Overview

Dataset statistics

Number of variables11
Number of observations87
Missing cells228
Missing cells (%)23.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.1 KiB
Average record size in memory95.5 B

Variable types

Categorical2
Text3
Numeric6

Dataset

Description사회복지관 현황_인허가
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=R9BK6ZSUETZCUKLT1W6V13845550&infSeq=1

Alerts

영업상태명 has constant value ""Constant
자격소유인원수(명) is highly overall correlated with 총인원수(명)High correlation
총인원수(명) is highly overall correlated with 자격소유인원수(명)High correlation
소재지우편번호 is highly overall correlated with WGS84위도 and 1 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 1 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
자격소유인원수(명) has 13 (14.9%) missing valuesMissing
총인원수(명) has 19 (21.8%) missing valuesMissing
소재지도로명주소 has 60 (69.0%) missing valuesMissing
소재지우편번호 has 44 (50.6%) missing valuesMissing
WGS84위도 has 46 (52.9%) missing valuesMissing
WGS84경도 has 46 (52.9%) missing valuesMissing
자격소유인원수(명) has 7 (8.0%) zerosZeros
총인원수(명) has 1 (1.1%) zerosZeros

Reproduction

Analysis started2023-12-10 21:20:23.850975
Analysis finished2023-12-10 21:20:27.675435
Duration3.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Memory size828.0 B
성남시
10 
부천시
10 
남양주시
8 
고양시
7 
시흥시
7 
Other values (20)
45 

Length

Max length4
Median length3
Mean length3.1034483
Min length3

Unique

Unique8 ?
Unique (%)9.2%

Sample

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

Common Values

ValueCountFrequency (%)
성남시 10
 
11.5%
부천시 10
 
11.5%
남양주시 8
 
9.2%
고양시 7
 
8.0%
시흥시 7
 
8.0%
안산시 5
 
5.7%
오산시 4
 
4.6%
안양시 4
 
4.6%
화성시 4
 
4.6%
광명시 3
 
3.4%
Other values (15) 25
28.7%

Length

2023-12-11T06:20:27.746118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성남시 10
 
11.5%
부천시 10
 
11.5%
남양주시 8
 
9.2%
고양시 7
 
8.0%
시흥시 7
 
8.0%
안산시 5
 
5.7%
오산시 4
 
4.6%
안양시 4
 
4.6%
화성시 4
 
4.6%
군포시 3
 
3.4%
Other values (15) 25
28.7%

사업장명
Text

Distinct84
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size828.0 B
2023-12-11T06:20:27.945942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length11.218391
Min length7

Characters and Unicode

Total characters976
Distinct characters128
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

Unique81 ?
Unique (%)93.1%

Sample

1st row고양시원당종합사회복지관
2nd row고양시문촌9종합사회복지관
3rd row고양시흰돌종합사회복지관
4th row고양시원흥종합사회복지관
5th row고양시일산종합사회복지관
ValueCountFrequency (%)
남양주시 7
 
6.6%
사회복지관 4
 
3.8%
성남위례종합사회복지관 2
 
1.9%
과천종합사회복지관 2
 
1.9%
서부희망케어센터 2
 
1.9%
무한돌봄 2
 
1.9%
합정종합사회복지관 2
 
1.9%
동부희망케어센터 2
 
1.9%
종합사회복지관 2
 
1.9%
안산시본오종합사회복지관 1
 
0.9%
Other values (80) 80
75.5%
2023-12-11T06:20:28.278064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 86
 
8.8%
복 85
 
8.7%
관 83
 
8.5%
사 83
 
8.5%
회 82
 
8.4%
합 77
 
7.9%
종 75
 
7.7%
시 38
 
3.9%
양 21
 
2.2%
19
 
1.9%
Other values (118) 327
33.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 944
96.7%
Space Separator 19
 
1.9%
Close Punctuation 5
 
0.5%
Open Punctuation 5
 
0.5%
Decimal Number 3
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 86
 
9.1%
복 85
 
9.0%
관 83
 
8.8%
사 83
 
8.8%
회 82
 
8.7%
합 77
 
8.2%
종 75
 
7.9%
시 38
 
4.0%
양 21
 
2.2%
부 16
 
1.7%
Other values (112) 298
31.6%
Decimal Number
ValueCountFrequency (%)
4 1
33.3%
7 1
33.3%
9 1
33.3%
Space Separator
ValueCountFrequency (%)
19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 944
96.7%
Common 32
 
3.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 86
 
9.1%
복 85
 
9.0%
관 83
 
8.8%
사 83
 
8.8%
회 82
 
8.7%
합 77
 
8.2%
종 75
 
7.9%
시 38
 
4.0%
양 21
 
2.2%
부 16
 
1.7%
Other values (112) 298
31.6%
Common
ValueCountFrequency (%)
19
59.4%
) 5
 
15.6%
( 5
 
15.6%
4 1
 
3.1%
7 1
 
3.1%
9 1
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 944
96.7%
ASCII 32
 
3.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 86
 
9.1%
복 85
 
9.0%
관 83
 
8.8%
사 83
 
8.8%
회 82
 
8.7%
합 77
 
8.2%
종 75
 
7.9%
시 38
 
4.0%
양 21
 
2.2%
부 16
 
1.7%
Other values (112) 298
31.6%
ASCII
ValueCountFrequency (%)
19
59.4%
) 5
 
15.6%
( 5
 
15.6%
4 1
 
3.1%
7 1
 
3.1%
9 1
 
3.1%

인허가일자
Real number (ℝ)

Distinct73
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20036012
Minimum19861216
Maximum20171218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2023-12-11T06:20:28.404940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19861216
5-th percentile19903609
Q119950866
median20030325
Q320145558
95-th percentile20160891
Maximum20171218
Range310002
Interquartile range (IQR)194692

Descriptive statistics

Standard deviation94496.774
Coefficient of variation (CV)0.0047163464
Kurtosis-1.3637087
Mean20036012
Median Absolute Deviation (MAD)80285
Skewness-0.022698582
Sum1.743133 × 109
Variance8.9296402 × 109
MonotonicityNot monotonic
2023-12-11T06:20:28.571102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20150304 7
 
8.0%
19951010 3
 
3.4%
20161101 2
 
2.3%
19920414 2
 
2.3%
19980326 2
 
2.3%
19921110 2
 
2.3%
20110610 2
 
2.3%
20101213 2
 
2.3%
19921226 1
 
1.1%
20011109 1
 
1.1%
Other values (63) 63
72.4%
ValueCountFrequency (%)
19861216 1
1.1%
19861231 1
1.1%
19870806 1
1.1%
19880701 1
1.1%
19900827 1
1.1%
19910101 1
1.1%
19920414 2
2.3%
19920604 1
1.1%
19921027 1
1.1%
19921110 2
2.3%
ValueCountFrequency (%)
20171218 1
1.1%
20170116 1
1.1%
20161101 2
2.3%
20160919 1
1.1%
20160825 1
1.1%
20160712 1
1.1%
20160601 1
1.1%
20160531 1
1.1%
20160422 1
1.1%
20151230 1
1.1%

영업상태명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size828.0 B
운영중
87 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
운영중 87
100.0%

Length

2023-12-11T06:20:28.680002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:20:28.853937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 87
100.0%

자격소유인원수(명)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct21
Distinct (%)28.4%
Missing13
Missing (%)14.9%
Infinite0
Infinite (%)0.0%
Mean10.675676
Minimum0
Maximum45
Zeros7
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2023-12-11T06:20:28.995994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17.25
median11
Q312
95-th percentile21.05
Maximum45
Range45
Interquartile range (IQR)4.75

Descriptive statistics

Standard deviation7.0441039
Coefficient of variation (CV)0.65982746
Kurtosis7.8131115
Mean10.675676
Median Absolute Deviation (MAD)2
Skewness1.912893
Sum790
Variance49.6194
MonotonicityNot monotonic
2023-12-11T06:20:29.130869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
12 13
14.9%
11 10
11.5%
0 7
8.0%
10 6
 
6.9%
13 6
 
6.9%
5 4
 
4.6%
9 4
 
4.6%
8 4
 
4.6%
7 3
 
3.4%
16 2
 
2.3%
Other values (11) 15
17.2%
(Missing) 13
14.9%
ValueCountFrequency (%)
0 7
8.0%
2 1
 
1.1%
3 1
 
1.1%
4 1
 
1.1%
5 4
4.6%
6 2
 
2.3%
7 3
3.4%
8 4
4.6%
9 4
4.6%
10 6
6.9%
ValueCountFrequency (%)
45 1
 
1.1%
30 2
 
2.3%
23 1
 
1.1%
20 1
 
1.1%
18 1
 
1.1%
16 2
 
2.3%
15 2
 
2.3%
14 2
 
2.3%
13 6
6.9%
12 13
14.9%

총인원수(명)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct29
Distinct (%)42.6%
Missing19
Missing (%)21.8%
Infinite0
Infinite (%)0.0%
Mean13.882353
Minimum0
Maximum48
Zeros1
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size915.0 B
2023-12-11T06:20:29.519319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.35
Q19
median13
Q317
95-th percentile30.65
Maximum48
Range48
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.5910912
Coefficient of variation (CV)0.61884979
Kurtosis3.4074643
Mean13.882353
Median Absolute Deviation (MAD)4
Skewness1.4709103
Sum944
Variance73.806848
MonotonicityNot monotonic
2023-12-11T06:20:29.697882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
11 7
 
8.0%
13 7
 
8.0%
9 6
 
6.9%
14 6
 
6.9%
18 4
 
4.6%
8 4
 
4.6%
5 4
 
4.6%
12 3
 
3.4%
17 3
 
3.4%
15 3
 
3.4%
Other values (19) 21
24.1%
(Missing) 19
21.8%
ValueCountFrequency (%)
0 1
 
1.1%
1 1
 
1.1%
2 2
 
2.3%
3 1
 
1.1%
4 1
 
1.1%
5 4
4.6%
7 1
 
1.1%
8 4
4.6%
9 6
6.9%
10 1
 
1.1%
ValueCountFrequency (%)
48 1
1.1%
36 1
1.1%
35 1
1.1%
31 1
1.1%
30 1
1.1%
27 1
1.1%
26 1
1.1%
24 1
1.1%
23 1
1.1%
21 1
1.1%

소재지도로명주소
Text

MISSING 

Distinct27
Distinct (%)100.0%
Missing60
Missing (%)69.0%
Memory size828.0 B
2023-12-11T06:20:29.935479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length21
Mean length18.888889
Min length14

Characters and Unicode

Total characters510
Distinct characters87
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

Unique27 ?
Unique (%)100.0%

Sample

1st row경기도 고양시 일산서구 고양대로 654
2nd row경기도 광명시 연서일로 4-3
3rd row경기도 광명시 오리로 1018
4th row경기도 구리시 벌말로129번길 50
5th row경기도 김포시 사우중로 100
ValueCountFrequency (%)
경기도 27
 
21.8%
성남시 6
 
4.8%
부천시 3
 
2.4%
분당구 3
 
2.4%
수원시 3
 
2.4%
광명시 2
 
1.6%
안산시 2
 
1.6%
9 2
 
1.6%
장안구 2
 
1.6%
시흥시 2
 
1.6%
Other values (69) 72
58.1%
2023-12-11T06:20:30.288146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
97
19.0%
시 30
 
5.9%
경 28
 
5.5%
도 28
 
5.5%
기 27
 
5.3%
로 27
 
5.3%
구 14
 
2.7%
1 14
 
2.7%
5 10
 
2.0%
남 9
 
1.8%
Other values (77) 226
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 323
63.3%
Space Separator 97
 
19.0%
Decimal Number 85
 
16.7%
Dash Punctuation 5
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 30
 
9.3%
경 28
 
8.7%
도 28
 
8.7%
기 27
 
8.4%
로 27
 
8.4%
구 14
 
4.3%
남 9
 
2.8%
원 9
 
2.8%
길 8
 
2.5%
성 8
 
2.5%
Other values (65) 135
41.8%
Decimal Number
ValueCountFrequency (%)
1 14
16.5%
5 10
11.8%
9 9
10.6%
2 9
10.6%
3 9
10.6%
0 9
10.6%
6 7
8.2%
4 7
8.2%
8 6
7.1%
7 5
 
5.9%
Space Separator
ValueCountFrequency (%)
97
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 323
63.3%
Common 187
36.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 30
 
9.3%
경 28
 
8.7%
도 28
 
8.7%
기 27
 
8.4%
로 27
 
8.4%
구 14
 
4.3%
남 9
 
2.8%
원 9
 
2.8%
길 8
 
2.5%
성 8
 
2.5%
Other values (65) 135
41.8%
Common
ValueCountFrequency (%)
97
51.9%
1 14
 
7.5%
5 10
 
5.3%
9 9
 
4.8%
2 9
 
4.8%
3 9
 
4.8%
0 9
 
4.8%
6 7
 
3.7%
4 7
 
3.7%
8 6
 
3.2%
Other values (2) 10
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 323
63.3%
ASCII 187
36.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
97
51.9%
1 14
 
7.5%
5 10
 
5.3%
9 9
 
4.8%
2 9
 
4.8%
3 9
 
4.8%
0 9
 
4.8%
6 7
 
3.7%
4 7
 
3.7%
8 6
 
3.2%
Other values (2) 10
 
5.3%
Hangul
ValueCountFrequency (%)
시 30
 
9.3%
경 28
 
8.7%
도 28
 
8.7%
기 27
 
8.4%
로 27
 
8.4%
구 14
 
4.3%
남 9
 
2.8%
원 9
 
2.8%
길 8
 
2.5%
성 8
 
2.5%
Other values (65) 135
41.8%

소재지지번주소
Text

Distinct81
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Memory size828.0 B
2023-12-11T06:20:30.581317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length26
Mean length18.563218
Min length10

Characters and Unicode

Total characters1615
Distinct characters160
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

Unique76 ?
Unique (%)87.4%

Sample

1st row경기도 고양시 덕양구 성사동
2nd row경기도 고양시 일산서구 주엽동 문촌마을9단지
3rd row경기도 고양시 일산동구 백석동
4th row경기도 고양시 덕양구 원흥동
5th row경기도 고양시 일산서구 일산동 620-3번지
ValueCountFrequency (%)
경기도 87
 
23.6%
성남시 10
 
2.7%
부천시 10
 
2.7%
남양주시 8
 
2.2%
고양시 7
 
1.9%
시흥시 7
 
1.9%
안산시 5
 
1.4%
분당구 5
 
1.4%
안양시 4
 
1.1%
오산시 4
 
1.1%
Other values (171) 221
60.1%
2023-12-11T06:20:30.981655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
281
 
17.4%
시 96
 
5.9%
도 92
 
5.7%
경 87
 
5.4%
기 87
 
5.4%
동 79
 
4.9%
지 54
 
3.3%
번 39
 
2.4%
구 32
 
2.0%
1 30
 
1.9%
Other values (150) 738
45.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1139
70.5%
Space Separator 281
 
17.4%
Decimal Number 168
 
10.4%
Dash Punctuation 21
 
1.3%
Uppercase Letter 4
 
0.2%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 96
 
8.4%
도 92
 
8.1%
경 87
 
7.6%
기 87
 
7.6%
동 79
 
6.9%
지 54
 
4.7%
번 39
 
3.4%
구 32
 
2.8%
양 28
 
2.5%
산 26
 
2.3%
Other values (133) 519
45.6%
Decimal Number
ValueCountFrequency (%)
1 30
17.9%
2 26
15.5%
3 23
13.7%
0 19
11.3%
5 14
8.3%
7 13
7.7%
4 12
 
7.1%
9 11
 
6.5%
6 11
 
6.5%
8 9
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
A 2
50.0%
B 1
25.0%
L 1
25.0%
Space Separator
ValueCountFrequency (%)
281
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1139
70.5%
Common 472
29.2%
Latin 4
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 96
 
8.4%
도 92
 
8.1%
경 87
 
7.6%
기 87
 
7.6%
동 79
 
6.9%
지 54
 
4.7%
번 39
 
3.4%
구 32
 
2.8%
양 28
 
2.5%
산 26
 
2.3%
Other values (133) 519
45.6%
Common
ValueCountFrequency (%)
281
59.5%
1 30
 
6.4%
2 26
 
5.5%
3 23
 
4.9%
- 21
 
4.4%
0 19
 
4.0%
5 14
 
3.0%
7 13
 
2.8%
4 12
 
2.5%
9 11
 
2.3%
Other values (4) 22
 
4.7%
Latin
ValueCountFrequency (%)
A 2
50.0%
B 1
25.0%
L 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1139
70.5%
ASCII 476
29.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
281
59.0%
1 30
 
6.3%
2 26
 
5.5%
3 23
 
4.8%
- 21
 
4.4%
0 19
 
4.0%
5 14
 
2.9%
7 13
 
2.7%
4 12
 
2.5%
9 11
 
2.3%
Other values (7) 26
 
5.5%
Hangul
ValueCountFrequency (%)
시 96
 
8.4%
도 92
 
8.1%
경 87
 
7.6%
기 87
 
7.6%
동 79
 
6.9%
지 54
 
4.7%
번 39
 
3.4%
구 32
 
2.8%
양 28
 
2.5%
산 26
 
2.3%
Other values (133) 519
45.6%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)95.3%
Missing44
Missing (%)50.6%
Infinite0
Infinite (%)0.0%
Mean14478.791
Minimum10111
Maximum18598
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2023-12-11T06:20:31.113014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10111
5-th percentile10846.8
Q113294
median14501
Q315378
95-th percentile18109.1
Maximum18598
Range8487
Interquartile range (IQR)2084

Descriptive statistics

Standard deviation2114.7314
Coefficient of variation (CV)0.14605719
Kurtosis-0.2037862
Mean14478.791
Median Absolute Deviation (MAD)1080
Skewness0.048988755
Sum622588
Variance4472089
MonotonicityNot monotonic
2023-12-11T06:20:31.246284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
15119 2
 
2.3%
16221 2
 
2.3%
12910 1
 
1.1%
12947 1
 
1.1%
14995 1
 
1.1%
14951 1
 
1.1%
15387 1
 
1.1%
15369 1
 
1.1%
14019 1
 
1.1%
12520 1
 
1.1%
Other values (31) 31
35.6%
(Missing) 44
50.6%
ValueCountFrequency (%)
10111 1
1.1%
10352 1
1.1%
10813 1
1.1%
11151 1
1.1%
11722 1
1.1%
11946 1
1.1%
12520 1
1.1%
12910 1
1.1%
12947 1
1.1%
13124 1
1.1%
ValueCountFrequency (%)
18598 1
1.1%
18143 1
1.1%
18131 1
1.1%
17912 1
1.1%
17877 1
1.1%
17730 1
1.1%
17131 1
1.1%
16230 1
1.1%
16221 2
2.3%
15387 1
1.1%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)100.0%
Missing46
Missing (%)52.9%
Infinite0
Infinite (%)0.0%
Mean37.415845
Minimum36.989549
Maximum37.904664
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2023-12-11T06:20:31.377958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.989549
5-th percentile37.130464
Q137.326213
median37.432938
Q337.50823
95-th percentile37.684516
Maximum37.904664
Range0.91511476
Interquartile range (IQR)0.18201776

Descriptive statistics

Standard deviation0.18516747
Coefficient of variation (CV)0.0049489053
Kurtosis1.041323
Mean37.415845
Median Absolute Deviation (MAD)0.094117418
Skewness0.18328501
Sum1534.0496
Variance0.034286993
MonotonicityNot monotonic
2023-12-11T06:20:31.522549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
37.159393162 1
 
1.1%
37.3686742469 1
 
1.1%
37.4329376376 1
 
1.1%
37.3262125923 1
 
1.1%
37.3333060087 1
 
1.1%
37.2874315328 1
 
1.1%
37.3292268125 1
 
1.1%
37.3930239652 1
 
1.1%
37.4807261501 1
 
1.1%
37.1384786317 1
 
1.1%
Other values (31) 31
35.6%
(Missing) 46
52.9%
ValueCountFrequency (%)
36.9895489439 1
1.1%
37.0654181063 1
1.1%
37.1304643221 1
1.1%
37.1384786317 1
1.1%
37.159393162 1
1.1%
37.1893953618 1
1.1%
37.2874315328 1
1.1%
37.2915990952 1
1.1%
37.2916498557 1
1.1%
37.2917686522 1
1.1%
ValueCountFrequency (%)
37.9046637048 1
1.1%
37.8573028098 1
1.1%
37.6845156468 1
1.1%
37.6230561834 1
1.1%
37.5891977367 1
1.1%
37.5692998982 1
1.1%
37.5417036317 1
1.1%
37.5270550555 1
1.1%
37.5197038371 1
1.1%
37.5086691284 1
1.1%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)100.0%
Missing46
Missing (%)52.9%
Infinite0
Infinite (%)0.0%
Mean126.9725
Minimum126.72228
Maximum127.58902
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2023-12-11T06:20:31.650076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.72228
5-th percentile126.76234
Q1126.80595
median126.91954
Q3127.11181
95-th percentile127.21486
Maximum127.58902
Range0.86673866
Interquartile range (IQR)0.3058576

Descriptive statistics

Standard deviation0.1889917
Coefficient of variation (CV)0.0014884459
Kurtosis0.95600092
Mean126.9725
Median Absolute Deviation (MAD)0.15103507
Skewness0.84469458
Sum5205.8725
Variance0.035717861
MonotonicityNot monotonic
2023-12-11T06:20:31.768966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
127.0778589217 1
 
1.1%
126.8133338254 1
 
1.1%
126.7927970966 1
 
1.1%
126.8059548544 1
 
1.1%
126.8136107423 1
 
1.1%
126.8610410001 1
 
1.1%
126.8584576095 1
 
1.1%
126.908373274 1
 
1.1%
127.5890221055 1
 
1.1%
127.0723454846 1
 
1.1%
Other values (31) 31
35.6%
(Missing) 46
52.9%
ValueCountFrequency (%)
126.7222834418 1
1.1%
126.7618280877 1
1.1%
126.7623397843 1
1.1%
126.7651814963 1
1.1%
126.7665287836 1
1.1%
126.7685040007 1
1.1%
126.7789955853 1
1.1%
126.7897090919 1
1.1%
126.7927970966 1
1.1%
126.7948822771 1
1.1%
ValueCountFrequency (%)
127.5890221055 1
1.1%
127.2157831453 1
1.1%
127.2148555155 1
1.1%
127.2116885764 1
1.1%
127.1894521151 1
1.1%
127.1660805057 1
1.1%
127.1590805562 1
1.1%
127.1537972864 1
1.1%
127.1463837779 1
1.1%
127.1155080199 1
1.1%

Interactions

2023-12-11T06:20:26.846452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:24.580044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:25.118032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:25.544154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:25.960696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:26.403110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:26.921195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:24.678224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:25.198151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:25.617965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:26.040047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:26.488386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:26.984317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:24.784665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:25.278504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:25.688265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:26.117359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:26.554363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:27.051739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:24.886076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:25.348980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:25.757144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:26.191277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:26.625748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:27.129546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:24.966575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:25.413053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:25.824700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:26.253646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:26.706194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:27.202967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:25.044008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:25.480021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:25.893705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:26.323714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:20:26.781146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:20:31.851288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명사업장명인허가일자자격소유인원수(명)총인원수(명)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
시군명1.0001.0000.8180.8470.6831.0001.0000.9830.9900.956
사업장명1.0001.0001.0000.0000.0001.0000.9741.0001.0001.000
인허가일자0.8181.0001.0000.4950.3011.0001.0000.5800.5000.249
자격소유인원수(명)0.8470.0000.4951.0000.7891.0000.9840.4780.7020.053
총인원수(명)0.6830.0000.3010.7891.0001.0000.0000.3420.8570.198
소재지도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지지번주소1.0000.9741.0000.9840.0001.0001.0001.0001.0001.000
소재지우편번호0.9831.0000.5800.4780.3421.0001.0001.0000.8390.788
WGS84위도0.9901.0000.5000.7020.8571.0001.0000.8391.0000.147
WGS84경도0.9561.0000.2490.0530.1981.0001.0000.7880.1471.000
2023-12-11T06:20:31.957358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자자격소유인원수(명)총인원수(명)소재지우편번호WGS84위도WGS84경도시군명
인허가일자1.000-0.019-0.164-0.2400.1480.0600.400
자격소유인원수(명)-0.0191.0000.546-0.2430.1640.1310.468
총인원수(명)-0.1640.5461.000-0.040-0.1210.1870.282
소재지우편번호-0.240-0.243-0.0401.000-0.770-0.1550.750
WGS84위도0.1480.164-0.121-0.7701.000-0.2600.783
WGS84경도0.0600.1310.187-0.155-0.2601.0000.671
시군명0.4000.4680.2820.7500.7830.6711.000

Missing values

2023-12-11T06:20:27.318882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:20:27.482145image/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.
2023-12-11T06:20:27.597328image/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

시군명사업장명인허가일자영업상태명자격소유인원수(명)총인원수(명)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0고양시고양시원당종합사회복지관19990208운영중1214<NA>경기도 고양시 덕양구 성사동<NA><NA><NA>
1고양시고양시문촌9종합사회복지관19951010운영중1214<NA>경기도 고양시 일산서구 주엽동 문촌마을9단지<NA><NA><NA>
2고양시고양시흰돌종합사회복지관19951010운영중1517<NA>경기도 고양시 일산동구 백석동<NA><NA><NA>
3고양시고양시원흥종합사회복지관20150204운영중<NA><NA><NA>경기도 고양시 덕양구 원흥동<NA><NA><NA>
4고양시고양시일산종합사회복지관20030102운영중3030경기도 고양시 일산서구 고양대로 654경기도 고양시 일산서구 일산동 620-3번지1035237.684516126.768504
5고양시고양시문촌7종합사회복지관19951010운영중1113<NA>경기도 고양시 일산서구 주엽동<NA><NA><NA>
6고양시고양시 덕양행신종합사회복지관20170116운영중<NA><NA><NA>경기도 고양시 덕양구 행신동<NA><NA><NA>
7과천시과천종합사회복지관19980326운영중1824<NA>경기도 과천시 별양동 과천타워<NA><NA><NA>
8과천시과천종합사회복지관19980326운영중3036<NA>경기도 과천시 별양동 1-17번지 과천타워 3층1383737.426985126.992
9광명시하안종합사회복지관19910101운영중105<NA>경기도 광명시 하안동 200번지 하안주공13단지아파트 1303동1430737.459848126.883816
시군명사업장명인허가일자영업상태명자격소유인원수(명)총인원수(명)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
77평택시부락종합사회복지관20010224운영중79경기도 평택시 서정로 303경기도 평택시 이충동 281번지1773037.065418127.067267
78평택시합정종합사회복지관19921110운영중59<NA>경기도 평택시 합정동 관리동 2층17877<NA><NA>
79평택시합정종합사회복지관19921110운영중22<NA>경기도 평택시 합정동 829번지1791236.989549127.091385
80포천시포천종합사회복지관20070921운영중011경기도 포천시 군내면 청성로 5경기도 포천시 군내면 하성북리 522-12번지1115137.904664127.211689
81하남시하남시종합사회복지관20050217운영중1321경기도 하남시 덕풍천서로 9경기도 하남시 신장동 521-5번지1294737.541704127.215783
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