This project involves analyzing and visualizing sensor data from Benin, Sierra Leone, and Togo. The project uses Python libraries such as pandas, matplotlib, seaborn, and scipy to perform various data analysis tasks and generate visualizations. Below is a step-by-step guide to understanding the code and its functionalities.
The project consists of several key steps:
- Data Loading: Load the data for Benin, Sierra Leone, and Togo from CSV files.
- Data Summarization: Summarize the data to get an overview of the sensor readings.
- Data Quality Check: Check the data for missing values and negative values.
- Time Series Plotting: Visualize the sensor readings over time.
- Cleaning Impact Analysis: Analyze the impact of cleaning on sensor readings.
- Correlation Analysis: Analyze the correlation between different sensor readings.
- Wind Analysis: Analyze the relationship between wind speed and direction.
- Temperature Analysis: Analyze the temperature variations over time.
- Histogram Plotting: Generate histograms to observe the distribution of sensor readings.
- Z-Score Analysis: Identify outliers in the data using Z-score.
- Bubble Chart Visualization: Create bubble charts to visualize relationships between multiple variables.
- Data Cleaning: Clean the data by handling missing values, removing outliers, and more.
To run this project, you need to have Python installed along with the following libraries:
pip install pandas matplotlib seaborn scipy