Overview

Dataset statistics

Number of variables28
Number of observations4
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1000.0 B
Average record size in memory250.0 B

Variable types

Categorical25
Boolean3

Alerts

bac has constant value "Neg"Constant
NECROP has constant value "N/realiz"Constant
DIABETES has constant value "False"Constant
ALCOOLISMO has constant value "False"Constant
MENTAL has constant value "False"Constant
motMudEsquema has constant value "Nulo"Constant
HISTOPATOL has constant value "N/realiz"Constant
Status_Resistencia has constant value "0"Constant
Cluster has constant value "0"Constant
faixaEtaria is highly overall correlated with FORMACLIN1 and 8 other fieldsHigh correlation
sexo is highly overall correlated with ESCOLARID and 3 other fieldsHigh correlation
ESCOLARID is highly overall correlated with sexo and 5 other fieldsHigh correlation
TIPOCUP is highly overall correlated with ESCOLARID and 3 other fieldsHigh correlation
sitAtual is highly overall correlated with BACOUTRO and 1 other fieldsHigh correlation
tipoCaso is highly overall correlated with ESCOLARID and 3 other fieldsHigh correlation
FORMACLIN1 is highly overall correlated with faixaEtaria and 3 other fieldsHigh correlation
classif is highly overall correlated with tipoCaso and 5 other fieldsHigh correlation
descoberta is highly overall correlated with sexo and 5 other fieldsHigh correlation
BACOUTRO is highly overall correlated with sexo and 8 other fieldsHigh correlation
cultEsc is highly overall correlated with faixaEtaria and 3 other fieldsHigh correlation
RX is highly overall correlated with BACOUTRO and 1 other fieldsHigh correlation
hiv is highly overall correlated with faixaEtaria and 1 other fieldsHigh correlation
aids is highly overall correlated with faixaEtaria and 1 other fieldsHigh correlation
DROGADICAO is highly overall correlated with faixaEtaria and 3 other fieldsHigh correlation
TABAGISMO is highly overall correlated with faixaEtaria and 3 other fieldsHigh correlation
tipoTrat is highly overall correlated with faixaEtaria and 3 other fieldsHigh correlation
idade is highly overall correlated with faixaEtaria and 1 other fieldsHigh correlation
Probabilidade is highly overall correlated with faixaEtaria and 17 other fieldsHigh correlation
sitAtual is uniformly distributedUniform
RX is uniformly distributedUniform
tipoTrat is uniformly distributedUniform
Probabilidade is uniformly distributedUniform
Probabilidade has unique valuesUnique

Reproduction

Analysis started2023-10-31 19:42:27.127663
Analysis finished2023-10-31 19:42:30.296644
Duration3.17 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

faixaEtaria
Categorical

Distinct3
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size64.0 B
30_39
2 
40_49
1 
20_29
1 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters20
Distinct characters6
Distinct categories2 ?
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 (%)50.0%

Sample

1st row40_49
2nd row30_39
3rd row30_39
4th row20_29

Common Values

ValueCountFrequency (%)
30_39 2
50.0%
40_49 1
25.0%
20_29 1
25.0%

Length

2023-10-31T16:42:30.372026image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:30.523283image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
30_39 2
50.0%
40_49 1
25.0%
20_29 1
25.0%

Most occurring characters

ValueCountFrequency (%)
3 4
20.0%
0 4
20.0%
_ 4
20.0%
9 4
20.0%
4 2
10.0%
2 2
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16
80.0%
Connector Punctuation 4
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 4
25.0%
0 4
25.0%
9 4
25.0%
4 2
12.5%
2 2
12.5%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 4
20.0%
0 4
20.0%
_ 4
20.0%
9 4
20.0%
4 2
10.0%
2 2
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 4
20.0%
0 4
20.0%
_ 4
20.0%
9 4
20.0%
4 2
10.0%
2 2
10.0%

sexo
Categorical

Distinct2
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size160.0 B
M
3 
F
1 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4
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

Unique1 ?
Unique (%)25.0%

Sample

1st rowM
2nd rowF
3rd rowM
4th rowM

Common Values

ValueCountFrequency (%)
M 3
75.0%
F 1
 
25.0%

Length

2023-10-31T16:42:30.646971image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:30.789038image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
m 3
75.0%
f 1
 
25.0%

Most occurring characters

ValueCountFrequency (%)
M 3
75.0%
F 1
 
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 3
75.0%
F 1
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 3
75.0%
F 1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 3
75.0%
F 1
 
25.0%

ESCOLARID
Categorical

Distinct3
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size64.0 B
De 8 a 11 anos
2 
De 4 a 7 anos
1 
De 12 a 14 anos
1 

Length

Max length15
Median length14.5
Mean length14
Min length13

Characters and Unicode

Total characters56
Distinct characters12
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st rowDe 8 a 11 anos
2nd rowDe 4 a 7 anos
3rd rowDe 12 a 14 anos
4th rowDe 8 a 11 anos

Common Values

ValueCountFrequency (%)
De 8 a 11 anos 2
50.0%
De 4 a 7 anos 1
25.0%
De 12 a 14 anos 1
25.0%

Length

2023-10-31T16:42:30.920337image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:31.088773image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
de 4
20.0%
a 4
20.0%
anos 4
20.0%
8 2
10.0%
11 2
10.0%
4 1
 
5.0%
7 1
 
5.0%
12 1
 
5.0%
14 1
 
5.0%

Most occurring characters

ValueCountFrequency (%)
16
28.6%
a 8
14.3%
1 6
 
10.7%
D 4
 
7.1%
e 4
 
7.1%
n 4
 
7.1%
o 4
 
7.1%
s 4
 
7.1%
8 2
 
3.6%
4 2
 
3.6%
Other values (2) 2
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24
42.9%
Space Separator 16
28.6%
Decimal Number 12
21.4%
Uppercase Letter 4
 
7.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8
33.3%
e 4
16.7%
n 4
16.7%
o 4
16.7%
s 4
16.7%
Decimal Number
ValueCountFrequency (%)
1 6
50.0%
8 2
 
16.7%
4 2
 
16.7%
7 1
 
8.3%
2 1
 
8.3%
Space Separator
ValueCountFrequency (%)
16
100.0%
Uppercase Letter
ValueCountFrequency (%)
D 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28
50.0%
Latin 28
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
16
57.1%
1 6
 
21.4%
8 2
 
7.1%
4 2
 
7.1%
7 1
 
3.6%
2 1
 
3.6%
Latin
ValueCountFrequency (%)
a 8
28.6%
D 4
14.3%
e 4
14.3%
n 4
14.3%
o 4
14.3%
s 4
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16
28.6%
a 8
14.3%
1 6
 
10.7%
D 4
 
7.1%
e 4
 
7.1%
n 4
 
7.1%
o 4
 
7.1%
s 4
 
7.1%
8 2
 
3.6%
4 2
 
3.6%
Other values (2) 2
 
3.6%

TIPOCUP
Categorical

Distinct2
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size64.0 B
Outra
3 
Dona de Casa
1 

Length

Max length12
Median length5
Mean length6.75
Min length5

Characters and Unicode

Total characters27
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)25.0%

Sample

1st rowOutra
2nd rowDona de Casa
3rd rowOutra
4th rowOutra

Common Values

ValueCountFrequency (%)
Outra 3
75.0%
Dona de Casa 1
 
25.0%

Length

2023-10-31T16:42:31.229246image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:31.397339image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
outra 3
50.0%
dona 1
 
16.7%
de 1
 
16.7%
casa 1
 
16.7%

Most occurring characters

ValueCountFrequency (%)
a 6
22.2%
O 3
11.1%
u 3
11.1%
t 3
11.1%
r 3
11.1%
2
 
7.4%
D 1
 
3.7%
o 1
 
3.7%
n 1
 
3.7%
d 1
 
3.7%
Other values (3) 3
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20
74.1%
Uppercase Letter 5
 
18.5%
Space Separator 2
 
7.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6
30.0%
u 3
15.0%
t 3
15.0%
r 3
15.0%
o 1
 
5.0%
n 1
 
5.0%
d 1
 
5.0%
e 1
 
5.0%
s 1
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
O 3
60.0%
D 1
 
20.0%
C 1
 
20.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 25
92.6%
Common 2
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6
24.0%
O 3
12.0%
u 3
12.0%
t 3
12.0%
r 3
12.0%
D 1
 
4.0%
o 1
 
4.0%
n 1
 
4.0%
d 1
 
4.0%
e 1
 
4.0%
Other values (2) 2
 
8.0%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6
22.2%
O 3
11.1%
u 3
11.1%
t 3
11.1%
r 3
11.1%
2
 
7.4%
D 1
 
3.7%
o 1
 
3.7%
n 1
 
3.7%
d 1
 
3.7%
Other values (3) 3
11.1%

sitAtual
Categorical

HIGH CORRELATION  UNIFORM 

Distinct2
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size64.0 B
Abandono
2 
Cura
2 

Length

Max length8
Median length6
Mean length6
Min length4

Characters and Unicode

Total characters24
Distinct characters9
Distinct categories2 ?
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 rowAbandono
2nd rowAbandono
3rd rowCura
4th rowCura

Common Values

ValueCountFrequency (%)
Abandono 2
50.0%
Cura 2
50.0%

Length

2023-10-31T16:42:31.542806image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:31.702801image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
abandono 2
50.0%
cura 2
50.0%

Most occurring characters

ValueCountFrequency (%)
a 4
16.7%
n 4
16.7%
o 4
16.7%
A 2
8.3%
b 2
8.3%
d 2
8.3%
C 2
8.3%
u 2
8.3%
r 2
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20
83.3%
Uppercase Letter 4
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4
20.0%
n 4
20.0%
o 4
20.0%
b 2
10.0%
d 2
10.0%
u 2
10.0%
r 2
10.0%
Uppercase Letter
ValueCountFrequency (%)
A 2
50.0%
C 2
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4
16.7%
n 4
16.7%
o 4
16.7%
A 2
8.3%
b 2
8.3%
d 2
8.3%
C 2
8.3%
u 2
8.3%
r 2
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4
16.7%
n 4
16.7%
o 4
16.7%
A 2
8.3%
b 2
8.3%
d 2
8.3%
C 2
8.3%
u 2
8.3%
r 2
8.3%

tipoCaso
Categorical

Distinct2
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size64.0 B
Novo
3 
Recidiva
1 

Length

Max length8
Median length4
Mean length5
Min length4

Characters and Unicode

Total characters20
Distinct characters9
Distinct categories2 ?
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 (%)25.0%

Sample

1st rowNovo
2nd rowNovo
3rd rowRecidiva
4th rowNovo

Common Values

ValueCountFrequency (%)
Novo 3
75.0%
Recidiva 1
 
25.0%

Length

2023-10-31T16:42:31.833852image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:31.993625image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
novo 3
75.0%
recidiva 1
 
25.0%

Most occurring characters

ValueCountFrequency (%)
o 6
30.0%
v 4
20.0%
N 3
15.0%
i 2
 
10.0%
R 1
 
5.0%
e 1
 
5.0%
c 1
 
5.0%
d 1
 
5.0%
a 1
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16
80.0%
Uppercase Letter 4
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 6
37.5%
v 4
25.0%
i 2
 
12.5%
e 1
 
6.2%
c 1
 
6.2%
d 1
 
6.2%
a 1
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
N 3
75.0%
R 1
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 6
30.0%
v 4
20.0%
N 3
15.0%
i 2
 
10.0%
R 1
 
5.0%
e 1
 
5.0%
c 1
 
5.0%
d 1
 
5.0%
a 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 6
30.0%
v 4
20.0%
N 3
15.0%
i 2
 
10.0%
R 1
 
5.0%
e 1
 
5.0%
c 1
 
5.0%
d 1
 
5.0%
a 1
 
5.0%

FORMACLIN1
Categorical

Distinct2
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size64.0 B
Pul
3 
Ganglionar Periferica
1 

Length

Max length21
Median length3
Mean length7.5
Min length3

Characters and Unicode

Total characters30
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)25.0%

Sample

1st rowGanglionar Periferica
2nd rowPul
3rd rowPul
4th rowPul

Common Values

ValueCountFrequency (%)
Pul 3
75.0%
Ganglionar Periferica 1
 
25.0%

Length

2023-10-31T16:42:32.124856image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:32.294657image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
pul 3
60.0%
ganglionar 1
 
20.0%
periferica 1
 
20.0%

Most occurring characters

ValueCountFrequency (%)
P 4
13.3%
l 4
13.3%
u 3
10.0%
a 3
10.0%
i 3
10.0%
r 3
10.0%
n 2
6.7%
e 2
6.7%
G 1
 
3.3%
g 1
 
3.3%
Other values (4) 4
13.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24
80.0%
Uppercase Letter 5
 
16.7%
Space Separator 1
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 4
16.7%
u 3
12.5%
a 3
12.5%
i 3
12.5%
r 3
12.5%
n 2
8.3%
e 2
8.3%
g 1
 
4.2%
o 1
 
4.2%
f 1
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
P 4
80.0%
G 1
 
20.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 29
96.7%
Common 1
 
3.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 4
13.8%
l 4
13.8%
u 3
10.3%
a 3
10.3%
i 3
10.3%
r 3
10.3%
n 2
6.9%
e 2
6.9%
G 1
 
3.4%
g 1
 
3.4%
Other values (3) 3
10.3%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 4
13.3%
l 4
13.3%
u 3
10.0%
a 3
10.0%
i 3
10.0%
r 3
10.0%
n 2
6.7%
e 2
6.7%
G 1
 
3.3%
g 1
 
3.3%
Other values (4) 4
13.3%

classif
Categorical

Distinct3
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size64.0 B
Pul
2 
Ext
1 
P+E
1 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st rowExt
2nd rowPul
3rd rowP+E
4th rowPul

Common Values

ValueCountFrequency (%)
Pul 2
50.0%
Ext 1
25.0%
P+E 1
25.0%

Length

2023-10-31T16:42:32.415141image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:32.562304image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
pul 2
50.0%
ext 1
25.0%
p+e 1
25.0%

Most occurring characters

ValueCountFrequency (%)
P 3
25.0%
u 2
16.7%
l 2
16.7%
E 2
16.7%
x 1
 
8.3%
t 1
 
8.3%
+ 1
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6
50.0%
Uppercase Letter 5
41.7%
Math Symbol 1
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 2
33.3%
l 2
33.3%
x 1
16.7%
t 1
16.7%
Uppercase Letter
ValueCountFrequency (%)
P 3
60.0%
E 2
40.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11
91.7%
Common 1
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 3
27.3%
u 2
18.2%
l 2
18.2%
E 2
18.2%
x 1
 
9.1%
t 1
 
9.1%
Common
ValueCountFrequency (%)
+ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 3
25.0%
u 2
16.7%
l 2
16.7%
E 2
16.7%
x 1
 
8.3%
t 1
 
8.3%
+ 1
 
8.3%

descoberta
Categorical

Distinct3
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size64.0 B
Elucidacao Diagn. em Internacao
2 
Investigacao de Contatos
1 
Demanda Ambulatorial
1 

Length

Max length31
Median length27.5
Mean length26.5
Min length20

Characters and Unicode

Total characters106
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st rowElucidacao Diagn. em Internacao
2nd rowInvestigacao de Contatos
3rd rowDemanda Ambulatorial
4th rowElucidacao Diagn. em Internacao

Common Values

ValueCountFrequency (%)
Elucidacao Diagn. em Internacao 2
50.0%
Investigacao de Contatos 1
25.0%
Demanda Ambulatorial 1
25.0%

Length

2023-10-31T16:42:32.694953image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:32.854390image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
elucidacao 2
15.4%
diagn 2
15.4%
em 2
15.4%
internacao 2
15.4%
investigacao 1
7.7%
de 1
7.7%
contatos 1
7.7%
demanda 1
7.7%
ambulatorial 1
7.7%

Most occurring characters

ValueCountFrequency (%)
a 17
16.0%
n 9
 
8.5%
9
 
8.5%
o 8
 
7.5%
c 7
 
6.6%
e 7
 
6.6%
i 6
 
5.7%
t 6
 
5.7%
d 4
 
3.8%
l 4
 
3.8%
Other values (13) 29
27.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 85
80.2%
Uppercase Letter 10
 
9.4%
Space Separator 9
 
8.5%
Other Punctuation 2
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 17
20.0%
n 9
10.6%
o 8
9.4%
c 7
8.2%
e 7
8.2%
i 6
 
7.1%
t 6
 
7.1%
d 4
 
4.7%
l 4
 
4.7%
m 4
 
4.7%
Other values (6) 13
15.3%
Uppercase Letter
ValueCountFrequency (%)
D 3
30.0%
I 3
30.0%
E 2
20.0%
C 1
 
10.0%
A 1
 
10.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 95
89.6%
Common 11
 
10.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 17
17.9%
n 9
 
9.5%
o 8
 
8.4%
c 7
 
7.4%
e 7
 
7.4%
i 6
 
6.3%
t 6
 
6.3%
d 4
 
4.2%
l 4
 
4.2%
m 4
 
4.2%
Other values (11) 23
24.2%
Common
ValueCountFrequency (%)
9
81.8%
. 2
 
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 17
16.0%
n 9
 
8.5%
9
 
8.5%
o 8
 
7.5%
c 7
 
6.6%
e 7
 
6.6%
i 6
 
5.7%
t 6
 
5.7%
d 4
 
3.8%
l 4
 
3.8%
Other values (13) 29
27.4%

bac
Categorical

Distinct1
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size64.0 B
Neg
4 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters3
Distinct categories2 ?
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 rowNeg
2nd rowNeg
3rd rowNeg
4th rowNeg

Common Values

ValueCountFrequency (%)
Neg 4
100.0%

Length

2023-10-31T16:42:32.997370image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:33.138061image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
neg 4
100.0%

Most occurring characters

ValueCountFrequency (%)
N 4
33.3%
e 4
33.3%
g 4
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8
66.7%
Uppercase Letter 4
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4
50.0%
g 4
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 4
33.3%
e 4
33.3%
g 4
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 4
33.3%
e 4
33.3%
g 4
33.3%

BACOUTRO
Categorical

Distinct3
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size64.0 B
Neg
2 
Pos
1 
N/realiz
1 

Length

Max length8
Median length3
Mean length4.25
Min length3

Characters and Unicode

Total characters17
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st rowPos
2nd rowN/realiz
3rd rowNeg
4th rowNeg

Common Values

ValueCountFrequency (%)
Neg 2
50.0%
Pos 1
25.0%
N/realiz 1
25.0%

Length

2023-10-31T16:42:33.257395image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:33.416284image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
neg 2
50.0%
pos 1
25.0%
n/realiz 1
25.0%

Most occurring characters

ValueCountFrequency (%)
N 3
17.6%
e 3
17.6%
g 2
11.8%
P 1
 
5.9%
o 1
 
5.9%
s 1
 
5.9%
/ 1
 
5.9%
r 1
 
5.9%
a 1
 
5.9%
l 1
 
5.9%
Other values (2) 2
11.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12
70.6%
Uppercase Letter 4
 
23.5%
Other Punctuation 1
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3
25.0%
g 2
16.7%
o 1
 
8.3%
s 1
 
8.3%
r 1
 
8.3%
a 1
 
8.3%
l 1
 
8.3%
i 1
 
8.3%
z 1
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
N 3
75.0%
P 1
 
25.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16
94.1%
Common 1
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 3
18.8%
e 3
18.8%
g 2
12.5%
P 1
 
6.2%
o 1
 
6.2%
s 1
 
6.2%
r 1
 
6.2%
a 1
 
6.2%
l 1
 
6.2%
i 1
 
6.2%
Common
ValueCountFrequency (%)
/ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 3
17.6%
e 3
17.6%
g 2
11.8%
P 1
 
5.9%
o 1
 
5.9%
s 1
 
5.9%
/ 1
 
5.9%
r 1
 
5.9%
a 1
 
5.9%
l 1
 
5.9%
Other values (2) 2
11.8%

cultEsc
Categorical

Distinct2
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size64.0 B
Pos
3 
Neg
1 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters6
Distinct categories2 ?
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 (%)25.0%

Sample

1st rowNeg
2nd rowPos
3rd rowPos
4th rowPos

Common Values

ValueCountFrequency (%)
Pos 3
75.0%
Neg 1
 
25.0%

Length

2023-10-31T16:42:33.546379image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:33.696142image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
pos 3
75.0%
neg 1
 
25.0%

Most occurring characters

ValueCountFrequency (%)
P 3
25.0%
o 3
25.0%
s 3
25.0%
N 1
 
8.3%
e 1
 
8.3%
g 1
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8
66.7%
Uppercase Letter 4
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3
37.5%
s 3
37.5%
e 1
 
12.5%
g 1
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
P 3
75.0%
N 1
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 3
25.0%
o 3
25.0%
s 3
25.0%
N 1
 
8.3%
e 1
 
8.3%
g 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 3
25.0%
o 3
25.0%
s 3
25.0%
N 1
 
8.3%
e 1
 
8.3%
g 1
 
8.3%

RX
Categorical

HIGH CORRELATION  UNIFORM 

Distinct2
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size64.0 B
Normal
2 
Susp TB
2 

Length

Max length7
Median length6.5
Mean length6.5
Min length6

Characters and Unicode

Total characters26
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
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 rowNormal
2nd rowNormal
3rd rowSusp TB
4th rowSusp TB

Common Values

ValueCountFrequency (%)
Normal 2
50.0%
Susp TB 2
50.0%

Length

2023-10-31T16:42:33.820443image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:33.971776image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
normal 2
33.3%
susp 2
33.3%
tb 2
33.3%

Most occurring characters

ValueCountFrequency (%)
N 2
 
7.7%
o 2
 
7.7%
r 2
 
7.7%
m 2
 
7.7%
a 2
 
7.7%
l 2
 
7.7%
S 2
 
7.7%
u 2
 
7.7%
s 2
 
7.7%
p 2
 
7.7%
Other values (3) 6
23.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16
61.5%
Uppercase Letter 8
30.8%
Space Separator 2
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 2
12.5%
r 2
12.5%
m 2
12.5%
a 2
12.5%
l 2
12.5%
u 2
12.5%
s 2
12.5%
p 2
12.5%
Uppercase Letter
ValueCountFrequency (%)
N 2
25.0%
S 2
25.0%
T 2
25.0%
B 2
25.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24
92.3%
Common 2
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 2
8.3%
o 2
8.3%
r 2
8.3%
m 2
8.3%
a 2
8.3%
l 2
8.3%
S 2
8.3%
u 2
8.3%
s 2
8.3%
p 2
8.3%
Other values (2) 4
16.7%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 2
 
7.7%
o 2
 
7.7%
r 2
 
7.7%
m 2
 
7.7%
a 2
 
7.7%
l 2
 
7.7%
S 2
 
7.7%
u 2
 
7.7%
s 2
 
7.7%
p 2
 
7.7%
Other values (3) 6
23.1%

NECROP
Categorical

Distinct1
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size64.0 B
N/realiz
4 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters32
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
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 rowN/realiz
2nd rowN/realiz
3rd rowN/realiz
4th rowN/realiz

Common Values

ValueCountFrequency (%)
N/realiz 4
100.0%

Length

2023-10-31T16:42:34.092538image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:34.243486image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
n/realiz 4
100.0%

Most occurring characters

ValueCountFrequency (%)
N 4
12.5%
/ 4
12.5%
r 4
12.5%
e 4
12.5%
a 4
12.5%
l 4
12.5%
i 4
12.5%
z 4
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24
75.0%
Uppercase Letter 4
 
12.5%
Other Punctuation 4
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 4
16.7%
e 4
16.7%
a 4
16.7%
l 4
16.7%
i 4
16.7%
z 4
16.7%
Uppercase Letter
ValueCountFrequency (%)
N 4
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 28
87.5%
Common 4
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 4
14.3%
r 4
14.3%
e 4
14.3%
a 4
14.3%
l 4
14.3%
i 4
14.3%
z 4
14.3%
Common
ValueCountFrequency (%)
/ 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 4
12.5%
/ 4
12.5%
r 4
12.5%
e 4
12.5%
a 4
12.5%
l 4
12.5%
i 4
12.5%
z 4
12.5%

hiv
Categorical

Distinct2
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size64.0 B
Pos
3 
Neg
1 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters12
Distinct characters6
Distinct categories2 ?
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 (%)25.0%

Sample

1st rowPos
2nd rowPos
3rd rowPos
4th rowNeg

Common Values

ValueCountFrequency (%)
Pos 3
75.0%
Neg 1
 
25.0%

Length

2023-10-31T16:42:34.353326image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:34.494861image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
pos 3
75.0%
neg 1
 
25.0%

Most occurring characters

ValueCountFrequency (%)
P 3
25.0%
o 3
25.0%
s 3
25.0%
N 1
 
8.3%
e 1
 
8.3%
g 1
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8
66.7%
Uppercase Letter 4
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3
37.5%
s 3
37.5%
e 1
 
12.5%
g 1
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
P 3
75.0%
N 1
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 3
25.0%
o 3
25.0%
s 3
25.0%
N 1
 
8.3%
e 1
 
8.3%
g 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 3
25.0%
o 3
25.0%
s 3
25.0%
N 1
 
8.3%
e 1
 
8.3%
g 1
 
8.3%

aids
Categorical

Distinct2
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size64.0 B
S
3 
N
1 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4
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

Unique1 ?
Unique (%)25.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowN

Common Values

ValueCountFrequency (%)
S 3
75.0%
N 1
 
25.0%

Length

2023-10-31T16:42:34.613436image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:34.762550image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
s 3
75.0%
n 1
 
25.0%

Most occurring characters

ValueCountFrequency (%)
S 3
75.0%
N 1
 
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 3
75.0%
N 1
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 3
75.0%
N 1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 3
75.0%
N 1
 
25.0%

DIABETES
Boolean

Distinct1
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size36.0 B
False
4 
ValueCountFrequency (%)
False 4
100.0%
2023-10-31T16:42:34.896108image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

ALCOOLISMO
Boolean

Distinct1
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size36.0 B
False
4 
ValueCountFrequency (%)
False 4
100.0%
2023-10-31T16:42:35.021482image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

MENTAL
Boolean

Distinct1
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size36.0 B
False
4 
ValueCountFrequency (%)
False 4
100.0%
2023-10-31T16:42:35.149768image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

DROGADICAO
Categorical

Distinct2
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size64.0 B
N
3 
S
1 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4
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

Unique1 ?
Unique (%)25.0%

Sample

1st rowS
2nd rowN
3rd rowN
4th rowN

Common Values

ValueCountFrequency (%)
N 3
75.0%
S 1
 
25.0%

Length

2023-10-31T16:42:35.273251image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:35.465683image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
n 3
75.0%
s 1
 
25.0%

Most occurring characters

ValueCountFrequency (%)
N 3
75.0%
S 1
 
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 3
75.0%
S 1
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 3
75.0%
S 1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 3
75.0%
S 1
 
25.0%

TABAGISMO
Categorical

Distinct2
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size64.0 B
N
3 
S
1 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4
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

Unique1 ?
Unique (%)25.0%

Sample

1st rowS
2nd rowN
3rd rowN
4th rowN

Common Values

ValueCountFrequency (%)
N 3
75.0%
S 1
 
25.0%

Length

2023-10-31T16:42:35.592490image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:35.756448image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
n 3
75.0%
s 1
 
25.0%

Most occurring characters

ValueCountFrequency (%)
N 3
75.0%
S 1
 
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 3
75.0%
S 1
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 3
75.0%
S 1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 3
75.0%
S 1
 
25.0%

motMudEsquema
Categorical

Distinct1
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size64.0 B
Nulo
4 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters16
Distinct characters4
Distinct categories2 ?
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 rowNulo
2nd rowNulo
3rd rowNulo
4th rowNulo

Common Values

ValueCountFrequency (%)
Nulo 4
100.0%

Length

2023-10-31T16:42:35.886027image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:36.041305image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
nulo 4
100.0%

Most occurring characters

ValueCountFrequency (%)
N 4
25.0%
u 4
25.0%
l 4
25.0%
o 4
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12
75.0%
Uppercase Letter 4
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 4
33.3%
l 4
33.3%
o 4
33.3%
Uppercase Letter
ValueCountFrequency (%)
N 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 4
25.0%
u 4
25.0%
l 4
25.0%
o 4
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 4
25.0%
u 4
25.0%
l 4
25.0%
o 4
25.0%

tipoTrat
Categorical

HIGH CORRELATION  UNIFORM 

Distinct2
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size64.0 B
Auto-Administrado
2 
Supervisionado
2 

Length

Max length17
Median length15.5
Mean length15.5
Min length14

Characters and Unicode

Total characters62
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
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 rowAuto-Administrado
2nd rowSupervisionado
3rd rowSupervisionado
4th rowAuto-Administrado

Common Values

ValueCountFrequency (%)
Auto-Administrado 2
50.0%
Supervisionado 2
50.0%

Length

2023-10-31T16:42:36.170774image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:37.186168image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
auto-administrado 2
50.0%
supervisionado 2
50.0%

Most occurring characters

ValueCountFrequency (%)
o 8
12.9%
i 8
12.9%
d 6
9.7%
A 4
 
6.5%
u 4
 
6.5%
t 4
 
6.5%
n 4
 
6.5%
s 4
 
6.5%
r 4
 
6.5%
a 4
 
6.5%
Other values (6) 12
19.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 54
87.1%
Uppercase Letter 6
 
9.7%
Dash Punctuation 2
 
3.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 8
14.8%
i 8
14.8%
d 6
11.1%
u 4
7.4%
t 4
7.4%
n 4
7.4%
s 4
7.4%
r 4
7.4%
a 4
7.4%
m 2
 
3.7%
Other values (3) 6
11.1%
Uppercase Letter
ValueCountFrequency (%)
A 4
66.7%
S 2
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 60
96.8%
Common 2
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 8
13.3%
i 8
13.3%
d 6
10.0%
A 4
 
6.7%
u 4
 
6.7%
t 4
 
6.7%
n 4
 
6.7%
s 4
 
6.7%
r 4
 
6.7%
a 4
 
6.7%
Other values (5) 10
16.7%
Common
ValueCountFrequency (%)
- 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 8
12.9%
i 8
12.9%
d 6
9.7%
A 4
 
6.5%
u 4
 
6.5%
t 4
 
6.5%
n 4
 
6.5%
s 4
 
6.5%
r 4
 
6.5%
a 4
 
6.5%
Other values (6) 12
19.4%

idade
Categorical

Distinct2
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size64.0 B
40_54
3 
23_39
1 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters20
Distinct characters7
Distinct categories2 ?
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 (%)25.0%

Sample

1st row40_54
2nd row40_54
3rd row40_54
4th row23_39

Common Values

ValueCountFrequency (%)
40_54 3
75.0%
23_39 1
 
25.0%

Length

2023-10-31T16:42:37.358382image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:37.533615image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
40_54 3
75.0%
23_39 1
 
25.0%

Most occurring characters

ValueCountFrequency (%)
4 6
30.0%
_ 4
20.0%
0 3
15.0%
5 3
15.0%
3 2
 
10.0%
2 1
 
5.0%
9 1
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16
80.0%
Connector Punctuation 4
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 6
37.5%
0 3
18.8%
5 3
18.8%
3 2
 
12.5%
2 1
 
6.2%
9 1
 
6.2%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 6
30.0%
_ 4
20.0%
0 3
15.0%
5 3
15.0%
3 2
 
10.0%
2 1
 
5.0%
9 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 6
30.0%
_ 4
20.0%
0 3
15.0%
5 3
15.0%
3 2
 
10.0%
2 1
 
5.0%
9 1
 
5.0%

HISTOPATOL
Categorical

Distinct1
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size64.0 B
N/realiz
4 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters32
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
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 rowN/realiz
2nd rowN/realiz
3rd rowN/realiz
4th rowN/realiz

Common Values

ValueCountFrequency (%)
N/realiz 4
100.0%

Length

2023-10-31T16:42:37.681323image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:37.878417image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
n/realiz 4
100.0%

Most occurring characters

ValueCountFrequency (%)
N 4
12.5%
/ 4
12.5%
r 4
12.5%
e 4
12.5%
a 4
12.5%
l 4
12.5%
i 4
12.5%
z 4
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24
75.0%
Uppercase Letter 4
 
12.5%
Other Punctuation 4
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 4
16.7%
e 4
16.7%
a 4
16.7%
l 4
16.7%
i 4
16.7%
z 4
16.7%
Uppercase Letter
ValueCountFrequency (%)
N 4
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 28
87.5%
Common 4
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 4
14.3%
r 4
14.3%
e 4
14.3%
a 4
14.3%
l 4
14.3%
i 4
14.3%
z 4
14.3%
Common
ValueCountFrequency (%)
/ 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 4
12.5%
/ 4
12.5%
r 4
12.5%
e 4
12.5%
a 4
12.5%
l 4
12.5%
i 4
12.5%
z 4
12.5%

Status_Resistencia
Categorical

Distinct1
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size64.0 B
0
4 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 4
100.0%

Length

2023-10-31T16:42:38.035605image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:38.183478image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4
100.0%

Most occurring characters

ValueCountFrequency (%)
0 4
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4
100.0%

Cluster
Categorical

Distinct1
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size64.0 B
0
4 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 4
100.0%

Length

2023-10-31T16:42:38.300368image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:38.439322image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
0 4
100.0%

Most occurring characters

ValueCountFrequency (%)
0 4
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4
100.0%

Probabilidade
Categorical

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct4
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size64.0 B
0.6120194388542883
1 
0.6292858855467693
1 
0.717662238501694
1 
0.6855705311276303
1 

Length

Max length18
Median length18
Mean length17.75
Min length17

Characters and Unicode

Total characters71
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row0.6120194388542883
2nd row0.6292858855467693
3rd row0.717662238501694
4th row0.6855705311276303

Common Values

ValueCountFrequency (%)
0.6120194388542883 1
25.0%
0.6292858855467693 1
25.0%
0.717662238501694 1
25.0%
0.6855705311276303 1
25.0%

Length

2023-10-31T16:42:38.561725image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-31T16:42:38.738831image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
0.6120194388542883 1
25.0%
0.6292858855467693 1
25.0%
0.717662238501694 1
25.0%
0.6855705311276303 1
25.0%

Most occurring characters

ValueCountFrequency (%)
6 9
12.7%
8 9
12.7%
0 8
11.3%
5 8
11.3%
2 7
9.9%
3 7
9.9%
1 6
8.5%
7 5
7.0%
. 4
5.6%
9 4
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67
94.4%
Other Punctuation 4
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 9
13.4%
8 9
13.4%
0 8
11.9%
5 8
11.9%
2 7
10.4%
3 7
10.4%
1 6
9.0%
7 5
7.5%
9 4
6.0%
4 4
6.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 71
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 9
12.7%
8 9
12.7%
0 8
11.3%
5 8
11.3%
2 7
9.9%
3 7
9.9%
1 6
8.5%
7 5
7.0%
. 4
5.6%
9 4
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 9
12.7%
8 9
12.7%
0 8
11.3%
5 8
11.3%
2 7
9.9%
3 7
9.9%
1 6
8.5%
7 5
7.0%
. 4
5.6%
9 4
5.6%

Correlations

2023-10-31T16:42:38.907470image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
faixaEtariasexoESCOLARIDTIPOCUPsitAtualtipoCasoFORMACLIN1classifdescobertaBACOUTROcultEscRXhivaidsDROGADICAOTABAGISMOtipoTratidadeProbabilidade
faixaEtaria1.0000.0000.0000.0000.0000.0000.7070.0000.0000.0000.7070.0000.7070.7070.7070.7070.7070.7071.000
sexo0.0001.0000.7070.0000.0000.0000.0000.0000.7070.7070.0000.0000.0000.0000.0000.0000.0000.0001.000
ESCOLARID0.0000.7071.0000.7070.0000.7070.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.7070.0001.000
TIPOCUP0.0000.0000.7071.0000.0000.0000.0000.0000.7070.7070.0000.0000.0000.0000.0000.0000.0000.0001.000
sitAtual0.0000.0000.0000.0001.0000.0000.0000.0000.0000.7070.0000.0000.0000.0000.0000.0000.0000.0001.000
tipoCaso0.0000.0000.7070.0000.0001.0000.0000.7070.7070.0000.0000.0000.0000.0000.0000.0000.0000.0001.000
FORMACLIN10.7070.0000.0000.0000.0000.0001.0000.7070.0000.7070.0000.0000.0000.0000.0000.0000.0000.0001.000
classif0.0000.0000.0000.0000.0000.7070.7071.0000.0000.0000.7070.0000.0000.0000.7070.7070.0000.0001.000
descoberta0.0000.7071.0000.7070.0000.7070.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.7070.0001.000
BACOUTRO0.0000.7070.0000.7070.7070.0000.7070.0000.0001.0000.7070.7070.0000.0000.7070.7070.0000.0001.000
cultEsc0.7070.0000.0000.0000.0000.0000.0000.7070.0000.7071.0000.0000.0000.0000.0000.0000.0000.0001.000
RX0.0000.0000.0000.0000.0000.0000.0000.0000.0000.7070.0001.0000.0000.0000.0000.0000.0000.0001.000
hiv0.7070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0001.000
aids0.7070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0001.000
DROGADICAO0.7070.0000.0000.0000.0000.0000.0000.7070.0000.7070.0000.0000.0000.0001.0000.0000.0000.0001.000
TABAGISMO0.7070.0000.0000.0000.0000.0000.0000.7070.0000.7070.0000.0000.0000.0000.0001.0000.0000.0001.000
tipoTrat0.7070.0000.7070.0000.0000.0000.0000.0000.7070.0000.0000.0000.0000.0000.0000.0001.0000.0001.000
idade0.7070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.000
Probabilidade1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-10-31T16:42:29.561507image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-31T16:42:30.089571image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

faixaEtariasexoESCOLARIDTIPOCUPsitAtualtipoCasoFORMACLIN1classifdescobertabacBACOUTROcultEscRXNECROPhivaidsDIABETESALCOOLISMOMENTALDROGADICAOTABAGISMOmotMudEsquematipoTratidadeHISTOPATOLStatus_ResistenciaClusterProbabilidade
37440_49MDe 8 a 11 anosOutraAbandonoNovoGanglionar PerifericaExtElucidacao Diagn. em InternacaoNegPosNegNormalN/realizPosSNNNSSNuloAuto-Administrado40_54N/realiz000.612019
96930_39FDe 4 a 7 anosDona de CasaAbandonoNovoPulPulInvestigacao de ContatosNegN/realizPosNormalN/realizPosSNNNNNNuloSupervisionado40_54N/realiz000.629286
25830_39MDe 12 a 14 anosOutraCuraRecidivaPulP+EDemanda AmbulatorialNegNegPosSusp TBN/realizPosSNNNNNNuloSupervisionado40_54N/realiz000.717662
124320_29MDe 8 a 11 anosOutraCuraNovoPulPulElucidacao Diagn. em InternacaoNegNegPosSusp TBN/realizNegNNNNNNNuloAuto-Administrado23_39N/realiz000.685571
faixaEtariasexoESCOLARIDTIPOCUPsitAtualtipoCasoFORMACLIN1classifdescobertabacBACOUTROcultEscRXNECROPhivaidsDIABETESALCOOLISMOMENTALDROGADICAOTABAGISMOmotMudEsquematipoTratidadeHISTOPATOLStatus_ResistenciaClusterProbabilidade
37440_49MDe 8 a 11 anosOutraAbandonoNovoGanglionar PerifericaExtElucidacao Diagn. em InternacaoNegPosNegNormalN/realizPosSNNNSSNuloAuto-Administrado40_54N/realiz000.612019
96930_39FDe 4 a 7 anosDona de CasaAbandonoNovoPulPulInvestigacao de ContatosNegN/realizPosNormalN/realizPosSNNNNNNuloSupervisionado40_54N/realiz000.629286
25830_39MDe 12 a 14 anosOutraCuraRecidivaPulP+EDemanda AmbulatorialNegNegPosSusp TBN/realizPosSNNNNNNuloSupervisionado40_54N/realiz000.717662
124320_29MDe 8 a 11 anosOutraCuraNovoPulPulElucidacao Diagn. em InternacaoNegNegPosSusp TBN/realizNegNNNNNNNuloAuto-Administrado23_39N/realiz000.685571