Working with SQL Queries in Pandas DataFrames

When working with pandas DataFrames, it’s common to need to execute SQL queries against a database. However, when iterating over a list of tables and executing separate queries for each table, things can get complicated quickly.

In this article, we’ll explore how to select all tables from a list in a pandas DataFrame and how to use f-strings to create dynamic SQL queries.

Introduction to Pandas DataFrames

Before we dive into the nitty-gritty of SQL queries, let’s take a quick look at what a pandas DataFrame is. A DataFrame is a two-dimensional table of data with columns of potentially different types. It’s similar to an Excel spreadsheet or a SQL table.

In pandas, DataFrames are created from other sources like CSV files, databases, or even web pages.

import pandas as pd

# Create a sample DataFrame
data = {'Name': ['John', 'Anna', 'Peter', 'Linda'],
        'Age': [28, 24, 35, 32],
        'Country': ['USA', 'UK', 'Australia', 'Germany']}
df = pd.DataFrame(data)

print(df)

Reading Data from a Database

In this article, we’ll be using pyodbc to connect to an SQL Server database.

First, you need to install the pyodbc library. You can do this with pip:

pip install pyodbc

Next, import the pyodbc library and create a connection string:

import pyodbc

# Create a connection string
cnxn_string = 'DRIVER=ODBC Driver 17 for SQL Server;'
                 'SERVER=server;'
                 'DATABASE=database;'
                 'TRUSTED_CONNECTION=yes;'

# Connect to the database
conn = pyodbc.connect(cnxn_string)

Now that we have a connection, let’s read data from the database using pandas:

import pandas as pd

# Read data from the database
select_all_tables_query = pd.read_sql_query("""SELECT table_name
FROM information_schema.tables
WHERE table_type = 'BASE TABLE'""", conn)

df_all_tables = pd.DataFrame(select_all_tables_query)

Selecting All Tables from a List in a Pandas DataFrame

In this section, we’ll explore how to select all tables from a list in a pandas DataFrame.

First, let’s take a look at the df_all_tables DataFrame:

print(df_all_tables)

As you can see, it contains two columns: table_name and an index. We want to iterate over each table name and execute a separate SQL query for each one.

Here’s how we can do that:

# Get the list of tables from the DataFrame
tables = df_all_tables['table_name']

# Iterate over each table in the list
for table in tables:
    # Create an f-string to create the dynamic SQL query
    sql_query = pd.read_sql_query(f"SELECT * FROM {table}", conn)
    
    # Save the query results to a new DataFrame
    df = pd.DataFrame(sql_query)

    # Write the DataFrame to a CSV file
    df.to_csv(r'C:\path\{table}', index=False)

Error Handling

When executing SQL queries, it’s always a good idea to handle any errors that might occur.

Here’s how you can do that:

try:
    sql_query = pd.read_sql_query(f"SELECT * FROM {table}", conn)
except pyodbc.Error as e:
    print(f"An error occurred: {e}")

Using F-Strings

Now, let’s take a look at how to use f-strings in our SQL queries.

F-strings are a way of formatting strings that allows you to embed expressions inside string literals. They’re similar to the % operator used for old-style string formatting.

Here’s an example:

name = 'John'
print(f'{name}')  # Prints John

var = 'foo'
print(f'{var}')  # Prints foo

In our SQL queries, we can use f-strings in a similar way to create dynamic query strings.

Here’s how it works:

# Create an f-string to create the dynamic SQL query
sql_query = pd.read_sql_query(f"SELECT * FROM {table}", conn)

By using f-strings, we can avoid having to concatenate variables and strings together manually. It makes our code cleaner and easier to read.

Conclusion

In this article, we explored how to select all tables from a list in a pandas DataFrame and how to use f-strings to create dynamic SQL queries.

We went over the basics of pandas DataFrames, connecting to an SQL Server database using pyodbc, reading data from the database, and executing separate SQL queries for each table.

We also discussed error handling and how to avoid common pitfalls like manually concatenating variables and strings together.

Last modified on 2024-02-23