Using pandasql to Assign Output to New Column in DataFrame

pandas and SQL are two powerful tools for data manipulation and analysis. The pandasql library, specifically, allows us to use SQL queries directly within our Python code to perform complex data operations. However, when working with pandas DataFrames, there are often times when we need to assign the output of a SQL query to a new column in another DataFrame.

In this article, we will explore how to achieve this using pandasql and discuss some key concepts, such as data types, join types, and optimization techniques.

Introduction to pandas and pandasql

pandas is an open-source library for data manipulation and analysis. It provides a powerful data structure called the DataFrame, which is ideal for tabular data. The DataFrame is composed of rows and columns, similar to a spreadsheet or SQL table.

pandasql, on the other hand, allows us to use SQL queries within our Python code. This enables us to perform complex data operations that would be difficult or impossible using only pandas alone. pandasql uses the pandas library as its underlying engine, which means it leverages pandas’ strengths in data manipulation and analysis.

Setting Up pandasql

To get started with pandasql, we need to import the pandasql library:

import pandasql as ps

This imports the ps alias for the pandasql module, which we will use throughout this article.

Creating Example DataFrames

Let’s create two example DataFrames: df1 and df2.

# Create df1
df1 = pd.DataFrame({"min":[10,10,21],
                   "max":[20, 20, 30],
                   "grade":['low', 'medium', "high"],
                   "class":['english', 'math', "english"]})

# Create df2
df2 = pd.DataFrame({"score":([15, 16, 25]),
                          "class":['english', 'math', "english"]})

These DataFrames represent two tables with similar columns.

SQL Query Using pandasql

Now that we have our DataFrames, let’s write a SQL query using pandasql to retrieve data from both tables. We will use an inner join to combine the data.

# Define the SQL query code
sqlcode = '''
select
df1.grade

from df2 
inner join df1 
on df2.score between df1.min and df1.max and df1.class = df2.class
'''

# Create a new DataFrame using pandasql
newdf = ps.sqldf(sqlcode,locals())

This SQL query retrieves the grade column from df1, joined to df2 on the condition that the score is between the minimum and maximum values in df1 for the same class.

Assigning Output to New Column

The original question asked how to assign the output of this SQL query to a new column in another DataFrame, df2. However, we quickly realize that this approach won’t work directly. The reason is that the result of the SQL query isn’t a Series; it’s a DataFrame.

To achieve our goal, we need to tweak our SQL query slightly.

Tweaking the SQL Query

Instead of using an inner join with df1 and df2, let’s use a left join.

# Define the tweaked SQL query code
sqlcode = '''
select
df2.*, df1.grade -- Notice the change
from df2 
left join df1 -- Notice the change
on (df2.score between df1.min and df1.max) and (df1.class = df2.class)
'''

# Create a new DataFrame using pandasql
newdf = ps.sqldf(sqlcode,locals())

By changing from an inner to a left join, we ensure that all rows in df2 are included in the result, even if there’s no matching row in df1.

Output

Let’s run this SQL query and see what output we get:

   score    class   grade
0     15  english     low
1     16     math  medium
2     25  english    high

The resulting DataFrame contains all rows from df2, with an additional column named grade containing the corresponding value from df1.

Conclusion

In this article, we explored how to use pandasql to assign output to a new column in another DataFrame. We saw that simply assigning the result of a SQL query to a new column won’t work directly, as the result isn’t a Series but rather a DataFrame.

By tweaking our SQL query slightly and using a left join instead of an inner join, we can achieve our goal and assign the desired output to a new column in another DataFrame. This example demonstrates the flexibility and power of pandasql for performing complex data operations within pandas DataFrames.

Last modified on 2024-01-29