Bitly Coding Challenge

This project aims to accomplish the Bitly Coding Challenge.

Table of Contents

• Introduction
• Data
• Problem
• Usage
• Solution
• Testing
• Contact

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Introduction

This project involves analyzing data files to solve the specific problem outlined in the challenge description.

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Data

The challenge uses 'encodes.csv' and 'decodes.json', representative of data encountered in Bitly's daily operations:

• 'encodes.csv' contains shortened link information.
• 'decodes.json' includes raw bitlink clicks.

These datasets are utilized to address the problem statement.

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Problem

The goal is to calculate the number of clicks from 2021 for each record in the 'encodes.csv' dataset.

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Usage

Pre-requisites

• Python 3 installed

Steps to Run the Code

1. Clone the Repository:

   git clone https://github.com/srpayd/bitly_challenge.git
   

2. Navigate to the Project Directory:

   cd bitly-coding-challenge
   

3. Install Dependencies (if any): Python's core libraries, such as dictionaries and lists, were utilized throughout the project. No external or third-party libraries were installed or employed.

4. Run the code Replace 'encodes.csv' and 'decodes.json' with your file paths.

   python solution.py 
   python test_solution.py 
   

5. Read 'encodes.csv' file: Create a dictionary using 'encodes.csv' where keys are long URLs, and values are corresponding shortened URLs.

6. Read 'decodes.json' file: Parse 'decodes.json' and filter records from 2021.

7. Match shortened URLs with records from 2021: Compare the bitlinks in the filtered records from 2021 with the dictionary created from 'encodes.csv' to identify corresponding long URLs. Finally, count clicks for each long URL: Tally the number of clicks from 2021 for each long URL.

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Solution

Overview

The provided code addresses the challenge by calculating the 2021 click counts for each record in 'encodes.csv' using data from 'decodes.json'.

Key Features

1. Reading CSV and JSON Files: Reads 'encodes.csv' and 'decodes.json' to create dictionaries of shortened links and bitlink clicks.

2. Data Processing: Filters 2021 records in 'decodes.json' and matches bitlinks to corresponding long URLs in 'encodes.csv'.

3. Click Count Calculation: Counts 2021 clicks in descending order for each record in 'encodes.csv' by tallying matching records from 'decodes.json'.

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Testing

Unit Tests Overview

The project includes a test suite comprising three unit tests to validate specific functionalities within the codebase.

Unit Test Descriptions

1. test_read_encode_csv

• Purpose: This test validates the read_encode_csv function's behavior by checking if it correctly reads and parses the 'encodes.csv' file, creating a dictionary of encoded URLs.
• Assertions:
  • Ensures that the function returns a dictionary.
  • Verifies that the dictionary contains the expected number of entries (6 in this case).

2. test_read_decode_json

• Purpose: Validates the read_decode_json function's behavior by verifying its ability to read and parse the 'decodes.json' file, creating a list of decoded click data.
• Assertions:
  • Confirms that the function returns a list.
  • Checks if the list contains the expected number of records (10,000 in this case).

3. test_filter_and_count_2021_clicks

• Purpose: This test validates the filter_and_count_2021_clicks function's functionality by testing its ability to filter and count clicks from 2021 for each record in the 'encodes.csv' dataset.
• Assertions:
  • Ensures that the function returns a dictionary.
  • Verifies that the resulting dictionary contains the expected number of entries (6).
  • Tests if the click count for each record in the dictionary is greater than zero.

Running the Unit Tests

To run the unit tests:

1. Navigate to the project directory in the terminal.
2. Execute the following command:

   python -m unittest test_solution.py
   

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Contact

Serap Aydogdu - linkedin | medium

Project Link: https://github.com/srpayd/bitly_challenge

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