Project Documentation: https://docs.google.com/document/d/1mx_qGRsQUyuWTqHC17Ub8YSaYts8m-8bIEt_Z1E_5so/edit?usp=sharing
Objective: Develop a Python program to analyze and visualize weather data. This project will help you understand data manipulation, working with external libraries, and basic data visualization techniques. Requirements:
- Python Knowledge: Basic understanding of Python syntax, functions, loops, and conditionals.
- Libraries: Use Pandas for data manipulation and Matplotlib or Seaborn for data visualization. Dataset: You can use any publicly available weather dataset. One such example is the Daily Global Historical Climatology Network (GHCN-Daily) dataset, which contains daily weather observations from thousands of land surface stations across the globe.
Steps:
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Data Collection: Download a weather dataset or access it through an API.
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Data Cleaning and Preparation: Load the data using Pandas. Clean the data by handling missing values, erroneous entries, and data types.
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Data Analysis: Calculate statistical summaries like mean, median, mode, minimum, and maximum temperature. Analyze more complex patterns like seasonal changes or yearly trends in temperature or precipitation.
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Data Visualization: Create line graphs to show temperature changes over time. Use bar charts to compare average monthly rainfall or temperatures. Optional: Create more complex visualizations like heatmaps or geographical plots if the dataset includes location data.
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Interpretation: Write a brief report interpreting the results of your analysis and visualizations. Discuss any interesting trends or patterns you observed.
Deliverables:
- Python Code: A well-documented Python script that performs data cleaning, analysis, and visualization.
- Report: A document or a set of slides that summarizes your methodology, findings, and interpretations of the data. Advanced Extensions (Optional): Perform comparative analysis between different years or locations. Use more advanced visualization techniques like interactive plots. Implement data fetching through a live API to get real-time weather data. Learning Outcomes: Understand how to manipulate and process data using Pandas. Gain experience in visualizing data and interpreting patterns in datasets. Learn to document and present findings in a coherent and understandable manner.
This project is designed to be flexible, allowing you to scale the complexity based on your comfort level with Python and data analysis. It provides a practical application of Python in a real-world context, enhancing your programming and data science skills.