SQL on Modin Dataframes#
Modin provides a SQL API that allows you to intermix SQL and pandas operations without copying the entire dataset into a new structure between the two. This is possible due to the architecture of Modin. Currently, Modin has a query compiler that acts as an intermediate layer between the query language (e.g. SQL, pandas) and the execution (See architecture documentation for details).
To execute SQL queries, Modin uses HDK engine (See Using HDK documentation for details) Thus, to execute SQL queries, pyhdk module must be installed.
A Short Example Using the Google Play Store#
import modin.pandas as pd import modin.experimental.sql as sql # read google play app store list from csv gstore_apps_df = pd.read_csv("https://tinyurl.com/googleplaystorecsv")
Imagine that you want to quickly select from ‘gstore_apps_df’ the columns App, Category, and Rating, where Price is ‘0’.
# You can then define the query that you want to perform query_str = "SELECT App, Category, Rating FROM gstore_apps WHERE Price = '0'" # And simply apply that query to a dataframe result_df = sql.query(query_str, gstore_apps=gstore_apps_df) # Or, in this case, where the query only requires one table, # you can also ignore the FROM part in the query string: sql_str = "SELECT App, Category, Rating WHERE Price = '0' "
Writing Complex Queries#
For complex queries, it’s recommended to use the HDK engine because it’s much more powerful, comparing to dfsql. Especially, if multiple data frames are involved.
Let’s explore a more complicated example.
gstore_reviews_df = pd.read_csv("https://tinyurl.com/googleplaystoreurcsv")
Say we want to retrieve the top 10 app categories ranked by best average ‘sentiment_polarity’ where the average ‘sentiment_subjectivity’ is less than 0.5.
Since ‘Category’ is on the gstore_apps_df and sentiment_polarity is on gstore_reviews_df, we need to join the two tables, and operate averages on that join.
# Single query with join and group by sql_str = """ SELECT category, AVG(sentiment_polarity) AS avg_sentiment_polarity, AVG(sentiment_subjectivity) AS avg_sentiment_subjectivity FROM ( SELECT category, CAST(sentiment as float) AS sentiment, CAST(sentiment_polarity AS float) AS sentiment_polarity, CAST(sentiment_subjectivity AS float) AS sentiment_subjectivity FROM gstore_apps_df INNER JOIN gstore_reviews_df ON gstore_apps_df.app = gstore_reviews_df.app ) sub GROUP BY category HAVING avg_sentiment_subjectivity < 0.5 ORDER BY avg_sentiment_polarity DESC LIMIT 10 """ # Run query using apps and reviews dataframes, # NOTE: that you simply pass the names of the tables in the query as arguments result_df = sql.query( sql_str, gstore_apps_df = gstore_apps_df, gstore_reviews_df = gstore_reviews_df)
Or, you can bring the best of doing this in python and run the query in multiple parts (it’s up to you).
# join the items and reviews result_df = sql.query(""" SELECT category, sentiment, sentiment_polarity, sentiment_subjectivity FROM gstore_apps_df INNER JOIN gstore_reviews_df ON gstore_apps_df.app = gstore_reviews_df.app""", gstore_apps_df=gstore_apps_df, gstore_reviews_df=gstore_reviews_df) # group by category and calculate averages result_df = sql.query(""" SELECT category, AVG(sentiment_polarity) AS avg_sentiment_polarity, AVG(sentiment_subjectivity) AS avg_sentiment_subjectivity FROM result_df GROUP BY category HAVING CAST(avg_sentiment_subjectivity AS float) < 0.5 ORDER BY avg_sentiment_polarity DESC LIMIT 10""", result_df=result_df)
If you have a cluster or even a computer with more than one CPU core, you can write SQL and Modin will run those queries in a distributed and optimized way.