EDA

Temperature Over Time (with Trend Line):

This line plot tracks the variation in temperature over time. It helps identify overall trends, such as whether temperatures are rising, falling, or fluctuating seasonally. This is useful for spotting long-term patterns and anomalies in the dataset.

Correlation Heatmap of Weather Features

The heatmap visualizes the strength of relationships between different weather variables, such as temperature, humidity, and wind speed. Darker or lighter squares indicate strong positive or negative correlations, helping to quickly identify which variables move together or in opposite directions.

Scatter Plot with Regression Line with Temperature vs Apparent Temperature

This scatter plot compares the actual temperature (what's recorded) to the apparent temperature (what it feels like). The regression line shows the overall relationship between the two, indicating how closely perceived temperature tracks actual temperature under different weather conditions.

Wind Speed Distribution by Precipitation Type

The box plot visualizes the spread and distribution of wind speeds under different precipitation types (rain or snow). It helps show whether wind speeds tend to increase or decrease during particular types of weather and reveals outliers (extreme wind speeds).

Humidity Distribution by Precipitation Type

The violin plot combines elements of a box plot and density plot to show the full distribution of humidity values for each precipitation type. This helps you understand not just the average humidity levels during different weather conditions but also the variability and probability density of humidity values.

Average Temperature by Precipitation Type

This bar plot shows the average temperature associated with each precipitation type, providing a quick way to compare the typical temperatures during rain and snow events. It helps to understand which weather conditions bring warmer or cooler temperatures.

Humidity Over Time (with Trend Line)

This time-series plot tracks the changes in humidity levels over time. It includes a trend line to highlight the general direction of changes, helping to identify any long-term trends (such as increasing or decreasing humidity over certain periods).

Pressure Distribution

This histogram shows the distribution of atmospheric pressure values across the dataset, indicating how often certain pressure ranges occur. The added density curve provides a smoothed outline of the distribution, making it easier to see the general shape and central tendency of the pressure readings.

Count of Weather Conditions

This count plot provides an overview of the frequency of different weather conditions recorded in the dataset (e.g., clear, cloudy, rainy). It allows you to quickly see which weather types are most and least common.

Wind Rose Plot

This specialized circular plot displays the frequency of wind coming from different directions at varying speeds. It helps understand the dominant wind directions and speeds in the dataset, making it easier to visualize wind patterns over time.

Wind Speed Over Time

The line plot shows how wind speed fluctuates over time. It helps identify periods of high or low wind activity, seasonal patterns in wind speed, or specific events like storms that may cause sudden changes in wind speed.

Hex Plot: Temperature vs Apparent Temperature

The hexagonal bin plot displays the relationship between temperature and apparent temperature, with the density of data points represented by color. This helps identify regions where the data is concentrated, making it easier to see patterns in large datasets with overlapping data points.

Time-Series Decomposition of Temperature

This plot breaks down the temperature time series into three components: trend (long-term movement), seasonality (regular, repeating patterns), and residuals (random noise or anomalies). This helps separate meaningful patterns from random fluctuations and is useful for understanding the underlying drivers of temperature changes.

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