Project-3-Python-For-Data-Analysis-Weather

End-to-end EDA project on hourly weather data (Canada 2012) | Exploring temperature, visibility, wind, and precipitation patterns using Python, Pandas, Matplotlib & Seaborn

🌦️ Exploratory Data Analysis — Weather Dataset

A structured EDA project on hourly weather observations from Canada (2012), completed as part of the Python for Data Analysis course.

📌 Objective

Explore weather patterns across all seasons, answer 15 analytical questions, and uncover key relationships between temperature, visibility, wind speed, humidity, and pressure.

📂 Dataset

• Source: Hourly Weather Observations — Canada 2012
• Records: 8,784 hourly readings
• Features: Date/Time, Temp_C, Dew Point Temp_C, Rel Hum_%, Wind Speed_km/h, Visibility_km, Press_kPa, Weather_Condition

🛠️ Tools & Libraries

Library	Purpose
Pandas	Data loading, cleaning, and exploration
Matplotlib	Base plotting
Seaborn	Statistical visualizations

📊 Analysis Structure

1. Imports & Setup
2. Load Dataset
3. First Look (head / tail)
4. Data Structure & Info
5. Data Quality Check
6. Descriptive Statistics
7. Column Renaming
8. Categorical Exploration
9. Analytical Questions (Q1 → Q15)
10. Visualizations
    • Univariate (Numerical + Categorical)
    • Bivariate (Num vs Num · Num vs Cat · Cat vs Cat)
    • Multivariate
    • Correlation Heatmap
    • Pairplot
11. Key Insights

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❓ 15 Analytical Questions

Q1 — Find all unique Wind Speed values

Find all distinct wind speed measurements recorded in the dataset.

Q2 — How many times was weather exactly 'Clear'?

Count the total occurrences of clear weather conditions.

Q3 — How many times was Wind Speed exactly 4 km/h?

Count records where wind speed equals 4 km/h precisely.

Q4 — Find all missing (null) values in the data

Identify and count all empty cells across all columns.

Q5 — Rename 'Weather' column to 'Weather_Condition'

Clean up column naming for clarity and avoid ambiguity.

Q6 — What is the mean Visibility?

Calculate the average visibility across all hourly readings.

Q7 — What is the standard deviation of Pressure?

Measure how much atmospheric pressure fluctuates in this dataset.

Q8 — What is the variance of Relative Humidity?

Quantify the spread of humidity values throughout the year.

Q9 — Find all instances where Snow was recorded

Filter and display all records with snow conditions.

Q10 — Wind Speed > 24 AND Visibility = 25

Find records meeting BOTH conditions simultaneously.

Q11 — Mean value of each column per Weather Condition

Group data by weather condition and calculate averages for all metrics.

Q12 — Min and Max values per Weather Condition

Find extreme values (lowest and highest) for each weather type.

Q13 — All records where Weather Condition is 'Fog'

Display all foggy weather observations in the dataset.

Q14 — Weather is 'Clear' OR Visibility > 40

Find records satisfying either condition (using OR operator).

Q15 — Complex filtering: (Clear AND Humidity > 50) OR Visibility > 40

⚠️ Operator Precedence: Demonstrates the importance of parentheses in conditional logic.
Find records that are either:

• Clear weather AND relative humidity exceeds 50%, OR
• Visibility is above 40 km

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💡 Key Insights

• Temperature and Dew Point are strongly correlated — near-linear relationship
• Fog and Snow produce the lowest visibility values in the dataset
• Winter dominates with extreme conditions; Summer is mostly clear
• Wind speed has minimal effect on visibility despite common assumption
• Pressure is the most stable variable — changes very slowly hour-to-hour
• High humidity consistently appears in foggy and rainy conditions

🚀 How to Run

git clone https://github.com/ammarelsayed-2a/Project-3-Python-For-Data-Analysis-Weather.git
cd Project-3-Python-For-Data-Analysis-Weather
jupyter notebook "Project 3 Weather.ipynb"

👤 Author

Ammar Elsayed — Python for Data Analysis | 2026
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