Complete guide to Python's sqlite3.Row.__getitem__ method covering row access, indexing, and usage patterns.
Last modified April 15, 2025
This comprehensive guide explores Python’s sqlite3.Row.getitem method, which provides indexed access to row values. We’ll cover basic usage, indexing options, and practical examples.
The sqlite3.Row.getitem method allows accessing row values by index or column name. It’s the underlying implementation for both index-based and name-based access to row data.
Key characteristics: it supports integer indices for positional access and string keys for column name access. It’s used internally when using square bracket notation on Row objects.
Here’s the simplest usage of getitem to access row values by index and column name.
basic_access.py
import sqlite3
with sqlite3.connect(’:memory:’) as conn: conn.row_factory = sqlite3.Row cursor = conn.cursor()
cursor.execute('''CREATE TABLE users (id INTEGER, name TEXT)''')
cursor.execute("INSERT INTO users VALUES (1, 'Alice')")
cursor.execute("SELECT * FROM users")
row = cursor.fetchone()
# Access by index
print(row.__getitem__(0)) # 1
# Access by column name
print(row.__getitem__('name')) # Alice
This example shows both index-based and name-based access using getitem. The row_factory is set to sqlite3.Row for named access.
Normally you’d use row[0] or row[’name’] syntax, which internally calls getitem.
This example demonstrates accessing multiple columns using getitem in a loop.
multiple_columns.py
import sqlite3
with sqlite3.connect(’:memory:’) as conn: conn.row_factory = sqlite3.Row cursor = conn.cursor()
cursor.execute('''CREATE TABLE products
(id INTEGER, name TEXT, price REAL)''')
cursor.execute("INSERT INTO products VALUES (1, 'Laptop', 999.99)")
cursor.execute("SELECT * FROM products")
row = cursor.fetchone()
# Access all columns by name
for col in row.keys():
print(f"{col}: {row.__getitem__(col)}")
The example shows how to iterate through all columns and access their values using getitem with column names. The keys method provides column names.
This pattern is useful when you need to process all columns without knowing their names in advance.
This example shows what happens when trying to access non-existent columns with getitem.
missing_columns.py
import sqlite3
with sqlite3.connect(’:memory:’) as conn: conn.row_factory = sqlite3.Row cursor = conn.cursor()
cursor.execute('''CREATE TABLE test (id INTEGER)''')
cursor.execute("INSERT INTO test VALUES (1)")
cursor.execute("SELECT * FROM test")
row = cursor.fetchone()
try:
# Access non-existent column
print(row.__getitem__('nonexistent'))
except IndexError as e:
print(f"Error: {e}")
Attempting to access a non-existent column with getitem raises an IndexError. This behavior is consistent with Python’s sequence protocol.
Always check column existence with keys or handle the potential exception when working with dynamic queries.
This example demonstrates using getitem with integer indices for positional access.
integer_indices.py
import sqlite3
with sqlite3.connect(’:memory:’) as conn: cursor = conn.cursor()
cursor.execute('''CREATE TABLE data (a INTEGER, b INTEGER, c INTEGER)''')
cursor.execute("INSERT INTO data VALUES (10, 20, 30)")
cursor.execute("SELECT * FROM data")
row = cursor.fetchone()
# Access by position
print(row.__getitem__(0)) # 10
print(row.__getitem__(2)) # 30
# Negative indices work too
print(row.__getitem__(-1)) # 30
The example shows standard sequence-style access using integer indices. Negative indices work as expected, counting from the end.
Positional access is faster than named access but less readable, especially with many columns.
This example shows how getitem interacts with other Row methods like keys and values.
combined_methods.py
import sqlite3
with sqlite3.connect(’:memory:’) as conn: conn.row_factory = sqlite3.Row cursor = conn.cursor()
cursor.execute('''CREATE TABLE events
(id INTEGER, name TEXT, date TEXT)''')
cursor.execute("INSERT INTO events VALUES (1, 'Meeting', '2025-01-01')")
cursor.execute("SELECT * FROM events")
row = cursor.fetchone()
# Combine with keys() and values()
for i, col in enumerate(row.keys()):
print(f"{col}: {row.__getitem__(i)} == {row.__getitem__(col)}")
The example demonstrates that index-based and name-based access return the same values when referring to the same column. The order matches the query’s column order.
This consistency is important when building dynamic data processing pipelines.
This example compares the performance of getitem with direct attribute access.
performance.py
import sqlite3 import timeit
with sqlite3.connect(’:memory:’) as conn: conn.row_factory = sqlite3.Row cursor = conn.cursor()
cursor.execute('''CREATE TABLE perf (a INTEGER, b INTEGER)''')
cursor.execute("INSERT INTO perf VALUES (1, 2)")
cursor.execute("SELECT * FROM perf")
row = cursor.fetchone()
# Time __getitem__ access
t1 = timeit.timeit(lambda: row.__getitem__('a'), number=100000)
# Time direct attribute access
t2 = timeit.timeit(lambda: row['a'], number=100000)
print(f"__getitem__: {t1:.3f}s")
print(f"Direct access: {t2:.3f}s")
The example shows that direct square bracket access (row[‘a’]) is slightly faster than calling getitem directly. Both methods are quite efficient.
The performance difference is negligible in most applications, so prefer the more readable syntax.
This advanced example demonstrates creating a custom Row subclass that extends getitem behavior.
custom_row.py
import sqlite3
class CustomRow(sqlite3.Row): def getitem(self, key): val = super().getitem(key) return f"Custom: {val}"
with sqlite3.connect(’:memory:’) as conn: conn.row_factory = CustomRow cursor = conn.cursor()
cursor.execute('''CREATE TABLE items (id INTEGER, name TEXT)''')
cursor.execute("INSERT INTO items VALUES (1, 'Widget')")
cursor.execute("SELECT * FROM items")
row = cursor.fetchone()
print(row.__getitem__('name')) # Custom: Widget
print(row['id']) # Custom: 1
The example shows how to subclass sqlite3.Row and modify getitem behavior. All access methods inherit the new behavior.
This technique is powerful for adding validation, transformation, or logging to row access operations.
Prefer named access: Use column names for readability
Handle missing columns: Check with keys() or catch exceptions
Use direct syntax: Prefer row[’name’] over getitem
Consider performance: Positional access is faster
Document column order: When using positional access
My name is Jan Bodnar, and I am a passionate programmer with extensive programming experience. I have been writing programming articles since 2007. To date, I have authored over 1,400 articles and 8 e-books. I possess more than ten years of experience in teaching programming.
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