Overview

Brought to you by YData

Dataset statistics

Number of variables67
Number of observations31862
Missing cells296663
Missing cells (%)13.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory104.1 MiB
Average record size in memory3.3 KiB

Variable types

Unsupported2
Numeric2
Categorical53
Text2
DateTime8

Alerts

INFLUENZA has constant value "A H3" Constant
Anosmia is highly overall correlated with DisgeusiaHigh correlation
Artralgias is highly overall correlated with Cefalea and 5 other fieldsHigh correlation
Asma is highly overall correlated with Diabetes and 9 other fieldsHigh correlation
Cefalea is highly overall correlated with Artralgias and 6 other fieldsHigh correlation
Cianosis is highly overall correlated with Conjuntivitis and 2 other fieldsHigh correlation
Conjuntivitis is highly overall correlated with Cianosis and 2 other fieldsHigh correlation
Diabetes is highly overall correlated with Asma and 9 other fieldsHigh correlation
Diagnóstico clínico de Neumonía is highly overall correlated with Diagnóstico probable and 1 other fieldsHigh correlation
Diagnóstico probable is highly overall correlated with Diagnóstico clínico de Neumonía and 1 other fieldsHigh correlation
Diarrea is highly overall correlated with Disnea and 1 other fieldsHigh correlation
Disgeusia is highly overall correlated with AnosmiaHigh correlation
Disnea is highly overall correlated with Cianosis and 5 other fieldsHigh correlation
Dolor abdminal is highly overall correlated with Artralgias and 5 other fieldsHigh correlation
Dolor torácico is highly overall correlated with Artralgias and 5 other fieldsHigh correlation
EPOC is highly overall correlated with Asma and 9 other fieldsHigh correlation
Enfermedad cardiaca is highly overall correlated with Asma and 9 other fieldsHigh correlation
Escalofríos is highly overall correlated with Artralgias and 5 other fieldsHigh correlation
Estatus del paciente is highly overall correlated with Estatus día previo and 2 other fieldsHigh correlation
Estatus día previo is highly overall correlated with Estatus del paciente and 2 other fieldsHigh correlation
Fecha de llegada al Estado is highly overall correlated with Procedencia and 3 other fieldsHigh correlation
Fiebre is highly overall correlated with Semana epidemiológica de defunciones positivasHigh correlation
Hipertensión is highly overall correlated with Asma and 9 other fieldsHigh correlation
Inmunosupresión is highly overall correlated with Asma and 10 other fieldsHigh correlation
Insuficiencia renal crónica is highly overall correlated with Asma and 9 other fieldsHigh correlation
Mialgias is highly overall correlated with Artralgias and 5 other fieldsHigh correlation
No consecutivo por inicio de sintomas is highly overall correlated with Semana epidemiológica de defunciones positivas and 1 other fieldsHigh correlation
Obesidad is highly overall correlated with Asma and 9 other fieldsHigh correlation
Odinofagia is highly overall correlated with Artralgias and 5 other fieldsHigh correlation
Otra condición is highly overall correlated with Asma and 10 other fieldsHigh correlation
Pacientes que requirieron intubación is highly overall correlated with VARIANTEHigh correlation
Polipnea is highly overall correlated with Cianosis and 5 other fieldsHigh correlation
Procedencia is highly overall correlated with Fecha de llegada al Estado and 2 other fieldsHigh correlation
REFUERZO is highly overall correlated with Fecha de llegada al Estado and 3 other fieldsHigh correlation
Resultado de laboratorio is highly overall correlated with Estatus del paciente and 3 other fieldsHigh correlation
Rinorrea is highly overall correlated with Disnea and 1 other fieldsHigh correlation
Semana epidemiológica de defunciones positivas is highly overall correlated with Cefalea and 8 other fieldsHigh correlation
Semana epidemiológica de resultados positivos is highly overall correlated with No consecutivo por inicio de sintomas and 3 other fieldsHigh correlation
Tabaquismo is highly overall correlated with Asma and 9 other fieldsHigh correlation
Tipo de manejo is highly overall correlated with Diagnóstico clínico de Neumonía and 3 other fieldsHigh correlation
Toma de muestra en el ESTADO is highly overall correlated with Semana epidemiológica de defunciones positivas and 1 other fieldsHigh correlation
VARIANTE is highly overall correlated with Fecha de llegada al Estado and 8 other fieldsHigh correlation
VIH/SIDA is highly overall correlated with Asma and 11 other fieldsHigh correlation
Vómito is highly overall correlated with Disnea and 1 other fieldsHigh correlation
Institución tratante is highly imbalanced (64.0%) Imbalance
Toma de muestra en el ESTADO is highly imbalanced (99.2%) Imbalance
Procedencia is highly imbalanced (99.8%) Imbalance
Fecha de llegada al Estado is highly imbalanced (99.9%) Imbalance
Estatus día previo is highly imbalanced (64.6%) Imbalance
Tipo de manejo is highly imbalanced (82.6%) Imbalance
Estatus del paciente is highly imbalanced (64.5%) Imbalance
Pacientes que requirieron intubación is highly imbalanced (98.6%) Imbalance
Pacientes que ingresaron a UCI is highly imbalanced (98.7%) Imbalance
Diagnóstico clínico de Neumonía is highly imbalanced (88.0%) Imbalance
Diagnóstico probable is highly imbalanced (83.2%) Imbalance
Disnea is highly imbalanced (73.8%) Imbalance
Irritabilidad is highly imbalanced (50.5%) Imbalance
Diarrea is highly imbalanced (69.1%) Imbalance
Dolor torácico is highly imbalanced (66.0%) Imbalance
Cefalea is highly imbalanced (50.5%) Imbalance
Polipnea is highly imbalanced (91.1%) Imbalance
Vómito is highly imbalanced (79.3%) Imbalance
Dolor abdminal is highly imbalanced (73.9%) Imbalance
Conjuntivitis is highly imbalanced (68.6%) Imbalance
Cianosis is highly imbalanced (68.7%) Imbalance
Anosmia is highly imbalanced (78.6%) Imbalance
Disgeusia is highly imbalanced (78.4%) Imbalance
Diabetes is highly imbalanced (79.1%) Imbalance
EPOC is highly imbalanced (96.7%) Imbalance
Asma is highly imbalanced (88.1%) Imbalance
Inmunosupresión is highly imbalanced (97.7%) Imbalance
Hipertensión is highly imbalanced (72.0%) Imbalance
VIH/SIDA is highly imbalanced (97.9%) Imbalance
Otra condición is highly imbalanced (92.6%) Imbalance
Enfermedad cardiaca is highly imbalanced (96.0%) Imbalance
Obesidad is highly imbalanced (71.0%) Imbalance
Insuficiencia renal crónica is highly imbalanced (94.8%) Imbalance
Tabaquismo is highly imbalanced (82.0%) Imbalance
Vacuna contra COVID19 is highly imbalanced (69.0%) Imbalance
REFUERZO is highly imbalanced (82.4%) Imbalance
VARIANTE is highly imbalanced (70.8%) Imbalance
No de caso positivo por inicio de síntomas has 9575 (30.1%) missing values Missing
Periodo mínimo de incubación (2 días) has 9288 (29.2%) missing values Missing
Periodo máximo de incubación (7 días) has 9288 (29.2%) missing values Missing
Fecha estimada de Alta Sanitaria has 9288 (29.2%) missing values Missing
Fecha de la defunción has 31667 (99.4%) missing values Missing
Semana epidemiológica de defunciones positivas has 31679 (99.4%) missing values Missing
Semana epidemiológica de resultados positivos has 9587 (30.1%) missing values Missing
Vacuna contra COVID19 has 20788 (65.2%) missing values Missing
Marca has 20788 (65.2%) missing values Missing
Fecha de última aplicación has 20790 (65.3%) missing values Missing
REFUERZO has 29963 (94.0%) missing values Missing
FECHA REFUERZO has 29963 (94.0%) missing values Missing
VARIANTE has 31784 (99.8%) missing values Missing
INFLUENZA has 31812 (99.8%) missing values Missing
No consecutivo por inicio de sintomas is uniformly distributed Uniform
No consecutivo por inicio de sintomas has unique values Unique
No de caso positivo por inicio de síntomas is an unsupported type, check if it needs cleaning or further analysis Unsupported
Fecha de resultado de laboratorio is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-03-14 09:36:14.146090
Analysis finished2025-03-14 09:36:39.219819
Duration25.07 seconds
Software versionydata-profiling vv4.14.0
Download configurationconfig.json

Variables

No de caso positivo por inicio de síntomas
Unsupported

Missing  Rejected  Unsupported 

Missing9575
Missing (%)30.1%
Memory size1.1 MiB

No consecutivo por inicio de sintomas
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct31862
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15931.5
Minimum1
Maximum31862
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size249.1 KiB
2025-03-14T09:36:39.432147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1594.05
Q17966.25
median15931.5
Q323896.75
95-th percentile30268.95
Maximum31862
Range31861
Interquartile range (IQR)15930.5

Descriptive statistics

Standard deviation9197.9115
Coefficient of variation (CV)0.57734121
Kurtosis-1.2
Mean15931.5
Median Absolute Deviation (MAD)7965.5
Skewness0
Sum5.0760945 × 108
Variance84601576
MonotonicityStrictly increasing
2025-03-14T09:36:39.762732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
21238 1
 
< 0.1%
21251 1
 
< 0.1%
21250 1
 
< 0.1%
21249 1
 
< 0.1%
21248 1
 
< 0.1%
21247 1
 
< 0.1%
21246 1
 
< 0.1%
21245 1
 
< 0.1%
21244 1
 
< 0.1%
Other values (31852) 31852
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
31862 1
< 0.1%
31861 1
< 0.1%
31860 1
< 0.1%
31859 1
< 0.1%
31858 1
< 0.1%
31857 1
< 0.1%
31856 1
< 0.1%
31855 1
< 0.1%
31854 1
< 0.1%
31853 1
< 0.1%

Institución tratante
Categorical

Imbalance 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
IMSS
23730 
SSyBS
6059 
ISSSTE
 
1787
PRIVADA
 
157
SEMAR
 
90
Other values (3)
 
39

Length

Max length7
Median length4
Mean length4.3189379
Min length3

Characters and Unicode

Total characters137610
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSSyBS
2nd rowIMSS
3rd rowIMSS
4th rowIMSS
5th rowIMSS

Common Values

ValueCountFrequency (%)
IMSS 23730
74.5%
SSyBS 6059
 
19.0%
ISSSTE 1787
 
5.6%
PRIVADA 157
 
0.5%
SEMAR 90
 
0.3%
DIF 36
 
0.1%
PEMEX 2
 
< 0.1%
SEDENA 1
 
< 0.1%

Length

2025-03-14T09:36:40.079048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:40.221839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
imss 23730
74.5%
ssybs 6059
 
19.0%
issste 1787
 
5.6%
privada 157
 
0.5%
semar 90
 
0.3%
dif 36
 
0.1%
pemex 2
 
< 0.1%
sedena 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
S 71089
51.7%
I 25710
 
18.7%
M 23822
 
17.3%
y 6059
 
4.4%
B 6059
 
4.4%
E 1883
 
1.4%
T 1787
 
1.3%
A 405
 
0.3%
R 247
 
0.2%
D 194
 
0.1%
Other values (5) 355
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 137610
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 71089
51.7%
I 25710
 
18.7%
M 23822
 
17.3%
y 6059
 
4.4%
B 6059
 
4.4%
E 1883
 
1.4%
T 1787
 
1.3%
A 405
 
0.3%
R 247
 
0.2%
D 194
 
0.1%
Other values (5) 355
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 137610
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 71089
51.7%
I 25710
 
18.7%
M 23822
 
17.3%
y 6059
 
4.4%
B 6059
 
4.4%
E 1883
 
1.4%
T 1787
 
1.3%
A 405
 
0.3%
R 247
 
0.2%
D 194
 
0.1%
Other values (5) 355
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 137610
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 71089
51.7%
I 25710
 
18.7%
M 23822
 
17.3%
y 6059
 
4.4%
B 6059
 
4.4%
E 1883
 
1.4%
T 1787
 
1.3%
A 405
 
0.3%
R 247
 
0.2%
D 194
 
0.1%
Other values (5) 355
 
0.3%
Distinct129
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2025-03-14T09:36:40.510077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length69
Median length63
Mean length19.000377
Min length5

Characters and Unicode

Total characters605390
Distinct characters40
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

Unique61 ?
Unique (%)0.2%

Sample

1st rowHOSPITAL GENERAL IXTLAHUACAN
2nd rowUMF 16 COLIMA
3rd rowUMF 11 COLIMA
4th rowUMF 2 MANZANILLO
5th rowUMF 19 COLIMA
ValueCountFrequency (%)
umf 12285
 
10.7%
colima 9644
 
8.4%
de 9131
 
7.9%
villa 8013
 
7.0%
alvarez 8013
 
7.0%
hgzmf 6474
 
5.6%
1 6474
 
5.6%
11 5619
 
4.9%
manzanillo 5104
 
4.4%
hospital 3818
 
3.3%
Other values (216) 40621
35.3%
2025-03-14T09:36:40.934082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
83362
13.8%
A 62423
 
10.3%
L 53624
 
8.9%
M 45127
 
7.5%
I 37337
 
6.2%
E 32371
 
5.3%
O 32036
 
5.3%
1 24980
 
4.1%
Z 22508
 
3.7%
F 21670
 
3.6%
Other values (30) 189952
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 605390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
83362
13.8%
A 62423
 
10.3%
L 53624
 
8.9%
M 45127
 
7.5%
I 37337
 
6.2%
E 32371
 
5.3%
O 32036
 
5.3%
1 24980
 
4.1%
Z 22508
 
3.7%
F 21670
 
3.6%
Other values (30) 189952
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 605390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
83362
13.8%
A 62423
 
10.3%
L 53624
 
8.9%
M 45127
 
7.5%
I 37337
 
6.2%
E 32371
 
5.3%
O 32036
 
5.3%
1 24980
 
4.1%
Z 22508
 
3.7%
F 21670
 
3.6%
Other values (30) 189952
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 605390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
83362
13.8%
A 62423
 
10.3%
L 53624
 
8.9%
M 45127
 
7.5%
I 37337
 
6.2%
E 32371
 
5.3%
O 32036
 
5.3%
1 24980
 
4.1%
Z 22508
 
3.7%
F 21670
 
3.6%
Other values (30) 189952
31.4%

Toma de muestra en el ESTADO
Categorical

High correlation  Imbalance 

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
SI
31783 
No, Ciudad De Mexico
 
30
No, Jalisco
 
14
No, Chihuahua
 
8
No, Michoacan
 
4
Other values (14)
 
23

Length

Max length20
Median length2
Mean length2.0328605
Min length2

Characters and Unicode

Total characters64771
Distinct characters38
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

Unique8 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
SI 31783
99.8%
No, Ciudad De Mexico 30
 
0.1%
No, Jalisco 14
 
< 0.1%
No, Chihuahua 8
 
< 0.1%
No, Michoacan 4
 
< 0.1%
No, Guanajuato 4
 
< 0.1%
No, Coahuila 3
 
< 0.1%
No, Oaxaca 2
 
< 0.1%
No, Campeche 2
 
< 0.1%
No, Queretaro 2
 
< 0.1%
Other values (9) 10
 
< 0.1%

Length

2025-03-14T09:36:41.072785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
si 31783
99.3%
no 79
 
0.2%
ciudad 30
 
0.1%
de 30
 
0.1%
mexico 30
 
0.1%
jalisco 14
 
< 0.1%
chihuahua 8
 
< 0.1%
michoacan 4
 
< 0.1%
guanajuato 4
 
< 0.1%
coahuila 3
 
< 0.1%
Other values (16) 20
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S 31785
49.1%
I 31783
49.1%
o 147
 
0.2%
143
 
0.2%
a 110
 
0.2%
i 95
 
0.1%
N 81
 
0.1%
, 79
 
0.1%
e 76
 
0.1%
u 63
 
0.1%
Other values (28) 409
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 64771
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 31785
49.1%
I 31783
49.1%
o 147
 
0.2%
143
 
0.2%
a 110
 
0.2%
i 95
 
0.1%
N 81
 
0.1%
, 79
 
0.1%
e 76
 
0.1%
u 63
 
0.1%
Other values (28) 409
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 64771
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 31785
49.1%
I 31783
49.1%
o 147
 
0.2%
143
 
0.2%
a 110
 
0.2%
i 95
 
0.1%
N 81
 
0.1%
, 79
 
0.1%
e 76
 
0.1%
u 63
 
0.1%
Other values (28) 409
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 64771
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 31785
49.1%
I 31783
49.1%
o 147
 
0.2%
143
 
0.2%
a 110
 
0.2%
i 95
 
0.1%
N 81
 
0.1%
, 79
 
0.1%
e 76
 
0.1%
u 63
 
0.1%
Other values (28) 409
 
0.6%
Distinct111
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2025-03-14T09:36:41.251284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length41
Median length35
Mean length14.82873
Min length9

Characters and Unicode

Total characters472473
Distinct characters56
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

Unique59 ?
Unique (%)0.2%

Sample

1st rowCOL, Ixtlahuacán
2nd rowCOL, Colima
3rd rowCOL, Villa de Álvarez
4th rowCOL, Manzanillo
5th rowCOL, Colima
ValueCountFrequency (%)
col 31522
38.8%
colima 11462
 
14.1%
de 8727
 
10.7%
villa 8673
 
10.7%
álvarez 8670
 
10.7%
manzanillo 5210
 
6.4%
tecomán 3285
 
4.0%
cuauhtémoc 1105
 
1.4%
coquimatlán 583
 
0.7%
comala 471
 
0.6%
Other values (163) 1580
 
1.9%
2025-03-14T09:36:41.634536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 49586
10.5%
49426
10.5%
C 45322
 
9.6%
a 43532
 
9.2%
, 31862
 
6.7%
L 31748
 
6.7%
O 31537
 
6.7%
i 26603
 
5.6%
o 22359
 
4.7%
e 21218
 
4.5%
Other values (46) 119280
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 472473
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 49586
10.5%
49426
10.5%
C 45322
 
9.6%
a 43532
 
9.2%
, 31862
 
6.7%
L 31748
 
6.7%
O 31537
 
6.7%
i 26603
 
5.6%
o 22359
 
4.7%
e 21218
 
4.5%
Other values (46) 119280
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 472473
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 49586
10.5%
49426
10.5%
C 45322
 
9.6%
a 43532
 
9.2%
, 31862
 
6.7%
L 31748
 
6.7%
O 31537
 
6.7%
i 26603
 
5.6%
o 22359
 
4.7%
e 21218
 
4.5%
Other values (46) 119280
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 472473
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 49586
10.5%
49426
10.5%
C 45322
 
9.6%
a 43532
 
9.2%
, 31862
 
6.7%
L 31748
 
6.7%
O 31537
 
6.7%
i 26603
 
5.6%
o 22359
 
4.7%
e 21218
 
4.5%
Other values (46) 119280
25.2%

Edad
Real number (ℝ)

Distinct106
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.062833
Minimum0
Maximum112
Zeros29
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size249.1 KiB
2025-03-14T09:36:41.762922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q125
median34
Q346
95-th percentile64
Maximum112
Range112
Interquartile range (IQR)21

Descriptive statistics

Standard deviation15.999255
Coefficient of variation (CV)0.44364941
Kurtosis0.5804396
Mean36.062833
Median Absolute Deviation (MAD)10
Skewness0.50055344
Sum1149034
Variance255.97615
MonotonicityNot monotonic
2025-03-14T09:36:41.967142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 939
 
2.9%
32 938
 
2.9%
26 932
 
2.9%
24 927
 
2.9%
27 916
 
2.9%
28 915
 
2.9%
31 886
 
2.8%
30 881
 
2.8%
25 876
 
2.7%
36 815
 
2.6%
Other values (96) 22837
71.7%
ValueCountFrequency (%)
0 29
 
0.1%
1 217
0.7%
2 170
0.5%
3 175
0.5%
4 120
0.4%
5 132
0.4%
6 121
0.4%
7 139
0.4%
8 131
0.4%
9 163
0.5%
ValueCountFrequency (%)
112 1
 
< 0.1%
108 3
< 0.1%
107 1
 
< 0.1%
106 1
 
< 0.1%
103 1
 
< 0.1%
102 3
< 0.1%
100 5
< 0.1%
98 3
< 0.1%
97 1
 
< 0.1%
96 1
 
< 0.1%

Sexo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
F
18204 
M
13658 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters31862
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 rowM
2nd rowF
3rd rowM
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
F 18204
57.1%
M 13658
42.9%

Length

2025-03-14T09:36:42.133335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:42.226262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
f 18204
57.1%
m 13658
42.9%

Most occurring characters

ValueCountFrequency (%)
F 18204
57.1%
M 13658
42.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 18204
57.1%
M 13658
42.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 18204
57.1%
M 13658
42.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 18204
57.1%
M 13658
42.9%
Distinct190
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size249.1 KiB
Minimum2021-12-06 00:00:00
Maximum2022-06-25 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-14T09:36:42.360796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-14T09:36:42.598849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct183
Distinct (%)0.8%
Missing9288
Missing (%)29.2%
Memory size249.1 KiB
Minimum2021-12-15 00:00:00
Maximum2022-06-23 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-14T09:36:42.792440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-14T09:36:42.988119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct187
Distinct (%)0.8%
Missing9288
Missing (%)29.2%
Memory size249.1 KiB
Minimum2021-12-20 00:00:00
Maximum2022-07-02 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-14T09:36:43.181995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-14T09:36:43.398763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct188
Distinct (%)0.8%
Missing9288
Missing (%)29.2%
Memory size249.1 KiB
Minimum2021-12-28 00:00:00
Maximum2022-07-11 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-14T09:36:43.601408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-14T09:36:43.803023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Procedencia
Categorical

High correlation  Imbalance 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
No aplica
31850 
Estados Unidos de América - SAN ANTONIO
 
2
TOMATLAN
 
2
Se desconoce
 
2
Estados Unidos de América - LOS ANGELES
 
1
Other values (5)
 
5

Length

Max length39
Median length9
Mean length9.005618
Min length8

Characters and Unicode

Total characters286937
Distinct characters32
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

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowNo aplica
2nd rowNo aplica
3rd rowNo aplica
4th rowNo aplica
5th rowNo aplica

Common Values

ValueCountFrequency (%)
No aplica 31850
> 99.9%
Estados Unidos de América - SAN ANTONIO 2
 
< 0.1%
TOMATLAN 2
 
< 0.1%
Se desconoce 2
 
< 0.1%
Estados Unidos de América - LOS ANGELES 1
 
< 0.1%
Estados Unidos de América - PASCO 1
 
< 0.1%
Estados Unidos de América - TEXAS 1
 
< 0.1%
Estados Unidos de América - WISCONSIN 1
 
< 0.1%
Otro - MEXICO 1
 
< 0.1%
Otro - JALISCO 1
 
< 0.1%

Length

2025-03-14T09:36:43.993055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:44.138866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 31850
50.0%
aplica 31850
50.0%
8
 
< 0.1%
estados 6
 
< 0.1%
unidos 6
 
< 0.1%
de 6
 
< 0.1%
américa 6
 
< 0.1%
otro 2
 
< 0.1%
desconoce 2
 
< 0.1%
se 2
 
< 0.1%
Other values (10) 13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 63712
22.2%
31889
11.1%
o 31868
11.1%
i 31862
11.1%
N 31861
11.1%
c 31860
11.1%
p 31850
11.1%
l 31850
11.1%
s 20
 
< 0.1%
d 20
 
< 0.1%
Other values (22) 145
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 286937
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 63712
22.2%
31889
11.1%
o 31868
11.1%
i 31862
11.1%
N 31861
11.1%
c 31860
11.1%
p 31850
11.1%
l 31850
11.1%
s 20
 
< 0.1%
d 20
 
< 0.1%
Other values (22) 145
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 286937
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 63712
22.2%
31889
11.1%
o 31868
11.1%
i 31862
11.1%
N 31861
11.1%
c 31860
11.1%
p 31850
11.1%
l 31850
11.1%
s 20
 
< 0.1%
d 20
 
< 0.1%
Other values (22) 145
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 286937
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 63712
22.2%
31889
11.1%
o 31868
11.1%
i 31862
11.1%
N 31861
11.1%
c 31860
11.1%
p 31850
11.1%
l 31850
11.1%
s 20
 
< 0.1%
d 20
 
< 0.1%
Other values (22) 145
 
0.1%

Fecha de llegada al Estado
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
No aplica
31856 
TOMATLAN
 
2
Se desconoce
 
2
Otro - MEXICO
 
1
Otro - JALISCO
 
1

Length

Max length14
Median length9
Mean length9.000408
Min length8

Characters and Unicode

Total characters286771
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

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowNo aplica
2nd rowNo aplica
3rd rowNo aplica
4th rowNo aplica
5th rowNo aplica

Common Values

ValueCountFrequency (%)
No aplica 31856
> 99.9%
TOMATLAN 2
 
< 0.1%
Se desconoce 2
 
< 0.1%
Otro - MEXICO 1
 
< 0.1%
Otro - JALISCO 1
 
< 0.1%

Length

2025-03-14T09:36:44.349880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:44.461851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 31856
50.0%
aplica 31856
50.0%
tomatlan 2
 
< 0.1%
se 2
 
< 0.1%
desconoce 2
 
< 0.1%
otro 2
 
< 0.1%
2
 
< 0.1%
mexico 1
 
< 0.1%
jalisco 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 63712
22.2%
o 31862
11.1%
31862
11.1%
c 31860
11.1%
N 31858
11.1%
p 31856
11.1%
l 31856
11.1%
i 31856
11.1%
O 6
 
< 0.1%
e 6
 
< 0.1%
Other values (16) 37
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 286771
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 63712
22.2%
o 31862
11.1%
31862
11.1%
c 31860
11.1%
N 31858
11.1%
p 31856
11.1%
l 31856
11.1%
i 31856
11.1%
O 6
 
< 0.1%
e 6
 
< 0.1%
Other values (16) 37
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 286771
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 63712
22.2%
o 31862
11.1%
31862
11.1%
c 31860
11.1%
N 31858
11.1%
p 31856
11.1%
l 31856
11.1%
i 31856
11.1%
O 6
 
< 0.1%
e 6
 
< 0.1%
Other values (16) 37
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 286771
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 63712
22.2%
o 31862
11.1%
31862
11.1%
c 31860
11.1%
N 31858
11.1%
p 31856
11.1%
l 31856
11.1%
i 31856
11.1%
O 6
 
< 0.1%
e 6
 
< 0.1%
Other values (16) 37
 
< 0.1%
Distinct177
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size249.1 KiB
Minimum2022-01-01 00:00:00
Maximum2022-06-26 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-14T09:36:44.632142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-14T09:36:44.837881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Estatus día previo
Categorical

High correlation  Imbalance 

Distinct9
Distinct (%)< 0.1%
Missing262
Missing (%)0.8%
Memory size2.2 MiB
Alta sanitaria
21048 
Seguimiento terminado
9361 
Seguimiento domiciliario
 
956
Defunción
 
194
En tratamiento
 
18
Other values (4)
 
23

Length

Max length24
Median length14
Mean length16.343608
Min length9

Characters and Unicode

Total characters516458
Distinct characters24
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSeguimiento terminado
2nd rowAlta sanitaria
3rd rowAlta sanitaria
4th rowAlta sanitaria
5th rowSeguimiento terminado

Common Values

ValueCountFrequency (%)
Alta sanitaria 21048
66.1%
Seguimiento terminado 9361
29.4%
Seguimiento domiciliario 956
 
3.0%
Defunción 194
 
0.6%
En tratamiento 18
 
0.1%
Caso grave 13
 
< 0.1%
Caso no grave 7
 
< 0.1%
Alta / defunción 2
 
< 0.1%
Defunción 1
 
< 0.1%
(Missing) 262
 
0.8%

Length

2025-03-14T09:36:45.020099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:45.156730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
alta 21050
33.4%
sanitaria 21048
33.4%
seguimiento 10317
16.4%
terminado 9361
14.9%
domiciliario 956
 
1.5%
defunción 197
 
0.3%
caso 20
 
< 0.1%
grave 20
 
< 0.1%
en 18
 
< 0.1%
tratamiento 18
 
< 0.1%
Other values (2) 9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 94587
18.3%
i 76130
14.7%
t 61830
12.0%
n 41163
8.0%
31415
 
6.1%
r 31403
 
6.1%
e 30230
 
5.9%
l 22006
 
4.3%
o 21635
 
4.2%
s 21068
 
4.1%
Other values (14) 84991
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 516458
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 94587
18.3%
i 76130
14.7%
t 61830
12.0%
n 41163
8.0%
31415
 
6.1%
r 31403
 
6.1%
e 30230
 
5.9%
l 22006
 
4.3%
o 21635
 
4.2%
s 21068
 
4.1%
Other values (14) 84991
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 516458
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 94587
18.3%
i 76130
14.7%
t 61830
12.0%
n 41163
8.0%
31415
 
6.1%
r 31403
 
6.1%
e 30230
 
5.9%
l 22006
 
4.3%
o 21635
 
4.2%
s 21068
 
4.1%
Other values (14) 84991
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 516458
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 94587
18.3%
i 76130
14.7%
t 61830
12.0%
n 41163
8.0%
31415
 
6.1%
r 31403
 
6.1%
e 30230
 
5.9%
l 22006
 
4.3%
o 21635
 
4.2%
s 21068
 
4.1%
Other values (14) 84991
16.5%

Tipo de manejo
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
Ambulatorio
31036 
Hospitalizado
 
826

Length

Max length13
Median length11
Mean length11.051849
Min length11

Characters and Unicode

Total characters352134
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 rowAmbulatorio
2nd rowAmbulatorio
3rd rowAmbulatorio
4th rowAmbulatorio
5th rowAmbulatorio

Common Values

ValueCountFrequency (%)
Ambulatorio 31036
97.4%
Hospitalizado 826
 
2.6%

Length

2025-03-14T09:36:45.363024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:45.472129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ambulatorio 31036
97.4%
hospitalizado 826
 
2.6%

Most occurring characters

ValueCountFrequency (%)
o 63724
18.1%
a 32688
9.3%
i 32688
9.3%
l 31862
9.0%
t 31862
9.0%
A 31036
8.8%
m 31036
8.8%
b 31036
8.8%
u 31036
8.8%
r 31036
8.8%
Other values (5) 4130
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 352134
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 63724
18.1%
a 32688
9.3%
i 32688
9.3%
l 31862
9.0%
t 31862
9.0%
A 31036
8.8%
m 31036
8.8%
b 31036
8.8%
u 31036
8.8%
r 31036
8.8%
Other values (5) 4130
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 352134
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 63724
18.1%
a 32688
9.3%
i 32688
9.3%
l 31862
9.0%
t 31862
9.0%
A 31036
8.8%
m 31036
8.8%
b 31036
8.8%
u 31036
8.8%
r 31036
8.8%
Other values (5) 4130
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 352134
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 63724
18.1%
a 32688
9.3%
i 32688
9.3%
l 31862
9.0%
t 31862
9.0%
A 31036
8.8%
m 31036
8.8%
b 31036
8.8%
u 31036
8.8%
r 31036
8.8%
Other values (5) 4130
 
1.2%

Estatus del paciente
Categorical

High correlation  Imbalance 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
Alta sanitaria
21184 
Seguimiento terminado
9467 
Seguimiento domiciliario
 
977
Defunción
 
194
En tratamiento
 
17
Other values (4)
 
23

Length

Max length24
Median length14
Mean length16.354121
Min length9

Characters and Unicode

Total characters521075
Distinct characters24
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSeguimiento terminado
2nd rowAlta sanitaria
3rd rowAlta sanitaria
4th rowAlta sanitaria
5th rowSeguimiento terminado

Common Values

ValueCountFrequency (%)
Alta sanitaria 21184
66.5%
Seguimiento terminado 9467
29.7%
Seguimiento domiciliario 977
 
3.1%
Defunción 194
 
0.6%
En tratamiento 17
 
0.1%
Caso grave 14
 
< 0.1%
Caso no grave 6
 
< 0.1%
Alta / defunción 2
 
< 0.1%
Defunción 1
 
< 0.1%

Length

2025-03-14T09:36:45.612391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:45.750123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
alta 21186
33.3%
sanitaria 21184
33.3%
seguimiento 10444
16.4%
terminado 9467
14.9%
domiciliario 977
 
1.5%
defunción 197
 
0.3%
caso 20
 
< 0.1%
grave 20
 
< 0.1%
en 17
 
< 0.1%
tratamiento 17
 
< 0.1%
Other values (2) 8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 95256
18.3%
i 76845
14.7%
t 62332
12.0%
n 41529
8.0%
31676
 
6.1%
r 31665
 
6.1%
e 30589
 
5.9%
l 22163
 
4.3%
o 21908
 
4.2%
s 21204
 
4.1%
Other values (14) 85908
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 521075
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 95256
18.3%
i 76845
14.7%
t 62332
12.0%
n 41529
8.0%
31676
 
6.1%
r 31665
 
6.1%
e 30589
 
5.9%
l 22163
 
4.3%
o 21908
 
4.2%
s 21204
 
4.1%
Other values (14) 85908
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 521075
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 95256
18.3%
i 76845
14.7%
t 62332
12.0%
n 41529
8.0%
31676
 
6.1%
r 31665
 
6.1%
e 30589
 
5.9%
l 22163
 
4.3%
o 21908
 
4.2%
s 21204
 
4.1%
Other values (14) 85908
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 521075
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 95256
18.3%
i 76845
14.7%
t 62332
12.0%
n 41529
8.0%
31676
 
6.1%
r 31665
 
6.1%
e 30589
 
5.9%
l 22163
 
4.3%
o 21908
 
4.2%
s 21204
 
4.1%
Other values (14) 85908
16.5%
Distinct76
Distinct (%)39.0%
Missing31667
Missing (%)99.4%
Memory size249.1 KiB
Minimum2022-01-01 00:00:00
Maximum2022-06-18 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-14T09:36:45.935398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-14T09:36:46.131701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Semana epidemiológica de defunciones positivas
Categorical

High correlation  Missing 

Distinct27
Distinct (%)14.8%
Missing31679
Missing (%)99.4%
Memory size1.9 MiB
Semana 6
31 
Semana 05
29 
Semana 04
15 
Semana 7
14 
Semana 5
13 
Other values (22)
81 

Length

Max length9
Median length9
Mean length8.5519126
Min length8

Characters and Unicode

Total characters1565
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

Unique7 ?
Unique (%)3.8%

Sample

1st rowSemana 5
2nd rowSemana 03
3rd rowSemana 04
4th rowSemana 1
5th rowSemana 2

Common Values

ValueCountFrequency (%)
Semana 6 31
 
0.1%
Semana 05 29
 
0.1%
Semana 04 15
 
< 0.1%
Semana 7 14
 
< 0.1%
Semana 5 13
 
< 0.1%
Semana 08 13
 
< 0.1%
Semana 9 11
 
< 0.1%
Semana 03 10
 
< 0.1%
Semana 3 5
 
< 0.1%
Semana 07 5
 
< 0.1%
Other values (17) 37
 
0.1%
(Missing) 31679
99.4%

Length

2025-03-14T09:36:46.313999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
semana 183
50.0%
6 31
 
8.5%
05 29
 
7.9%
04 15
 
4.1%
7 14
 
3.8%
5 13
 
3.6%
08 13
 
3.6%
9 11
 
3.0%
03 10
 
2.7%
3 5
 
1.4%
Other values (18) 42
 
11.5%

Most occurring characters

ValueCountFrequency (%)
a 366
23.4%
m 183
11.7%
n 183
11.7%
183
11.7%
e 183
11.7%
S 182
11.6%
0 82
 
5.2%
5 44
 
2.8%
6 35
 
2.2%
4 23
 
1.5%
Other values (7) 101
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1565
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 366
23.4%
m 183
11.7%
n 183
11.7%
183
11.7%
e 183
11.7%
S 182
11.6%
0 82
 
5.2%
5 44
 
2.8%
6 35
 
2.2%
4 23
 
1.5%
Other values (7) 101
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1565
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 366
23.4%
m 183
11.7%
n 183
11.7%
183
11.7%
e 183
11.7%
S 182
11.6%
0 82
 
5.2%
5 44
 
2.8%
6 35
 
2.2%
4 23
 
1.5%
Other values (7) 101
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1565
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 366
23.4%
m 183
11.7%
n 183
11.7%
183
11.7%
e 183
11.7%
S 182
11.6%
0 82
 
5.2%
5 44
 
2.8%
6 35
 
2.2%
4 23
 
1.5%
Other values (7) 101
 
6.5%

Semana epidemiológica de resultados positivos
Categorical

High correlation  Missing 

Distinct33
Distinct (%)0.1%
Missing9587
Missing (%)30.1%
Memory size2.0 MiB
Semana 5
4221 
Semana 04
3087 
Semana 02
2807 
Semana 3
2568 
Semana 6
2505 
Other values (28)
7087 

Length

Max length9
Median length8
Mean length8.472009
Min length8

Characters and Unicode

Total characters188714
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSemana 01
2nd rowSemana 02
3rd rowSemana 01
4th rowSemana 01
5th rowSemana 01

Common Values

ValueCountFrequency (%)
Semana 5 4221
13.2%
Semana 04 3087
 
9.7%
Semana 02 2807
 
8.8%
Semana 3 2568
 
8.1%
Semana 6 2505
 
7.9%
Semana 03 1205
 
3.8%
Semana 7 1083
 
3.4%
Semana 01 801
 
2.5%
Semana 8 635
 
2.0%
Semana 25 606
 
1.9%
Other values (23) 2757
 
8.7%
(Missing) 9587
30.1%

Length

2025-03-14T09:36:46.458245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
semana 22274
50.0%
5 4221
 
9.5%
04 3087
 
6.9%
02 2807
 
6.3%
3 2568
 
5.8%
6 2505
 
5.6%
03 1205
 
2.7%
7 1083
 
2.4%
01 832
 
1.9%
8 635
 
1.4%
Other values (23) 3332
 
7.5%

Most occurring characters

ValueCountFrequency (%)
a 44550
23.6%
m 22275
11.8%
n 22275
11.8%
e 22275
11.8%
22274
11.8%
S 22244
11.8%
0 8311
 
4.4%
5 5021
 
2.7%
2 4872
 
2.6%
4 4083
 
2.2%
Other values (7) 10534
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 188714
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 44550
23.6%
m 22275
11.8%
n 22275
11.8%
e 22275
11.8%
22274
11.8%
S 22244
11.8%
0 8311
 
4.4%
5 5021
 
2.7%
2 4872
 
2.6%
4 4083
 
2.2%
Other values (7) 10534
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 188714
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 44550
23.6%
m 22275
11.8%
n 22275
11.8%
e 22275
11.8%
22274
11.8%
S 22244
11.8%
0 8311
 
4.4%
5 5021
 
2.7%
2 4872
 
2.6%
4 4083
 
2.2%
Other values (7) 10534
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 188714
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 44550
23.6%
m 22275
11.8%
n 22275
11.8%
e 22275
11.8%
22274
11.8%
S 22244
11.8%
0 8311
 
4.4%
5 5021
 
2.7%
2 4872
 
2.6%
4 4083
 
2.2%
Other values (7) 10534
 
5.6%

Fecha de resultado de laboratorio
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size1.7 MiB

Resultado de laboratorio
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
SARS-COV-2
22275 
Negativo
9580 
Pendiente
 
7

Length

Max length10
Median length10
Mean length9.398437
Min length8

Characters and Unicode

Total characters299453
Distinct characters19
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 rowNegativo
2nd rowSARS-COV-2
3rd rowSARS-COV-2
4th rowSARS-COV-2
5th rowNegativo

Common Values

ValueCountFrequency (%)
SARS-COV-2 22275
69.9%
Negativo 9580
30.1%
Pendiente 7
 
< 0.1%

Length

2025-03-14T09:36:46.623338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:46.730498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sars-cov-2 22275
69.9%
negativo 9580
30.1%
pendiente 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
S 44550
14.9%
- 44550
14.9%
A 22275
 
7.4%
R 22275
 
7.4%
C 22275
 
7.4%
O 22275
 
7.4%
V 22275
 
7.4%
2 22275
 
7.4%
e 9601
 
3.2%
t 9587
 
3.2%
Other values (9) 57515
19.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 299453
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 44550
14.9%
- 44550
14.9%
A 22275
 
7.4%
R 22275
 
7.4%
C 22275
 
7.4%
O 22275
 
7.4%
V 22275
 
7.4%
2 22275
 
7.4%
e 9601
 
3.2%
t 9587
 
3.2%
Other values (9) 57515
19.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 299453
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 44550
14.9%
- 44550
14.9%
A 22275
 
7.4%
R 22275
 
7.4%
C 22275
 
7.4%
O 22275
 
7.4%
V 22275
 
7.4%
2 22275
 
7.4%
e 9601
 
3.2%
t 9587
 
3.2%
Other values (9) 57515
19.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 299453
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 44550
14.9%
- 44550
14.9%
A 22275
 
7.4%
R 22275
 
7.4%
C 22275
 
7.4%
O 22275
 
7.4%
V 22275
 
7.4%
2 22275
 
7.4%
e 9601
 
3.2%
t 9587
 
3.2%
Other values (9) 57515
19.2%

Pacientes que requirieron intubación
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
31822 
SI
 
40

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters63724
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 (%)
NO 31822
99.9%
SI 40
 
0.1%

Length

2025-03-14T09:36:46.854155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:46.941981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 31822
99.9%
si 40
 
0.1%

Most occurring characters

ValueCountFrequency (%)
N 31822
49.9%
O 31822
49.9%
S 40
 
0.1%
I 40
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63724
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 31822
49.9%
O 31822
49.9%
S 40
 
0.1%
I 40
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63724
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 31822
49.9%
O 31822
49.9%
S 40
 
0.1%
I 40
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63724
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 31822
49.9%
O 31822
49.9%
S 40
 
0.1%
I 40
 
0.1%

Pacientes que ingresaron a UCI
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
31825 
SI
 
37

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters63724
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 (%)
NO 31825
99.9%
SI 37
 
0.1%

Length

2025-03-14T09:36:47.051770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:47.138731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 31825
99.9%
si 37
 
0.1%

Most occurring characters

ValueCountFrequency (%)
N 31825
49.9%
O 31825
49.9%
S 37
 
0.1%
I 37
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63724
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 31825
49.9%
O 31825
49.9%
S 37
 
0.1%
I 37
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63724
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 31825
49.9%
O 31825
49.9%
S 37
 
0.1%
I 37
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63724
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 31825
49.9%
O 31825
49.9%
S 37
 
0.1%
I 37
 
0.1%

Diagnóstico clínico de Neumonía
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
31343 
SI
 
519

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters63724
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 (%)
NO 31343
98.4%
SI 519
 
1.6%

Length

2025-03-14T09:36:47.253880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:47.346196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 31343
98.4%
si 519
 
1.6%

Most occurring characters

ValueCountFrequency (%)
N 31343
49.2%
O 31343
49.2%
S 519
 
0.8%
I 519
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63724
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 31343
49.2%
O 31343
49.2%
S 519
 
0.8%
I 519
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63724
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 31343
49.2%
O 31343
49.2%
S 519
 
0.8%
I 519
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63724
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 31343
49.2%
O 31343
49.2%
S 519
 
0.8%
I 519
 
0.8%

Diagnóstico probable
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
ENFERMEDAD TIPO INFLUENZA (ETI)
31071 
INFECCION RESPIRATORIA AGUDA GRAVE (IRAG)
 
791

Length

Max length41
Median length31
Mean length31.248258
Min length31

Characters and Unicode

Total characters995632
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 rowENFERMEDAD TIPO INFLUENZA (ETI)
2nd rowENFERMEDAD TIPO INFLUENZA (ETI)
3rd rowENFERMEDAD TIPO INFLUENZA (ETI)
4th rowENFERMEDAD TIPO INFLUENZA (ETI)
5th rowENFERMEDAD TIPO INFLUENZA (ETI)

Common Values

ValueCountFrequency (%)
ENFERMEDAD TIPO INFLUENZA (ETI) 31071
97.5%
INFECCION RESPIRATORIA AGUDA GRAVE (IRAG) 791
 
2.5%

Length

2025-03-14T09:36:47.489189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:47.581831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
enfermedad 31071
24.2%
tipo 31071
24.2%
influenza 31071
24.2%
eti 31071
24.2%
infeccion 791
 
0.6%
respiratoria 791
 
0.6%
aguda 791
 
0.6%
grave 791
 
0.6%
irag 791
 
0.6%

Most occurring characters

ValueCountFrequency (%)
E 157728
15.8%
I 97168
9.8%
96377
9.7%
N 94795
9.5%
A 66888
 
6.7%
F 62933
 
6.3%
D 62933
 
6.3%
T 62933
 
6.3%
R 35026
 
3.5%
O 32653
 
3.3%
Other values (11) 226198
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 995632
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 157728
15.8%
I 97168
9.8%
96377
9.7%
N 94795
9.5%
A 66888
 
6.7%
F 62933
 
6.3%
D 62933
 
6.3%
T 62933
 
6.3%
R 35026
 
3.5%
O 32653
 
3.3%
Other values (11) 226198
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 995632
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 157728
15.8%
I 97168
9.8%
96377
9.7%
N 94795
9.5%
A 66888
 
6.7%
F 62933
 
6.3%
D 62933
 
6.3%
T 62933
 
6.3%
R 35026
 
3.5%
O 32653
 
3.3%
Other values (11) 226198
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 995632
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 157728
15.8%
I 97168
9.8%
96377
9.7%
N 94795
9.5%
A 66888
 
6.7%
F 62933
 
6.3%
D 62933
 
6.3%
T 62933
 
6.3%
R 35026
 
3.5%
O 32653
 
3.3%
Other values (11) 226198
22.7%

Fiebre
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
SI
19987 
NO
11873 
SE IGNORA
 
2

Length

Max length9
Median length2
Mean length2.0004394
Min length2

Characters and Unicode

Total characters63738
Distinct characters9
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 rowSI
3rd rowNO
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
SI 19987
62.7%
NO 11873
37.3%
SE IGNORA 2
 
< 0.1%

Length

2025-03-14T09:36:47.724790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:47.830340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
si 19987
62.7%
no 11873
37.3%
se 2
 
< 0.1%
ignora 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
S 19989
31.4%
I 19989
31.4%
N 11875
18.6%
O 11875
18.6%
E 2
 
< 0.1%
2
 
< 0.1%
G 2
 
< 0.1%
R 2
 
< 0.1%
A 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63738
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 19989
31.4%
I 19989
31.4%
N 11875
18.6%
O 11875
18.6%
E 2
 
< 0.1%
2
 
< 0.1%
G 2
 
< 0.1%
R 2
 
< 0.1%
A 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63738
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 19989
31.4%
I 19989
31.4%
N 11875
18.6%
O 11875
18.6%
E 2
 
< 0.1%
2
 
< 0.1%
G 2
 
< 0.1%
R 2
 
< 0.1%
A 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63738
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 19989
31.4%
I 19989
31.4%
N 11875
18.6%
O 11875
18.6%
E 2
 
< 0.1%
2
 
< 0.1%
G 2
 
< 0.1%
R 2
 
< 0.1%
A 2
 
< 0.1%

Tos
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
SI
24095 
NO
7767 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters63724
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 rowSI
3rd rowSI
4th rowSI
5th rowNO

Common Values

ValueCountFrequency (%)
SI 24095
75.6%
NO 7767
 
24.4%

Length

2025-03-14T09:36:47.949308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:48.131499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
si 24095
75.6%
no 7767
 
24.4%

Most occurring characters

ValueCountFrequency (%)
S 24095
37.8%
I 24095
37.8%
N 7767
 
12.2%
O 7767
 
12.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63724
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 24095
37.8%
I 24095
37.8%
N 7767
 
12.2%
O 7767
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63724
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 24095
37.8%
I 24095
37.8%
N 7767
 
12.2%
O 7767
 
12.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63724
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 24095
37.8%
I 24095
37.8%
N 7767
 
12.2%
O 7767
 
12.2%

Odinofagia
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
SI
19248 
NO
12595 
SE IGNORA
 
19

Length

Max length9
Median length2
Mean length2.0041743
Min length2

Characters and Unicode

Total characters63857
Distinct characters9
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 rowSI
3rd rowSI
4th rowSI
5th rowNO

Common Values

ValueCountFrequency (%)
SI 19248
60.4%
NO 12595
39.5%
SE IGNORA 19
 
0.1%

Length

2025-03-14T09:36:48.362130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:48.466239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
si 19248
60.4%
no 12595
39.5%
se 19
 
0.1%
ignora 19
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S 19267
30.2%
I 19267
30.2%
N 12614
19.8%
O 12614
19.8%
E 19
 
< 0.1%
19
 
< 0.1%
G 19
 
< 0.1%
R 19
 
< 0.1%
A 19
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63857
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 19267
30.2%
I 19267
30.2%
N 12614
19.8%
O 12614
19.8%
E 19
 
< 0.1%
19
 
< 0.1%
G 19
 
< 0.1%
R 19
 
< 0.1%
A 19
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63857
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 19267
30.2%
I 19267
30.2%
N 12614
19.8%
O 12614
19.8%
E 19
 
< 0.1%
19
 
< 0.1%
G 19
 
< 0.1%
R 19
 
< 0.1%
A 19
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63857
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 19267
30.2%
I 19267
30.2%
N 12614
19.8%
O 12614
19.8%
E 19
 
< 0.1%
19
 
< 0.1%
G 19
 
< 0.1%
R 19
 
< 0.1%
A 19
 
< 0.1%

Disnea
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
29200 
SI
 
2661
SE IGNORA
 
1

Length

Max length9
Median length2
Mean length2.0002197
Min length2

Characters and Unicode

Total characters63731
Distinct characters9
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNO
2nd rowNO
3rd rowSI
4th rowNO
5th rowSI

Common Values

ValueCountFrequency (%)
NO 29200
91.6%
SI 2661
 
8.4%
SE IGNORA 1
 
< 0.1%

Length

2025-03-14T09:36:48.601223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:48.731295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 29200
91.6%
si 2661
 
8.4%
se 1
 
< 0.1%
ignora 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 29201
45.8%
O 29201
45.8%
S 2662
 
4.2%
I 2662
 
4.2%
E 1
 
< 0.1%
1
 
< 0.1%
G 1
 
< 0.1%
R 1
 
< 0.1%
A 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63731
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 29201
45.8%
O 29201
45.8%
S 2662
 
4.2%
I 2662
 
4.2%
E 1
 
< 0.1%
1
 
< 0.1%
G 1
 
< 0.1%
R 1
 
< 0.1%
A 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63731
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 29201
45.8%
O 29201
45.8%
S 2662
 
4.2%
I 2662
 
4.2%
E 1
 
< 0.1%
1
 
< 0.1%
G 1
 
< 0.1%
R 1
 
< 0.1%
A 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63731
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 29201
45.8%
O 29201
45.8%
S 2662
 
4.2%
I 2662
 
4.2%
E 1
 
< 0.1%
1
 
< 0.1%
G 1
 
< 0.1%
R 1
 
< 0.1%
A 1
 
< 0.1%

Irritabilidad
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
24435 
SI
7424 
SE IGNORA
 
3

Length

Max length9
Median length2
Mean length2.0006591
Min length2

Characters and Unicode

Total characters63745
Distinct characters9
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 (%)
NO 24435
76.7%
SI 7424
 
23.3%
SE IGNORA 3
 
< 0.1%

Length

2025-03-14T09:36:48.962979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:49.197303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 24435
76.7%
si 7424
 
23.3%
se 3
 
< 0.1%
ignora 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 24438
38.3%
O 24438
38.3%
S 7427
 
11.7%
I 7427
 
11.7%
E 3
 
< 0.1%
3
 
< 0.1%
G 3
 
< 0.1%
R 3
 
< 0.1%
A 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63745
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 24438
38.3%
O 24438
38.3%
S 7427
 
11.7%
I 7427
 
11.7%
E 3
 
< 0.1%
3
 
< 0.1%
G 3
 
< 0.1%
R 3
 
< 0.1%
A 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63745
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 24438
38.3%
O 24438
38.3%
S 7427
 
11.7%
I 7427
 
11.7%
E 3
 
< 0.1%
3
 
< 0.1%
G 3
 
< 0.1%
R 3
 
< 0.1%
A 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63745
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 24438
38.3%
O 24438
38.3%
S 7427
 
11.7%
I 7427
 
11.7%
E 3
 
< 0.1%
3
 
< 0.1%
G 3
 
< 0.1%
R 3
 
< 0.1%
A 3
 
< 0.1%

Diarrea
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
28474 
SI
3386 
SE IGNORA
 
2

Length

Max length9
Median length2
Mean length2.0004394
Min length2

Characters and Unicode

Total characters63738
Distinct characters9
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 (%)
NO 28474
89.4%
SI 3386
 
10.6%
SE IGNORA 2
 
< 0.1%

Length

2025-03-14T09:36:49.334668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:49.743932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 28474
89.4%
si 3386
 
10.6%
se 2
 
< 0.1%
ignora 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 28476
44.7%
O 28476
44.7%
S 3388
 
5.3%
I 3388
 
5.3%
E 2
 
< 0.1%
2
 
< 0.1%
G 2
 
< 0.1%
R 2
 
< 0.1%
A 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63738
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 28476
44.7%
O 28476
44.7%
S 3388
 
5.3%
I 3388
 
5.3%
E 2
 
< 0.1%
2
 
< 0.1%
G 2
 
< 0.1%
R 2
 
< 0.1%
A 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63738
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 28476
44.7%
O 28476
44.7%
S 3388
 
5.3%
I 3388
 
5.3%
E 2
 
< 0.1%
2
 
< 0.1%
G 2
 
< 0.1%
R 2
 
< 0.1%
A 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63738
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 28476
44.7%
O 28476
44.7%
S 3388
 
5.3%
I 3388
 
5.3%
E 2
 
< 0.1%
2
 
< 0.1%
G 2
 
< 0.1%
R 2
 
< 0.1%
A 2
 
< 0.1%

Dolor torácico
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
27985 
SI
3858 
SE IGNORA
 
19

Length

Max length9
Median length2
Mean length2.0041743
Min length2

Characters and Unicode

Total characters63857
Distinct characters9
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 rowSI
5th rowNO

Common Values

ValueCountFrequency (%)
NO 27985
87.8%
SI 3858
 
12.1%
SE IGNORA 19
 
0.1%

Length

2025-03-14T09:36:50.049805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:50.225409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 27985
87.8%
si 3858
 
12.1%
se 19
 
0.1%
ignora 19
 
0.1%

Most occurring characters

ValueCountFrequency (%)
N 28004
43.9%
O 28004
43.9%
S 3877
 
6.1%
I 3877
 
6.1%
E 19
 
< 0.1%
19
 
< 0.1%
G 19
 
< 0.1%
R 19
 
< 0.1%
A 19
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63857
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 28004
43.9%
O 28004
43.9%
S 3877
 
6.1%
I 3877
 
6.1%
E 19
 
< 0.1%
19
 
< 0.1%
G 19
 
< 0.1%
R 19
 
< 0.1%
A 19
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63857
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 28004
43.9%
O 28004
43.9%
S 3877
 
6.1%
I 3877
 
6.1%
E 19
 
< 0.1%
19
 
< 0.1%
G 19
 
< 0.1%
R 19
 
< 0.1%
A 19
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63857
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 28004
43.9%
O 28004
43.9%
S 3877
 
6.1%
I 3877
 
6.1%
E 19
 
< 0.1%
19
 
< 0.1%
G 19
 
< 0.1%
R 19
 
< 0.1%
A 19
 
< 0.1%

Escalofríos
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
20305 
SI
11536 
SE IGNORA
 
21

Length

Max length9
Median length2
Mean length2.0046136
Min length2

Characters and Unicode

Total characters63871
Distinct characters9
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 rowSI
3rd rowNO
4th rowNO
5th rowSI

Common Values

ValueCountFrequency (%)
NO 20305
63.7%
SI 11536
36.2%
SE IGNORA 21
 
0.1%

Length

2025-03-14T09:36:50.420547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:50.637610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 20305
63.7%
si 11536
36.2%
se 21
 
0.1%
ignora 21
 
0.1%

Most occurring characters

ValueCountFrequency (%)
N 20326
31.8%
O 20326
31.8%
S 11557
18.1%
I 11557
18.1%
E 21
 
< 0.1%
21
 
< 0.1%
G 21
 
< 0.1%
R 21
 
< 0.1%
A 21
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63871
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 20326
31.8%
O 20326
31.8%
S 11557
18.1%
I 11557
18.1%
E 21
 
< 0.1%
21
 
< 0.1%
G 21
 
< 0.1%
R 21
 
< 0.1%
A 21
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63871
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 20326
31.8%
O 20326
31.8%
S 11557
18.1%
I 11557
18.1%
E 21
 
< 0.1%
21
 
< 0.1%
G 21
 
< 0.1%
R 21
 
< 0.1%
A 21
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63871
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 20326
31.8%
O 20326
31.8%
S 11557
18.1%
I 11557
18.1%
E 21
 
< 0.1%
21
 
< 0.1%
G 21
 
< 0.1%
R 21
 
< 0.1%
A 21
 
< 0.1%

Cefalea
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
SI
24510 
NO
7335 
SE IGNORA
 
17

Length

Max length9
Median length2
Mean length2.0037349
Min length2

Characters and Unicode

Total characters63843
Distinct characters9
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 rowSI
3rd rowNO
4th rowSI
5th rowSI

Common Values

ValueCountFrequency (%)
SI 24510
76.9%
NO 7335
 
23.0%
SE IGNORA 17
 
0.1%

Length

2025-03-14T09:36:50.833180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:50.957214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
si 24510
76.9%
no 7335
 
23.0%
se 17
 
0.1%
ignora 17
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S 24527
38.4%
I 24527
38.4%
N 7352
 
11.5%
O 7352
 
11.5%
E 17
 
< 0.1%
17
 
< 0.1%
G 17
 
< 0.1%
R 17
 
< 0.1%
A 17
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63843
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 24527
38.4%
I 24527
38.4%
N 7352
 
11.5%
O 7352
 
11.5%
E 17
 
< 0.1%
17
 
< 0.1%
G 17
 
< 0.1%
R 17
 
< 0.1%
A 17
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63843
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 24527
38.4%
I 24527
38.4%
N 7352
 
11.5%
O 7352
 
11.5%
E 17
 
< 0.1%
17
 
< 0.1%
G 17
 
< 0.1%
R 17
 
< 0.1%
A 17
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63843
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 24527
38.4%
I 24527
38.4%
N 7352
 
11.5%
O 7352
 
11.5%
E 17
 
< 0.1%
17
 
< 0.1%
G 17
 
< 0.1%
R 17
 
< 0.1%
A 17
 
< 0.1%

Mialgias
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
SI
17182 
NO
14663 
SE IGNORA
 
17

Length

Max length9
Median length2
Mean length2.0037349
Min length2

Characters and Unicode

Total characters63843
Distinct characters9
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 rowSI
3rd rowNO
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
SI 17182
53.9%
NO 14663
46.0%
SE IGNORA 17
 
0.1%

Length

2025-03-14T09:36:51.232938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:51.434215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
si 17182
53.9%
no 14663
46.0%
se 17
 
0.1%
ignora 17
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S 17199
26.9%
I 17199
26.9%
N 14680
23.0%
O 14680
23.0%
E 17
 
< 0.1%
17
 
< 0.1%
G 17
 
< 0.1%
R 17
 
< 0.1%
A 17
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63843
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 17199
26.9%
I 17199
26.9%
N 14680
23.0%
O 14680
23.0%
E 17
 
< 0.1%
17
 
< 0.1%
G 17
 
< 0.1%
R 17
 
< 0.1%
A 17
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63843
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 17199
26.9%
I 17199
26.9%
N 14680
23.0%
O 14680
23.0%
E 17
 
< 0.1%
17
 
< 0.1%
G 17
 
< 0.1%
R 17
 
< 0.1%
A 17
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63843
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 17199
26.9%
I 17199
26.9%
N 14680
23.0%
O 14680
23.0%
E 17
 
< 0.1%
17
 
< 0.1%
G 17
 
< 0.1%
R 17
 
< 0.1%
A 17
 
< 0.1%

Artralgias
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
16897 
SI
14947 
SE IGNORA
 
18

Length

Max length9
Median length2
Mean length2.0039546
Min length2

Characters and Unicode

Total characters63850
Distinct characters9
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 rowSI
3rd rowNO
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
NO 16897
53.0%
SI 14947
46.9%
SE IGNORA 18
 
0.1%

Length

2025-03-14T09:36:51.651417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:51.870998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 16897
53.0%
si 14947
46.9%
se 18
 
0.1%
ignora 18
 
0.1%

Most occurring characters

ValueCountFrequency (%)
N 16915
26.5%
O 16915
26.5%
S 14965
23.4%
I 14965
23.4%
E 18
 
< 0.1%
18
 
< 0.1%
G 18
 
< 0.1%
R 18
 
< 0.1%
A 18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63850
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 16915
26.5%
O 16915
26.5%
S 14965
23.4%
I 14965
23.4%
E 18
 
< 0.1%
18
 
< 0.1%
G 18
 
< 0.1%
R 18
 
< 0.1%
A 18
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63850
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 16915
26.5%
O 16915
26.5%
S 14965
23.4%
I 14965
23.4%
E 18
 
< 0.1%
18
 
< 0.1%
G 18
 
< 0.1%
R 18
 
< 0.1%
A 18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63850
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 16915
26.5%
O 16915
26.5%
S 14965
23.4%
I 14965
23.4%
E 18
 
< 0.1%
18
 
< 0.1%
G 18
 
< 0.1%
R 18
 
< 0.1%
A 18
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
21073 
SI
10785 
SE IGNORA
 
4

Length

Max length9
Median length2
Mean length2.0008788
Min length2

Characters and Unicode

Total characters63752
Distinct characters9
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 rowSI
3rd rowNO
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
NO 21073
66.1%
SI 10785
33.8%
SE IGNORA 4
 
< 0.1%

Length

2025-03-14T09:36:52.113194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:52.300827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 21073
66.1%
si 10785
33.8%
se 4
 
< 0.1%
ignora 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 21077
33.1%
O 21077
33.1%
S 10789
16.9%
I 10789
16.9%
E 4
 
< 0.1%
4
 
< 0.1%
G 4
 
< 0.1%
R 4
 
< 0.1%
A 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63752
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 21077
33.1%
O 21077
33.1%
S 10789
16.9%
I 10789
16.9%
E 4
 
< 0.1%
4
 
< 0.1%
G 4
 
< 0.1%
R 4
 
< 0.1%
A 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63752
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 21077
33.1%
O 21077
33.1%
S 10789
16.9%
I 10789
16.9%
E 4
 
< 0.1%
4
 
< 0.1%
G 4
 
< 0.1%
R 4
 
< 0.1%
A 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63752
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 21077
33.1%
O 21077
33.1%
S 10789
16.9%
I 10789
16.9%
E 4
 
< 0.1%
4
 
< 0.1%
G 4
 
< 0.1%
R 4
 
< 0.1%
A 4
 
< 0.1%

Rinorrea
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
SI
18417 
NO
13443 
SE IGNORA
 
2

Length

Max length9
Median length2
Mean length2.0004394
Min length2

Characters and Unicode

Total characters63738
Distinct characters9
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 rowSI
3rd rowNO
4th rowSI
5th rowNO

Common Values

ValueCountFrequency (%)
SI 18417
57.8%
NO 13443
42.2%
SE IGNORA 2
 
< 0.1%

Length

2025-03-14T09:36:52.493341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:52.670061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
si 18417
57.8%
no 13443
42.2%
se 2
 
< 0.1%
ignora 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
S 18419
28.9%
I 18419
28.9%
N 13445
21.1%
O 13445
21.1%
E 2
 
< 0.1%
2
 
< 0.1%
G 2
 
< 0.1%
R 2
 
< 0.1%
A 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63738
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 18419
28.9%
I 18419
28.9%
N 13445
21.1%
O 13445
21.1%
E 2
 
< 0.1%
2
 
< 0.1%
G 2
 
< 0.1%
R 2
 
< 0.1%
A 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63738
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 18419
28.9%
I 18419
28.9%
N 13445
21.1%
O 13445
21.1%
E 2
 
< 0.1%
2
 
< 0.1%
G 2
 
< 0.1%
R 2
 
< 0.1%
A 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63738
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 18419
28.9%
I 18419
28.9%
N 13445
21.1%
O 13445
21.1%
E 2
 
< 0.1%
2
 
< 0.1%
G 2
 
< 0.1%
R 2
 
< 0.1%
A 2
 
< 0.1%

Polipnea
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
31228 
SI
 
633
SE IGNORA
 
1

Length

Max length9
Median length2
Mean length2.0002197
Min length2

Characters and Unicode

Total characters63731
Distinct characters9
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

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
NO 31228
98.0%
SI 633
 
2.0%
SE IGNORA 1
 
< 0.1%

Length

2025-03-14T09:36:52.930759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:53.123407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 31228
98.0%
si 633
 
2.0%
se 1
 
< 0.1%
ignora 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 31229
49.0%
O 31229
49.0%
S 634
 
1.0%
I 634
 
1.0%
E 1
 
< 0.1%
1
 
< 0.1%
G 1
 
< 0.1%
R 1
 
< 0.1%
A 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63731
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 31229
49.0%
O 31229
49.0%
S 634
 
1.0%
I 634
 
1.0%
E 1
 
< 0.1%
1
 
< 0.1%
G 1
 
< 0.1%
R 1
 
< 0.1%
A 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63731
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 31229
49.0%
O 31229
49.0%
S 634
 
1.0%
I 634
 
1.0%
E 1
 
< 0.1%
1
 
< 0.1%
G 1
 
< 0.1%
R 1
 
< 0.1%
A 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63731
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 31229
49.0%
O 31229
49.0%
S 634
 
1.0%
I 634
 
1.0%
E 1
 
< 0.1%
1
 
< 0.1%
G 1
 
< 0.1%
R 1
 
< 0.1%
A 1
 
< 0.1%

Vómito
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
29946 
SI
 
1914
SE IGNORA
 
2

Length

Max length9
Median length2
Mean length2.0004394
Min length2

Characters and Unicode

Total characters63738
Distinct characters9
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 (%)
NO 29946
94.0%
SI 1914
 
6.0%
SE IGNORA 2
 
< 0.1%

Length

2025-03-14T09:36:53.270252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:53.374492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 29946
94.0%
si 1914
 
6.0%
se 2
 
< 0.1%
ignora 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 29948
47.0%
O 29948
47.0%
S 1916
 
3.0%
I 1916
 
3.0%
E 2
 
< 0.1%
2
 
< 0.1%
G 2
 
< 0.1%
R 2
 
< 0.1%
A 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63738
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 29948
47.0%
O 29948
47.0%
S 1916
 
3.0%
I 1916
 
3.0%
E 2
 
< 0.1%
2
 
< 0.1%
G 2
 
< 0.1%
R 2
 
< 0.1%
A 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63738
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 29948
47.0%
O 29948
47.0%
S 1916
 
3.0%
I 1916
 
3.0%
E 2
 
< 0.1%
2
 
< 0.1%
G 2
 
< 0.1%
R 2
 
< 0.1%
A 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63738
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 29948
47.0%
O 29948
47.0%
S 1916
 
3.0%
I 1916
 
3.0%
E 2
 
< 0.1%
2
 
< 0.1%
G 2
 
< 0.1%
R 2
 
< 0.1%
A 2
 
< 0.1%

Dolor abdminal
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
29245 
SI
 
2600
SE IGNORA
 
17

Length

Max length9
Median length2
Mean length2.0037349
Min length2

Characters and Unicode

Total characters63843
Distinct characters9
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 rowSI

Common Values

ValueCountFrequency (%)
NO 29245
91.8%
SI 2600
 
8.2%
SE IGNORA 17
 
0.1%

Length

2025-03-14T09:36:53.502919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:53.607103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 29245
91.7%
si 2600
 
8.2%
se 17
 
0.1%
ignora 17
 
0.1%

Most occurring characters

ValueCountFrequency (%)
N 29262
45.8%
O 29262
45.8%
S 2617
 
4.1%
I 2617
 
4.1%
E 17
 
< 0.1%
17
 
< 0.1%
G 17
 
< 0.1%
R 17
 
< 0.1%
A 17
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63843
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 29262
45.8%
O 29262
45.8%
S 2617
 
4.1%
I 2617
 
4.1%
E 17
 
< 0.1%
17
 
< 0.1%
G 17
 
< 0.1%
R 17
 
< 0.1%
A 17
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63843
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 29262
45.8%
O 29262
45.8%
S 2617
 
4.1%
I 2617
 
4.1%
E 17
 
< 0.1%
17
 
< 0.1%
G 17
 
< 0.1%
R 17
 
< 0.1%
A 17
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63843
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 29262
45.8%
O 29262
45.8%
S 2617
 
4.1%
I 2617
 
4.1%
E 17
 
< 0.1%
17
 
< 0.1%
G 17
 
< 0.1%
R 17
 
< 0.1%
A 17
 
< 0.1%

Conjuntivitis
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
28390 
SI
3470 
SE IGNORA
 
2

Length

Max length9
Median length2
Mean length2.0004394
Min length2

Characters and Unicode

Total characters63738
Distinct characters9
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 rowSI

Common Values

ValueCountFrequency (%)
NO 28390
89.1%
SI 3470
 
10.9%
SE IGNORA 2
 
< 0.1%

Length

2025-03-14T09:36:53.740721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:53.843793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 28390
89.1%
si 3470
 
10.9%
se 2
 
< 0.1%
ignora 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 28392
44.5%
O 28392
44.5%
S 3472
 
5.4%
I 3472
 
5.4%
E 2
 
< 0.1%
2
 
< 0.1%
G 2
 
< 0.1%
R 2
 
< 0.1%
A 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63738
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 28392
44.5%
O 28392
44.5%
S 3472
 
5.4%
I 3472
 
5.4%
E 2
 
< 0.1%
2
 
< 0.1%
G 2
 
< 0.1%
R 2
 
< 0.1%
A 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63738
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 28392
44.5%
O 28392
44.5%
S 3472
 
5.4%
I 3472
 
5.4%
E 2
 
< 0.1%
2
 
< 0.1%
G 2
 
< 0.1%
R 2
 
< 0.1%
A 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63738
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 28392
44.5%
O 28392
44.5%
S 3472
 
5.4%
I 3472
 
5.4%
E 2
 
< 0.1%
2
 
< 0.1%
G 2
 
< 0.1%
R 2
 
< 0.1%
A 2
 
< 0.1%

Cianosis
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
28406 
SI
3454 
SE IGNORA
 
2

Length

Max length9
Median length2
Mean length2.0004394
Min length2

Characters and Unicode

Total characters63738
Distinct characters9
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 rowSI

Common Values

ValueCountFrequency (%)
NO 28406
89.2%
SI 3454
 
10.8%
SE IGNORA 2
 
< 0.1%

Length

2025-03-14T09:36:54.005860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:54.116339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 28406
89.1%
si 3454
 
10.8%
se 2
 
< 0.1%
ignora 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 28408
44.6%
O 28408
44.6%
S 3456
 
5.4%
I 3456
 
5.4%
E 2
 
< 0.1%
2
 
< 0.1%
G 2
 
< 0.1%
R 2
 
< 0.1%
A 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63738
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 28408
44.6%
O 28408
44.6%
S 3456
 
5.4%
I 3456
 
5.4%
E 2
 
< 0.1%
2
 
< 0.1%
G 2
 
< 0.1%
R 2
 
< 0.1%
A 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63738
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 28408
44.6%
O 28408
44.6%
S 3456
 
5.4%
I 3456
 
5.4%
E 2
 
< 0.1%
2
 
< 0.1%
G 2
 
< 0.1%
R 2
 
< 0.1%
A 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63738
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 28408
44.6%
O 28408
44.6%
S 3456
 
5.4%
I 3456
 
5.4%
E 2
 
< 0.1%
2
 
< 0.1%
G 2
 
< 0.1%
R 2
 
< 0.1%
A 2
 
< 0.1%

Inicio súbito
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
18886 
SI
12973 
SE IGNORA
 
3

Length

Max length9
Median length2
Mean length2.0006591
Min length2

Characters and Unicode

Total characters63745
Distinct characters9
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 rowSI
3rd rowNO
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
NO 18886
59.3%
SI 12973
40.7%
SE IGNORA 3
 
< 0.1%

Length

2025-03-14T09:36:54.254972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:54.365663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 18886
59.3%
si 12973
40.7%
se 3
 
< 0.1%
ignora 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 18889
29.6%
O 18889
29.6%
S 12976
20.4%
I 12976
20.4%
E 3
 
< 0.1%
3
 
< 0.1%
G 3
 
< 0.1%
R 3
 
< 0.1%
A 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63745
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 18889
29.6%
O 18889
29.6%
S 12976
20.4%
I 12976
20.4%
E 3
 
< 0.1%
3
 
< 0.1%
G 3
 
< 0.1%
R 3
 
< 0.1%
A 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63745
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 18889
29.6%
O 18889
29.6%
S 12976
20.4%
I 12976
20.4%
E 3
 
< 0.1%
3
 
< 0.1%
G 3
 
< 0.1%
R 3
 
< 0.1%
A 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63745
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 18889
29.6%
O 18889
29.6%
S 12976
20.4%
I 12976
20.4%
E 3
 
< 0.1%
3
 
< 0.1%
G 3
 
< 0.1%
R 3
 
< 0.1%
A 3
 
< 0.1%

Anosmia
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
30087 
SI
 
1565
SE IGNORA
 
210

Length

Max length9
Median length2
Mean length2.0461365
Min length2

Characters and Unicode

Total characters65194
Distinct characters9
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 rowNO
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
NO 30087
94.4%
SI 1565
 
4.9%
SE IGNORA 210
 
0.7%

Length

2025-03-14T09:36:54.499101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:54.600684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 30087
93.8%
si 1565
 
4.9%
se 210
 
0.7%
ignora 210
 
0.7%

Most occurring characters

ValueCountFrequency (%)
N 30297
46.5%
O 30297
46.5%
S 1775
 
2.7%
I 1775
 
2.7%
E 210
 
0.3%
210
 
0.3%
G 210
 
0.3%
R 210
 
0.3%
A 210
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65194
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 30297
46.5%
O 30297
46.5%
S 1775
 
2.7%
I 1775
 
2.7%
E 210
 
0.3%
210
 
0.3%
G 210
 
0.3%
R 210
 
0.3%
A 210
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65194
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 30297
46.5%
O 30297
46.5%
S 1775
 
2.7%
I 1775
 
2.7%
E 210
 
0.3%
210
 
0.3%
G 210
 
0.3%
R 210
 
0.3%
A 210
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65194
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 30297
46.5%
O 30297
46.5%
S 1775
 
2.7%
I 1775
 
2.7%
E 210
 
0.3%
210
 
0.3%
G 210
 
0.3%
R 210
 
0.3%
A 210
 
0.3%

Disgeusia
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
30057 
SI
 
1599
SE IGNORA
 
206

Length

Max length9
Median length2
Mean length2.0452577
Min length2

Characters and Unicode

Total characters65166
Distinct characters9
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 (%)
NO 30057
94.3%
SI 1599
 
5.0%
SE IGNORA 206
 
0.6%

Length

2025-03-14T09:36:54.728163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:54.829983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 30057
93.7%
si 1599
 
5.0%
se 206
 
0.6%
ignora 206
 
0.6%

Most occurring characters

ValueCountFrequency (%)
N 30263
46.4%
O 30263
46.4%
S 1805
 
2.8%
I 1805
 
2.8%
E 206
 
0.3%
206
 
0.3%
G 206
 
0.3%
R 206
 
0.3%
A 206
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65166
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 30263
46.4%
O 30263
46.4%
S 1805
 
2.8%
I 1805
 
2.8%
E 206
 
0.3%
206
 
0.3%
G 206
 
0.3%
R 206
 
0.3%
A 206
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65166
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 30263
46.4%
O 30263
46.4%
S 1805
 
2.8%
I 1805
 
2.8%
E 206
 
0.3%
206
 
0.3%
G 206
 
0.3%
R 206
 
0.3%
A 206
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65166
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 30263
46.4%
O 30263
46.4%
S 1805
 
2.8%
I 1805
 
2.8%
E 206
 
0.3%
206
 
0.3%
G 206
 
0.3%
R 206
 
0.3%
A 206
 
0.3%

Diabetes
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
29947 
SI
 
1901
SE IGNORA
 
14

Length

Max length9
Median length2
Mean length2.0030758
Min length2

Characters and Unicode

Total characters63822
Distinct characters9
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 rowSI
3rd rowNO
4th rowNO
5th rowSI

Common Values

ValueCountFrequency (%)
NO 29947
94.0%
SI 1901
 
6.0%
SE IGNORA 14
 
< 0.1%

Length

2025-03-14T09:36:54.954772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:55.067170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 29947
93.9%
si 1901
 
6.0%
se 14
 
< 0.1%
ignora 14
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 29961
46.9%
O 29961
46.9%
S 1915
 
3.0%
I 1915
 
3.0%
E 14
 
< 0.1%
14
 
< 0.1%
G 14
 
< 0.1%
R 14
 
< 0.1%
A 14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63822
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 29961
46.9%
O 29961
46.9%
S 1915
 
3.0%
I 1915
 
3.0%
E 14
 
< 0.1%
14
 
< 0.1%
G 14
 
< 0.1%
R 14
 
< 0.1%
A 14
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63822
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 29961
46.9%
O 29961
46.9%
S 1915
 
3.0%
I 1915
 
3.0%
E 14
 
< 0.1%
14
 
< 0.1%
G 14
 
< 0.1%
R 14
 
< 0.1%
A 14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63822
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 29961
46.9%
O 29961
46.9%
S 1915
 
3.0%
I 1915
 
3.0%
E 14
 
< 0.1%
14
 
< 0.1%
G 14
 
< 0.1%
R 14
 
< 0.1%
A 14
 
< 0.1%

EPOC
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
31682 
SI
 
169
SE IGNORA
 
11

Length

Max length9
Median length2
Mean length2.0024167
Min length2

Characters and Unicode

Total characters63801
Distinct characters9
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 (%)
NO 31682
99.4%
SI 169
 
0.5%
SE IGNORA 11
 
< 0.1%

Length

2025-03-14T09:36:55.195803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:55.301945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 31682
99.4%
si 169
 
0.5%
se 11
 
< 0.1%
ignora 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 31693
49.7%
O 31693
49.7%
S 180
 
0.3%
I 180
 
0.3%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63801
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 31693
49.7%
O 31693
49.7%
S 180
 
0.3%
I 180
 
0.3%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63801
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 31693
49.7%
O 31693
49.7%
S 180
 
0.3%
I 180
 
0.3%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63801
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 31693
49.7%
O 31693
49.7%
S 180
 
0.3%
I 180
 
0.3%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Asma
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
30959 
SI
 
892
SE IGNORA
 
11

Length

Max length9
Median length2
Mean length2.0024167
Min length2

Characters and Unicode

Total characters63801
Distinct characters9
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 (%)
NO 30959
97.2%
SI 892
 
2.8%
SE IGNORA 11
 
< 0.1%

Length

2025-03-14T09:36:55.439919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:55.555423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 30959
97.1%
si 892
 
2.8%
se 11
 
< 0.1%
ignora 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 30970
48.5%
O 30970
48.5%
S 903
 
1.4%
I 903
 
1.4%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63801
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 30970
48.5%
O 30970
48.5%
S 903
 
1.4%
I 903
 
1.4%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63801
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 30970
48.5%
O 30970
48.5%
S 903
 
1.4%
I 903
 
1.4%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63801
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 30970
48.5%
O 30970
48.5%
S 903
 
1.4%
I 903
 
1.4%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Inmunosupresión
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
31748 
SI
 
103
SE IGNORA
 
11

Length

Max length9
Median length2
Mean length2.0024167
Min length2

Characters and Unicode

Total characters63801
Distinct characters9
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 (%)
NO 31748
99.6%
SI 103
 
0.3%
SE IGNORA 11
 
< 0.1%

Length

2025-03-14T09:36:55.689062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:55.790351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 31748
99.6%
si 103
 
0.3%
se 11
 
< 0.1%
ignora 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 31759
49.8%
O 31759
49.8%
S 114
 
0.2%
I 114
 
0.2%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63801
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 31759
49.8%
O 31759
49.8%
S 114
 
0.2%
I 114
 
0.2%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63801
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 31759
49.8%
O 31759
49.8%
S 114
 
0.2%
I 114
 
0.2%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63801
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 31759
49.8%
O 31759
49.8%
S 114
 
0.2%
I 114
 
0.2%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Hipertensión
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
28952 
SI
2899 
SE IGNORA
 
11

Length

Max length9
Median length2
Mean length2.0024167
Min length2

Characters and Unicode

Total characters63801
Distinct characters9
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 rowSI

Common Values

ValueCountFrequency (%)
NO 28952
90.9%
SI 2899
 
9.1%
SE IGNORA 11
 
< 0.1%

Length

2025-03-14T09:36:55.916038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:56.032951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 28952
90.8%
si 2899
 
9.1%
se 11
 
< 0.1%
ignora 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 28963
45.4%
O 28963
45.4%
S 2910
 
4.6%
I 2910
 
4.6%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63801
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 28963
45.4%
O 28963
45.4%
S 2910
 
4.6%
I 2910
 
4.6%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63801
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 28963
45.4%
O 28963
45.4%
S 2910
 
4.6%
I 2910
 
4.6%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63801
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 28963
45.4%
O 28963
45.4%
S 2910
 
4.6%
I 2910
 
4.6%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

VIH/SIDA
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
31757 
SI
 
94
SE IGNORA
 
11

Length

Max length9
Median length2
Mean length2.0024167
Min length2

Characters and Unicode

Total characters63801
Distinct characters9
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 (%)
NO 31757
99.7%
SI 94
 
0.3%
SE IGNORA 11
 
< 0.1%

Length

2025-03-14T09:36:56.180811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:56.283226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 31757
99.6%
si 94
 
0.3%
se 11
 
< 0.1%
ignora 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 31768
49.8%
O 31768
49.8%
S 105
 
0.2%
I 105
 
0.2%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63801
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 31768
49.8%
O 31768
49.8%
S 105
 
0.2%
I 105
 
0.2%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63801
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 31768
49.8%
O 31768
49.8%
S 105
 
0.2%
I 105
 
0.2%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63801
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 31768
49.8%
O 31768
49.8%
S 105
 
0.2%
I 105
 
0.2%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Otra condición
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
31372 
SI
 
478
SE IGNORA
 
12

Length

Max length9
Median length2
Mean length2.0026364
Min length2

Characters and Unicode

Total characters63808
Distinct characters9
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 (%)
NO 31372
98.5%
SI 478
 
1.5%
SE IGNORA 12
 
< 0.1%

Length

2025-03-14T09:36:56.412714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:56.520381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 31372
98.4%
si 478
 
1.5%
se 12
 
< 0.1%
ignora 12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 31384
49.2%
O 31384
49.2%
S 490
 
0.8%
I 490
 
0.8%
E 12
 
< 0.1%
12
 
< 0.1%
G 12
 
< 0.1%
R 12
 
< 0.1%
A 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63808
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 31384
49.2%
O 31384
49.2%
S 490
 
0.8%
I 490
 
0.8%
E 12
 
< 0.1%
12
 
< 0.1%
G 12
 
< 0.1%
R 12
 
< 0.1%
A 12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63808
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 31384
49.2%
O 31384
49.2%
S 490
 
0.8%
I 490
 
0.8%
E 12
 
< 0.1%
12
 
< 0.1%
G 12
 
< 0.1%
R 12
 
< 0.1%
A 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63808
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 31384
49.2%
O 31384
49.2%
S 490
 
0.8%
I 490
 
0.8%
E 12
 
< 0.1%
12
 
< 0.1%
G 12
 
< 0.1%
R 12
 
< 0.1%
A 12
 
< 0.1%

Enfermedad cardiaca
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
31633 
SI
 
218
SE IGNORA
 
11

Length

Max length9
Median length2
Mean length2.0024167
Min length2

Characters and Unicode

Total characters63801
Distinct characters9
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 (%)
NO 31633
99.3%
SI 218
 
0.7%
SE IGNORA 11
 
< 0.1%

Length

2025-03-14T09:36:56.647244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:56.752193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 31633
99.2%
si 218
 
0.7%
se 11
 
< 0.1%
ignora 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 31644
49.6%
O 31644
49.6%
S 229
 
0.4%
I 229
 
0.4%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63801
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 31644
49.6%
O 31644
49.6%
S 229
 
0.4%
I 229
 
0.4%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63801
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 31644
49.6%
O 31644
49.6%
S 229
 
0.4%
I 229
 
0.4%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63801
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 31644
49.6%
O 31644
49.6%
S 229
 
0.4%
I 229
 
0.4%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Obesidad
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
28807 
SI
3044 
SE IGNORA
 
11

Length

Max length9
Median length2
Mean length2.0024167
Min length2

Characters and Unicode

Total characters63801
Distinct characters9
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 rowSI
3rd rowNO
4th rowNO
5th rowSI

Common Values

ValueCountFrequency (%)
NO 28807
90.4%
SI 3044
 
9.6%
SE IGNORA 11
 
< 0.1%

Length

2025-03-14T09:36:56.880251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:56.983277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 28807
90.4%
si 3044
 
9.6%
se 11
 
< 0.1%
ignora 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 28818
45.2%
O 28818
45.2%
S 3055
 
4.8%
I 3055
 
4.8%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63801
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 28818
45.2%
O 28818
45.2%
S 3055
 
4.8%
I 3055
 
4.8%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63801
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 28818
45.2%
O 28818
45.2%
S 3055
 
4.8%
I 3055
 
4.8%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63801
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 28818
45.2%
O 28818
45.2%
S 3055
 
4.8%
I 3055
 
4.8%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Insuficiencia renal crónica
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
31545 
SI
 
305
SE IGNORA
 
12

Length

Max length9
Median length2
Mean length2.0026364
Min length2

Characters and Unicode

Total characters63808
Distinct characters9
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 (%)
NO 31545
99.0%
SI 305
 
1.0%
SE IGNORA 12
 
< 0.1%

Length

2025-03-14T09:36:57.145376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:57.244385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 31545
99.0%
si 305
 
1.0%
se 12
 
< 0.1%
ignora 12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 31557
49.5%
O 31557
49.5%
S 317
 
0.5%
I 317
 
0.5%
E 12
 
< 0.1%
12
 
< 0.1%
G 12
 
< 0.1%
R 12
 
< 0.1%
A 12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63808
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 31557
49.5%
O 31557
49.5%
S 317
 
0.5%
I 317
 
0.5%
E 12
 
< 0.1%
12
 
< 0.1%
G 12
 
< 0.1%
R 12
 
< 0.1%
A 12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63808
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 31557
49.5%
O 31557
49.5%
S 317
 
0.5%
I 317
 
0.5%
E 12
 
< 0.1%
12
 
< 0.1%
G 12
 
< 0.1%
R 12
 
< 0.1%
A 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63808
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 31557
49.5%
O 31557
49.5%
S 317
 
0.5%
I 317
 
0.5%
E 12
 
< 0.1%
12
 
< 0.1%
G 12
 
< 0.1%
R 12
 
< 0.1%
A 12
 
< 0.1%

Tabaquismo
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
NO
30297 
SI
 
1554
SE IGNORA
 
11

Length

Max length9
Median length2
Mean length2.0024167
Min length2

Characters and Unicode

Total characters63801
Distinct characters9
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 rowNO

Common Values

ValueCountFrequency (%)
NO 30297
95.1%
SI 1554
 
4.9%
SE IGNORA 11
 
< 0.1%

Length

2025-03-14T09:36:57.381032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:57.830676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 30297
95.1%
si 1554
 
4.9%
se 11
 
< 0.1%
ignora 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 30308
47.5%
O 30308
47.5%
S 1565
 
2.5%
I 1565
 
2.5%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63801
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 30308
47.5%
O 30308
47.5%
S 1565
 
2.5%
I 1565
 
2.5%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63801
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 30308
47.5%
O 30308
47.5%
S 1565
 
2.5%
I 1565
 
2.5%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63801
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 30308
47.5%
O 30308
47.5%
S 1565
 
2.5%
I 1565
 
2.5%
E 11
 
< 0.1%
11
 
< 0.1%
G 11
 
< 0.1%
R 11
 
< 0.1%
A 11
 
< 0.1%

Vacuna contra COVID19
Categorical

Imbalance  Missing 

Distinct3
Distinct (%)< 0.1%
Missing20788
Missing (%)65.2%
Memory size2.0 MiB
COMPLETA
9960 
INCOMPLETA
1077 
SE IGNORA
 
37

Length

Max length10
Median length8
Mean length8.1978508
Min length8

Characters and Unicode

Total characters90783
Distinct characters14
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 rowCOMPLETA
2nd rowCOMPLETA
3rd rowCOMPLETA
4th rowCOMPLETA
5th rowCOMPLETA

Common Values

ValueCountFrequency (%)
COMPLETA 9960
31.3%
INCOMPLETA 1077
 
3.4%
SE IGNORA 37
 
0.1%
(Missing) 20788
65.2%

Length

2025-03-14T09:36:57.957918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:58.071729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
completa 9960
89.6%
incompleta 1077
 
9.7%
se 37
 
0.3%
ignora 37
 
0.3%

Most occurring characters

ValueCountFrequency (%)
O 11074
12.2%
E 11074
12.2%
A 11074
12.2%
C 11037
12.2%
M 11037
12.2%
P 11037
12.2%
L 11037
12.2%
T 11037
12.2%
I 1114
 
1.2%
N 1114
 
1.2%
Other values (4) 148
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90783
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 11074
12.2%
E 11074
12.2%
A 11074
12.2%
C 11037
12.2%
M 11037
12.2%
P 11037
12.2%
L 11037
12.2%
T 11037
12.2%
I 1114
 
1.2%
N 1114
 
1.2%
Other values (4) 148
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90783
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 11074
12.2%
E 11074
12.2%
A 11074
12.2%
C 11037
12.2%
M 11037
12.2%
P 11037
12.2%
L 11037
12.2%
T 11037
12.2%
I 1114
 
1.2%
N 1114
 
1.2%
Other values (4) 148
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90783
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 11074
12.2%
E 11074
12.2%
A 11074
12.2%
C 11037
12.2%
M 11037
12.2%
P 11037
12.2%
L 11037
12.2%
T 11037
12.2%
I 1114
 
1.2%
N 1114
 
1.2%
Other values (4) 148
 
0.2%

Marca
Categorical

Missing 

Distinct11
Distinct (%)0.1%
Missing20788
Missing (%)65.2%
Memory size2.0 MiB
AstraZeneca
5982 
Pfizer BioNTech
3136 
CanSino
1109 
Sinovac
 
500
Se desconoce
 
146
Other values (6)
 
201

Length

Max length27
Median length11
Mean length11.569442
Min length7

Characters and Unicode

Total characters128120
Distinct characters34
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAstraZeneca
2nd rowAstraZeneca
3rd rowPfizer BioNTech
4th rowAstraZeneca
5th rowAstraZeneca

Common Values

ValueCountFrequency (%)
AstraZeneca 5982
 
18.8%
Pfizer BioNTech 3136
 
9.8%
CanSino 1109
 
3.5%
Sinovac 500
 
1.6%
Se desconoce 146
 
0.5%
Sinopharma 119
 
0.4%
Moderna 32
 
0.1%
Janssen (Johnson & Johnson) 24
 
0.1%
Novavax 14
 
< 0.1%
Gamaleya 11
 
< 0.1%
(Missing) 20788
65.2%

Length

2025-03-14T09:36:58.226241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
astrazeneca 5982
41.5%
pfizer 3137
21.7%
biontech 3137
21.7%
cansino 1109
 
7.7%
sinovac 500
 
3.5%
se 146
 
1.0%
desconoce 146
 
1.0%
sinopharma 119
 
0.8%
johnson 48
 
0.3%
moderna 32
 
0.2%
Other values (4) 73
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e 18743
14.6%
a 13928
 
10.9%
c 9911
 
7.7%
r 9270
 
7.2%
n 9141
 
7.1%
i 8002
 
6.2%
s 6224
 
4.9%
A 5982
 
4.7%
t 5982
 
4.7%
Z 5982
 
4.7%
Other values (24) 34955
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 128120
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 18743
14.6%
a 13928
 
10.9%
c 9911
 
7.7%
r 9270
 
7.2%
n 9141
 
7.1%
i 8002
 
6.2%
s 6224
 
4.9%
A 5982
 
4.7%
t 5982
 
4.7%
Z 5982
 
4.7%
Other values (24) 34955
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 128120
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 18743
14.6%
a 13928
 
10.9%
c 9911
 
7.7%
r 9270
 
7.2%
n 9141
 
7.1%
i 8002
 
6.2%
s 6224
 
4.9%
A 5982
 
4.7%
t 5982
 
4.7%
Z 5982
 
4.7%
Other values (24) 34955
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 128120
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 18743
14.6%
a 13928
 
10.9%
c 9911
 
7.7%
r 9270
 
7.2%
n 9141
 
7.1%
i 8002
 
6.2%
s 6224
 
4.9%
A 5982
 
4.7%
t 5982
 
4.7%
Z 5982
 
4.7%
Other values (24) 34955
27.3%
Distinct445
Distinct (%)4.0%
Missing20790
Missing (%)65.3%
Memory size249.1 KiB
Minimum2021-01-01 00:00:00
Maximum2022-12-28 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-14T09:36:58.412120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-14T09:36:58.612170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Ocupación
Categorical

Distinct18
Distinct (%)0.1%
Missing141
Missing (%)0.4%
Memory size2.0 MiB
EMPLEADOS
16145 
ESTUDIANTES
3099 
HOGAR
2636 
OTROS
2470 
DESEMPLEADOS
 
1403
Other values (13)
5968 

Length

Max length46
Median length9
Mean length9.8094007
Min length5

Characters and Unicode

Total characters311164
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 rowCAMPESINOS
2nd rowOTROS
3rd rowMEDICOS
4th rowEMPLEADOS
5th rowEMPLEADOS

Common Values

ValueCountFrequency (%)
EMPLEADOS 16145
50.7%
ESTUDIANTES 3099
 
9.7%
HOGAR 2636
 
8.3%
OTROS 2470
 
7.8%
DESEMPLEADOS 1403
 
4.4%
MAESTROS 1310
 
4.1%
ENFERMERAS 1064
 
3.3%
MEDICOS 721
 
2.3%
OTROS TRABAJADORES DE LA SALUD 713
 
2.2%
JUBILADO / PENSIONADO 667
 
2.1%
Other values (8) 1493
 
4.7%

Length

2025-03-14T09:36:58.795640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
empleados 16145
42.7%
otros 3642
 
9.6%
estudiantes 3099
 
8.2%
hogar 2636
 
7.0%
desempleados 1403
 
3.7%
maestros 1310
 
3.5%
enfermeras 1064
 
2.8%
de 986
 
2.6%
medicos 721
 
1.9%
trabajadores 713
 
1.9%
Other values (20) 6053
 
16.0%

Most occurring characters

ValueCountFrequency (%)
E 52851
17.0%
S 38928
12.5%
O 35301
11.3%
A 32499
10.4%
D 26777
8.6%
M 21667
7.0%
L 19926
 
6.4%
P 19084
 
6.1%
T 13030
 
4.2%
R 12938
 
4.2%
Other values (11) 38163
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 311164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 52851
17.0%
S 38928
12.5%
O 35301
11.3%
A 32499
10.4%
D 26777
8.6%
M 21667
7.0%
L 19926
 
6.4%
P 19084
 
6.1%
T 13030
 
4.2%
R 12938
 
4.2%
Other values (11) 38163
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 311164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 52851
17.0%
S 38928
12.5%
O 35301
11.3%
A 32499
10.4%
D 26777
8.6%
M 21667
7.0%
L 19926
 
6.4%
P 19084
 
6.1%
T 13030
 
4.2%
R 12938
 
4.2%
Other values (11) 38163
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 311164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 52851
17.0%
S 38928
12.5%
O 35301
11.3%
A 32499
10.4%
D 26777
8.6%
M 21667
7.0%
L 19926
 
6.4%
P 19084
 
6.1%
T 13030
 
4.2%
R 12938
 
4.2%
Other values (11) 38163
12.3%

REFUERZO
Categorical

High correlation  Imbalance  Missing 

Distinct7
Distinct (%)0.4%
Missing29963
Missing (%)94.0%
Memory size2.0 MiB
AstraZeneca
1751 
Se desconoce
 
106
Pfizer BioNTech
 
27
Moderna
 
8
CanSino
 
5
Other values (2)
 
2

Length

Max length27
Median length11
Mean length11.091627
Min length7

Characters and Unicode

Total characters21063
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

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
AstraZeneca 1751
 
5.5%
Se desconoce 106
 
0.3%
Pfizer BioNTech 27
 
0.1%
Moderna 8
 
< 0.1%
CanSino 5
 
< 0.1%
Janssen (Johnson & Johnson) 1
 
< 0.1%
Sinovac 1
 
< 0.1%
(Missing) 29963
94.0%

Length

2025-03-14T09:36:58.957359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:59.095909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
astrazeneca 1751
86.0%
se 106
 
5.2%
desconoce 106
 
5.2%
pfizer 27
 
1.3%
biontech 27
 
1.3%
moderna 8
 
0.4%
cansino 5
 
0.2%
johnson 2
 
0.1%
janssen 1
 
< 0.1%
1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 3883
18.4%
a 3517
16.7%
c 1991
9.5%
n 1882
8.9%
s 1861
8.8%
r 1786
8.5%
A 1751
8.3%
Z 1751
8.3%
t 1751
8.3%
o 257
 
1.2%
Other values (18) 633
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3883
18.4%
a 3517
16.7%
c 1991
9.5%
n 1882
8.9%
s 1861
8.8%
r 1786
8.5%
A 1751
8.3%
Z 1751
8.3%
t 1751
8.3%
o 257
 
1.2%
Other values (18) 633
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3883
18.4%
a 3517
16.7%
c 1991
9.5%
n 1882
8.9%
s 1861
8.8%
r 1786
8.5%
A 1751
8.3%
Z 1751
8.3%
t 1751
8.3%
o 257
 
1.2%
Other values (18) 633
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3883
18.4%
a 3517
16.7%
c 1991
9.5%
n 1882
8.9%
s 1861
8.8%
r 1786
8.5%
A 1751
8.3%
Z 1751
8.3%
t 1751
8.3%
o 257
 
1.2%
Other values (18) 633
 
3.0%

FECHA REFUERZO
Date

Missing 

Distinct191
Distinct (%)10.1%
Missing29963
Missing (%)94.0%
Memory size249.1 KiB
Minimum2021-02-15 00:00:00
Maximum2022-06-19 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-14T09:36:59.290590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-14T09:36:59.485654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

VARIANTE
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)2.6%
Missing31784
Missing (%)99.8%
Memory size1.9 MiB
OMICRON
74 
DELTA
 
4

Length

Max length7
Median length7
Mean length6.8974359
Min length5

Characters and Unicode

Total characters538
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 rowDELTA
2nd rowOMICRON
3rd rowOMICRON
4th rowOMICRON
5th rowDELTA

Common Values

ValueCountFrequency (%)
OMICRON 74
 
0.2%
DELTA 4
 
< 0.1%
(Missing) 31784
99.8%

Length

2025-03-14T09:36:59.663349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:59.770880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
omicron 74
94.9%
delta 4
 
5.1%

Most occurring characters

ValueCountFrequency (%)
O 148
27.5%
M 74
13.8%
I 74
13.8%
C 74
13.8%
R 74
13.8%
N 74
13.8%
D 4
 
0.7%
E 4
 
0.7%
L 4
 
0.7%
T 4
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 538
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 148
27.5%
M 74
13.8%
I 74
13.8%
C 74
13.8%
R 74
13.8%
N 74
13.8%
D 4
 
0.7%
E 4
 
0.7%
L 4
 
0.7%
T 4
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 538
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 148
27.5%
M 74
13.8%
I 74
13.8%
C 74
13.8%
R 74
13.8%
N 74
13.8%
D 4
 
0.7%
E 4
 
0.7%
L 4
 
0.7%
T 4
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 538
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 148
27.5%
M 74
13.8%
I 74
13.8%
C 74
13.8%
R 74
13.8%
N 74
13.8%
D 4
 
0.7%
E 4
 
0.7%
L 4
 
0.7%
T 4
 
0.7%

INFLUENZA
Categorical

Constant  Missing 

Distinct1
Distinct (%)2.0%
Missing31812
Missing (%)99.8%
Memory size1.9 MiB
A H3
50 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters200
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 rowA H3
2nd rowA H3
3rd rowA H3
4th rowA H3
5th rowA H3

Common Values

ValueCountFrequency (%)
A H3 50
 
0.2%
(Missing) 31812
99.8%

Length

2025-03-14T09:36:59.878064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-14T09:36:59.957913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a 50
50.0%
h3 50
50.0%

Most occurring characters

ValueCountFrequency (%)
A 50
25.0%
50
25.0%
H 50
25.0%
3 50
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 50
25.0%
50
25.0%
H 50
25.0%
3 50
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 50
25.0%
50
25.0%
H 50
25.0%
3 50
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 50
25.0%
50
25.0%
H 50
25.0%
3 50
25.0%

Interactions

2025-03-14T09:36:35.673380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-14T09:36:35.338898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-14T09:36:35.831831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-14T09:36:35.515344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-14T09:37:00.133070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AnosmiaArtralgiasAsmaAtaque al estado generalCefaleaCianosisConjuntivitisDiabetesDiagnóstico clínico de NeumoníaDiagnóstico probableDiarreaDisgeusiaDisneaDolor abdminalDolor torácicoEPOCEdadEnfermedad cardiacaEscalofríosEstatus del pacienteEstatus día previoFecha de llegada al EstadoFiebreHipertensiónInicio súbitoInmunosupresiónInstitución tratanteInsuficiencia renal crónicaIrritabilidadMarcaMialgiasNo consecutivo por inicio de sintomasObesidadOcupaciónOdinofagiaOtra condiciónPacientes que ingresaron a UCIPacientes que requirieron intubaciónPolipneaProcedenciaREFUERZOResultado de laboratorioRinorreaSemana epidemiológica de defunciones positivasSemana epidemiológica de resultados positivosSexoTabaquismoTipo de manejoToma de muestra en el ESTADOTosVARIANTEVIH/SIDAVacuna contra COVID19Vómito
Anosmia1.0000.1720.0300.0970.1550.0790.0790.0250.0210.0340.0410.7720.0590.1900.1780.0280.0360.0280.1640.0480.0490.0040.0180.0300.0290.0280.1150.0280.0520.0340.1670.0470.0300.0600.1560.0270.0050.0290.0540.0000.0000.0440.0420.1410.0420.0020.0300.0450.0640.0290.0000.0280.0040.070
Artralgias0.1721.0000.0000.2590.6570.0680.0680.0000.0380.0360.0340.1680.0050.5670.6120.0090.1120.0070.6080.0440.0450.0000.0930.0000.0240.0000.0680.0000.0530.0310.8310.0320.0350.0860.6170.0050.0390.0370.0080.0000.0000.0460.0140.1640.0490.0270.0240.0530.0000.0410.0000.0000.0000.118
Asma0.0300.0001.0000.1070.0000.1510.1510.6270.0120.0120.1510.0300.2150.0520.0610.7070.0200.7070.0470.0020.0050.0000.0000.7070.2460.7070.0500.6770.1240.0120.0000.0140.7080.0350.0490.6770.0000.0000.2140.0000.1260.0160.1510.0000.0090.0350.7070.0040.1660.0000.0000.7070.0080.151
Ataque al estado general0.0970.2590.1071.0000.2640.2500.2500.1020.0960.1050.2530.0990.3540.3510.3250.1090.0520.1090.3180.0850.0860.0000.0440.1090.2380.1080.1620.1060.2040.1190.2660.0990.1150.0720.3330.1020.0330.0300.3560.0000.1870.0730.2530.0000.0900.0110.1060.1140.0630.0470.1650.1070.0360.251
Cefalea0.1550.6570.0000.2641.0000.0350.0350.0170.0710.0900.0120.1570.0260.5420.5900.0200.1270.0150.5690.0380.0380.0000.1350.0150.0390.0100.0550.0240.0310.0440.6760.0460.0080.1020.5950.0000.0470.0790.0350.0000.0080.0240.0250.6560.0620.0600.0000.0970.0110.0340.0000.0000.0000.122
Cianosis0.0790.0680.1510.2500.0351.0000.9990.1340.0270.0270.3550.0920.5050.2460.1470.1510.0410.1510.1480.0650.0660.0040.0150.1510.2900.1510.0530.1450.3160.0310.0660.0480.1510.0610.1220.1440.0040.0000.5000.0000.0050.0590.3650.0000.0520.0100.1530.0350.0900.0360.0000.1510.0000.354
Conjuntivitis0.0790.0680.1510.2500.0350.9991.0000.1340.0270.0270.3550.0920.5040.2460.1470.1510.0410.1510.1480.0650.0660.0040.0150.1510.2900.1510.0540.1450.3160.0320.0660.0470.1510.0610.1220.1440.0040.0000.5000.0000.0110.0590.3650.0000.0520.0100.1530.0350.0900.0360.0000.1510.0000.354
Diabetes0.0250.0000.6270.1020.0170.1340.1341.0000.1770.1970.1340.0260.2060.0460.0510.6290.2310.6300.0420.0760.0770.0000.0140.6830.2180.6280.0470.6150.1090.0320.0000.0310.6330.1520.0450.6000.0290.0390.1950.0000.0550.0170.1340.0000.0320.0150.6270.1990.1460.0110.2830.6270.0100.134
Diagnóstico clínico de Neumonía0.0210.0380.0120.0960.0710.0270.0270.1771.0000.7050.0140.0160.3580.0520.0850.1650.3090.1010.0350.4220.4260.0000.0310.1550.0250.0890.0610.2190.0000.0460.0400.0700.0390.2020.0720.0130.1960.1530.2410.0000.0520.0400.0710.0000.1070.0380.0360.7050.0310.0000.2150.0260.0000.036
Diagnóstico probable0.0340.0360.0120.1050.0900.0270.0270.1970.7051.0000.0100.0310.4650.0420.1070.1770.3750.1630.0370.4800.4830.0000.0240.1820.0240.1010.1160.2620.0000.0560.0440.0910.0410.2500.1010.0130.1990.2020.3140.0000.0970.0630.0880.2390.0940.0570.0440.8990.0250.0000.2750.0290.0080.035
Diarrea0.0410.0340.1510.2530.0120.3550.3550.1340.0140.0101.0000.0490.5010.1870.1280.1510.0280.1510.1210.0320.0320.0000.0220.1510.2890.1510.0460.1440.2910.0270.0230.0320.1510.0280.1170.1440.0000.0000.5020.0000.0000.0310.3540.1710.0360.0000.1520.0050.0900.0270.0000.1510.0000.385
Disgeusia0.7720.1680.0300.0990.1570.0920.0920.0260.0160.0310.0491.0000.0660.1930.1850.0280.0370.0280.1730.0470.0470.0000.0200.0300.0340.0280.1120.0280.0580.0370.1720.0470.0310.0600.1590.0270.0040.0300.0550.0000.0000.0450.0480.1700.0460.0140.0320.0430.0650.0420.0000.0280.0000.073
Disnea0.0590.0050.2150.3540.0260.5050.5040.2060.3580.4650.5010.0661.0000.1770.2380.2250.1290.2240.1590.1740.1750.0000.0250.2290.4080.2180.0740.2270.4190.0510.0110.0830.2150.0980.1730.2060.0990.1080.7220.0000.1360.0460.5010.0090.0840.0210.2180.4540.1320.0230.2530.2140.0400.502
Dolor abdminal0.1900.5670.0520.3510.5420.2460.2460.0460.0520.0420.1870.1930.1771.0000.5930.0520.0650.0520.5650.0280.0270.0000.0480.0510.1020.0520.0520.0530.1080.0110.5830.0420.0510.0600.5510.0490.0390.0370.1780.0000.0000.0240.1220.2960.0320.0140.0570.0580.0230.0190.0000.0510.0170.278
Dolor torácico0.1780.6120.0610.3250.5900.1470.1470.0510.0850.1070.1280.1850.2380.5931.0000.0620.0770.0550.6420.0610.0610.0000.0200.0590.0930.0500.0780.0530.1550.0600.6300.0470.0520.0770.5950.0510.0770.0460.1800.0000.0370.0390.1150.1180.0630.0290.0590.1150.0310.0620.0000.0500.0150.233
EPOC0.0280.0090.7070.1090.0200.1510.1510.6290.1650.1770.1510.0280.2250.0520.0621.0000.1640.7120.0460.0780.0820.0000.0390.7100.2460.7080.0510.6800.1230.0270.0110.0390.7070.0740.0550.6770.0480.0950.2200.0000.0000.0330.1520.2860.0630.0080.7080.1840.1660.0020.0920.7080.0000.151
Edad0.0360.1120.0200.0520.1270.0410.0410.2310.3090.3750.0280.0370.1290.0650.0770.1641.0000.1160.0810.1180.1190.0000.0700.2790.0190.0290.0520.1120.0400.0800.118-0.0400.0770.2940.1070.0420.0880.0710.1160.0000.0730.1180.0450.0000.0660.0770.0580.3810.0000.0650.2010.0000.0950.087
Enfermedad cardiaca0.0280.0070.7070.1090.0150.1510.1510.6300.1010.1630.1510.0280.2240.0520.0550.7120.1161.0000.0460.0640.0640.0000.0100.7120.2460.7080.0530.6810.1230.0280.0030.0300.7080.0680.0500.6770.0180.0170.2240.0000.0520.0250.1510.2240.0060.0160.7070.1640.1660.0040.3240.7070.0000.151
Escalofríos0.1640.6080.0470.3180.5690.1480.1480.0420.0350.0370.1210.1730.1590.5650.6420.0460.0810.0461.0000.0360.0370.0000.1090.0470.0980.0460.0820.0440.2180.0550.6250.0250.0510.0790.5740.0440.0720.0350.1550.0000.0090.0370.1250.1700.0460.0220.0520.0520.0250.0710.0000.0460.0150.222
Estatus del paciente0.0480.0440.0020.0850.0380.0650.0650.0760.4220.4800.0320.0470.1740.0280.0610.0780.1180.0640.0361.0000.9550.0000.0760.0870.0460.0540.1030.1090.0460.0610.0400.2390.0180.0820.0490.0000.1240.1270.1270.0000.0960.7150.0400.0000.3520.0490.0190.5090.1820.0920.0330.0160.0160.029
Estatus día previo0.0490.0450.0050.0860.0380.0660.0660.0770.4260.4830.0320.0470.1750.0270.0610.0820.1190.0640.0370.9551.0000.0000.0760.0870.0480.0540.1000.1110.0460.0610.0400.2440.0160.0830.0480.0010.1060.1270.1280.0000.1030.7020.0410.0000.3530.0470.0170.5110.1820.0900.0330.0170.0200.030
Fecha de llegada al Estado0.0040.0000.0000.0000.0000.0040.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0120.0000.0090.0020.0000.0000.0000.0001.0001.0000.0000.0001.0000.0530.0050.0140.0000.0000.0001.0000.0000.0000.000
Fiebre0.0180.0930.0000.0440.1350.0150.0150.0140.0310.0240.0220.0200.0250.0480.0200.0390.0700.0100.1090.0760.0760.0001.0000.0220.0310.0000.0390.0080.0180.0670.0800.0390.0090.0660.0150.0060.0000.1120.0210.0000.0000.0710.0250.6530.0710.0270.0000.0240.0000.0470.0000.0080.0220.035
Hipertensión0.0300.0000.7070.1090.0150.1510.1510.6830.1550.1820.1510.0300.2290.0510.0590.7100.2790.7120.0470.0870.0870.0000.0221.0000.2460.7070.0540.6920.1230.0380.0000.0320.7170.1700.0490.6770.0270.0370.2180.0000.0710.0000.1510.0000.0300.0200.7070.1840.1660.0150.2000.7070.0190.151
Inicio súbito0.0290.0240.2460.2380.0390.2900.2900.2180.0250.0240.2890.0340.4080.1020.0930.2460.0190.2460.0980.0460.0480.0030.0310.2461.0000.2460.0760.2360.2360.0560.0580.0670.2470.0480.1270.2360.0000.0200.4090.0000.1740.0380.2950.0000.0810.0000.2460.0230.2470.0460.0000.2460.0190.289
Inmunosupresión0.0280.0000.7070.1080.0100.1510.1510.6280.0890.1010.1510.0280.2180.0520.0500.7080.0290.7080.0460.0540.0540.0000.0000.7070.2461.0000.0490.6790.1230.0180.0000.0160.7070.0210.0500.6780.0290.0110.2150.0000.1090.0000.1590.0440.0270.0090.7070.1160.1670.0111.0000.7110.0040.151
Institución tratante0.1150.0680.0500.1620.0550.0530.0540.0470.0610.1160.0460.1120.0740.0520.0780.0510.0520.0530.0820.1030.1000.0000.0390.0540.0760.0491.0000.0480.0850.2090.0860.0850.0550.2170.0910.0620.0560.0470.0770.0880.1400.1730.0910.2250.1020.0520.0610.1060.3040.0530.0000.0560.0430.039
Insuficiencia renal crónica0.0280.0000.6770.1060.0240.1450.1450.6150.2190.2620.1440.0280.2270.0530.0530.6800.1120.6810.0440.1090.1110.0000.0080.6920.2360.6790.0481.0000.1180.0360.0120.0190.6770.1000.0540.6480.0000.0220.2210.0000.1030.0190.1460.2430.0270.0340.6770.2620.1580.0000.0000.6770.0000.146
Irritabilidad0.0520.0530.1240.2040.0310.3160.3160.1090.0000.0000.2910.0580.4190.1080.1550.1230.0400.1230.2180.0460.0460.0000.0180.1230.2360.1230.0850.1181.0000.0570.0410.0610.1230.0700.0990.1180.0000.0000.4100.0000.0370.0390.2890.0000.0840.0000.1240.0000.0730.0140.0000.1230.0350.291
Marca0.0340.0310.0120.1190.0440.0310.0320.0320.0460.0560.0270.0370.0510.0110.0600.0270.0800.0280.0550.0610.0610.0000.0670.0380.0560.0180.2090.0360.0571.0000.0540.0580.0110.2340.0670.0350.0000.0000.0360.0610.3090.0810.0740.0000.0660.0620.0360.0920.0480.0230.0000.0000.3680.017
Mialgias0.1670.8310.0000.2660.6760.0660.0660.0000.0400.0440.0230.1720.0110.5830.6300.0110.1180.0030.6250.0400.0400.0000.0800.0000.0580.0000.0860.0120.0410.0541.0000.0300.0350.0950.6440.0000.0410.0400.0080.0000.0000.0330.0330.0900.0470.0350.0240.0600.0000.0540.0000.0000.0140.122
No consecutivo por inicio de sintomas0.0470.0320.0140.0990.0460.0480.0470.0310.0700.0910.0320.0470.0830.0420.0470.039-0.0400.0300.0250.2390.2440.0120.0390.0320.0670.0160.0850.0190.0610.0580.0301.0000.0330.0780.0300.0000.0100.0310.0190.0100.0930.3210.0470.5310.7130.0340.0300.0920.0120.0920.0600.0050.0570.060
Obesidad0.0300.0350.7080.1150.0080.1510.1510.6330.0390.0410.1510.0310.2150.0510.0520.7070.0770.7080.0510.0180.0160.0000.0090.7170.2470.7070.0550.6770.1230.0110.0350.0331.0000.0620.0580.6770.0070.0170.2130.0000.0660.0060.1510.0000.0440.0270.7090.0390.1660.0160.0000.7070.0130.151
Ocupación0.0600.0860.0350.0720.1020.0610.0610.1520.2020.2500.0280.0600.0980.0600.0770.0740.2940.0680.0790.0820.0830.0090.0660.1700.0480.0210.2170.1000.0700.2340.0950.0780.0621.0000.0990.0220.0510.0610.0790.0110.1010.1170.0710.0100.0640.3090.0690.2510.0080.0580.0000.0200.0940.057
Odinofagia0.1560.6170.0490.3330.5950.1220.1220.0450.0720.1010.1170.1590.1730.5510.5950.0550.1070.0500.5740.0490.0480.0020.0150.0490.1270.0500.0910.0540.0990.0670.6440.0300.0580.0991.0000.0460.0520.0500.1650.0000.0190.0280.1630.1270.0400.0420.0480.1070.0260.1090.1000.0500.0480.233
Otra condición0.0270.0050.6770.1020.0000.1440.1440.6000.0130.0130.1440.0270.2060.0490.0510.6770.0420.6770.0440.0000.0010.0000.0060.6770.2360.6780.0620.6480.1180.0350.0000.0000.6770.0220.0461.0000.0000.0000.2040.0000.1300.0110.1440.0000.0000.0290.6770.0230.1680.0001.0000.6770.0000.144
Pacientes que ingresaron a UCI0.0050.0390.0000.0330.0470.0040.0040.0290.1960.1990.0000.0040.0990.0390.0770.0480.0880.0180.0720.1240.1060.0000.0000.0270.0000.0290.0560.0000.0000.0000.0410.0100.0070.0510.0520.0001.0000.3240.0870.0000.0000.0620.0240.4520.1330.0130.0000.2060.0000.0050.0000.0000.0000.000
Pacientes que requirieron intubación0.0290.0370.0000.0300.0790.0000.0000.0390.1530.2020.0000.0300.1080.0370.0460.0950.0710.0170.0350.1270.1270.0000.1120.0370.0200.0110.0470.0220.0000.0000.0400.0310.0170.0610.0500.0000.3241.0000.1150.0000.0000.0320.0280.4260.2270.0120.0000.2140.0000.0001.0000.0120.0000.112
Polipnea0.0540.0080.2140.3560.0350.5000.5000.1950.2410.3140.5020.0550.7220.1780.1800.2200.1160.2240.1550.1270.1280.0000.0210.2180.4090.2150.0770.2210.4100.0360.0080.0190.2130.0790.1650.2040.0870.1151.0000.0000.1100.0150.5010.1620.0310.0190.2140.3050.1290.0020.0000.2130.0000.504
Procedencia0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0880.0000.0000.0610.0000.0100.0000.0110.0000.0000.0000.0000.0001.0001.0000.0000.0001.0000.0350.0050.0060.0000.0000.0000.0000.0000.0000.000
REFUERZO0.0000.0000.1260.1870.0080.0050.0110.0550.0520.0970.0000.0000.1360.0000.0370.0000.0730.0520.0090.0960.1031.0000.0000.0710.1740.1090.1400.1030.0370.3090.0000.0930.0660.1010.0190.1300.0000.0000.1101.0001.0000.2400.0461.0000.1440.0000.0440.0960.0950.0001.0000.0280.1870.000
Resultado de laboratorio0.0440.0460.0160.0730.0240.0590.0590.0170.0400.0630.0310.0450.0460.0240.0390.0330.1180.0250.0370.7150.7020.0000.0710.0000.0380.0000.1730.0190.0390.0810.0330.3210.0060.1170.0280.0110.0620.0320.0150.0000.2401.0000.0100.0001.0000.0220.0110.0640.0000.0901.0000.0100.0110.030
Rinorrea0.0420.0140.1510.2530.0250.3650.3650.1340.0710.0880.3540.0480.5010.1220.1150.1520.0450.1510.1250.0400.0410.0000.0250.1510.2950.1590.0910.1460.2890.0740.0330.0470.1510.0710.1630.1440.0240.0280.5010.0000.0460.0101.0000.2500.0540.0260.1510.0950.0910.1470.0000.1500.0270.354
Semana epidemiológica de defunciones positivas0.1410.1640.0000.0000.6560.0000.0000.0000.0000.2390.1710.1700.0090.2960.1180.2860.0000.2240.1700.0000.0001.0000.6530.0000.0000.0440.2250.2430.0000.0000.0900.5310.0000.0100.1270.0000.4520.4260.1621.0001.0000.0000.2501.0000.5230.0000.0920.2261.0000.1590.4291.0000.1250.151
Semana epidemiológica de resultados positivos0.0420.0490.0090.0900.0620.0520.0520.0320.1070.0940.0360.0460.0840.0320.0630.0630.0660.0060.0460.3520.3530.0530.0710.0300.0810.0270.1020.0270.0840.0660.0470.7130.0440.0640.0400.0000.1330.2270.0310.0350.1441.0000.0540.5231.0000.0370.0270.1010.0680.0870.5170.0000.0630.051
Sexo0.0020.0270.0350.0110.0600.0100.0100.0150.0380.0570.0000.0140.0210.0140.0290.0080.0770.0160.0220.0490.0470.0050.0270.0200.0000.0090.0520.0340.0000.0620.0350.0340.0270.3090.0420.0290.0130.0120.0190.0050.0000.0220.0260.0000.0371.0000.1140.0560.0160.0210.0340.0200.0350.029
Tabaquismo0.0300.0240.7070.1060.0000.1530.1530.6270.0360.0440.1520.0320.2180.0570.0590.7080.0580.7070.0520.0190.0170.0140.0000.7070.2460.7070.0610.6770.1240.0360.0240.0300.7090.0690.0480.6770.0000.0000.2140.0060.0440.0110.1510.0920.0270.1141.0000.0380.1660.0000.0000.7070.0410.151
Tipo de manejo0.0450.0530.0040.1140.0970.0350.0350.1990.7050.8990.0050.0430.4540.0580.1150.1840.3810.1640.0520.5090.5110.0000.0240.1840.0230.1160.1060.2620.0000.0920.0600.0920.0390.2510.1070.0230.2060.2140.3050.0000.0960.0640.0950.2260.1010.0560.0381.0000.0110.0000.2750.0270.0000.034
Toma de muestra en el ESTADO0.0640.0000.1660.0630.0110.0900.0900.1460.0310.0250.0900.0650.1320.0230.0310.1660.0000.1660.0250.1820.1820.0000.0000.1660.2470.1670.3040.1580.0730.0480.0000.0120.1660.0080.0260.1680.0000.0000.1290.0000.0950.0000.0911.0000.0680.0160.1660.0111.0000.0001.0000.1660.0000.091
Tos0.0290.0410.0000.0470.0340.0360.0360.0110.0000.0000.0270.0420.0230.0190.0620.0020.0650.0040.0710.0920.0900.0000.0470.0150.0460.0110.0530.0000.0140.0230.0540.0920.0160.0580.1090.0000.0050.0000.0020.0000.0000.0900.1470.1590.0870.0210.0000.0000.0001.0000.0000.0000.0210.042
VARIANTE0.0000.0000.0000.1650.0000.0000.0000.2830.2150.2750.0000.0000.2530.0000.0000.0920.2010.3240.0000.0330.0331.0000.0000.2000.0001.0000.0000.0000.0000.0000.0000.0600.0000.0000.1001.0000.0001.0000.0000.0001.0001.0000.0000.4290.5170.0340.0000.2751.0000.0001.0001.0000.0000.000
VIH/SIDA0.0280.0000.7070.1070.0000.1510.1510.6270.0260.0290.1510.0280.2140.0510.0500.7080.0000.7070.0460.0160.0170.0000.0080.7070.2460.7110.0560.6770.1230.0000.0000.0050.7070.0200.0500.6770.0000.0120.2130.0000.0280.0100.1501.0000.0000.0200.7070.0270.1660.0001.0001.0000.0000.151
Vacuna contra COVID190.0040.0000.0080.0360.0000.0000.0000.0100.0000.0080.0000.0000.0400.0170.0150.0000.0950.0000.0150.0160.0200.0000.0220.0190.0190.0040.0430.0000.0350.3680.0140.0570.0130.0940.0480.0000.0000.0000.0000.0000.1870.0110.0270.1250.0630.0350.0410.0000.0000.0210.0000.0001.0000.049
Vómito0.0700.1180.1510.2510.1220.3540.3540.1340.0360.0350.3850.0730.5020.2780.2330.1510.0870.1510.2220.0290.0300.0000.0350.1510.2890.1510.0390.1460.2910.0170.1220.0600.1510.0570.2330.1440.0000.1120.5040.0000.0000.0300.3540.1510.0510.0290.1510.0340.0910.0420.0000.1510.0491.000

Missing values

2025-03-14T09:36:36.340700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-14T09:36:37.139377image/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.
2025-03-14T09:36:38.579512image/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

No de caso positivo por inicio de síntomasNo consecutivo por inicio de sintomasInstitución tratanteUnidad notificanteToma de muestra en el ESTADOMunicipio de residenciaEdadSexoFecha de inicio de síntomasPeriodo mínimo de incubación (2 días)Periodo máximo de incubación (7 días)Fecha estimada de Alta SanitariaProcedenciaFecha de llegada al EstadoFecha de toma de muestraEstatus día previoTipo de manejoEstatus del pacienteFecha de la defunciónSemana epidemiológica de defunciones positivasSemana epidemiológica de resultados positivosFecha de resultado de laboratorioResultado de laboratorioPacientes que requirieron intubaciónPacientes que ingresaron a UCIDiagnóstico clínico de NeumoníaDiagnóstico probableFiebreTosOdinofagiaDisneaIrritabilidadDiarreaDolor torácicoEscalofríosCefaleaMialgiasArtralgiasAtaque al estado generalRinorreaPolipneaVómitoDolor abdminalConjuntivitisCianosisInicio súbitoAnosmiaDisgeusiaDiabetesEPOCAsmaInmunosupresiónHipertensiónVIH/SIDAOtra condiciónEnfermedad cardiacaObesidadInsuficiencia renal crónicaTabaquismoVacuna contra COVID19MarcaFecha de última aplicaciónOcupaciónREFUERZOFECHA REFUERZOVARIANTEINFLUENZA
0NaN1SSyBSHOSPITAL GENERAL IXTLAHUACANSICOL, Ixtlahuacán53M2021-12-06NaTNaTNaTNo aplicaNo aplica2022-01-03Seguimiento terminadoAmbulatorioSeguimiento terminadoNaTNaNNaN2022-01-04 00:00:00NegativoNONONOENFERMEDAD TIPO INFLUENZA (ETI)SISISINONONONOSISISISINONONONONONONOSISINONONONONONONONONOSINONONaNNaNNaTCAMPESINOSNaNNaTNaNNaN
112IMSSUMF 16 COLIMASICOL, Colima55F2021-12-132021-12-152021-12-202021-12-28No aplicaNo aplica2022-01-03Alta sanitariaAmbulatorioAlta sanitariaNaTNaNSemana 012022-01-05 00:00:00SARS-COV-2NONONOENFERMEDAD TIPO INFLUENZA (ETI)SISISINONONONOSISISISISISINONONONONOSINONOSINONONONONONONOSINONONaNNaNNaTOTROSNaNNaTNaNNaN
223IMSSUMF 11 COLIMASICOL, Villa de Álvarez35M2021-12-132021-12-152021-12-202021-12-28No aplicaNo aplica2022-01-14Alta sanitariaAmbulatorioAlta sanitariaNaTNaNSemana 022022-01-15 00:00:00SARS-COV-2NONONOENFERMEDAD TIPO INFLUENZA (ETI)NOSISISINOSISINONONONONONONONONONONONONONONONONONONONONONONONOSINaNNaNNaTMEDICOSNaNNaTNaNNaN
334IMSSUMF 2 MANZANILLOSICOL, Manzanillo33F2021-12-202021-12-222021-12-272022-01-04No aplicaNo aplica2022-01-03Alta sanitariaAmbulatorioAlta sanitariaNaTNaNSemana 012022-01-04 00:00:00SARS-COV-2NONONOENFERMEDAD TIPO INFLUENZA (ETI)NOSISINONONOSINOSINONONOSINONONONONONONONONONONONONONONONONONONONaNNaNNaTEMPLEADOSNaNNaTNaNNaN
4NaN5IMSSUMF 19 COLIMASICOL, Colima50M2021-12-20NaTNaTNaTNo aplicaNo aplica2022-01-04Seguimiento terminadoAmbulatorioSeguimiento terminadoNaTNaNNaN2022-01-05 00:00:00NegativoNONONOENFERMEDAD TIPO INFLUENZA (ETI)NONONOSINOSINOSISINONONONONONOSISISINONONOSINONONOSINONONOSINONONaNNaNNaTEMPLEADOSNaNNaTNaNNaN
5NaN6SSyBSHOSPITAL GENERAL IXTLAHUACANSICOL, Ixtlahuacán50F2021-12-21NaTNaTNaTNo aplicaNo aplica2022-01-03Seguimiento terminadoAmbulatorioSeguimiento terminadoNaTNaNNaN2022-01-04 00:00:00NegativoNONONOENFERMEDAD TIPO INFLUENZA (ETI)SISISINONONONOSINOSINOSINONONONONONOSINOSISINONONONONONONOSINONONaNNaNNaTDESEMPLEADOSNaNNaTNaNNaN
6NaN7IMSSUMF 17 MANZANILLOSICOL, Manzanillo49M2021-12-22NaTNaTNaTNo aplicaNo aplica2022-01-03Seguimiento terminadoAmbulatorioSeguimiento terminadoNaTNaNNaN2022-01-04 00:00:00NegativoNONONOENFERMEDAD TIPO INFLUENZA (ETI)SISISINONONONONOSINONONOSINONONONONONONONOSINONONOSINONONONONONONaNNaNNaTOTROSNaNNaTNaNNaN
748IMSSHGZMF 1 VILLA DE ALVAREZSICOL, Villa de Álvarez23F2021-12-222021-12-242021-12-292022-01-06No aplicaNo aplica2022-01-03Alta sanitariaAmbulatorioAlta sanitariaNaTNaNSemana 012022-01-04 00:00:00SARS-COV-2NONONOENFERMEDAD TIPO INFLUENZA (ETI)SISINONOSINONONOSINONONONONONONOSISINONONONONONONONONONONONONONONaNNaNNaTEMPLEADOSNaNNaTNaNNaN
8NaN9SSyBSHOSPITAL GENERAL IXTLAHUACANSICOL, Ixtlahuacán40F2021-12-22NaTNaTNaTNo aplicaNo aplica2022-01-03Seguimiento terminadoAmbulatorioSeguimiento terminadoNaTNaNNaN2022-01-04 00:00:00NegativoNONONOENFERMEDAD TIPO INFLUENZA (ETI)SISINONONONONOSINONONOSISINONONONONOSINONONONONONONONONONONONOSINaNNaNNaTHOGARNaNNaTNaNNaN
9510IMSSUMF 19 COLIMASICOL, Colima48F2021-12-232021-12-252021-12-302022-01-07No aplicaNo aplica2022-01-05Alta sanitariaAmbulatorioAlta sanitariaNaTNaNSemana 012022-01-06 00:00:00SARS-COV-2NONONOENFERMEDAD TIPO INFLUENZA (ETI)SISINONONONONOSISISISINONONONONOSISINONONONONONONONONONONONONONONaNNaNNaTOTROSNaNNaTNaNNaN
No de caso positivo por inicio de síntomasNo consecutivo por inicio de sintomasInstitución tratanteUnidad notificanteToma de muestra en el ESTADOMunicipio de residenciaEdadSexoFecha de inicio de síntomasPeriodo mínimo de incubación (2 días)Periodo máximo de incubación (7 días)Fecha estimada de Alta SanitariaProcedenciaFecha de llegada al EstadoFecha de toma de muestraEstatus día previoTipo de manejoEstatus del pacienteFecha de la defunciónSemana epidemiológica de defunciones positivasSemana epidemiológica de resultados positivosFecha de resultado de laboratorioResultado de laboratorioPacientes que requirieron intubaciónPacientes que ingresaron a UCIDiagnóstico clínico de NeumoníaDiagnóstico probableFiebreTosOdinofagiaDisneaIrritabilidadDiarreaDolor torácicoEscalofríosCefaleaMialgiasArtralgiasAtaque al estado generalRinorreaPolipneaVómitoDolor abdminalConjuntivitisCianosisInicio súbitoAnosmiaDisgeusiaDiabetesEPOCAsmaInmunosupresiónHipertensiónVIH/SIDAOtra condiciónEnfermedad cardiacaObesidadInsuficiencia renal crónicaTabaquismoVacuna contra COVID19MarcaFecha de última aplicaciónOcupaciónREFUERZOFECHA REFUERZOVARIANTEINFLUENZA
31852NaN31853IMSSHGZMF 1 VILLA DE ALVAREZSICOL, Colima42F2022-06-24NaTNaTNaTNo aplicaNo aplica2022-06-24NaNAmbulatorioSeguimiento terminadoNaTNaNNaN2022-06-27 00:00:00NegativoNONONOENFERMEDAD TIPO INFLUENZA (ETI)NONOSINONONONONOSINONOSINONONONONONONONONONONONONONONONONONONONONaNNaNNaTEMPLEADOSNaNNaTNaNNaN
318532207931854IMSSHGZMF 1 VILLA DE ALVAREZSICOL, Colima8F2022-06-252022-06-232022-07-022022-07-11No aplicaNo aplica2022-06-26NaNAmbulatorioSeguimiento domiciliarioNaTNaNSemana 262022-06-27 00:00:00SARS-COV-2NONONOENFERMEDAD TIPO INFLUENZA (ETI)NOSISINOSINONOSISISISINOSINONOSINONONONONONONONONONONONONONONONONaNNaNNaTESTUDIANTESNaNNaTNaNNaN
31854NaN31855IMSSHGSMF 4 TECOMANSICOL, Tecomán15F2022-06-25NaTNaTNaTNo aplicaNo aplica2022-06-26NaNAmbulatorioSeguimiento terminadoNaTNaNNaN2022-06-27 00:00:00NegativoNONONOENFERMEDAD TIPO INFLUENZA (ETI)NONONONONONONONOSISISINONONOSINONONONONONONONONONONONONONONONONONaNNaNNaTDESEMPLEADOSNaNNaTNaNNaN
31855NaN31856IMSSHGSMF 4 TECOMANSICOL, Tecomán33F2022-06-25NaTNaTNaTNo aplicaNo aplica2022-06-25NaNAmbulatorioSeguimiento terminadoNaTNaNNaN2022-06-27 00:00:00NegativoNONONOENFERMEDAD TIPO INFLUENZA (ETI)NONOSINONONONONOSISISINONONONONOSISINONONONONONONONONONONONONONOCOMPLETAAstraZeneca2021-10-26EMPLEADOSNaNNaTNaNNaN
318562208031857IMSSHGZMF 1 VILLA DE ALVAREZSICOL, Comala12M2022-06-252022-06-232022-07-022022-07-11No aplicaNo aplica2022-06-26NaNAmbulatorioSeguimiento domiciliarioNaTNaNSemana 262022-06-27 00:00:00SARS-COV-2NONONOENFERMEDAD TIPO INFLUENZA (ETI)SISINONONONONONOSINONOSISINONONOSISISINONONONONONONONONONONONONONaNNaNNaTESTUDIANTESNaNNaTNaNNaN
31857NaN31858IMSSHGSMF 4 TECOMANSICOL, Tecomán40M2022-06-25NaTNaTNaTNo aplicaNo aplica2022-06-26NaNAmbulatorioSeguimiento terminadoNaTNaNNaN2022-06-27 00:00:00NegativoNONONOENFERMEDAD TIPO INFLUENZA (ETI)SISISINONONONONOSINONONONONONONONONONONONONONONONONONONOSINONONONaNNaNNaTEMPLEADOSNaNNaTNaNNaN
318582208131859IMSSHGZMF 1 VILLA DE ALVAREZSICOL, Colima38F2022-06-252022-06-232022-07-022022-07-11No aplicaNo aplica2022-06-26NaNAmbulatorioSeguimiento domiciliarioNaTNaNSemana 262022-06-27 00:00:00SARS-COV-2NONONOENFERMEDAD TIPO INFLUENZA (ETI)NONOSINONONONOSISISISINOSINONONOSISISINONONONONONONONONONONONONONaNNaNNaTENFERMERASNaNNaTNaNNaN
31859NaN31860IMSSHGSMF 4 TECOMANSICOL, Tecomán26F2022-06-25NaTNaTNaTNo aplicaNo aplica2022-06-25NaNAmbulatorioSeguimiento terminadoNaTNaNNaN2022-06-27 00:00:00NegativoNONONOENFERMEDAD TIPO INFLUENZA (ETI)SINOSINONONONOSISISISINONONONONONONONONONONONONONONONONONONONONOCOMPLETASinovac2021-08-13OTROSAstraZeneca2022-01-11NaNNaN
318602208231861IMSSHGSMF 4 TECOMANSICOL, Tecomán34F2022-06-252022-06-232022-07-022022-07-11No aplicaNo aplica2022-06-25NaNAmbulatorioSeguimiento domiciliarioNaTNaNSemana 262022-06-27 00:00:00SARS-COV-2NONONOENFERMEDAD TIPO INFLUENZA (ETI)NONOSINONONONONONONONONONONONONONONONONONOSINONONONONONONONONONOCOMPLETASinovac2021-10-13HOGARNaNNaTNaNNaN
31861NaN31862IMSSHGZMF 1 VILLA DE ALVAREZSICOL, Colima16F2022-06-25NaTNaTNaTNo aplicaNo aplica2022-06-26NaNAmbulatorioSeguimiento terminadoNaTNaNNaN2022-06-27 00:00:00NegativoNONONOENFERMEDAD TIPO INFLUENZA (ETI)NOSISINOSISINONOSINONOSISISINONONONOSINONONONONONONONONONONONONONaNNaNNaTESTUDIANTESNaNNaTNaNNaN