Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory644.5 KiB
Average record size in memory66.0 B

Variable types

Text2
Numeric2
Categorical2
DateTime1

Dataset

Description고유번호,구분코드,구분명,측정일자,측정수위,통신상태,위치정보
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-2527/S/1/datasetView.do

Alerts

통신상태 has constant value ""Constant
구분코드 is highly overall correlated with 구분명High correlation
구분명 is highly overall correlated with 구분코드High correlation
측정수위 has 423 (4.2%) zerosZeros

Reproduction

Analysis started2024-05-11 00:26:47.272256
Analysis finished2024-05-11 00:26:51.184469
Duration3.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고유번호
Text

Distinct264
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T00:26:51.976235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters70000
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

Unique0 ?
Unique (%)0.0%

Sample

1st row11-0001
2nd row14-0004
3rd row01-0003
4th row23-0007
5th row23-0005
ValueCountFrequency (%)
20-0018 46
 
0.5%
14-0004 46
 
0.5%
12-0004 46
 
0.5%
11-0004 45
 
0.4%
16-0011 45
 
0.4%
12-0002 45
 
0.4%
16-0003 45
 
0.4%
12-0007 45
 
0.4%
01-0001 44
 
0.4%
17-0006 44
 
0.4%
Other values (254) 9549
95.5%
2024-05-11T00:26:53.743657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 30379
43.4%
- 10000
 
14.3%
1 9993
 
14.3%
2 6140
 
8.8%
3 2763
 
3.9%
5 2697
 
3.9%
4 1807
 
2.6%
7 1769
 
2.5%
8 1649
 
2.4%
6 1494
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60000
85.7%
Dash Punctuation 10000
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30379
50.6%
1 9993
 
16.7%
2 6140
 
10.2%
3 2763
 
4.6%
5 2697
 
4.5%
4 1807
 
3.0%
7 1769
 
2.9%
8 1649
 
2.7%
6 1494
 
2.5%
9 1309
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30379
43.4%
- 10000
 
14.3%
1 9993
 
14.3%
2 6140
 
8.8%
3 2763
 
3.9%
5 2697
 
3.9%
4 1807
 
2.6%
7 1769
 
2.5%
8 1649
 
2.4%
6 1494
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30379
43.4%
- 10000
 
14.3%
1 9993
 
14.3%
2 6140
 
8.8%
3 2763
 
3.9%
5 2697
 
3.9%
4 1807
 
2.6%
7 1769
 
2.5%
8 1649
 
2.4%
6 1494
 
2.1%

구분코드
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.4474
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T00:26:54.590516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median16
Q320
95-th percentile25
Maximum25
Range24
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.5032963
Coefficient of variation (CV)0.42099617
Kurtosis-0.64514922
Mean15.4474
Median Absolute Deviation (MAD)4
Skewness-0.48829179
Sum154474
Variance42.292863
MonotonicityNot monotonic
2024-05-11T00:26:55.587071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
20 886
 
8.9%
25 749
 
7.5%
23 729
 
7.3%
14 670
 
6.7%
16 574
 
5.7%
18 573
 
5.7%
17 535
 
5.3%
13 500
 
5.0%
22 497
 
5.0%
19 497
 
5.0%
Other values (13) 3790
37.9%
ValueCountFrequency (%)
1 163
 
1.6%
2 222
2.2%
3 338
3.4%
5 477
4.8%
7 371
3.7%
8 228
2.3%
9 74
 
0.7%
10 298
3.0%
11 414
4.1%
12 313
3.1%
ValueCountFrequency (%)
25 749
7.5%
24 79
 
0.8%
23 729
7.3%
22 497
5.0%
21 323
 
3.2%
20 886
8.9%
19 497
5.0%
18 573
5.7%
17 535
5.3%
16 574
5.7%

구분명
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
동작
886 
강동
749 
강남
729 
마포
670 
강서
 
574
Other values (18)
6392 

Length

Max length3
Median length2
Mean length2.0775
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row노원
2nd row마포
3rd row종로
4th row강남
5th row강남

Common Values

ValueCountFrequency (%)
동작 886
 
8.9%
강동 749
 
7.5%
강남 729
 
7.3%
마포 670
 
6.7%
강서 574
 
5.7%
금천 573
 
5.7%
구로 535
 
5.3%
서대문 500
 
5.0%
서초 497
 
5.0%
영등포 497
 
5.0%
Other values (13) 3790
37.9%

Length

2024-05-11T00:26:56.301238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
동작 886
 
8.9%
강동 749
 
7.5%
강남 729
 
7.3%
마포 670
 
6.7%
강서 574
 
5.7%
금천 573
 
5.7%
구로 535
 
5.3%
서대문 500
 
5.0%
서초 497
 
5.0%
영등포 497
 
5.0%
Other values (13) 3790
37.9%

측정일자
Date

Distinct293
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2024-05-11 08:27:00
Maximum2024-05-11 09:24:00
2024-05-11T00:26:56.917271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:26:57.573062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

측정수위
Real number (ℝ)

ZEROS 

Distinct63
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.142742
Minimum0
Maximum1.56
Zeros423
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T00:26:58.278997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.05
median0.1
Q30.18
95-th percentile0.4
Maximum1.56
Range1.56
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.17742725
Coefficient of variation (CV)1.2429926
Kurtosis30.673829
Mean0.142742
Median Absolute Deviation (MAD)0.06
Skewness4.6624353
Sum1427.42
Variance0.031480429
MonotonicityNot monotonic
2024-05-11T00:26:59.096124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05 714
 
7.1%
0.04 638
 
6.4%
0.06 532
 
5.3%
0.03 485
 
4.9%
0.07 475
 
4.8%
0.13 434
 
4.3%
0.0 423
 
4.2%
0.01 414
 
4.1%
0.18 398
 
4.0%
0.11 391
 
3.9%
Other values (53) 5096
51.0%
ValueCountFrequency (%)
0.0 423
4.2%
0.01 414
4.1%
0.02 354
3.5%
0.03 485
4.9%
0.04 638
6.4%
0.05 714
7.1%
0.06 532
5.3%
0.07 475
4.8%
0.08 377
3.8%
0.09 330
3.3%
ValueCountFrequency (%)
1.56 40
0.4%
1.55 18
0.2%
1.54 18
0.2%
1.39 1
 
< 0.1%
1.07 1
 
< 0.1%
0.93 42
0.4%
0.81 10
 
0.1%
0.8 10
 
0.1%
0.79 16
 
0.2%
0.58 8
 
0.1%

통신상태
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
통신양호
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
통신양호 10000
100.0%

Length

2024-05-11T00:26:59.712413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T00:27:00.115209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
통신양호 10000
100.0%

위치정보
Text

Distinct264
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T00:27:00.786999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length107
Median length52
Mean length36.0406
Min length11

Characters and Unicode

Total characters360406
Distinct characters426
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

Unique0 ?
Unique (%)0.0%

Sample

1st row노원구 동일로173가길 29앞 맨홀(동일로173가길29~동일로173가길28간 중앙차선내, 공릉초등교북축)
2nd row마포구 동교로 79앞 맨홀(대창전기조명앞, 성산초등교 월편)
3rd row종로구 자하문로 21 앞 맨홀(영해빌딩앞코너 측구측, 백운동천 하수박스)
4th row서울특별시 강남구 성수대교남단 교차로 윤호병원 보도 앞
5th row테헤란로435앞 맨홀(대종빌딩~삼영빌딩간 횡단보도앞 테헤란로측)
ValueCountFrequency (%)
앞 5510
 
7.8%
서울특별시 5325
 
7.5%
도로 1958
 
2.8%
위치 1356
 
1.9%
동작구 886
 
1.3%
강동구 714
 
1.0%
도로에 694
 
1.0%
강남구 691
 
1.0%
마포구 670
 
0.9%
강서구 654
 
0.9%
Other values (939) 52181
73.9%
2024-05-11T00:27:01.943368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
61871
 
17.2%
로 13303
 
3.7%
구 12045
 
3.3%
앞 11134
 
3.1%
1 9664
 
2.7%
동 8644
 
2.4%
서 8256
 
2.3%
2 6937
 
1.9%
시 6096
 
1.7%
도 6061
 
1.7%
Other values (416) 216395
60.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 234562
65.1%
Space Separator 61871
 
17.2%
Decimal Number 44000
 
12.2%
Close Punctuation 4679
 
1.3%
Open Punctuation 4679
 
1.3%
Dash Punctuation 2889
 
0.8%
Math Symbol 2824
 
0.8%
Other Punctuation 2693
 
0.7%
Uppercase Letter 1534
 
0.4%
Lowercase Letter 640
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 13303
 
5.7%
구 12045
 
5.1%
앞 11134
 
4.7%
동 8644
 
3.7%
서 8256
 
3.5%
시 6096
 
2.6%
도 6061
 
2.6%
울 5651
 
2.4%
별 5410
 
2.3%
특 5325
 
2.3%
Other values (373) 152637
65.1%
Uppercase Letter
ValueCountFrequency (%)
K 337
22.0%
T 265
17.3%
S 225
14.7%
C 220
14.3%
G 85
 
5.5%
J 78
 
5.1%
A 75
 
4.9%
M 74
 
4.8%
D 66
 
4.3%
H 38
 
2.5%
Other values (2) 71
 
4.6%
Decimal Number
ValueCountFrequency (%)
1 9664
22.0%
2 6937
15.8%
3 5061
11.5%
4 3705
 
8.4%
6 3612
 
8.2%
5 3462
 
7.9%
9 3110
 
7.1%
0 3102
 
7.0%
7 3046
 
6.9%
8 2301
 
5.2%
Other Punctuation
ValueCountFrequency (%)
, 1875
69.6%
& 272
 
10.1%
; 230
 
8.5%
: 108
 
4.0%
/ 70
 
2.6%
. 69
 
2.6%
# 69
 
2.6%
Lowercase Letter
ValueCountFrequency (%)
t 230
35.9%
g 115
18.0%
l 115
18.0%
m 76
 
11.9%
o 66
 
10.3%
e 38
 
5.9%
Math Symbol
ValueCountFrequency (%)
~ 1410
49.9%
< 707
25.0%
> 707
25.0%
Space Separator
ValueCountFrequency (%)
61871
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4679
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4679
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2889
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 234562
65.1%
Common 123670
34.3%
Latin 2174
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 13303
 
5.7%
구 12045
 
5.1%
앞 11134
 
4.7%
동 8644
 
3.7%
서 8256
 
3.5%
시 6096
 
2.6%
도 6061
 
2.6%
울 5651
 
2.4%
별 5410
 
2.3%
특 5325
 
2.3%
Other values (373) 152637
65.1%
Common
ValueCountFrequency (%)
61871
50.0%
1 9664
 
7.8%
2 6937
 
5.6%
3 5061
 
4.1%
) 4679
 
3.8%
( 4679
 
3.8%
4 3705
 
3.0%
6 3612
 
2.9%
5 3462
 
2.8%
9 3110
 
2.5%
Other values (15) 16890
 
13.7%
Latin
ValueCountFrequency (%)
K 337
15.5%
T 265
12.2%
t 230
10.6%
S 225
10.3%
C 220
10.1%
g 115
 
5.3%
l 115
 
5.3%
G 85
 
3.9%
J 78
 
3.6%
m 76
 
3.5%
Other values (8) 428
19.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 234562
65.1%
ASCII 125844
34.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
61871
49.2%
1 9664
 
7.7%
2 6937
 
5.5%
3 5061
 
4.0%
) 4679
 
3.7%
( 4679
 
3.7%
4 3705
 
2.9%
6 3612
 
2.9%
5 3462
 
2.8%
9 3110
 
2.5%
Other values (33) 19064
 
15.1%
Hangul
ValueCountFrequency (%)
로 13303
 
5.7%
구 12045
 
5.1%
앞 11134
 
4.7%
동 8644
 
3.7%
서 8256
 
3.5%
시 6096
 
2.6%
도 6061
 
2.6%
울 5651
 
2.4%
별 5410
 
2.3%
특 5325
 
2.3%
Other values (373) 152637
65.1%

Interactions

2024-05-11T00:26:49.423003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:26:48.815257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:26:49.706587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:26:49.101532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T00:27:02.393447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분코드구분명측정수위
구분코드1.0001.0000.387
구분명1.0001.0000.553
측정수위0.3870.5531.000
2024-05-11T00:27:02.720326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분코드측정수위구분명
구분코드1.0000.1220.999
측정수위0.1221.0000.258
구분명0.9990.2581.000

Missing values

2024-05-11T00:26:50.328088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T00:26:50.924160image/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.

Sample

고유번호구분코드구분명측정일자측정수위통신상태위치정보
325211-000111노원2024-05-11 09:15:00.00.19통신양호노원구 동일로173가길 29앞 맨홀(동일로173가길29~동일로173가길28간 중앙차선내, 공릉초등교북축)
530314-000414마포2024-05-11 08:29:00.00.13통신양호마포구 동교로 79앞 맨홀(대창전기조명앞, 성산초등교 월편)
14801-00031종로2024-05-11 08:49:29.00.01통신양호종로구 자하문로 21 앞 맨홀(영해빌딩앞코너 측구측, 백운동천 하수박스)
1298423-000723강남2024-05-11 08:49:00.00.09통신양호서울특별시 강남구 성수대교남단 교차로 윤호병원 보도 앞
1289123-000523강남2024-05-11 08:28:00.01.56통신양호테헤란로435앞 맨홀(대종빌딩~삼영빌딩간 횡단보도앞 테헤란로측)
1491225-001925강동2024-05-11 08:59:00.00.1통신양호강동구 둔촌동 6-13 일자산 2체육관 앞
845117-001417구로2024-05-11 09:09:00.00.12통신양호서울특별시 구로구 구로동 1124-77 깔깔거리 사조회참치 앞
56102-00062중2024-05-11 08:29:00.00.04통신양호서울특별시 중구 청계천로 106 주변
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1468725-001525강동2024-05-11 08:56:00.00.2통신양호강동구 암사동 554-1 강변그대가리버뷰아파트 102동 앞
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1163521-000421관악2024-05-11 08:29:00.00.1통신양호관악구 봉천로 536-1앞 맨홀(피닉스빌딩앞 중앙차선내)
169605-00115광진2024-05-11 08:34:00.00.2통신양호서울특별시 광진구 아차산로 503 청구아파트101동 앞 도로변
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