How About I leave ChatGPT to explain code!

Code

import pandas as pd

numbers = list(range(1,100))
svs = ['sv' + str(num) for num in numbers]
print(svs)
df_concatenated = pd.DataFrame()

for sv in svs:
    qs = f'''SELECT MIN(load_dt), MAX(load_dt)
    FROM schema_1.curated_{svs}'''
    df = aws.execute_query_df(db, qs)
    df_concatenated = pd.concat([df_concatenated, df], axis = 0)
df_concatenated

ChatGPT’s explanation

This is a Python code that uses the Pandas library to extract data from a SQL database and create a concatenated DataFrame.

The code starts by importing the Pandas library using the alias “pd”.

Next, it creates a list of numbers from 1 to 99 using the range() function and stores it in the variable “numbers”.

After that, it creates a list of strings called “svs” by concatenating the string “sv” with each number in the “numbers” list using a list comprehension.

The code then prints the “svs” list to the console.

The next line creates an empty Pandas DataFrame called “df_concatenated” using the pd.DataFrame() method.

Then, a for loop is used to iterate over each “sv” in the “svs” list. Inside the loop, a SQL query string is created using f-strings (formatted strings) and stored in the variable “qs”. The query selects the minimum and maximum “load_dt” values from a table in a SQL database, where the table name is derived from the current “sv”.

The query is then executed using a function called “aws.execute_query_df” which returns a Pandas DataFrame containing the query results. The DataFrame is concatenated with the previous data using the pd.concat() method, which combines the new data with the existing data along the rows (axis=0).

Finally, the loop ends and the concatenated DataFrame is stored in the “df_concatenated” variable. The code does not return or print this DataFrame, so it will not be visible in the console.

Review

It isn’t too bad as it explained the code really well. However, I believe a human who wrote this code still has the better context for explanation. The language model missed a couple of things:

• The query was actually executed using AWS API that connects to RedShift, which is AWS’ data warehouse solution.
• Actually Jupyter notebooks, which is where this code is executed does not need ‘print’ to actually print a dataframe.

Overall, its a decent attempt!