Weather Data Analysis with Python

This project presents an exploratory data analysis (EDA) of a weather dataset using Python and Pandas. The analysis covers general data profiling and answers to specific analytical questions regarding weather conditions, wind speed, humidity, visibility, and more.

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Key Objectives

• Understand the structure and properties of weather data
• Perform exploratory data analysis using Pandas
• Answer specific business questions using filtering, grouping, and aggregation
• Extract insights related to weather patterns

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Tools & Libraries Used

• Python
• Pandas for data manipulation and analysis
• Jupyter Notebook for interactive coding

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Dataset Columns

• Date/Time
• Temp_C
• Dew Point Temp_C
• Rel Hum_%
• Wind Speed_km/h
• Visibility_km
• Press_kPa
• Weather

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Key Questions Answered

1. Find all unique wind speed values
2. Find number of times when weather is exactly clear
3. Find number of times wind speed was exactly 4 km/h
4. Find all null values in the dataset
5. Rename column Weather to Weather Conditions
6. Find the mean visibility
7. Find standard deviation of pressure
8. Find variance of relative humidity
9. Find all instances when snow was recorded
10. Find records when wind speed > 24 and visibility = 25
11. Find mean of each column grouped by weather condition

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How to Use

1. Clone this repository:

   git clone https://github.com/Saad-learning/weather-data-analysis.git

2. Place your dataset at the specified path or update the path in the notebook:

   data = pd.read_csv(r'F:\Porfolio Project\Python\Weather\file.csv)

3. Run the Weather_analysis.ipynb notebook in Jupyter.

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Future Enhancements

• Add visualizations using Matplotlib or Seaborn
• Handle missing values and outliers more deeply
• Build predictive models for forecasting temperature or weather events

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Author

Saad Attia
Aspiring Data Analyst | Passionate about weather data and analytics
LinkedIn • GitHub