Overview

Dataset statistics

Number of variables8
Number of observations175
Missing cells17
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.4 KiB
Average record size in memory66.8 B

Variable types

Categorical3
Text3
Numeric2

Dataset

Description서울특별시 성북구 경로당 현황 데이터를 제공하고 있습니다. 이 데이터에는 경로당명, 주소, 전화번호 정보 포함되어 있습니다 .
Author서울특별시 성북구
URLhttps://www.data.go.kr/data/15040320/fileData.do

Alerts

기준일자 has constant value ""Constant
위도 is highly overall correlated with 행정동High correlation
경도 is highly overall correlated with 행정동High correlation
행정동 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
전화번호 has 17 (9.7%) missing valuesMissing
경로당명 has unique valuesUnique
주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 05:48:16.592703
Analysis finished2023-12-12 05:48:17.552286
Duration0.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
사립
123 
구립
52 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row구립
2nd row사립
3rd row구립
4th row사립
5th row구립

Common Values

ValueCountFrequency (%)
사립 123
70.3%
구립 52
29.7%

Length

2023-12-12T14:48:17.623719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:48:17.747282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사립 123
70.3%
구립 52
29.7%

경로당명
Text

UNIQUE 

Distinct175
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-12T14:48:17.930600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length15
Mean length10.605714
Min length5

Characters and Unicode

Total characters1856
Distinct characters196
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique175 ?
Unique (%)100.0%

Sample

1st row동소문동경로당
2nd row북정경로당
3rd row성북동경로당
4th row성북동할머니경로당
5th row성암경로당
ValueCountFrequency (%)
동소문동경로당 1
 
0.6%
종암동s.k(아)경로당 1
 
0.6%
종암2동s.k(아)제2경로당 1
 
0.6%
성북힐스테이트경로당 1
 
0.6%
동일하이빌뉴시티경로당 1
 
0.6%
래미안길음센터피스아파트경로당 1
 
0.6%
남종경로당 1
 
0.6%
래미안세레니티경로당 1
 
0.6%
종암동제1경로당 1
 
0.6%
종암1동현대아이파크경로당 1
 
0.6%
Other values (167) 167
94.4%
2023-12-12T14:48:18.276715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 177
 
9.5%
로 174
 
9.4%
당 174
 
9.4%
아 107
 
5.8%
) 93
 
5.0%
( 93
 
5.0%
동 78
 
4.2%
2 36
 
1.9%
암 34
 
1.8%
정 33
 
1.8%
Other values (186) 857
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1544
83.2%
Close Punctuation 93
 
5.0%
Open Punctuation 93
 
5.0%
Decimal Number 91
 
4.9%
Uppercase Letter 17
 
0.9%
Lowercase Letter 9
 
0.5%
Dash Punctuation 4
 
0.2%
Other Punctuation 3
 
0.2%
Space Separator 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 177
 
11.5%
로 174
 
11.3%
당 174
 
11.3%
아 107
 
6.9%
동 78
 
5.1%
암 34
 
2.2%
정 33
 
2.1%
릉 30
 
1.9%
이 23
 
1.5%
제 21
 
1.4%
Other values (158) 693
44.9%
Decimal Number
ValueCountFrequency (%)
2 36
39.6%
1 31
34.1%
3 11
 
12.1%
4 4
 
4.4%
8 3
 
3.3%
7 2
 
2.2%
9 2
 
2.2%
0 1
 
1.1%
5 1
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
S 6
35.3%
K 4
23.5%
L 2
 
11.8%
H 2
 
11.8%
E 1
 
5.9%
V 1
 
5.9%
I 1
 
5.9%
Lowercase Letter
ValueCountFrequency (%)
e 3
33.3%
l 2
22.2%
s 1
 
11.1%
h 1
 
11.1%
v 1
 
11.1%
i 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
. 2
66.7%
: 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 93
100.0%
Open Punctuation
ValueCountFrequency (%)
( 93
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1544
83.2%
Common 286
 
15.4%
Latin 26
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 177
 
11.5%
로 174
 
11.3%
당 174
 
11.3%
아 107
 
6.9%
동 78
 
5.1%
암 34
 
2.2%
정 33
 
2.1%
릉 30
 
1.9%
이 23
 
1.5%
제 21
 
1.4%
Other values (158) 693
44.9%
Common
ValueCountFrequency (%)
) 93
32.5%
( 93
32.5%
2 36
 
12.6%
1 31
 
10.8%
3 11
 
3.8%
- 4
 
1.4%
4 4
 
1.4%
8 3
 
1.0%
. 2
 
0.7%
7 2
 
0.7%
Other values (5) 7
 
2.4%
Latin
ValueCountFrequency (%)
S 6
23.1%
K 4
15.4%
e 3
11.5%
L 2
 
7.7%
H 2
 
7.7%
l 2
 
7.7%
s 1
 
3.8%
h 1
 
3.8%
v 1
 
3.8%
i 1
 
3.8%
Other values (3) 3
11.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1544
83.2%
ASCII 312
 
16.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 177
 
11.5%
로 174
 
11.3%
당 174
 
11.3%
아 107
 
6.9%
동 78
 
5.1%
암 34
 
2.2%
정 33
 
2.1%
릉 30
 
1.9%
이 23
 
1.5%
제 21
 
1.4%
Other values (158) 693
44.9%
ASCII
ValueCountFrequency (%)
) 93
29.8%
( 93
29.8%
2 36
 
11.5%
1 31
 
9.9%
3 11
 
3.5%
S 6
 
1.9%
K 4
 
1.3%
- 4
 
1.3%
4 4
 
1.3%
e 3
 
1.0%
Other values (18) 27
 
8.7%

주소
Text

UNIQUE 

Distinct175
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-12T14:48:18.572810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length38
Mean length26.074286
Min length17

Characters and Unicode

Total characters4563
Distinct characters174
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique175 ?
Unique (%)100.0%

Sample

1st row서울특별시 성북구 동소문로13길 39-20
2nd row서울특별시 성북구 성북로23길 129-5
3rd row서울특별시 성북구 성북로 41-12
4th row서울특별시 성북구 성북로23길 158
5th row서울특별시 성북구 성북로16가길 4
ValueCountFrequency (%)
서울특별시 175
 
19.9%
성북구 175
 
19.9%
1층 27
 
3.1%
관리동 14
 
1.6%
북악산로 9
 
1.0%
내 8
 
0.9%
정릉로 7
 
0.8%
길음로 6
 
0.7%
15 5
 
0.6%
6 5
 
0.6%
Other values (353) 450
51.1%
2023-12-12T14:48:18.993172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
719
 
15.8%
1 206
 
4.5%
북 195
 
4.3%
성 185
 
4.1%
서 179
 
3.9%
시 177
 
3.9%
구 175
 
3.8%
울 175
 
3.8%
별 175
 
3.8%
특 175
 
3.8%
Other values (164) 2202
48.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2808
61.5%
Decimal Number 827
 
18.1%
Space Separator 719
 
15.8%
Other Punctuation 98
 
2.1%
Open Punctuation 37
 
0.8%
Close Punctuation 37
 
0.8%
Dash Punctuation 32
 
0.7%
Uppercase Letter 4
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
북 195
 
6.9%
성 185
 
6.6%
서 179
 
6.4%
시 177
 
6.3%
구 175
 
6.2%
울 175
 
6.2%
별 175
 
6.2%
특 175
 
6.2%
로 170
 
6.1%
길 147
 
5.2%
Other values (143) 1055
37.6%
Decimal Number
ValueCountFrequency (%)
1 206
24.9%
2 143
17.3%
3 80
 
9.7%
4 72
 
8.7%
0 70
 
8.5%
5 62
 
7.5%
8 55
 
6.7%
9 51
 
6.2%
6 49
 
5.9%
7 39
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
K 1
25.0%
S 1
25.0%
B 1
25.0%
C 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 97
99.0%
@ 1
 
1.0%
Space Separator
ValueCountFrequency (%)
719
100.0%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%
Close Punctuation
ValueCountFrequency (%)
) 37
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2808
61.5%
Common 1750
38.4%
Latin 5
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
북 195
 
6.9%
성 185
 
6.6%
서 179
 
6.4%
시 177
 
6.3%
구 175
 
6.2%
울 175
 
6.2%
별 175
 
6.2%
특 175
 
6.2%
로 170
 
6.1%
길 147
 
5.2%
Other values (143) 1055
37.6%
Common
ValueCountFrequency (%)
719
41.1%
1 206
 
11.8%
2 143
 
8.2%
, 97
 
5.5%
3 80
 
4.6%
4 72
 
4.1%
0 70
 
4.0%
5 62
 
3.5%
8 55
 
3.1%
9 51
 
2.9%
Other values (6) 195
 
11.1%
Latin
ValueCountFrequency (%)
K 1
20.0%
S 1
20.0%
B 1
20.0%
e 1
20.0%
C 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2808
61.5%
ASCII 1755
38.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
719
41.0%
1 206
 
11.7%
2 143
 
8.1%
, 97
 
5.5%
3 80
 
4.6%
4 72
 
4.1%
0 70
 
4.0%
5 62
 
3.5%
8 55
 
3.1%
9 51
 
2.9%
Other values (11) 200
 
11.4%
Hangul
ValueCountFrequency (%)
북 195
 
6.9%
성 185
 
6.6%
서 179
 
6.4%
시 177
 
6.3%
구 175
 
6.2%
울 175
 
6.2%
별 175
 
6.2%
특 175
 
6.2%
로 170
 
6.1%
길 147
 
5.2%
Other values (143) 1055
37.6%

전화번호
Text

MISSING 

Distinct158
Distinct (%)100.0%
Missing17
Missing (%)9.7%
Memory size1.5 KiB
2023-12-12T14:48:19.300741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length11.132911
Min length11

Characters and Unicode

Total characters1759
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

Unique158 ?
Unique (%)100.0%

Sample

1st row02-926-8365
2nd row02-744-8507
3rd row02-762-0296
4th row02-742-1054
5th row02-762-6645
ValueCountFrequency (%)
02-928-7867 1
 
0.6%
02-942-3331 1
 
0.6%
02-921-5408 1
 
0.6%
02-982-1278 1
 
0.6%
02-914-6033 1
 
0.6%
02-910-9191 1
 
0.6%
02-941-7438 1
 
0.6%
02-921-8188 1
 
0.6%
02-928-1519 1
 
0.6%
02-913-4011 1
 
0.6%
Other values (148) 148
93.7%
2023-12-12T14:48:19.858443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 316
18.0%
2 288
16.4%
0 246
14.0%
9 211
12.0%
1 161
9.2%
4 108
 
6.1%
6 106
 
6.0%
8 90
 
5.1%
7 84
 
4.8%
5 76
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1443
82.0%
Dash Punctuation 316
 
18.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 288
20.0%
0 246
17.0%
9 211
14.6%
1 161
11.2%
4 108
 
7.5%
6 106
 
7.3%
8 90
 
6.2%
7 84
 
5.8%
5 76
 
5.3%
3 73
 
5.1%
Dash Punctuation
ValueCountFrequency (%)
- 316
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1759
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 316
18.0%
2 288
16.4%
0 246
14.0%
9 211
12.0%
1 161
9.2%
4 108
 
6.1%
6 106
 
6.0%
8 90
 
5.1%
7 84
 
4.8%
5 76
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1759
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 316
18.0%
2 288
16.4%
0 246
14.0%
9 211
12.0%
1 161
9.2%
4 108
 
6.1%
6 106
 
6.0%
8 90
 
5.1%
7 84
 
4.8%
5 76
 
4.3%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2021-01-01
175 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-01-01
2nd row2021-01-01
3rd row2021-01-01
4th row2021-01-01
5th row2021-01-01

Common Values

ValueCountFrequency (%)
2021-01-01 175
100.0%

Length

2023-12-12T14:48:20.044826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:48:20.186623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-01-01 175
100.0%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct160
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.602691
Minimum37.580549
Maximum37.622766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T14:48:20.332610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.580549
5-th percentile37.584082
Q137.597447
median37.602954
Q337.609442
95-th percentile37.617505
Maximum37.622766
Range0.04221713
Interquartile range (IQR)0.011994715

Descriptive statistics

Standard deviation0.0096004399
Coefficient of variation (CV)0.00025531258
Kurtosis-0.31452912
Mean37.602691
Median Absolute Deviation (MAD)0.00639915
Skewness-0.32308764
Sum6580.4709
Variance9.2168447 × 10-5
MonotonicityNot monotonic
2023-12-12T14:48:20.514446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.59447253 3
 
1.7%
37.60994709 2
 
1.1%
37.61132386 2
 
1.1%
37.60935341 2
 
1.1%
37.59899275 2
 
1.1%
37.60944177 2
 
1.1%
37.60269651 2
 
1.1%
37.59816479 2
 
1.1%
37.59630307 2
 
1.1%
37.60084117 2
 
1.1%
Other values (150) 154
88.0%
ValueCountFrequency (%)
37.58054875 1
0.6%
37.58093044 1
0.6%
37.58267087 1
0.6%
37.58277838 1
0.6%
37.582797 1
0.6%
37.58287311 1
0.6%
37.583328 1
0.6%
37.58342042 1
0.6%
37.58385991 1
0.6%
37.58417735 1
0.6%
ValueCountFrequency (%)
37.62276588 1
0.6%
37.62274041 1
0.6%
37.62144228 1
0.6%
37.61937475 1
0.6%
37.61898673 1
0.6%
37.61889318 1
0.6%
37.61857737 1
0.6%
37.61821238 1
0.6%
37.61767831 1
0.6%
37.61743013 1
0.6%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct160
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.02492
Minimum126.99145
Maximum127.06899
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-12T14:48:20.697755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.99145
5-th percentile127.00312
Q1127.01362
median127.02099
Q3127.03632
95-th percentile127.05501
Maximum127.06899
Range0.0775488
Interquartile range (IQR)0.02269685

Descriptive statistics

Standard deviation0.015845845
Coefficient of variation (CV)0.00012474595
Kurtosis0.0079504714
Mean127.02492
Median Absolute Deviation (MAD)0.0100108
Skewness0.65609523
Sum22229.361
Variance0.0002510908
MonotonicityNot monotonic
2023-12-12T14:48:20.909296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.012706 3
 
1.7%
127.0369136 2
 
1.1%
127.0208476 2
 
1.1%
127.0249739 2
 
1.1%
127.0231395 2
 
1.1%
127.0193237 2
 
1.1%
127.020948 2
 
1.1%
127.0297513 2
 
1.1%
127.032513 2
 
1.1%
127.0379642 2
 
1.1%
Other values (150) 154
88.0%
ValueCountFrequency (%)
126.9914458 1
0.6%
126.9918337 1
0.6%
126.9978758 1
0.6%
127.0006038 1
0.6%
127.0007755 1
0.6%
127.0023057 1
0.6%
127.0025079 1
0.6%
127.0025561 1
0.6%
127.0029912 1
0.6%
127.0031768 1
0.6%
ValueCountFrequency (%)
127.0689946 1
0.6%
127.0667784 1
0.6%
127.0657342 1
0.6%
127.065616 1
0.6%
127.0632125 1
0.6%
127.0622674 1
0.6%
127.0593557 1
0.6%
127.057609 1
0.6%
127.0553574 1
0.6%
127.0548669 1
0.6%

행정동
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
종암동
18 
길음1동
16 
정릉2동
14 
정릉1동
12 
정릉4동
11 
Other values (15)
104 

Length

Max length4
Median length4
Mean length3.6628571
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
종암동 18
 
10.3%
길음1동 16
 
9.1%
정릉2동 14
 
8.0%
정릉1동 12
 
6.9%
정릉4동 11
 
6.3%
월곡2동 10
 
5.7%
월곡1동 10
 
5.7%
삼선동 9
 
5.1%
돈암1동 9
 
5.1%
돈암2동 9
 
5.1%
Other values (10) 57
32.6%

Length

2023-12-12T14:48:21.072963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
종암동 18
 
10.3%
길음1동 16
 
9.1%
정릉2동 14
 
8.0%
정릉1동 12
 
6.9%
정릉4동 11
 
6.3%
월곡2동 10
 
5.7%
월곡1동 10
 
5.7%
삼선동 9
 
5.1%
돈암1동 9
 
5.1%
돈암2동 9
 
5.1%
Other values (10) 57
32.6%

Interactions

2023-12-12T14:48:17.115554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:48:16.932408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:48:17.244433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:48:17.010550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:48:21.172726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분위도경도행정동
구분1.0000.1240.0000.517
위도0.1241.0000.6590.928
경도0.0000.6591.0000.960
행정동0.5170.9280.9601.000
2023-12-12T14:48:21.288159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동구분
행정동1.0000.388
구분0.3881.000
2023-12-12T14:48:21.727342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도구분행정동
위도1.0000.2280.0910.573
경도0.2281.0000.0000.660
구분0.0910.0001.0000.388
행정동0.5730.6600.3881.000

Missing values

2023-12-12T14:48:17.379195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:48:17.507224image/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

구분경로당명주소전화번호기준일자위도경도행정동
0구립동소문동경로당서울특별시 성북구 동소문로13길 39-2002-926-83652021-01-0137.592909127.011004성북동
1사립북정경로당서울특별시 성북구 성북로23길 129-502-744-85072021-01-0137.592719126.991446성북동
2구립성북동경로당서울특별시 성북구 성북로 41-1202-762-02962021-01-0137.59143127.002556성북동
3사립성북동할머니경로당서울특별시 성북구 성북로23길 15802-742-10542021-01-0137.592269126.991834성북동
4구립성암경로당서울특별시 성북구 성북로16가길 402-762-66452021-01-0137.593988127.000604성북동
5사립돈암동일하이빌아파트경로당서울특별시 성북구 보문로38길 11, 102동 4층02-926-20732021-01-0137.591663127.014818삼선동
6구립삼선동제1경로당서울특별시 성북구 삼선교로14길 10902-742-46472021-01-0137.583328127.01069삼선동
7구립삼선동제2경로당서울특별시 성북구 보문로29길 86, 3층02-743-98362021-01-0137.586409127.013653삼선동
8사립삼선코오롱(아)경로당서울특별시 성북구 삼선교로23길 2302-928-78672021-01-0137.590846127.013018삼선동
9사립삼선푸르지오(아)경로당서울특별시 성북구 보문로29다길 31 단지내02-766-08062021-01-0137.584177127.012619삼선동
구분경로당명주소전화번호기준일자위도경도행정동
165사립참누리(아)경로당서울특별시 성북구 한천로101길 18, 207동 1층02-942-05062021-01-0137.622766127.049299장위3동
166사립꿈의숲코오롱하늘채아파트경로당서울특별시 성북구 돌곶이로 22002-919-93782021-01-0137.618987127.046529장위3동
167사립래미안석관(아)경로당서울특별시 성북구 화랑로 214, 래미안석관(아)복리시설1층02-965-53752021-01-0137.609159127.054867석관동
168구립석관동제1경로당서울특별시 성북구 한천로74길 102-963-99572021-01-0137.610821127.063213석관동
169사립석관동두산(아)경로당서울특별시 성북구 화랑로48길 1602-967-89722021-01-0137.613763127.068995석관동
170사립석관1동삼성(아)경로당서울특별시 성북구 한천로 68길 51, 삼성(아) 관리동<NA>2021-01-0137.610245127.065616석관동
171사립석관1동코오롱(아)경로당서울특별시 성북구 한천로 50902-968-04752021-01-0137.606986127.065734석관동
172구립석관동제2경로당서울특별시 성북구 돌곶이로21길 602-963-76602021-01-0137.609802127.057609석관동
173사립중앙하이츠(아)경로당서울특별시 성북구 한천로76길 7302-959-15582021-01-0137.61105127.066778석관동
174사립석관래미안아트리치경로당서울특별시 성북구 돌곶이로8길 22<NA>2021-01-0137.606543127.062267석관동