Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells9051
Missing cells (%)12.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory673.8 KiB
Average record size in memory69.0 B

Variable types

Numeric4
Categorical1
Text2

Dataset

Description달서구에서 5년간 발생한 붋법주정차 단속의 누적 건수를 제공하는 데이터 입니다. 발생한 년도, 주소지, 1년간 누적 발생건수를 제공하고 있습니다.
Author대구광역시 달서구
URLhttps://www.data.go.kr/data/15110082/fileData.do

Alerts

순번 is highly overall correlated with 단속발생High correlation
단속발생 is highly overall correlated with 순번High correlation
현장정보 has 9051 (90.5%) missing valuesMissing
순번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 17:06:31.155158
Analysis finished2023-12-12 17:06:33.923112
Duration2.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6295.6997
Minimum1
Maximum12572
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:06:34.004794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile647.9
Q13160.75
median6293.5
Q39438.25
95-th percentile11933.05
Maximum12572
Range12571
Interquartile range (IQR)6277.5

Descriptive statistics

Standard deviation3621.6479
Coefficient of variation (CV)0.5752574
Kurtosis-1.1982185
Mean6295.6997
Median Absolute Deviation (MAD)3139.5
Skewness-0.0026572683
Sum62956997
Variance13116333
MonotonicityNot monotonic
2023-12-13T02:06:34.159624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5948 1
 
< 0.1%
2082 1
 
< 0.1%
5092 1
 
< 0.1%
12249 1
 
< 0.1%
394 1
 
< 0.1%
5566 1
 
< 0.1%
5737 1
 
< 0.1%
2190 1
 
< 0.1%
1768 1
 
< 0.1%
5716 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
15 1
< 0.1%
ValueCountFrequency (%)
12572 1
< 0.1%
12571 1
< 0.1%
12570 1
< 0.1%
12569 1
< 0.1%
12567 1
< 0.1%
12565 1
< 0.1%
12564 1
< 0.1%
12562 1
< 0.1%
12561 1
< 0.1%
12560 1
< 0.1%

단속발생
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020
2623 
2019
2540 
2021
2303 
2018
1565 
2017
969 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2020 2623
26.2%
2019 2540
25.4%
2021 2303
23.0%
2018 1565
15.7%
2017 969
 
9.7%

Length

2023-12-13T02:06:34.368185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:06:34.492707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 2623
26.2%
2019 2540
25.4%
2021 2303
23.0%
2018 1565
15.7%
2017 969
 
9.7%

단속발생소재지
Text

Distinct5673
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T02:06:34.897141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length18.9103
Min length15

Characters and Unicode

Total characters189103
Distinct characters56
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3170 ?
Unique (%)31.7%

Sample

1st row대구광역시 달서구 신당동 1723-
2nd row대구광역시 달서구 감삼동 563-9
3rd row대구광역시 달서구 송현동 1037-3
4th row대구광역시 달서구 성당동 635
5th row대구광역시 달서구 신당동 1788
ValueCountFrequency (%)
대구광역시 10000
25.0%
달서구 10000
25.0%
이곡동 1014
 
2.5%
상인동 860
 
2.1%
두류동 818
 
2.0%
감삼동 709
 
1.8%
진천동 680
 
1.7%
송현동 626
 
1.6%
성당동 562
 
1.4%
본리동 524
 
1.3%
Other values (5047) 14207
35.5%
2023-12-13T02:06:35.759091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
30000
15.9%
구 20000
 
10.6%
대 10566
 
5.6%
광 10000
 
5.3%
역 10000
 
5.3%
시 10000
 
5.3%
달 10000
 
5.3%
서 10000
 
5.3%
동 10000
 
5.3%
1 9481
 
5.0%
Other values (46) 59056
31.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 109676
58.0%
Decimal Number 42047
 
22.2%
Space Separator 30000
 
15.9%
Dash Punctuation 7377
 
3.9%
Other Punctuation 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 20000
18.2%
대 10566
9.6%
광 10000
9.1%
역 10000
9.1%
시 10000
9.1%
달 10000
9.1%
서 10000
9.1%
동 10000
9.1%
곡 1319
 
1.2%
천 1171
 
1.1%
Other values (32) 16620
15.2%
Decimal Number
ValueCountFrequency (%)
1 9481
22.5%
2 4962
11.8%
5 4276
10.2%
3 3989
9.5%
4 3772
 
9.0%
0 3708
 
8.8%
6 3222
 
7.7%
7 2994
 
7.1%
8 2842
 
6.8%
9 2801
 
6.7%
Space Separator
ValueCountFrequency (%)
30000
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7377
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 109676
58.0%
Common 79427
42.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 20000
18.2%
대 10566
9.6%
광 10000
9.1%
역 10000
9.1%
시 10000
9.1%
달 10000
9.1%
서 10000
9.1%
동 10000
9.1%
곡 1319
 
1.2%
천 1171
 
1.1%
Other values (32) 16620
15.2%
Common
ValueCountFrequency (%)
30000
37.8%
1 9481
 
11.9%
- 7377
 
9.3%
2 4962
 
6.2%
5 4276
 
5.4%
3 3989
 
5.0%
4 3772
 
4.7%
0 3708
 
4.7%
6 3222
 
4.1%
7 2994
 
3.8%
Other values (4) 5646
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 109676
58.0%
ASCII 79427
42.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30000
37.8%
1 9481
 
11.9%
- 7377
 
9.3%
2 4962
 
6.2%
5 4276
 
5.4%
3 3989
 
5.0%
4 3772
 
4.7%
0 3708
 
4.7%
6 3222
 
4.1%
7 2994
 
3.8%
Other values (4) 5646
 
7.1%
Hangul
ValueCountFrequency (%)
구 20000
18.2%
대 10566
9.6%
광 10000
9.1%
역 10000
9.1%
시 10000
9.1%
달 10000
9.1%
서 10000
9.1%
동 10000
9.1%
곡 1319
 
1.2%
천 1171
 
1.1%
Other values (32) 16620
15.2%

누적발생건수
Real number (ℝ)

Distinct486
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.3553
Minimum1
Maximum6756
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:06:35.934273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q310
95-th percentile145
Maximum6756
Range6755
Interquartile range (IQR)9

Descriptive statistics

Standard deviation259.54021
Coefficient of variation (CV)5.8513912
Kurtosis229.00683
Mean44.3553
Median Absolute Deviation (MAD)1
Skewness13.429803
Sum443553
Variance67361.123
MonotonicityNot monotonic
2023-12-13T02:06:36.097207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3884
38.8%
2 1342
 
13.4%
3 671
 
6.7%
4 445
 
4.5%
5 327
 
3.3%
6 262
 
2.6%
7 187
 
1.9%
8 181
 
1.8%
9 146
 
1.5%
10 132
 
1.3%
Other values (476) 2423
24.2%
ValueCountFrequency (%)
1 3884
38.8%
2 1342
 
13.4%
3 671
 
6.7%
4 445
 
4.5%
5 327
 
3.3%
6 262
 
2.6%
7 187
 
1.9%
8 181
 
1.8%
9 146
 
1.5%
10 132
 
1.3%
ValueCountFrequency (%)
6756 1
< 0.1%
5763 1
< 0.1%
5456 1
< 0.1%
5318 1
< 0.1%
5091 1
< 0.1%
5061 1
< 0.1%
4889 1
< 0.1%
4719 1
< 0.1%
4586 1
< 0.1%
4575 1
< 0.1%

현장정보
Text

MISSING 

Distinct296
Distinct (%)31.2%
Missing9051
Missing (%)90.5%
Memory size156.2 KiB
2023-12-13T02:06:36.409326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length15.377239
Min length13

Characters and Unicode

Total characters14593
Distinct characters52
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

Unique21 ?
Unique (%)2.2%

Sample

1st row용산동 417(1).jpg
2nd row도원동 1463(1).jpg
3rd row이곡동 1251(1).jpg
4th row진천동 602(1).jpg
5th row상인동 1558(1).jpg
ValueCountFrequency (%)
상인동 139
 
7.3%
이곡동 130
 
6.8%
용산동 94
 
5.0%
월성동 59
 
3.1%
두류동 56
 
3.0%
본리동 53
 
2.8%
도원동 50
 
2.6%
장기동 48
 
2.5%
신당동 45
 
2.4%
진천동 43
 
2.3%
Other values (309) 1181
62.2%
2023-12-13T02:06:36.985032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1908
13.1%
p 949
 
6.5%
동 949
 
6.5%
949
 
6.5%
( 949
 
6.5%
) 949
 
6.5%
. 949
 
6.5%
j 949
 
6.5%
g 949
 
6.5%
2 422
 
2.9%
Other values (42) 4671
32.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4696
32.2%
Lowercase Letter 2847
19.5%
Other Letter 2833
19.4%
Space Separator 949
 
6.5%
Open Punctuation 949
 
6.5%
Close Punctuation 949
 
6.5%
Other Punctuation 949
 
6.5%
Dash Punctuation 421
 
2.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 949
33.5%
곡 147
 
5.2%
인 139
 
4.9%
상 139
 
4.9%
이 130
 
4.6%
산 108
 
3.8%
성 101
 
3.6%
용 94
 
3.3%
당 87
 
3.1%
천 86
 
3.0%
Other values (24) 853
30.1%
Decimal Number
ValueCountFrequency (%)
1 1908
40.6%
2 422
 
9.0%
5 385
 
8.2%
3 364
 
7.8%
0 324
 
6.9%
4 313
 
6.7%
9 261
 
5.6%
8 242
 
5.2%
6 240
 
5.1%
7 237
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
p 949
33.3%
j 949
33.3%
g 949
33.3%
Space Separator
ValueCountFrequency (%)
949
100.0%
Open Punctuation
ValueCountFrequency (%)
( 949
100.0%
Close Punctuation
ValueCountFrequency (%)
) 949
100.0%
Other Punctuation
ValueCountFrequency (%)
. 949
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 421
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8913
61.1%
Latin 2847
 
19.5%
Hangul 2833
 
19.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 949
33.5%
곡 147
 
5.2%
인 139
 
4.9%
상 139
 
4.9%
이 130
 
4.6%
산 108
 
3.8%
성 101
 
3.6%
용 94
 
3.3%
당 87
 
3.1%
천 86
 
3.0%
Other values (24) 853
30.1%
Common
ValueCountFrequency (%)
1 1908
21.4%
949
10.6%
( 949
10.6%
) 949
10.6%
. 949
10.6%
2 422
 
4.7%
- 421
 
4.7%
5 385
 
4.3%
3 364
 
4.1%
0 324
 
3.6%
Other values (5) 1293
14.5%
Latin
ValueCountFrequency (%)
p 949
33.3%
j 949
33.3%
g 949
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11760
80.6%
Hangul 2833
 
19.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1908
16.2%
p 949
8.1%
949
8.1%
( 949
8.1%
) 949
8.1%
. 949
8.1%
j 949
8.1%
g 949
8.1%
2 422
 
3.6%
- 421
 
3.6%
Other values (8) 2366
20.1%
Hangul
ValueCountFrequency (%)
동 949
33.5%
곡 147
 
5.2%
인 139
 
4.9%
상 139
 
4.9%
이 130
 
4.6%
산 108
 
3.8%
성 101
 
3.6%
용 94
 
3.3%
당 87
 
3.1%
천 86
 
3.0%
Other values (24) 853
30.1%

위도
Real number (ℝ)

Distinct5508
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.836984
Minimum35.792977
Maximum35.865587
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:06:37.180022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.792977
5-th percentile35.807761
Q135.820209
median35.840771
Q335.852601
95-th percentile35.859208
Maximum35.865587
Range0.0726104
Interquartile range (IQR)0.0323917

Descriptive statistics

Standard deviation0.01735139
Coefficient of variation (CV)0.0004841755
Kurtosis-1.1532964
Mean35.836984
Median Absolute Deviation (MAD)0.0136627
Skewness-0.40073161
Sum358369.84
Variance0.00030107072
MonotonicityNot monotonic
2023-12-13T02:06:37.374604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.8541031 48
 
0.5%
35.8540324 46
 
0.5%
35.8581548 9
 
0.1%
35.8581327 8
 
0.1%
35.8574936 8
 
0.1%
35.8515766 7
 
0.1%
35.8405887 7
 
0.1%
35.8529616 7
 
0.1%
35.8341396 7
 
0.1%
35.845711 7
 
0.1%
Other values (5498) 9846
98.5%
ValueCountFrequency (%)
35.7929765 1
< 0.1%
35.7941575 1
< 0.1%
35.7943128 1
< 0.1%
35.7950575 1
< 0.1%
35.795155 1
< 0.1%
35.7952302 2
< 0.1%
35.7954076 1
< 0.1%
35.7954515 1
< 0.1%
35.7957994 1
< 0.1%
35.7958198 2
< 0.1%
ValueCountFrequency (%)
35.8655869 1
< 0.1%
35.8653998 2
< 0.1%
35.8651906 1
< 0.1%
35.8651124 1
< 0.1%
35.8646802 2
< 0.1%
35.8645944 1
< 0.1%
35.8642743 1
< 0.1%
35.8642482 1
< 0.1%
35.8641273 1
< 0.1%
35.8640552 1
< 0.1%

경도
Real number (ℝ)

Distinct5527
Distinct (%)55.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.5306
Minimum128.4735
Maximum128.57587
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:06:37.557954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.4735
5-th percentile128.49396
Q1128.51781
median128.53209
Q3128.54581
95-th percentile128.56011
Maximum128.57587
Range0.1023686
Interquartile range (IQR)0.027996825

Descriptive statistics

Standard deviation0.019894042
Coefficient of variation (CV)0.00015478059
Kurtosis-0.27615943
Mean128.5306
Median Absolute Deviation (MAD)0.01400235
Skewness-0.30947883
Sum1285306
Variance0.00039577291
MonotonicityNot monotonic
2023-12-13T02:06:37.736247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.505778 48
 
0.5%
128.511411 46
 
0.5%
128.508212 9
 
0.1%
128.508848 8
 
0.1%
128.5582648 8
 
0.1%
128.529302 7
 
0.1%
128.5507689 7
 
0.1%
128.537711 7
 
0.1%
128.5445954 7
 
0.1%
128.5287338 7
 
0.1%
Other values (5517) 9846
98.5%
ValueCountFrequency (%)
128.4734994 1
 
< 0.1%
128.4740301 1
 
< 0.1%
128.4742832 2
< 0.1%
128.4747903 1
 
< 0.1%
128.4751407 2
< 0.1%
128.4754096 3
< 0.1%
128.4755387 1
 
< 0.1%
128.4757736 1
 
< 0.1%
128.4757741 2
< 0.1%
128.4759616 1
 
< 0.1%
ValueCountFrequency (%)
128.575868 1
 
< 0.1%
128.575075 1
 
< 0.1%
128.5744056 1
 
< 0.1%
128.5742916 3
< 0.1%
128.5742462 1
 
< 0.1%
128.5742223 3
< 0.1%
128.5742086 1
 
< 0.1%
128.5742078 2
< 0.1%
128.5741641 2
< 0.1%
128.5741599 3
< 0.1%

Interactions

2023-12-13T02:06:33.191749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:06:31.876958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:06:32.280596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:06:32.715263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:06:33.298516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:06:31.963851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:06:32.371897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:06:32.826056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:06:33.434217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:06:32.083643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:06:32.485059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:06:32.982243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:06:33.612985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:06:32.189534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:06:32.608839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:06:33.093124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:06:37.858169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번단속발생누적발생건수위도경도
순번1.0000.9980.1460.1500.123
단속발생0.9981.0000.0730.1260.082
누적발생건수0.1460.0731.0000.0140.006
위도0.1500.1260.0141.0000.616
경도0.1230.0820.0060.6161.000
2023-12-13T02:06:37.969681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번누적발생건수위도경도단속발생
순번1.000-0.190-0.005-0.0140.933
누적발생건수-0.1901.0000.052-0.0290.030
위도-0.0050.0521.000-0.0700.053
경도-0.014-0.029-0.0701.0000.034
단속발생0.9330.0300.0530.0341.000

Missing values

2023-12-13T02:06:33.749077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:06:33.868980image/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

순번단속발생단속발생소재지누적발생건수현장정보위도경도
594759482019대구광역시 달서구 신당동 1723-1<NA>35.85606128.494247
588458852019대구광역시 달서구 감삼동 563-91<NA>35.846342128.535968
471647172019대구광역시 달서구 송현동 1037-32<NA>35.836853128.554495
466346642019대구광역시 달서구 성당동 6352<NA>35.84323128.551013
467746782019대구광역시 달서구 신당동 17882<NA>35.855818128.498894
636463652019대구광역시 달서구 감삼동 101-51<NA>35.852017128.543188
164416452018대구광역시 달서구 이곡동 134717<NA>35.860742128.512891
912391242020대구광역시 달서구 성당동 525-131<NA>35.844257128.55545
8088092017대구광역시 달서구 두류동 490-51<NA>35.855951128.553873
805280532020대구광역시 달서구 상인동 15292<NA>35.814583128.545486
순번단속발생단속발생소재지누적발생건수현장정보위도경도
697469752020대구광역시 달서구 죽전동 365-319<NA>35.858456128.536727
791179122020대구광역시 달서구 호산동 357-1153<NA>35.849602128.485524
818981902020대구광역시 달서구 갈산동 358-802<NA>35.835886128.498649
4434442017대구광역시 달서구 감삼동 102-34<NA>35.852391128.544452
10994109952021대구광역시 달서구 상인동 123-13<NA>35.821525128.531946
515951602019대구광역시 달서구 신당동 1723-91<NA>35.855809128.495414
473347342019대구광역시 달서구 장기동 599-62<NA>35.843652128.531002
204220432018대구광역시 달서구 두류동 1227-34<NA>35.861598128.571498
510751082019대구광역시 달서구 감삼동 599-511<NA>35.84933128.532726
479948002019대구광역시 달서구 장기동 4182<NA>35.849658128.524482