CIS 5450 Homework 2: SQL and DuckDB
Due: Friday, October 11 2024, 10:00pm EST Worth 95 points in total (25 manually graded)
Welcome to Homework 2! By now, you should be familiar with the world of data science and the Pandas library. This assignment focuses on helping you get to grips with two new tools: SQL and DuckDB.
Through this homework, we will be working with SQL by exploring a Indego dataset containing bike rides, stations and weather data. We will then expand our exploration with DuckDB and inish by comparing the two with Pandas.
We are introducing a lot of new things in this homework, and this is often where students start to get lost. Thus, we strongly encourage you to review the slides/material as you work through this assignment.
Before you begin:
· Be sure to click "Copy to Drive" to make sure you're working on your own personal version of the homework
· Check the pinned FAQ post on Ed for updates! If you have been stuck, chances are other students have also faced similar problems. Y Part 0: Libraries and Set Up Jargon
import pandas as pd
!pip3 install penngrader-client !pip install sqlalchemy==1.4.46 !pip install pandasql
!pip install geopy
!pip install -U kaleido
from penngrader.grader import *
import pandas as pd
import datetime as dt
import geopy.distance as gp
import matplotlib.image as mpimg import plotly.express as px
# import re
import pandasql as ps #SQL on Pandas Dataframe
import nltk
nltk.download('punkt') import duckdb
from wordcloud import WordCloud
from matplotlib.dates import date2num import matplotlib.pyplot as plt
from PIL import Image
# from collections import Counter
# import random
# Three datasets we're using
! wget -nc https://storage.googleapis.com/penn-cis5450/indego_trips.csv
! wget -nc https://storage.googleapis.com/penn-cis5450/indego_stations.csv
! wget -nc https://storage.googleapis.com/penn-cis5450/weather_2022_PHL.csv
PennGrader Setup
# PLEASE ENSURE YOUR PENN-ID IS ENTERED CORRECTLY. IF NOT, THE AUTOGRADER WON'T KNOW
# TO ASSIGN POINTS TO YOU IN OUR BACKEND
STUDENT_ID = # YOUR PENN-ID GOES HERE AS AN INTEGER #
SECRET = STUDENT_ID
%%writefile config.yaml
grader_api_url: 'https://23whrwph9h.execute-api.us-east-1.amazonaws.com/default/Grader23'
grader_api_key: 'flfkE736fA6Z8GxMDJe2q8Kfk8UDqjsG3GVqOFOa'
grader = PennGrader('config.yaml', 'cis5450_fall24_HW2', STUDENT_ID, SECRET)
Biking in Philadelphia
I'm sure in your time in Philadelphia so far you've come across these blue bikes and stations. Indego is the company responsible for this bike sharing ride system, and they make data on bike trips available to the public. This data can not only be useful to get information of how people in Philly use bikes, but it can give information on the most visited places in the city which can be useful for city planners and business owners.
In this homework, we'll be exploring some data about bikes including: · Trips: data about bike trips during theirst week of October 2022. · Stations: data about bike stations, their ID and Name.
· Weather: data about the weather in Philadelphia during 2022.
We'll be parsing this data into dataframes and relations, and then exploring how to query and assemble the tables into results. We will primarily be using DuckDB, but for some of the initial questions, we will ask you to perform. the same operations in Pandas as well, so as to familiarize you with the differences and similarities of the two.
Part 1: Load & Process our Datasets [15 points total] Before we get into the data, we irst need to load and clean our datasets.
Metadata
You'll be working with three CSV iles:
indego_trips.csv
indego_stations.csv weather_2022_PHL.csv
The ile indego_trips.csv contains data about each trip, like the origin station, destination station and duration. The ile indego_stations.csv includes information about stations and their status in January 2023.
The ile weather_2022_PHL.csv has one row per day during 2022 and shows weather information. TODO:
· Load indego_trips.csv and save the data to a dataframe. called trips_df .
· Load indego_stations.csv and save the data to a dataframe. called stations_df .
· Load weather_2022_PHL.csv and save the data to a dataframe. called weather_df .
# TODO: Import the datasets to pandas dataframes -- make sure the dataframes are named correctly!
# view trips_df using .head() to make sure the import was successful
# view stations_df using .head() to make sure the import was successful
# view weather_df using .head() to make sure the import was successful
1.1 Data Preprocessing
Next, we are going to want to clean up our dataframes, namely trips_df and stations_df , by 1) ixing columns, 2) changing datatypes, 3) handling nulls.
First, let us view theirst few rows of trips_df . You may also call .info() and additionally check the cardinality of each column to view the speciics of the dataframe. This is a good irst step to take for Exploratory Data Analysis (EDA).
1.1.1 Cleaning trips_df [8 points]
.info() gives us meaningful information regarding columns, their types, and the amount of nulls, based on which we can now clean our dataframe.
Perform. these steps and save results on a new dataframe. trips_cleaned_df TODO:
· Drop the column plan_duration . We already have that information in the column passholder_type , which is more understandable. · Drop the rows where end_station is 3000. This is a virtual station used for maintainance, and doesn't represent a real trip.
· Drop all rows with null values. · Cast the columns:
o start_time , end_time , trip_route_category , passholder_type , bike_type as string. (Cast to 'string'and not 'str') o bike_id as int.
· Sort results by trip_id ascending
· Reset and drop the index and save results as trips_cleaned_df
After performing these steps, trips_cleaned_df should have the following schema: Final Schema:
trip_id duration start_time end_time start_station start_lat start_lon end_station end_lat end_lon bike_id trip_route_category passholder_type bike_type
#view info of trips_df
# TODO: drop plan_duration
# TODO: drop rows with irrelevant end_station
# TODO: drop rows with null values
# TODO: cast the types of the columns
# TODO: sort the results
# TODO: drop the index and save the results
# 4 points
grader.grade(test_case_id = 'test_cleaning_trips', answer = trips_cleaned_df)
Now we are going to clean up the start_time and end_time columns so that they are easier to use. We will be using Regex in this section to
separate out the date and the time from the entries. TODO:
· Fill in the Regex patterns to retrieve irst the date, and then the time, found in each entry.
· Extract the relevant parts of each column and populate new columns called the following: date_start , time_start , date_end , time_end . Note that the datetime type does contain both date and time, but that we are wanting to explore your Regex capabilities :)
· Cast the new date columns as datetime64[ns], using pd.to_datetime() and the format as '%m/%d/%Y' · Remove columns start_time , end_time
#view trips_cleaned_df using .head()
# TODO: cast the types of start_time and end_time to 'string'
# TODO: fill in the pattern to retrieve the date of each entry
# HINT: think about the unique syntax of the date section, the different variations it can appear in
date_pattern =
# TODO: fill in the pattern to retrieve the time of each entry
# HINT: think about the unique syntax of the time section, the different variations it can appear in
time_pattern =
# TODO: populate columns date_start, time_start, date_end, time_end
# TODO: cast the date columns to as datetime64[ns]
# TODO: drop the start_time and end_time
# 4 points
grader.grade(test_case_id = 'test_regex_trips', answer = trips_cleaned_df)
1.1.2 Processing Stations [3 Points]
stations_df contains information on Indego stations across the city. We will clean this df by removing Inactive stations and stations created after October 2022.
Perform. these steps and assign the cleaned dataframe. to stations_cleaned_df . TODO:
· Drop the stations that have an Inactive status.
· Cast column day of go live_date as datetime64[ns].
· Drop the stations that were created after 10/7/2022 since this is the last date of rides we are analyzing.
· Drop the columns day of go live_date and status
· Create a new column called is_west_philly that is True if zone is 2 or 3 and False otherwise.
· Save the resulting dataframe. as stations_cleaned_df , and sort it by station_id ascending
After performing these steps, stations_cleaned_df should have the following schema: Final Schema:
station_id station_name zone is_west_philly
#view info of stations_df
# TODO: Drop the stations that have an Inactive status.
# TODO: Cast column day_of_go_live_date as datetime64[ns].
# TODO: Drop the stations that were created after 10/7/2022.
# TODO: Drop day_of_go_live_date and status columns
# TODO: Create a new column called is_west_philly that is True if zone is 2 or 3 and False otherwise.
# TODO: Sort by station_id ascending
# TODO: Reset and drop the index, and save the resulting dataframe as stations_cleaned_df
# 3 points
grader.grade(test_case_id = 'test_cleaning_stations', answer = stations_cleaned_df)
Y 1.1.3 Cleaning the weather [4 Points]
Then, let's clean weather_df and make it usable. We are going to make two different datasets, one for the actual data, and another for the record-holding data.
TODO:
· Create actual_weather_cleaned_df and only keep the following 5 columns:
o date , actual_mean_temp , actual_min_temp , actual_max_temp , actual_precipitation · Create record_weather_cleaned_df and only keep the following 4 columns:
o date , record_min_temp , record_max_temp , record_precipitation Then for both datasets:
· Convert column date into type datetime64[ns] .
· Keep only the rows from 9/1/2022 to 10/31/2022, inclusive. · Sort by column date descending.
· Reset and drop the index.
After performing these steps, actual_weather_cleaned_df should have the following schema: Final Schema:
date actual_mean_temp actual_min_temp actual_max_temp actual_precipitation
... and record_weather_cleaned_df should have the following schema: Final Schema:
date record_min_temp record_max_temp record_precipitation
#view info of weather_df
# TODO: create actual_weather_cleaned_df
# TODO: create record_weather_cleaned_df
# TODO: for both datasets, convert column 'date' into type datetime64[ns]
# TODO: for both datasets, keep only the rows from 9/1/2022 to 10/31/2022, inclusive
# TODO: for both datasets, sort by column 'date' descending
# TODO: for both datasets, reset and drop the index
# 4 points
grader.grade(test_case_id = 'test_cleaning_weather', answer = [actual_weather_cleaned_df, record_weather_cleaned_df])
Part 2: DuckDB [55 points total]
IMPORTANT: Pay VERY CLOSE attention to this style guide! 命命命
The typical low to use duckdb is as follows:
1. Write a SQL query in the form. of a string
。 String Syntax: use triple quotes """<your query>""" to write multi-line strings
。Aliases are your friend: if there are very long table names or you ind yourself needed to declare the source (common during join tasks), it's almost always optimal to alias your tables with short INTUITIVE alias names
o New Clauses New Line: each of the main SQL clauses ( SELECT , FROM , WHERE , etc.) should begin on a new line o Use Indentation: if there are many components for a single clause, separate them out with new indented lines.
Example below:
"""
SELECT ltn.some_id, SUM(stn.some_value) AS total
FROM long_table_name AS ltn
INNER JOIN short_table_name AS stn
ON ltn.common_key = stn.common_key INNER JOIN med_table_name AS mtn
ON ltn.other_key = mtn.other_key
WHERE ltn.col1 > value
AND stn.col2 <= another_value
AND mtn.col3 != something_else
GROUP ORDER """
ltn.some_id total
2. Run the query using duckdb.sql(your_query)
Duckdband pandasql are convenient in that they allow you to reference the dataframes that are currently deined in your notebook, so you will be able to fully utilize the dataframes that you have created above!
Given that it is a brand new language, we wanted to give you a chance to directly compare the similarities/differences of the pandas that you already know and the SQL that you are about to learn. SQL queries may take up to a minute to run.
ΔWARNING: DO NOT USE PANDAS FOR SQL QUESTIONS OR VICE-VERSA! OTHERWISE, YOU WON'T RECEIVE CREDITS FOR THESE QUESTIONS
#TODO: Run this cell to understand how Duck DB connects to local dataframes and queries them
test_duckdb_query = """
SELECT *
FROM actual_weather_cleaned_df LIMIT 10
"""
duckdb.sql(test_duckdb_query)
2.1 Comparing Rides by Passholders [17 points]
2.1.1 How many rides were taken per passholder type? [3 points]
The dataframe trips_cleaned_df contains information for each ride. We want to know which of these rides were taken per passholder type (Day Pass, Indego30, Indego365), and how many such rides were taken.
TODO:
· Create a DuckDB query that counts the number of trips taken per passholder type.
· The resulting table should have the column passholder_type that contains the pass names and another column trip_count that is the count of the number of trips taken on that pass.
· Sort this table by trip_count in descending order.
Note: Do not modify the lines where we are calling the Duck DB SQL engine, as this line converts your results to a Pandas dataframe. for our test cases.
Hint: It would be helpful to use the GROUP BY and ORDER BY statements in SQL. Documentation for the GROUP BY statement can be found here and ORDER BY statement can be found here.
Final Schema:
passholder_type trip_count
# TODO: Use Duck DB to get the total number of trips by passholder typ
passholder_count_query = """
"""
passholder_count = duckdb.sql(passholder_count_query).df()
# 3 Points
2.1.2 What was the average distance between the start and endpoint of the ride by passholder type? [5 points] We are now interested in what the average displacement was between the start and end of the ride by each passholder type.
TODO:
· Create a (Python) function called calculate_distance that returns the distance in miles between two longitude/latitude coordinates. The input parameters should be start_lat , start_lon , end_lat , end_lon
· Create a function in Duck DB using the create_function functionality in Duck DB. Name this function distance . The input/output datatypes for this function should all be float .
· Use this function inside a Duck DB query to ind the average trip displacement by passholder type for non-roundtrip rides from trips_cleaned_df .
· Sort the table by the average displacement in descending order.
Documentation on using Python functions in Duck DB can be found here. The "Python Function API" and "Creating Functions" sections would be most helpful.
Hint: Use the GeoPy Distance module to help create your function, which has already been imported for you at the top of this notebook! Documentation on this module can be found here.
Final Schema:
passholder_type avg_displacement
# TODO: Finish the rest of the calculate_distance function by using the GeoPy Distance module.
def calculate_distance(start_lat, start_lon, end_lat, end_lon):
pass
The following test case is not graded but given to you all as a spot check. If you don't receive a hint from the test case you are good to go!
# 0 point
grader.grade(test_case_id = 'test_distance_function', answer = calculate_distance(30.4515, -91.1871, 29.9509, -90.0758))
# DO NOT EDIT
try:
duckdb.remove_function("distance") except:
pass
# TODO: Use the calculate_distance function to create a Duck DB function with the correct parameters
# TODO: Use Duck DB to find the average displacement for non-roundtrip rides for each passholder type.
distance_query = """
"""
passholder_distance = duckdb.sql(distance_query).df()
passholder_distance
# 5 points
grader.grade(test_case_id = 'test_passholder_distance', answer = passholder_distance)
2.1.3 What is the distribution of electric vs standard bikes for each type of pass? [4 points]
TODO:
· Create a DuckDB query that inds the percentage of rides that used an electric bike for each type of pass from trips_cleaned_df .
· The resulting table should have the column passholder_type and electric_pct , which shows what percentage of the rides taken for that pass were on electric bikes.
· For the electric_pct column, scale the percentages to be between 0 and 100 (e.g. 40.4% = 40.4) and rounded to 1 decimal place. · Sort the table by electric_pct in descending order.
· Do not modify the variable name of the query string (passholder_bike_type_query), as this variable will be passed into the test case.
Hint: For documentation on rounding in SQL, see the documentation here. For documentation on the CASE statement, see the documentation here.
Final Schema:
passholder_type electric_pct
# TODO: Create a Duck DB query that finds the percentage of rides on an electric bike for each type of passholder.
passholder_bike_type_query = """
"""
passholder_bike_type = duckdb.sql(passholder_bike_type_query).df()
passholder_bike_type
# 4 points
grader.grade(test_case_id = 'test_passholder_bike_type', answer = (passholder_bike_type, passholder_bike_type_query))
2.1.4 What are the most popular rides for each day of the week? [4 points]
We want to understand what are the most popular days of the week for bike rides for each type of pass in trips_cleaned_df . TODO:
· Create a query using Duck DB that returns a table with the following schema:
Day of Week Number of Days Day Pass Indego30 Indego365
· The Day of Week column indicates the speciic day of the week when the rides occurred. The Day Pass , Indego30 , and Indego365 columns represent the total number of rides for each type of pass on that particular day of the week. For example, if a row lists "Monday" in the Day of Week column, the value in the Day Pass column should relect the total number of rides taken with a Day Pass on all Mondays
throughout the dataset.
· Number of Days is the number of dates in our dataset that fall on that row's day of the week. · Sort the dataframe. to start from Monday and end on Sunday.
Hint: Look at the documentation here to extract day of week names in SQL. Look at the documentation here to igure out how to count the distinct dates for each day of the week in the Number of Days column.
Hint: If we are deining a column alias in SQL as a string with spaces (e.g. "Day of Week"), we have to put it in double quotes inside of the query. To reference a string, only single quotes are needed (e.g. 'Monday').
Hint: To sort the dataframe. by the correct days of the week, consider using a CASE statement to assign each day of the week to a number, then sorting by this assignment.
# TODO: Create a Duck DB query in the schema specified above that counts the number of bike rides per passholder type on ever
day_of_week_query = """
"""
day_of_week = duckdb.sql(day_of_week_query).df()
day_of_week
# 4 points
grader.grade(test_case_id = 'test_day_of_week_count', answer = day_of_week)
Relect: What do you think? [1 point]
Which day of the week did Indego30 passholders ride the most? What can you say about the distribution of the number of rides for each day of the week? Do we have enough data to draw a fair conclusion about our data or rider behavior?
# TODO: Answer the above reflection questions.
your_answer = "[Your answer here]"
# 1 point
grader.grade(test_case_id = 'test_reflection', answer = your_answer)
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