Overview

Dataset statistics

Number of variables17
Number of observations413
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory57.8 KiB
Average record size in memory143.3 B

Variable types

Numeric7
Categorical3
Text6
DateTime1

Dataset

Description대구광역시 달서구 내 공동주택 아파트 현황에 대한 내용 및 정보가 담겨있음.[공동주택(아파트) 위치, 동수, 층수, 사용승인정보]
Author대구광역시 달서구
URLhttps://www.data.go.kr/data/15054183/fileData.do

Alerts

주관부서 has constant value ""Constant
기준일자 has constant value ""Constant
위도 is highly overall correlated with 행정동High correlation
경도 is highly overall correlated with 행정동High correlation
동수 is highly overall correlated with 최고층수 and 2 other fieldsHigh correlation
최고층수 is highly overall correlated with 동수 and 2 other fieldsHigh correlation
세대수 is highly overall correlated with 동수 and 2 other fieldsHigh correlation
인구수 is highly overall correlated with 동수 and 2 other fieldsHigh correlation
행정동 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
아파트명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 14:39:09.902775
Analysis finished2023-12-12 14:39:15.814055
Duration5.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct406
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.831974
Minimum35.796589
Maximum35.863255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-12T23:39:15.883195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.796589
5-th percentile35.807295
Q135.81526
median35.835143
Q335.847008
95-th percentile35.857032
Maximum35.863255
Range0.06666629
Interquartile range (IQR)0.031748

Descriptive statistics

Standard deviation0.017084589
Coefficient of variation (CV)0.0004767973
Kurtosis-1.3285325
Mean35.831974
Median Absolute Deviation (MAD)0.01625603
Skewness-0.026245602
Sum14798.605
Variance0.00029188317
MonotonicityNot monotonic
2023-12-12T23:39:16.014638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.82432872 2
 
0.5%
35.80666027 2
 
0.5%
35.85443821 2
 
0.5%
35.81411597 2
 
0.5%
35.8538435 2
 
0.5%
35.80748027 2
 
0.5%
35.85552528 2
 
0.5%
35.80463244 1
 
0.2%
35.82084121 1
 
0.2%
35.8164463 1
 
0.2%
Other values (396) 396
95.9%
ValueCountFrequency (%)
35.79658888 1
0.2%
35.79879724 1
0.2%
35.79926113 1
0.2%
35.80153911 1
0.2%
35.80243205 1
0.2%
35.8026595 1
0.2%
35.80459825 1
0.2%
35.80463244 1
0.2%
35.80487593 1
0.2%
35.80543847 1
0.2%
ValueCountFrequency (%)
35.86325517 1
0.2%
35.86060121 1
0.2%
35.86057949 1
0.2%
35.86054826 1
0.2%
35.86027148 1
0.2%
35.8599818 1
0.2%
35.85928041 1
0.2%
35.85908412 1
0.2%
35.85907713 1
0.2%
35.85907574 1
0.2%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct406
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.53379
Minimum128.47368
Maximum128.57375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-12T23:39:16.183897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.47368
5-th percentile128.51148
Q1128.5263
median128.53418
Q3128.54252
95-th percentile128.55514
Maximum128.57375
Range0.1000651
Interquartile range (IQR)0.0162212

Descriptive statistics

Standard deviation0.014254361
Coefficient of variation (CV)0.00011089971
Kurtosis1.9194188
Mean128.53379
Median Absolute Deviation (MAD)0.0080719
Skewness-0.3885305
Sum53084.457
Variance0.00020318681
MonotonicityNot monotonic
2023-12-12T23:39:16.588776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.5422882 2
 
0.5%
128.5408067 2
 
0.5%
128.5195932 2
 
0.5%
128.5464015 2
 
0.5%
128.497451 2
 
0.5%
128.5328013 2
 
0.5%
128.5174791 2
 
0.5%
128.5487042 1
 
0.2%
128.5280103 1
 
0.2%
128.5251371 1
 
0.2%
Other values (396) 396
95.9%
ValueCountFrequency (%)
128.4736813 1
0.2%
128.4745535 1
0.2%
128.4788722 1
0.2%
128.4904566 1
0.2%
128.497451 2
0.5%
128.4988252 1
0.2%
128.5003974 1
0.2%
128.5004427 1
0.2%
128.5020437 1
0.2%
128.5038409 1
0.2%
ValueCountFrequency (%)
128.5737464 1
0.2%
128.5734507 1
0.2%
128.5717682 1
0.2%
128.5710592 1
0.2%
128.5702245 1
0.2%
128.5698397 1
0.2%
128.5692835 1
0.2%
128.5692365 1
0.2%
128.5689917 1
0.2%
128.5661628 1
0.2%

행정동
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
진천동
71 
본리동
39 
본동
30 
성당동
23 
감삼동
23 
Other values (18)
227 

Length

Max length6
Median length3
Mean length3.3753027
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row성당동
2nd row송현2동
3rd row두류1.2동
4th row성당동
5th row본동

Common Values

ValueCountFrequency (%)
진천동 71
17.2%
본리동 39
 
9.4%
본동 30
 
7.3%
성당동 23
 
5.6%
감삼동 23
 
5.6%
상인1동 23
 
5.6%
송현2동 22
 
5.3%
상인2동 22
 
5.3%
월성1동 20
 
4.8%
용산1동 19
 
4.6%
Other values (13) 121
29.3%

Length

2023-12-12T23:39:16.720312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
진천동 71
17.2%
본리동 39
 
9.4%
본동 30
 
7.3%
성당동 23
 
5.6%
감삼동 23
 
5.6%
상인1동 23
 
5.6%
송현2동 22
 
5.3%
상인2동 22
 
5.3%
월성1동 20
 
4.8%
용산1동 19
 
4.6%
Other values (13) 121
29.3%

지번
Text

Distinct410
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2023-12-12T23:39:17.066149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length24
Mean length17.951574
Min length15

Characters and Unicode

Total characters7414
Distinct characters50
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique407 ?
Unique (%)98.5%

Sample

1st row대구광역시 달서구 성당동 726-1
2nd row대구광역시 달서구 송현동156-3
3rd row대구광역시 달서구 두류동779-3
4th row대구광역시 달서구 성당동 70
5th row대구광역시 달서구 본동755
ValueCountFrequency (%)
대구광역시 413
27.5%
달서구 413
27.5%
본리동 35
 
2.3%
진천동 30
 
2.0%
감삼동 24
 
1.6%
상인동 22
 
1.5%
성당동 18
 
1.2%
월성동 17
 
1.1%
본동 16
 
1.1%
대곡동 15
 
1.0%
Other values (422) 497
33.1%
2023-12-12T23:39:17.594922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1087
14.7%
구 826
 
11.1%
대 437
 
5.9%
서 413
 
5.6%
달 413
 
5.6%
동 413
 
5.6%
시 413
 
5.6%
역 413
 
5.6%
광 413
 
5.6%
1 351
 
4.7%
Other values (40) 2235
30.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4513
60.9%
Decimal Number 1575
 
21.2%
Space Separator 1087
 
14.7%
Dash Punctuation 235
 
3.2%
Other Punctuation 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 826
18.3%
대 437
9.7%
서 413
9.2%
달 413
9.2%
동 413
9.2%
시 413
9.2%
역 413
9.2%
광 413
9.2%
천 69
 
1.5%
본 69
 
1.5%
Other values (27) 634
14.0%
Decimal Number
ValueCountFrequency (%)
1 351
22.3%
2 216
13.7%
4 164
10.4%
3 149
9.5%
5 146
9.3%
0 114
 
7.2%
9 114
 
7.2%
7 113
 
7.2%
8 107
 
6.8%
6 101
 
6.4%
Space Separator
ValueCountFrequency (%)
1087
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 235
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4513
60.9%
Common 2901
39.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 826
18.3%
대 437
9.7%
서 413
9.2%
달 413
9.2%
동 413
9.2%
시 413
9.2%
역 413
9.2%
광 413
9.2%
천 69
 
1.5%
본 69
 
1.5%
Other values (27) 634
14.0%
Common
ValueCountFrequency (%)
1087
37.5%
1 351
 
12.1%
- 235
 
8.1%
2 216
 
7.4%
4 164
 
5.7%
3 149
 
5.1%
5 146
 
5.0%
0 114
 
3.9%
9 114
 
3.9%
7 113
 
3.9%
Other values (3) 212
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4513
60.9%
ASCII 2901
39.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1087
37.5%
1 351
 
12.1%
- 235
 
8.1%
2 216
 
7.4%
4 164
 
5.7%
3 149
 
5.1%
5 146
 
5.0%
0 114
 
3.9%
9 114
 
3.9%
7 113
 
3.9%
Other values (3) 212
 
7.3%
Hangul
ValueCountFrequency (%)
구 826
18.3%
대 437
9.7%
서 413
9.2%
달 413
9.2%
동 413
9.2%
시 413
9.2%
역 413
9.2%
광 413
9.2%
천 69
 
1.5%
본 69
 
1.5%
Other values (27) 634
14.0%

도로명주소
Text

Distinct408
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2023-12-12T23:39:17.954574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length24
Mean length18.271186
Min length15

Characters and Unicode

Total characters7546
Distinct characters84
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique403 ?
Unique (%)97.6%

Sample

1st row대구광역시 달서구 장기로 116
2nd row대구광역시 달서구 학산로50길 18-6
3rd row대구광역시 달서구 성당로 231
4th row대구광역시 달서구 성당로35길 40
5th row대구광역시 달서구 와룡로10길 14
ValueCountFrequency (%)
대구광역시 413
25.0%
달서구 413
25.0%
월배로 16
 
1.0%
상화로 10
 
0.6%
장기로 9
 
0.5%
19 9
 
0.5%
40 9
 
0.5%
와룡로 9
 
0.5%
대명천로47길 8
 
0.5%
상인서로 8
 
0.5%
Other values (342) 749
45.3%
2023-12-12T23:39:18.559071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1240
16.4%
구 863
 
11.4%
대 456
 
6.0%
서 443
 
5.9%
달 432
 
5.7%
광 413
 
5.5%
역 413
 
5.5%
시 413
 
5.5%
로 396
 
5.2%
1 270
 
3.6%
Other values (74) 2207
29.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4912
65.1%
Decimal Number 1368
 
18.1%
Space Separator 1240
 
16.4%
Dash Punctuation 25
 
0.3%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 863
17.6%
대 456
9.3%
서 443
9.0%
달 432
8.8%
광 413
8.4%
역 413
8.4%
시 413
8.4%
로 396
8.1%
길 224
 
4.6%
월 74
 
1.5%
Other values (61) 785
16.0%
Decimal Number
ValueCountFrequency (%)
1 270
19.7%
2 189
13.8%
3 186
13.6%
4 133
9.7%
5 126
9.2%
7 114
8.3%
0 106
 
7.7%
9 86
 
6.3%
6 84
 
6.1%
8 74
 
5.4%
Space Separator
ValueCountFrequency (%)
1240
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4912
65.1%
Common 2634
34.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 863
17.6%
대 456
9.3%
서 443
9.0%
달 432
8.8%
광 413
8.4%
역 413
8.4%
시 413
8.4%
로 396
8.1%
길 224
 
4.6%
월 74
 
1.5%
Other values (61) 785
16.0%
Common
ValueCountFrequency (%)
1240
47.1%
1 270
 
10.3%
2 189
 
7.2%
3 186
 
7.1%
4 133
 
5.0%
5 126
 
4.8%
7 114
 
4.3%
0 106
 
4.0%
9 86
 
3.3%
6 84
 
3.2%
Other values (3) 100
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4912
65.1%
ASCII 2634
34.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1240
47.1%
1 270
 
10.3%
2 189
 
7.2%
3 186
 
7.1%
4 133
 
5.0%
5 126
 
4.8%
7 114
 
4.3%
0 106
 
4.0%
9 86
 
3.3%
6 84
 
3.2%
Other values (3) 100
 
3.8%
Hangul
ValueCountFrequency (%)
구 863
17.6%
대 456
9.3%
서 443
9.0%
달 432
8.8%
광 413
8.4%
역 413
8.4%
시 413
8.4%
로 396
8.1%
길 224
 
4.6%
월 74
 
1.5%
Other values (61) 785
16.0%

아파트명
Text

UNIQUE 

Distinct413
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2023-12-12T23:39:18.913116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length7.401937
Min length3

Characters and Unicode

Total characters3057
Distinct characters273
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

Unique413 ?
Unique (%)100.0%

Sample

1st row성남아파트
2nd row송림아파트
3rd row서남아파트
4th row교직원아파트
5th row대영아파트(가)
ValueCountFrequency (%)
101동 12
 
2.6%
102동 11
 
2.4%
a동 8
 
1.7%
b동 4
 
0.9%
우인그레이스(가 3
 
0.6%
월송아파트 3
 
0.6%
감삼용궁 3
 
0.6%
삼영로얄팔레스 3
 
0.6%
103동 3
 
0.6%
진천그린팰리스 2
 
0.4%
Other values (398) 410
88.7%
2023-12-12T23:39:19.420793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
트 124
 
4.1%
성 122
 
4.0%
아 121
 
4.0%
파 115
 
3.8%
타 76
 
2.5%
운 76
 
2.5%
동 75
 
2.5%
1 67
 
2.2%
스 55
 
1.8%
서 50
 
1.6%
Other values (263) 2176
71.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2746
89.8%
Decimal Number 164
 
5.4%
Space Separator 49
 
1.6%
Uppercase Letter 30
 
1.0%
Open Punctuation 26
 
0.9%
Close Punctuation 26
 
0.9%
Dash Punctuation 8
 
0.3%
Lowercase Letter 8
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
트 124
 
4.5%
성 122
 
4.4%
아 121
 
4.4%
파 115
 
4.2%
타 76
 
2.8%
운 76
 
2.8%
동 75
 
2.7%
스 55
 
2.0%
서 50
 
1.8%
월 47
 
1.7%
Other values (239) 1885
68.6%
Decimal Number
ValueCountFrequency (%)
1 67
40.9%
2 41
25.0%
0 27
16.5%
3 12
 
7.3%
5 5
 
3.0%
6 4
 
2.4%
4 3
 
1.8%
7 2
 
1.2%
8 2
 
1.2%
9 1
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
A 11
36.7%
C 5
16.7%
K 4
 
13.3%
B 4
 
13.3%
D 2
 
6.7%
H 1
 
3.3%
L 1
 
3.3%
S 1
 
3.3%
R 1
 
3.3%
Space Separator
ValueCountFrequency (%)
49
100.0%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 26
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2746
89.8%
Common 273
 
8.9%
Latin 38
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
트 124
 
4.5%
성 122
 
4.4%
아 121
 
4.4%
파 115
 
4.2%
타 76
 
2.8%
운 76
 
2.8%
동 75
 
2.7%
스 55
 
2.0%
서 50
 
1.8%
월 47
 
1.7%
Other values (239) 1885
68.6%
Common
ValueCountFrequency (%)
1 67
24.5%
49
17.9%
2 41
15.0%
0 27
9.9%
( 26
 
9.5%
) 26
 
9.5%
3 12
 
4.4%
- 8
 
2.9%
5 5
 
1.8%
6 4
 
1.5%
Other values (4) 8
 
2.9%
Latin
ValueCountFrequency (%)
A 11
28.9%
e 8
21.1%
C 5
13.2%
K 4
 
10.5%
B 4
 
10.5%
D 2
 
5.3%
H 1
 
2.6%
L 1
 
2.6%
S 1
 
2.6%
R 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2746
89.8%
ASCII 311
 
10.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
트 124
 
4.5%
성 122
 
4.4%
아 121
 
4.4%
파 115
 
4.2%
타 76
 
2.8%
운 76
 
2.8%
동 75
 
2.7%
스 55
 
2.0%
서 50
 
1.8%
월 47
 
1.7%
Other values (239) 1885
68.6%
ASCII
ValueCountFrequency (%)
1 67
21.5%
49
15.8%
2 41
13.2%
0 27
8.7%
( 26
 
8.4%
) 26
 
8.4%
3 12
 
3.9%
A 11
 
3.5%
- 8
 
2.6%
e 8
 
2.6%
Other values (14) 36
11.6%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0605327
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-12T23:39:19.602332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q36
95-th percentile13
Maximum27
Range26
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.2556321
Coefficient of variation (CV)1.0480477
Kurtosis4.4380679
Mean4.0605327
Median Absolute Deviation (MAD)1
Skewness1.9278715
Sum1677
Variance18.110405
MonotonicityNot monotonic
2023-12-12T23:39:19.738686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 183
44.3%
3 41
 
9.9%
6 29
 
7.0%
2 28
 
6.8%
4 25
 
6.1%
5 21
 
5.1%
8 16
 
3.9%
7 16
 
3.9%
10 13
 
3.1%
9 8
 
1.9%
Other values (11) 33
 
8.0%
ValueCountFrequency (%)
1 183
44.3%
2 28
 
6.8%
3 41
 
9.9%
4 25
 
6.1%
5 21
 
5.1%
6 29
 
7.0%
7 16
 
3.9%
8 16
 
3.9%
9 8
 
1.9%
10 13
 
3.1%
ValueCountFrequency (%)
27 1
 
0.2%
24 1
 
0.2%
21 1
 
0.2%
19 2
 
0.5%
18 2
 
0.5%
17 4
1.0%
15 4
1.0%
14 3
0.7%
13 4
1.0%
12 5
1.2%

층수
Text

Distinct96
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2023-12-12T23:39:20.001909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.5762712
Min length1

Characters and Unicode

Total characters1064
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

Unique60 ?
Unique (%)14.5%

Sample

1st row4~5
2nd row5
3rd row5
4th row5
5th row5
ValueCountFrequency (%)
5 58
 
14.0%
10 48
 
11.6%
15 45
 
10.9%
9 22
 
5.3%
20 21
 
5.1%
8 15
 
3.6%
18~20 10
 
2.4%
13 10
 
2.4%
7 9
 
2.2%
12 9
 
2.2%
Other values (86) 166
40.2%
2023-12-12T23:39:20.521410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 272
25.6%
2 158
14.8%
5 139
13.1%
0 130
12.2%
~ 118
11.1%
8 53
 
5.0%
3 49
 
4.6%
9 47
 
4.4%
7 36
 
3.4%
6 34
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 946
88.9%
Math Symbol 118
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 272
28.8%
2 158
16.7%
5 139
14.7%
0 130
13.7%
8 53
 
5.6%
3 49
 
5.2%
9 47
 
5.0%
7 36
 
3.8%
6 34
 
3.6%
4 28
 
3.0%
Math Symbol
ValueCountFrequency (%)
~ 118
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1064
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 272
25.6%
2 158
14.8%
5 139
13.1%
0 130
12.2%
~ 118
11.1%
8 53
 
5.0%
3 49
 
4.6%
9 47
 
4.4%
7 36
 
3.4%
6 34
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1064
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 272
25.6%
2 158
14.8%
5 139
13.1%
0 130
12.2%
~ 118
11.1%
8 53
 
5.0%
3 49
 
4.6%
9 47
 
4.4%
7 36
 
3.4%
6 34
 
3.2%

최고층수
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.978208
Minimum5
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-12T23:39:20.702308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q19
median15
Q320
95-th percentile30
Maximum48
Range43
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.005278
Coefficient of variation (CV)0.53446166
Kurtosis0.97431636
Mean14.978208
Median Absolute Deviation (MAD)5
Skewness0.91167966
Sum6186
Variance64.084475
MonotonicityNot monotonic
2023-12-12T23:39:20.865966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
5 59
14.3%
20 57
13.8%
15 54
13.1%
10 48
11.6%
9 22
 
5.3%
19 15
 
3.6%
8 15
 
3.6%
30 12
 
2.9%
18 11
 
2.7%
12 10
 
2.4%
Other values (24) 110
26.6%
ValueCountFrequency (%)
5 59
14.3%
6 9
 
2.2%
7 9
 
2.2%
8 15
 
3.6%
9 22
 
5.3%
10 48
11.6%
11 9
 
2.2%
12 10
 
2.4%
13 10
 
2.4%
14 7
 
1.7%
ValueCountFrequency (%)
48 1
 
0.2%
45 1
 
0.2%
43 2
 
0.5%
38 2
 
0.5%
37 1
 
0.2%
34 1
 
0.2%
33 1
 
0.2%
32 2
 
0.5%
31 2
 
0.5%
30 12
2.9%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct259
Distinct (%)62.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean386.55932
Minimum9
Maximum2420
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-12T23:39:21.042724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile15.6
Q124
median201
Q3585
95-th percentile1400
Maximum2420
Range2411
Interquartile range (IQR)561

Descriptive statistics

Standard deviation474.8028
Coefficient of variation (CV)1.2282793
Kurtosis2.7754107
Mean386.55932
Median Absolute Deviation (MAD)183
Skewness1.667691
Sum159649
Variance225437.7
MonotonicityNot monotonic
2023-12-12T23:39:21.210799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 29
 
7.0%
16 18
 
4.4%
19 17
 
4.1%
29 7
 
1.7%
24 7
 
1.7%
12 6
 
1.5%
40 6
 
1.5%
14 6
 
1.5%
20 6
 
1.5%
15 5
 
1.2%
Other values (249) 306
74.1%
ValueCountFrequency (%)
9 1
 
0.2%
10 1
 
0.2%
12 6
 
1.5%
13 2
 
0.5%
14 6
 
1.5%
15 5
 
1.2%
16 18
4.4%
17 1
 
0.2%
18 29
7.0%
19 17
4.1%
ValueCountFrequency (%)
2420 1
0.2%
2364 1
0.2%
2160 1
0.2%
2134 1
0.2%
2092 1
0.2%
1999 1
0.2%
1877 1
0.2%
1840 1
0.2%
1824 1
0.2%
1740 1
0.2%

인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct312
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean974.44068
Minimum11
Maximum6918
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-12T23:39:21.373257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile29.6
Q155
median478
Q31510
95-th percentile3234.8
Maximum6918
Range6907
Interquartile range (IQR)1455

Descriptive statistics

Standard deviation1215.0377
Coefficient of variation (CV)1.2469078
Kurtosis3.5751038
Mean974.44068
Median Absolute Deviation (MAD)438
Skewness1.7502079
Sum402444
Variance1476316.6
MonotonicityNot monotonic
2023-12-12T23:39:21.523721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 11
 
2.7%
47 7
 
1.7%
50 6
 
1.5%
30 6
 
1.5%
40 6
 
1.5%
54 5
 
1.2%
42 5
 
1.2%
25 5
 
1.2%
36 4
 
1.0%
43 4
 
1.0%
Other values (302) 354
85.7%
ValueCountFrequency (%)
11 1
 
0.2%
12 1
 
0.2%
14 1
 
0.2%
20 2
 
0.5%
22 1
 
0.2%
25 5
1.2%
26 1
 
0.2%
27 2
 
0.5%
28 3
0.7%
29 4
1.0%
ValueCountFrequency (%)
6918 1
0.2%
6426 1
0.2%
5951 1
0.2%
5908 1
0.2%
5496 1
0.2%
4938 1
0.2%
4726 1
0.2%
4524 1
0.2%
4435 1
0.2%
4434 1
0.2%

건축연도
Real number (ℝ)

Distinct45
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2000.9564
Minimum1978
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-12T23:39:21.710779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1978
5-th percentile1981
Q11994
median2002
Q32008
95-th percentile2019
Maximum2022
Range44
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.042638
Coefficient of variation (CV)0.0055186797
Kurtosis-0.52475214
Mean2000.9564
Median Absolute Deviation (MAD)7
Skewness-0.092204975
Sum826395
Variance121.93984
MonotonicityIncreasing
2023-12-12T23:39:21.868458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
2002 41
 
9.9%
2003 28
 
6.8%
1997 27
 
6.5%
2016 18
 
4.4%
1993 18
 
4.4%
1981 17
 
4.1%
1980 16
 
3.9%
1994 15
 
3.6%
2008 13
 
3.1%
2000 12
 
2.9%
Other values (35) 208
50.4%
ValueCountFrequency (%)
1978 1
 
0.2%
1979 3
 
0.7%
1980 16
3.9%
1981 17
4.1%
1982 3
 
0.7%
1983 1
 
0.2%
1984 1
 
0.2%
1985 4
 
1.0%
1986 3
 
0.7%
1987 3
 
0.7%
ValueCountFrequency (%)
2022 11
2.7%
2021 3
 
0.7%
2020 5
 
1.2%
2019 9
2.2%
2018 5
 
1.2%
2017 3
 
0.7%
2016 18
4.4%
2015 11
2.7%
2014 6
 
1.5%
2013 6
 
1.5%

사용승인일
Date

Distinct375
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
Minimum1978-02-04 00:00:00
Maximum2022-12-30 00:00:00
2023-12-12T23:39:22.051642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:22.228912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

관리실전화
Text

Distinct289
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2023-12-12T23:39:22.610290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters4956
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

Unique282 ?
Unique (%)68.3%

Sample

1st row053-656-0990
2nd row053-627-5392
3rd row053-627-1456
4th row053-627-5208
5th row053-628-0974
ValueCountFrequency (%)
000-000-0000 117
28.3%
053-639-8100 3
 
0.7%
053-644-1825 3
 
0.7%
053-637-1590 2
 
0.5%
053-634-5577 2
 
0.5%
053-631-6407 2
 
0.5%
053-644-8737 2
 
0.5%
053-651-8771 1
 
0.2%
053-634-1900 1
 
0.2%
053-644-3116 1
 
0.2%
Other values (279) 279
67.6%
2023-12-12T23:39:23.181414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1631
32.9%
- 826
16.7%
5 575
 
11.6%
3 557
 
11.2%
6 342
 
6.9%
2 202
 
4.1%
4 199
 
4.0%
1 182
 
3.7%
8 170
 
3.4%
7 155
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4130
83.3%
Dash Punctuation 826
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1631
39.5%
5 575
 
13.9%
3 557
 
13.5%
6 342
 
8.3%
2 202
 
4.9%
4 199
 
4.8%
1 182
 
4.4%
8 170
 
4.1%
7 155
 
3.8%
9 117
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 826
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4956
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1631
32.9%
- 826
16.7%
5 575
 
11.6%
3 557
 
11.2%
6 342
 
6.9%
2 202
 
4.1%
4 199
 
4.0%
1 182
 
3.7%
8 170
 
3.4%
7 155
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4956
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1631
32.9%
- 826
16.7%
5 575
 
11.6%
3 557
 
11.2%
6 342
 
6.9%
2 202
 
4.1%
4 199
 
4.0%
1 182
 
3.7%
8 170
 
3.4%
7 155
 
3.1%

팩스번호
Text

Distinct235
Distinct (%)56.9%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2023-12-12T23:39:23.557746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters4956
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

Unique230 ?
Unique (%)55.7%

Sample

1st row000-000-0000
2nd row000-000-0000
3rd row000-000-0000
4th row000-000-0000
5th row000-000-0000
ValueCountFrequency (%)
000-000-0000 175
42.4%
053-634-1950 2
 
0.5%
053-642-4870 2
 
0.5%
053-632-1591 2
 
0.5%
053-644-8738 2
 
0.5%
053-633-5616 1
 
0.2%
053-592-8526 1
 
0.2%
053-621-3607 1
 
0.2%
053-526-9921 1
 
0.2%
053-562-1214 1
 
0.2%
Other values (225) 225
54.5%
2023-12-12T23:39:24.061828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2095
42.3%
- 826
 
16.7%
3 473
 
9.5%
5 398
 
8.0%
6 238
 
4.8%
2 225
 
4.5%
4 169
 
3.4%
8 165
 
3.3%
1 150
 
3.0%
7 117
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4130
83.3%
Dash Punctuation 826
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2095
50.7%
3 473
 
11.5%
5 398
 
9.6%
6 238
 
5.8%
2 225
 
5.4%
4 169
 
4.1%
8 165
 
4.0%
1 150
 
3.6%
7 117
 
2.8%
9 100
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 826
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4956
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2095
42.3%
- 826
 
16.7%
3 473
 
9.5%
5 398
 
8.0%
6 238
 
4.8%
2 225
 
4.5%
4 169
 
3.4%
8 165
 
3.3%
1 150
 
3.0%
7 117
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4956
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2095
42.3%
- 826
 
16.7%
3 473
 
9.5%
5 398
 
8.0%
6 238
 
4.8%
2 225
 
4.5%
4 169
 
3.4%
8 165
 
3.3%
1 150
 
3.0%
7 117
 
2.4%

주관부서
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
건축과
413 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건축과 413
100.0%

Length

2023-12-12T23:39:24.200952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:39:24.319963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건축과 413
100.0%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2022-12-31
413 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-12-31
2nd row2022-12-31
3rd row2022-12-31
4th row2022-12-31
5th row2022-12-31

Common Values

ValueCountFrequency (%)
2022-12-31 413
100.0%

Length

2023-12-12T23:39:24.433213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:39:24.531022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-12-31 413
100.0%

Interactions

2023-12-12T23:39:14.807645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:10.655457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:11.334736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:12.010774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:12.702012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:13.368170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:14.021804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:14.906742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:10.767449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:11.423074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:12.116053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:12.798413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:13.481936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:14.139807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:15.009058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:10.874558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:11.519770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:12.202351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:12.892183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:13.569275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:14.254975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:15.117081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:10.974079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:11.609478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:12.296145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:12.985945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:13.655186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:14.395578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:15.205815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:11.065567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:11.705909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:12.394331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:13.064952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:13.741461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:14.492580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:15.296560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:11.160927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:11.815232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:12.503295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:13.150635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:13.820565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:14.599807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:15.376356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:11.255621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:11.928273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:12.612988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:13.261922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:13.908812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:14.707738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:39:24.605234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도행정동동수층수최고층수세대수인구수건축연도
위도1.0000.6330.9160.5330.7150.6150.4120.4230.655
경도0.6331.0000.8920.2680.6790.4950.4360.4350.511
행정동0.9160.8921.0000.6540.7670.7100.6100.5870.728
동수0.5330.2680.6541.0000.6760.5590.9080.9110.544
층수0.7150.6790.7670.6761.0001.0000.8030.8980.868
최고층수0.6150.4950.7100.5591.0001.0000.6580.6800.834
세대수0.4120.4360.6100.9080.8030.6581.0000.9470.588
인구수0.4230.4350.5870.9110.8980.6800.9471.0000.580
건축연도0.6550.5110.7280.5440.8680.8340.5880.5801.000
2023-12-12T23:39:24.721444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도동수최고층수세대수인구수건축연도행정동
위도1.0000.125-0.014-0.138-0.034-0.062-0.0300.649
경도0.1251.000-0.235-0.411-0.271-0.327-0.1450.596
동수-0.014-0.2351.0000.6060.9220.900-0.1110.267
최고층수-0.138-0.4110.6061.0000.6920.7320.4030.349
세대수-0.034-0.2710.9220.6921.0000.958-0.1340.271
인구수-0.062-0.3270.9000.7320.9581.000-0.0950.256
건축연도-0.030-0.145-0.1110.403-0.134-0.0951.0000.366
행정동0.6490.5960.2670.3490.2710.2560.3661.000

Missing values

2023-12-12T23:39:15.509436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:39:15.738735image/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

위도경도행정동지번도로명주소아파트명동수층수최고층수세대수인구수건축연도사용승인일관리실전화팩스번호주관부서기준일자
035.843607128.548397성당동대구광역시 달서구 성당동 726-1대구광역시 달서구 장기로 116성남아파트44~5515320619781978-02-04053-656-0990000-000-0000건축과2022-12-31
135.833321128.55224송현2동대구광역시 달서구 송현동156-3대구광역시 달서구 학산로50길 18-6송림아파트155408419791979-03-26053-627-5392000-000-0000건축과2022-12-31
235.853053128.573451두류1.2동대구광역시 달서구 두류동779-3대구광역시 달서구 성당로 231서남아파트155162719791979-06-12053-627-1456000-000-0000건축과2022-12-31
335.850896128.570224성당동대구광역시 달서구 성당동 70대구광역시 달서구 성당로35길 40교직원아파트355544219791979-10-26053-627-5208000-000-0000건축과2022-12-31
435.836524128.538243본동대구광역시 달서구 본동755대구광역시 달서구 와룡로10길 14대영아파트(가)1555011019801980-01-10053-628-0974000-000-0000건축과2022-12-31
535.832054128.544068송현2동대구광역시 달서구 송현동593-1대구광역시 달서구 송현로25길 74성한아파트155214819801980-04-24053-634-3833000-000-0000건축과2022-12-31
635.83136128.544151송현2동대구광역시 달서구 송현동592대구광역시 달서구 송현로 161송현아파트3558017719801980-05-17053-631-8658000-000-0000건축과2022-12-31
735.833857128.536088본동대구광역시 달서구 본동943-2대구광역시 달서구 구마로14길 81대동아파트155377919801980-05-30053-824-5180000-000-0000건축과2022-12-31
835.855775128.544418죽전동대구광역시 달서구 감삼동 33-2대구광역시 달서구 당산로45길 57일신아파트 11동155406319801980-05-30053-555-1204000-000-0000건축과2022-12-31
935.838128.540699본리동대구광역시 달서구 본리동 351-2대구광역시 달서구 와룡로14길 70대영아파트(나)155408919801980-07-15053-475-5274000-000-0000건축과2022-12-31
위도경도행정동지번도로명주소아파트명동수층수최고층수세대수인구수건축연도사용승인일관리실전화팩스번호주관부서기준일자
40335.81526128.5248진천동대구광역시 달서구 진천동 843대구광역시 달서구 진천로16길 26진천역라온프라이빗센텀342~4343585151020222022-05-26053-637-7300053-637-7301건축과2022-12-31
40435.83033128.5238월성1동대구광역시 달서구 월성동 1894대구광역시 달서구 월성로 100월성삼정그린코아에듀파크1226~30301392308920222022-06-15053-641-0967053-641-0968건축과2022-12-31
40535.83849128.5557성당동대구광역시 달서구 성당동 223-3대구광역시 달서구 대명천로 33성당태왕아너스메트로332~333322245320222022-06-28053-656-4600053-656-4601건축과2022-12-31
40635.84399128.5359감삼동대구광역시 달서구 감삼동 704대구광역시 달서구 와룡로 123죽전역동화아이위시336~373739296120222022-07-20053-565-2990053-565-2991건축과2022-12-31
40735.8179128.5274진천동대구광역시 달서구 진천동 66-1대구광역시 달서구 진천로18길 90월배역 우인그레이스11414481220222022-08-17000-000-0000000-000-0000건축과2022-12-31
40835.84942128.5375감삼동대구광역시 달서구 감삼동 141-5대구광역시 달서구 와룡로 186빌리브 스카이 주상복합3484850471720222022-08-12053-553-5200053-553-5201건축과2022-12-31
40935.84693128.5338감삼동대구광역시 달서구 감삼동 709대구광역시 달서구 와룡로31길 26힐스테이트 죽전역 더퍼스트332~4343391108220222022-09-30053-525-5200053-525-5201건축과2022-12-31
41035.847008128.5319감삼동대구광역시 달서구 감삼동 582-5대구광역시 달서구 달구벌대로304길 60죽전역화성파크드림아파트220~383814443220222022-10-25053-551-1133053-551-1134건축과2022-12-31
41135.853992128.571059두류1.2동대구광역시 달서구 두류동 803-44대구광역시 달서구 파도고개로 70두류파크KCC스위첸917~2424785133120222022-11-29053-214-2022053-214-2023건축과2022-12-31
41235.852629128.55024두류3동대구광역시 달서구 두류동 631-40대구광역시 달서구 당산로36길 11두류센트레빌더시티518~272733347520222022-12-30053-267-1080000-000-0000건축과2022-12-31