Overview

Dataset statistics

Number of variables18
Number of observations58
Missing cells1
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.7 KiB
Average record size in memory154.3 B

Variable types

Numeric8
Text5
DateTime4
Categorical1

Dataset

Description인천광역시 서구 주택사업계획승인현황에 대한 데이터로 승인번호, 사업승인일, 착공예정일 등의 정보가 포함되어 있습니다.
Author인천광역시 서구
URLhttps://www.data.go.kr/data/15090931/fileData.do

Alerts

주용도 has constant value ""Constant
데이터기준일자 has constant value ""Constant
대지면적(제곱미터) is highly overall correlated with 건축면적(제곱미터) and 4 other fieldsHigh correlation
건축면적(제곱미터) is highly overall correlated with 대지면적(제곱미터) and 3 other fieldsHigh correlation
연면적(제곱미터) is highly overall correlated with 대지면적(제곱미터) and 3 other fieldsHigh correlation
건폐율(퍼센트) is highly overall correlated with 대지면적(제곱미터)High correlation
주건축물수(동) is highly overall correlated with 대지면적(제곱미터) and 3 other fieldsHigh correlation
총세대수 is highly overall correlated with 대지면적(제곱미터) and 3 other fieldsHigh correlation
시공자 has 1 (1.7%) missing valuesMissing
대지위치 has unique valuesUnique
대지면적(제곱미터) has unique valuesUnique
건축면적(제곱미터) has unique valuesUnique
연면적(제곱미터) has unique valuesUnique
건폐율(퍼센트) has unique valuesUnique
총세대수 has unique valuesUnique

Reproduction

Analysis started2023-12-12 04:03:16.332670
Analysis finished2023-12-12 04:03:24.684154
Duration8.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

승인연도
Real number (ℝ)

Distinct6
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.931
Minimum2018
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size654.0 B
2023-12-12T13:03:24.736101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2018
5-th percentile2018
Q12019
median2019
Q32021.75
95-th percentile2023
Maximum2023
Range5
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation1.6634573
Coefficient of variation (CV)0.00082352182
Kurtosis-0.97665333
Mean2019.931
Median Absolute Deviation (MAD)1
Skewness0.65786935
Sum117156
Variance2.7670901
MonotonicityIncreasing
2023-12-12T13:03:24.872305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2019 24
41.4%
2018 10
17.2%
2022 9
 
15.5%
2023 6
 
10.3%
2020 5
 
8.6%
2021 4
 
6.9%
ValueCountFrequency (%)
2018 10
17.2%
2019 24
41.4%
2020 5
 
8.6%
2021 4
 
6.9%
2022 9
 
15.5%
2023 6
 
10.3%
ValueCountFrequency (%)
2023 6
 
10.3%
2022 9
 
15.5%
2021 4
 
6.9%
2020 5
 
8.6%
2019 24
41.4%
2018 10
17.2%

승인번호
Text

Distinct57
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size596.0 B
2023-12-12T13:03:25.156601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length21.948276
Min length21

Characters and Unicode

Total characters1273
Distinct characters24
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

Unique56 ?
Unique (%)96.6%

Sample

1st row2018-주택관리과-주택건설사업계획승인-1
2nd row2018-주택관리과-주택건설사업계획승인-2
3rd row2018-주택관리과-주택건설사업계획승인-3
4th row2018-주택관리과-주택건설사업계획승인-4
5th row2018-주택관리과-주택건설사업계획승인-5
ValueCountFrequency (%)
2021-주택과-주택건설사업계획승인-1 2
 
3.4%
2018-주택관리과-주택건설사업계획승인-1 1
 
1.7%
2019-주택과-주택건설사업계획승인-6 1
 
1.7%
2019-주택과-주택건설사업계획승인-8 1
 
1.7%
2019-주택과-주택건설사업계획승인-9 1
 
1.7%
2019-주택과-주택건설사업계획승인-10 1
 
1.7%
2020-주택과-주택건설사업계획승인-1 1
 
1.7%
2020-주택과-주택건설사업계획승인-2 1
 
1.7%
2020-주택과-주택건설사업계획승인-3 1
 
1.7%
2020-주택과-주택건설사업계획승인-4 1
 
1.7%
Other values (47) 47
81.0%
2023-12-12T13:03:25.584912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 174
13.7%
주 116
 
9.1%
택 116
 
9.1%
2 99
 
7.8%
0 66
 
5.2%
업 58
 
4.6%
인 58
 
4.6%
승 58
 
4.6%
계 58
 
4.6%
획 58
 
4.6%
Other values (14) 412
32.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 802
63.0%
Decimal Number 297
 
23.3%
Dash Punctuation 174
 
13.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 116
14.5%
택 116
14.5%
업 58
7.2%
인 58
7.2%
승 58
7.2%
계 58
7.2%
획 58
7.2%
사 58
7.2%
설 58
7.2%
건 58
7.2%
Other values (3) 106
13.2%
Decimal Number
ValueCountFrequency (%)
2 99
33.3%
0 66
22.2%
1 54
18.2%
9 28
 
9.4%
8 14
 
4.7%
3 14
 
4.7%
4 7
 
2.4%
5 6
 
2.0%
6 5
 
1.7%
7 4
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 174
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 802
63.0%
Common 471
37.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 116
14.5%
택 116
14.5%
업 58
7.2%
인 58
7.2%
승 58
7.2%
계 58
7.2%
획 58
7.2%
사 58
7.2%
설 58
7.2%
건 58
7.2%
Other values (3) 106
13.2%
Common
ValueCountFrequency (%)
- 174
36.9%
2 99
21.0%
0 66
 
14.0%
1 54
 
11.5%
9 28
 
5.9%
8 14
 
3.0%
3 14
 
3.0%
4 7
 
1.5%
5 6
 
1.3%
6 5
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 802
63.0%
ASCII 471
37.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 174
36.9%
2 99
21.0%
0 66
 
14.0%
1 54
 
11.5%
9 28
 
5.9%
8 14
 
3.0%
3 14
 
3.0%
4 7
 
1.5%
5 6
 
1.3%
6 5
 
1.1%
Hangul
ValueCountFrequency (%)
주 116
14.5%
택 116
14.5%
업 58
7.2%
인 58
7.2%
승 58
7.2%
계 58
7.2%
획 58
7.2%
사 58
7.2%
설 58
7.2%
건 58
7.2%
Other values (3) 106
13.2%

사업승인일
Date

Distinct54
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Memory size596.0 B
Minimum2018-05-18 00:00:00
Maximum2023-09-07 00:00:00
2023-12-12T13:03:25.711870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:25.856679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

착공예정일
Date

Distinct54
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Memory size596.0 B
Minimum2018-08-29 00:00:00
Maximum2023-12-01 00:00:00
2023-12-12T13:03:25.985479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:26.107987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

사용검사예정일
Date

Distinct53
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Memory size596.0 B
Minimum2019-07-29 00:00:00
Maximum2027-04-19 00:00:00
2023-12-12T13:03:26.249548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:26.418023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

대지위치
Text

UNIQUE 

Distinct58
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size596.0 B
2023-12-12T13:03:26.804375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length31
Mean length25.655172
Min length17

Characters and Unicode

Total characters1488
Distinct characters69
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

Unique58 ?
Unique (%)100.0%

Sample

1st row인천광역시 서구 오류동 1722-1
2nd row인천광역시 서구 원당동 1022
3rd row인천광역시 서구 가정동 루원시티 주상 1BL
4th row인천광역시 서구 당하동 검단신도시 AA4
5th row인천광역시 서구 마전동 마전택지지구 17 2
ValueCountFrequency (%)
인천광역시 58
18.8%
서구 58
18.8%
검단신도시 21
 
6.8%
원당동 19
 
6.2%
당하동 11
 
3.6%
불로동 11
 
3.6%
인천검단신도시 6
 
1.9%
가정동 6
 
1.9%
외 5
 
1.6%
루원시티 5
 
1.6%
Other values (90) 108
35.1%
2023-12-12T13:03:27.403509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
250
 
16.8%
시 96
 
6.5%
구 78
 
5.2%
인 69
 
4.6%
천 69
 
4.6%
역 64
 
4.3%
서 60
 
4.0%
동 59
 
4.0%
광 58
 
3.9%
1 56
 
3.8%
Other values (59) 629
42.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 985
66.2%
Space Separator 250
 
16.8%
Decimal Number 148
 
9.9%
Uppercase Letter 84
 
5.6%
Dash Punctuation 21
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 96
 
9.7%
구 78
 
7.9%
인 69
 
7.0%
천 69
 
7.0%
역 64
 
6.5%
서 60
 
6.1%
동 59
 
6.0%
광 58
 
5.9%
검 35
 
3.6%
단 35
 
3.6%
Other values (42) 362
36.8%
Decimal Number
ValueCountFrequency (%)
1 56
37.8%
2 28
18.9%
0 12
 
8.1%
3 11
 
7.4%
9 9
 
6.1%
5 8
 
5.4%
8 7
 
4.7%
4 7
 
4.7%
7 7
 
4.7%
6 3
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
A 45
53.6%
B 25
29.8%
L 8
 
9.5%
C 3
 
3.6%
R 3
 
3.6%
Space Separator
ValueCountFrequency (%)
250
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 985
66.2%
Common 419
28.2%
Latin 84
 
5.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 96
 
9.7%
구 78
 
7.9%
인 69
 
7.0%
천 69
 
7.0%
역 64
 
6.5%
서 60
 
6.1%
동 59
 
6.0%
광 58
 
5.9%
검 35
 
3.6%
단 35
 
3.6%
Other values (42) 362
36.8%
Common
ValueCountFrequency (%)
250
59.7%
1 56
 
13.4%
2 28
 
6.7%
- 21
 
5.0%
0 12
 
2.9%
3 11
 
2.6%
9 9
 
2.1%
5 8
 
1.9%
8 7
 
1.7%
4 7
 
1.7%
Other values (2) 10
 
2.4%
Latin
ValueCountFrequency (%)
A 45
53.6%
B 25
29.8%
L 8
 
9.5%
C 3
 
3.6%
R 3
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 985
66.2%
ASCII 503
33.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
250
49.7%
1 56
 
11.1%
A 45
 
8.9%
2 28
 
5.6%
B 25
 
5.0%
- 21
 
4.2%
0 12
 
2.4%
3 11
 
2.2%
9 9
 
1.8%
L 8
 
1.6%
Other values (7) 38
 
7.6%
Hangul
ValueCountFrequency (%)
시 96
 
9.7%
구 78
 
7.9%
인 69
 
7.0%
천 69
 
7.0%
역 64
 
6.5%
서 60
 
6.1%
동 59
 
6.0%
광 58
 
5.9%
검 35
 
3.6%
단 35
 
3.6%
Other values (42) 362
36.8%

주용도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size596.0 B
공동주택
58 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공동주택 58
100.0%

Length

2023-12-12T13:03:27.550176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:03:27.655860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공동주택 58
100.0%

대지면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct58
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46216.5
Minimum1305
Maximum107917
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size654.0 B
2023-12-12T13:03:27.772274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1305
5-th percentile9799.15
Q129351.25
median42975.5
Q364818
95-th percentile88935.05
Maximum107917
Range106612
Interquartile range (IQR)35466.75

Descriptive statistics

Standard deviation25023.575
Coefficient of variation (CV)0.54144244
Kurtosis-0.30903626
Mean46216.5
Median Absolute Deviation (MAD)19532.5
Skewness0.42689757
Sum2680557
Variance6.2617929 × 108
MonotonicityNot monotonic
2023-12-12T13:03:27.916598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16308 1
 
1.7%
46063 1
 
1.7%
19487 1
 
1.7%
41342 1
 
1.7%
31541 1
 
1.7%
18169 1
 
1.7%
34047 1
 
1.7%
65934 1
 
1.7%
18448 1
 
1.7%
19958 1
 
1.7%
Other values (48) 48
82.8%
ValueCountFrequency (%)
1305 1
1.7%
2929 1
1.7%
8423 1
1.7%
10042 1
1.7%
16308 1
1.7%
16700 1
1.7%
18169 1
1.7%
18448 1
1.7%
19487 1
1.7%
19759 1
1.7%
ValueCountFrequency (%)
107917 1
1.7%
103068 1
1.7%
90709 1
1.7%
88622 1
1.7%
86485 1
1.7%
85540 1
1.7%
76618 1
1.7%
74207 1
1.7%
73250 1
1.7%
70422 1
1.7%

건축면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct58
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8157.8793
Minimum511
Maximum21001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size654.0 B
2023-12-12T13:03:28.064405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum511
5-th percentile2625.9
Q15877
median7632.5
Q310461.75
95-th percentile13942.25
Maximum21001
Range20490
Interquartile range (IQR)4584.75

Descriptive statistics

Standard deviation3841.8982
Coefficient of variation (CV)0.47094325
Kurtosis1.0174392
Mean8157.8793
Median Absolute Deviation (MAD)2424
Skewness0.55728429
Sum473157
Variance14760182
MonotonicityNot monotonic
2023-12-12T13:03:28.196332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3614 1
 
1.7%
7529 1
 
1.7%
2907 1
 
1.7%
6648 1
 
1.7%
5487 1
 
1.7%
2643 1
 
1.7%
5610 1
 
1.7%
10057 1
 
1.7%
7216 1
 
1.7%
6931 1
 
1.7%
Other values (48) 48
82.8%
ValueCountFrequency (%)
511 1
1.7%
585 1
1.7%
2529 1
1.7%
2643 1
1.7%
2907 1
1.7%
3365 1
1.7%
3614 1
1.7%
3631 1
1.7%
4131 1
1.7%
5017 1
1.7%
ValueCountFrequency (%)
21001 1
1.7%
14421 1
1.7%
14397 1
1.7%
13862 1
1.7%
13818 1
1.7%
13651 1
1.7%
13298 1
1.7%
12822 1
1.7%
12315 1
1.7%
11737 1
1.7%

연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct58
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161230.6
Minimum2667
Maximum466221
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size654.0 B
2023-12-12T13:03:28.323964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2667
5-th percentile31270.35
Q1100671
median144349.5
Q3203118
95-th percentile353122.6
Maximum466221
Range463554
Interquartile range (IQR)102447

Descriptive statistics

Standard deviation94497.877
Coefficient of variation (CV)0.58610385
Kurtosis1.0608138
Mean161230.6
Median Absolute Deviation (MAD)54992.5
Skewness0.89092523
Sum9351375
Variance8.9298488 × 109
MonotonicityNot monotonic
2023-12-12T13:03:28.448853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55436 1
 
1.7%
152551 1
 
1.7%
63358 1
 
1.7%
127751 1
 
1.7%
102900 1
 
1.7%
54931 1
 
1.7%
118236 1
 
1.7%
202971 1
 
1.7%
87531 1
 
1.7%
96168 1
 
1.7%
Other values (48) 48
82.8%
ValueCountFrequency (%)
2667 1
1.7%
5484 1
1.7%
16845 1
1.7%
33816 1
1.7%
50378 1
1.7%
54931 1
1.7%
55436 1
1.7%
63358 1
1.7%
72806 1
1.7%
75929 1
1.7%
ValueCountFrequency (%)
466221 1
1.7%
365609 1
1.7%
359212 1
1.7%
352048 1
1.7%
313503 1
1.7%
288381 1
1.7%
284250 1
1.7%
259943 1
1.7%
254020 1
1.7%
243660 1
1.7%

용적률(퍼센트)
Real number (ℝ)

Distinct56
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean233.56078
Minimum69.13
Maximum439.9853
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size654.0 B
2023-12-12T13:03:28.610950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum69.13
5-th percentile177.2355
Q1196.48
median214.985
Q3235.8025
95-th percentile439.0875
Maximum439.9853
Range370.8553
Interquartile range (IQR)39.3225

Descriptive statistics

Standard deviation74.215256
Coefficient of variation (CV)0.31775564
Kurtosis3.2906885
Mean233.56078
Median Absolute Deviation (MAD)19.35
Skewness1.6665931
Sum13546.525
Variance5507.9042
MonotonicityNot monotonic
2023-12-12T13:03:29.058921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
214.98 2
 
3.4%
184.85 2
 
3.4%
229.99 1
 
1.7%
194.85 1
 
1.7%
216.96 1
 
1.7%
194.44 1
 
1.7%
224.59 1
 
1.7%
209.81 1
 
1.7%
329.86 1
 
1.7%
329.65 1
 
1.7%
Other values (46) 46
79.3%
ValueCountFrequency (%)
69.13 1
1.7%
119.66 1
1.7%
162.93 1
1.7%
179.76 1
1.7%
179.97 1
1.7%
183.69 1
1.7%
183.71 1
1.7%
184.8 1
1.7%
184.85 2
3.4%
186.98 1
1.7%
ValueCountFrequency (%)
439.9853 1
1.7%
439.89 1
1.7%
439.64 1
1.7%
438.99 1
1.7%
435.59 1
1.7%
329.86 1
1.7%
329.65 1
1.7%
279.98 1
1.7%
276.83 1
1.7%
249.71 1
1.7%

건폐율(퍼센트)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct58
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.615234
Minimum12.58
Maximum169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size654.0 B
2023-12-12T13:03:29.214821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12.58
5-th percentile13.1365
Q114.6375
median17.465
Q326.65
95-th percentile157.75
Maximum169
Range156.42
Interquartile range (IQR)12.0125

Descriptive statistics

Standard deviation40.536075
Coefficient of variation (CV)1.2428571
Kurtosis6.6482263
Mean32.615234
Median Absolute Deviation (MAD)3.9
Skewness2.8134969
Sum1891.6836
Variance1643.1734
MonotonicityNot monotonic
2023-12-12T13:03:29.359045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.16 1
 
1.7%
16.35 1
 
1.7%
14.92 1
 
1.7%
168.0 1
 
1.7%
17.4 1
 
1.7%
14.55 1
 
1.7%
16.48 1
 
1.7%
15.2 1
 
1.7%
38.12 1
 
1.7%
34.73 1
 
1.7%
Other values (48) 48
82.8%
ValueCountFrequency (%)
12.58 1
1.7%
12.64 1
1.7%
12.89 1
1.7%
13.18 1
1.7%
13.26 1
1.7%
13.36 1
1.7%
13.41 1
1.7%
13.72 1
1.7%
13.96 1
1.7%
13.98 1
1.7%
ValueCountFrequency (%)
169.0 1
1.7%
168.0 1
1.7%
162.0 1
1.7%
157.0 1
1.7%
144.0 1
1.7%
57.71 1
1.7%
39.95 1
1.7%
39.15 1
1.7%
38.12 1
1.7%
36.6 1
1.7%

주건축물수(동)
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.258621
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size654.0 B
2023-12-12T13:03:29.477363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.85
Q18
median11
Q315.75
95-th percentile19.15
Maximum21
Range20
Interquartile range (IQR)7.75

Descriptive statistics

Standard deviation4.7703777
Coefficient of variation (CV)0.42370889
Kurtosis-0.49415389
Mean11.258621
Median Absolute Deviation (MAD)3
Skewness0.10806301
Sum653
Variance22.756503
MonotonicityNot monotonic
2023-12-12T13:03:29.581584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
17 7
12.1%
9 6
10.3%
8 5
 
8.6%
11 5
 
8.6%
10 5
 
8.6%
12 4
 
6.9%
16 4
 
6.9%
5 3
 
5.2%
6 3
 
5.2%
13 3
 
5.2%
Other values (8) 13
22.4%
ValueCountFrequency (%)
1 2
 
3.4%
4 1
 
1.7%
5 3
5.2%
6 3
5.2%
7 3
5.2%
8 5
8.6%
9 6
10.3%
10 5
8.6%
11 5
8.6%
12 4
6.9%
ValueCountFrequency (%)
21 2
 
3.4%
20 1
 
1.7%
19 1
 
1.7%
17 7
12.1%
16 4
6.9%
15 1
 
1.7%
14 2
 
3.4%
13 3
5.2%
12 4
6.9%
11 5
8.6%

총세대수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct58
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean954.53448
Minimum43
Maximum2426
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size654.0 B
2023-12-12T13:03:29.707900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile224.55
Q1633.25
median881
Q31229.75
95-th percentile1877.35
Maximum2426
Range2383
Interquartile range (IQR)596.5

Descriptive statistics

Standard deviation524.8271
Coefficient of variation (CV)0.54982518
Kurtosis1.2120083
Mean954.53448
Median Absolute Deviation (MAD)289
Skewness0.8961928
Sum55363
Variance275443.48
MonotonicityNot monotonic
2023-12-12T13:03:29.841951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
420 1
 
1.7%
875 1
 
1.7%
437 1
 
1.7%
745 1
 
1.7%
765 1
 
1.7%
370 1
 
1.7%
810 1
 
1.7%
1172 1
 
1.7%
447 1
 
1.7%
483 1
 
1.7%
Other values (48) 48
82.8%
ValueCountFrequency (%)
43 1
1.7%
49 1
1.7%
120 1
1.7%
243 1
1.7%
370 1
1.7%
372 1
1.7%
420 1
1.7%
430 1
1.7%
437 1
1.7%
447 1
1.7%
ValueCountFrequency (%)
2426 1
1.7%
2379 1
1.7%
2378 1
1.7%
1789 1
1.7%
1734 1
1.7%
1540 1
1.7%
1500 1
1.7%
1448 1
1.7%
1425 1
1.7%
1417 1
1.7%

사업주체
Text

Distinct55
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Memory size596.0 B
2023-12-12T13:03:30.031008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length21
Mean length9.8448276
Min length3

Characters and Unicode

Total characters571
Distinct characters118
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

Unique52 ?
Unique (%)89.7%

Sample

1st row우방산업(주)
2nd row티에스주택 주식회사
3rd row아시아신탁(주)
4th row(주)유승종합건설
5th row마전지구지역주택조합
ValueCountFrequency (%)
주식회사 4
 
6.2%
대방건설(주 2
 
3.1%
주)하나자산신탁 2
 
3.1%
주)제이에스글로벌 2
 
3.1%
주)호반건설 2
 
3.1%
제일건설주식회사 1
 
1.6%
우방산업(주 1
 
1.6%
㈜금강주택 1
 
1.6%
주식회사인천검단피에프브이 1
 
1.6%
주)우미대한제28호위탁관리부동산투자회사 1
 
1.6%
Other values (47) 47
73.4%
2023-12-12T13:03:30.457474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 50
 
8.8%
( 31
 
5.4%
) 31
 
5.4%
건 18
 
3.2%
설 18
 
3.2%
이 16
 
2.8%
에 16
 
2.8%
사 16
 
2.8%
회 16
 
2.8%
식 14
 
2.5%
Other values (108) 345
60.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 480
84.1%
Open Punctuation 31
 
5.4%
Close Punctuation 31
 
5.4%
Other Symbol 13
 
2.3%
Space Separator 7
 
1.2%
Decimal Number 6
 
1.1%
Other Punctuation 3
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 50
 
10.4%
건 18
 
3.8%
설 18
 
3.8%
이 16
 
3.3%
에 16
 
3.3%
사 16
 
3.3%
회 16
 
3.3%
식 14
 
2.9%
신 14
 
2.9%
탁 13
 
2.7%
Other values (99) 289
60.2%
Decimal Number
ValueCountFrequency (%)
2 2
33.3%
1 2
33.3%
8 1
16.7%
3 1
16.7%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 13
100.0%
Space Separator
ValueCountFrequency (%)
7
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 493
86.3%
Common 78
 
13.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 50
 
10.1%
건 18
 
3.7%
설 18
 
3.7%
이 16
 
3.2%
에 16
 
3.2%
사 16
 
3.2%
회 16
 
3.2%
식 14
 
2.8%
신 14
 
2.8%
㈜ 13
 
2.6%
Other values (100) 302
61.3%
Common
ValueCountFrequency (%)
( 31
39.7%
) 31
39.7%
7
 
9.0%
, 3
 
3.8%
2 2
 
2.6%
1 2
 
2.6%
8 1
 
1.3%
3 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 480
84.1%
ASCII 78
 
13.7%
None 13
 
2.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 50
 
10.4%
건 18
 
3.8%
설 18
 
3.8%
이 16
 
3.3%
에 16
 
3.3%
사 16
 
3.3%
회 16
 
3.3%
식 14
 
2.9%
신 14
 
2.9%
탁 13
 
2.7%
Other values (99) 289
60.2%
ASCII
ValueCountFrequency (%)
( 31
39.7%
) 31
39.7%
7
 
9.0%
, 3
 
3.8%
2 2
 
2.6%
1 2
 
2.6%
8 1
 
1.3%
3 1
 
1.3%
None
ValueCountFrequency (%)
㈜ 13
100.0%

설계자
Text

Distinct42
Distinct (%)72.4%
Missing0
Missing (%)0.0%
Memory size596.0 B
2023-12-12T13:03:30.691461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length13.741379
Min length8

Characters and Unicode

Total characters797
Distinct characters83
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)58.6%

Sample

1st row(주)패스건축사사무소
2nd row(주)토담건축사사무소
3rd row(주)이안디자인 건축사사무소
4th row(주)유선엔지니어링건축사사무소
5th row(주)성도건축사사무소
ValueCountFrequency (%)
에이앤유디자인그룹건축사사무소(주 8
 
12.5%
㈜나산종합건축사사무소 3
 
4.7%
건축사사무소 3
 
4.7%
주)원양건축사사무소 3
 
4.7%
주)희림종합건축사사무소 2
 
3.1%
주)토담건축사사무소 2
 
3.1%
주)유선엔지니어링건축사사무소 2
 
3.1%
㈜해안종합건축사사무소 2
 
3.1%
주)현대건축사사무소 2
 
3.1%
㈜엠에이피한터인 1
 
1.6%
Other values (36) 36
56.2%
2023-12-12T13:03:31.116546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 121
15.2%
건 62
 
7.8%
축 61
 
7.7%
소 60
 
7.5%
무 60
 
7.5%
주 42
 
5.3%
( 41
 
5.1%
) 41
 
5.1%
종 21
 
2.6%
합 20
 
2.5%
Other values (73) 268
33.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 692
86.8%
Open Punctuation 41
 
5.1%
Close Punctuation 41
 
5.1%
Other Symbol 15
 
1.9%
Space Separator 6
 
0.8%
Other Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 121
17.5%
건 62
 
9.0%
축 61
 
8.8%
소 60
 
8.7%
무 60
 
8.7%
주 42
 
6.1%
종 21
 
3.0%
합 20
 
2.9%
이 15
 
2.2%
인 15
 
2.2%
Other values (68) 215
31.1%
Open Punctuation
ValueCountFrequency (%)
( 41
100.0%
Close Punctuation
ValueCountFrequency (%)
) 41
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 15
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 707
88.7%
Common 90
 
11.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 121
17.1%
건 62
 
8.8%
축 61
 
8.6%
소 60
 
8.5%
무 60
 
8.5%
주 42
 
5.9%
종 21
 
3.0%
합 20
 
2.8%
㈜ 15
 
2.1%
이 15
 
2.1%
Other values (69) 230
32.5%
Common
ValueCountFrequency (%)
( 41
45.6%
) 41
45.6%
6
 
6.7%
, 2
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 692
86.8%
ASCII 90
 
11.3%
None 15
 
1.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 121
17.5%
건 62
 
9.0%
축 61
 
8.8%
소 60
 
8.7%
무 60
 
8.7%
주 42
 
6.1%
종 21
 
3.0%
합 20
 
2.9%
이 15
 
2.2%
인 15
 
2.2%
Other values (68) 215
31.1%
ASCII
ValueCountFrequency (%)
( 41
45.6%
) 41
45.6%
6
 
6.7%
, 2
 
2.2%
None
ValueCountFrequency (%)
㈜ 15
100.0%

시공자
Text

MISSING 

Distinct40
Distinct (%)70.2%
Missing1
Missing (%)1.7%
Memory size596.0 B
2023-12-12T13:03:31.332937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length7.2105263
Min length4

Characters and Unicode

Total characters411
Distinct characters74
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)49.1%

Sample

1st row우방산업(주)
2nd row(주)호반산업
3rd row에스케이에코플랜트(주)
4th row(주)유승종합건설
5th row양우건설(주)
ValueCountFrequency (%)
주)대우건설 5
 
8.3%
주)우미개발 4
 
6.7%
대방건설(주 3
 
5.0%
주식회사 3
 
5.0%
㈜호반건설 3
 
5.0%
제일건설주식회사 3
 
5.0%
제일건설㈜ 2
 
3.3%
우미건설(주 2
 
3.3%
한신공영(주 2
 
3.3%
주)금성백조주택 2
 
3.3%
Other values (28) 31
51.7%
2023-12-12T13:03:31.719502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 49
 
11.9%
) 38
 
9.2%
건 38
 
9.2%
( 38
 
9.2%
설 36
 
8.8%
우 15
 
3.6%
㈜ 12
 
2.9%
대 12
 
2.9%
8
 
1.9%
미 8
 
1.9%
Other values (64) 157
38.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 315
76.6%
Close Punctuation 38
 
9.2%
Open Punctuation 38
 
9.2%
Other Symbol 12
 
2.9%
Space Separator 8
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 49
 
15.6%
건 38
 
12.1%
설 36
 
11.4%
우 15
 
4.8%
대 12
 
3.8%
미 8
 
2.5%
식 6
 
1.9%
회 6
 
1.9%
사 6
 
1.9%
합 5
 
1.6%
Other values (60) 134
42.5%
Close Punctuation
ValueCountFrequency (%)
) 38
100.0%
Open Punctuation
ValueCountFrequency (%)
( 38
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 12
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 327
79.6%
Common 84
 
20.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 49
 
15.0%
건 38
 
11.6%
설 36
 
11.0%
우 15
 
4.6%
㈜ 12
 
3.7%
대 12
 
3.7%
미 8
 
2.4%
식 6
 
1.8%
회 6
 
1.8%
사 6
 
1.8%
Other values (61) 139
42.5%
Common
ValueCountFrequency (%)
) 38
45.2%
( 38
45.2%
8
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 315
76.6%
ASCII 84
 
20.4%
None 12
 
2.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 49
 
15.6%
건 38
 
12.1%
설 36
 
11.4%
우 15
 
4.8%
대 12
 
3.8%
미 8
 
2.5%
식 6
 
1.9%
회 6
 
1.9%
사 6
 
1.9%
합 5
 
1.6%
Other values (60) 134
42.5%
ASCII
ValueCountFrequency (%)
) 38
45.2%
( 38
45.2%
8
 
9.5%
None
ValueCountFrequency (%)
㈜ 12
100.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size596.0 B
Minimum2023-09-30 00:00:00
Maximum2023-09-30 00:00:00
2023-12-12T13:03:31.853487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:31.970898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T13:03:23.497035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:17.619411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:18.515842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:19.254006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:19.952236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:20.693940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:21.553601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:22.665809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:23.611491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:17.725194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:18.600626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:19.353205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:20.052634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:20.791570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:21.641253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:22.766611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:23.740626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:17.841198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:18.702870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:19.430030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:20.151004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:20.877730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:21.739934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:22.860246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:23.820048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:17.962981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:18.777499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:19.505761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:20.231865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:20.970933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:21.848007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:22.945112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:23.911007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:18.068070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:18.855960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:19.578464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:20.303491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:21.077760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:21.967216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:23.038469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:24.030737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:18.181158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:18.956658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:19.666166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:20.385538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:21.219792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:22.081991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:23.156840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:24.122214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:18.290025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:19.068640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:19.768791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:20.480975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:21.339849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:22.184201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:23.266843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:24.248360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:18.420800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:19.161419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:19.866892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:20.579618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:21.458591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:22.577542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:03:23.358253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:03:32.058291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승인연도승인번호사업승인일착공예정일사용검사예정일대지위치대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)용적률(퍼센트)건폐율(퍼센트)주건축물수(동)총세대수사업주체설계자시공자
승인연도1.0001.0001.0001.0000.9681.0000.0000.0940.0970.2700.0000.0000.0000.9220.1870.854
승인번호1.0001.0000.9900.9900.9901.0000.8720.9190.9260.0000.9060.9250.9540.9910.9980.996
사업승인일1.0000.9901.0000.9930.9891.0000.9600.8560.9270.8840.9490.9200.0000.9900.9970.988
착공예정일1.0000.9900.9931.0000.9861.0000.8000.0000.0000.9670.9820.8160.8230.9690.9740.979
사용검사예정일0.9680.9900.9890.9861.0001.0000.8610.7610.8650.9360.9320.8890.0000.9420.9920.989
대지위치1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
대지면적(제곱미터)0.0000.8720.9600.8000.8611.0001.0000.7910.8530.5310.0000.8840.8740.7210.6830.000
건축면적(제곱미터)0.0940.9190.8560.0000.7611.0000.7911.0000.8450.7560.2440.8090.8390.9680.7160.932
연면적(제곱미터)0.0970.9260.9270.0000.8651.0000.8530.8451.0000.4070.1380.7380.9660.9850.7980.791
용적률(퍼센트)0.2700.0000.8840.9670.9361.0000.5310.7560.4071.0000.5130.5580.4280.9630.7990.982
건폐율(퍼센트)0.0000.9060.9490.9820.9321.0000.0000.2440.1380.5131.0000.0000.2481.0000.9050.504
주건축물수(동)0.0000.9250.9200.8160.8891.0000.8840.8090.7380.5580.0001.0000.7790.9460.8740.883
총세대수0.0000.9540.0000.8230.0001.0000.8740.8390.9660.4280.2480.7791.0000.9860.6680.894
사업주체0.9220.9910.9900.9690.9421.0000.7210.9680.9850.9631.0000.9460.9861.0000.9981.000
설계자0.1870.9980.9970.9740.9921.0000.6830.7160.7980.7990.9050.8740.6680.9981.0000.955
시공자0.8540.9960.9880.9790.9891.0000.0000.9320.7910.9820.5040.8830.8941.0000.9551.000
2023-12-12T13:03:32.240641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승인연도대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)용적률(퍼센트)건폐율(퍼센트)주건축물수(동)총세대수
승인연도1.000-0.076-0.110-0.0930.179-0.119-0.157-0.142
대지면적(제곱미터)-0.0761.0000.7850.907-0.165-0.5570.8540.921
건축면적(제곱미터)-0.1100.7851.0000.8980.163-0.1630.7770.870
연면적(제곱미터)-0.0930.9070.8981.0000.144-0.3940.8260.982
용적률(퍼센트)0.179-0.1650.1630.1441.0000.272-0.1140.100
건폐율(퍼센트)-0.119-0.557-0.163-0.3940.2721.000-0.361-0.432
주건축물수(동)-0.1570.8540.7770.826-0.114-0.3611.0000.857
총세대수-0.1420.9210.8700.9820.100-0.4320.8571.000

Missing values

2023-12-12T13:03:24.378254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:03:24.597605image/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

승인연도승인번호사업승인일착공예정일사용검사예정일대지위치주용도대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)용적률(퍼센트)건폐율(퍼센트)주건축물수(동)총세대수사업주체설계자시공자데이터기준일자
020182018-주택관리과-주택건설사업계획승인-12018-05-182018-08-292021-03-30인천광역시 서구 오류동 1722-1공동주택16308361455436229.9922.168420우방산업(주)(주)패스건축사사무소우방산업(주)2023-09-30
120182018-주택관리과-주택건설사업계획승인-22018-07-262018-09-282021-06-03인천광역시 서구 원당동 1022공동주택596679183177663208.7715.39161168티에스주택 주식회사(주)토담건축사사무소(주)호반산업2023-09-30
220182018-주택관리과-주택건설사업계획승인-32018-08-022018-09-282022-01-15인천광역시 서구 가정동 루원시티 주상 1BL공동주택7042221001466221439.6429.82202378아시아신탁(주)(주)이안디자인 건축사사무소에스케이에코플랜트(주)2023-09-30
320182018-주택관리과-주택건설사업계획승인-42018-08-032018-10-232021-09-18인천광역시 서구 당하동 검단신도시 AA4공동주택571227836154592184.8513.7212938(주)유승종합건설(주)유선엔지니어링건축사사무소(주)유승종합건설2023-09-30
420182018-주택관리과-주택건설사업계획승인-52018-08-092019-06-172022-02-28인천광역시 서구 마전동 마전택지지구 17 2공동주택23841520975929215.6421.858545마전지구지역주택조합(주)성도건축사사무소양우건설(주)2023-09-30
520182018-주택관리과-주택건설사업계획승인-62018-08-292018-10-122019-07-29인천광역시 서구 당하동 956-1공동주택2929585266769.1319.98149서남영(주)에이비라인건축사사무소(주)이인건설2023-09-30
620182018-주택관리과-주택건설사업계획승인-72018-11-012018-12-242022-01-17인천광역시 서구 원당동 검단신도시 택지개발지구 AB15-1블록공동주택6508710257199125211.8315.76171268우미건설(주)에이앤유디자인그룹건축사사무소(주)우미건설(주)2023-09-30
720182018-주택관리과-주택건설사업계획승인-82018-11-022019-02-132021-08-31인천광역시 서구 원당동 1018공동주택8554011339284250207.9713.26171540(주)대우건설(주)하우드엔지니어링종합건축사사무소(주)대우건설2023-09-30
820182018-주택관리과-주택건설사업계획승인-92018-11-072018-12-102021-08-31인천광역시 서구 당하동 1254-1공동주택457326404144028214.9614.011936대한토지신탁(주)(주)티씨엠씨건축사사무소한신공영(주)2023-09-30
920182018-주택관리과-주택건설사업계획승인-102018-12-072019-03-122022-03-03인천광역시 서구 당하동 인천검단신도시 AB-4블럭공동주택6944812315221097209.9817.73171279대방건설(주)(주)현대건축사사무소대방건설(주)2023-09-30
승인연도승인번호사업승인일착공예정일사용검사예정일대지위치주용도대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)용적률(퍼센트)건폐율(퍼센트)주건축물수(동)총세대수사업주체설계자시공자데이터기준일자
4820222022-주택과-주택건설사업계획승인-62022-07-052023-06-262026-11-29인천광역시 서구 연희동 166-10 외 70 필지공동주택6580110625243020249.7116.15131370(주)호반건설, 주식회사 하나자산신탁㈜희림종합건축사사무소㈜호반건설2023-09-30
4920222022-주택과-주택건설사업계획승인-72022-10-112023-05-082026-02-28인천광역시 서구 불로동 검단신도시 AB19블록공동주택429776000144671224.8813.9612856(주)호반건설㈜디드종합건축사사무소㈜호반건설2023-09-30
5020222022-주택과-주택건설사업계획승인-82022-10-302023-03-242026-03-31인천광역시 서구 불로동 검단신도시 AA20블록공동주택380826933140100194.618.212781대방개발기업주식회사㈜엠에이피한터인 종합건축사사무소대방건설㈜2023-09-30
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5620232023-주택과-주택건설사업계획승인-52023-08-102023-10-012026-08-31인천광역시 서구 불로동 검단신도시 AA22공동주택640118092191476196.4212.64111048인천검단3차피에프브이(주)(주)산하에코종합건설제일건설㈜㈜나산종합건축사사무소제일건설㈜2023-09-30
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