Overview

Dataset statistics

Number of variables12
Number of observations146
Missing cells207
Missing cells (%)11.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.8 KiB
Average record size in memory103.9 B

Variable types

Categorical3
Text3
Numeric6

Dataset

Description장애인 공동 생활가정 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=8WF675LN459F52J6688L1368631&infSeq=1

Alerts

소재지우편번호 is highly overall correlated with WGS84위도 and 1 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
시군명 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
영업상태명 is highly overall correlated with WGS84위도 and 1 other fieldsHigh correlation
영업상태명 is highly imbalanced (89.6%)Imbalance
총인원수(명) has 11 (7.5%) missing valuesMissing
소재지도로명주소 has 58 (39.7%) missing valuesMissing
소재지우편번호 has 42 (28.8%) missing valuesMissing
WGS84위도 has 48 (32.9%) missing valuesMissing
WGS84경도 has 48 (32.9%) missing valuesMissing
총인원수(명) has 2 (1.4%) zerosZeros

Reproduction

Analysis started2023-12-10 21:45:11.749502
Analysis finished2023-12-10 21:45:15.595935
Duration3.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
안산시
18 
고양시
17 
성남시
16 
용인시
14 
안성시
12 
Other values (17)
69 

Length

Max length4
Median length3
Mean length3.0342466
Min length3

Unique

Unique3 ?
Unique (%)2.1%

Sample

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

Common Values

ValueCountFrequency (%)
안산시 18
12.3%
고양시 17
11.6%
성남시 16
11.0%
용인시 14
9.6%
안성시 12
8.2%
화성시 10
 
6.8%
시흥시 9
 
6.2%
광주시 8
 
5.5%
부천시 7
 
4.8%
포천시 6
 
4.1%
Other values (12) 29
19.9%

Length

2023-12-11T06:45:15.647935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
안산시 18
12.3%
고양시 17
11.6%
성남시 16
11.0%
용인시 14
9.6%
안성시 12
8.2%
화성시 10
 
6.8%
시흥시 9
 
6.2%
광주시 8
 
5.5%
부천시 7
 
4.8%
포천시 6
 
4.1%
Other values (12) 29
19.9%

사업장명
Text

Distinct142
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-11T06:45:15.865601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length6.2739726
Min length2

Characters and Unicode

Total characters916
Distinct characters186
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

Unique138 ?
Unique (%)94.5%

Sample

1st row한마음의 집
2nd row한울그룹홈
3rd row한소망의집
4th row홀트자활의집
5th row미리내집(여)
ValueCountFrequency (%)
집 13
 
7.0%
장애인공동생활가정 4
 
2.2%
b 3
 
1.6%
그룹홈 3
 
1.6%
남양그룹홈 3
 
1.6%
자오쉼터 3
 
1.6%
평안밀알장애인공동생활가정 3
 
1.6%
아름다운동행2 2
 
1.1%
두드림 2
 
1.1%
1호 2
 
1.1%
Other values (140) 147
79.5%
2023-12-11T06:45:16.236487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
집 41
 
4.5%
39
 
4.3%
의 32
 
3.5%
동 29
 
3.2%
가 28
 
3.1%
홈 26
 
2.8%
공 25
 
2.7%
활 25
 
2.7%
정 24
 
2.6%
생 23
 
2.5%
Other values (176) 624
68.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 833
90.9%
Space Separator 39
 
4.3%
Decimal Number 18
 
2.0%
Uppercase Letter 10
 
1.1%
Close Punctuation 8
 
0.9%
Open Punctuation 8
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
집 41
 
4.9%
의 32
 
3.8%
동 29
 
3.5%
가 28
 
3.4%
홈 26
 
3.1%
공 25
 
3.0%
활 25
 
3.0%
정 24
 
2.9%
생 23
 
2.8%
그 21
 
2.5%
Other values (164) 559
67.1%
Decimal Number
ValueCountFrequency (%)
2 9
50.0%
1 6
33.3%
3 1
 
5.6%
5 1
 
5.6%
4 1
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
B 4
40.0%
A 3
30.0%
C 2
20.0%
D 1
 
10.0%
Space Separator
ValueCountFrequency (%)
39
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 833
90.9%
Common 73
 
8.0%
Latin 10
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
집 41
 
4.9%
의 32
 
3.8%
동 29
 
3.5%
가 28
 
3.4%
홈 26
 
3.1%
공 25
 
3.0%
활 25
 
3.0%
정 24
 
2.9%
생 23
 
2.8%
그 21
 
2.5%
Other values (164) 559
67.1%
Common
ValueCountFrequency (%)
39
53.4%
2 9
 
12.3%
) 8
 
11.0%
( 8
 
11.0%
1 6
 
8.2%
3 1
 
1.4%
5 1
 
1.4%
4 1
 
1.4%
Latin
ValueCountFrequency (%)
B 4
40.0%
A 3
30.0%
C 2
20.0%
D 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 833
90.9%
ASCII 83
 
9.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
집 41
 
4.9%
의 32
 
3.8%
동 29
 
3.5%
가 28
 
3.4%
홈 26
 
3.1%
공 25
 
3.0%
활 25
 
3.0%
정 24
 
2.9%
생 23
 
2.8%
그 21
 
2.5%
Other values (164) 559
67.1%
ASCII
ValueCountFrequency (%)
39
47.0%
2 9
 
10.8%
) 8
 
9.6%
( 8
 
9.6%
1 6
 
7.2%
B 4
 
4.8%
A 3
 
3.6%
C 2
 
2.4%
D 1
 
1.2%
3 1
 
1.2%
Other values (2) 2
 
2.4%

인허가일자
Real number (ℝ)

Distinct123
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20062619
Minimum19960401
Maximum20150904
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T06:45:16.356308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19960401
5-th percentile19993130
Q120040331
median20060872
Q320087898
95-th percentile20130267
Maximum20150904
Range190503
Interquartile range (IQR)47566.5

Descriptive statistics

Standard deviation38085.008
Coefficient of variation (CV)0.0018983069
Kurtosis-0.1318366
Mean20062619
Median Absolute Deviation (MAD)20545.5
Skewness-0.13730932
Sum2.9291424 × 109
Variance1.4504678 × 109
MonotonicityNot monotonic
2023-12-11T06:45:16.478011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20050119 5
 
3.4%
20050801 3
 
2.1%
20050820 3
 
2.1%
20040910 2
 
1.4%
20020813 2
 
1.4%
20061024 2
 
1.4%
20070202 2
 
1.4%
20040331 2
 
1.4%
20080901 2
 
1.4%
20110503 2
 
1.4%
Other values (113) 121
82.9%
ValueCountFrequency (%)
19960401 1
0.7%
19970201 1
0.7%
19981012 1
0.7%
19981218 1
0.7%
19990101 1
0.7%
19990325 1
0.7%
19990407 1
0.7%
19990701 1
0.7%
20000417 1
0.7%
20001016 1
0.7%
ValueCountFrequency (%)
20150904 1
0.7%
20140930 1
0.7%
20131011 1
0.7%
20131002 1
0.7%
20130826 1
0.7%
20130711 1
0.7%
20130709 1
0.7%
20130315 1
0.7%
20130124 1
0.7%
20130114 1
0.7%

영업상태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
운영중
144 
휴업 등
 
2

Length

Max length4
Median length3
Mean length3.0136986
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
운영중 144
98.6%
휴업 등 2
 
1.4%

Length

2023-12-11T06:45:16.590741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:45:16.668910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 144
97.3%
휴업 2
 
1.4%
등 2
 
1.4%

입소정원(명)
Real number (ℝ)

Distinct10
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0273973
Minimum4
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T06:45:16.740429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q14
median4
Q35
95-th percentile9
Maximum15
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.1621994
Coefficient of variation (CV)0.43008326
Kurtosis7.2983673
Mean5.0273973
Median Absolute Deviation (MAD)0
Skewness2.6287587
Sum734
Variance4.6751063
MonotonicityNot monotonic
2023-12-11T06:45:16.826174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4 103
70.5%
5 16
 
11.0%
8 10
 
6.8%
7 6
 
4.1%
9 3
 
2.1%
15 2
 
1.4%
12 2
 
1.4%
10 2
 
1.4%
6 1
 
0.7%
13 1
 
0.7%
ValueCountFrequency (%)
4 103
70.5%
5 16
 
11.0%
6 1
 
0.7%
7 6
 
4.1%
8 10
 
6.8%
9 3
 
2.1%
10 2
 
1.4%
12 2
 
1.4%
13 1
 
0.7%
15 2
 
1.4%
ValueCountFrequency (%)
15 2
 
1.4%
13 1
 
0.7%
12 2
 
1.4%
10 2
 
1.4%
9 3
 
2.1%
8 10
 
6.8%
7 6
 
4.1%
6 1
 
0.7%
5 16
 
11.0%
4 103
70.5%

자격소유인원수(명)
Categorical

Distinct4
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
1
74 
2
35 
0
30 
<NA>
 
7

Length

Max length4
Median length1
Mean length1.1438356
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 74
50.7%
2 35
24.0%
0 30
20.5%
<NA> 7
 
4.8%

Length

2023-12-11T06:45:16.923461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:45:17.012743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 74
50.7%
2 35
24.0%
0 30
20.5%
na 7
 
4.8%

총인원수(명)
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)5.2%
Missing11
Missing (%)7.5%
Infinite0
Infinite (%)0.0%
Mean1.6
Minimum0
Maximum10
Zeros2
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T06:45:17.090684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0523605
Coefficient of variation (CV)0.65772533
Kurtosis30.232403
Mean1.6
Median Absolute Deviation (MAD)0
Skewness4.2943865
Sum216
Variance1.1074627
MonotonicityNot monotonic
2023-12-11T06:45:17.191777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 73
50.0%
2 48
32.9%
3 8
 
5.5%
0 2
 
1.4%
4 2
 
1.4%
10 1
 
0.7%
5 1
 
0.7%
(Missing) 11
 
7.5%
ValueCountFrequency (%)
0 2
 
1.4%
1 73
50.0%
2 48
32.9%
3 8
 
5.5%
4 2
 
1.4%
5 1
 
0.7%
10 1
 
0.7%
ValueCountFrequency (%)
10 1
 
0.7%
5 1
 
0.7%
4 2
 
1.4%
3 8
 
5.5%
2 48
32.9%
1 73
50.0%
0 2
 
1.4%

소재지도로명주소
Text

MISSING 

Distinct73
Distinct (%)83.0%
Missing58
Missing (%)39.7%
Memory size1.3 KiB
2023-12-11T06:45:17.371328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length24
Mean length21.329545
Min length15

Characters and Unicode

Total characters1877
Distinct characters144
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

Unique61 ?
Unique (%)69.3%

Sample

1st row경기도 고양시 일산서구 대산로223번길 16-10
2nd row경기도 고양시 일산동구 성현로93번길 25-19
3rd row경기도 고양시 덕양구 보광로162번길 14
4th row경기도 고양시 덕양구 푸른마을로120번길 34
5th row경기도 고양시 덕양구 읍내로 28
ValueCountFrequency (%)
경기도 88
 
20.9%
고양시 12
 
2.9%
화성시 8
 
1.9%
성남시 8
 
1.9%
안성시 8
 
1.9%
안산시 8
 
1.9%
광주시 7
 
1.7%
포천시 6
 
1.4%
시흥시 6
 
1.4%
기흥구 6
 
1.4%
Other values (171) 264
62.7%
2023-12-11T06:45:17.665990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
333
17.7%
1 109
 
5.8%
도 103
 
5.5%
기 94
 
5.0%
시 94
 
5.0%
경 89
 
4.7%
로 65
 
3.5%
길 65
 
3.5%
2 57
 
3.0%
- 40
 
2.1%
Other values (134) 828
44.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1141
60.8%
Decimal Number 363
 
19.3%
Space Separator 333
 
17.7%
Dash Punctuation 40
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 103
 
9.0%
기 94
 
8.2%
시 94
 
8.2%
경 89
 
7.8%
로 65
 
5.7%
길 65
 
5.7%
번 36
 
3.2%
성 35
 
3.1%
양 35
 
3.1%
구 35
 
3.1%
Other values (122) 490
42.9%
Decimal Number
ValueCountFrequency (%)
1 109
30.0%
2 57
15.7%
6 34
 
9.4%
7 30
 
8.3%
3 28
 
7.7%
4 27
 
7.4%
5 24
 
6.6%
9 23
 
6.3%
8 16
 
4.4%
0 15
 
4.1%
Space Separator
ValueCountFrequency (%)
333
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1141
60.8%
Common 736
39.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 103
 
9.0%
기 94
 
8.2%
시 94
 
8.2%
경 89
 
7.8%
로 65
 
5.7%
길 65
 
5.7%
번 36
 
3.2%
성 35
 
3.1%
양 35
 
3.1%
구 35
 
3.1%
Other values (122) 490
42.9%
Common
ValueCountFrequency (%)
333
45.2%
1 109
 
14.8%
2 57
 
7.7%
- 40
 
5.4%
6 34
 
4.6%
7 30
 
4.1%
3 28
 
3.8%
4 27
 
3.7%
5 24
 
3.3%
9 23
 
3.1%
Other values (2) 31
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1141
60.8%
ASCII 736
39.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
333
45.2%
1 109
 
14.8%
2 57
 
7.7%
- 40
 
5.4%
6 34
 
4.6%
7 30
 
4.1%
3 28
 
3.8%
4 27
 
3.7%
5 24
 
3.3%
9 23
 
3.1%
Other values (2) 31
 
4.2%
Hangul
ValueCountFrequency (%)
도 103
 
9.0%
기 94
 
8.2%
시 94
 
8.2%
경 89
 
7.8%
로 65
 
5.7%
길 65
 
5.7%
번 36
 
3.2%
성 35
 
3.1%
양 35
 
3.1%
구 35
 
3.1%
Other values (122) 490
42.9%

소재지지번주소
Text

Distinct124
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-11T06:45:17.879071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length37
Mean length21.60274
Min length10

Characters and Unicode

Total characters3154
Distinct characters148
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique104 ?
Unique (%)71.2%

Sample

1st row경기도 고양시 덕양구 관산동 101호, 102호(관산동, 나동아파트) 106동
2nd row경기도 고양시 일산서구 대화동 2157-10번지
3rd row경기도 고양시 일산동구 성석동 992-4번지
4th row경기도 고양시 일산서구 탄현동 121번지
5th row경기도 고양시 덕양구 벽제동 541-3번지
ValueCountFrequency (%)
경기도 146
 
20.8%
안산시 18
 
2.6%
고양시 17
 
2.4%
성남시 16
 
2.3%
용인시 14
 
2.0%
안성시 12
 
1.7%
상록구 11
 
1.6%
중원구 11
 
1.6%
화성시 10
 
1.4%
기흥구 10
 
1.4%
Other values (265) 437
62.3%
2023-12-11T06:45:18.200535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
557
 
17.7%
도 158
 
5.0%
기 157
 
5.0%
시 153
 
4.9%
경 146
 
4.6%
동 123
 
3.9%
1 123
 
3.9%
지 111
 
3.5%
번 106
 
3.4%
- 84
 
2.7%
Other values (138) 1436
45.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1936
61.4%
Decimal Number 569
 
18.0%
Space Separator 557
 
17.7%
Dash Punctuation 84
 
2.7%
Other Punctuation 4
 
0.1%
Uppercase Letter 2
 
0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 158
 
8.2%
기 157
 
8.1%
시 153
 
7.9%
경 146
 
7.5%
동 123
 
6.4%
지 111
 
5.7%
번 106
 
5.5%
구 71
 
3.7%
양 50
 
2.6%
성 48
 
2.5%
Other values (121) 813
42.0%
Decimal Number
ValueCountFrequency (%)
1 123
21.6%
0 83
14.6%
2 76
13.4%
4 59
10.4%
3 49
 
8.6%
5 45
 
7.9%
8 38
 
6.7%
7 33
 
5.8%
6 32
 
5.6%
9 31
 
5.4%
Other Punctuation
ValueCountFrequency (%)
, 3
75.0%
/ 1
 
25.0%
Space Separator
ValueCountFrequency (%)
557
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 84
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1936
61.4%
Common 1216
38.6%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 158
 
8.2%
기 157
 
8.1%
시 153
 
7.9%
경 146
 
7.5%
동 123
 
6.4%
지 111
 
5.7%
번 106
 
5.5%
구 71
 
3.7%
양 50
 
2.6%
성 48
 
2.5%
Other values (121) 813
42.0%
Common
ValueCountFrequency (%)
557
45.8%
1 123
 
10.1%
- 84
 
6.9%
0 83
 
6.8%
2 76
 
6.2%
4 59
 
4.9%
3 49
 
4.0%
5 45
 
3.7%
8 38
 
3.1%
7 33
 
2.7%
Other values (6) 69
 
5.7%
Latin
ValueCountFrequency (%)
A 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1936
61.4%
ASCII 1218
38.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
557
45.7%
1 123
 
10.1%
- 84
 
6.9%
0 83
 
6.8%
2 76
 
6.2%
4 59
 
4.8%
3 49
 
4.0%
5 45
 
3.7%
8 38
 
3.1%
7 33
 
2.7%
Other values (7) 71
 
5.8%
Hangul
ValueCountFrequency (%)
도 158
 
8.2%
기 157
 
8.1%
시 153
 
7.9%
경 146
 
7.5%
동 123
 
6.4%
지 111
 
5.7%
번 106
 
5.5%
구 71
 
3.7%
양 50
 
2.6%
성 48
 
2.5%
Other values (121) 813
42.0%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct81
Distinct (%)77.9%
Missing42
Missing (%)28.8%
Infinite0
Infinite (%)0.0%
Mean14205.731
Minimum10239
Maximum18540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T06:45:18.317012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10239
5-th percentile10267.6
Q111589.75
median14739
Q316967
95-th percentile18255
Maximum18540
Range8301
Interquartile range (IQR)5377.25

Descriptive statistics

Standard deviation2690.8136
Coefficient of variation (CV)0.18941747
Kurtosis-1.2835844
Mean14205.731
Median Absolute Deviation (MAD)2334.5
Skewness-0.026948824
Sum1477396
Variance7240477.7
MonotonicityNot monotonic
2023-12-11T06:45:18.418788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15015 5
 
3.4%
18540 3
 
2.1%
18255 3
 
2.1%
17562 3
 
2.1%
12772 2
 
1.4%
16967 2
 
1.4%
14771 2
 
1.4%
15631 2
 
1.4%
15247 2
 
1.4%
16056 2
 
1.4%
Other values (71) 78
53.4%
(Missing) 42
28.8%
ValueCountFrequency (%)
10239 2
1.4%
10250 2
1.4%
10267 2
1.4%
10271 2
1.4%
10275 1
0.7%
10317 2
1.4%
10377 1
0.7%
10407 1
0.7%
10478 1
0.7%
10511 1
0.7%
ValueCountFrequency (%)
18540 3
2.1%
18441 1
 
0.7%
18255 3
2.1%
18251 1
 
0.7%
17819 1
 
0.7%
17591 1
 
0.7%
17564 1
 
0.7%
17562 3
2.1%
17553 1
 
0.7%
17547 1
 
0.7%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct81
Distinct (%)82.7%
Missing48
Missing (%)32.9%
Infinite0
Infinite (%)0.0%
Mean37.437308
Minimum36.98552
Maximum37.99511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T06:45:18.524007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.98552
5-th percentile37.008131
Q137.280678
median37.375138
Q337.64811
95-th percentile37.86771
Maximum37.99511
Range1.0095901
Interquartile range (IQR)0.36743194

Descriptive statistics

Standard deviation0.24585744
Coefficient of variation (CV)0.0065671774
Kurtosis-0.46336308
Mean37.437308
Median Absolute Deviation (MAD)0.11714606
Skewness0.33629849
Sum3668.8562
Variance0.060445881
MonotonicityNot monotonic
2023-12-11T06:45:18.633086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.2239715049 3
 
2.1%
37.2153357619 3
 
2.1%
37.342660191 3
 
2.1%
36.9907417817 2
 
1.4%
37.7281292872 2
 
1.4%
37.6862692608 2
 
1.4%
37.7015420381 2
 
1.4%
37.7084788807 2
 
1.4%
37.3465157716 2
 
1.4%
37.3425942417 2
 
1.4%
Other values (71) 75
51.4%
(Missing) 48
32.9%
ValueCountFrequency (%)
36.9855199868 1
0.7%
36.9902553342 1
0.7%
36.9907417817 2
1.4%
36.995702269 1
0.7%
37.0103245504 1
0.7%
37.0578635487 1
0.7%
37.0765364989 1
0.7%
37.1222750746 1
0.7%
37.1267346767 1
0.7%
37.1891915115 1
0.7%
ValueCountFrequency (%)
37.9951100709 1
0.7%
37.9571048622 1
0.7%
37.9327747899 1
0.7%
37.9321031514 1
0.7%
37.904556138 1
0.7%
37.8612078385 1
0.7%
37.8556964038 1
0.7%
37.8534244353 1
0.7%
37.8111630502 1
0.7%
37.7931435059 1
0.7%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct81
Distinct (%)82.7%
Missing48
Missing (%)32.9%
Infinite0
Infinite (%)0.0%
Mean126.9944
Minimum126.69024
Maximum127.50578
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T06:45:18.737668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.69024
5-th percentile126.72025
Q1126.8117
median126.91755
Q3127.15548
95-th percentile127.3554
Maximum127.50578
Range0.81553632
Interquartile range (IQR)0.34377468

Descriptive statistics

Standard deviation0.20936317
Coefficient of variation (CV)0.0016486016
Kurtosis-0.86467058
Mean126.9944
Median Absolute Deviation (MAD)0.16083979
Skewness0.42968085
Sum12445.451
Variance0.043832936
MonotonicityNot monotonic
2023-12-11T06:45:18.847093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.7882040789 3
 
2.1%
126.8525390582 3
 
2.1%
126.6906610664 3
 
2.1%
127.1578826451 2
 
1.4%
126.9097992475 2
 
1.4%
126.812798015 2
 
1.4%
126.7656822613 2
 
1.4%
126.7961769176 2
 
1.4%
126.8206597899 2
 
1.4%
126.6902440616 2
 
1.4%
Other values (71) 75
51.4%
(Missing) 48
32.9%
ValueCountFrequency (%)
126.6902440616 2
1.4%
126.6906610664 3
2.1%
126.7254721653 1
 
0.7%
126.7530277989 1
 
0.7%
126.7628608142 1
 
0.7%
126.7656822613 2
1.4%
126.7708580607 1
 
0.7%
126.7718944095 1
 
0.7%
126.7732299912 1
 
0.7%
126.7857315066 1
 
0.7%
ValueCountFrequency (%)
127.5057803771 1
0.7%
127.4899654466 1
0.7%
127.4497537415 1
0.7%
127.4061833025 1
0.7%
127.366949184 1
0.7%
127.3533567488 1
0.7%
127.2709652229 1
0.7%
127.2591786101 1
0.7%
127.2579824268 1
0.7%
127.2569416257 1
0.7%

Interactions

2023-12-11T06:45:14.591111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:12.300238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:12.785030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:13.282665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:13.742347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:14.160359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:14.669792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:12.393945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:12.864854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:13.377850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:13.817283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:14.233289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:14.745378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:12.469570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:12.940736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:13.458276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:13.886086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:14.308095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:14.824074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:12.547725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:13.014688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:13.528277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:13.958122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:14.376335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:14.892173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:12.619477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:13.089203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:13.594711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:14.017401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:14.440671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:14.972674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:12.695452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:13.184447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:13.666518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:14.082461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:45:14.509654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:45:18.930696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명인허가일자영업상태명입소정원(명)자격소유인원수(명)총인원수(명)소재지도로명주소소재지우편번호WGS84위도WGS84경도
시군명1.0000.3970.0000.4720.6950.4871.0000.9950.9810.957
인허가일자0.3971.0000.3610.0000.4550.2250.7820.4110.0000.000
영업상태명0.0000.3611.0000.0000.1200.000NaN0.000NaNNaN
입소정원(명)0.4720.0000.0001.0000.5600.5240.9830.0000.4500.324
자격소유인원수(명)0.6950.4550.1200.5601.0000.4720.9840.5770.3520.485
총인원수(명)0.4870.2250.0000.5240.4721.0000.9900.3770.4450.546
소재지도로명주소1.0000.782NaN0.9830.9840.9901.0001.0001.0001.000
소재지우편번호0.9950.4110.0000.0000.5770.3771.0001.0000.9040.890
WGS84위도0.9810.000NaN0.4500.3520.4451.0000.9041.0000.656
WGS84경도0.9570.000NaN0.3240.4850.5461.0000.8900.6561.000
2023-12-11T06:45:19.035718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자격소유인원수(명)시군명영업상태명
자격소유인원수(명)1.0000.4490.197
시군명0.4491.0000.000
영업상태명0.1970.0001.000
2023-12-11T06:45:19.109640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자입소정원(명)총인원수(명)소재지우편번호WGS84위도WGS84경도시군명영업상태명자격소유인원수(명)
인허가일자1.000-0.198-0.2370.076-0.036-0.0090.1590.2690.286
입소정원(명)-0.1981.0000.4480.147-0.092-0.1190.1920.0000.289
총인원수(명)-0.2370.4481.000-0.068-0.027-0.0970.2450.0000.317
소재지우편번호0.0760.147-0.0681.000-0.9280.0240.9060.0000.422
WGS84위도-0.036-0.092-0.027-0.9281.000-0.0640.7231.0000.215
WGS84경도-0.009-0.119-0.0970.024-0.0641.0000.6321.0000.319
시군명0.1590.1920.2450.9060.7230.6321.0000.0000.449
영업상태명0.2690.0000.0000.0001.0001.0000.0001.0000.197
자격소유인원수(명)0.2860.2890.3170.4220.2150.3190.4490.1971.000

Missing values

2023-12-11T06:45:15.276517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:45:15.408829image/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.
2023-12-11T06:45:15.530627image/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

시군명사업장명인허가일자영업상태명입소정원(명)자격소유인원수(명)총인원수(명)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0고양시한마음의 집20020201운영중402<NA>경기도 고양시 덕양구 관산동 101호, 102호(관산동, 나동아파트) 106동<NA><NA><NA>
1고양시한울그룹홈20050804운영중401경기도 고양시 일산서구 대산로223번길 16-10경기도 고양시 일산서구 대화동 2157-10번지1037737.678407126.753028
2고양시한소망의집20060605운영중402경기도 고양시 일산동구 성현로93번길 25-19경기도 고양시 일산동구 성석동 992-4번지1025037.708479126.796177
3고양시홀트자활의집20080111운영중502<NA>경기도 고양시 일산서구 탄현동 121번지1023937.701542126.765682
4고양시미리내집(여)20050801운영중502경기도 고양시 덕양구 보광로162번길 14경기도 고양시 덕양구 벽제동 541-3번지1026737.728129126.909799
5고양시고양장애인공동생활가정20040202운영중402경기도 고양시 덕양구 푸른마을로120번길 34경기도 고양시 덕양구 고양동 1121번지1027137.717551126.903189
6고양시성가정공동생활가정19990101운영중412경기도 고양시 덕양구 읍내로 28경기도 고양시 덕양구 고양동 240-6번지 현대아파트 106동 2001호1027537.701074126.901324
7고양시기쁨20101201운영중411경기도 고양시 일산동구 견달산로225번길 21-91경기도 고양시 일산동구 식사동 290-42번지1031737.686269126.812798
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