Overview

Dataset statistics

Number of variables12
Number of observations227
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.5 KiB
Average record size in memory101.6 B

Variable types

Numeric5
Categorical5
Text2

Dataset

Description대구도시공사에서 제공하는 산업단지 미분양 현황 정보(구분, 토지이용, 소재지, 지번, 블록명, 지목, 면적(제곱미터, 평), 평단가(원), 공급예정가(원), 상태)를 제공합니다.
Author대구도시개발공사
URLhttps://www.data.go.kr/data/15042923/fileData.do

Alerts

상태 has constant value ""Constant
구분 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
토지이용 is highly overall correlated with 면적(제곱미터) and 5 other fieldsHigh correlation
소재지 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
지목 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
연번 is highly overall correlated with 면적(제곱미터) and 5 other fieldsHigh correlation
면적(제곱미터) is highly overall correlated with 연번 and 5 other fieldsHigh correlation
면적(평) is highly overall correlated with 연번 and 5 other fieldsHigh correlation
평단가(원) is highly overall correlated with 연번 and 5 other fieldsHigh correlation
공급예정가(원) is highly overall correlated with 면적(제곱미터) and 2 other fieldsHigh correlation
소재지 is highly imbalanced (53.6%)Imbalance
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-14 19:33:29.084196
Analysis finished2024-03-14 19:33:36.763266
Duration7.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct227
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114
Minimum1
Maximum227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T04:33:37.083998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12.3
Q157.5
median114
Q3170.5
95-th percentile215.7
Maximum227
Range226
Interquartile range (IQR)113

Descriptive statistics

Standard deviation65.673435
Coefficient of variation (CV)0.57608276
Kurtosis-1.2
Mean114
Median Absolute Deviation (MAD)57
Skewness0
Sum25878
Variance4313
MonotonicityStrictly increasing
2024-03-15T04:33:37.508019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
144 1
 
0.4%
146 1
 
0.4%
147 1
 
0.4%
148 1
 
0.4%
149 1
 
0.4%
150 1
 
0.4%
151 1
 
0.4%
152 1
 
0.4%
153 1
 
0.4%
Other values (217) 217
95.6%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
227 1
0.4%
226 1
0.4%
225 1
0.4%
224 1
0.4%
223 1
0.4%
222 1
0.4%
221 1
0.4%
220 1
0.4%
219 1
0.4%
218 1
0.4%

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
금호워터폴리스
171 
대구국가산업단지
40 
수성의료지구
 
16

Length

Max length8
Median length7
Mean length7.1057269
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
금호워터폴리스 171
75.3%
대구국가산업단지 40
 
17.6%
수성의료지구 16
 
7.0%

Length

2024-03-15T04:33:37.954716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T04:33:38.292579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
금호워터폴리스 171
75.3%
대구국가산업단지 40
 
17.6%
수성의료지구 16
 
7.0%

토지이용
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
산업시설용지
120 
상업용지
29 
지원시설용지
28 
복합용지
14 
연구시설용지
 
11
Other values (12)
25 

Length

Max length10
Median length6
Mean length5.6431718
Min length4

Unique

Unique7 ?
Unique (%)3.1%

Sample

1st row통신시설용지
2nd row산업시설용지
3rd row산업시설용지
4th row산업시설용지
5th row산업시설용지

Common Values

ValueCountFrequency (%)
산업시설용지 120
52.9%
상업용지 29
 
12.8%
지원시설용지 28
 
12.3%
복합용지 14
 
6.2%
연구시설용지 11
 
4.8%
주차장용지 5
 
2.2%
단독주택지 5
 
2.2%
산업시설용지(물류) 3
 
1.3%
가스공급시설용지 3
 
1.3%
공공청사 2
 
0.9%
Other values (7) 7
 
3.1%

Length

2024-03-15T04:33:38.672063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
산업시설용지 120
52.9%
상업용지 29
 
12.8%
지원시설용지 28
 
12.3%
복합용지 14
 
6.2%
연구시설용지 11
 
4.8%
주차장용지 5
 
2.2%
단독주택지 5
 
2.2%
가스공급시설용지 3
 
1.3%
산업시설용지(물류 3
 
1.3%
공공청사 2
 
0.9%
Other values (7) 7
 
3.1%

소재지
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
대구광역시 북구 검단동
171 
대구광역시 달성군 구지면 응암리
38 
대구광역시 수성구 대흥동
 
16
대구광역시 달성군 구지면 고봉리
 
1
대구광역시 달성군 구지명 응암리
 
1

Length

Max length17
Median length12
Mean length12.951542
Min length12

Unique

Unique2 ?
Unique (%)0.9%

Sample

1st row대구광역시 수성구 대흥동
2nd row대구광역시 수성구 대흥동
3rd row대구광역시 수성구 대흥동
4th row대구광역시 수성구 대흥동
5th row대구광역시 수성구 대흥동

Common Values

ValueCountFrequency (%)
대구광역시 북구 검단동 171
75.3%
대구광역시 달성군 구지면 응암리 38
 
16.7%
대구광역시 수성구 대흥동 16
 
7.0%
대구광역시 달성군 구지면 고봉리 1
 
0.4%
대구광역시 달성군 구지명 응암리 1
 
0.4%

Length

2024-03-15T04:33:39.001324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T04:33:39.209955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 227
31.5%
북구 171
23.7%
검단동 171
23.7%
달성군 40
 
5.5%
구지면 39
 
5.4%
응암리 39
 
5.4%
수성구 16
 
2.2%
대흥동 16
 
2.2%
고봉리 1
 
0.1%
구지명 1
 
0.1%

지번
Text

Distinct57
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-03-15T04:33:39.853887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.7180617
Min length3

Characters and Unicode

Total characters844
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

Unique56 ?
Unique (%)24.7%

Sample

1st row887
2nd row892-10
3rd row892-1
4th row892-3
5th row892-8
ValueCountFrequency (%)
0-0 171
75.3%
1266-22 1
 
0.4%
1278-4 1
 
0.4%
1278-5 1
 
0.4%
1282-5 1
 
0.4%
1282-6 1
 
0.4%
1282-10 1
 
0.4%
1282-2 1
 
0.4%
1282-15 1
 
0.4%
1282-16 1
 
0.4%
Other values (47) 47
 
20.7%
2024-03-15T04:33:40.907686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 351
41.6%
- 223
26.4%
1 64
 
7.6%
2 63
 
7.5%
8 43
 
5.1%
6 27
 
3.2%
7 21
 
2.5%
9 19
 
2.3%
4 13
 
1.5%
3 10
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 621
73.6%
Dash Punctuation 223
 
26.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 351
56.5%
1 64
 
10.3%
2 63
 
10.1%
8 43
 
6.9%
6 27
 
4.3%
7 21
 
3.4%
9 19
 
3.1%
4 13
 
2.1%
3 10
 
1.6%
5 10
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 223
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 351
41.6%
- 223
26.4%
1 64
 
7.6%
2 63
 
7.5%
8 43
 
5.1%
6 27
 
3.2%
7 21
 
2.5%
9 19
 
2.3%
4 13
 
1.5%
3 10
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 351
41.6%
- 223
26.4%
1 64
 
7.6%
2 63
 
7.5%
8 43
 
5.1%
6 27
 
3.2%
7 21
 
2.5%
9 19
 
2.3%
4 13
 
1.5%
3 10
 
1.2%

블럭명
Text

Distinct224
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-03-15T04:33:42.147927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length6.5814978
Min length6

Characters and Unicode

Total characters1494
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique222 ?
Unique (%)97.8%

Sample

1st row55B-0L
2nd row61B-11L
3rd row61B-2L
4th row61B-4L
5th row61B-9L
ValueCountFrequency (%)
n1b-0l 3
 
1.3%
55b-0l 2
 
0.9%
b4b-5l 1
 
0.4%
b4b-6l 1
 
0.4%
a9b-9l 1
 
0.4%
b1b-1l 1
 
0.4%
b1b-2l 1
 
0.4%
b3b-1l 1
 
0.4%
b3b-2l 1
 
0.4%
b3b-3l 1
 
0.4%
Other values (214) 214
94.3%
2024-03-15T04:33:44.574107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 241
16.1%
- 227
15.2%
L 227
15.2%
1 150
10.0%
3 100
6.7%
2 100
6.7%
A 88
 
5.9%
4 53
 
3.5%
6 52
 
3.5%
7 47
 
3.1%
Other values (13) 209
14.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 642
43.0%
Uppercase Letter 625
41.8%
Dash Punctuation 227
 
15.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 241
38.6%
L 227
36.3%
A 88
 
14.1%
H 28
 
4.5%
G 25
 
4.0%
C 5
 
0.8%
I 4
 
0.6%
N 3
 
0.5%
K 1
 
0.2%
E 1
 
0.2%
Other values (2) 2
 
0.3%
Decimal Number
ValueCountFrequency (%)
1 150
23.4%
3 100
15.6%
2 100
15.6%
4 53
 
8.3%
6 52
 
8.1%
7 47
 
7.3%
0 43
 
6.7%
5 42
 
6.5%
9 33
 
5.1%
8 22
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 227
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 869
58.2%
Latin 625
41.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 241
38.6%
L 227
36.3%
A 88
 
14.1%
H 28
 
4.5%
G 25
 
4.0%
C 5
 
0.8%
I 4
 
0.6%
N 3
 
0.5%
K 1
 
0.2%
E 1
 
0.2%
Other values (2) 2
 
0.3%
Common
ValueCountFrequency (%)
- 227
26.1%
1 150
17.3%
3 100
11.5%
2 100
11.5%
4 53
 
6.1%
6 52
 
6.0%
7 47
 
5.4%
0 43
 
4.9%
5 42
 
4.8%
9 33
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1494
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 241
16.1%
- 227
15.2%
L 227
15.2%
1 150
10.0%
3 100
6.7%
2 100
6.7%
A 88
 
5.9%
4 53
 
3.5%
6 52
 
3.5%
7 47
 
3.1%
Other values (13) 209
14.0%

지목
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
장
120 
대
97 
잡
 
5
차
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row잡
2nd row장
3rd row장
4th row장
5th row장

Common Values

ValueCountFrequency (%)
장 120
52.9%
대 97
42.7%
잡 5
 
2.2%
차 5
 
2.2%

Length

2024-03-15T04:33:45.016913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T04:33:45.309659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
장 120
52.9%
대 97
42.7%
잡 5
 
2.2%
차 5
 
2.2%

면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct166
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3243.4018
Minimum20
Maximum82808.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T04:33:45.794145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile602.1
Q11024
median1650
Q33255
95-th percentile11554.19
Maximum82808.3
Range82788.3
Interquartile range (IQR)2231

Descriptive statistics

Standard deviation6305.8494
Coefficient of variation (CV)1.9442085
Kurtosis113.32785
Mean3243.4018
Median Absolute Deviation (MAD)665
Skewness9.4045967
Sum736252.2
Variance39763737
MonotonicityNot monotonic
2024-03-15T04:33:46.110603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650.0 14
 
6.2%
1480.0 10
 
4.4%
1602.0 7
 
3.1%
1000.0 6
 
2.6%
962.0 4
 
1.8%
1101.0 4
 
1.8%
999.0 4
 
1.8%
1024.0 3
 
1.3%
290.0 3
 
1.3%
3255.0 3
 
1.3%
Other values (156) 169
74.4%
ValueCountFrequency (%)
20.0 2
0.9%
66.0 1
 
0.4%
290.0 3
1.3%
311.0 1
 
0.4%
324.0 1
 
0.4%
570.0 1
 
0.4%
584.0 1
 
0.4%
587.0 1
 
0.4%
594.0 1
 
0.4%
621.0 1
 
0.4%
ValueCountFrequency (%)
82808.3 1
0.4%
25484.9 1
0.4%
19436.0 1
0.4%
16530.0 1
0.4%
12553.6 1
0.4%
12539.8 1
0.4%
12526.6 1
0.4%
12513.8 1
0.4%
12454.7 1
0.4%
12441.6 1
0.4%

면적(평)
Real number (ℝ)

HIGH CORRELATION 

Distinct146
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean982.81938
Minimum6
Maximum25093
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T04:33:46.479192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile182.4
Q1310
median500
Q3986
95-th percentile3501.4
Maximum25093
Range25087
Interquartile range (IQR)676

Descriptive statistics

Standard deviation1910.8548
Coefficient of variation (CV)1.9442583
Kurtosis113.32261
Mean982.81938
Median Absolute Deviation (MAD)202
Skewness9.4043272
Sum223100
Variance3651366
MonotonicityNot monotonic
2024-03-15T04:33:47.023103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500 15
 
6.6%
448 10
 
4.4%
303 10
 
4.4%
485 8
 
3.5%
334 5
 
2.2%
292 4
 
1.8%
1730 4
 
1.8%
312 3
 
1.3%
298 3
 
1.3%
310 3
 
1.3%
Other values (136) 162
71.4%
ValueCountFrequency (%)
6 2
0.9%
20 1
 
0.4%
88 3
1.3%
94 1
 
0.4%
98 1
 
0.4%
173 1
 
0.4%
177 1
 
0.4%
178 1
 
0.4%
180 1
 
0.4%
188 1
 
0.4%
ValueCountFrequency (%)
25093 1
0.4%
7723 1
0.4%
5890 1
0.4%
5009 1
0.4%
3804 1
0.4%
3800 1
0.4%
3796 1
0.4%
3792 1
0.4%
3774 1
0.4%
3770 1
0.4%

평단가(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct179
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5117034
Minimum0
Maximum15019048
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T04:33:47.361424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile966583.4
Q14264243.5
median4478034
Q36560494
95-th percentile11454498
Maximum15019048
Range15019048
Interquartile range (IQR)2296250.5

Descriptive statistics

Standard deviation2820571.7
Coefficient of variation (CV)0.55121222
Kurtosis0.9945792
Mean5117034
Median Absolute Deviation (MAD)1157193
Skewness0.75138237
Sum1.1615667 × 109
Variance7.9556247 × 1012
MonotonicityNot monotonic
2024-03-15T04:33:47.625733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4439820 13
 
5.7%
4444955 10
 
4.4%
4443979 7
 
3.1%
4528713 4
 
1.8%
4347043 3
 
1.3%
7774519 3
 
1.3%
12304516 3
 
1.3%
4612662 3
 
1.3%
9414585 2
 
0.9%
4344284 2
 
0.9%
Other values (169) 177
78.0%
ValueCountFrequency (%)
0 1
0.4%
965789 1
0.4%
966401 1
0.4%
966419 1
0.4%
966423 1
0.4%
966459 1
0.4%
966540 1
0.4%
966543 1
0.4%
966549 1
0.4%
966553 1
0.4%
ValueCountFrequency (%)
15019048 1
 
0.4%
12780556 1
 
0.4%
12432030 1
 
0.4%
12365421 1
 
0.4%
12359260 1
 
0.4%
12348163 1
 
0.4%
12304516 3
1.3%
12276929 1
 
0.4%
12054012 2
0.9%
10055631 1
 
0.4%

공급예정가(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct181
Distinct (%)79.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4761572 × 109
Minimum0
Maximum8.7652585 × 1010
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-15T04:33:48.117332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.348466 × 108
Q11.6722025 × 109
median2.24202 × 109
Q33.6752665 × 109
95-th percentile6.4352091 × 109
Maximum8.7652585 × 1010
Range8.7652585 × 1010
Interquartile range (IQR)2.003064 × 109

Descriptive statistics

Standard deviation6.8447278 × 109
Coefficient of variation (CV)1.9690502
Kurtosis110.62426
Mean3.4761572 × 109
Median Absolute Deviation (MAD)8.03983 × 108
Skewness9.853551
Sum7.8908768 × 1011
Variance4.6850299 × 1019
MonotonicityNot monotonic
2024-03-15T04:33:48.566210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2219910000 12
 
5.3%
1991340000 10
 
4.4%
2155330000 7
 
3.1%
1372200000 4
 
1.8%
1540629000 3
 
1.3%
2425650000 3
 
1.3%
4286184000 3
 
1.3%
3814400000 3
 
1.3%
2193360000 2
 
0.9%
2720815000 2
 
0.9%
Other values (171) 178
78.4%
ValueCountFrequency (%)
0 1
0.4%
37600000 1
0.4%
38000000 1
0.4%
71412000 1
0.4%
292634000 1
0.4%
495900000 2
0.9%
536475000 1
0.4%
540850000 1
0.4%
665820000 1
0.4%
919574000 1
0.4%
ValueCountFrequency (%)
87652585000 1
0.4%
48846150000 1
0.4%
29348360000 1
0.4%
13567224000 1
0.4%
8525000000 1
0.4%
7736125000 1
0.4%
7525573000 1
0.4%
7465215000 1
0.4%
7023709000 1
0.4%
6610560000 1
0.4%

상태
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
미분양
227 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미분양 227
100.0%

Length

2024-03-15T04:33:48.965298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T04:33:49.243283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미분양 227
100.0%

Interactions

2024-03-15T04:33:34.223795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:29.873472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:30.982188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:32.104951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:33.027011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:34.492110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:30.117841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:31.216197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:32.242424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:33.174170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:34.758505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:30.348883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:31.447979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:32.382638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:33.326624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:35.043849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:30.616694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:31.690243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:32.526487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:33.496665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:35.387256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:30.784930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:31.955794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:32.784541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:33.844084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T04:33:49.436407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분토지이용소재지지번지목면적(제곱미터)면적(평)평단가(원)공급예정가(원)
연번1.0000.8880.8240.8960.4590.7070.5430.5430.7540.062
구분0.8881.0000.6591.0001.0000.0870.4310.4310.9260.156
토지이용0.8240.6591.0000.8040.8571.0000.9050.9050.8340.829
소재지0.8961.0000.8041.0001.0000.0000.8930.8930.6660.000
지번0.4591.0000.8571.0001.0000.0000.9510.9510.4700.166
지목0.7070.0871.0000.0000.0001.0000.0000.0000.7110.000
면적(제곱미터)0.5430.4310.9050.8930.9510.0001.0001.0000.3430.963
면적(평)0.5430.4310.9050.8930.9510.0001.0001.0000.3430.963
평단가(원)0.7540.9260.8340.6660.4700.7110.3430.3431.0000.041
공급예정가(원)0.0620.1560.8290.0000.1660.0000.9630.9630.0411.000
2024-03-15T04:33:49.737317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분토지이용소재지지목
구분1.0000.4450.9960.082
토지이용0.4451.0000.5630.970
소재지0.9960.5631.0000.000
지목0.0820.9700.0001.000
2024-03-15T04:33:49.907348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번면적(제곱미터)면적(평)평단가(원)공급예정가(원)구분토지이용소재지지목
연번1.000-0.557-0.5570.8390.0790.8180.4960.5750.502
면적(제곱미터)-0.5571.0001.000-0.6030.5440.3600.7300.5550.000
면적(평)-0.5571.0001.000-0.6040.5440.3600.7300.5550.000
평단가(원)0.839-0.603-0.6041.0000.1940.6740.5170.4600.541
공급예정가(원)0.0790.5440.5440.1941.0000.1170.5990.0000.000
구분0.8180.3600.3600.6740.1171.0000.4450.9960.082
토지이용0.4960.7300.7300.5170.5990.4451.0000.5630.970
소재지0.5750.5550.5550.4600.0000.9960.5631.0000.000
지목0.5020.0000.0000.5410.0000.0820.9700.0001.000

Missing values

2024-03-15T04:33:35.998306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T04:33:36.548633image/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

연번구분토지이용소재지지번블럭명지목면적(제곱미터)면적(평)평단가(원)공급예정가(원)상태
01수성의료지구통신시설용지대구광역시 수성구 대흥동88755B-0L잡66.020357060071412000미분양
12수성의료지구산업시설용지대구광역시 수성구 대흥동892-1061B-11L장881.62673482813929911000미분양
23수성의료지구산업시설용지대구광역시 수성구 대흥동892-161B-2L장870.72643584708946363000미분양
34수성의료지구산업시설용지대구광역시 수성구 대흥동892-361B-4L장2099.363635856432280469000미분양
45수성의료지구산업시설용지대구광역시 수성구 대흥동892-861B-9L장871.82643483235919574000미분양
56수성의료지구산업시설용지대구광역시 수성구 대흥동893-362B-4L장2254.368335817952446366000미분양
67수성의료지구산업시설용지대구광역시 수성구 대흥동893-462B-5L장2247.168135808402438552000미분양
78수성의료지구산업시설용지대구광역시 수성구 대흥동893-562B-6L장2235.367735830832425747000미분양
89수성의료지구공공용지대구광역시 수성구 대흥동890-263B-2L대1000.030335709571082000000미분양
910수성의료지구산업시설용지대구광역시 수성구 대흥동847-169B-2L장1040.131535191681108538000미분양
연번구분토지이용소재지지번블럭명지목면적(제곱미터)면적(평)평단가(원)공급예정가(원)상태
217218금호워터폴리스주차장용지대구광역시 북구 검단동0-0I3B-0L차3161.095854278135199845000미분양
218219금호워터폴리스주차장용지대구광역시 북구 검단동0-0I4B-0L차3150.095554259165181750000미분양
219220금호워터폴리스주차장용지대구광역시 북구 검단동0-0I7B-0L차1101.033449874641665813000미분양
220221금호워터폴리스주차장용지대구광역시 북구 검단동0-0I8B-0L차1650.050049929002496450000미분양
221222금호워터폴리스유치원용지대구광역시 북구 검단동0-0J1B-0L대5000.0151556270638525000000미분양
222223금호워터폴리스공공청사대구광역시 북구 검단동0-0K2B-0L대1200.036449879121815600000미분양
223224금호워터폴리스가스공급시설용지대구광역시 북구 검단동0-0N1B-0L잡3306.0100264338326446700000미분양
224225금호워터폴리스가스공급시설용지대구광역시 북구 검단동0-0N1B-0L잡20.06633333338000000미분양
225226금호워터폴리스가스공급시설용지대구광역시 북구 검단동0-0N1B-0L잡20.06626666737600000미분양
226227금호워터폴리스오수중계펌프장대구광역시 북구 검단동0-0O2B-0L잡2221.067349931253360373000미분양