Your final project will be two submissions: A python file in $.$py or $.$ipynb and a paper in $.$pdf format explaining your data available with visuals, your process to clean the data and process and then the results. The attached rubric will guide you on grading criteria. Please use VS Code.

Python File:

Should submit working code

Well commented or documented on what you did or were trying to do

Bring in associated data using pandas

Examine data as needed to understand whats available

If applicable, make changes, joins or manipulate data

Complete task outlined in Project use case $1-3$

Paper sections should include:

Introduction

Data Available

Process

Results

Improvements $($what would you do differently with additional data$)$

Include citations if needed

Minimum length $2-4$ pages

$$

Data Cleansing Final Project Instructions:

There is a table in a database called addresses. Each row in the table holds anaddress. And has the columns:

id: the unique ID for the row

street: which should hold the street for the address

city: the city

state: the state

zip: a string representing the zip code

country: the country

latitude: a floating point number representing the latitude for the address

longitude: a floating point number representing the longitude for the address

default map: a boolean

The columns latitude, longitude and default map can be ignored. Not all the columns in the table are correct. For example instead of saying $$East Providence$($a city in RI$),$ the city column for certain addresses may read $$East Providence,$.$

In addition, many of the addresses are repeats.

This table is provided to you as a csv file.

Your task is to write python code to:

$1.$ Write python code to create a csv called corrected$\_$addresses.csv where each address in the addresses table is corrected i$.$e$.$ all issues in columns street, city, state, zip, country are corrected. The columns for this csv should be:

$$ id: the unique ID for the row

$$ street: which should hold the street for the address

$$ city: the city

$$ state: the state

$$ zip: a string representing the zip code

$$ country: the country

$2.$ write python code to create a csv called corrected$\_$unique$\_$addresses.csv which will

only contain unique addresses from corrected$\_$addresses.csv

$3.$ write python code to create a csv called linker$\_$to$\_$corrected$\_$unique$\_$addresses.csv

which contains $2$ columns:

$$ original$\_$id: the id for each row in the addresses.csv file

$$ cu$\_$id : the id for the corresponding corrected unique address in the corrected$\_$unique$\_$addresses.csv table

$4.$ Load the $3$ csvs into tables in a postgres database or spreadsheet called normalized$\_$addresses. And then create a dump of the postgres database using the pg$\_$dump command.

Please submit back:

$$ the code developed $($and documented$)$ and

$$ the data dump file resulting from the script

$$