Overview

Dataset statistics

Number of variables6
Number of observations643
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.5 KiB
Average record size in memory50.2 B

Variable types

Numeric2
Categorical2
Text2

Dataset

Description경상남도 하수처리시설 현황으로, 일 사용량, 시군명, 하수처리장명, 시설용량, 소재지에 관한 데이터를 제공합니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3076631

Alerts

연번 is highly overall correlated with 일 사용량 구분 and 1 other fieldsHigh correlation
시설용량 is highly overall correlated with 일 사용량 구분High correlation
일 사용량 구분 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
시군 is highly overall correlated with 연번High correlation
일 사용량 구분 is highly imbalanced (65.9%)Imbalance
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:42:09.993001
Analysis finished2023-12-10 23:42:11.139615
Duration1.15 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct643
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean322
Minimum1
Maximum643
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-12-11T08:42:11.221021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile33.1
Q1161.5
median322
Q3482.5
95-th percentile610.9
Maximum643
Range642
Interquartile range (IQR)321

Descriptive statistics

Standard deviation185.76239
Coefficient of variation (CV)0.57690184
Kurtosis-1.2
Mean322
Median Absolute Deviation (MAD)161
Skewness0
Sum207046
Variance34507.667
MonotonicityStrictly increasing
2023-12-11T08:42:11.412658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
404 1
 
0.2%
426 1
 
0.2%
427 1
 
0.2%
428 1
 
0.2%
429 1
 
0.2%
430 1
 
0.2%
431 1
 
0.2%
432 1
 
0.2%
433 1
 
0.2%
Other values (633) 633
98.4%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
643 1
0.2%
642 1
0.2%
641 1
0.2%
640 1
0.2%
639 1
0.2%
638 1
0.2%
637 1
0.2%
636 1
0.2%
635 1
0.2%
634 1
0.2%

일 사용량 구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
500㎥/일 미만
568 
500㎥/일 이상
73 
500㎥/일 이상
 
2

Length

Max length10
Median length9
Mean length9.0031104
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row500㎥/일 이상
2nd row500㎥/일 이상
3rd row500㎥/일 이상
4th row500㎥/일 이상
5th row500㎥/일 이상

Common Values

ValueCountFrequency (%)
500㎥/일 미만 568
88.3%
500㎥/일 이상 73
 
11.4%
500㎥/일 이상 2
 
0.3%

Length

2023-12-11T08:42:11.562933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:42:11.677797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
500㎥/일 643
50.0%
미만 568
44.2%
이상 75
 
5.8%

시군
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
하동군
67 
남해군
65 
함양군
54 
산청군
51 
진주시
43 
Other values (13)
363 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
하동군 67
 
10.4%
남해군 65
 
10.1%
함양군 54
 
8.4%
산청군 51
 
7.9%
진주시 43
 
6.7%
창녕군 42
 
6.5%
거제시 40
 
6.2%
밀양시 37
 
5.8%
사천시 32
 
5.0%
고성군 31
 
4.8%
Other values (8) 181
28.1%

Length

2023-12-11T08:42:11.786979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
하동군 67
 
10.4%
남해군 65
 
10.1%
함양군 54
 
8.4%
산청군 51
 
7.9%
진주시 43
 
6.7%
창녕군 42
 
6.5%
거제시 40
 
6.2%
밀양시 37
 
5.8%
사천시 32
 
5.0%
고성군 31
 
4.8%
Other values (8) 181
28.1%

처리장명
Text

Distinct597
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
2023-12-11T08:42:12.259428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length2
Mean length2.2830482
Min length2

Characters and Unicode

Total characters1468
Distinct characters250
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique559 ?
Unique (%)86.9%

Sample

1st row덕동
2nd row진해
3rd row북면
4th row웅동
5th row대산
ValueCountFrequency (%)
평촌 4
 
0.6%
신촌 4
 
0.6%
신기 3
 
0.5%
갈전 3
 
0.5%
입석 3
 
0.5%
대산 3
 
0.5%
소규모 3
 
0.5%
동촌 2
 
0.3%
당산 2
 
0.3%
신안 2
 
0.3%
Other values (589) 620
95.5%
2023-12-11T08:42:12.855685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 48
 
3.3%
대 43
 
2.9%
동 37
 
2.5%
촌 36
 
2.5%
신 34
 
2.3%
곡 34
 
2.3%
천 32
 
2.2%
평 26
 
1.8%
정 26
 
1.8%
리 26
 
1.8%
Other values (240) 1126
76.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1434
97.7%
Decimal Number 12
 
0.8%
Close Punctuation 7
 
0.5%
Open Punctuation 7
 
0.5%
Space Separator 6
 
0.4%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 48
 
3.3%
대 43
 
3.0%
동 37
 
2.6%
촌 36
 
2.5%
신 34
 
2.4%
곡 34
 
2.4%
천 32
 
2.2%
평 26
 
1.8%
정 26
 
1.8%
리 26
 
1.8%
Other values (231) 1092
76.2%
Decimal Number
ValueCountFrequency (%)
2 5
41.7%
1 4
33.3%
3 1
 
8.3%
5 1
 
8.3%
4 1
 
8.3%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Other Punctuation
ValueCountFrequency (%)
· 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1434
97.7%
Common 34
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 48
 
3.3%
대 43
 
3.0%
동 37
 
2.6%
촌 36
 
2.5%
신 34
 
2.4%
곡 34
 
2.4%
천 32
 
2.2%
평 26
 
1.8%
정 26
 
1.8%
리 26
 
1.8%
Other values (231) 1092
76.2%
Common
ValueCountFrequency (%)
) 7
20.6%
( 7
20.6%
6
17.6%
2 5
14.7%
1 4
11.8%
· 2
 
5.9%
3 1
 
2.9%
5 1
 
2.9%
4 1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1434
97.7%
ASCII 32
 
2.2%
None 2
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 48
 
3.3%
대 43
 
3.0%
동 37
 
2.6%
촌 36
 
2.5%
신 34
 
2.4%
곡 34
 
2.4%
천 32
 
2.2%
평 26
 
1.8%
정 26
 
1.8%
리 26
 
1.8%
Other values (231) 1092
76.2%
ASCII
ValueCountFrequency (%)
) 7
21.9%
( 7
21.9%
6
18.8%
2 5
15.6%
1 4
12.5%
3 1
 
3.1%
5 1
 
3.1%
4 1
 
3.1%
None
ValueCountFrequency (%)
· 2
100.0%

시설용량
Real number (ℝ)

HIGH CORRELATION 

Distinct126
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2567.6719
Minimum10
Maximum500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-12-11T08:42:13.028304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile30
Q146
median68
Q3145
95-th percentile3980
Maximum500000
Range499990
Interquartile range (IQR)99

Descriptive statistics

Standard deviation23274.061
Coefficient of variation (CV)9.0642662
Kurtosis336.69328
Mean2567.6719
Median Absolute Deviation (MAD)28
Skewness16.957488
Sum1651013
Variance5.4168192 × 108
MonotonicityNot monotonic
2023-12-11T08:42:13.167620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 72
 
11.2%
60 50
 
7.8%
30 46
 
7.2%
40 42
 
6.5%
45 34
 
5.3%
70 30
 
4.7%
100 29
 
4.5%
80 24
 
3.7%
120 15
 
2.3%
110 14
 
2.2%
Other values (116) 287
44.6%
ValueCountFrequency (%)
10 2
 
0.3%
12 1
 
0.2%
15 2
 
0.3%
16 1
 
0.2%
20 10
 
1.6%
25 7
 
1.1%
28 1
 
0.2%
30 46
7.2%
32 1
 
0.2%
35 10
 
1.6%
ValueCountFrequency (%)
500000 1
0.2%
190000 1
0.2%
146000 1
0.2%
145000 1
0.2%
97000 1
0.2%
60000 1
0.2%
54000 1
0.2%
43000 1
0.2%
30000 2
0.3%
26000 1
0.2%

소재지
Text

Distinct639
Distinct (%)99.5%
Missing1
Missing (%)0.2%
Memory size5.2 KiB
2023-12-11T08:42:13.462409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length32.5
Mean length18.238318
Min length1

Characters and Unicode

Total characters11709
Distinct characters240
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

Unique636 ?
Unique (%)99.1%

Sample

1st row경상남도 창원시 마산합포구 가포로 739
2nd row진해구 천자로 101(덕산동)
3rd row의창구 신촌본포로 333번길 42(월계리)
4th row경상남도 창원시 진해구 남양동 403
5th row의창구 유등로332번길 86-20(유등리)
ValueCountFrequency (%)
경상남도 155
 
6.1%
경남 141
 
5.5%
남해군 65
 
2.5%
산청군 50
 
2.0%
함양군 32
 
1.3%
통영시 27
 
1.1%
거창군 23
 
0.9%
합천군 22
 
0.9%
창녕군 21
 
0.8%
하동군 17
 
0.7%
Other values (1339) 2001
78.3%
2023-12-11T08:42:13.857276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2520
21.5%
리 606
 
5.2%
1 594
 
5.1%
면 565
 
4.8%
- 441
 
3.8%
남 438
 
3.7%
2 336
 
2.9%
경 299
 
2.6%
3 271
 
2.3%
군 259
 
2.2%
Other values (230) 5380
45.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6101
52.1%
Decimal Number 2590
22.1%
Space Separator 2520
21.5%
Dash Punctuation 441
 
3.8%
Open Punctuation 22
 
0.2%
Close Punctuation 22
 
0.2%
Other Punctuation 13
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 606
 
9.9%
면 565
 
9.3%
남 438
 
7.2%
경 299
 
4.9%
군 259
 
4.2%
상 205
 
3.4%
산 197
 
3.2%
도 183
 
3.0%
동 130
 
2.1%
시 122
 
2.0%
Other values (214) 3097
50.8%
Decimal Number
ValueCountFrequency (%)
1 594
22.9%
2 336
13.0%
3 271
10.5%
4 238
9.2%
6 224
 
8.6%
5 214
 
8.3%
8 194
 
7.5%
9 179
 
6.9%
7 174
 
6.7%
0 166
 
6.4%
Other Punctuation
ValueCountFrequency (%)
, 12
92.3%
. 1
 
7.7%
Space Separator
ValueCountFrequency (%)
2520
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 441
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6101
52.1%
Common 5608
47.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 606
 
9.9%
면 565
 
9.3%
남 438
 
7.2%
경 299
 
4.9%
군 259
 
4.2%
상 205
 
3.4%
산 197
 
3.2%
도 183
 
3.0%
동 130
 
2.1%
시 122
 
2.0%
Other values (214) 3097
50.8%
Common
ValueCountFrequency (%)
2520
44.9%
1 594
 
10.6%
- 441
 
7.9%
2 336
 
6.0%
3 271
 
4.8%
4 238
 
4.2%
6 224
 
4.0%
5 214
 
3.8%
8 194
 
3.5%
9 179
 
3.2%
Other values (6) 397
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6101
52.1%
ASCII 5608
47.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2520
44.9%
1 594
 
10.6%
- 441
 
7.9%
2 336
 
6.0%
3 271
 
4.8%
4 238
 
4.2%
6 224
 
4.0%
5 214
 
3.8%
8 194
 
3.5%
9 179
 
3.2%
Other values (6) 397
 
7.1%
Hangul
ValueCountFrequency (%)
리 606
 
9.9%
면 565
 
9.3%
남 438
 
7.2%
경 299
 
4.9%
군 259
 
4.2%
상 205
 
3.4%
산 197
 
3.2%
도 183
 
3.0%
동 130
 
2.1%
시 122
 
2.0%
Other values (214) 3097
50.8%

Interactions

2023-12-11T08:42:10.641932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:42:10.398617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:42:10.758634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:42:10.538254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:42:13.952649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번일 사용량 구분시군시설용량
연번1.0000.7750.9450.243
일 사용량 구분0.7751.0000.5190.634
시군0.9450.5191.0000.288
시설용량0.2430.6340.2881.000
2023-12-11T08:42:14.030628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군일 사용량 구분
시군1.0000.276
일 사용량 구분0.2761.000
2023-12-11T08:42:14.108351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시설용량일 사용량 구분시군
연번1.000-0.3600.6510.753
시설용량-0.3601.0000.5950.149
일 사용량 구분0.6510.5951.0000.276
시군0.7530.1490.2761.000

Missing values

2023-12-11T08:42:10.967014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:42:11.097559image/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

연번일 사용량 구분시군처리장명시설용량소재지
01500㎥/일 이상창원시덕동500000경상남도 창원시 마산합포구 가포로 739
12500㎥/일 이상창원시진해60000진해구 천자로 101(덕산동)
23500㎥/일 이상창원시북면24000의창구 신촌본포로 333번길 42(월계리)
34500㎥/일 이상창원시웅동20000경상남도 창원시 진해구 남양동 403
45500㎥/일 이상창원시대산13000의창구 유등로332번길 86-20(유등리)
56500㎥/일 이상창원시진동물재생센터4000마산합포구 진동면 미더덕로 406-158(고현리)
67500㎥/일 이상진주시진주190000경상남도 진주시 남강로 1607 (초전동)
78500㎥/일 이상진주시문산7100경상남도 진주시 문산읍 소문리 1842
89500㎥/일 이상진주시사봉2400경상남도 진주시 일반성면 운천리 335-1
910500㎥/일 이상진주시대곡1900경상남도 진주시 대곡면 가정리 94
연번일 사용량 구분시군처리장명시설용량소재지
633634500㎥/일 미만합천군매안110경상남도 합천군 가야면 매안리 253
634635500㎥/일 미만합천군유전85경남 합천군 대병면 유전리 922-2
635636500㎥/일 미만합천군덕곡75경상남도 합천군 덕곡면 율지리 314-1번지
636637500㎥/일 미만합천군권빈70경남 합천군 봉산면 권빈리 884-1
637638500㎥/일 미만합천군두심70가회면 둔내리 40-1번지
638639500㎥/일 미만합천군봉계68경상남도 합천군 봉산면 봉계리 914-2
639640500㎥/일 미만합천군계산50경남 합천군 봉산면 계산리 712
640641500㎥/일 미만합천군마장50가야면 치인리 242-4
641642500㎥/일 미만합천군용주50경상남도 합천군 용주면 용지리 370-1번지
642643500㎥/일 미만합천군구평46경상남도 합천군 가회면 함방리 689-1