Overview

Dataset statistics

Number of variables14
Number of observations1038
Missing cells1553
Missing cells (%)10.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory119.7 KiB
Average record size in memory118.1 B

Variable types

Text7
Numeric6
Categorical1

Dataset

Description경상남도 하천관리 시스템의 하천기본정보 데이터로, 하천관리코드, 하천코드, 하천명, 전체유역면적, 시점측점번호, 종점측점번호 등에 대한 정보를 제공합니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15093474

Alerts

전체유역면적 is highly overall correlated with 총유로연장 and 1 other fieldsHigh correlation
총유로연장 is highly overall correlated with 전체유역면적 and 1 other fieldsHigh correlation
하천총연장 is highly overall correlated with 지정연월일High correlation
하천기본계획 하천연장 is highly overall correlated with 전체유역면적 and 1 other fieldsHigh correlation
지정연월일 is highly overall correlated with 하천총연장High correlation
지정연월일 is highly imbalanced (86.9%)Imbalance
전체유역면적 has 106 (10.2%) missing valuesMissing
총유로연장 has 141 (13.6%) missing valuesMissing
하천총연장 has 127 (12.2%) missing valuesMissing
하천기본계획 하천연장 has 74 (7.1%) missing valuesMissing
고시번호 has 379 (36.5%) missing valuesMissing
시점명 has 68 (6.6%) missing valuesMissing
종점명 has 59 (5.7%) missing valuesMissing
시점측점번호 has 266 (25.6%) missing valuesMissing
종점측점번호 has 266 (25.6%) missing valuesMissing
기본계획수립년도 has 67 (6.5%) missing valuesMissing
하천기본계획 하천연장 is highly skewed (γ1 = 31.04802648)Skewed
총유로연장 has 14 (1.3%) zerosZeros
하천총연장 has 506 (48.7%) zerosZeros
하천기본계획 하천연장 has 13 (1.3%) zerosZeros

Reproduction

Analysis started2023-12-10 23:53:26.136991
Analysis finished2023-12-10 23:53:31.199971
Duration5.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

하천관리코드
Text

Distinct1037
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
2023-12-11T08:53:31.366203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters19722
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1036 ?
Unique (%)99.8%

Sample

1st row20262602008F99Q9901
2nd row20266402012F03Q0301
3rd row20276202009F02Q0101
4th row24200302013F02Q0101
5th row20270702015F02Q0101
ValueCountFrequency (%)
20254902021f02q0101 2
 
0.2%
20248502009f01q0101 1
 
0.1%
20250901996f01q0101 1
 
0.1%
20252002019f02q0101 1
 
0.1%
20252002008f02q0101 1
 
0.1%
20251902019f01q0101 1
 
0.1%
20251802019f02q0101 1
 
0.1%
20251802010f01q0101 1
 
0.1%
20251702019f02q0101 1
 
0.1%
20251602019f02q0101 1
 
0.1%
Other values (1027) 1027
98.9%
2023-12-11T08:53:31.735291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6795
34.5%
2 3733
18.9%
1 3563
18.1%
F 1038
 
5.3%
Q 1038
 
5.3%
9 778
 
3.9%
7 603
 
3.1%
4 508
 
2.6%
5 480
 
2.4%
6 450
 
2.3%
Other values (2) 736
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17646
89.5%
Uppercase Letter 2076
 
10.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6795
38.5%
2 3733
21.2%
1 3563
20.2%
9 778
 
4.4%
7 603
 
3.4%
4 508
 
2.9%
5 480
 
2.7%
6 450
 
2.6%
3 439
 
2.5%
8 297
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
F 1038
50.0%
Q 1038
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17646
89.5%
Latin 2076
 
10.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6795
38.5%
2 3733
21.2%
1 3563
20.2%
9 778
 
4.4%
7 603
 
3.4%
4 508
 
2.9%
5 480
 
2.7%
6 450
 
2.6%
3 439
 
2.5%
8 297
 
1.7%
Latin
ValueCountFrequency (%)
F 1038
50.0%
Q 1038
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19722
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6795
34.5%
2 3733
18.9%
1 3563
18.1%
F 1038
 
5.3%
Q 1038
 
5.3%
9 778
 
3.9%
7 603
 
3.1%
4 508
 
2.6%
5 480
 
2.4%
6 450
 
2.3%
Other values (2) 736
 
3.7%

하천코드
Real number (ℝ)

Distinct625
Distinct (%)60.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2268424.1
Minimum2012600
Maximum4022800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2023-12-11T08:53:31.888936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2012600
5-th percentile2023070
Q12024432.5
median2026295
Q32720127.5
95-th percentile2721361.5
Maximum4022800
Range2010200
Interquartile range (IQR)695695

Descriptive statistics

Standard deviation421590.63
Coefficient of variation (CV)0.18585177
Kurtosis5.8238049
Mean2268424.1
Median Absolute Deviation (MAD)2340
Skewness2.1896069
Sum2.3546242 × 109
Variance1.7773866 × 1011
MonotonicityNot monotonic
2023-12-11T08:53:32.071734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4022690 7
 
0.7%
2027200 7
 
0.7%
2420050 6
 
0.6%
2520010 6
 
0.6%
2026260 6
 
0.6%
2721000 6
 
0.6%
2026580 5
 
0.5%
2026340 5
 
0.5%
2720370 5
 
0.5%
2027620 5
 
0.5%
Other values (615) 980
94.4%
ValueCountFrequency (%)
2012600 3
0.3%
2012950 2
0.2%
2014040 1
 
0.1%
2014210 1
 
0.1%
2022560 4
0.4%
2022570 3
0.3%
2022590 3
0.3%
2022700 3
0.3%
2022710 2
0.2%
2022720 1
 
0.1%
ValueCountFrequency (%)
4022800 1
0.1%
4022790 2
0.2%
4022780 1
0.1%
4022770 1
0.1%
4022760 1
0.1%
4022750 1
0.1%
4022740 1
0.1%
4022730 1
0.1%
4022720 1
0.1%
4022710 1
0.1%

하천명
Text

Distinct558
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
2023-12-11T08:53:32.402581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length2.9932563
Min length2

Characters and Unicode

Total characters3107
Distinct characters216
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

Unique305 ?
Unique (%)29.4%

Sample

1st row계성천
2nd row운정천
3rd row다방천
4th row주중천
5th row신곡천
ValueCountFrequency (%)
대산천 12
 
1.2%
대곡천 11
 
1.1%
덕곡천 8
 
0.8%
동천 7
 
0.7%
단장천 7
 
0.7%
횡천강 7
 
0.7%
사천강 6
 
0.6%
계성천 6
 
0.6%
조만강 6
 
0.6%
회야강 6
 
0.6%
Other values (548) 962
92.7%
2023-12-11T08:53:32.892560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 1068
34.4%
곡 119
 
3.8%
산 77
 
2.5%
대 64
 
2.1%
양 48
 
1.5%
동 42
 
1.4%
강 39
 
1.3%
전 38
 
1.2%
정 37
 
1.2%
덕 35
 
1.1%
Other values (206) 1540
49.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3098
99.7%
Open Punctuation 3
 
0.1%
Close Punctuation 3
 
0.1%
Decimal Number 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 1068
34.5%
곡 119
 
3.8%
산 77
 
2.5%
대 64
 
2.1%
양 48
 
1.5%
동 42
 
1.4%
강 39
 
1.3%
전 38
 
1.2%
정 37
 
1.2%
덕 35
 
1.1%
Other values (202) 1531
49.4%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
2 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3098
99.7%
Common 9
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 1068
34.5%
곡 119
 
3.8%
산 77
 
2.5%
대 64
 
2.1%
양 48
 
1.5%
동 42
 
1.4%
강 39
 
1.3%
전 38
 
1.2%
정 37
 
1.2%
덕 35
 
1.1%
Other values (202) 1531
49.4%
Common
ValueCountFrequency (%)
( 3
33.3%
) 3
33.3%
1 2
22.2%
2 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3098
99.7%
ASCII 9
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 1068
34.5%
곡 119
 
3.8%
산 77
 
2.5%
대 64
 
2.1%
양 48
 
1.5%
동 42
 
1.4%
강 39
 
1.3%
전 38
 
1.2%
정 37
 
1.2%
덕 35
 
1.1%
Other values (202) 1531
49.4%
ASCII
ValueCountFrequency (%)
( 3
33.3%
) 3
33.3%
1 2
22.2%
2 1
 
11.1%

전체유역면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct679
Distinct (%)72.9%
Missing106
Missing (%)10.2%
Infinite0
Infinite (%)0.0%
Mean28.452865
Minimum0
Maximum500.47
Zeros5
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2023-12-11T08:53:33.021771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.6755
Q15.485
median10.42
Q324.7
95-th percentile124.939
Maximum500.47
Range500.47
Interquartile range (IQR)19.215

Descriptive statistics

Standard deviation53.044324
Coefficient of variation (CV)1.8642876
Kurtosis26.308287
Mean28.452865
Median Absolute Deviation (MAD)6.42
Skewness4.5409113
Sum26518.07
Variance2813.7003
MonotonicityNot monotonic
2023-12-11T08:53:33.153327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.2 5
 
0.5%
0.0 5
 
0.5%
20.2 4
 
0.4%
2.79 4
 
0.4%
24.7 4
 
0.4%
4.0 4
 
0.4%
9.57 4
 
0.4%
32.89 4
 
0.4%
4.34 4
 
0.4%
3.31 4
 
0.4%
Other values (669) 890
85.7%
(Missing) 106
 
10.2%
ValueCountFrequency (%)
0.0 5
0.5%
1.34 1
 
0.1%
1.48 1
 
0.1%
1.62 1
 
0.1%
1.74 1
 
0.1%
1.75 1
 
0.1%
1.79 1
 
0.1%
1.86 1
 
0.1%
2.04 3
0.3%
2.07 1
 
0.1%
ValueCountFrequency (%)
500.47 1
 
0.1%
481.46 1
 
0.1%
424.62 1
 
0.1%
381.11 1
 
0.1%
358.4 2
0.2%
350.13 3
0.3%
275.13 1
 
0.1%
265.05 1
 
0.1%
243.22 1
 
0.1%
238.85 1
 
0.1%

총유로연장
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct505
Distinct (%)56.3%
Missing141
Missing (%)13.6%
Infinite0
Infinite (%)0.0%
Mean8.8716388
Minimum0
Maximum158.43
Zeros14
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2023-12-11T08:53:33.324611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.808
Q14.24
median6
Q39.55
95-th percentile25.424
Maximum158.43
Range158.43
Interquartile range (IQR)5.31

Descriptive statistics

Standard deviation10.264364
Coefficient of variation (CV)1.1569862
Kurtosis78.712282
Mean8.8716388
Median Absolute Deviation (MAD)2.23
Skewness7.1002318
Sum7957.86
Variance105.35717
MonotonicityNot monotonic
2023-12-11T08:53:33.469573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.0 14
 
1.3%
0.0 14
 
1.3%
8.0 11
 
1.1%
3.0 10
 
1.0%
4.5 10
 
1.0%
5.2 10
 
1.0%
7.4 8
 
0.8%
4.0 8
 
0.8%
5.5 7
 
0.7%
9.0 7
 
0.7%
Other values (495) 798
76.9%
(Missing) 141
 
13.6%
ValueCountFrequency (%)
0.0 14
1.3%
0.25 1
 
0.1%
1.2 1
 
0.1%
1.5 1
 
0.1%
1.8 1
 
0.1%
2.2 1
 
0.1%
2.22 1
 
0.1%
2.24 1
 
0.1%
2.25 2
 
0.2%
2.3 1
 
0.1%
ValueCountFrequency (%)
158.43 1
0.1%
118.0 1
0.1%
113.29 1
0.1%
69.8 1
0.1%
69.6 1
0.1%
51.39 1
0.1%
44.94 1
0.1%
43.3 1
0.1%
43.15 2
0.2%
41.69 1
0.1%

하천총연장
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct178
Distinct (%)19.5%
Missing127
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean4.8997366
Minimum0
Maximum510.36
Zeros506
Zeros (%)48.7%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2023-12-11T08:53:33.658413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile16.65
Maximum510.36
Range510.36
Interquartile range (IQR)4

Descriptive statistics

Standard deviation29.729592
Coefficient of variation (CV)6.0675899
Kurtosis274.30586
Mean4.8997366
Median Absolute Deviation (MAD)0
Skewness16.260604
Sum4463.66
Variance883.84863
MonotonicityNot monotonic
2023-12-11T08:53:33.820710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 506
48.7%
2.0 21
 
2.0%
4.0 18
 
1.7%
2.5 15
 
1.4%
3.2 11
 
1.1%
7.0 11
 
1.1%
3.0 10
 
1.0%
4.5 10
 
1.0%
2.2 10
 
1.0%
2.8 9
 
0.9%
Other values (168) 290
27.9%
(Missing) 127
 
12.2%
ValueCountFrequency (%)
0.0 506
48.7%
0.3 1
 
0.1%
0.74 1
 
0.1%
0.9 1
 
0.1%
1.0 1
 
0.1%
1.17 1
 
0.1%
1.3 1
 
0.1%
1.44 2
 
0.2%
1.5 3
 
0.3%
1.53 1
 
0.1%
ValueCountFrequency (%)
510.36 3
0.3%
41.8 1
 
0.1%
40.8 5
0.5%
38.0 1
 
0.1%
34.0 1
 
0.1%
30.0 4
0.4%
28.6 1
 
0.1%
28.0 1
 
0.1%
26.6 3
0.3%
25.61 1
 
0.1%

하천기본계획 하천연장
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct437
Distinct (%)45.3%
Missing74
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean50.198948
Minimum0
Maximum44306
Zeros13
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2023-12-11T08:53:33.972946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.82
Q12.095
median3.2
Q35.1325
95-th percentile10.697
Maximum44306
Range44306
Interquartile range (IQR)3.0375

Descriptive statistics

Standard deviation1426.8685
Coefficient of variation (CV)28.42427
Kurtosis963.98662
Mean50.198948
Median Absolute Deviation (MAD)1.355
Skewness31.048026
Sum48391.786
Variance2035953.6
MonotonicityNot monotonic
2023-12-11T08:53:34.097348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.0 24
 
2.3%
4.0 24
 
2.3%
2.5 22
 
2.1%
3.0 19
 
1.8%
3.2 16
 
1.5%
2.2 14
 
1.3%
0.0 13
 
1.3%
2.8 13
 
1.3%
3.5 12
 
1.2%
5.0 12
 
1.2%
Other values (427) 795
76.6%
(Missing) 74
 
7.1%
ValueCountFrequency (%)
0.0 13
1.3%
0.19 1
 
0.1%
0.2 3
 
0.3%
0.23 1
 
0.1%
0.25 1
 
0.1%
0.26 2
 
0.2%
0.3 2
 
0.2%
0.325 1
 
0.1%
0.36 1
 
0.1%
0.38 1
 
0.1%
ValueCountFrequency (%)
44306.0 1
0.1%
41.8 1
0.1%
35.4 1
0.1%
31.2 1
0.1%
23.0 1
0.1%
19.7 1
0.1%
19.5 2
0.2%
19.2 1
0.1%
19.0 2
0.2%
18.5 1
0.1%

지정연월일
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
<NA>
975 
1982-11-29
 
53
2020-05-14
 
2
2020-05-01
 
2
1966-10-10
 
2
Other values (3)
 
4

Length

Max length10
Median length4
Mean length4.3641618
Min length4

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 975
93.9%
1982-11-29 53
 
5.1%
2020-05-14 2
 
0.2%
2020-05-01 2
 
0.2%
1966-10-10 2
 
0.2%
2021-09-02 2
 
0.2%
1966-04-22 1
 
0.1%
2020-07-16 1
 
0.1%

Length

2023-12-11T08:53:34.226759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:53:34.362582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 975
93.9%
1982-11-29 53
 
5.1%
2020-05-14 2
 
0.2%
2020-05-01 2
 
0.2%
1966-10-10 2
 
0.2%
2021-09-02 2
 
0.2%
1966-04-22 1
 
0.1%
2020-07-16 1
 
0.1%

고시번호
Text

MISSING 

Distinct122
Distinct (%)18.5%
Missing379
Missing (%)36.5%
Memory size8.2 KiB
2023-12-11T08:53:34.589996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length17.694992
Min length6

Characters and Unicode

Total characters11661
Distinct characters24
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

Unique45 ?
Unique (%)6.8%

Sample

1st row경상남도 고시 제2009-400호
2nd row경상남도 고시 제2013-264호
3rd row경상남도 고시 제2015-575호
4th row경상남도 고시 제2008-650호
5th row경상남도 고시 제2011-35호
ValueCountFrequency (%)
고시 627
31.9%
경상남도 610
31.0%
제2010-493호 38
 
1.9%
제2019-299호 36
 
1.8%
제2010-431호 36
 
1.8%
제2008-650호 26
 
1.3%
제2019-295호 26
 
1.3%
제2010-46호 26
 
1.3%
제 26
 
1.3%
제2005-326호 23
 
1.2%
Other values (112) 494
25.1%
2023-12-11T08:53:34.948592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1310
 
11.2%
0 1177
 
10.1%
2 945
 
8.1%
1 664
 
5.7%
경 658
 
5.6%
- 657
 
5.6%
남 656
 
5.6%
도 656
 
5.6%
제 656
 
5.6%
호 655
 
5.6%
Other values (14) 3627
31.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5221
44.8%
Decimal Number 4473
38.4%
Space Separator 1310
 
11.2%
Dash Punctuation 657
 
5.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 658
12.6%
남 656
12.6%
도 656
12.6%
제 656
12.6%
호 655
12.5%
고 653
12.5%
시 653
12.5%
상 626
12.0%
공 3
 
0.1%
북 2
 
< 0.1%
Other values (2) 3
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 1177
26.3%
2 945
21.1%
1 664
14.8%
4 377
 
8.4%
3 307
 
6.9%
5 286
 
6.4%
9 268
 
6.0%
6 224
 
5.0%
8 126
 
2.8%
7 99
 
2.2%
Space Separator
ValueCountFrequency (%)
1310
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 657
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6440
55.2%
Hangul 5221
44.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1310
20.3%
0 1177
18.3%
2 945
14.7%
1 664
10.3%
- 657
10.2%
4 377
 
5.9%
3 307
 
4.8%
5 286
 
4.4%
9 268
 
4.2%
6 224
 
3.5%
Other values (2) 225
 
3.5%
Hangul
ValueCountFrequency (%)
경 658
12.6%
남 656
12.6%
도 656
12.6%
제 656
12.6%
호 655
12.5%
고 653
12.5%
시 653
12.5%
상 626
12.0%
공 3
 
0.1%
북 2
 
< 0.1%
Other values (2) 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6440
55.2%
Hangul 5221
44.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1310
20.3%
0 1177
18.3%
2 945
14.7%
1 664
10.3%
- 657
10.2%
4 377
 
5.9%
3 307
 
4.8%
5 286
 
4.4%
9 268
 
4.2%
6 224
 
3.5%
Other values (2) 225
 
3.5%
Hangul
ValueCountFrequency (%)
경 658
12.6%
남 656
12.6%
도 656
12.6%
제 656
12.6%
호 655
12.5%
고 653
12.5%
시 653
12.5%
상 626
12.0%
공 3
 
0.1%
북 2
 
< 0.1%
Other values (2) 3
 
0.1%

시점명
Text

MISSING 

Distinct924
Distinct (%)95.3%
Missing68
Missing (%)6.6%
Memory size8.2 KiB
2023-12-11T08:53:35.314272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length34
Mean length18.579381
Min length3

Characters and Unicode

Total characters18022
Distinct characters300
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique882 ?
Unique (%)90.9%

Sample

1st row경상남도 창녕군 창녕읍 옥천리
2nd row경상남도 양산시 동면 사송리 246-4
3rd row경남 김해 대동 주중
4th row경상남도 밀양시 상동면 신곡리
5th row경상남도 함안군 산인면 운곡리
ValueCountFrequency (%)
경상남도 715
 
16.7%
경남 140
 
3.3%
진주시 62
 
1.5%
합천군 61
 
1.4%
함양군 54
 
1.3%
고성군 52
 
1.2%
밀양시 51
 
1.2%
창녕군 49
 
1.1%
양산시 48
 
1.1%
합천 48
 
1.1%
Other values (1520) 2991
70.0%
2023-12-11T08:53:35.757056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3306
 
18.3%
남 943
 
5.2%
경 862
 
4.8%
상 807
 
4.5%
리 781
 
4.3%
도 761
 
4.2%
면 677
 
3.8%
군 496
 
2.8%
시 370
 
2.1%
산 327
 
1.8%
Other values (290) 8692
48.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13051
72.4%
Space Separator 3306
 
18.3%
Decimal Number 1203
 
6.7%
Dash Punctuation 154
 
0.9%
Open Punctuation 127
 
0.7%
Close Punctuation 127
 
0.7%
Uppercase Letter 21
 
0.1%
Other Punctuation 16
 
0.1%
Math Symbol 9
 
< 0.1%
Lowercase Letter 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 943
 
7.2%
경 862
 
6.6%
상 807
 
6.2%
리 781
 
6.0%
도 761
 
5.8%
면 677
 
5.2%
군 496
 
3.8%
시 370
 
2.8%
산 327
 
2.5%
지 313
 
2.4%
Other values (268) 6714
51.4%
Decimal Number
ValueCountFrequency (%)
1 280
23.3%
2 147
12.2%
4 118
9.8%
6 105
 
8.7%
3 102
 
8.5%
8 98
 
8.1%
5 98
 
8.1%
7 97
 
8.1%
0 85
 
7.1%
9 73
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
N 13
61.9%
O 6
28.6%
B 1
 
4.8%
X 1
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 14
87.5%
, 2
 
12.5%
Space Separator
ValueCountFrequency (%)
3306
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 154
100.0%
Open Punctuation
ValueCountFrequency (%)
( 127
100.0%
Close Punctuation
ValueCountFrequency (%)
) 127
100.0%
Math Symbol
ValueCountFrequency (%)
+ 9
100.0%
Lowercase Letter
ValueCountFrequency (%)
o 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13051
72.4%
Common 4942
 
27.4%
Latin 29
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 943
 
7.2%
경 862
 
6.6%
상 807
 
6.2%
리 781
 
6.0%
도 761
 
5.8%
면 677
 
5.2%
군 496
 
3.8%
시 370
 
2.8%
산 327
 
2.5%
지 313
 
2.4%
Other values (268) 6714
51.4%
Common
ValueCountFrequency (%)
3306
66.9%
1 280
 
5.7%
- 154
 
3.1%
2 147
 
3.0%
( 127
 
2.6%
) 127
 
2.6%
4 118
 
2.4%
6 105
 
2.1%
3 102
 
2.1%
8 98
 
2.0%
Other values (7) 378
 
7.6%
Latin
ValueCountFrequency (%)
N 13
44.8%
o 8
27.6%
O 6
20.7%
B 1
 
3.4%
X 1
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13049
72.4%
ASCII 4971
 
27.6%
Compat Jamo 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3306
66.5%
1 280
 
5.6%
- 154
 
3.1%
2 147
 
3.0%
( 127
 
2.6%
) 127
 
2.6%
4 118
 
2.4%
6 105
 
2.1%
3 102
 
2.1%
8 98
 
2.0%
Other values (12) 407
 
8.2%
Hangul
ValueCountFrequency (%)
남 943
 
7.2%
경 862
 
6.6%
상 807
 
6.2%
리 781
 
6.0%
도 761
 
5.8%
면 677
 
5.2%
군 496
 
3.8%
시 370
 
2.8%
산 327
 
2.5%
지 313
 
2.4%
Other values (267) 6712
51.4%
Compat Jamo
ValueCountFrequency (%)
ㄴ 2
100.0%

종점명
Text

MISSING 

Distinct898
Distinct (%)91.7%
Missing59
Missing (%)5.7%
Memory size8.2 KiB
2023-12-11T08:53:36.042723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length33
Mean length21.402451
Min length2

Characters and Unicode

Total characters20953
Distinct characters285
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique836 ?
Unique (%)85.4%

Sample

1st row경상남도 창녕군 창녕읍 옥천리
2nd row경상남도 양산시 동면 내송리 762
3rd row경남 김해 대동 서낙동강(국가) 합류점
4th row경상남도 밀양시 상동면 동창천(지방) 합류점
5th row경상남도 함안군 대산면 함안천(국가) 합류점
ValueCountFrequency (%)
경상남도 705
 
15.2%
합류점 359
 
7.7%
경남 138
 
3.0%
진주시 62
 
1.3%
합천군 60
 
1.3%
함양군 52
 
1.1%
창녕군 51
 
1.1%
산청군 50
 
1.1%
밀양시 50
 
1.1%
합천 49
 
1.1%
Other values (1415) 3061
66.0%
2023-12-11T08:53:36.501410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3661
 
17.5%
남 1027
 
4.9%
경 850
 
4.1%
상 779
 
3.7%
도 754
 
3.6%
천 724
 
3.5%
합 718
 
3.4%
면 669
 
3.2%
리 605
 
2.9%
류 596
 
2.8%
Other values (275) 10570
50.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15259
72.8%
Space Separator 3661
 
17.5%
Decimal Number 919
 
4.4%
Open Punctuation 452
 
2.2%
Close Punctuation 452
 
2.2%
Dash Punctuation 135
 
0.6%
Other Punctuation 28
 
0.1%
Uppercase Letter 27
 
0.1%
Lowercase Letter 14
 
0.1%
Math Symbol 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 1027
 
6.7%
경 850
 
5.6%
상 779
 
5.1%
도 754
 
4.9%
천 724
 
4.7%
합 718
 
4.7%
면 669
 
4.4%
리 605
 
4.0%
류 596
 
3.9%
점 589
 
3.9%
Other values (251) 7948
52.1%
Decimal Number
ValueCountFrequency (%)
1 197
21.4%
2 124
13.5%
3 91
9.9%
4 82
8.9%
7 80
8.7%
5 76
 
8.3%
0 70
 
7.6%
9 69
 
7.5%
8 65
 
7.1%
6 65
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
N 18
66.7%
O 7
 
25.9%
B 1
 
3.7%
X 1
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 18
64.3%
, 9
32.1%
\ 1
 
3.6%
Lowercase Letter
ValueCountFrequency (%)
o 12
85.7%
m 2
 
14.3%
Space Separator
ValueCountFrequency (%)
3661
100.0%
Open Punctuation
ValueCountFrequency (%)
( 452
100.0%
Close Punctuation
ValueCountFrequency (%)
) 452
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 135
100.0%
Math Symbol
ValueCountFrequency (%)
+ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15258
72.8%
Common 5653
 
27.0%
Latin 41
 
0.2%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 1027
 
6.7%
경 850
 
5.6%
상 779
 
5.1%
도 754
 
4.9%
천 724
 
4.7%
합 718
 
4.7%
면 669
 
4.4%
리 605
 
4.0%
류 596
 
3.9%
점 589
 
3.9%
Other values (250) 7947
52.1%
Common
ValueCountFrequency (%)
3661
64.8%
( 452
 
8.0%
) 452
 
8.0%
1 197
 
3.5%
- 135
 
2.4%
2 124
 
2.2%
3 91
 
1.6%
4 82
 
1.5%
7 80
 
1.4%
5 76
 
1.3%
Other values (8) 303
 
5.4%
Latin
ValueCountFrequency (%)
N 18
43.9%
o 12
29.3%
O 7
 
17.1%
m 2
 
4.9%
B 1
 
2.4%
X 1
 
2.4%
Han
ValueCountFrequency (%)
海 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15257
72.8%
ASCII 5694
 
27.2%
Compat Jamo 1
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3661
64.3%
( 452
 
7.9%
) 452
 
7.9%
1 197
 
3.5%
- 135
 
2.4%
2 124
 
2.2%
3 91
 
1.6%
4 82
 
1.4%
7 80
 
1.4%
5 76
 
1.3%
Other values (14) 344
 
6.0%
Hangul
ValueCountFrequency (%)
남 1027
 
6.7%
경 850
 
5.6%
상 779
 
5.1%
도 754
 
4.9%
천 724
 
4.7%
합 718
 
4.7%
면 669
 
4.4%
리 605
 
4.0%
류 596
 
3.9%
점 589
 
3.9%
Other values (249) 7946
52.1%
Compat Jamo
ValueCountFrequency (%)
ㄴ 1
100.0%
CJK
ValueCountFrequency (%)
海 1
100.0%

시점측점번호
Text

MISSING 

Distinct430
Distinct (%)55.7%
Missing266
Missing (%)25.6%
Memory size8.2 KiB
2023-12-11T08:53:36.759992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length9.0272021
Min length3

Characters and Unicode

Total characters6969
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique378 ?
Unique (%)49.0%

Sample

1st row0069+0029
2nd row0000+0000
3rd row0043+0173
4th row0053+0053
5th row0000+0000
ValueCountFrequency (%)
0000+0000 235
30.4%
0018+0000 8
 
1.0%
0030+0000 8
 
1.0%
0025+0000 7
 
0.9%
0028+0000 6
 
0.8%
0033+0000 6
 
0.8%
0020+0000 6
 
0.8%
0040+0000 5
 
0.6%
0029+0000 4
 
0.5%
0032+0000 4
 
0.5%
Other values (420) 483
62.6%
2023-12-11T08:53:37.201229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4597
66.0%
+ 770
 
11.0%
2 283
 
4.1%
1 254
 
3.6%
3 211
 
3.0%
5 178
 
2.6%
4 174
 
2.5%
6 134
 
1.9%
7 134
 
1.9%
8 121
 
1.7%
Other values (3) 113
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6187
88.8%
Math Symbol 770
 
11.0%
Other Punctuation 10
 
0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4597
74.3%
2 283
 
4.6%
1 254
 
4.1%
3 211
 
3.4%
5 178
 
2.9%
4 174
 
2.8%
6 134
 
2.2%
7 134
 
2.2%
8 121
 
2.0%
9 101
 
1.6%
Math Symbol
ValueCountFrequency (%)
+ 770
100.0%
Other Punctuation
ValueCountFrequency (%)
. 10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6969
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4597
66.0%
+ 770
 
11.0%
2 283
 
4.1%
1 254
 
3.6%
3 211
 
3.0%
5 178
 
2.6%
4 174
 
2.5%
6 134
 
1.9%
7 134
 
1.9%
8 121
 
1.7%
Other values (3) 113
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6969
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4597
66.0%
+ 770
 
11.0%
2 283
 
4.1%
1 254
 
3.6%
3 211
 
3.0%
5 178
 
2.6%
4 174
 
2.5%
6 134
 
1.9%
7 134
 
1.9%
8 121
 
1.7%
Other values (3) 113
 
1.6%

종점측점번호
Text

MISSING 

Distinct208
Distinct (%)26.9%
Missing266
Missing (%)25.6%
Memory size8.2 KiB
2023-12-11T08:53:37.506990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length8.9339378
Min length1

Characters and Unicode

Total characters6897
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique185 ?
Unique (%)24.0%

Sample

1st row0042+0034
2nd row0000+0000
3rd row0020+0100
4th row0000+0000
5th row0015+0930
ValueCountFrequency (%)
0000+0000 529
68.5%
0020+0000 8
 
1.0%
0030+0000 5
 
0.6%
0033+0000 4
 
0.5%
0005+0000 3
 
0.4%
0010+0000 3
 
0.4%
0080+0000 3
 
0.4%
0002+0250 2
 
0.3%
0006+0000 2
 
0.3%
0031+0000 2
 
0.3%
Other values (198) 211
 
27.3%
2023-12-11T08:53:37.928367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5423
78.6%
+ 755
 
10.9%
1 129
 
1.9%
3 97
 
1.4%
2 88
 
1.3%
4 76
 
1.1%
5 74
 
1.1%
8 69
 
1.0%
9 59
 
0.9%
7 56
 
0.8%
Other values (3) 71
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6125
88.8%
Math Symbol 755
 
10.9%
Dash Punctuation 13
 
0.2%
Other Punctuation 4
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5423
88.5%
1 129
 
2.1%
3 97
 
1.6%
2 88
 
1.4%
4 76
 
1.2%
5 74
 
1.2%
8 69
 
1.1%
9 59
 
1.0%
7 56
 
0.9%
6 54
 
0.9%
Math Symbol
ValueCountFrequency (%)
+ 755
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6897
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5423
78.6%
+ 755
 
10.9%
1 129
 
1.9%
3 97
 
1.4%
2 88
 
1.3%
4 76
 
1.1%
5 74
 
1.1%
8 69
 
1.0%
9 59
 
0.9%
7 56
 
0.8%
Other values (3) 71
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6897
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5423
78.6%
+ 755
 
10.9%
1 129
 
1.9%
3 97
 
1.4%
2 88
 
1.3%
4 76
 
1.1%
5 74
 
1.1%
8 69
 
1.0%
9 59
 
0.9%
7 56
 
0.8%
Other values (3) 71
 
1.0%

기본계획수립년도
Real number (ℝ)

MISSING 

Distinct33
Distinct (%)3.4%
Missing67
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean2009.1761
Minimum1986
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2023-12-11T08:53:38.078983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1986
5-th percentile1994
Q12005
median2010
Q32014
95-th percentile2020
Maximum2023
Range37
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.6841573
Coefficient of variation (CV)0.0038245315
Kurtosis0.34249232
Mean2009.1761
Median Absolute Deviation (MAD)5
Skewness-0.66057843
Sum1950910
Variance59.046274
MonotonicityNot monotonic
2023-12-11T08:53:38.190697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
2010 126
 
12.1%
2019 90
 
8.7%
2008 68
 
6.6%
2005 63
 
6.1%
2013 57
 
5.5%
2004 51
 
4.9%
2011 50
 
4.8%
2012 40
 
3.9%
2014 37
 
3.6%
2006 30
 
2.9%
Other values (23) 359
34.6%
(Missing) 67
 
6.5%
ValueCountFrequency (%)
1986 16
1.5%
1988 2
 
0.2%
1992 1
 
0.1%
1993 10
1.0%
1994 22
2.1%
1995 21
2.0%
1996 15
1.4%
1997 16
1.5%
1998 3
 
0.3%
1999 5
 
0.5%
ValueCountFrequency (%)
2023 1
 
0.1%
2022 15
 
1.4%
2021 28
 
2.7%
2020 12
 
1.2%
2019 90
8.7%
2018 18
 
1.7%
2017 20
 
1.9%
2016 20
 
1.9%
2015 29
 
2.8%
2014 37
3.6%

Interactions

2023-12-11T08:53:29.776393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:26.991265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:27.633711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:28.156307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:28.707972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:29.265417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:29.886542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:27.091924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:27.730095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:28.258465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:28.802425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:29.363722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:29.974603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:27.202415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:27.809070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:28.340723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:28.897304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:29.444209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:30.065808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:27.339844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:27.897674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:28.431059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:29.015398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:29.525443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:30.160030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:27.450198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:27.971681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:28.518308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:29.103693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:29.604462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:30.258593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:27.534679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:28.061046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:28.605355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:29.179271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:53:29.683613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:53:38.287856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
하천코드전체유역면적총유로연장하천총연장하천기본계획 하천연장지정연월일기본계획수립년도
하천코드1.0000.1790.2120.0000.0000.6400.234
전체유역면적0.1791.0000.8590.0000.0000.0000.285
총유로연장0.2120.8591.0000.0000.0000.1600.173
하천총연장0.0000.0000.0001.0000.000NaN0.089
하천기본계획 하천연장0.0000.0000.0000.0001.0000.0000.161
지정연월일0.6400.0000.160NaN0.0001.0000.499
기본계획수립년도0.2340.2850.1730.0890.1610.4991.000
2023-12-11T08:53:38.408196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
하천코드전체유역면적총유로연장하천총연장하천기본계획 하천연장기본계획수립년도지정연월일
하천코드1.000-0.093-0.1440.064-0.149-0.1510.486
전체유역면적-0.0931.0000.8710.1810.533-0.1560.000
총유로연장-0.1440.8711.0000.1420.530-0.1470.083
하천총연장0.0640.1810.1421.0000.1480.3421.000
하천기본계획 하천연장-0.1490.5330.5300.1481.000-0.1710.000
기본계획수립년도-0.151-0.156-0.1470.342-0.1711.0000.355
지정연월일0.4860.0000.0831.0000.0000.3551.000

Missing values

2023-12-11T08:53:30.404791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:53:30.603637image/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.
2023-12-11T08:53:31.081840image/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

하천관리코드하천코드하천명전체유역면적총유로연장하천총연장하천기본계획 하천연장지정연월일고시번호시점명종점명시점측점번호종점측점번호기본계획수립년도
020262602008F99Q99012026260계성천34.849.770.013.29<NA><NA>경상남도 창녕군 창녕읍 옥천리경상남도 창녕군 창녕읍 옥천리0069+00290042+00342008
120266402012F03Q03012026640운정천19.237.50.04.56<NA><NA><NA><NA>0000+00000000+00002012
220276202009F02Q01012027620다방천20.288.986.83.48<NA>경상남도 고시 제2009-400호경상남도 양산시 동면 사송리 246-4경상남도 양산시 동면 내송리 762<NA><NA>2009
324200302013F02Q01012420030주중천8.626.624.04.8<NA>경상남도 고시 제2013-264호경남 김해 대동 주중경남 김해 대동 서낙동강(국가) 합류점<NA><NA>2013
420270702015F02Q01012027070신곡천6.624.38<NA>2.0<NA>경상남도 고시 제2015-575호경상남도 밀양시 상동면 신곡리경상남도 밀양시 상동면 동창천(지방) 합류점<NA><NA>2015
520257102008F02Q01012025710향양천42.0412.640.05.4<NA><NA><NA><NA>0043+01730020+01002008
620261702004F01Q01012026170운곡천10.825.830.05.35<NA><NA>경상남도 함안군 산인면 운곡리경상남도 함안군 대산면 함안천(국가) 합류점0053+00530000+00002004
720272002012F02Q01022027200단장천350.1343.1540.815.97<NA><NA>경남 밀양시 단장면 범도리 1439번지 집앞보경남 밀양시 활성동 458번지 밀양강 합류부0000+00000015+09302012
824201402011F99Q99012420140구산천1.484.40.00.23<NA><NA>경상남도 김해시 부원동경상남도 김해시 부원동0306+00001022+00002011
927201802015F02Q01012720180내동천16.467.565.24.26<NA><NA>경상남도 창원시 의창구 중동BOX4호교경상남도 창원시 의창구 팔용동 창원천 합류점0000+00000000+00002015
하천관리코드하천코드하천명전체유역면적총유로연장하천총연장하천기본계획 하천연장지정연월일고시번호시점명종점명시점측점번호종점측점번호기본계획수립년도
102820254302022F02Q01012025430덕곡천4.223.882.572.571982-11-29경상남도 고시 제2022-595호경상남도 진주시 명석 덕곡 484번지 일원경상남도 진주시 명석 관지 (나불천 합류점)0020+05700000+00002022
102927214302022F02Q01012721430삼화천5.244.371.441.441982-11-29경상남도 고시 제2022-595호경상남도 남해군 삼동 봉화 742-2번지 일원경상남도 남해군 삼동 봉화 화천 합류점0010+04400000+00002022
103020267202022F03Q01012026720수다천6.064.723.21.751982-11-29경남271호경상남도 창녕군 부곡면 수다리 1191경상남도 창녕군 부곡면 수다리 19(청도천합류점)0001+07500000+00002022
103120239402022F03Q01012023940신당천2.272.731.781.751982-11-29경남271호경상남도 창녕군 대합면 신당리 산14경상남도 창녕군 대합면 신당리 987-2(평지천합류점)0000-00200001+07522022
103220258002022F02Q01012025800진성천20.6210.78.278.271982-11-29경상남도 고시 제2022-595호경상남도 진주시 일반성 남산 1264번지 일원경상남도 진주시 사봉 무촌 반성천 합류점0080+02670000+00002022
103325200502021F03Q01012520050소주천2.363.091.681.722021-09-02경상남도 고시 제2021-465호양산시 소주동양산시 소주동 회야강 합류점0001+06800000-00412021
103425200102021F03Q01012520010회야강66.3115.9510.610.822021-09-02경상남도 고시 제2021-465호양산시 평산동 장홍저수지양산시 용당동 회야교0011+01400000+00002021
103524200502023F02Q01012420050조만강133.9918.5716.36.51982-11-29경상남도 고시 제2023-25호경상남도 김해시 무계동 426-18(No.79)경상남도 김해시 화목동 15-272(No.14)0790+00000140+00002023
103620268102022F02Q01012026810진례천15.428.435.3244306.01982-11-29경상남도 고시 제2022-599호경남 김해시 진례면 산본리 1025-1경남 김해시 진례면초전리 7 화포천(지방) 합류점0050+00320000+00002022
103720247302021F02Q01012024730대곡천12.858.255.45.41982-11-29경상남도 고시 제2021-663호경상남도 합천군 쌍백면 대곡리 336경상남도 합천군 쌍백면 평구리 대현천(지방) 합류점0005+04000000-00432021