Ambrosial: Swiggy Order History Analysis Package

Ambrosial is an Object-Oriented Programming (OOP) based installable Python package designed for comprehensive analysis of Swiggy order history data. This package offers a robust set of features for fetching, processing, and visualizing order data from the popular food delivery platform Swiggy.

Features

• Automated Data Retrieval: Utilizes browser-cookies to fetch and update Swiggy order history automatically.
• Data Preprocessing: Handles data preprocessing, loading, caching, and conversion to DataClasses.
• Comprehensive Analysis: Generates analysis in both textual format and through various types of graphs.
• Visualization Options: Supports multiple graph types including Barplots, CalendarPlots, HeatMaps, Interactive Location HeatMaps, Word Clouds, Scatterplots, and Regression Lines.
• High-Quality Code: 100% type-annotated code with test coverage of more than 80%.

Installation

git clone https://github.com/DistilledCode/ambrosial.git
cd ambrosial
python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
pip install .

Additional Installation Options

Installing with development dependencies

If you want to contribute to the project or run tests, you can install the development dependencies:

pip install .[dev]

This will install additional packages for development, including pre-commit hooks, linters, and formatters.

Installing with test dependencies

To run the tests, you can install the test dependencies:

pip install .[test]

This will install pytest and any other testing-related packages.

Verifying the Installation

After installation, you can verify that Ambrosial is correctly installed by running:

python -c "import ambrosial; print(ambrosial.__version__)"

This should print the version number of Ambrosial.

Project Structure

src/
├── ambrosial/
│   ├── swan/
│   ├── swich/
│   └── swiggy/

Basic Setup

Tip

You can also refer to advanced examples

To start using the package, import the necessary modules and create instances of the main classes:

from ambrosial.swiggy import Swiggy
from ambrosial.swan import SwiggyAnalytics
from ambrosial.swich import SwiggyChart

# Create instances
swiggy = Swiggy()
swan = SwiggyAnalytics(swiggy)
swich = SwiggyChart(swan)

Fetching and Loading Data

Before creating visualizations, you need to fetch or load your Swiggy order data:

# Fetch new data (if you haven't already)
swiggy.fetch_orders()
swiggy.saveb()  # Save data for future use

# Or load previously saved data
swiggy.loadb()

Creating Visualizations

Now you can use the SwiggyChart instance to create various visualizations:

1. Bar Plot

# Create a bar plot of top 10 most ordered items
swich.barplot.restaurant_deltime()

2. Calendar Plot

# Create a calendar plot showing order frequency
swich.calplot.order_count()

3. GitHub-style Contribution Map

# Create a GitHub-style map of order history
swich.ghubmap.order_amount()

4. Heat Map

# Create a heat map of order timings
swich.heatmap.order_count()

5. Interactive Location Map

# Create an interactive map of order locations
swich.map.count_density()

6. Regression Plot

# Create a regression plot of order values over time
swich.regplot.ordamt_ordfee()

7. Word Cloud

# Create a word cloud of restaurant names
swich.wcloud.restaurant_name()

Customizing Visualizations

Many visualization methods allow for customization. For example:

# Customize the bar plot
swich.barplot.top_items(
    top_n=15,
    title="Top 15 Most Ordered Items",
    color_palette="viridis"
)

# Customize the heat map
swich.heatmap.order_timings(
    cmap="YlOrRd",
    title="Order Timing Heatmap",
    figsize=(12, 8)
)

Combining with Analytics

You can use the SwiggyAnalytics instance to get data for custom visualizations:

# Get top restaurants data
top_restaurants = swan.restaurants.top_restaurants(top_n=5)

# Use this data to create a custom visualization
import matplotlib.pyplot as plt

plt.figure(figsize=(10, 6))
plt.bar(top_restaurants['name'], top_restaurants['order_count'])
plt.title("Top 5 Most Ordered From Restaurants")
plt.xlabel("Restaurant Name")
plt.ylabel("Number of Orders")
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()

Saving Visualizations

Most visualization methods in the SwiggyChart class likely have options to save the generated plots. For example:

swich.barplot.top_items(top_n=10, save_path="top_items.png")
swich.calplot.order_frequency(save_path="order_frequency.svg")

Remember to check the documentation or source code of each visualization method for specific parameters and options available.

Example Visualizations

Order Total vs. Order Fee Relationship

Punctuality vs. Order Delivery Time

Order Total vs. Order Fee Relationship

Average Delivery Times for Various Restaurants

Average Amount Spent on Menu Items

Average Spending at Various Restaurants

Weekly Order Count and Amount Heatmaps

Total Offer Availed Calendar for January 2022

Order Count, Discount, and Spending Trends (2021-2022)

Monthly Amount Spent Heatmap Calendar View

Monthly Order Count Heatmap Calendar View

Cuisine Word Cloud

Item Name Word Cloud

Restaurant Name Word Cloud

License

This project is licensed under the MIT License - see the LICENSE file for details.