Purchase-Analytics

Table of Contents

1. Problem
2. Steps to submit your solution
3. Input Dataset
4. Instructions
5. Output
6. Tips on getting an interview
7. Questions?

Problem

Instacart has published a dataset containing 3 million Instacart orders.

For this challenge, we want you to calculate, for each department, the number of times a product was requested, number of times a product was requested for the first time and a ratio of those two numbers.

Steps to submit your solution

• To submit your entry please use the link you received in your coding challenge invite email
• You will only be able to submit through the link one time
• Do NOT attach a file - we will not admit solutions which are attached files
• Do NOT send your solution over an email - We are unable to accept coding challenges that way

Creating private repositories

To avoid plagiarism and any wrongdoing, we request you to submit a private repository of your code. Both GitHub and Bitbucket offer free unlimited private repositories at no extra cost.

• Create a private repository on GitHub or Bitbucket with the given repository structure. Here is how you will be sharing your private repositories for us to see once you are ready to submit.
• Add "insight-cc-bot" as a collaborator in your project.
  • How to add collaborators on GitHub?
  • How to add users and groups as collaborators in Bitbucket?
• We will NOT be grading submissions we do not have access to.

Submitting a link to your repository

• Use the submission box to enter the link to your GitHub or Bitbucket repo ONLY
• Link to the specific repo for this project, not your general profile
• Put any comments in the README inside your project repo, not in the submission box

Input Datasets

For this challenge, we have two separate input data sources, order_products.csv and products.csv.

You can assume each line of the file order_products.csv holds data on one request. The file contains data of the form

order_id,product_id,add_to_cart_order,reordered
2,33120,1,1
2,28985,2,1
2,9327,3,0
2,45918,4,1
3,17668,1,1
3,46667,2,1
3,17461,4,1
3,32665,3,1
4,46842,1,0

where

• order_id: unique identifier of order
• product_id: unique identifier of product
• add_to_cart_order: sequence order in which each product was added to shopping cart
• reordered: flag indicating if the product has been ordered by this user at some point in the past. The field is 1 if the user has ordered it in the past and 0 if the user has not. While data engineers should validate their data, for the purposes of this challenge, you can take the reordered flag at face value and assume it accurately reflects whether the product has been ordered by the user before.

The file products.csv holds data on every product, and looks something like this:

product_id,product_name,aisle_id,department_id
9327,Garlic Powder,104,13
17461,Air Chilled Organic Boneless Skinless Chicken Breasts,35,12
17668,Unsweetened Chocolate Almond Breeze Almond Milk,91,16
28985,Michigan Organic Kale,83,4
32665,Organic Ezekiel 49 Bread Cinnamon Raisin,112,3
33120,Organic Egg Whites,86,16
45918,Coconut Butter,19,13
46667,Organic Ginger Root,83,4
46842,Plain Pre-Sliced Bagels,93,3

where

• product_id: unique identifier of the product
• product_name: name of the product
• aisle_id: identifier of aisle in which product is located
• department_id: identifier of department

Expected Output

Given the two input files in the input directory, your program should create an output file, report.csv, in the output directory that, for each department, surfaces the following statistics:

number_of_orders. How many times was a product requested from this department? (If the same product was ordered multiple times, we count it as multiple requests)

number_of_first_orders. How many of those requests contain products ordered for the first time?

percentage. What is the percentage of requests containing products ordered for the first time compared with the total number of requests for products from that department? (e.g., number_of_first_orders divided by number_of_orders)

For example, with the input files given above, the correct output file is

department_id,number_of_orders,number_of_first_orders,percentage
3,2,1,0.50
4,2,0,0.00
12,1,0,0.00
13,2,1,0.50
16,2,0,0.00

The output file should adhere to the following rules

• It is listed in ascending order by department_id
• A department_id should be listed only if number_of_orders is greater than 0
• percentage should be rounded to the second decimal

The examples input and out files are provided in the insight_testsuite/tests/test_1/input and insight_testsuite/tests/test_1/output folders, respectively.

Instructions

We designed this coding challenge to assess your coding skills and your understanding of computer science fundamentals. They are both prerequisites of becoming a data engineer. To solve this challenge you might pick a programing language of your choice (preferably Python, Scala, Java, or C/C++ because they are commonly used and will help us better assess you), but you are only allowed to use the default data structures that come with that programming language (you might use I/O libraries). For example, you can code in Python, but you should not use Pandas or any other external libraries.

The objective here is to see if you can implement the solution using basic data structure building blocks and software engineering best practices (by writing clean, modular, and well-tested code).

Tips on getting an interview

Writing clean, scalable and well-tested code

As a data engineer, it’s important that you write clean, well-documented code that scales for a large amount of data. For this reason, it’s important to ensure that your solution works well for a large number of records, rather than just the above example.

Here you can find large datasets to test your code (see here for its data dictionary). You can test your code using the files order_products_train.csv and order_products_prior.csv together with the file products.csv. Note, we will use it to test the full functionality of your code, along with other tests.

It's also important to use software engineering best practices like unit tests, especially since data is not always clean and predictable.

Before submitting your solution you should summarize your approach and run instructions (if any) in your README.

You may write your solution in any mainstream programming language, such as C, C++, C#, Go, Java, Python, Ruby, or Scala. Once completed, submit a link of your Github or Bitbucket repo with your source code.

In addition to the source code, the top-most directory of your repo must include the input and output directories, and a shell script named run.sh that compiles and runs the program(s) that implement(s) the required features.

If your solution requires additional libraries, environments, or dependencies, you must specify these in your README documentation. See the figure below for the required structure of the top-most directory in your repo, or simply clone this repo.

Repo directory structure

The directory structure for your repo should look like this:

├── README.md
├── run.sh
├── src
│   └── purchase_analytics.py
├── input
│   └── products.csv
|   └── order_products.csv
├── output
|   └── report.csv
├── insight_testsuite
    └── run_tests.sh
    └── tests
        └── test_1
        |   ├── input
        |   │   └── products.csv
        |   │   └── order_products.csv
        |   |__ output
        |   │   └── report.csv
        ├── your-own-test_1
            ├── input
            │   └── your-own-products.csv
            |   └── your-own-order_products.csv
            |── output
                └── report.csv

Don't fork this repo and don't use this README instead of your own. The content of src does not need to be a single file called purchase_analytics.py, which is only an example. Instead, you should include your own source files and give them expressive names.

Testing your directory structure and output format

To make sure that your code has the correct directory structure and the format of the output files are correct, we have included a test script called run_tests.sh in the insight_testsuite folder.

The tests are stored simply as text files under the insight_testsuite/tests folder. Each test should have a separate folder with an input folder for products.csv and order_products.csv and an output folder for report.csv.

You can run the test with the following command from within the insight_testsuite folder:

insight_testsuite~$ ./run_tests.sh

On a failed test, the output of run_tests.sh should look like:

[FAIL]: test_1
[Thu Mar 30 16:28:01 PDT 2017] 0 of 1 tests passed

On success:

[PASS]: test_1
[Thu Mar 30 16:25:57 PDT 2017] 1 of 1 tests passed

One test has been provided as a way to check your formatting and simulate how we will be running tests when you submit your solution. We urge you to write your own additional tests. test_1 is only intended to alert you if the directory structure or the output for this test is incorrect.

Your submission must pass at least the provided test in order to pass the coding challenge.

For a limited time we also are making available a website (no longer available) that will allow you to simulate the environment in which we will test your code. It has been primarily tested on Python code but could be used for Java and C++ repos. Keep in mind that if you need to compile your code (e.g., javac, make), that compilation needs to happen in the run.sh file of your code repository. For Python programmers, you are able to use Python2 or Python3 but if you use the later, specify python3 in your run.sh script.

Questions?

Email us at [email protected]