What Exactly is Pandasql?

Pandasql is a Python library designed to enable SQL query execution on Pandas DataFrames. It serves as a connector between SQL and Python, allowing you to harness the power of SQL for data manipulation directly within your Python scripts.

Let’s jump into practical examples!

Example 1: Basic SQL Query

Imagine you have a DataFrame that holds customer and order data. To calculate the total revenue from each customer, you can leverage Pandasql to write a SQL query like this:

import pandas as pd

import pandasql as ps

# Sample DataFrame

data = {'CustomerID': [1, 2, 3, 4],

'OrderID': [101, 102, 103, 104],

'Amount': [50, 75, 100, 125]}

df = pd.DataFrame(data)

# SQL query using Pandasql

query = "SELECT CustomerID, SUM(Amount) AS TotalRevenue FROM df GROUP BY CustomerID"

result = ps.sqldf(query, locals())

print(result)

In this snippet, we start by importing the necessary libraries, then define a SQL query to compute the total revenue grouped by each customer ID. The output is stored in the result DataFrame.

Example 2: Merging SQL with Pandas

Pandasql allows for SQL queries and integrates seamlessly with Pandas operations. For instance, if you wish to filter customers who spent over $75, you can do so by combining SQL with Pandas:

import pandas as pd

import pandasql as ps

# Sample DataFrame

data = {'CustomerID': [1, 2, 3, 4],

'OrderID': [101, 102, 103, 104],

'Amount': [50, 75, 100, 125]}

df = pd.DataFrame(data)

# SQL query with Pandasql

query = "SELECT * FROM df WHERE Amount > 75"

result = ps.sqldf(query, locals())

print(result)

This example filters the DataFrame to include only those entries where the Amount exceeds $75.

Example 3: Joining DataFrames

SQL’s ability to perform joins is one of its most powerful features, and Pandasql facilitates this with ease. Suppose you have two DataFrames—one for customer data and another for orders—and want to join them for a comprehensive view:

import pandas as pd

import pandasql as ps

# Sample DataFrames

customers = pd.DataFrame({'CustomerID': [1, 2, 3, 4],

'Name': ['Alice', 'Bob', 'Charlie', 'David']})

orders = pd.DataFrame({'OrderID': [101, 102, 103, 104],

'CustomerID': [1, 2, 3, 4],

'Amount': [50, 75, 100, 125]})

# SQL query with Pandasql to perform an inner join

query = "SELECT * FROM customers INNER JOIN orders ON customers.CustomerID = orders.CustomerID"

result = ps.sqldf(query, locals())

print(result)

In this case, we utilize SQL’s INNER JOIN to merge both DataFrames based on the CustomerID.

Overview of pandas.read_sql

The pandas.read_sql function is versatile for reading SQL data into Pandas DataFrames, with various customization options available:

• sql: The SQL query or table name to execute.
• con: Connection details to the database.
• index_col: Specify which column(s) should serve as the DataFrame index.
• coerce_float: Converts non-string, non-numeric objects to floats if possible.
• params: Parameters to pass for the SQL execution.
• parse_dates: Specify columns to parse as dates.
• chunksize: Returns an iterator with the specified number of rows per chunk.
• dtype: Define data types for the DataFrame.

In summary, pandas.read_sql is a flexible tool that can read SQL data into Pandas, making it highly adaptable for various use cases.