WeatherReport Analytical Skills using Pandas,Excel,PowerBi,SQL

Weather Data Analysis Skills

This repository demonstrates my ability to work with data analysis queries using Excel, Power BI, SQL, and Pandas. Below are specific queries and the techniques applied to solve each in different tools. Each solution illustrates data handling, analysis, and aggregation skills across these platforms.

***#Skills and Queries Solved:

1. Unique Wind Speed Values: Extracted unique values using DISTINCT in SQL, .unique() in Pandas, and custom filtering in Power BI and Excel.
2. Weather Clear Count: Counted instances where weather was "Clear" using filtering and counting methods across tools.
3. Wind Speed of 4 km/h Count: Applied conditional counting.
4. Identifying Null Values: Used IS NULL or .isnull() to identify missing data.
5. Column Renaming: Renamed columns for clarity across tools.
6. Mean Visibility: Calculated average visibility values.
7. Pressure Standard Deviation: Measured data dispersion in Pressure.
8. Humidity Variance: Determined variance of Relative Humidity values.
9. Records with Snow: Used "Contains" filtering to identify snow records.
10. High Wind Speed and Fixed Visibility: Combined conditions to locate specific weather records.
11. Mean by Weather Condition: Aggregated mean values based on weather type.
12. Min/Max by Weather Condition: Determined minimum and maximum values per weather type.
13. Fog Condition Records: Filtered records to display Fog condition.
14. Clear Weather or High Visibility: Combined "OR" conditions for filtering.
15. Complex Conditions: Applied complex AND/OR conditions.

Each query showcases my ability to manipulate and analyze data, adapting to various tool capabilities. The individual files in this repository provide a detailed view of Excel,PowerBi,Sql,Pandas specific solution.