Overview

Dataset statistics

Number of variables7
Number of observations21
Missing cells27
Missing cells (%)18.4%
Duplicate rows1
Duplicate rows (%)4.8%
Total size in memory1.3 KiB
Average record size in memory62.3 B

Variable types

Text5
Unsupported1
Categorical1

Alerts

Dataset has 1 (4.8%) duplicate rowsDuplicates
문화원현황(2015.10.27) has 5 (23.8%) missing valuesMissing
Unnamed: 1 has 2 (9.5%) missing valuesMissing
Unnamed: 2 has 5 (23.8%) missing valuesMissing
Unnamed: 3 has 5 (23.8%) missing valuesMissing
Unnamed: 4 has 5 (23.8%) missing valuesMissing
Unnamed: 5 has 5 (23.8%) missing valuesMissing
Unnamed: 3 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 02:40:56.607620
Analysis finished2024-03-14 02:40:57.112336
Duration0.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

문화원현황(2015.10.27)
Text

MISSING 

Distinct16
Distinct (%)100.0%
Missing5
Missing (%)23.8%
Memory size300.0 B
2024-03-14T11:40:57.210644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length2
Mean length2.625
Min length2

Characters and Unicode

Total characters42
Distinct characters32
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

Unique16 ?
Unique (%)100.0%

Sample

1st row문화원명
2nd row전라북도문화원연합회
3rd row전주
4th row군산
5th row익산
ValueCountFrequency (%)
김제 1
 
6.2%
전주 1
 
6.2%
군산 1
 
6.2%
익산 1
 
6.2%
정읍 1
 
6.2%
남원 1
 
6.2%
전라북도문화원연합회 1
 
6.2%
진안 1
 
6.2%
문화원명 1
 
6.2%
무주 1
 
6.2%
Other values (6) 6
37.5%
2024-03-14T11:40:57.475186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 3
 
7.1%
원 3
 
7.1%
안 2
 
4.8%
전 2
 
4.8%
산 2
 
4.8%
창 2
 
4.8%
화 2
 
4.8%
문 2
 
4.8%
수 1
 
2.4%
명 1
 
2.4%
Other values (22) 22
52.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 3
 
7.1%
원 3
 
7.1%
안 2
 
4.8%
전 2
 
4.8%
산 2
 
4.8%
창 2
 
4.8%
화 2
 
4.8%
문 2
 
4.8%
수 1
 
2.4%
명 1
 
2.4%
Other values (22) 22
52.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 3
 
7.1%
원 3
 
7.1%
안 2
 
4.8%
전 2
 
4.8%
산 2
 
4.8%
창 2
 
4.8%
화 2
 
4.8%
문 2
 
4.8%
수 1
 
2.4%
명 1
 
2.4%
Other values (22) 22
52.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 3
 
7.1%
원 3
 
7.1%
안 2
 
4.8%
전 2
 
4.8%
산 2
 
4.8%
창 2
 
4.8%
화 2
 
4.8%
문 2
 
4.8%
수 1
 
2.4%
명 1
 
2.4%
Other values (22) 22
52.4%

Unnamed: 1
Text

MISSING 

Distinct19
Distinct (%)100.0%
Missing2
Missing (%)9.5%
Memory size300.0 B
2024-03-14T11:40:57.762914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length14.263158
Min length3

Characters and Unicode

Total characters271
Distinct characters80
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

Unique19 ?
Unique (%)100.0%

Sample

1st row주 소
2nd row전주시 완산구 경원동 1가 104-5
3rd row전주시 덕진구 진북동 364-7
4th row군산시 나운1동 769번지
5th row익산시 어양동 산 94번지
ValueCountFrequency (%)
남원시 2
 
3.1%
전주시 2
 
3.1%
순창군 1
 
1.5%
346 1
 
1.5%
장수군 1
 
1.5%
장수읍 1
 
1.5%
한누리로 1
 
1.5%
393 1
 
1.5%
한누리전당 1
 
1.5%
가람관 1
 
1.5%
Other values (53) 53
81.5%
2024-03-14T11:40:58.118702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
46
 
17.0%
1 12
 
4.4%
읍 9
 
3.3%
3 8
 
3.0%
9 8
 
3.0%
시 8
 
3.0%
군 8
 
3.0%
동 8
 
3.0%
- 7
 
2.6%
4 7
 
2.6%
Other values (70) 150
55.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 155
57.2%
Decimal Number 57
 
21.0%
Space Separator 46
 
17.0%
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
 
5.8%
시 8
 
5.2%
군 8
 
5.2%
동 8
 
5.2%
로 7
 
4.5%
주 6
 
3.9%
산 5
 
3.2%
진 4
 
2.6%
장 4
 
2.6%
창 4
 
2.6%
Other values (56) 92
59.4%
Decimal Number
ValueCountFrequency (%)
1 12
21.1%
3 8
14.0%
9 8
14.0%
4 7
12.3%
6 6
10.5%
2 4
 
7.0%
0 4
 
7.0%
5 4
 
7.0%
7 3
 
5.3%
8 1
 
1.8%
Space Separator
ValueCountFrequency (%)
46
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 155
57.2%
Common 116
42.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
읍 9
 
5.8%
시 8
 
5.2%
군 8
 
5.2%
동 8
 
5.2%
로 7
 
4.5%
주 6
 
3.9%
산 5
 
3.2%
진 4
 
2.6%
장 4
 
2.6%
창 4
 
2.6%
Other values (56) 92
59.4%
Common
ValueCountFrequency (%)
46
39.7%
1 12
 
10.3%
3 8
 
6.9%
9 8
 
6.9%
- 7
 
6.0%
4 7
 
6.0%
6 6
 
5.2%
2 4
 
3.4%
0 4
 
3.4%
5 4
 
3.4%
Other values (4) 10
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 155
57.2%
ASCII 116
42.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
46
39.7%
1 12
 
10.3%
3 8
 
6.9%
9 8
 
6.9%
- 7
 
6.0%
4 7
 
6.0%
6 6
 
5.2%
2 4
 
3.4%
0 4
 
3.4%
5 4
 
3.4%
Other values (4) 10
 
8.6%
Hangul
ValueCountFrequency (%)
읍 9
 
5.8%
시 8
 
5.2%
군 8
 
5.2%
동 8
 
5.2%
로 7
 
4.5%
주 6
 
3.9%
산 5
 
3.2%
진 4
 
2.6%
장 4
 
2.6%
창 4
 
2.6%
Other values (56) 92
59.4%

Unnamed: 2
Text

MISSING 

Distinct15
Distinct (%)93.8%
Missing5
Missing (%)23.8%
Memory size300.0 B
2024-03-14T11:40:58.272105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters48
Distinct characters31
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

Unique14 ?
Unique (%)87.5%

Sample

1st row원 장
2nd row나종우
3rd row나종우
4th row이진원
5th row김태현
ValueCountFrequency (%)
나종우 2
 
11.8%
원 1
 
5.9%
장 1
 
5.9%
이진원 1
 
5.9%
김태현 1
 
5.9%
김영수 1
 
5.9%
김찬기 1
 
5.9%
김선유 1
 
5.9%
임원규 1
 
5.9%
이재명 1
 
5.9%
Other values (6) 6
35.3%
2024-03-14T11:40:58.530089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 7
 
14.6%
원 4
 
8.3%
나 2
 
4.2%
종 2
 
4.2%
기 2
 
4.2%
철 2
 
4.2%
영 2
 
4.2%
규 2
 
4.2%
이 2
 
4.2%
우 2
 
4.2%
Other values (21) 21
43.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 47
97.9%
Space Separator 1
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 7
 
14.9%
원 4
 
8.5%
나 2
 
4.3%
종 2
 
4.3%
기 2
 
4.3%
철 2
 
4.3%
영 2
 
4.3%
규 2
 
4.3%
이 2
 
4.3%
우 2
 
4.3%
Other values (20) 20
42.6%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 47
97.9%
Common 1
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 7
 
14.9%
원 4
 
8.5%
나 2
 
4.3%
종 2
 
4.3%
기 2
 
4.3%
철 2
 
4.3%
영 2
 
4.3%
규 2
 
4.3%
이 2
 
4.3%
우 2
 
4.3%
Other values (20) 20
42.6%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 47
97.9%
ASCII 1
 
2.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 7
 
14.9%
원 4
 
8.5%
나 2
 
4.3%
종 2
 
4.3%
기 2
 
4.3%
철 2
 
4.3%
영 2
 
4.3%
규 2
 
4.3%
이 2
 
4.3%
우 2
 
4.3%
Other values (20) 20
42.6%
ASCII
ValueCountFrequency (%)
1
100.0%

Unnamed: 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing5
Missing (%)23.8%
Memory size300.0 B

Unnamed: 4
Text

MISSING 

Distinct13
Distinct (%)81.2%
Missing5
Missing (%)23.8%
Memory size300.0 B
2024-03-14T11:40:58.672348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.75
Min length4

Characters and Unicode

Total characters124
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

Unique12 ?
Unique (%)75.0%

Sample

1st row설립년도
2nd row91.03.24
3rd row65. 4. 3
4th row94. 9. 3
5th row94. 9.12
ValueCountFrequency (%)
94 5
16.7%
3 5
16.7%
9 4
13.3%
4 2
 
6.7%
5.14 1
 
3.3%
87 1
 
3.3%
65.02.25 1
 
3.3%
65.01.26 1
 
3.3%
64.12.24 1
 
3.3%
89.11.25 1
 
3.3%
Other values (8) 8
26.7%
2024-03-14T11:40:58.943789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 30
24.2%
14
11.3%
9 13
10.5%
4 12
 
9.7%
1 10
 
8.1%
2 10
 
8.1%
3 8
 
6.5%
6 7
 
5.6%
5 7
 
5.6%
0 4
 
3.2%
Other values (6) 9
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 76
61.3%
Other Punctuation 30
 
24.2%
Space Separator 14
 
11.3%
Other Letter 4
 
3.2%

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 Letter
ValueCountFrequency (%)
설 1
25.0%
립 1
25.0%
년 1
25.0%
도 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 30
100.0%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 120
96.8%
Hangul 4
 
3.2%

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%
Hangul
ValueCountFrequency (%)
설 1
25.0%
립 1
25.0%
년 1
25.0%
도 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120
96.8%
Hangul 4
 
3.2%

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%
Hangul
ValueCountFrequency (%)
설 1
25.0%
립 1
25.0%
년 1
25.0%
도 1
25.0%

Unnamed: 5
Text

MISSING 

Distinct16
Distinct (%)100.0%
Missing5
Missing (%)23.8%
Memory size300.0 B
2024-03-14T11:40:59.090042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.75
Min length4

Characters and Unicode

Total characters124
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row전화번호
2nd row287-5509
3rd row255-3360
4th row451-2138
5th row835-0120
ValueCountFrequency (%)
547-4659 1
 
6.2%
255-3360 1
 
6.2%
451-2138 1
 
6.2%
835-0120 1
 
6.2%
532-0222 1
 
6.2%
633-1582 1
 
6.2%
287-5509 1
 
6.2%
433-1674 1
 
6.2%
전화번호 1
 
6.2%
324-1300 1
 
6.2%
Other values (6) 6
37.5%
2024-03-14T11:40:59.343838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 20
16.1%
3 17
13.7%
5 15
12.1%
- 15
12.1%
0 11
8.9%
1 11
8.9%
4 10
8.1%
6 9
7.3%
8 5
 
4.0%
9 4
 
3.2%
Other values (5) 7
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 105
84.7%
Dash Punctuation 15
 
12.1%
Other Letter 4
 
3.2%

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%
Other Letter
ValueCountFrequency (%)
전 1
25.0%
화 1
25.0%
번 1
25.0%
호 1
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 120
96.8%
Hangul 4
 
3.2%

Most frequent character per script

Common
ValueCountFrequency (%)
2 20
16.7%
3 17
14.2%
5 15
12.5%
- 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%
Hangul
ValueCountFrequency (%)
전 1
25.0%
화 1
25.0%
번 1
25.0%
호 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120
96.8%
Hangul 4
 
3.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 20
16.7%
3 17
14.2%
5 15
12.5%
- 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%
Hangul
ValueCountFrequency (%)
전 1
25.0%
화 1
25.0%
번 1
25.0%
호 1
25.0%

Unnamed: 6
Categorical

Distinct3
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size300.0 B
 
15 
<NA>
5 
비고
 
1

Length

Max length4
Median length2
Mean length2.4761905
Min length2

Unique

Unique1 ?
Unique (%)4.8%

Sample

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

Common Values

ValueCountFrequency (%)
  15
71.4%
<NA> 5
 
23.8%
비고 1
 
4.8%

Length

2024-03-14T11:40:59.455669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:40:59.553704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5
83.3%
비고 1
 
16.7%

Correlations

2024-03-14T11:40:59.614248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
문화원현황(2015.10.27)Unnamed: 1Unnamed: 2Unnamed: 4Unnamed: 5Unnamed: 6
문화원현황(2015.10.27)1.0001.0001.0001.0001.0001.000
Unnamed: 11.0001.0001.0001.0001.0001.000
Unnamed: 21.0001.0001.0000.8831.0001.000
Unnamed: 41.0001.0000.8831.0001.0001.000
Unnamed: 51.0001.0001.0001.0001.0001.000
Unnamed: 61.0001.0001.0001.0001.0001.000

Missing values

2024-03-14T11:40:56.868440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:40:56.953201image/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.
2024-03-14T11:40:57.046621image/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

문화원현황(2015.10.27)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6
0문화원명주 소원 장회원수설립년도전화번호비고
1전라북도문화원연합회전주시 완산구 경원동 1가 104-5나종우594691.03.24287-5509
2<NA><NA><NA>NaN<NA><NA><NA>
3<NA><NA><NA>NaN<NA><NA><NA>
4전주전주시 덕진구 진북동 364-7나종우52065. 4. 3255-3360
5군산군산시 나운1동 769번지이진원31194. 9. 3451-2138
6익산익산시 어양동 산 94번지김태현52494. 9.12835-0120
7정읍정읍시 연지동 252-151김영수67064.11. 4532-0222
8남원남원시 하정동 192-4김찬기27394. 9. 3633-1582
9<NA>(남원시 의회별관 3층)<NA>NaN<NA><NA><NA>
문화원현황(2015.10.27)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6
11완주완주군 고산면 읍내리 35-30임원규29094. 9. 3263-0222
12진안진안군 진안읍 중앙로 66이재명27892. 5.14433-1674
13무주무주군 무주읍 한풍루로 346김내생53089.11.25324-1300
14장수장수군 장수읍 한누리로 393강철규28194. 9. 3351-5349
15<NA>(한누리전당 가람관 2층)<NA>NaN<NA><NA><NA>
16임실임실군 임실읍 호국로 1640최성미43264.12.24642-2211
17<NA>(임실공설운동장내)<NA>NaN<NA><NA><NA>
18순창순창군 순창읍 장류로 407-11김기곤28065.01.26653-2069
19고창고창읍 천변북로 119번지송영래46465.02.25564-2340
20부안부안군 부안읍 매창로 89김원철32187. 5.28583-2101

Duplicate rows

Most frequently occurring

문화원현황(2015.10.27)Unnamed: 1Unnamed: 2Unnamed: 4Unnamed: 5Unnamed: 6# duplicates
0<NA><NA><NA><NA><NA><NA>2