Power BI Report on Weather Data Analysis

Research Questions for Power BI Report on Weather Data Analysis Open Dataset

Your dataset includes the following columns:

• Formatted Date
• Summary
• Precip Type (Rain/Snow)
• Temperature (C)
• Apparent Temperature (C)
• Humidity
• Wind Speed (km/h)
• Wind Bearing (degrees)
• Visibility (km)
• Loud Cover
• Pressure (millibars)
• Daily Summary

Research Questions and Report Steps

1️⃣ Research Question: How does temperature vary across different months and seasons?

Steps to Generate the Report:

1. Transform Data in Power Query:
• Extract Month and Season from Formatted Date.
• Use M Query to create a Month column:

  Month = Date.MonthName([Formatted Date])

• Use M Query to create a Season column:

• M
  
  Season = 
      if Date.Month([Formatted Date]) in {12,1,2} then "Winter"
      else if Date.Month([Formatted Date]) in {3,4,5} then "Spring"
      else if Date.Month([Formatted Date]) in {6,7,8} then "Summer"
      else "Fall"

• 2.Visualize in Power BI:

• Line Chart: X-axis → Month, Y-axis → Average Temperature
• Bar Chart: Compare average temperature across different season

2️⃣ Research Question: What is the relationship between humidity and apparent temperature?

Steps to Generate the Report:

1. Transform Data in Power Query:
   • Standardize column names (remove spaces, check for null values).
2. Visualize in Power BI:
   • Scatter Plot: X-axis → Humidity, Y-axis → Apparent Temperature
   • Heatmap: Show correlation between humidity and apparent temperature

---

3️⃣ Research Question: How does weather condition impact visibility?

Steps to Generate the Report:

1. Transform Data in Power Query:
• Create a column to categorize Visibility Ranges:

  M
  

• VisibilityCategory = 

      if [Visibility (km)] > 10 then "Clear"

      else if [Visibility (km)] >= 5 then "Moderate"

      else "Low"
2. Visualize in Power BI:
• Bar Chart: Show average visibility across different weather summaries
• Pie Chart: Proportion of Clear, Moderate, and Low visibility days

4️⃣ Research Question: What is the effect of wind speed on temperature?

Steps to Generate the Report:

1. Transform Data in Power Query:
• Create a column Wind Speed Category:

  M
  
  

• WindCategory = 

      if [Wind Speed (km/h)] < 10 then "Low"

      else if [Wind Speed (km/h)] >= 10 and [Wind Speed (km/h)] < 30 then "Moderate"

      else "High"
2. Visualize in Power BI:
• Bar Chart: Compare temperature across wind speed categories
• Scatter Plot: X-axis → Wind Speed, Y-axis → Temperature

5️⃣ Research Question: How does pressure influence weather conditions?

Steps to Generate the Report:

1. Transform Data in Power Query:
• Create a column Pressure Category:

  M
  

• PressureCategory = 

      if [Pressure (millibars)] < 1000 then "Low Pressure"

      else if [Pressure (millibars)] >= 1000 and [Pressure (millibars)] < 1020 then "Normal Pressure"

      else "High Pressure"
2. Visualize in Power BI:
• Bar Chart: Compare pressure across different weather summaries
• Line Chart: Show pressure variation over time

Additional Research Questions for M Query-Based Transformations

1️⃣ How can we classify days based on temperature ranges?
M Query:

M

TemperatureCategory = 
    if [Temperature (C)] >= 30 then "Hot"
    else if [Temperature (C)] >= 20 then "Warm"
    else if [Temperature (C)] >= 10 then "Cool"
    else "Cold"

Usage: Use it in bar charts to compare the number of hot, warm, cool, and cold days.

2️⃣ How can we determine extreme weather conditions?
M Query:

M

ExtremeWeather = 
    if [Humidity] > 0.9 and [Wind Speed (km/h)] > 50 and [Precip Type] = "Rain" then "Stormy"
    else if [Temperature (C)] < 0 and [Precip Type] = "Snow" then "Blizzard"
    else "Normal"

Usage: Use it to filter and analyze extreme weather events in Power BI.

Final Steps in Power BI:

1. Load the transformed data into Power BI.
2. Use appropriate visualizations (line charts, scatter plots, bar charts, heatmaps).
3. Create a dashboard showing key insights:
   • Seasonal temperature trends
   • Relationship between humidity and apparent temperature
   • Impact of wind speed on temperature
   • Pressure variations and weather conditions