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✖️ DON'T CREATE NESTED LISTS WITH THE * OPERATOR.

Because of this, you create a list containing multiple references to the very same inner list.
It looks like you're making a grid, but modifying one row will surprisingly change all of them. This is because the outer list just holds copies of the reference, not copies of the list itself.
Correct — use a list comprehension to ensure each inner list is a new, independent object.

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# hidden error — all inner lists are the same object
matrix = [[]] * 3  # seems to create a 3x0 matrix

# append to the first row
matrix[0].append(99)

# all rows were modified!
print(matrix)  # [[99], [99], [99]]

# ✅ correct version — use a list comprehension
matrix_fixed = [[] for _ in range(3)]

# append to the first row
matrix_fixed[0].append(99)

# only the first row is modified, as expected
print(matrix_fixed)  # [[99], [], []]

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By: @DataScienceQ ✨

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Data Setup

#### pandas

import pandas as pd

data = {
    'product_id': [101, 102, 103, 104, 105, 106, 107],
    'product_name': ['Laptop', 'Mouse', 'Keyboard', 'Monitor', 'Webcam', 'Microphone', 'Speakers'],
    'category': ['Electronics', 'Electronics', 'Electronics', 'Electronics', 'Peripherals', 'Peripherals', 'Audio'],
    'price': [1200.00, 25.00, 75.00, 300.00, 50.00, 80.00, 150.00],
    'stock_quantity': [50, 200, 150, 70, 100, 60, 40]
}
df_pd = pd.DataFrame(data)

#### polars

import polars as pl

data = {
    'product_id': [101, 102, 103, 104, 105, 106, 107],
    'product_name': ['Laptop', 'Mouse', 'Keyboard', 'Monitor', 'Webcam', 'Microphone', 'Speakers'],
    'category': ['Electronics', 'Electronics', 'Electronics', 'Electronics', 'Peripherals', 'Peripherals', 'Audio'],
    'price': [1200.00, 25.00, 75.00, 300.00, 50.00, 80.00, 150.00],
    'stock_quantity': [50, 200, 150, 70, 100, 60, 40]
}
df_pl = pl.DataFrame(data)

#### SQL (Conceptual Table Structure and Data)

-- CREATE TABLE products (
--     product_id INT PRIMARY KEY,
--     product_name VARCHAR(255),
--     category VARCHAR(255),
--     price DECIMAL(10, 2),
--     stock_quantity INT
-- );

-- INSERT INTO products VALUES
-- (101, 'Laptop', 'Electronics', 1200.00, 50),
-- (102, 'Mouse', 'Electronics', 25.00, 200),
-- (103, 'Keyboard', 'Electronics', 75.00, 150),
-- (104, 'Monitor', 'Electronics', 300.00, 70),
-- (105, 'Webcam', 'Peripherals', 50.00, 100),
-- (106, 'Microphone', 'Peripherals', 80.00, 60),
-- (107, 'Speakers', 'Audio', 150.00, 40);

---

Select All Columns (SELECT *)

#### pandas

# Select all columns
print(df_pd)

#### polars

# Select all columns
print(df_pl)
# Or explicitly select all columns:
# print(df_pl.select(pl.all()))

#### SQL

-- Select all columns
SELECT *
FROM products;

---

Select Specific Columns (SELECT col1, col2)

#### pandas

# Select 'product_name' and 'price'
print(df_pd[['product_name', 'price']])

#### polars

# Select 'product_name' and 'price'
print(df_pl.select('product_name', 'price'))
# Or using pl.col:
# print(df_pl.select(pl.col('product_name'), pl.col('price')))

#### SQL

-- Select product_name and price
SELECT product_name, price
FROM products;

---

Select Unique Values (SELECT DISTINCT)

#### pandas

# Select unique categories
print(df_pd['category'].unique())

# Select unique combinations of 'category' and 'price'
print(df_pd[['category', 'price']].drop_duplicates())

#### polars

# Select unique categories
print(df_pl.select('category').unique())

# Select unique combinations of 'category' and 'price'
print(df_pl.select('category', 'price').unique())

#### SQL

-- Select unique categories
SELECT DISTINCT category
FROM products;

-- Select unique combinations of category and price
SELECT DISTINCT category, price
FROM products;

---

Filter Rows (WHERE)

#### pandas

# Select products with price greater than 100
print(df_pd[df_pd['price'] > 100])

# Select products in 'Electronics' category
print(df_pd[df_pd['category'] == 'Electronics'])

# Select products in 'Electronics' category with stock less than 100
print(df_pd[(df_pd['category'] == 'Electronics') & (df_pd['stock_quantity'] < 100)])

#### polars

# Select products with price greater than 100
print(df_pl.filter(pl.col('price') > 100))

# Select products in 'Electronics' category
print(df_pl.filter(pl.col('category') == 'Electronics'))

# Select products in 'Electronics' category with stock less than 100
print(df_pl.filter((pl.col('category') == 'Electronics') & (pl.col('stock_quantity') < 100)))

#### SQL

-- Select products with price greater than 100
SELECT *
FROM products
WHERE price > 100;

-- Select products in 'Electronics' category
SELECT *
FROM products
WHERE category = 'Electronics';

-- Select products in 'Electronics' category with stock less than 100
SELECT *
FROM products
WHERE category = 'Electronics' AND stock_quantity < 100;

---

Order Rows (ORDER BY)

#### pandas

# Order products by price in ascending order
print(df_pd.sort_values('price'))

# Order products by price in descending order
print(df_pd.sort_values('price', ascending=False))

# Order by category (ASC) then by price (DESC)
print(df_pd.sort_values(by=['category', 'price'], ascending=[True, False]))

#### polars

# Order products by price in ascending order
print(df_pl.sort('price'))

# Order products by price in descending order
print(df_pl.sort('price', descending=True))

# Order by category (ASC) then by price (DESC)
print(df_pl.sort(by=['category', 'price'], descending=[False, True]))

#### SQL

-- Order products by price in ascending order
SELECT *
FROM products
ORDER BY price ASC;

-- Order products by price in descending order
SELECT *
FROM products
ORDER BY price DESC;

-- Order by category (ASC) then by price (DESC)
SELECT *
FROM products
ORDER BY category ASC, price DESC;

---

Limit Rows (LIMIT)

#### pandas

# Get the first 3 products
print(df_pd.head(3))

#### polars

# Get the first 3 products
print(df_pl.head(3))
# Or using limit:
# print(df_pl.limit(3))

#### SQL

-- Get the first 3 products
SELECT *
FROM products
LIMIT 3; -- Use TOP 3 for SQL Server: SELECT TOP 3 * FROM products;

---

Rename Columns (AS)

#### pandas

# Select 'product_name' as 'Item' and 'price' as 'Unit_Price'
print(df_pd[['product_name', 'price']].rename(columns={'product_name': 'Item', 'price': 'Unit_Price'}))

#### polars

# Select 'product_name' as 'Item' and 'price' as 'Unit_Price'
print(df_pl.select(
    pl.col('product_name').alias('Item'),
    pl.col('price').alias('Unit_Price')
))

#### SQL

-- Select product_name as Item and price as Unit_Price
SELECT product_name AS Item, price AS Unit_Price
FROM products;

---

Combined Operations (Filter, Select, Order, Limit)

#### pandas

# Select 'product_name', 'price', and 'stock_quantity' for 'Electronics' products,
# where price is > 50, ordered by 'price' descending, then get the top 2
result_pd = (
    df_pd[df_pd['category'] == 'Electronics']
    .loc[lambda df: df['price'] > 50]
    [['product_name', 'price', 'stock_quantity']]
    .sort_values(by='price', ascending=False)
    .head(2)
)
print(result_pd)

#### polars

# Select 'product_name', 'price', and 'stock_quantity' for 'Electronics' products,
# where price is > 50, ordered by 'price' descending, then get the top 2
result_pl = (
    df_pl.filter(pl.col('category') == 'Electronics')
    .filter(pl.col('price') > 50)
    .select('product_name', 'price', 'stock_quantity')
    .sort('price', descending=True)
    .head(2)
)
print(result_pl)

#### SQL

-- Select product_name, price, and stock_quantity for 'Electronics' products,
-- where price is > 50, ordered by price descending, then get the top 2
SELECT product_name, price, stock_quantity
FROM products
WHERE category = 'Electronics' AND price > 50
ORDER BY price DESC
LIMIT 2;

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By: @DataScienceQ ✨