Overview

Dataset statistics

Number of variables19
Number of observations30
Missing cells232
Missing cells (%)40.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 KiB
Average record size in memory162.4 B

Variable types

Numeric2
Categorical8
DateTime1
Text7
Boolean1

Dataset

Description샘플 데이터
Author경기도일자리재단
URLhttps://www.bigdata-region.kr/#/dataset/0bf9a293-f3c4-4dc9-8f8c-4b4afd2704c1

Alerts

회원유형명 has constant value ""Constant
성별코드 has constant value ""Constant
직업명 has constant value ""Constant
시도명 has constant value ""Constant
시군구명 has constant value ""Constant
동명 has constant value ""Constant
가입경로명 has constant value ""Constant
가입목표명 has constant value ""Constant
창업진단정보번호 is highly overall correlated with 창업역량전체평균점수 and 2 other fieldsHigh correlation
창업역량점수 is highly overall correlated with 창업역량수준명High correlation
창업분야명 is highly overall correlated with 데이터기준일자High correlation
창업역량전체평균점수 is highly overall correlated with 창업진단정보번호 and 1 other fieldsHigh correlation
창업분야평균점수 is highly overall correlated with 창업진단정보번호High correlation
창업역량수준명 is highly overall correlated with 창업역량점수High correlation
데이터기준일자 is highly overall correlated with 창업진단정보번호 and 2 other fieldsHigh correlation
창업단계명 is highly imbalanced (78.9%)Imbalance
창업분야평균점수 is highly imbalanced (64.7%)Imbalance
출생년도 is highly imbalanced (78.9%)Imbalance
우편번호 is highly imbalanced (78.9%)Imbalance
회원유형명 has 29 (96.7%) missing valuesMissing
성별코드 has 29 (96.7%) missing valuesMissing
직업명 has 29 (96.7%) missing valuesMissing
시도명 has 29 (96.7%) missing valuesMissing
시군구명 has 29 (96.7%) missing valuesMissing
동명 has 29 (96.7%) missing valuesMissing
가입경로명 has 29 (96.7%) missing valuesMissing
가입목표명 has 29 (96.7%) missing valuesMissing
창업진단정보번호 has unique valuesUnique
등록일시 has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:17:09.051868
Analysis finished2023-12-10 14:17:11.869448
Duration2.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

창업진단정보번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12400.5
Minimum12386
Maximum12415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:17:11.945865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12386
5-th percentile12387.45
Q112393.25
median12400.5
Q312407.75
95-th percentile12413.55
Maximum12415
Range29
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation8.8034084
Coefficient of variation (CV)0.00070992367
Kurtosis-1.2
Mean12400.5
Median Absolute Deviation (MAD)7.5
Skewness0
Sum372015
Variance77.5
MonotonicityStrictly increasing
2023-12-10T23:17:12.172662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
12386 1
 
3.3%
12402 1
 
3.3%
12415 1
 
3.3%
12414 1
 
3.3%
12413 1
 
3.3%
12412 1
 
3.3%
12411 1
 
3.3%
12410 1
 
3.3%
12409 1
 
3.3%
12408 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
12386 1
3.3%
12387 1
3.3%
12388 1
3.3%
12389 1
3.3%
12390 1
3.3%
12391 1
3.3%
12392 1
3.3%
12393 1
3.3%
12394 1
3.3%
12395 1
3.3%
ValueCountFrequency (%)
12415 1
3.3%
12414 1
3.3%
12413 1
3.3%
12412 1
3.3%
12411 1
3.3%
12410 1
3.3%
12409 1
3.3%
12408 1
3.3%
12407 1
3.3%
12406 1
3.3%

창업분야명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
서비스업 분야
19 
도·소매업 분야
8 
제조업 분야
3 

Length

Max length8
Median length7
Mean length7.1666667
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서비스업 분야
2nd row서비스업 분야
3rd row서비스업 분야
4th row서비스업 분야
5th row서비스업 분야

Common Values

ValueCountFrequency (%)
서비스업 분야 19
63.3%
도·소매업 분야 8
26.7%
제조업 분야 3
 
10.0%

Length

2023-12-10T23:17:12.417813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:17:12.605531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
분야 30
50.0%
서비스업 19
31.7%
도·소매업 8
 
13.3%
제조업 3
 
5.0%

창업단계명
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
창업준비
29 
창업초기
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
창업준비 29
96.7%
창업초기 1
 
3.3%

Length

2023-12-10T23:17:12.790131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:17:12.957086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
창업준비 29
96.7%
창업초기 1
 
3.3%

창업역량점수
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.69
Minimum2.98
Maximum4.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:17:13.128872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.98
5-th percentile2.998
Q13.3925
median3.72
Q33.975
95-th percentile4.3605
Maximum4.46
Range1.48
Interquartile range (IQR)0.5825

Descriptive statistics

Standard deviation0.41425463
Coefficient of variation (CV)0.11226413
Kurtosis-0.6721074
Mean3.69
Median Absolute Deviation (MAD)0.27
Skewness-0.086981154
Sum110.7
Variance0.1716069
MonotonicityNot monotonic
2023-12-10T23:17:13.319968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
3.24 2
 
6.7%
3.8 2
 
6.7%
2.98 2
 
6.7%
3.87 2
 
6.7%
4.0 2
 
6.7%
3.98 1
 
3.3%
4.02 1
 
3.3%
3.63 1
 
3.3%
4.41 1
 
3.3%
3.92 1
 
3.3%
Other values (15) 15
50.0%
ValueCountFrequency (%)
2.98 2
6.7%
3.02 1
3.3%
3.09 1
3.3%
3.24 2
6.7%
3.28 1
3.3%
3.37 1
3.3%
3.46 1
3.3%
3.5 1
3.3%
3.54 1
3.3%
3.63 1
3.3%
ValueCountFrequency (%)
4.46 1
3.3%
4.41 1
3.3%
4.3 1
3.3%
4.22 1
3.3%
4.02 1
3.3%
4.0 2
6.7%
3.98 1
3.3%
3.96 1
3.3%
3.92 1
3.3%
3.87 2
6.7%

창업역량전체평균점수
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
0.02
16 
0.03
14 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.02
2nd row0.02
3rd row0.02
4th row0.02
5th row0.02

Common Values

ValueCountFrequency (%)
0.02 16
53.3%
0.03 14
46.7%

Length

2023-12-10T23:17:13.501336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:17:13.646660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.02 16
53.3%
0.03 14
46.7%

창업분야평균점수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
0.01
28 
0.0
 
2

Length

Max length4
Median length4
Mean length3.9333333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.01
4th row0.01
5th row0.01

Common Values

ValueCountFrequency (%)
0.01 28
93.3%
0.0 2
 
6.7%

Length

2023-12-10T23:17:13.815850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:17:13.969458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.01 28
93.3%
0.0 2
 
6.7%

창업역량수준명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
전문가수준 Level 6
18 
숙련자수준 Level 5
7 
최고전문가수준 Level 7
3 
적응자수준 Level 4
2 

Length

Max length15
Median length13
Mean length13.2
Min length13

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전문가수준 Level 6
2nd row전문가수준 Level 6
3rd row전문가수준 Level 6
4th row전문가수준 Level 6
5th row숙련자수준 Level 5

Common Values

ValueCountFrequency (%)
전문가수준 Level 6 18
60.0%
숙련자수준 Level 5 7
 
23.3%
최고전문가수준 Level 7 3
 
10.0%
적응자수준 Level 4 2
 
6.7%

Length

2023-12-10T23:17:14.168361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:17:14.359214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
level 30
33.3%
전문가수준 18
20.0%
6 18
20.0%
숙련자수준 7
 
7.8%
5 7
 
7.8%
최고전문가수준 3
 
3.3%
7 3
 
3.3%
적응자수준 2
 
2.2%
4 2
 
2.2%

등록일시
Date

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2014-05-26 00:29:00
Maximum2014-06-08 06:42:00
2023-12-10T23:17:14.531342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:17:14.756493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)

회원유형명
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing29
Missing (%)96.7%
Memory size372.0 B
2023-12-10T23:17:14.961554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6
Distinct characters6
Distinct categories2 ?
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 row일반 학습자
ValueCountFrequency (%)
일반 1
50.0%
학습자 1
50.0%
2023-12-10T23:17:15.356132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
일 1
16.7%
반 1
16.7%
1
16.7%
학 1
16.7%
습 1
16.7%
자 1
16.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5
83.3%
Space Separator 1
 
16.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
일 1
20.0%
반 1
20.0%
학 1
20.0%
습 1
20.0%
자 1
20.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5
83.3%
Common 1
 
16.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
일 1
20.0%
반 1
20.0%
학 1
20.0%
습 1
20.0%
자 1
20.0%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5
83.3%
ASCII 1
 
16.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
일 1
20.0%
반 1
20.0%
학 1
20.0%
습 1
20.0%
자 1
20.0%
ASCII
ValueCountFrequency (%)
1
100.0%

출생년도
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
29 
1966
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 29
96.7%
1966 1
 
3.3%

Length

2023-12-10T23:17:15.572276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:17:15.725059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 29
96.7%
1966 1
 
3.3%

성별코드
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing29
Missing (%)96.7%
Memory size192.0 B
False
 
1
(Missing)
29 
ValueCountFrequency (%)
False 1
 
3.3%
(Missing) 29
96.7%
2023-12-10T23:17:15.848166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

직업명
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing29
Missing (%)96.7%
Memory size372.0 B
2023-12-10T23:17:15.946846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row주부
ValueCountFrequency (%)
주부 1
100.0%
2023-12-10T23:17:16.268540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 1
50.0%
부 1
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 1
50.0%
부 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 1
50.0%
부 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 1
50.0%
부 1
50.0%

우편번호
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
<NA>
29 
16698
 
1

Length

Max length5
Median length4
Mean length4.0333333
Min length4

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 29
96.7%
16698 1
 
3.3%

Length

2023-12-10T23:17:16.797765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:17:16.965238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 29
96.7%
16698 1
 
3.3%

시도명
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing29
Missing (%)96.7%
Memory size372.0 B
2023-12-10T23:17:17.087494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row경기
ValueCountFrequency (%)
경기 1
100.0%
2023-12-10T23:17:17.408756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 1
50.0%
기 1
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 1
50.0%
기 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 1
50.0%
기 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 1
50.0%
기 1
50.0%

시군구명
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing29
Missing (%)96.7%
Memory size372.0 B
2023-12-10T23:17:17.629512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
Distinct characters7
Distinct categories2 ?
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 row수원시 영통구
ValueCountFrequency (%)
수원시 1
50.0%
영통구 1
50.0%
2023-12-10T23:17:18.041183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 1
14.3%
원 1
14.3%
시 1
14.3%
1
14.3%
영 1
14.3%
통 1
14.3%
구 1
14.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6
85.7%
Space Separator 1
 
14.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 1
16.7%
원 1
16.7%
시 1
16.7%
영 1
16.7%
통 1
16.7%
구 1
16.7%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6
85.7%
Common 1
 
14.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 1
16.7%
원 1
16.7%
시 1
16.7%
영 1
16.7%
통 1
16.7%
구 1
16.7%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6
85.7%
ASCII 1
 
14.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 1
16.7%
원 1
16.7%
시 1
16.7%
영 1
16.7%
통 1
16.7%
구 1
16.7%
ASCII
ValueCountFrequency (%)
1
100.0%

동명
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing29
Missing (%)96.7%
Memory size372.0 B
2023-12-10T23:17:18.281703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
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

Unique1 ?
Unique (%)100.0%

Sample

1st row영통동
ValueCountFrequency (%)
영통동 1
100.0%
2023-12-10T23:17:18.644762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
영 1
33.3%
통 1
33.3%
동 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 1
33.3%
통 1
33.3%
동 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 1
33.3%
통 1
33.3%
동 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
영 1
33.3%
통 1
33.3%
동 1
33.3%

가입경로명
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing29
Missing (%)96.7%
Memory size372.0 B
2023-12-10T23:17:18.863501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters12
Distinct characters12
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 row인터넷(취업포털;검색)
ValueCountFrequency (%)
인터넷(취업포털;검색 1
100.0%
2023-12-10T23:17:19.295571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
인 1
8.3%
터 1
8.3%
넷 1
8.3%
( 1
8.3%
취 1
8.3%
업 1
8.3%
포 1
8.3%
털 1
8.3%
; 1
8.3%
검 1
8.3%
Other values (2) 2
16.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9
75.0%
Open Punctuation 1
 
8.3%
Other Punctuation 1
 
8.3%
Close Punctuation 1
 
8.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 1
11.1%
터 1
11.1%
넷 1
11.1%
취 1
11.1%
업 1
11.1%
포 1
11.1%
털 1
11.1%
검 1
11.1%
색 1
11.1%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Other Punctuation
ValueCountFrequency (%)
; 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9
75.0%
Common 3
 
25.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 1
11.1%
터 1
11.1%
넷 1
11.1%
취 1
11.1%
업 1
11.1%
포 1
11.1%
털 1
11.1%
검 1
11.1%
색 1
11.1%
Common
ValueCountFrequency (%)
( 1
33.3%
; 1
33.3%
) 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9
75.0%
ASCII 3
 
25.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
인 1
11.1%
터 1
11.1%
넷 1
11.1%
취 1
11.1%
업 1
11.1%
포 1
11.1%
털 1
11.1%
검 1
11.1%
색 1
11.1%
ASCII
ValueCountFrequency (%)
( 1
33.3%
; 1
33.3%
) 1
33.3%

가입목표명
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing29
Missing (%)96.7%
Memory size372.0 B
2023-12-10T23:17:19.554109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length31
Mean length31
Min length31

Characters and Unicode

Total characters31
Distinct characters22
Distinct categories5 ?
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 row재 취업을 위해(육아; 가사 등으로 직업 경력 단절 등)
ValueCountFrequency (%)
재 1
11.1%
취업을 1
11.1%
위해(육아 1
11.1%
가사 1
11.1%
등으로 1
11.1%
직업 1
11.1%
경력 1
11.1%
단절 1
11.1%
등 1
11.1%
2023-12-10T23:17:20.044418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8
25.8%
업 2
 
6.5%
등 2
 
6.5%
재 1
 
3.2%
사 1
 
3.2%
절 1
 
3.2%
단 1
 
3.2%
력 1
 
3.2%
경 1
 
3.2%
직 1
 
3.2%
Other values (12) 12
38.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20
64.5%
Space Separator 8
 
25.8%
Other Punctuation 1
 
3.2%
Open Punctuation 1
 
3.2%
Close Punctuation 1
 
3.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 2
 
10.0%
등 2
 
10.0%
재 1
 
5.0%
사 1
 
5.0%
절 1
 
5.0%
단 1
 
5.0%
력 1
 
5.0%
경 1
 
5.0%
직 1
 
5.0%
로 1
 
5.0%
Other values (8) 8
40.0%
Space Separator
ValueCountFrequency (%)
8
100.0%
Other Punctuation
ValueCountFrequency (%)
; 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20
64.5%
Common 11
35.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 2
 
10.0%
등 2
 
10.0%
재 1
 
5.0%
사 1
 
5.0%
절 1
 
5.0%
단 1
 
5.0%
력 1
 
5.0%
경 1
 
5.0%
직 1
 
5.0%
로 1
 
5.0%
Other values (8) 8
40.0%
Common
ValueCountFrequency (%)
8
72.7%
; 1
 
9.1%
( 1
 
9.1%
) 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20
64.5%
ASCII 11
35.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8
72.7%
; 1
 
9.1%
( 1
 
9.1%
) 1
 
9.1%
Hangul
ValueCountFrequency (%)
업 2
 
10.0%
등 2
 
10.0%
재 1
 
5.0%
사 1
 
5.0%
절 1
 
5.0%
단 1
 
5.0%
력 1
 
5.0%
경 1
 
5.0%
직 1
 
5.0%
로 1
 
5.0%
Other values (8) 8
40.0%

데이터기준일자
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2014-06-03
5 
2014-05-26
4 
2014-05-28
4 
2014-06-05
4 
2014-06-02
3 
Other values (8)
10 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique6 ?
Unique (%)20.0%

Sample

1st row2014-05-26
2nd row2014-05-26
3rd row2014-05-26
4th row2014-05-26
5th row2014-05-27

Common Values

ValueCountFrequency (%)
2014-06-03 5
16.7%
2014-05-26 4
13.3%
2014-05-28 4
13.3%
2014-06-05 4
13.3%
2014-06-02 3
10.0%
2014-05-27 2
 
6.7%
2014-06-06 2
 
6.7%
2014-05-29 1
 
3.3%
2014-05-30 1
 
3.3%
2014-06-01 1
 
3.3%
Other values (3) 3
10.0%

Length

2023-12-10T23:17:20.278317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2014-06-03 5
16.7%
2014-05-26 4
13.3%
2014-05-28 4
13.3%
2014-06-05 4
13.3%
2014-06-02 3
10.0%
2014-05-27 2
 
6.7%
2014-06-06 2
 
6.7%
2014-05-29 1
 
3.3%
2014-05-30 1
 
3.3%
2014-06-01 1
 
3.3%
Other values (3) 3
10.0%

Interactions

2023-12-10T23:17:10.625051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:17:10.291186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:17:10.790437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:17:10.461791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:17:20.417931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
창업진단정보번호창업분야명창업단계명창업역량점수창업역량전체평균점수창업분야평균점수창업역량수준명등록일시데이터기준일자
창업진단정보번호1.0000.0000.0000.0000.9940.6430.0001.0000.872
창업분야명0.0001.0000.0000.0000.0000.0000.1141.0000.817
창업단계명0.0000.0001.0000.0000.0000.0000.0001.0000.398
창업역량점수0.0000.0000.0001.0000.0000.0000.8721.0000.359
창업역량전체평균점수0.9940.0000.0000.0001.0000.0000.0001.0001.000
창업분야평균점수0.6430.0000.0000.0000.0001.0000.0001.0000.351
창업역량수준명0.0000.1140.0000.8720.0000.0001.0001.0000.631
등록일시1.0001.0001.0001.0001.0001.0001.0001.0001.000
데이터기준일자0.8720.8170.3980.3591.0000.3510.6311.0001.000
2023-12-10T23:17:20.638319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
창업역량전체평균점수우편번호창업분야평균점수출생년도창업분야명데이터기준일자창업단계명창업역량수준명
창업역량전체평균점수1.000NaN0.000NaN0.0000.7790.0000.000
우편번호NaN1.000NaNNaNNaNNaNNaNNaN
창업분야평균점수0.000NaN1.000NaN0.0000.2290.0000.000
출생년도NaNNaNNaN1.000NaNNaNNaNNaN
창업분야명0.000NaN0.000NaN1.0000.5330.0000.086
데이터기준일자0.779NaN0.229NaN0.5331.0000.2670.324
창업단계명0.000NaN0.000NaN0.0000.2671.0000.000
창업역량수준명0.000NaN0.000NaN0.0860.3240.0001.000
2023-12-10T23:17:20.865120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
창업진단정보번호창업역량점수창업분야명창업단계명창업역량전체평균점수창업분야평균점수창업역량수준명출생년도우편번호데이터기준일자
창업진단정보번호1.0000.1050.0000.0000.7890.5870.000NaNNaN0.572
창업역량점수0.1051.0000.0000.0000.0000.0000.639NaNNaN0.038
창업분야명0.0000.0001.0000.0000.0000.0000.086NaNNaN0.533
창업단계명0.0000.0000.0001.0000.0000.0000.000NaNNaN0.267
창업역량전체평균점수0.7890.0000.0000.0001.0000.0000.000NaNNaN0.779
창업분야평균점수0.5870.0000.0000.0000.0001.0000.000NaNNaN0.229
창업역량수준명0.0000.6390.0860.0000.0000.0001.000NaNNaN0.324
출생년도NaNNaNNaNNaNNaNNaNNaN1.000NaNNaN
우편번호NaNNaNNaNNaNNaNNaNNaNNaN1.000NaN
데이터기준일자0.5720.0380.5330.2670.7790.2290.324NaNNaN1.000

Missing values

2023-12-10T23:17:11.030492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:17:11.413322image/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.
2023-12-10T23:17:11.710251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

창업진단정보번호창업분야명창업단계명창업역량점수창업역량전체평균점수창업분야평균점수창업역량수준명등록일시회원유형명출생년도성별코드직업명우편번호시도명시군구명동명가입경로명가입목표명데이터기준일자
012386서비스업 분야창업준비3.980.020.0전문가수준 Level 62014-05-26 00:29<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-26
112387서비스업 분야창업준비3.870.020.0전문가수준 Level 62014-05-26 05:43<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-26
212388서비스업 분야창업준비3.870.020.01전문가수준 Level 62014-05-26 17:59<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-26
312389서비스업 분야창업준비3.650.020.01전문가수준 Level 62014-05-26 23:34<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-26
412390서비스업 분야창업준비3.240.020.01숙련자수준 Level 52014-05-27 12:12<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-27
512391서비스업 분야창업준비3.370.020.01숙련자수준 Level 52014-05-27 22:36<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-27
612392도·소매업 분야창업준비3.090.020.01숙련자수준 Level 52014-05-28 01:21<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-28
712393서비스업 분야창업준비3.50.020.01전문가수준 Level 62014-05-28 11:06<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-28
812394도·소매업 분야창업준비3.80.020.01전문가수준 Level 62014-05-28 15:32<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-28
912395도·소매업 분야창업준비4.460.020.01최고전문가수준 Level 72014-05-28 23:30<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-05-28
창업진단정보번호창업분야명창업단계명창업역량점수창업역량전체평균점수창업분야평균점수창업역량수준명등록일시회원유형명출생년도성별코드직업명우편번호시도명시군구명동명가입경로명가입목표명데이터기준일자
2012406도·소매업 분야창업준비3.960.030.01전문가수준 Level 62014-06-03 22:43<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-06-03
2112407제조업 분야창업준비3.740.030.01전문가수준 Level 62014-06-04 08:51<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-06-04
2212408서비스업 분야창업준비3.70.030.01전문가수준 Level 62014-06-05 01:57<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-06-05
2312409서비스업 분야창업준비4.00.030.01전문가수준 Level 62014-06-05 05:33<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-06-05
2412410도·소매업 분야창업준비3.240.030.01숙련자수준 Level 52014-06-05 05:56<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-06-05
2512411도·소매업 분야창업준비3.460.030.01숙련자수준 Level 52014-06-05 08:09<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-06-05
2612412서비스업 분야창업준비3.670.030.01전문가수준 Level 62014-06-06 00:17<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-06-06
2712413서비스업 분야창업초기3.920.030.01전문가수준 Level 62014-06-06 04:44<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-06-06
2812414서비스업 분야창업준비4.410.030.01최고전문가수준 Level 72014-06-07 14:23<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-06-07
2912415제조업 분야창업준비3.630.030.01전문가수준 Level 62014-06-08 06:42<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2014-06-08