Weather Forecast dataset

Here are detailed instructions to help users understand and analyze this dataset:

1. Column Descriptions:

   • STATION: Station code or identifier.
   • NAME: Name of the weather station (New Delhi Safdarjung, India, in this case).
   • DATE: Date of the recorded weather data.
   • PRCP: Precipitation in inches. If the value is 0, there was no precipitation. Non-zero values indicate the amount of precipitation.
   • TAVG: Average temperature in Fahrenheit.
   • TMAX: Maximum temperature in Fahrenheit.
   • TMIN: Minimum temperature in Fahrenheit.

2. Missing Data:

   • Look for missing values in any of the columns. Handle them appropriately based on the analysis (e.g., interpolation, removal, or filling with mean/median).

3. Data Types:

   • Ensure that the data types of each column are appropriate (e.g., date as a datetime object, temperatures as floats, etc.).

4. Descriptive Statistics:

   • Calculate descriptive statistics for each column, such as mean, median, standard deviation, and quartiles, to get an overview of the data distribution.

5. Temperature Trends:

   • Analyze temperature trends over time. Look for patterns, seasonality, and any significant deviations.

6. Precipitation Analysis:

   • Examine the precipitation data. Identify days with significant precipitation, and check if there are patterns or trends.

7. Temperature Extremes:

   • Identify days with unusually high or low temperatures. Check for any extreme weather events.

8. Visualizations:

   • Create visualizations such as line charts for temperature trends, bar charts for precipitation, and scatter plots for relationships between variables.

9. Monthly and Seasonal Analysis:

   • Aggregate data on a monthly or seasonal basis to observe broader patterns.

10. Correlation Analysis:

    • Check for correlations between different weather variables (e.g., temperature and precipitation).

11. Outlier Detection:

    • Identify and investigate any outliers in the dataset that may affect the analysis.

12. Data Range:

    • Check if the dataset covers a specific time range, and ensure it's suitable for the intended analysis.

These instructions provide a comprehensive guide to understanding and exploring the given weather dataset for New Delhi Safdarjung, India. Users can use statistical and visual analysis techniques to derive insights from the data.