Overview

Dataset statistics

Number of variables7
Number of observations21
Missing cells33
Missing cells (%)22.4%
Duplicate rows1
Duplicate rows (%)4.8%
Total size in memory1.3 KiB
Average record size in memory62.3 B

Variable types

Text6
Categorical1

Alerts

Dataset has 1 (4.8%) duplicate rowsDuplicates
문화원명 has 6 (28.6%) missing valuesMissing
주 소 has 3 (14.3%) missing valuesMissing
원 장 has 6 (28.6%) missing valuesMissing
회원수 has 6 (28.6%) missing valuesMissing
설립년도 has 6 (28.6%) missing valuesMissing
전화번호 has 6 (28.6%) missing valuesMissing

Reproduction

Analysis started2024-03-14 02:40:51.904375
Analysis finished2024-03-14 02:40:52.462108
Duration0.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

문화원명
Text

MISSING 

Distinct15
Distinct (%)100.0%
Missing6
Missing (%)28.6%
Memory size300.0 B
2024-03-14T11:40:52.571364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length2
Mean length2.5333333
Min length2

Characters and Unicode

Total characters38
Distinct characters31
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

Unique15 ?
Unique (%)100.0%

Sample

1st row전라북도문화원연합회
2nd row전주
3rd row군산
4th row익산
5th row정읍
ValueCountFrequency (%)
전라북도문화원연합회 1
 
6.7%
전주 1
 
6.7%
군산 1
 
6.7%
익산 1
 
6.7%
정읍 1
 
6.7%
남원 1
 
6.7%
김제 1
 
6.7%
완주 1
 
6.7%
진안 1
 
6.7%
무주 1
 
6.7%
Other values (5) 5
33.3%
2024-03-14T11:40:52.817586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 3
 
7.9%
전 2
 
5.3%
창 2
 
5.3%
안 2
 
5.3%
원 2
 
5.3%
산 2
 
5.3%
완 1
 
2.6%
진 1
 
2.6%
무 1
 
2.6%
수 1
 
2.6%
Other values (21) 21
55.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 38
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 3
 
7.9%
전 2
 
5.3%
창 2
 
5.3%
안 2
 
5.3%
원 2
 
5.3%
산 2
 
5.3%
완 1
 
2.6%
진 1
 
2.6%
무 1
 
2.6%
수 1
 
2.6%
Other values (21) 21
55.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 38
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 3
 
7.9%
전 2
 
5.3%
창 2
 
5.3%
안 2
 
5.3%
원 2
 
5.3%
산 2
 
5.3%
완 1
 
2.6%
진 1
 
2.6%
무 1
 
2.6%
수 1
 
2.6%
Other values (21) 21
55.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 38
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 3
 
7.9%
전 2
 
5.3%
창 2
 
5.3%
안 2
 
5.3%
원 2
 
5.3%
산 2
 
5.3%
완 1
 
2.6%
진 1
 
2.6%
무 1
 
2.6%
수 1
 
2.6%
Other values (21) 21
55.3%

주 소
Text

MISSING 

Distinct18
Distinct (%)100.0%
Missing3
Missing (%)14.3%
Memory size300.0 B
2024-03-14T11:40:53.009816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17.5
Mean length14.888889
Min length10

Characters and Unicode

Total characters268
Distinct characters79
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)100.0%

Sample

1st row전주시 완산구 경원동 1가 104-5
2nd row전주시 덕진구 진북동 364-7
3rd row군산시 나운1동 769번지
4th row익산시 어양동 산 94번지
5th row정읍시 연지동 252-151
ValueCountFrequency (%)
전주시 2
 
3.2%
남원시 2
 
3.2%
완주군 1
 
1.6%
순창군 1
 
1.6%
장수군 1
 
1.6%
장수읍 1
 
1.6%
한누리로 1
 
1.6%
393 1
 
1.6%
한누리전당 1
 
1.6%
가람관 1
 
1.6%
Other values (51) 51
81.0%
2024-03-14T11:40:53.421356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
45
 
16.8%
1 12
 
4.5%
읍 9
 
3.4%
동 8
 
3.0%
군 8
 
3.0%
9 8
 
3.0%
3 8
 
3.0%
시 8
 
3.0%
- 7
 
2.6%
로 7
 
2.6%
Other values (69) 148
55.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 153
57.1%
Decimal Number 57
 
21.3%
Space Separator 45
 
16.8%
Dash Punctuation 7
 
2.6%
Open Punctuation 3
 
1.1%
Close Punctuation 3
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
읍 9
 
5.9%
동 8
 
5.2%
군 8
 
5.2%
시 8
 
5.2%
로 7
 
4.6%
주 5
 
3.3%
산 5
 
3.3%
창 4
 
2.6%
장 4
 
2.6%
지 4
 
2.6%
Other values (55) 91
59.5%
Decimal Number
ValueCountFrequency (%)
1 12
21.1%
9 8
14.0%
3 8
14.0%
4 7
12.3%
6 6
10.5%
0 4
 
7.0%
2 4
 
7.0%
5 4
 
7.0%
7 3
 
5.3%
8 1
 
1.8%
Space Separator
ValueCountFrequency (%)
45
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 153
57.1%
Common 115
42.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
읍 9
 
5.9%
동 8
 
5.2%
군 8
 
5.2%
시 8
 
5.2%
로 7
 
4.6%
주 5
 
3.3%
산 5
 
3.3%
창 4
 
2.6%
장 4
 
2.6%
지 4
 
2.6%
Other values (55) 91
59.5%
Common
ValueCountFrequency (%)
45
39.1%
1 12
 
10.4%
9 8
 
7.0%
3 8
 
7.0%
- 7
 
6.1%
4 7
 
6.1%
6 6
 
5.2%
0 4
 
3.5%
2 4
 
3.5%
5 4
 
3.5%
Other values (4) 10
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 153
57.1%
ASCII 115
42.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
45
39.1%
1 12
 
10.4%
9 8
 
7.0%
3 8
 
7.0%
- 7
 
6.1%
4 7
 
6.1%
6 6
 
5.2%
0 4
 
3.5%
2 4
 
3.5%
5 4
 
3.5%
Other values (4) 10
 
8.7%
Hangul
ValueCountFrequency (%)
읍 9
 
5.9%
동 8
 
5.2%
군 8
 
5.2%
시 8
 
5.2%
로 7
 
4.6%
주 5
 
3.3%
산 5
 
3.3%
창 4
 
2.6%
장 4
 
2.6%
지 4
 
2.6%
Other values (55) 91
59.5%

원 장
Text

MISSING 

Distinct14
Distinct (%)93.3%
Missing6
Missing (%)28.6%
Memory size300.0 B
2024-03-14T11:40:53.578651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45
Distinct characters29
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

Unique13 ?
Unique (%)86.7%

Sample

1st row나종우
2nd row나종우
3rd row이진원
4th row김태현
5th row김영수
ValueCountFrequency (%)
나종우 2
13.3%
이진원 1
 
6.7%
김태현 1
 
6.7%
김영수 1
 
6.7%
김찬기 1
 
6.7%
김선유 1
 
6.7%
임원규 1
 
6.7%
이재명 1
 
6.7%
김내생 1
 
6.7%
강철규 1
 
6.7%
Other values (4) 4
26.7%
2024-03-14T11:40:53.875451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 7
 
15.6%
원 3
 
6.7%
나 2
 
4.4%
기 2
 
4.4%
규 2
 
4.4%
종 2
 
4.4%
철 2
 
4.4%
영 2
 
4.4%
이 2
 
4.4%
우 2
 
4.4%
Other values (19) 19
42.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 45
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 7
 
15.6%
원 3
 
6.7%
나 2
 
4.4%
기 2
 
4.4%
규 2
 
4.4%
종 2
 
4.4%
철 2
 
4.4%
영 2
 
4.4%
이 2
 
4.4%
우 2
 
4.4%
Other values (19) 19
42.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 45
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 7
 
15.6%
원 3
 
6.7%
나 2
 
4.4%
기 2
 
4.4%
규 2
 
4.4%
종 2
 
4.4%
철 2
 
4.4%
영 2
 
4.4%
이 2
 
4.4%
우 2
 
4.4%
Other values (19) 19
42.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 45
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 7
 
15.6%
원 3
 
6.7%
나 2
 
4.4%
기 2
 
4.4%
규 2
 
4.4%
종 2
 
4.4%
철 2
 
4.4%
영 2
 
4.4%
이 2
 
4.4%
우 2
 
4.4%
Other values (19) 19
42.2%

회원수
Text

MISSING 

Distinct15
Distinct (%)100.0%
Missing6
Missing (%)28.6%
Memory size300.0 B
2024-03-14T11:40:54.060661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.1333333
Min length3

Characters and Unicode

Total characters47
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

Unique15 ?
Unique (%)100.0%

Sample

1st row5,946
2nd row520
3rd row311
4th row524
5th row670
ValueCountFrequency (%)
5,946 1
 
6.7%
520 1
 
6.7%
311 1
 
6.7%
524 1
 
6.7%
670 1
 
6.7%
273 1
 
6.7%
772 1
 
6.7%
290 1
 
6.7%
278 1
 
6.7%
530 1
 
6.7%
Other values (5) 5
33.3%
2024-03-14T11:40:54.479279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 10
21.3%
4 5
10.6%
0 5
10.6%
3 5
10.6%
7 5
10.6%
5 4
 
8.5%
1 4
 
8.5%
6 3
 
6.4%
8 3
 
6.4%
9 2
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46
97.9%
Other Punctuation 1
 
2.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 10
21.7%
4 5
10.9%
0 5
10.9%
3 5
10.9%
7 5
10.9%
5 4
 
8.7%
1 4
 
8.7%
6 3
 
6.5%
8 3
 
6.5%
9 2
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 10
21.3%
4 5
10.6%
0 5
10.6%
3 5
10.6%
7 5
10.6%
5 4
 
8.5%
1 4
 
8.5%
6 3
 
6.4%
8 3
 
6.4%
9 2
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 10
21.3%
4 5
10.6%
0 5
10.6%
3 5
10.6%
7 5
10.6%
5 4
 
8.5%
1 4
 
8.5%
6 3
 
6.4%
8 3
 
6.4%
9 2
 
4.3%

설립년도
Text

MISSING 

Distinct12
Distinct (%)80.0%
Missing6
Missing (%)28.6%
Memory size300.0 B
2024-03-14T11:40:54.684310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters120
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)73.3%

Sample

1st row91.03.24
2nd row65. 4. 3
3rd row94. 9. 3
4th row94. 9.12
5th row64.11. 4
ValueCountFrequency (%)
94 5
17.2%
3 5
17.2%
9 4
13.8%
4 2
 
6.9%
5.14 1
 
3.4%
87 1
 
3.4%
65.02.25 1
 
3.4%
65.01.26 1
 
3.4%
64.12.24 1
 
3.4%
89.11.25 1
 
3.4%
Other values (7) 7
24.1%
2024-03-14T11:40:54.945964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 30
25.0%
14
11.7%
9 13
10.8%
4 12
 
10.0%
1 10
 
8.3%
2 10
 
8.3%
3 8
 
6.7%
6 7
 
5.8%
5 7
 
5.8%
0 4
 
3.3%
Other values (2) 5
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 76
63.3%
Other Punctuation 30
 
25.0%
Space Separator 14
 
11.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 13
17.1%
4 12
15.8%
1 10
13.2%
2 10
13.2%
3 8
10.5%
6 7
9.2%
5 7
9.2%
0 4
 
5.3%
8 3
 
3.9%
7 2
 
2.6%
Other Punctuation
ValueCountFrequency (%)
. 30
100.0%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 30
25.0%
14
11.7%
9 13
10.8%
4 12
 
10.0%
1 10
 
8.3%
2 10
 
8.3%
3 8
 
6.7%
6 7
 
5.8%
5 7
 
5.8%
0 4
 
3.3%
Other values (2) 5
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 30
25.0%
14
11.7%
9 13
10.8%
4 12
 
10.0%
1 10
 
8.3%
2 10
 
8.3%
3 8
 
6.7%
6 7
 
5.8%
5 7
 
5.8%
0 4
 
3.3%
Other values (2) 5
 
4.2%

전화번호
Text

MISSING 

Distinct15
Distinct (%)100.0%
Missing6
Missing (%)28.6%
Memory size300.0 B
2024-03-14T11:40:55.090498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters120
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

Unique15 ?
Unique (%)100.0%

Sample

1st row287-5509
2nd row255-3360
3rd row451-2138
4th row835-0120
5th row532-0222
ValueCountFrequency (%)
287-5509 1
 
6.7%
255-3360 1
 
6.7%
451-2138 1
 
6.7%
835-0120 1
 
6.7%
532-0222 1
 
6.7%
633-1582 1
 
6.7%
547-4659 1
 
6.7%
263-0222 1
 
6.7%
433-1674 1
 
6.7%
324-1300 1
 
6.7%
Other values (5) 5
33.3%
2024-03-14T11:40:55.664566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 20
16.7%
3 17
14.2%
- 15
12.5%
5 15
12.5%
0 11
9.2%
1 11
9.2%
4 10
8.3%
6 9
7.5%
8 5
 
4.2%
9 4
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 105
87.5%
Dash Punctuation 15
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 20
19.0%
3 17
16.2%
5 15
14.3%
0 11
10.5%
1 11
10.5%
4 10
9.5%
6 9
8.6%
8 5
 
4.8%
9 4
 
3.8%
7 3
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 20
16.7%
3 17
14.2%
- 15
12.5%
5 15
12.5%
0 11
9.2%
1 11
9.2%
4 10
8.3%
6 9
7.5%
8 5
 
4.2%
9 4
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 20
16.7%
3 17
14.2%
- 15
12.5%
5 15
12.5%
0 11
9.2%
1 11
9.2%
4 10
8.3%
6 9
7.5%
8 5
 
4.2%
9 4
 
3.3%

비고
Categorical

Distinct2
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
?
15 
<NA>
6 

Length

Max length4
Median length2
Mean length2.5714286
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
? 15
71.4%
<NA> 6
 
28.6%

Length

2024-03-14T11:40:55.774108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:40:55.870879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
15
71.4%
na 6
 
28.6%

Correlations

2024-03-14T11:40:55.923185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
문화원명주 소원 장회원수설립년도전화번호
문화원명1.0001.0001.0001.0001.0001.000
주 소1.0001.0001.0001.0001.0001.000
원 장1.0001.0001.0001.0000.8671.000
회원수1.0001.0001.0001.0001.0001.000
설립년도1.0001.0000.8671.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.000

Missing values

2024-03-14T11:40:52.177818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:40:52.279420image/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.
2024-03-14T11:40:52.380355image/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

문화원명주 소원 장회원수설립년도전화번호비고
0전라북도문화원연합회전주시 완산구 경원동 1가 104-5나종우5,94691.03.24287-5509?
1<NA><NA><NA><NA><NA><NA><NA>
2<NA><NA><NA><NA><NA><NA><NA>
3전주전주시 덕진구 진북동 364-7나종우52065. 4. 3255-3360?
4군산군산시 나운1동 769번지이진원31194. 9. 3451-2138?
5익산익산시 어양동 산 94번지김태현52494. 9.12835-0120?
6정읍정읍시 연지동 252-151김영수67064.11. 4532-0222?
7남원남원시 하정동 192-4김찬기27394. 9. 3633-1582?
8<NA>(남원시 의회별관 3층)<NA><NA><NA><NA><NA>
9김제김제시 서암동 399-1김선유77267.03.31547-4659?
문화원명주 소원 장회원수설립년도전화번호비고
11진안진안군 진안읍 중앙로 66이재명27892. 5.14433-1674?
12무주무주군 무주읍 한풍루로 346김내생53089.11.25324-1300?
13장수장수군 장수읍 한누리로 393강철규28194. 9. 3351-5349?
14<NA>(한누리전당 가람관 2층)<NA><NA><NA><NA><NA>
15임실임실군 임실읍 호국로 1640최성미43264.12.24642-2211?
16<NA>(임실공설운동장내)<NA><NA><NA><NA><NA>
17순창순창군 순창읍 장류로 407-11김기곤28065.01.26653-2069?
18고창고창읍 천변북로 119번지송영래46465.02.25564-2340?
19부안부안군 부안읍 매창로 89김원철32187. 5.28583-2101?
20<NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

문화원명주 소원 장회원수설립년도전화번호비고# duplicates
0<NA><NA><NA><NA><NA><NA><NA>3