Overview

Dataset statistics

Number of variables13
Number of observations116
Missing cells72
Missing cells (%)4.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.4 KiB
Average record size in memory109.1 B

Variable types

Text5
Categorical4
Numeric4

Dataset

Description시설명,시설코드,시설종류명(시설유형),시설종류상세명(시설종류),자치구(시)구분,시설장명,시군구코드,시군구명,시설주소,정원(수용인원),현인원,전화번호,우편번호
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-20412/S/1/datasetView.do

Alerts

시설종류상세명(시설종류) has constant value ""Constant
자치구(시)구분 has constant value ""Constant
시군구코드 is highly overall correlated with 우편번호 and 1 other fieldsHigh correlation
우편번호 is highly overall correlated with 시군구코드High correlation
시군구명 is highly overall correlated with 시군구코드High correlation
정원(수용인원) has 40 (34.5%) missing valuesMissing
현인원 has 32 (27.6%) missing valuesMissing
시설코드 has unique valuesUnique
정원(수용인원) has 35 (30.2%) zerosZeros
현인원 has 4 (3.4%) zerosZeros

Reproduction

Analysis started2024-05-11 00:52:50.427768
Analysis finished2024-05-11 00:52:57.761736
Duration7.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설명
Text

Distinct112
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-05-11T00:52:58.159387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length9.7327586
Min length6

Characters and Unicode

Total characters1129
Distinct characters154
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique109 ?
Unique (%)94.0%

Sample

1st row성북구립 상월곡실버복지센터
2nd row서초구립양재노인종합복지관
3rd row강서구립봉제산노인복지센터
4th row시립중랑노인종합복지관
5th row강남구립강남노인종합복지관
ValueCountFrequency (%)
성북구립 5
 
3.9%
금천한내어르신복지센터 3
 
2.4%
수락노인종합복지관 2
 
1.6%
금천어르신복지센터 2
 
1.6%
구립 2
 
1.6%
성동구립 1
 
0.8%
시니어아카데미 1
 
0.8%
시흥중앙교회 1
 
0.8%
인왕노인복지관 1
 
0.8%
압구정노인복지관 1
 
0.8%
Other values (108) 108
85.0%
2024-05-11T00:52:59.200615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 93
 
8.2%
복 92
 
8.1%
노 73
 
6.5%
인 72
 
6.4%
관 71
 
6.3%
립 41
 
3.6%
구 36
 
3.2%
종 34
 
3.0%
합 33
 
2.9%
터 32
 
2.8%
Other values (144) 552
48.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1093
96.8%
Decimal Number 23
 
2.0%
Space Separator 11
 
1.0%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 93
 
8.5%
복 92
 
8.4%
노 73
 
6.7%
인 72
 
6.6%
관 71
 
6.5%
립 41
 
3.8%
구 36
 
3.3%
종 34
 
3.1%
합 33
 
3.0%
터 32
 
2.9%
Other values (135) 516
47.2%
Decimal Number
ValueCountFrequency (%)
0 6
26.1%
5 5
21.7%
2 4
17.4%
1 4
17.4%
3 3
13.0%
7 1
 
4.3%
Space Separator
ValueCountFrequency (%)
11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1093
96.8%
Common 36
 
3.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 93
 
8.5%
복 92
 
8.4%
노 73
 
6.7%
인 72
 
6.6%
관 71
 
6.5%
립 41
 
3.8%
구 36
 
3.3%
종 34
 
3.1%
합 33
 
3.0%
터 32
 
2.9%
Other values (135) 516
47.2%
Common
ValueCountFrequency (%)
11
30.6%
0 6
16.7%
5 5
13.9%
2 4
 
11.1%
1 4
 
11.1%
3 3
 
8.3%
7 1
 
2.8%
( 1
 
2.8%
) 1
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1093
96.8%
ASCII 36
 
3.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 93
 
8.5%
복 92
 
8.4%
노 73
 
6.7%
인 72
 
6.6%
관 71
 
6.5%
립 41
 
3.8%
구 36
 
3.3%
종 34
 
3.1%
합 33
 
3.0%
터 32
 
2.9%
Other values (135) 516
47.2%
ASCII
ValueCountFrequency (%)
11
30.6%
0 6
16.7%
5 5
13.9%
2 4
 
11.1%
1 4
 
11.1%
3 3
 
8.3%
7 1
 
2.8%
( 1
 
2.8%
) 1
 
2.8%

시설코드
Text

UNIQUE 

Distinct116
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-05-11T00:53:00.472884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length5
Mean length5.1034483
Min length4

Characters and Unicode

Total characters592
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique116 ?
Unique (%)100.0%

Sample

1st rowA0495
2nd rowA0724
3rd rowA0922
4th rowA1022
5th rowA1165
ValueCountFrequency (%)
a0495 1
 
0.9%
f14041 1
 
0.9%
g3369 1
 
0.9%
g2855 1
 
0.9%
g2529 1
 
0.9%
g2071 1
 
0.9%
g1783 1
 
0.9%
g1782 1
 
0.9%
g1685 1
 
0.9%
g1437 1
 
0.9%
Other values (106) 106
91.4%
2024-05-11T00:53:01.977095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 70
11.8%
A 61
10.3%
0 56
9.5%
7 51
8.6%
4 49
8.3%
9 48
8.1%
1 47
7.9%
6 44
7.4%
5 44
7.4%
3 40
6.8%
Other values (7) 82
13.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 476
80.4%
Uppercase Letter 116
 
19.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 70
14.7%
0 56
11.8%
7 51
10.7%
4 49
10.3%
9 48
10.1%
1 47
9.9%
6 44
9.2%
5 44
9.2%
3 40
8.4%
8 27
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
A 61
52.6%
G 29
25.0%
F 12
 
10.3%
T 9
 
7.8%
P 3
 
2.6%
C 1
 
0.9%
Z 1
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 476
80.4%
Latin 116
 
19.6%

Most frequent character per script

Common
ValueCountFrequency (%)
2 70
14.7%
0 56
11.8%
7 51
10.7%
4 49
10.3%
9 48
10.1%
1 47
9.9%
6 44
9.2%
5 44
9.2%
3 40
8.4%
8 27
 
5.7%
Latin
ValueCountFrequency (%)
A 61
52.6%
G 29
25.0%
F 12
 
10.3%
T 9
 
7.8%
P 3
 
2.6%
C 1
 
0.9%
Z 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 70
11.8%
A 61
10.3%
0 56
9.5%
7 51
8.6%
4 49
8.3%
9 48
8.1%
1 47
7.9%
6 44
7.4%
5 44
7.4%
3 40
6.8%
Other values (7) 82
13.9%

시설종류명(시설유형)
Categorical

Distinct3
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
(노인) 노인복지관(소규모)
50 
(노인) 노인복지관
47 
(노인) 노인교실
19 

Length

Max length15
Median length10
Mean length11.991379
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(노인) 노인복지관(소규모)
2nd row(노인) 노인복지관
3rd row(노인) 노인복지관(소규모)
4th row(노인) 노인복지관
5th row(노인) 노인복지관

Common Values

ValueCountFrequency (%)
(노인) 노인복지관(소규모) 50
43.1%
(노인) 노인복지관 47
40.5%
(노인) 노인교실 19
 
16.4%

Length

2024-05-11T00:53:02.443950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T00:53:02.910226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노인 116
50.0%
노인복지관(소규모 50
21.6%
노인복지관 47
20.3%
노인교실 19
 
8.2%

시설종류상세명(시설종류)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
노인여가복지시설
116 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
노인여가복지시설 116
100.0%

Length

2024-05-11T00:53:03.417036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T00:53:03.762146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노인여가복지시설 116
100.0%

자치구(시)구분
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
자치구
116 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
자치구 116
100.0%

Length

2024-05-11T00:53:04.305508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T00:53:04.639395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자치구 116
100.0%

시설장명
Text

Distinct100
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-05-11T00:53:05.410124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.1034483
Min length2

Characters and Unicode

Total characters360
Distinct characters110
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)78.4%

Sample

1st row김경회
2nd row전경아
3rd row서순애
4th row최귀선
5th row고영한
ValueCountFrequency (%)
재단테스트 6
 
5.2%
조영표 4
 
3.4%
김종가 3
 
2.6%
오주호 2
 
1.7%
김동호 2
 
1.7%
범현 2
 
1.7%
장지연 2
 
1.7%
김미성 2
 
1.7%
최은영 2
 
1.7%
이계철 1
 
0.9%
Other values (90) 90
77.6%
2024-05-11T00:53:06.824955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 30
 
8.3%
이 18
 
5.0%
영 18
 
5.0%
박 12
 
3.3%
현 11
 
3.1%
은 10
 
2.8%
재 9
 
2.5%
성 9
 
2.5%
조 8
 
2.2%
경 7
 
1.9%
Other values (100) 228
63.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 359
99.7%
Space Separator 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 30
 
8.4%
이 18
 
5.0%
영 18
 
5.0%
박 12
 
3.3%
현 11
 
3.1%
은 10
 
2.8%
재 9
 
2.5%
성 9
 
2.5%
조 8
 
2.2%
경 7
 
1.9%
Other values (99) 227
63.2%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 359
99.7%
Common 1
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 30
 
8.4%
이 18
 
5.0%
영 18
 
5.0%
박 12
 
3.3%
현 11
 
3.1%
은 10
 
2.8%
재 9
 
2.5%
성 9
 
2.5%
조 8
 
2.2%
경 7
 
1.9%
Other values (99) 227
63.2%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 359
99.7%
ASCII 1
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 30
 
8.4%
이 18
 
5.0%
영 18
 
5.0%
박 12
 
3.3%
현 11
 
3.1%
은 10
 
2.8%
재 9
 
2.5%
성 9
 
2.5%
조 8
 
2.2%
경 7
 
1.9%
Other values (99) 227
63.2%
ASCII
ValueCountFrequency (%)
1
100.0%

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.145694 × 109
Minimum1.111 × 109
Maximum1.174 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-11T00:53:07.357206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.111 × 109
5-th percentile1.11925 × 109
Q11.132 × 109
median1.1485 × 109
Q31.159 × 109
95-th percentile1.16875 × 109
Maximum1.174 × 109
Range63000000
Interquartile range (IQR)27000000

Descriptive statistics

Standard deviation16626429
Coefficient of variation (CV)0.014512103
Kurtosis-1.0021621
Mean1.145694 × 109
Median Absolute Deviation (MAD)13500000
Skewness-0.21733658
Sum1.329005 × 1011
Variance2.7643814 × 1014
MonotonicityNot monotonic
2024-05-11T00:53:07.883503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1154500000 14
 
12.1%
1159000000 12
 
10.3%
1168000000 8
 
6.9%
1129000000 7
 
6.0%
1138000000 7
 
6.0%
1165000000 6
 
5.2%
1132000000 6
 
5.2%
1135000000 6
 
5.2%
1126000000 5
 
4.3%
1120000000 5
 
4.3%
Other values (15) 40
34.5%
ValueCountFrequency (%)
1111000000 2
 
1.7%
1114000000 2
 
1.7%
1117000000 2
 
1.7%
1120000000 5
4.3%
1121500000 1
 
0.9%
1123000000 1
 
0.9%
1126000000 5
4.3%
1129000000 7
6.0%
1130500000 2
 
1.7%
1132000000 6
5.2%
ValueCountFrequency (%)
1174000000 3
 
2.6%
1171000000 3
 
2.6%
1168000000 8
6.9%
1165000000 6
5.2%
1162000000 1
 
0.9%
1159000000 12
10.3%
1156000000 4
 
3.4%
1154500000 14
12.1%
1153000000 2
 
1.7%
1150000000 5
 
4.3%

시군구명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
금천구
14 
동작구
12 
강남구
8 
은평구
7 
성북구
7 
Other values (20)
68 

Length

Max length4
Median length3
Mean length3.0603448
Min length2

Unique

Unique3 ?
Unique (%)2.6%

Sample

1st row성북구
2nd row서초구
3rd row강서구
4th row중랑구
5th row강남구

Common Values

ValueCountFrequency (%)
금천구 14
 
12.1%
동작구 12
 
10.3%
강남구 8
 
6.9%
은평구 7
 
6.0%
성북구 7
 
6.0%
도봉구 6
 
5.2%
서초구 6
 
5.2%
노원구 6
 
5.2%
성동구 5
 
4.3%
중랑구 5
 
4.3%
Other values (15) 40
34.5%

Length

2024-05-11T00:53:08.528979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
금천구 14
 
12.1%
동작구 12
 
10.3%
강남구 8
 
6.9%
은평구 7
 
6.0%
성북구 7
 
6.0%
도봉구 6
 
5.2%
서초구 6
 
5.2%
노원구 6
 
5.2%
성동구 5
 
4.3%
중랑구 5
 
4.3%
Other values (15) 40
34.5%

시설주소
Text

Distinct106
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-05-11T00:53:09.461556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length37
Mean length25.827586
Min length17

Characters and Unicode

Total characters2996
Distinct characters189
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)88.8%

Sample

1st row서울특별시 성북구 화랑로18길 6 (상월곡동)
2nd row서울특별시 서초구 강남대로30길 73-7양재노인종합복지관 (양재동)
3rd row서울특별시 강서구 초록마을로15길 12
4th row서울특별시 중랑구 겸재로9길 45(면목동)
5th row서울특별시 강남구 삼성로 628강남노인종합복지관 (삼성동)
ValueCountFrequency (%)
서울특별시 116
 
21.6%
금천구 14
 
2.6%
동작구 12
 
2.2%
노량진로32길79 9
 
1.7%
본동 9
 
1.7%
독산동 8
 
1.5%
강남구 8
 
1.5%
성북구 7
 
1.3%
은평구 7
 
1.3%
도봉구 6
 
1.1%
Other values (269) 342
63.6%
2024-05-11T00:53:10.997144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
427
 
14.3%
서 143
 
4.8%
시 129
 
4.3%
구 124
 
4.1%
동 119
 
4.0%
울 118
 
3.9%
별 118
 
3.9%
특 118
 
3.9%
로 116
 
3.9%
길 88
 
2.9%
Other values (179) 1496
49.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1900
63.4%
Decimal Number 471
 
15.7%
Space Separator 427
 
14.3%
Close Punctuation 79
 
2.6%
Open Punctuation 79
 
2.6%
Dash Punctuation 25
 
0.8%
Other Punctuation 14
 
0.5%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 143
 
7.5%
시 129
 
6.8%
구 124
 
6.5%
동 119
 
6.3%
울 118
 
6.2%
별 118
 
6.2%
특 118
 
6.2%
로 116
 
6.1%
길 88
 
4.6%
강 28
 
1.5%
Other values (163) 799
42.1%
Decimal Number
ValueCountFrequency (%)
1 84
17.8%
2 70
14.9%
3 60
12.7%
5 47
10.0%
7 41
8.7%
6 41
8.7%
9 37
7.9%
8 33
 
7.0%
0 30
 
6.4%
4 28
 
5.9%
Space Separator
ValueCountFrequency (%)
427
100.0%
Close Punctuation
ValueCountFrequency (%)
) 79
100.0%
Open Punctuation
ValueCountFrequency (%)
( 79
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%
Other Punctuation
ValueCountFrequency (%)
, 14
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1900
63.4%
Common 1096
36.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 143
 
7.5%
시 129
 
6.8%
구 124
 
6.5%
동 119
 
6.3%
울 118
 
6.2%
별 118
 
6.2%
특 118
 
6.2%
로 116
 
6.1%
길 88
 
4.6%
강 28
 
1.5%
Other values (163) 799
42.1%
Common
ValueCountFrequency (%)
427
39.0%
1 84
 
7.7%
) 79
 
7.2%
( 79
 
7.2%
2 70
 
6.4%
3 60
 
5.5%
5 47
 
4.3%
7 41
 
3.7%
6 41
 
3.7%
9 37
 
3.4%
Other values (6) 131
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1900
63.4%
ASCII 1096
36.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
427
39.0%
1 84
 
7.7%
) 79
 
7.2%
( 79
 
7.2%
2 70
 
6.4%
3 60
 
5.5%
5 47
 
4.3%
7 41
 
3.7%
6 41
 
3.7%
9 37
 
3.4%
Other values (6) 131
 
12.0%
Hangul
ValueCountFrequency (%)
서 143
 
7.5%
시 129
 
6.8%
구 124
 
6.5%
동 119
 
6.3%
울 118
 
6.2%
별 118
 
6.2%
특 118
 
6.2%
로 116
 
6.1%
길 88
 
4.6%
강 28
 
1.5%
Other values (163) 799
42.1%

정원(수용인원)
Real number (ℝ)

MISSING  ZEROS 

Distinct25
Distinct (%)32.9%
Missing40
Missing (%)34.5%
Infinite0
Infinite (%)0.0%
Mean863.89474
Minimum0
Maximum18000
Zeros35
Zeros (%)30.2%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-11T00:53:11.490770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17
Q3571.25
95-th percentile2350
Maximum18000
Range18000
Interquartile range (IQR)571.25

Descriptive statistics

Standard deviation2764.3942
Coefficient of variation (CV)3.1999202
Kurtosis25.410545
Mean863.89474
Median Absolute Deviation (MAD)17
Skewness4.9245377
Sum65656
Variance7641875.4
MonotonicityNot monotonic
2024-05-11T00:53:11.992066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 35
30.2%
17 9
 
7.8%
1000 5
 
4.3%
300 3
 
2.6%
700 2
 
1.7%
110 2
 
1.7%
250 2
 
1.7%
1986 1
 
0.9%
2500 1
 
0.9%
400 1
 
0.9%
Other values (15) 15
 
12.9%
(Missing) 40
34.5%
ValueCountFrequency (%)
0 35
30.2%
17 9
 
7.8%
27 1
 
0.9%
80 1
 
0.9%
110 2
 
1.7%
190 1
 
0.9%
250 2
 
1.7%
300 3
 
2.6%
400 1
 
0.9%
470 1
 
0.9%
ValueCountFrequency (%)
18000 1
0.9%
13000 1
0.9%
10140 1
0.9%
2500 1
0.9%
2300 1
0.9%
2000 1
0.9%
1986 1
0.9%
1695 1
0.9%
1300 1
0.9%
1205 1
0.9%

현인원
Real number (ℝ)

MISSING  ZEROS 

Distinct53
Distinct (%)63.1%
Missing32
Missing (%)27.6%
Infinite0
Infinite (%)0.0%
Mean1655.6786
Minimum0
Maximum20980
Zeros4
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-11T00:53:12.534914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.7
Q1200
median500
Q31240
95-th percentile7538.7
Maximum20980
Range20980
Interquartile range (IQR)1040

Descriptive statistics

Standard deviation3737.9529
Coefficient of variation (CV)2.2576561
Kurtosis16.625898
Mean1655.6786
Median Absolute Deviation (MAD)400
Skewness4.0397867
Sum139077
Variance13972292
MonotonicityNot monotonic
2024-05-11T00:53:13.128335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 7
 
6.0%
250 6
 
5.2%
0 4
 
3.4%
1000 4
 
3.4%
350 3
 
2.6%
500 3
 
2.6%
120 3
 
2.6%
100 2
 
1.7%
1220 2
 
1.7%
400 2
 
1.7%
Other values (43) 48
41.4%
(Missing) 32
27.6%
ValueCountFrequency (%)
0 4
3.4%
27 1
 
0.9%
45 1
 
0.9%
60 1
 
0.9%
80 1
 
0.9%
100 2
1.7%
110 1
 
0.9%
120 3
2.6%
130 2
1.7%
150 1
 
0.9%
ValueCountFrequency (%)
20980 1
0.9%
18448 1
0.9%
18000 1
0.9%
10140 1
0.9%
8052 1
0.9%
4630 1
0.9%
4020 1
0.9%
3240 1
0.9%
3200 1
0.9%
2880 1
0.9%

전화번호
Text

Distinct105
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-05-11T00:53:13.880857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length11.094828
Min length9

Characters and Unicode

Total characters1287
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)87.1%

Sample

1st row02-963-1082
2nd row02-578-1515
3rd row02-2605-1553
4th row02-493-9966
5th row02-549-7070
ValueCountFrequency (%)
070-7163-0907 9
 
7.8%
02-892-5060 2
 
1.7%
02-2116-3753 2
 
1.7%
028975104 2
 
1.7%
02-866-1144 1
 
0.9%
02-806-1120 1
 
0.9%
0226433352 1
 
0.9%
02-707-1006 1
 
0.9%
02-733-9225 1
 
0.9%
02-435-0842 1
 
0.9%
Other values (95) 95
81.9%
2024-05-11T00:53:15.394310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 254
19.7%
2 190
14.8%
- 183
14.2%
9 95
 
7.4%
3 89
 
6.9%
6 88
 
6.8%
7 85
 
6.6%
1 83
 
6.4%
5 80
 
6.2%
8 70
 
5.4%
Other values (2) 70
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1103
85.7%
Dash Punctuation 183
 
14.2%
Close Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 254
23.0%
2 190
17.2%
9 95
 
8.6%
3 89
 
8.1%
6 88
 
8.0%
7 85
 
7.7%
1 83
 
7.5%
5 80
 
7.3%
8 70
 
6.3%
4 69
 
6.3%
Dash Punctuation
ValueCountFrequency (%)
- 183
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 254
19.7%
2 190
14.8%
- 183
14.2%
9 95
 
7.4%
3 89
 
6.9%
6 88
 
6.8%
7 85
 
6.6%
1 83
 
6.4%
5 80
 
6.2%
8 70
 
5.4%
Other values (2) 70
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 254
19.7%
2 190
14.8%
- 183
14.2%
9 95
 
7.4%
3 89
 
6.9%
6 88
 
6.8%
7 85
 
6.6%
1 83
 
6.4%
5 80
 
6.2%
8 70
 
5.4%
Other values (2) 70
 
5.4%

우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct103
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16398.793
Minimum1082
Maximum158600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-11T00:53:15.895604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1082
5-th percentile1404.5
Q13269.25
median5729
Q37348.75
95-th percentile153028.5
Maximum158600
Range157518
Interquartile range (IQR)4079.5

Descriptive statistics

Standard deviation39346.914
Coefficient of variation (CV)2.3993786
Kurtosis8.4850095
Mean16398.793
Median Absolute Deviation (MAD)2277.5
Skewness3.2006601
Sum1902260
Variance1.5481796 × 109
MonotonicityNot monotonic
2024-05-11T00:53:16.395393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6908 9
 
7.8%
6793 2
 
1.7%
7342 2
 
1.7%
1616 2
 
1.7%
153600 2
 
1.7%
8585 2
 
1.7%
8557 1
 
0.9%
3635 1
 
0.9%
2191 1
 
0.9%
8624 1
 
0.9%
Other values (93) 93
80.2%
ValueCountFrequency (%)
1082 1
0.9%
1156 1
0.9%
1305 1
0.9%
1314 1
0.9%
1370 1
0.9%
1394 1
0.9%
1408 1
0.9%
1480 1
0.9%
1616 2
1.7%
1697 1
0.9%
ValueCountFrequency (%)
158600 1
0.9%
157928 1
0.9%
157884 1
0.9%
153847 1
0.9%
153600 2
1.7%
152838 1
0.9%
135822 1
0.9%
134878 1
0.9%
8708 1
0.9%
8656 1
0.9%

Interactions

2024-05-11T00:52:55.231755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:52:52.262958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:52:53.309933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:52:54.326223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:52:55.515280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:52:52.508168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:52:53.580704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:52:54.520344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:52:55.788830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:52:52.765918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:52:53.847952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:52:54.719102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:52:56.047421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:52:53.015893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:52:54.105931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:52:54.883243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T00:53:16.812852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설종류명(시설유형)시설장명시군구코드시군구명정원(수용인원)현인원우편번호
시설종류명(시설유형)1.0000.8770.3400.5600.0000.1680.207
시설장명0.8771.0000.9940.9971.0001.0000.000
시군구코드0.3400.9941.0001.0000.4580.2850.461
시군구명0.5600.9971.0001.0000.1950.2610.366
정원(수용인원)0.0001.0000.4580.1951.0000.7930.000
현인원0.1681.0000.2850.2610.7931.0000.000
우편번호0.2070.0000.4610.3660.0000.0001.000
2024-05-11T00:53:17.335394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명시설종류명(시설유형)
시군구명1.0000.310
시설종류명(시설유형)0.3101.000
2024-05-11T00:53:17.692631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드정원(수용인원)현인원우편번호시설종류명(시설유형)시군구명
시군구코드1.0000.0870.0020.6210.2230.927
정원(수용인원)0.0871.0000.308-0.0920.0000.054
현인원0.0020.3081.000-0.2700.1070.082
우편번호0.621-0.092-0.2701.0000.0590.183
시설종류명(시설유형)0.2230.0000.1070.0591.0000.310
시군구명0.9270.0540.0820.1830.3101.000

Missing values

2024-05-11T00:52:56.472953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T00:52:57.077781image/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.
2024-05-11T00:52:57.558826image/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

시설명시설코드시설종류명(시설유형)시설종류상세명(시설종류)자치구(시)구분시설장명시군구코드시군구명시설주소정원(수용인원)현인원전화번호우편번호
0성북구립 상월곡실버복지센터A0495(노인) 노인복지관(소규모)노인여가복지시설자치구김경회1129000000성북구서울특별시 성북구 화랑로18길 6 (상월곡동)<NA>20002-963-10822793
1서초구립양재노인종합복지관A0724(노인) 노인복지관노인여가복지시설자치구전경아1165000000서초구서울특별시 서초구 강남대로30길 73-7양재노인종합복지관 (양재동)<NA>122002-578-15156741
2강서구립봉제산노인복지센터A0922(노인) 노인복지관(소규모)노인여가복지시설자치구서순애1150000000강서구서울특별시 강서구 초록마을로15길 12<NA>25002-2605-15537676
3시립중랑노인종합복지관A1022(노인) 노인복지관노인여가복지시설자치구최귀선1126000000중랑구서울특별시 중랑구 겸재로9길 45(면목동)180001800002-493-99662134
4강남구립강남노인종합복지관A1165(노인) 노인복지관노인여가복지시설자치구고영한1168000000강남구서울특별시 강남구 삼성로 628강남노인종합복지관 (삼성동)<NA>115002-549-70706085
5강북노인종합복지관A1174(노인) 노인복지관노인여가복지시설자치구김나현1130500000강북구서울특별시 강북구 삼양로92길 40-0강북노인종합복지관808002-999-91791082
6북가좌노인복지관A1599(노인) 노인복지관(소규모)노인여가복지시설자치구이은희1141000000서대문구서울특별시 서대문구 증가로20길 43북가좌2동노인복지센터11011002-376-50403668
7양천어르신종합복지관A1732(노인) 노인복지관노인여가복지시설자치구한승호1147000000양천구서울특별시 양천구 목동로3길 106서울특별시 양천구 목동로3길106 (신정동)<NA><NA>02-2649-88158098
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9시립은평노인종합복지관A2077(노인) 노인복지관노인여가복지시설자치구이지은1138000000은평구서울특별시 은평구 연서로 4150160002-385-13513310
시설명시설코드시설종류명(시설유형)시설종류상세명(시설종류)자치구(시)구분시설장명시군구코드시군구명시설주소정원(수용인원)현인원전화번호우편번호
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107테스트재노인2T222(노인) 노인복지관노인여가복지시설자치구재단테스트1159000000동작구서울특별시 동작구 노량진로32길79 (본동)17<NA>070-7163-09076908
108테스트재노인3T223(노인) 노인복지관노인여가복지시설자치구재단테스트1159000000동작구서울특별시 동작구 노량진로32길79 (본동)17<NA>070-7163-09076908
109테스트재노인소1T224(노인) 노인복지관(소규모)노인여가복지시설자치구재단테스트1159000000동작구서울특별시 동작구 노량진로32길79 (본동)17<NA>070-7163-09076908
110테스트재노인소2T225(노인) 노인복지관(소규모)노인여가복지시설자치구재단테스트1159000000동작구서울특별시 동작구 노량진로32길79 (본동)17<NA>070-7163-09076908
111테스트재노인소3T226(노인) 노인복지관(소규모)노인여가복지시설자치구재단테스트1159000000동작구서울특별시 동작구 노량진로32길79 (본동)17<NA>070-7163-09076908
112테스트노인복지관2T2402(노인) 노인복지관노인여가복지시설자치구김종가1159000000동작구서울특별시 동작구 노량진로32길79 (본동)17<NA>070-7163-09076908
113테스트노인복지관3T2403(노인) 노인복지관노인여가복지시설자치구김종가1159000000동작구서울특별시 동작구 노량진로32길79 (본동)17<NA>070-7163-09076908
114테스트노인복지관소1T2404(노인) 노인복지관(소규모)노인여가복지시설자치구김종가1159000000동작구서울특별시 동작구 노량진로32길79 (본동)17<NA>070-7163-09076908
115서초구립방배노인종합복지관Z0629(노인) 노인복지관노인여가복지시설자치구이창열1165000000서초구서울특별시 서초구 방배천로 481000171102-581-79926677