Overview

Dataset statistics

Number of variables9
Number of observations1028
Missing cells1752
Missing cells (%)18.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory77.4 KiB
Average record size in memory77.1 B

Variable types

Numeric5
Text3
Categorical1

Dataset

Description한강권역 수위 관측소 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=OI14019YGY7KD60CYS4D24634637&infSeq=1

Alerts

주의보수위값(m) is highly overall correlated with 경보수위값(m)High correlation
경보수위값(m) is highly overall correlated with 주의보수위값(m)High correlation
주소 has 23 (2.2%) missing valuesMissing
상세주소 has 41 (4.0%) missing valuesMissing
영점표고값(El.m) has 183 (17.8%) missing valuesMissing
주의보수위값(m) has 532 (51.8%) missing valuesMissing
경보수위값(m) has 532 (51.8%) missing valuesMissing
계획홍수위값(m) has 441 (42.9%) missing valuesMissing
수위관측소구분 has unique valuesUnique
영점표고값(El.m) has 66 (6.4%) zerosZeros
주의보수위값(m) has 42 (4.1%) zerosZeros
경보수위값(m) has 42 (4.1%) zerosZeros
계획홍수위값(m) has 72 (7.0%) zerosZeros

Reproduction

Analysis started2023-12-10 22:25:47.831878
Analysis finished2023-12-10 22:25:51.026927
Duration3.2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

수위관측소구분
Real number (ℝ)

UNIQUE 

Distinct1028
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2405929.2
Minimum1001602
Maximum9870002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2023-12-11T07:25:51.093423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001602
5-th percentile1003647.4
Q11022642.8
median2018667.5
Q33101604.2
95-th percentile5003604.7
Maximum9870002
Range8868400
Interquartile range (IQR)2078961.5

Descriptive statistics

Standard deviation1332416.9
Coefficient of variation (CV)0.55380553
Kurtosis3.0801884
Mean2405929.2
Median Absolute Deviation (MAD)996028.5
Skewness1.2960744
Sum2.4732952 × 109
Variance1.7753348 × 1012
MonotonicityNot monotonic
2023-12-11T07:25:51.215989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1003635 1
 
0.1%
3301623 1
 
0.1%
2401620 1
 
0.1%
2401650 1
 
0.1%
2401613 1
 
0.1%
2401616 1
 
0.1%
1004688 1
 
0.1%
1005697 1
 
0.1%
1006690 1
 
0.1%
1005695 1
 
0.1%
Other values (1018) 1018
99.0%
ValueCountFrequency (%)
1001602 1
0.1%
1001603 1
0.1%
1001605 1
0.1%
1001607 1
0.1%
1001610 1
0.1%
1001613 1
0.1%
1001615 1
0.1%
1001620 1
0.1%
1001622 1
0.1%
1001625 1
0.1%
ValueCountFrequency (%)
9870002 1
0.1%
9870001 1
0.1%
9000307 1
0.1%
8888888 1
0.1%
8000007 1
0.1%
8000005 1
0.1%
8000002 1
0.1%
8000001 1
0.1%
6004670 1
0.1%
6003620 1
0.1%

수위관측소명
Text

Distinct1014
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
2023-12-11T07:25:51.485907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length8
Mean length7.209144
Min length2

Characters and Unicode

Total characters7411
Distinct characters298
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1001 ?
Unique (%)97.4%

Sample

1st row가대교
2nd row가북
3rd row가음
4th row가평군(가평교)
5th row가평군(신청평대교)
ValueCountFrequency (%)
경천 3
 
0.3%
동상 2
 
0.2%
부안댐 2
 
0.2%
예당 2
 
0.2%
삽교호 2
 
0.2%
대아 2
 
0.2%
청천 2
 
0.2%
장남 2
 
0.2%
동화 2
 
0.2%
수어댐 2
 
0.2%
Other values (1004) 1009
98.0%
2023-12-11T07:25:51.893159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 827
 
11.2%
( 827
 
11.2%
교 631
 
8.5%
군 402
 
5.4%
시 398
 
5.4%
천 226
 
3.0%
주 178
 
2.4%
산 147
 
2.0%
대 133
 
1.8%
리 112
 
1.5%
Other values (288) 3530
47.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5699
76.9%
Close Punctuation 827
 
11.2%
Open Punctuation 827
 
11.2%
Decimal Number 56
 
0.8%
Space Separator 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 631
 
11.1%
군 402
 
7.1%
시 398
 
7.0%
천 226
 
4.0%
주 178
 
3.1%
산 147
 
2.6%
대 133
 
2.3%
리 112
 
2.0%
동 109
 
1.9%
양 108
 
1.9%
Other values (281) 3255
57.1%
Decimal Number
ValueCountFrequency (%)
2 28
50.0%
1 21
37.5%
3 5
 
8.9%
4 2
 
3.6%
Close Punctuation
ValueCountFrequency (%)
) 827
100.0%
Open Punctuation
ValueCountFrequency (%)
( 827
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5699
76.9%
Common 1712
 
23.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 631
 
11.1%
군 402
 
7.1%
시 398
 
7.0%
천 226
 
4.0%
주 178
 
3.1%
산 147
 
2.6%
대 133
 
2.3%
리 112
 
2.0%
동 109
 
1.9%
양 108
 
1.9%
Other values (281) 3255
57.1%
Common
ValueCountFrequency (%)
) 827
48.3%
( 827
48.3%
2 28
 
1.6%
1 21
 
1.2%
3 5
 
0.3%
2
 
0.1%
4 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5699
76.9%
ASCII 1712
 
23.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 827
48.3%
( 827
48.3%
2 28
 
1.6%
1 21
 
1.2%
3 5
 
0.3%
2
 
0.1%
4 2
 
0.1%
Hangul
ValueCountFrequency (%)
교 631
 
11.1%
군 402
 
7.1%
시 398
 
7.0%
천 226
 
4.0%
주 178
 
3.1%
산 147
 
2.6%
대 133
 
2.3%
리 112
 
2.0%
동 109
 
1.9%
양 108
 
1.9%
Other values (281) 3255
57.1%

관할기관명
Categorical

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
환경부
640 
한국수자원공사
244 
한국농어촌공사
88 
기타
 
28
한국수력원자력
 
27

Length

Max length7
Median length3
Mean length4.3715953
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row한국수자원공사
2nd row한국농어촌공사
3rd row한국농어촌공사
4th row환경부
5th row환경부

Common Values

ValueCountFrequency (%)
환경부 640
62.3%
한국수자원공사 244
 
23.7%
한국농어촌공사 88
 
8.6%
기타 28
 
2.7%
한국수력원자력 27
 
2.6%
안전행정부 1
 
0.1%

Length

2023-12-11T07:25:52.008801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:25:52.107281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
환경부 640
62.3%
한국수자원공사 244
 
23.7%
한국농어촌공사 88
 
8.6%
기타 28
 
2.7%
한국수력원자력 27
 
2.6%
안전행정부 1
 
0.1%

주소
Text

MISSING 

Distinct379
Distinct (%)37.7%
Missing23
Missing (%)2.2%
Memory size8.2 KiB
2023-12-11T07:25:52.364469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length8
Mean length8.8547264
Min length3

Characters and Unicode

Total characters8899
Distinct characters202
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

Unique232 ?
Unique (%)23.1%

Sample

1st row충청북도 단양군 가곡면
2nd row경상남도 거창군 가북면
3rd row경상북도 의성군 가음면
4th row경기도 가평군
5th row경기도 가평군
ValueCountFrequency (%)
경상북도 164
 
7.3%
강원도 129
 
5.7%
경기도 113
 
5.0%
전라북도 110
 
4.9%
경상남도 102
 
4.5%
전라남도 101
 
4.5%
충청북도 88
 
3.9%
충청남도 81
 
3.6%
완주군 20
 
0.9%
울산광역시 19
 
0.8%
Other values (405) 1324
58.8%
2023-12-11T07:25:52.765649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1247
 
14.0%
도 902
 
10.1%
군 549
 
6.2%
시 482
 
5.4%
경 402
 
4.5%
북 381
 
4.3%
남 315
 
3.5%
상 289
 
3.2%
전 235
 
2.6%
청 212
 
2.4%
Other values (192) 3885
43.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7617
85.6%
Space Separator 1247
 
14.0%
Decimal Number 30
 
0.3%
Dash Punctuation 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 902
 
11.8%
군 549
 
7.2%
시 482
 
6.3%
경 402
 
5.3%
북 381
 
5.0%
남 315
 
4.1%
상 289
 
3.8%
전 235
 
3.1%
청 212
 
2.8%
라 212
 
2.8%
Other values (181) 3638
47.8%
Decimal Number
ValueCountFrequency (%)
1 9
30.0%
2 7
23.3%
3 4
13.3%
8 2
 
6.7%
9 2
 
6.7%
6 2
 
6.7%
0 2
 
6.7%
5 1
 
3.3%
4 1
 
3.3%
Space Separator
ValueCountFrequency (%)
1247
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7617
85.6%
Common 1282
 
14.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 902
 
11.8%
군 549
 
7.2%
시 482
 
6.3%
경 402
 
5.3%
북 381
 
5.0%
남 315
 
4.1%
상 289
 
3.8%
전 235
 
3.1%
청 212
 
2.8%
라 212
 
2.8%
Other values (181) 3638
47.8%
Common
ValueCountFrequency (%)
1247
97.3%
1 9
 
0.7%
2 7
 
0.5%
- 5
 
0.4%
3 4
 
0.3%
8 2
 
0.2%
9 2
 
0.2%
6 2
 
0.2%
0 2
 
0.2%
5 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7617
85.6%
ASCII 1282
 
14.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1247
97.3%
1 9
 
0.7%
2 7
 
0.5%
- 5
 
0.4%
3 4
 
0.3%
8 2
 
0.2%
9 2
 
0.2%
6 2
 
0.2%
0 2
 
0.2%
5 1
 
0.1%
Hangul
ValueCountFrequency (%)
도 902
 
11.8%
군 549
 
7.2%
시 482
 
6.3%
경 402
 
5.3%
북 381
 
5.0%
남 315
 
4.1%
상 289
 
3.8%
전 235
 
3.1%
청 212
 
2.8%
라 212
 
2.8%
Other values (181) 3638
47.8%

상세주소
Text

MISSING 

Distinct964
Distinct (%)97.7%
Missing41
Missing (%)4.0%
Memory size8.2 KiB
2023-12-11T07:25:53.089956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length30
Mean length11.802432
Min length2

Characters and Unicode

Total characters11649
Distinct characters338
Distinct categories7 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique946 ?
Unique (%)95.8%

Sample

1st row가대리 154-1 도
2nd row박암리 479
3rd row양지리 173-2
4th row가평읍 읍내리 가평교
5th row청평면 삼회리 산90 신청평대교
ValueCountFrequency (%)
하류 19
 
0.7%
산 11
 
0.4%
상류 10
 
0.4%
천전리 8
 
0.3%
舊 7
 
0.3%
완산구 7
 
0.3%
1 6
 
0.2%
양지리 6
 
0.2%
충청남도 6
 
0.2%
신북읍 6
 
0.2%
Other values (2242) 2711
96.9%
2023-12-11T07:25:53.573064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1890
 
16.2%
리 817
 
7.0%
교 552
 
4.7%
면 515
 
4.4%
1 412
 
3.5%
- 271
 
2.3%
동 266
 
2.3%
산 240
 
2.1%
2 216
 
1.9%
( 193
 
1.7%
Other values (328) 6277
53.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7333
62.9%
Space Separator 1890
 
16.2%
Decimal Number 1766
 
15.2%
Dash Punctuation 271
 
2.3%
Open Punctuation 193
 
1.7%
Close Punctuation 193
 
1.7%
Uppercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 817
 
11.1%
교 552
 
7.5%
면 515
 
7.0%
동 266
 
3.6%
산 240
 
3.3%
천 176
 
2.4%
읍 143
 
2.0%
대 139
 
1.9%
구 98
 
1.3%
성 93
 
1.3%
Other values (311) 4294
58.6%
Decimal Number
ValueCountFrequency (%)
1 412
23.3%
2 216
12.2%
3 167
9.5%
4 165
9.3%
5 148
 
8.4%
0 147
 
8.3%
6 139
 
7.9%
8 131
 
7.4%
7 126
 
7.1%
9 115
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
T 1
33.3%
P 1
33.3%
A 1
33.3%
Space Separator
ValueCountFrequency (%)
1890
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 271
100.0%
Open Punctuation
ValueCountFrequency (%)
( 193
100.0%
Close Punctuation
ValueCountFrequency (%)
) 193
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7326
62.9%
Common 4313
37.0%
Han 7
 
0.1%
Latin 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 817
 
11.2%
교 552
 
7.5%
면 515
 
7.0%
동 266
 
3.6%
산 240
 
3.3%
천 176
 
2.4%
읍 143
 
2.0%
대 139
 
1.9%
구 98
 
1.3%
성 93
 
1.3%
Other values (310) 4287
58.5%
Common
ValueCountFrequency (%)
1890
43.8%
1 412
 
9.6%
- 271
 
6.3%
2 216
 
5.0%
( 193
 
4.5%
) 193
 
4.5%
3 167
 
3.9%
4 165
 
3.8%
5 148
 
3.4%
0 147
 
3.4%
Other values (4) 511
 
11.8%
Latin
ValueCountFrequency (%)
T 1
33.3%
P 1
33.3%
A 1
33.3%
Han
ValueCountFrequency (%)
舊 7
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7326
62.9%
ASCII 4316
37.1%
CJK 7
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1890
43.8%
1 412
 
9.5%
- 271
 
6.3%
2 216
 
5.0%
( 193
 
4.5%
) 193
 
4.5%
3 167
 
3.9%
4 165
 
3.8%
5 148
 
3.4%
0 147
 
3.4%
Other values (7) 514
 
11.9%
Hangul
ValueCountFrequency (%)
리 817
 
11.2%
교 552
 
7.5%
면 515
 
7.0%
동 266
 
3.6%
산 240
 
3.3%
천 176
 
2.4%
읍 143
 
2.0%
대 139
 
1.9%
구 98
 
1.3%
성 93
 
1.3%
Other values (310) 4287
58.5%
CJK
ValueCountFrequency (%)
舊 7
100.0%

영점표고값(El.m)
Real number (ℝ)

MISSING  ZEROS 

Distinct760
Distinct (%)89.9%
Missing183
Missing (%)17.8%
Infinite0
Infinite (%)0.0%
Mean76.731191
Minimum-7.03
Maximum724.782
Zeros66
Zeros (%)6.4%
Negative62
Negative (%)6.0%
Memory size9.2 KiB
2023-12-11T07:25:53.725683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-7.03
5-th percentile-0.7456
Q14.702
median35.561
Q3108
95-th percentile283.5848
Maximum724.782
Range731.812
Interquartile range (IQR)103.298

Descriptive statistics

Standard deviation104.42718
Coefficient of variation (CV)1.3609482
Kurtosis7.5625386
Mean76.731191
Median Absolute Deviation (MAD)35.516
Skewness2.3688672
Sum64837.856
Variance10905.036
MonotonicityNot monotonic
2023-12-11T07:25:53.921874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 66
 
6.4%
273.5 5
 
0.5%
108.0 3
 
0.3%
62.921 2
 
0.2%
94.0 2
 
0.2%
26.5 2
 
0.2%
0.96 2
 
0.2%
3.09 2
 
0.2%
-1.09 2
 
0.2%
11.15 2
 
0.2%
Other values (750) 757
73.6%
(Missing) 183
 
17.8%
ValueCountFrequency (%)
-7.03 1
0.1%
-6.86 1
0.1%
-6.46 1
0.1%
-6.16 1
0.1%
-5.18 1
0.1%
-4.62 1
0.1%
-4.57 1
0.1%
-4.01 1
0.1%
-3.215 1
0.1%
-2.86 1
0.1%
ValueCountFrequency (%)
724.782 1
0.1%
718.848 1
0.1%
687.824 1
0.1%
590.873 1
0.1%
550.39 1
0.1%
526.963 1
0.1%
521.655 1
0.1%
518.78 1
0.1%
511.589 1
0.1%
491.02 1
0.1%

주의보수위값(m)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct95
Distinct (%)19.2%
Missing532
Missing (%)51.8%
Infinite0
Infinite (%)0.0%
Mean5.5225806
Minimum0
Maximum73
Zeros42
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2023-12-11T07:25:54.362638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.3
median4.5
Q36
95-th percentile11.075
Maximum73
Range73
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation6.2396981
Coefficient of variation (CV)1.1298519
Kurtosis42.301415
Mean5.5225806
Median Absolute Deviation (MAD)1.5
Skewness5.5927114
Sum2739.2
Variance38.933833
MonotonicityNot monotonic
2023-12-11T07:25:54.564517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.0 43
 
4.2%
0.0 42
 
4.1%
4.5 38
 
3.7%
3.5 36
 
3.5%
3.0 26
 
2.5%
5.0 23
 
2.2%
6.0 22
 
2.1%
5.5 20
 
1.9%
2.5 18
 
1.8%
7.0 13
 
1.3%
Other values (85) 215
20.9%
(Missing) 532
51.8%
ValueCountFrequency (%)
0.0 42
4.1%
1.3 1
 
0.1%
1.5 1
 
0.1%
2.0 10
 
1.0%
2.3 3
 
0.3%
2.4 1
 
0.1%
2.5 18
1.8%
2.6 2
 
0.2%
2.7 4
 
0.4%
2.8 3
 
0.3%
ValueCountFrequency (%)
73.0 1
0.1%
48.6 1
0.1%
44.7 1
0.1%
42.5 1
0.1%
42.2 1
0.1%
37.0 1
0.1%
34.3 1
0.1%
33.0 1
0.1%
30.9 1
0.1%
27.2 1
0.1%

경보수위값(m)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct109
Distinct (%)22.0%
Missing532
Missing (%)51.8%
Infinite0
Infinite (%)0.0%
Mean6.6268145
Minimum0
Maximum74
Zeros42
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2023-12-11T07:25:54.743342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median5.5
Q37.5
95-th percentile13.075
Maximum74
Range74
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation6.4911553
Coefficient of variation (CV)0.97952875
Kurtosis35.571888
Mean6.6268145
Median Absolute Deviation (MAD)1.6
Skewness4.9519298
Sum3286.9
Variance42.135098
MonotonicityNot monotonic
2023-12-11T07:25:54.922170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 42
 
4.1%
5.0 38
 
3.7%
5.5 32
 
3.1%
6.0 28
 
2.7%
4.5 27
 
2.6%
4.0 26
 
2.5%
8.0 17
 
1.7%
3.5 17
 
1.7%
7.0 17
 
1.7%
7.5 12
 
1.2%
Other values (99) 240
23.3%
(Missing) 532
51.8%
ValueCountFrequency (%)
0.0 42
4.1%
1.8 1
 
0.1%
2.0 1
 
0.1%
2.5 6
 
0.6%
2.7 1
 
0.1%
2.8 3
 
0.3%
2.9 1
 
0.1%
3.0 9
 
0.9%
3.1 4
 
0.4%
3.2 3
 
0.3%
ValueCountFrequency (%)
74.0 1
0.1%
49.1 1
0.1%
46.3 1
0.1%
43.5 1
0.1%
43.0 1
0.1%
38.5 1
0.1%
34.9 1
0.1%
34.5 1
0.1%
32.7 1
0.1%
27.8 1
0.1%

계획홍수위값(m)
Real number (ℝ)

MISSING  ZEROS 

Distinct448
Distinct (%)76.3%
Missing441
Missing (%)42.9%
Infinite0
Infinite (%)0.0%
Mean20.239506
Minimum0
Maximum351.42
Zeros72
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2023-12-11T07:25:55.107811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.82
median7.14
Q313.45
95-th percentile101.658
Maximum351.42
Range351.42
Interquartile range (IQR)8.63

Descriptive statistics

Standard deviation41.617923
Coefficient of variation (CV)2.0562717
Kurtosis24.628206
Mean20.239506
Median Absolute Deviation (MAD)3.04
Skewness4.489778
Sum11880.59
Variance1732.0515
MonotonicityNot monotonic
2023-12-11T07:25:55.291647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 72
 
7.0%
4.94 4
 
0.4%
5.38 4
 
0.4%
6.11 3
 
0.3%
4.38 3
 
0.3%
4.31 3
 
0.3%
6.98 2
 
0.2%
8.65 2
 
0.2%
4.09 2
 
0.2%
6.56 2
 
0.2%
Other values (438) 490
47.7%
(Missing) 441
42.9%
ValueCountFrequency (%)
0.0 72
7.0%
1.24 1
 
0.1%
1.64 1
 
0.1%
3.22 1
 
0.1%
3.33 1
 
0.1%
3.4 1
 
0.1%
3.43 2
 
0.2%
3.5 1
 
0.1%
3.62 1
 
0.1%
3.63 1
 
0.1%
ValueCountFrequency (%)
351.42 1
0.1%
346.78 1
0.1%
324.6 1
0.1%
293.99 1
0.1%
212.15 1
0.1%
207.2 1
0.1%
204.92 1
0.1%
191.13 1
0.1%
189.7 1
0.1%
180.73 1
0.1%

Interactions

2023-12-11T07:25:50.225821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:48.463006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:48.876968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:49.330342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:49.735667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:50.306487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:48.533357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:48.964643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:49.406421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:49.829754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:50.386497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:48.632711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:49.080760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:49.487198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:49.944406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:50.481068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:48.711451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:49.180571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:49.570840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:50.030771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:50.574546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:48.794826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:49.255004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:49.652267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:25:50.132254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:25:55.419453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수위관측소구분관할기관명영점표고값(El.m)주의보수위값(m)경보수위값(m)계획홍수위값(m)
수위관측소구분1.0000.2790.1840.0000.0000.298
관할기관명0.2791.0000.4570.0630.0480.000
영점표고값(El.m)0.1840.4571.0000.0000.0000.609
주의보수위값(m)0.0000.0630.0001.0000.9980.317
경보수위값(m)0.0000.0480.0000.9981.0000.330
계획홍수위값(m)0.2980.0000.6090.3170.3301.000
2023-12-11T07:25:55.569626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수위관측소구분영점표고값(El.m)주의보수위값(m)경보수위값(m)계획홍수위값(m)관할기관명
수위관측소구분1.000-0.338-0.095-0.1030.0700.142
영점표고값(El.m)-0.3381.000-0.404-0.407-0.1050.206
주의보수위값(m)-0.095-0.4041.0000.9880.4650.039
경보수위값(m)-0.103-0.4070.9881.0000.4780.028
계획홍수위값(m)0.070-0.1050.4650.4781.0000.000
관할기관명0.1420.2060.0390.0280.0001.000

Missing values

2023-12-11T07:25:50.696418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:25:50.825490image/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.
2023-12-11T07:25:50.953322image/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

수위관측소구분수위관측소명관할기관명주소상세주소영점표고값(El.m)주의보수위값(m)경보수위값(m)계획홍수위값(m)
01003635가대교한국수자원공사충청북도 단양군 가곡면가대리 154-1 도<NA><NA><NA><NA>
12015650가북한국농어촌공사경상남도 거창군 가북면박암리 479<NA><NA><NA><NA>
22008629가음한국농어촌공사경상북도 의성군 가음면양지리 173-2<NA><NA><NA><NA>
31013655가평군(가평교)환경부경기도 가평군가평읍 읍내리 가평교51.8573.04.05.18
41015645가평군(신청평대교)환경부경기도 가평군청평면 삼회리 산90 신청평대교22.61210.012.515.46
51015644가평군(청평교)환경부경기도 가평군청평면 청평리 청평교31.2224.55.58.59
61015640가평군(청평댐)환경부경기도 가평군설악면 회곡리 745-270.67<NA><NA>51.33
71013652가평천한국수력원자력경기도 가평군북면 개곡리<NA><NA><NA><NA>
81302670강릉시(송림교)환경부강원도 강릉시 연곡면송림리 송림교3.0233.54.54.94
91302648강릉시(회산교)환경부강원도 강릉시회산동 회산교15.6313.03.54.76
수위관측소구분수위관측소명관할기관명주소상세주소영점표고값(El.m)주의보수위값(m)경보수위값(m)계획홍수위값(m)
10181006630횡성군(오산교)환경부강원도 횡성군 공근면오산리 오산교118.3915.06.06.95
10191006610횡성군(율동리)한국수자원공사강원도 횡성군갑천면 매일리 799186.458<NA><NA><NA>
10201006660횡성군(전천교)환경부강원도 횡성군횡성읍 교항리 전천교107.8763.54.55.86
10211006612횡성군(포동2교)한국수자원공사강원도 횡성군 갑천면포동리 포동2교176.592<NA><NA><NA>
10221006650횡성군(횡성교)환경부강원도 횡성군횡성읍 읍하리 횡성교107.0533.54.55.31
10231006615횡성군(횡성댐)한국수자원공사강원도 횡성군갑천면 대관대리 천72-6<NA><NA><NA><NA>
10244009628효곡한국농어촌공사전라남도 구례군간전면 금산리 127<NA><NA><NA><NA>
10251004635후영교한국수력원자력충청북도 괴산군청천면 후영리 후영교<NA><NA><NA><NA>
10261002690후포환경부강원도 영월군 남면북쌍리193.466<NA><NA><NA>
10271007649흑천한국수력원자력경기도 양평군양평읍 원덕리<NA><NA><NA><NA>