Overview

Dataset statistics

Number of variables25
Number of observations2423
Missing cells352
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory513.6 KiB
Average record size in memory217.1 B

Variable types

Categorical4
Text6
Numeric15

Dataset

Description년도별 전국 지자체 하수도 보급률(수계, 인구, 면적, 하수처리 1,2,3차처리내 공공하수처리인구, 하수처리구역내 1,2,3차처리 내 폐수처리인구, 하수처리구역외 미처리인구, 오수처리인구, 정화조인구, 인구보급률, 고도처리인구 보급률, 하수도설치율)
Author한국환경공단
URLhttps://www.data.go.kr/data/15053336/fileData.do

Alerts

하수처리구역 내 물리적처리 공공하수처리인구 has constant value ""Constant
하수처리구역 내 물리적처리 폐수처리인구 is highly imbalanced (99.2%)Imbalance
지류 has 190 (7.8%) missing valuesMissing
세부단위유역 has 80 (3.3%) missing valuesMissing
중권역 has 30 (1.2%) missing valuesMissing
소권역 has 30 (1.2%) missing valuesMissing
하수처리구역 내 생물학적처리 공공하수처리인구 is highly skewed (γ1 = 42.8120678)Skewed
하수처리구역 내 생물학적처리 폐수처리인구 is highly skewed (γ1 = 39.06876087)Skewed
하수처리구역 내 고도처리 폐수처리인구 is highly skewed (γ1 = 29.21714384)Skewed
총면적 has 51 (2.1%) zerosZeros
하수처리구역 내 생물학적처리 공공하수처리인구 has 2111 (87.1%) zerosZeros
하수처리구역 내 고도처리 공공하수처리인구 has 139 (5.7%) zerosZeros
하수처리구역 내 생물학적처리 폐수처리인구 has 2414 (99.6%) zerosZeros
하수처리구역 내 고도처리 폐수처리인구 has 2382 (98.3%) zerosZeros
미접속인구 has 1918 (79.2%) zerosZeros
하수처리구역 내 면적 has 121 (5.0%) zerosZeros
하수처리구역 외 미처리인구 has 1997 (82.4%) zerosZeros
하수처리구역 외 오수처리인구 has 887 (36.6%) zerosZeros
하수처리구역 외 정화조인구 has 698 (28.8%) zerosZeros
하수처리구역 외 면적 has 327 (13.5%) zerosZeros
공공하수처리구역 인구보급률 has 56 (2.3%) zerosZeros
고도처리인구 보급률 has 134 (5.5%) zerosZeros
하수도설치율 has 209 (8.6%) zerosZeros

Reproduction

Analysis started2024-04-06 08:06:08.178679
Analysis finished2024-04-06 08:06:09.640482
Duration1.46 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

Distinct17
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
경기도
555 
경상북도
330 
경상남도
305 
전라남도
297 
전라북도
243 
Other values (12)
693 

Length

Max length7
Median length4
Mean length3.8023112
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 555
22.9%
경상북도 330
13.6%
경상남도 305
12.6%
전라남도 297
12.3%
전라북도 243
10.0%
충청남도 208
 
8.6%
강원도 193
 
8.0%
충청북도 153
 
6.3%
제주특별자치도 43
 
1.8%
서울특별시 25
 
1.0%
Other values (7) 71
 
2.9%

Length

2024-04-06T17:06:09.795351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 555
22.9%
경상북도 330
13.6%
경상남도 305
12.6%
전라남도 297
12.3%
전라북도 243
10.0%
충청남도 208
 
8.6%
강원도 193
 
8.0%
충청북도 153
 
6.3%
제주특별자치도 43
 
1.8%
서울특별시 25
 
1.0%
Other values (7) 71
 
2.9%

구군
Text

Distinct206
Distinct (%)8.6%
Missing22
Missing (%)0.9%
Memory size19.1 KiB
2024-04-06T17:06:10.481692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0149938
Min length2

Characters and Unicode

Total characters7239
Distinct characters132
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)2.0%

Sample

1st row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
창원시 55
 
2.3%
성남시 50
 
2.1%
수원시 44
 
1.8%
청주시 43
 
1.8%
고양시 39
 
1.6%
용인시 38
 
1.6%
전주시 35
 
1.5%
안양시 31
 
1.3%
천안시 31
 
1.3%
진주시 30
 
1.2%
Other values (196) 2005
83.5%
2024-04-06T17:06:11.578075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 1564
21.6%
군 834
 
11.5%
주 386
 
5.3%
천 286
 
4.0%
양 216
 
3.0%
산 209
 
2.9%
성 207
 
2.9%
안 191
 
2.6%
원 159
 
2.2%
남 133
 
1.8%
Other values (122) 3054
42.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7239
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 1564
21.6%
군 834
 
11.5%
주 386
 
5.3%
천 286
 
4.0%
양 216
 
3.0%
산 209
 
2.9%
성 207
 
2.9%
안 191
 
2.6%
원 159
 
2.2%
남 133
 
1.8%
Other values (122) 3054
42.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7239
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 1564
21.6%
군 834
 
11.5%
주 386
 
5.3%
천 286
 
4.0%
양 216
 
3.0%
산 209
 
2.9%
성 207
 
2.9%
안 191
 
2.6%
원 159
 
2.2%
남 133
 
1.8%
Other values (122) 3054
42.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7239
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 1564
21.6%
군 834
 
11.5%
주 386
 
5.3%
천 286
 
4.0%
양 216
 
3.0%
산 209
 
2.9%
성 207
 
2.9%
안 191
 
2.6%
원 159
 
2.2%
남 133
 
1.8%
Other values (122) 3054
42.2%

행정구역명
Text

Distinct2156
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
2024-04-06T17:06:12.178753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.1481634
Min length2

Characters and Unicode

Total characters7628
Distinct characters316
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1991 ?
Unique (%)82.2%

Sample

1st row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
중앙동 26
 
1.1%
남면 12
 
0.5%
서면 9
 
0.4%
북면 8
 
0.3%
중구 6
 
0.2%
동구 6
 
0.2%
금성면 5
 
0.2%
동면 5
 
0.2%
교동 5
 
0.2%
서구 5
 
0.2%
Other values (2146) 2336
96.4%
2024-04-06T17:06:13.109932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
면 1148
 
15.0%
동 1110
 
14.6%
읍 218
 
2.9%
산 206
 
2.7%
2 125
 
1.6%
1 125
 
1.6%
성 122
 
1.6%
천 116
 
1.5%
남 110
 
1.4%
구 105
 
1.4%
Other values (306) 4243
55.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7293
95.6%
Decimal Number 324
 
4.2%
Other Punctuation 11
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 1148
 
15.7%
동 1110
 
15.2%
읍 218
 
3.0%
산 206
 
2.8%
성 122
 
1.7%
천 116
 
1.6%
남 110
 
1.5%
구 105
 
1.4%
서 91
 
1.2%
양 90
 
1.2%
Other values (296) 3977
54.5%
Decimal Number
ValueCountFrequency (%)
2 125
38.6%
1 125
38.6%
3 46
 
14.2%
4 13
 
4.0%
5 7
 
2.2%
6 4
 
1.2%
7 2
 
0.6%
9 1
 
0.3%
8 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
· 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7293
95.6%
Common 335
 
4.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 1148
 
15.7%
동 1110
 
15.2%
읍 218
 
3.0%
산 206
 
2.8%
성 122
 
1.7%
천 116
 
1.6%
남 110
 
1.5%
구 105
 
1.4%
서 91
 
1.2%
양 90
 
1.2%
Other values (296) 3977
54.5%
Common
ValueCountFrequency (%)
2 125
37.3%
1 125
37.3%
3 46
 
13.7%
4 13
 
3.9%
· 11
 
3.3%
5 7
 
2.1%
6 4
 
1.2%
7 2
 
0.6%
9 1
 
0.3%
8 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7293
95.6%
ASCII 324
 
4.2%
None 11
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
면 1148
 
15.7%
동 1110
 
15.2%
읍 218
 
3.0%
산 206
 
2.8%
성 122
 
1.7%
천 116
 
1.6%
남 110
 
1.5%
구 105
 
1.4%
서 91
 
1.2%
양 90
 
1.2%
Other values (296) 3977
54.5%
ASCII
ValueCountFrequency (%)
2 125
38.6%
1 125
38.6%
3 46
 
14.2%
4 13
 
4.0%
5 7
 
2.2%
6 4
 
1.2%
7 2
 
0.6%
9 1
 
0.3%
8 1
 
0.3%
None
ValueCountFrequency (%)
· 11
100.0%

수계
Categorical

Distinct10
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
한강
822 
낙동강
638 
금강
466 
영산강 섬진강
405 
<NA>
 
30
Other values (5)
 
62

Length

Max length11
Median length2
Mean length3.2587701
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
한강 822
33.9%
낙동강 638
26.3%
금강 466
19.2%
영산강 섬진강 405
16.7%
<NA> 30
 
1.2%
한강 금강 20
 
0.8%
영산강 섬진강 금강 16
 
0.7%
낙동강 한강 11
 
0.5%
낙동강 금강 8
 
0.3%
낙동강 영산강 섬진강 7
 
0.3%

Length

2024-04-06T17:06:13.387917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:06:13.576179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한강 853
29.3%
낙동강 664
22.8%
금강 510
17.5%
영산강 428
14.7%
섬진강 428
14.7%
na 30
 
1.0%

지류
Text

MISSING 

Distinct595
Distinct (%)26.6%
Missing190
Missing (%)7.8%
Memory size19.1 KiB
2024-04-06T17:06:14.139632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length3
Mean length3.2888491
Min length2

Characters and Unicode

Total characters7344
Distinct characters248
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

Unique348 ?
Unique (%)15.6%

Sample

1st row중랑천
2nd row중랑천
3rd row중랑천
4th row중랑천
5th row중랑천
ValueCountFrequency (%)
안양천 66
 
2.8%
탄천 62
 
2.6%
금강 58
 
2.5%
영산강 56
 
2.4%
황구지천 54
 
2.3%
남해 41
 
1.7%
서해 41
 
1.7%
형산강 36
 
1.5%
내성천 34
 
1.4%
지방2급 32
 
1.4%
Other values (575) 1873
79.6%
2024-04-06T17:06:15.156647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 1755
23.9%
강 520
 
7.1%
208
 
2.8%
지 208
 
2.8%
산 201
 
2.7%
안 191
 
2.6%
남 158
 
2.2%
양 150
 
2.0%
해 137
 
1.9%
동 125
 
1.7%
Other values (238) 3691
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6926
94.3%
Space Separator 208
 
2.8%
Decimal Number 78
 
1.1%
Open Punctuation 66
 
0.9%
Close Punctuation 66
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 1755
25.3%
강 520
 
7.5%
지 208
 
3.0%
산 201
 
2.9%
안 191
 
2.8%
남 158
 
2.3%
양 150
 
2.2%
해 137
 
2.0%
동 125
 
1.8%
방 119
 
1.7%
Other values (233) 3362
48.5%
Decimal Number
ValueCountFrequency (%)
2 74
94.9%
1 4
 
5.1%
Space Separator
ValueCountFrequency (%)
208
100.0%
Open Punctuation
ValueCountFrequency (%)
( 66
100.0%
Close Punctuation
ValueCountFrequency (%)
) 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6926
94.3%
Common 418
 
5.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 1755
25.3%
강 520
 
7.5%
지 208
 
3.0%
산 201
 
2.9%
안 191
 
2.8%
남 158
 
2.3%
양 150
 
2.2%
해 137
 
2.0%
동 125
 
1.8%
방 119
 
1.7%
Other values (233) 3362
48.5%
Common
ValueCountFrequency (%)
208
49.8%
2 74
 
17.7%
( 66
 
15.8%
) 66
 
15.8%
1 4
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6926
94.3%
ASCII 418
 
5.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 1755
25.3%
강 520
 
7.5%
지 208
 
3.0%
산 201
 
2.9%
안 191
 
2.8%
남 158
 
2.3%
양 150
 
2.2%
해 137
 
2.0%
동 125
 
1.8%
방 119
 
1.7%
Other values (233) 3362
48.5%
ASCII
ValueCountFrequency (%)
208
49.8%
2 74
 
17.7%
( 66
 
15.8%
) 66
 
15.8%
1 4
 
1.0%

세부단위유역
Text

MISSING 

Distinct99
Distinct (%)4.2%
Missing80
Missing (%)3.3%
Memory size19.1 KiB
2024-04-06T17:06:15.599312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length4.5463082
Min length2

Characters and Unicode

Total characters10652
Distinct characters41
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)0.8%

Sample

1st row한강본류
2nd row한강본류
3rd row한강본류
4th row한강본류
5th row한강본류
ValueCountFrequency (%)
한강본류 310
 
11.9%
안성천 173
 
6.6%
삽교천 148
 
5.7%
새만금 140
 
5.4%
남해서부 132
 
5.1%
서부경남 122
 
4.7%
남해동부 120
 
4.6%
금강하류 114
 
4.4%
동부경남 96
 
3.7%
서해남부 91
 
3.5%
Other values (20) 1167
44.7%
2024-04-06T17:06:16.292341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
강 1107
 
10.4%
류 1049
 
9.8%
남 804
 
7.5%
부 714
 
6.7%
동 556
 
5.2%
한 539
 
5.1%
해 496
 
4.7%
하 406
 
3.8%
천 398
 
3.7%
상 349
 
3.3%
Other values (31) 4234
39.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10316
96.8%
Space Separator 270
 
2.5%
Other Punctuation 66
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
강 1107
 
10.7%
류 1049
 
10.2%
남 804
 
7.8%
부 714
 
6.9%
동 556
 
5.4%
한 539
 
5.2%
해 496
 
4.8%
하 406
 
3.9%
천 398
 
3.9%
상 349
 
3.4%
Other values (29) 3898
37.8%
Space Separator
ValueCountFrequency (%)
270
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10316
96.8%
Common 336
 
3.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
강 1107
 
10.7%
류 1049
 
10.2%
남 804
 
7.8%
부 714
 
6.9%
동 556
 
5.4%
한 539
 
5.2%
해 496
 
4.8%
하 406
 
3.9%
천 398
 
3.9%
상 349
 
3.4%
Other values (29) 3898
37.8%
Common
ValueCountFrequency (%)
270
80.4%
/ 66
 
19.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10316
96.8%
ASCII 336
 
3.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
강 1107
 
10.7%
류 1049
 
10.2%
남 804
 
7.8%
부 714
 
6.9%
동 556
 
5.4%
한 539
 
5.2%
해 496
 
4.8%
하 406
 
3.9%
천 398
 
3.9%
상 349
 
3.4%
Other values (29) 3898
37.8%
ASCII
ValueCountFrequency (%)
270
80.4%
/ 66
 
19.6%

중권역
Text

MISSING 

Distinct392
Distinct (%)16.4%
Missing30
Missing (%)1.2%
Memory size19.1 KiB
2024-04-06T17:06:16.803241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length18
Mean length4.7768491
Min length2

Characters and Unicode

Total characters11431
Distinct characters112
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique165 ?
Unique (%)6.9%

Sample

1st row한강서울 한강고양
2nd row한강서울
3rd row한강서울
4th row한강서울
5th row한강잠실 한강서울
ValueCountFrequency (%)
한강서울 183
 
6.0%
안성천 131
 
4.3%
만경강 86
 
2.8%
한강고양 82
 
2.7%
미호천 77
 
2.5%
낙동강남해 69
 
2.3%
삽교천 65
 
2.1%
금강공주 56
 
1.8%
와탄천 55
 
1.8%
금강서해 52
 
1.7%
Other values (106) 2196
72.0%
2024-04-06T17:06:17.578149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
강 1472
 
12.9%
천 985
 
8.6%
659
 
5.8%
한 449
 
3.9%
남 380
 
3.3%
댐 364
 
3.2%
서 297
 
2.6%
동 288
 
2.5%
하 284
 
2.5%
해 238
 
2.1%
Other values (102) 6015
52.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10772
94.2%
Space Separator 659
 
5.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
강 1472
 
13.7%
천 985
 
9.1%
한 449
 
4.2%
남 380
 
3.5%
댐 364
 
3.4%
서 297
 
2.8%
동 288
 
2.7%
하 284
 
2.6%
해 238
 
2.2%
류 237
 
2.2%
Other values (101) 5778
53.6%
Space Separator
ValueCountFrequency (%)
659
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10772
94.2%
Common 659
 
5.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
강 1472
 
13.7%
천 985
 
9.1%
한 449
 
4.2%
남 380
 
3.5%
댐 364
 
3.4%
서 297
 
2.8%
동 288
 
2.7%
하 284
 
2.6%
해 238
 
2.2%
류 237
 
2.2%
Other values (101) 5778
53.6%
Common
ValueCountFrequency (%)
659
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10772
94.2%
ASCII 659
 
5.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
강 1472
 
13.7%
천 985
 
9.1%
한 449
 
4.2%
남 380
 
3.5%
댐 364
 
3.4%
서 297
 
2.8%
동 288
 
2.7%
하 284
 
2.6%
해 238
 
2.2%
류 237
 
2.2%
Other values (101) 5778
53.6%
ASCII
ValueCountFrequency (%)
659
100.0%

소권역
Text

MISSING 

Distinct1440
Distinct (%)60.2%
Missing30
Missing (%)1.2%
Memory size19.1 KiB
2024-04-06T17:06:18.199472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length65
Median length34
Mean length8.8307564
Min length2

Characters and Unicode

Total characters21132
Distinct characters237
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1139 ?
Unique (%)47.6%

Sample

1st row홍제천 창릉천 홍제천합류전 청계천
2nd row홍제천합류전 청계천
3rd row한강대교수위표 홍제천합류전
4th row한강대교수위표 중랑천합류전 청계천 중랑천하류
5th row중랑천합류전 잠실수중보 중랑천하류
ValueCountFrequency (%)
황구지천상류 52
 
1.1%
안양천중류 44
 
0.9%
성남수위표 40
 
0.9%
남천 37
 
0.8%
계양천합류후 35
 
0.8%
안양천상류 34
 
0.7%
무심천 31
 
0.7%
청계천 30
 
0.6%
화정천 27
 
0.6%
탄천상류 26
 
0.6%
Other values (785) 4295
92.3%
2024-04-06T17:06:19.322276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 3339
 
15.8%
2258
 
10.7%
류 1623
 
7.7%
하 690
 
3.3%
강 584
 
2.8%
수 558
 
2.6%
상 518
 
2.5%
위 497
 
2.4%
표 449
 
2.1%
합 381
 
1.8%
Other values (227) 10235
48.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18863
89.3%
Space Separator 2258
 
10.7%
Other Punctuation 6
 
< 0.1%
Decimal Number 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 3339
 
17.7%
류 1623
 
8.6%
하 690
 
3.7%
강 584
 
3.1%
수 558
 
3.0%
상 518
 
2.7%
위 497
 
2.6%
표 449
 
2.4%
합 381
 
2.0%
댐 361
 
1.9%
Other values (224) 9863
52.3%
Space Separator
ValueCountFrequency (%)
2258
100.0%
Other Punctuation
ValueCountFrequency (%)
· 6
100.0%
Decimal Number
ValueCountFrequency (%)
2 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18863
89.3%
Common 2269
 
10.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 3339
 
17.7%
류 1623
 
8.6%
하 690
 
3.7%
강 584
 
3.1%
수 558
 
3.0%
상 518
 
2.7%
위 497
 
2.6%
표 449
 
2.4%
합 381
 
2.0%
댐 361
 
1.9%
Other values (224) 9863
52.3%
Common
ValueCountFrequency (%)
2258
99.5%
· 6
 
0.3%
2 5
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18863
89.3%
ASCII 2263
 
10.7%
None 6
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 3339
 
17.7%
류 1623
 
8.6%
하 690
 
3.7%
강 584
 
3.1%
수 558
 
3.0%
상 518
 
2.7%
위 497
 
2.6%
표 449
 
2.4%
합 381
 
2.0%
댐 361
 
1.9%
Other values (224) 9863
52.3%
ASCII
ValueCountFrequency (%)
2258
99.8%
2 5
 
0.2%
None
ValueCountFrequency (%)
· 6
100.0%

총인구
Real number (ℝ)

Distinct2244
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21763.392
Minimum0
Maximum663965
Zeros20
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size21.4 KiB
2024-04-06T17:06:19.619718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1396.9
Q12768.5
median6657
Q319812.5
95-th percentile54877.1
Maximum663965
Range663965
Interquartile range (IQR)17044

Descriptive statistics

Standard deviation58949.969
Coefficient of variation (CV)2.7086756
Kurtosis44.520904
Mean21763.392
Median Absolute Deviation (MAD)4783
Skewness6.3263962
Sum52732700
Variance3.4750989 × 109
MonotonicityNot monotonic
2024-04-06T17:06:19.973629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20
 
0.8%
1818 3
 
0.1%
1931 3
 
0.1%
4864 3
 
0.1%
2821 3
 
0.1%
2273 3
 
0.1%
3223 3
 
0.1%
1861 3
 
0.1%
7437 3
 
0.1%
1448 3
 
0.1%
Other values (2234) 2376
98.1%
ValueCountFrequency (%)
0 20
0.8%
109 1
 
< 0.1%
185 1
 
< 0.1%
191 1
 
< 0.1%
620 1
 
< 0.1%
642 1
 
< 0.1%
678 1
 
< 0.1%
707 1
 
< 0.1%
714 1
 
< 0.1%
718 1
 
< 0.1%
ValueCountFrequency (%)
663965 1
< 0.1%
579768 1
< 0.1%
566676 1
< 0.1%
554629 1
< 0.1%
537800 1
< 0.1%
529200 1
< 0.1%
514946 1
< 0.1%
500546 1
< 0.1%
499449 1
< 0.1%
477173 1
< 0.1%

총면적
Real number (ℝ)

ZEROS 

Distinct2092
Distinct (%)86.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.375965
Minimum0
Maximum758.142
Zeros51
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size21.4 KiB
2024-04-06T17:06:20.279187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.68
Q15.0895
median32.27
Q359.462
95-th percentile126.0877
Maximum758.142
Range758.142
Interquartile range (IQR)54.3725

Descriptive statistics

Standard deviation47.866637
Coefficient of variation (CV)1.1568706
Kurtosis29.680935
Mean41.375965
Median Absolute Deviation (MAD)27.182
Skewness3.5392571
Sum100253.96
Variance2291.215
MonotonicityNot monotonic
2024-04-06T17:06:20.560510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 51
 
2.1%
1.0 13
 
0.5%
0.8 10
 
0.4%
1.3 8
 
0.3%
0.69 6
 
0.2%
2.0 5
 
0.2%
4.0 4
 
0.2%
3.8 4
 
0.2%
0.66 4
 
0.2%
10.8 4
 
0.2%
Other values (2082) 2314
95.5%
ValueCountFrequency (%)
0.0 51
2.1%
0.08 1
 
< 0.1%
0.177 1
 
< 0.1%
0.193 1
 
< 0.1%
0.2 1
 
< 0.1%
0.21 1
 
< 0.1%
0.24 1
 
< 0.1%
0.27 1
 
< 0.1%
0.28 1
 
< 0.1%
0.3 2
 
0.1%
ValueCountFrequency (%)
758.142 1
< 0.1%
447.98 1
< 0.1%
426.86 1
< 0.1%
411.32 1
< 0.1%
349.08 1
< 0.1%
331.0 1
< 0.1%
315.14 1
< 0.1%
298.38 1
< 0.1%
290.26 1
< 0.1%
276.79 1
< 0.1%

하수처리구역 내 물리적처리 공공하수처리인구
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0
2423 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 2423
100.0%

Length

2024-04-06T17:06:20.858225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:06:21.045210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2423
100.0%

하수처리구역 내 생물학적처리 공공하수처리인구
Real number (ℝ)

SKEWED  ZEROS 

Distinct282
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173.91704
Minimum0
Maximum144195
Zeros2111
Zeros (%)87.1%
Negative0
Negative (%)0.0%
Memory size21.4 KiB
2024-04-06T17:06:21.249302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile488
Maximum144195
Range144195
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3080.2123
Coefficient of variation (CV)17.710813
Kurtosis1979.1958
Mean173.91704
Median Absolute Deviation (MAD)0
Skewness42.812068
Sum421401
Variance9487707.8
MonotonicityNot monotonic
2024-04-06T17:06:21.522892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2111
87.1%
100 3
 
0.1%
187 3
 
0.1%
139 2
 
0.1%
352 2
 
0.1%
240 2
 
0.1%
108 2
 
0.1%
250 2
 
0.1%
488 2
 
0.1%
564 2
 
0.1%
Other values (272) 292
 
12.1%
ValueCountFrequency (%)
0 2111
87.1%
10 1
 
< 0.1%
12 1
 
< 0.1%
14 1
 
< 0.1%
20 2
 
0.1%
22 1
 
< 0.1%
28 1
 
< 0.1%
30 1
 
< 0.1%
35 1
 
< 0.1%
36 1
 
< 0.1%
ValueCountFrequency (%)
144195 1
< 0.1%
24629 1
< 0.1%
23690 1
< 0.1%
18567 1
< 0.1%
14284 1
< 0.1%
12660 1
< 0.1%
10050 1
< 0.1%
7703 1
< 0.1%
6465 1
< 0.1%
3202 1
< 0.1%

하수처리구역 내 고도처리 공공하수처리인구
Real number (ℝ)

ZEROS 

Distinct2121
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20282.519
Minimum0
Maximum663965
Zeros139
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size21.4 KiB
2024-04-06T17:06:21.788521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11309.5
median5133
Q318352
95-th percentile52808.7
Maximum663965
Range663965
Interquartile range (IQR)17042.5

Descriptive statistics

Standard deviation58512.072
Coefficient of variation (CV)2.8848523
Kurtosis45.207888
Mean20282.519
Median Absolute Deviation (MAD)4679
Skewness6.3607202
Sum49144544
Variance3.4236626 × 109
MonotonicityNot monotonic
2024-04-06T17:06:22.078872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 139
 
5.7%
1003 4
 
0.2%
983 3
 
0.1%
603 3
 
0.1%
1721 3
 
0.1%
715 3
 
0.1%
553 3
 
0.1%
841 3
 
0.1%
816 3
 
0.1%
1085 3
 
0.1%
Other values (2111) 2256
93.1%
ValueCountFrequency (%)
0 139
5.7%
10 1
 
< 0.1%
28 1
 
< 0.1%
29 1
 
< 0.1%
44 1
 
< 0.1%
68 1
 
< 0.1%
73 1
 
< 0.1%
76 1
 
< 0.1%
90 1
 
< 0.1%
98 1
 
< 0.1%
ValueCountFrequency (%)
663965 1
< 0.1%
579768 1
< 0.1%
561576 1
< 0.1%
554629 1
< 0.1%
537800 1
< 0.1%
521262 1
< 0.1%
514946 1
< 0.1%
500546 1
< 0.1%
499449 1
< 0.1%
477173 1
< 0.1%

하수처리구역 내 물리적처리 폐수처리인구
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0
2420 
1472
 
1
30
 
1
28
 
1

Length

Max length4
Median length1
Mean length1.0020636
Min length1

Unique

Unique3 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 2420
99.9%
1472 1
 
< 0.1%
30 1
 
< 0.1%
28 1
 
< 0.1%

Length

2024-04-06T17:06:22.398278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:06:22.680175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2420
99.9%
1472 1
 
< 0.1%
30 1
 
< 0.1%
28 1
 
< 0.1%

하수처리구역 내 생물학적처리 폐수처리인구
Real number (ℝ)

SKEWED  ZEROS 

Distinct10
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8518366
Minimum0
Maximum9849
Zeros2414
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size21.4 KiB
2024-04-06T17:06:22.860204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9849
Range9849
Interquartile range (IQR)0

Descriptive statistics

Standard deviation225.38424
Coefficient of variation (CV)32.893989
Kurtosis1606.5328
Mean6.8518366
Median Absolute Deviation (MAD)0
Skewness39.068761
Sum16602
Variance50798.054
MonotonicityNot monotonic
2024-04-06T17:06:23.071774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 2414
99.6%
5058 1
 
< 0.1%
259 1
 
< 0.1%
173 1
 
< 0.1%
231 1
 
< 0.1%
58 1
 
< 0.1%
88 1
 
< 0.1%
386 1
 
< 0.1%
500 1
 
< 0.1%
9849 1
 
< 0.1%
ValueCountFrequency (%)
0 2414
99.6%
58 1
 
< 0.1%
88 1
 
< 0.1%
173 1
 
< 0.1%
231 1
 
< 0.1%
259 1
 
< 0.1%
386 1
 
< 0.1%
500 1
 
< 0.1%
5058 1
 
< 0.1%
9849 1
 
< 0.1%
ValueCountFrequency (%)
9849 1
 
< 0.1%
5058 1
 
< 0.1%
500 1
 
< 0.1%
386 1
 
< 0.1%
259 1
 
< 0.1%
231 1
 
< 0.1%
173 1
 
< 0.1%
88 1
 
< 0.1%
58 1
 
< 0.1%
0 2414
99.6%

하수처리구역 내 고도처리 폐수처리인구
Real number (ℝ)

SKEWED  ZEROS 

Distinct38
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.872472
Minimum0
Maximum48502
Zeros2382
Zeros (%)98.3%
Negative0
Negative (%)0.0%
Memory size21.4 KiB
2024-04-06T17:06:23.326210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum48502
Range48502
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1221.445
Coefficient of variation (CV)16.994616
Kurtosis1056.9488
Mean71.872472
Median Absolute Deviation (MAD)0
Skewness29.217144
Sum174147
Variance1491928
MonotonicityNot monotonic
2024-04-06T17:06:23.565083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 2382
98.3%
44 2
 
0.1%
3 2
 
0.1%
2 2
 
0.1%
1 2
 
0.1%
48502 1
 
< 0.1%
4209 1
 
< 0.1%
54 1
 
< 0.1%
133 1
 
< 0.1%
197 1
 
< 0.1%
Other values (28) 28
 
1.2%
ValueCountFrequency (%)
0 2382
98.3%
1 2
 
0.1%
2 2
 
0.1%
3 2
 
0.1%
4 1
 
< 0.1%
7 1
 
< 0.1%
44 2
 
0.1%
54 1
 
< 0.1%
55 1
 
< 0.1%
58 1
 
< 0.1%
ValueCountFrequency (%)
48502 1
< 0.1%
15528 1
< 0.1%
14275 1
< 0.1%
13602 1
< 0.1%
13157 1
< 0.1%
11718 1
< 0.1%
10060 1
< 0.1%
8731 1
< 0.1%
6994 1
< 0.1%
5771 1
< 0.1%

미접속인구
Real number (ℝ)

ZEROS 

Distinct279
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.768882
Minimum0
Maximum13281
Zeros1918
Zeros (%)79.2%
Negative0
Negative (%)0.0%
Memory size21.4 KiB
2024-04-06T17:06:23.798182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile286.8
Maximum13281
Range13281
Interquartile range (IQR)0

Descriptive statistics

Standard deviation572.75896
Coefficient of variation (CV)6.600972
Kurtosis239.73927
Mean86.768882
Median Absolute Deviation (MAD)0
Skewness13.90787
Sum210241
Variance328052.83
MonotonicityNot monotonic
2024-04-06T17:06:24.048180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1918
79.2%
3 12
 
0.5%
1 11
 
0.5%
20 9
 
0.4%
30 9
 
0.4%
2 9
 
0.4%
5 9
 
0.4%
10 8
 
0.3%
9 7
 
0.3%
8 7
 
0.3%
Other values (269) 424
 
17.5%
ValueCountFrequency (%)
0 1918
79.2%
1 11
 
0.5%
2 9
 
0.4%
3 12
 
0.5%
4 6
 
0.2%
5 9
 
0.4%
6 3
 
0.1%
7 6
 
0.2%
8 7
 
0.3%
9 7
 
0.3%
ValueCountFrequency (%)
13281 1
< 0.1%
10897 1
< 0.1%
8869 1
< 0.1%
8244 1
< 0.1%
7808 1
< 0.1%
6434 1
< 0.1%
6050 1
< 0.1%
5376 1
< 0.1%
5025 1
< 0.1%
4047 1
< 0.1%

하수처리구역 내 면적
Real number (ℝ)

ZEROS 

Distinct1501
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3776438
Minimum0
Maximum126.21
Zeros121
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size21.4 KiB
2024-04-06T17:06:24.307877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.011
Q10.5565
median1.38
Q33.055
95-th percentile13.5469
Maximum126.21
Range126.21
Interquartile range (IQR)2.4985

Descriptive statistics

Standard deviation7.0185642
Coefficient of variation (CV)2.0779468
Kurtosis70.146257
Mean3.3776438
Median Absolute Deviation (MAD)0.992
Skewness6.7231812
Sum8184.031
Variance49.260244
MonotonicityNot monotonic
2024-04-06T17:06:24.605789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 121
 
5.0%
0.8 19
 
0.8%
1.0 15
 
0.6%
1.4 13
 
0.5%
0.4 13
 
0.5%
0.9 12
 
0.5%
0.6 12
 
0.5%
0.3 11
 
0.5%
0.1 11
 
0.5%
1.9 10
 
0.4%
Other values (1491) 2186
90.2%
ValueCountFrequency (%)
0.0 121
5.0%
0.01 1
 
< 0.1%
0.02 1
 
< 0.1%
0.025 1
 
< 0.1%
0.026 1
 
< 0.1%
0.03 1
 
< 0.1%
0.033 1
 
< 0.1%
0.04 2
 
0.1%
0.041 1
 
< 0.1%
0.042 2
 
0.1%
ValueCountFrequency (%)
126.21 1
< 0.1%
82.7 1
< 0.1%
76.22 1
< 0.1%
69.75 1
< 0.1%
63.93 1
< 0.1%
61.65 1
< 0.1%
61.12 1
< 0.1%
60.0 1
< 0.1%
55.953 1
< 0.1%
55.8 1
< 0.1%

하수처리구역 외 미처리인구
Real number (ℝ)

ZEROS 

Distinct323
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.98019
Minimum0
Maximum3622
Zeros1997
Zeros (%)82.4%
Negative0
Negative (%)0.0%
Memory size21.4 KiB
2024-04-06T17:06:24.940501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile637.4
Maximum3622
Range3622
Interquartile range (IQR)0

Descriptive statistics

Standard deviation325.14723
Coefficient of variation (CV)3.6956869
Kurtosis36.639682
Mean87.98019
Median Absolute Deviation (MAD)0
Skewness5.4536512
Sum213176
Variance105720.72
MonotonicityNot monotonic
2024-04-06T17:06:25.519398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1997
82.4%
16 6
 
0.2%
50 6
 
0.2%
24 5
 
0.2%
21 5
 
0.2%
30 4
 
0.2%
10 4
 
0.2%
29 4
 
0.2%
25 4
 
0.2%
40 4
 
0.2%
Other values (313) 384
 
15.8%
ValueCountFrequency (%)
0 1997
82.4%
1 1
 
< 0.1%
2 2
 
0.1%
4 2
 
0.1%
5 1
 
< 0.1%
6 2
 
0.1%
7 2
 
0.1%
8 3
 
0.1%
9 2
 
0.1%
10 4
 
0.2%
ValueCountFrequency (%)
3622 1
< 0.1%
3476 1
< 0.1%
3467 1
< 0.1%
3158 1
< 0.1%
2923 1
< 0.1%
2599 1
< 0.1%
2527 1
< 0.1%
2516 1
< 0.1%
2508 1
< 0.1%
2404 1
< 0.1%

하수처리구역 외 오수처리인구
Real number (ℝ)

ZEROS 

Distinct826
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean399.66942
Minimum0
Maximum18388
Zeros887
Zeros (%)36.6%
Negative0
Negative (%)0.0%
Memory size21.4 KiB
2024-04-06T17:06:25.972773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median93
Q3364.5
95-th percentile1768.4
Maximum18388
Range18388
Interquartile range (IQR)364.5

Descriptive statistics

Standard deviation1029.8312
Coefficient of variation (CV)2.5767075
Kurtosis80.144058
Mean399.66942
Median Absolute Deviation (MAD)93
Skewness7.3209747
Sum968399
Variance1060552.3
MonotonicityNot monotonic
2024-04-06T17:06:26.318575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 887
36.6%
20 12
 
0.5%
200 10
 
0.4%
3 8
 
0.3%
32 8
 
0.3%
138 8
 
0.3%
52 8
 
0.3%
1 8
 
0.3%
140 7
 
0.3%
50 7
 
0.3%
Other values (816) 1460
60.3%
ValueCountFrequency (%)
0 887
36.6%
1 8
 
0.3%
2 5
 
0.2%
3 8
 
0.3%
4 5
 
0.2%
5 6
 
0.2%
6 5
 
0.2%
7 6
 
0.2%
8 4
 
0.2%
9 5
 
0.2%
ValueCountFrequency (%)
18388 1
< 0.1%
12488 1
< 0.1%
12434 1
< 0.1%
12418 1
< 0.1%
11634 1
< 0.1%
9605 1
< 0.1%
9373 1
< 0.1%
8469 1
< 0.1%
8115 1
< 0.1%
7384 1
< 0.1%

하수처리구역 외 정화조인구
Real number (ℝ)

ZEROS 

Distinct1106
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean653.18201
Minimum0
Maximum26813
Zeros698
Zeros (%)28.8%
Negative0
Negative (%)0.0%
Memory size21.4 KiB
2024-04-06T17:06:26.580073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median265
Q3867.5
95-th percentile2313
Maximum26813
Range26813
Interquartile range (IQR)867.5

Descriptive statistics

Standard deviation1289.4525
Coefficient of variation (CV)1.974109
Kurtosis158.94582
Mean653.18201
Median Absolute Deviation (MAD)265
Skewness9.5808546
Sum1582660
Variance1662687.7
MonotonicityNot monotonic
2024-04-06T17:06:26.983275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 698
28.8%
3 10
 
0.4%
28 9
 
0.4%
35 8
 
0.3%
24 8
 
0.3%
11 6
 
0.2%
56 6
 
0.2%
4 6
 
0.2%
2 6
 
0.2%
594 6
 
0.2%
Other values (1096) 1660
68.5%
ValueCountFrequency (%)
0 698
28.8%
1 3
 
0.1%
2 6
 
0.2%
3 10
 
0.4%
4 6
 
0.2%
5 5
 
0.2%
6 3
 
0.1%
7 4
 
0.2%
9 2
 
0.1%
10 3
 
0.1%
ValueCountFrequency (%)
26813 1
< 0.1%
26022 1
< 0.1%
20046 1
< 0.1%
12977 1
< 0.1%
9461 1
< 0.1%
8712 1
< 0.1%
7550 1
< 0.1%
7507 1
< 0.1%
7273 1
< 0.1%
6896 1
< 0.1%

하수처리구역 외 면적
Real number (ℝ)

ZEROS 

Distinct1917
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.998322
Minimum0
Maximum631.932
Zeros327
Zeros (%)13.5%
Negative0
Negative (%)0.0%
Memory size21.4 KiB
2024-04-06T17:06:27.270283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.6335
median28.8
Q355.801
95-th percentile123.5194
Maximum631.932
Range631.932
Interquartile range (IQR)54.1675

Descriptive statistics

Standard deviation46.64651
Coefficient of variation (CV)1.227594
Kurtosis20.413446
Mean37.998322
Median Absolute Deviation (MAD)27.141
Skewness3.0992706
Sum92069.933
Variance2175.8969
MonotonicityNot monotonic
2024-04-06T17:06:27.547138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 327
 
13.5%
0.1 13
 
0.5%
0.6 8
 
0.3%
0.2 5
 
0.2%
3.0 5
 
0.2%
0.04 5
 
0.2%
1.0 4
 
0.2%
4.0 4
 
0.2%
3.38 4
 
0.2%
1.6 4
 
0.2%
Other values (1907) 2044
84.4%
ValueCountFrequency (%)
0.0 327
13.5%
0.001 1
 
< 0.1%
0.004 1
 
< 0.1%
0.007 1
 
< 0.1%
0.01 1
 
< 0.1%
0.012 1
 
< 0.1%
0.013 1
 
< 0.1%
0.014 2
 
0.1%
0.02 3
 
0.1%
0.026 1
 
< 0.1%
ValueCountFrequency (%)
631.932 1
< 0.1%
447.4 1
< 0.1%
406.46 1
< 0.1%
406.106 1
< 0.1%
344.18 1
< 0.1%
320.6 1
< 0.1%
312.44 1
< 0.1%
298.226 1
< 0.1%
286.89 1
< 0.1%
272.98 1
< 0.1%

공공하수처리구역 인구보급률
Real number (ℝ)

ZEROS 

Distinct711
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.648783
Minimum0
Maximum100
Zeros56
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size21.4 KiB
2024-04-06T17:06:27.889114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17.51
Q156
median89.3
Q399.9
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)43.9

Descriptive statistics

Standard deviation28.63219
Coefficient of variation (CV)0.37848844
Kurtosis-0.085800441
Mean75.648783
Median Absolute Deviation (MAD)10.7
Skewness-1.0339971
Sum183297
Variance819.80229
MonotonicityNot monotonic
2024-04-06T17:06:28.558349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 586
 
24.2%
0.0 56
 
2.3%
99.9 33
 
1.4%
99.8 27
 
1.1%
99.4 20
 
0.8%
99.5 18
 
0.7%
99.6 16
 
0.7%
99.0 15
 
0.6%
99.7 13
 
0.5%
98.3 13
 
0.5%
Other values (701) 1626
67.1%
ValueCountFrequency (%)
0.0 56
2.3%
1.0 1
 
< 0.1%
1.4 2
 
0.1%
2.8 1
 
< 0.1%
3.4 1
 
< 0.1%
3.9 1
 
< 0.1%
5.4 1
 
< 0.1%
5.6 1
 
< 0.1%
5.8 1
 
< 0.1%
5.9 1
 
< 0.1%
ValueCountFrequency (%)
100.0 586
24.2%
99.9 33
 
1.4%
99.8 27
 
1.1%
99.7 13
 
0.5%
99.6 16
 
0.7%
99.5 18
 
0.7%
99.4 20
 
0.8%
99.3 12
 
0.5%
99.2 10
 
0.4%
99.1 11
 
0.5%

고도처리인구 보급률
Real number (ℝ)

ZEROS 

Distinct727
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.015023
Minimum0
Maximum100
Zeros134
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size21.4 KiB
2024-04-06T17:06:28.810299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150.9
median85.9
Q399.6
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)48.7

Descriptive statistics

Standard deviation31.778729
Coefficient of variation (CV)0.44127916
Kurtosis-0.379129
Mean72.015023
Median Absolute Deviation (MAD)14.1
Skewness-0.94132284
Sum174492.4
Variance1009.8876
MonotonicityNot monotonic
2024-04-06T17:06:29.084025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 519
 
21.4%
0.0 134
 
5.5%
99.8 36
 
1.5%
99.9 31
 
1.3%
99.4 22
 
0.9%
99.5 18
 
0.7%
99.6 17
 
0.7%
99.0 16
 
0.7%
99.3 16
 
0.7%
98.8 15
 
0.6%
Other values (717) 1599
66.0%
ValueCountFrequency (%)
0.0 134
5.5%
0.6 1
 
< 0.1%
0.8 1
 
< 0.1%
1.0 1
 
< 0.1%
1.4 1
 
< 0.1%
1.6 1
 
< 0.1%
2.9 1
 
< 0.1%
3.0 1
 
< 0.1%
3.4 1
 
< 0.1%
3.9 1
 
< 0.1%
ValueCountFrequency (%)
100.0 519
21.4%
99.9 31
 
1.3%
99.8 36
 
1.5%
99.7 11
 
0.5%
99.6 17
 
0.7%
99.5 18
 
0.7%
99.4 22
 
0.9%
99.3 16
 
0.7%
99.2 11
 
0.5%
99.1 13
 
0.5%

하수도설치율
Real number (ℝ)

ZEROS 

Distinct803
Distinct (%)33.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.937887
Minimum0
Maximum100
Zeros209
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size21.4 KiB
2024-04-06T17:06:29.388242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q135.75
median69.1
Q393.5
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)57.75

Descriptive statistics

Standard deviation33.463091
Coefficient of variation (CV)0.54026853
Kurtosis-1.0548256
Mean61.937887
Median Absolute Deviation (MAD)27.5
Skewness-0.50568504
Sum150075.5
Variance1119.7785
MonotonicityNot monotonic
2024-04-06T17:06:29.652259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 252
 
10.4%
0.0 209
 
8.6%
99.0 25
 
1.0%
99.8 23
 
0.9%
99.9 22
 
0.9%
92.3 11
 
0.5%
99.4 10
 
0.4%
97.0 10
 
0.4%
99.5 10
 
0.4%
99.2 10
 
0.4%
Other values (793) 1841
76.0%
ValueCountFrequency (%)
0.0 209
8.6%
0.8 3
 
0.1%
0.9 1
 
< 0.1%
1.0 1
 
< 0.1%
1.2 2
 
0.1%
1.3 1
 
< 0.1%
1.8 2
 
0.1%
2.0 1
 
< 0.1%
2.6 1
 
< 0.1%
2.8 1
 
< 0.1%
ValueCountFrequency (%)
100.0 252
10.4%
99.9 22
 
0.9%
99.8 23
 
0.9%
99.7 9
 
0.4%
99.6 9
 
0.4%
99.5 10
 
0.4%
99.4 10
 
0.4%
99.3 5
 
0.2%
99.2 10
 
0.4%
99.1 6
 
0.2%

Sample

시도구군행정구역명수계지류세부단위유역중권역소권역총인구총면적하수처리구역 내 물리적처리 공공하수처리인구하수처리구역 내 생물학적처리 공공하수처리인구하수처리구역 내 고도처리 공공하수처리인구하수처리구역 내 물리적처리 폐수처리인구하수처리구역 내 생물학적처리 폐수처리인구하수처리구역 내 고도처리 폐수처리인구미접속인구하수처리구역 내 면적하수처리구역 외 미처리인구하수처리구역 외 오수처리인구하수처리구역 외 정화조인구하수처리구역 외 면적공공하수처리구역 인구보급률고도처리인구 보급률하수도설치율
0서울특별시종로구종로구한강<NA>한강본류한강서울 한강고양홍제천 창릉천 홍제천합류전 청계천15378923.8600153789000015.10008.76100.0100.0100.0
1서울특별시중구중구한강중랑천한강본류한강서울홍제천합류전 청계천1317879.920013178700009.90000.02100.0100.0100.0
2서울특별시용산구용산구한강중랑천한강본류한강서울한강대교수위표 홍제천합류전23728521.800237285000017.10004.7100.0100.0100.0
3서울특별시성동구성동구한강<NA>한강본류한강서울한강대교수위표 중랑천합류전 청계천 중랑천하류29267216.7400292672000013.00003.74100.0100.0100.0
4서울특별시광진구광진구한강중랑천한강본류한강잠실 한강서울중랑천합류전 잠실수중보 중랑천하류35262716.9600352627000012.20004.76100.0100.0100.0
5서울특별시동대문구동대문구한강중랑천한강본류한강서울청계천 중랑천하류35200614.1300352006000013.60000.53100.0100.0100.0
6서울특별시중랑구중랑구한강중랑천한강본류한강서울중랑천하류39188518.3900391885000012.90005.49100.0100.0100.0
7서울특별시성북구성북구한강중랑천한강본류한강서울청계천 중랑천하류44014224.5100440142000018.70005.81100.0100.0100.0
8서울특별시강북구강북구한강중랑천한강본류한강서울청계천 중랑천하류30256323.5200302563000011.300012.22100.0100.0100.0
9서울특별시도봉구도봉구한강중랑천한강본류한강서울중랑천하류31937320.6800319373000011.20009.48100.0100.0100.0
시도구군행정구역명수계지류세부단위유역중권역소권역총인구총면적하수처리구역 내 물리적처리 공공하수처리인구하수처리구역 내 생물학적처리 공공하수처리인구하수처리구역 내 고도처리 공공하수처리인구하수처리구역 내 물리적처리 폐수처리인구하수처리구역 내 생물학적처리 폐수처리인구하수처리구역 내 고도처리 폐수처리인구미접속인구하수처리구역 내 면적하수처리구역 외 미처리인구하수처리구역 외 오수처리인구하수처리구역 외 정화조인구하수처리구역 외 면적공공하수처리구역 인구보급률고도처리인구 보급률하수도설치율
2413제주특별자치도서귀포시중앙동영산강 섬진강영산강제주제주남해도순천33640.3200336400000.320000.0100.0100.0100.0
2414제주특별자치도서귀포시천지동영산강 섬진강영산강제주제주남해도순천 신례천35541.8300352600001.07600280.75499.299.299.2
2415제주특별자치도서귀포시효돈동영산강 섬진강영산강제주제주남해신례천53916.6200533700002.50700544.11399.099.099.0
2416제주특별자치도서귀포시영천동영산강 섬진강영산강제주제주남해신례천534746.1800531500006.935003239.24599.499.499.4
2417제주특별자치도서귀포시동홍동영산강 섬진강영산강제주제주남해도순천2399814.3002387800003.9040012010.39699.599.599.5
2418제주특별자치도서귀포시서홍동영산강 섬진강영산강제주제주남해도순천1119613.34001114000001.56015511.7899.599.599.5
2419제주특별자치도서귀포시대륜동영산강 섬진강영산강제주제주남해도순천 신례천1551322.24001549700005.144001617.09699.999.999.9
2420제주특별자치도서귀포시대천동영산강 섬진강영산강제주제주남해예래천 도순천1399950.89001397100003.253002847.63799.899.899.8
2421제주특별자치도서귀포시중문동영산강 섬진강영산강제주제주남해예래천 도순천1223456.69001220900004.582012452.10899.899.899.8
2422제주특별자치도서귀포시예래동영산강 섬진강영산강제주제주남해예래천 창고천409337.7200407300007.141002030.57999.599.599.5