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Python Natural Prompts
Data Science and Analytics
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About
This dataset provides a collection of Python code prompts used for analyzing and reporting various forest and timber-related data, focusing on sustainability, wildlife habitats, and forest health. The dataset serves as a resource for individuals interested in working with data related to forest management, timber production, wildlife, and environmental sustainability.
This dataset is designed for applications in data analysis, reporting, and decision-making, especially for projects aiming to assess or improve forestry practices and sustainability indicators.
Dataset Features
- PN_ID: Unique identifier for each prompt.
- Prompt: The main task or objective to be performed, such as generating reports, analyzing data, or visualizing trends.
- Task_Type_Description: A brief description of the task type, for instance, whether it involves generating reports, visualizing data, or analyzing growth trends.
- Task_Description: A detailed explanation of the specific task, outlining what data is needed and what the expected outcome should be.
- Code: A sample Python code that can be used to complete the task, including the necessary data manipulations, calculations, and output
Distribution
- Data Volume: The dataset includes 13631 rows (one per task or prompt), and each row contains 5 key columns (PN_ID, Prompt, Task_Type_Description, Task_Description, Code).
- Format: CSV file containing textual data and Python code examples.
Usage
This dataset is ideal for applications in the following areas:
- Environmental Data Analysis: The prompts can guide users in performing analysis on forest-related data, such as growth rates, timber production, and sustainability metrics.
- Data Reporting: The dataset helps in generating reports or dashboards related to forest health and management, making it valuable for forest managers and sustainability analysts.
- Python Learning: The Python code examples are suitable for individuals learning data analysis with Python, particularly in the context of environmental and forest data.
Coverage
- Geographic Coverage: Global
- Time Range: The data used in the tasks refers to general concepts, without a specific time range.
License
CC0 (Public Domain)
Who Can Use It
- Data Scientists: To develop machine learning models for environmental prediction and analysis.
- Researchers: For conducting studies on forest health, timber production, and sustainable forestry practices.
- Businesses: For leveraging insights into sustainable practices, carbon sequestration, and timber production efficiency in forestry-related industries.
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