Overview

Dataset statistics

Number of variables19
Number of observations136
Missing cells272
Missing cells (%)10.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.9 KiB
Average record size in memory165.0 B

Variable types

Categorical5
Numeric8
Text2
DateTime2
Unsupported2

Dataset

Description농업기반시설 시설제원 현황 (취입보)
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=F4OED4HBNFLMPK7FE80128239763&infSeq=1

Alerts

홍수면적(ha) has constant value ""Constant
만수면적(ha) has constant value ""Constant
표준코드 is highly overall correlated with 시군명High correlation
유역면적(ha) is highly overall correlated with 언체길이(m)High correlation
한발빈도 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
언체길이(m) is highly overall correlated with 유역면적(ha)High correlation
시군명 is highly overall correlated with 표준코드High correlation
구분 is highly overall correlated with 한발빈도 and 1 other fieldsHigh correlation
언체구조 is highly overall correlated with 한발빈도 and 1 other fieldsHigh correlation
언체구조 is highly imbalanced (72.0%)Imbalance
WGS84위도 has 136 (100.0%) missing valuesMissing
WGS84경도 has 136 (100.0%) missing valuesMissing
표준코드 has unique valuesUnique
WGS84위도 is an unsupported type, check if it needs cleaning or further analysisUnsupported
WGS84경도 is an unsupported type, check if it needs cleaning or further analysisUnsupported
유역면적(ha) has 12 (8.8%) zerosZeros

Reproduction

Analysis started2023-12-10 21:40:09.176645
Analysis finished2023-12-10 21:40:17.092435
Duration7.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
파주시
21 
안성시
15 
화성시
14 
양주시
13 
이천시
12 
Other values (13)
61 

Length

Max length4
Median length3
Mean length3.0220588
Min length3

Unique

Unique2 ?
Unique (%)1.5%

Sample

1st row고양시
2nd row고양시
3rd row광명시
4th row광명시
5th row광주시

Common Values

ValueCountFrequency (%)
파주시 21
15.4%
안성시 15
11.0%
화성시 14
10.3%
양주시 13
9.6%
이천시 12
8.8%
양평군 12
8.8%
광주시 12
8.8%
용인시 7
 
5.1%
포천시 7
 
5.1%
수원시 5
 
3.7%
Other values (8) 18
13.2%

Length

2023-12-11T06:40:17.173997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
파주시 21
15.4%
안성시 15
11.0%
화성시 14
10.3%
양주시 13
9.6%
이천시 12
8.8%
양평군 12
8.8%
광주시 12
8.8%
용인시 7
 
5.1%
포천시 7
 
5.1%
수원시 5
 
3.7%
Other values (8) 18
13.2%

표준코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct136
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1584993 × 109
Minimum4.11105 × 109
Maximum4.1830502 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T06:40:17.313650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.11105 × 109
5-th percentile4.11905 × 109
Q14.15105 × 109
median4.15505 × 109
Q34.17505 × 109
95-th percentile4.1830502 × 109
Maximum4.1830502 × 109
Range72000198
Interquartile range (IQR)24000018

Descriptive statistics

Standard deviation19915616
Coefficient of variation (CV)0.0047891354
Kurtosis-0.13633617
Mean4.1584993 × 109
Median Absolute Deviation (MAD)16000002
Skewness-0.71788773
Sum5.6555591 × 1011
Variance3.9663177 × 1014
MonotonicityNot monotonic
2023-12-11T06:40:17.473734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4147050004 1
 
0.7%
4151050004 1
 
0.7%
4153050063 1
 
0.7%
4153050062 1
 
0.7%
4151050014 1
 
0.7%
4148050003 1
 
0.7%
4148050001 1
 
0.7%
4151050017 1
 
0.7%
4151050027 1
 
0.7%
4173050010 1
 
0.7%
Other values (126) 126
92.6%
ValueCountFrequency (%)
4111050007 1
0.7%
4111050008 1
0.7%
4111050009 1
0.7%
4111050010 1
0.7%
4111050011 1
0.7%
4113050001 1
0.7%
4113050002 1
0.7%
4121050004 1
0.7%
4122050002 1
0.7%
4122050004 1
0.7%
ValueCountFrequency (%)
4183050205 1
0.7%
4183050203 1
0.7%
4183050201 1
0.7%
4183050200 1
0.7%
4183050199 1
0.7%
4183050180 1
0.7%
4183050178 1
0.7%
4183050177 1
0.7%
4183050014 1
0.7%
4183050013 1
0.7%

시설명
Text

Distinct131
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-11T06:40:17.807742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.2794118
Min length1

Characters and Unicode

Total characters310
Distinct characters140
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique126 ?
Unique (%)92.6%

Sample

1st row선우궁
2nd row휴암
3rd row가학
4th row옥련
5th row노곡
ValueCountFrequency (%)
중 2
 
1.5%
청용 2
 
1.5%
금곡 2
 
1.5%
지월 2
 
1.5%
영천 2
 
1.5%
금파1 1
 
0.7%
백석 1
 
0.7%
발랑 1
 
0.7%
맵 1
 
0.7%
도마산 1
 
0.7%
Other values (121) 121
89.0%
2023-12-11T06:40:18.593900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 14
 
4.5%
1 9
 
2.9%
천 7
 
2.3%
하 6
 
1.9%
상 6
 
1.9%
곡 6
 
1.9%
정 6
 
1.9%
리 6
 
1.9%
송 6
 
1.9%
2 6
 
1.9%
Other values (130) 238
76.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 288
92.9%
Decimal Number 17
 
5.5%
Open Punctuation 2
 
0.6%
Close Punctuation 2
 
0.6%
Uppercase Letter 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 14
 
4.9%
천 7
 
2.4%
하 6
 
2.1%
상 6
 
2.1%
곡 6
 
2.1%
정 6
 
2.1%
리 6
 
2.1%
송 6
 
2.1%
마 6
 
2.1%
담 5
 
1.7%
Other values (124) 220
76.4%
Decimal Number
ValueCountFrequency (%)
1 9
52.9%
2 6
35.3%
3 2
 
11.8%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 288
92.9%
Common 21
 
6.8%
Latin 1
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 14
 
4.9%
천 7
 
2.4%
하 6
 
2.1%
상 6
 
2.1%
곡 6
 
2.1%
정 6
 
2.1%
리 6
 
2.1%
송 6
 
2.1%
마 6
 
2.1%
담 5
 
1.7%
Other values (124) 220
76.4%
Common
ValueCountFrequency (%)
1 9
42.9%
2 6
28.6%
3 2
 
9.5%
( 2
 
9.5%
) 2
 
9.5%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 288
92.9%
ASCII 22
 
7.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 14
 
4.9%
천 7
 
2.4%
하 6
 
2.1%
상 6
 
2.1%
곡 6
 
2.1%
정 6
 
2.1%
리 6
 
2.1%
송 6
 
2.1%
마 6
 
2.1%
담 5
 
1.7%
Other values (124) 220
76.4%
ASCII
ValueCountFrequency (%)
1 9
40.9%
2 6
27.3%
3 2
 
9.1%
( 2
 
9.1%
) 2
 
9.1%
B 1
 
4.5%

소재지주소
Text

Distinct100
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-11T06:40:18.913017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length15
Mean length14.544118
Min length11

Characters and Unicode

Total characters1978
Distinct characters138
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

Unique74 ?
Unique (%)54.4%

Sample

1st row경기도 고양시 덕양구 관산동
2nd row경기도 고양시 일산동구 사리현동
3rd row경기도 광명시 가학동
4th row경기도 광명시 옥길동
5th row경기도 광주시 도척면 노곡리
ValueCountFrequency (%)
경기도 136
25.8%
파주시 21
 
4.0%
안성시 15
 
2.8%
화성시 14
 
2.7%
양주시 13
 
2.5%
광주시 12
 
2.3%
이천시 12
 
2.3%
양평군 12
 
2.3%
용인시 7
 
1.3%
처인구 7
 
1.3%
Other values (156) 279
52.8%
2023-12-11T06:40:19.358585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
392
19.8%
도 145
 
7.3%
기 138
 
7.0%
경 136
 
6.9%
시 124
 
6.3%
리 104
 
5.3%
면 77
 
3.9%
주 52
 
2.6%
동 47
 
2.4%
성 38
 
1.9%
Other values (128) 725
36.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1586
80.2%
Space Separator 392
 
19.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 145
 
9.1%
기 138
 
8.7%
경 136
 
8.6%
시 124
 
7.8%
리 104
 
6.6%
면 77
 
4.9%
주 52
 
3.3%
동 47
 
3.0%
성 38
 
2.4%
양 33
 
2.1%
Other values (127) 692
43.6%
Space Separator
ValueCountFrequency (%)
392
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1586
80.2%
Common 392
 
19.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 145
 
9.1%
기 138
 
8.7%
경 136
 
8.6%
시 124
 
7.8%
리 104
 
6.6%
면 77
 
4.9%
주 52
 
3.3%
동 47
 
3.0%
성 38
 
2.4%
양 33
 
2.1%
Other values (127) 692
43.6%
Common
ValueCountFrequency (%)
392
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1586
80.2%
ASCII 392
 
19.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
392
100.0%
Hangul
ValueCountFrequency (%)
도 145
 
9.1%
기 138
 
8.7%
경 136
 
8.6%
시 124
 
7.8%
리 104
 
6.6%
면 77
 
4.9%
주 52
 
3.3%
동 47
 
3.0%
성 38
 
2.4%
양 33
 
2.1%
Other values (127) 692
43.6%

착공년월일
Date

Distinct98
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Minimum1925-01-31 00:00:00
Maximum2010-11-08 00:00:00
2023-12-11T06:40:19.522259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:19.683201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

준공년월일
Date

Distinct111
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Minimum1925-12-31 00:00:00
Maximum2012-05-30 00:00:00
2023-12-11T06:40:19.860071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:20.065347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
보조수원
72 
부속시설
59 
주수원
 
5

Length

Max length4
Median length4
Mean length3.9632353
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부속시설
2nd row주수원
3rd row보조수원
4th row보조수원
5th row보조수원

Common Values

ValueCountFrequency (%)
보조수원 72
52.9%
부속시설 59
43.4%
주수원 5
 
3.7%

Length

2023-12-11T06:40:20.234554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:40:20.336701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
보조수원 72
52.9%
부속시설 59
43.4%
주수원 5
 
3.7%

유역면적(ha)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct109
Distinct (%)80.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4759.3551
Minimum0
Maximum63000
Zeros12
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T06:40:20.485846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1418.75
median1276
Q33207.5
95-th percentile22706.25
Maximum63000
Range63000
Interquartile range (IQR)2788.75

Descriptive statistics

Standard deviation10019.886
Coefficient of variation (CV)2.1053034
Kurtosis17.495681
Mean4759.3551
Median Absolute Deviation (MAD)1076
Skewness3.8665637
Sum647272.3
Variance1.0039812 × 108
MonotonicityNot monotonic
2023-12-11T06:40:20.650151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 12
 
8.8%
300.0 2
 
1.5%
3500.0 2
 
1.5%
450.0 2
 
1.5%
2435.0 2
 
1.5%
1360.0 2
 
1.5%
1270.0 2
 
1.5%
263.0 2
 
1.5%
200.0 2
 
1.5%
14000.0 2
 
1.5%
Other values (99) 106
77.9%
ValueCountFrequency (%)
0.0 12
8.8%
20.9 1
 
0.7%
23.4 1
 
0.7%
49.0 1
 
0.7%
72.0 1
 
0.7%
105.0 1
 
0.7%
148.0 1
 
0.7%
200.0 2
 
1.5%
250.0 1
 
0.7%
263.0 2
 
1.5%
ValueCountFrequency (%)
63000.0 2
1.5%
42000.0 1
0.7%
37100.0 1
0.7%
24400.0 1
0.7%
23810.0 1
0.7%
23325.0 1
0.7%
22500.0 1
0.7%
21000.0 1
0.7%
19010.0 2
1.5%
15600.0 1
0.7%

홍수면적(ha)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
0
136 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 136
100.0%

Length

2023-12-11T06:40:20.816332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:40:20.915339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 136
100.0%

만수면적(ha)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
0
136 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 136
100.0%

Length

2023-12-11T06:40:21.022870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:40:21.132052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 136
100.0%

수혜면적(ha)
Real number (ℝ)

Distinct71
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.697206
Minimum1
Maximum714
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T06:40:21.285870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q110
median25
Q353.25
95-th percentile213.25
Maximum714
Range713
Interquartile range (IQR)43.25

Descriptive statistics

Standard deviation94.575939
Coefficient of variation (CV)1.7290817
Kurtosis22.832085
Mean54.697206
Median Absolute Deviation (MAD)18
Skewness4.3070077
Sum7438.82
Variance8944.6082
MonotonicityNot monotonic
2023-12-11T06:40:21.470459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.0 13
 
9.6%
10.0 9
 
6.6%
30.0 9
 
6.6%
50.0 8
 
5.9%
15.0 6
 
4.4%
7.0 6
 
4.4%
40.0 5
 
3.7%
80.0 4
 
2.9%
65.0 3
 
2.2%
3.0 3
 
2.2%
Other values (61) 70
51.5%
ValueCountFrequency (%)
1.0 1
 
0.7%
1.5 1
 
0.7%
2.4 1
 
0.7%
2.5 1
 
0.7%
2.7 1
 
0.7%
3.0 3
2.2%
3.3 1
 
0.7%
3.5 1
 
0.7%
4.0 1
 
0.7%
5.0 3
2.2%
ValueCountFrequency (%)
714.0 1
0.7%
541.52 1
0.7%
379.1 1
0.7%
300.0 1
0.7%
277.0 1
0.7%
270.0 1
0.7%
250.0 1
0.7%
201.0 1
0.7%
200.0 1
0.7%
180.0 1
0.7%

한발빈도
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6617647
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T06:40:21.609338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q310
95-th percentile10
Maximum50
Range49
Interquartile range (IQR)5

Descriptive statistics

Standard deviation8.2365226
Coefficient of variation (CV)0.95090584
Kurtosis16.804955
Mean8.6617647
Median Absolute Deviation (MAD)0
Skewness3.6218312
Sum1178
Variance67.840305
MonotonicityNot monotonic
2023-12-11T06:40:21.737760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
10 85
62.5%
1 32
 
23.5%
5 9
 
6.6%
7 4
 
2.9%
50 4
 
2.9%
3 1
 
0.7%
20 1
 
0.7%
ValueCountFrequency (%)
1 32
 
23.5%
3 1
 
0.7%
5 9
 
6.6%
7 4
 
2.9%
10 85
62.5%
20 1
 
0.7%
50 4
 
2.9%
ValueCountFrequency (%)
50 4
 
2.9%
20 1
 
0.7%
10 85
62.5%
7 4
 
2.9%
5 9
 
6.6%
3 1
 
0.7%
1 32
 
23.5%

언체구조
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
콘크리트
126 
기타
 
7
토언제균일형
 
3

Length

Max length6
Median length4
Mean length3.9411765
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
콘크리트 126
92.6%
기타 7
 
5.1%
토언제균일형 3
 
2.2%

Length

2023-12-11T06:40:21.883016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:40:22.024762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
콘크리트 126
92.6%
기타 7
 
5.1%
토언제균일형 3
 
2.2%

언체길이(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)67.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.581618
Minimum5
Maximum234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T06:40:22.144703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile12.75
Q127.025
median41
Q362
95-th percentile155.5
Maximum234
Range229
Interquartile range (IQR)34.975

Descriptive statistics

Standard deviation45.057184
Coefficient of variation (CV)0.81064902
Kurtosis2.9466189
Mean55.581618
Median Absolute Deviation (MAD)17.7
Skewness1.7682312
Sum7559.1
Variance2030.1498
MonotonicityNot monotonic
2023-12-11T06:40:22.281437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.0 7
 
5.1%
40.0 6
 
4.4%
38.0 4
 
2.9%
22.0 4
 
2.9%
62.0 4
 
2.9%
17.0 3
 
2.2%
28.0 3
 
2.2%
55.0 3
 
2.2%
45.0 3
 
2.2%
100.0 3
 
2.2%
Other values (82) 96
70.6%
ValueCountFrequency (%)
5.0 1
0.7%
7.4 1
0.7%
7.5 1
0.7%
10.0 2
1.5%
11.5 1
0.7%
12.0 1
0.7%
13.0 1
0.7%
13.3 1
0.7%
14.5 1
0.7%
15.7 1
0.7%
ValueCountFrequency (%)
234.0 1
0.7%
204.0 1
0.7%
193.0 1
0.7%
186.0 1
0.7%
174.0 1
0.7%
170.0 1
0.7%
160.0 1
0.7%
154.0 1
0.7%
150.0 1
0.7%
148.0 1
0.7%

언체높이(m)
Real number (ℝ)

Distinct26
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1389706
Minimum0.4
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T06:40:22.410099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.5
Q10.9
median1
Q31.4
95-th percentile1.9
Maximum3
Range2.6
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.41703056
Coefficient of variation (CV)0.36614691
Kurtosis2.1181715
Mean1.1389706
Median Absolute Deviation (MAD)0.2
Skewness0.9508718
Sum154.9
Variance0.17391449
MonotonicityNot monotonic
2023-12-11T06:40:22.533461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1.0 33
24.3%
1.5 14
10.3%
1.2 14
10.3%
0.9 9
 
6.6%
1.3 8
 
5.9%
0.8 8
 
5.9%
0.5 7
 
5.1%
0.7 5
 
3.7%
0.6 5
 
3.7%
1.7 4
 
2.9%
Other values (16) 29
21.3%
ValueCountFrequency (%)
0.4 2
 
1.5%
0.5 7
 
5.1%
0.6 5
 
3.7%
0.65 2
 
1.5%
0.7 5
 
3.7%
0.8 8
 
5.9%
0.85 2
 
1.5%
0.9 9
 
6.6%
1.0 33
24.3%
1.1 3
 
2.2%
ValueCountFrequency (%)
3.0 1
 
0.7%
2.15 1
 
0.7%
2.1 1
 
0.7%
2.0 3
2.2%
1.9 3
2.2%
1.8 1
 
0.7%
1.7 4
2.9%
1.65 1
 
0.7%
1.6 2
1.5%
1.55 2
1.5%

마루폭
Real number (ℝ)

Distinct35
Distinct (%)25.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6425
Minimum0.3
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T06:40:22.666525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.5
Q10.7375
median1.1
Q31.6625
95-th percentile4.25
Maximum12
Range11.7
Interquartile range (IQR)0.925

Descriptive statistics

Standard deviation1.8211042
Coefficient of variation (CV)1.1087392
Kurtosis16.345798
Mean1.6425
Median Absolute Deviation (MAD)0.45
Skewness3.702413
Sum223.38
Variance3.3164204
MonotonicityNot monotonic
2023-12-11T06:40:22.789867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1.0 19
14.0%
0.5 17
12.5%
1.5 13
 
9.6%
0.8 9
 
6.6%
0.6 7
 
5.1%
1.3 7
 
5.1%
1.2 7
 
5.1%
2.0 7
 
5.1%
2.5 5
 
3.7%
0.7 4
 
2.9%
Other values (25) 41
30.1%
ValueCountFrequency (%)
0.3 1
 
0.7%
0.4 4
 
2.9%
0.45 1
 
0.7%
0.5 17
12.5%
0.6 7
 
5.1%
0.7 4
 
2.9%
0.75 1
 
0.7%
0.8 9
6.6%
0.9 3
 
2.2%
1.0 19
14.0%
ValueCountFrequency (%)
12.0 2
1.5%
8.0 2
1.5%
7.0 1
0.7%
5.03 1
0.7%
5.0 1
0.7%
4.0 2
1.5%
3.8 1
0.7%
3.7 1
0.7%
3.65 1
0.7%
3.4 1
0.7%

일류수심
Real number (ℝ)

Distinct22
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1048529
Minimum0.3
Maximum4.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T06:40:22.915008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.5
Q10.5
median1
Q31.4
95-th percentile2.125
Maximum4.5
Range4.2
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.62267878
Coefficient of variation (CV)0.56358522
Kurtosis7.6366944
Mean1.1048529
Median Absolute Deviation (MAD)0.4
Skewness2.059323
Sum150.26
Variance0.38772887
MonotonicityNot monotonic
2023-12-11T06:40:23.015821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.5 34
25.0%
1.0 26
19.1%
1.2 20
14.7%
1.4 13
 
9.6%
1.1 7
 
5.1%
1.5 7
 
5.1%
1.3 5
 
3.7%
0.3 4
 
2.9%
2.5 3
 
2.2%
2.0 2
 
1.5%
Other values (12) 15
11.0%
ValueCountFrequency (%)
0.3 4
 
2.9%
0.5 34
25.0%
0.6 2
 
1.5%
0.65 1
 
0.7%
0.7 1
 
0.7%
0.8 1
 
0.7%
1.0 26
19.1%
1.1 7
 
5.1%
1.2 20
14.7%
1.3 5
 
3.7%
ValueCountFrequency (%)
4.5 1
 
0.7%
3.5 1
 
0.7%
3.2 1
 
0.7%
2.5 3
2.2%
2.2 1
 
0.7%
2.1 1
 
0.7%
2.0 2
1.5%
1.81 1
 
0.7%
1.8 2
1.5%
1.7 2
1.5%

WGS84위도
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing136
Missing (%)100.0%
Memory size1.3 KiB

WGS84경도
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing136
Missing (%)100.0%
Memory size1.3 KiB

Interactions

2023-12-11T06:40:15.755841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:10.177383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:10.845329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:11.442029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:12.476355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:13.207060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:14.061968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:14.924528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:15.861452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:10.265157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:10.920074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:11.523674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:12.564928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:13.347212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:14.182651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:15.040381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:15.969801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:10.344815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:10.993273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:11.873441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:12.673555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:13.497254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:14.305266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:15.145165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:16.088531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:10.427895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:11.076289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:11.971084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:12.770394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:13.631611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:14.433500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:15.258878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:16.178392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:10.511790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:11.146506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:12.055910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:12.868045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:13.720771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:14.545238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:15.338825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:16.286145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:10.587121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:11.224187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:12.176352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:12.950809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:13.794909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:14.644955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:15.421941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:16.378801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:10.680726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:11.294466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:12.292463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:13.046148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:13.878085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:14.733887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:15.539505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:16.486113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:10.779413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:11.371676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:12.386076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:13.125985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:13.977061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:14.830044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:40:15.666186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:40:23.106752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명표준코드소재지주소착공년월일구분유역면적(ha)수혜면적(ha)한발빈도언체구조언체길이(m)언체높이(m)마루폭일류수심
시군명1.0000.9971.0000.9060.7020.3620.0000.5810.4170.4550.0000.3930.709
표준코드0.9971.0001.0000.6400.5590.3150.3060.3330.0000.2660.1980.2220.573
소재지주소1.0001.0001.0000.9470.7810.9980.9880.8970.8160.9860.8460.9520.957
착공년월일0.9060.6400.9471.0000.0000.9370.9710.6320.0000.8240.7150.8360.955
구분0.7020.5590.7810.0001.0000.0000.0000.6640.8510.2260.0000.0000.512
유역면적(ha)0.3620.3150.9980.9370.0001.0000.7300.0000.0000.7270.1600.2710.749
수혜면적(ha)0.0000.3060.9880.9710.0000.7301.0000.0000.0000.6540.8160.8010.560
한발빈도0.5810.3330.8970.6320.6640.0000.0001.0000.5870.4070.0000.0000.340
언체구조0.4170.0000.8160.0000.8510.0000.0000.5871.0000.0000.1370.0000.000
언체길이(m)0.4550.2660.9860.8240.2260.7270.6540.4070.0001.0000.3160.3650.497
언체높이(m)0.0000.1980.8460.7150.0000.1600.8160.0000.1370.3161.0000.7720.321
마루폭0.3930.2220.9520.8360.0000.2710.8010.0000.0000.3650.7721.0000.260
일류수심0.7090.5730.9570.9550.5120.7490.5600.3400.0000.4970.3210.2601.000
2023-12-11T06:40:23.232652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
언체구조시군명구분
언체구조1.0000.1970.534
시군명0.1971.0000.406
구분0.5340.4061.000
2023-12-11T06:40:23.333384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
표준코드유역면적(ha)수혜면적(ha)한발빈도언체길이(m)언체높이(m)마루폭일류수심시군명구분언체구조
표준코드1.000-0.098-0.422-0.064-0.080-0.130-0.1960.0030.8700.2950.000
유역면적(ha)-0.0981.0000.328-0.1100.5740.0830.233-0.0360.1600.0000.000
수혜면적(ha)-0.4220.3281.0000.2020.3960.2870.2940.1530.0000.0000.000
한발빈도-0.064-0.1100.2021.0000.2070.046-0.0530.4590.3360.6900.596
언체길이(m)-0.0800.5740.3960.2071.0000.1360.1750.1990.1840.1320.000
언체높이(m)-0.1300.0830.2870.0460.1361.0000.166-0.0660.0000.0000.067
마루폭-0.1960.2330.294-0.0530.1750.1661.0000.0580.1660.0000.000
일류수심0.003-0.0360.1530.4590.199-0.0660.0581.0000.3030.2550.000
시군명0.8700.1600.0000.3360.1840.0000.1660.3031.0000.4060.197
구분0.2950.0000.0000.6900.1320.0000.0000.2550.4061.0000.534
언체구조0.0000.0000.0000.5960.0000.0670.0000.0000.1970.5341.000

Missing values

2023-12-11T06:40:16.632062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:40:16.951620image/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

시군명표준코드시설명소재지주소착공년월일준공년월일구분유역면적(ha)홍수면적(ha)만수면적(ha)수혜면적(ha)한발빈도언체구조언체길이(m)언체높이(m)마루폭일류수심WGS84위도WGS84경도
0고양시4147050004선우궁경기도 고양시 덕양구 관산동1966-06-011966-12-30부속시설10150.00054.010콘크리트145.01.01.01.8<NA><NA>
1고양시4147050005휴암경기도 고양시 일산동구 사리현동1966-06-011967-01-01주수원13680.000201.010콘크리트150.01.30.82.1<NA><NA>
2광명시4139050001가학경기도 광명시 가학동1977-01-011978-12-31보조수원749.00010.01콘크리트40.01.50.70.5<NA><NA>
3광명시4121050004옥련경기도 광명시 옥길동1983-09-011984-12-31보조수원598.0005.33콘크리트28.00.91.60.5<NA><NA>
4광주시4179050034노곡경기도 광주시 도척면 노곡리1976-09-181977-04-10보조수원0.0009.05콘크리트10.01.00.51.2<NA><NA>
5광주시4179050008담안1호경기도 광주시 장지동1976-01-011976-12-25보조수원0.00012.01콘크리트42.01.00.81.1<NA><NA>
6광주시4179050067서하경기도 광주시 초월읍 서하리1989-03-201989-12-20부속시설37100.0007.010콘크리트154.00.81.03.2<NA><NA>
7광주시4179050037송들경기도 광주시 도척면 유정리1977-01-011977-04-20부속시설1080.0007.01콘크리트30.01.40.750.5<NA><NA>
8광주시4179050035여세경기도 광주시 도척면 노곡리1977-09-291977-11-26부속시설1130.0006.010콘크리트38.00.91.00.5<NA><NA>
9광주시4179050007역경기도 광주시 장지동1957-02-031957-05-06부속시설890.00050.010콘크리트40.01.31.01.5<NA><NA>
시군명표준코드시설명소재지주소착공년월일준공년월일구분유역면적(ha)홍수면적(ha)만수면적(ha)수혜면적(ha)한발빈도언체구조언체길이(m)언체높이(m)마루폭일류수심WGS84위도WGS84경도
126화성시4175050015매곡경기도 화성시 팔탄면 매곡리1957-12-011958-03-01부속시설5200.00022.01콘크리트48.51.20.50.5<NA><NA>
127화성시4175050002반정경기도 화성시 반정동1945-01-011945-12-31부속시설2300.00030.05콘크리트27.51.81.950.5<NA><NA>
128화성시4175050020발산경기도 화성시 정남면 괘랑리1966-06-011967-08-30보조수원19010.000105.010콘크리트170.01.151.650.5<NA><NA>
129화성시4175050017발안경기도 화성시 향남읍 발안리1957-01-011958-12-31부속시설3240.00020.01콘크리트49.10.81.50.5<NA><NA>
130화성시4175050007보름앞경기도 화성시 매송면 원리1980-01-011980-03-31부속시설384.00019.01콘크리트12.00.53.00.5<NA><NA>
131화성시4175050004송산경기도 화성시 진안동1945-01-011945-03-31부속시설1400.00030.01콘크리트27.21.11.00.5<NA><NA>
132화성시4175050023신리경기도 화성시 정남면 신리1982-01-011982-12-31부속시설2300.00010.01콘크리트18.01.00.50.5<NA><NA>
133화성시4175050025정림경기도 화성시 정남면 발산리1977-10-091977-11-25부속시설19010.000180.01콘크리트68.00.73.650.5<NA><NA>
134화성시4175050014지월경기도 화성시 팔탄면 지월리1970-10-011971-12-31부속시설2900.00017.01콘크리트51.10.851.00.5<NA><NA>
135화성시4175050003황계경기도 화성시 반정동1945-01-011945-03-31부속시설2450.00050.01콘크리트34.41.61.30.5<NA><NA>