QTM 350 - Data Science Computing

Lecture 18 - SQLite and Python

Danilo Freire
danilo.freire@emory.edu

Emory University

Hello! Great to see you again! 😊

Lecture overview 📚

Lecture overview

Last time we learned:

  • What are relational databases and why they are still important in data science
  • How to create databases, tables, and insert data using basic SQL commands
  • How to perform queries with SELECT, WHERE and ORDER BY
  • Group data with GROUP BY and filter groups with HAVING
  • Statistics with COUNT, SUM, AVG, MIN, and MAX
  • That was a nice revision! 🤓
  • SQLite is generally easier to set up as it’s often file-based or in-memory

Today we will learn:

  • How to connect SQLite with Python, because we love Python! 🐍 😂
  • We will use the built-in sqlite3 library together with pandas
  • Other SQL commands, such as LIKE, IN, BETWEEN, CASE, window functions, and string functions like SUBSTR and LENGTH
  • How to fill missing data with COALESCE
  • How to use database cursors and fetch results (fetchall, fetchone)
  • If time allows, we will also see how to pivot tables using CASE
  • Let’s get started! 🚀

Connecting SQLite with Python 🐍

Connecting and Creating a Cursor

  • To interact with an SQLite database from Python, we first need to establish a connection and then create a cursor.
  • The connection links our script to the database file.
  • The cursor is like a handle we use to send SQL commands and manage results.
import sqlite3

db_file = 'lecture18.db' # Database filename

# 1. Connect to the database (creates file if needed)
conn = sqlite3.connect(db_file) 

# 2. Create a cursor object to execute commands
cur = conn.cursor()

Executing SQL in Python

Creating Tables

  • We define the structure of our data using CREATE TABLE.
  • Use cursor.execute() to run the command and conn.commit() to save the change.
# Use triple quotes for multi-line SQL
# Drop table if exist 
sql_drop = '''
DROP TABLE IF EXISTS drivers; 
'''

# Execute the SQL command
cur.execute(sql_drop)

# Create a new table named 'drivers'
sql_create = '''
CREATE TABLE drivers (
    driver_id INTEGER PRIMARY KEY AUTOINCREMENT,
    driver_name TEXT,
    team TEXT,
    nationality TEXT,
    victories INTEGER
);
'''
# Execute the SQL command
cur.execute(sql_create)

# Commit the transaction to save the table
conn.commit() 

# Check if the table was created
cur.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='drivers';")
<sqlite3.Cursor at 0x103d31bc0>

Inserting Data

  • Once the table exists, we populate it using INSERT INTO.
  • Again, cursor.execute() runs the command, and conn.commit() makes the insertions permanent.
# Insert a few rows
cur.execute("INSERT INTO drivers (driver_name, team, nationality, victories) VALUES ('Lewis Hamilton', 'Mercedes', 'British', 103);")
cur.execute("INSERT INTO drivers (driver_name, team, nationality, victories) VALUES ('Max Verstappen', 'Red Bull Racing', 'Dutch', 55);")
cur.execute("INSERT INTO drivers (driver_name, team, nationality, victories) VALUES ('Fernando Alonso', 'Aston Martin', NULL, NULL);")
cur.execute("INSERT INTO drivers (driver_name, team, nationality) VALUES ('Charles Leclerc', 'Ferrari', 'Monégasque');") 

# Commit the insertions
conn.commit()

Fetching All Results (fetchall) and One Result (fetchone)

  • After a SELECT query, fetchall() retrieves all results at once into a list of tuples.
  • Useful if you need the full dataset immediately, but can use more memory for large results.
cur.execute('SELECT driver_id, driver_name, team FROM drivers') 
all_rows = cur.fetchall() # Get the list of tuples

# Print the fetched data
for row in all_rows:
    print(row) 
(1, 'Lewis Hamilton', 'Mercedes')
(2, 'Max Verstappen', 'Red Bull Racing')
(3, 'Fernando Alonso', 'Aston Martin')
(4, 'Charles Leclerc', 'Ferrari')
  • fetchone() retrieves rows one by one. Each call gets the next available row.
  • Returns None when no more rows are left. Good for processing sequentially or just getting the top item.
cur.execute('SELECT driver_id, driver_name FROM drivers ORDER BY driver_id')

row1 = cur.fetchone()
print(row1) # First row

row2 = cur.fetchone()
print(row2) # Second row
(1, 'Lewis Hamilton')
(2, 'Max Verstappen')

Iterating Over the Cursor

  • A more “Pythonic” and memory-efficient way to process results row-by-row is to iterate directly over the cursor.
# Execute the query
query = "SELECT driver_name, team FROM drivers WHERE driver_name LIKE 'M%'"

# Loop directly over the cursor object after execute
for row_tuple in cur.execute(query):
     print(row_tuple) 
('Max Verstappen', 'Red Bull Racing')

Filtering Data

IN and BETWEEN Operators

  • Use IN to check if a column’s value matches any value in a specified list.
query_in = "SELECT driver_name, team FROM drivers WHERE team IN ('Ferrari', 'Mercedes')" 
cur.execute(query_in)
for row in cur.fetchall():
    print(row)
('Lewis Hamilton', 'Mercedes')
('Charles Leclerc', 'Ferrari')
  • NOT IN works similarly to exclude values in the list.
query_not_in = "SELECT driver_name, team FROM drivers WHERE team NOT IN ('Ferrari', 'Mercedes')" 
cur.execute(query_not_in)
for row in cur.fetchall():
    print(row)
('Max Verstappen', 'Red Bull Racing')
('Fernando Alonso', 'Aston Martin')
  • BETWEEN checks if a value is within a specified range. Inclusive of the endpoints.
query_between = "SELECT driver_name, victories FROM drivers WHERE victories BETWEEN 10 AND 60" 
cur.execute(query_between)
for row in cur.fetchall():
    print(row)
('Max Verstappen', 55)
  • NOT BETWEEN excludes values within the specified range.

LIKE Operator: Basic Patterns

  • LIKE enables simple pattern matching in text strings.
    • %: Matches any sequence (including none) of characters.
    • _: Matches exactly one character.
# Starts with 'L'
cur.execute("SELECT driver_name FROM drivers WHERE driver_name LIKE 'L%'")
for row in cur.fetchall(): print(row)
('Lewis Hamilton',)
# Ends with 'Racing'
cur.execute("SELECT team FROM drivers WHERE team LIKE '%Racing'")
for row in cur.fetchall(): print(row)
('Red Bull Racing',)
# 'o' as the second letter
cur.execute("SELECT driver_name FROM drivers WHERE driver_name LIKE '_o%'")
for row in cur.fetchall(): print(row)

LIKE Operator: Case Sensitivity and NOT LIKE

  • SQLite LIKE is case-insensitive for ASCII by default. Use LOWER()/UPPER() for reliable, explicit case handling.
# Case-insensitive search for names starting with 'l'
cur.execute("SELECT driver_name FROM drivers WHERE LOWER(driver_name) LIKE 'l%'")
for row in cur.fetchall(): print(row)
('Lewis Hamilton',)
  • You can also use COLLATE NOCASE to make a specific comparison case-insensitive. This is a more general (and recommended) approach.
# Case-insensitive search for names starting with 'l' using COLLATE
cur.execute("SELECT driver_name FROM drivers WHERE driver_name LIKE 'l%' COLLATE NOCASE")

for row in cur.fetchall(): print(row)
('Lewis Hamilton',)
  • Use NOT LIKE to find strings that do not match the pattern.

Try it yourself! 🤓

  • Practice time! Use cur.execute() and fetching/looping.
  • Find all drivers whose names start with ‘M’.
  • Find drivers whose nationality has exactly 7 characters (LENGTH() function might be useful).
  • List drivers whose names start with ‘L’ or ‘M’.
  • Find drivers with 1 to 10 victories (BETWEEN).
  • Appendix 01

Handling Missing Data

Finding NULLs (IS NULL) and COALESCE

  • SQL represents missing values as NULL. Use IS NULL to find them. IS NOT NULL finds rows with values.
cur.execute("SELECT driver_name, victories FROM drivers WHERE victories IS NULL")
for row in cur.fetchall(): print(row)
('Fernando Alonso', None)
('Charles Leclerc', None)
  • COALESCE(value1, value2, ...) is useful for replacing NULLs with a default value. It returns the first non-NULL expression in its arguments.
# If 'victories' is NULL, display 0 instead.
cur.execute("SELECT driver_name, COALESCE(victories, 0) AS victories_filled FROM drivers")
for row in cur.fetchall(): print(row)
('Lewis Hamilton', 103)
('Max Verstappen', 55)
('Fernando Alonso', 0)
('Charles Leclerc', 0)

COALESCE with Subqueries

  • The default value in COALESCE can be dynamic, calculated by a subquery.
query_coalesce_sub = '''
SELECT 
  driver_name,
  COALESCE(victories, 
    -- Subquery calculates average victories from non-NULL rows
    (SELECT CAST(AVG(victories) AS INTEGER) 
     FROM drivers 
     WHERE victories IS NOT NULL) 
  ) AS victories_imputed
FROM drivers; 
''' 
cur.execute(query_coalesce_sub)
for row in cur.fetchall(): print(row)
('Lewis Hamilton', 103)
('Max Verstappen', 55)
('Fernando Alonso', 79)
('Charles Leclerc', 79)
  • Here, NULL victories are replaced by the average of non-NULL victories.

Window Functions

Introduction to Window Functions

  • Window functions compute values across a set of rows (a “window”) related to the current row, without collapsing them like GROUP BY.
  • Essential for ranking, running totals, moving averages, etc.
  • Basic syntax involves the OVER() clause.
  • Requires SQLite version 3.25.0 or newer.
# Add more data for better examples
more_drivers_data = [
    ('Valtteri Bottas', 'Mercedes', 'Finnish', 10),
    ('Sergio Perez', 'Red Bull Racing', 'Mexican', 5),
    ('Lando Norris', 'McLaren', 'British', 2),
    ('Esteban Ocon', 'Alpine', 'French', 1) 
]
# Check if data already exists to avoid duplicates 
cur.execute("SELECT COUNT(*) FROM drivers WHERE driver_name = 'Valtteri Bottas'")
if cur.fetchone()[0] == 0:
    cur.executemany('INSERT INTO drivers (driver_name, team, nationality, victories) VALUES (?, ?, ?, ?)', more_drivers_data)
    conn.commit()
    print(f"Added {len(more_drivers_data)} more drivers.")
else:
    print("Additional drivers already exist.")


# Check SQLite Version
cur.execute("SELECT sqlite_version();")
print(f"SQLite Version: {cur.fetchone()[0]}")
Added 4 more drivers.
SQLite Version: 3.46.0

Window Functions: AVG and RANK

  • Let’s see common window functions in action:
  • AVG(...) OVER (): Average over the entire query result.
  • AVG(...) OVER (PARTITION BY col): Average within groups (partitions) defined by col.
  • RANK() OVER (ORDER BY col): Assigns rank based on col (gaps possible for ties).
query_window_avg_rank = '''
SELECT 
    driver_name, team, victories,
    ROUND(AVG(victories) OVER (), 2) AS avg_overall, 
    ROUND(AVG(victories) OVER (PARTITION BY team), 2) AS avg_team, 
    RANK() OVER (ORDER BY victories DESC) AS rank_overall 
FROM drivers
WHERE victories IS NOT NULL 
ORDER BY rank_overall; 
'''
cur.execute(query_window_avg_rank)
for row in cur.fetchall(): print(row)
('Lewis Hamilton', 'Mercedes', 103, 29.33, 56.5, 1)
('Max Verstappen', 'Red Bull Racing', 55, 29.33, 30.0, 2)
('Valtteri Bottas', 'Mercedes', 10, 29.33, 56.5, 3)
('Sergio Perez', 'Red Bull Racing', 5, 29.33, 30.0, 4)
('Lando Norris', 'McLaren', 2, 29.33, 2.0, 5)
('Esteban Ocon', 'Alpine', 1, 29.33, 1.0, 6)

Window Functions vs. GROUP BY

  • A key difference: GROUP BY reduces the number of rows to one per group. Window functions maintain all original rows and add new columns based on the window calculation.
# GROUP BY example (summarizes)
cur.execute("SELECT team, ROUND(AVG(victories), 2) FROM drivers WHERE victories IS NOT NULL GROUP BY team")
print("--- GROUP BY Output (Collapsed) ---")
for row in cur.fetchall(): print(row)

# Window Function example (adds detail to each row)
cur.execute('''
    SELECT driver_name, team, victories, 
           ROUND(AVG(victories) OVER (PARTITION BY team), 2) as avg_in_team
    FROM drivers 
    WHERE victories IS NOT NULL ORDER BY team, victories DESC
''')
print("\n--- Window Function Output (All Rows) ---")
for row in cur.fetchall(): print(row)
--- GROUP BY Output (Collapsed) ---
('Alpine', 1.0)
('McLaren', 2.0)
('Mercedes', 56.5)
('Red Bull Racing', 30.0)

--- Window Function Output (All Rows) ---
('Esteban Ocon', 'Alpine', 1, 1.0)
('Lando Norris', 'McLaren', 2, 2.0)
('Lewis Hamilton', 'Mercedes', 103, 56.5)
('Valtteri Bottas', 'Mercedes', 10, 56.5)
('Max Verstappen', 'Red Bull Racing', 55, 30.0)
('Sergio Perez', 'Red Bull Racing', 5, 30.0)

Try it yourself! 🤓

  • Use a window function:
  • Select driver_name, nationality, victories.
  • Add rank_nationality: rank drivers by victories within each nationality. (PARTITION BY needed).
  • Exclude NULL victories.
  • Order by nationality, then rank_nationality.
  • Use cur.execute() and fetching/looping.
  • Appendix 02

String Manipulation

Basic String Functions

  • SQLite provides standard functions for text processing.
  • LENGTH(str), UPPER(str), LOWER(str).
  • SUBSTR(str, start, length) extracts a portion of the string (start is 1-indexed).
query_str1 = '''
SELECT driver_name, 
    LENGTH(driver_name) AS len,
    UPPER(driver_name) AS upper,
    LOWER(driver_name) AS lower,
    SUBSTR(driver_name, 1, 4) AS first_four -- Get first 4 characters
FROM drivers LIMIT 4;
'''
cur.execute(query_str1)
for row in cur.fetchall(): print(row)
('Lewis Hamilton', 14, 'LEWIS HAMILTON', 'lewis hamilton', 'Lewi')
('Max Verstappen', 14, 'MAX VERSTAPPEN', 'max verstappen', 'Max ')
('Fernando Alonso', 15, 'FERNANDO ALONSO', 'fernando alonso', 'Fern')
('Charles Leclerc', 15, 'CHARLES LECLERC', 'charles leclerc', 'Char')

More String Functions

  • TRIM(str) removes leading/trailing whitespace.
  • REPLACE(str, find, replace) substitutes text.
  • INSTR(str, find) finds the starting position of find within str (0 if not found).
  • The || operator concatenates (joins) strings.
query_str2 = '''
SELECT driver_name, 
    REPLACE(driver_name, ' ', '_') AS replaced_space,
    INSTR(LOWER(driver_name), 'a') AS first_a_pos, 
    'Driver: ' || driver_name AS labelled_name -- Concatenation
FROM drivers LIMIT 4;
'''
cur.execute(query_str2)
for row in cur.fetchall(): print(row)
('Lewis Hamilton', 'Lewis_Hamilton', 8, 'Driver: Lewis Hamilton')
('Max Verstappen', 'Max_Verstappen', 2, 'Driver: Max Verstappen')
('Fernando Alonso', 'Fernando_Alonso', 5, 'Driver: Fernando Alonso')
('Charles Leclerc', 'Charles_Leclerc', 3, 'Driver: Charles Leclerc')

Conditional Logic: CASE

Basic CASE Statement

  • The CASE statement is SQL’s way of implementing if-then-else logic within a query.
  • CASE WHEN condition THEN result ELSE default_result END
query_case1 = '''
SELECT driver_name, victories,
    CASE 
        WHEN victories > 50 THEN 'Legend' 
        ELSE 'Great Driver (or N/A)'     
    END AS category 
FROM drivers;
'''
cur.execute(query_case1)
for row in cur.fetchall(): print(row)
('Lewis Hamilton', 103, 'Legend')
('Max Verstappen', 55, 'Legend')
('Fernando Alonso', None, 'Great Driver (or N/A)')
('Charles Leclerc', None, 'Great Driver (or N/A)')
('Valtteri Bottas', 10, 'Great Driver (or N/A)')
('Sergio Perez', 5, 'Great Driver (or N/A)')
('Lando Norris', 2, 'Great Driver (or N/A)')
('Esteban Ocon', 1, 'Great Driver (or N/A)')

CASE with Multiple Conditions

  • You can chain multiple WHEN clauses. The first condition that evaluates to true determines the result for that row. The optional ELSE handles cases where no WHEN is true.
query_case2 = '''
SELECT driver_name, victories,
    CASE 
        WHEN victories > 100 THEN 'All-Time Great'
        WHEN victories > 50 THEN 'Legend'
        WHEN victories >= 10 THEN 'Race Winner'
        WHEN victories > 0 THEN 'Podium Potential'
        ELSE 'Data Missing or Zero Wins' 
    END AS status
FROM drivers ORDER BY victories DESC NULLS LAST;
'''
cur.execute(query_case2)
for row in cur.fetchall(): print(row)
('Lewis Hamilton', 103, 'All-Time Great')
('Max Verstappen', 55, 'Legend')
('Valtteri Bottas', 10, 'Race Winner')
('Sergio Perez', 5, 'Podium Potential')
('Lando Norris', 2, 'Podium Potential')
('Esteban Ocon', 1, 'Podium Potential')
('Fernando Alonso', None, 'Data Missing or Zero Wins')
('Charles Leclerc', None, 'Data Missing or Zero Wins')

CASE for Conditional NULL Handling

  • CASE offers more nuanced control over handling NULLs compared to just COALESCE, allowing checks on other columns.
query_case_null = '''
SELECT driver_name, 
    -- Fill nationality based on name if NULL
    CASE 
        WHEN nationality IS NULL AND driver_name = 'Fernando Alonso' THEN 'Spanish'
        WHEN nationality IS NULL THEN 'Unknown' 
        ELSE nationality
    END AS nationality_filled,
    -- Fill victories based on team if NULL
    CASE 
        WHEN victories IS NULL AND team = 'Aston Martin' THEN 32 -- Educated guess! 
        WHEN victories IS NULL THEN 0 
        ELSE victories
    END AS victories_filled
FROM drivers WHERE driver_name LIKE 'F%' OR driver_name LIKE 'L%'; -- Limit output
'''
cur.execute(query_case_null)
for row in cur.fetchall(): print(row)
('Lewis Hamilton', 'British', 103)
('Fernando Alonso', 'Spanish', 32)
('Lando Norris', 'British', 2)

Try it yourself! 🤓

  • Use CASE to create driver_level:
    • ‘Beginner’ if victories < 10 (and not NULL)
    • ‘Intermediate’ if 10 <= victories <= 50
    • ‘Expert’ if victories > 50
    • ‘Unknown’ if victories is NULL Handle NULL first!
  • Select driver_name, victories, driver_level.
  • Use cur.execute() and fetching/looping.
  • Appendix 03

Using SQL with pandas 🐼

Reading Data with pandas.read_sql

  • Reading data using loops and fetchall() works, but pandas makes it much easier for analysis.
  • pandas.read_sql() executes a SELECT query and loads the results directly into a DataFrame.
import pandas as pd # Import pandas

# Pass the SQL query and the connection object
df = pd.read_sql('SELECT * FROM drivers', conn) 
print(df) 
   driver_id      driver_name             team nationality  victories
0          1   Lewis Hamilton         Mercedes     British      103.0
1          2   Max Verstappen  Red Bull Racing       Dutch       55.0
2          3  Fernando Alonso     Aston Martin        None        NaN
3          4  Charles Leclerc          Ferrari  Monégasque        NaN
4          5  Valtteri Bottas         Mercedes     Finnish       10.0
5          6     Sergio Perez  Red Bull Racing     Mexican        5.0
6          7     Lando Norris          McLaren     British        2.0
7          8     Esteban Ocon           Alpine      French        1.0
  • This combines execution and fetching into one step, returning a structured table.

read_sql with Any SELECT Query

  • You can use complex SELECT statements, including joins, filtering, ordering, etc., within read_sql.
query_pd = """
SELECT driver_name, team, victories 
FROM drivers 
WHERE victories > 5 OR victories IS NULL
ORDER BY team
"""
df_filtered_ordered = pd.read_sql(query_pd, conn)
print(df_filtered_ordered)
       driver_name             team  victories
0  Fernando Alonso     Aston Martin        NaN
1  Charles Leclerc          Ferrari        NaN
2   Lewis Hamilton         Mercedes      103.0
3  Valtteri Bottas         Mercedes       10.0
4   Max Verstappen  Red Bull Racing       55.0
  • pandas handles retrieving all the data specified by your SQL query.

Manipulating DataFrames from SQL

  • Once loaded, the DataFrame behaves like any other pandas DataFrame. Apply all your data manipulation skills!
# df was loaded previously
avg_vic_pd = df.groupby('team')['victories'].mean().dropna()
print(avg_vic_pd)
team
Alpine              1.0
McLaren             2.0
Mercedes           56.5
Red Bull Racing    30.0
Name: victories, dtype: float64
# Example using pandas .query() method for filtering
print(df.query('victories > 50 and nationality == "British"'))
   driver_id     driver_name      team nationality  victories
0          1  Lewis Hamilton  Mercedes     British      103.0

Writing DataFrames to SQL (to_sql)

  • The reverse is also easy: DataFrame.to_sql() writes the DataFrame’s contents into a database table.
# Example: Create a DataFrame with British drivers
df_british = df[df['nationality'] == 'British'].copy()

# Write to a new table named 'british_drivers'
# index=False: [Important!]{.alert} Prevents pandas index from becoming a DB column.
df_british.to_sql('british_drivers', conn, if_exists='replace', index=False)

# Verify by reading it back using pandas
print(pd.read_sql('SELECT * FROM british_drivers', conn))
   driver_id     driver_name      team nationality  victories
0          1  Lewis Hamilton  Mercedes     British      103.0
1          7    Lando Norris   McLaren     British        2.0

Try it yourself! 🤓

    1. Create employees table (id, name, dept, salary) using cursor.
    1. Insert data for Alice (HR, 50k), Bob (IT, 60k), Charlie (HR, 70k), David (IT, 80k) using cursor. Commit.
    1. Read the full employees table into df_employees using pd.read_sql(). Print it.
    1. Using df_employees, calculate and print the average salary per department.
  • Appendix 04

Pivot tables in SQL 📄

Pivot Setup: Student Scores

  • Pivoting reshapes data from “long” to “wide” format. SQLite lacks a dedicated PIVOT command, so we use conditional aggregation.
  • Let’s set up the student_scores data first.
# Create and populate student_scores table
cur.execute('DROP TABLE IF EXISTS student_scores;')
cur.execute('CREATE TABLE student_scores (student_name TEXT, subject TEXT, score INTEGER, term TEXT);')
scores_data = [
    ('Alice', 'Maths', 90, 'Q1'), ('Alice', 'Maths', 80, 'Q2'), ('Alice', 'Science', 80, 'Q1'), ('Alice', 'Science', 75, 'Q2'),
    ('Bob', 'Maths', 80, 'Q1'), ('Bob', 'Maths', 100, 'Q2'), ('Bob', 'Science', 80, 'Q1'), ('Bob', 'Science', 70, 'Q2'),
    ('Charles', 'Maths', 70, 'Q1'), ('Charles', 'Maths', 75, 'Q2'), ('Charles', 'Science', 90, 'Q1'), ('Charles', 'Science', 85, 'Q2')
]
cur.executemany('INSERT INTO student_scores VALUES (?, ?, ?, ?)', scores_data)
conn.commit()
print("Created and populated student_scores.")

# Display raw data using pandas
print(pd.read_sql('SELECT * FROM student_scores ORDER BY student_name, term, subject', conn))
Created and populated student_scores.
   student_name  subject  score term
0         Alice    Maths     90   Q1
1         Alice  Science     80   Q1
2         Alice    Maths     80   Q2
3         Alice  Science     75   Q2
4           Bob    Maths     80   Q1
5           Bob  Science     80   Q1
6           Bob    Maths    100   Q2
7           Bob  Science     70   Q2
8       Charles    Maths     70   Q1
9       Charles  Science     90   Q1
10      Charles    Maths     75   Q2
11      Charles  Science     85   Q2

Pivoting with CASE and GROUP BY

  • The technique involves using an aggregate function (AVG, SUM, etc.) around a CASE statement for each desired new column. GROUP BY ensures one row per student.
pivot_query = '''
SELECT 
    student_name,
    -- Avg Maths: Only average score WHEN subject is Maths
    AVG(CASE WHEN subject = 'Maths' THEN score END) as Avg_Maths, 
    -- Avg Science: Only average score WHEN subject is Science
    AVG(CASE WHEN subject = 'Science' THEN score END) as Avg_Science
FROM student_scores
GROUP BY student_name 
ORDER BY student_name;
'''
# Display the pivoted result using pandas
df_pivot = pd.read_sql(pivot_query, conn)
print(df_pivot)
  student_name  Avg_Maths  Avg_Science
0        Alice       85.0         77.5
1          Bob       90.0         75.0
2      Charles       72.5         87.5
  • AVG ignores NULLs, which are produced by the CASE statement when the subject doesn’t match.

Conclusion 📚

Conclusion

  • sqlite3 provides the basic tools to interact with SQLite from Python.
  • Key steps: connect(), cursor(), execute(), commit().
  • Fetch results using fetchone(), fetchall(), or by iterating the cursor.
  • Common SQL commands work as expected.
  • Window functions require modern SQLite versions.
  • Use CASE + GROUP BY for pivoting.
  • pandas.read_sql() greatly simplifies loading data for analysis.
  • df.to_sql() saves DataFrames back to the database.

And that’s all for today! 🚀

Have a great day! 😊


SQLite connection closed.

Appendix 01

Exercise Solutions (Using standard sqlite3 fetching)

import sqlite3 
db_file = 'lecture18.db' 
conn = sqlite3.connect(db_file) 
cur = conn.cursor() 

# Find M names
cur.execute("SELECT * FROM drivers WHERE driver_name LIKE 'M%';")
print("--- Drivers starting with M ---")
for row in cur.fetchall(): print(row)

# Find 7 char nationalities
cur.execute("SELECT * FROM drivers WHERE LENGTH(nationality) = 7;")
print("\n--- Nationalities with 7 chars ---")
for row in cur.fetchall(): print(row)

# Find L or M names
cur.execute("SELECT * FROM drivers WHERE driver_name LIKE 'L%' OR driver_name LIKE 'M%';")
print("\n--- Drivers starting with L or M ---")
for row in cur.fetchall(): print(row)

# Find 1-10 wins
cur.execute("SELECT * FROM drivers WHERE victories BETWEEN 1 AND 10;")
print("\n--- Drivers with 1-10 wins ---")
for row in cur.fetchall(): print(row)

conn.close()
--- Drivers starting with M ---
(2, 'Max Verstappen', 'Red Bull Racing', 'Dutch', 55)

--- Nationalities with 7 chars ---
(1, 'Lewis Hamilton', 'Mercedes', 'British', 103)
(5, 'Valtteri Bottas', 'Mercedes', 'Finnish', 10)
(6, 'Sergio Perez', 'Red Bull Racing', 'Mexican', 5)
(7, 'Lando Norris', 'McLaren', 'British', 2)

--- Drivers starting with L or M ---
(1, 'Lewis Hamilton', 'Mercedes', 'British', 103)
(2, 'Max Verstappen', 'Red Bull Racing', 'Dutch', 55)
(7, 'Lando Norris', 'McLaren', 'British', 2)

--- Drivers with 1-10 wins ---
(5, 'Valtteri Bottas', 'Mercedes', 'Finnish', 10)
(6, 'Sergio Perez', 'Red Bull Racing', 'Mexican', 5)
(7, 'Lando Norris', 'McLaren', 'British', 2)
(8, 'Esteban Ocon', 'Alpine', 'French', 1)

Back to exercise

Appendix 02

Exercise Solution (Using standard sqlite3 fetching)

import sqlite3
db_file = 'lecture18.db'
conn = sqlite3.connect(db_file)
cur = conn.cursor() 

# Query and display using standard fetch
query_rank_nationality = '''
SELECT 
    driver_name, nationality, victories,
    RANK() OVER (PARTITION BY nationality ORDER BY victories DESC) AS rank_nationality
FROM drivers
WHERE victories IS NOT NULL 
ORDER BY nationality, rank_nationality; 
'''
cur.execute(query_rank_nationality)
print("\n--- Drivers ranked by victories within nationality ---")
for row in cur.fetchall(): print(row)

conn.close()

--- Drivers ranked by victories within nationality ---
('Lewis Hamilton', 'British', 103, 1)
('Lando Norris', 'British', 2, 2)
('Max Verstappen', 'Dutch', 55, 1)
('Valtteri Bottas', 'Finnish', 10, 1)
('Esteban Ocon', 'French', 1, 1)
('Sergio Perez', 'Mexican', 5, 1)

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Appendix 03

Exercise Solution (Using standard sqlite3 fetching)

import sqlite3
db_file = 'lecture18.db'
conn = sqlite3.connect(db_file)
cur = conn.cursor()

query_driver_level = '''
SELECT 
    driver_name, victories,
    CASE 
        WHEN victories IS NULL THEN 'Unknown' 
        WHEN victories > 50 THEN 'Expert'
        WHEN victories BETWEEN 10 AND 50 THEN 'Intermediate' 
        WHEN victories < 10 THEN 'Beginner' 
    END AS driver_level
FROM drivers
ORDER BY victories DESC NULLS LAST; 
'''
cur.execute(query_driver_level)
print("--- Driver classification using CASE ---")
for row in cur.fetchall(): print(row)

conn.close()
--- Driver classification using CASE ---
('Lewis Hamilton', 103, 'Expert')
('Max Verstappen', 55, 'Expert')
('Valtteri Bottas', 10, 'Intermediate')
('Sergio Perez', 5, 'Beginner')
('Lando Norris', 2, 'Beginner')
('Esteban Ocon', 1, 'Beginner')
('Fernando Alonso', None, 'Unknown')
('Charles Leclerc', None, 'Unknown')

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Appendix 04

Exercise Solution (Using pandas as requested)

import pandas as pd 
import sqlite3
import os

db_file = 'lecture18.db' 
conn = sqlite3.connect(db_file)
cur = conn.cursor() 

# 1. Create employees table
cur.execute('DROP TABLE IF EXISTS employees;') 
cur.execute('''
CREATE TABLE employees (
    employee_id INTEGER PRIMARY KEY AUTOINCREMENT,
    employee_name TEXT, department TEXT, salary INTEGER
);''')

# 2. Insert data
employee_data = [
    ('Alice', 'HR', 50000), ('Bob', 'IT', 60000),
    ('Charlie', 'HR', 70000), ('David', 'IT', 80000)
]
cur.executemany('INSERT INTO employees (employee_name, department, salary) VALUES (?, ?, ?)', employee_data)
conn.commit()
print("Employees table created and populated.")

# --- Pandas part starts here ---
# 3. Read data with pandas
df_employees = pd.read_sql('SELECT * FROM employees', conn)
print("\n--- Employees DataFrame ---")
print(df_employees)

# 4. Compute average salary by department using pandas
avg_salary_dept = df_employees.groupby('department')['salary'].mean()
print("\n--- Average salary by department (pandas) ---")
print(avg_salary_dept)

conn.close()
Employees table created and populated.

--- Employees DataFrame ---
   employee_id employee_name department  salary
0            1         Alice         HR   50000
1            2           Bob         IT   60000
2            3       Charlie         HR   70000
3            4         David         IT   80000

--- Average salary by department (pandas) ---
department
HR    60000.0
IT    70000.0
Name: salary, dtype: float64

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Appendix 05

Cleaning an SQLite Database

  • Option 1 (Easiest): Delete the database file. Find .db file (lecture18.db) or use code:
import os
db_file_to_delete = 'lecture18.db' 
if os.path.exists(db_file_to_delete):
    try:
        # Ensure connection is closed first!
        os.remove(db_file_to_delete)
        print(f"Deleted database file: {db_file_to_delete}")
    except OSError as e:
        print(f"Error deleting file {db_file_to_delete}: {e}")
  • Option 2: Drop all tables programmatically.
import sqlite3
db_file_to_clean = 'lecture18.db' 
conn_clean = sqlite3.connect(db_file_to_clean)
cur_clean = conn_clean.cursor()
cur_clean.execute("SELECT name FROM sqlite_master WHERE type='table' AND name NOT LIKE 'sqlite_%';")
tables = cur_clean.fetchall()
print(f"Tables to drop: {tables}")
for table_name_tuple in tables:
    cur_clean.execute(f"DROP TABLE IF EXISTS {table_name_tuple[0]}")
    print(f"Dropped table: {table_name_tuple[0]}")
conn_clean.commit()
conn_clean.close()
print("Finished dropping tables.")

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