Overview

Dataset statistics

Number of variables9
Number of observations90
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.0 KiB
Average record size in memory79.5 B

Variable types

Numeric6
Text3

Dataset

Description경상남도 김해시 소하천 현황에 대한 데이터로 소하천명,기점,종점,지정연장,위도,경도 등의 항목을 제공합니다.
Author경상남도 김해시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15092325

Alerts

순번 is highly overall correlated with 경도(기점) and 1 other fieldsHigh correlation
위도(기점) is highly overall correlated with 위도(종점)High correlation
경도(기점) is highly overall correlated with 순번 and 1 other fieldsHigh correlation
위도(종점) is highly overall correlated with 위도(기점)High correlation
경도(종점) is highly overall correlated with 순번 and 1 other fieldsHigh correlation
순번 has unique valuesUnique
소하천명 has unique valuesUnique
기점 has unique valuesUnique
종점 has unique valuesUnique
위도(기점) has unique valuesUnique
경도(기점) has unique valuesUnique
위도(종점) has unique valuesUnique
경도(종점) has unique valuesUnique

Reproduction

Analysis started2023-12-10 22:46:03.756541
Analysis finished2023-12-10 22:46:07.677053
Duration3.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct90
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.5
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-11T07:46:07.756476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.45
Q123.25
median45.5
Q367.75
95-th percentile85.55
Maximum90
Range89
Interquartile range (IQR)44.5

Descriptive statistics

Standard deviation26.124701
Coefficient of variation (CV)0.57416925
Kurtosis-1.2
Mean45.5
Median Absolute Deviation (MAD)22.5
Skewness0
Sum4095
Variance682.5
MonotonicityStrictly increasing
2023-12-11T07:46:07.895663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.1%
69 1
 
1.1%
67 1
 
1.1%
66 1
 
1.1%
65 1
 
1.1%
64 1
 
1.1%
63 1
 
1.1%
62 1
 
1.1%
61 1
 
1.1%
60 1
 
1.1%
Other values (80) 80
88.9%
ValueCountFrequency (%)
1 1
1.1%
2 1
1.1%
3 1
1.1%
4 1
1.1%
5 1
1.1%
6 1
1.1%
7 1
1.1%
8 1
1.1%
9 1
1.1%
10 1
1.1%
ValueCountFrequency (%)
90 1
1.1%
89 1
1.1%
88 1
1.1%
87 1
1.1%
86 1
1.1%
85 1
1.1%
84 1
1.1%
83 1
1.1%
82 1
1.1%
81 1
1.1%

소하천명
Text

UNIQUE 

Distinct90
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size852.0 B
2023-12-11T07:46:08.176568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.3222222
Min length2

Characters and Unicode

Total characters299
Distinct characters86
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

Unique90 ?
Unique (%)100.0%

Sample

1st row안평천
2nd row우동천
3rd row오척천
4th row방동천
5th row부곡천
ValueCountFrequency (%)
안평천 1
 
1.1%
신곡천 1
 
1.1%
다시골천 1
 
1.1%
후포천 1
 
1.1%
백운동천 1
 
1.1%
학운동천 1
 
1.1%
용성천 1
 
1.1%
안양천 1
 
1.1%
신안천 1
 
1.1%
선곡천 1
 
1.1%
Other values (80) 80
88.9%
2023-12-11T07:46:08.588855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 91
30.4%
동 10
 
3.3%
곡 9
 
3.0%
안 8
 
2.7%
1 7
 
2.3%
전 7
 
2.3%
용 6
 
2.0%
내 6
 
2.0%
2 6
 
2.0%
촌 5
 
1.7%
Other values (76) 144
48.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 285
95.3%
Decimal Number 14
 
4.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 91
31.9%
동 10
 
3.5%
곡 9
 
3.2%
안 8
 
2.8%
전 7
 
2.5%
용 6
 
2.1%
내 6
 
2.1%
촌 5
 
1.8%
계 5
 
1.8%
사 4
 
1.4%
Other values (73) 134
47.0%
Decimal Number
ValueCountFrequency (%)
1 7
50.0%
2 6
42.9%
3 1
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 285
95.3%
Common 14
 
4.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 91
31.9%
동 10
 
3.5%
곡 9
 
3.2%
안 8
 
2.8%
전 7
 
2.5%
용 6
 
2.1%
내 6
 
2.1%
촌 5
 
1.8%
계 5
 
1.8%
사 4
 
1.4%
Other values (73) 134
47.0%
Common
ValueCountFrequency (%)
1 7
50.0%
2 6
42.9%
3 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 285
95.3%
ASCII 14
 
4.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 91
31.9%
동 10
 
3.5%
곡 9
 
3.2%
안 8
 
2.8%
전 7
 
2.5%
용 6
 
2.1%
내 6
 
2.1%
촌 5
 
1.8%
계 5
 
1.8%
사 4
 
1.4%
Other values (73) 134
47.0%
ASCII
ValueCountFrequency (%)
1 7
50.0%
2 6
42.9%
3 1
 
7.1%

기점
Text

UNIQUE 

Distinct90
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size852.0 B
2023-12-11T07:46:08.932362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length21.1
Min length18

Characters and Unicode

Total characters1899
Distinct characters83
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

Unique90 ?
Unique (%)100.0%

Sample

1st row경상남도 김해시 진영읍 의전리 445-1
2nd row경상남도 김해시 진영읍 우동리 106-4
3rd row경상남도 김해시 진영읍 하계리 682-5
4th row경상남도 김해시 진영읍 하계리 120-2
5th row경상남도 김해시 진영읍 진영리 409-1
ValueCountFrequency (%)
경상남도 90
20.0%
김해시 90
20.0%
상동면 18
 
4.0%
생림면 17
 
3.8%
진례면 15
 
3.3%
한림면 12
 
2.7%
주촌면 8
 
1.8%
진영읍 8
 
1.8%
여차리 5
 
1.1%
안곡리 4
 
0.9%
Other values (141) 184
40.8%
2023-12-11T07:46:09.384478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
361
19.0%
상 108
 
5.7%
시 91
 
4.8%
경 90
 
4.7%
남 90
 
4.7%
도 90
 
4.7%
김 90
 
4.7%
해 90
 
4.7%
리 81
 
4.3%
면 73
 
3.8%
Other values (73) 735
38.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1167
61.5%
Space Separator 361
 
19.0%
Decimal Number 329
 
17.3%
Dash Punctuation 42
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 108
 
9.3%
시 91
 
7.8%
경 90
 
7.7%
남 90
 
7.7%
도 90
 
7.7%
김 90
 
7.7%
해 90
 
7.7%
리 81
 
6.9%
면 73
 
6.3%
동 43
 
3.7%
Other values (61) 321
27.5%
Decimal Number
ValueCountFrequency (%)
1 64
19.5%
2 43
13.1%
5 41
12.5%
3 33
10.0%
0 30
9.1%
6 29
8.8%
4 27
8.2%
7 25
 
7.6%
9 23
 
7.0%
8 14
 
4.3%
Space Separator
ValueCountFrequency (%)
361
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1167
61.5%
Common 732
38.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 108
 
9.3%
시 91
 
7.8%
경 90
 
7.7%
남 90
 
7.7%
도 90
 
7.7%
김 90
 
7.7%
해 90
 
7.7%
리 81
 
6.9%
면 73
 
6.3%
동 43
 
3.7%
Other values (61) 321
27.5%
Common
ValueCountFrequency (%)
361
49.3%
1 64
 
8.7%
2 43
 
5.9%
- 42
 
5.7%
5 41
 
5.6%
3 33
 
4.5%
0 30
 
4.1%
6 29
 
4.0%
4 27
 
3.7%
7 25
 
3.4%
Other values (2) 37
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1167
61.5%
ASCII 732
38.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
361
49.3%
1 64
 
8.7%
2 43
 
5.9%
- 42
 
5.7%
5 41
 
5.6%
3 33
 
4.5%
0 30
 
4.1%
6 29
 
4.0%
4 27
 
3.7%
7 25
 
3.4%
Other values (2) 37
 
5.1%
Hangul
ValueCountFrequency (%)
상 108
 
9.3%
시 91
 
7.8%
경 90
 
7.7%
남 90
 
7.7%
도 90
 
7.7%
김 90
 
7.7%
해 90
 
7.7%
리 81
 
6.9%
면 73
 
6.3%
동 43
 
3.7%
Other values (61) 321
27.5%

종점
Text

UNIQUE 

Distinct90
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size852.0 B
2023-12-11T07:46:09.714416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length24
Mean length21.444444
Min length16

Characters and Unicode

Total characters1930
Distinct characters87
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

Unique90 ?
Unique (%)100.0%

Sample

1st row경상남도 김해시 진영읍 의전리 174-4
2nd row경상남도 김해시 진영읍 우동리 315
3rd row경상남도 김해시 진영읍 우동리 814-3
4th row경상남도 김해시 진영읍 방동리 341-13
5th row경상남도 김해시 진영읍 진영리 642-4
ValueCountFrequency (%)
경상남도 90
20.0%
김해시 89
19.7%
생림면 17
 
3.8%
상동면 17
 
3.8%
진례면 15
 
3.3%
한림면 12
 
2.7%
진영읍 8
 
1.8%
주촌면 7
 
1.6%
여차리 5
 
1.1%
봉림리 4
 
0.9%
Other values (143) 187
41.5%
2023-12-11T07:46:10.160744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
364
18.9%
상 108
 
5.6%
경 90
 
4.7%
남 90
 
4.7%
도 90
 
4.7%
김 90
 
4.7%
해 90
 
4.7%
시 90
 
4.7%
1 80
 
4.1%
리 80
 
4.1%
Other values (77) 758
39.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1164
60.3%
Space Separator 364
 
18.9%
Decimal Number 346
 
17.9%
Dash Punctuation 56
 
2.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 108
 
9.3%
경 90
 
7.7%
남 90
 
7.7%
도 90
 
7.7%
김 90
 
7.7%
해 90
 
7.7%
시 90
 
7.7%
리 80
 
6.9%
면 72
 
6.2%
동 46
 
4.0%
Other values (65) 318
27.3%
Decimal Number
ValueCountFrequency (%)
1 80
23.1%
4 41
11.8%
3 40
11.6%
2 38
11.0%
5 33
9.5%
6 29
 
8.4%
7 26
 
7.5%
0 23
 
6.6%
8 22
 
6.4%
9 14
 
4.0%
Space Separator
ValueCountFrequency (%)
364
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1164
60.3%
Common 766
39.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 108
 
9.3%
경 90
 
7.7%
남 90
 
7.7%
도 90
 
7.7%
김 90
 
7.7%
해 90
 
7.7%
시 90
 
7.7%
리 80
 
6.9%
면 72
 
6.2%
동 46
 
4.0%
Other values (65) 318
27.3%
Common
ValueCountFrequency (%)
364
47.5%
1 80
 
10.4%
- 56
 
7.3%
4 41
 
5.4%
3 40
 
5.2%
2 38
 
5.0%
5 33
 
4.3%
6 29
 
3.8%
7 26
 
3.4%
0 23
 
3.0%
Other values (2) 36
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1164
60.3%
ASCII 766
39.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
364
47.5%
1 80
 
10.4%
- 56
 
7.3%
4 41
 
5.4%
3 40
 
5.2%
2 38
 
5.0%
5 33
 
4.3%
6 29
 
3.8%
7 26
 
3.4%
0 23
 
3.0%
Other values (2) 36
 
4.7%
Hangul
ValueCountFrequency (%)
상 108
 
9.3%
경 90
 
7.7%
남 90
 
7.7%
도 90
 
7.7%
김 90
 
7.7%
해 90
 
7.7%
시 90
 
7.7%
리 80
 
6.9%
면 72
 
6.2%
동 46
 
4.0%
Other values (65) 318
27.3%

지정연장(km)
Real number (ℝ)

Distinct68
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0915556
Minimum0.35
Maximum2.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-11T07:46:10.504534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.35
5-th percentile0.4545
Q10.6225
median0.955
Q31.45
95-th percentile2.205
Maximum2.8
Range2.45
Interquartile range (IQR)0.8275

Descriptive statistics

Standard deviation0.57246345
Coefficient of variation (CV)0.52444738
Kurtosis0.2150943
Mean1.0915556
Median Absolute Deviation (MAD)0.355
Skewness0.93971238
Sum98.24
Variance0.32771441
MonotonicityNot monotonic
2023-12-11T07:46:10.616864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.85 5
 
5.6%
0.65 3
 
3.3%
0.55 3
 
3.3%
1.01 3
 
3.3%
0.48 2
 
2.2%
1.03 2
 
2.2%
0.63 2
 
2.2%
0.78 2
 
2.2%
0.5 2
 
2.2%
0.52 2
 
2.2%
Other values (58) 64
71.1%
ValueCountFrequency (%)
0.35 1
1.1%
0.4 2
2.2%
0.42 1
1.1%
0.45 1
1.1%
0.46 1
1.1%
0.47 1
1.1%
0.48 2
2.2%
0.49 1
1.1%
0.5 2
2.2%
0.52 2
2.2%
ValueCountFrequency (%)
2.8 1
1.1%
2.62 1
1.1%
2.35 1
1.1%
2.31 1
1.1%
2.25 1
1.1%
2.15 2
2.2%
2.03 1
1.1%
1.85 1
1.1%
1.82 2
2.2%
1.81 1
1.1%

위도(기점)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct90
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.285857
Minimum35.166082
Maximum35.374154
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-11T07:46:10.728906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.166082
5-th percentile35.198731
Q135.252863
median35.287429
Q335.321787
95-th percentile35.358949
Maximum35.374154
Range0.20807145
Interquartile range (IQR)0.068923727

Descriptive statistics

Standard deviation0.049104623
Coefficient of variation (CV)0.0013916234
Kurtosis-0.3842098
Mean35.285857
Median Absolute Deviation (MAD)0.034658725
Skewness-0.33313948
Sum3175.7271
Variance0.002411264
MonotonicityNot monotonic
2023-12-11T07:46:10.855340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.27108467 1
 
1.1%
35.30689567 1
 
1.1%
35.34013582 1
 
1.1%
35.34517224 1
 
1.1%
35.35358693 1
 
1.1%
35.35740745 1
 
1.1%
35.34769132 1
 
1.1%
35.34417567 1
 
1.1%
35.33082917 1
 
1.1%
35.3741539 1
 
1.1%
Other values (80) 80
88.9%
ValueCountFrequency (%)
35.16608245 1
1.1%
35.16971692 1
1.1%
35.17550234 1
1.1%
35.18240193 1
1.1%
35.19612085 1
1.1%
35.20192036 1
1.1%
35.2192625 1
1.1%
35.22142565 1
1.1%
35.22160441 1
1.1%
35.22184678 1
1.1%
ValueCountFrequency (%)
35.3741539 1
1.1%
35.36910464 1
1.1%
35.36373674 1
1.1%
35.3627285 1
1.1%
35.36021063 1
1.1%
35.35740745 1
1.1%
35.35451344 1
1.1%
35.35358693 1
1.1%
35.35290755 1
1.1%
35.34769132 1
1.1%

경도(기점)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct90
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.83586
Minimum128.72058
Maximum128.97748
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-11T07:46:10.969968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.72058
5-th percentile128.73444
Q1128.78731
median128.84436
Q3128.88257
95-th percentile128.93503
Maximum128.97748
Range0.2568902
Interquartile range (IQR)0.095267375

Descriptive statistics

Standard deviation0.063688656
Coefficient of variation (CV)0.00049433951
Kurtosis-0.73676311
Mean128.83586
Median Absolute Deviation (MAD)0.0433798
Skewness0.029488337
Sum11595.228
Variance0.0040562449
MonotonicityNot monotonic
2023-12-11T07:46:11.082623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.7524907 1
 
1.1%
128.887332 1
 
1.1%
128.9231646 1
 
1.1%
128.9152185 1
 
1.1%
128.9038995 1
 
1.1%
128.8856808 1
 
1.1%
128.8903661 1
 
1.1%
128.8881495 1
 
1.1%
128.8979376 1
 
1.1%
128.8495008 1
 
1.1%
Other values (80) 80
88.9%
ValueCountFrequency (%)
128.7205849 1
1.1%
128.7219176 1
1.1%
128.7327226 1
1.1%
128.7332837 1
1.1%
128.7340155 1
1.1%
128.7349532 1
1.1%
128.7353296 1
1.1%
128.7391001 1
1.1%
128.7397567 1
1.1%
128.7429905 1
1.1%
ValueCountFrequency (%)
128.9774751 1
1.1%
128.9604093 1
1.1%
128.9522408 1
1.1%
128.9457417 1
1.1%
128.9353214 1
1.1%
128.9346804 1
1.1%
128.934009 1
1.1%
128.9332315 1
1.1%
128.9269798 1
1.1%
128.9231646 1
1.1%

위도(종점)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct90
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.286169
Minimum35.173122
Maximum35.378822
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-11T07:46:11.205773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.173122
5-th percentile35.195321
Q135.254455
median35.290977
Q335.31858
95-th percentile35.362686
Maximum35.378822
Range0.20569998
Interquartile range (IQR)0.064125087

Descriptive statistics

Standard deviation0.049471379
Coefficient of variation (CV)0.0014020048
Kurtosis-0.37622271
Mean35.286169
Median Absolute Deviation (MAD)0.033916965
Skewness-0.32935433
Sum3175.7552
Variance0.0024474173
MonotonicityNot monotonic
2023-12-11T07:46:11.312955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.26730649 1
 
1.1%
35.30450184 1
 
1.1%
35.3457893 1
 
1.1%
35.35107904 1
 
1.1%
35.36239251 1
 
1.1%
35.35783478 1
 
1.1%
35.34698826 1
 
1.1%
35.34271801 1
 
1.1%
35.33687974 1
 
1.1%
35.37039399 1
 
1.1%
Other values (80) 80
88.9%
ValueCountFrequency (%)
35.17312224 1
1.1%
35.17496557 1
1.1%
35.17600289 1
1.1%
35.1776757 1
1.1%
35.19403709 1
1.1%
35.1968893 1
1.1%
35.20038258 1
1.1%
35.21454792 1
1.1%
35.22438924 1
1.1%
35.22524173 1
1.1%
ValueCountFrequency (%)
35.37882222 1
1.1%
35.37039399 1
1.1%
35.36791673 1
1.1%
35.36369855 1
1.1%
35.36292622 1
1.1%
35.36239251 1
1.1%
35.35867942 1
1.1%
35.35783478 1
1.1%
35.35321543 1
1.1%
35.35107904 1
1.1%

경도(종점)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct90
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.83644
Minimum128.7135
Maximum128.98788
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-11T07:46:11.413606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.7135
5-th percentile128.7471
Q1128.77714
median128.83334
Q3128.88484
95-th percentile128.94501
Maximum128.98788
Range0.2743814
Interquartile range (IQR)0.10770202

Descriptive statistics

Standard deviation0.064774108
Coefficient of variation (CV)0.00050276234
Kurtosis-0.65794572
Mean128.83644
Median Absolute Deviation (MAD)0.05718935
Skewness0.1168495
Sum11595.279
Variance0.004195685
MonotonicityNot monotonic
2023-12-11T07:46:11.530287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.7537213 1
 
1.1%
128.9024815 1
 
1.1%
128.9274966 1
 
1.1%
128.9218944 1
 
1.1%
128.899868 1
 
1.1%
128.8950866 1
 
1.1%
128.8954935 1
 
1.1%
128.8943214 1
 
1.1%
128.8932577 1
 
1.1%
128.8485943 1
 
1.1%
Other values (80) 80
88.9%
ValueCountFrequency (%)
128.7135003 1
1.1%
128.7142355 1
1.1%
128.7158895 1
1.1%
128.719761 1
1.1%
128.7464794 1
1.1%
128.747861 1
1.1%
128.7483207 1
1.1%
128.7509966 1
1.1%
128.753309 1
1.1%
128.7537213 1
1.1%
ValueCountFrequency (%)
128.9878817 1
1.1%
128.9563071 1
1.1%
128.9549178 1
1.1%
128.9486039 1
1.1%
128.9452864 1
1.1%
128.9446705 1
1.1%
128.9372872 1
1.1%
128.9317302 1
1.1%
128.9292907 1
1.1%
128.9290027 1
1.1%

Interactions

2023-12-11T07:46:06.909949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:04.152074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:04.534414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:04.995684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:05.895321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:06.422360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:07.003850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:04.212918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:04.611891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:05.145326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:05.984833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:06.509506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:07.078101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:04.276161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:04.691114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:05.303802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:06.057789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:06.597128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:07.149966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:04.337828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:04.764548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:05.455296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:06.127353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:06.677607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:07.238312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:04.402084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:04.835193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:05.626444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:06.205186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:06.755158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:07.322201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:04.463796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:04.901215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:05.763328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:06.316002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:06.818853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:46:11.614609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번소하천명기점종점지정연장(km)위도(기점)경도(기점)위도(종점)경도(종점)
순번1.0001.0001.0001.0000.2680.8750.9220.8740.896
소하천명1.0001.0001.0001.0001.0001.0001.0001.0001.000
기점1.0001.0001.0001.0001.0001.0001.0001.0001.000
종점1.0001.0001.0001.0001.0001.0001.0001.0001.000
지정연장(km)0.2681.0001.0001.0001.0000.2570.3820.0000.442
위도(기점)0.8751.0001.0001.0000.2571.0000.7020.9870.520
경도(기점)0.9221.0001.0001.0000.3820.7021.0000.6720.963
위도(종점)0.8741.0001.0001.0000.0000.9870.6721.0000.579
경도(종점)0.8961.0001.0001.0000.4420.5200.9630.5791.000
2023-12-11T07:46:11.711956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번지정연장(km)위도(기점)경도(기점)위도(종점)경도(종점)
순번1.000-0.0590.1990.7500.1960.740
지정연장(km)-0.0591.000-0.315-0.195-0.302-0.178
위도(기점)0.199-0.3151.0000.4570.9920.440
경도(기점)0.750-0.1950.4571.0000.4420.988
위도(종점)0.196-0.3020.9920.4421.0000.422
경도(종점)0.740-0.1780.4400.9880.4221.000

Missing values

2023-12-11T07:46:07.461027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:46:07.623932image/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

순번소하천명기점종점지정연장(km)위도(기점)경도(기점)위도(종점)경도(종점)
01안평천경상남도 김해시 진영읍 의전리 445-1경상남도 김해시 진영읍 의전리 174-40.4835.271085128.75249135.267306128.753721
12우동천경상남도 김해시 진영읍 우동리 106-4경상남도 김해시 진영읍 우동리 3151.2635.271012128.72191835.281171128.719761
23오척천경상남도 김해시 진영읍 하계리 682-5경상남도 김해시 진영읍 우동리 814-32.3135.27541128.7353335.282219128.714236
34방동천경상남도 김해시 진영읍 하계리 120-2경상남도 김해시 진영읍 방동리 341-132.1535.286859128.73328435.284926128.7135
45부곡천경상남도 김해시 진영읍 진영리 409-1경상남도 김해시 진영읍 진영리 642-41.8135.297161128.72058535.309818128.71589
56내룡1천경상남도 김해시 진영읍 내룡리 626경상남도 김해시 진영읍 내룡리 471-50.4535.284025128.74601535.286677128.748321
67내룡2천경상남도 김해시 진영읍 내룡리 659경상남도 김해시 진영읍 내룡리 471-200.5535.28585128.7429935.286588128.747861
78경계천경상남도 김해시 진영읍 신용리 700-74경상남도 김해시 진영읍 신용리 700-141.6235.302695128.75602935.305522128.772052
89양동천경상남도 김해시 주촌면 양동리 14경상남도 김해시 장유1동 유하동 468-12.835.234384128.79546135.194037128.821904
910윗내삼천경상남도 김해시 주촌면 내삼리 176경상남도 김해시 주촌면 내삼리 3090.5735.242396128.80586835.241876128.811189
순번소하천명기점종점지정연장(km)위도(기점)경도(기점)위도(종점)경도(종점)
8081수안천경상남도 김해시 대동면 수안리 102경상남도 김해시 대동면 수안리 275-61.0335.234757128.9346835.227127128.937287
8182구산천경상남도 김해시 부원동 920-2경상남도 김해시 부원동 3411.0935.223549128.8847235.214548128.887801
8283곡내천경상남도 김해시 북부동 삼계동 1390경상남도 김해시 북부동 삼계동 1113-100.4935.271436128.85528635.270589128.860286
8384영운천경상남도 김해시 삼안동 삼방동 1061-2경상남도 김해시 삼안동 삼방동 8121.335.267193128.89529235.259131128.90168
8485무계1천경상남도 김해시 장유1동 무계동 552-1경상남도 김해시 장유1동 신문동 12390.6535.196121128.8061735.196889128.812996
8586무계2천경상남도 김해시 장유1동 무계동 200경상남도 김해시 장유1동 무계동 140.8635.20192128.81648835.200383128.823812
8687구관동천경상남도 김해시 장유3동 율하동 332경상남도 김해시 장유3동 율하동 1325-41.135.169717128.80572535.174966128.815242
8788신리천경상남도 김해시 장유3동 율하동 1099경상남도 김해시 장유3동 율하동 1372-51.535.166082128.81203835.176003128.821119
8889뜰천경상남도 김해시 장유3동 장유동 655-5경상남도 김해시 장유3동 응달동 1621.6135.175502128.84631835.177676128.829464
8990장유천경상남도 김해시 장유3동 장유동 161경상남도 김해시 장유3동 장유동 350.935.182402128.85128635.173122128.82704