Overview

Dataset statistics

Number of variables18
Number of observations500
Missing cells2251
Missing cells (%)25.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory75.3 KiB
Average record size in memory154.3 B

Variable types

Text7
Numeric5
Categorical4
Unsupported2

Dataset

Description샘플 데이터
Author국토교통부(open.eais.go.kr)
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=28

Alerts

대지_구분_코드 is highly imbalanced (89.8%)Imbalance
대표_여부 is highly imbalanced (87.3%)Imbalance
도로명_대지_위치 has 87 (17.4%) missing valuesMissing
특수지_명 has 495 (99.0%) missing valuesMissing
블록 has 500 (100.0%) missing valuesMissing
로트 has 500 (100.0%) missing valuesMissing
지역지구구역_코드 has 227 (45.4%) missing valuesMissing
지역지구구역_코드_명 has 235 (47.0%) missing valuesMissing
기타_지역지구구역 has 207 (41.4%) missing valuesMissing
관리_건축물대장_PK has unique valuesUnique
대지_위치 has unique valuesUnique
블록 is an unsupported type, check if it needs cleaning or further analysisUnsupported
로트 is an unsupported type, check if it needs cleaning or further analysisUnsupported
번 has 9 (1.8%) zerosZeros
지 has 150 (30.0%) zerosZeros

Reproduction

Analysis started2023-12-10 15:03:35.203257
Analysis finished2023-12-10 15:03:45.599173
Duration10.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리_건축물대장_PK
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:03:45.879258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length11
Mean length11.572
Min length7

Characters and Unicode

Total characters5786
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

Unique500 ?
Unique (%)100.0%

Sample

1st row42770-7782
2nd row11140-7908
3rd row44200-100267026
4th row26350-22494
5th row41670-100215629
ValueCountFrequency (%)
42770-7782 1
 
0.2%
44200-26907 1
 
0.2%
46780-7598 1
 
0.2%
41820-4227 1
 
0.2%
47930-11458 1
 
0.2%
46150-27911 1
 
0.2%
46840-30126 1
 
0.2%
26440-100236954 1
 
0.2%
45113-144314 1
 
0.2%
46770-26620 1
 
0.2%
Other values (490) 490
98.0%
2023-12-11T00:03:46.555841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 922
15.9%
0 878
15.2%
4 687
11.9%
2 587
10.1%
- 500
8.6%
7 433
7.5%
3 408
7.1%
8 393
6.8%
5 381
6.6%
6 344
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5286
91.4%
Dash Punctuation 500
 
8.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 922
17.4%
0 878
16.6%
4 687
13.0%
2 587
11.1%
7 433
8.2%
3 408
7.7%
8 393
7.4%
5 381
7.2%
6 344
 
6.5%
9 253
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5786
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 922
15.9%
0 878
15.2%
4 687
11.9%
2 587
10.1%
- 500
8.6%
7 433
7.5%
3 408
7.1%
8 393
6.8%
5 381
6.6%
6 344
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 922
15.9%
0 878
15.2%
4 687
11.9%
2 587
10.1%
- 500
8.6%
7 433
7.5%
3 408
7.1%
8 393
6.8%
5 381
6.6%
6 344
 
5.9%

대지_위치
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:03:47.171971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length27
Mean length22.496
Min length16

Characters and Unicode

Total characters11248
Distinct characters260
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

Unique500 ?
Unique (%)100.0%

Sample

1st row경상남도 산청군 오부면 중촌리 320번지
2nd row경상북도 성주군 성주읍 성산리 1430번지
3rd row전라남도 화순군 동면 운농리 404-1번지
4th row경상북도 상주시 이안면 소암리 427번지
5th row전라남도 나주시 산포면 매성리 산 52-5번지
ValueCountFrequency (%)
경상남도 97
 
4.1%
경기도 67
 
2.8%
전라남도 66
 
2.8%
경상북도 54
 
2.3%
전라북도 40
 
1.7%
서울특별시 39
 
1.7%
충청남도 23
 
1.0%
강원도 21
 
0.9%
창원시 19
 
0.8%
부산광역시 18
 
0.8%
Other values (1379) 1907
81.1%
2023-12-11T00:03:48.118998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1853
 
16.5%
지 518
 
4.6%
번 501
 
4.5%
도 417
 
3.7%
1 375
 
3.3%
- 338
 
3.0%
시 333
 
3.0%
리 302
 
2.7%
동 269
 
2.4%
2 268
 
2.4%
Other values (250) 6074
54.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7110
63.2%
Decimal Number 1947
 
17.3%
Space Separator 1853
 
16.5%
Dash Punctuation 338
 
3.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 518
 
7.3%
번 501
 
7.0%
도 417
 
5.9%
시 333
 
4.7%
리 302
 
4.2%
동 269
 
3.8%
남 258
 
3.6%
면 227
 
3.2%
경 227
 
3.2%
군 196
 
2.8%
Other values (238) 3862
54.3%
Decimal Number
ValueCountFrequency (%)
1 375
19.3%
2 268
13.8%
3 230
11.8%
5 181
9.3%
4 176
9.0%
6 170
8.7%
8 153
7.9%
7 152
7.8%
0 134
 
6.9%
9 108
 
5.5%
Space Separator
ValueCountFrequency (%)
1853
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 338
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7110
63.2%
Common 4138
36.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 518
 
7.3%
번 501
 
7.0%
도 417
 
5.9%
시 333
 
4.7%
리 302
 
4.2%
동 269
 
3.8%
남 258
 
3.6%
면 227
 
3.2%
경 227
 
3.2%
군 196
 
2.8%
Other values (238) 3862
54.3%
Common
ValueCountFrequency (%)
1853
44.8%
1 375
 
9.1%
- 338
 
8.2%
2 268
 
6.5%
3 230
 
5.6%
5 181
 
4.4%
4 176
 
4.3%
6 170
 
4.1%
8 153
 
3.7%
7 152
 
3.7%
Other values (2) 242
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7110
63.2%
ASCII 4138
36.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1853
44.8%
1 375
 
9.1%
- 338
 
8.2%
2 268
 
6.5%
3 230
 
5.6%
5 181
 
4.4%
4 176
 
4.3%
6 170
 
4.1%
8 153
 
3.7%
7 152
 
3.7%
Other values (2) 242
 
5.8%
Hangul
ValueCountFrequency (%)
지 518
 
7.3%
번 501
 
7.0%
도 417
 
5.9%
시 333
 
4.7%
리 302
 
4.2%
동 269
 
3.8%
남 258
 
3.6%
면 227
 
3.2%
경 227
 
3.2%
군 196
 
2.8%
Other values (238) 3862
54.3%

도로명_대지_위치
Text

MISSING 

Distinct413
Distinct (%)100.0%
Missing87
Missing (%)17.4%
Memory size4.0 KiB
2023-12-11T00:03:48.865162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length25
Mean length18.917676
Min length14

Characters and Unicode

Total characters7813
Distinct characters268
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

Unique413 ?
Unique (%)100.0%

Sample

1st row전라남도 영암군 원봉소길 50
2nd row경기도 성남시 분당구 안골로 50-8
3rd row서울특별시 강서구 화곡로13길 36
4th row경기도 고양시 일산서구 일청로91번길 24
5th row경상북도 경주시 순금1길 57-5
ValueCountFrequency (%)
경상남도 75
 
4.4%
경기도 72
 
4.3%
전라남도 48
 
2.8%
전라북도 43
 
2.5%
서울특별시 28
 
1.7%
경상북도 24
 
1.4%
강원도 20
 
1.2%
제주특별자치도 20
 
1.2%
부산광역시 20
 
1.2%
창원시 13
 
0.8%
Other values (889) 1330
78.6%
2023-12-11T00:03:49.863780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1281
 
16.4%
1 336
 
4.3%
도 331
 
4.2%
시 303
 
3.9%
길 301
 
3.9%
로 256
 
3.3%
2 247
 
3.2%
- 195
 
2.5%
3 194
 
2.5%
남 181
 
2.3%
Other values (258) 4188
53.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4750
60.8%
Decimal Number 1587
 
20.3%
Space Separator 1281
 
16.4%
Dash Punctuation 195
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 331
 
7.0%
시 303
 
6.4%
길 301
 
6.3%
로 256
 
5.4%
남 181
 
3.8%
경 176
 
3.7%
구 151
 
3.2%
군 133
 
2.8%
상 118
 
2.5%
산 113
 
2.4%
Other values (246) 2687
56.6%
Decimal Number
ValueCountFrequency (%)
1 336
21.2%
2 247
15.6%
3 194
12.2%
5 148
9.3%
4 135
8.5%
6 128
 
8.1%
8 109
 
6.9%
7 104
 
6.6%
9 99
 
6.2%
0 87
 
5.5%
Space Separator
ValueCountFrequency (%)
1281
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 195
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4750
60.8%
Common 3063
39.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 331
 
7.0%
시 303
 
6.4%
길 301
 
6.3%
로 256
 
5.4%
남 181
 
3.8%
경 176
 
3.7%
구 151
 
3.2%
군 133
 
2.8%
상 118
 
2.5%
산 113
 
2.4%
Other values (246) 2687
56.6%
Common
ValueCountFrequency (%)
1281
41.8%
1 336
 
11.0%
2 247
 
8.1%
- 195
 
6.4%
3 194
 
6.3%
5 148
 
4.8%
4 135
 
4.4%
6 128
 
4.2%
8 109
 
3.6%
7 104
 
3.4%
Other values (2) 186
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4750
60.8%
ASCII 3063
39.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1281
41.8%
1 336
 
11.0%
2 247
 
8.1%
- 195
 
6.4%
3 194
 
6.3%
5 148
 
4.8%
4 135
 
4.4%
6 128
 
4.2%
8 109
 
3.6%
7 104
 
3.4%
Other values (2) 186
 
6.1%
Hangul
ValueCountFrequency (%)
도 331
 
7.0%
시 303
 
6.4%
길 301
 
6.3%
로 256
 
5.4%
남 181
 
3.8%
경 176
 
3.7%
구 151
 
3.2%
군 133
 
2.8%
상 118
 
2.5%
산 113
 
2.4%
Other values (246) 2687
56.6%

시군구_코드
Real number (ℝ)

Distinct180
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41195.636
Minimum11110
Maximum50130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:03:50.200752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11545
Q141390
median45190
Q347290
95-th percentile48880
Maximum50130
Range39020
Interquartile range (IQR)5900

Descriptive statistics

Standard deviation10221.134
Coefficient of variation (CV)0.24811205
Kurtosis2.5138866
Mean41195.636
Median Absolute Deviation (MAD)3070
Skewness-1.8510663
Sum20597818
Variance1.0447157 × 108
MonotonicityNot monotonic
2023-12-11T00:03:50.587265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50130 10
 
2.0%
41500 9
 
1.8%
45130 8
 
1.6%
46780 8
 
1.6%
48740 8
 
1.6%
48170 8
 
1.6%
48240 8
 
1.6%
50110 7
 
1.4%
48220 7
 
1.4%
41220 7
 
1.4%
Other values (170) 420
84.0%
ValueCountFrequency (%)
11110 2
 
0.4%
11170 1
 
0.2%
11215 2
 
0.4%
11230 3
0.6%
11260 2
 
0.4%
11305 1
 
0.2%
11350 1
 
0.2%
11440 2
 
0.4%
11470 1
 
0.2%
11500 6
1.2%
ValueCountFrequency (%)
50130 10
2.0%
50110 7
1.4%
48890 5
1.0%
48880 5
1.0%
48870 4
 
0.8%
48860 7
1.4%
48850 2
 
0.4%
48840 1
 
0.2%
48820 1
 
0.2%
48740 8
1.6%

법정동_코드
Real number (ℝ)

Distinct191
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23818.052
Minimum10100
Maximum46023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:03:51.011786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10100
5-th percentile10200
Q110800
median25179
Q334026
95-th percentile39030.2
Maximum46023
Range35923
Interquartile range (IQR)23226

Descriptive statistics

Standard deviation11228.559
Coefficient of variation (CV)0.47143063
Kurtosis-1.584271
Mean23818.052
Median Absolute Deviation (MAD)11847.5
Skewness-0.037476296
Sum11909026
Variance1.2608055 × 108
MonotonicityNot monotonic
2023-12-11T00:03:51.309317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10300 25
 
5.0%
10200 22
 
4.4%
10100 20
 
4.0%
10800 14
 
2.8%
10600 13
 
2.6%
10700 12
 
2.4%
10500 11
 
2.2%
10400 10
 
2.0%
25021 10
 
2.0%
33023 7
 
1.4%
Other values (181) 356
71.2%
ValueCountFrequency (%)
10100 20
4.0%
10200 22
4.4%
10300 25
5.0%
10400 10
 
2.0%
10500 11
2.2%
10600 13
2.6%
10700 12
2.4%
10800 14
2.8%
10900 2
 
0.4%
11000 4
 
0.8%
ValueCountFrequency (%)
46023 1
0.2%
46021 1
0.2%
45022 1
0.2%
44026 1
0.2%
43033 1
0.2%
43030 1
0.2%
43022 1
0.2%
42029 1
0.2%
42023 1
0.2%
41031 1
0.2%

대지_구분_코드
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
489 
1
 
10
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 489
97.8%
1 10
 
2.0%
2 1
 
0.2%

Length

2023-12-11T00:03:51.930708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:03:52.277151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 489
97.8%
1 10
 
2.0%
2 1
 
0.2%

번
Real number (ℝ)

ZEROS 

Distinct399
Distinct (%)79.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean515.938
Minimum0
Maximum6132
Zeros9
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:03:52.510159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25.75
Q1172.25
median384.5
Q3686.5
95-th percentile1416.1
Maximum6132
Range6132
Interquartile range (IQR)514.25

Descriptive statistics

Standard deviation548.72085
Coefficient of variation (CV)1.0635403
Kurtosis28.138481
Mean515.938
Median Absolute Deviation (MAD)239.5
Skewness3.9282345
Sum257969
Variance301094.57
MonotonicityNot monotonic
2023-12-11T00:03:52.795907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
1.8%
160 4
 
0.8%
8 4
 
0.8%
476 3
 
0.6%
405 3
 
0.6%
321 3
 
0.6%
274 3
 
0.6%
245 3
 
0.6%
249 3
 
0.6%
56 3
 
0.6%
Other values (389) 462
92.4%
ValueCountFrequency (%)
0 9
1.8%
1 1
 
0.2%
3 2
 
0.4%
6 1
 
0.2%
7 1
 
0.2%
8 4
0.8%
12 1
 
0.2%
13 2
 
0.4%
16 1
 
0.2%
18 1
 
0.2%
ValueCountFrequency (%)
6132 1
0.2%
4212 1
0.2%
3543 1
0.2%
2793 1
0.2%
2647 1
0.2%
2399 1
0.2%
2280 1
0.2%
2082 1
0.2%
2068 1
0.2%
2032 1
0.2%

지
Real number (ℝ)

ZEROS 

Distinct65
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.594
Minimum0
Maximum302
Zeros150
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:03:53.088191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q38.25
95-th percentile56.1
Maximum302
Range302
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation27.895977
Coefficient of variation (CV)2.4060701
Kurtosis35.620467
Mean11.594
Median Absolute Deviation (MAD)2
Skewness5.1090365
Sum5797
Variance778.18554
MonotonicityNot monotonic
2023-12-11T00:03:53.393490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 150
30.0%
1 68
13.6%
2 52
 
10.4%
3 30
 
6.0%
4 24
 
4.8%
5 20
 
4.0%
6 16
 
3.2%
9 13
 
2.6%
8 8
 
1.6%
7 7
 
1.4%
Other values (55) 112
22.4%
ValueCountFrequency (%)
0 150
30.0%
1 68
13.6%
2 52
 
10.4%
3 30
 
6.0%
4 24
 
4.8%
5 20
 
4.0%
6 16
 
3.2%
7 7
 
1.4%
8 8
 
1.6%
9 13
 
2.6%
ValueCountFrequency (%)
302 1
0.2%
208 1
0.2%
178 1
0.2%
166 1
0.2%
157 1
0.2%
150 1
0.2%
127 1
0.2%
115 2
0.4%
108 1
0.2%
100 1
0.2%

특수지_명
Text

MISSING 

Distinct5
Distinct (%)100.0%
Missing495
Missing (%)99.0%
Memory size4.0 KiB
2023-12-11T00:03:53.752564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length15
Mean length13.4
Min length7

Characters and Unicode

Total characters67
Distinct characters48
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st row경상남도 하동군 청암면 묵계리 1537블럭 5노트
2nd row신항 북 '컨' 배후물류부지
3rd row5B 1-3L
4th row수도권매립지2-1공구
5th row진장,명촌지구
ValueCountFrequency (%)
경상남도 1
 
7.1%
하동군 1
 
7.1%
청암면 1
 
7.1%
묵계리 1
 
7.1%
1537블럭 1
 
7.1%
5노트 1
 
7.1%
신항 1
 
7.1%
북 1
 
7.1%
컨 1
 
7.1%
배후물류부지 1
 
7.1%
Other values (4) 4
28.6%
2023-12-11T00:03:54.919658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9
 
13.4%
지 3
 
4.5%
1 3
 
4.5%
5 3
 
4.5%
3 2
 
3.0%
' 2
 
3.0%
도 2
 
3.0%
구 2
 
3.0%
- 2
 
3.0%
경 1
 
1.5%
Other values (38) 38
56.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 41
61.2%
Decimal Number 10
 
14.9%
Space Separator 9
 
13.4%
Other Punctuation 3
 
4.5%
Dash Punctuation 2
 
3.0%
Uppercase Letter 2
 
3.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 3
 
7.3%
도 2
 
4.9%
구 2
 
4.9%
경 1
 
2.4%
물 1
 
2.4%
류 1
 
2.4%
부 1
 
2.4%
수 1
 
2.4%
권 1
 
2.4%
배 1
 
2.4%
Other values (27) 27
65.9%
Decimal Number
ValueCountFrequency (%)
1 3
30.0%
5 3
30.0%
3 2
20.0%
2 1
 
10.0%
7 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
' 2
66.7%
, 1
33.3%
Uppercase Letter
ValueCountFrequency (%)
B 1
50.0%
L 1
50.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 41
61.2%
Common 24
35.8%
Latin 2
 
3.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 3
 
7.3%
도 2
 
4.9%
구 2
 
4.9%
경 1
 
2.4%
물 1
 
2.4%
류 1
 
2.4%
부 1
 
2.4%
수 1
 
2.4%
권 1
 
2.4%
배 1
 
2.4%
Other values (27) 27
65.9%
Common
ValueCountFrequency (%)
9
37.5%
1 3
 
12.5%
5 3
 
12.5%
3 2
 
8.3%
' 2
 
8.3%
- 2
 
8.3%
2 1
 
4.2%
, 1
 
4.2%
7 1
 
4.2%
Latin
ValueCountFrequency (%)
B 1
50.0%
L 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 41
61.2%
ASCII 26
38.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9
34.6%
1 3
 
11.5%
5 3
 
11.5%
3 2
 
7.7%
' 2
 
7.7%
- 2
 
7.7%
B 1
 
3.8%
L 1
 
3.8%
2 1
 
3.8%
, 1
 
3.8%
Hangul
ValueCountFrequency (%)
지 3
 
7.3%
도 2
 
4.9%
구 2
 
4.9%
경 1
 
2.4%
물 1
 
2.4%
류 1
 
2.4%
부 1
 
2.4%
수 1
 
2.4%
권 1
 
2.4%
배 1
 
2.4%
Other values (27) 27
65.9%

블록
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing500
Missing (%)100.0%
Memory size4.5 KiB

로트
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing500
Missing (%)100.0%
Memory size4.5 KiB

지역지구구역_구분_코드
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
260 
3
127 
2
113 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row3
5th row1

Common Values

ValueCountFrequency (%)
1 260
52.0%
3 127
25.4%
2 113
22.6%

Length

2023-12-11T00:03:55.181004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:03:55.437207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 260
52.0%
3 127
25.4%
2 113
22.6%

지역지구구역_구분_코드_명
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
용도지역코드
264 
용도지구코드
121 
용도구역코드
115 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row용도구역코드
2nd row용도지역코드
3rd row용도지역코드
4th row용도지역코드
5th row용도구역코드

Common Values

ValueCountFrequency (%)
용도지역코드 264
52.8%
용도지구코드 121
24.2%
용도구역코드 115
23.0%

Length

2023-12-11T00:03:55.688794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:03:55.912839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
용도지역코드 264
52.8%
용도지구코드 121
24.2%
용도구역코드 115
23.0%

지역지구구역_코드
Text

MISSING 

Distinct76
Distinct (%)27.8%
Missing227
Missing (%)45.4%
Memory size4.0 KiB
2023-12-11T00:03:56.466872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.2307692
Min length2

Characters and Unicode

Total characters1155
Distinct characters26
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

Unique32 ?
Unique (%)11.7%

Sample

1st rowZ200
2nd row1020
3rd row0230
4th row0230
5th rowUQA001
ValueCountFrequency (%)
1020 33
 
12.1%
z200 14
 
5.1%
uqa122 14
 
5.1%
1330 13
 
4.8%
0230 13
 
4.8%
1022 13
 
4.8%
uqb100 11
 
4.0%
070 9
 
3.3%
1120 9
 
3.3%
1021 8
 
2.9%
Other values (66) 136
49.8%
2023-12-11T00:03:57.298377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 387
33.5%
1 221
19.1%
2 195
16.9%
3 76
 
6.6%
U 63
 
5.5%
Q 58
 
5.0%
A 29
 
2.5%
4 21
 
1.8%
Z 19
 
1.6%
9 19
 
1.6%
Other values (16) 67
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 946
81.9%
Uppercase Letter 209
 
18.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 63
30.1%
Q 58
27.8%
A 29
13.9%
Z 19
 
9.1%
B 15
 
7.2%
M 7
 
3.3%
N 5
 
2.4%
E 2
 
1.0%
H 2
 
1.0%
C 2
 
1.0%
Other values (6) 7
 
3.3%
Decimal Number
ValueCountFrequency (%)
0 387
40.9%
1 221
23.4%
2 195
20.6%
3 76
 
8.0%
4 21
 
2.2%
9 19
 
2.0%
7 15
 
1.6%
8 8
 
0.8%
5 2
 
0.2%
6 2
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 946
81.9%
Latin 209
 
18.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 63
30.1%
Q 58
27.8%
A 29
13.9%
Z 19
 
9.1%
B 15
 
7.2%
M 7
 
3.3%
N 5
 
2.4%
E 2
 
1.0%
H 2
 
1.0%
C 2
 
1.0%
Other values (6) 7
 
3.3%
Common
ValueCountFrequency (%)
0 387
40.9%
1 221
23.4%
2 195
20.6%
3 76
 
8.0%
4 21
 
2.2%
9 19
 
2.0%
7 15
 
1.6%
8 8
 
0.8%
5 2
 
0.2%
6 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1155
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 387
33.5%
1 221
19.1%
2 195
16.9%
3 76
 
6.6%
U 63
 
5.5%
Q 58
 
5.0%
A 29
 
2.5%
4 21
 
1.8%
Z 19
 
1.6%
9 19
 
1.6%
Other values (16) 67
 
5.8%

지역지구구역_코드_명
Text

MISSING 

Distinct53
Distinct (%)20.0%
Missing235
Missing (%)47.0%
Memory size4.0 KiB
2023-12-11T00:03:57.762676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length6
Mean length6.2981132
Min length4

Characters and Unicode

Total characters1669
Distinct characters80
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)7.2%

Sample

1st row일반주거지역
2nd row제1종일반주거지역
3rd row자연녹지지역
4th row준공업지역
5th row제1종지구단위계획구역
ValueCountFrequency (%)
일반주거지역 46
17.3%
자연녹지지역 23
 
8.6%
계획관리지역 20
 
7.5%
제2종일반주거지역 15
 
5.6%
제1종일반주거지역 14
 
5.3%
농림지역 13
 
4.9%
일반상업지역 11
 
4.1%
준주거지역 7
 
2.6%
준농림지역 7
 
2.6%
제1종지구단위계획구역 6
 
2.3%
Other values (44) 104
39.1%
2023-12-11T00:03:58.504695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 281
16.8%
역 225
 
13.5%
일 94
 
5.6%
반 94
 
5.6%
주 93
 
5.6%
거 91
 
5.5%
구 72
 
4.3%
제 46
 
2.8%
관 41
 
2.5%
종 39
 
2.3%
Other values (70) 593
35.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1628
97.5%
Decimal Number 40
 
2.4%
Space Separator 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 281
17.3%
역 225
13.8%
일 94
 
5.8%
반 94
 
5.8%
주 93
 
5.7%
거 91
 
5.6%
구 72
 
4.4%
제 46
 
2.8%
관 41
 
2.5%
종 39
 
2.4%
Other values (65) 552
33.9%
Decimal Number
ValueCountFrequency (%)
1 21
52.5%
2 16
40.0%
4 2
 
5.0%
3 1
 
2.5%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1628
97.5%
Common 41
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 281
17.3%
역 225
13.8%
일 94
 
5.8%
반 94
 
5.8%
주 93
 
5.7%
거 91
 
5.6%
구 72
 
4.4%
제 46
 
2.8%
관 41
 
2.5%
종 39
 
2.4%
Other values (65) 552
33.9%
Common
ValueCountFrequency (%)
1 21
51.2%
2 16
39.0%
4 2
 
4.9%
3 1
 
2.4%
1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1628
97.5%
ASCII 41
 
2.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 281
17.3%
역 225
13.8%
일 94
 
5.8%
반 94
 
5.8%
주 93
 
5.7%
거 91
 
5.6%
구 72
 
4.4%
제 46
 
2.8%
관 41
 
2.5%
종 39
 
2.4%
Other values (65) 552
33.9%
ASCII
ValueCountFrequency (%)
1 21
51.2%
2 16
39.0%
4 2
 
4.9%
3 1
 
2.4%
1
 
2.4%

대표_여부
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
486 
0
 
12
<NA>
 
2

Length

Max length4
Median length1
Mean length1.012
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 486
97.2%
0 12
 
2.4%
<NA> 2
 
0.4%

Length

2023-12-11T00:03:58.784326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:03:58.942865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 486
97.2%
0 12
 
2.4%
na 2
 
0.4%

기타_지역지구구역
Text

MISSING 

Distinct88
Distinct (%)30.0%
Missing207
Missing (%)41.4%
Memory size4.0 KiB
2023-12-11T00:03:59.293212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length6.0784983
Min length2

Characters and Unicode

Total characters1781
Distinct characters101
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)16.4%

Sample

1st row준주거지역
2nd row자연환경보전지역
3rd row준농림지역
4th row제1종일반주거지역
5th row제1종지구단위계획구역
ValueCountFrequency (%)
일반주거지역 31
 
10.5%
관리지역 25
 
8.5%
제2종일반주거지역 18
 
6.1%
준농림지역 14
 
4.8%
계획관리지역 12
 
4.1%
준농림 11
 
3.7%
자연녹지지역 11
 
3.7%
자연취락지구 9
 
3.1%
2종일반주거지역 9
 
3.1%
개발제한구역 8
 
2.7%
Other values (78) 146
49.7%
2023-12-11T00:03:59.869990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 254
 
14.3%
역 208
 
11.7%
일 96
 
5.4%
반 96
 
5.4%
주 94
 
5.3%
구 91
 
5.1%
거 90
 
5.1%
종 52
 
2.9%
관 49
 
2.8%
제 49
 
2.8%
Other values (91) 702
39.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1709
96.0%
Decimal Number 57
 
3.2%
Open Punctuation 5
 
0.3%
Close Punctuation 5
 
0.3%
Uppercase Letter 2
 
0.1%
Space Separator 1
 
0.1%
Other Punctuation 1
 
0.1%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 254
14.9%
역 208
 
12.2%
일 96
 
5.6%
반 96
 
5.6%
주 94
 
5.5%
구 91
 
5.3%
거 90
 
5.3%
종 52
 
3.0%
관 49
 
2.9%
제 49
 
2.9%
Other values (77) 630
36.9%
Decimal Number
ValueCountFrequency (%)
2 32
56.1%
1 16
28.1%
3 3
 
5.3%
4 2
 
3.5%
7 2
 
3.5%
8 1
 
1.8%
0 1
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
U 1
50.0%
D 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1709
96.0%
Common 69
 
3.9%
Latin 3
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 254
14.9%
역 208
 
12.2%
일 96
 
5.6%
반 96
 
5.6%
주 94
 
5.5%
구 91
 
5.3%
거 90
 
5.3%
종 52
 
3.0%
관 49
 
2.9%
제 49
 
2.9%
Other values (77) 630
36.9%
Common
ValueCountFrequency (%)
2 32
46.4%
1 16
23.2%
( 5
 
7.2%
) 5
 
7.2%
3 3
 
4.3%
4 2
 
2.9%
7 2
 
2.9%
8 1
 
1.4%
1
 
1.4%
, 1
 
1.4%
Latin
ValueCountFrequency (%)
m 1
33.3%
U 1
33.3%
D 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1709
96.0%
ASCII 72
 
4.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 254
14.9%
역 208
 
12.2%
일 96
 
5.6%
반 96
 
5.6%
주 94
 
5.5%
구 91
 
5.3%
거 90
 
5.3%
종 52
 
3.0%
관 49
 
2.9%
제 49
 
2.9%
Other values (77) 630
36.9%
ASCII
ValueCountFrequency (%)
2 32
44.4%
1 16
22.2%
( 5
 
6.9%
) 5
 
6.9%
3 3
 
4.2%
4 2
 
2.8%
7 2
 
2.8%
8 1
 
1.4%
1
 
1.4%
, 1
 
1.4%
Other values (4) 4
 
5.6%

생성_일자
Real number (ℝ)

Distinct197
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20116803
Minimum20090318
Maximum20160528
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:04:00.097732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090318
5-th percentile20090323
Q120110415
median20111106
Q320121116
95-th percentile20150902
Maximum20160528
Range70210
Interquartile range (IQR)10701

Descriptive statistics

Standard deviation17518.244
Coefficient of variation (CV)0.00087082643
Kurtosis0.1834481
Mean20116803
Median Absolute Deviation (MAD)691.5
Skewness0.91512006
Sum1.0058401 × 1010
Variance3.0688886 × 108
MonotonicityNot monotonic
2023-12-11T00:04:00.342041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20110420 47
 
9.4%
20110415 36
 
7.2%
20110418 27
 
5.4%
20111124 27
 
5.4%
20111117 27
 
5.4%
20111123 21
 
4.2%
20111021 15
 
3.0%
20111125 12
 
2.4%
20100105 10
 
2.0%
20090321 8
 
1.6%
Other values (187) 270
54.0%
ValueCountFrequency (%)
20090318 2
 
0.4%
20090319 7
1.4%
20090320 7
1.4%
20090321 8
1.6%
20090323 2
 
0.4%
20090325 2
 
0.4%
20090421 1
 
0.2%
20090529 1
 
0.2%
20090630 1
 
0.2%
20090709 1
 
0.2%
ValueCountFrequency (%)
20160528 1
0.2%
20160518 1
0.2%
20160511 1
0.2%
20160510 1
0.2%
20160506 1
0.2%
20160429 1
0.2%
20160412 1
0.2%
20160407 2
0.4%
20160311 1
0.2%
20160224 2
0.4%

Interactions

2023-12-11T00:03:43.277293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:37.504630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:38.957243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:40.852112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:42.120166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:43.510704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:37.845102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:39.271528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:41.168399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:42.413306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:43.766471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:38.109677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:39.566517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:41.394244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:42.611911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:43.979765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:38.360668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:40.333219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:41.671436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:42.863561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:44.200938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:38.606938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:40.598701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:41.891056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:03:43.064408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T00:04:00.517891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구_코드법정동_코드대지_구분_코드번지특수지_명지역지구구역_구분_코드지역지구구역_구분_코드_명지역지구구역_코드지역지구구역_코드_명대표_여부기타_지역지구구역생성_일자
시군구_코드1.0000.2940.0000.0000.2411.0000.0000.0000.0000.0000.0000.0000.000
법정동_코드0.2941.0000.0000.0700.0931.0000.0000.1480.2500.0000.0990.4270.000
대지_구분_코드0.0000.0001.0000.1220.000NaN0.0000.0000.2060.4380.0200.6970.062
번0.0000.0700.1221.0000.0001.0000.0000.0670.6430.1040.2240.6550.254
지0.2410.0930.0000.0001.000NaN0.0320.0370.3630.0000.0000.3930.073
특수지_명1.0001.000NaN1.000NaN1.0001.0001.0001.0000.000NaN1.0001.000
지역지구구역_구분_코드0.0000.0000.0000.0000.0321.0001.0000.2000.0000.3510.0000.1750.096
지역지구구역_구분_코드_명0.0000.1480.0000.0670.0371.0000.2001.0000.0000.0000.0210.2970.050
지역지구구역_코드0.0000.2500.2060.6430.3631.0000.0000.0001.0000.7550.0000.6930.364
지역지구구역_코드_명0.0000.0000.4380.1040.0000.0000.3510.0000.7551.0000.0000.0000.000
대표_여부0.0000.0990.0200.2240.000NaN0.0000.0210.0000.0001.0000.1420.150
기타_지역지구구역0.0000.4270.6970.6550.3931.0000.1750.2970.6930.0000.1421.0000.000
생성_일자0.0000.0000.0620.2540.0731.0000.0960.0500.3640.0000.1500.0001.000
2023-12-11T00:04:00.768912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대지_구분_코드지역지구구역_구분_코드_명대표_여부지역지구구역_구분_코드
대지_구분_코드1.0000.0000.0320.000
지역지구구역_구분_코드_명0.0001.0000.0350.063
대표_여부0.0320.0351.0000.000
지역지구구역_구분_코드0.0000.0630.0001.000
2023-12-11T00:04:00.937255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구_코드법정동_코드번지생성_일자대지_구분_코드지역지구구역_구분_코드지역지구구역_구분_코드_명대표_여부
시군구_코드1.0000.0930.0100.0320.0530.0000.0000.0000.000
법정동_코드0.0931.0000.002-0.058-0.0200.0000.0000.0810.083
번0.0100.0021.000-0.014-0.0360.0770.0000.0410.167
지0.032-0.058-0.0141.000-0.0020.0000.0190.0230.000
생성_일자0.053-0.020-0.036-0.0021.0000.0390.0470.0360.111
대지_구분_코드0.0000.0000.0770.0000.0391.0000.0000.0000.032
지역지구구역_구분_코드0.0000.0000.0000.0190.0470.0001.0000.0630.000
지역지구구역_구분_코드_명0.0000.0810.0410.0230.0360.0000.0631.0000.035
대표_여부0.0000.0830.1670.0000.1110.0320.0000.0351.000

Missing values

2023-12-11T00:03:44.518240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T00:03:45.000993image/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.
2023-12-11T00:03:45.431452image/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

관리_건축물대장_PK대지_위치도로명_대지_위치시군구_코드법정동_코드대지_구분_코드번지특수지_명블록로트지역지구구역_구분_코드지역지구구역_구분_코드_명지역지구구역_코드지역지구구역_코드_명대표_여부기타_지역지구구역생성_일자
042770-7782경상남도 산청군 오부면 중촌리 320번지전라남도 영암군 원봉소길 50451301070001515<NA><NA><NA>1용도구역코드Z200<NA>1<NA>20110418
111140-7908경상북도 성주군 성주읍 성산리 1430번지<NA>461503402304771<NA><NA><NA>1용도지역코드1020일반주거지역1<NA>20111123
244200-100267026전라남도 화순군 동면 운농리 404-1번지경기도 성남시 분당구 안골로 50-8448103302715142<NA><NA><NA>3용도지역코드0230제1종일반주거지역1<NA>20111021
326350-22494경상북도 상주시 이안면 소암리 427번지서울특별시 강서구 화곡로13길 364673025026071227<NA><NA><NA>3용도지역코드0230자연녹지지역1준주거지역20121121
441670-100215629전라남도 나주시 산포면 매성리 산 52-5번지경기도 고양시 일산서구 일청로91번길 24477301370001453<NA><NA><NA>1용도구역코드<NA>준공업지역1<NA>20110415
548220-12600경상남도 진주시 호탄동 620-2번지경상북도 경주시 순금1길 57-54723025026056114<NA><NA><NA>1용도지역코드<NA>제1종지구단위계획구역1자연환경보전지역20090325
642800-2310서울특별시 관악구 신림동 533-18번지충청남도 청양군 만수로 1573112303302701559<NA><NA><NA>3용도지구코드<NA><NA>1<NA>20140731
742730-14232서울특별시 관악구 신림동 540-21번지부산광역시 부산진구 엄광로 263-10282453302301139<NA><NA><NA>3용도지역코드UQA001농림지역1준농림지역20111124
827140-5635경기도 부천시 오정구 작동 65-3번지경상남도 함양군 내천길 8-6451903302501101<NA><NA><NA>2용도지역코드9900<NA>1<NA>20110415
947170-100183363부산광역시 부산진구 전포동 355-33번지<NA>1168010300016076<NA><NA><NA>1용도지역코드<NA><NA>1제1종일반주거지역20091208
관리_건축물대장_PK대지_위치도로명_대지_위치시군구_코드법정동_코드대지_구분_코드번지특수지_명블록로트지역지구구역_구분_코드지역지구구역_구분_코드_명지역지구구역_코드지역지구구역_코드_명대표_여부기타_지역지구구역생성_일자
49048860-33768전라남도 완도군 군외면 당인리 81번지전라북도 부안군 창북중앙길 54487403903601552<NA><NA><NA>1용도지역코드<NA>준농림지역1<NA>20150516
49126440-100251359경상북도 영천시 신녕면 완전리 689번지전라북도 장수군 성재길 8-52638037022114160<NA><NA><NA>1용도지역코드112배출시설설치제한지역1일반공업지역20110420
49248860-32044대구광역시 중구 남산동 2208-20번지서울특별시 강서구 양천로62길 29-10421103102104120<NA><NA><NA>2용도지역코드011자연녹지지역1<NA>20110418
49342210-4523전라남도 여수시 신기동 125-6번지강원도 횡성군 청정로 85227170119000420<NA><NA><NA>1용도지구코드<NA>농업진흥구역1농업진흥구역20121116
49450130-14644경상북도 안동시 일직면 조탑리 386번지부산광역시 사상구 백양대로907번가길 334825010100002<NA><NA><NA>2용도지구코드301일반상업지역<NA>일반주거지역20090421
49541220-26293전라북도 순창군 유등면 건곡리 970번지서울특별시 강남구 헌릉로569길 39467803904806680<NA><NA><NA>1용도구역코드0240택지개발사업구역1<NA>20110420
49641285-119831전라남도 여수시 관문동 292-4번지인천광역시 남구 석정로324번길 11-2344710370380918<NA><NA><NA>3용도구역코드Z200일반상업지역1<NA>20110420
49741610-20891경기도 안산시 단원구 원시동 735-3번지전라남도 함평군 손불중앙길 424812511100027013<NA><NA><NA>1용도지역코드010고도지구1<NA>20100705
49846170-48616경상남도 산청군 금서면 방곡리 815-2번지전라북도 남원시 사방로 129483103403004779<NA><NA><NA>1용도지역코드<NA>자연환경보전지역1제1종지구단위계획구역20111117
49941273-107812전라남도 순천시 남정동 560-8번지경상남도 사천시 벌리2길 39-1141111250280511<NA><NA><NA>1용도지역코드<NA><NA>1<NA>20100119