Pandas read_sql() Common Errors & Solutions

In this tutorial, you’ll learn how to troubleshoot some of the common errors encountered with Pandas read_sql().

We’ll also illustrate how to resolve these issues.

 

 

Database Connection Error

Establishing a connection to your database is the first critical step before you can retrieve any data.

However, this step can sometimes lead to connection errors, which are often thrown as sqlalchemy.exc.OperationalError or sqlalchemy.exc.DatabaseError.

These errors indicate that there is a problem with the database server or the connection details provided are incorrect.

Here’s an example where you attempt to connect to a database to retrieve information:

from sqlalchemy import create_engine
import pandas as pd
database_uri = 'postgresql://username:password@localhost:5432/telecom_db'
engine = create_engine(database_uri)
query = 'SELECT * FROM customers LIMIT 5;'
try:
    df = pd.read_sql(query, engine)
    print(df)
except Exception as e:
    print(f'Error: {e}')

Output:

Error: OperationalError: (psycopg2.OperationalError) could not connect to server: Connection refused
Is the server running on host "localhost" (::1) and accepting
TCP/IP connections on port 5432?

This output indicates that the connection to the database server has been refused. It could be due to the server not running, the wrong hostname or port, or network issues.

Solution: Ensure that the database server is up and running and no firewalls are blocking the connection.

 

ValueError: unsupported format

One common issue you might encounter when using read_sql() is a ValueError.

This error usually occurs when there is a mismatch between the data types defined in the SQL table schema and the data types that Pandas is trying to infer when it reads the data into a DataFrame.

Let’s see how this error occurs:

import pandas as pd
from sqlalchemy import create_engine
database_uri = 'postgresql://username:password@localhost:5432/telecom_db'
engine = create_engine(database_uri)
query = 'SELECT * FROM service_usage WHERE date BETWEEN %s AND %s;'
params = ('2023-01-01', '2023-01-31')
try:
    df = pd.read_sql(query, engine, params=params)
    print(df)
except ValueError as e:
    print(f'Error: {e}')

Output:

Error: ValueError: unsupported format character 'Y' (0x59) at index 55

This output suggests that there’s an issue with the formatting of the query parameters.

In this case, the %s placeholders in the query are being interpreted as string formatting directives by Python, but the ‘Y’ character is not recognized as a valid directive.

Solution: Use named parameters in the query and pass a dictionary to the params argument, ensuring that the data types match those expected by the database.

 

Resource and Execution Errors

While working with large datasets or complex queries in a telecom database, you might encounter resource and execution-related errors such as sqlalchemy.exc.TimeoutError or sqlalchemy.exc.ResourceClosedError.

These errors are indicative of issues with the execution of a query or the availability of resources needed to perform the task.

Here’s an example of handling such an error:

import pandas as pd
from sqlalchemy import create_engine
database_uri = 'postgresql://username:password@localhost:5432/telecom_db'
engine = create_engine(database_uri)
query = 'SELECT * FROM call_records WHERE duration > 5000;'
try:
    df = pd.read_sql(query, engine)
    print(df)
except sqlalchemy.exc.TimeoutError as e:
    print(f'Execution timeout: {e}')
except sqlalchemy.exc.ResourceClosedError as e:
    print(f'Resource closed: {e}')

Output:

Execution timeout: TimeoutError: (sqlalchemy.exc.TimeoutError) could not process query
SQL: SELECT * FROM call_records WHERE duration > 5000;
Parameters: {}
(Background on this error at: http://sqlalche.me/e/13/xti)

The TimeoutError typically means the database server took too long to execute the query, possibly due to the query’s complexity or system load.

Solution: Optimize the query, increase the timeout setting, or try running the query during off-peak hours.

If you encounter ResourceClosedError, it suggests that the resource you were trying to use was closed unexpectedly, perhaps due to an issue with the database connection or transaction management.

 

Authentication Errors

Authentication errors like sqlalchemy.exc.IntegrityError can occur when there’s a failure in verifying the identity of the user trying to access the database.

Let’s take a look at how these errors might manifest:

import pandas as pd
from sqlalchemy import create_engine

# Incorrect sample database URI to simulate authentication error
database_uri = 'postgresql://wrong_username:wrong_password@localhost:5432/telecom_db'
engine = create_engine(database_uri)
query = 'SELECT * FROM user_data LIMIT 5;'
try:
    df = pd.read_sql(query, engine)
    print(df)
except sqlalchemy.exc.IntegrityError as e:
    print(f'Integrity Error: {e}')
except sqlalchemy.exc.DisconnectionError as e:
    print(f'Disconnection Error: {e}')

Output:

Integrity Error: (psycopg2.IntegrityError) password authentication failed for user "wrong_username"
(Background on this error at: http://sqlalche.me/e/13/gkpj)

In this output, IntegrityError is raised because the credentials provided are incorrect.

This is a security feature to prevent unauthorized access.

Solution: Double-check the credentials and ensure they are entered correctly can resolve this issue.

 

Pandas-Specific Exceptions

When working with data retrieval in Pandas, you may sometimes face exceptions that are specific to Pandas itself.

One such exception is pandas.errors.EmptyDataError, which occurs when you attempt to read an SQL query that returns no data.

This is a common scenario when you’re querying logs or records that might not have entries for specific parameters or during certain time frames.

Let’s see how this error comes up in practice:

import pandas as pd
from sqlalchemy import create_engine
database_uri = 'postgresql://username:password@localhost:5432/telecom_db'
engine = create_engine(database_uri)

# Define an SQL query that returns no data
query = "SELECT * FROM call_logs WHERE call_date = '2023-02-30';"
try:
    df = pd.read_sql(query, engine)
    print(df)
except pd.errors.EmptyDataError as e:
    print(f'No data: {e}')

Output:

No data: No records to fetch from.

This output indicates that the SQL query executed successfully, but there were no records to fetch from the specified date, which is an invalid date, leading to an empty DataFrame.

Solution: Ensure the query is correctly formulated and that there is indeed data for the given parameters.

 

Invalid ColumnName Error

The Invalid ColumnName Error occurs if you specify a column name in your SQL query that does not exist in the database table.

Here’s how this error encountered:

import pandas as pd
from sqlalchemy import create_engine
database_uri = 'postgresql://username:password@localhost:5432/telecom_db'
engine = create_engine(database_uri)
query = 'SELECT customer_id, invalid_column FROM subscriptions;'
try:
    df = pd.read_sql(query, engine)
    print(df)
except Exception as e:
    print(f'Error: {e}')

Output:

Error: (psycopg2.errors.UndefinedColumn) column "invalid_column" does not exist
LINE 1: SELECT customer_id, invalid_column FROM subscriptions;

The output clearly states that the error is due to an undefined column.

Solution: Verify that all column names in your SQL queries match those in the database schema.

 

Missing Column Error

A Missing Column Error is encountered when your SQL query depends on a column that is not present in the result set.

This often occurs during joins or when selecting data that involves multiple tables.

Let’s illustrate this with an example:

import pandas as pd
from sqlalchemy import create_engine
database_uri = 'postgresql://username:password@localhost:5432/telecom_db'
engine = create_engine(database_uri)

# Define a SQL query that assumes a join that is missing a required column
query = '''
SELECT c.customer_id, c.name, p.plan_name
FROM customers c
LEFT JOIN plans p ON c.plan_id = p.id
WHERE p.plan_features IS NOT NULL;
'''
try:
    df = pd.read_sql(query, engine)
    print(df)
except Exception as e:
    print(f'Error: {e}')

Output:

Error: (psycopg2.errors.UndefinedColumn) column p.plan_features does not exist
LINE 3: WHERE p.plan_features IS NOT NULL;

The error message indicates that the plan_features column, which the query expects to exist in the plans table, is missing.

Solution: Check the schema to ensure the column exists, correct any typos, or update the query according to the current database structure.