Overview

Dataset statistics

Number of variables35
Number of observations100
Missing cells28
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.2 KiB
Average record size in memory299.3 B

Variable types

Text8
Categorical11
Numeric16

Alerts

lclas has constant value ""Constant
mlsfc has constant value ""Constant
lst_updt_dt has constant value ""Constant
data_orgn has constant value ""Constant
FILE_NAME has constant value ""Constant
base_ymd has constant value ""Constant
ctprvn_nm is highly imbalanced (65.6%)Imbalance
telno has 4 (4.0%) missing valuesMissing
hmpg has 24 (24.0%) missing valuesMissing
id has unique valuesUnique
fclt_name has unique valuesUnique
prfplc_id has unique valuesUnique
tot_advtk_cnt has unique valuesUnique
opn_cnt has 16 (16.0%) zerosZeros

Reproduction

Analysis started2023-12-10 09:59:22.834114
Analysis finished2023-12-10 09:59:24.135432
Duration1.3 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

id
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:59:24.467422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters1600
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
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 rowKC486PC19N000001
2nd rowKC486PC19N000002
3rd rowKC486PC19N000003
4th rowKC486PC19N000004
5th rowKC486PC19N000005
ValueCountFrequency (%)
kc486pc19n000001 1
 
1.0%
kc486pc19n000063 1
 
1.0%
kc486pc19n000074 1
 
1.0%
kc486pc19n000073 1
 
1.0%
kc486pc19n000072 1
 
1.0%
kc486pc19n000071 1
 
1.0%
kc486pc19n000070 1
 
1.0%
kc486pc19n000069 1
 
1.0%
kc486pc19n000068 1
 
1.0%
kc486pc19n000067 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T18:59:25.112088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 419
26.2%
C 200
12.5%
1 121
 
7.6%
4 120
 
7.5%
8 120
 
7.5%
6 120
 
7.5%
9 120
 
7.5%
K 100
 
6.2%
P 100
 
6.2%
N 100
 
6.2%
Other values (4) 80
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1100
68.8%
Uppercase Letter 500
31.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 419
38.1%
1 121
 
11.0%
4 120
 
10.9%
8 120
 
10.9%
6 120
 
10.9%
9 120
 
10.9%
3 20
 
1.8%
5 20
 
1.8%
7 20
 
1.8%
2 20
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
C 200
40.0%
K 100
20.0%
P 100
20.0%
N 100
20.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1100
68.8%
Latin 500
31.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 419
38.1%
1 121
 
11.0%
4 120
 
10.9%
8 120
 
10.9%
6 120
 
10.9%
9 120
 
10.9%
3 20
 
1.8%
5 20
 
1.8%
7 20
 
1.8%
2 20
 
1.8%
Latin
ValueCountFrequency (%)
C 200
40.0%
K 100
20.0%
P 100
20.0%
N 100
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 419
26.2%
C 200
12.5%
1 121
 
7.6%
4 120
 
7.5%
8 120
 
7.5%
6 120
 
7.5%
9 120
 
7.5%
K 100
 
6.2%
P 100
 
6.2%
N 100
 
6.2%
Other values (4) 80
 
5.0%

lclas
Categorical

CONSTANT 

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

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
문화시설 100
100.0%

Length

2023-12-10T18:59:25.393083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:59:25.666063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
문화시설 100
100.0%

mlsfc
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공연장 100
100.0%

Length

2023-12-10T18:59:25.828974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:59:26.034573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공연장 100
100.0%

fclt_name
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:59:26.468129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length22
Mean length9.4
Min length3

Characters and Unicode

Total characters940
Distinct characters219
Distinct categories7 ?
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 row순위아트홀1관 [대학로]
2nd row바탕골소극장
3rd row컬처스페이스 엔유(구. 쁘티첼 씨어터)
4th row콘텐츠박스(구. 르메이에르 씨어터)
5th row삼형제극장(환상극장)
ValueCountFrequency (%)
대학로 8
 
5.1%
3
 
1.9%
씨어터 2
 
1.3%
극장 2
 
1.3%
아트홀 2
 
1.3%
신세계 2
 
1.3%
국립극단 2
 
1.3%
문화홀 2
 
1.3%
국립극장 1
 
0.6%
드림시어터 1
 
0.6%
Other values (132) 132
84.1%
2023-12-10T18:59:27.335775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
57
 
6.1%
44
 
4.7%
42
 
4.5%
33
 
3.5%
33
 
3.5%
32
 
3.4%
25
 
2.7%
22
 
2.3%
21
 
2.2%
) 19
 
2.0%
Other values (209) 612
65.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 780
83.0%
Space Separator 57
 
6.1%
Close Punctuation 26
 
2.8%
Open Punctuation 26
 
2.8%
Uppercase Letter 24
 
2.6%
Other Punctuation 21
 
2.2%
Decimal Number 6
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
44
 
5.6%
42
 
5.4%
33
 
4.2%
33
 
4.2%
32
 
4.1%
25
 
3.2%
22
 
2.8%
21
 
2.7%
18
 
2.3%
17
 
2.2%
Other values (184) 493
63.2%
Uppercase Letter
ValueCountFrequency (%)
K 5
20.8%
T 3
12.5%
S 3
12.5%
C 3
12.5%
J 2
 
8.3%
G 2
 
8.3%
H 1
 
4.2%
N 1
 
4.2%
F 1
 
4.2%
D 1
 
4.2%
Other values (2) 2
 
8.3%
Decimal Number
ValueCountFrequency (%)
1 2
33.3%
8 1
16.7%
4 1
16.7%
2 1
16.7%
3 1
16.7%
Other Punctuation
ValueCountFrequency (%)
. 19
90.5%
, 1
 
4.8%
& 1
 
4.8%
Close Punctuation
ValueCountFrequency (%)
) 19
73.1%
] 7
 
26.9%
Open Punctuation
ValueCountFrequency (%)
( 19
73.1%
[ 7
 
26.9%
Space Separator
ValueCountFrequency (%)
57
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 780
83.0%
Common 136
 
14.5%
Latin 24
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
44
 
5.6%
42
 
5.4%
33
 
4.2%
33
 
4.2%
32
 
4.1%
25
 
3.2%
22
 
2.8%
21
 
2.7%
18
 
2.3%
17
 
2.2%
Other values (184) 493
63.2%
Common
ValueCountFrequency (%)
57
41.9%
) 19
 
14.0%
. 19
 
14.0%
( 19
 
14.0%
[ 7
 
5.1%
] 7
 
5.1%
1 2
 
1.5%
8 1
 
0.7%
, 1
 
0.7%
4 1
 
0.7%
Other values (3) 3
 
2.2%
Latin
ValueCountFrequency (%)
K 5
20.8%
T 3
12.5%
S 3
12.5%
C 3
12.5%
J 2
 
8.3%
G 2
 
8.3%
H 1
 
4.2%
N 1
 
4.2%
F 1
 
4.2%
D 1
 
4.2%
Other values (2) 2
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 780
83.0%
ASCII 160
 
17.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
57
35.6%
) 19
 
11.9%
. 19
 
11.9%
( 19
 
11.9%
[ 7
 
4.4%
] 7
 
4.4%
K 5
 
3.1%
T 3
 
1.9%
S 3
 
1.9%
C 3
 
1.9%
Other values (15) 18
 
11.2%
Hangul
ValueCountFrequency (%)
44
 
5.6%
42
 
5.4%
33
 
4.2%
33
 
4.2%
32
 
4.1%
25
 
3.2%
22
 
2.8%
21
 
2.7%
18
 
2.3%
17
 
2.2%
Other values (184) 493
63.2%

ctprvn_nm
Categorical

IMBALANCE 

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울특별시
82 
경기도
 
5
대구광역시
 
4
대전광역시
 
1
부산광역시
 
1
Other values (7)
 
7

Length

Max length7
Median length5
Mean length4.87
Min length3

Unique

Unique9 ?
Unique (%)9.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 82
82.0%
경기도 5
 
5.0%
대구광역시 4
 
4.0%
대전광역시 1
 
1.0%
부산광역시 1
 
1.0%
울산광역시 1
 
1.0%
강원도 1
 
1.0%
세종특별자치시 1
 
1.0%
충청남도 1
 
1.0%
전라북도 1
 
1.0%
Other values (2) 2
 
2.0%

Length

2023-12-10T18:59:27.658907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울특별시 82
82.0%
경기도 5
 
5.0%
대구광역시 4
 
4.0%
대전광역시 1
 
1.0%
부산광역시 1
 
1.0%
울산광역시 1
 
1.0%
강원도 1
 
1.0%
세종특별자치시 1
 
1.0%
충청남도 1
 
1.0%
전라북도 1
 
1.0%
Other values (2) 2
 
2.0%

sgnr_nm
Categorical

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
종로구
48 
중구
10 
구로구
 
4
서초구
 
4
강남구
 
4
Other values (24)
30 

Length

Max length7
Median length3
Mean length3.04
Min length2

Unique

Unique19 ?
Unique (%)19.0%

Sample

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

Common Values

ValueCountFrequency (%)
종로구 48
48.0%
중구 10
 
10.0%
구로구 4
 
4.0%
서초구 4
 
4.0%
강남구 4
 
4.0%
용산구 3
 
3.0%
양천구 2
 
2.0%
광진구 2
 
2.0%
남구 2
 
2.0%
마포구 2
 
2.0%
Other values (19) 19
 
19.0%

Length

2023-12-10T18:59:27.912500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
종로구 48
46.2%
중구 10
 
9.6%
구로구 4
 
3.8%
서초구 4
 
3.8%
강남구 4
 
3.8%
용산구 3
 
2.9%
남구 2
 
1.9%
마포구 2
 
1.9%
수원시 2
 
1.9%
광진구 2
 
1.9%
Other values (22) 23
22.1%

legaldong_cd
Real number (ℝ)

Distinct56
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5667452 × 109
Minimum1.1110119 × 109
Maximum4.7130124 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:28.161365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110119 × 109
5-th percentile1.1110168 × 109
Q11.1110168 × 109
median1.1140129 × 109
Q31.1650108 × 109
95-th percentile4.1194259 × 109
Maximum4.7130124 × 109
Range3.6020005 × 109
Interquartile range (IQR)53994000

Descriptive statistics

Standard deviation1.0021464 × 109
Coefficient of variation (CV)0.63963582
Kurtosis2.7310082
Mean1.5667452 × 109
Median Absolute Deviation (MAD)2996100
Skewness2.050661
Sum1.5667452 × 1011
Variance1.0042974 × 1018
MonotonicityNot monotonic
2023-12-10T18:59:28.428671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1111016800 32
32.0%
1111017100 5
 
5.0%
1111016900 4
 
4.0%
1153010200 3
 
3.0%
1165010800 3
 
3.0%
1147010200 2
 
2.0%
1144012000 2
 
2.0%
1114013100 1
 
1.0%
3611025028 1
 
1.0%
1117013100 1
 
1.0%
Other values (46) 46
46.0%
ValueCountFrequency (%)
1111011900 1
 
1.0%
1111013000 1
 
1.0%
1111016000 1
 
1.0%
1111016500 1
 
1.0%
1111016600 1
 
1.0%
1111016800 32
32.0%
1111016900 4
 
4.0%
1111017000 1
 
1.0%
1111017100 5
 
5.0%
1111017200 1
 
1.0%
ValueCountFrequency (%)
4713012400 1
1.0%
4518010500 1
1.0%
4476025028 1
1.0%
4211011200 1
1.0%
4127310100 1
1.0%
4119010900 1
1.0%
4113510700 1
1.0%
4111514100 1
1.0%
4111113000 1
1.0%
3611025028 1
1.0%
Distinct55
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:59:28.830935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.2
Min length2

Characters and Unicode

Total characters320
Distinct characters79
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 (%)47.0%

Sample

1st row동숭동
2nd row동숭동
3rd row동숭동
4th row동숭동
5th row동숭동
ValueCountFrequency (%)
동숭동 32
32.0%
명륜2가 5
 
5.0%
혜화동 4
 
4.0%
서초동 3
 
3.0%
구로동 3
 
3.0%
연지동 2
 
2.0%
서교동 2
 
2.0%
목동 2
 
2.0%
달동 1
 
1.0%
흥인동 1
 
1.0%
Other values (45) 45
45.0%
2023-12-10T18:59:29.777840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
122
38.1%
32
 
10.0%
18
 
5.6%
10
 
3.1%
2 10
 
3.1%
7
 
2.2%
6
 
1.9%
5
 
1.6%
4
 
1.2%
4
 
1.2%
Other values (69) 102
31.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 302
94.4%
Decimal Number 18
 
5.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
122
40.4%
32
 
10.6%
18
 
6.0%
10
 
3.3%
7
 
2.3%
6
 
2.0%
5
 
1.7%
4
 
1.3%
4
 
1.3%
4
 
1.3%
Other values (64) 90
29.8%
Decimal Number
ValueCountFrequency (%)
2 10
55.6%
1 4
 
22.2%
4 2
 
11.1%
6 1
 
5.6%
3 1
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 302
94.4%
Common 18
 
5.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
122
40.4%
32
 
10.6%
18
 
6.0%
10
 
3.3%
7
 
2.3%
6
 
2.0%
5
 
1.7%
4
 
1.3%
4
 
1.3%
4
 
1.3%
Other values (64) 90
29.8%
Common
ValueCountFrequency (%)
2 10
55.6%
1 4
 
22.2%
4 2
 
11.1%
6 1
 
5.6%
3 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 302
94.4%
ASCII 18
 
5.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
122
40.4%
32
 
10.6%
18
 
6.0%
10
 
3.3%
7
 
2.3%
6
 
2.0%
5
 
1.7%
4
 
1.3%
4
 
1.3%
4
 
1.3%
Other values (64) 90
29.8%
ASCII
ValueCountFrequency (%)
2 10
55.6%
1 4
 
22.2%
4 2
 
11.1%
6 1
 
5.6%
3 1
 
5.6%

adstrd_cd
Real number (ℝ)

Distinct49
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5667918 × 109
Minimum1.111053 × 109
Maximum4.7130621 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:30.103978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.111053 × 109
5-th percentile1.111064 × 109
Q11.111064 × 109
median1.114055 × 109
Q31.165056 × 109
95-th percentile4.1194883 × 109
Maximum4.7130621 × 109
Range3.6020091 × 109
Interquartile range (IQR)53992050

Descriptive statistics

Standard deviation1.0021448 × 109
Coefficient of variation (CV)0.63961582
Kurtosis2.7310069
Mean1.5667918 × 109
Median Absolute Deviation (MAD)2991000
Skewness2.0506608
Sum1.5667918 × 1011
Variance1.0042943 × 1018
MonotonicityNot monotonic
2023-12-10T18:59:30.368273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1111064000 34
34.0%
1111065000 11
 
11.0%
1114057000 3
 
3.0%
1114055000 3
 
3.0%
1153056000 2
 
2.0%
1147051000 2
 
2.0%
1165053000 2
 
2.0%
1144066000 2
 
2.0%
4476025000 1
 
1.0%
1114058000 1
 
1.0%
Other values (39) 39
39.0%
ValueCountFrequency (%)
1111053000 1
 
1.0%
1111061500 1
 
1.0%
1111063000 1
 
1.0%
1111064000 34
34.0%
1111065000 11
 
11.0%
1114052000 1
 
1.0%
1114055000 3
 
3.0%
1114057000 3
 
3.0%
1114058000 1
 
1.0%
1114061500 1
 
1.0%
ValueCountFrequency (%)
4713062100 1
1.0%
4518057000 1
1.0%
4476025000 1
1.0%
4211062000 1
1.0%
4127352500 1
1.0%
4119074400 1
1.0%
4113564000 1
1.0%
4111573000 1
1.0%
4111157100 1
1.0%
3611025000 1
1.0%

adstrd_nm
Categorical

Distinct49
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
이화동
34 
혜화동
11 
필동
 
3
명동
 
3
구로제5동
 
2
Other values (44)
47 

Length

Max length11
Median length3
Mean length3.3
Min length2

Unique

Unique41 ?
Unique (%)41.0%

Sample

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

Common Values

ValueCountFrequency (%)
이화동 34
34.0%
혜화동 11
 
11.0%
필동 3
 
3.0%
명동 3
 
3.0%
구로제5동 2
 
2.0%
목1동 2
 
2.0%
서초3동 2
 
2.0%
서교동 2
 
2.0%
신도림동 1
 
1.0%
상일동 1
 
1.0%
Other values (39) 39
39.0%

Length

2023-12-10T18:59:30.708058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
이화동 34
34.0%
혜화동 11
 
11.0%
필동 3
 
3.0%
명동 3
 
3.0%
구로제5동 2
 
2.0%
목1동 2
 
2.0%
서초3동 2
 
2.0%
서교동 2
 
2.0%
장충동 1
 
1.0%
야탑3동 1
 
1.0%
Other values (39) 39
39.0%

rdnmaddr_cd
Real number (ℝ)

Distinct65
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5667669 × 1011
Minimum1.1110201 × 1011
Maximum4.7130331 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:30.973902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110201 × 1011
5-th percentile1.111031 × 1011
Q11.111041 × 1011
median1.114031 × 1011
Q31.1650416 × 1011
95-th percentile4.1194468 × 1011
Maximum4.7130331 × 1011
Range3.602013 × 1011
Interquartile range (IQR)5.4000633 × 109

Descriptive statistics

Standard deviation1.0021453 × 1011
Coefficient of variation (CV)0.63962631
Kurtosis2.7310094
Mean1.5667669 × 1011
Median Absolute Deviation (MAD)3.0000099 × 108
Skewness2.0506613
Sum1.5667669 × 1013
Variance1.0042952 × 1022
MonotonicityNot monotonic
2023-12-10T18:59:31.258986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111104100075 10
 
10.0%
111104100035 9
 
9.0%
111104100043 6
 
6.0%
111104100245 5
 
5.0%
111104100033 4
 
4.0%
111103005008 3
 
3.0%
111404103165 2
 
2.0%
116502000003 2
 
2.0%
111103100008 2
 
2.0%
111103100002 2
 
2.0%
Other values (55) 55
55.0%
ValueCountFrequency (%)
111102005001 1
 
1.0%
111103005008 3
 
3.0%
111103100002 2
 
2.0%
111103100008 2
 
2.0%
111103100010 1
 
1.0%
111103100022 1
 
1.0%
111104100033 4
4.0%
111104100034 1
 
1.0%
111104100035 9
9.0%
111104100036 1
 
1.0%
ValueCountFrequency (%)
471303305039 1
1.0%
451803270061 1
1.0%
447603259024 1
1.0%
421103218037 1
1.0%
412733190080 1
1.0%
411903184016 1
1.0%
411352000005 1
1.0%
411154328473 1
1.0%
411113174010 1
1.0%
361104574153 1
1.0%
Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:59:32.007835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length28
Mean length24.05
Min length18

Characters and Unicode

Total characters2405
Distinct characters162
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)98.0%

Sample

1st row서울특별시 종로구 대학로10길 11 (동숭동)
2nd row서울특별시 종로구 대학로10길 5 (동숭동)
3rd row서울특별시 종로구 대학로12길 73 (동숭동)
4th row서울특별시 종로구 동숭길 55 (동숭동)
5th row서울특별시 종로구 이화장길 72 (동숭동)
ValueCountFrequency (%)
서울특별시 82
 
16.3%
종로구 48
 
9.5%
동숭동 32
 
6.3%
동숭길 10
 
2.0%
중구 10
 
2.0%
대학로12길 9
 
1.8%
대학로8가길 6
 
1.2%
이화장길 5
 
1.0%
명륜2가 5
 
1.0%
경기도 5
 
1.0%
Other values (239) 292
57.9%
2023-12-10T18:59:33.116084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
404
 
16.8%
147
 
6.1%
138
 
5.7%
106
 
4.4%
100
 
4.2%
) 98
 
4.1%
( 98
 
4.1%
95
 
4.0%
83
 
3.5%
83
 
3.5%
Other values (152) 1053
43.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1472
61.2%
Space Separator 404
 
16.8%
Decimal Number 325
 
13.5%
Close Punctuation 98
 
4.1%
Open Punctuation 98
 
4.1%
Dash Punctuation 7
 
0.3%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
147
 
10.0%
138
 
9.4%
106
 
7.2%
100
 
6.8%
95
 
6.5%
83
 
5.6%
83
 
5.6%
83
 
5.6%
55
 
3.7%
52
 
3.5%
Other values (137) 530
36.0%
Decimal Number
ValueCountFrequency (%)
1 64
19.7%
2 61
18.8%
3 38
11.7%
5 30
9.2%
4 27
8.3%
7 23
 
7.1%
6 23
 
7.1%
0 23
 
7.1%
8 21
 
6.5%
9 15
 
4.6%
Space Separator
ValueCountFrequency (%)
404
100.0%
Close Punctuation
ValueCountFrequency (%)
) 98
100.0%
Open Punctuation
ValueCountFrequency (%)
( 98
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1472
61.2%
Common 933
38.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
147
 
10.0%
138
 
9.4%
106
 
7.2%
100
 
6.8%
95
 
6.5%
83
 
5.6%
83
 
5.6%
83
 
5.6%
55
 
3.7%
52
 
3.5%
Other values (137) 530
36.0%
Common
ValueCountFrequency (%)
404
43.3%
) 98
 
10.5%
( 98
 
10.5%
1 64
 
6.9%
2 61
 
6.5%
3 38
 
4.1%
5 30
 
3.2%
4 27
 
2.9%
7 23
 
2.5%
6 23
 
2.5%
Other values (5) 67
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1472
61.2%
ASCII 933
38.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
404
43.3%
) 98
 
10.5%
( 98
 
10.5%
1 64
 
6.9%
2 61
 
6.5%
3 38
 
4.1%
5 30
 
3.2%
4 27
 
2.9%
7 23
 
2.5%
6 23
 
2.5%
Other values (5) 67
 
7.2%
Hangul
ValueCountFrequency (%)
147
 
10.0%
138
 
9.4%
106
 
7.2%
100
 
6.8%
95
 
6.5%
83
 
5.6%
83
 
5.6%
83
 
5.6%
55
 
3.7%
52
 
3.5%
Other values (137) 530
36.0%

zip_cd
Real number (ℝ)

Distinct66
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9167.74
Minimum2864
Maximum56161
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:33.390836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2864
5-th percentile3075
Q13086
median4045.5
Q36903
95-th percentile41602.85
Maximum56161
Range53297
Interquartile range (IQR)3817

Descriptive statistics

Standard deviation12140.969
Coefficient of variation (CV)1.3243143
Kurtosis4.380873
Mean9167.74
Median Absolute Deviation (MAD)961.5
Skewness2.3308328
Sum916774
Variance1.4740313 × 108
MonotonicityNot monotonic
2023-12-10T18:59:33.645931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3086 25
25.0%
3084 5
 
5.0%
3088 4
 
4.0%
6757 2
 
2.0%
3077 2
 
2.0%
3075 2
 
2.0%
13514 1
 
1.0%
2864 1
 
1.0%
4569 1
 
1.0%
4621 1
 
1.0%
Other values (56) 56
56.0%
ValueCountFrequency (%)
2864 1
 
1.0%
3066 1
 
1.0%
3068 1
 
1.0%
3074 1
 
1.0%
3075 2
 
2.0%
3077 2
 
2.0%
3079 1
 
1.0%
3082 1
 
1.0%
3084 5
5.0%
3085 1
 
1.0%
ValueCountFrequency (%)
56161 1
1.0%
48058 1
1.0%
44695 1
1.0%
42409 1
1.0%
41942 1
1.0%
41585 1
1.0%
41229 1
1.0%
38089 1
1.0%
35204 1
1.0%
33156 1
1.0%
Distinct78
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:59:34.342626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters800
Distinct characters15
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

Unique68 ?
Unique (%)68.0%

Sample

1st row다사560537
2nd row다사560537
3rd row다사562536
4th row다사562536
5th row다사562533
ValueCountFrequency (%)
다사561537 6
 
6.0%
다사562536 4
 
4.0%
다사560537 4
 
4.0%
다사562535 3
 
3.0%
다사562533 3
 
3.0%
다사561538 3
 
3.0%
다사560538 3
 
3.0%
다사561539 2
 
2.0%
다사560539 2
 
2.0%
다사558540 2
 
2.0%
Other values (68) 68
68.0%
2023-12-10T18:59:35.229831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 172
21.5%
92
11.5%
89
11.1%
6 75
9.4%
3 68
 
8.5%
4 66
 
8.2%
1 43
 
5.4%
7 39
 
4.9%
0 37
 
4.6%
8 35
 
4.4%
Other values (5) 84
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 600
75.0%
Other Letter 200
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 172
28.7%
6 75
12.5%
3 68
 
11.3%
4 66
 
11.0%
1 43
 
7.2%
7 39
 
6.5%
0 37
 
6.2%
8 35
 
5.8%
9 34
 
5.7%
2 31
 
5.2%
Other Letter
ValueCountFrequency (%)
92
46.0%
89
44.5%
11
 
5.5%
5
 
2.5%
3
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 600
75.0%
Hangul 200
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 172
28.7%
6 75
12.5%
3 68
 
11.3%
4 66
 
11.0%
1 43
 
7.2%
7 39
 
6.5%
0 37
 
6.2%
8 35
 
5.8%
9 34
 
5.7%
2 31
 
5.2%
Hangul
ValueCountFrequency (%)
92
46.0%
89
44.5%
11
 
5.5%
5
 
2.5%
3
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 600
75.0%
Hangul 200
 
25.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 172
28.7%
6 75
12.5%
3 68
 
11.3%
4 66
 
11.0%
1 43
 
7.2%
7 39
 
6.5%
0 37
 
6.2%
8 35
 
5.8%
9 34
 
5.7%
2 31
 
5.2%
Hangul
ValueCountFrequency (%)
92
46.0%
89
44.5%
11
 
5.5%
5
 
2.5%
3
 
1.5%

x_cd
Real number (ℝ)

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.370021
Minimum35.168931
Maximum37.873274
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:35.516954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.168931
5-th percentile35.866676
Q137.508292
median37.568884
Q337.58228
95-th percentile37.584738
Maximum37.873274
Range2.704343
Interquartile range (IQR)0.07398775

Descriptive statistics

Standard deviation0.54001411
Coefficient of variation (CV)0.014450463
Kurtosis6.1956845
Mean37.370021
Median Absolute Deviation (MAD)0.0150185
Skewness-2.7219309
Sum3737.0021
Variance0.29161524
MonotonicityNot monotonic
2023-12-10T18:59:36.207412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.58186 2
 
2.0%
37.581006 2
 
2.0%
37.571818 1
 
1.0%
37.582986 1
 
1.0%
37.565951 1
 
1.0%
37.583671 1
 
1.0%
37.584886 1
 
1.0%
37.552932 1
 
1.0%
37.582276 1
 
1.0%
37.583781 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
35.168931 1
1.0%
35.537351 1
1.0%
35.570561 1
1.0%
35.855196 1
1.0%
35.862288 1
1.0%
35.866907 1
1.0%
35.877939 1
1.0%
35.883454 1
1.0%
36.275971 1
1.0%
36.36682 1
1.0%
ValueCountFrequency (%)
37.873274 1
1.0%
37.590535 1
1.0%
37.587125 1
1.0%
37.586268 1
1.0%
37.584886 1
1.0%
37.58473 1
1.0%
37.584578 1
1.0%
37.583991 1
1.0%
37.583915 1
1.0%
37.583891 1
1.0%

y_cd
Real number (ℝ)

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.13403
Minimum126.72566
Maximum129.3261
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:36.609724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.72566
5-th percentile126.87136
Q1126.99744
median127.00306
Q3127.00947
95-th percentile128.59246
Maximum129.3261
Range2.600448
Interquartile range (IQR)0.01203675

Descriptive statistics

Standard deviation0.49660427
Coefficient of variation (CV)0.0039061475
Kurtosis10.348494
Mean127.13403
Median Absolute Deviation (MAD)0.006678
Skewness3.3458557
Sum12713.403
Variance0.2466158
MonotonicityNot monotonic
2023-12-10T18:59:36.961404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.003964 2
 
2.0%
127.000985 1
 
1.0%
127.002636 1
 
1.0%
127.014783 1
 
1.0%
127.002548 1
 
1.0%
127.000278 1
 
1.0%
126.99989 1
 
1.0%
127.002759 1
 
1.0%
126.998549 1
 
1.0%
127.003549 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
126.725655 1
1.0%
126.744789 1
1.0%
126.822573 1
1.0%
126.849022 1
1.0%
126.871296 1
1.0%
126.871365 1
1.0%
126.886648 1
1.0%
126.888978 1
1.0%
126.889229 1
1.0%
126.890258 1
1.0%
ValueCountFrequency (%)
129.326103 1
1.0%
129.206626 1
1.0%
129.129839 1
1.0%
128.629223 1
1.0%
128.597267 1
1.0%
128.592208 1
1.0%
128.58324 1
1.0%
127.727786 1
1.0%
127.384955 1
1.0%
127.287582 1
1.0%

x_utmk_cd
Real number (ℝ)

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean967850.33
Minimum931566
Maximum1165548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:37.236004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum931566
5-th percentile944455.75
Q1955625
median956125.5
Q3956645.5
95-th percentile1098609.9
Maximum1165548
Range233982
Interquartile range (IQR)1020.5

Descriptive statistics

Standard deviation44644.424
Coefficient of variation (CV)0.046127405
Kurtosis10.432169
Mean967850.33
Median Absolute Deviation (MAD)559.5
Skewness3.3573542
Sum96785033
Variance1.9931246 × 109
MonotonicityNot monotonic
2023-12-10T18:59:37.522158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
956080 2
 
2.0%
956204 2
 
2.0%
956259 2
 
2.0%
955935 1
 
1.0%
956087 1
 
1.0%
957150 1
 
1.0%
955880 1
 
1.0%
955827 1
 
1.0%
956098 1
 
1.0%
955727 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
931566 1
1.0%
933247 1
1.0%
939979 1
1.0%
941013 1
1.0%
944451 1
1.0%
944456 1
1.0%
945779 1
1.0%
945990 1
1.0%
946021 1
1.0%
946111 1
1.0%
ValueCountFrequency (%)
1165548 1
1.0%
1154089 1
1.0%
1148427 1
1.0%
1101934 1
1.0%
1099063 1
1.0%
1098586 1
1.0%
1097811 1
1.0%
1020034 1
1.0%
989680 1
1.0%
981002 1
1.0%

y_utmk_cd
Real number (ℝ)

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1930285.1
Minimum1687178
Maximum1985964
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:37.911331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1687178
5-th percentile1764190.1
Q11945621.2
median1952291.5
Q31953770.5
95-th percentile1954044.9
Maximum1985964
Range298786
Interquartile range (IQR)8149.25

Descriptive statistics

Standard deviation59724.646
Coefficient of variation (CV)0.030940841
Kurtosis6.1756295
Mean1930285.1
Median Absolute Deviation (MAD)1659.5
Skewness-2.7190522
Sum1.9302852 × 108
Variance3.5670334 × 109
MonotonicityNot monotonic
2023-12-10T18:59:38.207592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1953724 2
 
2.0%
1953629 2
 
2.0%
1953770 2
 
2.0%
1952611 1
 
1.0%
1953849 1
 
1.0%
1951954 1
 
1.0%
1953925 1
 
1.0%
1954061 1
 
1.0%
1950516 1
 
1.0%
1953939 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
1687178 1
1.0%
1728355 1
1.0%
1730699 1
1.0%
1762614 1
1.0%
1763927 1
1.0%
1764204 1
1.0%
1765184 1
1.0%
1765758 1
1.0%
1808900 1
1.0%
1818826 1
1.0%
ValueCountFrequency (%)
1985964 1
1.0%
1954688 1
1.0%
1954310 1
1.0%
1954210 1
1.0%
1954061 1
1.0%
1954044 1
1.0%
1954026 1
1.0%
1953961 1
1.0%
1953953 1
1.0%
1953949 1
1.0%

prfplc_id
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:59:38.828326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters800
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
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 rowFC001228
2nd rowFC001174
3rd rowFC001459
4th rowFC000841
5th rowFC001119
ValueCountFrequency (%)
fc001228 1
 
1.0%
fc000629 1
 
1.0%
fc001091 1
 
1.0%
fc000014 1
 
1.0%
fc001202 1
 
1.0%
fc000773 1
 
1.0%
fc000003 1
 
1.0%
fc001218 1
 
1.0%
fc001275 1
 
1.0%
fc001384 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T18:59:39.718614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 309
38.6%
F 100
 
12.5%
C 100
 
12.5%
1 86
 
10.8%
2 36
 
4.5%
3 33
 
4.1%
8 30
 
3.8%
7 25
 
3.1%
9 24
 
3.0%
5 22
 
2.8%
Other values (2) 35
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 600
75.0%
Uppercase Letter 200
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 309
51.5%
1 86
 
14.3%
2 36
 
6.0%
3 33
 
5.5%
8 30
 
5.0%
7 25
 
4.2%
9 24
 
4.0%
5 22
 
3.7%
6 18
 
3.0%
4 17
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
F 100
50.0%
C 100
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 600
75.0%
Latin 200
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 309
51.5%
1 86
 
14.3%
2 36
 
6.0%
3 33
 
5.5%
8 30
 
5.0%
7 25
 
4.2%
9 24
 
4.0%
5 22
 
3.7%
6 18
 
3.0%
4 17
 
2.8%
Latin
ValueCountFrequency (%)
F 100
50.0%
C 100
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 309
38.6%
F 100
 
12.5%
C 100
 
12.5%
1 86
 
10.8%
2 36
 
4.5%
3 33
 
4.1%
8 30
 
3.8%
7 25
 
3.1%
9 24
 
3.0%
5 22
 
2.8%
Other values (2) 35
 
4.4%

opn_year
Real number (ℝ)

Distinct34
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.01
Minimum1951
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:40.268234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1951
5-th percentile1980.95
Q12003.75
median2009
Q32014
95-th percentile2017
Maximum2018
Range67
Interquartile range (IQR)10.25

Descriptive statistics

Standard deviation11.353365
Coefficient of variation (CV)0.005659675
Kurtosis5.7590462
Mean2006.01
Median Absolute Deviation (MAD)5
Skewness-2.0909033
Sum200601
Variance128.89889
MonotonicityNot monotonic
2023-12-10T18:59:40.517597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
2009 10
 
10.0%
2016 10
 
10.0%
2005 7
 
7.0%
2014 6
 
6.0%
2010 6
 
6.0%
2011 5
 
5.0%
2017 5
 
5.0%
2008 5
 
5.0%
2003 5
 
5.0%
2007 4
 
4.0%
Other values (24) 37
37.0%
ValueCountFrequency (%)
1951 1
1.0%
1973 1
1.0%
1975 1
1.0%
1978 1
1.0%
1980 1
1.0%
1981 1
1.0%
1985 1
1.0%
1986 1
1.0%
1988 1
1.0%
1989 1
1.0%
ValueCountFrequency (%)
2018 3
 
3.0%
2017 5
5.0%
2016 10
10.0%
2015 2
 
2.0%
2014 6
6.0%
2013 4
 
4.0%
2012 2
 
2.0%
2011 5
5.0%
2010 6
6.0%
2009 10
10.0%

chartr
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
민간(대학로)
43 
민간(대학로 외)
25 
공공(문예회관)
17 
국립
공공(기타)

Length

Max length9
Median length8
Mean length7.16
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row민간(대학로)
2nd row민간(대학로)
3rd row민간(대학로)
4th row민간(대학로)
5th row민간(대학로)

Common Values

ValueCountFrequency (%)
민간(대학로) 43
43.0%
민간(대학로 외) 25
25.0%
공공(문예회관) 17
 
17.0%
국립 9
 
9.0%
공공(기타) 6
 
6.0%

Length

2023-12-10T18:59:40.922216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:59:41.166492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
민간(대학로 68
54.4%
25
 
20.0%
공공(문예회관 17
 
13.6%
국립 9
 
7.2%
공공(기타 6
 
4.8%

seats
Real number (ℝ)

Distinct89
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2344.76
Minimum20
Maximum163246
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:41.467223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile75.2
Q1154
median376.5
Q3968.5
95-th percentile2815
Maximum163246
Range163226
Interquartile range (IQR)814.5

Descriptive statistics

Standard deviation16279.458
Coefficient of variation (CV)6.9429103
Kurtosis99.322737
Mean2344.76
Median Absolute Deviation (MAD)276.5
Skewness9.9501908
Sum234476
Variance2.6502076 × 108
MonotonicityNot monotonic
2023-12-10T18:59:41.751627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 4
 
4.0%
450 2
 
2.0%
270 2
 
2.0%
124 2
 
2.0%
302 2
 
2.0%
1100 2
 
2.0%
30 2
 
2.0%
154 2
 
2.0%
200 2
 
2.0%
150 1
 
1.0%
Other values (79) 79
79.0%
ValueCountFrequency (%)
20 1
1.0%
30 2
2.0%
50 1
1.0%
60 1
1.0%
76 1
1.0%
80 1
1.0%
83 1
1.0%
90 1
1.0%
97 1
1.0%
99 1
1.0%
ValueCountFrequency (%)
163246 1
1.0%
5098 1
1.0%
5081 1
1.0%
3414 1
1.0%
3062 1
1.0%
2802 1
1.0%
2286 1
1.0%
2194 1
1.0%
2189 1
1.0%
1998 1
1.0%

prfplc_cnt
Real number (ℝ)

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.82
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:41.985658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4.05
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3734863
Coefficient of variation (CV)0.75466281
Kurtosis5.0292637
Mean1.82
Median Absolute Deviation (MAD)0
Skewness2.0981354
Sum182
Variance1.8864646
MonotonicityNot monotonic
2023-12-10T18:59:42.195398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 63
63.0%
2 14
 
14.0%
3 11
 
11.0%
4 7
 
7.0%
5 3
 
3.0%
7 1
 
1.0%
8 1
 
1.0%
ValueCountFrequency (%)
1 63
63.0%
2 14
 
14.0%
3 11
 
11.0%
4 7
 
7.0%
5 3
 
3.0%
7 1
 
1.0%
8 1
 
1.0%
ValueCountFrequency (%)
8 1
 
1.0%
7 1
 
1.0%
5 3
 
3.0%
4 7
 
7.0%
3 11
 
11.0%
2 14
 
14.0%
1 63
63.0%

telno
Text

MISSING 

Distinct93
Distinct (%)96.9%
Missing4
Missing (%)4.0%
Memory size932.0 B
2023-12-10T18:59:42.872392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.6875
Min length11

Characters and Unicode

Total characters1122
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 (%)93.8%

Sample

1st row02-766-7667
2nd row02-764-8760
3rd row02-766-7667
4th row02-747-2232
5th row02-6326-1333
ValueCountFrequency (%)
02-766-7667 2
 
2.1%
00-1644-2003 2
 
2.1%
02-739-8288 2
 
2.1%
044-301-3523 1
 
1.0%
032-500-2000 1
 
1.0%
02-747-5773 1
 
1.0%
070-4106-8889 1
 
1.0%
02-2230-6601 1
 
1.0%
02-779-1595 1
 
1.0%
02-764-7304 1
 
1.0%
Other values (83) 83
86.5%
2023-12-10T18:59:43.695969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 242
21.6%
- 192
17.1%
2 140
12.5%
7 87
 
7.8%
3 84
 
7.5%
1 84
 
7.5%
6 74
 
6.6%
4 66
 
5.9%
8 61
 
5.4%
5 61
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 930
82.9%
Dash Punctuation 192
 
17.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 242
26.0%
2 140
15.1%
7 87
 
9.4%
3 84
 
9.0%
1 84
 
9.0%
6 74
 
8.0%
4 66
 
7.1%
8 61
 
6.6%
5 61
 
6.6%
9 31
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1122
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 242
21.6%
- 192
17.1%
2 140
12.5%
7 87
 
7.8%
3 84
 
7.5%
1 84
 
7.5%
6 74
 
6.6%
4 66
 
5.9%
8 61
 
5.4%
5 61
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1122
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 242
21.6%
- 192
17.1%
2 140
12.5%
7 87
 
7.8%
3 84
 
7.5%
1 84
 
7.5%
6 74
 
6.6%
4 66
 
5.9%
8 61
 
5.4%
5 61
 
5.4%

hmpg
Text

MISSING 

Distinct76
Distinct (%)100.0%
Missing24
Missing (%)24.0%
Memory size932.0 B
2023-12-10T18:59:44.145816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length107
Median length46
Mean length34.039474
Min length13

Characters and Unicode

Total characters2587
Distinct characters64
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)100.0%

Sample

1st rowhttp://cafe.naver.com/teum2013
2nd rowhttp://www.jeongdong.or.kr
3rd rowhttp://www.theateryong.or.kr
4th rowhttp://www.hongikartcenter.com/main.do
5th rowhttp://cafe.naver.com/primearthall/
ValueCountFrequency (%)
http://www.ntck.or.kr 2
 
2.6%
http://www.guroartsvalley.or.kr 1
 
1.3%
https://tcsanwoollim.modoo.at 1
 
1.3%
http://www.mhicon.com/board/bbs/board.php?bo_table=art 1
 
1.3%
http://www.ntok.go.kr 1
 
1.3%
http://club.cyworld.com/dtc-gep 1
 
1.3%
https://www.facebook.com/sharthall 1
 
1.3%
http://www.doosanartcenter.com 1
 
1.3%
http://www.ckarthall.com/default 1
 
1.3%
http://www.ktchamberhall.com 1
 
1.3%
Other values (65) 65
85.5%
2023-12-10T18:59:44.828651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 252
 
9.7%
/ 243
 
9.4%
. 208
 
8.0%
w 170
 
6.6%
r 160
 
6.2%
o 149
 
5.8%
a 143
 
5.5%
h 117
 
4.5%
e 117
 
4.5%
c 108
 
4.2%
Other values (54) 920
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1939
75.0%
Other Punctuation 538
 
20.8%
Decimal Number 51
 
2.0%
Uppercase Letter 32
 
1.2%
Math Symbol 9
 
0.3%
Dash Punctuation 8
 
0.3%
Connector Punctuation 5
 
0.2%
Other Letter 5
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 252
13.0%
w 170
 
8.8%
r 160
 
8.3%
o 149
 
7.7%
a 143
 
7.4%
h 117
 
6.0%
e 117
 
6.0%
c 108
 
5.6%
p 107
 
5.5%
n 76
 
3.9%
Other values (16) 540
27.8%
Uppercase Letter
ValueCountFrequency (%)
T 4
12.5%
I 4
12.5%
D 4
12.5%
C 3
9.4%
S 3
9.4%
M 3
9.4%
U 2
6.2%
P 2
6.2%
F 2
6.2%
N 1
 
3.1%
Other values (4) 4
12.5%
Decimal Number
ValueCountFrequency (%)
0 16
31.4%
1 9
17.6%
6 6
 
11.8%
2 6
 
11.8%
3 5
 
9.8%
4 3
 
5.9%
5 3
 
5.9%
9 1
 
2.0%
8 1
 
2.0%
7 1
 
2.0%
Other Punctuation
ValueCountFrequency (%)
/ 243
45.2%
. 208
38.7%
: 74
 
13.8%
? 5
 
0.9%
% 4
 
0.7%
& 4
 
0.7%
Other Letter
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Math Symbol
ValueCountFrequency (%)
= 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1971
76.2%
Common 611
 
23.6%
Hangul 5
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 252
12.8%
w 170
 
8.6%
r 160
 
8.1%
o 149
 
7.6%
a 143
 
7.3%
h 117
 
5.9%
e 117
 
5.9%
c 108
 
5.5%
p 107
 
5.4%
n 76
 
3.9%
Other values (30) 572
29.0%
Common
ValueCountFrequency (%)
/ 243
39.8%
. 208
34.0%
: 74
 
12.1%
0 16
 
2.6%
1 9
 
1.5%
= 9
 
1.5%
- 8
 
1.3%
6 6
 
1.0%
2 6
 
1.0%
_ 5
 
0.8%
Other values (9) 27
 
4.4%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2582
99.8%
Hangul 5
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 252
 
9.8%
/ 243
 
9.4%
. 208
 
8.1%
w 170
 
6.6%
r 160
 
6.2%
o 149
 
5.8%
a 143
 
5.5%
h 117
 
4.5%
e 117
 
4.5%
c 108
 
4.2%
Other values (49) 915
35.4%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

seat_capa
Real number (ℝ)

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean730.53
Minimum20
Maximum7009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:45.143442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile75.2
Q1154
median336.5
Q3893
95-th percentile2669
Maximum7009
Range6989
Interquartile range (IQR)739

Descriptive statistics

Standard deviation1024.4438
Coefficient of variation (CV)1.4023296
Kurtosis15.214836
Mean730.53
Median Absolute Deviation (MAD)236.5
Skewness3.3659386
Sum73053
Variance1049485.2
MonotonicityNot monotonic
2023-12-10T18:59:45.431684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 4
 
4.0%
270 3
 
3.0%
450 2
 
2.0%
1100 2
 
2.0%
200 2
 
2.0%
30 2
 
2.0%
302 2
 
2.0%
154 2
 
2.0%
124 2
 
2.0%
50 1
 
1.0%
Other values (78) 78
78.0%
ValueCountFrequency (%)
20 1
1.0%
30 2
2.0%
50 1
1.0%
60 1
1.0%
76 1
1.0%
80 1
1.0%
83 1
1.0%
90 1
1.0%
97 1
1.0%
99 1
1.0%
ValueCountFrequency (%)
7009 1
1.0%
4374 1
1.0%
3414 1
1.0%
3288 1
1.0%
2802 1
1.0%
2662 1
1.0%
2194 1
1.0%
2189 1
1.0%
1998 1
1.0%
1829 1
1.0%

prf_cnt
Real number (ℝ)

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.44
Minimum1
Maximum587
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:45.747758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q311
95-th percentile72.25
Maximum587
Range586
Interquartile range (IQR)8

Descriptive statistics

Standard deviation72.926464
Coefficient of variation (CV)3.2498424
Kurtosis41.558611
Mean22.44
Median Absolute Deviation (MAD)3
Skewness6.1176357
Sum2244
Variance5318.2691
MonotonicityNot monotonic
2023-12-10T18:59:45.998276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 15
15.0%
4 13
13.0%
7 10
10.0%
3 9
9.0%
2 9
9.0%
10 7
 
7.0%
5 5
 
5.0%
8 4
 
4.0%
11 3
 
3.0%
12 3
 
3.0%
Other values (19) 22
22.0%
ValueCountFrequency (%)
1 15
15.0%
2 9
9.0%
3 9
9.0%
4 13
13.0%
5 5
 
5.0%
6 1
 
1.0%
7 10
10.0%
8 4
 
4.0%
9 1
 
1.0%
10 7
7.0%
ValueCountFrequency (%)
587 1
1.0%
372 1
1.0%
180 1
1.0%
168 1
1.0%
115 1
1.0%
70 1
1.0%
69 1
1.0%
51 1
1.0%
27 1
1.0%
26 1
1.0%

opn_cnt
Real number (ℝ)

ZEROS 

Distinct27
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.91
Minimum0
Maximum585
Zeros16
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:46.231393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q39
95-th percentile71.3
Maximum585
Range585
Interquartile range (IQR)8

Descriptive statistics

Standard deviation72.773635
Coefficient of variation (CV)3.4803269
Kurtosis41.641638
Mean20.91
Median Absolute Deviation (MAD)3
Skewness6.1194339
Sum2091
Variance5296.0019
MonotonicityNot monotonic
2023-12-10T18:59:46.460155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 16
16.0%
1 16
16.0%
2 8
 
8.0%
4 8
 
8.0%
3 7
 
7.0%
6 6
 
6.0%
5 6
 
6.0%
7 5
 
5.0%
16 3
 
3.0%
9 3
 
3.0%
Other values (17) 22
22.0%
ValueCountFrequency (%)
0 16
16.0%
1 16
16.0%
2 8
8.0%
3 7
7.0%
4 8
8.0%
5 6
 
6.0%
6 6
 
6.0%
7 5
 
5.0%
8 1
 
1.0%
9 3
 
3.0%
ValueCountFrequency (%)
585 1
1.0%
368 1
1.0%
177 1
1.0%
167 1
1.0%
115 1
1.0%
69 1
1.0%
68 1
1.0%
48 1
1.0%
26 2
2.0%
24 1
1.0%

stag_cnt
Real number (ℝ)

Distinct89
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean297.72
Minimum7
Maximum1696
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:46.723827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile22.75
Q169.75
median188.5
Q3384
95-th percentile919.5
Maximum1696
Range1689
Interquartile range (IQR)314.25

Descriptive statistics

Standard deviation313.24093
Coefficient of variation (CV)1.0521326
Kurtosis3.920339
Mean297.72
Median Absolute Deviation (MAD)128
Skewness1.8399174
Sum29772
Variance98119.88
MonotonicityNot monotonic
2023-12-10T18:59:47.051020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107 3
 
3.0%
54 2
 
2.0%
169 2
 
2.0%
25 2
 
2.0%
91 2
 
2.0%
460 2
 
2.0%
144 2
 
2.0%
175 2
 
2.0%
284 2
 
2.0%
138 2
 
2.0%
Other values (79) 79
79.0%
ValueCountFrequency (%)
7 1
1.0%
8 1
1.0%
13 1
1.0%
14 1
1.0%
18 1
1.0%
23 1
1.0%
24 1
1.0%
25 2
2.0%
27 1
1.0%
28 1
1.0%
ValueCountFrequency (%)
1696 1
1.0%
1244 1
1.0%
1070 1
1.0%
1012 1
1.0%
1005 1
1.0%
915 1
1.0%
876 1
1.0%
866 1
1.0%
848 1
1.0%
778 1
1.0%

tot_advtk_cnt
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31950.99
Minimum298
Maximum458835
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:47.337836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum298
5-th percentile805.05
Q12068.25
median9020.5
Q332901
95-th percentile118540.95
Maximum458835
Range458537
Interquartile range (IQR)30832.75

Descriptive statistics

Standard deviation66205.286
Coefficient of variation (CV)2.0720887
Kurtosis26.56435
Mean31950.99
Median Absolute Deviation (MAD)7996
Skewness4.7599769
Sum3195099
Variance4.3831399 × 109
MonotonicityNot monotonic
2023-12-10T18:59:47.669513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
147632 1
 
1.0%
2997 1
 
1.0%
298 1
 
1.0%
3000 1
 
1.0%
20993 1
 
1.0%
812 1
 
1.0%
1648 1
 
1.0%
30035 1
 
1.0%
1397 1
 
1.0%
650 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
298 1
1.0%
392 1
1.0%
428 1
1.0%
650 1
1.0%
673 1
1.0%
812 1
1.0%
814 1
1.0%
836 1
1.0%
863 1
1.0%
881 1
1.0%
ValueCountFrequency (%)
458835 1
1.0%
404566 1
1.0%
149239 1
1.0%
147632 1
1.0%
126292 1
1.0%
118133 1
1.0%
100831 1
1.0%
91599 1
1.0%
91565 1
1.0%
87527 1
1.0%

prv_rep_div
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
68 
공공
32 

Length

Max length4
Median length4
Mean length3.36
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 68
68.0%
공공 32
32.0%

Length

2023-12-10T18:59:48.191760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:59:48.799234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 68
68.0%
공공 32
32.0%

lst_updt_dt
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20191113
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20191113
2nd row20191113
3rd row20191113
4th row20191113
5th row20191113

Common Values

ValueCountFrequency (%)
20191113 100
100.0%

Length

2023-12-10T18:59:48.967761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:59:49.124235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20191113 100
100.0%

data_orgn
Categorical

CONSTANT 

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

Length

Max length15
Median length15
Mean length15
Min length15

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKOPIS 공연예술통합전산망
2nd rowKOPIS 공연예술통합전산망
3rd rowKOPIS 공연예술통합전산망
4th rowKOPIS 공연예술통합전산망
5th rowKOPIS 공연예술통합전산망

Common Values

ValueCountFrequency (%)
KOPIS 공연예술통합전산망 100
100.0%

Length

2023-12-10T18:59:49.295740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:59:49.476854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kopis 100
50.0%
공연예술통합전산망 100
50.0%

FILE_NAME
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
KC_486_WNTY_KOPIS_PRFPLC_2019
100 

Length

Max length29
Median length29
Mean length29
Min length29

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKC_486_WNTY_KOPIS_PRFPLC_2019
2nd rowKC_486_WNTY_KOPIS_PRFPLC_2019
3rd rowKC_486_WNTY_KOPIS_PRFPLC_2019
4th rowKC_486_WNTY_KOPIS_PRFPLC_2019
5th rowKC_486_WNTY_KOPIS_PRFPLC_2019

Common Values

ValueCountFrequency (%)
KC_486_WNTY_KOPIS_PRFPLC_2019 100
100.0%

Length

2023-12-10T18:59:49.652643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:59:49.809959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kc_486_wnty_kopis_prfplc_2019 100
100.0%

base_ymd
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20191125
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20191125
2nd row20191125
3rd row20191125
4th row20191125
5th row20191125

Common Values

ValueCountFrequency (%)
20191125 100
100.0%

Length

2023-12-10T18:59:49.978151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:59:50.132924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20191125 100
100.0%

Sample

idlclasmlsfcfclt_namectprvn_nmsgnr_nmlegaldong_cdlegalemd_nmadstrd_cdadstrd_nmrdnmaddr_cdrdnm_addrzip_cdgrid_cdx_cdy_cdx_utmk_cdy_utmk_cdprfplc_idopn_yearchartrseatsprfplc_cnttelnohmpgseat_capaprf_cntopn_cntstag_cnttot_advtk_cntprv_rep_divlst_updt_dtdata_orgnFILE_NAMEbase_ymd
0KC486PC19N000001문화시설공연장순위아트홀1관 [대학로]서울특별시종로구1111016800동숭동1111064000이화동111104100033서울특별시 종로구 대학로10길 11 (동숭동)3086다사56053737.581852127.0027879561001953723FC0012282014민간(대학로)221102-766-7667<NA>221101696147632<NA>20191113KOPIS 공연예술통합전산망KC_486_WNTY_KOPIS_PRFPLC_201920191125
1KC486PC19N000002문화시설공연장바탕골소극장서울특별시종로구1111016800동숭동1111064000이화동111104100033서울특별시 종로구 대학로10길 5 (동숭동)3086다사56053737.58186127.002569560801953724FC0011741986민간(대학로)200102-764-8760<NA>200101070118133<NA>20191113KOPIS 공연예술통합전산망KC_486_WNTY_KOPIS_PRFPLC_201920191125
2KC486PC19N000003문화시설공연장컬처스페이스 엔유(구. 쁘티첼 씨어터)서울특별시종로구1111016800동숭동1111064000이화동111104100035서울특별시 종로구 대학로12길 73 (동숭동)3086다사56253637.580763127.0039459562021953602FC0014592010민간(대학로)314102-766-7667<NA>314101244126292<NA>20191113KOPIS 공연예술통합전산망KC_486_WNTY_KOPIS_PRFPLC_201920191125
3KC486PC19N000004문화시설공연장콘텐츠박스(구. 르메이에르 씨어터)서울특별시종로구1111016800동숭동1111064000이화동111104100075서울특별시 종로구 동숭길 55 (동숭동)3086다사56253637.580783127.004169562211953604FC0008412006민간(대학로)193102-747-2232<NA>1931084843638<NA>20191113KOPIS 공연예술통합전산망KC_486_WNTY_KOPIS_PRFPLC_201920191125
4KC486PC19N000005문화시설공연장삼형제극장(환상극장)서울특별시종로구1111016800동숭동1111064000이화동111104100245서울특별시 종로구 이화장길 72 (동숭동)3088다사56253337.578462127.004619562591953346FC0011192009민간(대학로)100102-6326-1333http://cafe.naver.com/teum20131001087613113<NA>20191113KOPIS 공연예술통합전산망KC_486_WNTY_KOPIS_PRFPLC_201920191125
5KC486PC19N000006문화시설공연장정동극장서울특별시중구1114016700정동1114052000소공동111404103276서울특별시 중구 정동길 43 (정동)4518다사53451937.565936126.9728449534461951972FC0000061995국립816202-751-1500http://www.jeongdong.or.kr342101033139639공공20191113KOPIS 공연예술통합전산망KC_486_WNTY_KOPIS_PRFPLC_201920191125
6KC486PC19N000007문화시설공연장극장용서울특별시용산구1117013500용산동6가1117069000서빙고동111703102004서울특별시 용산구 서빙고로 137 (용산동6가)4383다사54047237.52372126.979949540471947285FC0000082005국립805100-1544-5955http://www.theateryong.or.kr8058730187527공공20191113KOPIS 공연예술통합전산망KC_486_WNTY_KOPIS_PRFPLC_201920191125
7KC486PC19N000008문화시설공연장홍익대 대학로 아트센터서울특별시종로구1111016600연건동1111064000이화동111103100002서울특별시 종로구 대학로 57 (연건동)3082다사55953137.57657127.0014999559831953138FC0012082013민간(대학로)852202-742-0300http://www.hongikartcenter.com/main.do85210739691599<NA>20191113KOPIS 공연예술통합전산망KC_486_WNTY_KOPIS_PRFPLC_201920191125
8KC486PC19N000009문화시설공연장프라임아트홀서울특별시구로구1153010200구로동1153056000구로제5동115303116007서울특별시 구로구 새말로 97 (구로동)8288다사46145437.507117126.8902589461111945490FC0002152007민간(대학로 외)400102-2111-1146http://cafe.naver.com/primearthall/40011633541696<NA>20191113KOPIS 공연예술통합전산망KC_486_WNTY_KOPIS_PRFPLC_201920191125
9KC486PC19N000010문화시설공연장동양예술극장(구. 아트센터K)서울특별시종로구1111016900혜화동1111065000혜화동111104100036서울특별시 종로구 대학로14길 29 (혜화동)3084다사56054037.584578127.0022649560551954026FC0010872015민간(대학로)622302-743-4667http://artcenterdyu.co.kr/622191661563985<NA>20191113KOPIS 공연예술통합전산망KC_486_WNTY_KOPIS_PRFPLC_201920191125
idlclasmlsfcfclt_namectprvn_nmsgnr_nmlegaldong_cdlegalemd_nmadstrd_cdadstrd_nmrdnmaddr_cdrdnm_addrzip_cdgrid_cdx_cdy_cdx_utmk_cdy_utmk_cdprfplc_idopn_yearchartrseatsprfplc_cnttelnohmpgseat_capaprf_cntopn_cntstag_cnttot_advtk_cntprv_rep_divlst_updt_dtdata_orgnFILE_NAMEbase_ymd
90KC486PC19N000091문화시설공연장안산문화예술의전당경기도안산시 단원구4127310100고잔동4127352500고잔동412733190080경기도 안산시 단원구 화랑로 312 (고잔동)15355다사39924737.319737126.8225739399791924743FC0000182004공공(문예회관)21943031-481-4000http://www.ansanart.com/219427267117504공공20191113KOPIS 공연예술통합전산망KC_486_WNTY_KOPIS_PRFPLC_201920191125
91KC486PC19N000092문화시설공연장영등포 타임스퀘어서울특별시영등포구1156010500영등포동4가1156053500영등포동115603118024서울특별시 영등포구 영중로 15 (영등포동4가)7305다사47346637.517244126.9037059473061946606FC0014032014민간(대학로 외)50100-1899-8778http://www.timessquare.co.kr/?controller=category&action=selectShopDetailInfoView&shopCd=00000152502048392<NA>20191113KOPIS 공연예술통합전산망KC_486_WNTY_KOPIS_PRFPLC_201920191125
92KC486PC19N000093문화시설공연장남산예술센터서울특별시중구1114014200예장동1114057000필동111403101005서울특별시 중구 소파로 138 (예장동)4628다사54851137.559033126.9884689548221951199FC0009302009공공(문예회관)473102-758-2150http://www.nsartscenter.or.kr/47377653697공공20191113KOPIS 공연예술통합전산망KC_486_WNTY_KOPIS_PRFPLC_201920191125
93KC486PC19N000094문화시설공연장한국방송회관 코바코홀 (구. 브로드홀)서울특별시양천구1147010200목동1147051000목1동114703114001서울특별시 양천구 목동동로 233 (목동)7995다사44447737.526995126.8713659444561947707FC0011071998공공(기타)2901070-7581-8902https://broadcast.kobaco.co.kr/main.jsp2901042532179공공20191113KOPIS 공연예술통합전산망KC_486_WNTY_KOPIS_PRFPLC_201920191125
94KC486PC19N000095문화시설공연장정읍 연지아트홀전라북도정읍시4518010500연지동4518057000연지동451803270061전라북도 정읍시 중앙로 73 (연지동)56161다마41030635.570561126.8490229410131730699FC0017122017공공(기타)2031063-539-7872<NA>2034481501공공20191113KOPIS 공연예술통합전산망KC_486_WNTY_KOPIS_PRFPLC_201920191125
95KC486PC19N000096문화시설공연장여우별아트홀대구광역시중구2711010500삼덕동1가2711054500삼덕동271104223085대구광역시 중구 동성로3길 35 (삼덕동1가)41942라마99063935.866907128.59726710990631763927FC0013932014민간(대학로 외)1561<NA>https://foxstarart.modoo.at/1561022841063<NA>20191113KOPIS 공연예술통합전산망KC_486_WNTY_KOPIS_PRFPLC_201920191125
96KC486PC19N000097문화시설공연장경주예술의전당경상북도경주시4713012400황성동4713062100황성동471303305039경상북도 경주시 알천북로 1 (황성동)38089마마54064235.862288129.20662611540891764204FC0005172010공공(문예회관)2286200-1588-4925http://www.gjartcenter.kr13861212279373공공20191113KOPIS 공연예술통합전산망KC_486_WNTY_KOPIS_PRFPLC_201920191125
97KC486PC19N000098문화시설공연장수원SK아트리움경기도수원시 장안구4111113000정자동4111157100정자1동411113174010경기도 수원시 장안구 이목로 24-25 (정자동)16336다사54423437.308904126.9858169544361923450FC0014382014공공(문예회관)12502031-250-5300http://www.suwonskartrium.or.kr/12501211538435공공20191113KOPIS 공연예술통합전산망KC_486_WNTY_KOPIS_PRFPLC_201920191125
98KC486PC19N000099문화시설공연장부평문화사랑방인천광역시부평구2823710600갈산동2823764200갈산2동282373008034인천광역시 부평구 주부토로 173 (갈산동)21335다사31545937.510681126.7256559315661945993FC0017962008공공(문예회관)1241032-500-2000http://www.bpcf.or.kr/culturelove/facility.asp124777836공공20191113KOPIS 공연예술통합전산망KC_486_WNTY_KOPIS_PRFPLC_201920191125
99KC486PC19N000100문화시설공연장삼일로창고극장서울특별시중구1114013100저동1가1114055000명동111404103175서울특별시 중구 삼일대로9길 12 (저동1가)4537다사54751637.563444126.9878999547741951688FC0002481975민간(대학로 외)100102-319-8020http://cafe.naver.com/samilro.cafe1007629673<NA>20191113KOPIS 공연예술통합전산망KC_486_WNTY_KOPIS_PRFPLC_201920191125