Overview

Dataset statistics

Number of variables12
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory109.3 B

Variable types

Numeric6
Text3
Categorical3

Dataset

Description수위계코드,수위계명,하천명,구청코드,구청명,송신지 자료수집 시각,수신서버 저장 시각,실시간 하천 수위값(m),제방고(m),계획홍수위(m),하상고(m),통제수위(m)
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-1167/S/1/datasetView.do

Alerts

송신지 자료수집 시각 is highly overall correlated with 수신서버 저장 시각High correlation
수신서버 저장 시각 is highly overall correlated with 송신지 자료수집 시각High correlation
수위계코드 is highly overall correlated with 구청코드 and 1 other fieldsHigh correlation
구청코드 is highly overall correlated with 수위계코드 and 1 other fieldsHigh correlation
실시간 하천 수위값(m) is highly overall correlated with 제방고(m) and 2 other fieldsHigh correlation
제방고(m) is highly overall correlated with 실시간 하천 수위값(m) and 2 other fieldsHigh correlation
계획홍수위(m) is highly overall correlated with 수위계코드 and 4 other fieldsHigh correlation
하상고(m) is highly overall correlated with 실시간 하천 수위값(m) and 2 other fieldsHigh correlation
통제수위(m) is highly imbalanced (53.3%)Imbalance
수위계코드 has unique valuesUnique
수위계명 has unique valuesUnique
실시간 하천 수위값(m) has unique valuesUnique

Reproduction

Analysis started2024-05-03 19:55:05.425356
Analysis finished2024-05-03 19:55:14.150760
Duration8.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

수위계코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1106.5238
Minimum101
Maximum2502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-05-03T19:55:14.310254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile102
Q1303
median901
Q32002
95-th percentile2303
Maximum2502
Range2401
Interquartile range (IQR)1699

Descriptive statistics

Standard deviation874.2707
Coefficient of variation (CV)0.79010564
Kurtosis-1.6201653
Mean1106.5238
Median Absolute Deviation (MAD)600
Skewness0.32986496
Sum23237
Variance764349.26
MonotonicityStrictly increasing
2024-05-03T19:55:14.771517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
101 1
 
4.8%
102 1
 
4.8%
2502 1
 
4.8%
2303 1
 
4.8%
2301 1
 
4.8%
2201 1
 
4.8%
2003 1
 
4.8%
2002 1
 
4.8%
2001 1
 
4.8%
1501 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
101 1
4.8%
102 1
4.8%
103 1
4.8%
301 1
4.8%
302 1
4.8%
303 1
4.8%
401 1
4.8%
402 1
4.8%
403 1
4.8%
801 1
4.8%
ValueCountFrequency (%)
2502 1
4.8%
2303 1
4.8%
2301 1
4.8%
2201 1
4.8%
2003 1
4.8%
2002 1
4.8%
2001 1
4.8%
1501 1
4.8%
1401 1
4.8%
902 1
4.8%

수위계명
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2024-05-03T19:55:15.155939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.3809524
Min length3

Characters and Unicode

Total characters71
Distinct characters40
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

Unique21 ?
Unique (%)100.0%

Sample

1st row여수대교
2nd row대곡교
3rd row탄천2교
4th row모래말옆
5th row노원교
ValueCountFrequency (%)
여수대교 1
 
4.8%
마장2교 1
 
4.8%
양산교 1
 
4.8%
신대방역 1
 
4.8%
기아대교 1
 
4.8%
광화교 1
 
4.8%
도림교 1
 
4.8%
고척교 1
 
4.8%
성산2교 1
 
4.8%
증산교 1
 
4.8%
Other values (11) 11
52.4%
2024-05-03T19:55:15.926373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 19
26.8%
대 4
 
5.6%
산 3
 
4.2%
2 3
 
4.2%
성 3
 
4.2%
계 2
 
2.8%
신 2
 
2.8%
월 2
 
2.8%
장 2
 
2.8%
여 1
 
1.4%
Other values (30) 30
42.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67
94.4%
Decimal Number 4
 
5.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 19
28.4%
대 4
 
6.0%
산 3
 
4.5%
성 3
 
4.5%
계 2
 
3.0%
신 2
 
3.0%
월 2
 
3.0%
장 2
 
3.0%
여 1
 
1.5%
고 1
 
1.5%
Other values (28) 28
41.8%
Decimal Number
ValueCountFrequency (%)
2 3
75.0%
1 1
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67
94.4%
Common 4
 
5.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 19
28.4%
대 4
 
6.0%
산 3
 
4.5%
성 3
 
4.5%
계 2
 
3.0%
신 2
 
3.0%
월 2
 
3.0%
장 2
 
3.0%
여 1
 
1.5%
고 1
 
1.5%
Other values (28) 28
41.8%
Common
ValueCountFrequency (%)
2 3
75.0%
1 1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67
94.4%
ASCII 4
 
5.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 19
28.4%
대 4
 
6.0%
산 3
 
4.5%
성 3
 
4.5%
계 2
 
3.0%
신 2
 
3.0%
월 2
 
3.0%
장 2
 
3.0%
여 1
 
1.5%
고 1
 
1.5%
Other values (28) 28
41.8%
ASCII
ValueCountFrequency (%)
2 3
75.0%
1 1
 
25.0%

하천명
Text

Distinct11
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Memory size300.0 B
2024-05-03T19:55:16.194573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length15.190476
Min length15

Characters and Unicode

Total characters319
Distinct characters23
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

Unique6 ?
Unique (%)28.6%

Sample

1st row탄천
2nd row탄천
3rd row탄천
4th row방학천
5th row중랑천
ValueCountFrequency (%)
탄천 4
19.0%
중랑천 4
19.0%
도림천 3
14.3%
우이천 2
9.5%
안양천 2
9.5%
방학천 1
 
4.8%
정릉천 1
 
4.8%
청계천 1
 
4.8%
불광천 1
 
4.8%
홍제천 1
 
4.8%
2024-05-03T19:55:16.983278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
260
81.5%
천 21
 
6.6%
탄 4
 
1.3%
중 4
 
1.3%
랑 4
 
1.3%
도 3
 
0.9%
림 3
 
0.9%
우 2
 
0.6%
이 2
 
0.6%
안 2
 
0.6%
Other values (13) 14
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Space Separator 260
81.5%
Other Letter 59
 
18.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 21
35.6%
탄 4
 
6.8%
중 4
 
6.8%
랑 4
 
6.8%
도 3
 
5.1%
림 3
 
5.1%
우 2
 
3.4%
이 2
 
3.4%
안 2
 
3.4%
양 2
 
3.4%
Other values (12) 12
20.3%
Space Separator
ValueCountFrequency (%)
260
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 260
81.5%
Hangul 59
 
18.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 21
35.6%
탄 4
 
6.8%
중 4
 
6.8%
랑 4
 
6.8%
도 3
 
5.1%
림 3
 
5.1%
우 2
 
3.4%
이 2
 
3.4%
안 2
 
3.4%
양 2
 
3.4%
Other values (12) 12
20.3%
Common
ValueCountFrequency (%)
260
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260
81.5%
Hangul 59
 
18.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
260
100.0%
Hangul
ValueCountFrequency (%)
천 21
35.6%
탄 4
 
6.8%
중 4
 
6.8%
랑 4
 
6.8%
도 3
 
5.1%
림 3
 
5.1%
우 2
 
3.4%
이 2
 
3.4%
안 2
 
3.4%
양 2
 
3.4%
Other values (12) 12
20.3%

구청코드
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.04762
Minimum101
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-05-03T19:55:17.253313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1103
median109
Q3120
95-th percentile123
Maximum125
Range24
Interquartile range (IQR)17

Descriptive statistics

Standard deviation8.7434329
Coefficient of variation (CV)0.078735888
Kurtosis-1.6206073
Mean111.04762
Median Absolute Deviation (MAD)6
Skewness0.32927126
Sum2332
Variance76.447619
MonotonicityIncreasing
2024-05-03T19:55:17.615110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
101 3
14.3%
103 3
14.3%
104 3
14.3%
120 3
14.3%
109 2
9.5%
123 2
9.5%
108 1
 
4.8%
114 1
 
4.8%
115 1
 
4.8%
122 1
 
4.8%
ValueCountFrequency (%)
101 3
14.3%
103 3
14.3%
104 3
14.3%
108 1
 
4.8%
109 2
9.5%
114 1
 
4.8%
115 1
 
4.8%
120 3
14.3%
122 1
 
4.8%
123 2
9.5%
ValueCountFrequency (%)
125 1
 
4.8%
123 2
9.5%
122 1
 
4.8%
120 3
14.3%
115 1
 
4.8%
114 1
 
4.8%
109 2
9.5%
108 1
 
4.8%
104 3
14.3%
103 3
14.3%

구청명
Text

Distinct11
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Memory size300.0 B
2024-05-03T19:55:17.977514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0952381
Min length3

Characters and Unicode

Total characters65
Distinct characters21
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

Unique5 ?
Unique (%)23.8%

Sample

1st row강남구
2nd row강남구
3rd row강남구
4th row도봉구
5th row도봉구
ValueCountFrequency (%)
강남구 3
14.3%
도봉구 3
14.3%
노원구 3
14.3%
구로구 3
14.3%
성동구 2
9.5%
관악구 2
9.5%
동대문구 1
 
4.8%
서대문구 1
 
4.8%
마포구 1
 
4.8%
금천구 1
 
4.8%
2024-05-03T19:55:18.750741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 24
36.9%
강 3
 
4.6%
도 3
 
4.6%
봉 3
 
4.6%
노 3
 
4.6%
원 3
 
4.6%
로 3
 
4.6%
동 3
 
4.6%
남 3
 
4.6%
대 2
 
3.1%
Other values (11) 15
23.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 65
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 24
36.9%
강 3
 
4.6%
도 3
 
4.6%
봉 3
 
4.6%
노 3
 
4.6%
원 3
 
4.6%
로 3
 
4.6%
동 3
 
4.6%
남 3
 
4.6%
대 2
 
3.1%
Other values (11) 15
23.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 65
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 24
36.9%
강 3
 
4.6%
도 3
 
4.6%
봉 3
 
4.6%
노 3
 
4.6%
원 3
 
4.6%
로 3
 
4.6%
동 3
 
4.6%
남 3
 
4.6%
대 2
 
3.1%
Other values (11) 15
23.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 65
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 24
36.9%
강 3
 
4.6%
도 3
 
4.6%
봉 3
 
4.6%
노 3
 
4.6%
원 3
 
4.6%
로 3
 
4.6%
동 3
 
4.6%
남 3
 
4.6%
대 2
 
3.1%
Other values (11) 15
23.1%

송신지 자료수집 시각
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2024-05-04 04:46:01
16 
2024-05-04 04:45:48
3 
2024-05-04 04:43:01
 
1
2024-05-04 04:46:20
 
1

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique2 ?
Unique (%)9.5%

Sample

1st row2024-05-04 04:46:01
2nd row2024-05-04 04:46:01
3rd row2024-05-04 04:43:01
4th row2024-05-04 04:46:01
5th row2024-05-04 04:46:01

Common Values

ValueCountFrequency (%)
2024-05-04 04:46:01 16
76.2%
2024-05-04 04:45:48 3
 
14.3%
2024-05-04 04:43:01 1
 
4.8%
2024-05-04 04:46:20 1
 
4.8%

Length

2024-05-03T19:55:19.158749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T19:55:19.487308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-05-04 21
50.0%
04:46:01 16
38.1%
04:45:48 3
 
7.1%
04:43:01 1
 
2.4%
04:46:20 1
 
2.4%

수신서버 저장 시각
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2024-05-04 04:46:01
16 
2024-05-04 04:45:48
3 
2024-05-04 04:43:01
 
1
2024-05-04 04:46:20
 
1

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique2 ?
Unique (%)9.5%

Sample

1st row2024-05-04 04:46:01
2nd row2024-05-04 04:46:01
3rd row2024-05-04 04:43:01
4th row2024-05-04 04:46:01
5th row2024-05-04 04:46:01

Common Values

ValueCountFrequency (%)
2024-05-04 04:46:01 16
76.2%
2024-05-04 04:45:48 3
 
14.3%
2024-05-04 04:43:01 1
 
4.8%
2024-05-04 04:46:20 1
 
4.8%

Length

2024-05-03T19:55:19.948516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T19:55:20.275008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-05-04 21
50.0%
04:46:01 16
38.1%
04:45:48 3
 
7.1%
04:43:01 1
 
2.4%
04:46:20 1
 
2.4%

실시간 하천 수위값(m)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.882381
Minimum4.66
Maximum33.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-05-03T19:55:20.628334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.66
5-th percentile4.92
Q16.74
median11.86
Q315.56
95-th percentile31.36
Maximum33.75
Range29.09
Interquartile range (IQR)8.82

Descriptive statistics

Standard deviation8.7997596
Coefficient of variation (CV)0.63387971
Kurtosis0.29936699
Mean13.882381
Median Absolute Deviation (MAD)5.12
Skewness1.1070589
Sum291.53
Variance77.435769
MonotonicityNot monotonic
2024-05-03T19:55:20.992946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
15.56 1
 
4.8%
11.86 1
 
4.8%
4.66 1
 
4.8%
31.36 1
 
4.8%
13.07 1
 
4.8%
10.19 1
 
4.8%
7.58 1
 
4.8%
6.3 1
 
4.8%
5.69 1
 
4.8%
11.68 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
4.66 1
4.8%
4.92 1
4.8%
5.69 1
4.8%
5.98 1
4.8%
6.3 1
4.8%
6.74 1
4.8%
7.58 1
4.8%
8.3 1
4.8%
10.19 1
4.8%
11.68 1
4.8%
ValueCountFrequency (%)
33.75 1
4.8%
31.36 1
4.8%
28.93 1
4.8%
21.76 1
4.8%
21.31 1
4.8%
15.56 1
4.8%
15.44 1
4.8%
14.0 1
4.8%
13.07 1
4.8%
12.45 1
4.8%

제방고(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.86619
Minimum14.28
Maximum40.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-05-03T19:55:21.395710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14.28
5-th percentile14.3
Q117.12
median20.09
Q324.63
95-th percentile35.4
Maximum40.02
Range25.74
Interquartile range (IQR)7.51

Descriptive statistics

Standard deviation7.2906971
Coefficient of variation (CV)0.33342329
Kurtosis0.77343969
Mean21.86619
Median Absolute Deviation (MAD)3.77
Skewness1.2385953
Sum459.19
Variance53.154265
MonotonicityNot monotonic
2024-05-03T19:55:21.774738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
21.43 2
 
9.5%
25.17 1
 
4.8%
18.34 1
 
4.8%
35.4 1
 
4.8%
17.55 1
 
4.8%
18.35 1
 
4.8%
14.3 1
 
4.8%
14.36 1
 
4.8%
15.44 1
 
4.8%
16.32 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
14.28 1
4.8%
14.3 1
4.8%
14.36 1
4.8%
15.44 1
4.8%
16.32 1
4.8%
17.12 1
4.8%
17.55 1
4.8%
18.34 1
4.8%
18.35 1
4.8%
19.49 1
4.8%
ValueCountFrequency (%)
40.02 1
4.8%
35.4 1
4.8%
33.19 1
4.8%
30.92 1
4.8%
25.17 1
4.8%
24.63 1
4.8%
21.43 2
9.5%
21.16 1
4.8%
20.2 1
4.8%
20.09 1
4.8%

계획홍수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.410476
Minimum12.76
Maximum39.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-05-03T19:55:22.009893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12.76
5-th percentile13.54
Q116
median17.05
Q323.89
95-th percentile34.52
Maximum39.32
Range26.56
Interquartile range (IQR)7.89

Descriptive statistics

Standard deviation7.4659336
Coefficient of variation (CV)0.36578929
Kurtosis0.98561438
Mean20.410476
Median Absolute Deviation (MAD)2.98
Skewness1.3666751
Sum428.62
Variance55.740165
MonotonicityNot monotonic
2024-05-03T19:55:22.372315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
17.05 3
 
14.3%
18.01 2
 
9.5%
24.25 1
 
4.8%
14.07 1
 
4.8%
16.0 1
 
4.8%
34.52 1
 
4.8%
16.36 1
 
4.8%
16.18 1
 
4.8%
12.76 1
 
4.8%
13.95 1
 
4.8%
Other values (8) 8
38.1%
ValueCountFrequency (%)
12.76 1
 
4.8%
13.54 1
 
4.8%
13.95 1
 
4.8%
14.07 1
 
4.8%
15.3 1
 
4.8%
16.0 1
 
4.8%
16.18 1
 
4.8%
16.36 1
 
4.8%
17.05 3
14.3%
18.01 2
9.5%
ValueCountFrequency (%)
39.32 1
 
4.8%
34.52 1
 
4.8%
32.81 1
 
4.8%
28.43 1
 
4.8%
24.25 1
 
4.8%
23.89 1
 
4.8%
20.12 1
 
4.8%
19.95 1
 
4.8%
18.01 2
9.5%
17.05 3
14.3%

하상고(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.201905
Minimum3.09
Maximum32.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-05-03T19:55:22.715887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.09
5-th percentile4
Q16.7
median10.8
Q315.2
95-th percentile31.05
Maximum32.87
Range29.78
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation9.0602564
Coefficient of variation (CV)0.68628403
Kurtosis0.15383057
Mean13.201905
Median Absolute Deviation (MAD)4.4
Skewness1.0552214
Sum277.24
Variance82.088246
MonotonicityNot monotonic
2024-05-03T19:55:23.043615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
15.2 2
 
9.5%
6.7 1
 
4.8%
5.2 1
 
4.8%
31.05 1
 
4.8%
11.95 1
 
4.8%
9.7 1
 
4.8%
7.59 1
 
4.8%
4.64 1
 
4.8%
3.09 1
 
4.8%
10.8 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
3.09 1
4.8%
4.0 1
4.8%
4.41 1
4.8%
4.64 1
4.8%
5.2 1
4.8%
6.7 1
4.8%
7.21 1
4.8%
7.59 1
4.8%
9.7 1
4.8%
10.76 1
4.8%
ValueCountFrequency (%)
32.87 1
4.8%
31.05 1
4.8%
29.39 1
4.8%
21.44 1
4.8%
20.95 1
4.8%
15.2 2
9.5%
13.17 1
4.8%
11.95 1
4.8%
11.92 1
4.8%
10.8 1
4.8%

통제수위(m)
Categorical

IMBALANCE 

Distinct5
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
0.0
17 
22.67
 
1
15.83
 
1
9.2
 
1
<NA>
 
1

Length

Max length5
Median length3
Mean length3.2380952
Min length3

Unique

Unique4 ?
Unique (%)19.0%

Sample

1st row22.67
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 17
81.0%
22.67 1
 
4.8%
15.83 1
 
4.8%
9.2 1
 
4.8%
<NA> 1
 
4.8%

Length

2024-05-03T19:55:23.464929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T19:55:23.807929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17
81.0%
22.67 1
 
4.8%
15.83 1
 
4.8%
9.2 1
 
4.8%
na 1
 
4.8%

Interactions

2024-05-03T19:55:12.383969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:06.184623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:07.483380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:08.584403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:09.935090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:11.204005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:12.522587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:06.323871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:07.640812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:08.737254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:10.173748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:11.349011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:12.665885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:06.551820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:07.940430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:08.975314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:10.403866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:11.504269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:12.812826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:06.832819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:08.138487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:09.220806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:10.644915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:11.655879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:12.972961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:07.087700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:08.293960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:09.457561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:10.881090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:11.798008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:13.115217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:07.326445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:08.443569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:09.694124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:11.022751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:55:12.018793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T19:55:23.998747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수위계코드수위계명하천명구청코드구청명송신지 자료수집 시각수신서버 저장 시각실시간 하천 수위값(m)제방고(m)계획홍수위(m)하상고(m)통제수위(m)
수위계코드1.0001.0000.8011.0001.0000.7840.7840.0000.5210.6980.0000.000
수위계명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
하천명0.8011.0001.0000.8010.9380.0000.0000.0000.0000.0000.0000.000
구청코드1.0001.0000.8011.0001.0000.7840.7840.0000.5210.6980.0000.000
구청명1.0001.0000.9381.0001.0000.8690.8690.3870.0000.1590.0000.386
송신지 자료수집 시각0.7841.0000.0000.7840.8691.0001.0000.0000.0000.0000.0000.000
수신서버 저장 시각0.7841.0000.0000.7840.8691.0001.0000.0000.0000.0000.0000.000
실시간 하천 수위값(m)0.0001.0000.0000.0000.3870.0000.0001.0000.8040.8320.9500.000
제방고(m)0.5211.0000.0000.5210.0000.0000.0000.8041.0000.9970.8410.000
계획홍수위(m)0.6981.0000.0000.6980.1590.0000.0000.8320.9971.0000.8720.423
하상고(m)0.0001.0000.0000.0000.0000.0000.0000.9500.8410.8721.0000.722
통제수위(m)0.0001.0000.0000.0000.3860.0000.0000.0000.0000.4230.7221.000
2024-05-03T19:55:24.335595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
송신지 자료수집 시각통제수위(m)수신서버 저장 시각
송신지 자료수집 시각1.0000.0001.000
통제수위(m)0.0001.0000.000
수신서버 저장 시각1.0000.0001.000
2024-05-03T19:55:24.712268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수위계코드구청코드실시간 하천 수위값(m)제방고(m)계획홍수위(m)하상고(m)송신지 자료수집 시각수신서버 저장 시각통제수위(m)
수위계코드1.0000.994-0.377-0.430-0.572-0.3050.3740.3740.000
구청코드0.9941.000-0.378-0.439-0.576-0.3080.3740.3740.000
실시간 하천 수위값(m)-0.377-0.3781.0000.6620.8150.9830.0000.0000.000
제방고(m)-0.430-0.4390.6621.0000.9250.6860.0000.0000.000
계획홍수위(m)-0.572-0.5760.8150.9251.0000.7930.0000.0000.241
하상고(m)-0.305-0.3080.9830.6860.7931.0000.0000.0000.446
송신지 자료수집 시각0.3740.3740.0000.0000.0000.0001.0001.0000.000
수신서버 저장 시각0.3740.3740.0000.0000.0000.0001.0001.0000.000
통제수위(m)0.0000.0000.0000.0000.2410.4460.0000.0001.000

Missing values

2024-05-03T19:55:13.406124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T19:55:13.937723image/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

수위계코드수위계명하천명구청코드구청명송신지 자료수집 시각수신서버 저장 시각실시간 하천 수위값(m)제방고(m)계획홍수위(m)하상고(m)통제수위(m)
0101여수대교탄천101강남구2024-05-04 04:46:012024-05-04 04:46:0115.5625.1724.2515.222.67
1102대곡교탄천101강남구2024-05-04 04:46:012024-05-04 04:46:0111.8620.0918.0110.760.0
2103탄천2교탄천101강남구2024-05-04 04:43:012024-05-04 04:43:015.9821.4318.014.410.0
3301모래말옆방학천103도봉구2024-05-04 04:46:012024-05-04 04:46:0121.3124.6323.8920.950.0
4302노원교중랑천103도봉구2024-05-04 04:46:012024-05-04 04:46:0121.7630.9228.4321.440.0
5303계성교우이천103도봉구2024-05-04 04:46:012024-05-04 04:46:0128.9333.1932.8129.390.0
6401장월교우이천104노원구2024-05-04 04:46:012024-05-04 04:46:0115.4420.220.1215.20.0
7402신의교중랑천104노원구2024-05-04 04:46:012024-05-04 04:46:0133.7540.0239.3232.870.0
8403월계1교중랑천104노원구2024-05-04 04:46:012024-05-04 04:46:0114.021.1619.9513.1715.83
9801용두교정릉천108동대문구2024-05-04 04:46:012024-05-04 04:46:0112.4517.1217.0511.920.0
수위계코드수위계명하천명구청코드구청명송신지 자료수집 시각수신서버 저장 시각실시간 하천 수위값(m)제방고(m)계획홍수위(m)하상고(m)통제수위(m)
11902마장2교청계천109성동구2024-05-04 04:46:012024-05-04 04:46:018.318.3417.057.210.0
121401증산교불광천114서대문구2024-05-04 04:46:012024-05-04 04:46:016.7414.2813.546.79.2
131501성산2교홍제천115마포구2024-05-04 04:46:012024-05-04 04:46:0111.6816.3215.310.80.0
142001고척교안양천120구로구2024-05-04 04:45:482024-05-04 04:45:485.6915.4414.073.090.0
152002도림교도림천120구로구2024-05-04 04:45:482024-05-04 04:45:486.314.3613.954.640.0
162003광화교목감천120구로구2024-05-04 04:45:482024-05-04 04:45:487.5814.312.767.590.0
172201기아대교안양천122금천구2024-05-04 04:46:012024-05-04 04:46:0110.1918.3516.189.70.0
182301신대방역도림천123관악구2024-05-04 04:46:012024-05-04 04:46:0113.0717.5516.3611.950.0
192303양산교도림천123관악구2024-05-04 04:46:012024-05-04 04:46:0131.3635.434.5231.05<NA>
202502봉은교탄천125송파구2024-05-04 04:46:202024-05-04 04:46:204.6621.4316.05.20.0