Overview

Dataset statistics

Number of variables9
Number of observations22
Missing cells11
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory82.0 B

Variable types

Numeric4
Text4
DateTime1

Dataset

Description경기도 광주시에서 현재 가동중인 하수종말 처리장 현황에 대한 데이터로 처리장명, 위치, 시설용량, 처리방법, 사업비, 사업기간 등을 제공합니다.
URLhttps://www.data.go.kr/data/3080471/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 시설용량 and 2 other fieldsHigh correlation
시설용량 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
차집관거 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
사업비(백만원) is highly overall correlated with 연번 and 1 other fieldsHigh correlation
차집관거 has 11 (50.0%) missing valuesMissing
연번 has unique valuesUnique
처리장명 has unique valuesUnique
위치 has unique valuesUnique
사업비(백만원) has unique valuesUnique

Reproduction

Analysis started2023-12-12 21:28:14.780325
Analysis finished2023-12-12 21:28:17.004988
Duration2.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T06:28:17.054854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.05
Q16.25
median11.5
Q316.75
95-th percentile20.95
Maximum22
Range21
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation6.4935866
Coefficient of variation (CV)0.5646597
Kurtosis-1.2
Mean11.5
Median Absolute Deviation (MAD)5.5
Skewness0
Sum253
Variance42.166667
MonotonicityStrictly increasing
2023-12-13T06:28:17.169470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 1
 
4.5%
13 1
 
4.5%
22 1
 
4.5%
21 1
 
4.5%
20 1
 
4.5%
19 1
 
4.5%
18 1
 
4.5%
17 1
 
4.5%
16 1
 
4.5%
15 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1 1
4.5%
2 1
4.5%
3 1
4.5%
4 1
4.5%
5 1
4.5%
6 1
4.5%
7 1
4.5%
8 1
4.5%
9 1
4.5%
10 1
4.5%
ValueCountFrequency (%)
22 1
4.5%
21 1
4.5%
20 1
4.5%
19 1
4.5%
18 1
4.5%
17 1
4.5%
16 1
4.5%
15 1
4.5%
14 1
4.5%
13 1
4.5%

처리장명
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-13T06:28:17.323311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length11.136364
Min length11

Characters and Unicode

Total characters245
Distinct characters45
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

Unique22 ?
Unique (%)100.0%

Sample

1st row경안 맑은물 복원센터
2nd row광주 맑은물 복원센터
3rd row곤지암 맑은물 복원센터
4th row양벌 맑은물 복원센터
5th row오포 맑은물 복원센터
ValueCountFrequency (%)
복원센터 22
33.3%
맑은물 22
33.3%
경안 1
 
1.5%
매산 1
 
1.5%
불당 1
 
1.5%
오전 1
 
1.5%
검복 1
 
1.5%
추곡 1
 
1.5%
검천 1
 
1.5%
귀여 1
 
1.5%
Other values (14) 14
21.2%
2023-12-13T06:28:17.596937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
44
18.0%
복 23
9.4%
원 23
9.4%
맑 22
9.0%
은 22
9.0%
물 22
9.0%
센 22
9.0%
터 22
9.0%
광 2
 
0.8%
동 2
 
0.8%
Other values (35) 41
16.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 201
82.0%
Space Separator 44
 
18.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
복 23
11.4%
원 23
11.4%
맑 22
10.9%
은 22
10.9%
물 22
10.9%
센 22
10.9%
터 22
10.9%
광 2
 
1.0%
동 2
 
1.0%
삼 2
 
1.0%
Other values (34) 39
19.4%
Space Separator
ValueCountFrequency (%)
44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 201
82.0%
Common 44
 
18.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
복 23
11.4%
원 23
11.4%
맑 22
10.9%
은 22
10.9%
물 22
10.9%
센 22
10.9%
터 22
10.9%
광 2
 
1.0%
동 2
 
1.0%
삼 2
 
1.0%
Other values (34) 39
19.4%
Common
ValueCountFrequency (%)
44
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 201
82.0%
ASCII 44
 
18.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
44
100.0%
Hangul
ValueCountFrequency (%)
복 23
11.4%
원 23
11.4%
맑 22
10.9%
은 22
10.9%
물 22
10.9%
센 22
10.9%
터 22
10.9%
광 2
 
1.0%
동 2
 
1.0%
삼 2
 
1.0%
Other values (34) 39
19.4%

위치
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-13T06:28:17.828752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length31.5
Mean length30.045455
Min length21

Characters and Unicode

Total characters661
Distinct characters80
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

Unique22 ?
Unique (%)100.0%

Sample

1st row경기도 광주시 남한산성면 해공로 427(하번천리 18-6)
2nd row경기도 광주시 초월읍 경수길 11(지월리 729-23)
3rd row경기도 광주시 초월읍 현산로 130-24(도평2리 17-1)
4th row경기도 광주시 청석로 149(양벌리 8-3)
5th row경기도 광주시 문현로 71(문형리 332)
ValueCountFrequency (%)
경기도 22
 
17.1%
광주시 22
 
17.1%
남한산성면 6
 
4.7%
남종면 5
 
3.9%
산수로 4
 
3.1%
도척면 3
 
2.3%
남한산성로 3
 
2.3%
초월읍 2
 
1.6%
퇴촌면 2
 
1.6%
23-1 1
 
0.8%
Other values (59) 59
45.7%
2023-12-13T06:28:18.200490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
107
 
16.2%
1 34
 
5.1%
도 29
 
4.4%
2 24
 
3.6%
경 23
 
3.5%
광 23
 
3.5%
기 22
 
3.3%
리 22
 
3.3%
주 22
 
3.3%
시 22
 
3.3%
Other values (70) 333
50.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 344
52.0%
Decimal Number 148
22.4%
Space Separator 107
 
16.2%
Open Punctuation 21
 
3.2%
Close Punctuation 21
 
3.2%
Dash Punctuation 20
 
3.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 29
 
8.4%
경 23
 
6.7%
광 23
 
6.7%
기 22
 
6.4%
리 22
 
6.4%
주 22
 
6.4%
시 22
 
6.4%
산 17
 
4.9%
면 16
 
4.7%
로 16
 
4.7%
Other values (56) 132
38.4%
Decimal Number
ValueCountFrequency (%)
1 34
23.0%
2 24
16.2%
4 16
10.8%
5 13
 
8.8%
3 13
 
8.8%
7 12
 
8.1%
0 11
 
7.4%
8 10
 
6.8%
6 8
 
5.4%
9 7
 
4.7%
Space Separator
ValueCountFrequency (%)
107
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 344
52.0%
Common 317
48.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 29
 
8.4%
경 23
 
6.7%
광 23
 
6.7%
기 22
 
6.4%
리 22
 
6.4%
주 22
 
6.4%
시 22
 
6.4%
산 17
 
4.9%
면 16
 
4.7%
로 16
 
4.7%
Other values (56) 132
38.4%
Common
ValueCountFrequency (%)
107
33.8%
1 34
 
10.7%
2 24
 
7.6%
( 21
 
6.6%
) 21
 
6.6%
- 20
 
6.3%
4 16
 
5.0%
5 13
 
4.1%
3 13
 
4.1%
7 12
 
3.8%
Other values (4) 36
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 344
52.0%
ASCII 317
48.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
107
33.8%
1 34
 
10.7%
2 24
 
7.6%
( 21
 
6.6%
) 21
 
6.6%
- 20
 
6.3%
4 16
 
5.0%
5 13
 
4.1%
3 13
 
4.1%
7 12
 
3.8%
Other values (4) 36
 
11.4%
Hangul
ValueCountFrequency (%)
도 29
 
8.4%
경 23
 
6.7%
광 23
 
6.7%
기 22
 
6.4%
리 22
 
6.4%
주 22
 
6.4%
시 22
 
6.4%
산 17
 
4.9%
면 16
 
4.7%
로 16
 
4.7%
Other values (56) 132
38.4%

시설용량
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8062.7273
Minimum60
Maximum71000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T06:28:18.328332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile81
Q1135
median550
Q34750
95-th percentile24900
Maximum71000
Range70940
Interquartile range (IQR)4615

Descriptive statistics

Standard deviation16366.538
Coefficient of variation (CV)2.0299009
Kurtosis10.5812
Mean8062.7273
Median Absolute Deviation (MAD)480
Skewness3.0391094
Sum177380
Variance2.6786355 × 108
MonotonicityDecreasing
2023-12-13T06:28:18.433474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
20000 2
 
9.1%
100 2
 
9.1%
130 2
 
9.1%
71000 1
 
4.5%
400 1
 
4.5%
60 1
 
4.5%
80 1
 
4.5%
150 1
 
4.5%
180 1
 
4.5%
200 1
 
4.5%
Other values (9) 9
40.9%
ValueCountFrequency (%)
60 1
4.5%
80 1
4.5%
100 2
9.1%
130 2
9.1%
150 1
4.5%
180 1
4.5%
200 1
4.5%
400 1
4.5%
500 1
4.5%
600 1
4.5%
ValueCountFrequency (%)
71000 1
4.5%
25000 1
4.5%
23000 1
4.5%
20000 2
9.1%
5000 1
4.5%
4000 1
4.5%
3650 1
4.5%
1900 1
4.5%
1200 1
4.5%
600 1
4.5%

차집관거
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)100.0%
Missing11
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean16.872727
Minimum0.8
Maximum53.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T06:28:18.586327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile1.45
Q14.1
median16.2
Q321.9
95-th percentile41.25
Maximum53.6
Range52.8
Interquartile range (IQR)17.8

Descriptive statistics

Standard deviation15.366658
Coefficient of variation (CV)0.91073944
Kurtosis2.4423598
Mean16.872727
Median Absolute Deviation (MAD)11.7
Skewness1.346945
Sum185.6
Variance236.13418
MonotonicityNot monotonic
2023-12-13T06:28:18.730952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
18.2 1
 
4.5%
53.6 1
 
4.5%
13.8 1
 
4.5%
16.2 1
 
4.5%
20.8 1
 
4.5%
28.9 1
 
4.5%
23.0 1
 
4.5%
3.7 1
 
4.5%
0.8 1
 
4.5%
4.5 1
 
4.5%
(Missing) 11
50.0%
ValueCountFrequency (%)
0.8 1
4.5%
2.1 1
4.5%
3.7 1
4.5%
4.5 1
4.5%
13.8 1
4.5%
16.2 1
4.5%
18.2 1
4.5%
20.8 1
4.5%
23.0 1
4.5%
28.9 1
4.5%
ValueCountFrequency (%)
53.6 1
4.5%
28.9 1
4.5%
23.0 1
4.5%
20.8 1
4.5%
18.2 1
4.5%
16.2 1
4.5%
13.8 1
4.5%
4.5 1
4.5%
3.7 1
4.5%
2.1 1
4.5%

처리방법
Text

Distinct14
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-13T06:28:18.982982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length19
Mean length11.136364
Min length4

Characters and Unicode

Total characters245
Distinct characters42
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)45.5%

Sample

1st rowPID+MCF, DeNiPho
2nd rowHBR-Ⅱ+생물막여과
3rd row산화구법+생물막여과
4th rowSBAF
5th row산화구법+생물막여과, KHBNR+기계여과, KHBNR+섬유상여과
ValueCountFrequency (%)
sbr(c-tech 6
22.2%
산화구법+생물막여과 3
11.1%
sbaf 2
 
7.4%
ksbnr 2
 
7.4%
denipho 2
 
7.4%
hbr-ⅱ+기계여과 2
 
7.4%
pid+mcf 1
 
3.7%
hbr-ⅱ+생물막여과 1
 
3.7%
khbnr+기계여과 1
 
3.7%
khbnr+섬유상여과 1
 
3.7%
Other values (6) 6
22.2%
2023-12-13T06:28:19.362357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 18
 
7.3%
R 17
 
6.9%
+ 14
 
5.7%
S 13
 
5.3%
여 12
 
4.9%
과 12
 
4.9%
- 10
 
4.1%
C 9
 
3.7%
e 8
 
3.3%
h 8
 
3.3%
Other values (32) 124
50.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 103
42.0%
Other Letter 67
27.3%
Lowercase Letter 26
 
10.6%
Math Symbol 14
 
5.7%
Dash Punctuation 10
 
4.1%
Close Punctuation 6
 
2.4%
Open Punctuation 6
 
2.4%
Other Punctuation 5
 
2.0%
Space Separator 5
 
2.0%
Letter Number 3
 
1.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 18
17.5%
R 17
16.5%
S 13
12.6%
C 9
8.7%
N 8
7.8%
K 7
 
6.8%
T 6
 
5.8%
H 5
 
4.9%
F 4
 
3.9%
D 4
 
3.9%
Other values (5) 12
11.7%
Other Letter
ValueCountFrequency (%)
여 12
17.9%
과 12
17.9%
기 6
9.0%
계 5
7.5%
막 4
 
6.0%
생 4
 
6.0%
법 4
 
6.0%
구 4
 
6.0%
화 4
 
6.0%
산 4
 
6.0%
Other values (5) 8
11.9%
Lowercase Letter
ValueCountFrequency (%)
e 8
30.8%
h 8
30.8%
c 6
23.1%
o 2
 
7.7%
i 2
 
7.7%
Math Symbol
ValueCountFrequency (%)
+ 14
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 132
53.9%
Hangul 67
27.3%
Common 46
 
18.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 18
13.6%
R 17
12.9%
S 13
9.8%
C 9
 
6.8%
e 8
 
6.1%
h 8
 
6.1%
N 8
 
6.1%
K 7
 
5.3%
T 6
 
4.5%
c 6
 
4.5%
Other values (11) 32
24.2%
Hangul
ValueCountFrequency (%)
여 12
17.9%
과 12
17.9%
기 6
9.0%
계 5
7.5%
막 4
 
6.0%
생 4
 
6.0%
법 4
 
6.0%
구 4
 
6.0%
화 4
 
6.0%
산 4
 
6.0%
Other values (5) 8
11.9%
Common
ValueCountFrequency (%)
+ 14
30.4%
- 10
21.7%
) 6
13.0%
( 6
13.0%
, 5
 
10.9%
5
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 175
71.4%
Hangul 67
 
27.3%
Number Forms 3
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 18
 
10.3%
R 17
 
9.7%
+ 14
 
8.0%
S 13
 
7.4%
- 10
 
5.7%
C 9
 
5.1%
e 8
 
4.6%
h 8
 
4.6%
N 8
 
4.6%
K 7
 
4.0%
Other values (16) 63
36.0%
Hangul
ValueCountFrequency (%)
여 12
17.9%
과 12
17.9%
기 6
9.0%
계 5
7.5%
막 4
 
6.0%
생 4
 
6.0%
법 4
 
6.0%
구 4
 
6.0%
화 4
 
6.0%
산 4
 
6.0%
Other values (5) 8
11.9%
Number Forms
ValueCountFrequency (%)
Ⅱ 3
100.0%

사업비(백만원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20102.545
Minimum1128
Maximum140117
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T06:28:19.522464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1128
5-th percentile1621.95
Q12428
median5650.5
Q329657.25
95-th percentile56393.4
Maximum140117
Range138989
Interquartile range (IQR)27229.25

Descriptive statistics

Standard deviation31290.975
Coefficient of variation (CV)1.5565678
Kurtosis10.458101
Mean20102.545
Median Absolute Deviation (MAD)3840
Skewness2.9613718
Sum442256
Variance9.7912513 × 108
MonotonicityNot monotonic
2023-12-13T06:28:19.654487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
140117 1
 
4.5%
8607 1
 
4.5%
2166 1
 
4.5%
1128 1
 
4.5%
1601 1
 
4.5%
2020 1
 
4.5%
2500 1
 
4.5%
2404 1
 
4.5%
2897 1
 
4.5%
2159 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1128 1
4.5%
1601 1
4.5%
2020 1
4.5%
2159 1
4.5%
2166 1
4.5%
2404 1
4.5%
2500 1
4.5%
2881 1
4.5%
2897 1
4.5%
3706 1
4.5%
ValueCountFrequency (%)
140117 1
4.5%
57235 1
4.5%
40403 1
4.5%
35373 1
4.5%
34728 1
4.5%
30544 1
4.5%
26997 1
4.5%
20794 1
4.5%
12695 1
4.5%
8607 1
4.5%

사업기간
Text

Distinct17
Distinct (%)77.3%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-13T06:28:19.821089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters506
Distinct characters13
Distinct categories4 ?
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 (%)68.2%

Sample

1st row1998-12-23 ~ 2021-03-10
2nd row1990-12-01 ~ 2011-11-11
3rd row1994-10-01 ~ 2011-09-23
4th row2011-07-22 ~ 2015-01-31
5th row1998-03-01 ~ 2011-09-23
ValueCountFrequency (%)
22
33.3%
2004-08-01 5
 
7.6%
2007-07-09 5
 
7.6%
2011-09-23 4
 
6.1%
2014-08-11 3
 
4.5%
2011-07-22 3
 
4.5%
2011-11-11 2
 
3.0%
2014-12-23 1
 
1.5%
2021-03-10 1
 
1.5%
1996-03-01 1
 
1.5%
Other values (19) 19
28.8%
2023-12-13T06:28:20.153505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 106
20.9%
- 88
17.4%
1 86
17.0%
2 56
11.1%
44
8.7%
9 35
 
6.9%
~ 22
 
4.3%
7 18
 
3.6%
4 14
 
2.8%
8 14
 
2.8%
Other values (3) 23
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 352
69.6%
Dash Punctuation 88
 
17.4%
Space Separator 44
 
8.7%
Math Symbol 22
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106
30.1%
1 86
24.4%
2 56
15.9%
9 35
 
9.9%
7 18
 
5.1%
4 14
 
4.0%
8 14
 
4.0%
3 13
 
3.7%
6 6
 
1.7%
5 4
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 88
100.0%
Space Separator
ValueCountFrequency (%)
44
100.0%
Math Symbol
ValueCountFrequency (%)
~ 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 506
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106
20.9%
- 88
17.4%
1 86
17.0%
2 56
11.1%
44
8.7%
9 35
 
6.9%
~ 22
 
4.3%
7 18
 
3.6%
4 14
 
2.8%
8 14
 
2.8%
Other values (3) 23
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 506
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106
20.9%
- 88
17.4%
1 86
17.0%
2 56
11.1%
44
8.7%
9 35
 
6.9%
~ 22
 
4.3%
7 18
 
3.6%
4 14
 
2.8%
8 14
 
2.8%
Other values (3) 23
 
4.5%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
Minimum2023-08-01 00:00:00
Maximum2023-08-01 00:00:00
2023-12-13T06:28:20.290654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:28:20.387564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T06:28:16.484096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:28:15.122842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:28:15.795337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:28:16.157151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:28:16.559598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:28:15.210772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:28:15.877401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:28:16.233658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:28:16.651135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:28:15.605742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:28:15.973544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:28:16.327654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:28:16.726594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:28:15.699125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:28:16.058710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:28:16.404988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:28:20.481942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번처리장명위치시설용량차집관거처리방법사업비(백만원)사업기간
연번1.0001.0001.0000.8260.0000.7010.8680.720
처리장명1.0001.0001.0001.0001.0001.0001.0001.000
위치1.0001.0001.0001.0001.0001.0001.0001.000
시설용량0.8261.0001.0001.0000.6570.8890.8211.000
차집관거0.0001.0001.0000.6571.0000.0000.7171.000
처리방법0.7011.0001.0000.8890.0001.0000.8841.000
사업비(백만원)0.8681.0001.0000.8210.7170.8841.0001.000
사업기간0.7201.0001.0001.0001.0001.0001.0001.000
2023-12-13T06:28:20.630601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시설용량차집관거사업비(백만원)
연번1.000-0.999-0.636-0.933
시설용량-0.9991.0000.6470.935
차집관거-0.6360.6471.0000.491
사업비(백만원)-0.9330.9350.4911.000

Missing values

2023-12-13T06:28:16.839472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:28:16.958882image/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

연번처리장명위치시설용량차집관거처리방법사업비(백만원)사업기간데이터기준일자
01경안 맑은물 복원센터경기도 광주시 남한산성면 해공로 427(하번천리 18-6)7100018.2PID+MCF, DeNiPho1401171998-12-23 ~ 2021-03-102023-08-01
12광주 맑은물 복원센터경기도 광주시 초월읍 경수길 11(지월리 729-23)2500053.6HBR-Ⅱ+생물막여과353731990-12-01 ~ 2011-11-112023-08-01
23곤지암 맑은물 복원센터경기도 광주시 초월읍 현산로 130-24(도평2리 17-1)2300013.8산화구법+생물막여과347281994-10-01 ~ 2011-09-232023-08-01
34양벌 맑은물 복원센터경기도 광주시 청석로 149(양벌리 8-3)2000016.2SBAF404032011-07-22 ~ 2015-01-312023-08-01
45오포 맑은물 복원센터경기도 광주시 문현로 71(문형리 332)2000020.8산화구법+생물막여과, KHBNR+기계여과, KHBNR+섬유상여과572351998-03-01 ~ 2011-09-232023-08-01
56삼리 맑은물 복원센터경기도 광주시 곤지암읍 평촌길 12-125(삼리 612-52)500028.9SBAF269972011-06-20 ~ 2014-08-152023-08-01
67도척 맑은물 복원센터경기도 광주시 도척면 궁평하천길 58(궁평리 2-10)400023.0산화구법+생물막여과207941996-12-30 ~ 2011-11-112023-08-01
78광동 맑은물 복원센터경기도 광주시 퇴촌면 산수로 1321(광동리 211-1)36503.7KIDEA+기계여과, DeNiPho305441991-07-20 ~ 2014-08-112023-08-01
89분원 맑은물 복원센터경기도 광주시 남종면 산수로 1704(분원리 225)1900<NA>SBR+여과기37061991-07-15 ~ 2012-03-092023-08-01
910남한산성 맑은물 복원센터경기도 광주시 남한산성면 남한산성로 700(산성리 44-6)12000.8산화구법+MCF50161994-04-01 ~ 2011-09-232023-08-01
연번처리장명위치시설용량차집관거처리방법사업비(백만원)사업기간데이터기준일자
1213삼성 맑은물 복원센터경기도 광주시 남종면 태허정로 404번길89 (삼성리 241-5)4002.1HBR-Ⅱ+기계여과, KNR86071996-03-01 ~ 1999-12-202023-08-01
1314방도 맑은물 복원센터경기도 광주시 도척면 마도로 19(방도리 5-2)200<NA>KSBNR62852011-07-22 ~ 2014-08-112023-08-01
1415엄미 맑은물 복원센터경기도 광주시 남한산성면 회안대로 1551(엄미리 105-1)180<NA>SBR(C-Tech)21592004-08-01 ~ 2007-07-092023-08-01
1516귀여 맑은물 복원센터경기도 광주시 남종면 산수로 1884(귀여리 313-1)150<NA>KSBNR+기계여과28971991-07-18 ~ 2008-02-062023-08-01
1617검천 맑은물 복원센터경기도 광주시 남종면 산수로 2468(검천리 320-5)130<NA>SBR(C-Tech)24042004-08-01 ~ 2007-07-092023-08-01
1718추곡 맑은물 복원센터경기도 광주시 도척면 도척로 1084-37(추곡리 241-2)130<NA>IC-SBR+사여과25002004-03-01 ~ 2007-10-152023-08-01
1819검복 맑은물 복원센터경기도 광주시 남한산성면 남한산성로 495(검복리 161)100<NA>SBR(C-Tech)20202004-08-01 ~ 2007-07-092023-08-01
1920오전 맑은물 복원센터경기도 광주시 남한산성면 남한산성로 184(오전리 276)100<NA>SBR(C-Tech)16012004-08-01 ~ 2007-07-092023-08-01
2021불당 맑은물 복원센터경기도 광주시 남한산성면 불당길 19(불당리 57)80<NA>SBR(C-Tech)11282004-08-01 ~ 2007-07-092023-08-01
2122수청 맑은물 복원센터경기도 광주시 남종면 수청리 205-760<NA>SBR(C-Tech)21662014-12-23 ~ 2016-01-282023-08-01