The py_spoo_url from spoo-me

🚀 Simple URL shortening with advanced analytics, emoji aliases, and more using spoo.me

📖 Table of Contents

📦 Installing
📥 Importing
✂️ Shortening URL
- For Non-emoji aliases
- 😎 For Emoji aliases
📊 URL Statistics
📤 Exporting Stats Data
🧳 Dependencies
🚨 Error Codes
🤝 Support and Issues
🤗 Contributing
📜 Licence

📦 Installing

You can install this package using pip:

pip install py_spoo_url

📥 Importing

from py_spoo_url import Shorten, Statistics

✂️ Shortening URL

For Non-emoji aliases

shortener = Shortener()
long_url = "https://www.example.com"
short_url = shortener.shorten(long_url, password="SuperSecretPassword@444", max_clicks=100)
# for custom alias, put `alias=<your_choice>`

print(f"Shortened URL: {short_url}")

😎 For Emoji aliases

shortener = Shortener()
long_url = "https://www.example.com"
emoji_url = shorten.emojify(long_url) # pass password and max-clicks as shown above if you want
# for custom emoji alias, put `emoji_alias=<random_emoji_sequence>`

print(f"Emojified URL: {emoji_url}")

Note: The emoji sequence must contain actual emojies like 😆🤯...

📊 URL Statistics

The Statistics class enables you to retrieve detailed statistics for a given short code.

🔧 Initializing the class

from spoo_me import Statistics

# Initialize Statistics with a short code
stats = Statistics(short_code="ga") # replace with the shortcode you want
# if the shortUrl is password protected you have to pass the password too

👀 Viewing the Basic Statistics

print(f"Total Clicks: {stats.total_clicks}")
print(f"Total Unique Clicks: {stats.total_unique_clicks}")
print(f"Average Daily Clicks: {stats.average_daily_clicks}")
print(f"Clicks Analysis: {stats.clicks_analysis}")
print(f"Browser Analysis: {stats.browsers_analysis}")
# ... and more (details below)

List of the analytics you can access

Method/Attribute	Description
total_clicks	Total number of clicks on the short URL.
total_unique_clicks	Total number of unique clicks on the short URL.
average_daily_clicks	Average number of clicks per day.
average_monthly_clicks	Average number of clicks per month.
average_weekly_clicks	Average number of clicks per week.
last_click	Information about the last click on the short URL.
last_click_browser	Browser used for the last click.
last_click_platform	Operating system used for the last click.
created_at	Date when the short URL was created.
creation_time	Time of day when the short URL was created.
browsers_analysis	Analysis of browsers used for clicks.
platforms_analysis	Analysis of operating systems used for clicks.
country_analysis	Analysis of countries from which clicks originated.
referrers_analysis	Analysis of referrers (sources) of clicks.
clicks_analysis	Detailed analysis of daily clicks.
unique_browsers_analysis	Analysis of unique browsers used for clicks.
unique_platforms_analysis	Analysis of unique operating systems for clicks.
unique_country_analysis	Analysis of unique countries from which clicks originated.
unique_referrers_analysis	Analysis of unique referrers (sources) of clicks.
unique_clicks_analysis	Detailed analysis of daily unique clicks.
expired	Indicates if the short URL has expired.
password	Password associated with the short URL (if any).

Example Usage

print(f"Creation Time: {stats.creation_time}")

📈 Generating Insightful Charts

plt = stats.make_chart(data="browsers_analysis", chart_type="bar") # this returns an object of matplotlib
plt.show()

# ... and more (see below)

# generating countries heatmaps
plt = stats.make_countries_heatmap()
plt.savefig("heatmap.png", format="png", bbox_inches="tight", pad_inches=0.5, dpi=300,)

plt = stats.make_unique_countries_heatmap()
plt.savefig("unique_heatmap.png", format="png", bbox_inches="tight", pad_inches=0.5, dpi=300,)

List of Available Charts

Method	Description
make_chart	Create various types of charts based on the data provided.

Parameters	Description
data	Type of data to visualize (e.g., 'browsers_analysis', see below).
chart_type	Type of chart to create (e.g., "bar", "pie", "line", see below).
days	Number of days to consider for time-based analysis. (only for `last_n_days_analysis` and `last_n_days_unique_analysis`)

Valid Data that can be passed to make the chart

'browsers_analysis'
'platforms_analysis'
'country_analysis'
'referrers_analysis'
'clicks_analysis'
'unique_browsers_analysis'
'unique_platforms_analysis'
'unique_country_analysis'
'unique_referrers_analysis'
'unique_clicks_analysis'
'last_n_days_analysis'
'last_n_days_unique_analysis'

Valid Chart types

'bar'
'pie'
'line'
'scatter'
'hist'
'box'
'area'

Usage Example

plt = stats.make_chart('browsers_analysis', chart_type="bar")
plt.show()

👀 Heatmap Preview

📤 Exporting Stats Data

You can export the statistical data to various file formats, including Excel, CSV, and JSON:

# Export data to Excel
stats.export_data(filename="stats_export.xlsx", filetype="xlsx")

# Export data to CSV and compress into a ZIP file
stats.export_data(filename="stats_export", filetype="csv")

# Export data to Json
stats.export_data(filename="stats_export.json", filetypes="json")

🧳 Dependencies

matplotlib: For creating charts and visualizations.
requests: For making HTTP requests to the Spoo.me API.
pandas: For handling and manipulating data in tabular form. 🐼
geopandas: For creating geographical visualizations. 🌎

All of the dependencies are automatically installed while installing the package but in case of any errors, you can install all of the dependencies listed in the requirements.txt file.

🚨 Error Codes

To see the error codes returned by the API, please visit https://spoo.me/api

🤝 Support and Issues

If you encounter any issues or have questions about using the Spoo.me Python package, please open an issue on the GitHub repository.

🤗 Contributing

Contributions are welcome! If you have ideas for improvements or new features, feel free to fork the repository, make your changes, and submit a pull request

📜 Licence

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

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