City-Wise Departmental Employee Count in Uttar Pradesh :
City | HR | Sales | IT | Finance | Marketing |
---|---|---|---|---|---|
Lucknow | 120 | 200 | 150 | 100 | 80 |
Kanpur | 95 | 180 | 130 | 110 | 90 |
Varanasi | 85 | 170 | 140 | 120 | 75 |
Agra | 100 | 150 | 125 | 90 | 85 |
Meerut | 75 | 160 | 110 | 105 | 70 |
Allahabad | 90 | 145 | 135 | 95 | 65 |
Ghaziabad | 105 | 190 | 155 | 115 | 95 |
Noida | 115 | 220 | 175 | 125 | 100 |
Aligarh | 70 | 140 | 100 | 80 | 60 |
Moradabad | 80 | 155 | 120 | 85 | 75 |
Related Activities:
- Use VLOOKUP to find the total count of employees in a specific city like “Kanpur.”
- Apply INDEX-MATCH to retrieve the count of employees in the “IT” department for “Noida.”
- Sort the Data by the “Marketing” column to rank cities by their employee count in that department.
- Filter Rows to display only cities with more than 150 employees in the “Sales” department.
- Create a Pivot Table to summarize total employees across all departments.
- Apply Conditional Formatting to highlight cities where the “HR” count is below 100.
- Calculate Row Totals to find the total employee count per city.
- Visualize Data using a bar chart comparing department-wise counts for all cities.
- Use Data Validation to restrict new entries in the “Department” columns to predefined values.
- Find the Highest Count in each department using the MAX function.
- Calculate Averages for employee counts across all departments using the AVERAGE function.
- Use Subtotals to display total counts for each department when the table is grouped by cities.
- Apply Filtering to display only cities with “Finance” employee counts exceeding 100.
- Analyze Trends by creating a stacked column chart to compare department-wise distribution across cities.
- Highlight Duplicates in department totals if repeated values exist across columns.