How to Pass Parameters to pandas read_sql with SQL Queries?

Pandas read_sql with parameterized SQL queries

The pandas.read_sql function is a powerful tool for fetching data from databases. When working with SQLAlchemy engines, effectively passing parameters to your SQL queries is crucial. This guide demonstrates two common approaches: positional and named parameters.

Method 1: Positional Parameters

This method uses a list or tuple to supply parameters to your SQL query. The placeholders in the SQL statement (%s) are replaced sequentially by the elements in the parameter list.

Example:

<code class="language-python">import pandas as pd
from datetime import datetime

# ... (database connection setup using SQLAlchemy) ...

query = ('SELECT "Timestamp", "Value" FROM "MyTable" '
         'WHERE "Timestamp" BETWEEN %s AND %s')
params = [datetime(2014, 6, 24, 16, 0), datetime(2014, 6, 24, 17, 0)]

df = pd.read_sql(query, db, params=params, index_col=['Timestamp'])</code>

Method 2: Named Parameters

Named parameters offer better readability and maintainability. However, the supported syntax depends on your database driver. With SQLAlchemy and psycopg2, the %(name)s syntax is necessary.

Example:

<code class="language-python">import pandas as pd
from datetime import datetime

# ... (database connection setup using SQLAlchemy) ...

query = ('SELECT "Timestamp", "Value" FROM "MyTable" '
         'WHERE "Timestamp" BETWEEN %(dstart)s AND %(dfinish)s')
params = {"dstart": datetime(2014, 6, 24, 16, 0), "dfinish": datetime(2014, 6, 24, 17, 0)}

df = pd.read_sql(query, db, params=params, index_col=['Timestamp'])</code>

Important Note: Always check your database driver's documentation to confirm the correct parameter syntax. Using the wrong syntax will lead to query errors. This ensures your parameterized queries execute correctly and securely.

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