Converting SQL query into pandas syntax - sql

Question

I am very new to Pandas. How do I convert the following query into pandas syntax. I am no longer querying a MS Access table, I am now querying a pandas DataFrame called df.
The query is:
SELECT
Short_ID,
SUM(IIF(Status = 'Completed', 1, 0))) / COUNT (Status) AS completion_metric
FROM
PROMIS_LT_Long_ID
GROUP BY
Short_ID;
The query results would be something like this:
Short_ID | completion_metric
---------+------------------
1004 | 0.125
1005 | 0
1004 | 0.5
I have created the pandas df with the following code and now I would like to query the pandas DataFrame and obtain the same result as the query above.
import pyodbc
import pandas as pd
def connect_to_db():
db_name = "imuscigrp"
conn = pyodbc.connect(r'DRIVER={SQL Server};SERVER=tcp:SQLDCB301P.uhn.ca\SQLDCB301P;DATABASE=imucsigrp'
r';UID=imucsigrp_data_team;PWD=Kidney123!')
cursor = conn.cursor()
return cursor, conn
def completion_metric():
SQL_Query = pd.read-sql_query('SELECT PROMIS_LT_Long_ID.Short_ID, PROMIS_LT_Long_ID.Status FROM PROMIS_LT_Long_ID', conn)
#converts SQL_Query into Pandas dataframe
df = pd.DataFrame(SQL_Query, columns = ["Short_ID", "Status"])
#querying the df to obtain longitudinal completion metric values
return
Any contributions will help, thank you

Answer 1

You can use some numpy functions for performing similar operations.
For example, numpy.where to replace the value based on a condition.
import numpy as np
df = pd.DataFrame(SQL_Query, columns = ["Short_ID", "Status"])
df["completion_metric"] = np.where(df.Status == "Completed", 1, 0)
Then numpy.average to compute an average value for the grouped data.
completion_metric = df.groupby("Short_ID").agg({"completion_metric": np.average})