Using a Python dict for a SQL INSERT statement

python

data-types

sql-injection

pandas

byAnton Shumikhin·Jan 8, 2025

Utilize Python dictionary unpacking with named placeholders in a parameterized SQL INSERT query for secure, dynamic insertion:

# Assuming `conn` is your active DB connection and `cursor` is its best friend
data_dict = {'column1': 'value1', 'column2': 'value2'}
placeholders = ', '.join(f'%({k})s' for k in data_dict)
query = f"INSERT INTO table_name ({', '.join(data_dict)}) VALUES ({placeholders})"
cursor.execute(query, data_dict)  # Insert... and it's gone!

This approach avoids SQL injection, and smoothly handles the data types in the dictionary, making it both secure and scalable for diverse data schemas.

Dynamically building SQL queries

Crafting an SQL INSERT statement from a Python dict requires both syntactical precision and security measures. It's essential to cloak column names, maintain the sequence of values to match the columns, and deal with Python version differences while handling data types.

Cloaking column names for safety

To avoid SQL injection, never interpolate user inputs directly into SQL queries. Escaping column names can involve database adapters or manual verification against a list of valid fields.

Python version differences in handling dict

Python 3's dict.values() returns a dict_values object, not a list. So, for Python 3, convert them to a list. Thankfully, Python 2's dict.values() returns a list, saving you extra keystrokes 🚀.

Managing data types

SQL is a stickler for data types, so your input dictionary should match those. Remember: Datetime objects require formatting according to your DBMS's dialect.

Constructing placeholders using list comprehension

Use Python's list comprehension to create placeholders while reducing verbosity like a ninja 🥷:

placeholders = ', '.join(['%s']*len(data_dict))
values = tuple(data_dict.values())

Using executemany() for large-scale operations

When dealing with large volumes of data, executemany() method is your secret weapon, minimizing server round trips and saving precious milliseconds.

Pandas for the win

If you're a Pandas user -- and really, who isn't? -- the DataFrame.to_sql() method can be a game-changer, especially when combined with a well-optimized SQL Alchemy engine.

Protecting sequence in dictionary

Since you can't rely on the order of dictionaries (unless you're using Python 3.7+), always process your list of dictionaries in a consistent order. This is especially vital when using the executemany() method.

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