Overview

Dataset statistics

Number of variables15
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.3 KiB
Average record size in memory126.3 B

Variable types

Categorical10
Numeric4
Text1

Alerts

dtbz_nm is highly overall correlated with busi_cd and 7 other fieldsHigh correlation
progrm_code is highly overall correlated with busi_cd and 9 other fieldsHigh correlation
ubz_nm is highly overall correlated with busi_cd and 12 other fieldsHigh correlation
dtbz_id is highly overall correlated with busi_cd and 9 other fieldsHigh correlation
dtlbz_id is highly overall correlated with busi_cd and 9 other fieldsHigh correlation
progrm_nm is highly overall correlated with busi_cd and 9 other fieldsHigh correlation
dtlbz_nm is highly overall correlated with busi_cd and 9 other fieldsHigh correlation
busi_cd is highly overall correlated with bsns_begin_de and 10 other fieldsHigh correlation
ubz_code is highly overall correlated with org_nm and 8 other fieldsHigh correlation
bsns_begin_de is highly overall correlated with busi_cd and 5 other fieldsHigh correlation
bsns_end_de is highly overall correlated with busi_yy and 3 other fieldsHigh correlation
busi_yy is highly overall correlated with busi_cd and 6 other fieldsHigh correlation
org_nm is highly overall correlated with busi_cd and 6 other fieldsHigh correlation
instt_code is highly overall correlated with busi_cd and 6 other fieldsHigh correlation
busi_yy is highly imbalanced (80.6%)Imbalance
ddtlbz_nm has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:53:02.035992
Analysis finished2023-12-10 09:53:15.724644
Duration13.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

busi_yy
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2017
97 
2021
 
3

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2021
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 97
97.0%
2021 3
 
3.0%

Length

2023-12-10T18:53:15.859113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:53:16.560463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 97
97.0%
2021 3
 
3.0%

org_nm
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
대한체육회
19 
서울올림픽기념국민체육진흥공단
 
6
(사)스페셜올림픽코리아
 
5
태권도진흥재단
 
4
사단법인 대한씨름협회
 
4
Other values (43)
62 

Length

Max length25
Median length16
Mean length9.41
Min length3

Unique

Unique29 ?
Unique (%)29.0%

Sample

1st row태권도진흥재단
2nd row서울올림픽기념국민체육진흥공단
3rd row(재)한국기원
4th row(재)한국기원
5th row(재)한국기원

Common Values

ValueCountFrequency (%)
대한체육회 19
19.0%
서울올림픽기념국민체육진흥공단 6
 
6.0%
(사)스페셜올림픽코리아 5
 
5.0%
태권도진흥재단 4
 
4.0%
사단법인 대한씨름협회 4
 
4.0%
(사단)한국배구연맹 3
 
3.0%
한국야구위원회 3
 
3.0%
대한장애인체육회 3
 
3.0%
(재)한국기원 3
 
3.0%
한국예술종합학교 산학협력단 3
 
3.0%
Other values (38) 47
47.0%

Length

2023-12-10T18:53:16.809012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대한체육회 20
 
17.2%
사단법인 7
 
6.0%
서울올림픽기념국민체육진흥공단 6
 
5.2%
사)스페셜올림픽코리아 5
 
4.3%
태권도진흥재단 4
 
3.4%
대한씨름협회 4
 
3.4%
산학협력단 4
 
3.4%
사단)한국배구연맹 3
 
2.6%
한국야구위원회 3
 
2.6%
대한장애인체육회 3
 
2.6%
Other values (43) 57
49.1%

busi_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9969907 × 1019
Minimum2.0210413 × 1016
Maximum2.0211123 × 1019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:53:17.065591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0210413 × 1016
5-th percentile2.0170206 × 1019
Q12.0170315 × 1019
median2.0170418 × 1019
Q32.0170624 × 1019
95-th percentile2.0171132 × 1019
Maximum2.0211123 × 1019
Range2.0190913 × 1019
Interquartile range (IQR)3.0925 × 1014

Descriptive statistics

Standard deviation2.0151291 × 1018
Coefficient of variation (CV)0.10090828
Kurtosis99.998328
Mean1.9969907 × 1019
Median Absolute Deviation (MAD)1.905 × 1014
Skewness-9.9998757
Sum1.9969907 × 1021
Variance4.0607452 × 1036
MonotonicityNot monotonic
2023-12-10T18:53:17.403270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.0170206e+19 7
 
7.0%
2.0170327e+19 5
 
5.0%
2.0170613e+19 5
 
5.0%
2.0170605e+19 4
 
4.0%
2.0170315e+19 4
 
4.0%
2.0170404e+19 3
 
3.0%
2.0170227e+19 3
 
3.0%
2.0170321e+19 3
 
3.0%
2.0170407e+19 3
 
3.0%
2.0170202e+19 3
 
3.0%
Other values (50) 60
60.0%
ValueCountFrequency (%)
2.0210413045034428e+16 1
 
1.0%
2.0170124e+19 1
 
1.0%
2.0170202e+19 3
3.0%
2.0170206e+19 7
7.0%
2.0170214e+19 1
 
1.0%
2.0170215e+19 1
 
1.0%
2.0170221e+19 1
 
1.0%
2.0170227e+19 3
3.0%
2.0170307e+19 1
 
1.0%
2.017031e+19 1
 
1.0%
ValueCountFrequency (%)
2.0211123000001184e+19 1
1.0%
2.021041600000088e+19 1
1.0%
2.0180213e+19 1
1.0%
2.0171222e+19 1
1.0%
2.0171204e+19 1
1.0%
2.0171128e+19 1
1.0%
2.0171107e+19 1
1.0%
2.0171106e+19 1
1.0%
2.0171024e+19 1
1.0%
2.0171019e+19 1
1.0%

ddtlbz_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:53:17.961701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length96
Median length30
Mean length18.53
Min length7

Characters and Unicode

Total characters1853
Distinct characters320
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row태권도원 운영관리비
2nd row2021년도 스포츠스타 체육교실
3rd row한.중 아마 친선 교류전
4th row소외계층 바둑보급
5th row상비군 대표강화 사업
ValueCountFrequency (%)
지원 16
 
4.3%
2017 11
 
2.9%
및 10
 
2.7%
사업 9
 
2.4%
2017년 7
 
1.9%
위한 6
 
1.6%
운영 5
 
1.3%
개발 4
 
1.1%
활성화 4
 
1.1%
개최 3
 
0.8%
Other values (271) 298
79.9%
2023-12-10T18:53:18.646106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
273
 
14.7%
스 37
 
2.0%
원 33
 
1.8%
지 32
 
1.7%
사 28
 
1.5%
츠 26
 
1.4%
2 26
 
1.4%
포 25
 
1.3%
육 25
 
1.3%
업 25
 
1.3%
Other values (310) 1323
71.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1361
73.4%
Space Separator 273
 
14.7%
Decimal Number 99
 
5.3%
Lowercase Letter 48
 
2.6%
Uppercase Letter 20
 
1.1%
Close Punctuation 18
 
1.0%
Open Punctuation 18
 
1.0%
Other Punctuation 11
 
0.6%
Dash Punctuation 3
 
0.2%
Connector Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 37
 
2.7%
원 33
 
2.4%
지 32
 
2.4%
사 28
 
2.1%
츠 26
 
1.9%
포 25
 
1.8%
육 25
 
1.8%
업 25
 
1.8%
화 24
 
1.8%
체 24
 
1.8%
Other values (265) 1082
79.5%
Lowercase Letter
ValueCountFrequency (%)
a 5
10.4%
e 5
10.4%
l 5
10.4%
d 4
8.3%
i 4
8.3%
o 4
8.3%
p 3
 
6.2%
s 3
 
6.2%
t 3
 
6.2%
m 3
 
6.2%
Other values (6) 9
18.8%
Uppercase Letter
ValueCountFrequency (%)
K 4
20.0%
W 3
15.0%
L 2
10.0%
S 2
10.0%
T 2
10.0%
O 1
 
5.0%
B 1
 
5.0%
F 1
 
5.0%
R 1
 
5.0%
A 1
 
5.0%
Other values (2) 2
10.0%
Decimal Number
ValueCountFrequency (%)
2 26
26.3%
0 24
24.2%
1 23
23.2%
7 21
21.2%
3 3
 
3.0%
5 2
 
2.0%
Other Punctuation
ValueCountFrequency (%)
: 2
18.2%
, 2
18.2%
; 2
18.2%
& 2
18.2%
? 2
18.2%
. 1
9.1%
Space Separator
ValueCountFrequency (%)
273
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1361
73.4%
Common 424
 
22.9%
Latin 68
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 37
 
2.7%
원 33
 
2.4%
지 32
 
2.4%
사 28
 
2.1%
츠 26
 
1.9%
포 25
 
1.8%
육 25
 
1.8%
업 25
 
1.8%
화 24
 
1.8%
체 24
 
1.8%
Other values (265) 1082
79.5%
Latin
ValueCountFrequency (%)
a 5
 
7.4%
e 5
 
7.4%
l 5
 
7.4%
d 4
 
5.9%
K 4
 
5.9%
i 4
 
5.9%
o 4
 
5.9%
p 3
 
4.4%
s 3
 
4.4%
t 3
 
4.4%
Other values (18) 28
41.2%
Common
ValueCountFrequency (%)
273
64.4%
2 26
 
6.1%
0 24
 
5.7%
1 23
 
5.4%
7 21
 
5.0%
) 18
 
4.2%
( 18
 
4.2%
- 3
 
0.7%
3 3
 
0.7%
: 2
 
0.5%
Other values (7) 13
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1361
73.4%
ASCII 492
 
26.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
273
55.5%
2 26
 
5.3%
0 24
 
4.9%
1 23
 
4.7%
7 21
 
4.3%
) 18
 
3.7%
( 18
 
3.7%
a 5
 
1.0%
e 5
 
1.0%
l 5
 
1.0%
Other values (35) 74
 
15.0%
Hangul
ValueCountFrequency (%)
스 37
 
2.7%
원 33
 
2.4%
지 32
 
2.4%
사 28
 
2.1%
츠 26
 
1.9%
포 25
 
1.8%
육 25
 
1.8%
업 25
 
1.8%
화 24
 
1.8%
체 24
 
1.8%
Other values (265) 1082
79.5%

dtlbz_id
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
201507251610307A0001
28 
201507255610301A0006
 
5
201507251610306A0012
 
5
201507251610306A0005
 
4
201507252610317A0004
 
4
Other values (43)
54 

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique36 ?
Unique (%)36.0%

Sample

1st row201007253610313A0002
2nd row201907251610301B0009
3rd row201507251610306A0005
4th row201507251610306A0005
5th row201507251610306A0005

Common Values

ValueCountFrequency (%)
201507251610307A0001 28
28.0%
201507255610301A0006 5
 
5.0%
201507251610306A0012 5
 
5.0%
201507251610306A0005 4
 
4.0%
201507252610317A0004 4
 
4.0%
201507252610317A0001 4
 
4.0%
201507252610317A0005 3
 
3.0%
20070395363030310003 3
 
3.0%
201507252610317A0002 2
 
2.0%
201507251610306A0010 2
 
2.0%
Other values (38) 40
40.0%

Length

2023-12-10T18:53:18.896128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
201507251610307a0001 28
28.0%
201507251610306a0012 5
 
5.0%
201507255610301a0006 5
 
5.0%
201507251610306a0005 4
 
4.0%
201507252610317a0004 4
 
4.0%
201507252610317a0001 4
 
4.0%
201507252610317a0005 3
 
3.0%
20070395363030310003 3
 
3.0%
201607253610311b0002 2
 
2.0%
201007253610313a0002 2
 
2.0%
Other values (38) 40
40.0%

dtlbz_nm
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
문화체육사업지원
28 
장애인체육법인단체 지원(스페셜올림픽코리아)
 
5
전통스포츠보급
 
5
바둑 및 마인드스포츠 등
 
4
프로스포츠 정책사업 및 공통사업 지원
 
4
Other values (43)
54 

Length

Max length23
Median length18
Mean length11.58
Min length5

Unique

Unique36 ?
Unique (%)36.0%

Sample

1st row태권도원 운영
2nd row맞춤형생활체육활동 지원
3rd row바둑 및 마인드스포츠 등
4th row바둑 및 마인드스포츠 등
5th row바둑 및 마인드스포츠 등

Common Values

ValueCountFrequency (%)
문화체육사업지원 28
28.0%
장애인체육법인단체 지원(스페셜올림픽코리아) 5
 
5.0%
전통스포츠보급 5
 
5.0%
바둑 및 마인드스포츠 등 4
 
4.0%
프로스포츠 정책사업 및 공통사업 지원 4
 
4.0%
유소년, 아마추어 스포츠활성화 지원 4
 
4.0%
프로스포츠활성화 지원 3
 
3.0%
스포츠융복합 기술개발 3
 
3.0%
체육진흥투표권 비발행 대상종목 지원 2
 
2.0%
안전한 스포츠환경 구축 2
 
2.0%
Other values (38) 40
40.0%

Length

2023-12-10T18:53:19.194880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
문화체육사업지원 28
 
13.3%
지원 26
 
12.3%
및 10
 
4.7%
장애인체육법인단체 5
 
2.4%
기술개발 5
 
2.4%
운영 5
 
2.4%
지원(스페셜올림픽코리아 5
 
2.4%
전통스포츠보급 5
 
2.4%
유소년 4
 
1.9%
등 4
 
1.9%
Other values (76) 114
54.0%

dtbz_id
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
201507251610307A
28 
201507251610306A
17 
201507252610317A
14 
201507255610301A
8 
2007039536303031
6 
Other values (15)
27 

Length

Max length16
Median length16
Mean length16
Min length16

Unique

Unique8 ?
Unique (%)8.0%

Sample

1st row201007253610313A
2nd row201907251610301B
3rd row201507251610306A
4th row201507251610306A
5th row201507251610306A

Common Values

ValueCountFrequency (%)
201507251610307A 28
28.0%
201507251610306A 17
17.0%
201507252610317A 14
14.0%
201507255610301A 8
 
8.0%
2007039536303031 6
 
6.0%
201707251610302B 4
 
4.0%
200907253610309A 4
 
4.0%
201607252610312B 3
 
3.0%
201607253610311B 2
 
2.0%
201407253660301A 2
 
2.0%
Other values (10) 12
12.0%

Length

2023-12-10T18:53:19.514806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
201507251610307a 28
28.0%
201507251610306a 17
17.0%
201507252610317a 14
14.0%
201507255610301a 8
 
8.0%
2007039536303031 6
 
6.0%
201707251610302b 4
 
4.0%
200907253610309a 4
 
4.0%
201607252610312b 3
 
3.0%
201007253610313a 2
 
2.0%
201607252620300a 2
 
2.0%
Other values (10) 12
12.0%

dtbz_nm
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
체육·문화예술사업의 지원
28 
생활체육 정보제공 및 종목보급
17 
주최단체지원
14 
장애인 체육활성화 지원
8 
스포츠산업기술기반조성(R&D)
6 
Other values (14)
27 

Length

Max length18
Median length17
Mean length12.07
Min length6

Unique

Unique7 ?
Unique (%)7.0%

Sample

1st row태권도진흥재단 운영(보조) 지원
2nd row생활체육 프로그램 지원
3rd row생활체육 정보제공 및 종목보급
4th row생활체육 정보제공 및 종목보급
5th row생활체육 정보제공 및 종목보급

Common Values

ValueCountFrequency (%)
체육·문화예술사업의 지원 28
28.0%
생활체육 정보제공 및 종목보급 17
17.0%
주최단체지원 14
14.0%
장애인 체육활성화 지원 8
 
8.0%
스포츠산업기술기반조성(R&D) 6
 
6.0%
생활체육 프로그램 지원 5
 
5.0%
태권도 진흥 4
 
4.0%
우수선수양성지원 3
 
3.0%
2018평창동계올림픽경기대회 지원 2
 
2.0%
전국(소년)체전 지원 2
 
2.0%
Other values (9) 11
 
11.0%

Length

2023-12-10T18:53:19.874815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지원 48
21.4%
체육·문화예술사업의 28
12.5%
생활체육 22
9.8%
및 18
 
8.0%
정보제공 17
 
7.6%
종목보급 17
 
7.6%
주최단체지원 14
 
6.2%
장애인 8
 
3.6%
체육활성화 8
 
3.6%
스포츠산업기술기반조성(r&d 6
 
2.7%
Other values (21) 38
17.0%

ubz_code
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5253.18
Minimum5161
Maximum5561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:53:20.175513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5161
5-th percentile5161
Q15161
median5162
Q35361
95-th percentile5561
Maximum5561
Range400
Interquartile range (IQR)200

Descriptive statistics

Standard deviation119.60916
Coefficient of variation (CV)0.022768906
Kurtosis1.0043764
Mean5253.18
Median Absolute Deviation (MAD)1
Skewness1.3116983
Sum525318
Variance14306.351
MonotonicityNot monotonic
2023-12-10T18:53:20.467071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5161 50
50.0%
5261 20
 
20.0%
5361 11
 
11.0%
5561 8
 
8.0%
5362 6
 
6.0%
5262 2
 
2.0%
5365 2
 
2.0%
5163 1
 
1.0%
ValueCountFrequency (%)
5161 50
50.0%
5163 1
 
1.0%
5261 20
 
20.0%
5262 2
 
2.0%
5361 11
 
11.0%
5362 6
 
6.0%
5365 2
 
2.0%
5561 8
 
8.0%
ValueCountFrequency (%)
5561 8
 
8.0%
5365 2
 
2.0%
5362 6
 
6.0%
5361 11
 
11.0%
5262 2
 
2.0%
5261 20
 
20.0%
5163 1
 
1.0%
5161 50
50.0%

ubz_nm
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
생활체육 활성화
50 
대한체육회 지원
19 
국제체육 지원
11 
장애인체육 육성
8 
스포츠산업 연구 및 기술개발(R&D)
6 
Other values (5)
6 

Length

Max length20
Median length8
Mean length8.64
Min length6

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row국제체육 지원
2nd row생활체육 활성화
3rd row생활체육 활성화
4th row생활체육 활성화
5th row생활체육 활성화

Common Values

ValueCountFrequency (%)
생활체육 활성화 50
50.0%
대한체육회 지원 19
 
19.0%
국제체육 지원 11
 
11.0%
장애인체육 육성 8
 
8.0%
스포츠산업 연구 및 기술개발(R&D) 6
 
6.0%
스포츠산업 활성화 2
 
2.0%
전문체육 육성 1
 
1.0%
전문체육육성 기반구축 1
 
1.0%
생활체육시설 지원 1
 
1.0%
시도전문체육 1
 
1.0%

Length

2023-12-10T18:53:20.771603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:53:21.052906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
활성화 52
24.6%
생활체육 50
23.7%
지원 31
14.7%
대한체육회 19
 
9.0%
국제체육 11
 
5.2%
육성 9
 
4.3%
장애인체육 8
 
3.8%
스포츠산업 8
 
3.8%
연구 6
 
2.8%
및 6
 
2.8%
Other values (6) 11
 
5.2%

progrm_code
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
5100
51 
5200
22 
5300
19 
5500
8 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5300
2nd row5100
3rd row5100
4th row5100
5th row5100

Common Values

ValueCountFrequency (%)
5100 51
51.0%
5200 22
22.0%
5300 19
 
19.0%
5500 8
 
8.0%

Length

2023-12-10T18:53:21.396059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:53:21.633641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5100 51
51.0%
5200 22
22.0%
5300 19
 
19.0%
5500 8
 
8.0%

progrm_nm
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
생활체육육성
51 
전문체육육성
22 
스포츠산업 육성 및 국제교류
19 
장애인체육육성
8 

Length

Max length15
Median length6
Mean length7.79
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row스포츠산업 육성 및 국제교류
2nd row생활체육육성
3rd row생활체육육성
4th row생활체육육성
5th row생활체육육성

Common Values

ValueCountFrequency (%)
생활체육육성 51
51.0%
전문체육육성 22
22.0%
스포츠산업 육성 및 국제교류 19
 
19.0%
장애인체육육성 8
 
8.0%

Length

2023-12-10T18:53:21.901548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:53:22.097222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활체육육성 51
32.5%
전문체육육성 22
14.0%
스포츠산업 19
 
12.1%
육성 19
 
12.1%
및 19
 
12.1%
국제교류 19
 
12.1%
장애인체육육성 8
 
5.1%

bsns_begin_de
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20171582
Minimum20170101
Maximum20211101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:53:22.336141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20170101
5-th percentile20170101
Q120170101
median20170302
Q320170601
95-th percentile20171133
Maximum20211101
Range41000
Interquartile range (IQR)500

Descriptive statistics

Standard deviation6892.5146
Coefficient of variation (CV)0.0003416943
Kurtosis29.772967
Mean20171582
Median Absolute Deviation (MAD)200.5
Skewness5.5767157
Sum2.0171582 × 109
Variance47506757
MonotonicityNot monotonic
2023-12-10T18:53:22.664318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20170101 40
40.0%
20170601 8
 
8.0%
20170301 7
 
7.0%
20170401 6
 
6.0%
20170501 5
 
5.0%
20170314 4
 
4.0%
20170801 3
 
3.0%
20170701 2
 
2.0%
20170201 2
 
2.0%
20170920 2
 
2.0%
Other values (20) 21
21.0%
ValueCountFrequency (%)
20170101 40
40.0%
20170102 1
 
1.0%
20170201 2
 
2.0%
20170301 7
 
7.0%
20170302 1
 
1.0%
20170314 4
 
4.0%
20170321 1
 
1.0%
20170323 1
 
1.0%
20170328 1
 
1.0%
20170330 1
 
1.0%
ValueCountFrequency (%)
20211101 1
1.0%
20210401 1
1.0%
20210101 1
1.0%
20171219 1
1.0%
20171201 1
1.0%
20171129 1
1.0%
20171106 1
1.0%
20171101 2
2.0%
20171013 1
1.0%
20171001 1
1.0%

bsns_end_de
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20175737
Minimum20170630
Maximum20220331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:53:23.027596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20170630
5-th percentile20171216
Q120171231
median20171231
Q320180228
95-th percentile20181595
Maximum20220331
Range49701
Interquartile range (IQR)8997

Descriptive statistics

Standard deviation8757.1296
Coefficient of variation (CV)0.00043404261
Kurtosis14.085225
Mean20175737
Median Absolute Deviation (MAD)0
Skewness3.3964434
Sum2.0175737 × 109
Variance76687319
MonotonicityNot monotonic
2023-12-10T18:53:23.288410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
20171231 57
57.0%
20180228 8
 
8.0%
20180331 6
 
6.0%
20171130 4
 
4.0%
20180531 4
 
4.0%
20180630 3
 
3.0%
20180430 3
 
3.0%
20180131 3
 
3.0%
20171229 2
 
2.0%
20210101 1
 
1.0%
Other values (9) 9
 
9.0%
ValueCountFrequency (%)
20170630 1
 
1.0%
20171130 4
 
4.0%
20171220 1
 
1.0%
20171229 2
 
2.0%
20171231 57
57.0%
20180131 3
 
3.0%
20180228 8
 
8.0%
20180331 6
 
6.0%
20180430 3
 
3.0%
20180531 4
 
4.0%
ValueCountFrequency (%)
20220331 1
 
1.0%
20220228 1
 
1.0%
20210101 1
 
1.0%
20191231 1
 
1.0%
20190430 1
 
1.0%
20181130 1
 
1.0%
20181031 1
 
1.0%
20180731 1
 
1.0%
20180630 3
3.0%
20180531 4
4.0%

instt_code
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
P000000
46 
B551013
19 
B551014
6 
B553722
5 
B552570
 
4
Other values (14)
20 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique9 ?
Unique (%)9.0%

Sample

1st rowB552570
2nd rowB551014
3rd rowP000000
4th rowP000000
5th rowP000000

Common Values

ValueCountFrequency (%)
P000000 46
46.0%
B551013 19
19.0%
B551014 6
 
6.0%
B553722 5
 
5.0%
B552570 4
 
4.0%
B551551 3
 
3.0%
B552852 2
 
2.0%
B551951 2
 
2.0%
B553475 2
 
2.0%
B552867 2
 
2.0%
Other values (9) 9
 
9.0%

Length

2023-12-10T18:53:23.543053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
p000000 46
46.0%
b551013 19
19.0%
b551014 6
 
6.0%
b553722 5
 
5.0%
b552570 4
 
4.0%
b551551 3
 
3.0%
b553475 2
 
2.0%
b552867 2
 
2.0%
b551951 2
 
2.0%
b552852 2
 
2.0%
Other values (9) 9
 
9.0%

Interactions

2023-12-10T18:53:13.389045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:04.891232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:09.506253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:11.449377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:14.405473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:06.456903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:10.827851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:12.722949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:14.614915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:07.700996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:11.047787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:12.968131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:14.788968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:08.735522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:11.243625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:13.197384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:53:23.775965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
busi_yyorg_nmbusi_cdddtlbz_nmdtlbz_iddtlbz_nmdtbz_iddtbz_nmubz_codeubz_nmprogrm_codeprogrm_nmbsns_begin_debsns_end_deinstt_code
busi_yy1.0000.814NaN1.0001.0001.0000.7700.2900.0000.9410.1400.1400.9611.0000.550
org_nm0.8141.000NaN1.0000.9280.9280.9400.9490.9930.9790.9870.9870.6810.9581.000
busi_cdNaNNaN1.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
ddtlbz_nm1.0001.000NaN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
dtlbz_id1.0000.928NaN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.8780.752
dtlbz_nm1.0000.928NaN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.8780.752
dtbz_id0.7700.940NaN1.0001.0001.0001.0001.0001.0000.9931.0001.0000.9550.8250.885
dtbz_nm0.2900.949NaN1.0001.0001.0001.0001.0001.0000.9741.0001.0000.9550.7590.938
ubz_code0.0000.993NaN1.0001.0001.0001.0001.0001.0001.0001.0001.0000.4180.6190.885
ubz_nm0.9410.979NaN1.0001.0001.0000.9930.9741.0001.0001.0001.0000.9380.8370.853
progrm_code0.1400.987NaN1.0001.0001.0001.0001.0001.0001.0001.0001.0000.4950.0000.839
progrm_nm0.1400.987NaN1.0001.0001.0001.0001.0001.0001.0001.0001.0000.4950.0000.839
bsns_begin_de0.9610.681NaN1.0001.0001.0000.9550.9550.4180.9380.4950.4951.0001.0000.469
bsns_end_de1.0000.958NaN1.0000.8780.8780.8250.7590.6190.8370.0000.0001.0001.0000.835
instt_code0.5501.000NaN1.0000.7520.7520.8850.9380.8850.8530.8390.8390.4690.8351.000
2023-12-10T18:53:24.166183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
dtbz_nmbusi_yyprogrm_codeinstt_codeubz_nmdtbz_iddtlbz_idorg_nmprogrm_nmdtlbz_nm
dtbz_nm1.0000.2300.9190.4890.8110.9940.8010.4770.9190.801
busi_yy0.2301.0000.0900.4450.7600.5660.7280.4910.0900.728
progrm_code0.9190.0901.0000.5860.9680.9130.7360.6421.0000.736
instt_code0.4890.4450.5861.0000.5050.4890.2150.8010.5860.215
ubz_nm0.8110.7600.9680.5051.0000.8040.7600.6380.9680.760
dtbz_id0.9940.5660.9130.4890.8041.0000.8060.4460.9130.806
dtlbz_id0.8010.7280.7360.2150.7600.8061.0000.3030.7361.000
org_nm0.4770.4910.6420.8010.6380.4460.3031.0000.6420.303
progrm_nm0.9190.0901.0000.5860.9680.9130.7360.6421.0000.736
dtlbz_nm0.8010.7280.7360.2150.7600.8061.0000.3030.7361.000
2023-12-10T18:53:24.419498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
busi_cdubz_codebsns_begin_debsns_end_debusi_yyorg_nmdtlbz_iddtlbz_nmdtbz_iddtbz_nmubz_nmprogrm_codeprogrm_nminstt_code
busi_cd1.000-0.0020.7770.2931.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
ubz_code-0.0021.000-0.220-0.0550.0000.6570.7400.7400.9180.9230.9730.9950.9950.554
bsns_begin_de0.777-0.2201.0000.2960.8260.4910.7280.7280.5660.2300.7600.0900.0900.445
bsns_end_de0.293-0.0550.2961.0000.9740.5870.4390.4390.4990.4320.6210.0000.0000.531
busi_yy1.0000.0000.8260.9741.0000.4910.7280.7280.5660.2300.7600.0900.0900.445
org_nm1.0000.6570.4910.5870.4911.0000.3030.3030.4460.4770.6380.6420.6420.801
dtlbz_id1.0000.7400.7280.4390.7280.3031.0001.0000.8060.8010.7600.7360.7360.215
dtlbz_nm1.0000.7400.7280.4390.7280.3031.0001.0000.8060.8010.7600.7360.7360.215
dtbz_id1.0000.9180.5660.4990.5660.4460.8060.8061.0000.9940.8040.9130.9130.489
dtbz_nm1.0000.9230.2300.4320.2300.4770.8010.8010.9941.0000.8110.9190.9190.489
ubz_nm1.0000.9730.7600.6210.7600.6380.7600.7600.8040.8111.0000.9680.9680.505
progrm_code1.0000.9950.0900.0000.0900.6420.7360.7360.9130.9190.9681.0001.0000.586
progrm_nm1.0000.9950.0900.0000.0900.6420.7360.7360.9130.9190.9681.0001.0000.586
instt_code1.0000.5540.4450.5310.4450.8010.2150.2150.4890.4890.5050.5860.5861.000

Missing values

2023-12-10T18:53:15.077730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:53:15.534831image/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

busi_yyorg_nmbusi_cdddtlbz_nmdtlbz_iddtlbz_nmdtbz_iddtbz_nmubz_codeubz_nmprogrm_codeprogrm_nmbsns_begin_debsns_end_deinstt_code
02017태권도진흥재단20170407000000000713태권도원 운영관리비201007253610313A0002태권도원 운영201007253610313A태권도진흥재단 운영(보조) 지원5361국제체육 지원5300스포츠산업 육성 및 국제교류2017010120171231B552570
12021서울올림픽기념국민체육진흥공단202104160000008787442021년도 스포츠스타 체육교실201907251610301B0009맞춤형생활체육활동 지원201907251610301B생활체육 프로그램 지원5161생활체육 활성화5100생활체육육성2021040120220228B551014
22017(재)한국기원20170613000000000005한.중 아마 친선 교류전201507251610306A0005바둑 및 마인드스포츠 등201507251610306A생활체육 정보제공 및 종목보급5161생활체육 활성화5100생활체육육성2017060120171231P000000
32017(재)한국기원20170613000000000007소외계층 바둑보급201507251610306A0005바둑 및 마인드스포츠 등201507251610306A생활체육 정보제공 및 종목보급5161생활체육 활성화5100생활체육육성2017060120171231P000000
42017(재)한국기원20170613000000000009상비군 대표강화 사업201507251610306A0005바둑 및 마인드스포츠 등201507251610306A생활체육 정보제공 및 종목보급5161생활체육 활성화5100생활체육육성2017020120171231P000000
52017한국예술종합학교 산학협력단20170524000000000245지역 예술꿈나무 발굴 사업201507251610307A0001문화체육사업지원201507251610307A체육·문화예술사업의 지원5161생활체육 활성화5100생활체육육성2017050120180228P000000
62017포항공과대학교 산학협력단20170313000000000875스포츠 방송 콘텐츠 개발을 위한 다시점 다채널 중계 시스템 기술개발20070395363030310002스포츠서비스 기술개발2007039536303031스포츠산업기술기반조성(R&D)5362스포츠산업 연구 및 기술개발(R&D)5300스포츠산업 육성 및 국제교류2017010120171231P000000
72021대한체육회 진천선수촌20211123000001182186동계종목 특별지원201507252610317A0009체육진흥투표권 비발행대상 종목 지원201507252610317A주최단체지원5261전문체육 육성5200전문체육육성2021110120220331P000000
82017(사)한국프로골프협회20170524000000000329경기장 안전관리 교육물 제작201507252610317A0004프로스포츠 정책사업 및 공통사업 지원201507252610317A주최단체지원5261대한체육회 지원5200전문체육육성2017010120171229P000000
92017대한체육회20170202000000000027어르신체육활동지원201707251610302B0008어르신체육활동 지원201707251610302B생활체육 프로그램 지원5161생활체육 활성화5100생활체육육성2017010120171231B551013
busi_yyorg_nmbusi_cdddtlbz_nmdtlbz_iddtlbz_nmdtbz_iddtbz_nmubz_codeubz_nmprogrm_codeprogrm_nmbsns_begin_debsns_end_deinstt_code
902017(사단)한국배구연맹20170605000000000044프로배구 통합워크샵 개최201507252610317A0004프로스포츠 정책사업 및 공통사업 지원201507252610317A주최단체지원5261대한체육회 지원5200전문체육육성2017010120171231P000000
912017대한체육회20170406000000000204개인정보보호 지원201507251610307A0001문화체육사업지원201507251610307A체육·문화예술사업의 지원5161생활체육 활성화5100생활체육육성2017040120171231B551013
922017서울올림픽기념국민체육진흥공단20170424000000000129레저스포츠 종목 체험 및 대회 개최201507251610306A0004레저스포츠보급201507251610306A생활체육 정보제공 및 종목보급5161생활체육 활성화5100생활체육육성2017010120171231B551014
932017대한체육회20170206000000000038전국소년체전 운영비201607252620300A0003전국소년체전 운영비201607252620300A전국(소년)체전 지원5262시도전문체육5200전문체육육성2017010120171231B551013
942017(사)스페셜올림픽코리아20170414000000000884SOK 선수등록시스템 고도화201507255610301A0006장애인체육법인단체 지원(스페셜올림픽코리아)201507255610301A장애인 체육활성화 지원5561장애인체육 육성5500장애인체육육성2017010120171231B553722
952017(사)스페셜올림픽코리아201711280000000001372017 스페셜올림픽 시상식201507255610301A0006장애인체육법인단체 지원(스페셜올림픽코리아)201507255610301A장애인 체육활성화 지원5561장애인체육 육성5500장애인체육육성2017112920171220B553722
962017(사)케이비엘20170321000000001188주최단체 공통사업 (케이비엘)201507252610317A0004프로스포츠 정책사업 및 공통사업 지원201507252610317A주최단체지원5261대한체육회 지원5200전문체육육성2017010120171231P000000
972017아쿠아즈20170623000000000173해양 안전을 위한 다이빙 컴퓨터 내장형 스마트 슈트개발20070395363030310003스포츠융복합 기술개발2007039536303031스포츠산업기술기반조성(R&D)5362스포츠산업 연구 및 기술개발(R&D)5300스포츠산업 육성 및 국제교류2017030120171231P000000
982017(주)에어패스20170818000000000214청소년용 실감 체험형 스포츠 통합플랫폼 기술 개발20070395363030310003스포츠융복합 기술개발2007039536303031스포츠산업기술기반조성(R&D)5362스포츠산업 연구 및 기술개발(R&D)5300스포츠산업 육성 및 국제교류2017080120180731P000000
992017대한체육회20170206000000000054생활체육지도자교육201507251610306A0009생활체육지도자교육201507251610306A생활체육 정보제공 및 종목보급5161생활체육 활성화5100생활체육육성2017010120171231B551013