Overview

Dataset statistics

Number of variables14
Number of observations417
Missing cells2050
Missing cells (%)35.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory48.6 KiB
Average record size in memory119.3 B

Variable types

Categorical3
Text3
Numeric3
Unsupported4
Boolean1

Dataset

Description전라남도 곡성군 도시계획정보시스템 (UPIS) 용도지구 결정조서 테이블 내용을 제공합니다(조서유형, 면적 변경 등 포함)
Author전라남도 곡성군
URLhttps://www.data.go.kr/data/15123613/fileData.do

Alerts

최초결정일자 is highly overall correlated with 면적 기정 and 5 other fieldsHigh correlation
비고 is highly overall correlated with 면적 변경 and 3 other fieldsHigh correlation
조서유형 is highly overall correlated with 최초결정일자 and 2 other fieldsHigh correlation
공간도형존재여부 is highly overall correlated with 면적 변경후 and 3 other fieldsHigh correlation
면적 기정 is highly overall correlated with 면적 변경 and 2 other fieldsHigh correlation
면적 변경 is highly overall correlated with 면적 기정 and 3 other fieldsHigh correlation
면적 변경후 is highly overall correlated with 면적 기정 and 3 other fieldsHigh correlation
최초결정일자 is highly imbalanced (87.6%)Imbalance
비고 is highly imbalanced (82.3%)Imbalance
도면번호 has 8 (1.9%) missing valuesMissing
면적 기정 has 265 (63.5%) missing valuesMissing
면적 변경 has 36 (8.6%) missing valuesMissing
면적 변경후 has 72 (17.3%) missing valuesMissing
연장 has 417 (100.0%) missing valuesMissing
폭 has 417 (100.0%) missing valuesMissing
제한내용 has 417 (100.0%) missing valuesMissing
최초결정일자정보 has 417 (100.0%) missing valuesMissing
연장 is an unsupported type, check if it needs cleaning or further analysisUnsupported
폭 is an unsupported type, check if it needs cleaning or further analysisUnsupported
제한내용 is an unsupported type, check if it needs cleaning or further analysisUnsupported
최초결정일자정보 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 00:01:44.928271
Analysis finished2023-12-12 00:01:46.803414
Duration1.88 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

조서유형
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
PMA0001
264 
PMA0003
81 
PMA0004
72 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPMA0004
2nd rowPMA0004
3rd rowPMA0004
4th rowPMA0004
5th rowPMA0004

Common Values

ValueCountFrequency (%)
PMA0001 264
63.3%
PMA0003 81
 
19.4%
PMA0004 72
 
17.3%

Length

2023-12-12T09:01:46.882486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:01:46.996800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
pma0001 264
63.3%
pma0003 81
 
19.4%
pma0004 72
 
17.3%

도면번호
Text

MISSING 

Distinct330
Distinct (%)80.7%
Missing8
Missing (%)1.9%
Memory size3.4 KiB
2023-12-12T09:01:47.364748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.8166259
Min length1

Characters and Unicode

Total characters1152
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique277 ?
Unique (%)67.7%

Sample

1st row7-31
2nd row7-32
3rd row7-33
4th row7-34
5th row7-35
ValueCountFrequency (%)
1 9
 
2.2%
3 6
 
1.5%
2 5
 
1.2%
10 5
 
1.2%
9 4
 
1.0%
6 4
 
1.0%
8 4
 
1.0%
5 3
 
0.7%
7 3
 
0.7%
4 3
 
0.7%
Other values (320) 363
88.8%
2023-12-12T09:01:47.917572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 183
15.9%
7 153
13.3%
3 147
12.8%
2 146
12.7%
0 102
8.9%
4 88
7.6%
6 83
7.2%
8 76
6.6%
- 71
 
6.2%
5 56
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1081
93.8%
Dash Punctuation 71
 
6.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 183
16.9%
7 153
14.2%
3 147
13.6%
2 146
13.5%
0 102
9.4%
4 88
8.1%
6 83
7.7%
8 76
7.0%
5 56
 
5.2%
9 47
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 71
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1152
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 183
15.9%
7 153
13.3%
3 147
12.8%
2 146
12.7%
0 102
8.9%
4 88
7.6%
6 83
7.2%
8 76
6.6%
- 71
 
6.2%
5 56
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 183
15.9%
7 153
13.3%
3 147
12.8%
2 146
12.7%
0 102
8.9%
4 88
7.6%
6 83
7.2%
8 76
6.6%
- 71
 
6.2%
5 56
 
4.9%

위치명
Text

Distinct396
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2023-12-12T09:01:48.344584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length12.443645
Min length3

Characters and Unicode

Total characters5189
Distinct characters131
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique376 ?
Unique (%)90.2%

Sample

1st row죽곡면 봉정리
2nd row죽곡면 동계리
3rd row죽곡면 원달리
4th row고달면 대사리
5th row고달면 백곡리
ValueCountFrequency (%)
일원 274
 
19.1%
입면 54
 
3.8%
곡성읍 42
 
2.9%
삼기면 42
 
2.9%
목사동면 39
 
2.7%
죽곡면 39
 
2.7%
겸면 35
 
2.4%
석곡면 34
 
2.4%
오곡면 32
 
2.2%
옥과면 31
 
2.2%
Other values (420) 809
56.5%
2023-12-12T09:01:48.923858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1017
19.6%
리 407
 
7.8%
면 358
 
6.9%
원 330
 
6.4%
일 326
 
6.3%
곡 179
 
3.4%
1 150
 
2.9%
3 137
 
2.6%
4 129
 
2.5%
2 122
 
2.4%
Other values (121) 2034
39.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3118
60.1%
Space Separator 1017
 
19.6%
Decimal Number 997
 
19.2%
Dash Punctuation 54
 
1.0%
Other Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 407
 
13.1%
면 358
 
11.5%
원 330
 
10.6%
일 326
 
10.5%
곡 179
 
5.7%
오 70
 
2.2%
산 65
 
2.1%
동 63
 
2.0%
입 58
 
1.9%
사 53
 
1.7%
Other values (108) 1209
38.8%
Decimal Number
ValueCountFrequency (%)
1 150
15.0%
3 137
13.7%
4 129
12.9%
2 122
12.2%
6 91
9.1%
5 87
8.7%
0 76
7.6%
9 71
7.1%
7 71
7.1%
8 63
6.3%
Space Separator
ValueCountFrequency (%)
1017
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 54
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3118
60.1%
Common 2071
39.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 407
 
13.1%
면 358
 
11.5%
원 330
 
10.6%
일 326
 
10.5%
곡 179
 
5.7%
오 70
 
2.2%
산 65
 
2.1%
동 63
 
2.0%
입 58
 
1.9%
사 53
 
1.7%
Other values (108) 1209
38.8%
Common
ValueCountFrequency (%)
1017
49.1%
1 150
 
7.2%
3 137
 
6.6%
4 129
 
6.2%
2 122
 
5.9%
6 91
 
4.4%
5 87
 
4.2%
0 76
 
3.7%
9 71
 
3.4%
7 71
 
3.4%
Other values (3) 120
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3118
60.1%
ASCII 2071
39.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1017
49.1%
1 150
 
7.2%
3 137
 
6.6%
4 129
 
6.2%
2 122
 
5.9%
6 91
 
4.4%
5 87
 
4.2%
0 76
 
3.7%
9 71
 
3.4%
7 71
 
3.4%
Other values (3) 120
 
5.8%
Hangul
ValueCountFrequency (%)
리 407
 
13.1%
면 358
 
11.5%
원 330
 
10.6%
일 326
 
10.5%
곡 179
 
5.7%
오 70
 
2.2%
산 65
 
2.1%
동 63
 
2.0%
입 58
 
1.9%
사 53
 
1.7%
Other values (108) 1209
38.8%

지역명
Text

Distinct345
Distinct (%)82.9%
Missing1
Missing (%)0.2%
Memory size3.4 KiB
2023-12-12T09:01:49.269298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length2
Mean length2.5793269
Min length2

Characters and Unicode

Total characters1073
Distinct characters188
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique277 ?
Unique (%)66.6%

Sample

1st row봉정리
2nd row동계리
3rd row원달리
4th row대사리
5th row백곡리
ValueCountFrequency (%)
시설용지지구 3
 
0.7%
기동 3
 
0.7%
평지 3
 
0.7%
헬스팜 2
 
0.5%
신기리 2
 
0.5%
석곡농공단지 2
 
0.5%
탑동 2
 
0.5%
반석 2
 
0.5%
묘천제1지구 2
 
0.5%
송전지구 2
 
0.5%
Other values (334) 393
94.5%
2023-12-12T09:01:49.829645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 76
 
7.1%
지 57
 
5.3%
구 43
 
4.0%
동 36
 
3.4%
정 34
 
3.2%
산 24
 
2.2%
평 23
 
2.1%
곡 21
 
2.0%
봉 20
 
1.9%
신 18
 
1.7%
Other values (178) 721
67.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1047
97.6%
Decimal Number 19
 
1.8%
Space Separator 4
 
0.4%
Uppercase Letter 2
 
0.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 76
 
7.3%
지 57
 
5.4%
구 43
 
4.1%
동 36
 
3.4%
정 34
 
3.2%
산 24
 
2.3%
평 23
 
2.2%
곡 21
 
2.0%
봉 20
 
1.9%
신 18
 
1.7%
Other values (172) 695
66.4%
Decimal Number
ValueCountFrequency (%)
2 8
42.1%
1 7
36.8%
3 4
21.1%
Space Separator
ValueCountFrequency (%)
4
100.0%
Uppercase Letter
ValueCountFrequency (%)
C 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1047
97.6%
Common 24
 
2.2%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 76
 
7.3%
지 57
 
5.4%
구 43
 
4.1%
동 36
 
3.4%
정 34
 
3.2%
산 24
 
2.3%
평 23
 
2.2%
곡 21
 
2.0%
봉 20
 
1.9%
신 18
 
1.7%
Other values (172) 695
66.4%
Common
ValueCountFrequency (%)
2 8
33.3%
1 7
29.2%
4
16.7%
3 4
16.7%
, 1
 
4.2%
Latin
ValueCountFrequency (%)
C 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1047
97.6%
ASCII 26
 
2.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 76
 
7.3%
지 57
 
5.4%
구 43
 
4.1%
동 36
 
3.4%
정 34
 
3.2%
산 24
 
2.3%
평 23
 
2.2%
곡 21
 
2.0%
봉 20
 
1.9%
신 18
 
1.7%
Other values (172) 695
66.4%
ASCII
ValueCountFrequency (%)
2 8
30.8%
1 7
26.9%
4
15.4%
3 4
15.4%
C 2
 
7.7%
, 1
 
3.8%

면적 기정
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct121
Distinct (%)79.6%
Missing265
Missing (%)63.5%
Infinite0
Infinite (%)0.0%
Mean222679.44
Minimum7700
Maximum6489000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-12T09:01:49.969988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7700
5-th percentile13364
Q136802.5
median70530
Q3106500
95-th percentile772450
Maximum6489000
Range6481300
Interquartile range (IQR)69697.5

Descriptive statistics

Standard deviation783256.15
Coefficient of variation (CV)3.5174157
Kurtosis53.189984
Mean222679.44
Median Absolute Deviation (MAD)34982.5
Skewness7.0315854
Sum33847275
Variance6.1349019 × 1011
MonotonicityNot monotonic
2023-12-12T09:01:50.125562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75000.0 5
 
1.2%
97000.0 4
 
1.0%
82000.0 3
 
0.7%
124000.0 3
 
0.7%
71000.0 3
 
0.7%
174000.0 3
 
0.7%
79000.0 3
 
0.7%
52000.0 3
 
0.7%
50000.0 3
 
0.7%
55000.0 2
 
0.5%
Other values (111) 120
28.8%
(Missing) 265
63.5%
ValueCountFrequency (%)
7700.0 1
0.2%
7900.0 1
0.2%
8750.0 1
0.2%
10160.0 1
0.2%
11000.0 1
0.2%
11520.0 1
0.2%
11610.0 1
0.2%
12000.0 1
0.2%
14480.0 1
0.2%
14800.0 1
0.2%
ValueCountFrequency (%)
6489000.0 1
0.2%
6451000.0 1
0.2%
1913000.0 1
0.2%
1878000.0 1
0.2%
1862000.0 1
0.2%
1323000.0 1
0.2%
1090540.0 1
0.2%
1081000.0 1
0.2%
520000.0 1
0.2%
404000.0 1
0.2%

면적 변경
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct347
Distinct (%)91.1%
Missing36
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean53086.942
Minimum70
Maximum1090540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-12T09:01:50.276120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile1570
Q117360
median31850
Q370840
95-th percentile155870
Maximum1090540
Range1090470
Interquartile range (IQR)53480

Descriptive statistics

Standard deviation72952.906
Coefficient of variation (CV)1.3742156
Kurtosis108.73762
Mean53086.942
Median Absolute Deviation (MAD)20850
Skewness8.2760451
Sum20226125
Variance5.3221264 × 109
MonotonicityNot monotonic
2023-12-12T09:01:50.467571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97000.0 4
 
1.0%
75000.0 4
 
1.0%
174000.0 3
 
0.7%
79000.0 3
 
0.7%
50000.0 3
 
0.7%
71000.0 3
 
0.7%
82000.0 3
 
0.7%
52000.0 3
 
0.7%
17170.0 2
 
0.5%
135000.0 2
 
0.5%
Other values (337) 351
84.2%
(Missing) 36
 
8.6%
ValueCountFrequency (%)
70.0 1
0.2%
101.0 1
0.2%
120.0 1
0.2%
270.0 1
0.2%
311.8 1
0.2%
320.0 1
0.2%
470.0 1
0.2%
501.0 1
0.2%
520.0 2
0.5%
779.0 1
0.2%
ValueCountFrequency (%)
1090540.0 1
0.2%
404000.0 1
0.2%
301190.0 1
0.2%
232942.0 1
0.2%
230600.0 1
0.2%
192000.0 1
0.2%
181010.0 1
0.2%
179140.0 1
0.2%
178930.0 1
0.2%
178000.0 1
0.2%

면적 변경후
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct312
Distinct (%)90.4%
Missing72
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean117479.57
Minimum3310
Maximum6489000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-12T09:01:50.659742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3310
5-th percentile10770
Q119340
median30430
Q365000
95-th percentile202826.8
Maximum6489000
Range6485690
Interquartile range (IQR)45660

Descriptive statistics

Standard deviation533531.14
Coefficient of variation (CV)4.5414802
Kurtosis117.984
Mean117479.57
Median Absolute Deviation (MAD)14920
Skewness10.35544
Sum40530452
Variance2.8465548 × 1011
MonotonicityNot monotonic
2023-12-12T09:01:50.826041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14800.0 3
 
0.7%
24830.0 2
 
0.5%
105320.0 2
 
0.5%
54170.0 2
 
0.5%
62220.0 2
 
0.5%
19480.0 2
 
0.5%
91480.0 2
 
0.5%
17280.0 2
 
0.5%
178930.0 2
 
0.5%
404000.0 2
 
0.5%
Other values (302) 324
77.7%
(Missing) 72
 
17.3%
ValueCountFrequency (%)
3310.0 1
0.2%
5140.0 1
0.2%
5310.0 1
0.2%
5970.0 1
0.2%
6430.0 1
0.2%
6700.0 1
0.2%
7380.0 2
0.5%
7700.0 1
0.2%
7900.0 1
0.2%
8650.0 1
0.2%
ValueCountFrequency (%)
6489000.0 1
0.2%
6469000.0 1
0.2%
2046000.0 1
0.2%
1913000.0 1
0.2%
1878000.0 1
0.2%
1458000.0 1
0.2%
1090540.0 1
0.2%
1081000.0 1
0.2%
859940.0 1
0.2%
520000.0 1
0.2%

연장
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing417
Missing (%)100.0%
Memory size3.8 KiB

폭
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing417
Missing (%)100.0%
Memory size3.8 KiB

제한내용
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing417
Missing (%)100.0%
Memory size3.8 KiB

최초결정일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
<NA>
397 
2002-05-01
 
9
2002-05-31
 
3
2003-06-01
 
3
2012-11-05
 
1
Other values (4)
 
4

Length

Max length10
Median length4
Mean length4.2877698
Min length4

Unique

Unique5 ?
Unique (%)1.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 397
95.2%
2002-05-01 9
 
2.2%
2002-05-31 3
 
0.7%
2003-06-01 3
 
0.7%
2012-11-05 1
 
0.2%
1997-08-06 1
 
0.2%
1997-06-01 1
 
0.2%
2015-11-19 1
 
0.2%
1992-12-16 1
 
0.2%

Length

2023-12-12T09:01:50.961193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:01:51.083241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 397
95.2%
2002-05-01 9
 
2.2%
2002-05-31 3
 
0.7%
2003-06-01 3
 
0.7%
2012-11-05 1
 
0.2%
1997-08-06 1
 
0.2%
1997-06-01 1
 
0.2%
2015-11-19 1
 
0.2%
1992-12-16 1
 
0.2%

최초결정일자정보
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing417
Missing (%)100.0%
Memory size3.8 KiB

비고
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
<NA>
393 
구적오차
 
11
번호변경
 
9
경계조정
 
3
경계확장
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 393
94.2%
구적오차 11
 
2.6%
번호변경 9
 
2.2%
경계조정 3
 
0.7%
경계확장 1
 
0.2%

Length

2023-12-12T09:01:51.257058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:01:51.701287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 393
94.2%
구적오차 11
 
2.6%
번호변경 9
 
2.2%
경계조정 3
 
0.7%
경계확장 1
 
0.2%

공간도형존재여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size549.0 B
True
338 
False
79 
ValueCountFrequency (%)
True 338
81.1%
False 79
 
18.9%
2023-12-12T09:01:51.818752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-12-12T09:01:45.950718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:01:45.401099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:01:45.678760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:01:46.039353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:01:45.485183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:01:45.780374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:01:46.136451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:01:45.565244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:01:45.864968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:01:51.912323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조서유형면적 기정면적 변경면적 변경후최초결정일자비고공간도형존재여부
조서유형1.0000.2680.1990.194NaNNaN0.701
면적 기정0.2681.0000.6791.0001.0000.0000.272
면적 변경0.1990.6791.0000.7891.000NaN0.190
면적 변경후0.1941.0000.7891.0001.0000.0000.792
최초결정일자NaN1.0001.0001.0001.0000.932NaN
비고NaN0.000NaN0.0000.9321.000NaN
공간도형존재여부0.7010.2720.1900.792NaNNaN1.000
2023-12-12T09:01:52.054688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최초결정일자비고조서유형공간도형존재여부
최초결정일자1.0000.6471.0001.000
비고0.6471.0001.0001.000
조서유형1.0001.0001.0000.947
공간도형존재여부1.0001.0000.9471.000
2023-12-12T09:01:52.197409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적 기정면적 변경면적 변경후조서유형최초결정일자비고공간도형존재여부
면적 기정1.0000.7180.9930.1740.8160.0000.180
면적 변경0.7181.0000.7740.1520.8041.0000.231
면적 변경후0.9930.7741.0000.2360.8160.0000.919
조서유형0.1740.1520.2361.0001.0001.0000.947
최초결정일자0.8160.8040.8161.0001.0000.6471.000
비고0.0001.0000.0001.0000.6471.0001.000
공간도형존재여부0.1800.2310.9190.9471.0001.0001.000

Missing values

2023-12-12T09:01:46.313614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:01:46.512676image/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.
2023-12-12T09:01:46.696568image/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

조서유형도면번호위치명지역명면적 기정면적 변경면적 변경후연장폭제한내용최초결정일자최초결정일자정보비고공간도형존재여부
0PMA00047-31죽곡면 봉정리봉정리170000.0170000.0<NA><NA><NA><NA><NA><NA><NA>N
1PMA00047-32죽곡면 동계리동계리71000.071000.0<NA><NA><NA><NA><NA><NA><NA>N
2PMA00047-33죽곡면 원달리원달리82000.082000.0<NA><NA><NA><NA><NA><NA><NA>N
3PMA00047-34고달면 대사리대사리134000.0134000.0<NA><NA><NA><NA><NA><NA><NA>N
4PMA00047-35고달면 백곡리백곡리75000.075000.0<NA><NA><NA><NA><NA><NA><NA>N
5PMA00047-36고달면 대사리대사리67000.067000.0<NA><NA><NA><NA><NA><NA><NA>N
6PMA00047-37고달면 백곡리백곡리72000.072000.0<NA><NA><NA><NA><NA><NA><NA>N
7PMA00047-38고달면 뇌죽리뇌죽리53000.053000.0<NA><NA><NA><NA><NA><NA><NA>N
8PMA00047-39고달면 목동리목동리174000.0174000.0<NA><NA><NA><NA><NA><NA><NA>N
9PMA00047-40고달면 고달리고달리56000.056000.0<NA><NA><NA><NA><NA><NA><NA>N
조서유형도면번호위치명지역명면적 기정면적 변경면적 변경후연장폭제한내용최초결정일자최초결정일자정보비고공간도형존재여부
407PMA0003273입면 금산리 278 일원내금22100.0779.022879.0<NA><NA><NA><NA><NA><NA>Y
408PMA0003278입면 삼오리 585일원삼오78370.0<NA>78370.0<NA><NA><NA><NA><NA><NA>Y
409PMA0003280입면 매월리 439일원매월15740.0<NA>15740.0<NA><NA><NA><NA><NA><NA>Y
410PMA0003283입면 만수리 539-2일원소운치11520.0320.011840.0<NA><NA><NA><NA><NA><NA>Y
411PMA0003285입면 만수리 68일원만수54560.0<NA>54560.0<NA><NA><NA><NA><NA><NA>Y
412PMA0003286입면 약천리 324일원약천67750.0<NA>67750.0<NA><NA><NA><NA><NA><NA>Y
413PMA0003288입면 입석리 240일원상입73580.0<NA>73580.0<NA><NA><NA><NA><NA><NA>Y
414PMA0003331오산면 운곡리 305일원세곡18540.0<NA>18540.0<NA><NA><NA><NA><NA><NA>Y
415PMA000310석곡면 연반리 산19번지 일원헬스팜301194.0101.0301093.0<NA><NA><NA>2015-11-19<NA><NA>Y
416PMA00033석곡면 연반리 697번지 일원석곡농공단지124000.0311.8124311.8<NA><NA><NA>1992-12-16<NA><NA>Y