Overview

Dataset statistics

Number of variables10
Number of observations322
Missing cells274
Missing cells (%)8.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.9 KiB
Average record size in memory85.4 B

Variable types

Numeric5
Categorical1
DateTime1
Text3

Dataset

Description심의일정순번,심의구분,심의년도,심의순번,심의일자,심의시작시간,심의종료시간,심의장소,심의대상사업,심의내용
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-2198/S/1/datasetView.do

Alerts

심의일정순번 is highly overall correlated with 심의년도High correlation
심의년도 is highly overall correlated with 심의일정순번High correlation
심의순번 has 34 (10.6%) missing valuesMissing
심의대상사업 has 18 (5.6%) missing valuesMissing
심의내용 has 222 (68.9%) missing valuesMissing
심의일정순번 has unique valuesUnique

Reproduction

Analysis started2024-05-11 05:25:36.313814
Analysis finished2024-05-11 05:25:44.137004
Duration7.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

심의일정순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct322
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161.5
Minimum1
Maximum322
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-05-11T14:25:44.281239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17.05
Q181.25
median161.5
Q3241.75
95-th percentile305.95
Maximum322
Range321
Interquartile range (IQR)160.5

Descriptive statistics

Standard deviation93.097619
Coefficient of variation (CV)0.57645585
Kurtosis-1.2
Mean161.5
Median Absolute Deviation (MAD)80.5
Skewness0
Sum52003
Variance8667.1667
MonotonicityStrictly decreasing
2024-05-11T14:25:44.555715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
322 1
 
0.3%
80 1
 
0.3%
102 1
 
0.3%
103 1
 
0.3%
104 1
 
0.3%
105 1
 
0.3%
106 1
 
0.3%
107 1
 
0.3%
108 1
 
0.3%
109 1
 
0.3%
Other values (312) 312
96.9%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
322 1
0.3%
321 1
0.3%
320 1
0.3%
319 1
0.3%
318 1
0.3%
317 1
0.3%
316 1
0.3%
315 1
0.3%
314 1
0.3%
313 1
0.3%

심의구분
Categorical

Distinct5
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
심의회의
189 
초안검토회의
124 
소위원회
 
3
기타
 
3
자문회의
 
3

Length

Max length6
Median length4
Mean length4.7515528
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
심의회의 189
58.7%
초안검토회의 124
38.5%
소위원회 3
 
0.9%
기타 3
 
0.9%
자문회의 3
 
0.9%

Length

2024-05-11T14:25:44.805399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:25:45.021662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
심의회의 189
58.7%
초안검토회의 124
38.5%
소위원회 3
 
0.9%
기타 3
 
0.9%
자문회의 3
 
0.9%

심의년도
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.972
Minimum2010
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-05-11T14:25:45.202847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2011
Q12014
median2017
Q32020
95-th percentile2023
Maximum2024
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.8088062
Coefficient of variation (CV)0.0018883783
Kurtosis-0.98134102
Mean2016.972
Median Absolute Deviation (MAD)3
Skewness0.040364239
Sum649465
Variance14.507005
MonotonicityDecreasing
2024-05-11T14:25:45.409648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2020 44
13.7%
2017 35
10.9%
2014 34
10.6%
2016 27
8.4%
2013 25
 
7.8%
2023 21
 
6.5%
2019 21
 
6.5%
2015 20
 
6.2%
2012 18
 
5.6%
2021 14
 
4.3%
Other values (5) 63
19.6%
ValueCountFrequency (%)
2010 12
 
3.7%
2011 13
 
4.0%
2012 18
5.6%
2013 25
7.8%
2014 34
10.6%
2015 20
6.2%
2016 27
8.4%
2017 35
10.9%
2018 14
 
4.3%
2019 21
6.5%
ValueCountFrequency (%)
2024 11
 
3.4%
2023 21
6.5%
2022 13
 
4.0%
2021 14
 
4.3%
2020 44
13.7%
2019 21
6.5%
2018 14
 
4.3%
2017 35
10.9%
2016 27
8.4%
2015 20
6.2%

심의순번
Real number (ℝ)

MISSING 

Distinct45
Distinct (%)15.6%
Missing34
Missing (%)10.6%
Infinite0
Infinite (%)0.0%
Mean14.319444
Minimum1
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-05-11T14:25:45.651788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median12
Q320
95-th percentile34
Maximum46
Range45
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.22544
Coefficient of variation (CV)0.71409473
Kurtosis0.088508504
Mean14.319444
Median Absolute Deviation (MAD)7
Skewness0.86084953
Sum4124
Variance104.55962
MonotonicityNot monotonic
2024-05-11T14:25:45.928277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
5 14
 
4.3%
3 14
 
4.3%
2 13
 
4.0%
12 13
 
4.0%
10 13
 
4.0%
7 12
 
3.7%
8 12
 
3.7%
4 12
 
3.7%
9 12
 
3.7%
6 12
 
3.7%
Other values (35) 161
50.0%
(Missing) 34
 
10.6%
ValueCountFrequency (%)
1 11
3.4%
2 13
4.0%
3 14
4.3%
4 12
3.7%
5 14
4.3%
6 12
3.7%
7 12
3.7%
8 12
3.7%
9 12
3.7%
10 13
4.0%
ValueCountFrequency (%)
46 1
0.3%
45 1
0.3%
43 1
0.3%
42 1
0.3%
41 1
0.3%
40 1
0.3%
39 1
0.3%
38 1
0.3%
37 1
0.3%
36 2
0.6%

심의일자
Date

Distinct311
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
Minimum2010-03-17 00:00:00
Maximum2024-05-10 00:00:00
2024-05-11T14:25:46.239317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:46.509778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

심의시작시간
Real number (ℝ)

Distinct8
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1584.1304
Minimum100
Maximum1700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-05-11T14:25:46.717772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile1600
Q11600
median1600
Q31600
95-th percentile1600
Maximum1700
Range1600
Interquartile range (IQR)0

Descriptive statistics

Standard deviation130.43907
Coefficient of variation (CV)0.082341116
Kurtosis107.07581
Mean1584.1304
Median Absolute Deviation (MAD)0
Skewness-9.9841696
Sum510090
Variance17014.351
MonotonicityNot monotonic
2024-05-11T14:25:46.903674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1600 302
93.8%
1500 8
 
2.5%
1700 3
 
0.9%
1630 3
 
0.9%
1400 2
 
0.6%
100 2
 
0.6%
900 1
 
0.3%
1000 1
 
0.3%
ValueCountFrequency (%)
100 2
 
0.6%
900 1
 
0.3%
1000 1
 
0.3%
1400 2
 
0.6%
1500 8
 
2.5%
1600 302
93.8%
1630 3
 
0.9%
1700 3
 
0.9%
ValueCountFrequency (%)
1700 3
 
0.9%
1630 3
 
0.9%
1600 302
93.8%
1500 8
 
2.5%
1400 2
 
0.6%
1000 1
 
0.3%
900 1
 
0.3%
100 2
 
0.6%

심의종료시간
Real number (ℝ)

Distinct8
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1789.3478
Minimum100
Maximum1800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-05-11T14:25:47.089802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile1800
Q11800
median1800
Q31800
95-th percentile1800
Maximum1800
Range1700
Interquartile range (IQR)0

Descriptive statistics

Standard deviation103.36041
Coefficient of variation (CV)0.057764291
Kurtosis227.10003
Mean1789.3478
Median Absolute Deviation (MAD)0
Skewness-14.383107
Sum576170
Variance10683.374
MonotonicityNot monotonic
2024-05-11T14:25:47.275459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1800 313
97.2%
1700 2
 
0.6%
1630 2
 
0.6%
1600 1
 
0.3%
100 1
 
0.3%
1500 1
 
0.3%
1200 1
 
0.3%
1710 1
 
0.3%
ValueCountFrequency (%)
100 1
 
0.3%
1200 1
 
0.3%
1500 1
 
0.3%
1600 1
 
0.3%
1630 2
 
0.6%
1700 2
 
0.6%
1710 1
 
0.3%
1800 313
97.2%
ValueCountFrequency (%)
1800 313
97.2%
1710 1
 
0.3%
1700 2
 
0.6%
1630 2
 
0.6%
1600 1
 
0.3%
1500 1
 
0.3%
1200 1
 
0.3%
100 1
 
0.3%

심의장소
Text

Distinct70
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-05-11T14:25:47.562272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length23
Mean length17.76087
Min length3

Characters and Unicode

Total characters5719
Distinct characters48
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

Unique37 ?
Unique (%)11.5%

Sample

1st row서소문1청사 11층 회의실
2nd row서소문1청사 11층 회의실
3rd row서소문1청사 11층 회의실
4th row서소문1청사 11층 회의실
5th row테스트
ValueCountFrequency (%)
회의실 268
20.1%
11층 219
16.4%
1동 169
12.6%
서소문청사 150
11.2%
서울시청 141
10.6%
서소문별관 51
 
3.8%
3층 47
 
3.5%
시청 40
 
3.0%
남산별관 32
 
2.4%
대회의실 31
 
2.3%
Other values (36) 188
14.1%
2024-05-11T14:25:48.046487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1015
17.7%
1 660
11.5%
서 439
 
7.7%
청 420
 
7.3%
층 314
 
5.5%
의 311
 
5.4%
실 308
 
5.4%
회 307
 
5.4%
문 271
 
4.7%
소 270
 
4.7%
Other values (38) 1404
24.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3883
67.9%
Space Separator 1015
 
17.7%
Decimal Number 818
 
14.3%
Other Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 439
11.3%
청 420
10.8%
층 314
 
8.1%
의 311
 
8.0%
실 308
 
7.9%
회 307
 
7.9%
문 271
 
7.0%
소 270
 
7.0%
시 211
 
5.4%
사 210
 
5.4%
Other values (29) 822
21.2%
Decimal Number
ValueCountFrequency (%)
1 660
80.7%
2 80
 
9.8%
3 49
 
6.0%
0 28
 
3.4%
4 1
 
0.1%
Space Separator
ValueCountFrequency (%)
1015
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3883
67.9%
Common 1836
32.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 439
11.3%
청 420
10.8%
층 314
 
8.1%
의 311
 
8.0%
실 308
 
7.9%
회 307
 
7.9%
문 271
 
7.0%
소 270
 
7.0%
시 211
 
5.4%
사 210
 
5.4%
Other values (29) 822
21.2%
Common
ValueCountFrequency (%)
1015
55.3%
1 660
35.9%
2 80
 
4.4%
3 49
 
2.7%
0 28
 
1.5%
, 1
 
0.1%
( 1
 
0.1%
) 1
 
0.1%
4 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3883
67.9%
ASCII 1836
32.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1015
55.3%
1 660
35.9%
2 80
 
4.4%
3 49
 
2.7%
0 28
 
1.5%
, 1
 
0.1%
( 1
 
0.1%
) 1
 
0.1%
4 1
 
0.1%
Hangul
ValueCountFrequency (%)
서 439
11.3%
청 420
10.8%
층 314
 
8.1%
의 311
 
8.0%
실 308
 
7.9%
회 307
 
7.9%
문 271
 
7.0%
소 270
 
7.0%
시 211
 
5.4%
사 210
 
5.4%
Other values (29) 822
21.2%

심의대상사업
Text

MISSING 

Distinct159
Distinct (%)52.3%
Missing18
Missing (%)5.6%
Memory size2.6 KiB
2024-05-11T14:25:48.504380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length36
Mean length20.292763
Min length3

Characters and Unicode

Total characters6169
Distinct characters248
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)17.4%

Sample

1st row상계주공5단지 재건축정비사업
2nd row강북3재정비촉진구역 도시정비형 재개발사업
3rd row세운재정비촉진지구 3구역 도시정비형 재개발사업(재협의)
4th row세운재정비촉진지구 5-1?3구역 도시정비형 재개발사업
5th row테스트
ValueCountFrequency (%)
도시환경정비사업 50
 
5.7%
주택재개발정비사업 47
 
5.3%
주택재건축정비사업 36
 
4.1%
증축 19
 
2.2%
신축 14
 
1.6%
개발사업 13
 
1.5%
교육연구시설 13
 
1.5%
신축공사 12
 
1.4%
재건축정비사업 12
 
1.4%
도시정비형 11
 
1.2%
Other values (283) 655
74.3%
2024-05-11T14:25:49.159484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
586
 
9.5%
사 290
 
4.7%
업 266
 
4.3%
정 253
 
4.1%
비 245
 
4.0%
구 212
 
3.4%
재 203
 
3.3%
역 172
 
2.8%
주 141
 
2.3%
축 130
 
2.1%
Other values (238) 3671
59.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5075
82.3%
Space Separator 586
 
9.5%
Decimal Number 280
 
4.5%
Open Punctuation 49
 
0.8%
Close Punctuation 47
 
0.8%
Dash Punctuation 46
 
0.7%
Uppercase Letter 44
 
0.7%
Other Punctuation 41
 
0.7%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 290
 
5.7%
업 266
 
5.2%
정 253
 
5.0%
비 245
 
4.8%
구 212
 
4.2%
재 203
 
4.0%
역 172
 
3.4%
주 141
 
2.8%
축 130
 
2.6%
시 128
 
2.5%
Other values (202) 3035
59.8%
Uppercase Letter
ValueCountFrequency (%)
G 6
13.6%
R 5
11.4%
C 5
11.4%
T 4
9.1%
B 4
9.1%
D 3
6.8%
E 3
6.8%
K 3
6.8%
S 2
 
4.5%
Q 2
 
4.5%
Other values (5) 7
15.9%
Decimal Number
ValueCountFrequency (%)
1 75
26.8%
3 52
18.6%
2 49
17.5%
4 34
12.1%
5 30
 
10.7%
6 16
 
5.7%
9 9
 
3.2%
7 9
 
3.2%
0 6
 
2.1%
Other Punctuation
ValueCountFrequency (%)
, 28
68.3%
? 8
 
19.5%
. 2
 
4.9%
/ 2
 
4.9%
& 1
 
2.4%
Open Punctuation
ValueCountFrequency (%)
( 48
98.0%
「 1
 
2.0%
Close Punctuation
ValueCountFrequency (%)
) 46
97.9%
」 1
 
2.1%
Space Separator
ValueCountFrequency (%)
586
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5075
82.3%
Common 1050
 
17.0%
Latin 44
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 290
 
5.7%
업 266
 
5.2%
정 253
 
5.0%
비 245
 
4.8%
구 212
 
4.2%
재 203
 
4.0%
역 172
 
3.4%
주 141
 
2.8%
축 130
 
2.6%
시 128
 
2.5%
Other values (202) 3035
59.8%
Common
ValueCountFrequency (%)
586
55.8%
1 75
 
7.1%
3 52
 
5.0%
2 49
 
4.7%
( 48
 
4.6%
) 46
 
4.4%
- 46
 
4.4%
4 34
 
3.2%
5 30
 
2.9%
, 28
 
2.7%
Other values (11) 56
 
5.3%
Latin
ValueCountFrequency (%)
G 6
13.6%
R 5
11.4%
C 5
11.4%
T 4
9.1%
B 4
9.1%
D 3
6.8%
E 3
6.8%
K 3
6.8%
S 2
 
4.5%
Q 2
 
4.5%
Other values (5) 7
15.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5075
82.3%
ASCII 1092
 
17.7%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
586
53.7%
1 75
 
6.9%
3 52
 
4.8%
2 49
 
4.5%
( 48
 
4.4%
) 46
 
4.2%
- 46
 
4.2%
4 34
 
3.1%
5 30
 
2.7%
, 28
 
2.6%
Other values (24) 98
 
9.0%
Hangul
ValueCountFrequency (%)
사 290
 
5.7%
업 266
 
5.2%
정 253
 
5.0%
비 245
 
4.8%
구 212
 
4.2%
재 203
 
4.0%
역 172
 
3.4%
주 141
 
2.8%
축 130
 
2.6%
시 128
 
2.5%
Other values (202) 3035
59.8%
None
ValueCountFrequency (%)
「 1
50.0%
」 1
50.0%

심의내용
Text

MISSING 

Distinct78
Distinct (%)78.0%
Missing222
Missing (%)68.9%
Memory size2.6 KiB
2024-05-11T14:25:49.507142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length165
Median length42
Mean length22.1
Min length1

Characters and Unicode

Total characters2210
Distinct characters207
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)69.0%

Sample

1st row북아현3 재정비촉진구역 주택재개발정비사업 환경영향평가 보완(소위원회)
2nd row북아현3 재정비촉진구역 주택재개발정비사업 환경영향평가서 심의
3rd row산호아파트 주택재건축정비사업 환경영향평가(재보완, 소위원회)
4th row소위원회
5th row잠실우성4차 주택재건축정비사업 소위원회
ValueCountFrequency (%)
환경영향평가 16
 
4.5%
주택재개발정비사업 14
 
4.0%
검토회의 14
 
4.0%
주택재건축정비사업 13
 
3.7%
환경영향평가서 12
 
3.4%
심의 10
 
2.8%
환경영향평가서초안 9
 
2.5%
입니다 8
 
2.3%
초안 8
 
2.3%
7
 
2.0%
Other values (162) 243
68.6%
2024-05-11T14:25:50.245620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
257
 
11.6%
경 68
 
3.1%
의 64
 
2.9%
환 62
 
2.8%
비 60
 
2.7%
가 59
 
2.7%
정 58
 
2.6%
재 56
 
2.5%
평 55
 
2.5%
사 55
 
2.5%
Other values (197) 1416
64.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1772
80.2%
Space Separator 260
 
11.8%
Decimal Number 92
 
4.2%
Other Punctuation 32
 
1.4%
Close Punctuation 18
 
0.8%
Open Punctuation 18
 
0.8%
Dash Punctuation 13
 
0.6%
Math Symbol 3
 
0.1%
Modifier Symbol 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 68
 
3.8%
의 64
 
3.6%
환 62
 
3.5%
비 60
 
3.4%
가 59
 
3.3%
정 58
 
3.3%
재 56
 
3.2%
평 55
 
3.1%
사 55
 
3.1%
향 54
 
3.0%
Other values (176) 1181
66.6%
Decimal Number
ValueCountFrequency (%)
1 25
27.2%
3 17
18.5%
2 14
15.2%
0 11
12.0%
5 6
 
6.5%
4 6
 
6.5%
6 5
 
5.4%
9 5
 
5.4%
7 2
 
2.2%
8 1
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 18
56.2%
, 11
34.4%
? 2
 
6.2%
: 1
 
3.1%
Space Separator
ValueCountFrequency (%)
257
98.8%
　 3
 
1.2%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1772
80.2%
Common 438
 
19.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 68
 
3.8%
의 64
 
3.6%
환 62
 
3.5%
비 60
 
3.4%
가 59
 
3.3%
정 58
 
3.3%
재 56
 
3.2%
평 55
 
3.1%
사 55
 
3.1%
향 54
 
3.0%
Other values (176) 1181
66.6%
Common
ValueCountFrequency (%)
257
58.7%
1 25
 
5.7%
) 18
 
4.1%
( 18
 
4.1%
. 18
 
4.1%
3 17
 
3.9%
2 14
 
3.2%
- 13
 
3.0%
0 11
 
2.5%
, 11
 
2.5%
Other values (11) 36
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1768
80.0%
ASCII 435
 
19.7%
Compat Jamo 4
 
0.2%
None 3
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
257
59.1%
1 25
 
5.7%
) 18
 
4.1%
( 18
 
4.1%
. 18
 
4.1%
3 17
 
3.9%
2 14
 
3.2%
- 13
 
3.0%
0 11
 
2.5%
, 11
 
2.5%
Other values (10) 33
 
7.6%
Hangul
ValueCountFrequency (%)
경 68
 
3.8%
의 64
 
3.6%
환 62
 
3.5%
비 60
 
3.4%
가 59
 
3.3%
정 58
 
3.3%
재 56
 
3.2%
평 55
 
3.1%
사 55
 
3.1%
향 54
 
3.1%
Other values (173) 1177
66.6%
None
ValueCountFrequency (%)
　 3
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅅ 2
50.0%
ㄴ 1
25.0%
ㄷ 1
25.0%

Interactions

2024-05-11T14:25:42.277893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:38.773201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:39.750957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:40.643272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:41.476069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:42.435228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:38.972186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:39.910727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:40.809720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:41.642124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:42.647024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:39.170645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:40.083060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:40.998078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:41.808735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:42.799418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:39.404778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:40.245528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:41.136610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:41.945207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:42.987946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:39.590404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:40.453604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:41.327030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:25:42.111965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T14:25:50.446909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
심의일정순번심의구분심의년도심의순번심의시작시간심의종료시간심의장소심의내용
심의일정순번1.0000.5030.9800.4180.2170.0830.9440.903
심의구분0.5031.0000.4340.2930.0000.0000.6540.993
심의년도0.9800.4341.0000.2210.0420.0000.9360.908
심의순번0.4180.2930.2211.0000.2700.1730.6610.723
심의시작시간0.2170.0000.0420.2701.0000.8160.8921.000
심의종료시간0.0830.0000.0000.1730.8161.0000.5801.000
심의장소0.9440.6540.9360.6610.8920.5801.0000.894
심의내용0.9030.9930.9080.7231.0001.0000.8941.000
2024-05-11T14:25:50.648367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
심의일정순번심의년도심의순번심의시작시간심의종료시간심의구분
심의일정순번1.0000.9960.123-0.0570.1060.230
심의년도0.9961.0000.042-0.0470.1140.193
심의순번0.1230.0421.000-0.105-0.0190.165
심의시작시간-0.057-0.047-0.1051.0000.4520.000
심의종료시간0.1060.114-0.0190.4521.0000.000
심의구분0.2300.1930.1650.0000.0001.000

Missing values

2024-05-11T14:25:43.196990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T14:25:43.427788image/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.
2024-05-11T14:25:44.044844image/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

심의일정순번심의구분심의년도심의순번심의일자심의시작시간심의종료시간심의장소심의대상사업심의내용
0322심의회의2024<NA>2024-05-1016001800서소문1청사 11층 회의실상계주공5단지 재건축정비사업<NA>
1321심의회의2024<NA>2024-04-2616001800서소문1청사 11층 회의실강북3재정비촉진구역 도시정비형 재개발사업<NA>
2320심의회의2024<NA>2024-04-1216001800서소문1청사 11층 회의실세운재정비촉진지구 3구역 도시정비형 재개발사업(재협의)<NA>
3319심의회의2024<NA>2024-03-2916001800서소문1청사 11층 회의실세운재정비촉진지구 5-1?3구역 도시정비형 재개발사업<NA>
4318심의회의2024<NA>2024-03-269001800테스트테스트<NA>
5317초안검토회의2024<NA>2024-03-2216001800서소문1청사 11층 회의실양동구역 제4-2?7지구 도시정비형 재개발사업<NA>
6316심의회의2024<NA>2024-03-1516001800서소문1청사 11층 회의실신정4재정비촉진구역 재건축정비사업<NA>
7315심의회의2024<NA>2024-03-0816001800서울특별시청 서소문청사 11층방화3재정비촉진구역 재건축정비사업<NA>
8314소위원회2024<NA>2024-02-2316001800서소문1청사 11층 회의실<NA>북아현3 재정비촉진구역 주택재개발정비사업 환경영향평가 보완(소위원회)
9313심의회의2024<NA>2024-02-1616001800서소문1청사 11층 회의실목1동 924외 2필지 복합시설 신축사업<NA>
심의일정순번심의구분심의년도심의순번심의일자심의시작시간심의종료시간심의장소심의대상사업심의내용
31210초안검토회의2010272010-12-0116001800남산별관 3층회의실동부청과 시장정비사업<NA>
3139심의회의2010322010-11-2416001800남산별관 3층 회의실고덕시영 아파트 주택재건축 정비사업<NA>
3148초안검토회의2010322010-11-2416001800남산별관 3층 회의실고덕시영 아파트 주택재건축 정비사업<NA>
3157심의회의2010312010-11-2216001800남산별관 3층 회의실<NA><NA>
3166심의회의2010332010-11-1716001800서울시청 남산별관 지하1층 회의실덕성여대 약학관 증축사업<NA>
3175초안검토회의2010142010-06-1116001800남산별관 3층 회의실고덕주공3단지 아파트 주택재건축정비사업환경영향평가서초안에 대한 검토회의입니다
3184심의회의2010152010-05-3116001800서울시청 남산 별관 3층 회의실공평구역 제1,2,4지구 도시환경정비사업보완서 심의 입니다
3193초안검토회의2010122010-05-3116001700서울시청 남산별관 3층 회의실공평구역 제1,2,4지구 도시환경정비사업보완서 심의
3202심의회의2010112010-05-0316001710서울시청 남산별관 3층 회의실공평구역 제1,2,4지구 도시환경정비사업환경영향평가서심의
3211초안검토회의201052010-03-1716001800서울시청 남산별관 2층 회의실공평구역 제1,2,4지구 도시환경정비사업<NA>