Overview

Dataset statistics

Number of variables7
Number of observations59
Missing cells54
Missing cells (%)13.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory61.2 B

Variable types

Numeric3
Text3
Categorical1

Alerts

계획홍수위값(m) is highly overall correlated with 제한수위값(El.m)High correlation
제한수위값(El.m) is highly overall correlated with 계획홍수위값(m)High correlation
주소 has 11 (18.6%) missing valuesMissing
상세주소 has 15 (25.4%) missing valuesMissing
계획홍수위값(m) has 7 (11.9%) missing valuesMissing
제한수위값(El.m) has 21 (35.6%) missing valuesMissing
댐관측소구분 has unique valuesUnique
댐관측소명 has unique valuesUnique

Reproduction

Analysis started2024-03-12 23:34:42.203743
Analysis finished2024-03-12 23:34:43.303878
Duration1.1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐관측소구분
Real number (ℝ)

UNIQUE 

Distinct59
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2754301.1
Minimum1001210
Maximum5101110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.0 B
2024-03-13T08:34:43.369340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001210
5-th percentile1004240.1
Q11651660
median2021210
Q34006369
95-th percentile5003637.1
Maximum5101110
Range4099900
Interquartile range (IQR)2354709

Descriptive statistics

Standard deviation1501706.1
Coefficient of variation (CV)0.54522222
Kurtosis-1.2491458
Mean2754301.1
Median Absolute Deviation (MAD)1009100
Skewness0.43987812
Sum1.6250376 × 108
Variance2.2551213 × 1012
MonotonicityStrictly increasing
2024-03-13T08:34:43.492534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001210 1
 
1.7%
1003110 1
 
1.7%
2503210 1
 
1.7%
2503220 1
 
1.7%
3001110 1
 
1.7%
3008110 1
 
1.7%
3008611 1
 
1.7%
3014410 1
 
1.7%
3203310 1
 
1.7%
3301611 1
 
1.7%
Other values (49) 49
83.1%
ValueCountFrequency (%)
1001210 1
1.7%
1003110 1
1.7%
1003611 1
1.7%
1004310 1
1.7%
1006110 1
1.7%
1009710 1
1.7%
1010310 1
1.7%
1010320 1
1.7%
1012110 1
1.7%
1013310 1
1.7%
ValueCountFrequency (%)
5101110 1
1.7%
5006621 1
1.7%
5003701 1
1.7%
5003630 1
1.7%
5003619 1
1.7%
5003410 1
1.7%
5002410 1
1.7%
5001701 1
1.7%
5001608 1
1.7%
5001604 1
1.7%

댐관측소명
Text

UNIQUE 

Distinct59
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size604.0 B
2024-03-13T08:34:43.727197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length4
Min length3

Characters and Unicode

Total characters236
Distinct characters72
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

Unique59 ?
Unique (%)100.0%

Sample

1st row광동댐
2nd row충주댐
3rd row충주조정지댐
4th row괴산댐
5th row횡성댐
ValueCountFrequency (%)
광동댐 1
 
1.7%
대곡댐 1
 
1.7%
연초댐 1
 
1.7%
구천댐 1
 
1.7%
용담댐 1
 
1.7%
대청댐 1
 
1.7%
대청댐조정지 1
 
1.7%
금강하구둑 1
 
1.7%
보령댐 1
 
1.7%
대아저수지 1
 
1.7%
Other values (49) 49
83.1%
2024-03-13T08:34:44.035904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
댐 45
19.1%
지 18
 
7.6%
조 12
 
5.1%
수 9
 
3.8%
양 8
 
3.4%
천 8
 
3.4%
절 7
 
3.0%
저 6
 
2.5%
담 6
 
2.5%
주 6
 
2.5%
Other values (62) 111
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 231
97.9%
Decimal Number 5
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
댐 45
19.5%
지 18
 
7.8%
조 12
 
5.2%
수 9
 
3.9%
양 8
 
3.5%
천 8
 
3.5%
절 7
 
3.0%
저 6
 
2.6%
담 6
 
2.6%
주 6
 
2.6%
Other values (59) 106
45.9%
Decimal Number
ValueCountFrequency (%)
1 2
40.0%
2 2
40.0%
3 1
20.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 231
97.9%
Common 5
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
댐 45
19.5%
지 18
 
7.8%
조 12
 
5.2%
수 9
 
3.9%
양 8
 
3.5%
천 8
 
3.5%
절 7
 
3.0%
저 6
 
2.6%
담 6
 
2.6%
주 6
 
2.6%
Other values (59) 106
45.9%
Common
ValueCountFrequency (%)
1 2
40.0%
2 2
40.0%
3 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 231
97.9%
ASCII 5
 
2.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
댐 45
19.5%
지 18
 
7.8%
조 12
 
5.2%
수 9
 
3.9%
양 8
 
3.5%
천 8
 
3.5%
절 7
 
3.0%
저 6
 
2.6%
담 6
 
2.6%
주 6
 
2.6%
Other values (59) 106
45.9%
ASCII
ValueCountFrequency (%)
1 2
40.0%
2 2
40.0%
3 1
20.0%

관할기관명
Categorical

Distinct4
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size604.0 B
한국수자원공사
42 
한국농어촌공사
10 
한국수력원자력
6 
환경부
 
1

Length

Max length7
Median length7
Mean length6.9322034
Min length3

Unique

Unique1 ?
Unique (%)1.7%

Sample

1st row한국수자원공사
2nd row한국수자원공사
3rd row한국수자원공사
4th row한국수력원자력
5th row한국수자원공사

Common Values

ValueCountFrequency (%)
한국수자원공사 42
71.2%
한국농어촌공사 10
 
16.9%
한국수력원자력 6
 
10.2%
환경부 1
 
1.7%

Length

2024-03-13T08:34:44.151093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:34:44.240175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한국수자원공사 42
71.2%
한국농어촌공사 10
 
16.9%
한국수력원자력 6
 
10.2%
환경부 1
 
1.7%

주소
Text

MISSING 

Distinct37
Distinct (%)77.1%
Missing11
Missing (%)18.6%
Memory size604.0 B
2024-03-13T08:34:44.416078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length8
Mean length8.8541667
Min length7

Characters and Unicode

Total characters425
Distinct characters72
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

Unique29 ?
Unique (%)60.4%

Sample

1st row강원도 삼척시
2nd row충청북도 충주시
3rd row충청북도 충주시
4th row충청북도 괴산군
5th row강원도 횡성군
ValueCountFrequency (%)
경상북도 10
 
9.2%
전라남도 8
 
7.3%
강원도 7
 
6.4%
전라북도 7
 
6.4%
경상남도 5
 
4.6%
안동시 4
 
3.7%
춘천시 3
 
2.8%
충청북도 3
 
2.8%
대덕구 2
 
1.8%
경기도 2
 
1.8%
Other values (50) 58
53.2%
2024-03-13T08:34:44.928047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
61
 
14.4%
도 44
 
10.4%
시 27
 
6.4%
군 24
 
5.6%
북 23
 
5.4%
경 20
 
4.7%
전 18
 
4.2%
남 15
 
3.5%
라 15
 
3.5%
상 15
 
3.5%
Other values (62) 163
38.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 364
85.6%
Space Separator 61
 
14.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 44
 
12.1%
시 27
 
7.4%
군 24
 
6.6%
북 23
 
6.3%
경 20
 
5.5%
전 18
 
4.9%
남 15
 
4.1%
라 15
 
4.1%
상 15
 
4.1%
천 13
 
3.6%
Other values (61) 150
41.2%
Space Separator
ValueCountFrequency (%)
61
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 364
85.6%
Common 61
 
14.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 44
 
12.1%
시 27
 
7.4%
군 24
 
6.6%
북 23
 
6.3%
경 20
 
5.5%
전 18
 
4.9%
남 15
 
4.1%
라 15
 
4.1%
상 15
 
4.1%
천 13
 
3.6%
Other values (61) 150
41.2%
Common
ValueCountFrequency (%)
61
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 364
85.6%
ASCII 61
 
14.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
61
100.0%
Hangul
ValueCountFrequency (%)
도 44
 
12.1%
시 27
 
7.4%
군 24
 
6.6%
북 23
 
6.3%
경 20
 
5.5%
전 18
 
4.9%
남 15
 
4.1%
라 15
 
4.1%
상 15
 
4.1%
천 13
 
3.6%
Other values (61) 150
41.2%

상세주소
Text

MISSING 

Distinct44
Distinct (%)100.0%
Missing15
Missing (%)25.4%
Memory size604.0 B
2024-03-13T08:34:45.148651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length7.7954545
Min length3

Characters and Unicode

Total characters343
Distinct characters100
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

Unique44 ?
Unique (%)100.0%

Sample

1st row숙암리 15
2nd row종민동
3rd row가금면 장천리
4th row칠성면 산막이옛길 43
5th row갑천면 대관대리
ValueCountFrequency (%)
취수탑 3
 
3.2%
임하면 2
 
2.1%
용수리 2
 
2.1%
도교 2
 
2.1%
대천리 1
 
1.1%
미산면 1
 
1.1%
성덕리 1
 
1.1%
성산면 1
 
1.1%
천426-1 1
 
1.1%
용호동 1
 
1.1%
Other values (79) 79
84.0%
2024-03-13T08:34:45.490123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
50
 
14.6%
면 32
 
9.3%
리 30
 
8.7%
동 12
 
3.5%
천 8
 
2.3%
산 8
 
2.3%
2 7
 
2.0%
수 7
 
2.0%
성 7
 
2.0%
대 7
 
2.0%
Other values (90) 175
51.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 255
74.3%
Space Separator 50
 
14.6%
Decimal Number 35
 
10.2%
Dash Punctuation 3
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 32
 
12.5%
리 30
 
11.8%
동 12
 
4.7%
천 8
 
3.1%
산 8
 
3.1%
수 7
 
2.7%
성 7
 
2.7%
대 7
 
2.7%
용 7
 
2.7%
로 5
 
2.0%
Other values (79) 132
51.8%
Decimal Number
ValueCountFrequency (%)
2 7
20.0%
4 6
17.1%
6 5
14.3%
3 4
11.4%
0 4
11.4%
1 4
11.4%
5 2
 
5.7%
7 2
 
5.7%
8 1
 
2.9%
Space Separator
ValueCountFrequency (%)
50
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 255
74.3%
Common 88
 
25.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 32
 
12.5%
리 30
 
11.8%
동 12
 
4.7%
천 8
 
3.1%
산 8
 
3.1%
수 7
 
2.7%
성 7
 
2.7%
대 7
 
2.7%
용 7
 
2.7%
로 5
 
2.0%
Other values (79) 132
51.8%
Common
ValueCountFrequency (%)
50
56.8%
2 7
 
8.0%
4 6
 
6.8%
6 5
 
5.7%
3 4
 
4.5%
0 4
 
4.5%
1 4
 
4.5%
- 3
 
3.4%
5 2
 
2.3%
7 2
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 255
74.3%
ASCII 88
 
25.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
50
56.8%
2 7
 
8.0%
4 6
 
6.8%
6 5
 
5.7%
3 4
 
4.5%
0 4
 
4.5%
1 4
 
4.5%
- 3
 
3.4%
5 2
 
2.3%
7 2
 
2.3%
Hangul
ValueCountFrequency (%)
면 32
 
12.5%
리 30
 
11.8%
동 12
 
4.7%
천 8
 
3.1%
산 8
 
3.1%
수 7
 
2.7%
성 7
 
2.7%
대 7
 
2.7%
용 7
 
2.7%
로 5
 
2.0%
Other values (79) 132
51.8%

계획홍수위값(m)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct52
Distinct (%)100.0%
Missing7
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean136.27885
Minimum4.62
Maximum675.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.0 B
2024-03-13T08:34:45.622010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.62
5-th percentile36.42
Q167.8475
median112.375
Q3179.25
95-th percentile292.095
Maximum675.3
Range670.68
Interquartile range (IQR)111.4025

Descriptive statistics

Standard deviation108.49898
Coefficient of variation (CV)0.7961542
Kurtosis11.204565
Mean136.27885
Median Absolute Deviation (MAD)51.975
Skewness2.7042439
Sum7086.5
Variance11772.028
MonotonicityNot monotonic
2024-03-13T08:34:45.738838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65.5 1
 
1.7%
152.6 1
 
1.7%
122.7 1
 
1.7%
41.2 1
 
1.7%
49.6 1
 
1.7%
94.54 1
 
1.7%
265.5 1
 
1.7%
80.0 1
 
1.7%
35.1 1
 
1.7%
4.62 1
 
1.7%
Other values (42) 42
71.2%
(Missing) 7
 
11.9%
ValueCountFrequency (%)
4.62 1
1.7%
27.0 1
1.7%
35.1 1
1.7%
37.5 1
1.7%
40.0 1
1.7%
41.2 1
1.7%
46.0 1
1.7%
48.41 1
1.7%
49.6 1
1.7%
52.0 1
1.7%
ValueCountFrequency (%)
675.3 1
1.7%
364.9 1
1.7%
324.6 1
1.7%
265.5 1
1.7%
264.6 1
1.7%
238.5 1
1.7%
210.2 1
1.7%
205.1 1
1.7%
198.6 1
1.7%
198.0 1
1.7%

제한수위값(El.m)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)100.0%
Missing21
Missing (%)35.6%
Infinite0
Infinite (%)0.0%
Mean132.22895
Minimum30
Maximum362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.0 B
2024-03-13T08:34:45.875320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile40.07
Q174.4
median118
Q3177.65
95-th percentile270.125
Maximum362
Range332
Interquartile range (IQR)103.25

Descriptive statistics

Standard deviation78.740689
Coefficient of variation (CV)0.59548753
Kurtosis0.92096083
Mean132.22895
Median Absolute Deviation (MAD)55.05
Skewness1.0004054
Sum5024.7
Variance6200.0962
MonotonicityNot monotonic
2024-03-13T08:34:45.986527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
319.0 1
 
1.7%
76.5 1
 
1.7%
30.0 1
 
1.7%
74.0 1
 
1.7%
116.0 1
 
1.7%
82.0 1
 
1.7%
52.7 1
 
1.7%
194.0 1
 
1.7%
108.5 1
 
1.7%
93.0 1
 
1.7%
Other values (28) 28
47.5%
(Missing) 21
35.6%
ValueCountFrequency (%)
30.0 1
1.7%
34.8 1
1.7%
41.0 1
1.7%
47.2 1
1.7%
48.0 1
1.7%
50.0 1
1.7%
52.7 1
1.7%
64.9 1
1.7%
70.5 1
1.7%
74.0 1
1.7%
ValueCountFrequency (%)
362.0 1
1.7%
319.0 1
1.7%
261.5 1
1.7%
236.0 1
1.7%
207.2 1
1.7%
204.0 1
1.7%
194.0 1
1.7%
193.5 1
1.7%
190.3 1
1.7%
178.2 1
1.7%

Interactions

2024-03-13T08:34:42.858938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:34:42.465472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:34:42.652017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:34:42.919315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:34:42.521111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:34:42.719049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:34:42.983641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:34:42.589945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:34:42.789590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T08:34:46.061697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐관측소구분댐관측소명관할기관명주소상세주소계획홍수위값(m)제한수위값(El.m)
댐관측소구분1.0001.0000.5381.0001.0000.3290.193
댐관측소명1.0001.0001.0001.0001.0001.0001.000
관할기관명0.5381.0001.0000.0001.0000.0000.000
주소1.0001.0000.0001.0001.0000.9440.932
상세주소1.0001.0001.0001.0001.0001.0001.000
계획홍수위값(m)0.3291.0000.0000.9441.0001.0000.994
제한수위값(El.m)0.1931.0000.0000.9321.0000.9941.000
2024-03-13T08:34:46.164253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐관측소구분계획홍수위값(m)제한수위값(El.m)관할기관명
댐관측소구분1.000-0.294-0.3750.377
계획홍수위값(m)-0.2941.0000.9980.000
제한수위값(El.m)-0.3750.9981.0000.000
관할기관명0.3770.0000.0001.000

Missing values

2024-03-13T08:34:43.072578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T08:34:43.162825image/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.
2024-03-13T08:34:43.245025image/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

댐관측소구분댐관측소명관할기관명주소상세주소계획홍수위값(m)제한수위값(El.m)
01001210광동댐한국수자원공사강원도 삼척시숙암리 15675.3<NA>
11003110충주댐한국수자원공사충청북도 충주시종민동145.0138.0
21003611충주조정지댐한국수자원공사충청북도 충주시가금면 장천리67.3<NA>
31004310괴산댐한국수력원자력충청북도 괴산군칠성면 산막이옛길 43136.93134.0
41006110횡성댐한국수자원공사강원도 횡성군갑천면 대관대리180.0178.2
51009710평화의댐한국수자원공사강원도 화천군화천읍 수하리264.6<NA>
61010310화천댐한국수력원자력강원도 화천군간동면 어룡동길 42183.0175.0
71010320춘천댐한국수력원자력강원도 춘천시신북읍 영서로 3741104.9102.0
81012110소양강댐한국수자원공사강원도 춘천시동면 월곡리198.0190.3
91013310의암댐한국수력원자력강원도 춘천시신동면 옛경춘로 62-1573.3670.5
댐관측소구분댐관측소명관할기관명주소상세주소계획홍수위값(m)제한수위값(El.m)
495001604담양2조절지한국수자원공사<NA><NA><NA><NA>
505001608담양1조절지한국수자원공사<NA><NA><NA><NA>
515001701담양홍수조절지한국수자원공사<NA><NA><NA><NA>
525002410장성댐한국농어촌공사전라남도 장성군장성읍 용강리 취수탑90.9590.0
535003410나주댐한국농어촌공사전라남도 나주시다도면 판촌리 취수탑 도교68.0364.9
545003619화순1조절지한국수자원공사<NA><NA><NA><NA>
555003630화순2조절지한국수자원공사<NA><NA><NA><NA>
565003701화순홍수조절지한국수자원공사<NA><NA><NA><NA>
575006621대동저수지한국농어촌공사전라남도 함평군대동면 운교리37.534.8
585101110장흥댐한국수자원공사전라남도 장흥군부산면 지천리82.879.0