Overview

Dataset statistics

Number of variables17
Number of observations2082
Missing cells1765
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory284.8 KiB
Average record size in memory140.1 B

Variable types

Text12
Numeric3
Categorical2

Dataset

Description기관명,건물명,IDX,주소,조사일자,전체면적(㎡),석면함유면적(㎡),담당부서,담당자명,연락처,건물용도,구조물,건축일자,층고(지상),층고(지하),옥탑,보고서
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-1119/S/1/datasetView.do

Alerts

층고(지상) is highly overall correlated with 층고(지하)High correlation
층고(지하) is highly overall correlated with 층고(지상)High correlation
옥탑 is highly imbalanced (59.4%)Imbalance
전체면적(㎡) has 176 (8.5%) missing valuesMissing
연락처 has 51 (2.4%) missing valuesMissing
구조물 has 239 (11.5%) missing valuesMissing
건축일자 has 256 (12.3%) missing valuesMissing
보고서 has 995 (47.8%) missing valuesMissing
IDX has unique valuesUnique
층고(지상) has 30 (1.4%) zerosZeros
층고(지하) has 1317 (63.3%) zerosZeros

Reproduction

Analysis started2024-05-18 00:35:50.469160
Analysis finished2024-05-18 00:36:01.194583
Duration10.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct469
Distinct (%)22.7%
Missing17
Missing (%)0.8%
Memory size16.4 KiB
2024-05-18T09:36:01.555252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length7.3646489
Min length3

Characters and Unicode

Total characters15208
Distinct characters339
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique288 ?
Unique (%)13.9%

Sample

1st row서남환경
2nd row서부공원녹지사업소(월드컵공원)
3rd row서부공원녹지사업소(월드컵공원)
4th row한남직업전문학교
5th row서울시립상계직업전문학교
ValueCountFrequency (%)
서남환경 182
 
8.1%
서울대공원 133
 
5.9%
중랑물재생센터 120
 
5.3%
난지물재생센터 86
 
3.8%
서울랜드 71
 
3.2%
탄천물재생센터 69
 
3.1%
강북아리수정수센터 57
 
2.5%
어린이대공원 51
 
2.3%
한강사업본부 42
 
1.9%
서울시 42
 
1.9%
Other values (498) 1398
62.1%
2024-05-18T09:36:02.761781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1004
 
6.6%
495
 
3.3%
487
 
3.2%
469
 
3.1%
469
 
3.1%
432
 
2.8%
421
 
2.8%
370
 
2.4%
347
 
2.3%
341
 
2.2%
Other values (329) 10373
68.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14608
96.1%
Space Separator 192
 
1.3%
Open Punctuation 137
 
0.9%
Close Punctuation 137
 
0.9%
Decimal Number 46
 
0.3%
Lowercase Letter 38
 
0.2%
Uppercase Letter 34
 
0.2%
Letter Number 7
 
< 0.1%
Other Punctuation 7
 
< 0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1004
 
6.9%
495
 
3.4%
487
 
3.3%
469
 
3.2%
469
 
3.2%
432
 
3.0%
421
 
2.9%
370
 
2.5%
347
 
2.4%
341
 
2.3%
Other values (291) 9773
66.9%
Uppercase Letter
ValueCountFrequency (%)
S 8
23.5%
H 8
23.5%
C 4
11.8%
O 2
 
5.9%
P 2
 
5.9%
M 2
 
5.9%
D 2
 
5.9%
I 1
 
2.9%
L 1
 
2.9%
B 1
 
2.9%
Other values (3) 3
 
8.8%
Lowercase Letter
ValueCountFrequency (%)
o 7
18.4%
i 6
15.8%
l 6
15.8%
e 6
15.8%
u 6
15.8%
t 2
 
5.3%
r 2
 
5.3%
y 1
 
2.6%
c 1
 
2.6%
a 1
 
2.6%
Decimal Number
ValueCountFrequency (%)
1 16
34.8%
2 14
30.4%
4 8
17.4%
3 3
 
6.5%
9 2
 
4.3%
5 1
 
2.2%
0 1
 
2.2%
6 1
 
2.2%
Other Punctuation
ValueCountFrequency (%)
/ 6
85.7%
, 1
 
14.3%
Space Separator
ValueCountFrequency (%)
192
100.0%
Open Punctuation
ValueCountFrequency (%)
( 137
100.0%
Close Punctuation
ValueCountFrequency (%)
) 137
100.0%
Letter Number
ValueCountFrequency (%)
7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14608
96.1%
Common 521
 
3.4%
Latin 79
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1004
 
6.9%
495
 
3.4%
487
 
3.3%
469
 
3.2%
469
 
3.2%
432
 
3.0%
421
 
2.9%
370
 
2.5%
347
 
2.4%
341
 
2.3%
Other values (291) 9773
66.9%
Latin
ValueCountFrequency (%)
S 8
10.1%
H 8
10.1%
o 7
 
8.9%
7
 
8.9%
i 6
 
7.6%
l 6
 
7.6%
e 6
 
7.6%
u 6
 
7.6%
C 4
 
5.1%
O 2
 
2.5%
Other values (14) 19
24.1%
Common
ValueCountFrequency (%)
192
36.9%
( 137
26.3%
) 137
26.3%
1 16
 
3.1%
2 14
 
2.7%
4 8
 
1.5%
/ 6
 
1.2%
3 3
 
0.6%
9 2
 
0.4%
- 2
 
0.4%
Other values (4) 4
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14608
96.1%
ASCII 593
 
3.9%
Number Forms 7
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1004
 
6.9%
495
 
3.4%
487
 
3.3%
469
 
3.2%
469
 
3.2%
432
 
3.0%
421
 
2.9%
370
 
2.5%
347
 
2.4%
341
 
2.3%
Other values (291) 9773
66.9%
ASCII
ValueCountFrequency (%)
192
32.4%
( 137
23.1%
) 137
23.1%
1 16
 
2.7%
2 14
 
2.4%
S 8
 
1.3%
H 8
 
1.3%
4 8
 
1.3%
o 7
 
1.2%
i 6
 
1.0%
Other values (27) 60
 
10.1%
Number Forms
ValueCountFrequency (%)
7
100.0%
Distinct1819
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
2024-05-18T09:36:04.181823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length23
Mean length9.1777137
Min length2

Characters and Unicode

Total characters19108
Distinct characters527
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1747 ?
Unique (%)83.9%

Sample

1st row영등포문화원
2nd row제1유입펌프동
3rd row장례식장
4th row서울여성플라자
5th row튼튼삐아제어린이집
ValueCountFrequency (%)
중랑물재생센터 119
 
3.3%
제1 99
 
2.8%
본관 83
 
2.3%
제3처리장 74
 
2.1%
제2처리장 73
 
2.0%
제1처리장 72
 
2.0%
화장실 50
 
1.4%
최초침전지계단실 43
 
1.2%
청사 39
 
1.1%
계단실 32
 
0.9%
Other values (1832) 2893
80.9%
2024-05-18T09:36:05.696820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1529
 
8.0%
686
 
3.6%
670
 
3.5%
576
 
3.0%
1 575
 
3.0%
500
 
2.6%
449
 
2.3%
442
 
2.3%
346
 
1.8%
341
 
1.8%
Other values (517) 12994
68.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14830
77.6%
Space Separator 1529
 
8.0%
Decimal Number 1374
 
7.2%
Uppercase Letter 532
 
2.8%
Close Punctuation 279
 
1.5%
Open Punctuation 278
 
1.5%
Dash Punctuation 119
 
0.6%
Other Punctuation 87
 
0.5%
Lowercase Letter 69
 
0.4%
Math Symbol 6
 
< 0.1%
Other values (2) 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
686
 
4.6%
670
 
4.5%
576
 
3.9%
500
 
3.4%
449
 
3.0%
442
 
3.0%
346
 
2.3%
341
 
2.3%
326
 
2.2%
321
 
2.2%
Other values (457) 10173
68.6%
Uppercase Letter
ValueCountFrequency (%)
B 182
34.2%
L 87
16.4%
A 74
13.9%
C 57
 
10.7%
D 34
 
6.4%
E 16
 
3.0%
S 12
 
2.3%
T 12
 
2.3%
F 9
 
1.7%
G 9
 
1.7%
Other values (13) 40
 
7.5%
Lowercase Letter
ValueCountFrequency (%)
e 13
18.8%
t 8
11.6%
a 8
11.6%
w 6
8.7%
n 5
 
7.2%
r 5
 
7.2%
o 4
 
5.8%
g 3
 
4.3%
i 3
 
4.3%
y 3
 
4.3%
Other values (7) 11
15.9%
Decimal Number
ValueCountFrequency (%)
1 575
41.8%
2 289
21.0%
3 173
 
12.6%
9 104
 
7.6%
4 75
 
5.5%
5 48
 
3.5%
0 41
 
3.0%
6 36
 
2.6%
7 20
 
1.5%
8 13
 
0.9%
Other Punctuation
ValueCountFrequency (%)
, 60
69.0%
/ 27
31.0%
Math Symbol
ValueCountFrequency (%)
+ 4
66.7%
~ 2
33.3%
Space Separator
ValueCountFrequency (%)
1529
100.0%
Close Punctuation
ValueCountFrequency (%)
) 279
100.0%
Open Punctuation
ValueCountFrequency (%)
( 278
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 119
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14831
77.6%
Common 3676
 
19.2%
Latin 601
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
686
 
4.6%
670
 
4.5%
576
 
3.9%
500
 
3.4%
449
 
3.0%
442
 
3.0%
346
 
2.3%
341
 
2.3%
326
 
2.2%
321
 
2.2%
Other values (458) 10174
68.6%
Latin
ValueCountFrequency (%)
B 182
30.3%
L 87
14.5%
A 74
12.3%
C 57
 
9.5%
D 34
 
5.7%
E 16
 
2.7%
e 13
 
2.2%
S 12
 
2.0%
T 12
 
2.0%
F 9
 
1.5%
Other values (30) 105
17.5%
Common
ValueCountFrequency (%)
1529
41.6%
1 575
 
15.6%
2 289
 
7.9%
) 279
 
7.6%
( 278
 
7.6%
3 173
 
4.7%
- 119
 
3.2%
9 104
 
2.8%
4 75
 
2.0%
, 60
 
1.6%
Other values (9) 195
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14830
77.6%
ASCII 4277
 
22.4%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1529
35.7%
1 575
 
13.4%
2 289
 
6.8%
) 279
 
6.5%
( 278
 
6.5%
B 182
 
4.3%
3 173
 
4.0%
- 119
 
2.8%
9 104
 
2.4%
L 87
 
2.0%
Other values (49) 662
15.5%
Hangul
ValueCountFrequency (%)
686
 
4.6%
670
 
4.5%
576
 
3.9%
500
 
3.4%
449
 
3.0%
442
 
3.0%
346
 
2.3%
341
 
2.3%
326
 
2.2%
321
 
2.2%
Other values (457) 10173
68.6%
None
ValueCountFrequency (%)
1
100.0%

IDX
Real number (ℝ)

UNIQUE 

Distinct2082
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1094.793
Minimum1
Maximum2595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2024-05-18T09:36:06.142928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile108.05
Q1534.25
median1097.5
Q31622.75
95-th percentile2070.95
Maximum2595
Range2594
Interquartile range (IQR)1088.5

Descriptive statistics

Standard deviation641.70383
Coefficient of variation (CV)0.5861417
Kurtosis-1.0184183
Mean1094.793
Median Absolute Deviation (MAD)544.5
Skewness0.081314492
Sum2279359
Variance411783.8
MonotonicityNot monotonic
2024-05-18T09:36:06.594718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2591 1
 
< 0.1%
734 1
 
< 0.1%
737 1
 
< 0.1%
740 1
 
< 0.1%
739 1
 
< 0.1%
736 1
 
< 0.1%
735 1
 
< 0.1%
743 1
 
< 0.1%
744 1
 
< 0.1%
742 1
 
< 0.1%
Other values (2072) 2072
99.5%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
2595 1
< 0.1%
2594 1
< 0.1%
2593 1
< 0.1%
2592 1
< 0.1%
2591 1
< 0.1%
2571 1
< 0.1%
2551 1
< 0.1%
2536 1
< 0.1%
2535 1
< 0.1%
2534 1
< 0.1%

주소
Text

Distinct811
Distinct (%)39.0%
Missing2
Missing (%)0.1%
Memory size16.4 KiB
2024-05-18T09:36:07.243815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length35
Mean length15.411538
Min length6

Characters and Unicode

Total characters32056
Distinct characters243
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique645 ?
Unique (%)31.0%

Sample

1st row서울특별시 영등포구신길로 275
2nd row강서구 양천로 201
3rd row서울특별시 강남구봉은사로114길 13
4th row서울특별시 동작구여의대방로54길 18
5th row서울특별시 성동구행당로79
ValueCountFrequency (%)
서울특별시 779
 
11.6%
서울시 517
 
7.7%
경기도 310
 
4.6%
강서구 193
 
2.9%
과천시 166
 
2.5%
성동구 164
 
2.4%
마곡동 157
 
2.3%
74 143
 
2.1%
강남구 101
 
1.5%
광진구 90
 
1.3%
Other values (1201) 4103
61.0%
2024-05-18T09:36:08.329624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5117
 
16.0%
1905
 
5.9%
1776
 
5.5%
1687
 
5.3%
1654
 
5.2%
1357
 
4.2%
1 1219
 
3.8%
- 881
 
2.7%
791
 
2.5%
791
 
2.5%
Other values (233) 14878
46.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19571
61.1%
Decimal Number 6452
 
20.1%
Space Separator 5117
 
16.0%
Dash Punctuation 881
 
2.7%
Other Punctuation 16
 
< 0.1%
Open Punctuation 8
 
< 0.1%
Close Punctuation 8
 
< 0.1%
Lowercase Letter 2
 
< 0.1%
Control 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1905
 
9.7%
1776
 
9.1%
1687
 
8.6%
1654
 
8.5%
1357
 
6.9%
791
 
4.0%
791
 
4.0%
449
 
2.3%
438
 
2.2%
431
 
2.2%
Other values (214) 8292
42.4%
Decimal Number
ValueCountFrequency (%)
1 1219
18.9%
4 776
12.0%
2 741
11.5%
7 663
10.3%
3 642
10.0%
5 553
8.6%
8 520
8.1%
6 475
 
7.4%
0 436
 
6.8%
9 427
 
6.6%
Other Punctuation
ValueCountFrequency (%)
, 15
93.8%
/ 1
 
6.2%
Lowercase Letter
ValueCountFrequency (%)
o 1
50.0%
r 1
50.0%
Space Separator
ValueCountFrequency (%)
5117
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 881
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19571
61.1%
Common 12483
38.9%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1905
 
9.7%
1776
 
9.1%
1687
 
8.6%
1654
 
8.5%
1357
 
6.9%
791
 
4.0%
791
 
4.0%
449
 
2.3%
438
 
2.2%
431
 
2.2%
Other values (214) 8292
42.4%
Common
ValueCountFrequency (%)
5117
41.0%
1 1219
 
9.8%
- 881
 
7.1%
4 776
 
6.2%
2 741
 
5.9%
7 663
 
5.3%
3 642
 
5.1%
5 553
 
4.4%
8 520
 
4.2%
6 475
 
3.8%
Other values (7) 896
 
7.2%
Latin
ValueCountFrequency (%)
o 1
50.0%
r 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 19571
61.1%
ASCII 12485
38.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5117
41.0%
1 1219
 
9.8%
- 881
 
7.1%
4 776
 
6.2%
2 741
 
5.9%
7 663
 
5.3%
3 642
 
5.1%
5 553
 
4.4%
8 520
 
4.2%
6 475
 
3.8%
Other values (9) 898
 
7.2%
Hangul
ValueCountFrequency (%)
1905
 
9.7%
1776
 
9.1%
1687
 
8.6%
1654
 
8.5%
1357
 
6.9%
791
 
4.0%
791
 
4.0%
449
 
2.3%
438
 
2.2%
431
 
2.2%
Other values (214) 8292
42.4%
Distinct375
Distinct (%)18.1%
Missing12
Missing (%)0.6%
Memory size16.4 KiB
2024-05-18T09:36:08.917400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length10
Mean length11.168116
Min length8

Characters and Unicode

Total characters23118
Distinct characters16
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95 ?
Unique (%)4.6%

Sample

1st row2077.07.29
2nd row2020.07.15
3rd row2020.07.15
4th row2020.07.15
5th row2020.06.26
ValueCountFrequency (%)
2012 172
 
6.7%
07 167
 
6.5%
16 167
 
6.5%
31 167
 
6.5%
2012.07.24~2012.08.07 83
 
3.2%
2012.06.19 45
 
1.7%
2012.07.09 41
 
1.6%
2012.06.07 41
 
1.6%
2012.06.22 36
 
1.4%
2012.07.03-04 32
 
1.2%
Other values (368) 1625
63.1%
2024-05-18T09:36:10.068315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5869
25.4%
. 4310
18.6%
1 4206
18.2%
2 3909
16.9%
7 1016
 
4.4%
6 703
 
3.0%
3 685
 
3.0%
9 523
 
2.3%
506
 
2.2%
8 461
 
2.0%
Other values (6) 930
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17886
77.4%
Other Punctuation 4355
 
18.8%
Space Separator 506
 
2.2%
Dash Punctuation 229
 
1.0%
Math Symbol 142
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5869
32.8%
1 4206
23.5%
2 3909
21.9%
7 1016
 
5.7%
6 703
 
3.9%
3 685
 
3.8%
9 523
 
2.9%
8 461
 
2.6%
4 264
 
1.5%
5 250
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 4310
99.0%
/ 38
 
0.9%
, 7
 
0.2%
Space Separator
ValueCountFrequency (%)
506
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 229
100.0%
Math Symbol
ValueCountFrequency (%)
~ 142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23118
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5869
25.4%
. 4310
18.6%
1 4206
18.2%
2 3909
16.9%
7 1016
 
4.4%
6 703
 
3.0%
3 685
 
3.0%
9 523
 
2.3%
506
 
2.2%
8 461
 
2.0%
Other values (6) 930
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23118
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5869
25.4%
. 4310
18.6%
1 4206
18.2%
2 3909
16.9%
7 1016
 
4.4%
6 703
 
3.0%
3 685
 
3.0%
9 523
 
2.3%
506
 
2.2%
8 461
 
2.0%
Other values (6) 930
 
4.0%

전체면적(㎡)
Text

MISSING 

Distinct1474
Distinct (%)77.3%
Missing176
Missing (%)8.5%
Memory size16.4 KiB
2024-05-18T09:36:10.928279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length6.021511
Min length2

Characters and Unicode

Total characters11477
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1297 ?
Unique (%)68.0%

Sample

1st row3356.83
2nd row5,215.4 ㎡
3rd row2283.65
4th row22519.96
5th row379.16
ValueCountFrequency (%)
0.00 38
 
2.0%
36.00 26
 
1.4%
27.00 21
 
1.1%
27.68 17
 
0.9%
23.00 12
 
0.6%
14.85 12
 
0.6%
6.60 12
 
0.6%
21.45 12
 
0.6%
4.00 10
 
0.5%
20.85 9
 
0.5%
Other values (1402) 1739
91.1%
2024-05-18T09:36:12.486743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1527
13.3%
1525
13.3%
. 1396
12.2%
1 999
8.7%
2 991
8.6%
6 731
6.4%
4 730
6.4%
5 711
6.2%
3 671
5.8%
8 668
5.8%
Other values (4) 1528
13.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8236
71.8%
Other Punctuation 1714
 
14.9%
Space Separator 1525
 
13.3%
Other Symbol 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1527
18.5%
1 999
12.1%
2 991
12.0%
6 731
8.9%
4 730
8.9%
5 711
8.6%
3 671
8.1%
8 668
8.1%
7 624
7.6%
9 584
 
7.1%
Other Punctuation
ValueCountFrequency (%)
. 1396
81.4%
, 318
 
18.6%
Space Separator
ValueCountFrequency (%)
1525
100.0%
Other Symbol
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11477
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1527
13.3%
1525
13.3%
. 1396
12.2%
1 999
8.7%
2 991
8.6%
6 731
6.4%
4 730
6.4%
5 711
6.2%
3 671
5.8%
8 668
5.8%
Other values (4) 1528
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11475
> 99.9%
CJK Compat 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1527
13.3%
1525
13.3%
. 1396
12.2%
1 999
8.7%
2 991
8.6%
6 731
6.4%
4 730
6.4%
5 711
6.2%
3 671
5.8%
8 668
5.8%
Other values (3) 1526
13.3%
CJK Compat
ValueCountFrequency (%)
2
100.0%
Distinct441
Distinct (%)21.2%
Missing1
Missing (%)< 0.1%
Memory size16.4 KiB
2024-05-18T09:36:13.663102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length3.1653051
Min length1

Characters and Unicode

Total characters6587
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique376 ?
Unique (%)18.1%

Sample

1st row1436.45
2nd row0
3rd row119.97
4th row1660.4
5th row352.51
ValueCountFrequency (%)
0 985
47.3%
0.00 540
25.9%
0.01 14
 
0.7%
1.00 13
 
0.6%
0.10 9
 
0.4%
0.02 7
 
0.3%
0.05 6
 
0.3%
0.11 5
 
0.2%
0.50 5
 
0.2%
0.16 4
 
0.2%
Other values (421) 494
23.7%
2024-05-18T09:36:15.364727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3205
48.7%
. 1061
 
16.1%
716
 
10.9%
1 319
 
4.8%
2 209
 
3.2%
5 185
 
2.8%
3 180
 
2.7%
4 157
 
2.4%
6 146
 
2.2%
8 141
 
2.1%
Other values (4) 268
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4806
73.0%
Other Punctuation 1064
 
16.2%
Space Separator 716
 
10.9%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3205
66.7%
1 319
 
6.6%
2 209
 
4.3%
5 185
 
3.8%
3 180
 
3.7%
4 157
 
3.3%
6 146
 
3.0%
8 141
 
2.9%
7 136
 
2.8%
9 128
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 1061
99.7%
, 3
 
0.3%
Space Separator
ValueCountFrequency (%)
716
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6587
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3205
48.7%
. 1061
 
16.1%
716
 
10.9%
1 319
 
4.8%
2 209
 
3.2%
5 185
 
2.8%
3 180
 
2.7%
4 157
 
2.4%
6 146
 
2.2%
8 141
 
2.1%
Other values (4) 268
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6586
> 99.9%
CJK Compat 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3205
48.7%
. 1061
 
16.1%
716
 
10.9%
1 319
 
4.8%
2 209
 
3.2%
5 185
 
2.8%
3 180
 
2.7%
4 157
 
2.4%
6 146
 
2.2%
8 141
 
2.1%
Other values (3) 267
 
4.1%
CJK Compat
ValueCountFrequency (%)
1
100.0%
Distinct441
Distinct (%)21.3%
Missing13
Missing (%)0.6%
Memory size16.4 KiB
2024-05-18T09:36:16.332085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length20
Mean length8.2266796
Min length1

Characters and Unicode

Total characters17021
Distinct characters291
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique245 ?
Unique (%)11.8%

Sample

1st row영등포문화원
2nd row(주)서남환경
3rd row공공의료추진반
4th row서울여성플라자/공간운영실 지원팀
5th row남경종합관리㈜
ValueCountFrequency (%)
수처리과 166
 
5.4%
㈜서남환경 151
 
4.9%
시설팀 143
 
4.6%
총무과 143
 
4.6%
시설과 104
 
3.4%
보수과 100
 
3.2%
중랑물재생센터 90
 
2.9%
서울대공원 83
 
2.7%
난지물재생센터 83
 
2.7%
장비회계팀 77
 
2.5%
Other values (443) 1956
63.2%
2024-05-18T09:36:18.021790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1076
 
6.3%
983
 
5.8%
694
 
4.1%
674
 
4.0%
634
 
3.7%
603
 
3.5%
506
 
3.0%
460
 
2.7%
408
 
2.4%
408
 
2.4%
Other values (281) 10575
62.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15528
91.2%
Space Separator 1076
 
6.3%
Other Symbol 229
 
1.3%
Close Punctuation 69
 
0.4%
Open Punctuation 66
 
0.4%
Uppercase Letter 37
 
0.2%
Dash Punctuation 5
 
< 0.1%
Other Punctuation 5
 
< 0.1%
Decimal Number 5
 
< 0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
983
 
6.3%
694
 
4.5%
674
 
4.3%
634
 
4.1%
603
 
3.9%
506
 
3.3%
460
 
3.0%
408
 
2.6%
408
 
2.6%
404
 
2.6%
Other values (259) 9754
62.8%
Uppercase Letter
ValueCountFrequency (%)
S 15
40.5%
H 6
 
16.2%
P 6
 
16.2%
A 4
 
10.8%
B 3
 
8.1%
C 1
 
2.7%
Y 1
 
2.7%
M 1
 
2.7%
Decimal Number
ValueCountFrequency (%)
1 1
20.0%
5 1
20.0%
3 1
20.0%
9 1
20.0%
2 1
20.0%
Other Punctuation
ValueCountFrequency (%)
? 3
60.0%
, 1
 
20.0%
/ 1
 
20.0%
Space Separator
ValueCountFrequency (%)
1076
100.0%
Other Symbol
ValueCountFrequency (%)
229
100.0%
Close Punctuation
ValueCountFrequency (%)
) 69
100.0%
Open Punctuation
ValueCountFrequency (%)
( 66
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15757
92.6%
Common 1227
 
7.2%
Latin 37
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
983
 
6.2%
694
 
4.4%
674
 
4.3%
634
 
4.0%
603
 
3.8%
506
 
3.2%
460
 
2.9%
408
 
2.6%
408
 
2.6%
404
 
2.6%
Other values (260) 9983
63.4%
Common
ValueCountFrequency (%)
1076
87.7%
) 69
 
5.6%
( 66
 
5.4%
- 5
 
0.4%
? 3
 
0.2%
1 1
 
0.1%
5 1
 
0.1%
3 1
 
0.1%
_ 1
 
0.1%
, 1
 
0.1%
Other values (3) 3
 
0.2%
Latin
ValueCountFrequency (%)
S 15
40.5%
H 6
 
16.2%
P 6
 
16.2%
A 4
 
10.8%
B 3
 
8.1%
C 1
 
2.7%
Y 1
 
2.7%
M 1
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15528
91.2%
ASCII 1264
 
7.4%
None 229
 
1.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1076
85.1%
) 69
 
5.5%
( 66
 
5.2%
S 15
 
1.2%
H 6
 
0.5%
P 6
 
0.5%
- 5
 
0.4%
A 4
 
0.3%
B 3
 
0.2%
? 3
 
0.2%
Other values (11) 11
 
0.9%
Hangul
ValueCountFrequency (%)
983
 
6.3%
694
 
4.5%
674
 
4.3%
634
 
4.1%
603
 
3.9%
506
 
3.3%
460
 
3.0%
408
 
2.6%
408
 
2.6%
404
 
2.6%
Other values (259) 9754
62.8%
None
ValueCountFrequency (%)
229
100.0%
Distinct653
Distinct (%)31.4%
Missing3
Missing (%)0.1%
Memory size16.4 KiB
2024-05-18T09:36:19.307734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length3
Mean length3.0909091
Min length2

Characters and Unicode

Total characters6426
Distinct characters201
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique422 ?
Unique (%)20.3%

Sample

1st row박노순
2nd row홍승표
3rd row김성주
4th row전명기
5th row양효시
ValueCountFrequency (%)
홍승표 158
 
7.4%
김근모 88
 
4.1%
박재춘 83
 
3.9%
박수현 79
 
3.7%
이지호 52
 
2.4%
한충규 51
 
2.4%
박정욱 47
 
2.2%
백공명 41
 
1.9%
정원호 33
 
1.5%
임영성 32
 
1.5%
Other values (654) 1470
68.9%
2024-05-18T09:36:20.708201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
318
 
4.9%
294
 
4.6%
268
 
4.2%
220
 
3.4%
189
 
2.9%
188
 
2.9%
170
 
2.6%
169
 
2.6%
167
 
2.6%
161
 
2.5%
Other values (191) 4282
66.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6354
98.9%
Space Separator 62
 
1.0%
Open Punctuation 3
 
< 0.1%
Other Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
318
 
5.0%
294
 
4.6%
268
 
4.2%
220
 
3.5%
189
 
3.0%
188
 
3.0%
170
 
2.7%
169
 
2.7%
167
 
2.6%
161
 
2.5%
Other values (186) 4210
66.3%
Space Separator
ValueCountFrequency (%)
62
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Decimal Number
ValueCountFrequency (%)
9 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6354
98.9%
Common 72
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
318
 
5.0%
294
 
4.6%
268
 
4.2%
220
 
3.5%
189
 
3.0%
188
 
3.0%
170
 
2.7%
169
 
2.7%
167
 
2.6%
161
 
2.5%
Other values (186) 4210
66.3%
Common
ValueCountFrequency (%)
62
86.1%
( 3
 
4.2%
, 3
 
4.2%
) 3
 
4.2%
9 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6354
98.9%
ASCII 72
 
1.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
318
 
5.0%
294
 
4.6%
268
 
4.2%
220
 
3.5%
189
 
3.0%
188
 
3.0%
170
 
2.7%
169
 
2.7%
167
 
2.6%
161
 
2.5%
Other values (186) 4210
66.3%
ASCII
ValueCountFrequency (%)
62
86.1%
( 3
 
4.2%
, 3
 
4.2%
) 3
 
4.2%
9 1
 
1.4%

연락처
Text

MISSING 

Distinct736
Distinct (%)36.2%
Missing51
Missing (%)2.4%
Memory size16.4 KiB
2024-05-18T09:36:21.567122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length26
Mean length11.30773
Min length1

Characters and Unicode

Total characters22966
Distinct characters21
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique523 ?
Unique (%)25.8%

Sample

1st row02-846-0155
2nd row02-3660-2119
3rd row02-2133-9232
4th row02-810-5292
5th row02-2295-0501
ValueCountFrequency (%)
02-3660-2119 157
 
7.7%
500-7413 98
 
4.8%
02-2211-2554 87
 
4.3%
02-300-8589 85
 
4.2%
02-3410-9709 51
 
2.5%
450-9319(010-4305-2605 44
 
2.2%
02-2290-6185 32
 
1.6%
2211-2515 29
 
1.4%
509-6181(010-7478-3254 26
 
1.3%
02-3660-2270 24
 
1.2%
Other values (726) 1403
68.9%
2024-05-18T09:36:22.833186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3509
15.3%
- 3465
15.1%
2 3214
14.0%
1 2383
10.4%
3 2028
8.8%
6 1593
6.9%
5 1539
6.7%
4 1510
6.6%
9 1399
 
6.1%
7 1065
 
4.6%
Other values (11) 1261
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19255
83.8%
Dash Punctuation 3465
 
15.1%
Close Punctuation 90
 
0.4%
Open Punctuation 86
 
0.4%
Other Punctuation 49
 
0.2%
Other Letter 14
 
0.1%
Space Separator 5
 
< 0.1%
Uppercase Letter 1
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3509
18.2%
2 3214
16.7%
1 2383
12.4%
3 2028
10.5%
6 1593
8.3%
5 1539
8.0%
4 1510
7.8%
9 1399
 
7.3%
7 1065
 
5.5%
8 1015
 
5.3%
Other Punctuation
ValueCountFrequency (%)
/ 46
93.9%
. 2
 
4.1%
: 1
 
2.0%
Other Letter
ValueCountFrequency (%)
8
57.1%
6
42.9%
Dash Punctuation
ValueCountFrequency (%)
- 3465
100.0%
Close Punctuation
ValueCountFrequency (%)
) 90
100.0%
Open Punctuation
ValueCountFrequency (%)
( 86
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Uppercase Letter
ValueCountFrequency (%)
F 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22951
99.9%
Hangul 14
 
0.1%
Latin 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3509
15.3%
- 3465
15.1%
2 3214
14.0%
1 2383
10.4%
3 2028
8.8%
6 1593
6.9%
5 1539
6.7%
4 1510
6.6%
9 1399
 
6.1%
7 1065
 
4.6%
Other values (8) 1246
 
5.4%
Hangul
ValueCountFrequency (%)
8
57.1%
6
42.9%
Latin
ValueCountFrequency (%)
F 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22952
99.9%
Hangul 14
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3509
15.3%
- 3465
15.1%
2 3214
14.0%
1 2383
10.4%
3 2028
8.8%
6 1593
6.9%
5 1539
6.7%
4 1510
6.6%
9 1399
 
6.1%
7 1065
 
4.6%
Other values (9) 1247
 
5.4%
Hangul
ValueCountFrequency (%)
8
57.1%
6
42.9%

건물용도
Categorical

Distinct12
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
업무시설
832 
물재생시설
467 
문화/복지시설
393 
<NA>
208 
주거시설
 
46
Other values (7)
136 

Length

Max length7
Median length4
Mean length4.8232469
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row문화/복지시설
2nd row물재생시설
3rd row의료시설
4th row업무시설
5th row업무시설

Common Values

ValueCountFrequency (%)
업무시설 832
40.0%
물재생시설 467
22.4%
문화/복지시설 393
18.9%
<NA> 208
 
10.0%
주거시설 46
 
2.2%
문화복지 30
 
1.4%
의료시설 28
 
1.3%
체육시설 26
 
1.2%
빗물펌프장 26
 
1.2%
상수도시설 10
 
0.5%
Other values (2) 16
 
0.8%

Length

2024-05-18T09:36:23.433704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
업무시설 832
40.0%
물재생시설 467
22.4%
문화/복지시설 393
18.9%
na 208
 
10.0%
주거시설 46
 
2.2%
문화복지 30
 
1.4%
의료시설 28
 
1.3%
체육시설 26
 
1.2%
빗물펌프장 26
 
1.2%
상수도시설 10
 
0.5%
Other values (2) 16
 
0.8%

구조물
Text

MISSING 

Distinct71
Distinct (%)3.9%
Missing239
Missing (%)11.5%
Memory size16.4 KiB
2024-05-18T09:36:24.027053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length7
Mean length6.6673901
Min length2

Characters and Unicode

Total characters12288
Distinct characters79
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)1.6%

Sample

1st row철근콘크리트
2nd row철근콘크리트조
3rd row철근콘크리트
4th row철근콘크리트
5th row철근콘크리트
ValueCountFrequency (%)
철근콘크리트조 1223
64.4%
시멘트벽돌조 116
 
6.1%
철골조 70
 
3.7%
철근콘크리트 66
 
3.5%
경량철골조 45
 
2.4%
조적조 37
 
1.9%
연와조 31
 
1.6%
철근콘크리트조/슬라브 29
 
1.5%
목조 26
 
1.4%
철근 23
 
1.2%
Other values (59) 233
 
12.3%
2024-05-18T09:36:25.141159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1771
14.4%
1565
12.7%
1539
12.5%
1426
11.6%
1416
11.5%
1413
11.5%
1394
11.3%
171
 
1.4%
144
 
1.2%
144
 
1.2%
Other values (69) 1305
10.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12070
98.2%
Other Punctuation 122
 
1.0%
Space Separator 61
 
0.5%
Uppercase Letter 30
 
0.2%
Dash Punctuation 4
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1771
14.7%
1565
13.0%
1539
12.8%
1426
11.8%
1416
11.7%
1413
11.7%
1394
11.5%
171
 
1.4%
144
 
1.2%
144
 
1.2%
Other values (60) 1087
9.0%
Other Punctuation
ValueCountFrequency (%)
/ 102
83.6%
. 16
 
13.1%
, 4
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
C 15
50.0%
R 13
43.3%
P 2
 
6.7%
Space Separator
ValueCountFrequency (%)
61
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12070
98.2%
Common 188
 
1.5%
Latin 30
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1771
14.7%
1565
13.0%
1539
12.8%
1426
11.8%
1416
11.7%
1413
11.7%
1394
11.5%
171
 
1.4%
144
 
1.2%
144
 
1.2%
Other values (60) 1087
9.0%
Common
ValueCountFrequency (%)
/ 102
54.3%
61
32.4%
. 16
 
8.5%
- 4
 
2.1%
, 4
 
2.1%
+ 1
 
0.5%
Latin
ValueCountFrequency (%)
C 15
50.0%
R 13
43.3%
P 2
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12070
98.2%
ASCII 218
 
1.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1771
14.7%
1565
13.0%
1539
12.8%
1426
11.8%
1416
11.7%
1413
11.7%
1394
11.5%
171
 
1.4%
144
 
1.2%
144
 
1.2%
Other values (60) 1087
9.0%
ASCII
ValueCountFrequency (%)
/ 102
46.8%
61
28.0%
. 16
 
7.3%
C 15
 
6.9%
R 13
 
6.0%
- 4
 
1.8%
, 4
 
1.8%
P 2
 
0.9%
+ 1
 
0.5%

건축일자
Text

MISSING 

Distinct205
Distinct (%)11.2%
Missing256
Missing (%)12.3%
Memory size16.4 KiB
2024-05-18T09:36:25.700139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length4
Mean length4.6308872
Min length1

Characters and Unicode

Total characters8456
Distinct characters14
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique138 ?
Unique (%)7.6%

Sample

1st row1995
2nd row1988.01.30
3rd row1989.10.10
4th row1989
5th row1984
ValueCountFrequency (%)
1988 171
 
9.4%
1999 117
 
6.4%
1997 111
 
6.1%
1984 81
 
4.4%
1998 65
 
3.6%
1995 61
 
3.3%
2005 53
 
2.9%
2009 52
 
2.8%
1983 51
 
2.8%
1980 49
 
2.7%
Other values (194) 1015
55.6%
2024-05-18T09:36:26.771296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 2261
26.7%
1 1765
20.9%
0 1237
14.6%
8 970
11.5%
2 685
 
8.1%
. 386
 
4.6%
7 359
 
4.2%
5 230
 
2.7%
3 197
 
2.3%
6 197
 
2.3%
Other values (4) 169
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8067
95.4%
Other Punctuation 386
 
4.6%
Connector Punctuation 1
 
< 0.1%
Dash Punctuation 1
 
< 0.1%
Other Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 2261
28.0%
1 1765
21.9%
0 1237
15.3%
8 970
12.0%
2 685
 
8.5%
7 359
 
4.5%
5 230
 
2.9%
3 197
 
2.4%
6 197
 
2.4%
4 166
 
2.1%
Other Punctuation
ValueCountFrequency (%)
. 386
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Other Letter
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8455
> 99.9%
Hangul 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
9 2261
26.7%
1 1765
20.9%
0 1237
14.6%
8 970
11.5%
2 685
 
8.1%
. 386
 
4.6%
7 359
 
4.2%
5 230
 
2.7%
3 197
 
2.3%
6 197
 
2.3%
Other values (3) 168
 
2.0%
Hangul
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8455
> 99.9%
Hangul 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 2261
26.7%
1 1765
20.9%
0 1237
14.6%
8 970
11.5%
2 685
 
8.1%
. 386
 
4.6%
7 359
 
4.2%
5 230
 
2.7%
3 197
 
2.3%
6 197
 
2.3%
Other values (3) 168
 
2.0%
Hangul
ValueCountFrequency (%)
1
100.0%

층고(지상)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8731988
Minimum0
Maximum15
Zeros30
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2024-05-18T09:36:27.400437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum15
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3778164
Coefficient of variation (CV)0.73554197
Kurtosis14.617222
Mean1.8731988
Median Absolute Deviation (MAD)0
Skewness2.7321436
Sum3900
Variance1.8983779
MonotonicityNot monotonic
2024-05-18T09:36:27.883842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 1109
53.3%
2 458
22.0%
3 281
 
13.5%
4 90
 
4.3%
5 69
 
3.3%
0 30
 
1.4%
6 27
 
1.3%
8 9
 
0.4%
7 4
 
0.2%
15 3
 
0.1%
Other values (2) 2
 
0.1%
ValueCountFrequency (%)
0 30
 
1.4%
1 1109
53.3%
2 458
22.0%
3 281
 
13.5%
4 90
 
4.3%
5 69
 
3.3%
6 27
 
1.3%
7 4
 
0.2%
8 9
 
0.4%
9 1
 
< 0.1%
ValueCountFrequency (%)
15 3
 
0.1%
11 1
 
< 0.1%
9 1
 
< 0.1%
8 9
 
0.4%
7 4
 
0.2%
6 27
 
1.3%
5 69
 
3.3%
4 90
 
4.3%
3 281
13.5%
2 458
22.0%

층고(지하)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.42603266
Minimum0
Maximum6
Zeros1317
Zeros (%)63.3%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2024-05-18T09:36:28.353051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.64252215
Coefficient of variation (CV)1.5081523
Kurtosis8.0409358
Mean0.42603266
Median Absolute Deviation (MAD)0
Skewness2.0671808
Sum887
Variance0.41283471
MonotonicityNot monotonic
2024-05-18T09:36:28.664740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1317
63.3%
1 678
32.6%
2 65
 
3.1%
3 13
 
0.6%
4 6
 
0.3%
5 2
 
0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 1317
63.3%
1 678
32.6%
2 65
 
3.1%
3 13
 
0.6%
4 6
 
0.3%
5 2
 
0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 2
 
0.1%
4 6
 
0.3%
3 13
 
0.6%
2 65
 
3.1%
1 678
32.6%
0 1317
63.3%

옥탑
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
0
1636 
1
425 
2
 
19
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row2
5th row0

Common Values

ValueCountFrequency (%)
0 1636
78.6%
1 425
 
20.4%
2 19
 
0.9%
3 2
 
0.1%

Length

2024-05-18T09:36:29.073571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:36:29.549196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1636
78.6%
1 425
 
20.4%
2 19
 
0.9%
3 2
 
0.1%

보고서
Text

MISSING 

Distinct1087
Distinct (%)100.0%
Missing995
Missing (%)47.8%
Memory size16.4 KiB
2024-05-18T09:36:30.271768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length126
Median length114
Mean length91.653174
Min length74

Characters and Unicode

Total characters99627
Distinct characters430
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1087 ?
Unique (%)100.0%

Sample

1st rowhttp://asbestos.seoul.go.kr/modgnb/down.jsp?fpath=00/21/94&fname=13.북서울 꿈의숲-접촉산화제어실.pdf
2nd rowhttp://asbestos.seoul.go.kr/modgnb/down.jsp?fpath=00/21/97&fname=25.수도박물관(경비실,화장실).pdf
3rd rowhttp://asbestos.seoul.go.kr/modgnb/down.jsp?fpath=00/22/10&fname=3.어린이도서관.pdf
4th rowhttp://asbestos.seoul.go.kr/modgnb/down.jsp?fpath=00/22/11&fname=공중화장실.JPG
5th rowhttp://asbestos.seoul.go.kr/modgnb/down.jsp?fpath=00/21/90&fname=1.보일러 및 변전실.pdf
ValueCountFrequency (%)
서울대공원 81
 
3.2%
최초침전지계단실 44
 
1.7%
어린이대공원 40
 
1.6%
관랑환기실 28
 
1.1%
없는경우.pdf 25
 
1.0%
계단실)_ver1.pdf 18
 
0.7%
서울랜드 11
 
0.4%
10
 
0.4%
지상.pdf 9
 
0.4%
서울영어마을 9
 
0.4%
Other values (1990) 2256
89.1%
2024-05-18T09:36:31.800322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 6522
 
6.5%
. 5960
 
6.0%
o 5438
 
5.5%
s 5436
 
5.5%
0 4785
 
4.8%
t 4352
 
4.4%
p 4347
 
4.4%
e 3341
 
3.4%
a 3265
 
3.3%
n 3263
 
3.3%
Other values (420) 52918
53.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 51346
51.5%
Other Punctuation 15775
 
15.8%
Decimal Number 14710
 
14.8%
Other Letter 10765
 
10.8%
Math Symbol 2189
 
2.2%
Space Separator 1444
 
1.4%
Dash Punctuation 1325
 
1.3%
Connector Punctuation 882
 
0.9%
Uppercase Letter 501
 
0.5%
Open Punctuation 345
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
406
 
3.8%
382
 
3.5%
254
 
2.4%
245
 
2.3%
227
 
2.1%
225
 
2.1%
223
 
2.1%
221
 
2.1%
216
 
2.0%
213
 
2.0%
Other values (353) 8153
75.7%
Lowercase Letter
ValueCountFrequency (%)
o 5438
10.6%
s 5436
10.6%
t 4352
 
8.5%
p 4347
 
8.5%
e 3341
 
6.5%
a 3265
 
6.4%
n 3263
 
6.4%
f 3260
 
6.3%
d 3259
 
6.3%
h 2175
 
4.2%
Other values (13) 13210
25.7%
Uppercase Letter
ValueCountFrequency (%)
B 193
38.5%
L 85
17.0%
A 40
 
8.0%
C 39
 
7.8%
T 34
 
6.8%
H 32
 
6.4%
D 29
 
5.8%
E 17
 
3.4%
F 5
 
1.0%
P 5
 
1.0%
Other values (11) 22
 
4.4%
Decimal Number
ValueCountFrequency (%)
0 4785
32.5%
1 2871
19.5%
2 2230
15.2%
3 894
 
6.1%
6 823
 
5.6%
8 697
 
4.7%
4 691
 
4.7%
5 664
 
4.5%
7 551
 
3.7%
9 504
 
3.4%
Other Punctuation
ValueCountFrequency (%)
/ 6522
41.3%
. 5960
37.8%
? 1087
 
6.9%
& 1087
 
6.9%
: 1087
 
6.9%
, 32
 
0.2%
Math Symbol
ValueCountFrequency (%)
= 2174
99.3%
~ 15
 
0.7%
Space Separator
ValueCountFrequency (%)
1444
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1325
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 882
100.0%
Open Punctuation
ValueCountFrequency (%)
( 345
100.0%
Close Punctuation
ValueCountFrequency (%)
) 345
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 51847
52.0%
Common 37015
37.2%
Hangul 10765
 
10.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
406
 
3.8%
382
 
3.5%
254
 
2.4%
245
 
2.3%
227
 
2.1%
225
 
2.1%
223
 
2.1%
221
 
2.1%
216
 
2.0%
213
 
2.0%
Other values (353) 8153
75.7%
Latin
ValueCountFrequency (%)
o 5438
 
10.5%
s 5436
 
10.5%
t 4352
 
8.4%
p 4347
 
8.4%
e 3341
 
6.4%
a 3265
 
6.3%
n 3263
 
6.3%
f 3260
 
6.3%
d 3259
 
6.3%
h 2175
 
4.2%
Other values (34) 13711
26.4%
Common
ValueCountFrequency (%)
/ 6522
17.6%
. 5960
16.1%
0 4785
12.9%
1 2871
 
7.8%
2 2230
 
6.0%
= 2174
 
5.9%
1444
 
3.9%
- 1325
 
3.6%
? 1087
 
2.9%
& 1087
 
2.9%
Other values (13) 7530
20.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88862
89.2%
Hangul 10765
 
10.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 6522
 
7.3%
. 5960
 
6.7%
o 5438
 
6.1%
s 5436
 
6.1%
0 4785
 
5.4%
t 4352
 
4.9%
p 4347
 
4.9%
e 3341
 
3.8%
a 3265
 
3.7%
n 3263
 
3.7%
Other values (57) 42153
47.4%
Hangul
ValueCountFrequency (%)
406
 
3.8%
382
 
3.5%
254
 
2.4%
245
 
2.3%
227
 
2.1%
225
 
2.1%
223
 
2.1%
221
 
2.1%
216
 
2.0%
213
 
2.0%
Other values (353) 8153
75.7%

Interactions

2024-05-18T09:35:57.440489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:35:54.516943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:35:55.827036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:35:57.855747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:35:54.926887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:35:56.351967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:35:58.309355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:35:55.394878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:35:56.945464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T09:36:32.361875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
IDX건물용도구조물층고(지상)층고(지하)옥탑
IDX1.0000.6270.7350.3940.2870.400
건물용도0.6271.0000.8050.3980.2260.332
구조물0.7350.8051.0000.0700.0000.000
층고(지상)0.3940.3980.0701.0000.4750.592
층고(지하)0.2870.2260.0000.4751.0000.398
옥탑0.4000.3320.0000.5920.3981.000
2024-05-18T09:36:32.868942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건물용도옥탑
건물용도1.0000.206
옥탑0.2061.000
2024-05-18T09:36:33.265264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
IDX층고(지상)층고(지하)건물용도옥탑
IDX1.000-0.450-0.2640.3260.250
층고(지상)-0.4501.0000.5640.1910.408
층고(지하)-0.2640.5641.0000.1130.283
건물용도0.3260.1910.1131.0000.206
옥탑0.2500.4080.2830.2061.000

Missing values

2024-05-18T09:35:59.013520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T09:36:00.043149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-18T09:36:00.708871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

기관명건물명IDX주소조사일자전체면적(㎡)석면함유면적(㎡)담당부서담당자명연락처건물용도구조물건축일자층고(지상)층고(지하)옥탑보고서
0<NA>영등포문화원2591서울특별시 영등포구신길로 275<NA>3356.831436.45영등포문화원박노순02-846-0155문화/복지시설철근콘크리트<NA>311<NA>
1서남환경제1유입펌프동2311강서구 양천로 201<NA>5,215.4 ㎡0(주)서남환경홍승표02-3660-2119물재생시설철근콘크리트조1995200<NA>
2<NA>장례식장2594서울특별시 강남구봉은사로114길 13<NA>2283.65119.97공공의료추진반김성주02-2133-9232의료시설철근콘크리트<NA>000<NA>
3<NA>서울여성플라자2571서울특별시 동작구여의대방로54길 18<NA>22519.961660.4서울여성플라자/공간운영실 지원팀전명기02-810-5292업무시설철근콘크리트<NA>532<NA>
4<NA>튼튼삐아제어린이집2595서울특별시 성동구행당로79<NA>379.16352.51남경종합관리㈜양효시02-2295-0501업무시설철근콘크리트<NA>200<NA>
5서부공원녹지사업소(월드컵공원)관사12271서울시 마포구 중동 38-6<NA>476.290시설과신재현300-5568주거시설<NA><NA>200<NA>
6서부공원녹지사업소(월드컵공원)관사32273서울시 마포구 중동 38-6<NA>479.140시설과신재현300-5568주거시설<NA><NA>200<NA>
7한남직업전문학교본관2392서울특별시 용산구한남대로 136<NA>3,254.708.10중부?남부기술교육원 중부캠퍼스 시설팀김문철02-361-5862업무시설철근콘크리트조1988.01.30310<NA>
8<NA>본관동2592서울특별시 강남구봉은사로114길 13<NA>3616.942329공공의료추진반김성주02-2133-9232의료시설철근콘크리트<NA>000<NA>
9서울시립상계직업전문학교본관2391서울특별시<NA><NA>1.33북부기술교육원 운영지원팀김종술02-2092-4741업무시설철근콘크리트조1989.10.10000<NA>
기관명건물명IDX주소조사일자전체면적(㎡)석면함유면적(㎡)담당부서담당자명연락처건물용도구조물건축일자층고(지상)층고(지하)옥탑보고서
2072서울시립노인요양원신관155서울시 노원구 상계1동 산512010.01.08353.280총무과 관리팀문수봉939-6176문화/복지시설철근콘크리트1991.10.30200http://asbestos.seoul.go.kr/modgnb/down.jsp?fpath=00/01/55&fname=77. 서울시립노인요양원1.pdf
2073서울시립성북노인종합복지관본관108서울특별시 성북구 종암동 66-252010.01.082595.020총무과남중배<NA>문화/복지시설철근콘크리트조1997.12.27511<NA>
2074서울시립노인요양원본관77서울시 노원구 상계1동 산512010.01.081167.840총무과 관리팀문수봉939-6176문화/복지시설철근콘크리트1991.10.30211http://asbestos.seoul.go.kr/modgnb/down.jsp?fpath=00/00/77&fname=77. 서울시립노인요양원.pdf
2075응봉공원암벽등반관리사무소655성동구 응봉동 269-42010..07.26121.810성동구청 공원녹지과김기배2286-5654업무시설철근콘크리트조2000200http://asbestos.seoul.go.kr/modgnb/down.jsp?fpath=00/06/55&fname=364. 응봉공원 암벽등반 관리사무소.pdf
2076서울시보건환경연구원관리동8서울특별시 경기도 과천시주암동 12010-03-19846.721636.72운영기획부김현철02-570-3346업무시설철근콘크리트조1980.09.08310<NA>
2077서울대공원종합관리사무소3경기도 과천시 대공원광장로 1022010-02-092722.60시설과류훈02-500-7413업무시설철근콘크리트조1983.04.09200<NA>
2078엘림복지회엘림직업전문학교-교직원숙소23경기도 군포시 고산로 5892010-01-14~151766.810시설과최홍순031-390-1031문화/복지시설철근콘크리트조1988.07.25610<NA>
2079서울특별시의회부속동160서울특별시 중구 태평로1가 60-12009.12.28~29<NA>15의정담당관송정미3702-1263업무시설철근콘크리트조<NA>400<NA>
2080서울특별시의회본관151서울특별시 중구세종대로 1252009.12.28~297,175.36786.64시설관리팀김자겸02-2180-7816업무시설철근콘크리트조1935.03.20910<NA>
2081서울특별시의회서울특별시의회 의원회관137서울특별시 중구 서소문동 37-12009.12.287587.80.01의정담당관차인석<NA>업무시설철근콘크리트조1971.11.15813<NA>