Overview

Dataset statistics

Number of variables11
Number of observations21
Missing cells4
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory96.3 B

Variable types

Numeric2
Text7
DateTime1
Categorical1

Dataset

Description경상남도 내 분뇨 공공처리시설 현황으로, 시군명, 처리장명, 시설용량, 처리공법, 소재지, 준공일, 방류수역(하수처리장, 지류, 본류), 운영구분(위탁/직역)에 대한 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15056380/fileData.do

Alerts

방류수역_하수처리장 has 4 (19.0%) missing valuesMissing
연번 has unique valuesUnique
처리장명 has unique valuesUnique
소재지 has unique valuesUnique
준공일 has unique valuesUnique

Reproduction

Analysis started2023-12-12 12:54:20.015520
Analysis finished2023-12-12 12:54:21.607181
Duration1.59 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T21:54:21.677766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q316
95-th percentile20
Maximum21
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.2048368
Coefficient of variation (CV)0.56407607
Kurtosis-1.2
Mean11
Median Absolute Deviation (MAD)5
Skewness0
Sum231
Variance38.5
MonotonicityStrictly increasing
2023-12-12T21:54:21.802367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 1
 
4.8%
2 1
 
4.8%
21 1
 
4.8%
20 1
 
4.8%
19 1
 
4.8%
18 1
 
4.8%
17 1
 
4.8%
16 1
 
4.8%
15 1
 
4.8%
14 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
1 1
4.8%
2 1
4.8%
3 1
4.8%
4 1
4.8%
5 1
4.8%
6 1
4.8%
7 1
4.8%
8 1
4.8%
9 1
4.8%
10 1
4.8%
ValueCountFrequency (%)
21 1
4.8%
20 1
4.8%
19 1
4.8%
18 1
4.8%
17 1
4.8%
16 1
4.8%
15 1
4.8%
14 1
4.8%
13 1
4.8%
12 1
4.8%

시군
Text

Distinct18
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T21:54:21.968960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters42
Distinct characters27
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

Unique16 ?
Unique (%)76.2%

Sample

1st row창원
2nd row창원
3rd row창원
4th row진주
5th row통영
ValueCountFrequency (%)
창원 3
 
14.3%
사천 2
 
9.5%
창녕 1
 
4.8%
거창 1
 
4.8%
함양 1
 
4.8%
산청 1
 
4.8%
하동 1
 
4.8%
남해 1
 
4.8%
고성 1
 
4.8%
함안 1
 
4.8%
Other values (8) 8
38.1%
2023-12-12T21:54:22.238638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
창 5
 
11.9%
천 3
 
7.1%
원 3
 
7.1%
양 3
 
7.1%
산 2
 
4.8%
사 2
 
4.8%
해 2
 
4.8%
함 2
 
4.8%
거 2
 
4.8%
고 1
 
2.4%
Other values (17) 17
40.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
창 5
 
11.9%
천 3
 
7.1%
원 3
 
7.1%
양 3
 
7.1%
산 2
 
4.8%
사 2
 
4.8%
해 2
 
4.8%
함 2
 
4.8%
거 2
 
4.8%
고 1
 
2.4%
Other values (17) 17
40.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
창 5
 
11.9%
천 3
 
7.1%
원 3
 
7.1%
양 3
 
7.1%
산 2
 
4.8%
사 2
 
4.8%
해 2
 
4.8%
함 2
 
4.8%
거 2
 
4.8%
고 1
 
2.4%
Other values (17) 17
40.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
창 5
 
11.9%
천 3
 
7.1%
원 3
 
7.1%
양 3
 
7.1%
산 2
 
4.8%
사 2
 
4.8%
해 2
 
4.8%
함 2
 
4.8%
거 2
 
4.8%
고 1
 
2.4%
Other values (17) 17
40.5%

처리장명
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T21:54:22.453261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.047619
Min length2

Characters and Unicode

Total characters43
Distinct characters30
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

Unique21 ?
Unique (%)100.0%

Sample

1st row창원
2nd row마산
3rd row진해
4th row진주
5th row통영
ValueCountFrequency (%)
창원 1
 
4.8%
의령 1
 
4.8%
거창 1
 
4.8%
함양 1
 
4.8%
산청 1
 
4.8%
하동 1
 
4.8%
남해 1
 
4.8%
고성 1
 
4.8%
남지 1
 
4.8%
함안 1
 
4.8%
Other values (11) 11
52.4%
2023-12-12T21:54:22.848878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
양 3
 
7.0%
천 3
 
7.0%
산 3
 
7.0%
해 3
 
7.0%
남 2
 
4.7%
함 2
 
4.7%
거 2
 
4.7%
창 2
 
4.7%
진 2
 
4.7%
영 1
 
2.3%
Other values (20) 20
46.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 43
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
양 3
 
7.0%
천 3
 
7.0%
산 3
 
7.0%
해 3
 
7.0%
남 2
 
4.7%
함 2
 
4.7%
거 2
 
4.7%
창 2
 
4.7%
진 2
 
4.7%
영 1
 
2.3%
Other values (20) 20
46.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 43
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
양 3
 
7.0%
천 3
 
7.0%
산 3
 
7.0%
해 3
 
7.0%
남 2
 
4.7%
함 2
 
4.7%
거 2
 
4.7%
창 2
 
4.7%
진 2
 
4.7%
영 1
 
2.3%
Other values (20) 20
46.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 43
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
양 3
 
7.0%
천 3
 
7.0%
산 3
 
7.0%
해 3
 
7.0%
남 2
 
4.7%
함 2
 
4.7%
거 2
 
4.7%
창 2
 
4.7%
진 2
 
4.7%
영 1
 
2.3%
Other values (20) 20
46.5%

시설용량
Real number (ℝ)

Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.90476
Minimum30
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T21:54:22.978600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile30
Q150
median60
Q3160
95-th percentile300
Maximum400
Range370
Interquartile range (IQR)110

Descriptive statistics

Standard deviation99.283385
Coefficient of variation (CV)0.86404935
Kurtosis2.248828
Mean114.90476
Median Absolute Deviation (MAD)30
Skewness1.6081286
Sum2413
Variance9857.1905
MonotonicityNot monotonic
2023-12-12T21:54:23.100515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
50 6
28.6%
30 2
 
9.5%
400 1
 
4.8%
205 1
 
4.8%
80 1
 
4.8%
220 1
 
4.8%
103 1
 
4.8%
45 1
 
4.8%
300 1
 
4.8%
60 1
 
4.8%
Other values (5) 5
23.8%
ValueCountFrequency (%)
30 2
 
9.5%
45 1
 
4.8%
50 6
28.6%
55 1
 
4.8%
60 1
 
4.8%
80 1
 
4.8%
95 1
 
4.8%
103 1
 
4.8%
130 1
 
4.8%
160 1
 
4.8%
ValueCountFrequency (%)
400 1
4.8%
300 1
4.8%
220 1
4.8%
205 1
4.8%
200 1
4.8%
160 1
4.8%
130 1
4.8%
103 1
4.8%
95 1
4.8%
80 1
4.8%

처리공법
Text

Distinct11
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T21:54:23.267017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length5.047619
Min length3

Characters and Unicode

Total characters106
Distinct characters39
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)38.1%

Sample

1st row전처리
2nd row전처리
3rd row전처리
4th row전처리
5th row액상부식공법
ValueCountFrequency (%)
전처리 6
28.6%
액상부식법 5
23.8%
a₂o 2
 
9.5%
액상부식공법 1
 
4.8%
협잡물제거+질소,인저감 1
 
4.8%
협잡물제거 1
 
4.8%
협잡물전처리 1
 
4.8%
rabc 1
 
4.8%
부상분리,호기성소화 1
 
4.8%
세일-바이오 1
 
4.8%
2023-12-12T21:54:23.587181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 9
 
8.5%
전 8
 
7.5%
처 8
 
7.5%
상 7
 
6.6%
부 7
 
6.6%
법 7
 
6.6%
액 6
 
5.7%
식 6
 
5.7%
A 3
 
2.8%
협 3
 
2.8%
Other values (29) 42
39.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89
84.0%
Uppercase Letter 9
 
8.5%
Other Punctuation 2
 
1.9%
Math Symbol 2
 
1.9%
Other Number 2
 
1.9%
Dash Punctuation 1
 
0.9%
Decimal Number 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 9
 
10.1%
전 8
 
9.0%
처 8
 
9.0%
상 7
 
7.9%
부 7
 
7.9%
법 7
 
7.9%
액 6
 
6.7%
식 6
 
6.7%
협 3
 
3.4%
잡 3
 
3.4%
Other values (19) 25
28.1%
Uppercase Letter
ValueCountFrequency (%)
A 3
33.3%
B 2
22.2%
O 2
22.2%
C 1
 
11.1%
R 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Other Number
ValueCountFrequency (%)
₂ 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89
84.0%
Latin 9
 
8.5%
Common 8
 
7.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 9
 
10.1%
전 8
 
9.0%
처 8
 
9.0%
상 7
 
7.9%
부 7
 
7.9%
법 7
 
7.9%
액 6
 
6.7%
식 6
 
6.7%
협 3
 
3.4%
잡 3
 
3.4%
Other values (19) 25
28.1%
Latin
ValueCountFrequency (%)
A 3
33.3%
B 2
22.2%
O 2
22.2%
C 1
 
11.1%
R 1
 
11.1%
Common
ValueCountFrequency (%)
, 2
25.0%
+ 2
25.0%
₂ 2
25.0%
- 1
12.5%
3 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89
84.0%
ASCII 15
 
14.2%
None 2
 
1.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 9
 
10.1%
전 8
 
9.0%
처 8
 
9.0%
상 7
 
7.9%
부 7
 
7.9%
법 7
 
7.9%
액 6
 
6.7%
식 6
 
6.7%
협 3
 
3.4%
잡 3
 
3.4%
Other values (19) 25
28.1%
ASCII
ValueCountFrequency (%)
A 3
20.0%
B 2
13.3%
, 2
13.3%
+ 2
13.3%
O 2
13.3%
- 1
 
6.7%
C 1
 
6.7%
R 1
 
6.7%
3 1
 
6.7%
None
ValueCountFrequency (%)
₂ 2
100.0%

소재지
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T21:54:23.836847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length23
Mean length21.142857
Min length16

Characters and Unicode

Total characters444
Distinct characters81
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

Unique21 ?
Unique (%)100.0%

Sample

1st row경상남도 창원시 성산구 창곡로 108-28
2nd row경상남도 창원시 마산합포구 덕동동 714
3rd row경상남도 창원시 진해구 행암동 24
4th row경상남도 진주시 초전동 190-1
5th row경상남도 통영시 명정동 869
ValueCountFrequency (%)
경상남도 21
 
20.2%
창원시 3
 
2.9%
사천시 2
 
1.9%
정양리 1
 
1.0%
하동읍 1
 
1.0%
138-1 1
 
1.0%
남변리 1
 
1.0%
남해읍 1
 
1.0%
남해군 1
 
1.0%
1-1 1
 
1.0%
Other values (71) 71
68.3%
2023-12-12T21:54:24.334394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
83
18.7%
남 29
 
6.5%
1 27
 
6.1%
경 22
 
5.0%
상 22
 
5.0%
도 21
 
4.7%
- 13
 
2.9%
리 12
 
2.7%
시 11
 
2.5%
군 10
 
2.3%
Other values (71) 194
43.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 268
60.4%
Space Separator 83
 
18.7%
Decimal Number 78
 
17.6%
Dash Punctuation 13
 
2.9%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 29
 
10.8%
경 22
 
8.2%
상 22
 
8.2%
도 21
 
7.8%
리 12
 
4.5%
시 11
 
4.1%
군 10
 
3.7%
동 10
 
3.7%
읍 9
 
3.4%
산 8
 
3.0%
Other values (57) 114
42.5%
Decimal Number
ValueCountFrequency (%)
1 27
34.6%
0 9
 
11.5%
2 8
 
10.3%
3 7
 
9.0%
9 6
 
7.7%
8 6
 
7.7%
5 5
 
6.4%
4 5
 
6.4%
7 4
 
5.1%
6 1
 
1.3%
Space Separator
ValueCountFrequency (%)
83
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 268
60.4%
Common 176
39.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 29
 
10.8%
경 22
 
8.2%
상 22
 
8.2%
도 21
 
7.8%
리 12
 
4.5%
시 11
 
4.1%
군 10
 
3.7%
동 10
 
3.7%
읍 9
 
3.4%
산 8
 
3.0%
Other values (57) 114
42.5%
Common
ValueCountFrequency (%)
83
47.2%
1 27
 
15.3%
- 13
 
7.4%
0 9
 
5.1%
2 8
 
4.5%
3 7
 
4.0%
9 6
 
3.4%
8 6
 
3.4%
5 5
 
2.8%
4 5
 
2.8%
Other values (4) 7
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 268
60.4%
ASCII 176
39.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
83
47.2%
1 27
 
15.3%
- 13
 
7.4%
0 9
 
5.1%
2 8
 
4.5%
3 7
 
4.0%
9 6
 
3.4%
8 6
 
3.4%
5 5
 
2.8%
4 5
 
2.8%
Other values (4) 7
 
4.0%
Hangul
ValueCountFrequency (%)
남 29
 
10.8%
경 22
 
8.2%
상 22
 
8.2%
도 21
 
7.8%
리 12
 
4.5%
시 11
 
4.1%
군 10
 
3.7%
동 10
 
3.7%
읍 9
 
3.4%
산 8
 
3.0%
Other values (57) 114
42.5%

준공일
Date

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
Minimum1982-05-31 00:00:00
Maximum2011-06-30 00:00:00
2023-12-12T21:54:24.470389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:24.613133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)

방류수역_하수처리장
Text

MISSING 

Distinct16
Distinct (%)94.1%
Missing4
Missing (%)19.0%
Memory size300.0 B
2023-12-12T21:54:24.815900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.2941176
Min length2

Characters and Unicode

Total characters39
Distinct characters27
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

Unique15 ?
Unique (%)88.2%

Sample

1st row마산창원
2nd row마산창원
3rd row진해
4th row진주
5th row삼천포
ValueCountFrequency (%)
마산창원 2
 
11.8%
양산 1
 
5.9%
진주 1
 
5.9%
삼천포 1
 
5.9%
사천 1
 
5.9%
화목 1
 
5.9%
밀양 1
 
5.9%
진해 1
 
5.9%
가야 1
 
5.9%
남지 1
 
5.9%
Other values (6) 6
35.3%
2023-12-12T21:54:25.238099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
창 3
 
7.7%
양 3
 
7.7%
산 3
 
7.7%
마 2
 
5.1%
남 2
 
5.1%
해 2
 
5.1%
천 2
 
5.1%
진 2
 
5.1%
원 2
 
5.1%
의 1
 
2.6%
Other values (17) 17
43.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 39
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
창 3
 
7.7%
양 3
 
7.7%
산 3
 
7.7%
마 2
 
5.1%
남 2
 
5.1%
해 2
 
5.1%
천 2
 
5.1%
진 2
 
5.1%
원 2
 
5.1%
의 1
 
2.6%
Other values (17) 17
43.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 39
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
창 3
 
7.7%
양 3
 
7.7%
산 3
 
7.7%
마 2
 
5.1%
남 2
 
5.1%
해 2
 
5.1%
천 2
 
5.1%
진 2
 
5.1%
원 2
 
5.1%
의 1
 
2.6%
Other values (17) 17
43.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 39
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
창 3
 
7.7%
양 3
 
7.7%
산 3
 
7.7%
마 2
 
5.1%
남 2
 
5.1%
해 2
 
5.1%
천 2
 
5.1%
진 2
 
5.1%
원 2
 
5.1%
의 1
 
2.6%
Other values (17) 17
43.6%

방류수역_지류
Text

Distinct18
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T21:54:25.425323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.7619048
Min length2

Characters and Unicode

Total characters58
Distinct characters27
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

Unique16 ?
Unique (%)76.2%

Sample

1st row낙동강
2nd row낙동강
3rd row낙동강
4th row남강
5th row북신만
ValueCountFrequency (%)
낙동강 3
 
14.3%
남강 2
 
9.5%
황강 1
 
4.8%
위천 1
 
4.8%
섬진강 1
 
4.8%
봉천 1
 
4.8%
고성천 1
 
4.8%
계성천 1
 
4.8%
신음천 1
 
4.8%
의령천 1
 
4.8%
Other values (8) 8
38.1%
2023-12-12T21:54:25.733895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 11
19.0%
강 9
15.5%
만 4
 
6.9%
낙 3
 
5.2%
동 3
 
5.2%
성 2
 
3.4%
봉 2
 
3.4%
양 2
 
3.4%
사 2
 
3.4%
남 2
 
3.4%
Other values (17) 18
31.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 58
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 11
19.0%
강 9
15.5%
만 4
 
6.9%
낙 3
 
5.2%
동 3
 
5.2%
성 2
 
3.4%
봉 2
 
3.4%
양 2
 
3.4%
사 2
 
3.4%
남 2
 
3.4%
Other values (17) 18
31.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 58
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 11
19.0%
강 9
15.5%
만 4
 
6.9%
낙 3
 
5.2%
동 3
 
5.2%
성 2
 
3.4%
봉 2
 
3.4%
양 2
 
3.4%
사 2
 
3.4%
남 2
 
3.4%
Other values (17) 18
31.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 58
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 11
19.0%
강 9
15.5%
만 4
 
6.9%
낙 3
 
5.2%
동 3
 
5.2%
성 2
 
3.4%
봉 2
 
3.4%
양 2
 
3.4%
사 2
 
3.4%
남 2
 
3.4%
Other values (17) 18
31.0%

방류수역_본류
Text

Distinct13
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T21:54:25.905345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.7142857
Min length2

Characters and Unicode

Total characters78
Distinct characters20
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

Unique9 ?
Unique (%)42.9%

Sample

1st row낙동강남해
2nd row낙동강남해
3rd row낙동강남해
4th row낙동강남해
5th row낙동강남해
ValueCountFrequency (%)
낙동강남해 6
28.6%
낙동강하구언 2
 
9.5%
남강 2
 
9.5%
남해 2
 
9.5%
죽천천 1
 
4.8%
가화천 1
 
4.8%
밀양강 1
 
4.8%
거제도 1
 
4.8%
낙동밀양 1
 
4.8%
남해동부 1
 
4.8%
Other values (3) 3
14.3%
2023-12-12T21:54:26.338148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
강 14
17.9%
남 12
15.4%
동 11
14.1%
낙 10
12.8%
해 9
11.5%
천 3
 
3.8%
하 2
 
2.6%
구 2
 
2.6%
언 2
 
2.6%
밀 2
 
2.6%
Other values (10) 11
14.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
강 14
17.9%
남 12
15.4%
동 11
14.1%
낙 10
12.8%
해 9
11.5%
천 3
 
3.8%
하 2
 
2.6%
구 2
 
2.6%
언 2
 
2.6%
밀 2
 
2.6%
Other values (10) 11
14.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
강 14
17.9%
남 12
15.4%
동 11
14.1%
낙 10
12.8%
해 9
11.5%
천 3
 
3.8%
하 2
 
2.6%
구 2
 
2.6%
언 2
 
2.6%
밀 2
 
2.6%
Other values (10) 11
14.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
강 14
17.9%
남 12
15.4%
동 11
14.1%
낙 10
12.8%
해 9
11.5%
천 3
 
3.8%
하 2
 
2.6%
구 2
 
2.6%
언 2
 
2.6%
밀 2
 
2.6%
Other values (10) 11
14.1%

운영
Categorical

Distinct2
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
위탁
15 
직영
6 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row위탁
2nd row직영
3rd row위탁
4th row직영
5th row위탁

Common Values

ValueCountFrequency (%)
위탁 15
71.4%
직영 6
 
28.6%

Length

2023-12-12T21:54:26.494023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:54:26.632021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
위탁 15
71.4%
직영 6
 
28.6%

Interactions

2023-12-12T21:54:20.675029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:20.473660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:21.155300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:54:20.573702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:54:26.717864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군처리장명시설용량처리공법소재지준공일방류수역_하수처리장방류수역_지류방류수역_본류운영
연번1.0000.8651.0000.1950.7041.0001.0001.0000.8650.4820.555
시군0.8651.0001.0000.5810.9261.0001.0001.0000.9950.9520.540
처리장명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시설용량0.1950.5811.0001.0000.0001.0001.0000.0000.0000.0000.330
처리공법0.7040.9261.0000.0001.0001.0001.0001.0000.9910.6520.000
소재지1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
준공일1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
방류수역_하수처리장1.0001.0001.0000.0001.0001.0001.0001.0001.0001.0000.000
방류수역_지류0.8650.9951.0000.0000.9911.0001.0001.0001.0000.9880.000
방류수역_본류0.4820.9521.0000.0000.6521.0001.0001.0000.9881.0000.558
운영0.5550.5401.0000.3300.0001.0001.0000.0000.0000.5581.000
2023-12-12T21:54:26.872824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시설용량운영
연번1.000-0.4950.216
시설용량-0.4951.0000.160
운영0.2160.1601.000

Missing values

2023-12-12T21:54:21.335698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:54:21.540987image/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

연번시군처리장명시설용량처리공법소재지준공일방류수역_하수처리장방류수역_지류방류수역_본류운영
01창원창원400전처리경상남도 창원시 성산구 창곡로 108-282000-12-11마산창원낙동강낙동강남해위탁
12창원마산205전처리경상남도 창원시 마산합포구 덕동동 7141982-05-31마산창원낙동강낙동강남해직영
23창원진해80전처리경상남도 창원시 진해구 행암동 242000-02-29진해낙동강낙동강남해위탁
34진주진주220전처리경상남도 진주시 초전동 190-11997-02-04진주남강낙동강남해직영
45통영통영103액상부식공법경상남도 통영시 명정동 8691996-10-12<NA>북신만낙동강남해위탁
56사천삼천포50협잡물제거+질소,인저감경상남도 사천시 사등동 114-11999-10-19삼천포사천만죽천천위탁
67사천사천45협잡물제거경상남도 사천시 사남면 방지리 2002003-06-05사천봉현천가화천위탁
78김해김해300전처리경상남도 김해시 화목동 1913-32000-09-22화목조만강낙동강하구언위탁
89밀양밀양60액상부식법경상남도 밀양시 상남면 기산리 35-12011-06-30밀양밀양강밀양강위탁
910거제거제160액상부식법경상남도 거제시 사등면 사곡리 산 2-51999-03-03<NA>사등만거제도직영
연번시군처리장명시설용량처리공법소재지준공일방류수역_하수처리장방류수역_지류방류수역_본류운영
1112의령의령30RABC경상남도 의령군 의령읍 무전리 782000-11-29의령의령천낙동강남해위탁
1213함안함안55A₂O경상남도 함안군 가야읍 남경길 107-1232006-07-28가야신음천남강위탁
1314창녕남지50부상분리,호기성소화경상남도 창녕군 남지읍 남지리 7-111992-03-16남지계성천낙동밀양위탁
1415고성고성50전처리경상남도 고성군 고성읍 송학리 1-12002-12-31고성고성천남해동부직영
1516남해남해50세일-바이오경상남도 남해군 남해읍 남변리 138-12006-02-28남해봉천남해직영
1617하동하동50전처리+B3공법경상남도 하동군 하동읍 신기리 102-212000-12-30하동섬진강남해직영
1718산청산청30액상부식법경상남도 산청군 산청읍 내리 149(산청대로 1381번길 130)2001-07-16<NA>남강남강위탁
1819함양함양130액상부식법경상남도 함양군 함양읍 용평리 915-12000-12-18함양위천남강댐위탁
1920거창거창95액상부식법경상남도 거창군 거창읍 양평리 290-12006-03-31거창황강낙동강위탁
2021합천합천50A₂O경상남도 합천군 대양면 정양리 502001-04-02<NA>아천천황강위탁