How to Use Pandas read_sql to Write and Run SQL?

A Brief Introduction to pandas.read_sql

What is Pandas ?

The Python library Pandas provides the capability to interpret SQL queries using its Pandas functions. The function in the Pandas library reads the results of a SQL query from SQL databases into Panda DataFrames.

This function is compatible with with various database systems such as PostgreSQL, MySQL, Microsoft SQL Server, Oracle, and SQLite.

Users can call directly or use and for reading SQL queries or tables/views, respectively.

Understanding the Functions of Pandas read_sql

There are three primary functions associated with :

• : This function reads data from a SQL statement or query and returns it as a DataFrame.
• : This function executes a SQL query and reads results into a DataFrame from a database.
• : This function reads an entire SQL table or view into a DataFrame from a database.

Understanding the Limitations of Pandas read_sql Function

While Pandas simplifies the extraction of SQL databases within the Python library, it's essential to recognize its limitations.

Significant Memory Consumption

The process can consume a significant amount of memory, primarily because of the storage requirements for the DataFrame and the processing of SQL query results. Adequate memory resources must be available to execute the operation smoothly.

Moreover, importing large datasets can lead to potential errors, and even with moderate data volumes, loading times may be sluggish.

If your goal is to enhance loading speeds, Pandas might not be the optimal choice for extracting SQL databases.

Slower Querying Compared to SQL

Querying data with Pandas is often slower than running the same query in SQL. SQL databases are optimized for data retrieval tasks, while Pandas is primarily designed for data manipulation and analysis in memory.

However, Pandas is great for complex data cleaning or analysis tasks, particularly when integrating with machine learning libraries like scikit-learn. Despite its slower performance compared to SQL, Pandas remains a valuable tool for its ease of use and flexibility in various data analysis workflows.

Using Pandas read_sql: LinkedIn SQL Interview Question Example

Let's practice how to effectively utilize and functions using the LinkedIn SQL Interview Question.

read_sql_query to Execute a SQL query

Let's start by executing a SQL query to retrieve data from the table. We'll select all columns and limit the results to the first five rows.

We'll then use the function to execute the SQL query and fetch the results into a DataFrame named .

Finally, we'll display the first five rows of the DataFrame using the function.

The output will show the candidate IDs and their corresponding skills:

read_sql_table to Read a SQL Table

Now, let's read the entire table into a DataFrame using the function.

We'll then execute the function to read the candidates table into the DataFrame .

By default, this function will read the entire table. However, for demonstration purposes, we'll display only the first ten rows of the DataFrame.

The output will show the first ten rows of the table, including candidate IDs and their respective skills.

Using Pandas read_sql: New York Times SQL Interview Question Example

Next, we'll demonstrate how to use the to filter rows based on specific criteria using the New York Times SQL Interview Question.

read_sql_query to Filter Rows

We'll start by executing a SQL query to filter rows from the table based on device types. We'll select all columns where the device type is either "laptop" or "mobile".

We'll then use the function to execute the SQL query and fetch the results into a DataFrame named .

Finally, we'll display the DataFrame to view the filtered rows.

The output will display viewer data filtered by device types "laptop" and "mobile."

Using Pandas read_sql: JPMorgan Chase SQL Interview Question Example

To demonstrate reading specific columns from a SQL table, we'll use a JPMorgan Chase SQL interview question as an example.

read_sql_table to Extract Specific Columns

We'll read the table, extracting only the and columns.

We'll then use the function to read the specified columns from the table into the DataFrame .

Finally, we'll display the first five rows of the DataFrame to view the extracted columns.

The output will show the card names and issued amounts from the table.

Conclusion

In this tutorial, we've explored various examples of using Pandas functions to execute SQL queries, read SQL tables, and filter rows based on specific criteria. By leveraging these functions, data analysts and scientists can efficiently interact with SQL databases and extract valuable insights for analysis.