AirBnB MongoDB Analysis

Instructions

A little assignment to practice importing and analyzing data within a MongoDB database.

The goals of this assignment are to:

• gain experience working with real world data sets
• import the text data file into MongoDB document-oriented database collection
• perform queries in MongoDB to gain insights present in the data

Part 1: Data selection and import

The data source

In this assignment, you will work with AirBnB listings data from a city/region of your choosing.

Save the data

Save the raw data file of your choice into the data directory.

1. Select a city or region of interest to you.
2. Download the appropriate data file - it will be named listings.csv.gz - this is a csv file compressed with gzip compressino.

Upload the data file to i6

In order to import this data into a MongoDB database, we must first upload it to our web server from which we will connect to the MongoDB sever.

1. Use a file transfer program, such as Cyberduck, to transfer this compressed file to your account on the i6.cims.nyu.edu web server.
2. Log into the i6 server using ssh, and unzip this file with the command, gunzip listings.csv.gz - this will result in a file named lsitings.csv in the same directory.

Data scrubbing

Take a look at the data in the data file. If you decide it needs any scrubbing, perform that scrubbing using any tool you wish. Save the scrubbed version in a file named listings_clean.csv.

If you use any Python or other programs to help scrub the data, include those program files in the main directory of the project.

Keep track of any changes you make to the data while scrubbing - you will include these details in a report.

Import the data into a MongoDB collection

Use the following command to import the data into a MongoDB collection on the database server. This import may take a few minutes, if the file is very large.

mongoimport --headerline --type=csv --db=your_db_name --collection=listings --host=your_db_host --file=listings.csv --username=your_db_username --password=your_db_password


mongoimport --headerline --type=csv --db=zq2076 --collection=listings --host=class-mongodb.cims.nyu.edu --file=listings.csv --username=zq2076 --password=694996797Qzy2140

mongoimport --headerline --type=csv --db=zq2076 --collection=listings --host=127.0.0.1 --file=listings.csv

• replace your_db_host with the host name of your database, class-mongodb.cims.nyu.edu.
• replace your_db_name with your database name, your NYU Net ID.
• replace your_db_username with your database username, your NYU Net ID.
• replace your_db_password with your database password.
• if you have scrubbed the data, replace the listings.csv file name with your scrubbed version, listings_clean.csv.

Part 2: Data analysis in MongoDB

Use the mongo command line client to run the following queries - save both the query and up to the first 3 result documents for each query to include in your report later.

1. show exactly two documents from the listings collection in any order
2. show exactly 10 documents in any order, but "prettyprint" in easier to read format, using the pretty() function.
3. choose two hosts (by reffering to their host_id values) who are superhosts (available in the host_is_superhost field), and show all of the listings offered by both of the two hosts
   • only show the name, price, neighbourhood, host_name, and host_is_superhost for each result
4. find all the unique host_name values (see the docs)
5. find all of the places that have more than 2 beds in a neighborhood of your choice (referred to as either the neighborhood or neighbourhood_group_cleansed fields in the data file), ordered by review_scores_rating descending
   • only show the name, beds, review_scores_rating, and price
   • if your data set only has blanks for all the neighborhood-related fields, or only one neighborhood value in all documents, you may pick another field to filter by - include an explanation and justification for this in your report.
   • if you run out of memory for this query, try filtering review_scores_rating that aren't empty ($ne); and lastly, if there's still an issue, you can set the beds to match exactly 2.
6. show the number of listings per host
7. find the average review_scores_rating per neighborhood, and only show the ones above a 95, sorted in descending order of rating (see the docs)
   • if your data set only has blanks in the neighborhood-related fields, or only one neighborhood value in all documents, you may pick another field to break down the listings by - include an explanation and justification for this in your report.

Write a report

Write a report which displays the data and the results in the file named README.md.

This report should be well-written and well-formatted using Markdown code - refer to this guide to using Markdown.

The report must include:

Data set details:

• The origin of your data set - what is it and where does it come from. Include a link to the URL of the source.
• What format the original data file was in (CSV, JSON, or other).
• Display some of the raw data from the original data file (the first 20 rows is enough - feel free to clip the text in fields to prevent line-wrapping). Use Markdown's ability to display tables - see the examples in the Markdown guide linked above.
• Describe any problems that were present in the data and the scrubbing tasks that were necessary to prepare your data set for import - include any scrubbing done in Python, a text editor, or any other tool. Be specific with examples of the problems in the original data and the way in which those were solved. Feel free to show small snippets of relevant code - see the examples of code "syntax highlighting" in the Markdown guide linked above.

Analysis:

• Describe each of the analyses you have performed. For each query, include:
  • a description of the query
  • the code used to perform it
  • up to the first three results in a preformatted text block (feel free to clip the text in fields to prevent line-wrapping)
  • describe any insights the analysis shows that may not be obvious to someone just viewing the raw data.

Part 3: Extra credit

For extra credit, you can optionally use Python to connect to the MongoDB database and perform some of the queries in your analysis.

Set up a Python virtual environment

A virtual environment is a sort of clean slate Python programming environment, within which you can install Python modules for your project in a way that does not conflict with other modules you or others have installed for other purposes.

On i6, create and activate a new virtual environment with the name .venv:

python3 -m venv .venv
source .venv/bin/activate

Install the pymongo module to allow Python to connect to MongoDB.

pip3 install pymongo

Set up your Python program

The code below shows how to connect from Python to a MongoDB database using pymongo module. Replace the database credentials below with the correct values for your account:

import pymongo
connection = pymongo.MongoClient("your_db_host", 27017,
                                username="your_db_username",
                                password="your_db_password",
                                authSource="your_db_name")
collection = connection["your_db_name"]]["your_db_collection_name"]

# the collection variable will be a reference to your collection
docs = collection.find({}).limit(10) # get the first 10 documents
print(docs)

Read the pymongo docs to learn more.

Recreate analyses

Reproduce one of your earlier queries:

• find all of the places that have more than 2 beds in a neighborhood of your choosing, ordered by review_scores_rating descending
• only show the name, beds, review_scores_rating, and price
• note that in pymongo, you'll have to quote all of your keys.

Requesting extra credit

If you believe you deserve extra credit, include a sub-heading at the bottom of your README.md document explaining why you believe you deserve it.

...

### Extra-credit

This assignment deserves extra credit because iste numquam eos et repudiandae sint enim. Rerum enim voluptas voluptatem consequuntur. Sed atque deserunt nihil eius neque et provident aspernatur. Incidunt iusto beatae illo minus vel. Quis sint sunt et facilis doloribus eligendi error est. Ipsum similique.
...

Submit your work

Each student must submit this assignment individually. Use Visual Studio Code to perform git stage, commit and push actions to submit. These actions are all available as menu items in Visual Studio Code's Source Control panel.

1. Type a short note about what you have done to the files in the Message area, and then type Command-Enter (Mac) or Control-Enter (Windows) to perform git stage and commit actions.
2. Click the ... icon next to the words, "Source Control" and select "Push" to perform the git push action. This will upload your work to your repository on GitHub.com.

Be sure to include the following:

• The original plain text data file which you downloaded from the Internet. Place this within the data directory.
• If you performed any scrubbing of the data, include the any Python programs you used for scrubbing the data file placed within the main project directory, as well as the scrubbed data file itself, named listings_clean.csv, also within the data directory.
• your report in the file named README.md.

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