Overview

Dataset statistics

Number of variables10
Number of observations42
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory90.0 B

Variable types

Numeric7
Text1
Categorical1
DateTime1

Dataset

Description전북특별자치도 진안군 도시계획정보시스템 공간시설 집행에 대한 데이터로 공간정보관리번호, X좌표 및 Y좌표 정보, 라벨명, 면적_도형, 길이_도형 등 정보를 제공합니다.
Author전북특별자치도 진안군
URLhttps://www.data.go.kr/data/15119133/fileData.do

Alerts

X좌표최소값 is highly overall correlated with X좌표최대값High correlation
Y좌표최소값 is highly overall correlated with Y좌표최대값High correlation
X좌표최대값 is highly overall correlated with X좌표최소값High correlation
Y좌표최대값 is highly overall correlated with Y좌표최소값High correlation
면적_도형 is highly overall correlated with 길이_도형High correlation
길이_도형 is highly overall correlated with 면적_도형High correlation
공간정보관리번호 has unique valuesUnique
X좌표최소값 has unique valuesUnique
X좌표최대값 has unique valuesUnique
현황도형 관리번호 has unique valuesUnique
면적_도형 has unique valuesUnique
길이_도형 has unique valuesUnique

Reproduction

Analysis started2024-03-14 08:41:42.743594
Analysis finished2024-03-14 08:41:52.327582
Duration9.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공간정보관리번호
Real number (ℝ)

UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201.5
Minimum181
Maximum222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-14T17:41:52.452047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum181
5-th percentile183.05
Q1191.25
median201.5
Q3211.75
95-th percentile219.95
Maximum222
Range41
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation12.267844
Coefficient of variation (CV)0.060882601
Kurtosis-1.2
Mean201.5
Median Absolute Deviation (MAD)10.5
Skewness0
Sum8463
Variance150.5
MonotonicityStrictly increasing
2024-03-14T17:41:52.701808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
181 1
 
2.4%
213 1
 
2.4%
205 1
 
2.4%
206 1
 
2.4%
207 1
 
2.4%
208 1
 
2.4%
209 1
 
2.4%
210 1
 
2.4%
211 1
 
2.4%
212 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
181 1
2.4%
182 1
2.4%
183 1
2.4%
184 1
2.4%
185 1
2.4%
186 1
2.4%
187 1
2.4%
188 1
2.4%
189 1
2.4%
190 1
2.4%
ValueCountFrequency (%)
222 1
2.4%
221 1
2.4%
220 1
2.4%
219 1
2.4%
218 1
2.4%
217 1
2.4%
216 1
2.4%
215 1
2.4%
214 1
2.4%
213 1
2.4%

X좌표최소값
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23835852
Minimum23101411
Maximum24708237
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-14T17:41:52.936321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23101411
5-th percentile23149080
Q123745603
median23847925
Q323948197
95-th percentile24343683
Maximum24708237
Range1606826
Interquartile range (IQR)202594

Descriptive statistics

Standard deviation321406.33
Coefficient of variation (CV)0.013484156
Kurtosis2.3625961
Mean23835852
Median Absolute Deviation (MAD)102750.5
Skewness0.22815944
Sum1.0011058 × 109
Variance1.0330203 × 1011
MonotonicityNot monotonic
2024-03-14T17:41:53.256707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
23752421 1
 
2.4%
23899727 1
 
2.4%
23718064 1
 
2.4%
23756470 1
 
2.4%
23730260 1
 
2.4%
23743330 1
 
2.4%
23774316 1
 
2.4%
23901454 1
 
2.4%
23914597 1
 
2.4%
23899988 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
23101411 1
2.4%
23134795 1
2.4%
23140694 1
2.4%
23308422 1
2.4%
23569634 1
2.4%
23598465 1
2.4%
23639134 1
2.4%
23660811 1
2.4%
23718064 1
2.4%
23730260 1
2.4%
ValueCountFrequency (%)
24708237 1
2.4%
24702485 1
2.4%
24346103 1
2.4%
24297694 1
2.4%
23994158 1
2.4%
23990984 1
2.4%
23989577 1
2.4%
23983530 1
2.4%
23977883 1
2.4%
23961293 1
2.4%

Y좌표최소값
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25383454
Minimum24295090
Maximum27253944
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-14T17:41:53.531286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24295090
5-th percentile24547670
Q125221539
median25248300
Q325401966
95-th percentile26652599
Maximum27253944
Range2958854
Interquartile range (IQR)180427.5

Descriptive statistics

Standard deviation594851.05
Coefficient of variation (CV)0.023434599
Kurtosis3.6810786
Mean25383454
Median Absolute Deviation (MAD)89294.5
Skewness1.5343749
Sum1.0661051 × 109
Variance3.5384777 × 1011
MonotonicityNot monotonic
2024-03-14T17:41:53.765840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
25199362 2
 
4.8%
25255084 1
 
2.4%
25220794 1
 
2.4%
24939078 1
 
2.4%
25234962 1
 
2.4%
25248453 1
 
2.4%
25233853 1
 
2.4%
25233506 1
 
2.4%
25248384 1
 
2.4%
25185093 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
24295090 1
2.4%
24383231 1
2.4%
24540789 1
2.4%
24678411 1
2.4%
24880335 1
2.4%
24939078 1
2.4%
25185093 1
2.4%
25199362 2
4.8%
25210860 1
2.4%
25220794 1
2.4%
ValueCountFrequency (%)
27253944 1
2.4%
27221092 1
2.4%
26675780 1
2.4%
26212168 1
2.4%
26184736 1
2.4%
25894666 1
2.4%
25582812 1
2.4%
25490767 1
2.4%
25477153 1
2.4%
25467629 1
2.4%

X좌표최대값
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23857411
Minimum23146017
Maximum24721752
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-14T17:41:54.000229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23146017
5-th percentile23173704
Q123760860
median23870491
Q323959620
95-th percentile24358144
Maximum24721752
Range1575735
Interquartile range (IQR)198761

Descriptive statistics

Standard deviation318406.21
Coefficient of variation (CV)0.013346218
Kurtosis2.3271639
Mean23857411
Median Absolute Deviation (MAD)108491
Skewness0.26515124
Sum1.0020113 × 109
Variance1.0138252 × 1011
MonotonicityNot monotonic
2024-03-14T17:41:54.251034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
23758135 1
 
2.4%
23915863 1
 
2.4%
23747373 1
 
2.4%
23779009 1
 
2.4%
23759719 1
 
2.4%
23771507 1
 
2.4%
23801884 1
 
2.4%
23915152 1
 
2.4%
23918035 1
 
2.4%
23903015 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
23146017 1
2.4%
23156443 1
2.4%
23162797 1
2.4%
23380947 1
2.4%
23577736 1
2.4%
23604230 1
2.4%
23645156 1
2.4%
23666718 1
2.4%
23747373 1
2.4%
23758135 1
2.4%
ValueCountFrequency (%)
24721752 1
2.4%
24721241 1
2.4%
24360309 1
2.4%
24317004 1
2.4%
24050946 1
2.4%
24031873 1
2.4%
24014155 1
2.4%
23995139 1
2.4%
23993080 1
2.4%
23992673 1
2.4%

Y좌표최대값
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25407324
Minimum24305381
Maximum27278994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-14T17:41:54.584840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24305381
5-th percentile24555654
Q125242875
median25281442
Q325453405
95-th percentile26669661
Maximum27278994
Range2973613
Interquartile range (IQR)210529.75

Descriptive statistics

Standard deviation599753.9
Coefficient of variation (CV)0.023605552
Kurtosis3.5564024
Mean25407324
Median Absolute Deviation (MAD)96280.5
Skewness1.4961759
Sum1.0671076 × 109
Variance3.5970474 × 1011
MonotonicityNot monotonic
2024-03-14T17:41:54.820563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
25219294 2
 
4.8%
25260766 1
 
2.4%
25223739 1
 
2.4%
24956850 1
 
2.4%
25279040 1
 
2.4%
25283962 1
 
2.4%
25270270 1
 
2.4%
25248943 1
 
2.4%
25310034 1
 
2.4%
25199362 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
24305381 1
2.4%
24389300 1
2.4%
24548829 1
2.4%
24685324 1
2.4%
24913900 1
2.4%
24956850 1
2.4%
25199362 1
2.4%
25219294 2
4.8%
25223739 1
2.4%
25242857 1
2.4%
ValueCountFrequency (%)
27278994 1
2.4%
27251641 1
2.4%
26690741 1
2.4%
26269146 1
2.4%
26221785 1
2.4%
25945319 1
2.4%
25626972 1
2.4%
25503857 1
2.4%
25480884 1
2.4%
25474736 1
2.4%
Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size464.0 B
2024-03-14T17:41:55.538278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters1008
Distinct characters14
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

Unique42 ?
Unique (%)100.0%

Sample

1st row45720UQ153PS201311220001
2nd row45720UQ153PS201707140002
3rd row45720UQ153PS201707140003
4th row45720UQ153PS201310070004
5th row45720UQ153PS201210260005
ValueCountFrequency (%)
45720uq153ps201311220001 1
 
2.4%
45720uq153ps201712150031 1
 
2.4%
45720uq153ps202200030041 1
 
2.4%
45720uq153ps201506110024 1
 
2.4%
45720uq153ps201707140025 1
 
2.4%
45720uq153ps201707140026 1
 
2.4%
45720uq153ps201707140027 1
 
2.4%
45720uq153ps201707140028 1
 
2.4%
45720uq153ps201707140029 1
 
2.4%
45720uq153ps201712150030 1
 
2.4%
Other values (32) 32
76.2%
2024-03-14T17:41:56.482667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 246
24.4%
1 130
12.9%
2 125
12.4%
5 107
10.6%
7 69
 
6.8%
3 66
 
6.5%
4 57
 
5.7%
U 42
 
4.2%
Q 42
 
4.2%
P 42
 
4.2%
Other values (4) 82
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 840
83.3%
Uppercase Letter 168
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 246
29.3%
1 130
15.5%
2 125
14.9%
5 107
12.7%
7 69
 
8.2%
3 66
 
7.9%
4 57
 
6.8%
8 20
 
2.4%
9 12
 
1.4%
6 8
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
U 42
25.0%
Q 42
25.0%
P 42
25.0%
S 42
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 840
83.3%
Latin 168
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 246
29.3%
1 130
15.5%
2 125
14.9%
5 107
12.7%
7 69
 
8.2%
3 66
 
7.9%
4 57
 
6.8%
8 20
 
2.4%
9 12
 
1.4%
6 8
 
1.0%
Latin
ValueCountFrequency (%)
U 42
25.0%
Q 42
25.0%
P 42
25.0%
S 42
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 246
24.4%
1 130
12.9%
2 125
12.4%
5 107
10.6%
7 69
 
6.8%
3 66
 
6.5%
4 57
 
5.7%
U 42
 
4.2%
Q 42
 
4.2%
P 42
 
4.2%
Other values (4) 82
 
8.1%

라벨명
Categorical

Distinct9
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Memory size464.0 B
기타 교통광장시설
14 
경관녹지
11 
완충녹지
기타 공원시설
공공공지
Other values (4)

Length

Max length9
Median length4
Mean length6.1428571
Min length4

Unique

Unique3 ?
Unique (%)7.1%

Sample

1st row경관녹지
2nd row경관녹지
3rd row경관녹지
4th row기타 공원시설
5th row공공공지

Common Values

ValueCountFrequency (%)
기타 교통광장시설 14
33.3%
경관녹지 11
26.2%
완충녹지 5
 
11.9%
기타 공원시설 4
 
9.5%
공공공지 3
 
7.1%
체육공원 2
 
4.8%
기타 광장시설 1
 
2.4%
근린공원 1
 
2.4%
기타 일반광장시설 1
 
2.4%

Length

2024-03-14T17:41:56.937629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T17:41:57.150268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 20
32.3%
교통광장시설 14
22.6%
경관녹지 11
17.7%
완충녹지 5
 
8.1%
공원시설 4
 
6.5%
공공공지 3
 
4.8%
체육공원 2
 
3.2%
광장시설 1
 
1.6%
근린공원 1
 
1.6%
일반광장시설 1
 
1.6%

면적_도형
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23937.68
Minimum560.55
Maximum367305.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-14T17:41:57.397436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum560.55
5-th percentile1228.357
Q12311.5325
median7922.31
Q326717.443
95-th percentile55625.182
Maximum367305.17
Range366744.62
Interquartile range (IQR)24405.91

Descriptive statistics

Standard deviation56917.531
Coefficient of variation (CV)2.377738
Kurtosis34.187888
Mean23937.68
Median Absolute Deviation (MAD)6532.995
Skewness5.610614
Sum1005382.6
Variance3.2396053 × 109
MonotonicityNot monotonic
2024-03-14T17:41:57.638185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
902.43 1
 
2.4%
5283.26 1
 
2.4%
16102.18 1
 
2.4%
44290.52 1
 
2.4%
37896.58 1
 
2.4%
5151.61 1
 
2.4%
14946.93 1
 
2.4%
13097.41 1
 
2.4%
1245.4 1
 
2.4%
3787.08 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
560.55 1
2.4%
902.43 1
2.4%
1227.46 1
2.4%
1245.4 1
2.4%
1266.06 1
2.4%
1512.57 1
2.4%
1718.21 1
2.4%
1987.8 1
2.4%
2068.53 1
2.4%
2128.22 1
2.4%
ValueCountFrequency (%)
367305.17 1
2.4%
57502.34 1
2.4%
55769.42 1
2.4%
52884.66 1
2.4%
46405.47 1
2.4%
44290.52 1
2.4%
42206.13 1
2.4%
37896.58 1
2.4%
32792.37 1
2.4%
29418.97 1
2.4%

길이_도형
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean853.01357
Minimum105.91
Maximum3391.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-14T17:41:57.981755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum105.91
5-th percentile167.064
Q1368.3275
median703.75
Q31222.23
95-th percentile2033.615
Maximum3391.06
Range3285.15
Interquartile range (IQR)853.9025

Descriptive statistics

Standard deviation678.07855
Coefficient of variation (CV)0.79492118
Kurtosis3.589788
Mean853.01357
Median Absolute Deviation (MAD)399.495
Skewness1.6189895
Sum35826.57
Variance459790.52
MonotonicityNot monotonic
2024-03-14T17:41:58.273426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
166.23 1
 
2.4%
995.82 1
 
2.4%
1737.12 1
 
2.4%
979.73 1
 
2.4%
1246.77 1
 
2.4%
724.0 1
 
2.4%
2252.84 1
 
2.4%
492.24 1
 
2.4%
449.32 1
 
2.4%
526.81 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
105.91 1
2.4%
135.43 1
2.4%
166.23 1
2.4%
182.91 1
2.4%
187.46 1
2.4%
204.48 1
2.4%
233.29 1
2.4%
268.91 1
2.4%
297.78 1
2.4%
310.73 1
2.4%
ValueCountFrequency (%)
3391.06 1
2.4%
2252.84 1
2.4%
2049.22 1
2.4%
1737.12 1
2.4%
1660.32 1
2.4%
1603.37 1
2.4%
1394.38 1
2.4%
1358.95 1
2.4%
1336.49 1
2.4%
1318.61 1
2.4%
Distinct10
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Memory size464.0 B
Minimum2013-01-02 00:00:00
Maximum2022-03-31 00:00:00
2024-03-14T17:41:58.606198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:58.909046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)

Interactions

2024-03-14T17:41:50.638850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:43.170687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:44.357569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:45.565666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:46.712622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:47.947237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:49.423203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:50.828904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:43.370026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:44.503483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:45.702646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:46.859923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:48.288632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:49.600161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:51.029320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:43.528326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:44.664985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:45.864068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:47.029563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:48.534785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:49.843320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:51.181113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:43.755805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:44.876070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:46.057493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:47.228413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:48.707355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:49.995524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:51.355767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:43.918728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:45.076667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:46.256613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:47.443878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:48.886519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:50.165901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:51.505963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:44.059454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:45.233565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:46.398448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:47.601769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:49.063114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:50.318216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:51.666628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:44.210342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:45.401027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:46.551968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:47.780054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:49.266119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:41:50.480445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T17:41:59.185561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공간정보관리번호X좌표최소값Y좌표최소값X좌표최대값Y좌표최대값현황도형 관리번호라벨명면적_도형길이_도형현황도형 생성일시
공간정보관리번호1.0000.5810.4530.4310.4531.0000.6360.5240.6150.918
X좌표최소값0.5811.0000.8120.9940.8361.0000.1780.0000.6150.528
Y좌표최소값0.4530.8121.0000.9240.9991.0000.8480.0000.0000.668
X좌표최대값0.4310.9940.9241.0000.8331.0000.3670.0000.6870.628
Y좌표최대값0.4530.8360.9990.8331.0001.0000.8160.0000.0000.580
현황도형 관리번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
라벨명0.6360.1780.8480.3670.8161.0001.0000.7340.1700.798
면적_도형0.5240.0000.0000.0000.0001.0000.7341.0000.8730.815
길이_도형0.6150.6150.0000.6870.0001.0000.1700.8731.0000.528
현황도형 생성일시0.9180.5280.6680.6280.5801.0000.7980.8150.5281.000
2024-03-14T17:41:59.528986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공간정보관리번호X좌표최소값Y좌표최소값X좌표최대값Y좌표최대값면적_도형길이_도형라벨명
공간정보관리번호1.0000.287-0.3820.272-0.3640.0010.1490.347
X좌표최소값0.2871.0000.4550.9820.4280.0060.0080.119
Y좌표최소값-0.3820.4551.0000.4840.9840.3210.2420.435
X좌표최대값0.2720.9820.4841.0000.4690.0930.1050.202
Y좌표최대값-0.3640.4280.9840.4691.0000.3990.3470.397
면적_도형0.0010.0060.3210.0930.3991.0000.8610.407
길이_도형0.1490.0080.2420.1050.3470.8611.0000.035
라벨명0.3470.1190.4350.2020.3970.4070.0351.000

Missing values

2024-03-14T17:41:51.879217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T17:41:52.227534image/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

공간정보관리번호X좌표최소값Y좌표최소값X좌표최대값Y좌표최대값현황도형 관리번호라벨명면적_도형길이_도형현황도형 생성일시
01812375242125255084237581352526076645720UQ153PS201311220001경관녹지902.43166.232015-07-15
11822375317725282804237642812530156045720UQ153PS201707140002경관녹지4947.57572.522017-08-28
21832376091025296572237780442530868245720UQ153PS201707140003경관녹지3499.23615.062017-08-28
31842470823727221092247212412725164145720UQ153PS201310070004기타 공원시설23472.11758.192015-07-14
41852384131225490767238468552550385745720UQ153PS201210260005공공공지2128.22297.782013-01-31
51862385453825467629238588032547473645720UQ153PS200906190006기타 교통광장시설1227.46182.912013-01-02
61872434610326675780243603092669074145720UQ153PS199907230007기타 광장시설8145.65701.942013-01-02
71882470248527253944247217522727899445720UQ153PS201206290008체육공원27799.22705.562013-01-31
81892382848325477153238334502548088445720UQ153PS201810050009기타 공원시설1266.06135.432018-11-20
91902382714725401197239367912546439245720UQ153PS202100880010체육공원367305.173391.062021-09-30
공간정보관리번호X좌표최소값Y좌표최소값X좌표최대값Y좌표최대값현황도형 관리번호라벨명면적_도형길이_도형현황도형 생성일시
322132389972725220794239158632524285745720UQ153PS201712150033경관녹지5283.26995.822019-08-22
332142390783925210860239174882521929445720UQ153PS201712150034기타 일반광장시설7291.91348.262019-08-22
342152399415825582812240509462562697245720UQ153PS201707140035공공공지57502.341603.372017-08-28
352162389456725245919239303462528384345720UQ153PS202200030036완충녹지8276.311660.322022-03-31
362172394883125244697239622942525404345720UQ153PS202200030037완충녹지1718.21444.292022-03-31
372182394629425223774239516002524293045720UQ153PS202200030038완충녹지2397.49450.072022-03-31
382192396129325248192239930802526369145720UQ153PS202200030039완충녹지6815.93811.172022-03-31
392202398353025228195239951392524769945720UQ153PS202200030040완충녹지8550.07814.512022-03-31
402212391679125247639239201852525953245720UQ153PS202200030041경관녹지1512.57268.912022-03-31
412222398957725248217239926732525155945720UQ153PS202200030042경관녹지560.55105.912022-03-31