Overview

Dataset statistics

Number of variables10
Number of observations903
Missing cells199
Missing cells (%)2.2%
Duplicate rows1
Duplicate rows (%)0.1%
Total size in memory73.3 KiB
Average record size in memory83.1 B

Variable types

Numeric2
Text2
Categorical6

Dataset

Description대구광역시 달서구 내 제설함(일명 적사함 또는 모래함)에 대한 내용 및 정보가 담겨있음. (좌표, 관리번호, 내용물, 위치, 규격, 전화번호)
URLhttps://www.data.go.kr/data/15035211/fileData.do

Alerts

Dataset has 1 (0.1%) duplicate rowsDuplicates
기준일자 is highly overall correlated with 위도 and 6 other fieldsHigh correlation
관리기관 is highly overall correlated with 위도 and 6 other fieldsHigh correlation
내용물 is highly overall correlated with 위도 and 6 other fieldsHigh correlation
전화번호 is highly overall correlated with 위도 and 6 other fieldsHigh correlation
규격(mm) is highly overall correlated with 위도 and 6 other fieldsHigh correlation
관리부서 is highly overall correlated with 위도 and 6 other fieldsHigh correlation
위도 is highly overall correlated with 내용물 and 5 other fieldsHigh correlation
경도 is highly overall correlated with 내용물 and 5 other fieldsHigh correlation
내용물 is highly imbalanced (69.1%)Imbalance
규격(mm) is highly imbalanced (69.1%)Imbalance
전화번호 is highly imbalanced (69.1%)Imbalance
관리기관 is highly imbalanced (69.1%)Imbalance
관리부서 is highly imbalanced (69.1%)Imbalance
기준일자 is highly imbalanced (69.1%)Imbalance
위도 has 50 (5.5%) missing valuesMissing
경도 has 50 (5.5%) missing valuesMissing
관리번호 has 50 (5.5%) missing valuesMissing
위치_도로명주소 has 49 (5.4%) missing valuesMissing

Reproduction

Analysis started2023-12-12 09:35:11.868732
Analysis finished2023-12-12 09:35:13.906997
Duration2.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct772
Distinct (%)90.5%
Missing50
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean35.83921
Minimum35.793781
Maximum35.865476
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.1 KiB
2023-12-12T18:35:14.394515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.793781
5-th percentile35.806856
Q135.827368
median35.843321
Q335.854631
95-th percentile35.860601
Maximum35.865476
Range0.07169512
Interquartile range (IQR)0.02726342

Descriptive statistics

Standard deviation0.017543247
Coefficient of variation (CV)0.00048949871
Kurtosis-0.75302973
Mean35.83921
Median Absolute Deviation (MAD)0.01261254
Skewness-0.5746551
Sum30570.846
Variance0.00030776552
MonotonicityNot monotonic
2023-12-12T18:35:14.601538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.85996425 9
 
1.0%
35.83573768 8
 
0.9%
35.81204253 4
 
0.4%
35.83192291 4
 
0.4%
35.85076 4
 
0.4%
35.83194467 4
 
0.4%
35.86211112 4
 
0.4%
35.80775324 3
 
0.3%
35.86060121 3
 
0.3%
35.85446105 3
 
0.3%
Other values (762) 807
89.4%
(Missing) 50
 
5.5%
ValueCountFrequency (%)
35.79378116 1
0.1%
35.79525393 1
0.1%
35.79544666 1
0.1%
35.79561634 1
0.1%
35.79568373 1
0.1%
35.7963157 1
0.1%
35.79660442 1
0.1%
35.7990852 1
0.1%
35.7993707 1
0.1%
35.79983761 1
0.1%
ValueCountFrequency (%)
35.86547628 1
 
0.1%
35.86479823 1
 
0.1%
35.86325517 1
 
0.1%
35.86293681 1
 
0.1%
35.86282937 1
 
0.1%
35.86236995 1
 
0.1%
35.86231162 1
 
0.1%
35.86224436 1
 
0.1%
35.86215714 2
0.2%
35.86211112 4
0.4%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct772
Distinct (%)90.5%
Missing50
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean128.5359
Minimum128.47368
Maximum128.57396
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.1 KiB
2023-12-12T18:35:14.842074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.47368
5-th percentile128.49914
Q1128.52081
median128.5391
Q3128.55077
95-th percentile128.56874
Maximum128.57396
Range0.1002786
Interquartile range (IQR)0.0299569

Descriptive statistics

Standard deviation0.020702794
Coefficient of variation (CV)0.00016106623
Kurtosis-0.43876745
Mean128.5359
Median Absolute Deviation (MAD)0.0133275
Skewness-0.44246242
Sum109641.13
Variance0.00042860567
MonotonicityNot monotonic
2023-12-12T18:35:15.037028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.5080191 9
 
1.0%
128.5224198 8
 
0.9%
128.5365056 4
 
0.4%
128.5331421 4
 
0.4%
128.5531853 4
 
0.4%
128.5343266 4
 
0.4%
128.5118529 4
 
0.4%
128.5521558 3
 
0.3%
128.5295257 3
 
0.3%
128.5134421 3
 
0.3%
Other values (762) 807
89.4%
(Missing) 50
 
5.5%
ValueCountFrequency (%)
128.4736813 1
0.1%
128.4771506 1
0.1%
128.4778796 1
0.1%
128.4787802 1
0.1%
128.4788667 1
0.1%
128.479042 1
0.1%
128.4790512 1
0.1%
128.4837443 1
0.1%
128.4870487 1
0.1%
128.4872359 1
0.1%
ValueCountFrequency (%)
128.5739599 1
0.1%
128.5737745 1
0.1%
128.5737249 1
0.1%
128.5734407 1
0.1%
128.5734299 1
0.1%
128.5732231 1
0.1%
128.5730688 1
0.1%
128.5729933 1
0.1%
128.5729791 1
0.1%
128.57295 1
0.1%

관리번호
Text

MISSING 

Distinct853
Distinct (%)100.0%
Missing50
Missing (%)5.5%
Memory size7.2 KiB
2023-12-12T18:35:15.488567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.8757327
Min length4

Characters and Unicode

Total characters4159
Distinct characters38
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

Unique853 ?
Unique (%)100.0%

Sample

1st row성당-1
2nd row성당-2
3rd row성당-3
4th row성당-4
5th row성당-5
ValueCountFrequency (%)
성당-27 1
 
0.1%
상인-14 1
 
0.1%
상인-42 1
 
0.1%
상인-43 1
 
0.1%
상인-44 1
 
0.1%
상인-45 1
 
0.1%
상인-46 1
 
0.1%
상인-47 1
 
0.1%
상인-48 1
 
0.1%
상인-49 1
 
0.1%
Other values (843) 843
98.8%
2023-12-12T18:35:16.096669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 853
20.5%
1 275
 
6.6%
2 220
 
5.3%
3 194
 
4.7%
4 179
 
4.3%
5 158
 
3.8%
6 134
 
3.2%
7 130
 
3.1%
현 120
 
2.9%
송 120
 
2.9%
Other values (28) 1776
42.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1706
41.0%
Decimal Number 1600
38.5%
Dash Punctuation 853
20.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
현 120
 
7.0%
송 120
 
7.0%
본 109
 
6.4%
이 107
 
6.3%
곡 107
 
6.3%
당 105
 
6.2%
두 89
 
5.2%
류 89
 
5.2%
상 80
 
4.7%
인 80
 
4.7%
Other values (17) 700
41.0%
Decimal Number
ValueCountFrequency (%)
1 275
17.2%
2 220
13.8%
3 194
12.1%
4 179
11.2%
5 158
9.9%
6 134
8.4%
7 130
8.1%
8 112
7.0%
9 101
 
6.3%
0 97
 
6.1%
Dash Punctuation
ValueCountFrequency (%)
- 853
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2453
59.0%
Hangul 1706
41.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
현 120
 
7.0%
송 120
 
7.0%
본 109
 
6.4%
이 107
 
6.3%
곡 107
 
6.3%
당 105
 
6.2%
두 89
 
5.2%
류 89
 
5.2%
상 80
 
4.7%
인 80
 
4.7%
Other values (17) 700
41.0%
Common
ValueCountFrequency (%)
- 853
34.8%
1 275
 
11.2%
2 220
 
9.0%
3 194
 
7.9%
4 179
 
7.3%
5 158
 
6.4%
6 134
 
5.5%
7 130
 
5.3%
8 112
 
4.6%
9 101
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2453
59.0%
Hangul 1706
41.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 853
34.8%
1 275
 
11.2%
2 220
 
9.0%
3 194
 
7.9%
4 179
 
7.3%
5 158
 
6.4%
6 134
 
5.5%
7 130
 
5.3%
8 112
 
4.6%
9 101
 
4.1%
Hangul
ValueCountFrequency (%)
현 120
 
7.0%
송 120
 
7.0%
본 109
 
6.4%
이 107
 
6.3%
곡 107
 
6.3%
당 105
 
6.2%
두 89
 
5.2%
류 89
 
5.2%
상 80
 
4.7%
인 80
 
4.7%
Other values (17) 700
41.0%

내용물
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
제설함(모래주머니, 제설제)
853 
<NA>
 
50

Length

Max length15
Median length15
Mean length14.390919
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row제설함(모래주머니, 제설제)
2nd row제설함(모래주머니, 제설제)
3rd row제설함(모래주머니, 제설제)
4th row제설함(모래주머니, 제설제)
5th row제설함(모래주머니, 제설제)

Common Values

ValueCountFrequency (%)
제설함(모래주머니, 제설제) 853
94.5%
<NA> 50
 
5.5%

Length

2023-12-12T18:35:16.287115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:35:16.447607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제설함(모래주머니 853
48.6%
제설제 853
48.6%
na 50
 
2.8%

위치_도로명주소
Text

MISSING 

Distinct794
Distinct (%)93.0%
Missing49
Missing (%)5.4%
Memory size7.2 KiB
2023-12-12T18:35:16.879462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length26
Mean length19.269321
Min length9

Characters and Unicode

Total characters16456
Distinct characters117
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique755 ?
Unique (%)88.4%

Sample

1st row대구광역시 달서구 두류공원로161
2nd row대구광역시 달서구 장기로25
3rd row대구광역시 달서구 장기로50
4th row대구광역시 달서구 장기로10길21
5th row대구광역시 달서구 당산로94
ValueCountFrequency (%)
대구광역시 854
25.5%
달서구 853
25.4%
맞은편 38
 
1.1%
선원로 33
 
1.0%
월배로 33
 
1.0%
상인동 31
 
0.9%
앞산순환로 27
 
0.8%
중흥로 26
 
0.8%
도원동 25
 
0.7%
달구벌대로 22
 
0.7%
Other values (765) 1410
42.1%
2023-12-12T18:35:17.458381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2538
15.4%
구 1792
 
10.9%
서 951
 
5.8%
대 928
 
5.6%
달 903
 
5.5%
광 854
 
5.2%
역 854
 
5.2%
시 854
 
5.2%
로 696
 
4.2%
1 632
 
3.8%
Other values (107) 5454
33.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10528
64.0%
Decimal Number 3105
 
18.9%
Space Separator 2538
 
15.4%
Dash Punctuation 140
 
0.9%
Open Punctuation 72
 
0.4%
Close Punctuation 72
 
0.4%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 1792
17.0%
서 951
9.0%
대 928
8.8%
달 903
8.6%
광 854
8.1%
역 854
8.1%
시 854
8.1%
로 696
 
6.6%
길 404
 
3.8%
동 161
 
1.5%
Other values (92) 2131
20.2%
Decimal Number
ValueCountFrequency (%)
1 632
20.4%
2 399
12.9%
3 383
12.3%
5 325
10.5%
4 293
9.4%
6 258
8.3%
7 229
 
7.4%
0 211
 
6.8%
9 207
 
6.7%
8 168
 
5.4%
Space Separator
ValueCountFrequency (%)
2538
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 140
100.0%
Open Punctuation
ValueCountFrequency (%)
( 72
100.0%
Close Punctuation
ValueCountFrequency (%)
) 72
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10528
64.0%
Common 5928
36.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 1792
17.0%
서 951
9.0%
대 928
8.8%
달 903
8.6%
광 854
8.1%
역 854
8.1%
시 854
8.1%
로 696
 
6.6%
길 404
 
3.8%
동 161
 
1.5%
Other values (92) 2131
20.2%
Common
ValueCountFrequency (%)
2538
42.8%
1 632
 
10.7%
2 399
 
6.7%
3 383
 
6.5%
5 325
 
5.5%
4 293
 
4.9%
6 258
 
4.4%
7 229
 
3.9%
0 211
 
3.6%
9 207
 
3.5%
Other values (5) 453
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10528
64.0%
ASCII 5928
36.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2538
42.8%
1 632
 
10.7%
2 399
 
6.7%
3 383
 
6.5%
5 325
 
5.5%
4 293
 
4.9%
6 258
 
4.4%
7 229
 
3.9%
0 211
 
3.6%
9 207
 
3.5%
Other values (5) 453
 
7.6%
Hangul
ValueCountFrequency (%)
구 1792
17.0%
서 951
9.0%
대 928
8.8%
달 903
8.6%
광 854
8.1%
역 854
8.1%
시 854
8.1%
로 696
 
6.6%
길 404
 
3.8%
동 161
 
1.5%
Other values (92) 2131
20.2%

규격(mm)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
1200
853 
<NA>
 
50

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1200
2nd row1200
3rd row1200
4th row1200
5th row1200

Common Values

ValueCountFrequency (%)
1200 853
94.5%
<NA> 50
 
5.5%

Length

2023-12-12T18:35:17.641580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:35:17.799998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1200 853
94.5%
na 50
 
5.5%

전화번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
053-667-3444
853 
<NA>
 
50

Length

Max length12
Median length12
Mean length11.557032
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row053-667-3444
2nd row053-667-3444
3rd row053-667-3444
4th row053-667-3444
5th row053-667-3444

Common Values

ValueCountFrequency (%)
053-667-3444 853
94.5%
<NA> 50
 
5.5%

Length

2023-12-12T18:35:17.969594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:35:18.105133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
053-667-3444 853
94.5%
na 50
 
5.5%

관리기관
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
대구광역시 달서구청
853 
<NA>
 
50

Length

Max length10
Median length10
Mean length9.6677741
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대구광역시 달서구청
2nd row대구광역시 달서구청
3rd row대구광역시 달서구청
4th row대구광역시 달서구청
5th row대구광역시 달서구청

Common Values

ValueCountFrequency (%)
대구광역시 달서구청 853
94.5%
<NA> 50
 
5.5%

Length

2023-12-12T18:35:18.242785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:35:18.365512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 853
48.6%
달서구청 853
48.6%
na 50
 
2.8%

관리부서
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
안전도시과
853 
<NA>
 
50

Length

Max length5
Median length5
Mean length4.944629
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
안전도시과 853
94.5%
<NA> 50
 
5.5%

Length

2023-12-12T18:35:18.487178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:35:18.614559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
안전도시과 853
94.5%
na 50
 
5.5%

기준일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
2023-01-01
853 
<NA>
 
50

Length

Max length10
Median length10
Mean length9.6677741
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-01-01
2nd row2023-01-01
3rd row2023-01-01
4th row2023-01-01
5th row2023-01-01

Common Values

ValueCountFrequency (%)
2023-01-01 853
94.5%
<NA> 50
 
5.5%

Length

2023-12-12T18:35:18.769939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:35:18.880090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-01-01 853
94.5%
na 50
 
5.5%

Interactions

2023-12-12T18:35:12.914349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:35:12.583035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:35:13.053142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:35:12.774626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:35:18.943405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도
위도1.0000.693
경도0.6931.000
2023-12-12T18:35:19.029650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일자관리기관내용물전화번호규격(mm)관리부서
기준일자1.0001.0001.0001.0001.0001.000
관리기관1.0001.0001.0001.0001.0001.000
내용물1.0001.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.000
규격(mm)1.0001.0001.0001.0001.0001.000
관리부서1.0001.0001.0001.0001.0001.000
2023-12-12T18:35:19.149880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도내용물규격(mm)전화번호관리기관관리부서기준일자
위도1.000-0.3061.0001.0001.0001.0001.0001.000
경도-0.3061.0001.0001.0001.0001.0001.0001.000
내용물1.0001.0001.0001.0001.0001.0001.0001.000
규격(mm)1.0001.0001.0001.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.0001.0001.000
관리기관1.0001.0001.0001.0001.0001.0001.0001.000
관리부서1.0001.0001.0001.0001.0001.0001.0001.000
기준일자1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-12T18:35:13.211682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:35:13.493925image/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.
2023-12-12T18:35:13.724738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

위도경도관리번호내용물위치_도로명주소규격(mm)전화번호관리기관관리부서기준일자
035.849999128.563697성당-1제설함(모래주머니, 제설제)대구광역시 달서구 두류공원로1611200053-667-3444대구광역시 달서구청안전도시과2023-01-01
135.841118128.556505성당-2제설함(모래주머니, 제설제)대구광역시 달서구 장기로251200053-667-3444대구광역시 달서구청안전도시과2023-01-01
235.843217128.554937성당-3제설함(모래주머니, 제설제)대구광역시 달서구 장기로501200053-667-3444대구광역시 달서구청안전도시과2023-01-01
335.844223128.555608성당-4제설함(모래주머니, 제설제)대구광역시 달서구 장기로10길211200053-667-3444대구광역시 달서구청안전도시과2023-01-01
435.845842128.544693성당-5제설함(모래주머니, 제설제)대구광역시 달서구 당산로941200053-667-3444대구광역시 달서구청안전도시과2023-01-01
535.846297128.551483성당-6제설함(모래주머니, 제설제)대구광역시 달서구 야외음악당로1031200053-667-3444대구광역시 달서구청안전도시과2023-01-01
635.848309128.549127성당-7제설함(모래주머니, 제설제)대구광역시 달서구 당산로30길301200053-667-3444대구광역시 달서구청안전도시과2023-01-01
735.845061128.55185성당-8제설함(모래주머니, 제설제)대구광역시 달서구 야외음악당로871200053-667-3444대구광역시 달서구청안전도시과2023-01-01
835.843615128.552604성당-9제설함(모래주머니, 제설제)대구광역시 달서구 장기로721200053-667-3444대구광역시 달서구청안전도시과2023-01-01
935.844461128.549057성당-10제설함(모래주머니, 제설제)대구광역시 달서구 장기로 22길 141200053-667-3444대구광역시 달서구청안전도시과2023-01-01
위도경도관리번호내용물위치_도로명주소규격(mm)전화번호관리기관관리부서기준일자
893<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
894<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
895<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
896<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
897<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
898<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
899<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
900<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
901<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
902<NA><NA><NA><NA>대구광역시 달서구<NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

위도경도관리번호내용물위치_도로명주소규격(mm)전화번호관리기관관리부서기준일자# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>49