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📊 Page View Time Series Visualizer

🔧 Project Overview

Visualize time series data using:

  • 🎨 Line charts
  • 📊 Bar charts
  • 🔄 Box plots

You will work with Pandas, Matplotlib, and Seaborn to analyze the number of daily page views on the freeCodeCamp.org forum between 2016-05-09 and 2019-12-03.

🔍 Objective

Gain insights into user behavior by identifying trends and seasonal patterns in forum visits.

📊 Data Description

The dataset contains daily page view counts from the freeCodeCamp forum. The cleaning process filters out extreme outliers (top 2.5% and bottom 2.5%).

  • File: fcc-forum-pageviews.csv
  • Columns:
    • date (Index): Date of page views.
    • value: Daily page view count.

🗒 Tasks

  1. Data Import and Cleaning

    • Import the data using Pandas.
    • Remove top and bottom 2.5% of page views.

  2. Visualizations

    • Line Plot

      • Visualize daily page views over time.
      • Include title, axis labels, and proper formatting.

    • Bar Chart

      • Display average daily page views per month grouped by year.
      • Add month legends and axis labels.

    • Box Plots

      • Show yearly trends and monthly seasonality.
      • Create two side-by-side box plots.

🎨 Visualizations

1. 🔮 Line Chart

Title: Daily freeCodeCamp Forum Page Views 5/2016-12/2019

2. 🌈 Bar Chart

  • X-Axis: Years
  • Y-Axis: Average Page Views

3. 🔢 Box Plots

  • Year-wise Trend: Highlights yearly distribution.
  • Month-wise Seasonality: Focuses on monthly patterns.

🛠️ Development

🗋 Files

  • time_series_visualizer.py: Main script.
  • main.py: Testing script.
  • test_module.py: Unit tests.

🎧 Instructions

  1. Clone the repository.

    git clone https://github.com/Alogyn/data-analysis-with-python-projects
cd data-analysis-with-python-projects/boilerplate-page-view-time-series-visualizer

  2. Install dependencies.

    pip install -r requirements.txt

  3. Run the script for development.

    python main.py

  4. Generate visualizations.

    • Ensure the fcc-forum-pageviews.csv file is in the working directory.

  5. Save and return the charts.

🔢 Testing

  • Use test_module.py to verify functionality.
  • Run tests with: 
    python -m unittest test_module.py

🔄 Key Features

  • ✅ Cleaned Time Series Data: Outliers removed for accurate trends.
  • 📊 Three Types of Visualizations: Line, Bar, and Box Plots.
  • 🏆 Python Libraries: Leveraging Pandas, Matplotlib, and Seaborn.

🔧 Tools Used

  • Languages: Python
  • Libraries: Pandas, Matplotlib, Seaborn
  • Environment: Gitpod Starter Code

🛠️ Future Improvements

  • Add interactivity to the visualizations.
  • Expand the analysis to include more metrics.

🎥 Example Outputs

Line Plot

Line Plot

Bar Chart

Bar Chart

Box Plots

Box Plots

🌐 License

This project is licensed under the MIT License.