Overview

Dataset statistics

Number of variables34
Number of observations100
Missing cells301
Missing cells (%)8.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.9 KiB
Average record size in memory285.3 B

Variable types

Numeric10
Categorical7
Unsupported1
Text13
Boolean3

Alerts

dues_unit_cn has constant value ""Constant
totar_yy has constant value ""Constant
esntl_id has 100 (100.0%) missing valuesMissing
fax_no has 2 (2.0%) missing valuesMissing
sbscrb_yy has 3 (3.0%) missing valuesMissing
theat_l_co has 70 (70.0%) missing valuesMissing
theat_m_co has 25 (25.0%) missing valuesMissing
theat_s_co has 31 (31.0%) missing valuesMissing
field_prfplc_co has 69 (69.0%) missing valuesMissing
sn has unique valuesUnique
fclty_nm has unique valuesUnique
addr has unique valuesUnique
totar_mg has unique valuesUnique
lo_val has unique valuesUnique
la_val has unique valuesUnique
esntl_id is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 10:18:53.646882
Analysis finished2023-12-10 10:18:54.723825
Duration1.08 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

sn
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.92
Minimum1
Maximum229
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:18:54.848303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.95
Q127.75
median53.5
Q378.25
95-th percentile98.05
Maximum229
Range228
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation41.344006
Coefficient of variation (CV)0.72635289
Kurtosis6.8272845
Mean56.92
Median Absolute Deviation (MAD)25.5
Skewness1.9966066
Sum5692
Variance1709.3269
MonotonicityNot monotonic
2023-12-10T19:18:55.032646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
9 1
1.0%
10 1
1.0%
11 1
1.0%
12 1
1.0%
ValueCountFrequency (%)
229 1
1.0%
228 1
1.0%
227 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%

branch_nm
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기
35 
서울 인천
32 
강원
16 
대전 충청 세종
14 
호남 제주
 
3

Length

Max length8
Median length2
Mean length3.89
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울 인천
2nd row호남 제주
3rd row서울 인천
4th row서울 인천
5th row서울 인천

Common Values

ValueCountFrequency (%)
경기 35
35.0%
서울 인천 32
32.0%
강원 16
16.0%
대전 충청 세종 14
 
14.0%
호남 제주 3
 
3.0%

Length

2023-12-10T19:18:55.244216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:18:55.423429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기 35
21.5%
서울 32
19.6%
인천 32
19.6%
강원 16
9.8%
대전 14
 
8.6%
충청 14
 
8.6%
세종 14
 
8.6%
호남 3
 
1.8%
제주 3
 
1.8%

area_lc
Categorical

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기
35 
서울
23 
강원
16 
인천
9 
충남
9 
Other values (2)
8 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울
2nd row제주
3rd row서울
4th row서울
5th row서울

Common Values

ValueCountFrequency (%)
경기 35
35.0%
서울 23
23.0%
강원 16
16.0%
인천 9
 
9.0%
충남 9
 
9.0%
대전 5
 
5.0%
제주 3
 
3.0%

Length

2023-12-10T19:18:55.650063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:18:55.816495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기 35
35.0%
서울 23
23.0%
강원 16
16.0%
인천 9
 
9.0%
충남 9
 
9.0%
대전 5
 
5.0%
제주 3
 
3.0%

esntl_id
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

instt_nm
Text

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:18:56.117766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length16
Mean length7.67
Min length3

Characters and Unicode

Total characters767
Distinct characters122
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)88.0%

Sample

1st row(재)광진문화재단
2nd row서귀포시청
3rd row(재)강남문화재단
4th row(재)구로문화재단
5th row(재)금천문화재단
ValueCountFrequency (%)
재)화성시문화재단 3
 
3.0%
평택시청 3
 
3.0%
강릉시청 2
 
2.0%
재)고양문화재단 2
 
2.0%
재)안양문화예술재단 2
 
2.0%
광주도시관리공사 1
 
1.0%
이천시청 1
 
1.0%
재)춘천시문화재단 1
 
1.0%
재)인제군문화재단 1
 
1.0%
재)원주문화재단 1
 
1.0%
Other values (84) 84
83.2%
2023-12-10T19:18:56.695983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
재 94
 
12.3%
단 56
 
7.3%
화 54
 
7.0%
문 51
 
6.6%
( 48
 
6.3%
) 48
 
6.3%
시 42
 
5.5%
청 32
 
4.2%
천 17
 
2.2%
구 11
 
1.4%
Other values (112) 314
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 669
87.2%
Open Punctuation 48
 
6.3%
Close Punctuation 48
 
6.3%
Space Separator 1
 
0.1%
Other Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
재 94
 
14.1%
단 56
 
8.4%
화 54
 
8.1%
문 51
 
7.6%
시 42
 
6.3%
청 32
 
4.8%
천 17
 
2.5%
구 11
 
1.6%
공 11
 
1.6%
관 11
 
1.6%
Other values (108) 290
43.3%
Open Punctuation
ValueCountFrequency (%)
( 48
100.0%
Close Punctuation
ValueCountFrequency (%)
) 48
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 670
87.4%
Common 97
 
12.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
재 94
 
14.0%
단 56
 
8.4%
화 54
 
8.1%
문 51
 
7.6%
시 42
 
6.3%
청 32
 
4.8%
천 17
 
2.5%
구 11
 
1.6%
공 11
 
1.6%
관 11
 
1.6%
Other values (109) 291
43.4%
Common
ValueCountFrequency (%)
( 48
49.5%
) 48
49.5%
1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 669
87.2%
ASCII 97
 
12.6%
None 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
재 94
 
14.1%
단 56
 
8.4%
화 54
 
8.1%
문 51
 
7.6%
시 42
 
6.3%
청 32
 
4.8%
천 17
 
2.5%
구 11
 
1.6%
공 11
 
1.6%
관 11
 
1.6%
Other values (108) 290
43.3%
ASCII
ValueCountFrequency (%)
( 48
49.5%
) 48
49.5%
1
 
1.0%
None
ValueCountFrequency (%)
㈜ 1
100.0%

fclty_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:18:57.060529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length16
Mean length7.22
Min length4

Characters and Unicode

Total characters722
Distinct characters153
Distinct categories5 ?
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 row나루아트센터
2nd row서귀포예술의전당
3rd row강남씨어터
4th row구로아트밸리예술극장
5th row금나래아트홀
ValueCountFrequency (%)
인천 2
 
2.0%
나루아트센터 1
 
1.0%
세종국악당 1
 
1.0%
인제하늘내린센터 1
 
1.0%
치악예술관 1
 
1.0%
영월문화예술회관 1
 
1.0%
포천반월아트홀 1
 
1.0%
서부문화예술회관 1
 
1.0%
북부문화예술회관 1
 
1.0%
남부문화예술회관 1
 
1.0%
Other values (91) 91
89.2%
2023-12-10T19:18:57.597061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
문 46
 
6.4%
화 42
 
5.8%
관 42
 
5.8%
예 40
 
5.5%
회 39
 
5.4%
술 34
 
4.7%
아 34
 
4.7%
트 31
 
4.3%
홀 18
 
2.5%
천 16
 
2.2%
Other values (143) 380
52.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 712
98.6%
Open Punctuation 3
 
0.4%
Close Punctuation 3
 
0.4%
Space Separator 2
 
0.3%
Uppercase Letter 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
문 46
 
6.5%
화 42
 
5.9%
관 42
 
5.9%
예 40
 
5.6%
회 39
 
5.5%
술 34
 
4.8%
아 34
 
4.8%
트 31
 
4.4%
홀 18
 
2.5%
천 16
 
2.2%
Other values (138) 370
52.0%
Uppercase Letter
ValueCountFrequency (%)
K 1
50.0%
S 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 712
98.6%
Common 8
 
1.1%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
문 46
 
6.5%
화 42
 
5.9%
관 42
 
5.9%
예 40
 
5.6%
회 39
 
5.5%
술 34
 
4.8%
아 34
 
4.8%
트 31
 
4.4%
홀 18
 
2.5%
천 16
 
2.2%
Other values (138) 370
52.0%
Common
ValueCountFrequency (%)
( 3
37.5%
) 3
37.5%
2
25.0%
Latin
ValueCountFrequency (%)
K 1
50.0%
S 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 712
98.6%
ASCII 10
 
1.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
문 46
 
6.5%
화 42
 
5.9%
관 42
 
5.9%
예 40
 
5.6%
회 39
 
5.5%
술 34
 
4.8%
아 34
 
4.8%
트 31
 
4.4%
홀 18
 
2.5%
천 16
 
2.2%
Other values (138) 370
52.0%
ASCII
ValueCountFrequency (%)
( 3
30.0%
) 3
30.0%
2
20.0%
K 1
 
10.0%
S 1
 
10.0%

rprsntv_nm
Text

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:18:58.085612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.99
Min length2

Characters and Unicode

Total characters299
Distinct characters100
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

Unique88 ?
Unique (%)88.0%

Sample

1st row김경남
2nd row양윤경
3rd row최병식
4th row이성
5th row정병재
ValueCountFrequency (%)
최형오 3
 
3.0%
정장선 3
 
3.0%
김한근 2
 
2.0%
이재준 2
 
2.0%
최대호 2
 
2.0%
백상균 1
 
1.0%
황인배 1
 
1.0%
최돈선 1
 
1.0%
최상기 1
 
1.0%
원창묵 1
 
1.0%
Other values (83) 83
83.0%
2023-12-10T19:18:58.705813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 20
 
6.7%
최 13
 
4.3%
이 13
 
4.3%
호 12
 
4.0%
정 10
 
3.3%
선 8
 
2.7%
재 8
 
2.7%
박 7
 
2.3%
종 7
 
2.3%
한 7
 
2.3%
Other values (90) 194
64.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 299
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 20
 
6.7%
최 13
 
4.3%
이 13
 
4.3%
호 12
 
4.0%
정 10
 
3.3%
선 8
 
2.7%
재 8
 
2.7%
박 7
 
2.3%
종 7
 
2.3%
한 7
 
2.3%
Other values (90) 194
64.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 299
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 20
 
6.7%
최 13
 
4.3%
이 13
 
4.3%
호 12
 
4.0%
정 10
 
3.3%
선 8
 
2.7%
재 8
 
2.7%
박 7
 
2.3%
종 7
 
2.3%
한 7
 
2.3%
Other values (90) 194
64.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 299
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 20
 
6.7%
최 13
 
4.3%
이 13
 
4.3%
호 12
 
4.0%
정 10
 
3.3%
선 8
 
2.7%
재 8
 
2.7%
박 7
 
2.3%
종 7
 
2.3%
한 7
 
2.3%
Other values (90) 194
64.9%

rspofc_cl
Categorical

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
이사장
35 
시장
17 
대표이사
13 
사장
7 
원장
5 
Other values (15)
23 

Length

Max length8
Median length7
Mean length2.97
Min length2

Unique

Unique11 ?
Unique (%)11.0%

Sample

1st row사장
2nd row시장
3rd row이사장
4th row이사장
5th row이사장

Common Values

ValueCountFrequency (%)
이사장 35
35.0%
시장 17
17.0%
대표이사 13
 
13.0%
사장 7
 
7.0%
원장 5
 
5.0%
군수 4
 
4.0%
관장 4
 
4.0%
구청장 2
 
2.0%
소장 2
 
2.0%
대표이사직무대행 1
 
1.0%
Other values (10) 10
 
10.0%

Length

2023-12-10T19:18:58.936809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
이사장 36
35.3%
시장 18
17.6%
대표이사 13
 
12.7%
사장 7
 
6.9%
원장 5
 
4.9%
군수 4
 
3.9%
관장 4
 
3.9%
구청장 3
 
2.9%
소장 2
 
2.0%
위원장 1
 
1.0%
Other values (9) 9
 
8.8%

dues_ct
Categorical

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
500
72 
400
21 
300
 
6
0
 
1

Length

Max length3
Median length3
Mean length2.98
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row500
2nd row400
3rd row500
4th row500
5th row500

Common Values

ValueCountFrequency (%)
500 72
72.0%
400 21
 
21.0%
300 6
 
6.0%
0 1
 
1.0%

Length

2023-12-10T19:18:59.142720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:18:59.342664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
500 72
72.0%
400 21
 
21.0%
300 6
 
6.0%
0 1
 
1.0%

dues_unit_cn
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
백만원
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
백만원 100
100.0%

Length

2023-12-10T19:18:59.525847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:18:59.671322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
백만원 100
100.0%

addr
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:18:59.984721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length32
Mean length20.81
Min length12

Characters and Unicode

Total characters2081
Distinct characters206
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
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 row서울특별시 광진구 능동로 76
2nd row제주특별자치도 서귀포시 태평로 270
3rd row서울시 강남구 삼성로628(삼성동66) 강남문화재단 4층
4th row서울특별시 구로구 가마산로25길 9-24
5th row서울특별시 금천구 시흥대로73길 금천구청 내
ValueCountFrequency (%)
경기도 35
 
7.6%
서울특별시 20
 
4.4%
강원도 16
 
3.5%
충청남도 9
 
2.0%
인천광역시 8
 
1.7%
중구 6
 
1.3%
대전광역시 5
 
1.1%
종로구 4
 
0.9%
서구 4
 
0.9%
서초구 3
 
0.7%
Other values (321) 348
76.0%
2023-12-10T19:19:00.625174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
358
 
17.2%
로 94
 
4.5%
시 93
 
4.5%
1 68
 
3.3%
도 66
 
3.2%
구 49
 
2.4%
동 46
 
2.2%
2 46
 
2.2%
서 40
 
1.9%
기 40
 
1.9%
Other values (196) 1181
56.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1326
63.7%
Space Separator 359
 
17.3%
Decimal Number 327
 
15.7%
Open Punctuation 28
 
1.3%
Close Punctuation 28
 
1.3%
Dash Punctuation 9
 
0.4%
Other Punctuation 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 94
 
7.1%
시 93
 
7.0%
도 66
 
5.0%
구 49
 
3.7%
동 46
 
3.5%
서 40
 
3.0%
기 40
 
3.0%
경 38
 
2.9%
천 28
 
2.1%
대 28
 
2.1%
Other values (180) 804
60.6%
Decimal Number
ValueCountFrequency (%)
1 68
20.8%
2 46
14.1%
5 38
11.6%
3 36
11.0%
4 31
9.5%
6 24
 
7.3%
0 23
 
7.0%
8 22
 
6.7%
9 21
 
6.4%
7 18
 
5.5%
Space Separator
ValueCountFrequency (%)
358
99.7%
  1
 
0.3%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1326
63.7%
Common 755
36.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 94
 
7.1%
시 93
 
7.0%
도 66
 
5.0%
구 49
 
3.7%
동 46
 
3.5%
서 40
 
3.0%
기 40
 
3.0%
경 38
 
2.9%
천 28
 
2.1%
대 28
 
2.1%
Other values (180) 804
60.6%
Common
ValueCountFrequency (%)
358
47.4%
1 68
 
9.0%
2 46
 
6.1%
5 38
 
5.0%
3 36
 
4.8%
4 31
 
4.1%
( 28
 
3.7%
) 28
 
3.7%
6 24
 
3.2%
0 23
 
3.0%
Other values (6) 75
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1326
63.7%
ASCII 754
36.2%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
358
47.5%
1 68
 
9.0%
2 46
 
6.1%
5 38
 
5.0%
3 36
 
4.8%
4 31
 
4.1%
( 28
 
3.7%
) 28
 
3.7%
6 24
 
3.2%
0 23
 
3.1%
Other values (5) 74
 
9.8%
Hangul
ValueCountFrequency (%)
로 94
 
7.1%
시 93
 
7.0%
도 66
 
5.0%
구 49
 
3.7%
동 46
 
3.5%
서 40
 
3.0%
기 40
 
3.0%
경 38
 
2.9%
천 28
 
2.1%
대 28
 
2.1%
Other values (180) 804
60.6%
None
ValueCountFrequency (%)
  1
100.0%

zip_no
Text

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:19:01.002679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.14
Min length5

Characters and Unicode

Total characters514
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

Unique92 ?
Unique (%)92.0%

Sample

1st row05065
2nd row63594
3rd row06085
4th row08301
5th row08611
ValueCountFrequency (%)
14093 2
 
2.0%
35204 2
 
2.0%
10471 2
 
2.0%
06757 2
 
2.0%
11012 1
 
1.0%
10894 1
 
1.0%
24624 1
 
1.0%
26447 1
 
1.0%
26227 1
 
1.0%
11151 1
 
1.0%
Other values (86) 86
86.0%
2023-12-10T19:19:01.478930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 83
16.1%
0 72
14.0%
2 72
14.0%
3 55
10.7%
4 54
10.5%
5 42
8.2%
6 38
7.4%
7 37
7.2%
8 31
 
6.0%
9 23
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 507
98.6%
Dash Punctuation 7
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 83
16.4%
0 72
14.2%
2 72
14.2%
3 55
10.8%
4 54
10.7%
5 42
8.3%
6 38
7.5%
7 37
7.3%
8 31
 
6.1%
9 23
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 514
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 83
16.1%
0 72
14.0%
2 72
14.0%
3 55
10.7%
4 54
10.5%
5 42
8.2%
6 38
7.4%
7 37
7.2%
8 31
 
6.0%
9 23
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 514
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 83
16.1%
0 72
14.0%
2 72
14.0%
3 55
10.7%
4 54
10.5%
5 42
8.2%
6 38
7.4%
7 37
7.2%
8 31
 
6.0%
9 23
 
4.5%

tel_no
Text

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:19:01.785888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length11.71
Min length9

Characters and Unicode

Total characters1171
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

Unique92 ?
Unique (%)92.0%

Sample

1st row02-2049-4700
2nd row064-760-3341
3rd row02-6712-0543
4th row02-2029-1700
5th row02-2627-2959
ValueCountFrequency (%)
1661-8990 4
 
4.0%
1577-7766 2
 
2.0%
031-689-5000 2
 
2.0%
041-661-8030 1
 
1.0%
031-950-1810 1
 
1.0%
033-259-5800 1
 
1.0%
033-460-8900 1
 
1.0%
033-737-4309 1
 
1.0%
033-375-6353 1
 
1.0%
031-540-6213 1
 
1.0%
Other values (85) 85
85.0%
2023-12-10T19:19:02.208352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 254
21.7%
- 192
16.4%
3 138
11.8%
2 104
8.9%
1 95
 
8.1%
6 75
 
6.4%
4 73
 
6.2%
5 64
 
5.5%
8 61
 
5.2%
7 60
 
5.1%
Other values (2) 55
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 978
83.5%
Dash Punctuation 192
 
16.4%
Math Symbol 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 254
26.0%
3 138
14.1%
2 104
10.6%
1 95
 
9.7%
6 75
 
7.7%
4 73
 
7.5%
5 64
 
6.5%
8 61
 
6.2%
7 60
 
6.1%
9 54
 
5.5%
Dash Punctuation
ValueCountFrequency (%)
- 192
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1171
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 254
21.7%
- 192
16.4%
3 138
11.8%
2 104
8.9%
1 95
 
8.1%
6 75
 
6.4%
4 73
 
6.2%
5 64
 
5.5%
8 61
 
5.2%
7 60
 
5.1%
Other values (2) 55
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1171
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 254
21.7%
- 192
16.4%
3 138
11.8%
2 104
8.9%
1 95
 
8.1%
6 75
 
6.4%
4 73
 
6.2%
5 64
 
5.5%
8 61
 
5.2%
7 60
 
5.1%
Other values (2) 55
 
4.7%

fax_no
Text

MISSING 

Distinct93
Distinct (%)94.9%
Missing2
Missing (%)2.0%
Memory size932.0 B
2023-12-10T19:19:02.574409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.969388
Min length11

Characters and Unicode

Total characters1173
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

Unique90 ?
Unique (%)91.8%

Sample

1st row02-2201-4800
2nd row064-760-3349
3rd row02-3774-0429
4th row02-2029-1706
5th row02-2627-2959
ValueCountFrequency (%)
070-8250-0396 4
 
4.1%
031-960-9619 2
 
2.0%
031-687-5000 2
 
2.0%
041-930-3493 1
 
1.0%
041-746-5959 1
 
1.0%
033-262-1371 1
 
1.0%
033-461-0378 1
 
1.0%
033-763-9631 1
 
1.0%
033-374-6353 1
 
1.0%
031-540-6211 1
 
1.0%
Other values (83) 83
84.7%
2023-12-10T19:19:03.077307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 196
16.7%
0 195
16.6%
3 139
11.8%
2 112
9.5%
1 91
7.8%
9 85
7.2%
4 84
7.2%
6 74
 
6.3%
8 72
 
6.1%
5 66
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 977
83.3%
Dash Punctuation 196
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 195
20.0%
3 139
14.2%
2 112
11.5%
1 91
9.3%
9 85
8.7%
4 84
8.6%
6 74
 
7.6%
8 72
 
7.4%
5 66
 
6.8%
7 59
 
6.0%
Dash Punctuation
ValueCountFrequency (%)
- 196
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1173
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 196
16.7%
0 195
16.6%
3 139
11.8%
2 112
9.5%
1 91
7.8%
9 85
7.2%
4 84
7.2%
6 74
 
6.3%
8 72
 
6.1%
5 66
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1173
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 196
16.7%
0 195
16.6%
3 139
11.8%
2 112
9.5%
1 91
7.8%
9 85
7.2%
4 84
7.2%
6 74
 
6.3%
8 72
 
6.1%
5 66
 
5.6%

hmpg_addr
Text

Distinct93
Distinct (%)93.9%
Missing1
Missing (%)1.0%
Memory size932.0 B
2023-12-10T19:19:03.480030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length77
Median length49
Mean length22.828283
Min length11

Characters and Unicode

Total characters2260
Distinct characters46
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)89.9%

Sample

1st rowwww.naruart.or.kr
2nd rowhttp://arts.seogwipo.go.kr
3rd rowwww.gfac.or.kr
4th rowwww.guroartsvalley.or.kr
5th rowhttp://www.gcfac.or.kr/
ValueCountFrequency (%)
www.hcf.or.kr 3
 
3.0%
http://www.pyeongtaek.go.kr/pyeongtaek/culture/calendarlist.do?mid=0601060100 3
 
3.0%
http://www.ayac.or.kr 2
 
2.0%
www.artgy.or.kr 2
 
2.0%
www.artic.or.kr 1
 
1.0%
www.nsart.or.kr 1
 
1.0%
www.hscf.or.kr 1
 
1.0%
http://www.cccf.or.kr 1
 
1.0%
www.injeart.or.kr 1
 
1.0%
www.wcf.or.kr 1
 
1.0%
Other values (83) 83
83.8%
2023-12-10T19:19:04.292460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 297
13.1%
w 274
 
12.1%
r 214
 
9.5%
o 147
 
6.5%
t 136
 
6.0%
/ 116
 
5.1%
k 111
 
4.9%
a 101
 
4.5%
c 93
 
4.1%
g 78
 
3.5%
Other values (36) 693
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1727
76.4%
Other Punctuation 452
 
20.0%
Decimal Number 60
 
2.7%
Uppercase Letter 10
 
0.4%
Math Symbol 7
 
0.3%
Connector Punctuation 4
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 274
15.9%
r 214
12.4%
o 147
 
8.5%
t 136
 
7.9%
k 111
 
6.4%
a 101
 
5.8%
c 93
 
5.4%
g 78
 
4.5%
e 78
 
4.5%
n 73
 
4.2%
Other values (14) 422
24.4%
Decimal Number
ValueCountFrequency (%)
0 24
40.0%
1 9
 
15.0%
6 9
 
15.0%
4 5
 
8.3%
3 4
 
6.7%
9 4
 
6.7%
7 2
 
3.3%
5 1
 
1.7%
8 1
 
1.7%
2 1
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 297
65.7%
/ 116
 
25.7%
: 31
 
6.9%
? 6
 
1.3%
# 1
 
0.2%
& 1
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
L 4
40.0%
I 3
30.0%
N 2
20.0%
B 1
 
10.0%
Math Symbol
ValueCountFrequency (%)
= 7
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1737
76.9%
Common 523
 
23.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 274
15.8%
r 214
12.3%
o 147
 
8.5%
t 136
 
7.8%
k 111
 
6.4%
a 101
 
5.8%
c 93
 
5.4%
g 78
 
4.5%
e 78
 
4.5%
n 73
 
4.2%
Other values (18) 432
24.9%
Common
ValueCountFrequency (%)
. 297
56.8%
/ 116
 
22.2%
: 31
 
5.9%
0 24
 
4.6%
1 9
 
1.7%
6 9
 
1.7%
= 7
 
1.3%
? 6
 
1.1%
4 5
 
1.0%
_ 4
 
0.8%
Other values (8) 15
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2260
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 297
13.1%
w 274
 
12.1%
r 214
 
9.5%
o 147
 
6.5%
t 136
 
6.0%
/ 116
 
5.1%
k 111
 
4.9%
a 101
 
4.5%
c 93
 
4.1%
g 78
 
3.5%
Other values (36) 693
30.7%

opnng_year_yy
Real number (ℝ)

Distinct36
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001.32
Minimum1951
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:04.948642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1951
5-th percentile1984.85
Q11993
median2004
Q32010.25
95-th percentile2015.05
Maximum2018
Range67
Interquartile range (IQR)17.25

Descriptive statistics

Standard deviation11.187005
Coefficient of variation (CV)0.0055898134
Kurtosis2.9058486
Mean2001.32
Median Absolute Deviation (MAD)8
Skewness-1.1620618
Sum200132
Variance125.14909
MonotonicityNot monotonic
2023-12-10T19:19:05.136078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
2004 9
 
9.0%
2011 7
 
7.0%
2005 5
 
5.0%
2012 5
 
5.0%
1995 5
 
5.0%
2008 4
 
4.0%
1990 4
 
4.0%
1991 4
 
4.0%
2009 4
 
4.0%
1992 3
 
3.0%
Other values (26) 50
50.0%
ValueCountFrequency (%)
1951 1
 
1.0%
1973 1
 
1.0%
1978 1
 
1.0%
1981 1
 
1.0%
1982 1
 
1.0%
1985 1
 
1.0%
1988 3
3.0%
1989 3
3.0%
1990 4
4.0%
1991 4
4.0%
ValueCountFrequency (%)
2018 2
 
2.0%
2017 2
 
2.0%
2016 1
 
1.0%
2015 2
 
2.0%
2014 3
3.0%
2013 3
3.0%
2012 5
5.0%
2011 7
7.0%
2010 3
3.0%
2009 4
4.0%

sbscrb_yy
Real number (ℝ)

MISSING 

Distinct23
Distinct (%)23.7%
Missing3
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean2007.8144
Minimum1996
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:05.371500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1996
5-th percentile1996
Q12003
median2009
Q32014
95-th percentile2018
Maximum2019
Range23
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.9870861
Coefficient of variation (CV)0.0034799462
Kurtosis-1.0842691
Mean2007.8144
Median Absolute Deviation (MAD)5
Skewness-0.22376384
Sum194758
Variance48.819373
MonotonicityNot monotonic
2023-12-10T19:19:05.567703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1996 11
 
11.0%
2009 10
 
10.0%
2004 7
 
7.0%
2013 6
 
6.0%
2016 6
 
6.0%
2003 5
 
5.0%
2017 5
 
5.0%
2006 5
 
5.0%
2014 5
 
5.0%
2007 4
 
4.0%
Other values (13) 33
33.0%
ValueCountFrequency (%)
1996 11
11.0%
1997 1
 
1.0%
1999 4
 
4.0%
2000 2
 
2.0%
2001 3
 
3.0%
2002 1
 
1.0%
2003 5
5.0%
2004 7
7.0%
2005 3
 
3.0%
2006 5
5.0%
ValueCountFrequency (%)
2019 2
 
2.0%
2018 4
4.0%
2017 5
5.0%
2016 6
6.0%
2015 4
4.0%
2014 5
5.0%
2013 6
6.0%
2012 4
4.0%
2011 3
3.0%
2010 1
 
1.0%

totar_mg
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16036.03
Minimum1632
Maximum133858.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:05.771222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1632
5-th percentile2084.11
Q15233.25
median9306.5
Q317440.125
95-th percentile44876.75
Maximum133858.4
Range132226.4
Interquartile range (IQR)12206.875

Descriptive statistics

Standard deviation20611.771
Coefficient of variation (CV)1.2853413
Kurtosis16.272302
Mean16036.03
Median Absolute Deviation (MAD)5119.6
Skewness3.58402
Sum1603603
Variance4.2484512 × 108
MonotonicityNot monotonic
2023-12-10T19:19:05.998112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18860.5 1
 
1.0%
11843.0 1
 
1.0%
4237.0 1
 
1.0%
9782.8 1
 
1.0%
13655.9 1
 
1.0%
7807.0 1
 
1.0%
3729.0 1
 
1.0%
10945.0 1
 
1.0%
5417.0 1
 
1.0%
5099.0 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1632.0 1
1.0%
1731.2 1
1.0%
1844.3 1
1.0%
1901.6 1
1.0%
2023.5 1
1.0%
2087.3 1
1.0%
2438.0 1
1.0%
2615.0 1
1.0%
2679.0 1
1.0%
2777.0 1
1.0%
ValueCountFrequency (%)
133858.4 1
1.0%
120733.8 1
1.0%
63396.0 1
1.0%
56351.2 1
1.0%
49907.0 1
1.0%
44612.0 1
1.0%
43744.3 1
1.0%
41056.0 1
1.0%
41054.3 1
1.0%
37900.3 1
1.0%

totar_yy
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
m²
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowm²
2nd rowm²
3rd rowm²
4th rowm²
5th rowm²

Common Values

ValueCountFrequency (%)
m² 100
100.0%

Length

2023-12-10T19:19:06.214027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:19:06.353981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m² 100
100.0%

theat_l_co
Text

MISSING 

Distinct29
Distinct (%)96.7%
Missing70
Missing (%)70.0%
Memory size932.0 B
2023-12-10T19:19:06.562415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.4666667
Min length3

Characters and Unicode

Total characters134
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

Unique28 ?
Unique (%)93.3%

Sample

1st row1,184
2nd row3,022
3rd row1,231
4th row850
5th row706
ValueCountFrequency (%)
1,184 2
 
6.7%
1,808 1
 
3.3%
1,001 1
 
3.3%
1,546 1
 
3.3%
998 1
 
3.3%
1,200 1
 
3.3%
991 1
 
3.3%
496 1
 
3.3%
1,068 1
 
3.3%
1,025 1
 
3.3%
Other values (19) 19
63.3%
2023-12-10T19:19:07.061221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 29
21.6%
, 22
16.4%
2 16
11.9%
0 12
9.0%
8 11
 
8.2%
5 10
 
7.5%
4 9
 
6.7%
9 9
 
6.7%
6 7
 
5.2%
3 5
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 112
83.6%
Other Punctuation 22
 
16.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 29
25.9%
2 16
14.3%
0 12
10.7%
8 11
 
9.8%
5 10
 
8.9%
4 9
 
8.0%
9 9
 
8.0%
6 7
 
6.2%
3 5
 
4.5%
7 4
 
3.6%
Other Punctuation
ValueCountFrequency (%)
, 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 134
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 29
21.6%
, 22
16.4%
2 16
11.9%
0 12
9.0%
8 11
 
8.2%
5 10
 
7.5%
4 9
 
6.7%
9 9
 
6.7%
6 7
 
5.2%
3 5
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 29
21.6%
, 22
16.4%
2 16
11.9%
0 12
9.0%
8 11
 
8.2%
5 10
 
7.5%
4 9
 
6.7%
9 9
 
6.7%
6 7
 
5.2%
3 5
 
3.7%

theat_m_co
Text

MISSING 

Distinct71
Distinct (%)94.7%
Missing25
Missing (%)25.0%
Memory size932.0 B
2023-12-10T19:19:07.476689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.0533333
Min length3

Characters and Unicode

Total characters229
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

Unique67 ?
Unique (%)89.3%

Sample

1st row601
2nd row802
3rd row232
4th row579
5th row566
ValueCountFrequency (%)
638 2
 
2.7%
600 2
 
2.7%
601 2
 
2.7%
786 2
 
2.7%
631 1
 
1.3%
686 1
 
1.3%
660 1
 
1.3%
395 1
 
1.3%
929 1
 
1.3%
910 1
 
1.3%
Other values (61) 61
81.3%
2023-12-10T19:19:08.198849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 32
14.0%
6 29
12.7%
3 24
10.5%
5 24
10.5%
9 21
9.2%
8 20
8.7%
7 20
8.7%
1 20
8.7%
4 19
8.3%
2 18
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 227
99.1%
Other Punctuation 2
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32
14.1%
6 29
12.8%
3 24
10.6%
5 24
10.6%
9 21
9.3%
8 20
8.8%
7 20
8.8%
1 20
8.8%
4 19
8.4%
2 18
7.9%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 229
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32
14.0%
6 29
12.7%
3 24
10.5%
5 24
10.5%
9 21
9.2%
8 20
8.7%
7 20
8.7%
1 20
8.7%
4 19
8.3%
2 18
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 229
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32
14.0%
6 29
12.7%
3 24
10.5%
5 24
10.5%
9 21
9.2%
8 20
8.7%
7 20
8.7%
1 20
8.7%
4 19
8.3%
2 18
7.9%

theat_s_co
Real number (ℝ)

MISSING 

Distinct57
Distinct (%)82.6%
Missing31
Missing (%)31.0%
Infinite0
Infinite (%)0.0%
Mean242.72464
Minimum50
Maximum638
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:08.455652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile108.8
Q1168
median221
Q3300
95-th percentile419.8
Maximum638
Range588
Interquartile range (IQR)132

Descriptive statistics

Standard deviation108.01237
Coefficient of variation (CV)0.44499963
Kurtosis1.7283958
Mean242.72464
Median Absolute Deviation (MAD)61
Skewness1.0241347
Sum16748
Variance11666.673
MonotonicityNot monotonic
2023-12-10T19:19:08.665156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 5
 
5.0%
300 3
 
3.0%
250 3
 
3.0%
180 3
 
3.0%
160 2
 
2.0%
167 2
 
2.0%
443 1
 
1.0%
128 1
 
1.0%
480 1
 
1.0%
303 1
 
1.0%
Other values (47) 47
47.0%
(Missing) 31
31.0%
ValueCountFrequency (%)
50 1
1.0%
74 1
1.0%
85 1
1.0%
108 1
1.0%
110 1
1.0%
112 1
1.0%
119 1
1.0%
128 1
1.0%
130 1
1.0%
138 1
1.0%
ValueCountFrequency (%)
638 1
1.0%
502 1
1.0%
480 1
1.0%
443 1
1.0%
385 1
1.0%
380 1
1.0%
379 1
1.0%
378 1
1.0%
374 1
1.0%
367 1
1.0%

field_prfplc_co
Text

MISSING 

Distinct20
Distinct (%)64.5%
Missing69
Missing (%)69.0%
Memory size932.0 B
2023-12-10T19:19:08.939272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.516129
Min length2

Characters and Unicode

Total characters109
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 (%)48.4%

Sample

1st row124
2nd row428
3rd row600
4th row627
5th row1,053
ValueCountFrequency (%)
200 5
16.1%
1,000 4
 
12.9%
600 3
 
9.7%
50 2
 
6.5%
300 2
 
6.5%
250 1
 
3.2%
1,200 1
 
3.2%
105 1
 
3.2%
1,598 1
 
3.2%
1,500 1
 
3.2%
Other values (10) 10
32.3%
2023-12-10T19:19:09.427377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 48
44.0%
2 11
 
10.1%
1 11
 
10.1%
, 9
 
8.3%
5 9
 
8.3%
4 6
 
5.5%
6 5
 
4.6%
3 4
 
3.7%
8 3
 
2.8%
7 2
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 100
91.7%
Other Punctuation 9
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 48
48.0%
2 11
 
11.0%
1 11
 
11.0%
5 9
 
9.0%
4 6
 
6.0%
6 5
 
5.0%
3 4
 
4.0%
8 3
 
3.0%
7 2
 
2.0%
9 1
 
1.0%
Other Punctuation
ValueCountFrequency (%)
, 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 109
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 48
44.0%
2 11
 
10.1%
1 11
 
10.1%
, 9
 
8.3%
5 9
 
8.3%
4 6
 
5.5%
6 5
 
4.6%
3 4
 
3.7%
8 3
 
2.8%
7 2
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 109
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 48
44.0%
2 11
 
10.1%
1 11
 
10.1%
, 9
 
8.3%
5 9
 
8.3%
4 6
 
5.5%
6 5
 
4.6%
3 4
 
3.7%
8 3
 
2.8%
7 2
 
1.8%

ehbll_nm
Boolean

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
True
76 
False
24 
ValueCountFrequency (%)
True 76
76.0%
False 24
 
24.0%
2023-12-10T19:19:09.628914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

eduspntd_nm
Boolean

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
False
57 
True
43 
ValueCountFrequency (%)
False 57
57.0%
True 43
43.0%
2023-12-10T19:19:09.752377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

cinema_nm
Boolean

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
False
88 
True
12 
ValueCountFrequency (%)
False 88
88.0%
True 12
 
12.0%
2023-12-10T19:19:09.881496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

ctprvn_cd
Real number (ℝ)

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.05
Minimum11
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:10.000422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q123
median31
Q332
95-th percentile34
Maximum39
Range28
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.8732659
Coefficient of variation (CV)0.34062441
Kurtosis-0.76952648
Mean26.05
Median Absolute Deviation (MAD)1
Skewness-0.88537645
Sum2605
Variance78.734848
MonotonicityNot monotonic
2023-12-10T19:19:10.145757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
31 35
35.0%
11 23
23.0%
32 16
16.0%
23 9
 
9.0%
34 9
 
9.0%
25 5
 
5.0%
39 3
 
3.0%
ValueCountFrequency (%)
11 23
23.0%
23 9
 
9.0%
25 5
 
5.0%
31 35
35.0%
32 16
16.0%
34 9
 
9.0%
39 3
 
3.0%
ValueCountFrequency (%)
39 3
 
3.0%
34 9
 
9.0%
32 16
16.0%
31 35
35.0%
25 5
 
5.0%
23 9
 
9.0%
11 23
23.0%

ctprvn_nm
Categorical

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기도
35 
서울특별시
23 
강원도
16 
인천광역시
9 
충청남도
9 
Other values (2)
8 

Length

Max length7
Median length3
Mean length3.95
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시
2nd row제주특별자치도
3rd row서울특별시
4th row서울특별시
5th row서울특별시

Common Values

ValueCountFrequency (%)
경기도 35
35.0%
서울특별시 23
23.0%
강원도 16
16.0%
인천광역시 9
 
9.0%
충청남도 9
 
9.0%
대전광역시 5
 
5.0%
제주특별자치도 3
 
3.0%

Length

2023-12-10T19:19:10.340996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:19:10.545151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 35
35.0%
서울특별시 23
23.0%
강원도 16
16.0%
인천광역시 9
 
9.0%
충청남도 9
 
9.0%
대전광역시 5
 
5.0%
제주특별자치도 3
 
3.0%

signgu_cd
Real number (ℝ)

Distinct83
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26177
Minimum11010
Maximum39020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:10.784409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11010
5-th percentile11020
Q123032.5
median31096.5
Q332030
95-th percentile34091.5
Maximum39020
Range28010
Interquartile range (IQR)8997.5

Descriptive statistics

Standard deviation8886.3719
Coefficient of variation (CV)0.33947251
Kurtosis-0.78021195
Mean26177
Median Absolute Deviation (MAD)1303.5
Skewness-0.88433566
Sum2617700
Variance78967605
MonotonicityNot monotonic
2023-12-10T19:19:11.025402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11010 4
 
4.0%
11020 3
 
3.0%
31240 3
 
3.0%
11220 3
 
3.0%
31070 3
 
3.0%
25030 3
 
3.0%
23010 2
 
2.0%
32030 2
 
2.0%
39010 2
 
2.0%
23040 2
 
2.0%
Other values (73) 73
73.0%
ValueCountFrequency (%)
11010 4
4.0%
11020 3
3.0%
11040 1
 
1.0%
11050 1
 
1.0%
11080 1
 
1.0%
11110 1
 
1.0%
11120 1
 
1.0%
11130 1
 
1.0%
11140 1
 
1.0%
11170 1
 
1.0%
ValueCountFrequency (%)
39020 1
1.0%
39010 2
2.0%
34340 1
1.0%
34310 1
1.0%
34080 1
1.0%
34070 1
1.0%
34060 1
1.0%
34050 1
1.0%
34030 1
1.0%
34020 1
1.0%

signgu_nm
Text

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:19:11.544109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.38
Min length2

Characters and Unicode

Total characters338
Distinct characters88
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

Unique71 ?
Unique (%)71.0%

Sample

1st row광진구
2nd row서귀포시
3rd row강남구
4th row구로구
5th row금천구
ValueCountFrequency (%)
중구 6
 
5.5%
종로구 4
 
3.6%
서구 4
 
3.6%
평택시 3
 
2.7%
서초구 3
 
2.7%
화성시 3
 
2.7%
수원시 2
 
1.8%
안양시 2
 
1.8%
고양시 2
 
1.8%
제주시 2
 
1.8%
Other values (77) 79
71.8%
2023-12-10T19:19:12.240743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 51
 
15.1%
구 49
 
14.5%
군 13
 
3.8%
천 12
 
3.6%
서 11
 
3.3%
양 10
 
3.0%
10
 
3.0%
주 9
 
2.7%
동 8
 
2.4%
안 8
 
2.4%
Other values (78) 157
46.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 328
97.0%
Space Separator 10
 
3.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 51
 
15.5%
구 49
 
14.9%
군 13
 
4.0%
천 12
 
3.7%
서 11
 
3.4%
양 10
 
3.0%
주 9
 
2.7%
동 8
 
2.4%
안 8
 
2.4%
성 8
 
2.4%
Other values (77) 149
45.4%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 328
97.0%
Common 10
 
3.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 51
 
15.5%
구 49
 
14.9%
군 13
 
4.0%
천 12
 
3.7%
서 11
 
3.4%
양 10
 
3.0%
주 9
 
2.7%
동 8
 
2.4%
안 8
 
2.4%
성 8
 
2.4%
Other values (77) 149
45.4%
Common
ValueCountFrequency (%)
10
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 328
97.0%
ASCII 10
 
3.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 51
 
15.5%
구 49
 
14.9%
군 13
 
4.0%
천 12
 
3.7%
서 11
 
3.4%
양 10
 
3.0%
주 9
 
2.7%
동 8
 
2.4%
안 8
 
2.4%
성 8
 
2.4%
Other values (77) 149
45.4%
ASCII
ValueCountFrequency (%)
10
100.0%

adstrd_cd
Real number (ℝ)

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2617751
Minimum1101063
Maximum3902054
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:12.449743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1101063
5-th percentile1102056.8
Q12303314
median3109713.5
Q33203056.8
95-th percentile3409199
Maximum3902054
Range2800991
Interquartile range (IQR)899742.75

Descriptive statistics

Standard deviation888630.44
Coefficient of variation (CV)0.33946332
Kurtosis-0.7801971
Mean2617751
Median Absolute Deviation (MAD)130297.5
Skewness-0.88434121
Sum2.617751 × 108
Variance7.8966406 × 1011
MonotonicityNot monotonic
2023-12-10T19:19:12.694737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2304066 2
 
2.0%
1122051 2
 
2.0%
2503069 2
 
2.0%
3120056 1
 
1.0%
3232011 1
 
1.0%
3201063 1
 
1.0%
3239011 1
 
1.0%
3202054 1
 
1.0%
3233011 1
 
1.0%
3127031 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
1101063 1
1.0%
1101064 1
1.0%
1101072 1
1.0%
1101073 1
1.0%
1102052 1
1.0%
1102057 1
1.0%
1102069 1
1.0%
1104055 1
1.0%
1105065 1
1.0%
1108083 1
1.0%
ValueCountFrequency (%)
3902054 1
1.0%
3901064 1
1.0%
3901052 1
1.0%
3434012 1
1.0%
3431011 1
1.0%
3408051 1
1.0%
3407033 1
1.0%
3406051 1
1.0%
3405051 1
1.0%
3403054 1
1.0%

adstrd_nm
Text

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:19:13.161260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.48
Min length2

Characters and Unicode

Total characters348
Distinct characters117
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)90.0%

Sample

1st row자양2동
2nd row천지동
3rd row삼성1동
4th row구로1동
5th row시흥1동
ValueCountFrequency (%)
중앙동 4
 
4.0%
만년동 2
 
2.0%
송도2동 2
 
2.0%
서초1동 2
 
2.0%
남양읍 1
 
1.0%
운정1동 1
 
1.0%
횡성읍 1
 
1.0%
효자1동 1
 
1.0%
인제읍 1
 
1.0%
명륜1동 1
 
1.0%
Other values (84) 84
84.0%
2023-12-10T19:19:13.859160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 85
24.4%
1 31
 
8.9%
읍 13
 
3.7%
천 8
 
2.3%
중 8
 
2.3%
2 6
 
1.7%
산 6
 
1.7%
양 5
 
1.4%
면 5
 
1.4%
정 5
 
1.4%
Other values (107) 176
50.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 303
87.1%
Decimal Number 43
 
12.4%
Other Punctuation 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 85
28.1%
읍 13
 
4.3%
천 8
 
2.6%
중 8
 
2.6%
산 6
 
2.0%
양 5
 
1.7%
면 5
 
1.7%
정 5
 
1.7%
성 5
 
1.7%
자 4
 
1.3%
Other values (100) 159
52.5%
Decimal Number
ValueCountFrequency (%)
1 31
72.1%
2 6
 
14.0%
3 3
 
7.0%
4 1
 
2.3%
6 1
 
2.3%
5 1
 
2.3%
Other Punctuation
ValueCountFrequency (%)
· 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 303
87.1%
Common 45
 
12.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 85
28.1%
읍 13
 
4.3%
천 8
 
2.6%
중 8
 
2.6%
산 6
 
2.0%
양 5
 
1.7%
면 5
 
1.7%
정 5
 
1.7%
성 5
 
1.7%
자 4
 
1.3%
Other values (100) 159
52.5%
Common
ValueCountFrequency (%)
1 31
68.9%
2 6
 
13.3%
3 3
 
6.7%
· 2
 
4.4%
4 1
 
2.2%
6 1
 
2.2%
5 1
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 303
87.1%
ASCII 43
 
12.4%
None 2
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 85
28.1%
읍 13
 
4.3%
천 8
 
2.6%
중 8
 
2.6%
산 6
 
2.0%
양 5
 
1.7%
면 5
 
1.7%
정 5
 
1.7%
성 5
 
1.7%
자 4
 
1.3%
Other values (100) 159
52.5%
ASCII
ValueCountFrequency (%)
1 31
72.1%
2 6
 
14.0%
3 3
 
7.0%
4 1
 
2.3%
6 1
 
2.3%
5 1
 
2.3%
None
ValueCountFrequency (%)
· 2
100.0%

lo_val
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.20754
Minimum126.4481
Maximum129.16026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:14.137682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.4481
5-th percentile126.62617
Q1126.89747
median127.03511
Q3127.29409
95-th percentile128.64021
Maximum129.16026
Range2.7121617
Interquartile range (IQR)0.39661815

Descriptive statistics

Standard deviation0.59930169
Coefficient of variation (CV)0.0047112122
Kurtosis2.7739235
Mean127.20754
Median Absolute Deviation (MAD)0.19980795
Skewness1.7710593
Sum12720.754
Variance0.35916251
MonotonicityNot monotonic
2023-12-10T19:19:14.404959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.070453 1
 
1.0%
127.435034 1
 
1.0%
127.9751991 1
 
1.0%
127.7293318 1
 
1.0%
128.164952 1
 
1.0%
127.9445539 1
 
1.0%
128.4681885 1
 
1.0%
127.211925 1
 
1.0%
126.9201636 1
 
1.0%
127.0650589 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
126.4481 1
1.0%
126.515522 1
1.0%
126.5349 1
1.0%
126.5507911 1
1.0%
126.614523 1
1.0%
126.6267833 1
1.0%
126.6294631 1
1.0%
126.6296708 1
1.0%
126.6349799 1
1.0%
126.6380371 1
1.0%
ValueCountFrequency (%)
129.1602617 1
1.0%
129.1151033 1
1.0%
128.9880281 1
1.0%
128.8954013 1
1.0%
128.8950537 1
1.0%
128.6268 1
1.0%
128.5884287 1
1.0%
128.4681885 1
1.0%
128.3909697 1
1.0%
128.164952 1
1.0%

la_val
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.244592
Minimum33.246333
Maximum38.245063
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:14.662005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.246333
5-th percentile36.187092
Q137.20018
median37.478192
Q337.583517
95-th percentile38.061487
Maximum38.245063
Range4.9987297
Interquartile range (IQR)0.38333743

Descriptive statistics

Standard deviation0.82253065
Coefficient of variation (CV)0.022084566
Kurtosis12.58319
Mean37.244592
Median Absolute Deviation (MAD)0.1641644
Skewness-3.2010367
Sum3724.4592
Variance0.67655667
MonotonicityNot monotonic
2023-12-10T19:19:14.925638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5374246 1
 
1.0%
37.272342 1
 
1.0%
37.4886377 1
 
1.0%
37.8726427 1
 
1.0%
38.0606741 1
 
1.0%
37.3354363 1
 
1.0%
37.1860192 1
 
1.0%
37.8960071 1
 
1.0%
36.990158 1
 
1.0%
37.0673793 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
33.2463334 1
1.0%
33.475061 1
1.0%
33.5044361 1
1.0%
36.0758285 1
1.0%
36.1120954 1
1.0%
36.1910394 1
1.0%
36.2729241 1
1.0%
36.3016877 1
1.0%
36.3224232 1
1.0%
36.333885 1
1.0%
ValueCountFrequency (%)
38.2450631 1
1.0%
38.213037 1
1.0%
38.1088132 1
1.0%
38.1087191 1
1.0%
38.076933 1
1.0%
38.0606741 1
1.0%
37.9052419 1
1.0%
37.8960071 1
1.0%
37.8726427 1
1.0%
37.8253879 1
1.0%

Sample

snbranch_nmarea_lcesntl_idinstt_nmfclty_nmrprsntv_nmrspofc_cldues_ctdues_unit_cnaddrzip_notel_nofax_nohmpg_addropnng_year_yysbscrb_yytotar_mgtotar_yytheat_l_cotheat_m_cotheat_s_cofield_prfplc_coehbll_nmeduspntd_nmcinema_nmctprvn_cdctprvn_nmsigngu_cdsigngu_nmadstrd_cdadstrd_nmlo_valla_val
01서울 인천서울<NA>(재)광진문화재단나루아트센터김경남사장500백만원서울특별시 광진구 능동로 760506502-2049-470002-2201-4800www.naruart.or.kr2005201618860.5m²<NA>601167<NA>YNN11서울특별시11050광진구1105065자양2동127.07045337.537425
1227호남 제주제주<NA>서귀포시청서귀포예술의전당양윤경시장400백만원제주특별자치도 서귀포시 태평로 27063594064-760-3341064-760-3349http://arts.seogwipo.go.kr201420148481.0m²<NA>802190124YYN39제주특별자치도39020서귀포시3902054천지동126.55079133.246333
23서울 인천서울<NA>(재)강남문화재단강남씨어터최병식이사장500백만원서울시 강남구 삼성로628(삼성동66) 강남문화재단 4층0608502-6712-054302-3774-0429www.gfac.or.kr2008201710794.7m²<NA>232138<NA>YNN11서울특별시11230강남구1123058삼성1동127.05216137.515918
34서울 인천서울<NA>(재)구로문화재단구로아트밸리예술극장이성이사장500백만원서울특별시 구로구 가마산로25길 9-240830102-2029-170002-2029-1706www.guroartsvalley.or.kr200820098799.8m²<NA>579153<NA>YYN11서울특별시11170구로구1117052구로1동126.88899237.496562
45서울 인천서울<NA>(재)금천문화재단금나래아트홀정병재이사장500백만원서울특별시 금천구 시흥대로73길 금천구청 내0861102-2627-295902-2627-2959http://www.gcfac.or.kr/200820136262.0m²<NA>566<NA><NA>YYN11서울특별시11180금천구1118057시흥1동126.90241237.440551
56서울 인천서울<NA>(재)두산연강재단두산아트센터장명호대표이사500백만원서울특별시 종로구 종로33길 15 두산아트센터0312902-708-500102-708-5010www.doosanartcenter.com/2007200918842.9m²<NA>620250<NA>YNN11서울특별시11010종로구1101063종로5·6가동127.00101637.571813
67서울 인천서울<NA>(재)마포문화재단(재)마포문화재단이국환대표이사업무대행500백만원서울특별시 마포구 대흥로20길 280413602-3274-862002-3274-8599http://www.mapoartcenter.or.kr2002200319317.0m²<NA>781180<NA>YYN11서울특별시11140마포구1114060대흥동126.94553637.549897
7228호남 제주제주<NA>제주시청제주아트센터고희범시장400백만원제주특별자치도 제주시 오남로 231690-162064-728-8953064-728-8959arts.jejusi.go.kr/201020109482.8m²1,184<NA><NA><NA>NNN39제주특별자치도39010제주시3901064오라동126.51552233.475061
89서울 인천서울<NA>(재)서초문화재단반포심산아트홀박동호대표이사500백만원서울시 서초구 사평대로 55(반포동) 심산기념문화센터 1층0655002-3477-246102-3476-5678www.seochocf.or.kr2011201713486.5m²<NA>405<NA><NA>YYY11서울특별시11220서초구1122056반포본동126.99053337.498862
910서울 인천서울<NA>(재)성동문화재단성동문화회관정원오이사장500백만원서울특별시 성동구 왕십리로 2810474402-2204-640102-2269-6499www.sdfac.or.kr200520153867.0m²<NA>520<NA><NA>YYN11서울특별시11040성동구1104055사근동127.03601837.559292
snbranch_nmarea_lcesntl_idinstt_nmfclty_nmrprsntv_nmrspofc_cldues_ctdues_unit_cnaddrzip_notel_nofax_nohmpg_addropnng_year_yysbscrb_yytotar_mgtotar_yytheat_l_cotheat_m_cotheat_s_cofield_prfplc_coehbll_nmeduspntd_nmcinema_nmctprvn_cdctprvn_nmsigngu_cdsigngu_nmadstrd_cdadstrd_nmlo_valla_val
9091대전 충청 세종대전<NA>서구청관저문예회관장종태구청장400백만원대전광역시 서구 관저동로 105번길 20(관저동, 1199번지)35378044-288-2790042-288-5911http://www.seogu.go.kr/art201220192023.5m²<NA><NA>254<NA>NNN25대전광역시25030서구2503072관저1동127.33952236.301688
9192대전 충청 세종충남<NA>(재)당진문화재단당진문예의전당문옥배관장500백만원충청남도 당진시 무수동2길 25-2131774041-350-2901~4041-352-6896http://www.dangjinart.kr/200520057585.6m²1,001300<NA>1,000YNN34충청남도34080당진시3408051당진1동126.63803736.890528
9293대전 충청 세종충남<NA>(재)천안문화재단천안예술의전당구만섭시장 권한대행500백만원충청남도 천안시 동남구 성남면 종합휴양지로 185331-7011566-0155041-900-7226http://www.cnac.or.kr2012200033755.0m²1,642443<NA>200YYN34충청남도34011천안시 동남구3401134성남면127.2252936.756081
9394대전 충청 세종충남<NA>계룡시청계룡문화예술의전당권용산소장400백만원충청남도 계룡시 엄사면 문화1로 1332829042-840-3707042-840-3709http://ac.gyeryong.go.kr201120129462.0m²<NA>718152<NA>YYN34충청남도34070계룡시3407033엄사면127.23414836.272924
9495대전 충청 세종충남<NA>공주시청공주문예회관김정섭시장400백만원충청남도 공주시 고마나루길532535041-840-2180041-840-2426http://acc.gongju.go.kr199020073825.0m²<NA>649119<NA>NNN34충청남도34020공주시3402058웅진동127.11171136.460169
9596대전 충청 세종충남<NA>금산군청금산다락원박진구원장300백만원충청남도 금산군 금산읍 금산로 1559 금산다락원312-912041-750-4424041-751-6523www.geumsan.go.kr/daragwon200420033657.0m²<NA>727250<NA>YNN34충청남도34310금산군3431011금산읍127.49162936.112095
9697대전 충청 세종충남<NA>논산시청논산문화예술회관황명선시장400백만원충청남도 논산시 시민로 270320-030041-746-5950041-746-5959www.arthall.nonsan.go.kr198520062615.0m²<NA>577180200YNN34충청남도34060논산시3406051취암동127.09513536.191039
9798대전 충청 세종충남<NA>보령시청보령문화예술회관김동일시장400백만원충청남도 보령시 성주산로 10133483041-930-3403041-930-3493www.brcn.go.kr/art.do200220026026.0m²<NA>798167<NA>YYN34충청남도34030보령시3403054대천4동126.61452336.333885
9899대전 충청 세종충남<NA>서산시청서산시문화회관맹정호시장400백만원충청남도 서산시 문화로 5431970041-661-8030041-661-8032http://www.seosan.go.kr/culture/index.do1990200710718.0m²<NA>601108<NA>YNN34충청남도34050서산시3405051부춘동126.448136.786117
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