Overview

Dataset statistics

Number of variables15
Number of observations874
Missing cells1802
Missing cells (%)13.7%
Duplicate rows4
Duplicate rows (%)0.5%
Total size in memory106.8 KiB
Average record size in memory125.2 B

Variable types

Categorical4
Text6
Numeric4
DateTime1

Dataset

Description성남시 내 설치되어 있는 제설함 현황으로, 동명, 설치장소, 주소, 위경도, 제설함 수, 관리기관 항목으로 구성되어 있습니다
URLhttps://www.data.go.kr/data/15043219/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 4 (0.5%) duplicate rowsDuplicates
시군명 is highly overall correlated with 모래 비치량 and 1 other fieldsHigh correlation
소금 비치량 is highly overall correlated with 위도 and 4 other fieldsHigh correlation
관리기관명 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
염화칼슘 비치량 is highly overall correlated with 소금 비치량 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 소금 비치량 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 소금 비치량 and 1 other fieldsHigh correlation
제설함 수(개) is highly overall correlated with 모래 비치량High correlation
모래 비치량 is highly overall correlated with 제설함 수(개) and 2 other fieldsHigh correlation
시군명 is highly imbalanced (70.7%)Imbalance
소금 비치량 is highly imbalanced (59.3%)Imbalance
관리번호 has 425 (48.6%) missing valuesMissing
설치장소명 has 56 (6.4%) missing valuesMissing
소재지도로명주소 has 154 (17.6%) missing valuesMissing
소재지지번주소 has 42 (4.8%) missing valuesMissing
위도 has 70 (8.0%) missing valuesMissing
경도 has 70 (8.0%) missing valuesMissing
제설함 수(개) has 10 (1.1%) missing valuesMissing
모래 비치량 has 682 (78.0%) missing valuesMissing
관리기관 전화번호 has 293 (33.5%) missing valuesMissing
모래 비치량 has 150 (17.2%) zerosZeros

Reproduction

Analysis started2023-12-11 23:30:19.430071
Analysis finished2023-12-11 23:30:22.799392
Duration3.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
성남시
829 
성남시
 
45

Length

Max length4
Median length3
Mean length3.0514874
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
성남시 829
94.9%
성남시 45
 
5.1%

Length

2023-12-12T08:30:22.875506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:30:22.980664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
성남시 874
100.0%

읍면동명
Text

Distinct58
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
2023-12-12T08:30:23.178993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length4
Mean length3.6407323
Min length3

Characters and Unicode

Total characters3182
Distinct characters58
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.9%

Sample

1st row신흥1동
2nd row신흥1동
3rd row신흥1동
4th row신흥1동
5th row신흥1동
ValueCountFrequency (%)
은행2동 46
 
5.2%
수진2동 40
 
4.6%
단대동 38
 
4.3%
태평4동 33
 
3.8%
시흥동 33
 
3.8%
분당동 30
 
3.4%
태평2동 29
 
3.3%
태평3동 27
 
3.1%
도촌동 27
 
3.1%
은행1동 26
 
3.0%
Other values (45) 548
62.5%
2023-12-12T08:30:23.596305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 876
27.5%
2 204
 
6.4%
1 128
 
4.0%
3 114
 
3.6%
평 111
 
3.5%
대 105
 
3.3%
태 101
 
3.2%
흥 93
 
2.9%
수 85
 
2.7%
신 76
 
2.4%
Other values (48) 1289
40.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2697
84.8%
Decimal Number 479
 
15.1%
Other Punctuation 3
 
0.1%
Space Separator 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 876
32.5%
평 111
 
4.1%
대 105
 
3.9%
태 101
 
3.7%
흥 93
 
3.4%
수 85
 
3.2%
신 76
 
2.8%
은 72
 
2.7%
행 72
 
2.7%
원 68
 
2.5%
Other values (42) 1038
38.5%
Decimal Number
ValueCountFrequency (%)
2 204
42.6%
1 128
26.7%
3 114
23.8%
4 33
 
6.9%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2697
84.8%
Common 485
 
15.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 876
32.5%
평 111
 
4.1%
대 105
 
3.9%
태 101
 
3.7%
흥 93
 
3.4%
수 85
 
3.2%
신 76
 
2.8%
은 72
 
2.7%
행 72
 
2.7%
원 68
 
2.5%
Other values (42) 1038
38.5%
Common
ValueCountFrequency (%)
2 204
42.1%
1 128
26.4%
3 114
23.5%
4 33
 
6.8%
, 3
 
0.6%
3
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2697
84.8%
ASCII 485
 
15.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 876
32.5%
평 111
 
4.1%
대 105
 
3.9%
태 101
 
3.7%
흥 93
 
3.4%
수 85
 
3.2%
신 76
 
2.8%
은 72
 
2.7%
행 72
 
2.7%
원 68
 
2.5%
Other values (42) 1038
38.5%
ASCII
ValueCountFrequency (%)
2 204
42.1%
1 128
26.4%
3 114
23.5%
4 33
 
6.8%
, 3
 
0.6%
3
 
0.6%

관리번호
Text

MISSING 

Distinct55
Distinct (%)12.2%
Missing425
Missing (%)48.6%
Memory size7.0 KiB
2023-12-12T08:30:23.903104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length1.6904232
Min length1

Characters and Unicode

Total characters759
Distinct characters13
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

Unique15 ?
Unique (%)3.3%

Sample

1st row1
2nd row2
3rd row3
4th row4
5th row5
ValueCountFrequency (%)
1 18
 
4.0%
8 18
 
4.0%
2 18
 
4.0%
9 18
 
4.0%
4 18
 
4.0%
6 18
 
4.0%
5 18
 
4.0%
7 17
 
3.8%
10 17
 
3.8%
11 17
 
3.8%
Other values (45) 272
60.6%
2023-12-12T08:30:24.285741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 205
27.0%
2 142
18.7%
3 84
11.1%
4 55
 
7.2%
5 44
 
5.8%
6 43
 
5.7%
8 40
 
5.3%
7 40
 
5.3%
9 39
 
5.1%
0 37
 
4.9%
Other values (3) 30
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 729
96.0%
Other Letter 20
 
2.6%
Dash Punctuation 10
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 205
28.1%
2 142
19.5%
3 84
11.5%
4 55
 
7.5%
5 44
 
6.0%
6 43
 
5.9%
8 40
 
5.5%
7 40
 
5.5%
9 39
 
5.3%
0 37
 
5.1%
Other Letter
ValueCountFrequency (%)
복 10
50.0%
정 10
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 739
97.4%
Hangul 20
 
2.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 205
27.7%
2 142
19.2%
3 84
11.4%
4 55
 
7.4%
5 44
 
6.0%
6 43
 
5.8%
8 40
 
5.4%
7 40
 
5.4%
9 39
 
5.3%
0 37
 
5.0%
Hangul
ValueCountFrequency (%)
복 10
50.0%
정 10
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 739
97.4%
Hangul 20
 
2.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 205
27.7%
2 142
19.2%
3 84
11.4%
4 55
 
7.4%
5 44
 
6.0%
6 43
 
5.8%
8 40
 
5.4%
7 40
 
5.4%
9 39
 
5.3%
0 37
 
5.0%
Hangul
ValueCountFrequency (%)
복 10
50.0%
정 10
50.0%

설치장소명
Text

MISSING 

Distinct709
Distinct (%)86.7%
Missing56
Missing (%)6.4%
Memory size7.0 KiB
2023-12-12T08:30:24.586342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length25
Mean length9.6075795
Min length2

Characters and Unicode

Total characters7859
Distinct characters394
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique672 ?
Unique (%)82.2%

Sample

1st row시민로 163번길 10
2nd row수정로188번길 16-1
3rd row수정로180번길 14-1
4th row수정로170번길 16-1
5th row수정남로98번길 8
ValueCountFrequency (%)
앞 170
 
9.9%
옆 36
 
2.1%
이매동 23
 
1.3%
입구 22
 
1.3%
심곡로 20
 
1.2%
맞은편 16
 
0.9%
사이 15
 
0.9%
도로 14
 
0.8%
사거리 13
 
0.8%
12
 
0.7%
Other values (923) 1375
80.1%
2023-12-12T08:30:25.028584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
924
 
11.8%
로 422
 
5.4%
1 330
 
4.2%
길 216
 
2.7%
2 213
 
2.7%
번 209
 
2.7%
앞 171
 
2.2%
동 161
 
2.0%
4 144
 
1.8%
3 131
 
1.7%
Other values (384) 4938
62.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5317
67.7%
Decimal Number 1369
 
17.4%
Space Separator 924
 
11.8%
Dash Punctuation 76
 
1.0%
Close Punctuation 57
 
0.7%
Open Punctuation 57
 
0.7%
Other Punctuation 31
 
0.4%
Math Symbol 16
 
0.2%
Uppercase Letter 9
 
0.1%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 422
 
7.9%
길 216
 
4.1%
번 209
 
3.9%
앞 171
 
3.2%
동 161
 
3.0%
이 112
 
2.1%
정 103
 
1.9%
남 94
 
1.8%
수 92
 
1.7%
교 89
 
1.7%
Other values (360) 3648
68.6%
Decimal Number
ValueCountFrequency (%)
1 330
24.1%
2 213
15.6%
4 144
10.5%
3 131
 
9.6%
5 108
 
7.9%
6 100
 
7.3%
8 97
 
7.1%
7 83
 
6.1%
9 82
 
6.0%
0 81
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
C 3
33.3%
A 2
22.2%
I 2
22.2%
B 1
 
11.1%
U 1
 
11.1%
Lowercase Letter
ValueCountFrequency (%)
u 1
33.3%
k 1
33.3%
c 1
33.3%
Space Separator
ValueCountFrequency (%)
924
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 76
100.0%
Close Punctuation
ValueCountFrequency (%)
) 57
100.0%
Open Punctuation
ValueCountFrequency (%)
( 57
100.0%
Other Punctuation
ValueCountFrequency (%)
, 31
100.0%
Math Symbol
ValueCountFrequency (%)
~ 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5317
67.7%
Common 2530
32.2%
Latin 12
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 422
 
7.9%
길 216
 
4.1%
번 209
 
3.9%
앞 171
 
3.2%
동 161
 
3.0%
이 112
 
2.1%
정 103
 
1.9%
남 94
 
1.8%
수 92
 
1.7%
교 89
 
1.7%
Other values (360) 3648
68.6%
Common
ValueCountFrequency (%)
924
36.5%
1 330
 
13.0%
2 213
 
8.4%
4 144
 
5.7%
3 131
 
5.2%
5 108
 
4.3%
6 100
 
4.0%
8 97
 
3.8%
7 83
 
3.3%
9 82
 
3.2%
Other values (6) 318
 
12.6%
Latin
ValueCountFrequency (%)
C 3
25.0%
A 2
16.7%
I 2
16.7%
u 1
 
8.3%
k 1
 
8.3%
B 1
 
8.3%
c 1
 
8.3%
U 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5317
67.7%
ASCII 2542
32.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
924
36.3%
1 330
 
13.0%
2 213
 
8.4%
4 144
 
5.7%
3 131
 
5.2%
5 108
 
4.2%
6 100
 
3.9%
8 97
 
3.8%
7 83
 
3.3%
9 82
 
3.2%
Other values (14) 330
 
13.0%
Hangul
ValueCountFrequency (%)
로 422
 
7.9%
길 216
 
4.1%
번 209
 
3.9%
앞 171
 
3.2%
동 161
 
3.0%
이 112
 
2.1%
정 103
 
1.9%
남 94
 
1.8%
수 92
 
1.7%
교 89
 
1.7%
Other values (360) 3648
68.6%

소재지도로명주소
Text

MISSING 

Distinct656
Distinct (%)91.1%
Missing154
Missing (%)17.6%
Memory size7.0 KiB
2023-12-12T08:30:25.456175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length32
Mean length22.011111
Min length1

Characters and Unicode

Total characters15848
Distinct characters167
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

Unique629 ?
Unique (%)87.4%

Sample

1st row경기도 성남시 수정구 시민로 163번길 10
2nd row경기도 성남시 수정구 수정로188번길 16-1
3rd row경기도 성남시 수정구 수정로180번길 14-1
4th row경기도 성남시 수정구 수정로170번길 16-1
5th row경기도 성남시 수정구 수정남로98번길 8
ValueCountFrequency (%)
성남시 701
19.8%
경기도 641
18.1%
수정구 328
 
9.3%
중원구 206
 
5.8%
분당구 154
 
4.4%
태평동 44
 
1.2%
11 24
 
0.7%
심곡로 19
 
0.5%
8 18
 
0.5%
중앙동 17
 
0.5%
Other values (670) 1385
39.2%
2023-12-12T08:30:26.341835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2873
18.1%
남 807
 
5.1%
성 772
 
4.9%
시 726
 
4.6%
구 701
 
4.4%
로 682
 
4.3%
도 667
 
4.2%
경 642
 
4.1%
기 641
 
4.0%
1 613
 
3.9%
Other values (157) 6724
42.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9895
62.4%
Space Separator 2873
 
18.1%
Decimal Number 2603
 
16.4%
Open Punctuation 137
 
0.9%
Close Punctuation 137
 
0.9%
Dash Punctuation 119
 
0.8%
Other Punctuation 84
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 807
 
8.2%
성 772
 
7.8%
시 726
 
7.3%
구 701
 
7.1%
로 682
 
6.9%
도 667
 
6.7%
경 642
 
6.5%
기 641
 
6.5%
길 490
 
5.0%
번 488
 
4.9%
Other values (142) 3279
33.1%
Decimal Number
ValueCountFrequency (%)
1 613
23.5%
2 399
15.3%
3 294
11.3%
4 240
 
9.2%
5 229
 
8.8%
6 206
 
7.9%
7 168
 
6.5%
9 167
 
6.4%
8 162
 
6.2%
0 125
 
4.8%
Space Separator
ValueCountFrequency (%)
2873
100.0%
Open Punctuation
ValueCountFrequency (%)
( 137
100.0%
Close Punctuation
ValueCountFrequency (%)
) 137
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 119
100.0%
Other Punctuation
ValueCountFrequency (%)
, 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9895
62.4%
Common 5953
37.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 807
 
8.2%
성 772
 
7.8%
시 726
 
7.3%
구 701
 
7.1%
로 682
 
6.9%
도 667
 
6.7%
경 642
 
6.5%
기 641
 
6.5%
길 490
 
5.0%
번 488
 
4.9%
Other values (142) 3279
33.1%
Common
ValueCountFrequency (%)
2873
48.3%
1 613
 
10.3%
2 399
 
6.7%
3 294
 
4.9%
4 240
 
4.0%
5 229
 
3.8%
6 206
 
3.5%
7 168
 
2.8%
9 167
 
2.8%
8 162
 
2.7%
Other values (5) 602
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9895
62.4%
ASCII 5953
37.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2873
48.3%
1 613
 
10.3%
2 399
 
6.7%
3 294
 
4.9%
4 240
 
4.0%
5 229
 
3.8%
6 206
 
3.5%
7 168
 
2.8%
9 167
 
2.8%
8 162
 
2.7%
Other values (5) 602
 
10.1%
Hangul
ValueCountFrequency (%)
남 807
 
8.2%
성 772
 
7.8%
시 726
 
7.3%
구 701
 
7.1%
로 682
 
6.9%
도 667
 
6.7%
경 642
 
6.5%
기 641
 
6.5%
길 490
 
5.0%
번 488
 
4.9%
Other values (142) 3279
33.1%

소재지지번주소
Text

MISSING 

Distinct784
Distinct (%)94.2%
Missing42
Missing (%)4.8%
Memory size7.0 KiB
2023-12-12T08:30:26.602266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length33
Mean length20.486779
Min length10

Characters and Unicode

Total characters17045
Distinct characters155
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique751 ?
Unique (%)90.3%

Sample

1st row경기도 성남시 수정구 신흥동 5197
2nd row경기도 성남시 수정구 신흥동 5332
3rd row경기도 성남시 수정구 신흥동 5351-3
4th row경기도 성남시 수정구 신흥동 5745
5th row경기도 성남시 수정구 신흥동 5735
ValueCountFrequency (%)
성남시 828
20.0%
경기도 781
18.8%
수정구 370
 
8.9%
분당구 247
 
6.0%
중원구 210
 
5.1%
태평동 100
 
2.4%
은행동 70
 
1.7%
신흥동 60
 
1.4%
수진동 39
 
0.9%
단대동 35
 
0.8%
Other values (846) 1408
33.9%
2023-12-12T08:30:27.005635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3327
19.5%
성 859
 
5.0%
구 859
 
5.0%
남 844
 
5.0%
시 839
 
4.9%
동 814
 
4.8%
도 795
 
4.7%
경 784
 
4.6%
기 781
 
4.6%
1 515
 
3.0%
Other values (145) 6628
38.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10193
59.8%
Space Separator 3327
 
19.5%
Decimal Number 3113
 
18.3%
Dash Punctuation 360
 
2.1%
Open Punctuation 24
 
0.1%
Close Punctuation 24
 
0.1%
Uppercase Letter 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 859
 
8.4%
구 859
 
8.4%
남 844
 
8.3%
시 839
 
8.2%
동 814
 
8.0%
도 795
 
7.8%
경 784
 
7.7%
기 781
 
7.7%
수 465
 
4.6%
정 405
 
4.0%
Other values (127) 2748
27.0%
Decimal Number
ValueCountFrequency (%)
1 515
16.5%
2 468
15.0%
3 389
12.5%
4 299
9.6%
5 288
9.3%
7 272
8.7%
6 239
7.7%
8 230
7.4%
9 209
6.7%
0 204
 
6.6%
Uppercase Letter
ValueCountFrequency (%)
Y 1
25.0%
W 1
25.0%
C 1
25.0%
A 1
25.0%
Space Separator
ValueCountFrequency (%)
3327
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 360
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10193
59.8%
Common 6848
40.2%
Latin 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 859
 
8.4%
구 859
 
8.4%
남 844
 
8.3%
시 839
 
8.2%
동 814
 
8.0%
도 795
 
7.8%
경 784
 
7.7%
기 781
 
7.7%
수 465
 
4.6%
정 405
 
4.0%
Other values (127) 2748
27.0%
Common
ValueCountFrequency (%)
3327
48.6%
1 515
 
7.5%
2 468
 
6.8%
3 389
 
5.7%
- 360
 
5.3%
4 299
 
4.4%
5 288
 
4.2%
7 272
 
4.0%
6 239
 
3.5%
8 230
 
3.4%
Other values (4) 461
 
6.7%
Latin
ValueCountFrequency (%)
Y 1
25.0%
W 1
25.0%
C 1
25.0%
A 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10193
59.8%
ASCII 6852
40.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3327
48.6%
1 515
 
7.5%
2 468
 
6.8%
3 389
 
5.7%
- 360
 
5.3%
4 299
 
4.4%
5 288
 
4.2%
7 272
 
4.0%
6 239
 
3.5%
8 230
 
3.4%
Other values (8) 465
 
6.8%
Hangul
ValueCountFrequency (%)
성 859
 
8.4%
구 859
 
8.4%
남 844
 
8.3%
시 839
 
8.2%
동 814
 
8.0%
도 795
 
7.8%
경 784
 
7.7%
기 781
 
7.7%
수 465
 
4.6%
정 405
 
4.0%
Other values (127) 2748
27.0%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct748
Distinct (%)93.0%
Missing70
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean37.424053
Minimum37.337504
Maximum37.469459
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-12-12T08:30:27.214160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.337504
5-th percentile37.362858
Q137.400666
median37.437982
Q337.449841
95-th percentile37.458655
Maximum37.469459
Range0.13195465
Interquartile range (IQR)0.04917501

Descriptive statistics

Standard deviation0.033635032
Coefficient of variation (CV)0.00089875439
Kurtosis-0.34728928
Mean37.424053
Median Absolute Deviation (MAD)0.015945135
Skewness-0.93563495
Sum30088.938
Variance0.0011313154
MonotonicityNot monotonic
2023-12-12T08:30:27.438198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.38188066 10
 
1.1%
37.4523562 5
 
0.6%
37.44773668 4
 
0.5%
37.45319444 4
 
0.5%
37.44578255 3
 
0.3%
37.44854378 3
 
0.3%
37.37352415 3
 
0.3%
37.37026853 3
 
0.3%
37.43667202 3
 
0.3%
37.37902063 3
 
0.3%
Other values (738) 763
87.3%
(Missing) 70
 
8.0%
ValueCountFrequency (%)
37.33750433 1
0.1%
37.33795136 1
0.1%
37.33820309 1
0.1%
37.33830195 1
0.1%
37.33831061 1
0.1%
37.33875809 1
0.1%
37.33901814 1
0.1%
37.33932247 1
0.1%
37.33967132 1
0.1%
37.33985005 1
0.1%
ValueCountFrequency (%)
37.46945898 1
0.1%
37.4685701 1
0.1%
37.46778443 1
0.1%
37.46713253 1
0.1%
37.46697207 1
0.1%
37.46675742 2
0.2%
37.46641706 1
0.1%
37.46636708 1
0.1%
37.46548318 1
0.1%
37.46480123 1
0.1%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct748
Distinct (%)93.0%
Missing70
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean127.13512
Minimum127.04431
Maximum127.18565
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-12-12T08:30:27.617256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.04431
5-th percentile127.09673
Q1127.12209
median127.13724
Q3127.15452
95-th percentile127.16841
Maximum127.18565
Range0.1413478
Interquartile range (IQR)0.032431225

Descriptive statistics

Standard deviation0.023439793
Coefficient of variation (CV)0.00018436914
Kurtosis0.27144376
Mean127.13512
Median Absolute Deviation (MAD)0.0166918
Skewness-0.62925527
Sum102216.63
Variance0.00054942389
MonotonicityNot monotonic
2023-12-12T08:30:27.804907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1200307 10
 
1.1%
127.1571054 5
 
0.6%
127.1558307 4
 
0.5%
127.1719052 4
 
0.5%
127.1492097 3
 
0.3%
127.1294247 3
 
0.3%
127.1191902 3
 
0.3%
127.1312315 3
 
0.3%
127.1550994 3
 
0.3%
127.137104 3
 
0.3%
Other values (738) 763
87.3%
(Missing) 70
 
8.0%
ValueCountFrequency (%)
127.0443066 1
0.1%
127.0471692 2
0.2%
127.0668435 1
0.1%
127.0693984 1
0.1%
127.071799 1
0.1%
127.0720306 1
0.1%
127.0722121 1
0.1%
127.073265 1
0.1%
127.0733424 1
0.1%
127.0734563 2
0.2%
ValueCountFrequency (%)
127.1856544 1
 
0.1%
127.1782214 1
 
0.1%
127.1759657 1
 
0.1%
127.1741776 1
 
0.1%
127.1738518 1
 
0.1%
127.1723557 1
 
0.1%
127.1719052 4
0.5%
127.1718627 1
 
0.1%
127.1716087 1
 
0.1%
127.171529 1
 
0.1%

제설함 수(개)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)1.6%
Missing10
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean1.2800926
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-12-12T08:30:27.954847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum26
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5835984
Coefficient of variation (CV)1.2370968
Kurtosis93.726106
Mean1.2800926
Median Absolute Deviation (MAD)0
Skewness8.4842853
Sum1106
Variance2.507784
MonotonicityNot monotonic
2023-12-12T08:30:28.085037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 812
92.9%
2 20
 
2.3%
6 5
 
0.6%
7 5
 
0.6%
3 4
 
0.5%
8 4
 
0.5%
5 3
 
0.3%
15 2
 
0.2%
10 2
 
0.2%
12 2
 
0.2%
Other values (4) 5
 
0.6%
(Missing) 10
 
1.1%
ValueCountFrequency (%)
1 812
92.9%
2 20
 
2.3%
3 4
 
0.5%
4 2
 
0.2%
5 3
 
0.3%
6 5
 
0.6%
7 5
 
0.6%
8 4
 
0.5%
9 1
 
0.1%
10 2
 
0.2%
ValueCountFrequency (%)
26 1
 
0.1%
15 2
 
0.2%
13 1
 
0.1%
12 2
 
0.2%
10 2
 
0.2%
9 1
 
0.1%
8 4
0.5%
7 5
0.6%
6 5
0.6%
5 3
0.3%

염화칼슘 비치량
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
60
236 
5
97 
3
92 
50
67 
4
66 
Other values (12)
316 

Length

Max length9
Median length8
Mean length2.3810069
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row10kg(3포)
2nd row10kg(3포)
3rd row10kg(3포)
4th row10kg(3포)
5th row10kg(3포)

Common Values

ValueCountFrequency (%)
60 236
27.0%
5 97
11.1%
3 92
 
10.5%
50 67
 
7.7%
4 66
 
7.6%
<NA> 62
 
7.1%
10 44
 
5.0%
6 41
 
4.7%
10kg X 5 38
 
4.3%
20 33
 
3.8%
Other values (7) 98
11.2%

Length

2023-12-12T08:30:28.232876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
60 236
24.1%
5 135
13.8%
3 92
 
9.4%
50 67
 
6.9%
4 66
 
6.7%
na 62
 
6.3%
10kg 52
 
5.3%
x 52
 
5.3%
10 44
 
4.5%
6 41
 
4.2%
Other values (8) 131
13.4%

소금 비치량
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
<NA>
724 
0
109 
10
 
40
20
 
1

Length

Max length4
Median length4
Mean length3.5320366
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 724
82.8%
0 109
 
12.5%
10 40
 
4.6%
20 1
 
0.1%

Length

2023-12-12T08:30:28.404446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:30:28.539992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 724
82.8%
0 109
 
12.5%
10 40
 
4.6%
20 1
 
0.1%

모래 비치량
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct12
Distinct (%)6.2%
Missing682
Missing (%)78.0%
Infinite0
Infinite (%)0.0%
Mean10.265625
Minimum0
Maximum299
Zeros150
Zeros (%)17.2%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-12-12T08:30:28.646188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile79.45
Maximum299
Range299
Interquartile range (IQR)0

Descriptive statistics

Standard deviation38.549856
Coefficient of variation (CV)3.7552371
Kurtosis33.07815
Mean10.265625
Median Absolute Deviation (MAD)0
Skewness5.4423012
Sum1971
Variance1486.0914
MonotonicityNot monotonic
2023-12-12T08:30:28.760516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 150
 
17.2%
10 28
 
3.2%
91 4
 
0.5%
39 2
 
0.2%
299 1
 
0.1%
130 1
 
0.1%
286 1
 
0.1%
195 1
 
0.1%
70 1
 
0.1%
104 1
 
0.1%
Other values (2) 2
 
0.2%
(Missing) 682
78.0%
ValueCountFrequency (%)
0 150
17.2%
10 28
 
3.2%
13 1
 
0.1%
39 2
 
0.2%
70 1
 
0.1%
91 4
 
0.5%
104 1
 
0.1%
130 1
 
0.1%
152 1
 
0.1%
195 1
 
0.1%
ValueCountFrequency (%)
299 1
 
0.1%
286 1
 
0.1%
195 1
 
0.1%
152 1
 
0.1%
130 1
 
0.1%
104 1
 
0.1%
91 4
0.5%
70 1
 
0.1%
39 2
0.2%
13 1
 
0.1%

관리기관명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
수정구 건설과
399 
분당구 건설과
251 
중원구 건설과
224 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수정구 건설과
2nd row수정구 건설과
3rd row수정구 건설과
4th row수정구 건설과
5th row수정구 건설과

Common Values

ValueCountFrequency (%)
수정구 건설과 399
45.7%
분당구 건설과 251
28.7%
중원구 건설과 224
25.6%

Length

2023-12-12T08:30:28.874722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:30:28.967333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건설과 874
50.0%
수정구 399
22.8%
분당구 251
 
14.4%
중원구 224
 
12.8%

관리기관 전화번호
Text

MISSING 

Distinct58
Distinct (%)10.0%
Missing293
Missing (%)33.5%
Memory size7.0 KiB
2023-12-12T08:30:29.125361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.628227
Min length4

Characters and Unicode

Total characters6756
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

Unique34 ?
Unique (%)5.9%

Sample

1st row031-729-5607
2nd row031-729-5607
3rd row031-729-5607
4th row031-729-5607
5th row031-729-5607
ValueCountFrequency (%)
031-729-6351 224
38.6%
031-729-7604 29
 
5.0%
031-729-5607 24
 
4.1%
031-729-7665 24
 
4.1%
031-729-7784 24
 
4.1%
031-729-5646 22
 
3.8%
031-729-7944 21
 
3.6%
031-729-7867 20
 
3.4%
031-729-7361 19
 
3.3%
5707 17
 
2.9%
Other values (48) 157
27.0%
2023-12-12T08:30:29.451701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1108
16.4%
7 922
13.6%
3 824
12.2%
1 820
12.1%
0 652
9.7%
9 633
9.4%
2 588
8.7%
6 473
7.0%
5 369
 
5.5%
4 235
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5648
83.6%
Dash Punctuation 1108
 
16.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 922
16.3%
3 824
14.6%
1 820
14.5%
0 652
11.5%
9 633
11.2%
2 588
10.4%
6 473
8.4%
5 369
6.5%
4 235
 
4.2%
8 132
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 1108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6756
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1108
16.4%
7 922
13.6%
3 824
12.2%
1 820
12.1%
0 652
9.7%
9 633
9.4%
2 588
8.7%
6 473
7.0%
5 369
 
5.5%
4 235
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1108
16.4%
7 922
13.6%
3 824
12.2%
1 820
12.1%
0 652
9.7%
9 633
9.4%
2 588
8.7%
6 473
7.0%
5 369
 
5.5%
4 235
 
3.5%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
Minimum2023-04-14 00:00:00
Maximum2023-04-14 00:00:00
2023-12-12T08:30:29.563293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:29.644733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T08:30:21.789010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:20.523599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:20.941298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:21.365674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:21.885456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:20.643053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:21.046406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:21.462576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:21.995696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:20.741422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:21.179054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:21.559858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:22.088012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:20.835670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:21.278476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:30:21.678374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:30:29.751233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명읍면동명관리번호위도경도제설함 수(개)염화칼슘 비치량소금 비치량모래 비치량관리기관명관리기관 전화번호
시군명1.0001.0000.2680.4360.6420.0000.452NaNNaN0.2420.250
읍면동명1.0001.0000.0000.9780.9560.8580.9941.0000.8501.0000.990
관리번호0.2680.0001.0000.0000.000NaN0.0000.000NaN0.0000.000
위도0.4360.9780.0001.0000.6500.1950.8090.8100.0000.7970.948
경도0.6420.9560.0000.6501.0000.5070.7560.9490.2680.7100.933
제설함 수(개)0.0000.858NaN0.1950.5071.0000.2720.0000.9950.1860.416
염화칼슘 비치량0.4520.9940.0000.8090.7560.2721.0000.9310.0000.9610.995
소금 비치량NaN1.0000.0000.8100.9490.0000.9311.000NaN0.1910.000
모래 비치량NaN0.850NaN0.0000.2680.9950.000NaN1.0000.3240.000
관리기관명0.2421.0000.0000.7970.7100.1860.9610.1910.3241.0001.000
관리기관 전화번호0.2500.9900.0000.9480.9330.4160.9950.0000.0001.0001.000
2023-12-12T08:30:29.902308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명소금 비치량관리기관명염화칼슘 비치량
시군명1.0001.0000.3940.353
소금 비치량1.0001.0000.3130.678
관리기관명0.3940.3131.0000.916
염화칼슘 비치량0.3530.6780.9161.000
2023-12-12T08:30:30.001488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도제설함 수(개)모래 비치량시군명염화칼슘 비치량소금 비치량관리기관명
위도1.0000.461-0.1850.1610.3330.4830.6060.684
경도0.4611.000-0.009-0.1140.4960.4190.7860.567
제설함 수(개)-0.185-0.0091.0000.5490.0000.1200.0000.117
모래 비치량0.161-0.1140.5491.0001.0000.0001.0000.239
시군명0.3330.4960.0001.0001.0000.3531.0000.394
염화칼슘 비치량0.4830.4190.1200.0000.3531.0000.6780.916
소금 비치량0.6060.7860.0001.0001.0000.6781.0000.313
관리기관명0.6840.5670.1170.2390.3940.9160.3131.000

Missing values

2023-12-12T08:30:22.207400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:30:22.459486image/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.
2023-12-12T08:30:22.659568image/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

시군명읍면동명관리번호설치장소명소재지도로명주소소재지지번주소위도경도제설함 수(개)염화칼슘 비치량소금 비치량모래 비치량관리기관명관리기관 전화번호데이터기준일자
0성남시신흥1동1시민로 163번길 10경기도 성남시 수정구 시민로 163번길 10경기도 성남시 수정구 신흥동 519737.442473127.142178110kg(3포)<NA><NA>수정구 건설과031-729-56072023-04-14
1성남시신흥1동2수정로188번길 16-1경기도 성남시 수정구 수정로188번길 16-1경기도 성남시 수정구 신흥동 5332<NA><NA>110kg(3포)<NA><NA>수정구 건설과031-729-56072023-04-14
2성남시신흥1동3수정로180번길 14-1경기도 성남시 수정구 수정로180번길 14-1경기도 성남시 수정구 신흥동 5351-337.442155127.140309110kg(3포)<NA><NA>수정구 건설과031-729-56072023-04-14
3성남시신흥1동4수정로170번길 16-1경기도 성남시 수정구 수정로170번길 16-1경기도 성남시 수정구 신흥동 574537.441742127.139639110kg(3포)<NA><NA>수정구 건설과031-729-56072023-04-14
4성남시신흥1동5수정남로98번길 8경기도 성남시 수정구 수정남로98번길 8경기도 성남시 수정구 신흥동 573537.441497127.138755110kg(3포)<NA><NA>수정구 건설과031-729-56072023-04-14
5성남시신흥1동6수정로154번길 13경기도 성남시 수정구 수정로154번길 13경기도 성남시 수정구 신흥동 570637.441477127.137818110kg(3포)<NA><NA>수정구 건설과031-729-56072023-04-14
6성남시신흥1동7수정로154번길 20경기도 성남시 수정구 수정로154번길 20경기도 성남시 수정구 신흥동 605137.440905127.137863110kg(3포)<NA><NA>수정구 건설과031-729-56072023-04-14
7성남시신흥1동8탄리로30번길 5경기도 성남시 수정구 탄리로30번길 5경기도 성남시 수정구 신흥동 642237.439274127.13852110kg(3포)<NA><NA>수정구 건설과031-729-56072023-04-14
8성남시신흥1동9탄리로30번길 16-1경기도 성남시 수정구 탄리로30번길 16-1경기도 성남시 수정구 신흥동 664237.439543127.139786110kg(3포)<NA><NA>수정구 건설과031-729-56072023-04-14
9성남시신흥1동10산성대로215번길 23경기도 성남시 수정구 산성대로215번길 23경기도 성남시 수정구 신흥동 621937.44002127.140391110kg(3포)<NA><NA>수정구 건설과031-729-56072023-04-14
시군명읍면동명관리번호설치장소명소재지도로명주소소재지지번주소위도경도제설함 수(개)염화칼슘 비치량소금 비치량모래 비치량관리기관명관리기관 전화번호데이터기준일자
864성남시분당동<NA>서현로<NA>경기도 성남시 분당구 분당동 199-137.36943127.148229154<NA><NA>분당구 건설과031-729-73612023-04-14
865성남시서현동<NA>새마을로<NA>경기도 성남시 분당구 서현동 33-637.383376127.14603884<NA><NA>분당구 건설과031-729-73612023-04-14
866성남시야탑동<NA>양현로<NA>경기도 성남시 분당구 야탑동 487-237.415687127.12259454<NA><NA>분당구 건설과031-729-73612023-04-14
867성남시야탑동<NA>돌마로<NA>경기도 성남시 분당구 야탑동 197-137.410839127.14366774<NA><NA>분당구 건설과031-729-73612023-04-14
868성남시야탑동<NA>판교로<NA>경기도 성남시 분당구 야탑동 198-137.406921127.14182874<NA><NA>분당구 건설과031-729-73612023-04-14
869성남시판교동<NA>성내미터널<NA>경기도 성남시 분당구 판교동 494-237.399691127.09432544<NA><NA>분당구 건설과031-729-73612023-04-14
870성남시백현동<NA>판교IC<NA>경기도 성남시 분당구 백현동 529-237.392562127.108685134<NA><NA>분당구 건설과031-729-73612023-04-14
871성남시하산운동<NA>두밀로<NA>경기도 성남시 분당구 하산운동 372-537.378085127.07588444<NA><NA>분당구 건설과031-729-73612023-04-14
872성남시동원동<NA>동막로<NA>경기도 성남시 분당구 동원동 293-337.352118127.085648124<NA><NA>분당구 건설과031-729-73612023-04-14
873성남시이매동<NA>돌마터널<NA>경기도 성남시 분당구 이매동 52-1537.395384127.13795104<NA><NA>분당구 건설과031-729-73612023-04-14

Duplicate rows

Most frequently occurring

시군명읍면동명관리번호설치장소명소재지도로명주소소재지지번주소위도경도제설함 수(개)염화칼슘 비치량소금 비치량모래 비치량관리기관명관리기관 전화번호데이터기준일자# duplicates
1성남시분당동<NA>불정로경기도 성남시 분당구 불정로312경기도 성남시 분당구 분당동 4337.370269127.13123113<NA><NA>분당구 건설과031-729-76042023-04-143
0성남시고등동<NA><NA><NA>경기도 성남시 수정구 상적동 190-5937.432002127.07345613<NA><NA>수정구 건설과<NA>2023-04-142
2성남시수내3동<NA>발이봉남로43번길(수내동)경기도 성남시 분당구 발이봉남로43번길(수내동)성남시 분당구 수내동 109-1637.362858127.12868615<NA><NA>분당구 건설과031-729-76652023-04-142
3성남시위례동<NA>대원사 입구<NA>경기도 성남시 수정구 창곡동 573-237.466757127.1535491<NA><NA><NA>수정구 건설과<NA>2023-04-142