Overview

Dataset statistics

Number of variables7
Number of observations30
Missing cells29
Missing cells (%)13.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory61.4 B

Variable types

Categorical1
Text2
DateTime3
Boolean1

Dataset

Description샘플 데이터
Author경기도일자리재단
URLhttps://www.bigdata-region.kr/#/dataset/71bb4fbb-50aa-4ff7-9a09-6cd2fcbd838b

Alerts

정보순번 has constant value ""Constant
변경사유내용 has constant value ""Constant
사용여부 has constant value ""Constant
변경사유내용 has 29 (96.7%) missing valuesMissing
상담키값 has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:52:30.961353
Analysis finished2023-12-10 13:52:31.695279
Duration0.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정보순번
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
1
30 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 30
100.0%

Length

2023-12-10T22:52:31.847768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:52:32.029852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 30
100.0%

변경사유내용
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing29
Missing (%)96.7%
Memory size372.0 B
2023-12-10T22:52:32.250112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters20
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row상담변경사유테스트!@#!#%@#$56
ValueCountFrequency (%)
상담변경사유테스트!@#!#%@#$56 1
100.0%
2023-12-10T22:52:32.874550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
# 3
15.0%
! 2
 
10.0%
@ 2
 
10.0%
상 1
 
5.0%
담 1
 
5.0%
변 1
 
5.0%
경 1
 
5.0%
사 1
 
5.0%
유 1
 
5.0%
테 1
 
5.0%
Other values (6) 6
30.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9
45.0%
Other Punctuation 8
40.0%
Decimal Number 2
 
10.0%
Currency Symbol 1
 
5.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 1
11.1%
담 1
11.1%
변 1
11.1%
경 1
11.1%
사 1
11.1%
유 1
11.1%
테 1
11.1%
스 1
11.1%
트 1
11.1%
Other Punctuation
ValueCountFrequency (%)
# 3
37.5%
! 2
25.0%
@ 2
25.0%
% 1
 
12.5%
Decimal Number
ValueCountFrequency (%)
5 1
50.0%
6 1
50.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11
55.0%
Hangul 9
45.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 1
11.1%
담 1
11.1%
변 1
11.1%
경 1
11.1%
사 1
11.1%
유 1
11.1%
테 1
11.1%
스 1
11.1%
트 1
11.1%
Common
ValueCountFrequency (%)
# 3
27.3%
! 2
18.2%
@ 2
18.2%
% 1
 
9.1%
$ 1
 
9.1%
5 1
 
9.1%
6 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11
55.0%
Hangul 9
45.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
# 3
27.3%
! 2
18.2%
@ 2
18.2%
% 1
 
9.1%
$ 1
 
9.1%
5 1
 
9.1%
6 1
 
9.1%
Hangul
ValueCountFrequency (%)
상 1
11.1%
담 1
11.1%
변 1
11.1%
경 1
11.1%
사 1
11.1%
유 1
11.1%
테 1
11.1%
스 1
11.1%
트 1
11.1%

등록일시
Date

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2018-07-05 12:42:00
Maximum2018-07-31 14:27:00
2023-12-10T22:52:33.080140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:52:33.367569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)

수정일시
Date

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2018-07-05 12:42:00
Maximum2018-07-31 14:27:00
2023-12-10T22:52:33.553907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:52:34.059651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)

사용여부
Boolean

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size162.0 B
True
30 
ValueCountFrequency (%)
True 30
100.0%
2023-12-10T22:52:34.227913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

상담키값
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:52:34.532949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.7666667
Min length6

Characters and Unicode

Total characters233
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

Unique30 ?
Unique (%)100.0%

Sample

1st row109_167_1
2nd row75_29_1
3rd row101_86_1
4th row97_45_1
5th row81_4_1
ValueCountFrequency (%)
109_167_1 1
 
3.3%
75_29_1 1
 
3.3%
120_229_1 1
 
3.3%
75_38_1 1
 
3.3%
80_64_1 1
 
3.3%
102_48_1 1
 
3.3%
101_155_1 1
 
3.3%
82_32_1 1
 
3.3%
107_52_1 1
 
3.3%
106_55_1 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T22:52:35.131234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 64
27.5%
_ 60
25.8%
0 21
 
9.0%
8 17
 
7.3%
7 14
 
6.0%
2 12
 
5.2%
9 11
 
4.7%
5 11
 
4.7%
3 10
 
4.3%
4 7
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 173
74.2%
Connector Punctuation 60
 
25.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 64
37.0%
0 21
 
12.1%
8 17
 
9.8%
7 14
 
8.1%
2 12
 
6.9%
9 11
 
6.4%
5 11
 
6.4%
3 10
 
5.8%
4 7
 
4.0%
6 6
 
3.5%
Connector Punctuation
ValueCountFrequency (%)
_ 60
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 233
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 64
27.5%
_ 60
25.8%
0 21
 
9.0%
8 17
 
7.3%
7 14
 
6.0%
2 12
 
5.2%
9 11
 
4.7%
5 11
 
4.7%
3 10
 
4.3%
4 7
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 233
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 64
27.5%
_ 60
25.8%
0 21
 
9.0%
8 17
 
7.3%
7 14
 
6.0%
2 12
 
5.2%
9 11
 
4.7%
5 11
 
4.7%
3 10
 
4.3%
4 7
 
3.0%

데이터기준일자
Date

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2018-07-05 00:00:00
Maximum2018-07-31 00:00:00
2023-12-10T22:52:35.428931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:52:35.623490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)

Correlations

2023-12-10T22:52:35.750500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록일시수정일시상담키값데이터기준일자
등록일시1.0001.0001.0001.000
수정일시1.0001.0001.0001.000
상담키값1.0001.0001.0001.000
데이터기준일자1.0001.0001.0001.000

Missing values

2023-12-10T22:52:31.327323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:52:31.615167image/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

정보순번변경사유내용등록일시수정일시사용여부상담키값데이터기준일자
01<NA>2018-07-24 11:382018-07-24 11:38Y109_167_12018-07-24
11<NA>2018-07-05 13:142018-07-05 13:14Y75_29_12018-07-05
21<NA>2018-07-19 16:342018-07-19 16:34Y101_86_12018-07-19
31<NA>2018-07-19 16:292018-07-19 16:29Y97_45_12018-07-19
41상담변경사유테스트!@#!#%@#$562018-07-05 12:422018-07-05 12:42Y81_4_12018-07-05
51<NA>2018-07-24 14:262018-07-24 14:41Y110_96_12018-07-24
61<NA>2018-07-23 13:312018-07-23 13:37Y103_49_12018-07-23
71<NA>2018-07-31 14:012018-07-31 14:01Y117_78_12018-07-31
81<NA>2018-07-31 14:062018-07-31 14:06Y119_79_12018-07-31
91<NA>2018-07-13 13:262018-07-13 13:26Y93_73_12018-07-13
정보순번변경사유내용등록일시수정일시사용여부상담키값데이터기준일자
201<NA>2018-07-10 16:332018-07-10 16:33Y88_28_12018-07-10
211<NA>2018-07-23 17:192018-07-23 17:19Y106_55_12018-07-23
221<NA>2018-07-23 14:102018-07-23 14:10Y107_52_12018-07-23
231<NA>2018-07-06 15:032018-07-06 16:12Y82_32_12018-07-06
241<NA>2018-07-19 16:342018-07-19 16:34Y101_155_12018-07-19
251<NA>2018-07-19 17:192018-07-19 17:19Y102_48_12018-07-19
261<NA>2018-07-05 13:572018-07-05 13:57Y80_64_12018-07-05
271<NA>2018-07-05 13:142018-07-05 13:14Y75_38_12018-07-05
281<NA>2018-07-31 14:272018-07-31 14:27Y120_229_12018-07-31
291<NA>2018-07-06 15:032018-07-06 16:12Y82_13_12018-07-06