Overview

Dataset statistics

Number of variables6
Number of observations251
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.9 KiB
Average record size in memory52.5 B

Variable types

Numeric4
Text2

Dataset

Description가뭄지수, 결과 등을 통하여 분석한 SGI 관측소 시설 제원정보에 대한 데이터 항목을 제공합니다.(항목 : 관측소코드, 관측소명, 행정동코드, 행정동명 등)
Author한국수자원공사
URLhttps://www.data.go.kr/data/15049848/fileData.do

Alerts

관측소코드 has unique valuesUnique
관측소명 has unique valuesUnique
행정구역코드 has unique valuesUnique
행정구역명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 05:08:17.404532
Analysis finished2023-12-12 05:08:20.026204
Duration2.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관측소코드
Real number (ℝ)

UNIQUE 

Distinct251
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57682.717
Minimum241
Maximum95537
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-12T14:08:20.122359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum241
5-th percentile9857
Q111773
median65597
Q384008.5
95-th percentile95517.5
Maximum95537
Range95296
Interquartile range (IQR)72235.5

Descriptive statistics

Standard deviation32243.859
Coefficient of variation (CV)0.55898648
Kurtosis-1.2388514
Mean57682.717
Median Absolute Deviation (MAD)18433
Skewness-0.66559698
Sum14478362
Variance1.0396665 × 109
MonotonicityNot monotonic
2023-12-12T14:08:20.329805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65586 1
 
0.4%
87268 1
 
0.4%
65594 1
 
0.4%
87267 1
 
0.4%
9881 1
 
0.4%
73547 1
 
0.4%
73548 1
 
0.4%
9250 1
 
0.4%
9883 1
 
0.4%
65041 1
 
0.4%
Other values (241) 241
96.0%
ValueCountFrequency (%)
241 1
0.4%
442 1
0.4%
2224 1
0.4%
4084 1
0.4%
4854 1
0.4%
5723 1
0.4%
6721 1
0.4%
6752 1
0.4%
7850 1
0.4%
8936 1
0.4%
ValueCountFrequency (%)
95537 1
0.4%
95536 1
0.4%
95534 1
0.4%
95533 1
0.4%
95531 1
0.4%
95528 1
0.4%
95527 1
0.4%
95526 1
0.4%
95524 1
0.4%
95523 1
0.4%

관측소명
Text

UNIQUE 

Distinct251
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-12T14:08:20.792633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.0517928
Min length4

Characters and Unicode

Total characters1017
Distinct characters190
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

Unique251 ?
Unique (%)100.0%

Sample

1st row군포당정
2nd row김포양촌
3rd row남양주별내
4th row동두천상패
5th row강릉연곡
ValueCountFrequency (%)
군포당정 1
 
0.4%
하동화개 1
 
0.4%
원주명륜 1
 
0.4%
세종부강 1
 
0.4%
세종조치원 1
 
0.4%
의령낙서 1
 
0.4%
의령봉수 1
 
0.4%
의령의령 1
 
0.4%
진주일반성 1
 
0.4%
진주초전 1
 
0.4%
Other values (241) 241
96.0%
2023-12-12T14:08:21.451100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 52
 
5.1%
천 42
 
4.1%
주 38
 
3.7%
성 30
 
2.9%
양 28
 
2.8%
평 24
 
2.4%
안 20
 
2.0%
진 19
 
1.9%
화 18
 
1.8%
서 17
 
1.7%
Other values (180) 729
71.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1016
99.9%
Connector Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 52
 
5.1%
천 42
 
4.1%
주 38
 
3.7%
성 30
 
3.0%
양 28
 
2.8%
평 24
 
2.4%
안 20
 
2.0%
진 19
 
1.9%
화 18
 
1.8%
서 17
 
1.7%
Other values (179) 728
71.7%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1016
99.9%
Common 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 52
 
5.1%
천 42
 
4.1%
주 38
 
3.7%
성 30
 
3.0%
양 28
 
2.8%
평 24
 
2.4%
안 20
 
2.0%
진 19
 
1.9%
화 18
 
1.8%
서 17
 
1.7%
Other values (179) 728
71.7%
Common
ValueCountFrequency (%)
_ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1016
99.9%
ASCII 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 52
 
5.1%
천 42
 
4.1%
주 38
 
3.7%
성 30
 
3.0%
양 28
 
2.8%
평 24
 
2.4%
안 20
 
2.0%
진 19
 
1.9%
화 18
 
1.8%
서 17
 
1.7%
Other values (179) 728
71.7%
ASCII
ValueCountFrequency (%)
_ 1
100.0%

행정구역코드
Real number (ℝ)

UNIQUE 

Distinct251
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4285715 × 109
Minimum1.129076 × 109
Maximum5.011066 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-12T14:08:21.660214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.129076 × 109
5-th percentile3.611029 × 109
Q14.2740285 × 109
median4.514037 × 109
Q34.7150525 × 109
95-th percentile4.885528 × 109
Maximum5.011066 × 109
Range3.88199 × 109
Interquartile range (IQR)4.41024 × 108

Descriptive statistics

Standard deviation4.6646811 × 108
Coefficient of variation (CV)0.10533151
Kurtosis13.188425
Mean4.4285715 × 109
Median Absolute Deviation (MAD)2.149883 × 108
Skewness-2.9579194
Sum1.1115714 × 1012
Variance2.175925 × 1017
MonotonicityNot monotonic
2023-12-12T14:08:21.881958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4141051000 1
 
0.4%
4889043000 1
 
0.4%
3611025000 1
 
0.4%
4872038000 1
 
0.4%
4872040000 1
 
0.4%
4872025000 1
 
0.4%
4817036000 1
 
0.4%
4817069500 1
 
0.4%
4874032000 1
 
0.4%
4874038000 1
 
0.4%
Other values (241) 241
96.0%
ValueCountFrequency (%)
1129076000 1
0.4%
2614052000 1
0.4%
2671025300 1
0.4%
2717055000 1
0.4%
2771031000 1
0.4%
2771036000 1
0.4%
2914074000 1
0.4%
2917068500 1
0.4%
3023058000 1
0.4%
3171025600 1
0.4%
ValueCountFrequency (%)
5011066000 1
0.4%
5011031000 1
0.4%
5011025900 1
0.4%
4889043000 1
0.4%
4889040000 1
0.4%
4889034000 1
0.4%
4888039000 1
0.4%
4888032000 1
0.4%
4888025000 1
0.4%
4887040000 1
0.4%

행정구역명
Text

UNIQUE 

Distinct251
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-12T14:08:22.185469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length10
Mean length10.087649
Min length8

Characters and Unicode

Total characters2532
Distinct characters195
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

Unique251 ?
Unique (%)100.0%

Sample

1st row경기도군포시군포1동
2nd row경기도김포시양촌읍
3rd row경기도남양주시별내면
4th row경기도동두천시상패동
5th row강원도강릉시연곡면
ValueCountFrequency (%)
경기도군포시군포1동 1
 
0.4%
경상남도하동군화개면 1
 
0.4%
경기도안성시안성1동 1
 
0.4%
세종특별자치시세종시부강면 1
 
0.4%
세종특별자치시세종시조치원읍 1
 
0.4%
경상남도의령군낙서면 1
 
0.4%
경상남도의령군봉수면 1
 
0.4%
경상남도의령군의령읍 1
 
0.4%
경상남도진주시일반성면 1
 
0.4%
경상남도진주시초장동 1
 
0.4%
Other values (241) 241
96.0%
2023-12-12T14:08:22.722509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 242
 
9.6%
면 154
 
6.1%
군 139
 
5.5%
시 130
 
5.1%
남 112
 
4.4%
경 110
 
4.3%
북 100
 
3.9%
상 80
 
3.2%
동 67
 
2.6%
전 64
 
2.5%
Other values (185) 1334
52.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2520
99.5%
Decimal Number 12
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 242
 
9.6%
면 154
 
6.1%
군 139
 
5.5%
시 130
 
5.2%
남 112
 
4.4%
경 110
 
4.4%
북 100
 
4.0%
상 80
 
3.2%
동 67
 
2.7%
전 64
 
2.5%
Other values (182) 1322
52.5%
Decimal Number
ValueCountFrequency (%)
1 10
83.3%
3 1
 
8.3%
2 1
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2520
99.5%
Common 12
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 242
 
9.6%
면 154
 
6.1%
군 139
 
5.5%
시 130
 
5.2%
남 112
 
4.4%
경 110
 
4.4%
북 100
 
4.0%
상 80
 
3.2%
동 67
 
2.7%
전 64
 
2.5%
Other values (182) 1322
52.5%
Common
ValueCountFrequency (%)
1 10
83.3%
3 1
 
8.3%
2 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2520
99.5%
ASCII 12
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 242
 
9.6%
면 154
 
6.1%
군 139
 
5.5%
시 130
 
5.2%
남 112
 
4.4%
경 110
 
4.4%
북 100
 
4.0%
상 80
 
3.2%
동 67
 
2.7%
전 64
 
2.5%
Other values (182) 1322
52.5%
ASCII
ValueCountFrequency (%)
1 10
83.3%
3 1
 
8.3%
2 1
 
8.3%

X좌표
Real number (ℝ)

Distinct250
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean259227.76
Minimum132086.16
Maximum432041.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-12T14:08:22.946440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum132086.16
5-th percentile160168.07
Q1196859.28
median245726.34
Q3319067.65
95-th percentile387118.98
Maximum432041.17
Range299955.01
Interquartile range (IQR)122208.37

Descriptive statistics

Standard deviation74056.751
Coefficient of variation (CV)0.28568218
Kurtosis-0.90947802
Mean259227.76
Median Absolute Deviation (MAD)56973.961
Skewness0.39783182
Sum65066167
Variance5.4844024 × 109
MonotonicityNot monotonic
2023-12-12T14:08:23.118637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
326409.5961 2
 
0.8%
208933.5168 1
 
0.4%
395069.6417 1
 
0.4%
158486.3572 1
 
0.4%
354478.9866 1
 
0.4%
404272.698 1
 
0.4%
246450.9787 1
 
0.4%
260240.6938 1
 
0.4%
175470.9311 1
 
0.4%
206525.5412 1
 
0.4%
Other values (240) 240
95.6%
ValueCountFrequency (%)
132086.1553 1
0.4%
133783.6193 1
0.4%
134072.5086 1
0.4%
134904.6385 1
0.4%
139885.4371 1
0.4%
141569.5473 1
0.4%
144278.5522 1
0.4%
151485.948 1
0.4%
151882.9633 1
0.4%
153572.3686 1
0.4%
ValueCountFrequency (%)
432041.1656 1
0.4%
414495.2898 1
0.4%
409882.2413 1
0.4%
408799.7549 1
0.4%
408609.1086 1
0.4%
408031.2293 1
0.4%
406126.9762 1
0.4%
404272.698 1
0.4%
399960.5829 1
0.4%
396026.3513 1
0.4%

Y좌표
Real number (ℝ)

Distinct250
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean311315.53
Minimum-18756.844
Maximum527496.45
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)1.2%
Memory size2.3 KiB
2023-12-12T14:08:23.295843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-18756.844
5-th percentile149066.87
Q1224751.25
median310457.42
Q3400212.85
95-th percentile485956.84
Maximum527496.45
Range546253.3
Interquartile range (IQR)175461.61

Descriptive statistics

Standard deviation110858.12
Coefficient of variation (CV)0.35609569
Kurtosis-0.47354102
Mean311315.53
Median Absolute Deviation (MAD)87589.314
Skewness-0.12359254
Sum78140197
Variance1.2289522 × 1010
MonotonicityNot monotonic
2023-12-12T14:08:23.483408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
386026.9414 2
 
0.8%
402863.8759 1
 
0.4%
294626.2982 1
 
0.4%
337597.7112 1
 
0.4%
298524.5361 1
 
0.4%
210244.2309 1
 
0.4%
160226.7644 1
 
0.4%
211270.5271 1
 
0.4%
171386.9102 1
 
0.4%
259247.4572 1
 
0.4%
Other values (240) 240
95.6%
ValueCountFrequency (%)
-18756.84443 1
0.4%
-7285.017339 1
0.4%
-5125.638631 1
0.4%
103893.4804 1
0.4%
104349.8526 1
0.4%
118155.6908 1
0.4%
119612.6027 1
0.4%
132547.5265 1
0.4%
132991.2984 1
0.4%
141840.1165 1
0.4%
ValueCountFrequency (%)
527496.4532 1
0.4%
525250.8636 1
0.4%
524542.1348 1
0.4%
521876.2775 1
0.4%
508288.014 1
0.4%
506922.8482 1
0.4%
506552.0641 1
0.4%
506281.063 1
0.4%
506131.6163 1
0.4%
497844.1394 1
0.4%

Interactions

2023-12-12T14:08:19.387084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:08:17.753992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:08:18.294212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:08:18.835119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:08:19.508980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:08:17.848264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:08:18.415828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:08:18.977695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:08:19.647348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:08:17.989010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:08:18.563696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:08:19.124986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:08:19.746104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:08:18.135837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:08:18.705002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:08:19.243402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:08:23.612112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소코드행정구역코드X좌표Y좌표
관측소코드1.0000.1200.0370.139
행정구역코드0.1201.0000.0000.204
X좌표0.0370.0001.0000.064
Y좌표0.1390.2040.0641.000
2023-12-12T14:08:23.778837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소코드행정구역코드X좌표Y좌표
관측소코드1.0000.1070.102-0.046
행정구역코드0.1071.000-0.076-0.001
X좌표0.102-0.0761.0000.107
Y좌표-0.046-0.0010.1071.000

Missing values

2023-12-12T14:08:19.883706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:08:19.987027image/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

관측소코드관측소명행정구역코드행정구역명X좌표Y좌표
065586군포당정4141051000경기도군포시군포1동208933.5168402863.8759
187236김포양촌4157025600경기도김포시양촌읍266747.6916434406.3847
284014남양주별내4136031000경기도남양주시별내면258642.0014411324.0472
39871동두천상패4125060000경기도동두천시상패동353931.0239484675.5106
484007강릉연곡4215037000강원도강릉시연곡면204816.9453470565.9089
582004강릉왕산4215032000강원도강릉시왕산면223836.0483506552.0641
69866강릉홍제4215051000강원도강릉시홍제동305610.419525250.8636
773502고성토성4282033000강원도고성군토성면376428.1724408989.2921
895515인제남면4281031000강원도인제군남면260606.934184666.8195
995517인제서화4281034000강원도인제군서화면366061.3818256702.6648
관측소코드관측소명행정구역코드행정구역명X좌표Y좌표
24173526곡성고달4672036000전라남도곡성군고달면304019.36389755.877
24282033곡성목사동4672034000전라남도곡성군목사동면319981.9738458820.5246
24395527광양봉강4623031000전라남도광양시봉강면213422.9538401429.497
24487257구례토지4673033000전라남도구례군토지면205376.9691407042.2888
24511774나주삼도4617054000전라남도나주시금남동283249.7539425212.235
2466721목포용당4611051000전라남도목포시용당1동326409.5961386026.9414
24765606함양마천4887031000경상남도함양군마천면328565.8154179583.0834
24895537제주조천5011025900제주특별자치도제주시조천읍302468.7366190465.3111
2499858대전문평3023058000대전광역시대덕구목상동169332.8409460809.2143
25087246하남하산곡4145051000경기도하남시천현동375034.7287222868.106