Overview

Dataset statistics

Number of variables16
Number of observations252
Missing cells23
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.4 KiB
Average record size in memory135.5 B

Variable types

Numeric7
Text4
DateTime1
Categorical4

Dataset

Description2020-12-22
Author지방행정인허가공개데이터
URLhttps://bigdata.busan.go.kr/data/bigDataDetailView.do?menuCode=M00000000007&hdfs_file_sn=20201222133205169000

Alerts

last_load_dttm has constant value ""Constant
reference_date is highly overall correlated with skey and 3 other fieldsHigh correlation
gugun is highly overall correlated with skey and 4 other fieldsHigh correlation
skey is highly overall correlated with gugun and 1 other fieldsHigh correlation
num_of_people is highly overall correlated with present_num_of_ppHigh correlation
present_num_of_pp is highly overall correlated with num_of_peopleHigh correlation
lat is highly overall correlated with gugunHigh correlation
lng is highly overall correlated with gugun and 1 other fieldsHigh correlation
instt_code is highly overall correlated with gugun and 1 other fieldsHigh correlation
type_of_facility is highly imbalanced (60.9%)Imbalance
present_num_of_pp has 3 (1.2%) missing valuesMissing
lat has 4 (1.6%) missing valuesMissing
lng has 4 (1.6%) missing valuesMissing
skey has unique valuesUnique

Reproduction

Analysis started2024-04-19 06:45:27.118290
Analysis finished2024-04-19 06:45:33.268300
Duration6.15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct252
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1741.7659
Minimum1556
Maximum1921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-19T15:45:33.342117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1556
5-th percentile1568.55
Q11618.75
median1741.5
Q31857.25
95-th percentile1908.45
Maximum1921
Range365
Interquartile range (IQR)238.5

Descriptive statistics

Standard deviation119.38249
Coefficient of variation (CV)0.068541069
Kurtosis-1.448781
Mean1741.7659
Median Absolute Deviation (MAD)119.5
Skewness-0.077593477
Sum438925
Variance14252.18
MonotonicityNot monotonic
2024-04-19T15:45:33.474781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1717 1
 
0.4%
1588 1
 
0.4%
1633 1
 
0.4%
1634 1
 
0.4%
1899 1
 
0.4%
1900 1
 
0.4%
1901 1
 
0.4%
1902 1
 
0.4%
1903 1
 
0.4%
1904 1
 
0.4%
Other values (242) 242
96.0%
ValueCountFrequency (%)
1556 1
0.4%
1557 1
0.4%
1558 1
0.4%
1559 1
0.4%
1560 1
0.4%
1561 1
0.4%
1562 1
0.4%
1563 1
0.4%
1564 1
0.4%
1565 1
0.4%
ValueCountFrequency (%)
1921 1
0.4%
1920 1
0.4%
1919 1
0.4%
1918 1
0.4%
1917 1
0.4%
1916 1
0.4%
1915 1
0.4%
1914 1
0.4%
1913 1
0.4%
1912 1
0.4%
Distinct243
Distinct (%)97.2%
Missing2
Missing (%)0.8%
Memory size2.1 KiB
2024-04-19T15:45:33.706566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length7.332
Min length2

Characters and Unicode

Total characters1833
Distinct characters248
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique236 ?
Unique (%)94.4%

Sample

1st row샘물지역아동센터
2nd row주례지역아동센터
3rd row학장지역아동센터
4th row희망지역아동센터
5th row아가페지역아동센터
ValueCountFrequency (%)
지역아동센터 14
 
5.1%
희망지역아동센터 2
 
0.7%
2
 
0.7%
꿈나무지역아동센터 2
 
0.7%
사랑지역아동센터 2
 
0.7%
샘물지역아동센터 2
 
0.7%
다원 2
 
0.7%
꿈샘지역아동센터 2
 
0.7%
낙동지역아동센터 2
 
0.7%
사랑의샘 1
 
0.4%
Other values (242) 242
88.6%
2024-04-19T15:45:34.078361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
171
 
9.3%
160
 
8.7%
160
 
8.7%
155
 
8.5%
153
 
8.3%
148
 
8.1%
25
 
1.4%
21
 
1.1%
16
 
0.9%
16
 
0.9%
Other values (238) 808
44.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1791
97.7%
Space Separator 25
 
1.4%
Decimal Number 8
 
0.4%
Uppercase Letter 5
 
0.3%
Open Punctuation 2
 
0.1%
Close Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
171
 
9.5%
160
 
8.9%
160
 
8.9%
155
 
8.7%
153
 
8.5%
148
 
8.3%
21
 
1.2%
16
 
0.9%
16
 
0.9%
15
 
0.8%
Other values (229) 776
43.3%
Decimal Number
ValueCountFrequency (%)
1 4
50.0%
8 2
25.0%
3 2
25.0%
Uppercase Letter
ValueCountFrequency (%)
L 2
40.0%
C 2
40.0%
H 1
20.0%
Space Separator
ValueCountFrequency (%)
25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1791
97.7%
Common 37
 
2.0%
Latin 5
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
171
 
9.5%
160
 
8.9%
160
 
8.9%
155
 
8.7%
153
 
8.5%
148
 
8.3%
21
 
1.2%
16
 
0.9%
16
 
0.9%
15
 
0.8%
Other values (229) 776
43.3%
Common
ValueCountFrequency (%)
25
67.6%
1 4
 
10.8%
( 2
 
5.4%
) 2
 
5.4%
8 2
 
5.4%
3 2
 
5.4%
Latin
ValueCountFrequency (%)
L 2
40.0%
C 2
40.0%
H 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1791
97.7%
ASCII 42
 
2.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
171
 
9.5%
160
 
8.9%
160
 
8.9%
155
 
8.7%
153
 
8.5%
148
 
8.3%
21
 
1.2%
16
 
0.9%
16
 
0.9%
15
 
0.8%
Other values (229) 776
43.3%
ASCII
ValueCountFrequency (%)
25
59.5%
1 4
 
9.5%
( 2
 
4.8%
) 2
 
4.8%
8 2
 
4.8%
3 2
 
4.8%
L 2
 
4.8%
C 2
 
4.8%
H 1
 
2.4%
Distinct242
Distinct (%)96.8%
Missing2
Missing (%)0.8%
Memory size2.1 KiB
2024-04-19T15:45:34.447262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters750
Distinct characters128
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique234 ?
Unique (%)93.6%

Sample

1st row박종칠
2nd row유재용
3rd row최인금
4th row김진아
5th row진영은
ValueCountFrequency (%)
나영찬 2
 
0.8%
정혜인 2
 
0.8%
김미연 2
 
0.8%
이은희 2
 
0.8%
박명숙 2
 
0.8%
김경희 2
 
0.8%
이정미 2
 
0.8%
김현희 2
 
0.8%
김분이 1
 
0.4%
황기석 1
 
0.4%
Other values (232) 232
92.8%
2024-04-19T15:45:34.882610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
64
 
8.5%
40
 
5.3%
36
 
4.8%
34
 
4.5%
31
 
4.1%
27
 
3.6%
26
 
3.5%
25
 
3.3%
20
 
2.7%
16
 
2.1%
Other values (118) 431
57.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 750
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
64
 
8.5%
40
 
5.3%
36
 
4.8%
34
 
4.5%
31
 
4.1%
27
 
3.6%
26
 
3.5%
25
 
3.3%
20
 
2.7%
16
 
2.1%
Other values (118) 431
57.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 750
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
64
 
8.5%
40
 
5.3%
36
 
4.8%
34
 
4.5%
31
 
4.1%
27
 
3.6%
26
 
3.5%
25
 
3.3%
20
 
2.7%
16
 
2.1%
Other values (118) 431
57.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 750
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
64
 
8.5%
40
 
5.3%
36
 
4.8%
34
 
4.5%
31
 
4.1%
27
 
3.6%
26
 
3.5%
25
 
3.3%
20
 
2.7%
16
 
2.1%
Other values (118) 431
57.5%

addr
Text

Distinct246
Distinct (%)98.4%
Missing2
Missing (%)0.8%
Memory size2.1 KiB
2024-04-19T15:45:35.173598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length39
Mean length27.792
Min length15

Characters and Unicode

Total characters6948
Distinct characters239
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

Unique242 ?
Unique (%)96.8%

Sample

1st row부산광역시 사상구 엄궁북로 4번길 32-10 (엄궁동)
2nd row부산광역시 사상구 가야대로 366번길 63-6 (주례동)
3rd row부산광역시 사상구 학감대로 49번길 28-70 (학장동) 학장종합사회복지관 3층
4th row부산광역시 사상구 사상로 275 (덕포동)
5th row부산광역시 사상구 새벽시장로63번길 42(감전동, 2층)
ValueCountFrequency (%)
부산광역시 204
 
16.4%
해운대구 26
 
2.1%
남구 24
 
1.9%
부산진구 24
 
1.9%
사상구 24
 
1.9%
동래구 23
 
1.8%
사하구 21
 
1.7%
북구 21
 
1.7%
동구 20
 
1.6%
연제구 13
 
1.0%
Other values (620) 846
67.9%
2024-04-19T15:45:35.618968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1009
 
14.5%
321
 
4.6%
257
 
3.7%
253
 
3.6%
246
 
3.5%
1 246
 
3.5%
239
 
3.4%
) 230
 
3.3%
( 230
 
3.3%
223
 
3.2%
Other values (229) 3694
53.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4097
59.0%
Decimal Number 1217
 
17.5%
Space Separator 1009
 
14.5%
Close Punctuation 230
 
3.3%
Open Punctuation 230
 
3.3%
Other Punctuation 96
 
1.4%
Dash Punctuation 63
 
0.9%
Uppercase Letter 4
 
0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
321
 
7.8%
257
 
6.3%
253
 
6.2%
246
 
6.0%
239
 
5.8%
223
 
5.4%
212
 
5.2%
205
 
5.0%
150
 
3.7%
145
 
3.5%
Other values (209) 1846
45.1%
Decimal Number
ValueCountFrequency (%)
1 246
20.2%
2 184
15.1%
3 160
13.1%
0 118
9.7%
4 107
8.8%
6 102
8.4%
5 89
 
7.3%
7 80
 
6.6%
9 67
 
5.5%
8 64
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 93
96.9%
. 2
 
2.1%
@ 1
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
A 3
75.0%
F 1
 
25.0%
Space Separator
ValueCountFrequency (%)
1009
100.0%
Close Punctuation
ValueCountFrequency (%)
) 230
100.0%
Open Punctuation
ValueCountFrequency (%)
( 230
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 63
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4097
59.0%
Common 2847
41.0%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
321
 
7.8%
257
 
6.3%
253
 
6.2%
246
 
6.0%
239
 
5.8%
223
 
5.4%
212
 
5.2%
205
 
5.0%
150
 
3.7%
145
 
3.5%
Other values (209) 1846
45.1%
Common
ValueCountFrequency (%)
1009
35.4%
1 246
 
8.6%
) 230
 
8.1%
( 230
 
8.1%
2 184
 
6.5%
3 160
 
5.6%
0 118
 
4.1%
4 107
 
3.8%
6 102
 
3.6%
, 93
 
3.3%
Other values (8) 368
 
12.9%
Latin
ValueCountFrequency (%)
A 3
75.0%
F 1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4097
59.0%
ASCII 2851
41.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1009
35.4%
1 246
 
8.6%
) 230
 
8.1%
( 230
 
8.1%
2 184
 
6.5%
3 160
 
5.6%
0 118
 
4.1%
4 107
 
3.8%
6 102
 
3.6%
, 93
 
3.3%
Other values (10) 372
 
13.0%
Hangul
ValueCountFrequency (%)
321
 
7.8%
257
 
6.3%
253
 
6.2%
246
 
6.0%
239
 
5.8%
223
 
5.4%
212
 
5.2%
205
 
5.0%
150
 
3.7%
145
 
3.5%
Other values (209) 1846
45.1%
Distinct230
Distinct (%)92.0%
Missing2
Missing (%)0.8%
Memory size2.1 KiB
Minimum1908-01-04 00:00:00
Maximum2020-08-01 00:00:00
2024-04-19T15:45:35.756374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:35.908886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

num_of_people
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)16.0%
Missing2
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean27.176
Minimum5
Maximum130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-19T15:45:36.035136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7
Q119
median25
Q329
95-th percentile50.55
Maximum130
Range125
Interquartile range (IQR)10

Descriptive statistics

Standard deviation16.338077
Coefficient of variation (CV)0.60119506
Kurtosis12.020469
Mean27.176
Median Absolute Deviation (MAD)6
Skewness2.7900998
Sum6794
Variance266.93276
MonotonicityNot monotonic
2024-04-19T15:45:36.164103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
29 65
25.8%
19 58
23.0%
25 25
 
9.9%
7 25
 
9.9%
35 14
 
5.6%
24 5
 
2.0%
39 5
 
2.0%
23 4
 
1.6%
33 4
 
1.6%
28 4
 
1.6%
Other values (30) 41
16.3%
ValueCountFrequency (%)
5 3
 
1.2%
7 25
9.9%
9 1
 
0.4%
10 1
 
0.4%
17 1
 
0.4%
19 58
23.0%
20 3
 
1.2%
21 1
 
0.4%
23 4
 
1.6%
24 5
 
2.0%
ValueCountFrequency (%)
130 1
0.4%
110 1
0.4%
105 1
0.4%
96 1
0.4%
80 2
0.8%
77 1
0.4%
75 1
0.4%
72 1
0.4%
66 1
0.4%
63 1
0.4%

present_num_of_pp
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)17.7%
Missing3
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean23.590361
Minimum1
Maximum106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-19T15:45:36.308560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q118
median24
Q329
95-th percentile37
Maximum106
Range105
Interquartile range (IQR)11

Descriptive statistics

Standard deviation12.513576
Coefficient of variation (CV)0.53045291
Kurtosis11.055493
Mean23.590361
Median Absolute Deviation (MAD)5
Skewness2.1433119
Sum5874
Variance156.58958
MonotonicityNot monotonic
2024-04-19T15:45:36.430992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
19 27
 
10.7%
29 24
 
9.5%
25 18
 
7.1%
18 14
 
5.6%
28 12
 
4.8%
26 11
 
4.4%
5 10
 
4.0%
23 10
 
4.0%
24 10
 
4.0%
7 9
 
3.6%
Other values (34) 104
41.3%
ValueCountFrequency (%)
1 1
 
0.4%
2 1
 
0.4%
3 4
 
1.6%
5 10
4.0%
6 5
2.0%
7 9
3.6%
8 1
 
0.4%
10 2
 
0.8%
13 1
 
0.4%
14 6
2.4%
ValueCountFrequency (%)
106 1
0.4%
86 1
0.4%
79 1
0.4%
70 1
0.4%
62 1
0.4%
50 1
0.4%
49 1
0.4%
48 1
0.4%
46 1
0.4%
45 1
0.4%

num_of_worker
Real number (ℝ)

Distinct20
Distinct (%)8.0%
Missing2
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean4.028
Minimum1
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-19T15:45:36.537997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q33
95-th percentile22.55
Maximum44
Range43
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.5952411
Coefficient of variation (CV)1.6373488
Kurtosis15.75891
Mean4.028
Median Absolute Deviation (MAD)0
Skewness3.9834318
Sum1007
Variance43.497205
MonotonicityNot monotonic
2024-04-19T15:45:36.642582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 164
65.1%
3 51
 
20.2%
4 9
 
3.6%
1 5
 
2.0%
25 3
 
1.2%
10 3
 
1.2%
23 2
 
0.8%
30 1
 
0.4%
27 1
 
0.4%
26 1
 
0.4%
Other values (10) 10
 
4.0%
(Missing) 2
 
0.8%
ValueCountFrequency (%)
1 5
 
2.0%
2 164
65.1%
3 51
 
20.2%
4 9
 
3.6%
5 1
 
0.4%
7 1
 
0.4%
10 3
 
1.2%
11 1
 
0.4%
20 1
 
0.4%
22 1
 
0.4%
ValueCountFrequency (%)
44 1
 
0.4%
40 1
 
0.4%
35 1
 
0.4%
34 1
 
0.4%
33 1
 
0.4%
30 1
 
0.4%
27 1
 
0.4%
26 1
 
0.4%
25 3
1.2%
23 2
0.8%

type_of_facility
Categorical

IMBALANCE 

Distinct10
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
지역아동센터
195 
공동생활가정
23 
아동양육시설
 
16
다함께돌봄센터
 
6
아동복지시설
 
5
Other values (5)
 
7

Length

Max length11
Median length6
Mean length6.0674603
Min length4

Unique

Unique3 ?
Unique (%)1.2%

Sample

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

Common Values

ValueCountFrequency (%)
지역아동센터 195
77.4%
공동생활가정 23
 
9.1%
아동양육시설 16
 
6.3%
다함께돌봄센터 6
 
2.4%
아동복지시설 5
 
2.0%
공동생활가정(생활) 2
 
0.8%
<NA> 2
 
0.8%
자립지원시설 1
 
0.4%
공동생활가정(그룹홈) 1
 
0.4%
학대피해아동쉼터 1
 
0.4%

Length

2024-04-19T15:45:37.030787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T15:45:37.174880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지역아동센터 195
77.4%
공동생활가정 23
 
9.1%
아동양육시설 16
 
6.3%
다함께돌봄센터 6
 
2.4%
아동복지시설 5
 
2.0%
공동생활가정(생활 2
 
0.8%
na 2
 
0.8%
자립지원시설 1
 
0.4%
공동생활가정(그룹홈 1
 
0.4%
학대피해아동쉼터 1
 
0.4%

tel
Text

Distinct250
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-04-19T15:45:37.408839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.09127
Min length12

Characters and Unicode

Total characters3047
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

Unique249 ?
Unique (%)98.8%

Sample

1st row051-322-0675
2nd row070-7763-1649
3rd row051-311-4014
4th row051-304-5098
5th row070-7543-4619
ValueCountFrequency (%)
051-123-1234 3
 
1.2%
051-508-2923 1
 
0.4%
051-817-8547 1
 
0.4%
051-333-1621 1
 
0.4%
051-508-1133 1
 
0.4%
051-897-2633 1
 
0.4%
051-894-9991 1
 
0.4%
070-7546-4926 1
 
0.4%
051-816-9055 1
 
0.4%
051-529-6692 1
 
0.4%
Other values (240) 240
95.2%
2024-04-19T15:45:37.786050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 504
16.5%
0 476
15.6%
5 423
13.9%
1 413
13.6%
2 209
6.9%
3 198
 
6.5%
6 192
 
6.3%
7 181
 
5.9%
4 164
 
5.4%
8 153
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2543
83.5%
Dash Punctuation 504
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 476
18.7%
5 423
16.6%
1 413
16.2%
2 209
8.2%
3 198
7.8%
6 192
7.6%
7 181
 
7.1%
4 164
 
6.4%
8 153
 
6.0%
9 134
 
5.3%
Dash Punctuation
ValueCountFrequency (%)
- 504
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3047
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 504
16.5%
0 476
15.6%
5 423
13.9%
1 413
13.6%
2 209
6.9%
3 198
 
6.5%
6 192
 
6.3%
7 181
 
5.9%
4 164
 
5.4%
8 153
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3047
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 504
16.5%
0 476
15.6%
5 423
13.9%
1 413
13.6%
2 209
6.9%
3 198
 
6.5%
6 192
 
6.3%
7 181
 
5.9%
4 164
 
5.4%
8 153
 
5.0%

lat
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct242
Distinct (%)97.6%
Missing4
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean35.160901
Minimum35.031229
Maximum35.339603
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-19T15:45:37.914579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.031229
5-th percentile35.07708
Q135.123086
median35.162769
Q335.199472
95-th percentile35.2459
Maximum35.339603
Range0.3083744
Interquartile range (IQR)0.076385773

Descriptive statistics

Standard deviation0.055698851
Coefficient of variation (CV)0.0015841133
Kurtosis0.33499554
Mean35.160901
Median Absolute Deviation (MAD)0.038756617
Skewness0.34117133
Sum8719.9035
Variance0.003102362
MonotonicityNot monotonic
2024-04-19T15:45:38.041179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.103758 2
 
0.8%
35.21359 2
 
0.8%
35.12308572674206 2
 
0.8%
35.0839222289 2
 
0.8%
35.1473429729 2
 
0.8%
35.210417 2
 
0.8%
35.20009 1
 
0.4%
35.2294405 1
 
0.4%
35.257562 1
 
0.4%
35.293395 1
 
0.4%
Other values (232) 232
92.1%
(Missing) 4
 
1.6%
ValueCountFrequency (%)
35.031229 1
0.4%
35.042547 1
0.4%
35.049109 1
0.4%
35.053556 1
0.4%
35.055065 1
0.4%
35.059122 1
0.4%
35.060213 1
0.4%
35.061277 1
0.4%
35.065157 1
0.4%
35.066334 1
0.4%
ValueCountFrequency (%)
35.3396034 1
0.4%
35.32552426 1
0.4%
35.32062941 1
0.4%
35.32004502 1
0.4%
35.31935363 1
0.4%
35.293395 1
0.4%
35.293337 1
0.4%
35.266354 1
0.4%
35.259886 1
0.4%
35.257562 1
0.4%

lng
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct242
Distinct (%)97.6%
Missing4
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean129.05756
Minimum128.81438
Maximum129.22567
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-19T15:45:38.176207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.81438
5-th percentile128.97271
Q1129.01362
median129.05511
Q3129.09635
95-th percentile129.17124
Maximum129.22567
Range0.411286
Interquartile range (IQR)0.082731929

Descriptive statistics

Standard deviation0.06485995
Coefficient of variation (CV)0.00050256608
Kurtosis0.98910321
Mean129.05756
Median Absolute Deviation (MAD)0.04139186
Skewness0.021082917
Sum32006.274
Variance0.0042068131
MonotonicityNot monotonic
2024-04-19T15:45:38.326378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.980943 2
 
0.8%
129.06395 2
 
0.8%
129.09635114006093 2
 
0.8%
129.0122328501 2
 
0.8%
128.9983032999 2
 
0.8%
129.02707 2
 
0.8%
129.09743 1
 
0.4%
129.0762614 1
 
0.4%
129.090458 1
 
0.4%
129.100009 1
 
0.4%
Other values (232) 232
92.1%
(Missing) 4
 
1.6%
ValueCountFrequency (%)
128.814384 1
0.4%
128.830894 1
0.4%
128.878345 1
0.4%
128.902814 1
0.4%
128.956367 1
0.4%
128.959391 1
0.4%
128.960501 1
0.4%
128.964789 1
0.4%
128.967403 1
0.4%
128.970707 1
0.4%
ValueCountFrequency (%)
129.22567 1
0.4%
129.22531135 1
0.4%
129.21741075 1
0.4%
129.21574004 1
0.4%
129.21492066 1
0.4%
129.21066199 1
0.4%
129.205707 1
0.4%
129.18861625 1
0.4%
129.18705373 1
0.4%
129.184258 1
0.4%

gugun
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
부산광역시 해운대구
26 
부산광역시 사상구
24 
부산광역시 부산진구
24 
부산광역시 남구
24 
부산광역시 동래구
23 
Other values (12)
131 

Length

Max length10
Median length9
Mean length8.8531746
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 사상구
2nd row부산광역시 사상구
3rd row부산광역시 사상구
4th row부산광역시 사상구
5th row부산광역시 사상구

Common Values

ValueCountFrequency (%)
부산광역시 해운대구 26
10.3%
부산광역시 사상구 24
9.5%
부산광역시 부산진구 24
9.5%
부산광역시 남구 24
9.5%
부산광역시 동래구 23
9.1%
부산광역시 북구 21
8.3%
부산광역시 사하구 21
8.3%
부산광역시 동구 20
7.9%
부산광역시 연제구 13
 
5.2%
부산광역시 영도구 12
 
4.8%
Other values (7) 44
17.5%

Length

2024-04-19T15:45:38.446413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 250
49.8%
해운대구 26
 
5.2%
사상구 24
 
4.8%
부산진구 24
 
4.8%
남구 24
 
4.8%
동래구 23
 
4.6%
북구 21
 
4.2%
사하구 21
 
4.2%
동구 20
 
4.0%
연제구 13
 
2.6%
Other values (8) 56
 
11.2%

reference_date
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2020-07-31
177 
2020-08-27
26 
2020-09-04
20 
2020-08-21
 
13
2020-08-31
 
9
Other values (2)
 
7

Length

Max length10
Median length10
Mean length9.952381
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-07-31
2nd row2020-07-31
3rd row2020-07-31
4th row2020-07-31
5th row2020-07-31

Common Values

ValueCountFrequency (%)
2020-07-31 177
70.2%
2020-08-27 26
 
10.3%
2020-09-04 20
 
7.9%
2020-08-21 13
 
5.2%
2020-08-31 9
 
3.6%
2020-09-01 5
 
2.0%
<NA> 2
 
0.8%

Length

2024-04-19T15:45:38.566730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T15:45:38.683767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-07-31 177
70.2%
2020-08-27 26
 
10.3%
2020-09-04 20
 
7.9%
2020-08-21 13
 
5.2%
2020-08-31 9
 
3.6%
2020-09-01 5
 
2.0%
na 2
 
0.8%

instt_code
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3324325.4
Minimum3250000
Maximum3400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-19T15:45:38.804248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3270000
Q13290000
median3320000
Q33350000
95-th percentile3390000
Maximum3400000
Range150000
Interquartile range (IQR)60000

Descriptive statistics

Standard deviation41099.69
Coefficient of variation (CV)0.012363317
Kurtosis-0.89502331
Mean3324325.4
Median Absolute Deviation (MAD)30000
Skewness0.30119642
Sum8.3773 × 108
Variance1.6891845 × 109
MonotonicityNot monotonic
2024-04-19T15:45:38.908052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3310000 26
10.3%
3330000 26
10.3%
3390000 24
9.5%
3290000 24
9.5%
3300000 23
9.1%
3320000 21
8.3%
3340000 21
8.3%
3270000 20
7.9%
3370000 13
 
5.2%
3280000 12
 
4.8%
Other values (6) 42
16.7%
ValueCountFrequency (%)
3250000 5
 
2.0%
3260000 7
 
2.8%
3270000 20
7.9%
3280000 12
4.8%
3290000 24
9.5%
3300000 23
9.1%
3310000 26
10.3%
3320000 21
8.3%
3330000 26
10.3%
3340000 21
8.3%
ValueCountFrequency (%)
3400000 11
4.4%
3390000 24
9.5%
3380000 9
 
3.6%
3370000 13
5.2%
3360000 5
 
2.0%
3350000 5
 
2.0%
3340000 21
8.3%
3330000 26
10.3%
3320000 21
8.3%
3310000 26
10.3%

last_load_dttm
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2020-12-22 13:38:23
252 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-22 13:38:23
2nd row2020-12-22 13:38:23
3rd row2020-12-22 13:38:23
4th row2020-12-22 13:38:23
5th row2020-12-22 13:38:23

Common Values

ValueCountFrequency (%)
2020-12-22 13:38:23 252
100.0%

Length

2024-04-19T15:45:39.022068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T15:45:39.102282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-22 252
50.0%
13:38:23 252
50.0%

Interactions

2024-04-19T15:45:32.027423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:27.798746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:28.553851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:29.236034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:29.828960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:30.440745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:31.361756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:32.172576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:27.941511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:28.647085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:29.316907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:29.910761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:30.524338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:31.443280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:32.271213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:28.064049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:28.755529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:29.396121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:29.990517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:30.607303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:31.545992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:32.358941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:28.176963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:28.858606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:29.482029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:30.072745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:30.689603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:31.642458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:32.440185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:28.282038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:28.970831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:29.571728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:30.181199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:30.766528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:31.720883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:32.527669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:28.374460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:29.059229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:29.657720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:30.266776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:30.868052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:31.807253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:32.614233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:28.457905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:29.140800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:29.736423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:30.344943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:30.952455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:45:31.899112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-19T15:45:39.162049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeynum_of_peoplepresent_num_of_ppnum_of_workertype_of_facilitylatlnggugunreference_dateinstt_code
skey1.0000.1850.0000.0000.4170.6610.7290.9660.8760.879
num_of_people0.1851.0000.9280.7950.8600.3900.2460.5560.0000.530
present_num_of_pp0.0000.9281.0000.8210.6820.3960.3200.4320.0000.557
num_of_worker0.0000.7950.8211.0000.6620.3970.2070.5620.0000.463
type_of_facility0.4170.8600.6820.6621.0000.2110.6290.5730.4480.458
lat0.6610.3900.3960.3970.2111.0000.7520.8750.5420.841
lng0.7290.2460.3200.2070.6290.7521.0000.9000.8180.880
gugun0.9660.5560.4320.5620.5730.8750.9001.0001.0001.000
reference_date0.8760.0000.0000.0000.4480.5420.8181.0001.0000.908
instt_code0.8790.5300.5570.4630.4580.8410.8801.0000.9081.000
2024-04-19T15:45:39.268742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
type_of_facilityreference_dategugun
type_of_facility1.0000.2400.274
reference_date0.2401.0000.979
gugun0.2740.9791.000
2024-04-19T15:45:39.357931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeynum_of_peoplepresent_num_of_ppnum_of_workerlatlnginstt_codetype_of_facilitygugunreference_date
skey1.0000.0890.042-0.0090.0830.1940.1110.1430.8460.665
num_of_people0.0891.0000.8860.290-0.102-0.041-0.0860.4430.2600.000
present_num_of_pp0.0420.8861.0000.213-0.048-0.053-0.0780.3940.1830.000
num_of_worker-0.0090.2900.2131.000-0.112-0.056-0.0260.4000.2260.000
lat0.083-0.102-0.048-0.1121.0000.4540.3190.0960.5860.319
lng0.194-0.041-0.053-0.0560.4541.0000.0180.3410.6420.617
instt_code0.111-0.086-0.078-0.0260.3190.0181.0000.2180.9870.768
type_of_facility0.1430.4430.3940.4000.0960.3410.2181.0000.2740.240
gugun0.8460.2600.1830.2260.5860.6420.9870.2741.0000.979
reference_date0.6650.0000.0000.0000.3190.6170.7680.2400.9791.000

Missing values

2024-04-19T15:45:32.771728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-19T15:45:32.958071image/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.
2024-04-19T15:45:33.113267image/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

skeyfacility_nmfacility_manageraddrinstallation_datenum_of_peoplepresent_num_of_ppnum_of_workertype_of_facilitytellatlnggugunreference_dateinstt_codelast_load_dttm
01717샘물지역아동센터박종칠부산광역시 사상구 엄궁북로 4번길 32-10 (엄궁동)2005-12-0723212지역아동센터051-322-067535.127663128.971123부산광역시 사상구2020-07-3133900002020-12-22 13:38:23
11718주례지역아동센터유재용부산광역시 사상구 가야대로 366번길 63-6 (주례동)2011-06-2419172지역아동센터070-7763-164935.148144129.013284부산광역시 사상구2020-07-3133900002020-12-22 13:38:23
21719학장지역아동센터최인금부산광역시 사상구 학감대로 49번길 28-70 (학장동) 학장종합사회복지관 3층2005-03-0725242지역아동센터051-311-401435.138652128.989744부산광역시 사상구2020-07-3133900002020-12-22 13:38:23
31720희망지역아동센터김진아부산광역시 사상구 사상로 275 (덕포동)2015-01-0819182지역아동센터051-304-509835.169386128.982244부산광역시 사상구2020-07-3133900002020-12-22 13:38:23
41721아가페지역아동센터진영은부산광역시 사상구 새벽시장로63번길 42(감전동, 2층)2020.04.0819192지역아동센터070-7543-461935.156991128.981484부산광역시 사상구2020-07-3133900002020-12-22 13:38:23
51722천사의집최민애부산광역시 사상구 엄궁북로 61, 401동 102호(엄궁동, 엄궁롯데캐슬아파트)2002-03-26763공동생활가정051-322-744235.128171128.977322부산광역시 사상구2020-07-3133900002020-12-22 13:38:23
61723예그린공동생활가정이은희부산광역시 사상구 대동로 98, 105동 102호(학장동,학장반도보라타운)2014-10-14774공동생활가정051-321-021635.13729128.979171부산광역시 사상구2020-07-3133900002020-12-22 13:38:23
71724에바다드림홈황용점부산광역시 사상구 동주로16번길 43(주례동)2014-12-31773공동생활가정051-322-971735.147343128.998303부산광역시 사상구2020-07-3133900002020-12-22 13:38:23
81725에바다리더홈조진선부산광역시 사상구 동주로16번길 43(주례동)2014-12-31773공동생활가정051-322-971635.147343128.998303부산광역시 사상구2020-07-3133900002020-12-22 13:38:23
91726사상구 다함께돌봄센터김남희부산광역시 사상구 낙동대로776번길 37(엄궁동)2019-03-0730252다함께돌봄센터051-316-266035.129088128.973139부산광역시 사상구2020-07-3133900002020-12-22 13:38:23
skeyfacility_nmfacility_manageraddrinstallation_datenum_of_peoplepresent_num_of_ppnum_of_workertype_of_facilitytellatlnggugunreference_dateinstt_codelast_load_dttm
2421845엘림김양자부산광역시 동구 초량상로79번길 27-4 (초량동)2004-10-0132323지역아동센터051-466-388035.119334129.03682부산광역시 동구2020-09-0432700002020-12-22 13:38:23
2431846미애원이광열부산광역시 동구 망양로 641 (수정동)1957-12-26411510아동양육시설051-468-029335.126199129.03651부산광역시 동구2020-09-0432700002020-12-22 13:38:23
2441847랄랄라가족그룹홈이승준부산광역시 동구 망양로476번길 11-1 (초량동)2009-06-18733공동생활가정051-464-827135.11649129.034532부산광역시 동구2020-09-0432700002020-12-22 13:38:23
2451888행복지역아동센터장준표부산광역시 기장군 기장읍 차성동로122번길 82008-10-3035253지역아동센터051-721-497735.248418129.217411부산광역시 기장군2020-07-3134000002020-12-22 13:38:23
2461889교리지역아동센터이진호부산광역시 기장군 기장읍 차성로417번길 25, 2층2010-04-2335353지역아동센터051-724-090735.256466129.21574부산광역시 기장군2020-07-3134000002020-12-22 13:38:23
2471890신정LCC지역아동센터남일식부산광역시 기장군 정관읍 용수로 102010-10-2137323지역아동센터051-727-019135.325524129.179798부산광역시 기장군2020-07-3134000002020-12-22 13:38:23
2481891이레지역아동센터박이숙부산광역시 기장군 정관읍 구연방곡로 10, 106동 103호 (정관 센트럴파크)2010-11-1519362지역아동센터051-724-007935.320045129.187054부산광역시 기장군2020-07-3134000002020-12-22 13:38:23
2491892꿈나무지역아동센터김창호부산광역시 기장군 기장읍 대청로36번길 11-122008-09-0129192지역아동센터051-723-121335.236924129.214921부산광역시 기장군2020-07-3134000002020-12-22 13:38:23
2501893무양지역아동센터김효진부산광역시 기장군 기장읍 대변로 117-3, A동 202호 (삼화그린빌라)2011-06-22981지역아동센터051-723-062135.228013129.225311부산광역시 기장군2020-07-3134000002020-12-22 13:38:23
2511894모전지역아동센터윤미영부산광역시 기장군 정관읍 정관1로 18, 105동 101호 (이지더원1차)2016-09-2819192지역아동센터051-728-039935.339603129.164009부산광역시 기장군2020-07-3134000002020-12-22 13:38:23