Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations54808
Missing cells6533
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.1 MiB
Average record size in memory346.9 B

Variable types

Numeric5
Categorical8

Dataset

DescriptionDataset de RH para predecir promoción.
CreatorSunny
URL

Variable descriptions

employee_idID único del empleado
departmentDepartamento
regionRegión de empleo
educationNivel educativo
genderGénero (f, m)
recruitment_channelCanal de reclutamiento
no_of_trainingsNº de capacitaciones año previo
ageEdad
previous_year_ratingCalificación año previo
length_of_serviceAños de servicio
awards_won?¿Ganó premio año previo? (0/1)
avg_training_scorePromedio evaluación de formación
is_promoted¿Promovido? (0/1)

Alerts

age is highly overall correlated with length_of_serviceHigh correlation
avg_training_score is highly overall correlated with departmentHigh correlation
department is highly overall correlated with avg_training_scoreHigh correlation
length_of_service is highly overall correlated with ageHigh correlation
awards_won? is highly imbalanced (84.1%)Imbalance
is_promoted is highly imbalanced (58.0%)Imbalance
education has 2409 (4.4%) missing valuesMissing
previous_year_rating has 4124 (7.5%) missing valuesMissing
employee_id has unique valuesUnique

Reproduction

Analysis started2025-09-25 03:16:04.248784
Analysis finished2025-09-25 03:16:20.443421
Duration16.19 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

employee_id
Real number (ℝ)

Unique 

ID único del empleado

Distinct54808
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39195.831
Minimum1
Maximum78298
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size428.3 KiB
2025-09-25T03:16:20.765936image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3916.35
Q119669.75
median39225.5
Q358730.5
95-th percentile74415.3
Maximum78298
Range78297
Interquartile range (IQR)39060.75

Descriptive statistics

Standard deviation22586.581
Coefficient of variation (CV)0.57624959
Kurtosis-1.1964792
Mean39195.831
Median Absolute Deviation (MAD)19531.5
Skewness-0.0031279472
Sum2.1482451 × 109
Variance5.1015366 × 108
MonotonicityNot monotonic
2025-09-25T03:16:21.363866image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
654381
 
< 0.1%
162231
 
< 0.1%
382501
 
< 0.1%
680861
 
< 0.1%
780801
 
< 0.1%
526541
 
< 0.1%
310091
 
< 0.1%
80861
 
< 0.1%
249761
 
< 0.1%
604681
 
< 0.1%
Other values (54798)54798
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
41
< 0.1%
51
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
121
< 0.1%
141
< 0.1%
ValueCountFrequency (%)
782981
< 0.1%
782971
< 0.1%
782961
< 0.1%
782941
< 0.1%
782921
< 0.1%
782911
< 0.1%
782901
< 0.1%
782891
< 0.1%
782881
< 0.1%
782871
< 0.1%

department
Categorical

High correlation 

Departamento

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
Sales & Marketing
16840 
Operations
11348 
Technology
7138 
Procurement
7138 
Analytics
5352 
Other values (4)
6992 

Length

Max length17
Median length11
Mean length11.469238
Min length2

Characters and Unicode

Total characters628606
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales & Marketing
2nd rowOperations
3rd rowSales & Marketing
4th rowSales & Marketing
5th rowTechnology

Common Values

ValueCountFrequency (%)
Sales & Marketing16840
30.7%
Operations11348
20.7%
Technology7138
13.0%
Procurement7138
13.0%
Analytics5352
 
9.8%
Finance2536
 
4.6%
HR2418
 
4.4%
Legal1039
 
1.9%
R&D999
 
1.8%

Length

2025-09-25T03:16:21.944515image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-25T03:16:22.285397image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
sales16840
19.0%
16840
19.0%
marketing16840
19.0%
operations11348
12.8%
technology7138
8.1%
procurement7138
8.1%
analytics5352
 
6.0%
finance2536
 
2.9%
hr2418
 
2.7%
legal1039
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e70017
 
11.1%
a53955
 
8.6%
n52888
 
8.4%
r42464
 
6.8%
t40678
 
6.5%
i36076
 
5.7%
33680
 
5.4%
s33540
 
5.3%
o32762
 
5.2%
l30369
 
4.8%
Other values (20)202177
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)628606
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e70017
 
11.1%
a53955
 
8.6%
n52888
 
8.4%
r42464
 
6.8%
t40678
 
6.5%
i36076
 
5.7%
33680
 
5.4%
s33540
 
5.3%
o32762
 
5.2%
l30369
 
4.8%
Other values (20)202177
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)628606
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e70017
 
11.1%
a53955
 
8.6%
n52888
 
8.4%
r42464
 
6.8%
t40678
 
6.5%
i36076
 
5.7%
33680
 
5.4%
s33540
 
5.3%
o32762
 
5.2%
l30369
 
4.8%
Other values (20)202177
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)628606
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e70017
 
11.1%
a53955
 
8.6%
n52888
 
8.4%
r42464
 
6.8%
t40678
 
6.5%
i36076
 
5.7%
33680
 
5.4%
s33540
 
5.3%
o32762
 
5.2%
l30369
 
4.8%
Other values (20)202177
32.2%

region
Categorical

Región de empleo

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
region_2
12343 
region_22
6428 
region_7
4843 
region_15
2808 
region_13
2648 
Other values (29)
25738 

Length

Max length9
Median length9
Mean length8.5917384
Min length8

Characters and Unicode

Total characters470896
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowregion_7
2nd rowregion_22
3rd rowregion_19
4th rowregion_23
5th rowregion_26

Common Values

ValueCountFrequency (%)
region_212343
22.5%
region_226428
 
11.7%
region_74843
 
8.8%
region_152808
 
5.1%
region_132648
 
4.8%
region_262260
 
4.1%
region_311935
 
3.5%
region_41703
 
3.1%
region_271659
 
3.0%
region_161465
 
2.7%
Other values (24)16716
30.5%

Length

2025-09-25T03:16:22.785771image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
region_212343
22.5%
region_226428
 
11.7%
region_74843
 
8.8%
region_152808
 
5.1%
region_132648
 
4.8%
region_262260
 
4.1%
region_311935
 
3.5%
region_41703
 
3.1%
region_271659
 
3.0%
region_161465
 
2.7%
Other values (24)16716
30.5%

Most occurring characters

ValueCountFrequency (%)
r54808
11.6%
g54808
11.6%
i54808
11.6%
o54808
11.6%
n54808
11.6%
_54808
11.6%
e54808
11.6%
236638
7.8%
116183
 
3.4%
38536
 
1.8%
Other values (7)25883
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)470896
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r54808
11.6%
g54808
11.6%
i54808
11.6%
o54808
11.6%
n54808
11.6%
_54808
11.6%
e54808
11.6%
236638
7.8%
116183
 
3.4%
38536
 
1.8%
Other values (7)25883
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)470896
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r54808
11.6%
g54808
11.6%
i54808
11.6%
o54808
11.6%
n54808
11.6%
_54808
11.6%
e54808
11.6%
236638
7.8%
116183
 
3.4%
38536
 
1.8%
Other values (7)25883
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)470896
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r54808
11.6%
g54808
11.6%
i54808
11.6%
o54808
11.6%
n54808
11.6%
_54808
11.6%
e54808
11.6%
236638
7.8%
116183
 
3.4%
38536
 
1.8%
Other values (7)25883
5.5%

education
Categorical

Missing 

Nivel educativo

Distinct3
Distinct (%)< 0.1%
Missing2409
Missing (%)4.4%
Memory size3.2 MiB
Bachelor's
36669 
Master's & above
14925 
Below Secondary
 
805

Length

Max length16
Median length10
Mean length11.785817
Min length10

Characters and Unicode

Total characters617565
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaster's & above
2nd rowBachelor's
3rd rowBachelor's
4th rowBachelor's
5th rowBachelor's

Common Values

ValueCountFrequency (%)
Bachelor's36669
66.9%
Master's & above14925
27.2%
Below Secondary805
 
1.5%
(Missing)2409
 
4.4%

Length

2025-09-25T03:16:23.051143image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-25T03:16:23.276511image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
bachelor's36669
44.2%
master's14925
18.0%
14925
18.0%
above14925
18.0%
below805
 
1.0%
secondary805
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e68129
11.0%
a67324
10.9%
s66519
10.8%
o53204
8.6%
r52399
8.5%
'51594
8.4%
B37474
 
6.1%
c37474
 
6.1%
l37474
 
6.1%
h36669
 
5.9%
Other values (11)109305
17.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)617565
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e68129
11.0%
a67324
10.9%
s66519
10.8%
o53204
8.6%
r52399
8.5%
'51594
8.4%
B37474
 
6.1%
c37474
 
6.1%
l37474
 
6.1%
h36669
 
5.9%
Other values (11)109305
17.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)617565
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e68129
11.0%
a67324
10.9%
s66519
10.8%
o53204
8.6%
r52399
8.5%
'51594
8.4%
B37474
 
6.1%
c37474
 
6.1%
l37474
 
6.1%
h36669
 
5.9%
Other values (11)109305
17.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)617565
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e68129
11.0%
a67324
10.9%
s66519
10.8%
o53204
8.6%
r52399
8.5%
'51594
8.4%
B37474
 
6.1%
c37474
 
6.1%
l37474
 
6.1%
h36669
 
5.9%
Other values (11)109305
17.7%

gender
Categorical

Género (f, m)

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
m
38496 
f
16312 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters54808
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowf
2nd rowm
3rd rowm
4th rowm
5th rowm

Common Values

ValueCountFrequency (%)
m38496
70.2%
f16312
29.8%

Length

2025-09-25T03:16:23.505538image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-25T03:16:23.702529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
m38496
70.2%
f16312
29.8%

Most occurring characters

ValueCountFrequency (%)
m38496
70.2%
f16312
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)54808
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m38496
70.2%
f16312
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)54808
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m38496
70.2%
f16312
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)54808
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m38496
70.2%
f16312
29.8%

recruitment_channel
Categorical

Canal de reclutamiento

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
other
30446 
sourcing
23220 
referred
 
1142

Length

Max length8
Median length5
Mean length6.3334915
Min length5

Characters and Unicode

Total characters347126
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsourcing
2nd rowother
3rd rowsourcing
4th rowother
5th rowother

Common Values

ValueCountFrequency (%)
other30446
55.6%
sourcing23220
42.4%
referred1142
 
2.1%

Length

2025-09-25T03:16:23.974545image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-25T03:16:24.195801image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
other30446
55.6%
sourcing23220
42.4%
referred1142
 
2.1%

Most occurring characters

ValueCountFrequency (%)
r57092
16.4%
o53666
15.5%
e33872
9.8%
t30446
8.8%
h30446
8.8%
s23220
6.7%
u23220
6.7%
c23220
6.7%
i23220
6.7%
n23220
6.7%
Other values (3)25504
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)347126
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r57092
16.4%
o53666
15.5%
e33872
9.8%
t30446
8.8%
h30446
8.8%
s23220
6.7%
u23220
6.7%
c23220
6.7%
i23220
6.7%
n23220
6.7%
Other values (3)25504
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)347126
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r57092
16.4%
o53666
15.5%
e33872
9.8%
t30446
8.8%
h30446
8.8%
s23220
6.7%
u23220
6.7%
c23220
6.7%
i23220
6.7%
n23220
6.7%
Other values (3)25504
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)347126
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r57092
16.4%
o53666
15.5%
e33872
9.8%
t30446
8.8%
h30446
8.8%
s23220
6.7%
u23220
6.7%
c23220
6.7%
i23220
6.7%
n23220
6.7%
Other values (3)25504
7.3%

no_of_trainings
Real number (ℝ)

Nº de capacitaciones año previo

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2530105
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size428.3 KiB
2025-09-25T03:16:24.397165image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.60926402
Coefficient of variation (CV)0.48624015
Kurtosis18.740082
Mean1.2530105
Median Absolute Deviation (MAD)0
Skewness3.4454339
Sum68675
Variance0.37120264
MonotonicityNot monotonic
2025-09-25T03:16:24.607669image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
144378
81.0%
27987
 
14.6%
31776
 
3.2%
4468
 
0.9%
5128
 
0.2%
644
 
0.1%
712
 
< 0.1%
85
 
< 0.1%
105
 
< 0.1%
95
 
< 0.1%
ValueCountFrequency (%)
144378
81.0%
27987
 
14.6%
31776
 
3.2%
4468
 
0.9%
5128
 
0.2%
644
 
0.1%
712
 
< 0.1%
85
 
< 0.1%
95
 
< 0.1%
105
 
< 0.1%
ValueCountFrequency (%)
105
 
< 0.1%
95
 
< 0.1%
85
 
< 0.1%
712
 
< 0.1%
644
 
0.1%
5128
 
0.2%
4468
 
0.9%
31776
 
3.2%
27987
 
14.6%
144378
81.0%

age
Real number (ℝ)

High correlation 

Edad

Distinct41
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.803915
Minimum20
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size428.3 KiB
2025-09-25T03:16:24.865324image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile25
Q129
median33
Q339
95-th percentile51
Maximum60
Range40
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.6601692
Coefficient of variation (CV)0.22009504
Kurtosis0.79235337
Mean34.803915
Median Absolute Deviation (MAD)4
Skewness1.0074318
Sum1907533
Variance58.678192
MonotonicityNot monotonic
2025-09-25T03:16:25.376214image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
303665
 
6.7%
323534
 
6.4%
313534
 
6.4%
293405
 
6.2%
333210
 
5.9%
283147
 
5.7%
343076
 
5.6%
272827
 
5.2%
352711
 
4.9%
362517
 
4.6%
Other values (31)23182
42.3%
ValueCountFrequency (%)
20113
 
0.2%
2198
 
0.2%
22231
 
0.4%
23428
 
0.8%
24845
 
1.5%
251299
 
2.4%
262060
3.8%
272827
5.2%
283147
5.7%
293405
6.2%
ValueCountFrequency (%)
60217
0.4%
59209
0.4%
58213
0.4%
57238
0.4%
56264
0.5%
55294
0.5%
54313
0.6%
53364
0.7%
52351
0.6%
51389
0.7%

previous_year_rating
Categorical

Missing 

Calificación año previo

Distinct5
Distinct (%)< 0.1%
Missing4124
Missing (%)7.5%
Memory size2.7 MiB
3.0
18618 
5.0
11741 
4.0
9877 
1.0
6223 
2.0
4225 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters152052
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.0
3rd row3.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.018618
34.0%
5.011741
21.4%
4.09877
18.0%
1.06223
 
11.4%
2.04225
 
7.7%
(Missing)4124
 
7.5%

Length

2025-09-25T03:16:25.660933image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-25T03:16:25.875230image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
3.018618
36.7%
5.011741
23.2%
4.09877
19.5%
1.06223
 
12.3%
2.04225
 
8.3%

Most occurring characters

ValueCountFrequency (%)
.50684
33.3%
050684
33.3%
318618
 
12.2%
511741
 
7.7%
49877
 
6.5%
16223
 
4.1%
24225
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)152052
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.50684
33.3%
050684
33.3%
318618
 
12.2%
511741
 
7.7%
49877
 
6.5%
16223
 
4.1%
24225
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)152052
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.50684
33.3%
050684
33.3%
318618
 
12.2%
511741
 
7.7%
49877
 
6.5%
16223
 
4.1%
24225
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)152052
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.50684
33.3%
050684
33.3%
318618
 
12.2%
511741
 
7.7%
49877
 
6.5%
16223
 
4.1%
24225
 
2.8%

length_of_service
Real number (ℝ)

High correlation 

Años de servicio

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8655123
Minimum1
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size428.3 KiB
2025-09-25T03:16:26.165155image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile15
Maximum37
Range36
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.2650942
Coefficient of variation (CV)0.72714776
Kurtosis4.4140314
Mean5.8655123
Median Absolute Deviation (MAD)2
Skewness1.7380615
Sum321477
Variance18.191028
MonotonicityNot monotonic
2025-09-25T03:16:26.451438image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
37033
12.8%
46836
12.5%
26684
12.2%
55832
10.6%
75551
10.1%
64734
8.6%
14547
8.3%
82883
5.3%
92629
 
4.8%
102193
 
4.0%
Other values (25)5886
10.7%
ValueCountFrequency (%)
14547
8.3%
26684
12.2%
37033
12.8%
46836
12.5%
55832
10.6%
64734
8.6%
75551
10.1%
82883
5.3%
92629
 
4.8%
102193
 
4.0%
ValueCountFrequency (%)
371
 
< 0.1%
344
 
< 0.1%
339
 
< 0.1%
3210
 
< 0.1%
3120
< 0.1%
3012
 
< 0.1%
2930
0.1%
2830
0.1%
2736
0.1%
2641
0.1%

awards_won?
Categorical

Imbalance 

¿Ganó premio año previo? (0/1)

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
0
53538 
1
 
1270

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters54808
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
053538
97.7%
11270
 
2.3%

Length

2025-09-25T03:16:26.714167image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-25T03:16:26.903333image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
053538
97.7%
11270
 
2.3%

Most occurring characters

ValueCountFrequency (%)
053538
97.7%
11270
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)54808
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
053538
97.7%
11270
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)54808
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
053538
97.7%
11270
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)54808
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
053538
97.7%
11270
 
2.3%

avg_training_score
Real number (ℝ)

High correlation 

Promedio evaluación de formación

Distinct61
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.38675
Minimum39
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size428.3 KiB
2025-09-25T03:16:27.148586image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile47
Q151
median60
Q376
95-th percentile86
Maximum99
Range60
Interquartile range (IQR)25

Descriptive statistics

Standard deviation13.371559
Coefficient of variation (CV)0.21095197
Kurtosis-1.0496493
Mean63.38675
Median Absolute Deviation (MAD)10
Skewness0.45190809
Sum3474101
Variance178.7986
MonotonicityNot monotonic
2025-09-25T03:16:27.451090image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
502716
 
5.0%
492681
 
4.9%
482437
 
4.4%
512347
 
4.3%
602155
 
3.9%
592064
 
3.8%
581898
 
3.5%
611879
 
3.4%
521856
 
3.4%
471746
 
3.2%
Other values (51)33029
60.3%
ValueCountFrequency (%)
392
 
< 0.1%
405
 
< 0.1%
4126
 
< 0.1%
4262
 
0.1%
43176
 
0.3%
44335
 
0.6%
45681
 
1.2%
461136
2.1%
471746
3.2%
482437
4.4%
ValueCountFrequency (%)
9935
 
0.1%
9837
 
0.1%
9749
 
0.1%
9648
 
0.1%
9545
 
0.1%
9465
 
0.1%
9384
0.2%
9299
0.2%
91117
0.2%
90185
0.3%

is_promoted
Categorical

Imbalance 

¿Promovido? (0/1)

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
0
50140 
1
 
4668

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters54808
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
050140
91.5%
14668
 
8.5%

Length

2025-09-25T03:16:27.716180image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-25T03:16:27.910047image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
050140
91.5%
14668
 
8.5%

Most occurring characters

ValueCountFrequency (%)
050140
91.5%
14668
 
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)54808
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
050140
91.5%
14668
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)54808
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
050140
91.5%
14668
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)54808
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
050140
91.5%
14668
 
8.5%

Interactions

2025-09-25T03:16:17.720939image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-09-25T03:16:08.138214image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-09-25T03:16:11.321202image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-09-25T03:16:13.466522image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-09-25T03:16:15.376785image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-09-25T03:16:17.999240image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-09-25T03:16:08.776959image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-09-25T03:16:11.773105image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-09-25T03:16:13.755573image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-09-25T03:16:15.854865image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-09-25T03:16:18.205450image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-09-25T03:16:09.745050image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-09-25T03:16:12.146119image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-09-25T03:16:14.107954image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-09-25T03:16:16.140252image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-09-25T03:16:18.476828image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-09-25T03:16:10.299418image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-09-25T03:16:12.482027image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-09-25T03:16:14.508000image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-09-25T03:16:16.757152image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-09-25T03:16:18.717934image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-09-25T03:16:10.760054image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-09-25T03:16:13.003610image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-09-25T03:16:14.874218image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-09-25T03:16:17.217731image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-09-25T03:16:28.083438image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ageavg_training_scoreawards_won?departmenteducationemployee_idgenderis_promotedlength_of_serviceno_of_trainingsprevious_year_ratingrecruitment_channelregion
age1.000-0.0410.0000.0750.463-0.0000.0410.0290.644-0.0860.0150.0300.147
avg_training_score-0.0411.0000.1630.5740.063-0.0000.1950.303-0.0290.0530.0910.0400.080
awards_won?0.0000.1631.0000.0060.0000.0050.0000.1960.0430.0000.0290.0020.017
department0.0750.5740.0061.0000.1240.0000.2860.0510.0460.0570.1090.0620.132
education0.4630.0630.0000.1241.0000.0120.0270.0260.1890.0270.0210.0270.181
employee_id-0.000-0.0000.0050.0000.0121.0000.0060.0000.001-0.0030.0080.0000.002
gender0.0410.1950.0000.2860.0270.0061.0000.0100.0280.0870.0270.0080.161
is_promoted0.0290.3030.1960.0510.0260.0000.0101.0000.0160.0220.1700.0180.090
length_of_service0.644-0.0290.0430.0460.1890.0010.0280.0161.000-0.0570.0060.0180.083
no_of_trainings-0.0860.0530.0000.0570.027-0.0030.0870.022-0.0571.0000.0400.0130.040
previous_year_rating0.0150.0910.0290.1090.0210.0080.0270.1700.0060.0401.0000.0500.051
recruitment_channel0.0300.0400.0020.0620.0270.0000.0080.0180.0180.0130.0501.0000.110
region0.1470.0800.0170.1320.1810.0020.1610.0900.0830.0400.0510.1101.000

Missing values

2025-09-25T03:16:19.045938image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-25T03:16:19.534933image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-09-25T03:16:20.132822image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

employee_iddepartmentregioneducationgenderrecruitment_channelno_of_trainingsageprevious_year_ratinglength_of_serviceawards_won?avg_training_scoreis_promoted
065438Sales & Marketingregion_7Master's & abovefsourcing1355.080490
165141Operationsregion_22Bachelor'smother1305.040600
27513Sales & Marketingregion_19Bachelor'smsourcing1343.070500
32542Sales & Marketingregion_23Bachelor'smother2391.0100500
448945Technologyregion_26Bachelor'smother1453.020730
558896Analyticsregion_2Bachelor'smsourcing2313.070850
620379Operationsregion_20Bachelor'sfother1313.050590
716290Operationsregion_34Master's & abovemsourcing1333.060630
873202Analyticsregion_20Bachelor'smother1284.050830
928911Sales & Marketingregion_1Master's & abovemsourcing1325.050540
employee_iddepartmentregioneducationgenderrecruitment_channelno_of_trainingsageprevious_year_ratinglength_of_serviceawards_won?avg_training_scoreis_promoted
5479840257Sales & Marketingregion_2Master's & abovefother2405.040510
5479968093Procurementregion_2Master's & abovefother1505.061670
5480039227HRregion_11Bachelor'smother2345.030520
5480112431Technologyregion_26Bachelor'sfsourcing131NaN10780
548026915Sales & Marketingregion_14Bachelor'smother2311.020490
548033030Technologyregion_14Bachelor'smsourcing1483.0170780
5480474592Operationsregion_27Master's & abovefother1372.060560
5480513918Analyticsregion_1Bachelor'smother1275.030790
5480613614Sales & Marketingregion_9NaNmsourcing1291.020450
5480751526HRregion_22Bachelor'smother1271.050490