Overview

Dataset statistics

Number of variables15
Number of observations75
Missing cells142
Missing cells (%)12.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.4 KiB
Average record size in memory128.8 B

Variable types

DateTime1
Categorical4
Text3
Numeric7

Dataset

Description경기도 비점오염 저감시설 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=0856ISDY12ITD4N45F9A11682702&infSeq=1

Alerts

집계년월 has constant value ""Constant
시설규모(㎥/일) is highly overall correlated with 국비(백만원) and 1 other fieldsHigh correlation
국비(백만원) is highly overall correlated with 시설규모(㎥/일) and 2 other fieldsHigh correlation
도비(백만원) is highly overall correlated with 시비(백만원)High correlation
시비(백만원) is highly overall correlated with 국비(백만원) and 2 other fieldsHigh correlation
소재지우편번호 is highly overall correlated with WGS84위도 and 2 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
시군명 is highly overall correlated with 소재지우편번호 and 3 other fieldsHigh correlation
하천명 is highly overall correlated with 시설규모(㎥/일) and 6 other fieldsHigh correlation
국비(백만원) has 18 (24.0%) missing valuesMissing
도비(백만원) has 42 (56.0%) missing valuesMissing
시비(백만원) has 26 (34.7%) missing valuesMissing
소재지우편번호 has 11 (14.7%) missing valuesMissing
소재지도로명주소 has 45 (60.0%) missing valuesMissing

Reproduction

Analysis started2023-12-10 21:32:33.014751
Analysis finished2023-12-10 21:32:39.124277
Duration6.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

집계년월
Date

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size732.0 B
Minimum2022-11-01 00:00:00
Maximum2022-11-01 00:00:00
2023-12-11T06:32:39.192442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:39.287622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

시군명
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size732.0 B
용인시
23 
광주시
20 
남양주시
9 
수원시
8 
성남시
4 
Other values (7)
11 

Length

Max length4
Median length3
Mean length3.12
Min length3

Unique

Unique5 ?
Unique (%)6.7%

Sample

1st row가평군
2nd row광주시
3rd row광주시
4th row광주시
5th row광주시

Common Values

ValueCountFrequency (%)
용인시 23
30.7%
광주시 20
26.7%
남양주시 9
 
12.0%
수원시 8
 
10.7%
성남시 4
 
5.3%
구리시 3
 
4.0%
안성시 3
 
4.0%
가평군 1
 
1.3%
부천시 1
 
1.3%
양평군 1
 
1.3%
Other values (2) 2
 
2.7%

Length

2023-12-11T06:32:39.386300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
용인시 23
30.7%
광주시 20
26.7%
남양주시 9
 
12.0%
수원시 8
 
10.7%
성남시 4
 
5.3%
구리시 3
 
4.0%
안성시 3
 
4.0%
가평군 1
 
1.3%
부천시 1
 
1.3%
양평군 1
 
1.3%
Other values (2) 2
 
2.7%

하천명
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Memory size732.0 B
경안천
40 
왕숙천
5 
탄천
 
4
남한강
 
4
<NA>
 
3
Other values (16)
19 

Length

Max length4
Median length3
Mean length3.04
Min length2

Unique

Unique14 ?
Unique (%)18.7%

Sample

1st row달전천
2nd row경안천
3rd row경안천
4th row경안천
5th row경안천

Common Values

ValueCountFrequency (%)
경안천 40
53.3%
왕숙천 5
 
6.7%
탄천 4
 
5.3%
남한강 4
 
5.3%
<NA> 3
 
4.0%
원천리천 3
 
4.0%
수원천 2
 
2.7%
홍릉천 1
 
1.3%
노곡천 1
 
1.3%
사능천 1
 
1.3%
Other values (11) 11
 
14.7%

Length

2023-12-11T06:32:39.534729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경안천 40
53.3%
왕숙천 5
 
6.7%
탄천 4
 
5.3%
남한강 4
 
5.3%
na 3
 
4.0%
원천리천 3
 
4.0%
수원천 2
 
2.7%
굴포천 1
 
1.3%
달전천 1
 
1.3%
청미천 1
 
1.3%
Other values (11) 11
 
14.7%

시설유형
Categorical

Distinct18
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Memory size732.0 B
인공습지
23 
장치형
9 
여과형
8 
저류형
7 
LID
6 
Other values (13)
22 

Length

Max length15
Median length14
Mean length5.1733333
Min length3

Unique

Unique7 ?
Unique (%)9.3%

Sample

1st row인공습지
2nd row장치형( HDS-FSF)
3rd row장치형
4th row장치형
5th row자연형(침투도랑)

Common Values

ValueCountFrequency (%)
인공습지 23
30.7%
장치형 9
 
12.0%
여과형 8
 
10.7%
저류형 7
 
9.3%
LID 6
 
8.0%
장치형(스톰시스) 3
 
4.0%
장치형(스톰필터) 3
 
4.0%
장치형( HDS-FSF) 3
 
4.0%
장치형(CDS-저류조) 2
 
2.7%
장치형(CDS-MFS) 2
 
2.7%
Other values (8) 9
 
12.0%

Length

2023-12-11T06:32:39.653126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
인공습지 23
28.7%
장치형 13
16.2%
여과형 8
 
10.0%
저류형 7
 
8.8%
lid 6
 
7.5%
장치형(스톰시스 3
 
3.8%
장치형(스톰필터 3
 
3.8%
hds-fsf 3
 
3.8%
침투형 2
 
2.5%
장치형(cds-mfs 2
 
2.5%
Other values (9) 10
12.5%

사업기간
Text

Distinct43
Distinct (%)57.3%
Missing0
Missing (%)0.0%
Memory size732.0 B
2023-12-11T06:32:39.856924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length7.8666667
Min length4

Characters and Unicode

Total characters590
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)36.0%

Sample

1st row2010~2011
2nd row2007~2008
3rd row2012
4th row2011
5th row2010
ValueCountFrequency (%)
2004~2005 9
 
12.0%
2007~2008 7
 
9.3%
2001~2002 4
 
5.3%
2005~2006 3
 
4.0%
2013~2016 3
 
4.0%
2010 2
 
2.7%
2010~2011 2
 
2.7%
2010~2013 2
 
2.7%
2005 2
 
2.7%
2016 2
 
2.7%
Other values (33) 39
52.0%
2023-12-11T06:32:40.238645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 209
35.4%
2 149
25.3%
1 77
 
13.1%
~ 55
 
9.3%
5 19
 
3.2%
8 17
 
2.9%
4 15
 
2.5%
6 14
 
2.4%
7 13
 
2.2%
3 12
 
2.0%
Other values (2) 10
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 532
90.2%
Math Symbol 58
 
9.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 209
39.3%
2 149
28.0%
1 77
 
14.5%
5 19
 
3.6%
8 17
 
3.2%
4 15
 
2.8%
6 14
 
2.6%
7 13
 
2.4%
3 12
 
2.3%
9 7
 
1.3%
Math Symbol
ValueCountFrequency (%)
~ 55
94.8%
∼ 3
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common 590
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 209
35.4%
2 149
25.3%
1 77
 
13.1%
~ 55
 
9.3%
5 19
 
3.2%
8 17
 
2.9%
4 15
 
2.5%
6 14
 
2.4%
7 13
 
2.2%
3 12
 
2.0%
Other values (2) 10
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 587
99.5%
Math Operators 3
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 209
35.6%
2 149
25.4%
1 77
 
13.1%
~ 55
 
9.4%
5 19
 
3.2%
8 17
 
2.9%
4 15
 
2.6%
6 14
 
2.4%
7 13
 
2.2%
3 12
 
2.0%
Math Operators
ValueCountFrequency (%)
∼ 3
100.0%

주요오염원정보
Categorical

Distinct12
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size732.0 B
시가지, 도로
25 
도로
15 
시가지, 농경지
14 
주차장
4 
시가지,도로
4 
Other values (7)
13 

Length

Max length8
Median length7
Mean length5.2533333
Min length2

Unique

Unique3 ?
Unique (%)4.0%

Sample

1st row시가지, 도로
2nd row시가지, 도로
3rd row교량
4th row교량
5th row주차장

Common Values

ValueCountFrequency (%)
시가지, 도로 25
33.3%
도로 15
20.0%
시가지, 농경지 14
18.7%
주차장 4
 
5.3%
시가지,도로 4
 
5.3%
대지, 밭 3
 
4.0%
도심 3
 
4.0%
교량 2
 
2.7%
도시 2
 
2.7%
시가지 1
 
1.3%
Other values (2) 2
 
2.7%

Length

2023-12-11T06:32:40.374722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
시가지 40
33.9%
도로 40
33.9%
농경지 14
 
11.9%
주차장 4
 
3.4%
시가지,도로 4
 
3.4%
대지 4
 
3.4%
밭 3
 
2.5%
도심 3
 
2.5%
교량 2
 
1.7%
도시 2
 
1.7%
Other values (2) 2
 
1.7%

시설규모(㎥/일)
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10572.533
Minimum14
Maximum100000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-11T06:32:40.497446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile25.2
Q1162
median2300
Q39426
95-th percentile63600
Maximum100000
Range99986
Interquartile range (IQR)9264

Descriptive statistics

Standard deviation21809.658
Coefficient of variation (CV)2.0628601
Kurtosis9.3851646
Mean10572.533
Median Absolute Deviation (MAD)2267
Skewness3.1229882
Sum792940
Variance4.7566116 × 108
MonotonicityNot monotonic
2023-12-11T06:32:40.659343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 3
 
4.0%
15 2
 
2.7%
96000 2
 
2.7%
7300 2
 
2.7%
8200 2
 
2.7%
10000 2
 
2.7%
39 2
 
2.7%
2300 2
 
2.7%
1545 1
 
1.3%
100000 1
 
1.3%
Other values (56) 56
74.7%
ValueCountFrequency (%)
14 1
1.3%
15 2
2.7%
21 1
1.3%
27 1
1.3%
30 1
1.3%
32 1
1.3%
33 1
1.3%
39 2
2.7%
40 1
1.3%
54 1
1.3%
ValueCountFrequency (%)
100000 1
1.3%
96000 2
2.7%
72000 1
1.3%
60000 1
1.3%
48000 1
1.3%
24800 1
1.3%
23600 1
1.3%
22300 1
1.3%
20000 1
1.3%
17400 1
1.3%

국비(백만원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct55
Distinct (%)96.5%
Missing18
Missing (%)24.0%
Infinite0
Infinite (%)0.0%
Mean1961.0526
Minimum8
Maximum12638
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-11T06:32:40.811777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile49
Q1187
median970
Q32002
95-th percentile8562
Maximum12638
Range12630
Interquartile range (IQR)1815

Descriptive statistics

Standard deviation2812.9577
Coefficient of variation (CV)1.4344122
Kurtosis4.2327498
Mean1961.0526
Median Absolute Deviation (MAD)839
Skewness2.1582907
Sum111780
Variance7912731.2
MonotonicityNot monotonic
2023-12-11T06:32:40.961098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94 2
 
2.7%
49 2
 
2.7%
187 1
 
1.3%
2052 1
 
1.3%
970 1
 
1.3%
1331 1
 
1.3%
2147 1
 
1.3%
1515 1
 
1.3%
273 1
 
1.3%
1090 1
 
1.3%
Other values (45) 45
60.0%
(Missing) 18
 
24.0%
ValueCountFrequency (%)
8 1
1.3%
34 1
1.3%
49 2
2.7%
55 1
1.3%
64 1
1.3%
65 1
1.3%
84 1
1.3%
94 2
2.7%
95 1
1.3%
115 1
1.3%
ValueCountFrequency (%)
12638 1
1.3%
9760 1
1.3%
8734 1
1.3%
8519 1
1.3%
7888 1
1.3%
7500 1
1.3%
5548 1
1.3%
4576 1
1.3%
3986 1
1.3%
3349 1
1.3%

도비(백만원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)97.0%
Missing42
Missing (%)56.0%
Infinite0
Infinite (%)0.0%
Mean410.9697
Minimum10
Maximum2130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-11T06:32:41.094797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile31.2
Q173
median232
Q3502
95-th percentile1573
Maximum2130
Range2120
Interquartile range (IQR)429

Descriptive statistics

Standard deviation511.91061
Coefficient of variation (CV)1.2456164
Kurtosis5.2602837
Mean410.9697
Median Absolute Deviation (MAD)194
Skewness2.2767376
Sum13562
Variance262052.47
MonotonicityNot monotonic
2023-12-11T06:32:41.566044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
32 2
 
2.7%
929 1
 
1.3%
534 1
 
1.3%
228 1
 
1.3%
140 1
 
1.3%
596 1
 
1.3%
1307 1
 
1.3%
502 1
 
1.3%
2130 1
 
1.3%
650 1
 
1.3%
Other values (22) 22
29.3%
(Missing) 42
56.0%
ValueCountFrequency (%)
10 1
1.3%
30 1
1.3%
32 2
2.7%
38 1
1.3%
40 1
1.3%
43 1
1.3%
65 1
1.3%
73 1
1.3%
120 1
1.3%
122 1
1.3%
ValueCountFrequency (%)
2130 1
1.3%
1972 1
1.3%
1307 1
1.3%
929 1
1.3%
650 1
1.3%
596 1
1.3%
588 1
1.3%
534 1
1.3%
502 1
1.3%
500 1
1.3%

시비(백만원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct48
Distinct (%)98.0%
Missing26
Missing (%)34.7%
Infinite0
Infinite (%)0.0%
Mean1173.7755
Minimum10
Maximum12042
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-11T06:32:41.720706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile38.8
Q1198
median502
Q31090
95-th percentile3907.4
Maximum12042
Range12032
Interquartile range (IQR)892

Descriptive statistics

Standard deviation2140.3791
Coefficient of variation (CV)1.8234995
Kurtosis16.330806
Mean1173.7755
Median Absolute Deviation (MAD)380
Skewness3.846497
Sum57515
Variance4581222.6
MonotonicityNot monotonic
2023-12-11T06:32:41.856979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
198 2
 
2.7%
1929 1
 
1.3%
523 1
 
1.3%
488 1
 
1.3%
2345 1
 
1.3%
954 1
 
1.3%
454 1
 
1.3%
541 1
 
1.3%
644 1
 
1.3%
1515 1
 
1.3%
Other values (38) 38
50.7%
(Missing) 26
34.7%
ValueCountFrequency (%)
10 1
1.3%
30 1
1.3%
38 1
1.3%
40 1
1.3%
45 1
1.3%
49 1
1.3%
65 1
1.3%
120 1
1.3%
122 1
1.3%
150 1
1.3%
ValueCountFrequency (%)
12042 1
1.3%
8734 1
1.3%
4183 1
1.3%
3494 1
1.3%
2530 1
1.3%
2345 1
1.3%
1972 1
1.3%
1929 1
1.3%
1591 1
1.3%
1515 1
1.3%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct56
Distinct (%)87.5%
Missing11
Missing (%)14.7%
Infinite0
Infinite (%)0.0%
Mean14689.172
Minimum11962
Maximum17531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-11T06:32:42.027556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11962
5-th percentile12211.9
Q112728
median13580
Q317043.25
95-th percentile17177.45
Maximum17531
Range5569
Interquartile range (IQR)4315.25

Descriptive statistics

Standard deviation2208.4122
Coefficient of variation (CV)0.15034287
Kurtosis-1.943972
Mean14689.172
Median Absolute Deviation (MAD)1463
Skewness0.097032857
Sum940107
Variance4877084.6
MonotonicityNot monotonic
2023-12-11T06:32:42.189284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17046 2
 
2.7%
17141 2
 
2.7%
12728 2
 
2.7%
12755 2
 
2.7%
13580 2
 
2.7%
12746 2
 
2.7%
17028 2
 
2.7%
12733 2
 
2.7%
17037 1
 
1.3%
12584 1
 
1.3%
Other values (46) 46
61.3%
(Missing) 11
 
14.7%
ValueCountFrequency (%)
11962 1
1.3%
12094 1
1.3%
12140 1
1.3%
12211 1
1.3%
12217 1
1.3%
12232 1
1.3%
12242 1
1.3%
12253 1
1.3%
12265 1
1.3%
12270 1
1.3%
ValueCountFrequency (%)
17531 1
1.3%
17530 1
1.3%
17520 1
1.3%
17180 1
1.3%
17163 1
1.3%
17152 1
1.3%
17151 1
1.3%
17147 1
1.3%
17142 1
1.3%
17141 2
2.7%

소재지지번주소
Text

Distinct74
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size732.0 B
2023-12-11T06:32:42.481656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length25
Mean length20.92
Min length15

Characters and Unicode

Total characters1569
Distinct characters118
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

Unique73 ?
Unique (%)97.3%

Sample

1st row경기도 가평군 가평읍 달전리 355
2nd row경기도 광주시 곤지암읍 곤지암리 346-1
3rd row경기도 광주시 초월읍 지월리 813번지
4th row경기도 광주시 퇴촌면 광동리 507
5th row경기도 광주시 남한산성면 산성리 437
ValueCountFrequency (%)
경기도 75
 
20.6%
용인시 23
 
6.3%
처인구 22
 
6.0%
광주시 20
 
5.5%
남양주시 9
 
2.5%
수원시 8
 
2.2%
포곡읍 6
 
1.6%
초월읍 5
 
1.4%
분당구 4
 
1.1%
성남시 4
 
1.1%
Other values (154) 188
51.6%
2023-12-11T06:32:43.027998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
289
 
18.4%
경 79
 
5.0%
기 76
 
4.8%
도 76
 
4.8%
시 73
 
4.7%
1 63
 
4.0%
- 50
 
3.2%
동 48
 
3.1%
인 46
 
2.9%
3 39
 
2.5%
Other values (108) 730
46.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 943
60.1%
Space Separator 289
 
18.4%
Decimal Number 287
 
18.3%
Dash Punctuation 50
 
3.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 79
 
8.4%
기 76
 
8.1%
도 76
 
8.1%
시 73
 
7.7%
동 48
 
5.1%
인 46
 
4.9%
지 39
 
4.1%
구 38
 
4.0%
리 32
 
3.4%
주 29
 
3.1%
Other values (96) 407
43.2%
Decimal Number
ValueCountFrequency (%)
1 63
22.0%
3 39
13.6%
4 34
11.8%
2 34
11.8%
5 29
10.1%
7 25
 
8.7%
0 20
 
7.0%
8 17
 
5.9%
6 16
 
5.6%
9 10
 
3.5%
Space Separator
ValueCountFrequency (%)
289
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 943
60.1%
Common 626
39.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 79
 
8.4%
기 76
 
8.1%
도 76
 
8.1%
시 73
 
7.7%
동 48
 
5.1%
인 46
 
4.9%
지 39
 
4.1%
구 38
 
4.0%
리 32
 
3.4%
주 29
 
3.1%
Other values (96) 407
43.2%
Common
ValueCountFrequency (%)
289
46.2%
1 63
 
10.1%
- 50
 
8.0%
3 39
 
6.2%
4 34
 
5.4%
2 34
 
5.4%
5 29
 
4.6%
7 25
 
4.0%
0 20
 
3.2%
8 17
 
2.7%
Other values (2) 26
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 943
60.1%
ASCII 626
39.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
289
46.2%
1 63
 
10.1%
- 50
 
8.0%
3 39
 
6.2%
4 34
 
5.4%
2 34
 
5.4%
5 29
 
4.6%
7 25
 
4.0%
0 20
 
3.2%
8 17
 
2.7%
Other values (2) 26
 
4.2%
Hangul
ValueCountFrequency (%)
경 79
 
8.4%
기 76
 
8.1%
도 76
 
8.1%
시 73
 
7.7%
동 48
 
5.1%
인 46
 
4.9%
지 39
 
4.1%
구 38
 
4.0%
리 32
 
3.4%
주 29
 
3.1%
Other values (96) 407
43.2%

소재지도로명주소
Text

MISSING 

Distinct29
Distinct (%)96.7%
Missing45
Missing (%)60.0%
Memory size732.0 B
2023-12-11T06:32:43.256320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length22
Mean length19.1
Min length14

Characters and Unicode

Total characters573
Distinct characters89
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

Unique28 ?
Unique (%)93.3%

Sample

1st row경기도 광주시 초월읍 지월로 17-66
2nd row경기도 광주시 곤지암읍 곤지암천로 288
3rd row경기도 광주시 파발로 176
4th row경기도 광주시 회안대로 785-5
5th row경기도 광주시 회안대로 777
ValueCountFrequency (%)
경기도 30
21.6%
광주시 9
 
6.5%
남양주시 7
 
5.0%
수원시 6
 
4.3%
용인시 4
 
2.9%
장안구 3
 
2.2%
파발로 3
 
2.2%
처인구 3
 
2.2%
초월읍 2
 
1.4%
성남시 2
 
1.4%
Other values (64) 70
50.4%
2023-12-11T06:32:43.608954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
109
19.0%
경 33
 
5.8%
기 31
 
5.4%
도 30
 
5.2%
시 30
 
5.2%
로 28
 
4.9%
1 23
 
4.0%
주 16
 
2.8%
구 13
 
2.3%
7 12
 
2.1%
Other values (79) 248
43.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 355
62.0%
Space Separator 109
 
19.0%
Decimal Number 101
 
17.6%
Dash Punctuation 8
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 33
 
9.3%
기 31
 
8.7%
도 30
 
8.5%
시 30
 
8.5%
로 28
 
7.9%
주 16
 
4.5%
구 13
 
3.7%
남 10
 
2.8%
광 9
 
2.5%
원 8
 
2.3%
Other values (67) 147
41.4%
Decimal Number
ValueCountFrequency (%)
1 23
22.8%
7 12
11.9%
2 11
10.9%
5 10
9.9%
6 9
 
8.9%
8 9
 
8.9%
3 8
 
7.9%
4 7
 
6.9%
0 6
 
5.9%
9 6
 
5.9%
Space Separator
ValueCountFrequency (%)
109
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 355
62.0%
Common 218
38.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 33
 
9.3%
기 31
 
8.7%
도 30
 
8.5%
시 30
 
8.5%
로 28
 
7.9%
주 16
 
4.5%
구 13
 
3.7%
남 10
 
2.8%
광 9
 
2.5%
원 8
 
2.3%
Other values (67) 147
41.4%
Common
ValueCountFrequency (%)
109
50.0%
1 23
 
10.6%
7 12
 
5.5%
2 11
 
5.0%
5 10
 
4.6%
6 9
 
4.1%
8 9
 
4.1%
3 8
 
3.7%
- 8
 
3.7%
4 7
 
3.2%
Other values (2) 12
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 355
62.0%
ASCII 218
38.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
109
50.0%
1 23
 
10.6%
7 12
 
5.5%
2 11
 
5.0%
5 10
 
4.6%
6 9
 
4.1%
8 9
 
4.1%
3 8
 
3.7%
- 8
 
3.7%
4 7
 
3.2%
Other values (2) 12
 
5.5%
Hangul
ValueCountFrequency (%)
경 33
 
9.3%
기 31
 
8.7%
도 30
 
8.5%
시 30
 
8.5%
로 28
 
7.9%
주 16
 
4.5%
구 13
 
3.7%
남 10
 
2.8%
광 9
 
2.5%
원 8
 
2.3%
Other values (67) 147
41.4%

WGS84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.370344
Minimum37.071377
Maximum37.814844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-11T06:32:43.771992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.071377
5-th percentile37.233695
Q137.254096
median37.349566
Q337.42039
95-th percentile37.641485
Maximum37.814844
Range0.74346698
Interquartile range (IQR)0.16629459

Descriptive statistics

Standard deviation0.15075509
Coefficient of variation (CV)0.0040340834
Kurtosis0.24461176
Mean37.370344
Median Absolute Deviation (MAD)0.09547017
Skewness0.68592901
Sum2802.7758
Variance0.022727096
MonotonicityNot monotonic
2023-12-11T06:32:43.928732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.25409561 12
 
16.0%
37.3733137 4
 
5.3%
37.56865139 2
 
2.7%
37.54143593 1
 
1.3%
37.25681921 1
 
1.3%
37.250871314 1
 
1.3%
37.30368069 1
 
1.3%
37.29956322 1
 
1.3%
37.29004388 1
 
1.3%
37.2633752 1
 
1.3%
Other values (50) 50
66.7%
ValueCountFrequency (%)
37.07137748 1
1.3%
37.07665442 1
1.3%
37.08232062 1
1.3%
37.22488914 1
1.3%
37.23746866 1
1.3%
37.23749235 1
1.3%
37.23780129 1
1.3%
37.23890847 1
1.3%
37.24460376 1
1.3%
37.250871314 1
1.3%
ValueCountFrequency (%)
37.81484446 1
1.3%
37.70743361 1
1.3%
37.65514031 1
1.3%
37.6550575 1
1.3%
37.63566788 1
1.3%
37.61653098 1
1.3%
37.61294679 1
1.3%
37.60119941 1
1.3%
37.59581055 1
1.3%
37.58403427 1
1.3%

WGS84경도
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.19089
Minimum126.81341
Maximum127.52022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-11T06:32:44.074514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.81341
5-th percentile127.00832
Q1127.0962
median127.19065
Q3127.25242
95-th percentile127.43515
Maximum127.52022
Range0.7068158
Interquartile range (IQR)0.1562268

Descriptive statistics

Standard deviation0.12411489
Coefficient of variation (CV)0.00097581586
Kurtosis0.87402967
Mean127.19089
Median Absolute Deviation (MAD)0.0707913
Skewness0.057460432
Sum9539.317
Variance0.015404506
MonotonicityNot monotonic
2023-12-11T06:32:44.278558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0961967 12
 
16.0%
127.1471537 4
 
5.3%
127.1214616 2
 
2.7%
127.3233013 1
 
1.3%
127.0471936 1
 
1.3%
127.0788600935 1
 
1.3%
127.0100331 1
 
1.3%
127.0043144 1
 
1.3%
126.9701778 1
 
1.3%
127.0285852 1
 
1.3%
Other values (50) 50
66.7%
ValueCountFrequency (%)
126.8134053 1
1.3%
126.9471772 1
1.3%
126.9701778 1
1.3%
127.0043144 1
1.3%
127.0100331 1
1.3%
127.0193114 1
1.3%
127.0285852 1
1.3%
127.0398042 1
1.3%
127.0471936 1
1.3%
127.0788600935 1
1.3%
ValueCountFrequency (%)
127.5202211 1
1.3%
127.4639339 1
1.3%
127.4563267 1
1.3%
127.443139 1
1.3%
127.4317215 1
1.3%
127.3467155 1
1.3%
127.3440197 1
1.3%
127.3377992 1
1.3%
127.3233013 1
1.3%
127.304283 1
1.3%

Interactions

2023-12-11T06:32:38.093957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:33.841639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:34.496767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:35.234973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:36.086742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:36.820500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:37.428289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:38.191466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:33.929915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:34.609276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:35.327593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:36.196867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:36.917735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:37.516098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:38.287050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:34.035895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:34.712013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:35.407232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:36.308717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:37.015441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:37.597332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:38.359301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:34.113827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:34.805414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:35.476898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:36.400739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:37.089746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:37.694681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:38.442233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:34.192206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:34.907597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:35.561107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:36.492853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:37.173111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:37.800935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:38.522346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:34.271475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:35.006840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:35.919549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:36.581898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:37.250723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:37.900193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:38.610378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:34.376210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:35.119811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:36.010026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:36.705760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:37.340107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:32:38.003360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:32:44.388703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명하천명시설유형사업기간주요오염원정보시설규모(㎥/일)국비(백만원)도비(백만원)시비(백만원)소재지우편번호소재지지번주소소재지도로명주소WGS84위도WGS84경도
시군명1.0000.9650.0000.9760.7560.1920.0000.2790.7080.9551.0001.0000.9080.930
하천명0.9651.0000.4480.9900.5670.8820.9160.0000.9040.9411.0001.0000.9120.893
시설유형0.0000.4481.0000.0000.7850.0000.6660.1500.3900.4680.9900.9800.4870.370
사업기간0.9760.9900.0001.0000.6340.8150.9400.9561.0000.9590.9000.9480.8730.930
주요오염원정보0.7560.5670.7850.6341.0000.0000.1710.0000.0000.6801.0001.0000.4770.588
시설규모(㎥/일)0.1920.8820.0000.8150.0001.0000.7740.2760.0000.5641.0001.0000.0000.528
국비(백만원)0.0000.9160.6660.9400.1710.7741.0000.5860.9290.4991.0001.0000.0000.444
도비(백만원)0.2790.0000.1500.9560.0000.2760.5861.0000.0950.0001.0001.0000.4620.000
시비(백만원)0.7080.9040.3901.0000.0000.0000.9290.0951.0000.6201.0001.0000.0000.664
소재지우편번호0.9550.9410.4680.9590.6800.5640.4990.0000.6201.0001.0001.0000.7460.847
소재지지번주소1.0001.0000.9900.9001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명주소1.0001.0000.9800.9481.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
WGS84위도0.9080.9120.4870.8730.4770.0000.0000.4620.0000.7461.0001.0001.0000.734
WGS84경도0.9300.8930.3700.9300.5880.5280.4440.0000.6640.8471.0001.0000.7341.000
2023-12-11T06:32:44.553912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
하천명시설유형주요오염원정보시군명
하천명1.0000.1290.2050.751
시설유형0.1291.0000.3740.000
주요오염원정보0.2050.3741.0000.303
시군명0.7510.0000.3031.000
2023-12-11T06:32:44.672472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설규모(㎥/일)국비(백만원)도비(백만원)시비(백만원)소재지우편번호WGS84위도WGS84경도시군명하천명시설유형주요오염원정보
시설규모(㎥/일)1.0000.6900.3130.3960.048-0.052-0.2440.0780.5690.0000.000
국비(백만원)0.6901.0000.3330.8060.118-0.147-0.4140.0000.5940.3320.046
도비(백만원)0.3130.3331.0000.5110.104-0.132-0.2120.0560.0000.0000.000
시비(백만원)0.3960.8060.5111.0000.323-0.319-0.2620.3240.5750.1870.000
소재지우편번호0.0480.1180.1040.3231.000-0.942-0.2580.8740.7000.1980.396
WGS84위도-0.052-0.147-0.132-0.319-0.9421.0000.2260.6750.6130.1590.215
WGS84경도-0.244-0.414-0.212-0.262-0.2580.2261.0000.7370.5720.1280.286
시군명0.0780.0000.0560.3240.8740.6750.7371.0000.7510.0000.303
하천명0.5690.5940.0000.5750.7000.6130.5720.7511.0000.1290.205
시설유형0.0000.3320.0000.1870.1980.1590.1280.0000.1291.0000.374
주요오염원정보0.0000.0460.0000.0000.3960.2150.2860.3030.2050.3741.000

Missing values

2023-12-11T06:32:38.739620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:32:38.908028image/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.
2023-12-11T06:32:39.041998image/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

집계년월시군명하천명시설유형사업기간주요오염원정보시설규모(㎥/일)국비(백만원)도비(백만원)시비(백만원)소재지우편번호소재지지번주소소재지도로명주소WGS84위도WGS84경도
02022-11가평군달전천인공습지2010~2011시가지, 도로50001750<NA>37512422경기도 가평군 가평읍 달전리 355<NA>37.814844127.520221
12022-11광주시경안천장치형( HDS-FSF)2007~2008시가지, 도로24800<NA>449449<NA>경기도 광주시 곤지암읍 곤지암리 346-1<NA>37.349566127.346716
22022-11광주시경안천장치형2012교량15<NA>222<NA>12728경기도 광주시 초월읍 지월리 813번지경기도 광주시 초월읍 지월로 17-6637.421478127.284414
32022-11광주시경안천장치형2011교량32<NA>203<NA>12711경기도 광주시 퇴촌면 광동리 507<NA>37.469885127.300444
42022-11광주시경안천자연형(침투도랑)2010주차장14<NA>101012700경기도 광주시 남한산성면 산성리 437<NA>37.476856127.186921
52022-11광주시경안천장치형(직접폭기 접촉산화법)2007~2008시가지, 도로2200<NA>404012755경기도 광주시 경안동 100<NA>37.414281127.255255
62022-11광주시경안천장치형( HDS-FSF)2007~2008시가지, 도로23600<NA>42742712803경기도 광주시 곤지암읍 곤지암리 555번지경기도 광주시 곤지암읍 곤지암천로 28837.349925127.34402
72022-11광주시경안천장치형( HDS-FSF)2007~2008시가지, 도로17400<NA>31531512755경기도 광주시 경안동 123-4번지경기도 광주시 파발로 17637.414807127.25398
82022-11광주시경안천장치형(CDS-MFS)2007~2008시가지, 도로2100<NA>383812746경기도 광주시 탄벌동 27-1번지경기도 광주시 회안대로 785-537.419303127.249725
92022-11광주시경안천장치형(CDS-MFS)2007~2008도로22300<NA>40340312746경기도 광주시 탄벌동 25번지경기도 광주시 회안대로 77737.418727127.249305
집계년월시군명하천명시설유형사업기간주요오염원정보시설규모(㎥/일)국비(백만원)도비(백만원)시비(백만원)소재지우편번호소재지지번주소소재지도로명주소WGS84위도WGS84경도
652022-11용인시경안천침투형2005도로137115<NA><NA>17028경기도 용인시 처인구 포곡읍 삼계리 277-12번지경기도 용인시 처인구 포곡읍 백옥대로 183537.279075127.228339
662022-11용인시경안천침투형2004~2005도로4064<NA><NA>17023경기도 용인시 처인구 포곡읍 전대리 117-1<NA>37.278825127.227233
672022-11용인시경안천식생형2004~2005도로398<NA><NA>17028경기도 용인시 처인구 포곡읍 삼계리 472-8<NA>37.281962127.231178
682022-11용인시경안천인공습지2015도로334748<NA><NA><NA>경기도 용인시 처인구 유방동 114-1<NA>37.256383127.211092
692022-11용인시진위천저류형2013~2020시가지, 도로11500126385961204217099경기도 용인시 기흥구 하갈동 128-1번지경기도 용인시 기흥구 하갈로 79-137.254096127.096197
702022-11용인시경안천인공습지2010~2013시가지, 농경지50004671403017043경기도 용인시 처인구 유방동 442-1<NA>37.254096127.096197
712022-11용인시경안천인공습지2010~2013시가지, 농경지850076222849<NA>경기도 용인시 처인구 포곡읍 둔전리 294-1<NA>37.254096127.096197
722022-11용인시청미천인공습지2016시가지, 농경지32001800<NA>120017180경기도 용인시 처인구 백암면 백봉리 1387-3<NA>37.254096127.096197
732022-11의왕시<NA>인공습지2011~2013시가지, 도로1478910675341233<NA>경기도 의왕시 월암동 501<NA>37.313651126.947177
742022-11이천시중리천인공습지2014~2019시가지, 도로119003182<NA>1591<NA>경기도 이천시 안흥동 450-1<NA>37.275797127.463934