Overview

Dataset statistics

Number of variables12
Number of observations34
Missing cells38
Missing cells (%)9.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory104.9 B

Variable types

Text5
Numeric4
DateTime1
Categorical1
Unsupported1

Alerts

우편번호 is highly overall correlated with WGS84위도High correlation
시설용량 is highly overall correlated with WGS84경도High correlation
WGS84위도 is highly overall correlated with 우편번호High correlation
WGS84경도 is highly overall correlated with 시설용량High correlation
도로명주소 has 1 (2.9%) missing valuesMissing
우편번호 has 1 (2.9%) missing valuesMissing
비고 has 34 (100.0%) missing valuesMissing
WGS84위도 has 1 (2.9%) missing valuesMissing
WGS84경도 has 1 (2.9%) missing valuesMissing
구분명 has unique valuesUnique
지번주소 has unique valuesUnique
비고 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 21:27:52.968018
Analysis finished2023-12-10 21:27:55.126945
Duration2.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Text

Distinct28
Distinct (%)82.4%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-11T06:27:55.239831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1176471
Min length3

Characters and Unicode

Total characters106
Distinct characters36
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

Unique23 ?
Unique (%)67.6%

Sample

1st row가평군
2nd row고양시
3rd row과천시
4th row광명시
5th row광주시
ValueCountFrequency (%)
포천시 3
 
8.8%
용인시 2
 
5.9%
여주시 2
 
5.9%
광주시 2
 
5.9%
남양주시 2
 
5.9%
양주시 1
 
2.9%
가평군 1
 
2.9%
안양시 1
 
2.9%
평택시 1
 
2.9%
파주시 1
 
2.9%
Other values (18) 18
52.9%
2023-12-11T06:27:55.528365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 32
30.2%
천 8
 
7.5%
주 8
 
7.5%
양 6
 
5.7%
포 4
 
3.8%
남 4
 
3.8%
군 3
 
2.8%
평 3
 
2.8%
광 3
 
2.8%
안 3
 
2.8%
Other values (26) 32
30.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 106
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 32
30.2%
천 8
 
7.5%
주 8
 
7.5%
양 6
 
5.7%
포 4
 
3.8%
남 4
 
3.8%
군 3
 
2.8%
평 3
 
2.8%
광 3
 
2.8%
안 3
 
2.8%
Other values (26) 32
30.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 106
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 32
30.2%
천 8
 
7.5%
주 8
 
7.5%
양 6
 
5.7%
포 4
 
3.8%
남 4
 
3.8%
군 3
 
2.8%
평 3
 
2.8%
광 3
 
2.8%
안 3
 
2.8%
Other values (26) 32
30.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 106
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 32
30.2%
천 8
 
7.5%
주 8
 
7.5%
양 6
 
5.7%
포 4
 
3.8%
남 4
 
3.8%
군 3
 
2.8%
평 3
 
2.8%
광 3
 
2.8%
안 3
 
2.8%
Other values (26) 32
30.2%

구분명
Text

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-11T06:27:55.753032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length12.588235
Min length9

Characters and Unicode

Total characters428
Distinct characters68
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)100.0%

Sample

1st row가평군 분뇨처리시설
2nd row고양시 일산공공하수처리시설
3rd row과천시 분뇨처리시설
4th row광명시 환경사업소(분뇨처리시설)
5th row광주시 수양분뇨처리시설
ValueCountFrequency (%)
분뇨처리시설 15
21.4%
포천시 3
 
4.3%
가축분뇨공공처리시설(분뇨 2
 
2.9%
가축분뇨 2
 
2.9%
분뇨 2
 
2.9%
병합처리시설 2
 
2.9%
여주시 2
 
2.9%
광주시 2
 
2.9%
용인 2
 
2.9%
가평군 1
 
1.4%
Other values (37) 37
52.9%
2023-12-11T06:27:56.086289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 57
13.3%
리 37
 
8.6%
36
 
8.4%
분 36
 
8.4%
뇨 36
 
8.4%
처 36
 
8.4%
설 35
 
8.2%
공 14
 
3.3%
천 8
 
1.9%
주 7
 
1.6%
Other values (58) 126
29.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 380
88.8%
Space Separator 36
 
8.4%
Close Punctuation 6
 
1.4%
Open Punctuation 6
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 57
15.0%
리 37
 
9.7%
분 36
 
9.5%
뇨 36
 
9.5%
처 36
 
9.5%
설 35
 
9.2%
공 14
 
3.7%
천 8
 
2.1%
주 7
 
1.8%
양 7
 
1.8%
Other values (55) 107
28.2%
Space Separator
ValueCountFrequency (%)
36
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 380
88.8%
Common 48
 
11.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 57
15.0%
리 37
 
9.7%
분 36
 
9.5%
뇨 36
 
9.5%
처 36
 
9.5%
설 35
 
9.2%
공 14
 
3.7%
천 8
 
2.1%
주 7
 
1.8%
양 7
 
1.8%
Other values (55) 107
28.2%
Common
ValueCountFrequency (%)
36
75.0%
) 6
 
12.5%
( 6
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 380
88.8%
ASCII 48
 
11.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 57
15.0%
리 37
 
9.7%
분 36
 
9.5%
뇨 36
 
9.5%
처 36
 
9.5%
설 35
 
9.2%
공 14
 
3.7%
천 8
 
2.1%
주 7
 
1.8%
양 7
 
1.8%
Other values (55) 107
28.2%
ASCII
ValueCountFrequency (%)
36
75.0%
) 6
 
12.5%
( 6
 
12.5%

도로명주소
Text

MISSING 

Distinct33
Distinct (%)100.0%
Missing1
Missing (%)2.9%
Memory size404.0 B
2023-12-11T06:27:56.351344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length19.272727
Min length15

Characters and Unicode

Total characters636
Distinct characters113
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)100.0%

Sample

1st row경기도 가평군 조종면 조종내길 57
2nd row경기도 고양시 일산서구 고양대로112번길 64
3rd row경기도 과천시 상하벌로 17
4th row경기도 광명시 부광로 387
5th row경기도 광주시 곤지암읍 경충대로311번길 36
ValueCountFrequency (%)
경기도 33
 
21.4%
포천시 3
 
1.9%
남양주시 2
 
1.3%
여주시 2
 
1.3%
광주시 2
 
1.3%
17 2
 
1.3%
용인시 2
 
1.3%
황무로 1
 
0.6%
이천시 1
 
0.6%
평택시 1
 
0.6%
Other values (105) 105
68.2%
2023-12-11T06:27:56.766749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
121
19.0%
경 35
 
5.5%
도 34
 
5.3%
기 34
 
5.3%
시 31
 
4.9%
로 30
 
4.7%
1 23
 
3.6%
0 19
 
3.0%
2 14
 
2.2%
7 13
 
2.0%
Other values (103) 282
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 392
61.6%
Space Separator 121
 
19.0%
Decimal Number 116
 
18.2%
Dash Punctuation 7
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 35
 
8.9%
도 34
 
8.7%
기 34
 
8.7%
시 31
 
7.9%
로 30
 
7.7%
천 11
 
2.8%
주 9
 
2.3%
구 8
 
2.0%
면 8
 
2.0%
양 8
 
2.0%
Other values (91) 184
46.9%
Decimal Number
ValueCountFrequency (%)
1 23
19.8%
0 19
16.4%
2 14
12.1%
7 13
11.2%
8 10
8.6%
3 9
 
7.8%
4 9
 
7.8%
6 8
 
6.9%
9 7
 
6.0%
5 4
 
3.4%
Space Separator
ValueCountFrequency (%)
121
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 392
61.6%
Common 244
38.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 35
 
8.9%
도 34
 
8.7%
기 34
 
8.7%
시 31
 
7.9%
로 30
 
7.7%
천 11
 
2.8%
주 9
 
2.3%
구 8
 
2.0%
면 8
 
2.0%
양 8
 
2.0%
Other values (91) 184
46.9%
Common
ValueCountFrequency (%)
121
49.6%
1 23
 
9.4%
0 19
 
7.8%
2 14
 
5.7%
7 13
 
5.3%
8 10
 
4.1%
3 9
 
3.7%
4 9
 
3.7%
6 8
 
3.3%
9 7
 
2.9%
Other values (2) 11
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 392
61.6%
ASCII 244
38.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
121
49.6%
1 23
 
9.4%
0 19
 
7.8%
2 14
 
5.7%
7 13
 
5.3%
8 10
 
4.1%
3 9
 
3.7%
4 9
 
3.7%
6 8
 
3.3%
9 7
 
2.9%
Other values (2) 11
 
4.5%
Hangul
ValueCountFrequency (%)
경 35
 
8.9%
도 34
 
8.7%
기 34
 
8.7%
시 31
 
7.9%
로 30
 
7.7%
천 11
 
2.8%
주 9
 
2.3%
구 8
 
2.0%
면 8
 
2.0%
양 8
 
2.0%
Other values (91) 184
46.9%

지번주소
Text

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-11T06:27:57.064339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length22
Mean length20.5
Min length15

Characters and Unicode

Total characters697
Distinct characters103
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)100.0%

Sample

1st row경기도 가평군 조종면 현리 567-10번지
2nd row경기도 고양시 일산서구 법곳동 740-5번지
3rd row경기도 과천시 과천동 249번지
4th row경기도 광명시 광명동 533-2번지
5th row경기도 광주시 곤지암읍 수양리 423번지
ValueCountFrequency (%)
경기도 34
 
21.5%
포천시 3
 
1.9%
고양시 2
 
1.3%
광주시 2
 
1.3%
여주시 2
 
1.3%
용인시 2
 
1.3%
남양주시 2
 
1.3%
용암리 1
 
0.6%
평택시 1
 
0.6%
갈산동 1
 
0.6%
Other values (108) 108
68.4%
2023-12-11T06:27:57.549346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
124
17.8%
기 35
 
5.0%
도 35
 
5.0%
지 35
 
5.0%
경 34
 
4.9%
번 33
 
4.7%
시 32
 
4.6%
1 23
 
3.3%
동 20
 
2.9%
2 18
 
2.6%
Other values (93) 308
44.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 442
63.4%
Space Separator 124
 
17.8%
Decimal Number 114
 
16.4%
Dash Punctuation 17
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 35
 
7.9%
도 35
 
7.9%
지 35
 
7.9%
경 34
 
7.7%
번 33
 
7.5%
시 32
 
7.2%
동 20
 
4.5%
리 16
 
3.6%
양 10
 
2.3%
천 9
 
2.0%
Other values (81) 183
41.4%
Decimal Number
ValueCountFrequency (%)
1 23
20.2%
2 18
15.8%
4 13
11.4%
5 12
10.5%
7 12
10.5%
6 10
8.8%
0 10
8.8%
3 8
 
7.0%
9 5
 
4.4%
8 3
 
2.6%
Space Separator
ValueCountFrequency (%)
124
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 442
63.4%
Common 255
36.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 35
 
7.9%
도 35
 
7.9%
지 35
 
7.9%
경 34
 
7.7%
번 33
 
7.5%
시 32
 
7.2%
동 20
 
4.5%
리 16
 
3.6%
양 10
 
2.3%
천 9
 
2.0%
Other values (81) 183
41.4%
Common
ValueCountFrequency (%)
124
48.6%
1 23
 
9.0%
2 18
 
7.1%
- 17
 
6.7%
4 13
 
5.1%
5 12
 
4.7%
7 12
 
4.7%
6 10
 
3.9%
0 10
 
3.9%
3 8
 
3.1%
Other values (2) 8
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 442
63.4%
ASCII 255
36.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
124
48.6%
1 23
 
9.0%
2 18
 
7.1%
- 17
 
6.7%
4 13
 
5.1%
5 12
 
4.7%
7 12
 
4.7%
6 10
 
3.9%
0 10
 
3.9%
3 8
 
3.1%
Other values (2) 8
 
3.1%
Hangul
ValueCountFrequency (%)
기 35
 
7.9%
도 35
 
7.9%
지 35
 
7.9%
경 34
 
7.7%
번 33
 
7.5%
시 32
 
7.2%
동 20
 
4.5%
리 16
 
3.6%
양 10
 
2.3%
천 9
 
2.0%
Other values (81) 183
41.4%

우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)100.0%
Missing1
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean13550.485
Minimum10425
Maximum18130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-11T06:27:57.715876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10425
5-th percentile10940.4
Q111717
median12728
Q315099
95-th percentile17697.4
Maximum18130
Range7705
Interquartile range (IQR)3382

Descriptive statistics

Standard deviation2363.5949
Coefficient of variation (CV)0.17442881
Kurtosis-0.8165376
Mean13550.485
Median Absolute Deviation (MAD)1571
Skewness0.69873902
Sum447166
Variance5586581.1
MonotonicityNot monotonic
2023-12-11T06:27:57.859588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
12438 1
 
2.9%
11717 1
 
2.9%
12642 1
 
2.9%
12611 1
 
2.9%
11008 1
 
2.9%
18130 1
 
2.9%
17022 1
 
2.9%
17099 1
 
2.9%
17349 1
 
2.9%
10425 1
 
2.9%
Other values (23) 23
67.6%
ValueCountFrequency (%)
10425 1
2.9%
10839 1
2.9%
11008 1
2.9%
11114 1
2.9%
11130 1
2.9%
11138 1
2.9%
11301 1
2.9%
11428 1
2.9%
11717 1
2.9%
11960 1
2.9%
ValueCountFrequency (%)
18130 1
2.9%
17923 1
2.9%
17547 1
2.9%
17349 1
2.9%
17099 1
2.9%
17022 1
2.9%
16648 1
2.9%
15607 1
2.9%
15099 1
2.9%
14400 1
2.9%

시설용량
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)58.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169.17647
Minimum20
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-11T06:27:58.000608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile30
Q166.25
median120
Q3205.5
95-th percentile500
Maximum500
Range480
Interquartile range (IQR)139.25

Descriptive statistics

Standard deviation151.44665
Coefficient of variation (CV)0.89519928
Kurtosis0.35231553
Mean169.17647
Median Absolute Deviation (MAD)65
Skewness1.2721373
Sum5752
Variance22936.089
MonotonicityNot monotonic
2023-12-11T06:27:58.134536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
120 4
 
11.8%
90 4
 
11.8%
40 4
 
11.8%
500 3
 
8.8%
30 2
 
5.9%
300 2
 
5.9%
150 2
 
5.9%
100 1
 
2.9%
60 1
 
2.9%
220 1
 
2.9%
Other values (10) 10
29.4%
ValueCountFrequency (%)
20 1
 
2.9%
30 2
5.9%
40 4
11.8%
50 1
 
2.9%
60 1
 
2.9%
85 1
 
2.9%
90 4
11.8%
95 1
 
2.9%
100 1
 
2.9%
120 4
11.8%
ValueCountFrequency (%)
500 3
8.8%
480 1
 
2.9%
400 1
 
2.9%
350 1
 
2.9%
300 2
5.9%
220 1
 
2.9%
162 1
 
2.9%
150 2
5.9%
140 1
 
2.9%
130 1
 
2.9%

설치일자
Date

Distinct33
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size404.0 B
Minimum1983-11-13 00:00:00
Maximum2016-08-23 00:00:00
2023-12-11T06:27:58.251721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:58.363459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)

처리방식
Categorical

Distinct10
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Memory size404.0 B
전처리(하수연계)
24 
B3(하수연계)
 
2
액상부식(단독처리)
 
1
호기성(하수연계)
 
1
액상부식(하수연계)
 
1
Other values (5)
5 

Length

Max length12
Median length9
Mean length9.0882353
Min length8

Unique

Unique8 ?
Unique (%)23.5%

Sample

1st row액상부식(단독처리)
2nd row전처리(하수연계)
3rd row전처리(하수연계)
4th row전처리(하수연계)
5th row호기성(하수연계)

Common Values

ValueCountFrequency (%)
전처리(하수연계) 24
70.6%
B3(하수연계) 2
 
5.9%
액상부식(단독처리) 1
 
2.9%
호기성(하수연계) 1
 
2.9%
액상부식(하수연계) 1
 
2.9%
BIOSUF(하수연계) 1
 
2.9%
막분리(단독처리) 1
 
2.9%
혐기성(하수연계) 1
 
2.9%
산화구(단독처리) 1
 
2.9%
호기성(단독처리) 1
 
2.9%

Length

2023-12-11T06:27:58.727645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:27:58.875614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전처리(하수연계 24
70.6%
b3(하수연계 2
 
5.9%
액상부식(단독처리 1
 
2.9%
호기성(하수연계 1
 
2.9%
액상부식(하수연계 1
 
2.9%
biosuf(하수연계 1
 
2.9%
막분리(단독처리 1
 
2.9%
혐기성(하수연계 1
 
2.9%
산화구(단독처리 1
 
2.9%
호기성(단독처리 1
 
2.9%

방류하천명
Text

Distinct22
Distinct (%)64.7%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-11T06:27:59.021526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.5
Min length3

Characters and Unicode

Total characters153
Distinct characters33
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

Unique13 ?
Unique (%)38.2%

Sample

1st row청 평 천
2nd row한 강
3rd row양 재 천
4th row안 양 천
5th row경 안 천
ValueCountFrequency (%)
천 27
29.3%
안 7
 
7.6%
한 6
 
6.5%
강 5
 
5.4%
경 4
 
4.3%
평 3
 
3.3%
남 3
 
3.3%
양 3
 
3.3%
포 3
 
3.3%
탄 3
 
3.3%
Other values (20) 28
30.4%
2023-12-11T06:27:59.337880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
58
37.9%
천 28
18.3%
안 7
 
4.6%
한 6
 
3.9%
강 5
 
3.3%
경 4
 
2.6%
평 3
 
2.0%
남 3
 
2.0%
양 3
 
2.0%
포 3
 
2.0%
Other values (23) 33
21.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 95
62.1%
Space Separator 58
37.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 28
29.5%
안 7
 
7.4%
한 6
 
6.3%
강 5
 
5.3%
경 4
 
4.2%
평 3
 
3.2%
남 3
 
3.2%
양 3
 
3.2%
포 3
 
3.2%
탄 3
 
3.2%
Other values (22) 30
31.6%
Space Separator
ValueCountFrequency (%)
58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 95
62.1%
Common 58
37.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 28
29.5%
안 7
 
7.4%
한 6
 
6.3%
강 5
 
5.3%
경 4
 
4.2%
평 3
 
3.2%
남 3
 
3.2%
양 3
 
3.2%
포 3
 
3.2%
탄 3
 
3.2%
Other values (22) 30
31.6%
Common
ValueCountFrequency (%)
58
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 95
62.1%
ASCII 58
37.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
58
100.0%
Hangul
ValueCountFrequency (%)
천 28
29.5%
안 7
 
7.4%
한 6
 
6.3%
강 5
 
5.3%
경 4
 
4.2%
평 3
 
3.2%
남 3
 
3.2%
양 3
 
3.2%
포 3
 
3.2%
탄 3
 
3.2%
Other values (22) 30
31.6%

비고
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing34
Missing (%)100.0%
Memory size438.0 B

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)100.0%
Missing1
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean37.524085
Minimum36.988099
Maximum38.024871
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-11T06:27:59.479725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.988099
5-th percentile37.081327
Q137.303138
median37.463631
Q337.71894
95-th percentile37.998838
Maximum38.024871
Range1.0367719
Interquartile range (IQR)0.4158021

Descriptive statistics

Standard deviation0.2857734
Coefficient of variation (CV)0.0076157328
Kurtosis-0.68978041
Mean37.524085
Median Absolute Deviation (MAD)0.17178278
Skewness0.15944427
Sum1238.2948
Variance0.081666438
MonotonicityNot monotonic
2023-12-11T06:27:59.648840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
37.815567976 1
 
2.9%
37.7189398989 1
 
2.9%
37.301796542 1
 
2.9%
37.3839285228 1
 
2.9%
38.0248705225 1
 
2.9%
37.1404782493 1
 
2.9%
37.3029182461 1
 
2.9%
37.2521134199 1
 
2.9%
37.2918278538 1
 
2.9%
37.6580715067 1
 
2.9%
Other values (23) 23
67.6%
ValueCountFrequency (%)
36.9880986184 1
2.9%
36.992600711 1
2.9%
37.1404782493 1
2.9%
37.2348788548 1
2.9%
37.2521134199 1
2.9%
37.2918278538 1
2.9%
37.301796542 1
2.9%
37.3029182461 1
2.9%
37.3031377988 1
2.9%
37.3342673547 1
2.9%
ValueCountFrequency (%)
38.0248705225 1
2.9%
38.000014087 1
2.9%
37.9980547209 1
2.9%
37.9450450425 1
2.9%
37.9314927389 1
2.9%
37.8634771986 1
2.9%
37.815567976 1
2.9%
37.8064002966 1
2.9%
37.7189398989 1
2.9%
37.6580715067 1
2.9%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)100.0%
Missing1
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean127.1295
Minimum126.69672
Maximum127.61928
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-11T06:27:59.782298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.69672
5-th percentile126.74471
Q1127.00238
median127.12438
Q3127.28547
95-th percentile127.53858
Maximum127.61928
Range0.92255461
Interquartile range (IQR)0.28308499

Descriptive statistics

Standard deviation0.24467797
Coefficient of variation (CV)0.0019246357
Kurtosis-0.41208022
Mean127.1295
Median Absolute Deviation (MAD)0.13735228
Skewness0.081940039
Sum4195.2733
Variance0.059867309
MonotonicityNot monotonic
2023-12-11T06:27:59.894335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
127.3530898787 1
 
2.9%
127.0519653948 1
 
2.9%
127.6192792584 1
 
2.9%
127.6114240084 1
 
2.9%
127.0476250275 1
 
2.9%
127.0645822452 1
 
2.9%
127.2264222089 1
 
2.9%
127.0964553873 1
 
2.9%
127.4750385174 1
 
2.9%
126.726226441 1
 
2.9%
Other values (23) 23
67.6%
ValueCountFrequency (%)
126.6967246521 1
2.9%
126.726226441 1
2.9%
126.7570377758 1
2.9%
126.7683958017 1
2.9%
126.787694255 1
2.9%
126.8431330896 1
2.9%
126.8906988769 1
2.9%
126.9870237463 1
2.9%
127.0023847069 1
2.9%
127.0476250275 1
2.9%
ValueCountFrequency (%)
127.6192792584 1
2.9%
127.6114240084 1
2.9%
127.4900100476 1
2.9%
127.4750385174 1
2.9%
127.3674833608 1
2.9%
127.3530898787 1
2.9%
127.3470474426 1
2.9%
127.3133543055 1
2.9%
127.2854696942 1
2.9%
127.2264222089 1
2.9%

Interactions

2023-12-11T06:27:54.390556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:53.417583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:53.735630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:54.091869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:54.471833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:53.502116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:53.869754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:54.170091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:54.574194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:53.574799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:53.954684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:54.239816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:54.644409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:53.652034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:54.025231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:54.321493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:27:59.980168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명구분명도로명주소지번주소우편번호시설용량설치일자처리방식방류하천명WGS84위도WGS84경도
시군명1.0001.0001.0001.0000.9900.9740.9620.0000.9800.9810.862
구분명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
우편번호0.9901.0001.0001.0001.0000.4450.8640.0000.9440.7700.550
시설용량0.9741.0001.0001.0000.4451.0000.9700.4860.7620.5670.654
설치일자0.9621.0001.0001.0000.8640.9701.0001.0000.8960.9701.000
처리방식0.0001.0001.0001.0000.0000.4861.0001.0000.0000.3390.000
방류하천명0.9801.0001.0001.0000.9440.7620.8960.0001.0000.9060.000
WGS84위도0.9811.0001.0001.0000.7700.5670.9700.3390.9061.0000.000
WGS84경도0.8621.0001.0001.0000.5500.6541.0000.0000.0000.0001.000
2023-12-11T06:28:00.110940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호시설용량WGS84위도WGS84경도처리방식
우편번호1.0000.285-0.900-0.0510.000
시설용량0.2851.000-0.151-0.6990.227
WGS84위도-0.900-0.1511.000-0.0640.118
WGS84경도-0.051-0.699-0.0641.0000.000
처리방식0.0000.2270.1180.0001.000

Missing values

2023-12-11T06:27:54.768630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:27:54.950961image/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.
2023-12-11T06:27:55.068021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시군명구분명도로명주소지번주소우편번호시설용량설치일자처리방식방류하천명비고WGS84위도WGS84경도
0가평군가평군 분뇨처리시설경기도 가평군 조종면 조종내길 57경기도 가평군 조종면 현리 567-10번지124381202003-05-18액상부식(단독처리)청 평 천<NA>37.815568127.35309
1고양시고양시 일산공공하수처리시설경기도 고양시 일산서구 고양대로112번길 64경기도 고양시 일산서구 법곳동 740-5번지104251621998-04-27전처리(하수연계)한 강<NA>37.658072126.726226
2과천시과천시 분뇨처리시설경기도 과천시 상하벌로 17경기도 과천시 과천동 249번지13815301993-12-30전처리(하수연계)양 재 천<NA>37.44869127.002385
3광명시광명시 환경사업소(분뇨처리시설)경기도 광명시 부광로 387경기도 광명시 광명동 533-2번지142993001983-11-13전처리(하수연계)안 양 천<NA>37.463631126.843133
4광주시광주시 수양분뇨처리시설경기도 광주시 곤지암읍 경충대로311번길 36경기도 광주시 곤지암읍 수양리 423번지12813401989-09-07호기성(하수연계)경 안 천<NA>37.334267127.367483
5광주시광주시 지월분뇨처리시설경기도 광주시 초월읍 경수길 11경기도 광주시 초월읍 지월리 729-23번지12728401994-12-31전처리(하수연계)경 안 천<NA>37.419889127.28547
6구리시구리 분뇨처리시설경기도 구리시 검배로 200경기도 구리시 수택동 89번지119601301993-03-10전처리(하수연계)왕 숙 천<NA>37.593177127.159115
7김포시김포시 분뇨처리시설<NA>경기도 고양시 금포로 1117-5<NA>1502004-12-31액상부식(하수연계)굴 포 천<NA><NA><NA>
8남양주시남양주 가축분뇨공공처리시설(분뇨)경기도 남양주시 진건읍 금강로380번길 67경기도 남양주시 진건읍 진관리 875번지12247852000-12-20B3(하수연계)왕 숙 천<NA>37.635414127.150755
9남양주시화도 분뇨처리시설경기도 남양주시 화도읍 폭포로 562경기도 남양주시 화도읍 금남리 580번지12194401993-08-31전처리(하수연계)묵 현 천<NA>37.631557127.347047
시군명구분명도로명주소지번주소우편번호시설용량설치일자처리방식방류하천명비고WGS84위도WGS84경도
24용인시용인 가축분뇨 분뇨 병합처리시설경기도 용인시 처인구 포곡읍 옥현로 58경기도 용인시 처인구 포곡읍 유운리 1-1번지170221202005-11-29전처리(하수연계)경 안 천<NA>37.302918127.226422
25용인시용인 분뇨처리시설경기도 용인시 기흥구 하갈로 79경기도 용인시 기흥구 하갈동 114번지17099902005-07-31전처리(하수연계)경 안 천<NA>37.252113127.096455
26의정부시의정부시 분뇨처리시설경기도 의정부시 장곡로 147경기도 의정부시 장암동 44-5번지117173002000-12-31전처리(하수연계)중 랑 천<NA>37.71894127.051965
27이천시이천 분뇨처리시설경기도 이천시 황무로 1081-200경기도 이천시 갈산동 2번지17349501986-12-30전처리(하수연계)복 하 천<NA>37.291828127.475039
28파주시파주시 분뇨처리시설경기도 파주시 파주읍 통일로 1089-100경기도 파주시 파주읍 봉암리 1001-1번지108392201997-11-04산화구(단독처리)문 산 천<NA>37.8064126.787694
29평택시통복위생(분뇨)처리시설경기도 평택시 평남로 200-35경기도 평택시 통복동 197-1번지179231501988-02-27전처리(하수연계)안 성 천<NA>36.992601127.067331
30포천시포천시 신평분뇨처리시설경기도 포천시 신북면 중앙로461번길 17경기도 포천시 신북면 신평리 647-1번지11138601998-01-13전처리(하수연계)포 천 천<NA>37.931493127.219945
31포천시포천시 영송분뇨처리시설경기도 포천시 영중면 가영로 380경기도 포천시 영중면 영송리 617번지11130901992-10-26호기성(단독처리)영 평 천<NA>37.998055127.205796
32포천시포천시 일이동분뇨처리시설경기도 포천시 일동면 수입로 396경기도 포천시 일동면 사직리 1400번지11114302010-05-12전처리(하수연계)영 평 천<NA>38.000014127.313354
33하남시하남 분뇨처리시설경기도 하남시 미사대로 710경기도 하남시 신장동 27번지129411002015-02-28전처리(하수연계)탄 천<NA>37.547544127.219372