[SOLVED] CIS 5450 Homework 2 SQL and DuckDB Python

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! 

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)