Overview

Dataset statistics

Number of variables11
Number of observations776
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory73.6 KiB
Average record size in memory97.2 B

Variable types

Numeric8
Text2
Categorical1

Dataset

Description지적재조사 사업을 처음에 진행할 때, 기초가 되는 연계요청 필지수, 토지소유자 정보, 토지현황조사, 건축물, 현지조사 항목, 토지이용계획에 관한 데이터 건수에 관한 데이터입니다.
Author국토교통부
URLhttps://www.data.go.kr/data/15123019/fileData.do

Alerts

연계수 is highly overall correlated with 요청필지수 and 4 other fieldsHigh correlation
요청필지수 is highly overall correlated with 연계수 and 1 other fieldsHigh correlation
토지소유자정보 is highly overall correlated with 연계수 and 4 other fieldsHigh correlation
토지현황조사 is highly overall correlated with 연계수 and 3 other fieldsHigh correlation
건축물 is highly overall correlated with 연계수 and 3 other fieldsHigh correlation
현지업데이트 is highly overall correlated with 연계수 and 3 other fieldsHigh correlation
토지이용 is highly imbalanced (92.6%)Imbalance
사업지구일련번호 has unique valuesUnique
현지업데이트 has 9 (1.2%) zerosZeros

Reproduction

Analysis started2023-12-12 02:50:29.243032
Analysis finished2023-12-12 02:50:37.673028
Duration8.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Real number (ℝ)

Distinct223
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44460.823
Minimum11140
Maximum51830
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2023-12-12T11:50:37.749319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11140
5-th percentile28196.25
Q143112
median46130
Q348139.25
95-th percentile51750
Maximum51830
Range40690
Interquartile range (IQR)5027.25

Descriptive statistics

Standard deviation6193.6671
Coefficient of variation (CV)0.13930617
Kurtosis3.0855335
Mean44460.823
Median Absolute Deviation (MAD)2380
Skewness-1.7419697
Sum34501599
Variance38361513
MonotonicityIncreasing
2023-12-12T11:50:37.901732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51230 19
 
2.4%
46830 18
 
2.3%
41220 16
 
2.1%
48240 14
 
1.8%
51810 13
 
1.7%
41500 12
 
1.5%
43740 12
 
1.5%
46870 11
 
1.4%
48820 10
 
1.3%
46170 10
 
1.3%
Other values (213) 641
82.6%
ValueCountFrequency (%)
11140 1
0.1%
26110 1
0.1%
26140 1
0.1%
26170 1
0.1%
26200 2
0.3%
26230 2
0.3%
26260 1
0.1%
26290 1
0.1%
26320 1
0.1%
26350 1
0.1%
ValueCountFrequency (%)
51830 1
 
0.1%
51820 2
 
0.3%
51810 13
1.7%
51800 4
 
0.5%
51790 7
0.9%
51780 5
 
0.6%
51770 3
 
0.4%
51760 2
 
0.3%
51750 5
 
0.6%
51730 6
0.8%
Distinct223
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
2023-12-12T11:50:38.220949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length8
Mean length8.6958763
Min length7

Characters and Unicode

Total characters6748
Distinct characters141
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

Unique63 ?
Unique (%)8.1%

Sample

1st row서울특별시 중구
2nd row부산광역시 중구
3rd row부산광역시 서구
4th row부산광역시 동구
5th row부산광역시 영도구
ValueCountFrequency (%)
전라남도 123
 
7.6%
경상남도 108
 
6.6%
경기도 107
 
6.6%
강원특별자치도 93
 
5.7%
충청북도 78
 
4.8%
경상북도 65
 
4.0%
충청남도 63
 
3.9%
전라북도 58
 
3.6%
청주시 21
 
1.3%
삼척시 19
 
1.2%
Other values (217) 891
54.8%
2023-12-12T11:50:38.601065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
850
 
12.6%
715
 
10.6%
427
 
6.3%
363
 
5.4%
325
 
4.8%
288
 
4.3%
219
 
3.2%
195
 
2.9%
184
 
2.7%
181
 
2.7%
Other values (131) 3001
44.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5898
87.4%
Space Separator 850
 
12.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
715
 
12.1%
427
 
7.2%
363
 
6.2%
325
 
5.5%
288
 
4.9%
219
 
3.7%
195
 
3.3%
184
 
3.1%
181
 
3.1%
178
 
3.0%
Other values (130) 2823
47.9%
Space Separator
ValueCountFrequency (%)
850
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5898
87.4%
Common 850
 
12.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
715
 
12.1%
427
 
7.2%
363
 
6.2%
325
 
5.5%
288
 
4.9%
219
 
3.7%
195
 
3.3%
184
 
3.1%
181
 
3.1%
178
 
3.0%
Other values (130) 2823
47.9%
Common
ValueCountFrequency (%)
850
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5898
87.4%
ASCII 850
 
12.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
850
100.0%
Hangul
ValueCountFrequency (%)
715
 
12.1%
427
 
7.2%
363
 
6.2%
325
 
5.5%
288
 
4.9%
219
 
3.7%
195
 
3.3%
184
 
3.1%
181
 
3.1%
178
 
3.0%
Other values (130) 2823
47.9%

사업지구일련번호
Real number (ℝ)

UNIQUE 

Distinct776
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8028.259
Minimum6921
Maximum8955
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2023-12-12T11:50:38.719390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6921
5-th percentile7270.75
Q17657.75
median8032.5
Q38407.25
95-th percentile8730.25
Maximum8955
Range2034
Interquartile range (IQR)749.5

Descriptive statistics

Standard deviation467.11231
Coefficient of variation (CV)0.058183512
Kurtosis-0.81853726
Mean8028.259
Median Absolute Deviation (MAD)375
Skewness-0.055509284
Sum6229929
Variance218193.91
MonotonicityNot monotonic
2023-12-12T11:50:38.839954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7812 1
 
0.1%
8471 1
 
0.1%
8247 1
 
0.1%
8242 1
 
0.1%
8243 1
 
0.1%
8244 1
 
0.1%
8248 1
 
0.1%
8249 1
 
0.1%
8642 1
 
0.1%
8592 1
 
0.1%
Other values (766) 766
98.7%
ValueCountFrequency (%)
6921 1
0.1%
6931 1
0.1%
6938 1
0.1%
6989 1
0.1%
7061 1
0.1%
7075 1
0.1%
7081 1
0.1%
7082 1
0.1%
7108 1
0.1%
7109 1
0.1%
ValueCountFrequency (%)
8955 1
0.1%
8951 1
0.1%
8950 1
0.1%
8949 1
0.1%
8947 1
0.1%
8946 1
0.1%
8945 1
0.1%
8943 1
0.1%
8942 1
0.1%
8941 1
0.1%
Distinct768
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
2023-12-12T11:50:39.097508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length5.6662371
Min length3

Characters and Unicode

Total characters4397
Distinct characters263
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique760 ?
Unique (%)97.9%

Sample

1st row신당9-1지구
2nd row영주동2지구
3rd row남부민1지구
4th row수정2지구
5th row청학2지구
ValueCountFrequency (%)
1지구 6
 
0.7%
가곡면 5
 
0.5%
지구 5
 
0.5%
청하면 5
 
0.5%
대동 4
 
0.4%
옥종 4
 
0.4%
지산면 4
 
0.4%
봉강면 3
 
0.3%
금산 3
 
0.3%
득량 3
 
0.3%
Other values (828) 874
95.4%
2023-12-12T11:50:39.455780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
797
18.1%
785
17.9%
1 266
 
6.0%
2 172
 
3.9%
143
 
3.3%
82
 
1.9%
63
 
1.4%
54
 
1.2%
3 47
 
1.1%
47
 
1.1%
Other values (253) 1941
44.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3649
83.0%
Decimal Number 566
 
12.9%
Space Separator 143
 
3.3%
Open Punctuation 13
 
0.3%
Close Punctuation 13
 
0.3%
Other Punctuation 12
 
0.3%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
797
21.8%
785
21.5%
82
 
2.2%
63
 
1.7%
54
 
1.5%
47
 
1.3%
46
 
1.3%
44
 
1.2%
42
 
1.2%
37
 
1.0%
Other values (237) 1652
45.3%
Decimal Number
ValueCountFrequency (%)
1 266
47.0%
2 172
30.4%
3 47
 
8.3%
4 31
 
5.5%
5 16
 
2.8%
0 11
 
1.9%
6 9
 
1.6%
9 5
 
0.9%
7 5
 
0.9%
8 4
 
0.7%
Other Punctuation
ValueCountFrequency (%)
, 7
58.3%
· 5
41.7%
Space Separator
ValueCountFrequency (%)
143
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3649
83.0%
Common 748
 
17.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
797
21.8%
785
21.5%
82
 
2.2%
63
 
1.7%
54
 
1.5%
47
 
1.3%
46
 
1.3%
44
 
1.2%
42
 
1.2%
37
 
1.0%
Other values (237) 1652
45.3%
Common
ValueCountFrequency (%)
1 266
35.6%
2 172
23.0%
143
19.1%
3 47
 
6.3%
4 31
 
4.1%
5 16
 
2.1%
( 13
 
1.7%
) 13
 
1.7%
0 11
 
1.5%
6 9
 
1.2%
Other values (6) 27
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3649
83.0%
ASCII 743
 
16.9%
None 5
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
797
21.8%
785
21.5%
82
 
2.2%
63
 
1.7%
54
 
1.5%
47
 
1.3%
46
 
1.3%
44
 
1.2%
42
 
1.2%
37
 
1.0%
Other values (237) 1652
45.3%
ASCII
ValueCountFrequency (%)
1 266
35.8%
2 172
23.1%
143
19.2%
3 47
 
6.3%
4 31
 
4.2%
5 16
 
2.2%
( 13
 
1.7%
) 13
 
1.7%
0 11
 
1.5%
6 9
 
1.2%
Other values (5) 22
 
3.0%
None
ValueCountFrequency (%)
· 5
100.0%

연계수
Real number (ℝ)

HIGH CORRELATION 

Distinct110
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.685567
Minimum1
Maximum447
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2023-12-12T11:50:39.572698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q112
median19
Q334.25
95-th percentile82.25
Maximum447
Range446
Interquartile range (IQR)22.25

Descriptive statistics

Standard deviation35.201233
Coefficient of variation (CV)1.185803
Kurtosis35.585774
Mean29.685567
Median Absolute Deviation (MAD)9
Skewness4.7299914
Sum23036
Variance1239.1268
MonotonicityNot monotonic
2023-12-12T11:50:39.698649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 39
 
5.0%
10 34
 
4.4%
15 32
 
4.1%
13 32
 
4.1%
18 28
 
3.6%
11 28
 
3.6%
9 28
 
3.6%
14 26
 
3.4%
19 25
 
3.2%
8 25
 
3.2%
Other values (100) 479
61.7%
ValueCountFrequency (%)
1 8
 
1.0%
2 4
 
0.5%
3 2
 
0.3%
4 11
 
1.4%
5 7
 
0.9%
6 13
 
1.7%
7 19
2.4%
8 25
3.2%
9 28
3.6%
10 34
4.4%
ValueCountFrequency (%)
447 1
 
0.1%
288 1
 
0.1%
242 1
 
0.1%
224 1
 
0.1%
201 1
 
0.1%
196 1
 
0.1%
191 1
 
0.1%
184 3
0.4%
179 1
 
0.1%
171 1
 
0.1%

요청필지수
Real number (ℝ)

HIGH CORRELATION 

Distinct722
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4237.7332
Minimum31
Maximum183814
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2023-12-12T11:50:39.814133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile282.25
Q1799.75
median1811.5
Q34285.5
95-th percentile14151.5
Maximum183814
Range183783
Interquartile range (IQR)3485.75

Descriptive statistics

Standard deviation9965.5945
Coefficient of variation (CV)2.3516333
Kurtosis158.46367
Mean4237.7332
Median Absolute Deviation (MAD)1290.5
Skewness10.556821
Sum3288481
Variance99313073
MonotonicityNot monotonic
2023-12-12T11:50:39.938929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
656 3
 
0.4%
341 3
 
0.4%
276 3
 
0.4%
1252 2
 
0.3%
863 2
 
0.3%
560 2
 
0.3%
651 2
 
0.3%
648 2
 
0.3%
298 2
 
0.3%
1008 2
 
0.3%
Other values (712) 753
97.0%
ValueCountFrequency (%)
31 1
0.1%
57 1
0.1%
62 1
0.1%
67 1
0.1%
68 1
0.1%
84 1
0.1%
105 1
0.1%
111 1
0.1%
116 1
0.1%
123 1
0.1%
ValueCountFrequency (%)
183814 1
0.1%
110356 1
0.1%
81617 1
0.1%
52458 1
0.1%
46939 1
0.1%
44144 1
0.1%
43693 1
0.1%
40230 1
0.1%
37850 1
0.1%
36891 1
0.1%

토지소유자정보
Real number (ℝ)

HIGH CORRELATION 

Distinct105
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.35567
Minimum1
Maximum443
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2023-12-12T11:50:40.109758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q111
median18
Q332.25
95-th percentile78
Maximum443
Range442
Interquartile range (IQR)21.25

Descriptive statistics

Standard deviation34.35552
Coefficient of variation (CV)1.2115926
Kurtosis37.726538
Mean28.35567
Median Absolute Deviation (MAD)9
Skewness4.8664992
Sum22004
Variance1180.3017
MonotonicityNot monotonic
2023-12-12T11:50:40.566259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 35
 
4.5%
13 34
 
4.4%
11 33
 
4.3%
14 33
 
4.3%
10 32
 
4.1%
7 29
 
3.7%
17 27
 
3.5%
9 26
 
3.4%
12 26
 
3.4%
8 25
 
3.2%
Other values (95) 476
61.3%
ValueCountFrequency (%)
1 9
 
1.2%
2 4
 
0.5%
3 6
 
0.8%
4 9
 
1.2%
5 12
 
1.5%
6 16
2.1%
7 29
3.7%
8 25
3.2%
9 26
3.4%
10 32
4.1%
ValueCountFrequency (%)
443 1
 
0.1%
281 1
 
0.1%
236 1
 
0.1%
219 1
 
0.1%
200 1
 
0.1%
196 1
 
0.1%
184 3
0.4%
175 1
 
0.1%
174 1
 
0.1%
170 1
 
0.1%

토지현황조사
Real number (ℝ)

HIGH CORRELATION 

Distinct102
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.103093
Minimum1
Maximum442
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2023-12-12T11:50:40.705877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q110
median16
Q330
95-th percentile73
Maximum442
Range441
Interquartile range (IQR)20

Descriptive statistics

Standard deviation33.168305
Coefficient of variation (CV)1.2706657
Kurtosis43.907103
Mean26.103093
Median Absolute Deviation (MAD)8
Skewness5.2958466
Sum20256
Variance1100.1365
MonotonicityNot monotonic
2023-12-12T11:50:40.861074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 41
 
5.3%
9 38
 
4.9%
13 37
 
4.8%
10 37
 
4.8%
8 32
 
4.1%
16 30
 
3.9%
15 28
 
3.6%
7 28
 
3.6%
19 26
 
3.4%
12 26
 
3.4%
Other values (92) 453
58.4%
ValueCountFrequency (%)
1 9
 
1.2%
2 6
 
0.8%
3 7
 
0.9%
4 11
 
1.4%
5 19
2.4%
6 19
2.4%
7 28
3.6%
8 32
4.1%
9 38
4.9%
10 37
4.8%
ValueCountFrequency (%)
442 1
0.1%
281 1
0.1%
235 1
0.1%
222 1
0.1%
198 1
0.1%
195 1
0.1%
184 1
0.1%
183 2
0.3%
172 1
0.1%
167 1
0.1%

건축물
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.005155
Minimum0
Maximum219
Zeros5
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2023-12-12T11:50:41.045790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median11
Q320
95-th percentile56.25
Maximum219
Range219
Interquartile range (IQR)14

Descriptive statistics

Standard deviation22.657221
Coefficient of variation (CV)1.2583741
Kurtosis21.145462
Mean18.005155
Median Absolute Deviation (MAD)6
Skewness3.8983767
Sum13972
Variance513.34965
MonotonicityNot monotonic
2023-12-12T11:50:41.177588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 51
 
6.6%
7 45
 
5.8%
9 43
 
5.5%
4 41
 
5.3%
8 40
 
5.2%
5 38
 
4.9%
11 32
 
4.1%
10 32
 
4.1%
3 30
 
3.9%
12 30
 
3.9%
Other values (77) 394
50.8%
ValueCountFrequency (%)
0 5
 
0.6%
1 17
 
2.2%
2 27
3.5%
3 30
3.9%
4 41
5.3%
5 38
4.9%
6 51
6.6%
7 45
5.8%
8 40
5.2%
9 43
5.5%
ValueCountFrequency (%)
219 1
0.1%
184 1
0.1%
176 1
0.1%
164 1
0.1%
146 1
0.1%
136 2
0.3%
115 1
0.1%
109 1
0.1%
108 1
0.1%
105 1
0.1%

현지업데이트
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct82
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.435567
Minimum0
Maximum217
Zeros9
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2023-12-12T11:50:41.310618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median10.5
Q319
95-th percentile56.25
Maximum217
Range217
Interquartile range (IQR)13

Descriptive statistics

Standard deviation22.580402
Coefficient of variation (CV)1.295077
Kurtosis21.119293
Mean17.435567
Median Absolute Deviation (MAD)5.5
Skewness3.8927446
Sum13530
Variance509.87455
MonotonicityNot monotonic
2023-12-12T11:50:41.438739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 52
 
6.7%
5 44
 
5.7%
9 43
 
5.5%
7 39
 
5.0%
8 37
 
4.8%
4 36
 
4.6%
3 35
 
4.5%
2 35
 
4.5%
10 34
 
4.4%
12 29
 
3.7%
Other values (72) 392
50.5%
ValueCountFrequency (%)
0 9
 
1.2%
1 24
3.1%
2 35
4.5%
3 35
4.5%
4 36
4.6%
5 44
5.7%
6 52
6.7%
7 39
5.0%
8 37
4.8%
9 43
5.5%
ValueCountFrequency (%)
217 1
0.1%
184 1
0.1%
176 1
0.1%
164 1
0.1%
145 1
0.1%
135 1
0.1%
133 1
0.1%
115 1
0.1%
107 1
0.1%
106 1
0.1%

토지이용
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
0
760 
1
 
11
2
 
3
4
 
1
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
0 760
97.9%
1 11
 
1.4%
2 3
 
0.4%
4 1
 
0.1%
6 1
 
0.1%

Length

2023-12-12T11:50:41.556055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:50:41.661341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 760
97.9%
1 11
 
1.4%
2 3
 
0.4%
4 1
 
0.1%
6 1
 
0.1%

Interactions

2023-12-12T11:50:36.464835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:29.985712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:31.081891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:32.025468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:32.796792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:33.985587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:34.908519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:35.692050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:36.619396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:30.164772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:31.204513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:32.126470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:32.919800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:34.115604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:35.036153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:35.792677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:36.743055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:30.348277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:31.347039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:32.237065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:33.023663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:34.247709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:35.163748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:35.892906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:36.854321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:30.476562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:31.456836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:32.328402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:33.109604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:34.359032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:35.266081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:35.991176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:36.978561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:30.625556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:31.572062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:32.450278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:33.226113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:34.486742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:35.360096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:36.086078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:37.062584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:30.741403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:31.681843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:32.536411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:33.328766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:34.596498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:35.450830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:36.172924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:37.148092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:30.843313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:31.836433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:32.618372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:33.419681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:34.698803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:35.534941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:36.257767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:37.269268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:30.969544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:31.930976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:32.711486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:33.521913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:34.815093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:35.615792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:50:36.364330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:50:41.737228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드사업지구일련번호연계수요청필지수토지소유자정보토지현황조사건축물현지업데이트토지이용
시군구코드1.0000.3350.1550.1420.1550.1650.2350.2300.000
사업지구일련번호0.3351.0000.0000.0000.0000.0000.1910.1890.000
연계수0.1550.0001.0000.4740.9980.9930.7510.7510.082
요청필지수0.1420.0000.4741.0000.4380.4330.5790.5780.000
토지소유자정보0.1550.0000.9980.4381.0000.9920.7570.7570.089
토지현황조사0.1650.0000.9930.4330.9921.0000.8310.8310.129
건축물0.2350.1910.7510.5790.7570.8311.0001.0000.312
현지업데이트0.2300.1890.7510.5780.7570.8311.0001.0000.316
토지이용0.0000.0000.0820.0000.0890.1290.3120.3161.000
2023-12-12T11:50:41.874157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드사업지구일련번호연계수요청필지수토지소유자정보토지현황조사건축물현지업데이트토지이용
시군구코드1.000-0.079-0.142-0.055-0.128-0.138-0.124-0.1100.000
사업지구일련번호-0.0791.000-0.156-0.028-0.153-0.122-0.103-0.0910.000
연계수-0.142-0.1561.0000.5260.9880.9560.7080.6650.050
요청필지수-0.055-0.0280.5261.0000.5190.4950.3920.3690.000
토지소유자정보-0.128-0.1530.9880.5191.0000.9670.7300.6900.054
토지현황조사-0.138-0.1220.9560.4950.9671.0000.7710.7290.079
건축물-0.124-0.1030.7080.3920.7300.7711.0000.9720.134
현지업데이트-0.110-0.0910.6650.3690.6900.7290.9721.0000.136
토지이용0.0000.0000.0500.0000.0540.0790.1340.1361.000

Missing values

2023-12-12T11:50:37.446518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:50:37.612075image/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

시군구코드시군구명사업지구일련번호사업지구명연계수요청필지수토지소유자정보토지현황조사건축물현지업데이트토지이용
011140서울특별시 중구7812신당9-1지구418433330
126110부산광역시 중구7422영주동2지구8387088660
226140부산광역시 서구8459남부민1지구8124475110
326170부산광역시 동구7616수정2지구203071201914130
426200부산광역시 영도구7593청학2지구341533292920180
526200부산광역시 영도구7594청학3지구3110782828980
626230부산광역시 부산진구7495당감1지구1317891313770
726230부산광역시 부산진구7494당감2지구1215351212881
826260부산광역시 동래구8700안락1지구15195011101290
926290부산광역시 남구7129우암3지구7113477660
시군구코드시군구명사업지구일련번호사업지구명연계수요청필지수토지소유자정보토지현황조사건축물현지업데이트토지이용
76651810강원특별자치도 인제군8232신남리5지구161750141311110
76751810강원특별자치도 인제군8233신남리8지구182732151412120
76851810강원특별자치도 인제군8221신남리9지구1511351210880
76951810강원특별자치도 인제군8222원통리21지구963187660
77051810강원특별자치도 인제군8223원통리22지구750465440
77151810강원특별자치도 인제군8224원통리23지구111622109770
77251810강원특별자치도 인제군8225원통리24지구174733161514140
77351820강원특별자치도 고성군8524삼포1지구304553303030300
77451820강원특별자치도 고성군8525삼포2지구132401131313130
77551830강원특별자치도 양양군7354광진2지구2834522819650