Overview

Dataset statistics

Number of variables4
Number of observations48
Missing cells0
Missing cells (%)0.0%
Duplicate rows16
Duplicate rows (%)33.3%
Total size in memory1.6 KiB
Average record size in memory34.8 B

Variable types

Categorical4

Alerts

생공_주의 has constant value ""Constant
생공_매우심함 has constant value ""Constant
Dataset has 16 (33.3%) duplicate rowsDuplicates
분석일자 is highly overall correlated with 생공_심함High correlation
생공_심함 is highly overall correlated with 분석일자High correlation

Reproduction

Analysis started2023-12-10 10:40:25.916402
Analysis finished2023-12-10 10:40:26.614911
Duration0.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분석일자
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size516.0 B
2019-01-10
 
3
2019-02-15
 
3
2019-03-11
 
3
2019-04-10
 
3
2019-05-10
 
3
Other values (11)
33 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-01-10
2nd row2019-01-10
3rd row2019-01-10
4th row2019-02-15
5th row2019-02-15

Common Values

ValueCountFrequency (%)
2019-01-10 3
 
6.2%
2019-02-15 3
 
6.2%
2019-03-11 3
 
6.2%
2019-04-10 3
 
6.2%
2019-05-10 3
 
6.2%
2019-06-10 3
 
6.2%
2019-07-10 3
 
6.2%
2019-08-10 3
 
6.2%
2019-09-10 3
 
6.2%
2019-10-14 3
 
6.2%
Other values (6) 18
37.5%

Length

2023-12-10T19:40:26.770803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-01-10 3
 
6.2%
2019-02-15 3
 
6.2%
2019-03-11 3
 
6.2%
2019-04-10 3
 
6.2%
2019-05-10 3
 
6.2%
2019-06-10 3
 
6.2%
2019-07-10 3
 
6.2%
2019-08-10 3
 
6.2%
2019-09-10 3
 
6.2%
2019-10-14 3
 
6.2%
Other values (6) 18
37.5%

생공_주의
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size516.0 B
없음
48 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
없음 48
100.0%

Length

2023-12-10T19:40:27.080185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:40:27.317656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
없음 48
100.0%

생공_심함
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size516.0 B
없음
36 
<b>[충남]</b> 보령. 서산. 당진. 서천. 청양. 홍성. 예산. 태안<br>
6 
<b>[충남] </b> 당진. 보령. 서산. 서천. 예산. 청양. 태안. 홍성 <br>
 
3
<b>[충남]</b> 보령. 서산. 당진. 서천. 청양. 홍성. 예산. 태안
 
3

Length

Max length50
Median length2
Mean length13
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
없음 36
75.0%
<b>[충남]</b> 보령. 서산. 당진. 서천. 청양. 홍성. 예산. 태안<br> 6
 
12.5%
<b>[충남] </b> 당진. 보령. 서산. 서천. 예산. 청양. 태안. 홍성 <br> 3
 
6.2%
<b>[충남]</b> 보령. 서산. 당진. 서천. 청양. 홍성. 예산. 태안 3
 
6.2%

Length

2023-12-10T19:40:27.572937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:40:27.786252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
없음 36
24.0%
보령 12
 
8.0%
서산 12
 
8.0%
당진 12
 
8.0%
서천 12
 
8.0%
청양 12
 
8.0%
홍성 12
 
8.0%
예산 12
 
8.0%
b>[충남]</b 9
 
6.0%
태안<br 6
 
4.0%
Other values (4) 15
10.0%

생공_매우심함
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size516.0 B
없음
48 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
없음 48
100.0%

Length

2023-12-10T19:40:28.081195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:40:28.339375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
없음 48
100.0%

Correlations

2023-12-10T19:40:28.462691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분석일자생공_심함
분석일자1.0001.000
생공_심함1.0001.000
2023-12-10T19:40:28.640076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생공_심함분석일자
생공_심함1.0000.853
분석일자0.8531.000
2023-12-10T19:40:28.884459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분석일자생공_심함
분석일자1.0000.853
생공_심함0.8531.000

Missing values

2023-12-10T19:40:26.304313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:40:26.538025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

분석일자생공_주의생공_심함생공_매우심함
02019-01-10없음없음없음
12019-01-10없음없음없음
22019-01-10없음없음없음
32019-02-15없음없음없음
42019-02-15없음없음없음
52019-02-15없음없음없음
62019-03-11없음없음없음
72019-03-11없음없음없음
82019-03-11없음없음없음
92019-04-10없음없음없음
분석일자생공_주의생공_심함생공_매우심함
382020-01-13없음없음없음
392020-02-11없음없음없음
402020-02-11없음없음없음
412020-02-11없음없음없음
422020-03-10없음없음없음
432020-03-10없음없음없음
442020-03-10없음없음없음
452020-04-10없음없음없음
462020-04-10없음없음없음
472020-04-10없음없음없음

Duplicate rows

Most frequently occurring

분석일자생공_주의생공_심함생공_매우심함# duplicates
02019-01-10없음없음없음3
12019-02-15없음없음없음3
22019-03-11없음없음없음3
32019-04-10없음없음없음3
42019-05-10없음없음없음3
52019-06-10없음없음없음3
62019-07-10없음없음없음3
72019-08-10없음없음없음3
82019-09-10없음<b>[충남] </b> 당진. 보령. 서산. 서천. 예산. 청양. 태안. 홍성 <br>없음3
92019-10-14없음<b>[충남]</b> 보령. 서산. 당진. 서천. 청양. 홍성. 예산. 태안없음3