| gender | department | department_name | division | assignment_category | employee_position_title | date_first_hired | year_first_hired | |
|---|---|---|---|---|---|---|---|---|
| 0 | F | POL | Department of Police | MSB Information Mgmt and Tech Division Records Management Section | Fulltime-Regular | Office Services Coordinator | 09/22/1986 | 1,986 |
| 1 | M | POL | Department of Police | ISB Major Crimes Division Fugitive Section | Fulltime-Regular | Master Police Officer | 09/12/1988 | 1,988 |
| 2 | F | HHS | Department of Health and Human Services | Adult Protective and Case Management Services | Fulltime-Regular | Social Worker IV | 11/19/1989 | 1,989 |
| 3 | M | COR | Correction and Rehabilitation | PRRS Facility and Security | Fulltime-Regular | Resident Supervisor II | 05/05/2014 | 2,014 |
| 4 | M | HCA | Department of Housing and Community Affairs | Affordable Housing Programs | Fulltime-Regular | Planning Specialist III | 03/05/2007 | 2,007 |
| 9,223 | F | HHS | Department of Health and Human Services | School Based Health Centers | Fulltime-Regular | Community Health Nurse II | 11/03/2015 | 2,015 |
| 9,224 | F | FRS | Fire and Rescue Services | Human Resources Division | Fulltime-Regular | Fire/Rescue Division Chief | 11/28/1988 | 1,988 |
| 9,225 | M | HHS | Department of Health and Human Services | Child and Adolescent Mental Health Clinic Services | Parttime-Regular | Medical Doctor IV - Psychiatrist | 04/30/2001 | 2,001 |
| 9,226 | M | CCL | County Council | Council Central Staff | Fulltime-Regular | Manager II | 09/05/2006 | 2,006 |
| 9,227 | M | DLC | Department of Liquor Control | Licensure, Regulation and Education | Fulltime-Regular | Alcohol/Tobacco Enforcement Specialist II | 01/30/2012 | 2,012 |
gender
ObjectDType- Null values
- 17 (0.2%)
- Unique values
- 2 (< 0.1%)
Most frequent values
department
ObjectDType- Null values
- 0 (0.0%)
- Unique values
- 37 (0.4%)
Most frequent values
department_name
ObjectDType- Null values
- 0 (0.0%)
- Unique values
- 37 (0.4%)
Most frequent values
division
ObjectDType- Null values
- 0 (0.0%)
- Unique values
-
694 (7.5%)
This column has a high cardinality (> 40).
Most frequent values
assignment_category
ObjectDType- Null values
- 0 (0.0%)
- Unique values
- 2 (< 0.1%)
Most frequent values
employee_position_title
ObjectDType- Null values
- 0 (0.0%)
- Unique values
-
443 (4.8%)
This column has a high cardinality (> 40).
Most frequent values
date_first_hired
ObjectDType- Null values
- 0 (0.0%)
- Unique values
-
2,264 (24.5%)
This column has a high cardinality (> 40).
Most frequent values
year_first_hired
Int64DType- Null values
- 0 (0.0%)
- Unique values
-
51 (0.6%)
This column has a high cardinality (> 40).
- Mean ± Std
- 2.00e+03 ± 9.33
- Median ± IQR
- 2,005 ± 14
- Min | Max
- 1,965 | 2,016
No columns match the selected filter: . You can change the column filter in the dropdown menu above.
| Column | Column name | dtype | Is sorted | Null values | Unique values | Mean | Std | Min | Median | Max |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | gender | ObjectDType | False | 17 (0.2%) | 2 (< 0.1%) | |||||
| 1 | department | ObjectDType | False | 0 (0.0%) | 37 (0.4%) | |||||
| 2 | department_name | ObjectDType | False | 0 (0.0%) | 37 (0.4%) | |||||
| 3 | division | ObjectDType | False | 0 (0.0%) | 694 (7.5%) | |||||
| 4 | assignment_category | ObjectDType | False | 0 (0.0%) | 2 (< 0.1%) | |||||
| 5 | employee_position_title | ObjectDType | False | 0 (0.0%) | 443 (4.8%) | |||||
| 6 | date_first_hired | ObjectDType | False | 0 (0.0%) | 2264 (24.5%) | |||||
| 7 | year_first_hired | Int64DType | False | 0 (0.0%) | 51 (0.6%) | 2.00e+03 | 9.33 | 1,965 | 2,005 | 2,016 |
No columns match the selected filter: . You can change the column filter in the dropdown menu above.
gender
ObjectDType- Null values
- 17 (0.2%)
- Unique values
- 2 (< 0.1%)
Most frequent values
department
ObjectDType- Null values
- 0 (0.0%)
- Unique values
- 37 (0.4%)
Most frequent values
department_name
ObjectDType- Null values
- 0 (0.0%)
- Unique values
- 37 (0.4%)
Most frequent values
division
ObjectDType- Null values
- 0 (0.0%)
- Unique values
-
694 (7.5%)
This column has a high cardinality (> 40).
Most frequent values
assignment_category
ObjectDType- Null values
- 0 (0.0%)
- Unique values
- 2 (< 0.1%)
Most frequent values
employee_position_title
ObjectDType- Null values
- 0 (0.0%)
- Unique values
-
443 (4.8%)
This column has a high cardinality (> 40).
Most frequent values
date_first_hired
ObjectDType- Null values
- 0 (0.0%)
- Unique values
-
2,264 (24.5%)
This column has a high cardinality (> 40).
Most frequent values
year_first_hired
Int64DType- Null values
- 0 (0.0%)
- Unique values
-
51 (0.6%)
This column has a high cardinality (> 40).
- Mean ± Std
- 2.00e+03 ± 9.33
- Median ± IQR
- 2,005 ± 14
- Min | Max
- 1,965 | 2,016
No columns match the selected filter: . You can change the column filter in the dropdown menu above.
| Column 1 | Column 2 | Cramér's V | Pearson's Correlation |
|---|---|---|---|
| department | department_name | 1.00 | |
| division | assignment_category | 0.606 | |
| assignment_category | employee_position_title | 0.490 | |
| division | employee_position_title | 0.458 | |
| department | employee_position_title | 0.415 | |
| department_name | employee_position_title | 0.415 | |
| department | division | 0.370 | |
| department_name | division | 0.370 | |
| department | assignment_category | 0.370 | |
| department_name | assignment_category | 0.370 | |
| gender | department_name | 0.369 | |
| gender | department | 0.369 | |
| employee_position_title | date_first_hired | 0.332 | |
| gender | employee_position_title | 0.253 | |
| gender | division | 0.247 | |
| gender | assignment_category | 0.227 | |
| department | date_first_hired | 0.156 | |
| department_name | date_first_hired | 0.156 | |
| date_first_hired | year_first_hired | 0.144 | |
| employee_position_title | year_first_hired | 0.144 |
The table below shows the strength of association between the most similar columns in the dataframe.
Cramér's V statistic is a number between 0 and 1.
When it is close to 1 the columns are strongly associated — they contain similar information.
In this case, one of them may be redundant and for some models (such as linear models) it might be beneficial to remove it.
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