Overview

Dataset statistics

Number of variables38
Number of observations964099
Missing cells27
Missing cells (%)< 0.1%
Duplicate rows110
Duplicate rows (%)< 0.1%
Total size in memory279.5 MiB
Average record size in memory304.0 B

Variable types

Categorical34
Numeric4

Alerts

Dataset has 110 (< 0.1%) duplicate rowsDuplicates
DT_NOTIFIC has a high cardinality: 6983 distinct valuesHigh cardinality
TRATAMENTO is highly overall correlated with FORMAHigh correlation
FORMA is highly overall correlated with TRATAMENTO and 1 other fieldsHigh correlation
AGRAVAIDS is highly overall correlated with AGRAVALCOO and 2 other fieldsHigh correlation
AGRAVALCOO is highly overall correlated with AGRAVAIDS and 2 other fieldsHigh correlation
AGRAVDIABE is highly overall correlated with AGRAVALCOO and 1 other fieldsHigh correlation
AGRAVDOENC is highly overall correlated with AGRAVAIDS and 2 other fieldsHigh correlation
BACILOSC_E is highly overall correlated with FORMAHigh correlation
BACILOSC_O is highly overall correlated with RIFAMPICIN and 8 other fieldsHigh correlation
HIV is highly overall correlated with AGRAVAIDSHigh correlation
RIFAMPICIN is highly overall correlated with BACILOSC_O and 8 other fieldsHigh correlation
ISONIAZIDA is highly overall correlated with BACILOSC_O and 8 other fieldsHigh correlation
ETAMBUTOL is highly overall correlated with BACILOSC_O and 8 other fieldsHigh correlation
ESTREPTOMI is highly overall correlated with BACILOSC_O and 8 other fieldsHigh correlation
PIRAZINAMI is highly overall correlated with BACILOSC_O and 8 other fieldsHigh correlation
ETIONAMIDA is highly overall correlated with BACILOSC_O and 8 other fieldsHigh correlation
OUTRAS is highly overall correlated with BACILOSC_O and 8 other fieldsHigh correlation
BACILOSC_1 is highly overall correlated with BACILOSC_2 and 4 other fieldsHigh correlation
BACILOSC_2 is highly overall correlated with BACILOSC_1 and 4 other fieldsHigh correlation
BACILOSC_3 is highly overall correlated with BACILOSC_1 and 4 other fieldsHigh correlation
BACILOSC_4 is highly overall correlated with BACILOSC_1 and 4 other fieldsHigh correlation
BACILOSC_5 is highly overall correlated with BACILOSC_1 and 4 other fieldsHigh correlation
BACILOSC_6 is highly overall correlated with BACILOSC_1 and 4 other fieldsHigh correlation
AGRAVDROGA is highly overall correlated with BACILOSC_O and 8 other fieldsHigh correlation
AGRAVTABAC is highly overall correlated with BACILOSC_O and 8 other fieldsHigh correlation
RAIOX_TORA is highly imbalanced (57.0%)Imbalance
TESTE_TUBE is highly imbalanced (65.8%)Imbalance
FORMA is highly imbalanced (63.9%)Imbalance
CULTURA_ES is highly imbalanced (50.3%)Imbalance
SITUA_ENCE is highly imbalanced (76.9%)Imbalance
DIAS_EM_TRATAMENTO has 15433 (1.6%) zerosZeros

Reproduction

Analysis started2023-06-06 17:57:35.448007
Analysis finished2023-06-06 17:59:55.576085
Duration2 minutes and 20.13 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

DT_NOTIFIC
Categorical

Distinct6983
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
28/03/2017
 
331
14/11/2018
 
331
08/05/2018
 
323
16/11/2016
 
321
17/07/2003
 
318
Other values (6978)
962475 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters9640990
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

Unique34 ?
Unique (%)< 0.1%

Sample

1st row06/01/2001
2nd row16/01/2001
3rd row16/01/2001
4th row22/01/2001
5th row03/01/2001

Common Values

ValueCountFrequency (%)
28/03/2017 331
 
< 0.1%
14/11/2018 331
 
< 0.1%
08/05/2018 323
 
< 0.1%
16/11/2016 321
 
< 0.1%
17/07/2003 318
 
< 0.1%
20/04/2016 317
 
< 0.1%
20/02/2017 313
 
< 0.1%
30/08/2018 313
 
< 0.1%
09/02/2018 312
 
< 0.1%
15/04/2014 312
 
< 0.1%
Other values (6973) 960908
99.7%

Length

2023-06-06T14:59:55.628869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
28/03/2017 331
 
< 0.1%
14/11/2018 331
 
< 0.1%
08/05/2018 323
 
< 0.1%
16/11/2016 321
 
< 0.1%
17/07/2003 318
 
< 0.1%
20/04/2016 317
 
< 0.1%
20/02/2017 313
 
< 0.1%
30/08/2018 313
 
< 0.1%
09/02/2018 312
 
< 0.1%
15/04/2014 312
 
< 0.1%
Other values (6973) 960908
99.7%

Most occurring characters

ValueCountFrequency (%)
0 2629134
27.3%
/ 1928198
20.0%
2 1610059
16.7%
1 1442046
15.0%
3 324476
 
3.4%
8 297283
 
3.1%
7 289352
 
3.0%
4 287179
 
3.0%
5 286971
 
3.0%
6 285377
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7712792
80.0%
Other Punctuation 1928198
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2629134
34.1%
2 1610059
20.9%
1 1442046
18.7%
3 324476
 
4.2%
8 297283
 
3.9%
7 289352
 
3.8%
4 287179
 
3.7%
5 286971
 
3.7%
6 285377
 
3.7%
9 260915
 
3.4%
Other Punctuation
ValueCountFrequency (%)
/ 1928198
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9640990
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2629134
27.3%
/ 1928198
20.0%
2 1610059
16.7%
1 1442046
15.0%
3 324476
 
3.4%
8 297283
 
3.1%
7 289352
 
3.0%
4 287179
 
3.0%
5 286971
 
3.0%
6 285377
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9640990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2629134
27.3%
/ 1928198
20.0%
2 1610059
16.7%
1 1442046
15.0%
3 324476
 
3.4%
8 297283
 
3.1%
7 289352
 
3.0%
4 287179
 
3.0%
5 286971
 
3.0%
6 285377
 
3.0%

CS_SEXO
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
1
626587 
0
337343 
2
 
169

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters964099
Distinct characters3
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 row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 626587
65.0%
0 337343
35.0%
2 169
 
< 0.1%

Length

2023-06-06T14:59:55.697946image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:55.783397image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1 626587
65.0%
0 337343
35.0%
2 169
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 626587
65.0%
0 337343
35.0%
2 169
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 964099
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 626587
65.0%
0 337343
35.0%
2 169
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 964099
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 626587
65.0%
0 337343
35.0%
2 169
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 964099
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 626587
65.0%
0 337343
35.0%
2 169
 
< 0.1%

CS_RACA
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4788108
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 MiB
2023-06-06T14:59:55.829108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q34
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5085872
Coefficient of variation (CV)0.7211048
Kurtosis0.37320886
Mean3.4788108
Median Absolute Deviation (MAD)2
Skewness1.0721364
Sum3353918
Variance6.2930095
MonotonicityNot monotonic
2023-06-06T14:59:55.880212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 400847
41.6%
1 299253
31.0%
9 125731
 
13.0%
2 118048
 
12.2%
5 11471
 
1.2%
3 8749
 
0.9%
ValueCountFrequency (%)
1 299253
31.0%
2 118048
 
12.2%
3 8749
 
0.9%
4 400847
41.6%
5 11471
 
1.2%
9 125731
 
13.0%
ValueCountFrequency (%)
9 125731
 
13.0%
5 11471
 
1.2%
4 400847
41.6%
3 8749
 
0.9%
2 118048
 
12.2%
1 299253
31.0%

TRATAMENTO
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3279964
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 MiB
2023-06-06T14:59:55.931877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.91687118
Coefficient of variation (CV)0.69041692
Kurtosis9.3902297
Mean1.3279964
Median Absolute Deviation (MAD)0
Skewness3.1303389
Sum1280320
Variance0.84065277
MonotonicityNot monotonic
2023-06-06T14:59:55.984021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 818553
84.9%
2 60792
 
6.3%
3 38353
 
4.0%
5 35312
 
3.7%
4 9138
 
0.9%
6 1849
 
0.2%
9 102
 
< 0.1%
ValueCountFrequency (%)
1 818553
84.9%
2 60792
 
6.3%
3 38353
 
4.0%
4 9138
 
0.9%
5 35312
 
3.7%
6 1849
 
0.2%
9 102
 
< 0.1%
ValueCountFrequency (%)
9 102
 
< 0.1%
6 1849
 
0.2%
5 35312
 
3.7%
4 9138
 
0.9%
3 38353
 
4.0%
2 60792
 
6.3%
1 818553
84.9%

RAIOX_TORA
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
1.0
745157 
4.0
163691 
2.0
 
45550
3.0
 
9582
9.0
 
119

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
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

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row4.0

Common Values

ValueCountFrequency (%)
1.0 745157
77.3%
4.0 163691
 
17.0%
2.0 45550
 
4.7%
3.0 9582
 
1.0%
9.0 119
 
< 0.1%

Length

2023-06-06T14:59:56.038135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:56.094928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 745157
77.3%
4.0 163691
 
17.0%
2.0 45550
 
4.7%
3.0 9582
 
1.0%
9.0 119
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
1 745157
25.8%
4 163691
 
5.7%
2 45550
 
1.6%
3 9582
 
0.3%
9 119
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
1 745157
38.6%
4 163691
 
8.5%
2 45550
 
2.4%
3 9582
 
0.5%
9 119
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
1 745157
25.8%
4 163691
 
5.7%
2 45550
 
1.6%
3 9582
 
0.3%
9 119
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
1 745157
25.8%
4 163691
 
5.7%
2 45550
 
1.6%
3 9582
 
0.3%
9 119
 
< 0.1%

TESTE_TUBE
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
4.0
821948 
3.0
89605 
1.0
 
36835
2.0
 
15301
9.0
 
410

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
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

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 821948
85.3%
3.0 89605
 
9.3%
1.0 36835
 
3.8%
2.0 15301
 
1.6%
9.0 410
 
< 0.1%

Length

2023-06-06T14:59:56.148290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:56.207332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
4.0 821948
85.3%
3.0 89605
 
9.3%
1.0 36835
 
3.8%
2.0 15301
 
1.6%
9.0 410
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
4 821948
28.4%
3 89605
 
3.1%
1 36835
 
1.3%
2 15301
 
0.5%
9 410
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
4 821948
42.6%
3 89605
 
4.6%
1 36835
 
1.9%
2 15301
 
0.8%
9 410
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
4 821948
28.4%
3 89605
 
3.1%
1 36835
 
1.3%
2 15301
 
0.5%
9 410
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
4 821948
28.4%
3 89605
 
3.1%
1 36835
 
1.3%
2 15301
 
0.5%
9 410
 
< 0.1%

FORMA
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
1.0
815686 
2.0
123293 
3.0
 
25005
9.0
 
115

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
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

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1.0 815686
84.6%
2.0 123293
 
12.8%
3.0 25005
 
2.6%
9.0 115
 
< 0.1%

Length

2023-06-06T14:59:56.258324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:56.318949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 815686
84.6%
2.0 123293
 
12.8%
3.0 25005
 
2.6%
9.0 115
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
1 815686
28.2%
2 123293
 
4.3%
3 25005
 
0.9%
9 115
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
1 815686
42.3%
2 123293
 
6.4%
3 25005
 
1.3%
9 115
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
1 815686
28.2%
2 123293
 
4.3%
3 25005
 
0.9%
9 115
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
1 815686
28.2%
2 123293
 
4.3%
3 25005
 
0.9%
9 115
 
< 0.1%

AGRAVAIDS
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
2.0
518380 
9.0
387995 
1.0
57723 
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
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 (%)< 0.1%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
2.0 518380
53.8%
9.0 387995
40.2%
1.0 57723
 
6.0%
3.0 1
 
< 0.1%

Length

2023-06-06T14:59:56.390127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:56.464030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 518380
53.8%
9.0 387995
40.2%
1.0 57723
 
6.0%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 518380
17.9%
9 387995
13.4%
1 57723
 
2.0%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
2 518380
26.9%
9 387995
20.1%
1 57723
 
3.0%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 518380
17.9%
9 387995
13.4%
1 57723
 
2.0%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 518380
17.9%
9 387995
13.4%
1 57723
 
2.0%
3 1
 
< 0.1%

AGRAVALCOO
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
2.0
523144 
9.0
324790 
1.0
116165 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
Distinct characters5
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 row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
2.0 523144
54.3%
9.0 324790
33.7%
1.0 116165
 
12.0%

Length

2023-06-06T14:59:56.529973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:56.600017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 523144
54.3%
9.0 324790
33.7%
1.0 116165
 
12.0%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 523144
18.1%
9 324790
 
11.2%
1 116165
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
2 523144
27.1%
9 324790
 
16.8%
1 116165
 
6.0%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 523144
18.1%
9 324790
 
11.2%
1 116165
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 523144
18.1%
9 324790
 
11.2%
1 116165
 
4.0%

AGRAVDIABE
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
2.0
568204 
9.0
339492 
1.0
 
56402
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
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 (%)< 0.1%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
2.0 568204
58.9%
9.0 339492
35.2%
1.0 56402
 
5.9%
3.0 1
 
< 0.1%

Length

2023-06-06T14:59:56.666300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:56.731607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 568204
58.9%
9.0 339492
35.2%
1.0 56402
 
5.9%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 568204
19.6%
9 339492
 
11.7%
1 56402
 
2.0%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
2 568204
29.5%
9 339492
 
17.6%
1 56402
 
2.9%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 568204
19.6%
9 339492
 
11.7%
1 56402
 
2.0%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 568204
19.6%
9 339492
 
11.7%
1 56402
 
2.0%
3 1
 
< 0.1%

AGRAVDOENC
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
2.0
601675 
9.0
344290 
1.0
 
18134

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
Distinct characters5
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 row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
2.0 601675
62.4%
9.0 344290
35.7%
1.0 18134
 
1.9%

Length

2023-06-06T14:59:56.790794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:56.854275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 601675
62.4%
9.0 344290
35.7%
1.0 18134
 
1.9%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 601675
20.8%
9 344290
 
11.9%
1 18134
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
2 601675
31.2%
9 344290
 
17.9%
1 18134
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 601675
20.8%
9 344290
 
11.9%
1 18134
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 601675
20.8%
9 344290
 
11.9%
1 18134
 
0.6%

AGRAVOUTRA
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
9.0
501303 
2.0
386942 
1.0
75854 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
Distinct characters5
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 row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.0 501303
52.0%
2.0 386942
40.1%
1.0 75854
 
7.9%

Length

2023-06-06T14:59:56.929781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:56.995616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.0 501303
52.0%
2.0 386942
40.1%
1.0 75854
 
7.9%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
9 501303
17.3%
2 386942
13.4%
1 75854
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
9 501303
26.0%
2 386942
20.1%
1 75854
 
3.9%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
9 501303
17.3%
2 386942
13.4%
1 75854
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
9 501303
17.3%
2 386942
13.4%
1 75854
 
2.6%

BACILOSC_E
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
1.0
525168 
3.0
217773 
2.0
211415 
4.0
 
9640
9.0
 
103

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
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

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1.0 525168
54.5%
3.0 217773
22.6%
2.0 211415
21.9%
4.0 9640
 
1.0%
9.0 103
 
< 0.1%

Length

2023-06-06T14:59:57.054308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:57.122976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 525168
54.5%
3.0 217773
22.6%
2.0 211415
21.9%
4.0 9640
 
1.0%
9.0 103
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
1 525168
18.2%
3 217773
 
7.5%
2 211415
 
7.3%
4 9640
 
0.3%
9 103
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
1 525168
27.2%
3 217773
 
11.3%
2 211415
 
11.0%
4 9640
 
0.5%
9 103
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
1 525168
18.2%
3 217773
 
7.5%
2 211415
 
7.3%
4 9640
 
0.3%
9 103
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
1 525168
18.2%
3 217773
 
7.5%
2 211415
 
7.3%
4 9640
 
0.3%
9 103
 
< 0.1%

BACILOS_E2
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
9.0
592351 
1.0
155845 
3.0
133793 
2.0
82110 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
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

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.0 592351
61.4%
1.0 155845
 
16.2%
3.0 133793
 
13.9%
2.0 82110
 
8.5%

Length

2023-06-06T14:59:57.177131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:57.233400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.0 592351
61.4%
1.0 155845
 
16.2%
3.0 133793
 
13.9%
2.0 82110
 
8.5%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
9 592351
20.5%
1 155845
 
5.4%
3 133793
 
4.6%
2 82110
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
9 592351
30.7%
1 155845
 
8.1%
3 133793
 
6.9%
2 82110
 
4.3%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
9 592351
20.5%
1 155845
 
5.4%
3 133793
 
4.6%
2 82110
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
9 592351
20.5%
1 155845
 
5.4%
3 133793
 
4.6%
2 82110
 
2.8%

BACILOSC_O
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
3.0
593447 
9.0
321216 
2.0
 
29609
1.0
 
19827

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
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

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3.0 593447
61.6%
9.0 321216
33.3%
2.0 29609
 
3.1%
1.0 19827
 
2.1%

Length

2023-06-06T14:59:57.293400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:57.360695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
3.0 593447
61.6%
9.0 321216
33.3%
2.0 29609
 
3.1%
1.0 19827
 
2.1%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
3 593447
20.5%
9 321216
 
11.1%
2 29609
 
1.0%
1 19827
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
3 593447
30.8%
9 321216
 
16.7%
2 29609
 
1.5%
1 19827
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
3 593447
20.5%
9 321216
 
11.1%
2 29609
 
1.0%
1 19827
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
3 593447
20.5%
9 321216
 
11.1%
2 29609
 
1.0%
1 19827
 
0.7%

CULTURA_ES
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
4.0
733646 
1.0
121293 
2.0
 
64506
3.0
 
42627
9.0
 
2027

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
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

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 733646
76.1%
1.0 121293
 
12.6%
2.0 64506
 
6.7%
3.0 42627
 
4.4%
9.0 2027
 
0.2%

Length

2023-06-06T14:59:57.420712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:57.480014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
4.0 733646
76.1%
1.0 121293
 
12.6%
2.0 64506
 
6.7%
3.0 42627
 
4.4%
9.0 2027
 
0.2%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
4 733646
25.4%
1 121293
 
4.2%
2 64506
 
2.2%
3 42627
 
1.5%
9 2027
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
4 733646
38.0%
1 121293
 
6.3%
2 64506
 
3.3%
3 42627
 
2.2%
9 2027
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
4 733646
25.4%
1 121293
 
4.2%
2 64506
 
2.2%
3 42627
 
1.5%
9 2027
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
4 733646
25.4%
1 121293
 
4.2%
2 64506
 
2.2%
3 42627
 
1.5%
9 2027
 
0.1%

HIV
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
2.0
478373 
4.0
350989 
3.0
68223 
1.0
65466 
9.0
 
1048

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
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

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
2.0 478373
49.6%
4.0 350989
36.4%
3.0 68223
 
7.1%
1.0 65466
 
6.8%
9.0 1048
 
0.1%

Length

2023-06-06T14:59:57.536368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:57.604933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 478373
49.6%
4.0 350989
36.4%
3.0 68223
 
7.1%
1.0 65466
 
6.8%
9.0 1048
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 478373
16.5%
4 350989
 
12.1%
3 68223
 
2.4%
1 65466
 
2.3%
9 1048
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
2 478373
24.8%
4 350989
 
18.2%
3 68223
 
3.5%
1 65466
 
3.4%
9 1048
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 478373
16.5%
4 350989
 
12.1%
3 68223
 
2.4%
1 65466
 
2.3%
9 1048
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 478373
16.5%
4 350989
 
12.1%
3 68223
 
2.4%
1 65466
 
2.3%
9 1048
 
< 0.1%

RIFAMPICIN
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
1.0
639766 
9.0
315812 
2.0
 
8521

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
Distinct characters5
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 row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 639766
66.4%
9.0 315812
32.8%
2.0 8521
 
0.9%

Length

2023-06-06T14:59:57.665172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:57.737116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 639766
66.4%
9.0 315812
32.8%
2.0 8521
 
0.9%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
1 639766
22.1%
9 315812
 
10.9%
2 8521
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
1 639766
33.2%
9 315812
 
16.4%
2 8521
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
1 639766
22.1%
9 315812
 
10.9%
2 8521
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
1 639766
22.1%
9 315812
 
10.9%
2 8521
 
0.3%

ISONIAZIDA
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
1.0
639620 
9.0
316061 
2.0
 
8418

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
Distinct characters5
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 row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 639620
66.3%
9.0 316061
32.8%
2.0 8418
 
0.9%

Length

2023-06-06T14:59:57.957915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:58.031814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 639620
66.3%
9.0 316061
32.8%
2.0 8418
 
0.9%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
1 639620
22.1%
9 316061
 
10.9%
2 8418
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
1 639620
33.2%
9 316061
 
16.4%
2 8418
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
1 639620
22.1%
9 316061
 
10.9%
2 8418
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
1 639620
22.1%
9 316061
 
10.9%
2 8418
 
0.3%

ETAMBUTOL
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
2.0
395454 
9.0
337218 
1.0
231427 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
Distinct characters5
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 row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 395454
41.0%
9.0 337218
35.0%
1.0 231427
24.0%

Length

2023-06-06T14:59:58.097643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:58.165663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 395454
41.0%
9.0 337218
35.0%
1.0 231427
24.0%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 395454
13.7%
9 337218
 
11.7%
1 231427
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
2 395454
20.5%
9 337218
 
17.5%
1 231427
 
12.0%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 395454
13.7%
9 337218
 
11.7%
1 231427
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 395454
13.7%
9 337218
 
11.7%
1 231427
 
8.0%

ESTREPTOMI
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
2.0
607795 
9.0
348534 
1.0
 
7770

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
Distinct characters5
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 row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 607795
63.0%
9.0 348534
36.2%
1.0 7770
 
0.8%

Length

2023-06-06T14:59:58.228743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:58.301014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 607795
63.0%
9.0 348534
36.2%
1.0 7770
 
0.8%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 607795
21.0%
9 348534
 
12.1%
1 7770
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
2 607795
31.5%
9 348534
 
18.1%
1 7770
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 607795
21.0%
9 348534
 
12.1%
1 7770
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 607795
21.0%
9 348534
 
12.1%
1 7770
 
0.3%

PIRAZINAMI
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
1.0
633834 
9.0
316875 
2.0
 
13390

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
Distinct characters5
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 row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 633834
65.7%
9.0 316875
32.9%
2.0 13390
 
1.4%

Length

2023-06-06T14:59:58.372040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:58.449972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 633834
65.7%
9.0 316875
32.9%
2.0 13390
 
1.4%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
1 633834
21.9%
9 316875
 
11.0%
2 13390
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
1 633834
32.9%
9 316875
 
16.4%
2 13390
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
1 633834
21.9%
9 316875
 
11.0%
2 13390
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
1 633834
21.9%
9 316875
 
11.0%
2 13390
 
0.5%

ETIONAMIDA
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
2.0
606141 
9.0
348992 
1.0
 
8966

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
Distinct characters5
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 row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 606141
62.9%
9.0 348992
36.2%
1.0 8966
 
0.9%

Length

2023-06-06T14:59:58.522231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:58.603814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 606141
62.9%
9.0 348992
36.2%
1.0 8966
 
0.9%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 606141
21.0%
9 348992
 
12.1%
1 8966
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
2 606141
31.4%
9 348992
 
18.1%
1 8966
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 606141
21.0%
9 348992
 
12.1%
1 8966
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 606141
21.0%
9 348992
 
12.1%
1 8966
 
0.3%

OUTRAS
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
2.0
566324 
9.0
388206 
1.0
 
9569

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
Distinct characters5
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 row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 566324
58.7%
9.0 388206
40.3%
1.0 9569
 
1.0%

Length

2023-06-06T14:59:58.666373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:58.729448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 566324
58.7%
9.0 388206
40.3%
1.0 9569
 
1.0%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 566324
19.6%
9 388206
13.4%
1 9569
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
2 566324
29.4%
9 388206
20.1%
1 9569
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 566324
19.6%
9 388206
13.4%
1 9569
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 566324
19.6%
9 388206
13.4%
1 9569
 
0.3%

TRAT_SUPER
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
2.0
339043 
1.0
316579 
9.0
308477 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
Distinct characters5
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 row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 339043
35.2%
1.0 316579
32.8%
9.0 308477
32.0%

Length

2023-06-06T14:59:58.785935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:58.848163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2.0 339043
35.2%
1.0 316579
32.8%
9.0 308477
32.0%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 339043
 
11.7%
1 316579
 
10.9%
9 308477
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
2 339043
 
17.6%
1 316579
 
16.4%
9 308477
 
16.0%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 339043
 
11.7%
1 316579
 
10.9%
9 308477
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
2 339043
 
11.7%
1 316579
 
10.9%
9 308477
 
10.7%

DOENCA_TRA
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
9.0
536016 
2.0
415302 
1.0
 
12780
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
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 (%)< 0.1%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
9.0 536016
55.6%
2.0 415302
43.1%
1.0 12780
 
1.3%
3.0 1
 
< 0.1%

Length

2023-06-06T14:59:58.909927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:58.976723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.0 536016
55.6%
2.0 415302
43.1%
1.0 12780
 
1.3%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
9 536016
18.5%
2 415302
14.4%
1 12780
 
0.4%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
9 536016
27.8%
2 415302
21.5%
1 12780
 
0.7%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
9 536016
18.5%
2 415302
14.4%
1 12780
 
0.4%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
9 536016
18.5%
2 415302
14.4%
1 12780
 
0.4%
3 1
 
< 0.1%

BACILOSC_1
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
9.0
370965 
3.0
248122 
1.0
156038 
2.0
155774 
4.0
 
33200

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
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

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.0 370965
38.5%
3.0 248122
25.7%
1.0 156038
16.2%
2.0 155774
16.2%
4.0 33200
 
3.4%

Length

2023-06-06T14:59:59.034390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:59.099816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.0 370965
38.5%
3.0 248122
25.7%
1.0 156038
16.2%
2.0 155774
16.2%
4.0 33200
 
3.4%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
9 370965
 
12.8%
3 248122
 
8.6%
1 156038
 
5.4%
2 155774
 
5.4%
4 33200
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
9 370965
 
19.2%
3 248122
 
12.9%
1 156038
 
8.1%
2 155774
 
8.1%
4 33200
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
9 370965
 
12.8%
3 248122
 
8.6%
1 156038
 
5.4%
2 155774
 
5.4%
4 33200
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
9 370965
 
12.8%
3 248122
 
8.6%
1 156038
 
5.4%
2 155774
 
5.4%
4 33200
 
1.1%

BACILOSC_2
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
3.0
368968 
2.0
319550 
9.0
180534 
1.0
61752 
4.0
 
33295

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
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

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3.0 368968
38.3%
2.0 319550
33.1%
9.0 180534
18.7%
1.0 61752
 
6.4%
4.0 33295
 
3.5%

Length

2023-06-06T14:59:59.156975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:59.214251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
3.0 368968
38.3%
2.0 319550
33.1%
9.0 180534
18.7%
1.0 61752
 
6.4%
4.0 33295
 
3.5%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
3 368968
 
12.8%
2 319550
 
11.0%
9 180534
 
6.2%
1 61752
 
2.1%
4 33295
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
3 368968
 
19.1%
2 319550
 
16.6%
9 180534
 
9.4%
1 61752
 
3.2%
4 33295
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
3 368968
 
12.8%
2 319550
 
11.0%
9 180534
 
6.2%
1 61752
 
2.1%
4 33295
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
3 368968
 
12.8%
2 319550
 
11.0%
9 180534
 
6.2%
1 61752
 
2.1%
4 33295
 
1.2%

BACILOSC_3
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
9.0
374345 
3.0
325485 
2.0
217622 
4.0
 
33354
1.0
 
13293

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
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

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.0 374345
38.8%
3.0 325485
33.8%
2.0 217622
22.6%
4.0 33354
 
3.5%
1.0 13293
 
1.4%

Length

2023-06-06T14:59:59.278251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:59.342648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.0 374345
38.8%
3.0 325485
33.8%
2.0 217622
22.6%
4.0 33354
 
3.5%
1.0 13293
 
1.4%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
9 374345
 
12.9%
3 325485
 
11.3%
2 217622
 
7.5%
4 33354
 
1.2%
1 13293
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
9 374345
 
19.4%
3 325485
 
16.9%
2 217622
 
11.3%
4 33354
 
1.7%
1 13293
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
9 374345
 
12.9%
3 325485
 
11.3%
2 217622
 
7.5%
4 33354
 
1.2%
1 13293
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
9 374345
 
12.9%
3 325485
 
11.3%
2 217622
 
7.5%
4 33354
 
1.2%
1 13293
 
0.5%

BACILOSC_4
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
3.0
403910 
2.0
317154 
9.0
198689 
4.0
 
33391
1.0
 
10955

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
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

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3.0 403910
41.9%
2.0 317154
32.9%
9.0 198689
20.6%
4.0 33391
 
3.5%
1.0 10955
 
1.1%

Length

2023-06-06T14:59:59.398375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:59.464050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
3.0 403910
41.9%
2.0 317154
32.9%
9.0 198689
20.6%
4.0 33391
 
3.5%
1.0 10955
 
1.1%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
3 403910
14.0%
2 317154
 
11.0%
9 198689
 
6.9%
4 33391
 
1.2%
1 10955
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
3 403910
20.9%
2 317154
 
16.4%
9 198689
 
10.3%
4 33391
 
1.7%
1 10955
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
3 403910
14.0%
2 317154
 
11.0%
9 198689
 
6.9%
4 33391
 
1.2%
1 10955
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
3 403910
14.0%
2 317154
 
11.0%
9 198689
 
6.9%
4 33391
 
1.2%
1 10955
 
0.4%

BACILOSC_5
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
9.0
389669 
3.0
330761 
2.0
206606 
4.0
 
33380
1.0
 
3683

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
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

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.0 389669
40.4%
3.0 330761
34.3%
2.0 206606
21.4%
4.0 33380
 
3.5%
1.0 3683
 
0.4%

Length

2023-06-06T14:59:59.529090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:59.609024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.0 389669
40.4%
3.0 330761
34.3%
2.0 206606
21.4%
4.0 33380
 
3.5%
1.0 3683
 
0.4%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
9 389669
13.5%
3 330761
 
11.4%
2 206606
 
7.1%
4 33380
 
1.2%
1 3683
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
9 389669
20.2%
3 330761
 
17.2%
2 206606
 
10.7%
4 33380
 
1.7%
1 3683
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
9 389669
13.5%
3 330761
 
11.4%
2 206606
 
7.1%
4 33380
 
1.2%
1 3683
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
9 389669
13.5%
3 330761
 
11.4%
2 206606
 
7.1%
4 33380
 
1.2%
1 3683
 
0.1%

BACILOSC_6
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
3.0
359221 
2.0
347432 
9.0
219752 
4.0
 
33383
1.0
 
4311

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
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

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3.0 359221
37.3%
2.0 347432
36.0%
9.0 219752
22.8%
4.0 33383
 
3.5%
1.0 4311
 
0.4%

Length

2023-06-06T14:59:59.674712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:59.742510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
3.0 359221
37.3%
2.0 347432
36.0%
9.0 219752
22.8%
4.0 33383
 
3.5%
1.0 4311
 
0.4%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
3 359221
 
12.4%
2 347432
 
12.0%
9 219752
 
7.6%
4 33383
 
1.2%
1 4311
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
3 359221
 
18.6%
2 347432
 
18.0%
9 219752
 
11.4%
4 33383
 
1.7%
1 4311
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
3 359221
 
12.4%
2 347432
 
12.0%
9 219752
 
7.6%
4 33383
 
1.2%
1 4311
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
3 359221
 
12.4%
2 347432
 
12.0%
9 219752
 
7.6%
4 33383
 
1.2%
1 4311
 
0.1%

SITUA_ENCE
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
1.0
927909 
3.0
 
36190

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
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 row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 927909
96.2%
3.0 36190
 
3.8%

Length

2023-06-06T14:59:59.807784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:59.867247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 927909
96.2%
3.0 36190
 
3.8%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
1 927909
32.1%
3 36190
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
1 927909
48.1%
3 36190
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
1 927909
32.1%
3 36190
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
1 927909
32.1%
3 36190
 
1.3%

AGRAVDROGA
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
9.0
666187 
2.0
262647 
1.0
 
35265

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
Distinct characters5
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 row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.0 666187
69.1%
2.0 262647
 
27.2%
1.0 35265
 
3.7%

Length

2023-06-06T14:59:59.922724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T14:59:59.983727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.0 666187
69.1%
2.0 262647
 
27.2%
1.0 35265
 
3.7%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
9 666187
23.0%
2 262647
 
9.1%
1 35265
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
9 666187
34.5%
2 262647
 
13.6%
1 35265
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
9 666187
23.0%
2 262647
 
9.1%
1 35265
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
9 666187
23.0%
2 262647
 
9.1%
1 35265
 
1.2%

AGRAVTABAC
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
9.0
664694 
2.0
236287 
1.0
 
63118

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2892297
Distinct characters5
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 row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.0 664694
68.9%
2.0 236287
 
24.5%
1.0 63118
 
6.5%

Length

2023-06-06T15:00:00.047124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-06T15:00:00.108135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9.0 664694
68.9%
2.0 236287
 
24.5%
1.0 63118
 
6.5%

Most occurring characters

ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
9 664694
23.0%
2 236287
 
8.2%
1 63118
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1928198
66.7%
Other Punctuation 964099
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 964099
50.0%
9 664694
34.5%
2 236287
 
12.3%
1 63118
 
3.3%
Other Punctuation
ValueCountFrequency (%)
. 964099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2892297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
9 664694
23.0%
2 236287
 
8.2%
1 63118
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2892297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 964099
33.3%
0 964099
33.3%
9 664694
23.0%
2 236287
 
8.2%
1 63118
 
2.2%

UF
Categorical

Distinct27
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size7.4 MiB
RJ
168495 
SP
92690 
BA
81470 
RS
72437 
PE
65629 
Other values (22)
483377 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1928196
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
RJ 168495
17.5%
SP 92690
 
9.6%
BA 81470
 
8.5%
RS 72437
 
7.5%
PE 65629
 
6.8%
MG 63477
 
6.6%
CE 55343
 
5.7%
PA 53016
 
5.5%
AM 39487
 
4.1%
PR 39427
 
4.1%
Other values (17) 232627
24.1%

Length

2023-06-06T15:00:00.164137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rj 168495
17.5%
sp 92690
 
9.6%
ba 81470
 
8.5%
rs 72437
 
7.5%
pe 65629
 
6.8%
mg 63477
 
6.6%
ce 55343
 
5.7%
pa 53016
 
5.5%
am 39487
 
4.1%
pr 39427
 
4.1%
Other values (17) 232627
24.1%

Most occurring characters

ValueCountFrequency (%)
R 308936
16.0%
P 284158
14.7%
A 237180
12.3%
S 236949
12.3%
M 170762
8.9%
J 168495
8.7%
E 151678
7.9%
B 97737
 
5.1%
C 89206
 
4.6%
G 76815
 
4.0%
Other values (7) 106280
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1928196
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 308936
16.0%
P 284158
14.7%
A 237180
12.3%
S 236949
12.3%
M 170762
8.9%
J 168495
8.7%
E 151678
7.9%
B 97737
 
5.1%
C 89206
 
4.6%
G 76815
 
4.0%
Other values (7) 106280
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 1928196
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 308936
16.0%
P 284158
14.7%
A 237180
12.3%
S 236949
12.3%
M 170762
8.9%
J 168495
8.7%
E 151678
7.9%
B 97737
 
5.1%
C 89206
 
4.6%
G 76815
 
4.0%
Other values (7) 106280
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1928196
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 308936
16.0%
P 284158
14.7%
A 237180
12.3%
S 236949
12.3%
M 170762
8.9%
J 168495
8.7%
E 151678
7.9%
B 97737
 
5.1%
C 89206
 
4.6%
G 76815
 
4.0%
Other values (7) 106280
 
5.5%

DIAS_EM_TRATAMENTO
Real number (ℝ)

Distinct1872
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean204.90569
Minimum0
Maximum4909
Zeros15433
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size7.4 MiB
2023-06-06T15:00:00.234371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile45
Q1181
median190
Q3221
95-th percentile339
Maximum4909
Range4909
Interquartile range (IQR)40

Descriptive statistics

Standard deviation103.44353
Coefficient of variation (CV)0.50483484
Kurtosis146.47058
Mean204.90569
Median Absolute Deviation (MAD)17
Skewness7.1690704
Sum1.9754937 × 108
Variance10700.565
MonotonicityNot monotonic
2023-06-06T15:00:00.316788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
182 43203
 
4.5%
184 34658
 
3.6%
183 34275
 
3.6%
181 29702
 
3.1%
189 24539
 
2.5%
185 22454
 
2.3%
186 18565
 
1.9%
188 17653
 
1.8%
180 17080
 
1.8%
187 16921
 
1.8%
Other values (1862) 705049
73.1%
ValueCountFrequency (%)
0 15433
1.6%
1 2592
 
0.3%
2 1770
 
0.2%
3 1652
 
0.2%
4 1473
 
0.2%
5 1307
 
0.1%
6 1291
 
0.1%
7 1326
 
0.1%
8 1026
 
0.1%
9 934
 
0.1%
ValueCountFrequency (%)
4909 1
< 0.1%
4879 1
< 0.1%
4570 1
< 0.1%
4443 1
< 0.1%
4407 1
< 0.1%
4358 1
< 0.1%
4294 1
< 0.1%
4163 1
< 0.1%
4042 1
< 0.1%
3989 1
< 0.1%

IDADE
Real number (ℝ)

Distinct116
Distinct (%)< 0.1%
Missing26
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean39.804771
Minimum0
Maximum120
Zeros60
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.4 MiB
2023-06-06T15:00:00.388768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q126
median38
Q352
95-th percentile71
Maximum120
Range120
Interquartile range (IQR)26

Descriptive statistics

Standard deviation17.085009
Coefficient of variation (CV)0.42922014
Kurtosis-0.30126314
Mean39.804771
Median Absolute Deviation (MAD)12
Skewness0.4304062
Sum38374705
Variance291.89754
MonotonicityNot monotonic
2023-06-06T15:00:00.466616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 23282
 
2.4%
23 22923
 
2.4%
25 22869
 
2.4%
26 22642
 
2.3%
27 22579
 
2.3%
22 22374
 
2.3%
28 22159
 
2.3%
29 21916
 
2.3%
30 21690
 
2.2%
21 21130
 
2.2%
Other values (106) 740509
76.8%
ValueCountFrequency (%)
0 60
 
< 0.1%
1 2505
0.3%
2 2098
0.2%
3 1816
0.2%
4 1683
0.2%
5 1605
0.2%
6 1527
0.2%
7 1472
0.2%
8 1556
0.2%
9 1581
0.2%
ValueCountFrequency (%)
120 1
 
< 0.1%
119 1
 
< 0.1%
115 1
 
< 0.1%
114 3
 
< 0.1%
112 1
 
< 0.1%
111 1
 
< 0.1%
110 2
 
< 0.1%
108 4
 
< 0.1%
107 6
 
< 0.1%
106 26
< 0.1%

Interactions

2023-06-06T14:59:47.863274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-06T14:59:45.666453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-06T14:59:46.438102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-06T14:59:47.145695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-06T14:59:48.044507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-06T14:59:45.885412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-06T14:59:46.613265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-06T14:59:47.329816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-06T14:59:48.230748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-06T14:59:46.090841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-06T14:59:46.795315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-06T14:59:47.496717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-06T14:59:48.415483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-06T14:59:46.274557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-06T14:59:46.955952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-06T14:59:47.677350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-06-06T15:00:00.565120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
CS_RACATRATAMENTODIAS_EM_TRATAMENTOIDADECS_SEXORAIOX_TORATESTE_TUBEFORMAAGRAVAIDSAGRAVALCOOAGRAVDIABEAGRAVDOENCAGRAVOUTRABACILOSC_EBACILOS_E2BACILOSC_OCULTURA_ESHIVRIFAMPICINISONIAZIDAETAMBUTOLESTREPTOMIPIRAZINAMIETIONAMIDAOUTRASTRAT_SUPERDOENCA_TRABACILOSC_1BACILOSC_2BACILOSC_3BACILOSC_4BACILOSC_5BACILOSC_6SITUA_ENCEAGRAVDROGAAGRAVTABACUF
CS_RACA1.0000.049-0.002-0.0450.0240.0390.0280.0360.1420.2110.1650.2020.1680.0300.0990.0750.0410.0980.0830.0820.1240.0720.0820.0710.0700.1450.0840.1280.0740.1260.0700.1230.0660.0280.0920.0910.238
TRATAMENTO0.0491.000-0.0270.0350.0360.0470.0410.5350.0720.1090.0680.0840.0580.4890.0430.0620.1230.0620.0880.0860.1620.0840.1050.0780.0660.0950.0550.0570.0390.0580.0370.0570.0350.2290.0900.0750.087
DIAS_EM_TRATAMENTO-0.002-0.0271.000-0.0110.0020.0030.0070.0070.0190.0250.0200.0250.0110.0080.0050.0170.0050.0170.0300.0290.0180.0390.0230.0310.0150.0060.0090.0150.0180.0170.0240.0230.0390.0100.0220.0220.016
IDADE-0.0450.035-0.0111.0000.0590.0390.0630.0430.0780.1210.1260.0330.0850.0890.0500.0270.0310.0870.0290.0290.0560.0270.0280.0250.0240.0260.0270.0420.0470.0390.0410.0350.0400.1440.0810.0560.039
CS_SEXO0.0240.0360.0020.0591.0000.0400.0460.0360.0280.1310.0240.0200.0120.0330.0160.0350.0340.0370.0340.0340.0350.0310.0340.0310.0300.0310.0310.0320.0290.0350.0290.0330.0280.0360.0660.0650.049
RAIOX_TORA0.0390.0470.0030.0390.0401.0000.1730.2260.0540.0480.0320.0350.0330.1170.0790.1000.0450.0500.0960.0960.0940.0920.0970.0920.0890.0790.0510.1000.1020.1030.1020.1020.1030.0210.0840.0840.123
TESTE_TUBE0.0280.0410.0070.0630.0460.1731.0000.0770.0640.0720.0530.0660.0150.0970.1340.1740.0420.0440.2020.2020.1940.1890.2010.1890.1770.1540.1190.0510.0400.0510.0370.0490.0370.0510.1880.1890.129
FORMA0.0360.5350.0070.0430.0360.2260.0771.0000.0920.0530.0280.0170.0210.5920.1280.1260.1440.0920.0180.0150.0130.0170.0180.0130.0200.0590.0090.2900.2930.2860.2900.2840.2920.0470.0340.0430.055
AGRAVAIDS0.1420.0720.0190.0780.0280.0540.0640.0921.0000.5380.4520.5560.3680.0650.1260.2510.1180.6010.3090.3070.3570.2760.3080.2750.2500.1350.0970.3130.1190.3200.1140.3110.1080.0840.3390.3350.187
AGRAVALCOO0.2110.1090.0250.1210.1310.0480.0720.0530.5381.0000.6410.6520.4320.0670.2170.2840.1330.2960.2880.2880.3630.2530.2870.2530.2300.1260.0980.4240.1530.4280.1460.4160.1330.0930.3620.3690.190
AGRAVDIABE0.1650.0680.0200.1260.0240.0320.0530.0280.4520.6411.0000.6690.4430.0420.1760.2390.1090.2470.2970.2970.3700.2610.2970.2610.2370.1250.0880.3490.1190.3530.1140.3430.1040.0640.3370.3360.152
AGRAVDOENC0.2020.0840.0250.0330.0200.0350.0660.0170.5560.6520.6691.0000.4510.0440.2200.2950.1360.3040.2990.2990.3740.2610.2980.2620.2370.1290.1090.4350.1470.4400.1400.4270.1280.0610.3400.3400.188
AGRAVOUTRA0.1680.0580.0110.0850.0120.0330.0150.0210.3680.4320.4430.4511.0000.0430.2750.0760.0500.1560.0760.0750.2100.0560.0760.0550.0470.0970.0590.3010.1040.2720.1000.2620.0940.0870.1070.1040.249
BACILOSC_E0.0300.4890.0080.0890.0330.1170.0970.5920.0650.0670.0420.0440.0431.0000.3610.1270.1460.0570.1090.1090.1050.1010.1090.1010.0930.1190.0580.2160.2010.1800.1900.1760.1910.0520.1000.1020.069
BACILOS_E20.0990.0430.0050.0500.0160.0790.1340.1280.1260.2170.1760.2200.2750.3611.0000.3190.0850.0550.3830.3830.4630.3690.3820.3690.3420.3390.2390.2490.1160.2190.1060.2130.1040.0770.3460.3480.164
BACILOSC_O0.0750.0620.0170.0270.0350.1000.1740.1260.2510.2840.2390.2950.0760.1270.3191.0000.1570.2310.6860.6860.6570.6430.6850.6420.5920.4290.3500.1990.1260.2130.1230.2070.1220.0480.6380.6410.277
CULTURA_ES0.0410.1230.0050.0310.0340.0450.0420.1440.1180.1330.1090.1360.0500.1460.0850.1571.0000.1400.1870.1870.1870.1720.1860.1720.1560.1090.0830.1000.0720.1110.0700.1070.0650.0230.1970.1930.184
HIV0.0980.0620.0170.0870.0370.0500.0440.0920.6010.2960.2470.3040.1560.0570.0550.2310.1401.0000.2830.2810.3040.2530.2820.2510.2300.1300.1130.1880.1090.1970.1060.1930.1010.0760.2910.2890.200
RIFAMPICIN0.0830.0880.0300.0290.0340.0960.2020.0180.3090.2880.2970.2990.0760.1090.3830.6860.1870.2831.0000.8640.6720.7280.7950.6700.6260.4330.4340.2430.1480.2620.1460.2540.1440.0990.6460.6490.334
ISONIAZIDA0.0820.0860.0290.0290.0340.0960.2020.0150.3070.2880.2970.2990.0750.1090.3830.6860.1870.2810.8641.0000.6720.7030.7760.6710.6240.4330.4340.2420.1480.2610.1460.2540.1440.0990.6460.6480.333
ETAMBUTOL0.1240.1620.0180.0560.0350.0940.1940.0130.3570.3630.3700.3740.2100.1050.4630.6570.1870.3040.6720.6721.0000.6940.6720.6880.6310.4330.4200.3230.1490.3300.1470.3200.1420.0650.6140.6170.319
ESTREPTOMI0.0720.0840.0390.0270.0310.0920.1890.0170.2760.2530.2610.2610.0560.1010.3690.6430.1720.2530.7280.7030.6941.0000.6710.7380.6550.4100.4130.2180.1330.2340.1320.2260.1320.0500.5990.6010.312
PIRAZINAMI0.0820.1050.0230.0280.0340.0970.2010.0180.3080.2870.2970.2980.0760.1090.3820.6850.1860.2820.7950.7760.6720.6711.0000.6570.6170.4320.4340.2420.1480.2610.1460.2530.1430.0810.6440.6470.333
ETIONAMIDA0.0710.0780.0310.0250.0310.0920.1890.0130.2750.2530.2610.2620.0550.1010.3690.6420.1720.2510.6700.6710.6880.7380.6571.0000.6420.4100.4120.2190.1330.2340.1320.2270.1310.0460.5980.6010.309
OUTRAS0.0700.0660.0150.0240.0300.0890.1770.0200.2500.2300.2370.2370.0470.0930.3420.5920.1560.2300.6260.6240.6310.6550.6170.6421.0000.3800.3820.2010.1220.2150.1210.2080.1190.0470.5490.5510.287
TRAT_SUPER0.1450.0950.0060.0260.0310.0790.1540.0590.1350.1260.1250.1290.0970.1190.3390.4290.1090.1300.4330.4330.4330.4100.4320.4100.3801.0000.3910.1920.1830.2010.1840.2000.1900.0470.3830.3840.274
DOENCA_TRA0.0840.0550.0090.0270.0310.0510.1190.0090.0970.0980.0880.1090.0590.0580.2390.3500.0830.1130.4340.4340.4200.4130.4340.4120.3820.3911.0000.0890.0730.0970.0740.0940.0760.0290.4000.4020.187
BACILOSC_10.1280.0570.0150.0420.0320.1000.0510.2900.3130.4240.3490.4350.3010.2160.2490.1990.1000.1880.2430.2420.3230.2180.2420.2190.2010.1920.0891.0000.6050.7090.5790.6890.5650.0740.2510.2540.140
BACILOSC_20.0740.0390.0180.0470.0290.1020.0400.2930.1190.1530.1190.1470.1040.2010.1160.1260.0720.1090.1480.1480.1490.1330.1480.1330.1220.1830.0730.6051.0000.6270.7300.6050.6790.1980.1600.1630.167
BACILOSC_30.1260.0580.0170.0390.0350.1030.0510.2860.3200.4280.3530.4400.2720.1800.2190.2130.1110.1970.2620.2610.3300.2340.2610.2340.2150.2010.0970.7090.6271.0000.6310.7470.5950.1080.2730.2750.160
BACILOSC_40.0700.0370.0240.0410.0290.1020.0370.2900.1140.1460.1140.1400.1000.1900.1060.1230.0700.1060.1460.1460.1470.1320.1460.1320.1210.1840.0740.5790.7300.6311.0000.6380.7200.2070.1580.1610.172
BACILOSC_50.1230.0570.0230.0350.0330.1020.0490.2840.3110.4160.3430.4270.2620.1760.2130.2070.1070.1930.2540.2540.3200.2260.2530.2270.2080.2000.0940.6890.6050.7470.6381.0000.6300.1120.2660.2680.167
BACILOSC_60.0660.0350.0390.0400.0280.1030.0370.2920.1080.1330.1040.1280.0940.1910.1040.1220.0650.1010.1440.1440.1420.1320.1430.1310.1190.1900.0760.5650.6790.5950.7200.6301.0000.2020.1540.1570.175
SITUA_ENCE0.0280.2290.0100.1440.0360.0210.0510.0470.0840.0930.0640.0610.0870.0520.0770.0480.0230.0760.0990.0990.0650.0500.0810.0460.0470.0470.0290.0740.1980.1080.2070.1120.2021.0000.0260.0420.047
AGRAVDROGA0.0920.0900.0220.0810.0660.0840.1880.0340.3390.3620.3370.3400.1070.1000.3460.6380.1970.2910.6460.6460.6140.5990.6440.5980.5490.3830.4000.2510.1600.2730.1580.2660.1540.0261.0000.7310.351
AGRAVTABAC0.0910.0750.0220.0560.0650.0840.1890.0430.3350.3690.3360.3400.1040.1020.3480.6410.1930.2890.6490.6480.6170.6010.6470.6010.5510.3840.4020.2540.1630.2750.1610.2680.1570.0420.7311.0000.349
UF0.2380.0870.0160.0390.0490.1230.1290.0550.1870.1900.1520.1880.2490.0690.1640.2770.1840.2000.3340.3330.3190.3120.3330.3090.2870.2740.1870.1400.1670.1600.1720.1670.1750.0470.3510.3491.000

Missing values

2023-06-06T14:59:48.833458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-06T14:59:51.138159image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-06T14:59:54.896764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DT_NOTIFICCS_SEXOCS_RACATRATAMENTORAIOX_TORATESTE_TUBEFORMAAGRAVAIDSAGRAVALCOOAGRAVDIABEAGRAVDOENCAGRAVOUTRABACILOSC_EBACILOS_E2BACILOSC_OCULTURA_ESHIVRIFAMPICINISONIAZIDAETAMBUTOLESTREPTOMIPIRAZINAMIETIONAMIDAOUTRASTRAT_SUPERDOENCA_TRABACILOSC_1BACILOSC_2BACILOSC_3BACILOSC_4BACILOSC_5BACILOSC_6SITUA_ENCEAGRAVDROGAAGRAVTABACUFDIAS_EM_TRATAMENTOIDADE
006/01/200119.01.01.04.01.09.09.09.09.09.03.09.03.04.04.01.01.02.02.01.02.02.02.02.09.03.09.03.09.03.01.09.09.0AC19566.0
116/01/200104.01.01.04.01.09.09.09.09.09.03.09.03.04.04.01.01.02.02.01.02.02.02.02.09.03.09.03.09.03.01.09.09.0AC18135.0
216/01/200114.01.01.04.01.09.09.09.09.09.03.09.03.04.04.01.01.02.02.01.02.02.02.02.09.03.09.03.09.03.01.09.09.0AC18154.0
322/01/200114.01.01.04.01.09.09.09.09.09.03.09.03.04.04.01.01.02.02.01.02.02.02.02.09.03.09.03.09.03.01.09.09.0AC18136.0
403/01/200109.01.04.04.01.09.09.09.09.09.03.09.03.04.04.01.01.02.02.01.02.02.02.02.09.03.09.03.09.03.01.09.09.0AC18314.0
509/01/200109.01.04.04.01.09.09.09.09.09.01.09.03.04.04.01.01.02.02.01.02.02.02.09.09.03.09.03.09.03.01.09.09.0AC18160.0
631/01/200119.03.04.04.01.09.09.09.09.09.01.09.03.04.04.01.01.01.02.01.02.02.02.02.09.03.09.03.09.03.01.09.09.0AC37646.0
706/02/200114.01.04.04.01.09.09.09.09.09.03.09.03.04.04.01.01.02.02.01.02.02.02.09.09.03.09.03.09.03.01.09.09.0AC18159.0
808/02/200114.01.04.04.01.09.09.09.09.09.01.09.03.04.04.01.01.02.02.01.02.02.02.09.09.03.09.03.09.03.01.09.09.0AC18118.0
901/02/200119.01.04.04.02.09.09.09.09.09.03.09.03.04.04.01.01.02.02.01.02.02.02.02.09.03.09.03.09.03.01.09.09.0AC18148.0
DT_NOTIFICCS_SEXOCS_RACATRATAMENTORAIOX_TORATESTE_TUBEFORMAAGRAVAIDSAGRAVALCOOAGRAVDIABEAGRAVDOENCAGRAVOUTRABACILOSC_EBACILOS_E2BACILOSC_OCULTURA_ESHIVRIFAMPICINISONIAZIDAETAMBUTOLESTREPTOMIPIRAZINAMIETIONAMIDAOUTRASTRAT_SUPERDOENCA_TRABACILOSC_1BACILOSC_2BACILOSC_3BACILOSC_4BACILOSC_5BACILOSC_6SITUA_ENCEAGRAVDROGAAGRAVTABACUFDIAS_EM_TRATAMENTOIDADE
96408926/06/201914.01.01.04.01.02.02.02.02.02.01.09.09.04.02.09.09.09.09.09.09.09.09.09.01.02.02.02.02.02.01.02.02.0TO25142.0
96409005/07/201911.01.01.04.01.02.02.02.02.02.01.09.09.04.02.09.09.09.09.09.09.09.09.09.02.02.02.02.02.02.01.02.02.0TO23724.0
96409111/07/201914.01.01.04.01.02.02.02.02.02.01.09.09.04.02.09.09.09.09.09.09.09.09.09.01.01.01.02.02.02.01.02.01.0TO19641.0
96409215/07/201914.01.01.04.01.02.02.01.02.02.01.09.09.04.02.09.09.09.09.09.09.09.09.09.01.01.02.02.02.02.01.02.02.0TO18353.0
96409322/07/201914.01.03.04.02.02.02.01.02.01.04.09.09.04.02.09.09.09.09.09.09.09.09.09.04.04.04.04.04.04.03.02.02.0TO19261.0
96409431/07/201904.01.01.04.01.02.02.02.02.02.01.09.09.04.02.09.09.09.09.09.09.09.09.09.01.01.02.02.02.02.01.02.02.0TO19032.0
96409517/08/201904.01.01.04.01.09.02.02.02.02.01.09.09.01.02.09.09.09.09.09.09.09.09.09.02.02.02.03.02.02.01.02.02.0TO21424.0
96409604/09/201914.01.01.04.01.02.02.02.02.02.01.09.09.04.02.09.09.09.09.09.09.09.09.09.02.02.02.03.03.03.01.02.02.0TO19622.0
96409727/09/201914.01.04.04.02.02.02.02.01.02.03.09.09.04.02.09.09.09.09.09.09.09.09.09.04.04.04.04.04.04.01.02.02.0TO17815.0
96409811/12/201914.01.01.04.01.02.02.02.02.09.02.09.09.04.04.09.09.09.09.09.09.09.09.09.03.09.09.09.09.09.03.02.02.0TO5487.0

Duplicate rows

Most frequently occurring

DT_NOTIFICCS_SEXOCS_RACATRATAMENTORAIOX_TORATESTE_TUBEFORMAAGRAVAIDSAGRAVALCOOAGRAVDIABEAGRAVDOENCAGRAVOUTRABACILOSC_EBACILOS_E2BACILOSC_OCULTURA_ESHIVRIFAMPICINISONIAZIDAETAMBUTOLESTREPTOMIPIRAZINAMIETIONAMIDAOUTRASTRAT_SUPERDOENCA_TRABACILOSC_1BACILOSC_2BACILOSC_3BACILOSC_4BACILOSC_5BACILOSC_6SITUA_ENCEAGRAVDROGAAGRAVTABACUFDIAS_EM_TRATAMENTOIDADE# duplicates
1807/02/200219.04.01.04.01.09.09.09.09.09.03.09.03.01.04.01.01.02.02.01.02.02.09.09.09.02.09.02.09.02.01.09.09.0PR19420.03
001/10/200911.01.01.04.01.02.02.02.02.02.02.02.03.02.02.01.01.02.02.01.02.02.02.02.02.02.02.02.02.02.01.09.09.0RS19528.02
101/11/200511.01.01.04.01.09.09.09.09.09.01.09.03.01.04.01.01.02.02.01.02.02.02.02.09.09.09.09.09.09.01.09.09.0RS075.02
201/12/200419.01.01.04.01.09.09.09.09.09.01.09.03.04.04.01.01.02.02.01.02.02.02.02.09.03.09.09.09.09.01.09.09.0BA20920.02
302/02/200901.01.01.04.01.09.09.09.09.01.01.03.03.04.04.02.02.02.02.02.02.02.09.02.09.09.09.09.09.09.03.09.09.0RJ048.02
402/05/201719.01.01.04.01.09.02.02.01.02.03.09.09.04.02.09.09.09.09.09.09.09.09.09.03.03.03.03.03.03.01.02.02.0SC18719.02
503/01/200514.01.01.04.01.09.09.09.09.09.01.09.03.04.04.01.01.02.02.01.02.02.02.02.09.03.09.03.09.03.01.09.09.0BA18216.02
603/04/200219.01.01.04.01.09.09.09.09.09.01.09.03.04.04.01.01.02.02.01.02.02.09.09.09.09.09.09.09.09.01.09.09.0RJ18149.02
703/05/200215.01.01.04.01.09.09.09.09.09.03.09.03.04.04.01.01.02.02.01.02.02.02.02.09.03.09.03.09.03.01.09.09.0AM1791.02
803/08/200911.01.01.02.01.02.01.02.02.02.01.01.03.04.03.01.01.02.02.01.02.02.02.02.03.01.02.03.02.03.01.09.09.0RJ18953.02