Overview

Dataset statistics

Number of variables6
Number of observations61
Missing cells6
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory51.2 B

Variable types

Numeric1
Categorical3
Text2

Alerts

orgnzt_stle_se_cd is highly overall correlated with crtfc_seHigh correlation
crtfc_se is highly overall correlated with orgnzt_stle_se_cdHigh correlation
crtfc_yy is highly overall correlated with chghy_dcHigh correlation
chghy_dc is highly overall correlated with crtfc_yyHigh correlation
crtfc_se is highly imbalanced (53.6%)Imbalance
regist_no has 6 (9.8%) missing valuesMissing
instt_nm has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:49:18.667242
Analysis finished2023-12-10 09:49:19.810482
Duration1.14 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

crtfc_yy
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.6393
Minimum2015
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-10T18:49:19.915030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12016
median2018
Q32019
95-th percentile2020
Maximum2020
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8976546
Coefficient of variation (CV)0.0009405321
Kurtosis-1.5436681
Mean2017.6393
Median Absolute Deviation (MAD)2
Skewness-0.12447226
Sum123076
Variance3.6010929
MonotonicityIncreasing
2023-12-10T18:49:20.142638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2020 14
23.0%
2019 13
21.3%
2015 12
19.7%
2016 10
16.4%
2017 7
11.5%
2018 5
 
8.2%
ValueCountFrequency (%)
2015 12
19.7%
2016 10
16.4%
2017 7
11.5%
2018 5
 
8.2%
2019 13
21.3%
2020 14
23.0%
ValueCountFrequency (%)
2020 14
23.0%
2019 13
21.3%
2018 5
 
8.2%
2017 7
11.5%
2016 10
16.4%
2015 12
19.7%

crtfc_se
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size620.0 B
문화예술후원우수기관
55 
문화예술후원매개단체
6 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row문화예술후원매개단체
2nd row문화예술후원매개단체
3rd row문화예술후원매개단체
4th row문화예술후원우수기관
5th row문화예술후원우수기관

Common Values

ValueCountFrequency (%)
문화예술후원우수기관 55
90.2%
문화예술후원매개단체 6
 
9.8%

Length

2023-12-10T18:49:20.383749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:49:20.676484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
문화예술후원우수기관 55
90.2%
문화예술후원매개단체 6
 
9.8%

regist_no
Text

MISSING 

Distinct51
Distinct (%)92.7%
Missing6
Missing (%)9.8%
Memory size620.0 B
2023-12-10T18:49:21.135303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.7636364
Min length3

Characters and Unicode

Total characters207
Distinct characters12
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

Unique47 ?
Unique (%)85.5%

Sample

1st row제1호
2nd row제3호
3rd row제1호
4th row제2호
5th row제3호
ValueCountFrequency (%)
제3호 2
 
3.6%
제6호 2
 
3.6%
제1호 2
 
3.6%
제4호 2
 
3.6%
제46호 1
 
1.8%
제47호 1
 
1.8%
제48호 1
 
1.8%
제39호 1
 
1.8%
제40호 1
 
1.8%
제41호 1
 
1.8%
Other values (41) 41
74.5%
2023-12-10T18:49:22.044981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 55
26.6%
호 55
26.6%
4 16
 
7.7%
3 15
 
7.2%
5 14
 
6.8%
2 13
 
6.3%
1 10
 
4.8%
6 8
 
3.9%
8 6
 
2.9%
0 5
 
2.4%
Other values (2) 10
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 110
53.1%
Decimal Number 97
46.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 16
16.5%
3 15
15.5%
5 14
14.4%
2 13
13.4%
1 10
10.3%
6 8
8.2%
8 6
 
6.2%
0 5
 
5.2%
7 5
 
5.2%
9 5
 
5.2%
Other Letter
ValueCountFrequency (%)
제 55
50.0%
호 55
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 110
53.1%
Common 97
46.9%

Most frequent character per script

Common
ValueCountFrequency (%)
4 16
16.5%
3 15
15.5%
5 14
14.4%
2 13
13.4%
1 10
10.3%
6 8
8.2%
8 6
 
6.2%
0 5
 
5.2%
7 5
 
5.2%
9 5
 
5.2%
Hangul
ValueCountFrequency (%)
제 55
50.0%
호 55
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 110
53.1%
ASCII 97
46.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 55
50.0%
호 55
50.0%
ASCII
ValueCountFrequency (%)
4 16
16.5%
3 15
15.5%
5 14
14.4%
2 13
13.4%
1 10
10.3%
6 8
8.2%
8 6
 
6.2%
0 5
 
5.2%
7 5
 
5.2%
9 5
 
5.2%

instt_nm
Text

UNIQUE 

Distinct61
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size620.0 B
2023-12-10T18:49:22.548409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length9
Mean length6.4098361
Min length3

Characters and Unicode

Total characters391
Distinct characters158
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)100.0%

Sample

1st row(사)경남메세나협회
2nd row(사)아르콘
3rd row(사)한국메세나협회
4th row㈜TBC
5th row㈜두산
ValueCountFrequency (%)
사)경남메세나협회 1
 
1.6%
㈜파라다이스 1
 
1.6%
한국마사회 1
 
1.6%
재)서울문화재단 1
 
1.6%
㈜아모레퍼시픽 1
 
1.6%
㈜오성정보통신 1
 
1.6%
한국철도공사 1
 
1.6%
㈜크라운해태홀딩스 1
 
1.6%
한전kdn 1
 
1.6%
한미약품㈜ 1
 
1.6%
Other values (51) 51
83.6%
2023-12-10T18:49:23.538618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㈜ 47
 
12.0%
한 12
 
3.1%
스 11
 
2.8%
아 9
 
2.3%
국 9
 
2.3%
이 9
 
2.3%
사 8
 
2.0%
( 7
 
1.8%
) 7
 
1.8%
나 6
 
1.5%
Other values (148) 266
68.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 310
79.3%
Other Symbol 47
 
12.0%
Uppercase Letter 18
 
4.6%
Open Punctuation 7
 
1.8%
Close Punctuation 7
 
1.8%
Dash Punctuation 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
한 12
 
3.9%
스 11
 
3.5%
아 9
 
2.9%
국 9
 
2.9%
이 9
 
2.9%
사 8
 
2.6%
나 6
 
1.9%
세 6
 
1.9%
대 6
 
1.9%
화 6
 
1.9%
Other values (133) 228
73.5%
Uppercase Letter
ValueCountFrequency (%)
K 4
22.2%
S 3
16.7%
C 2
11.1%
B 2
11.1%
N 1
 
5.6%
D 1
 
5.6%
O 1
 
5.6%
I 1
 
5.6%
L 1
 
5.6%
T 1
 
5.6%
Other Symbol
ValueCountFrequency (%)
㈜ 47
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 357
91.3%
Latin 18
 
4.6%
Common 16
 
4.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
㈜ 47
 
13.2%
한 12
 
3.4%
스 11
 
3.1%
아 9
 
2.5%
국 9
 
2.5%
이 9
 
2.5%
사 8
 
2.2%
나 6
 
1.7%
세 6
 
1.7%
대 6
 
1.7%
Other values (134) 234
65.5%
Latin
ValueCountFrequency (%)
K 4
22.2%
S 3
16.7%
C 2
11.1%
B 2
11.1%
N 1
 
5.6%
D 1
 
5.6%
O 1
 
5.6%
I 1
 
5.6%
L 1
 
5.6%
T 1
 
5.6%
Common
ValueCountFrequency (%)
( 7
43.8%
) 7
43.8%
- 2
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 310
79.3%
None 47
 
12.0%
ASCII 34
 
8.7%

Most frequent character per block

None
ValueCountFrequency (%)
㈜ 47
100.0%
Hangul
ValueCountFrequency (%)
한 12
 
3.9%
스 11
 
3.5%
아 9
 
2.9%
국 9
 
2.9%
이 9
 
2.9%
사 8
 
2.6%
나 6
 
1.9%
세 6
 
1.9%
대 6
 
1.9%
화 6
 
1.9%
Other values (133) 228
73.5%
ASCII
ValueCountFrequency (%)
( 7
20.6%
) 7
20.6%
K 4
11.8%
S 3
8.8%
C 2
 
5.9%
B 2
 
5.9%
- 2
 
5.9%
N 1
 
2.9%
D 1
 
2.9%
O 1
 
2.9%
Other values (4) 4
11.8%

orgnzt_stle_se_cd
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size620.0 B
대기업
23 
중견기업
15 
중소기업
10 
공기업
6 
사단법인
4 
Other values (2)
3 

Length

Max length4
Median length4
Mean length3.5245902
Min length3

Unique

Unique1 ?
Unique (%)1.6%

Sample

1st row사단법인
2nd row사단법인
3rd row사단법인
4th row중소기업
5th row대기업

Common Values

ValueCountFrequency (%)
대기업 23
37.7%
중견기업 15
24.6%
중소기업 10
16.4%
공기업 6
 
9.8%
사단법인 4
 
6.6%
재단법인 2
 
3.3%
공공기관 1
 
1.6%

Length

2023-12-10T18:49:23.898406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:49:24.524002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대기업 23
37.7%
중견기업 15
24.6%
중소기업 10
16.4%
공기업 6
 
9.8%
사단법인 4
 
6.6%
재단법인 2
 
3.3%
공공기관 1
 
1.6%

chghy_dc
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size620.0 B
<NA>
42 
신규 인증
14 
인증취소
5 

Length

Max length5
Median length4
Mean length4.2295082
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 42
68.9%
신규 인증 14
 
23.0%
인증취소 5
 
8.2%

Length

2023-12-10T18:49:24.800614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:49:25.012146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 42
56.0%
신규 14
 
18.7%
인증 14
 
18.7%
인증취소 5
 
6.7%

Interactions

2023-12-10T18:49:19.186995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:49:25.146950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
crtfc_yycrtfc_seregist_noinstt_nmorgnzt_stle_se_cdchghy_dc
crtfc_yy1.0000.0001.0001.0000.1601.000
crtfc_se0.0001.0000.0001.0001.0000.000
regist_no1.0000.0001.0001.0000.000NaN
instt_nm1.0001.0001.0001.0001.0001.000
orgnzt_stle_se_cd0.1601.0000.0001.0001.0000.502
chghy_dc1.0000.000NaN1.0000.5021.000
2023-12-10T18:49:25.341213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
orgnzt_stle_se_cdchghy_dccrtfc_se
orgnzt_stle_se_cd1.0000.2950.957
chghy_dc0.2951.0000.000
crtfc_se0.9570.0001.000
2023-12-10T18:49:25.495006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
crtfc_yycrtfc_seorgnzt_stle_se_cdchghy_dc
crtfc_yy1.0000.0000.1390.939
crtfc_se0.0001.0000.9570.000
orgnzt_stle_se_cd0.1390.9571.0000.295
chghy_dc0.9390.0000.2951.000

Missing values

2023-12-10T18:49:19.463183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:49:19.722191image/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

crtfc_yycrtfc_seregist_noinstt_nmorgnzt_stle_se_cdchghy_dc
02015문화예술후원매개단체제1호(사)경남메세나협회사단법인<NA>
12015문화예술후원매개단체<NA>(사)아르콘사단법인인증취소
22015문화예술후원매개단체제3호(사)한국메세나협회사단법인<NA>
32015문화예술후원우수기관제1호㈜TBC중소기업<NA>
42015문화예술후원우수기관제2호㈜두산대기업<NA>
52015문화예술후원우수기관제3호벽산엔지니어링㈜중견기업<NA>
62015문화예술후원우수기관제4호㈜신세계대기업<NA>
72015문화예술후원우수기관<NA>㈜희망이음중소기업인증취소
82015문화예술후원우수기관제6호올림푸스한국㈜중견기업<NA>
92015문화예술후원우수기관제7호㈜종근당홀딩스중견기업<NA>
crtfc_yycrtfc_seregist_noinstt_nmorgnzt_stle_se_cdchghy_dc
512020문화예술후원우수기관제51호페르노리카코리아㈜중견기업신규 인증
522020문화예술후원우수기관제52호롯데정밀화학㈜대기업신규 인증
532020문화예술후원우수기관제53호메트라이프생명보험㈜대기업신규 인증
542020문화예술후원우수기관제54호본아이에프㈜중견기업신규 인증
552020문화예술후원우수기관제55호샘표식품㈜중견기업신규 인증
562020문화예술후원우수기관제56호에쓰-오일(S-OIL)㈜대기업신규 인증
572020문화예술후원우수기관제57호스테들러코리아㈜중소기업신규 인증
582020문화예술후원우수기관제58호티스케이프㈜중소기업신규 인증
592020문화예술후원우수기관제59호무학㈜중견기업신규 인증
602020문화예술후원우수기관제60호한국남부발전㈜공기업신규 인증