Data Analytics

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1. What is the primary purpose of the pandas library? A. Working with unstructured multimedia data B. Creating and manipulating structured tabular data C. Building machine learning models D. Visualizing neural networks Correct answer: B. 2. Which pandas object is one-dimensional and enforces a homogeneous data type? A. DataFrame B. Index C. Series D. Panel Correct answer: C. 3. How can a pd.Series be best compared to an Excel structure? A. Entire worksheet B. Row C. Column D. Pivot table Correct answer: C. 4. Which object in pandas represents labels for rows or columns? A. Series B. DataFrame C. Index D. ndarray Correct answer: C. 5. What happens if no index is provided when creating a pd.Series? A. An error is raised B. A random index is created C. A RangeIndex starting at 0 is created D. Index values must be inferred manually Correct answer: C. 6. Which argument is used to explicitly set the data type of a pd.Series? A. type= B. data_type= C. dtype= D. astype= Correct answer: C. 7. What is the default value of the name attribute of a pd.Series if not provided? A. Empty string B. Undefined C. None D. "Series" Correct answer: C. 8. Which structure allows heterogeneous column data types? A. Series B. Index C. ndarray D. DataFrame Correct answer: D. 9. When constructing a DataFrame from a dictionary, what do the dictionary keys represent? A. Row labels B. Index levels C. Column labels D. Data types Correct answer: C. 10. Which attribute returns the number of rows in a pd.Series? A. size B. shape C. len() D. index Correct answer: B. 11. What does the pd.Series.shape attribute return? A. An integer B. A list C. A one-element tuple D. A two-element tuple Correct answer: C. 12. Which attribute of a DataFrame returns a Series of column data types? A. dtype B. dtypes C. types D. schema Correct answer: B. 13. What does len(df) return for a DataFrame? A. Number of columns B. Total number of elements C. Number of rows D. Size of memory used Correct answer: C. 14. In basic DataFrame selection using df["a"], what is returned? A. A DataFrame B. A scalar C. A NumPy array D. A Series Correct answer: D. 15. What does df[["a"]] return? A. A Series B. A DataFrame C. A scalar D. A NumPy array Correct answer: B. 16. When using [] with a Series that has a non-default integer index, selection is done by: A. Position B. Order of insertion C. Label D. Data type Correct answer: C. 17. Which method should be used for explicit position-based selection in a Series? A. loc B. at C. iloc D. ix Correct answer: C. 18. What does ser.iloc[1] return? A. All rows with label 1 B. The value at position 1 C. A slice of the Series D. A DataFrame Correct answer: B. 19. How many indexers are required when using DataFrame.iloc? A. One B. Two C. Three D. Unlimited Correct answer: B. 20. What does df.iloc[:, 0] return? A. First row B. First column as a Series C. First column as a DataFrame D. Entire DataFrame Correct answer: B. 21. Which method performs label-based selection in a Series? A. iloc B. at C. loc D. take Correct answer: C. 22. What is a key difference between slicing with loc and iloc? A. loc excludes the stop value B. iloc includes labels C. loc includes the stop label D. iloc works only with strings Correct answer: C. 23. Which operation may raise a KeyError when using loc? A. Slicing with ordered unique labels B. Selecting existing labels C. Slicing with non-unique unordered labels D. Selecting with lists Correct answer: C. 24. In a DataFrame, df.loc["Jack", :] selects: A. All rows named Jack B. All columns named Jack C. All columns for the row labeled Jack D. Only numeric columns Correct answer: C.

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# Select products in 'Electronics' or 'Audio' categories
print("Products in Electronics or Audio:")
print(df_pl.filter(pl.col('category').is_in(['Electronics', 'Audio'])))

# Select products with price between 50 and 200 (inclusive)
print("\nProducts with price between 50 and 200:")
print(df_pl.filter(pl.col('price').is_between(50, 200)))

#### SQL

-- Select products in 'Electronics' or 'Audio' categories
SELECT *
FROM products
WHERE category IN ('Electronics', 'Audio');

-- Select products with price between 50 and 200 (inclusive)
SELECT *
FROM products
WHERE price BETWEEN 50 AND 200;

# Create 'price_level' based on price and 'stock_status'
def get_price_level(price):
    if price > 200:
        return 'High'
    elif price > 50:
        return 'Medium'
    else:
        return 'Low'

def get_stock_status(stock):
    if stock == 0:
        return 'Out of Stock'
    elif stock < 50:
        return 'Low Stock'
    else:
        return 'In Stock'

result_pd = df_pd.assign(
    price_level=df_pd['price'].apply(get_price_level),
    stock_status=df_pd['stock_quantity'].apply(get_stock_status)
)[['product_name', 'price', 'price_level', 'stock_quantity', 'stock_status']]
print(result_pd)

#### polars

# Create 'price_level' based on price and 'stock_status'
result_pl = df_pl.select(
    'product_name',
    'price',
    pl.when(pl.col('price') > 200).then(pl.lit('High'))
    .when(pl.col('price') > 50).then(pl.lit('Medium'))
    .otherwise(pl.lit('Low'))
    .alias('price_level'),
    'stock_quantity',
    pl.when(pl.col('stock_quantity') == 0).then(pl.lit('Out of Stock'))
    .when(pl.col('stock_quantity') < 50).then(pl.lit('Low Stock'))
    .otherwise(pl.lit('In Stock'))
    .alias('stock_status')
)
print(result_pl)

#### SQL

-- Create price_level and stock_status based on conditions
SELECT
    product_name,
    price,
    CASE
        WHEN price > 200 THEN 'High'
        WHEN price > 50 THEN 'Medium'
        ELSE 'Low'
    END AS price_level,
    stock_quantity,
    CASE
        WHEN stock_quantity = 0 THEN 'Out of Stock'
        WHEN stock_quantity < 50 THEN 'Low Stock'
        ELSE 'In Stock'
    END AS stock_status
FROM products;

--- String Transformations in Select #### pandas

# Select product_name in uppercase and first 3 characters of category
result_pd = df_pd.assign(
    product_name_upper=df_pd['product_name'].str.upper(),
    category_prefix=df_pd['category'].str.slice(0, 3)
)[['product_name', 'product_name_upper', 'category', 'category_prefix']]
print(result_pd)

#### polars

# Select product_name in uppercase and first 3 characters of category
result_pl = df_pl.select(
    'product_name',
    pl.col('product_name').str.to_uppercase().alias('product_name_upper'),
    'category',
    pl.col('category').str.slice(0, 3).alias('category_prefix')
)
print(result_pl)

#### SQL

-- Select product_name in uppercase and first 3 characters of category
SELECT
    product_name,
    UPPER(product_name) AS product_name_upper,
    category,
    SUBSTRING(category, 1, 3) AS category_prefix -- Or LEFT(category, 3) in some SQL dialects
FROM products;

--- Selecting with Advanced Filtering (IN, BETWEEN equivalents) #### pandas

# Select products in 'Electronics' or 'Audio' categories
print("Products in Electronics or Audio:")
print(df_pd[df_pd['category'].isin(['Electronics', 'Audio'])])

# Select products with price between 50 and 200 (inclusive)
print("\nProducts with price between 50 and 200:")
print(df_pd[df_pd['price'].between(50, 200)])

#### polars

Selecting with Transformations and Conditional Logic #### Data Setup #### pandas

import pandas as pd

data = {
    'product_id': [101, 102, 103, 104, 105, 106, 107, 108],
    'product_name': ['Laptop', 'Mouse', 'Keyboard', 'Monitor', 'Webcam', 'Microphone', 'Speakers', 'Charger'],
    'category': ['Electronics', 'Electronics', 'Electronics', 'Electronics', 'Peripherals', 'Peripherals', 'Audio', 'Accessories'],
    'price': [1200.00, 25.00, 75.00, 300.00, 50.00, 80.00, 150.00, 15.00],
    'stock_quantity': [50, 200, 150, 70, 100, 60, 40, 0]
}
df_pd = pd.DataFrame(data)

#### polars

import polars as pl

data = {
    'product_id': [101, 102, 103, 104, 105, 106, 107, 108],
    'product_name': ['Laptop', 'Mouse', 'Keyboard', 'Monitor', 'Webcam', 'Microphone', 'Speakers', 'Charger'],
    'category': ['Electronics', 'Electronics', 'Electronics', 'Electronics', 'Peripherals', 'Peripherals', 'Audio', 'Accessories'],
    'price': [1200.00, 25.00, 75.00, 300.00, 50.00, 80.00, 150.00, 15.00],
    'stock_quantity': [50, 200, 150, 70, 100, 60, 40, 0]
}
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),
-- (108, 'Charger', 'Accessories', 15.00, 0);

--- Creating New Columns with Expressions (SELECT col1, col2 + col3 AS new_col) #### pandas

# Select 'product_name', 'price', and calculate 'total_inventory_value'
result_pd = df_pd.assign(
    total_inventory_value=df_pd['price'] * df_pd['stock_quantity'],
    discounted_price=df_pd['price'] * 0.9
)[['product_name', 'price', 'total_inventory_value', 'discounted_price']]
print(result_pd)

#### polars

# Select 'product_name', 'price', and calculate 'total_inventory_value'
result_pl = df_pl.select(
    'product_name',
    'price',
    (pl.col('price') * pl.col('stock_quantity')).alias('total_inventory_value'),
    (pl.col('price') * 0.9).alias('discounted_price')
)
print(result_pl)

#### SQL

-- Select product_name, price, and calculate total_inventory_value and discounted_price
SELECT
    product_name,
    price,
    price * stock_quantity AS total_inventory_value,
    price * 0.9 AS discounted_price
FROM products;

--- Conditional Column Creation (CASE WHEN equivalent) #### pandas

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