Overview

Dataset statistics

Number of variables8
Number of observations273
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.7 KiB
Average record size in memory66.5 B

Variable types

Numeric2
Categorical5
Text1

Dataset

Description한강수계 1단계 소규모개발사업의 오염총량관리를 위한 환경정책기본법 팔당호 상수원 수질보전 특별대책지역 Ⅰ권역내에 해당하는 단위유역 현황
Author환경부 국립환경과학원
URLhttps://www.data.go.kr/data/15070554/fileData.do

Alerts

시도 has constant value ""Constant
특대유역 is highly overall correlated with 행정구역코드 and 3 other fieldsHigh correlation
읍면동 is highly overall correlated with 행정구역코드 and 3 other fieldsHigh correlation
단위유역 is highly overall correlated with 행정구역코드 and 3 other fieldsHigh correlation
시군구 is highly overall correlated with 행정구역코드 and 3 other fieldsHigh correlation
행정구역코드 is highly overall correlated with 시군구 and 3 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 05:24:21.397947
Analysis finished2023-12-12 05:24:22.409334
Duration1.01 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정구역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct228
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1685912 × 109
Minimum4.1360256 × 109
Maximum4.183041 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T14:24:22.488329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1360256 × 109
5-th percentile4.136036 × 109
Q14.1610256 × 109
median4.173033 × 109
Q34.183025 × 109
95-th percentile4.1830374 × 109
Maximum4.183041 × 109
Range47015412
Interquartile range (IQR)21999404

Descriptive statistics

Standard deviation13452039
Coefficient of variation (CV)0.0032269987
Kurtosis0.14223336
Mean4.1685912 × 109
Median Absolute Deviation (MAD)10001996
Skewness-0.81576513
Sum1.1380254 × 1012
Variance1.8095734 × 1014
MonotonicityNot monotonic
2023-12-12T14:24:22.634401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4161025329 4
 
1.5%
4173033029 3
 
1.1%
4182032521 3
 
1.1%
4183031026 2
 
0.7%
4173034021 2
 
0.7%
4161034022 2
 
0.7%
4183034022 2
 
0.7%
4183034023 2
 
0.7%
4161036023 2
 
0.7%
4161036022 2
 
0.7%
Other values (218) 249
91.2%
ValueCountFrequency (%)
4136025621 1
0.4%
4136025622 1
0.4%
4136025624 1
0.4%
4136025625 2
0.7%
4136025626 2
0.7%
4136025627 1
0.4%
4136025628 1
0.4%
4136025629 1
0.4%
4136025630 1
0.4%
4136036021 1
0.4%
ValueCountFrequency (%)
4183041033 1
0.4%
4183041032 1
0.4%
4183041031 1
0.4%
4183041030 1
0.4%
4183041029 1
0.4%
4183041028 1
0.4%
4183041027 1
0.4%
4183041026 1
0.4%
4183041025 1
0.4%
4183041024 2
0.7%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
경기도
273 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기도
2nd row경기도
3rd row경기도
4th row경기도
5th row경기도

Common Values

ValueCountFrequency (%)
경기도 273
100.0%

Length

2023-12-12T14:24:22.782377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:24:22.909843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 273
100.0%

시군구
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
광주시
94 
양평군
78 
여주군
64 
남양주시
19 
용인시처인구
 
9

Length

Max length6
Median length3
Mean length3.1684982
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row용인시처인구
2nd row용인시처인구
3rd row용인시처인구
4th row용인시처인구
5th row용인시처인구

Common Values

ValueCountFrequency (%)
광주시 94
34.4%
양평군 78
28.6%
여주군 64
23.4%
남양주시 19
 
7.0%
용인시처인구 9
 
3.3%
가평군 9
 
3.3%

Length

2023-12-12T14:24:23.046202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:24:23.173360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광주시 94
34.4%
양평군 78
28.6%
여주군 64
23.4%
남양주시 19
 
7.0%
용인시처인구 9
 
3.3%
가평군 9
 
3.3%

읍면동
Categorical

HIGH CORRELATION 

Distinct37
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
대신면
20 
양서면
 
17
흥천면
 
17
실촌읍
 
16
초월읍
 
16
Other values (32)
187 

Length

Max length3
Median length3
Mean length2.985348
Min length2

Unique

Unique12 ?
Unique (%)4.4%

Sample

1st row모현면
2nd row모현면
3rd row모현면
4th row모현면
5th row모현면

Common Values

ValueCountFrequency (%)
대신면 20
 
7.3%
양서면 17
 
6.2%
흥천면 17
 
6.2%
실촌읍 16
 
5.9%
초월읍 16
 
5.9%
퇴촌면 15
 
5.5%
양평읍 15
 
5.5%
개군면 14
 
5.1%
금사면 12
 
4.4%
화도읍 11
 
4.0%
Other values (27) 120
44.0%

Length

2023-12-12T14:24:23.309090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대신면 20
 
7.3%
흥천면 17
 
6.2%
양서면 17
 
6.2%
실촌읍 16
 
5.9%
초월읍 16
 
5.9%
퇴촌면 15
 
5.5%
양평읍 15
 
5.5%
개군면 14
 
5.1%
금사면 12
 
4.4%
화도읍 11
 
4.0%
Other values (27) 120
44.0%

법정리
Text

Distinct209
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2023-12-12T14:24:23.640061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.007326
Min length2

Characters and Unicode

Total characters821
Distinct characters144
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

Unique166 ?
Unique (%)60.8%

Sample

1st row갈담리
2nd row초부리
3rd row일산리
4th row매산리
5th row동림리
ValueCountFrequency (%)
일산리 15
 
5.5%
도곡리 5
 
1.8%
지월리 4
 
1.5%
상백리 3
 
1.1%
수입리 3
 
1.1%
청평리 3
 
1.1%
삼봉리 2
 
0.7%
운심리 2
 
0.7%
귀백리 2
 
0.7%
용담리 2
 
0.7%
Other values (199) 232
85.0%
2023-12-12T14:24:24.381754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 273
33.3%
산 28
 
3.4%
일 17
 
2.1%
도 15
 
1.8%
대 15
 
1.8%
신 14
 
1.7%
수 13
 
1.6%
천 12
 
1.5%
상 10
 
1.2%
지 10
 
1.2%
Other values (134) 414
50.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 821
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 273
33.3%
산 28
 
3.4%
일 17
 
2.1%
도 15
 
1.8%
대 15
 
1.8%
신 14
 
1.7%
수 13
 
1.6%
천 12
 
1.5%
상 10
 
1.2%
지 10
 
1.2%
Other values (134) 414
50.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 821
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 273
33.3%
산 28
 
3.4%
일 17
 
2.1%
도 15
 
1.8%
대 15
 
1.8%
신 14
 
1.7%
수 13
 
1.6%
천 12
 
1.5%
상 10
 
1.2%
지 10
 
1.2%
Other values (134) 414
50.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 821
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 273
33.3%
산 28
 
3.4%
일 17
 
2.1%
도 15
 
1.8%
대 15
 
1.8%
신 14
 
1.7%
수 13
 
1.6%
천 12
 
1.5%
상 10
 
1.2%
지 10
 
1.2%
Other values (134) 414
50.4%

단위유역
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
한강F
121 
한강E
59 
경안B
53 
경안A
 
10
복하A
 
10
Other values (4)
20 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row경안A
2nd row경안A
3rd row경안A
4th row경안A
5th row경안A

Common Values

ValueCountFrequency (%)
한강F 121
44.3%
한강E 59
21.6%
경안B 53
19.4%
경안A 10
 
3.7%
복하A 10
 
3.7%
양화A 9
 
3.3%
흑천A 8
 
2.9%
조종A 2
 
0.7%
북한D 1
 
0.4%

Length

2023-12-12T14:24:24.530599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:24:24.648505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한강f 121
44.3%
한강e 59
21.6%
경안b 53
19.4%
경안a 10
 
3.7%
복하a 10
 
3.7%
양화a 9
 
3.3%
흑천a 8
 
2.9%
조종a 2
 
0.7%
북한d 1
 
0.4%

특대유역
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
한강F08
39 
한강E01
30 
한강E02
29 
한강F01
21 
경안B03
20 
Other values (15)
134 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row경안A01
2nd row경안A01
3rd row경안A01
4th row경안A01
5th row경안A02

Common Values

ValueCountFrequency (%)
한강F08 39
14.3%
한강E01 30
11.0%
한강E02 29
10.6%
한강F01 21
 
7.7%
경안B03 20
 
7.3%
경안B02 18
 
6.6%
한강F05 17
 
6.2%
한강F02 16
 
5.9%
경안B01 15
 
5.5%
복하A01 10
 
3.7%
Other values (10) 58
21.2%

Length

2023-12-12T14:24:24.775252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한강f08 39
14.3%
한강e01 30
11.0%
한강e02 29
10.6%
한강f01 21
 
7.7%
경안b03 20
 
7.3%
경안b02 18
 
6.6%
한강f05 17
 
6.2%
한강f02 16
 
5.9%
경안b01 15
 
5.5%
복하a01 10
 
3.7%
Other values (10) 58
21.2%

면적편입율(%)
Real number (ℝ)

Distinct89
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.271795
Minimum3.14
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T14:24:24.929245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.14
5-th percentile10.686
Q180.37
median100
Q3100
95-th percentile100
Maximum100
Range96.86
Interquartile range (IQR)19.63

Descriptive statistics

Standard deviation29.74118
Coefficient of variation (CV)0.35715791
Kurtosis0.96331879
Mean83.271795
Median Absolute Deviation (MAD)0
Skewness-1.5766
Sum22733.2
Variance884.5378
MonotonicityNot monotonic
2023-12-12T14:24:25.084796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 185
67.8%
28.66 1
 
0.4%
91.2 1
 
0.4%
37.77 1
 
0.4%
93.51 1
 
0.4%
96.86 1
 
0.4%
41.06 1
 
0.4%
47.38 1
 
0.4%
93.29 1
 
0.4%
18.78 1
 
0.4%
Other values (79) 79
28.9%
ValueCountFrequency (%)
3.14 1
0.4%
4.87 1
0.4%
5.41 1
0.4%
6.03 1
0.4%
6.49 1
0.4%
6.71 1
0.4%
7.17 1
0.4%
7.35 1
0.4%
8.25 1
0.4%
8.28 1
0.4%
ValueCountFrequency (%)
100.0 185
67.8%
96.86 1
 
0.4%
95.13 1
 
0.4%
94.59 1
 
0.4%
93.97 1
 
0.4%
93.51 1
 
0.4%
93.29 1
 
0.4%
92.65 1
 
0.4%
91.72 1
 
0.4%
91.2 1
 
0.4%

Interactions

2023-12-12T14:24:22.012014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:24:21.832587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:24:22.105972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:24:21.924361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:24:25.188388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역코드시군구읍면동단위유역특대유역면적편입율(%)
행정구역코드1.0001.0001.0000.8140.9700.000
시군구1.0001.0001.0000.8530.9340.000
읍면동1.0001.0001.0000.9220.9620.000
단위유역0.8140.8530.9221.0001.0000.265
특대유역0.9700.9340.9621.0001.0000.438
면적편입율(%)0.0000.0000.0000.2650.4381.000
2023-12-12T14:24:25.303777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특대유역읍면동단위유역시군구
특대유역1.0000.6400.9790.757
읍면동0.6401.0000.6230.940
단위유역0.9790.6231.0000.624
시군구0.7570.9400.6241.000
2023-12-12T14:24:25.417574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역코드면적편입율(%)시군구읍면동단위유역특대유역
행정구역코드1.000-0.1120.9980.9380.6430.759
면적편입율(%)-0.1121.0000.0000.0000.1230.149
시군구0.9980.0001.0000.9400.6240.757
읍면동0.9380.0000.9401.0000.6230.640
단위유역0.6430.1230.6240.6231.0000.979
특대유역0.7590.1490.7570.6400.9791.000

Missing values

2023-12-12T14:24:22.236796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:24:22.362571image/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

행정구역코드시도시군구읍면동법정리단위유역특대유역면적편입율(%)
04146131022경기도용인시처인구모현면갈담리경안A경안A01100.0
14146131023경기도용인시처인구모현면초부리경안A경안A01100.0
24146131024경기도용인시처인구모현면일산리경안A경안A0130.45
34146131025경기도용인시처인구모현면매산리경안A경안A01100.0
44146131026경기도용인시처인구모현면동림리경안A경안A02100.0
54146131027경기도용인시처인구모현면능원리경안A경안A02100.0
64146131028경기도용인시처인구모현면오산리경안A경안A02100.0
74161025022경기도광주시오포읍신현리경안A경안A02100.0
84161025023경기도광주시오포읍능평리경안A경안A02100.0
94161025024경기도광주시오포읍문형리경안A경안A02100.0
행정구역코드시도시군구읍면동법정리단위유역특대유역면적편입율(%)
2634183033027경기도양평군양서면대심리한강F한강F0833.5
2644183035021경기도양평군서종면문호리한강F한강F087.35
2654183025027경기도양평군양평읍도곡리흑천A흑천A0131.94
2664183025028경기도양평군양평읍대흥리흑천A흑천A0189.06
2674183025029경기도양평군양평읍봉성리흑천A흑천A01100.0
2684183025030경기도양평군양평읍원덕리흑천A흑천A01100.0
2694183025031경기도양평군양평읍회현리흑천A흑천A0139.61
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