Overview

Dataset statistics

Number of variables14
Number of observations13
Missing cells99
Missing cells (%)54.4%
Duplicate rows1
Duplicate rows (%)7.7%
Total size in memory2.5 KiB
Average record size in memory195.8 B

Variable types

Text1
Categorical8
Unsupported1
Numeric4

Alerts

Dataset has 1 (7.7%) duplicate rowsDuplicates
2024-04-01 00:00:00 is highly overall correlated with 2024-05-01 00:00:00 and 10 other fieldsHigh correlation
2024-05-01 00:00:00 is highly overall correlated with 2024-04-01 00:00:00 and 10 other fieldsHigh correlation
2024-06-01 00:00:00 is highly overall correlated with 2024-04-01 00:00:00 and 10 other fieldsHigh correlation
2024-07-01 00:00:00 is highly overall correlated with 2024-04-01 00:00:00 and 10 other fieldsHigh correlation
2024-08-01 00:00:00 is highly overall correlated with 2024-04-01 00:00:00 and 10 other fieldsHigh correlation
2024-09-01 00:00:00 is highly overall correlated with 2024-04-01 00:00:00 and 10 other fieldsHigh correlation
2024-10-01 00:00:00 is highly overall correlated with 2024-04-01 00:00:00 and 10 other fieldsHigh correlation
2024-11-01 00:00:00 is highly overall correlated with 2024-04-01 00:00:00 and 10 other fieldsHigh correlation
2025-01-01 00:00:00 is highly overall correlated with 2024-04-01 00:00:00 and 10 other fieldsHigh correlation
2025-02-01 00:00:00 is highly overall correlated with 2024-04-01 00:00:00 and 10 other fieldsHigh correlation
2025-03-01 00:00:00 is highly overall correlated with 2024-04-01 00:00:00 and 10 other fieldsHigh correlation
2025-04-01 00:00:00 is highly overall correlated with 2024-04-01 00:00:00 and 10 other fieldsHigh correlation
Unnamed: 0 has 4 (30.8%) missing valuesMissing
2024-04-01 00:00:00 has 8 (61.5%) missing valuesMissing
2024-05-01 00:00:00 has 8 (61.5%) missing valuesMissing
2024-06-01 00:00:00 has 8 (61.5%) missing valuesMissing
2024-07-01 00:00:00 has 8 (61.5%) missing valuesMissing
2024-08-01 00:00:00 has 8 (61.5%) missing valuesMissing
2024-09-01 00:00:00 has 8 (61.5%) missing valuesMissing
2024-10-01 00:00:00 has 8 (61.5%) missing valuesMissing
2024-11-01 00:00:00 has 8 (61.5%) missing valuesMissing
2024-12-01 00:00:00 has 7 (53.8%) missing valuesMissing
2025-01-01 00:00:00 has 6 (46.2%) missing valuesMissing
2025-02-01 00:00:00 has 6 (46.2%) missing valuesMissing
2025-03-01 00:00:00 has 6 (46.2%) missing valuesMissing
2025-04-01 00:00:00 has 6 (46.2%) missing valuesMissing
2024-04-01 00:00:00 is uniformly distributedUniform
2024-05-01 00:00:00 is uniformly distributedUniform
2024-06-01 00:00:00 is uniformly distributedUniform
2024-07-01 00:00:00 is uniformly distributedUniform
2024-08-01 00:00:00 is uniformly distributedUniform
2024-09-01 00:00:00 is uniformly distributedUniform
2024-10-01 00:00:00 is uniformly distributedUniform
2024-11-01 00:00:00 is uniformly distributedUniform
2024-12-01 00:00:00 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2025-11-26 17:56:38.201042
Analysis finished2025-11-26 17:56:47.166853
Duration8.97 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Unnamed: 0
Text

Missing 

Distinct7
Distinct (%)77.8%
Missing4
Missing (%)30.8%
Memory size774.0 B
2025-11-26T14:56:47.748877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length9
Mean length8.1111111
Min length2

Characters and Unicode

Total characters73
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)55.6%

Sample

1st rowMetas
2nd rowVendas
3rd rowVisitas Realizadas
4th rowRealizado
5th rowVendas
ValueCountFrequency (%)
vendas2
18.2%
visitas2
18.2%
realizadas2
18.2%
metas1
9.1%
realizado1
9.1%
hc1
9.1%
closer1
9.1%
sdr1
9.1%
2025-11-26T14:56:48.834289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a13
17.8%
s10
13.7%
e7
9.6%
i7
9.6%
d5
 
6.8%
V4
 
5.5%
l4
 
5.5%
R4
 
5.5%
t3
 
4.1%
z3
 
4.1%
Other values (9)13
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)73
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a13
17.8%
s10
13.7%
e7
9.6%
i7
9.6%
d5
 
6.8%
V4
 
5.5%
l4
 
5.5%
R4
 
5.5%
t3
 
4.1%
z3
 
4.1%
Other values (9)13
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)73
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a13
17.8%
s10
13.7%
e7
9.6%
i7
9.6%
d5
 
6.8%
V4
 
5.5%
l4
 
5.5%
R4
 
5.5%
t3
 
4.1%
z3
 
4.1%
Other values (9)13
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)73
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a13
17.8%
s10
13.7%
e7
9.6%
i7
9.6%
d5
 
6.8%
V4
 
5.5%
l4
 
5.5%
R4
 
5.5%
t3
 
4.1%
z3
 
4.1%
Other values (9)13
17.8%

2024-04-01 00:00:00
Categorical

High correlation  Missing  Uniform 

Distinct5
Distinct (%)100.0%
Missing8
Missing (%)61.5%
Memory size849.0 B
625.0
1 
875.0
1 
548.0
1 
748.0
1 
17.0
1 

Length

Max length5
Median length5
Mean length4.8
Min length4

Characters and Unicode

Total characters24
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st row625.0
2nd row875.0
3rd row548.0
4th row748.0
5th row17.0

Common Values

ValueCountFrequency (%)
625.01
 
7.7%
875.01
 
7.7%
548.01
 
7.7%
748.01
 
7.7%
17.01
 
7.7%
(Missing)8
61.5%

Length

2025-11-26T14:56:49.861367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T14:56:50.089286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
625.01
20.0%
875.01
20.0%
548.01
20.0%
748.01
20.0%
17.01
20.0%

Most occurring characters

ValueCountFrequency (%)
.5
20.8%
05
20.8%
53
12.5%
83
12.5%
73
12.5%
42
 
8.3%
61
 
4.2%
21
 
4.2%
11
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.5
20.8%
05
20.8%
53
12.5%
83
12.5%
73
12.5%
42
 
8.3%
61
 
4.2%
21
 
4.2%
11
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.5
20.8%
05
20.8%
53
12.5%
83
12.5%
73
12.5%
42
 
8.3%
61
 
4.2%
21
 
4.2%
11
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.5
20.8%
05
20.8%
53
12.5%
83
12.5%
73
12.5%
42
 
8.3%
61
 
4.2%
21
 
4.2%
11
 
4.2%

2024-05-01 00:00:00
Categorical

High correlation  Missing  Uniform 

Distinct5
Distinct (%)100.0%
Missing8
Missing (%)61.5%
Memory size849.0 B
625.0
1 
810.0
1 
494.0
1 
747.0
1 
17.0
1 

Length

Max length5
Median length5
Mean length4.8
Min length4

Characters and Unicode

Total characters24
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st row625.0
2nd row810.0
3rd row494.0
4th row747.0
5th row17.0

Common Values

ValueCountFrequency (%)
625.01
 
7.7%
810.01
 
7.7%
494.01
 
7.7%
747.01
 
7.7%
17.01
 
7.7%
(Missing)8
61.5%

Length

2025-11-26T14:56:50.384148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T14:56:50.593182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
625.01
20.0%
810.01
20.0%
494.01
20.0%
747.01
20.0%
17.01
20.0%

Most occurring characters

ValueCountFrequency (%)
06
25.0%
.5
20.8%
43
12.5%
73
12.5%
12
 
8.3%
61
 
4.2%
21
 
4.2%
51
 
4.2%
81
 
4.2%
91
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
06
25.0%
.5
20.8%
43
12.5%
73
12.5%
12
 
8.3%
61
 
4.2%
21
 
4.2%
51
 
4.2%
81
 
4.2%
91
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
06
25.0%
.5
20.8%
43
12.5%
73
12.5%
12
 
8.3%
61
 
4.2%
21
 
4.2%
51
 
4.2%
81
 
4.2%
91
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
06
25.0%
.5
20.8%
43
12.5%
73
12.5%
12
 
8.3%
61
 
4.2%
21
 
4.2%
51
 
4.2%
81
 
4.2%
91
 
4.2%

2024-06-01 00:00:00
Categorical

High correlation  Missing  Uniform 

Distinct5
Distinct (%)100.0%
Missing8
Missing (%)61.5%
Memory size849.0 B
625.0
1 
870.0
1 
556.0
1 
750.0
1 
17.0
1 

Length

Max length5
Median length5
Mean length4.8
Min length4

Characters and Unicode

Total characters24
Distinct characters8
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

Unique5 ?
Unique (%)100.0%

Sample

1st row625.0
2nd row870.0
3rd row556.0
4th row750.0
5th row17.0

Common Values

ValueCountFrequency (%)
625.01
 
7.7%
870.01
 
7.7%
556.01
 
7.7%
750.01
 
7.7%
17.01
 
7.7%
(Missing)8
61.5%

Length

2025-11-26T14:56:50.865838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T14:56:51.097548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
625.01
20.0%
870.01
20.0%
556.01
20.0%
750.01
20.0%
17.01
20.0%

Most occurring characters

ValueCountFrequency (%)
07
29.2%
.5
20.8%
54
16.7%
73
12.5%
62
 
8.3%
21
 
4.2%
81
 
4.2%
11
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07
29.2%
.5
20.8%
54
16.7%
73
12.5%
62
 
8.3%
21
 
4.2%
81
 
4.2%
11
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07
29.2%
.5
20.8%
54
16.7%
73
12.5%
62
 
8.3%
21
 
4.2%
81
 
4.2%
11
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07
29.2%
.5
20.8%
54
16.7%
73
12.5%
62
 
8.3%
21
 
4.2%
81
 
4.2%
11
 
4.2%

2024-07-01 00:00:00
Categorical

High correlation  Missing  Uniform 

Distinct5
Distinct (%)100.0%
Missing8
Missing (%)61.5%
Memory size849.0 B
625.0
1 
888.0
1 
537.0
1 
795.0
1 
16.0
1 

Length

Max length5
Median length5
Mean length4.8
Min length4

Characters and Unicode

Total characters24
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st row625.0
2nd row888.0
3rd row537.0
4th row795.0
5th row16.0

Common Values

ValueCountFrequency (%)
625.01
 
7.7%
888.01
 
7.7%
537.01
 
7.7%
795.01
 
7.7%
16.01
 
7.7%
(Missing)8
61.5%

Length

2025-11-26T14:56:51.355413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T14:56:51.557673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
625.01
20.0%
888.01
20.0%
537.01
20.0%
795.01
20.0%
16.01
20.0%

Most occurring characters

ValueCountFrequency (%)
.5
20.8%
05
20.8%
53
12.5%
83
12.5%
62
 
8.3%
72
 
8.3%
21
 
4.2%
31
 
4.2%
91
 
4.2%
11
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.5
20.8%
05
20.8%
53
12.5%
83
12.5%
62
 
8.3%
72
 
8.3%
21
 
4.2%
31
 
4.2%
91
 
4.2%
11
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.5
20.8%
05
20.8%
53
12.5%
83
12.5%
62
 
8.3%
72
 
8.3%
21
 
4.2%
31
 
4.2%
91
 
4.2%
11
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.5
20.8%
05
20.8%
53
12.5%
83
12.5%
62
 
8.3%
72
 
8.3%
21
 
4.2%
31
 
4.2%
91
 
4.2%
11
 
4.2%

2024-08-01 00:00:00
Categorical

High correlation  Missing  Uniform 

Distinct5
Distinct (%)100.0%
Missing8
Missing (%)61.5%
Memory size849.0 B
625.0
1 
922.0
1 
549.0
1 
739.0
1 
15.0
1 

Length

Max length5
Median length5
Mean length4.8
Min length4

Characters and Unicode

Total characters24
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st row625.0
2nd row922.0
3rd row549.0
4th row739.0
5th row15.0

Common Values

ValueCountFrequency (%)
625.01
 
7.7%
922.01
 
7.7%
549.01
 
7.7%
739.01
 
7.7%
15.01
 
7.7%
(Missing)8
61.5%

Length

2025-11-26T14:56:51.800624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T14:56:51.980191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
625.01
20.0%
922.01
20.0%
549.01
20.0%
739.01
20.0%
15.01
20.0%

Most occurring characters

ValueCountFrequency (%)
.5
20.8%
05
20.8%
23
12.5%
53
12.5%
93
12.5%
61
 
4.2%
41
 
4.2%
71
 
4.2%
31
 
4.2%
11
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.5
20.8%
05
20.8%
23
12.5%
53
12.5%
93
12.5%
61
 
4.2%
41
 
4.2%
71
 
4.2%
31
 
4.2%
11
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.5
20.8%
05
20.8%
23
12.5%
53
12.5%
93
12.5%
61
 
4.2%
41
 
4.2%
71
 
4.2%
31
 
4.2%
11
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.5
20.8%
05
20.8%
23
12.5%
53
12.5%
93
12.5%
61
 
4.2%
41
 
4.2%
71
 
4.2%
31
 
4.2%
11
 
4.2%

2024-09-01 00:00:00
Categorical

High correlation  Missing  Uniform 

Distinct5
Distinct (%)100.0%
Missing8
Missing (%)61.5%
Memory size849.0 B
640.0
1 
903.0
1 
506.0
1 
694.0
1 
15.0
1 

Length

Max length5
Median length5
Mean length4.8
Min length4

Characters and Unicode

Total characters24
Distinct characters8
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

Unique5 ?
Unique (%)100.0%

Sample

1st row640.0
2nd row903.0
3rd row506.0
4th row694.0
5th row15.0

Common Values

ValueCountFrequency (%)
640.01
 
7.7%
903.01
 
7.7%
506.01
 
7.7%
694.01
 
7.7%
15.01
 
7.7%
(Missing)8
61.5%

Length

2025-11-26T14:56:52.230008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T14:56:52.407930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
640.01
20.0%
903.01
20.0%
506.01
20.0%
694.01
20.0%
15.01
20.0%

Most occurring characters

ValueCountFrequency (%)
08
33.3%
.5
20.8%
63
 
12.5%
42
 
8.3%
92
 
8.3%
52
 
8.3%
31
 
4.2%
11
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
08
33.3%
.5
20.8%
63
 
12.5%
42
 
8.3%
92
 
8.3%
52
 
8.3%
31
 
4.2%
11
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
08
33.3%
.5
20.8%
63
 
12.5%
42
 
8.3%
92
 
8.3%
52
 
8.3%
31
 
4.2%
11
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
08
33.3%
.5
20.8%
63
 
12.5%
42
 
8.3%
92
 
8.3%
52
 
8.3%
31
 
4.2%
11
 
4.2%

2024-10-01 00:00:00
Categorical

High correlation  Missing  Uniform 

Distinct5
Distinct (%)100.0%
Missing8
Missing (%)61.5%
Memory size849.0 B
625.0
1 
920.0
1 
567.0
1 
705.0
1 
15.0
1 

Length

Max length5
Median length5
Mean length4.8
Min length4

Characters and Unicode

Total characters24
Distinct characters8
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

Unique5 ?
Unique (%)100.0%

Sample

1st row625.0
2nd row920.0
3rd row567.0
4th row705.0
5th row15.0

Common Values

ValueCountFrequency (%)
625.01
 
7.7%
920.01
 
7.7%
567.01
 
7.7%
705.01
 
7.7%
15.01
 
7.7%
(Missing)8
61.5%

Length

2025-11-26T14:56:52.652159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T14:56:52.828652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
625.01
20.0%
920.01
20.0%
567.01
20.0%
705.01
20.0%
15.01
20.0%

Most occurring characters

ValueCountFrequency (%)
07
29.2%
.5
20.8%
54
16.7%
62
 
8.3%
22
 
8.3%
72
 
8.3%
91
 
4.2%
11
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07
29.2%
.5
20.8%
54
16.7%
62
 
8.3%
22
 
8.3%
72
 
8.3%
91
 
4.2%
11
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07
29.2%
.5
20.8%
54
16.7%
62
 
8.3%
22
 
8.3%
72
 
8.3%
91
 
4.2%
11
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07
29.2%
.5
20.8%
54
16.7%
62
 
8.3%
22
 
8.3%
72
 
8.3%
91
 
4.2%
11
 
4.2%

2024-11-01 00:00:00
Categorical

High correlation  Missing  Uniform 

Distinct5
Distinct (%)100.0%
Missing8
Missing (%)61.5%
Memory size849.0 B
625.0
1 
838.0
1 
467.0
1 
556.0
1 
15.0
1 

Length

Max length5
Median length5
Mean length4.8
Min length4

Characters and Unicode

Total characters24
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st row625.0
2nd row838.0
3rd row467.0
4th row556.0
5th row15.0

Common Values

ValueCountFrequency (%)
625.01
 
7.7%
838.01
 
7.7%
467.01
 
7.7%
556.01
 
7.7%
15.01
 
7.7%
(Missing)8
61.5%

Length

2025-11-26T14:56:53.090943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T14:56:53.264281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
625.01
20.0%
838.01
20.0%
467.01
20.0%
556.01
20.0%
15.01
20.0%

Most occurring characters

ValueCountFrequency (%)
.5
20.8%
05
20.8%
54
16.7%
63
12.5%
82
 
8.3%
21
 
4.2%
31
 
4.2%
41
 
4.2%
71
 
4.2%
11
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.5
20.8%
05
20.8%
54
16.7%
63
12.5%
82
 
8.3%
21
 
4.2%
31
 
4.2%
41
 
4.2%
71
 
4.2%
11
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.5
20.8%
05
20.8%
54
16.7%
63
12.5%
82
 
8.3%
21
 
4.2%
31
 
4.2%
41
 
4.2%
71
 
4.2%
11
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.5
20.8%
05
20.8%
54
16.7%
63
12.5%
82
 
8.3%
21
 
4.2%
31
 
4.2%
41
 
4.2%
71
 
4.2%
11
 
4.2%

2024-12-01 00:00:00
Unsupported

Missing  Rejected  Unsupported 

Missing7
Missing (%)53.8%
Memory size655.0 B

2025-01-01 00:00:00
Real number (ℝ)

High correlation  Missing 

Distinct7
Distinct (%)100.0%
Missing6
Missing (%)46.2%
Infinite0
Infinite (%)0.0%
Mean433.57143
Minimum5
Maximum932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2025-11-26T14:56:53.466665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8
Q117.5
median513
Q3775
95-th percentile914.9
Maximum932
Range927
Interquartile range (IQR)757.5

Descriptive statistics

Standard deviation415.89336
Coefficient of variation (CV)0.95922686
Kurtosis-2.3300534
Mean433.57143
Median Absolute Deviation (MAD)419
Skewness0.0052721924
Sum3035
Variance172967.29
MonotonicityNot monotonic
2025-11-26T14:56:53.665360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6751
 
7.7%
8751
 
7.7%
5131
 
7.7%
9321
 
7.7%
201
 
7.7%
51
 
7.7%
151
 
7.7%
(Missing)6
46.2%
ValueCountFrequency (%)
51
7.7%
151
7.7%
201
7.7%
5131
7.7%
6751
7.7%
8751
7.7%
9321
7.7%
ValueCountFrequency (%)
9321
7.7%
8751
7.7%
6751
7.7%
5131
7.7%
201
7.7%
151
7.7%
51
7.7%

2025-02-01 00:00:00
Real number (ℝ)

High correlation  Missing 

Distinct7
Distinct (%)100.0%
Missing6
Missing (%)46.2%
Infinite0
Infinite (%)0.0%
Mean452.42857
Minimum7
Maximum1086
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2025-11-26T14:56:53.862980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile9.4
Q118.5
median523
Q3757
95-th percentile1041.9
Maximum1086
Range1079
Interquartile range (IQR)738.5

Descriptive statistics

Standard deviation453.35558
Coefficient of variation (CV)1.002049
Kurtosis-1.7794432
Mean452.42857
Median Absolute Deviation (MAD)501
Skewness0.30022993
Sum3167
Variance205531.29
MonotonicityNot monotonic
2025-11-26T14:56:54.075979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5751
 
7.7%
10861
 
7.7%
5231
 
7.7%
9391
 
7.7%
221
 
7.7%
71
 
7.7%
151
 
7.7%
(Missing)6
46.2%
ValueCountFrequency (%)
71
7.7%
151
7.7%
221
7.7%
5231
7.7%
5751
7.7%
9391
7.7%
10861
7.7%
ValueCountFrequency (%)
10861
7.7%
9391
7.7%
5751
7.7%
5231
7.7%
221
7.7%
151
7.7%
71
7.7%

2025-03-01 00:00:00
Real number (ℝ)

High correlation  Missing 

Distinct7
Distinct (%)100.0%
Missing6
Missing (%)46.2%
Infinite0
Infinite (%)0.0%
Mean392.42857
Minimum7
Maximum863
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2025-11-26T14:56:54.268528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile8.8
Q116.5
median520
Q3662
95-th percentile839.3
Maximum863
Range856
Interquartile range (IQR)645.5

Descriptive statistics

Standard deviation375.06482
Coefficient of variation (CV)0.95575309
Kurtosis-2.2089581
Mean392.42857
Median Absolute Deviation (MAD)343
Skewness0.02649783
Sum2747
Variance140673.62
MonotonicityNot monotonic
2025-11-26T14:56:54.488686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5401
 
7.7%
8631
 
7.7%
5201
 
7.7%
7841
 
7.7%
201
 
7.7%
71
 
7.7%
131
 
7.7%
(Missing)6
46.2%
ValueCountFrequency (%)
71
7.7%
131
7.7%
201
7.7%
5201
7.7%
5401
7.7%
7841
7.7%
8631
7.7%
ValueCountFrequency (%)
8631
7.7%
7841
7.7%
5401
7.7%
5201
7.7%
201
7.7%
131
7.7%
71
7.7%

2025-04-01 00:00:00
Real number (ℝ)

High correlation  Missing 

Distinct7
Distinct (%)100.0%
Missing6
Missing (%)46.2%
Infinite0
Infinite (%)0.0%
Mean364.42857
Minimum6
Maximum863
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size236.0 B
2025-11-26T14:56:54.678301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile9.3
Q120
median374
Q3634
95-th percentile822.5
Maximum863
Range857
Interquartile range (IQR)614

Descriptive statistics

Standard deviation359.95965
Coefficient of variation (CV)0.98773719
Kurtosis-1.928876
Mean364.42857
Median Absolute Deviation (MAD)354
Skewness0.24822424
Sum2551
Variance129570.95
MonotonicityNot monotonic
2025-11-26T14:56:54.973002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5401
 
7.7%
8631
 
7.7%
3741
 
7.7%
7281
 
7.7%
231
 
7.7%
61
 
7.7%
171
 
7.7%
(Missing)6
46.2%
ValueCountFrequency (%)
61
7.7%
171
7.7%
231
7.7%
3741
7.7%
5401
7.7%
7281
7.7%
8631
7.7%
ValueCountFrequency (%)
8631
7.7%
7281
7.7%
5401
7.7%
3741
7.7%
231
7.7%
171
7.7%
61
7.7%

Interactions

2025-11-26T14:56:44.368797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:56:40.853340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:56:42.451694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:56:43.509742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:56:44.575158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:56:41.261113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:56:42.677346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:56:43.775038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:56:44.814832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:56:41.935538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:56:42.952380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:56:43.983251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:56:45.031435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:56:42.198917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:56:43.207198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T14:56:44.149130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-26T14:56:55.193160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2024-04-01 00:00:002024-05-01 00:00:002024-06-01 00:00:002024-07-01 00:00:002024-08-01 00:00:002024-09-01 00:00:002024-10-01 00:00:002024-11-01 00:00:002025-01-01 00:00:002025-02-01 00:00:002025-03-01 00:00:002025-04-01 00:00:00
2024-04-01 00:00:001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-05-01 00:00:001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-06-01 00:00:001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-07-01 00:00:001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-08-01 00:00:001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-09-01 00:00:001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-10-01 00:00:001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-11-01 00:00:001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2025-01-01 00:00:001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9640.9640.964
2025-02-01 00:00:001.0001.0001.0001.0001.0001.0001.0001.0000.9641.0001.0001.000
2025-03-01 00:00:001.0001.0001.0001.0001.0001.0001.0001.0000.9641.0001.0001.000
2025-04-01 00:00:001.0001.0001.0001.0001.0001.0001.0001.0000.9641.0001.0001.000

Missing values

2025-11-26T14:56:45.397334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-26T14:56:45.852431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-26T14:56:46.491770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 02024-04-01 00:00:002024-05-01 00:00:002024-06-01 00:00:002024-07-01 00:00:002024-08-01 00:00:002024-09-01 00:00:002024-10-01 00:00:002024-11-01 00:00:002024-12-01 00:00:002025-01-01 00:00:002025-02-01 00:00:002025-03-01 00:00:002025-04-01 00:00:00
0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1MetasNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2Vendas625.0625.0625.0625.0625.0640.0625.0625.0480675.0575.0540.0540.0
3Visitas Realizadas875.0810.0870.0888.0922.0903.0920.0838.0612875.01086.0863.0863.0
4NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
5RealizadoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6Vendas548.0494.0556.0537.0549.0506.0567.0467.0304513.0523.0520.0374.0
7Visitas Realizadas748.0747.0750.0795.0739.0694.0705.0556.0445932.0939.0784.0728.0
8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9HC17.017.017.016.015.015.015.015.01920.022.020.023.0
Unnamed: 02024-04-01 00:00:002024-05-01 00:00:002024-06-01 00:00:002024-07-01 00:00:002024-08-01 00:00:002024-09-01 00:00:002024-10-01 00:00:002024-11-01 00:00:002024-12-01 00:00:002025-01-01 00:00:002025-02-01 00:00:002025-03-01 00:00:002025-04-01 00:00:00
3Visitas Realizadas875.0810.0870.0888.0922.0903.0920.0838.0612875.01086.0863.0863.0
4NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
5RealizadoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6Vendas548.0494.0556.0537.0549.0506.0567.0467.0304513.0523.0520.0374.0
7Visitas Realizadas748.0747.0750.0795.0739.0694.0705.0556.0445932.0939.0784.0728.0
8NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9HC17.017.017.016.015.015.015.015.01920.022.020.023.0
10CloserNaNNaNNaNNaNNaNNaNNaNNaNNaN5.07.07.06.0
11SDRNaNNaNNaNNaNNaNNaNNaNNaNNaN15.015.013.017.0
12NaNNaNNaNNaNNaNNaNNaNNaNNaN* antes de Jan/25 não havia divisão entre closer e SDRNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

Unnamed: 02024-04-01 00:00:002024-05-01 00:00:002024-06-01 00:00:002024-07-01 00:00:002024-08-01 00:00:002024-09-01 00:00:002024-10-01 00:00:002024-11-01 00:00:002025-01-01 00:00:002025-02-01 00:00:002025-03-01 00:00:002025-04-01 00:00:00# duplicates
0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4