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https://github.com/filip-copija/temptrends
A comprehensive app analyzing global temperature trends, using Python for data processing and visualization to understand climate changes and regional differences.
https://github.com/filip-copija/temptrends
analysis jupyter-notebook kaggle python temperature
Last synced: 13 days ago
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A comprehensive app analyzing global temperature trends, using Python for data processing and visualization to understand climate changes and regional differences.
- Host: GitHub
- URL: https://github.com/filip-copija/temptrends
- Owner: filip-copija
- Created: 2024-07-09T08:17:22.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-07-09T08:21:29.000Z (7 months ago)
- Last Synced: 2024-11-12T05:08:25.057Z (2 months ago)
- Topics: analysis, jupyter-notebook, kaggle, python, temperature
- Language: Jupyter Notebook
- Homepage:
- Size: 2.96 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Global Temperature Trends Analysis
## Description
A comprehensive app analyzing global temperature trends from 1743-2013, using Python for data processing and visualization to understand climate changes and regional differences.
## Project Overview
The Global Temperature Trends Analysis project aims to explore and understand the changes in average temperatures worldwide over a period of 270 years. The project utilizes advanced data processing and analysis techniques to uncover long-term climate trends, identify anomalies, and compare regional temperature changes. The dataset used in this analysis is sourced from Kaggle, ensuring a broad and reliable range of data.
## Goals
- **Understanding Temperature Trends:** Analyze long-term trends in global temperature changes.
- **Identifying Anomalies:** Detect years that deviate from overall trends, indicating potential extreme climatic events.
- **Regional Comparisons:** Compare temperature changes across different regions of the world.
- **Climate Change Conclusions:** Draw conclusions about global warming and its impact on Earth's climate.
## Techniques and Tools Used
- **Python:** Programming language used for data processing and analysis.
- **Python Libraries:** Utilization of pandas for data manipulation, Matplotlib, and Seaborn for visualization.
- **Statistical Analysis:** Applying statistical methods for data analysis to interpret the results.
## Project Structure
- **data/**: Contains the dataset `GlobalLandTemperaturesByMajorCity.csv`.
- **notebooks/**: Jupyter notebooks containing the analysis and visualizations.
- **README.md**: Documentation of the project.
## Installation
1. Clone the repository:
```bash
git clone
cd
```
2. Install the required libraries:
```bash
pip install -r requirements.txt
```
## Running the Analysis
To run the analysis, open and execute the Jupyter notebook `notebook.ipynb` in your Jupyter environment.
## Key Analyses
1. **Average Temperature Trends Over Time for Selected Cities**
2. **Comparison of Average Temperatures for Selected Cities**
3. **Distribution of Average Temperatures**
4. **Heat Map of Average Temperatures Worldwide**
## Conclusion
This project offers insights into historical temperature trends, helping to understand the broader context of climate change. Through data visualization and statistical analysis, it highlights significant patterns and anomalies that inform our understanding of global warming and its regional impacts.