Overview

Dataset statistics

Number of variables19
Number of observations27176
Missing cells13178
Missing cells (%)2.6%
Duplicate rows64
Duplicate rows (%)0.2%
Total size in memory22.4 MiB
Average record size in memory864.8 B

Variable types

Categorical11
Text3
Numeric3
Boolean2

Alerts

Muertos has constant value "False"Constant
Material has constant value "False"Constant
Dataset has 64 (0.2%) duplicate rowsDuplicates
FRECUENCIA is highly overall correlated with PonderacionHigh correlation
GRAVEDAD_ACCIDENTE is highly overall correlated with HeridosHigh correlation
Heridos is highly overall correlated with GRAVEDAD_ACCIDENTE and 1 other fieldsHigh correlation
HoraP1 is highly overall correlated with PonderacionHigh correlation
HoraP2 is highly overall correlated with PonderacionHigh correlation
Ponderacion is highly overall correlated with FRECUENCIA and 4 other fieldsHigh correlation
Riesgo is highly overall correlated with PonderacionHigh correlation
tipo_principal is highly overall correlated with tipo_secundariaHigh correlation
tipo_secundaria is highly overall correlated with tipo_principalHigh correlation
HoraP2 is highly imbalanced (52.3%)Imbalance
CLASE_ACCIDENTE is highly imbalanced (81.7%)Imbalance
tipo_secundaria has 6360 (23.4%) missing valuesMissing
num_secundaria has 6360 (23.4%) missing valuesMissing

Reproduction

Analysis started2025-11-28 04:35:31.593515
Analysis finished2025-11-28 04:35:37.380125
Duration5.79 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Jornada
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Tarde
10598 
Morning
9044 
Noche
7534 

Length

Max length7
Median length5
Mean length5.6655873
Min length5

Characters and Unicode

Total characters153968
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 rowTarde
2nd rowNoche
3rd rowTarde
4th rowNoche
5th rowNoche

Common Values

ValueCountFrequency (%)
Tarde10598
39.0%
Morning9044
33.3%
Noche7534
27.7%

Length

2025-11-28T04:35:37.687381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T04:35:37.784450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
tarde10598
39.0%
morning9044
33.3%
noche7534
27.7%

Most occurring characters

ValueCountFrequency (%)
r19642
12.8%
e18132
11.8%
n18088
11.7%
o16578
10.8%
T10598
6.9%
d10598
6.9%
a10598
6.9%
M9044
5.9%
i9044
5.9%
g9044
5.9%
Other values (3)22602
14.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)153968
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r19642
12.8%
e18132
11.8%
n18088
11.7%
o16578
10.8%
T10598
6.9%
d10598
6.9%
a10598
6.9%
M9044
5.9%
i9044
5.9%
g9044
5.9%
Other values (3)22602
14.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)153968
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r19642
12.8%
e18132
11.8%
n18088
11.7%
o16578
10.8%
T10598
6.9%
d10598
6.9%
a10598
6.9%
M9044
5.9%
i9044
5.9%
g9044
5.9%
Other values (3)22602
14.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)153968
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r19642
12.8%
e18132
11.8%
n18088
11.7%
o16578
10.8%
T10598
6.9%
d10598
6.9%
a10598
6.9%
M9044
5.9%
i9044
5.9%
g9044
5.9%
Other values (3)22602
14.7%

HoraP1
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
No
24179 
Si
2997 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters54352
Distinct characters4
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 rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No24179
89.0%
Si2997
 
11.0%

Length

2025-11-28T04:35:37.886142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T04:35:37.956347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no24179
89.0%
si2997
 
11.0%

Most occurring characters

ValueCountFrequency (%)
N24179
44.5%
o24179
44.5%
S2997
 
5.5%
i2997
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)54352
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N24179
44.5%
o24179
44.5%
S2997
 
5.5%
i2997
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)54352
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N24179
44.5%
o24179
44.5%
S2997
 
5.5%
i2997
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)54352
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N24179
44.5%
o24179
44.5%
S2997
 
5.5%
i2997
 
5.5%

HoraP2
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
No
24390 
Si
2786 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters54352
Distinct characters4
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 rowNo
2nd rowNo
3rd rowSi
4th rowNo
5th rowSi

Common Values

ValueCountFrequency (%)
No24390
89.7%
Si2786
 
10.3%

Length

2025-11-28T04:35:38.063132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T04:35:38.177514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no24390
89.7%
si2786
 
10.3%

Most occurring characters

ValueCountFrequency (%)
N24390
44.9%
o24390
44.9%
S2786
 
5.1%
i2786
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)54352
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N24390
44.9%
o24390
44.9%
S2786
 
5.1%
i2786
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)54352
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N24390
44.9%
o24390
44.9%
S2786
 
5.1%
i2786
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)54352
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N24390
44.9%
o24390
44.9%
S2786
 
5.1%
i2786
 
5.1%

GRAVEDAD_ACCIDENTE
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Material
15467 
Heridos
11410 
Muertos
 
299

Length

Max length8
Median length8
Mean length7.5691419
Min length7

Characters and Unicode

Total characters205699
Distinct characters12
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 rowHeridos
2nd rowMaterial
3rd rowHeridos
4th rowMaterial
5th rowHeridos

Common Values

ValueCountFrequency (%)
Material15467
56.9%
Heridos11410
42.0%
Muertos299
 
1.1%

Length

2025-11-28T04:35:38.319340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T04:35:38.425472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
material15467
56.9%
heridos11410
42.0%
muertos299
 
1.1%

Most occurring characters

ValueCountFrequency (%)
a30934
15.0%
e27176
13.2%
r27176
13.2%
i26877
13.1%
t15766
7.7%
M15766
7.7%
l15467
7.5%
o11709
 
5.7%
s11709
 
5.7%
H11410
 
5.5%
Other values (2)11709
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)205699
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a30934
15.0%
e27176
13.2%
r27176
13.2%
i26877
13.1%
t15766
7.7%
M15766
7.7%
l15467
7.5%
o11709
 
5.7%
s11709
 
5.7%
H11410
 
5.5%
Other values (2)11709
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)205699
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a30934
15.0%
e27176
13.2%
r27176
13.2%
i26877
13.1%
t15766
7.7%
M15766
7.7%
l15467
7.5%
o11709
 
5.7%
s11709
 
5.7%
H11410
 
5.5%
Other values (2)11709
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)205699
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a30934
15.0%
e27176
13.2%
r27176
13.2%
i26877
13.1%
t15766
7.7%
M15766
7.7%
l15467
7.5%
o11709
 
5.7%
s11709
 
5.7%
H11410
 
5.5%
Other values (2)11709
 
5.7%

CLASE_ACCIDENTE
Categorical

Imbalance 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Choque
25149 
Atropello
 
1530
Caida Ocupante
 
217
Volcamiento
 
136
Otro
 
131

Length

Max length14
Median length6
Mean length6.2491169
Min length4

Characters and Unicode

Total characters169826
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 rowChoque
2nd rowChoque
3rd rowAtropello
4th rowChoque
5th rowCaida Ocupante

Common Values

ValueCountFrequency (%)
Choque25149
92.5%
Atropello1530
 
5.6%
Caida Ocupante217
 
0.8%
Volcamiento136
 
0.5%
Otro131
 
0.5%
Incendio13
 
< 0.1%

Length

2025-11-28T04:35:38.560574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T04:35:38.685200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
choque25149
91.8%
atropello1530
 
5.6%
caida217
 
0.8%
ocupante217
 
0.8%
volcamiento136
 
0.5%
otro131
 
0.5%
incendio13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o28625
16.9%
e27045
15.9%
C25366
14.9%
u25366
14.9%
h25149
14.8%
q25149
14.8%
l3196
 
1.9%
t2014
 
1.2%
p1747
 
1.0%
r1661
 
1.0%
Other values (11)4508
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)169826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o28625
16.9%
e27045
15.9%
C25366
14.9%
u25366
14.9%
h25149
14.8%
q25149
14.8%
l3196
 
1.9%
t2014
 
1.2%
p1747
 
1.0%
r1661
 
1.0%
Other values (11)4508
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)169826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o28625
16.9%
e27045
15.9%
C25366
14.9%
u25366
14.9%
h25149
14.8%
q25149
14.8%
l3196
 
1.9%
t2014
 
1.2%
p1747
 
1.0%
r1661
 
1.0%
Other values (11)4508
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)169826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o28625
16.9%
e27045
15.9%
C25366
14.9%
u25366
14.9%
h25149
14.8%
q25149
14.8%
l3196
 
1.9%
t2014
 
1.2%
p1747
 
1.0%
r1661
 
1.0%
Other values (11)4508
 
2.7%
Distinct11481
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2025-11-28T04:35:39.223439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length71
Median length63
Mean length12.570503
Min length5

Characters and Unicode

Total characters341616
Distinct characters44
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

Unique7946 ?
Unique (%)29.2%

Sample

1st row15 SUR CL 110
2nd rowAV CIRCUNVALAR CL 90B
3rd rowCL 17 CR 22
4th rowAV CIRCUNVALAR CL 110 CR 43
5th rowAV CORDIALIDAD CR 5A
ValueCountFrequency (%)
cl23635
21.0%
cr23586
20.9%
1101984
 
1.8%
381956
 
1.7%
401805
 
1.6%
av1666
 
1.5%
451638
 
1.5%
301632
 
1.4%
via1485
 
1.3%
431328
 
1.2%
Other values (1408)51910
46.1%
2025-11-28T04:35:40.003147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
85449
25.0%
C51496
15.1%
R28414
 
8.3%
L26165
 
7.7%
417643
 
5.2%
115837
 
4.6%
512641
 
3.7%
312330
 
3.6%
011011
 
3.2%
79751
 
2.9%
Other values (34)70879
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)341616
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
85449
25.0%
C51496
15.1%
R28414
 
8.3%
L26165
 
7.7%
417643
 
5.2%
115837
 
4.6%
512641
 
3.7%
312330
 
3.6%
011011
 
3.2%
79751
 
2.9%
Other values (34)70879
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)341616
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
85449
25.0%
C51496
15.1%
R28414
 
8.3%
L26165
 
7.7%
417643
 
5.2%
115837
 
4.6%
512641
 
3.7%
312330
 
3.6%
011011
 
3.2%
79751
 
2.9%
Other values (34)70879
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)341616
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
85449
25.0%
C51496
15.1%
R28414
 
8.3%
L26165
 
7.7%
417643
 
5.2%
115837
 
4.6%
512641
 
3.7%
312330
 
3.6%
011011
 
3.2%
79751
 
2.9%
Other values (34)70879
20.7%

tipo_principal
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing229
Missing (%)0.8%
Memory size1.3 MiB
CL
15333 
CR
10872 
NOMBRE
 
522
AV
 
220

Length

Max length6
Median length2
Mean length2.0774854
Min length2

Characters and Unicode

Total characters55982
Distinct characters10
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 rowCL
2nd rowCL
3rd rowCL
4th rowCL
5th rowCR

Common Values

ValueCountFrequency (%)
CL15333
56.4%
CR10872
40.0%
NOMBRE522
 
1.9%
AV220
 
0.8%
(Missing)229
 
0.8%

Length

2025-11-28T04:35:40.184438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T04:35:40.295652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
cl15333
56.9%
cr10872
40.3%
nombre522
 
1.9%
av220
 
0.8%

Most occurring characters

ValueCountFrequency (%)
C26205
46.8%
L15333
27.4%
R11394
20.4%
N522
 
0.9%
O522
 
0.9%
M522
 
0.9%
B522
 
0.9%
E522
 
0.9%
A220
 
0.4%
V220
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)55982
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C26205
46.8%
L15333
27.4%
R11394
20.4%
N522
 
0.9%
O522
 
0.9%
M522
 
0.9%
B522
 
0.9%
E522
 
0.9%
A220
 
0.4%
V220
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)55982
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C26205
46.8%
L15333
27.4%
R11394
20.4%
N522
 
0.9%
O522
 
0.9%
M522
 
0.9%
B522
 
0.9%
E522
 
0.9%
A220
 
0.4%
V220
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)55982
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C26205
46.8%
L15333
27.4%
R11394
20.4%
N522
 
0.9%
O522
 
0.9%
M522
 
0.9%
B522
 
0.9%
E522
 
0.9%
A220
 
0.4%
V220
 
0.4%
Distinct467
Distinct (%)1.7%
Missing229
Missing (%)0.8%
Memory size1.3 MiB
2025-11-28T04:35:40.822226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length2
Mean length2.2495268
Min length1

Characters and Unicode

Total characters60618
Distinct characters33
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

Unique115 ?
Unique (%)0.4%

Sample

1st row110
2nd row90B
3rd row17
4th row110
5th row5A
ValueCountFrequency (%)
1101717
 
6.3%
381332
 
4.9%
301225
 
4.5%
451145
 
4.2%
46736
 
2.7%
43706
 
2.6%
44569
 
2.1%
19562
 
2.1%
72492
 
1.8%
40480
 
1.8%
Other values (463)18365
67.2%
2025-11-28T04:35:41.593875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48225
13.6%
18224
13.6%
56114
10.1%
35967
9.8%
05579
9.2%
84863
8.0%
74689
7.7%
63875
6.4%
93353
5.5%
23282
 
5.4%
Other values (23)6447
10.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)60618
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
48225
13.6%
18224
13.6%
56114
10.1%
35967
9.8%
05579
9.2%
84863
8.0%
74689
7.7%
63875
6.4%
93353
5.5%
23282
 
5.4%
Other values (23)6447
10.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)60618
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
48225
13.6%
18224
13.6%
56114
10.1%
35967
9.8%
05579
9.2%
84863
8.0%
74689
7.7%
63875
6.4%
93353
5.5%
23282
 
5.4%
Other values (23)6447
10.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)60618
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
48225
13.6%
18224
13.6%
56114
10.1%
35967
9.8%
05579
9.2%
84863
8.0%
74689
7.7%
63875
6.4%
93353
5.5%
23282
 
5.4%
Other values (23)6447
10.6%

tipo_secundaria
Categorical

High correlation  Missing 

Distinct3
Distinct (%)< 0.1%
Missing6360
Missing (%)23.4%
Memory size1.4 MiB
CR
12534 
CL
8258 
AV
 
24

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters41632
Distinct characters5
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 rowCR
2nd rowCR
3rd rowCR
4th rowCR
5th rowCL

Common Values

ValueCountFrequency (%)
CR12534
46.1%
CL8258
30.4%
AV24
 
0.1%
(Missing)6360
23.4%

Length

2025-11-28T04:35:41.758947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T04:35:41.829994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
cr12534
60.2%
cl8258
39.7%
av24
 
0.1%

Most occurring characters

ValueCountFrequency (%)
C20792
49.9%
R12534
30.1%
L8258
 
19.8%
A24
 
0.1%
V24
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)41632
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C20792
49.9%
R12534
30.1%
L8258
 
19.8%
A24
 
0.1%
V24
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)41632
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C20792
49.9%
R12534
30.1%
L8258
 
19.8%
A24
 
0.1%
V24
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)41632
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C20792
49.9%
R12534
30.1%
L8258
 
19.8%
A24
 
0.1%
V24
 
0.1%

num_secundaria
Text

Missing 

Distinct483
Distinct (%)2.3%
Missing6360
Missing (%)23.4%
Memory size1.2 MiB
2025-11-28T04:35:42.228539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.0974251
Min length1

Characters and Unicode

Total characters43660
Distinct characters28
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

Unique95 ?
Unique (%)0.5%

Sample

1st row22
2nd row43
3rd row14
4th row53
5th row110
ValueCountFrequency (%)
38501
 
2.4%
43480
 
2.3%
46414
 
2.0%
45382
 
1.8%
44381
 
1.8%
8338
 
1.6%
6328
 
1.6%
53317
 
1.5%
50315
 
1.5%
47302
 
1.5%
Other values (473)17058
81.9%
2025-11-28T04:35:42.772897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
46143
14.1%
14792
11.0%
54695
10.8%
34604
10.5%
23909
9.0%
63710
8.5%
73402
7.8%
83211
7.4%
02693
6.2%
92685
6.1%
Other values (18)3816
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)43660
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
46143
14.1%
14792
11.0%
54695
10.8%
34604
10.5%
23909
9.0%
63710
8.5%
73402
7.8%
83211
7.4%
02693
6.2%
92685
6.1%
Other values (18)3816
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)43660
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
46143
14.1%
14792
11.0%
54695
10.8%
34604
10.5%
23909
9.0%
63710
8.5%
73402
7.8%
83211
7.4%
02693
6.2%
92685
6.1%
Other values (18)3816
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)43660
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
46143
14.1%
14792
11.0%
54695
10.8%
34604
10.5%
23909
9.0%
63710
8.5%
73402
7.8%
83211
7.4%
02693
6.2%
92685
6.1%
Other values (18)3816
8.7%

FRECUENCIA
Real number (ℝ)

High correlation 

Distinct37
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7622167
Minimum1
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size212.4 KiB
2025-11-28T04:35:42.896752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile17
Maximum77
Range76
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.6020631
Coefficient of variation (CV)1.8063149
Kurtosis28.400605
Mean4.7622167
Median Absolute Deviation (MAD)1
Skewness4.7988629
Sum129418
Variance73.99549
MonotonicityNot monotonic
2025-11-28T04:35:43.023513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
112393
45.6%
23718
 
13.7%
32280
 
8.4%
41688
 
6.2%
51350
 
5.0%
6888
 
3.3%
7700
 
2.6%
8504
 
1.9%
9441
 
1.6%
12384
 
1.4%
Other values (27)2830
 
10.4%
ValueCountFrequency (%)
112393
45.6%
23718
 
13.7%
32280
 
8.4%
41688
 
6.2%
51350
 
5.0%
6888
 
3.3%
7700
 
2.6%
8504
 
1.9%
9441
 
1.6%
10330
 
1.2%
ValueCountFrequency (%)
7777
0.3%
6666
0.2%
5555
 
0.2%
53159
0.6%
4949
 
0.2%
4141
 
0.2%
3333
 
0.1%
3264
0.2%
2958
 
0.2%
2828
 
0.1%

Heridos
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
no
15766 
si
11410 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters54352
Distinct characters4
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 rowsi
2nd rowno
3rd rowsi
4th rowno
5th rowsi

Common Values

ValueCountFrequency (%)
no15766
58.0%
si11410
42.0%

Length

2025-11-28T04:35:43.148190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T04:35:43.218400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no15766
58.0%
si11410
42.0%

Most occurring characters

ValueCountFrequency (%)
n15766
29.0%
o15766
29.0%
s11410
21.0%
i11410
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)54352
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n15766
29.0%
o15766
29.0%
s11410
21.0%
i11410
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)54352
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n15766
29.0%
o15766
29.0%
s11410
21.0%
i11410
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)54352
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n15766
29.0%
o15766
29.0%
s11410
21.0%
i11410
21.0%

Muertos
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
False
27176 
ValueCountFrequency (%)
False27176
100.0%
2025-11-28T04:35:43.260814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Material
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
False
27176 
ValueCountFrequency (%)
False27176
100.0%
2025-11-28T04:35:43.297754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

YEAR
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.3547
Minimum2018
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size212.4 KiB
2025-11-28T04:35:43.352806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2018
5-th percentile2018
Q12019
median2020
Q32022
95-th percentile2024
Maximum2025
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9595961
Coefficient of variation (CV)0.00096992673
Kurtosis-0.62803888
Mean2020.3547
Median Absolute Deviation (MAD)1
Skewness0.54160441
Sum54905160
Variance3.8400167
MonotonicityNot monotonic
2025-11-28T04:35:43.440628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
20185898
21.7%
20195645
20.8%
20214700
17.3%
20223683
13.6%
20203281
12.1%
20231674
 
6.2%
20241482
 
5.5%
2025813
 
3.0%
ValueCountFrequency (%)
20185898
21.7%
20195645
20.8%
20203281
12.1%
20214700
17.3%
20223683
13.6%
20231674
 
6.2%
20241482
 
5.5%
2025813
 
3.0%
ValueCountFrequency (%)
2025813
 
3.0%
20241482
 
5.5%
20231674
 
6.2%
20223683
13.6%
20214700
17.3%
20203281
12.1%
20195645
20.8%
20185898
21.7%

MES
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
February
2593 
March
2571 
January
2457 
December
2334 
May
2235 
Other values (7)
14986 

Length

Max length9
Median length7
Mean length6.1809685
Min length3

Characters and Unicode

Total characters167974
Distinct characters26
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 rowOctober
2nd rowSeptember
3rd rowJuly
4th rowNovember
5th rowFebruary

Common Values

ValueCountFrequency (%)
February2593
9.5%
March2571
9.5%
January2457
9.0%
December2334
8.6%
May2235
8.2%
October2223
8.2%
June2220
8.2%
July2170
8.0%
April2122
7.8%
November2122
7.8%
Other values (2)4129
15.2%

Length

2025-11-28T04:35:43.558978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
february2593
9.5%
march2571
9.5%
january2457
9.0%
december2334
8.6%
may2235
8.2%
october2223
8.2%
june2220
8.2%
july2170
8.0%
april2122
7.8%
november2122
7.8%
Other values (2)4129
15.2%

Most occurring characters

ValueCountFrequency (%)
e24600
14.6%
r21121
12.6%
u13486
 
8.0%
a12313
 
7.3%
b11378
 
6.8%
y9455
 
5.6%
c7128
 
4.2%
J6847
 
4.1%
m6562
 
3.9%
t6352
 
3.8%
Other values (16)48732
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)167974
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e24600
14.6%
r21121
12.6%
u13486
 
8.0%
a12313
 
7.3%
b11378
 
6.8%
y9455
 
5.6%
c7128
 
4.2%
J6847
 
4.1%
m6562
 
3.9%
t6352
 
3.8%
Other values (16)48732
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)167974
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e24600
14.6%
r21121
12.6%
u13486
 
8.0%
a12313
 
7.3%
b11378
 
6.8%
y9455
 
5.6%
c7128
 
4.2%
J6847
 
4.1%
m6562
 
3.9%
t6352
 
3.8%
Other values (16)48732
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)167974
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e24600
14.6%
r21121
12.6%
u13486
 
8.0%
a12313
 
7.3%
b11378
 
6.8%
y9455
 
5.6%
c7128
 
4.2%
J6847
 
4.1%
m6562
 
3.9%
t6352
 
3.8%
Other values (16)48732
29.0%

DIA
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Tue
4233 
Fri
4125 
Wed
4069 
Mon
4000 
Thu
3976 
Other values (2)
6773 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters81528
Distinct characters15
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 rowWed
2nd rowThu
3rd rowTue
4th rowTue
5th rowTue

Common Values

ValueCountFrequency (%)
Tue4233
15.6%
Fri4125
15.2%
Wed4069
15.0%
Mon4000
14.7%
Thu3976
14.6%
Sat3968
14.6%
Sun2805
10.3%

Length

2025-11-28T04:35:43.682677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T04:35:43.772144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
tue4233
15.6%
fri4125
15.2%
wed4069
15.0%
mon4000
14.7%
thu3976
14.6%
sat3968
14.6%
sun2805
10.3%

Most occurring characters

ValueCountFrequency (%)
u11014
13.5%
e8302
10.2%
T8209
10.1%
n6805
 
8.3%
S6773
 
8.3%
F4125
 
5.1%
r4125
 
5.1%
i4125
 
5.1%
W4069
 
5.0%
d4069
 
5.0%
Other values (5)19912
24.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)81528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u11014
13.5%
e8302
10.2%
T8209
10.1%
n6805
 
8.3%
S6773
 
8.3%
F4125
 
5.1%
r4125
 
5.1%
i4125
 
5.1%
W4069
 
5.0%
d4069
 
5.0%
Other values (5)19912
24.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)81528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u11014
13.5%
e8302
10.2%
T8209
10.1%
n6805
 
8.3%
S6773
 
8.3%
F4125
 
5.1%
r4125
 
5.1%
i4125
 
5.1%
W4069
 
5.0%
d4069
 
5.0%
Other values (5)19912
24.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)81528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u11014
13.5%
e8302
10.2%
T8209
10.1%
n6805
 
8.3%
S6773
 
8.3%
F4125
 
5.1%
r4125
 
5.1%
i4125
 
5.1%
W4069
 
5.0%
d4069
 
5.0%
Other values (5)19912
24.4%

Ponderacion
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4262088
Minimum4
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size212.4 KiB
2025-11-28T04:35:43.891958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q15.33
median6.33
Q37.33
95-th percentile9.67
Maximum13
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6622131
Coefficient of variation (CV)0.25866154
Kurtosis-0.17963293
Mean6.4262088
Median Absolute Deviation (MAD)1
Skewness0.60481345
Sum174638.65
Variance2.7629523
MonotonicityNot monotonic
2025-11-28T04:35:43.992573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
6.333294
12.1%
4.332735
 
10.1%
5.332269
 
8.3%
61922
 
7.1%
71798
 
6.6%
6.671795
 
6.6%
5.671514
 
5.6%
51488
 
5.5%
7.671468
 
5.4%
7.331433
 
5.3%
Other values (16)7460
27.5%
ValueCountFrequency (%)
41404
5.2%
4.332735
10.1%
4.671131
 
4.2%
51488
5.5%
5.332269
8.3%
5.671514
5.6%
61922
7.1%
6.333294
12.1%
6.671795
6.6%
71798
6.6%
ValueCountFrequency (%)
131
 
< 0.1%
1239
 
0.1%
11.6711
 
< 0.1%
11.3342
 
0.2%
1131
 
0.1%
10.67248
 
0.9%
10.33135
 
0.5%
10553
2.0%
9.67667
2.5%
9.33349
1.3%

Riesgo
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Moderado
14206 
Bajo
12463 
Alto
 
507

Length

Max length8
Median length8
Mean length6.0909626
Min length4

Characters and Unicode

Total characters165528
Distinct characters11
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 rowBajo
2nd rowBajo
3rd rowModerado
4th rowBajo
5th rowModerado

Common Values

ValueCountFrequency (%)
Moderado14206
52.3%
Bajo12463
45.9%
Alto507
 
1.9%

Length

2025-11-28T04:35:44.345047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T04:35:44.422909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
moderado14206
52.3%
bajo12463
45.9%
alto507
 
1.9%

Most occurring characters

ValueCountFrequency (%)
o41382
25.0%
d28412
17.2%
a26669
16.1%
M14206
 
8.6%
e14206
 
8.6%
r14206
 
8.6%
B12463
 
7.5%
j12463
 
7.5%
A507
 
0.3%
l507
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)165528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o41382
25.0%
d28412
17.2%
a26669
16.1%
M14206
 
8.6%
e14206
 
8.6%
r14206
 
8.6%
B12463
 
7.5%
j12463
 
7.5%
A507
 
0.3%
l507
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)165528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o41382
25.0%
d28412
17.2%
a26669
16.1%
M14206
 
8.6%
e14206
 
8.6%
r14206
 
8.6%
B12463
 
7.5%
j12463
 
7.5%
A507
 
0.3%
l507
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)165528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o41382
25.0%
d28412
17.2%
a26669
16.1%
M14206
 
8.6%
e14206
 
8.6%
r14206
 
8.6%
B12463
 
7.5%
j12463
 
7.5%
A507
 
0.3%
l507
 
0.3%

Interactions

2025-11-28T04:35:36.317145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T04:35:35.598689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T04:35:35.959457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T04:35:36.426369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T04:35:35.729308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T04:35:36.077870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T04:35:36.535136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T04:35:35.852245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-28T04:35:36.207339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-28T04:35:44.501564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CLASE_ACCIDENTEDIAFRECUENCIAGRAVEDAD_ACCIDENTEHeridosHoraP1HoraP2JornadaMESPonderacionRiesgoYEARtipo_principaltipo_secundaria
CLASE_ACCIDENTE1.0000.0180.0050.2300.2850.0350.0280.0620.0000.1500.1890.0650.0140.000
DIA0.0181.0000.0100.0640.0760.0570.0440.0800.0210.0230.0000.0210.0070.000
FRECUENCIA0.0050.0101.0000.0640.0870.0020.0170.0130.0130.5300.175-0.1320.0960.108
GRAVEDAD_ACCIDENTE0.2300.0640.0641.0001.0000.0280.0280.0870.0120.4240.1720.3450.0350.014
Heridos0.2850.0760.0871.0001.0000.0240.0230.0970.0130.5840.2150.4750.0340.019
HoraP10.0350.0570.0020.0280.0241.0000.1190.4980.0160.5240.3240.0060.0000.000
HoraP20.0280.0440.0170.0280.0230.1191.0000.3380.0170.6230.4290.0200.0110.019
Jornada0.0620.0800.0130.0870.0970.4980.3381.0000.0250.2750.1160.0500.0010.000
MES0.0000.0210.0130.0120.0130.0160.0170.0251.0000.0120.0170.0960.0180.011
Ponderacion0.1500.0230.5300.4240.5840.5240.6230.2750.0121.0000.9460.0620.0550.056
Riesgo0.1890.0000.1750.1720.2150.3240.4290.1160.0170.9461.0000.0560.0520.043
YEAR0.0650.021-0.1320.3450.4750.0060.0200.0500.0960.0620.0561.0000.0350.023
tipo_principal0.0140.0070.0960.0350.0340.0000.0110.0010.0180.0550.0520.0351.0000.702
tipo_secundaria0.0000.0000.1080.0140.0190.0000.0190.0000.0110.0560.0430.0230.7021.000

Missing values

2025-11-28T04:35:36.724696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-28T04:35:36.952373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-28T04:35:37.258317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

JornadaHoraP1HoraP2GRAVEDAD_ACCIDENTECLASE_ACCIDENTEdireccion_normalizadatipo_principalnum_principaltipo_secundarianum_secundariaFRECUENCIAHeridosMuertosMaterialYEARMESDIAPonderacionRiesgo
0TardeNoNoHeridosChoque15 SUR CL 110CL110NaNNaN1sinono2018OctoberWed5.33Bajo
1NocheNoNoMaterialChoqueAV CIRCUNVALAR CL 90BCL90BNaNNaN1nonono2019SeptemberThu4.67Bajo
2TardeNoSiHeridosAtropelloCL 17 CR 22CL17CR221sinono2018JulyTue9.67Moderado
3NocheNoNoMaterialChoqueAV CIRCUNVALAR CL 110 CR 43CL110CR431nonono2021NovemberTue4.67Bajo
4NocheNoSiHeridosCaida OcupanteAV CORDIALIDAD CR 5ACR5ANaNNaN1sinono2024FebruaryTue9.33Moderado
5MorningSiNoHeridosChoqueAV 100 CR 14AV100CR141sinono2018JuneTue8.00Moderado
6MorningSiNoMaterialChoqueAV 110 CR 53AV110CR531nonono2019AugustThu7.00Moderado
7MorningNoNoHeridosChoqueAV 110 CL 110AV110CL1102sinono2021MarchMon6.00Bajo
8MorningNoNoMaterialChoqueAV 110 CL 110AV110CL1102nonono2021MarchMon5.00Bajo
9TardeNoSiHeridosChoqueAV 110 CL 30AV110CL301sinono2019SeptemberWed8.33Moderado
JornadaHoraP1HoraP2GRAVEDAD_ACCIDENTECLASE_ACCIDENTEdireccion_normalizadatipo_principalnum_principaltipo_secundarianum_secundariaFRECUENCIAHeridosMuertosMaterialYEARMESDIAPonderacionRiesgo
27166NocheNoNoMaterialChoqueVIA JUAN MINA PUENTE LA PROSPERIDADNOMBREVIA JUAN MINANaNNaN1nonono2022JanuarySun4.67Bajo
27167TardeNoNoMaterialChoqueVIA JUAN MINA VIA 11 5 168NOMBREVIA JUAN MINANaNNaN1nonono2022JulyThu4.33Bajo
27168MorningSiNoMaterialChoqueVIA JUAN MINA VIA 11 CR 8CR8NaNNaN1nonono2022MayThu7.00Moderado
27169NocheNoNoMaterialChoqueVIA JUAN TUBARA KM 8NaNNaNNaNNaN1nonono2021SeptemberTue4.67Bajo
27170TardeNoNoHeridosChoqueVIA LA PLAYA COLEGIO PIES DESCALZOSNaNNaNNaNNaN1sinono2023AugustFri5.33Bajo
27171NocheNoNoMaterialChoqueVIA LA PLAYA KM 2,5NaNNaNNaNNaN1nonono2018JulySun4.67Bajo
27172MorningNoNoMaterialChoqueVIA LA PROSPERIDAD ENTRADA A VILLA CAMPESTRENOMBREVIA LA PROSPERIDADNaNNaN1nonono2021OctoberThu4.00Bajo
27173NocheNoSiHeridosChoqueVIA LAS FLORES A LA PLAYA A 250 METROSNaNNaNNaNNaN1sinono2018SeptemberSun8.67Moderado
27174MorningNoNoHeridosOtroVIA PROSPERIDAD SENTIDO LA PLAYA LAS FLORESNaNNaNNaNNaN1sinono2024JulyMon5.00Bajo
27175MorningNoNoMuertosCaida OcupanteVIA QUE CONDUCE VEREDA LAS NUBES AV 110AV110NaNNaN1nonono2020AugustTue6.67Moderado

Duplicate rows

Most frequently occurring

JornadaHoraP1HoraP2GRAVEDAD_ACCIDENTECLASE_ACCIDENTEdireccion_normalizadatipo_principalnum_principaltipo_secundarianum_secundariaFRECUENCIAHeridosMuertosMaterialYEARMESDIAPonderacionRiesgo# duplicates
56TardeNoNoMaterialChoqueCR 38 CL 57CR38CL578nonono2018AprilWed6.33Moderado3
0MorningNoNoHeridosChoqueCL 99 CR 57CL99CR573sinono2022FebruaryMon7.00Moderado2
1MorningNoNoMaterialChoqueCL 110 CR 12CL110CR1213nonono2021JuneSat6.00Bajo2
2MorningNoNoMaterialChoqueCL 110 CR 27CL110CR2733nonono2019DecemberFri6.00Bajo2
3MorningNoNoMaterialChoqueCL 110 CR 6CL110CR666nonono2019SeptemberMon6.00Bajo2
4MorningNoNoMaterialChoqueCL 17 CR 8CL17CR825nonono2018MayWed6.00Bajo2
5MorningNoNoMaterialChoqueCL 30 CR 19CL30CR193nonono2020DecemberMon6.00Bajo2
6MorningNoNoMaterialChoqueCL 30 CR 45CL30CR4512nonono2020NovemberFri6.00Bajo2
7MorningNoNoMaterialChoqueCL 30 CR 8CL30CR853nonono2018JuneFri6.00Bajo2
8MorningNoNoMaterialChoqueCL 56 CR 9CL56CR94nonono2020MarchMon6.00Bajo2