()

3- Statistical summary:
df.describe()

𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐕𝐚𝐥𝐮𝐞𝐬:

1- Identify missing values:
df.isnull().sum()

2- Visualize missing values:
sns.heatmap(df.isnull(), cbar=False, cmap='viridis')
plt.show()

𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧:

1- Histograms:
df.hist(bins=30, figsize=(20, 15))
plt.show()

2 - Box plots:
plt.figure(figsize=(10, 6))
sns.boxplot(data=df)
plt.xticks(rotation=90)
plt.show()

3- Pair plots:
sns.pairplot(df)
plt.show()

4- Correlation matrix and heatmap:
correlation_matrix = df.corr()
plt.figure(figsize=(12, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.show()

𝐂𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐜𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬:
Count plots for categorical features:

plt.figure(figsize=(10, 6))
sns.countplot(x='categorical_column', data=df)
plt.show()

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👨‍💻 FREE Resources to Learn & Practice Python

1. https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course
2. https://www.hackerrank.com/domains/python
3. https://www.hackerearth.com/practice/python/getting-started/numbers/practice-problems/
4. https://learnpython.org/
5. https://www.w3schools.com/python/python_exercises.asp
6. https://news.1rj.ru/str/pythonfreebootcamp/134
7. https://news.1rj.ru/str/pythonanalyst/26
8. https://pythonbasics.org/exercises/
9. https://news.1rj.ru/str/pythondevelopersindia/300
10. https://docs.python.org/3/
11. https://news.1rj.ru/str/pythonspecialist/33

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Writing Python Lists

A-Z of Data Science Part-1

A-Z of Data Science Part-2

❤️ Learning Path for ML

🗓️ Python Basics You Should Know 🐍

✅ 1. Variables & Data Types 
Variables store data. Data types show what kind of data it is.

# String (text)
name = "Alice"

# Integer (whole number)
age = 25

# Float (decimal)
height = 5.6

# Boolean (True/False)
is_student = True

🔹 Use type() to check data type:

print(type(name))  # <class 'str'>

✅ 2. Lists and Tuples
⦁ List = changeable collection

fruits = ["apple", "banana", "cherry"]
print(fruits)  # banana
fruits.append("orange")  # add item

⦁ Tuple = fixed collection (cannot change items)

colors = ("red", "green", "blue")
print(colors)  # red

✅ 3. Dictionaries 
Store data as key-value pairs.

person = {
  "name": "John",
  "age": 22,
  "city": "Seoul"
}
print(person["name"])  # John

✅ 4. Conditional Statements (if-else) 
Make decisions.

age = 20
if age >= 18:
    print("Adult")
else:
    print("Minor")

🔹 Use elif for multiple conditions:

if age < 13:
    print("Child")
elif age < 18:
    print("Teenager")
else:
    print("Adult")

✅ 5. Loops 
Repeat code.

⦁ For Loop – fixed repeats

for i in range(3):
    print("Hello", i)

⦁ While Loop – repeats while true

count = 1
while count <= 3:
    print("Count is", count)
    count += 1

✅ 6. Functions 
Reusable code blocks.

def greet(name):
    print("Hello", name)

greet("Alice")  # Hello Alice

🔹 Return result:

def add(a, b):
    return a + b

print(add(3, 5))  # 8

✅ 7. Input / Output 
Get user input and show messages.

name = input("Enter your name: ")
print("Hi", name)

🧪 Mini Projects

1. Number Guessing Game

import random
num = random.randint(1, 10)
guess = int(input("Guess a number (1-10): "))
if guess == num:
    print("Correct!")
else:
    print("Wrong, number was", num)

2. To-Do List

todo = []
todo.append("Buy milk")
todo.append("Study Python")
print(todo)

🛠️ Recommended Tools
⦁ Google Colab (online)
⦁ Jupyter Notebook
⦁ Python IDLE or VS Code

💡 Practice a bit daily, start simple, and focus on basics — they matter most!

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