How to Read a SQLite Database File Using Polars Package in Python

I’ll explore a common Polars read_sql issue with SQLite connections package in python, explain its cause, and provide two fixes. Then, I’ll take it further by adding practical utility functions to the code.

The Initial Code and the Error

Consider the following code snippet that you might run to read data from a SQLite file:

 sqlite3
import polars as pl

conn = sqlite3.connect('database.sqlite')
df = pl.read_sql("SELECT * from table_name", conn)

print(df)

Running this results in the error:

: 'sqlite3.Connection' object has no attribute 'split'

Explanation of the Error

Polars’ read_sql function expects the connection parameter to be a connection string or an object that behaves like one (for example, a SQLAlchemy engine) that supports the method .split(). The built-in sqlite3.Connection object does not have this method, hence the error. In other words, Polars cannot interpret the raw SQLite connection as it would a connection string.

Correct the Error

Use Pandas as a Bridge

One simple solution is to use Pandas to read the data from the SQLite database and then convert the resulting DataFrame into a Polars DataFrame. Here’s how you can do that:

sqlite3
import pandas as pd
import polars as pl

# Establish a connection using sqlite3
conn = sqlite3.connect('database.sqlite')

# Define your query
query = "SELECT * FROM table_name"

# Use Pandas to read the SQL query
df_pd = pd.read_sql_query(query, conn)

# Convert the Pandas DataFrame to a Polars DataFrame
df = pl.from_pandas(df_pd)

print(df)

This approach sidesteps the issue by leveraging Pandas’ extensive support for SQL queries.

Use a SQLAlchemy Engine

Polars’ read_sql works well when provided with a connection string or a SQLAlchemy engine. To use this approach, install SQLAlchemy if you haven’t already:

install sqlalchemy

Then use the following code:

sqlalchemy import create_engine
import polars as pl

# Create a SQLAlchemy engine for SQLite
engine = create_engine("sqlite:///database.sqlite")

# Define your query
query = "SELECT * FROM table_name"

# Directly read the SQL query with Polars using the engine
df = pl.read_sql(query, engine)

print(df)

This method provides the connection in the form that Polars expects and avoids the original error.

Adding More Practice Functionality

Let’s expand the code to include additional functions that are useful for a software project:

• Function for reading data: A helper function to create a Polars DataFrame from a query.
• Data preview and schema display: To get a quick overview of the table.
• Basic filtering operation: To illustrate common DataFrame operations.

Expanded Code Example

sqlalchemy import create_engine
import polars as pl
import pandas as pd
import sqlite3

# Option 1: Using SQLAlchemy Engine with Polars directly
def read_data_with_engine(db_file: str, query: str) -> pl.DataFrame:
    """
    Read data from a SQLite file using a SQLAlchemy engine.
    
    :param db_file: Path to the SQLite database file.
    :param query: SQL query to execute.
    :return: Polars DataFrame with the query result.
    """
    # Create SQLAlchemy engine (using sqlite:/// URI format)
    engine = create_engine(f"sqlite:///{db_file}")
    df = pl.read_sql(query, engine)
    return df

# Option 2: Using sqlite3 with Pandas as an intermediate step.
def read_data_with_pandas(db_file: str, query: str) -> pl.DataFrame:
    """
    Read data from a SQLite file using sqlite3 and Pandas,
    then convert the result into a Polars DataFrame.
    
    :param db_file: Path to the SQLite database file.
    :param query: SQL query to execute.
    :return: Polars DataFrame with the query result.
    """
    # Connect using sqlite3
    conn = sqlite3.connect(db_file)
    df_pd = pd.read_sql_query(query, conn)
    df = pl.from_pandas(df_pd)
    return df

def display_dataframe_info(df: pl.DataFrame):
    """
    Display basic information about the DataFrame.
    
    :param df: The Polars DataFrame.
    """
    # Print the DataFrame's head (first 5 rows)
    print("Preview of the DataFrame:")
    print(df.head())
    
    # Show DataFrame schema
    print("\nDataFrame Schema:")
    print(df.schema)
    
    # Show DataFrame statistics (if numerical data is present)
    try:
        print("\nDescriptive Statistics:")
        print(df.describe())
    except Exception as e:
        print("\nCould not generate statistics:", e)

def filter_data(df: pl.DataFrame, column: str, min_value) -> pl.DataFrame:
    """
    Filter the DataFrame for rows where the value in a specified column is >= min_value.
    
    :param df: The Polars DataFrame.
    :param column: Column name to filter on.
    :param min_value: The minimum value for filtering.
    :return: Filtered Polars DataFrame.
    """
    filtered_df = df.filter(pl.col(column) >= min_value)
    return filtered_df

# ----------------------
# Example Usage
# ----------------------
if __name__ == '__main__':
    db_file = 'database.sqlite'
    query = "SELECT * FROM table_name"
    
    print("Attempting to read data using SQLAlchemy engine with Polars...")
    try:
        df = read_data_with_engine(db_file, query)
        display_dataframe_info(df)
    except Exception as e:
        print("Error using SQLAlchemy engine method:", e)
    
    print("\nAttempting to read data using Pandas intermediary method...")
    try:
        df_alt = read_data_with_pandas(db_file, query)
        display_dataframe_info(df_alt)
        
        # Example additional practice: filter rows from the first method
        # (Assuming that there is a numeric column named 'value')
        filtered_df = filter_data(df_alt, 'value', 10)
        print("\nFiltered DataFrame (rows where 'value' >= 10):")
        print(filtered_df)
    except Exception as e:
        print("Error using Pandas intermediary method:", e)

Explanation

1. Dual Data Reading Functions:
   • read_data_with_engine: Uses SQLAlchemy to provide a connection that Polars can directly use.
   • read_data_with_pandas: Uses sqlite3 and Pandas to read the SQLite database, then converts the data to a Polars DataFrame.
2. DataFrame Information:
   The display_dataframe_info function prints a preview, the schema, and attempts to show descriptive statistics. This is valuable during development to verify that the data was loaded correctly.
3. Data Filtering:
   The filter_data function demonstrates a common data operation: filtering rows based on a condition (in the example, filtering rows where a column named value is greater than or equal to 10).
4. Error Handling:
   Both methods are wrapped in try/except blocks. This is good practice when writing production code, as it provides clearer feedback when something goes wrong.

Conclusion

By understanding the requirements of Polars’ read_sql function and adjusting our approach either by converting a Pandas DataFrame or using a SQLAlchemy engine we can effectively work around the 'split' attribute error. The extended code offers additional practice with reading data, displaying key insights, and applying basic data transformations, all useful in a software project development context.