Overview

Dataset statistics

Number of variables9
Number of observations43
Missing cells10
Missing cells (%)2.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory78.1 B

Variable types

Numeric3
Text4
Categorical2

Dataset

Description충청북도 제천시의 하수종말처리장에 관한 데이터로서 처리장명, 위치, 시설용량, 차집관거 등으로 구성되어 있는 데이터입니다.
URLhttps://www.data.go.kr/data/15122276/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
시설용량 is highly overall correlated with 처리방법High correlation
사업비(백만원) is highly overall correlated with 처리방법High correlation
처리방법 is highly overall correlated with 시설용량 and 1 other fieldsHigh correlation
차집관거 has 10 (23.3%) missing valuesMissing
연번 has unique valuesUnique
처리장명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 17:56:35.438203
Analysis finished2023-12-12 17:56:37.322652
Duration1.88 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22
Minimum1
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T02:56:37.420635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.1
Q111.5
median22
Q332.5
95-th percentile40.9
Maximum43
Range42
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.556539
Coefficient of variation (CV)0.57075176
Kurtosis-1.2
Mean22
Median Absolute Deviation (MAD)11
Skewness0
Sum946
Variance157.66667
MonotonicityStrictly increasing
2023-12-13T02:56:37.632658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1 1
 
2.3%
2 1
 
2.3%
25 1
 
2.3%
26 1
 
2.3%
27 1
 
2.3%
28 1
 
2.3%
29 1
 
2.3%
30 1
 
2.3%
31 1
 
2.3%
32 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
1 1
2.3%
2 1
2.3%
3 1
2.3%
4 1
2.3%
5 1
2.3%
6 1
2.3%
7 1
2.3%
8 1
2.3%
9 1
2.3%
10 1
2.3%
ValueCountFrequency (%)
43 1
2.3%
42 1
2.3%
41 1
2.3%
40 1
2.3%
39 1
2.3%
38 1
2.3%
37 1
2.3%
36 1
2.3%
35 1
2.3%
34 1
2.3%

처리장명
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-13T02:56:37.955276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.1395349
Min length3

Characters and Unicode

Total characters178
Distinct characters66
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

Unique43 ?
Unique (%)100.0%

Sample

1st row제천하수처리장
2nd row송학공공
3rd row덕산공공
4th row봉양공공
5th row송계2
ValueCountFrequency (%)
제천하수처리장 1
 
2.3%
백운매촌 1
 
2.3%
수산오티 1
 
2.3%
송학초장 1
 
2.3%
송학오미 1
 
2.3%
금성성내3 1
 
2.3%
백운애련 1
 
2.3%
봉양마곡 1
 
2.3%
금성사곡 1
 
2.3%
상촌공공 1
 
2.3%
Other values (33) 33
76.7%
2023-12-13T02:56:38.525726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
공 18
 
10.1%
수 15
 
8.4%
산 11
 
6.2%
성 9
 
5.1%
금 7
 
3.9%
청 7
 
3.9%
풍 7
 
3.9%
한 6
 
3.4%
곡 6
 
3.4%
송 5
 
2.8%
Other values (56) 87
48.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 175
98.3%
Decimal Number 3
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 18
 
10.3%
수 15
 
8.6%
산 11
 
6.3%
성 9
 
5.1%
금 7
 
4.0%
청 7
 
4.0%
풍 7
 
4.0%
한 6
 
3.4%
곡 6
 
3.4%
송 5
 
2.9%
Other values (54) 84
48.0%
Decimal Number
ValueCountFrequency (%)
2 2
66.7%
3 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 175
98.3%
Common 3
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 18
 
10.3%
수 15
 
8.6%
산 11
 
6.3%
성 9
 
5.1%
금 7
 
4.0%
청 7
 
4.0%
풍 7
 
4.0%
한 6
 
3.4%
곡 6
 
3.4%
송 5
 
2.9%
Other values (54) 84
48.0%
Common
ValueCountFrequency (%)
2 2
66.7%
3 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 175
98.3%
ASCII 3
 
1.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
공 18
 
10.3%
수 15
 
8.6%
산 11
 
6.3%
성 9
 
5.1%
금 7
 
4.0%
청 7
 
4.0%
풍 7
 
4.0%
한 6
 
3.4%
곡 6
 
3.4%
송 5
 
2.9%
Other values (54) 84
48.0%
ASCII
ValueCountFrequency (%)
2 2
66.7%
3 1
33.3%

위치
Text

Distinct41
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-13T02:56:38.870781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length21.162791
Min length16

Characters and Unicode

Total characters910
Distinct characters82
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

Unique39 ?
Unique (%)90.7%

Sample

1st row충청북도 제천시 내토로12길 64(천남동)
2nd row충청북도 제천시 송학면 선돌로 4길 5
3rd row충청북도 제천시 덕산면 월악로14길 4-51
4th row충청북도 제천시 봉양읍 국사봉로 79
5th row충청북도 제천시 한수면 미륵송계로 1933
ValueCountFrequency (%)
충청북도 43
20.9%
제천시 42
20.4%
청풍면 7
 
3.4%
한수면 6
 
2.9%
수산면 5
 
2.4%
금성면 4
 
1.9%
송학면 3
 
1.5%
덕산면 3
 
1.5%
34 2
 
1.0%
인삼로 2
 
1.0%
Other values (79) 89
43.2%
2023-12-13T02:56:39.335053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
163
17.9%
청 56
 
6.2%
천 47
 
5.2%
도 45
 
4.9%
시 45
 
4.9%
충 43
 
4.7%
북 43
 
4.7%
제 43
 
4.7%
면 32
 
3.5%
1 29
 
3.2%
Other values (72) 364
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 576
63.3%
Space Separator 163
 
17.9%
Decimal Number 152
 
16.7%
Dash Punctuation 17
 
1.9%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 56
 
9.7%
천 47
 
8.2%
도 45
 
7.8%
시 45
 
7.8%
충 43
 
7.5%
북 43
 
7.5%
제 43
 
7.5%
면 32
 
5.6%
로 27
 
4.7%
길 19
 
3.3%
Other values (58) 176
30.6%
Decimal Number
ValueCountFrequency (%)
1 29
19.1%
5 23
15.1%
2 19
12.5%
4 17
11.2%
3 13
8.6%
6 12
7.9%
9 10
 
6.6%
0 10
 
6.6%
7 10
 
6.6%
8 9
 
5.9%
Space Separator
ValueCountFrequency (%)
163
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 576
63.3%
Common 334
36.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 56
 
9.7%
천 47
 
8.2%
도 45
 
7.8%
시 45
 
7.8%
충 43
 
7.5%
북 43
 
7.5%
제 43
 
7.5%
면 32
 
5.6%
로 27
 
4.7%
길 19
 
3.3%
Other values (58) 176
30.6%
Common
ValueCountFrequency (%)
163
48.8%
1 29
 
8.7%
5 23
 
6.9%
2 19
 
5.7%
- 17
 
5.1%
4 17
 
5.1%
3 13
 
3.9%
6 12
 
3.6%
9 10
 
3.0%
0 10
 
3.0%
Other values (4) 21
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 576
63.3%
ASCII 334
36.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
163
48.8%
1 29
 
8.7%
5 23
 
6.9%
2 19
 
5.7%
- 17
 
5.1%
4 17
 
5.1%
3 13
 
3.9%
6 12
 
3.6%
9 10
 
3.0%
0 10
 
3.0%
Other values (4) 21
 
6.3%
Hangul
ValueCountFrequency (%)
청 56
 
9.7%
천 47
 
8.2%
도 45
 
7.8%
시 45
 
7.8%
충 43
 
7.5%
북 43
 
7.5%
제 43
 
7.5%
면 32
 
5.6%
로 27
 
4.7%
길 19
 
3.3%
Other values (58) 176
30.6%

시설용량
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)65.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1798.7907
Minimum20
Maximum70000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T02:56:39.486349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile30.4
Q149
median60
Q3175
95-th percentile1140
Maximum70000
Range69980
Interquartile range (IQR)126

Descriptive statistics

Standard deviation10652.185
Coefficient of variation (CV)5.921859
Kurtosis42.931294
Mean1798.7907
Median Absolute Deviation (MAD)25
Skewness6.5498335
Sum77348
Variance1.1346904 × 108
MonotonicityNot monotonic
2023-12-13T02:56:39.649049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
50 5
 
11.6%
60 5
 
11.6%
70 3
 
7.0%
160 2
 
4.7%
130 2
 
4.7%
45 2
 
4.7%
35 2
 
4.7%
40 2
 
4.7%
70000 1
 
2.3%
20 1
 
2.3%
Other values (18) 18
41.9%
ValueCountFrequency (%)
20 1
 
2.3%
25 1
 
2.3%
30 1
 
2.3%
34 1
 
2.3%
35 2
 
4.7%
40 2
 
4.7%
45 2
 
4.7%
48 1
 
2.3%
50 5
11.6%
58 1
 
2.3%
ValueCountFrequency (%)
70000 1
2.3%
1500 1
2.3%
1200 1
2.3%
600 1
2.3%
500 1
2.3%
295 1
2.3%
270 1
2.3%
260 1
2.3%
200 1
2.3%
190 1
2.3%

차집관거
Text

MISSING 

Distinct25
Distinct (%)75.8%
Missing10
Missing (%)23.3%
Memory size476.0 B
2023-12-13T02:56:39.861255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.9090909
Min length3

Characters and Unicode

Total characters162
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)63.6%

Sample

1st row100,000
2nd row14,000
3rd row13,352
4th row21,218
5th row2,231
ValueCountFrequency (%)
1,100 5
 
15.2%
4,500 3
 
9.1%
3,000 2
 
6.1%
500 2
 
6.1%
4,200 1
 
3.0%
3,200 1
 
3.0%
4,202 1
 
3.0%
2,300 1
 
3.0%
1,700 1
 
3.0%
2,600 1
 
3.0%
Other values (15) 15
45.5%
2023-12-13T02:56:40.243469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 63
38.9%
, 29
17.9%
1 21
 
13.0%
2 13
 
8.0%
3 12
 
7.4%
4 8
 
4.9%
5 6
 
3.7%
7 4
 
2.5%
8 3
 
1.9%
6 3
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 133
82.1%
Other Punctuation 29
 
17.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 63
47.4%
1 21
 
15.8%
2 13
 
9.8%
3 12
 
9.0%
4 8
 
6.0%
5 6
 
4.5%
7 4
 
3.0%
8 3
 
2.3%
6 3
 
2.3%
Other Punctuation
ValueCountFrequency (%)
, 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 162
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 63
38.9%
, 29
17.9%
1 21
 
13.0%
2 13
 
8.0%
3 12
 
7.4%
4 8
 
4.9%
5 6
 
3.7%
7 4
 
2.5%
8 3
 
1.9%
6 3
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 63
38.9%
, 29
17.9%
1 21
 
13.0%
2 13
 
8.0%
3 12
 
7.4%
4 8
 
4.9%
5 6
 
3.7%
7 4
 
2.5%
8 3
 
1.9%
6 3
 
1.9%

처리방법
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Memory size476.0 B
KM-SBR
20 
DMBR
5 
A2EBC
3 
FNR
3 
고효율합병정화조+생물막여과
 
2
Other values (9)
10 

Length

Max length16
Median length6
Mean length6.5813953
Min length3

Unique

Unique8 ?
Unique (%)18.6%

Sample

1st rowNPR
2nd row선회와류식
3rd rowDMBR
4th rowDMBR + 선회와류식(증설)
5th rowKM-SBR

Common Values

ValueCountFrequency (%)
KM-SBR 20
46.5%
DMBR 5
 
11.6%
A2EBC 3
 
7.0%
FNR 3
 
7.0%
고효율합병정화조+생물막여과 2
 
4.7%
JC-SBR 2
 
4.7%
NPR 1
 
2.3%
선회와류식 1
 
2.3%
DMBR + 선회와류식(증설) 1
 
2.3%
흡수성바이오휠터+약품처리 1
 
2.3%
Other values (4) 4
 
9.3%

Length

2023-12-13T02:56:40.411838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
km-sbr 20
44.4%
dmbr 6
 
13.3%
a2ebc 3
 
6.7%
fnr 3
 
6.7%
고효율합병정화조+생물막여과 2
 
4.4%
jc-sbr 2
 
4.4%
npr 1
 
2.2%
선회와류식 1
 
2.2%
1
 
2.2%
선회와류식(증설 1
 
2.2%
Other values (5) 5
 
11.1%

사업비(백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3688.4651
Minimum129
Maximum59097
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T02:56:40.613666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum129
5-th percentile170.3
Q1296.5
median700
Q32226
95-th percentile12191
Maximum59097
Range58968
Interquartile range (IQR)1929.5

Descriptive statistics

Standard deviation9652.6923
Coefficient of variation (CV)2.6169943
Kurtosis27.14463
Mean3688.4651
Median Absolute Deviation (MAD)533
Skewness4.9500329
Sum158604
Variance93174469
MonotonicityNot monotonic
2023-12-13T02:56:40.817577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
222 2
 
4.7%
280 2
 
4.7%
2777 1
 
2.3%
331 1
 
2.3%
700 1
 
2.3%
345 1
 
2.3%
1445 1
 
2.3%
1760 1
 
2.3%
1653 1
 
2.3%
4324 1
 
2.3%
Other values (31) 31
72.1%
ValueCountFrequency (%)
129 1
2.3%
149 1
2.3%
167 1
2.3%
200 1
2.3%
222 2
4.7%
224 1
2.3%
259 1
2.3%
280 2
4.7%
291 1
2.3%
302 1
2.3%
ValueCountFrequency (%)
59097 1
2.3%
23756 1
2.3%
12341 1
2.3%
10841 1
2.3%
6243 1
2.3%
5872 1
2.3%
5496 1
2.3%
4324 1
2.3%
2777 1
2.3%
2689 1
2.3%

사업기간
Text

Distinct33
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-13T02:56:41.090573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters989
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

Unique31 ?
Unique (%)72.1%

Sample

1st row1991-03-16 ~ 2009-01-01
2nd row2002-09-27 ~ 2004-09-30
3rd row2006-09-20 ~ 2011-12-15
4th row2006-09-20 ~ 2011-12-15
5th row2020-05-21 ~ 2021-11-20
ValueCountFrequency (%)
43
33.1%
2006-09-20 10
 
7.7%
2011-12-15 10
 
7.7%
2001-09-14 3
 
2.3%
1998-10-30 2
 
1.5%
1999-07-17 2
 
1.5%
2005-09-25 2
 
1.5%
1998-12-29 2
 
1.5%
2012-06-27 1
 
0.8%
2002-09-17 1
 
0.8%
Other values (54) 54
41.5%
2023-12-13T02:56:41.463701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 212
21.4%
- 172
17.4%
2 148
15.0%
1 131
13.2%
87
8.8%
9 82
 
8.3%
~ 43
 
4.3%
6 24
 
2.4%
5 24
 
2.4%
3 21
 
2.1%
Other values (3) 45
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 687
69.5%
Dash Punctuation 172
 
17.4%
Space Separator 87
 
8.8%
Math Symbol 43
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 212
30.9%
2 148
21.5%
1 131
19.1%
9 82
 
11.9%
6 24
 
3.5%
5 24
 
3.5%
3 21
 
3.1%
7 18
 
2.6%
8 14
 
2.0%
4 13
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 172
100.0%
Space Separator
ValueCountFrequency (%)
87
100.0%
Math Symbol
ValueCountFrequency (%)
~ 43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 989
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 212
21.4%
- 172
17.4%
2 148
15.0%
1 131
13.2%
87
8.8%
9 82
 
8.3%
~ 43
 
4.3%
6 24
 
2.4%
5 24
 
2.4%
3 21
 
2.1%
Other values (3) 45
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 989
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 212
21.4%
- 172
17.4%
2 148
15.0%
1 131
13.2%
87
8.8%
9 82
 
8.3%
~ 43
 
4.3%
6 24
 
2.4%
5 24
 
2.4%
3 21
 
2.1%
Other values (3) 45
 
4.6%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-09-01
43 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-09-01
2nd row2023-09-01
3rd row2023-09-01
4th row2023-09-01
5th row2023-09-01

Common Values

ValueCountFrequency (%)
2023-09-01 43
100.0%

Length

2023-12-13T02:56:41.625853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:56:41.724715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-09-01 43
100.0%

Interactions

2023-12-13T02:56:36.768570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:56:35.891972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:56:36.480979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:56:36.859421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:56:36.289258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:56:36.564214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:56:36.955575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:56:36.391877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:56:36.670490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:56:41.790589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번처리장명위치시설용량차집관거처리방법사업비(백만원)사업기간
연번1.0001.0000.9700.0000.5690.6920.0000.892
처리장명1.0001.0001.0001.0001.0001.0001.0001.000
위치0.9701.0001.0001.0000.9830.9901.0000.987
시설용량0.0001.0001.0001.0001.0001.0001.0001.000
차집관거0.5691.0000.9831.0001.0000.9241.0000.466
처리방법0.6921.0000.9901.0000.9241.0000.8820.963
사업비(백만원)0.0001.0001.0001.0001.0000.8821.0000.000
사업기간0.8921.0000.9871.0000.4660.9630.0001.000
2023-12-13T02:56:41.901299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시설용량사업비(백만원)처리방법
연번1.000-0.4690.1680.379
시설용량-0.4691.0000.4240.841
사업비(백만원)0.1680.4241.0000.631
처리방법0.3790.8410.6311.000

Missing values

2023-12-13T02:56:37.085205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:56:37.252240image/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
01제천하수처리장충청북도 제천시 내토로12길 64(천남동)70000100,000NPR590971991-03-16 ~ 2009-01-012023-09-01
12송학공공충청북도 제천시 송학면 선돌로 4길 5120014,000선회와류식108412002-09-27 ~ 2004-09-302023-09-01
23덕산공공충청북도 제천시 덕산면 월악로14길 4-5150013,352DMBR123412006-09-20 ~ 2011-12-152023-09-01
34봉양공공충청북도 제천시 봉양읍 국사봉로 79150021,218DMBR + 선회와류식(증설)237562006-09-20 ~ 2011-12-152023-09-01
45송계2충청북도 제천시 한수면 미륵송계로 19336002,231KM-SBR54962020-05-21 ~ 2021-11-202023-09-01
56금성성내2충청북도 제천시 금성면 청풍호로 1542-13160<NA>흡수성바이오휠터+약품처리3752000-09-29 ~ 2001-06-292023-09-01
67금성중전충청북도 제천시 금성면 신담길 190-948<NA>KM-SBR2591998-12-29 ~ 1999-07-122023-09-01
78금성포전충청북도 제천시 금성면 중포길 8768<NA>고효율합병정화조+생물막여과2801998-12-30 ~ 1999-07-132023-09-01
89금성문화충청북도 제천시금성면 국사봉로28길 34190<NA>KM-SBR4951995-12-20 ~ 1996-09-122023-09-01
910금성큰말충청북도 제천시 금성면국사봉로28길 34170<NA>KM-SBR2912001-09-14 ~ 2002-04-302023-09-01
연번처리장명위치시설용량차집관거처리방법사업비(백만원)사업기간데이터기준일자
3334백운공공충청북도 제천시 원월리 52-11804,500DMBR58722006-09-20 ~ 2011-12-152023-09-01
3435수산적곡충청북도 제천시 적곡리 107-3502,600KM-SBR27772006-09-20 ~ 2011-12-152023-09-01
3536한삼포공공충청북도 제천시 도곡리 606-1501,700KM-SBR26892006-09-20 ~ 2011-12-152023-09-01
3637음실공공충청북도 제천시 도곡리 355-4502,300DMBR21432006-09-20 ~ 2011-12-152023-09-01
3738월림공공충청북도 제천시 월림리 185130<NA>KM-SBR62432006-09-20 ~ 2011-12-152023-09-01
3839한수탄지충청북도 제천시 한수면 월악로 1414604,202JC-SBR18512010-09-20 ~ 2012-06-272023-09-01
3940수산대전충청북도 제천시 수산면 인삼로 580303,000IC-SBR13932011- 4-29 ~ 2012-12-262023-09-01
4041덕산수산충청북도 제천시 덕산면 후청골2길 12-65454,500FNR23092012-05-25 ~ 2014-07-082023-09-01
4142청풍연곡충청북도 제천시 청풍면 읍리 산4-52703,200H-SBR10412013-10-08 ~ 2014-11-302023-09-01
4243한수덕곡충청북도 제천시 한수면 봉화재길 638201,400JC-SBR11202014-12-23 ~ 2016-03-172023-09-01