Overview

Dataset statistics

Number of variables12
Number of observations23
Missing cells44
Missing cells (%)15.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory108.7 B

Variable types

Text4
Numeric7
Categorical1

Dataset

Description장애인단기보호시설 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=50FHIG7BRRVGNUM501I213925181&infSeq=1

Alerts

영업상태명 has constant value ""Constant
인허가일자 is highly overall correlated with WGS84경도High correlation
소재지우편번호 is highly overall correlated with WGS84위도High correlation
WGS84위도 is highly overall correlated with 소재지우편번호High correlation
WGS84경도 is highly overall correlated with 인허가일자High correlation
자격소유인원수(명) has 3 (13.0%) missing valuesMissing
총인원수(명) has 2 (8.7%) missing valuesMissing
소재지도로명주소 has 11 (47.8%) missing valuesMissing
소재지우편번호 has 10 (43.5%) missing valuesMissing
WGS84위도 has 9 (39.1%) missing valuesMissing
WGS84경도 has 9 (39.1%) missing valuesMissing
소재지지번주소 has unique valuesUnique
자격소유인원수(명) has 1 (4.3%) zerosZeros

Reproduction

Analysis started2023-12-10 22:27:02.556184
Analysis finished2023-12-10 22:27:08.429653
Duration5.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Text

Distinct13
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-11T07:27:08.526027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1304348
Min length3

Characters and Unicode

Total characters72
Distinct characters19
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

Unique6 ?
Unique (%)26.1%

Sample

1st row고양시
2nd row고양시
3rd row고양시
4th row광명시
5th row광주시
ValueCountFrequency (%)
고양시 3
13.0%
안산시 3
13.0%
파주시 3
13.0%
광주시 2
8.7%
남양주시 2
8.7%
안양시 2
8.7%
이천시 2
8.7%
광명시 1
 
4.3%
군포시 1
 
4.3%
동두천시 1
 
4.3%
Other values (3) 3
13.0%
2023-12-11T07:27:08.823598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 21
29.2%
양 8
 
11.1%
주 7
 
9.7%
안 5
 
6.9%
천 4
 
5.6%
고 3
 
4.2%
군 3
 
4.2%
산 3
 
4.2%
파 3
 
4.2%
광 3
 
4.2%
Other values (9) 12
16.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 72
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 21
29.2%
양 8
 
11.1%
주 7
 
9.7%
안 5
 
6.9%
천 4
 
5.6%
고 3
 
4.2%
군 3
 
4.2%
산 3
 
4.2%
파 3
 
4.2%
광 3
 
4.2%
Other values (9) 12
16.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 72
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 21
29.2%
양 8
 
11.1%
주 7
 
9.7%
안 5
 
6.9%
천 4
 
5.6%
고 3
 
4.2%
군 3
 
4.2%
산 3
 
4.2%
파 3
 
4.2%
광 3
 
4.2%
Other values (9) 12
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 72
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 21
29.2%
양 8
 
11.1%
주 7
 
9.7%
안 5
 
6.9%
천 4
 
5.6%
고 3
 
4.2%
군 3
 
4.2%
산 3
 
4.2%
파 3
 
4.2%
광 3
 
4.2%
Other values (9) 12
16.7%

사업장명
Text

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-11T07:27:09.008248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length18
Mean length10.043478
Min length4

Characters and Unicode

Total characters231
Distinct characters67
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

Unique21 ?
Unique (%)91.3%

Sample

1st row고양시장애인주간보호센터 부설 단기보호시설
2nd row나너우리센터
3rd row우림누리
4th row라마의 집
5th row광주시 장애인 단기보호시설(북부권)
ValueCountFrequency (%)
사랑의빛 2
 
6.2%
장애인 2
 
6.2%
단기보호센터 2
 
6.2%
장애인단기보호센터 1
 
3.1%
나너우리센터 1
 
3.1%
이천시장애인단기보호센터(효양동산 1
 
3.1%
엘리엘동산 1
 
3.1%
햇살마당 1
 
3.1%
창인단기보호시설 1
 
3.1%
안양시수리장애인단기보호시설 1
 
3.1%
Other values (19) 19
59.4%
2023-12-11T07:27:09.378244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
단 15
 
6.5%
보 15
 
6.5%
기 15
 
6.5%
호 15
 
6.5%
시 14
 
6.1%
애 10
 
4.3%
인 10
 
4.3%
9
 
3.9%
장 9
 
3.9%
설 8
 
3.5%
Other values (57) 111
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 216
93.5%
Space Separator 9
 
3.9%
Close Punctuation 3
 
1.3%
Open Punctuation 3
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
단 15
 
6.9%
보 15
 
6.9%
기 15
 
6.9%
호 15
 
6.9%
시 14
 
6.5%
애 10
 
4.6%
인 10
 
4.6%
장 9
 
4.2%
설 8
 
3.7%
터 8
 
3.7%
Other values (54) 97
44.9%
Space Separator
ValueCountFrequency (%)
9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 216
93.5%
Common 15
 
6.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
단 15
 
6.9%
보 15
 
6.9%
기 15
 
6.9%
호 15
 
6.9%
시 14
 
6.5%
애 10
 
4.6%
인 10
 
4.6%
장 9
 
4.2%
설 8
 
3.7%
터 8
 
3.7%
Other values (54) 97
44.9%
Common
ValueCountFrequency (%)
9
60.0%
) 3
 
20.0%
( 3
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 216
93.5%
ASCII 15
 
6.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
단 15
 
6.9%
보 15
 
6.9%
기 15
 
6.9%
호 15
 
6.9%
시 14
 
6.5%
애 10
 
4.6%
인 10
 
4.6%
장 9
 
4.2%
설 8
 
3.7%
터 8
 
3.7%
Other values (54) 97
44.9%
ASCII
ValueCountFrequency (%)
9
60.0%
) 3
 
20.0%
( 3
 
20.0%

인허가일자
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20068163
Minimum19970115
Maximum20131220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T07:27:09.503003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19970115
5-th percentile20002214
Q120040859
median20071231
Q320105573
95-th percentile20121121
Maximum20131220
Range161105
Interquartile range (IQR)64714

Descriptive statistics

Standard deviation44538.646
Coefficient of variation (CV)0.0022193683
Kurtosis-0.66506996
Mean20068163
Median Absolute Deviation (MAD)30524
Skewness-0.37778923
Sum4.6156775 × 108
Variance1.9836909 × 109
MonotonicityNot monotonic
2023-12-11T07:27:09.648043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
20100427 2
 
8.7%
20120227 1
 
4.3%
20080905 1
 
4.3%
20001226 1
 
4.3%
20121101 1
 
4.3%
20121123 1
 
4.3%
20060218 1
 
4.3%
19970115 1
 
4.3%
20031105 1
 
4.3%
20041026 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
19970115 1
4.3%
20001226 1
4.3%
20011101 1
4.3%
20020501 1
4.3%
20031105 1
4.3%
20040707 1
4.3%
20041011 1
4.3%
20041026 1
4.3%
20050126 1
4.3%
20051111 1
4.3%
ValueCountFrequency (%)
20131220 1
4.3%
20121123 1
4.3%
20121101 1
4.3%
20120227 1
4.3%
20120102 1
4.3%
20110719 1
4.3%
20100427 2
8.7%
20090804 1
4.3%
20081219 1
4.3%
20080905 1
4.3%

영업상태명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
운영중
23 

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 23
100.0%

Length

2023-12-11T07:27:09.796012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:27:09.889576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 23
100.0%

입소정원(명)
Real number (ℝ)

Distinct6
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.391304
Minimum10
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T07:27:09.969162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q110
median10
Q312.5
95-th percentile20
Maximum30
Range20
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation4.8592847
Coefficient of variation (CV)0.3921528
Kurtosis7.6197811
Mean12.391304
Median Absolute Deviation (MAD)0
Skewness2.6700056
Sum285
Variance23.612648
MonotonicityNot monotonic
2023-12-11T07:27:10.078402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
10 15
65.2%
12 2
 
8.7%
20 2
 
8.7%
13 2
 
8.7%
30 1
 
4.3%
15 1
 
4.3%
ValueCountFrequency (%)
10 15
65.2%
12 2
 
8.7%
13 2
 
8.7%
15 1
 
4.3%
20 2
 
8.7%
30 1
 
4.3%
ValueCountFrequency (%)
30 1
 
4.3%
20 2
 
8.7%
15 1
 
4.3%
13 2
 
8.7%
12 2
 
8.7%
10 15
65.2%

자격소유인원수(명)
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)30.0%
Missing3
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean2.9
Minimum0
Maximum6
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T07:27:10.184278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.95
Q12
median3
Q34
95-th percentile4.1
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3726655
Coefficient of variation (CV)0.47333292
Kurtosis0.54159774
Mean2.9
Median Absolute Deviation (MAD)1
Skewness-0.07325795
Sum58
Variance1.8842105
MonotonicityNot monotonic
2023-12-11T07:27:10.282323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 6
26.1%
3 6
26.1%
2 4
17.4%
1 2
 
8.7%
0 1
 
4.3%
6 1
 
4.3%
(Missing) 3
13.0%
ValueCountFrequency (%)
0 1
 
4.3%
1 2
 
8.7%
2 4
17.4%
3 6
26.1%
4 6
26.1%
6 1
 
4.3%
ValueCountFrequency (%)
6 1
 
4.3%
4 6
26.1%
3 6
26.1%
2 4
17.4%
1 2
 
8.7%
0 1
 
4.3%

총인원수(명)
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)28.6%
Missing2
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean3.8571429
Minimum2
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T07:27:10.382996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median4
Q34
95-th percentile6
Maximum9
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5259657
Coefficient of variation (CV)0.39562075
Kurtosis5.9674809
Mean3.8571429
Median Absolute Deviation (MAD)1
Skewness1.944385
Sum81
Variance2.3285714
MonotonicityNot monotonic
2023-12-11T07:27:10.506670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 10
43.5%
3 5
21.7%
2 3
 
13.0%
5 1
 
4.3%
6 1
 
4.3%
9 1
 
4.3%
(Missing) 2
 
8.7%
ValueCountFrequency (%)
2 3
 
13.0%
3 5
21.7%
4 10
43.5%
5 1
 
4.3%
6 1
 
4.3%
9 1
 
4.3%
ValueCountFrequency (%)
9 1
 
4.3%
6 1
 
4.3%
5 1
 
4.3%
4 10
43.5%
3 5
21.7%
2 3
 
13.0%

소재지도로명주소
Text

MISSING 

Distinct12
Distinct (%)100.0%
Missing11
Missing (%)47.8%
Memory size316.0 B
2023-12-11T07:27:10.700663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22.5
Mean length19.5
Min length14

Characters and Unicode

Total characters234
Distinct characters62
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

Unique12 ?
Unique (%)100.0%

Sample

1st row경기도 광주시 사기막길95번길 59
2nd row경기도 군포시 군포로 444
3rd row경기도 남양주시 수동면 비룡로1742번길 95
4th row경기도 동두천시 상패로 64
5th row경기도 성남시 중원구 순환로226번길 8
ValueCountFrequency (%)
경기도 12
 
21.4%
상록구 2
 
3.6%
안산시 2
 
3.6%
안양시 2
 
3.6%
만안구 2
 
3.6%
14 2
 
3.6%
39 1
 
1.8%
22 1
 
1.8%
장내로140번길 1
 
1.8%
냉천로 1
 
1.8%
Other values (30) 30
53.6%
2023-12-11T07:27:11.014905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
44
18.8%
기 13
 
5.6%
경 12
 
5.1%
도 12
 
5.1%
시 11
 
4.7%
길 9
 
3.8%
4 8
 
3.4%
로 8
 
3.4%
안 7
 
3.0%
5 6
 
2.6%
Other values (52) 104
44.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 148
63.2%
Space Separator 44
 
18.8%
Decimal Number 41
 
17.5%
Dash Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 13
 
8.8%
경 12
 
8.1%
도 12
 
8.1%
시 11
 
7.4%
길 9
 
6.1%
로 8
 
5.4%
안 7
 
4.7%
번 5
 
3.4%
구 5
 
3.4%
군 4
 
2.7%
Other values (40) 62
41.9%
Decimal Number
ValueCountFrequency (%)
4 8
19.5%
5 6
14.6%
9 6
14.6%
2 6
14.6%
1 4
9.8%
8 3
 
7.3%
6 3
 
7.3%
3 2
 
4.9%
0 2
 
4.9%
7 1
 
2.4%
Space Separator
ValueCountFrequency (%)
44
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 148
63.2%
Common 86
36.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 13
 
8.8%
경 12
 
8.1%
도 12
 
8.1%
시 11
 
7.4%
길 9
 
6.1%
로 8
 
5.4%
안 7
 
4.7%
번 5
 
3.4%
구 5
 
3.4%
군 4
 
2.7%
Other values (40) 62
41.9%
Common
ValueCountFrequency (%)
44
51.2%
4 8
 
9.3%
5 6
 
7.0%
9 6
 
7.0%
2 6
 
7.0%
1 4
 
4.7%
8 3
 
3.5%
6 3
 
3.5%
3 2
 
2.3%
0 2
 
2.3%
Other values (2) 2
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 148
63.2%
ASCII 86
36.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
44
51.2%
4 8
 
9.3%
5 6
 
7.0%
9 6
 
7.0%
2 6
 
7.0%
1 4
 
4.7%
8 3
 
3.5%
6 3
 
3.5%
3 2
 
2.3%
0 2
 
2.3%
Other values (2) 2
 
2.3%
Hangul
ValueCountFrequency (%)
기 13
 
8.8%
경 12
 
8.1%
도 12
 
8.1%
시 11
 
7.4%
길 9
 
6.1%
로 8
 
5.4%
안 7
 
4.7%
번 5
 
3.4%
구 5
 
3.4%
군 4
 
2.7%
Other values (40) 62
41.9%

소재지지번주소
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-11T07:27:11.221742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length24
Mean length18.869565
Min length11

Characters and Unicode

Total characters434
Distinct characters84
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

Unique23 ?
Unique (%)100.0%

Sample

1st row경기도 고양시 일산서구 일산동
2nd row경기도 고양시 일산동구 설문동
3rd row경기도 고양시 일산동구 성석동
4th row경기도 광명시 광명동 374-80번지
5th row경기도 광주시 탄벌동 674-1번지
ValueCountFrequency (%)
경기도 23
23.0%
고양시 3
 
3.0%
파주시 3
 
3.0%
상록구 3
 
3.0%
안산시 3
 
3.0%
안양시 2
 
2.0%
만안구 2
 
2.0%
일산동구 2
 
2.0%
광주시 2
 
2.0%
안양동 2
 
2.0%
Other values (52) 55
55.0%
2023-12-11T07:27:11.528258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
77
 
17.7%
경 23
 
5.3%
도 23
 
5.3%
기 23
 
5.3%
시 21
 
4.8%
동 21
 
4.8%
지 14
 
3.2%
번 13
 
3.0%
1 12
 
2.8%
2 10
 
2.3%
Other values (74) 197
45.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 293
67.5%
Space Separator 77
 
17.7%
Decimal Number 55
 
12.7%
Dash Punctuation 9
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 23
 
7.8%
도 23
 
7.8%
기 23
 
7.8%
시 21
 
7.2%
동 21
 
7.2%
지 14
 
4.8%
번 13
 
4.4%
양 10
 
3.4%
구 9
 
3.1%
안 9
 
3.1%
Other values (62) 127
43.3%
Decimal Number
ValueCountFrequency (%)
1 12
21.8%
2 10
18.2%
7 8
14.5%
4 5
9.1%
3 5
9.1%
0 4
 
7.3%
5 4
 
7.3%
6 4
 
7.3%
9 2
 
3.6%
8 1
 
1.8%
Space Separator
ValueCountFrequency (%)
77
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 293
67.5%
Common 141
32.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 23
 
7.8%
도 23
 
7.8%
기 23
 
7.8%
시 21
 
7.2%
동 21
 
7.2%
지 14
 
4.8%
번 13
 
4.4%
양 10
 
3.4%
구 9
 
3.1%
안 9
 
3.1%
Other values (62) 127
43.3%
Common
ValueCountFrequency (%)
77
54.6%
1 12
 
8.5%
2 10
 
7.1%
- 9
 
6.4%
7 8
 
5.7%
4 5
 
3.5%
3 5
 
3.5%
0 4
 
2.8%
5 4
 
2.8%
6 4
 
2.8%
Other values (2) 3
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 293
67.5%
ASCII 141
32.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
77
54.6%
1 12
 
8.5%
2 10
 
7.1%
- 9
 
6.4%
7 8
 
5.7%
4 5
 
3.5%
3 5
 
3.5%
0 4
 
2.8%
5 4
 
2.8%
6 4
 
2.8%
Other values (2) 3
 
2.1%
Hangul
ValueCountFrequency (%)
경 23
 
7.8%
도 23
 
7.8%
기 23
 
7.8%
시 21
 
7.2%
동 21
 
7.2%
지 14
 
4.8%
번 13
 
4.4%
양 10
 
3.4%
구 9
 
3.1%
안 9
 
3.1%
Other values (62) 127
43.3%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)100.0%
Missing10
Missing (%)43.5%
Infinite0
Infinite (%)0.0%
Mean13119.231
Minimum10932
Maximum15875
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T07:27:11.636723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10932
5-th percentile10981.8
Q112024
median12748
Q314091
95-th percentile15647
Maximum15875
Range4943
Interquartile range (IQR)2067

Descriptive statistics

Standard deviation1704.5571
Coefficient of variation (CV)0.12992813
Kurtosis-1.1807692
Mean13119.231
Median Absolute Deviation (MAD)1343
Skewness0.35095315
Sum170550
Variance2905514.9
MonotonicityNot monotonic
2023-12-11T07:27:11.762421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
12748 1
 
4.3%
15875 1
 
4.3%
12029 1
 
4.3%
12024 1
 
4.3%
11338 1
 
4.3%
13204 1
 
4.3%
15495 1
 
4.3%
15275 1
 
4.3%
13996 1
 
4.3%
14091 1
 
4.3%
Other values (3) 3
 
13.0%
(Missing) 10
43.5%
ValueCountFrequency (%)
10932 1
4.3%
11015 1
4.3%
11338 1
4.3%
12024 1
4.3%
12029 1
4.3%
12528 1
4.3%
12748 1
4.3%
13204 1
4.3%
13996 1
4.3%
14091 1
4.3%
ValueCountFrequency (%)
15875 1
4.3%
15495 1
4.3%
15275 1
4.3%
14091 1
4.3%
13996 1
4.3%
13204 1
4.3%
12748 1
4.3%
12528 1
4.3%
12029 1
4.3%
12024 1
4.3%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)100.0%
Missing9
Missing (%)39.1%
Infinite0
Infinite (%)0.0%
Mean37.565626
Minimum37.307954
Maximum38.119831
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T07:27:11.854029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.307954
5-th percentile37.326518
Q137.387995
median37.457601
Q337.746151
95-th percentile37.981761
Maximum38.119831
Range0.81187725
Interquartile range (IQR)0.35815685

Descriptive statistics

Standard deviation0.24809265
Coefficient of variation (CV)0.0066042464
Kurtosis0.15564738
Mean37.565626
Median Absolute Deviation (MAD)0.11624962
Skewness1.0136757
Sum525.91876
Variance0.061549961
MonotonicityNot monotonic
2023-12-11T07:27:11.950750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
37.4716850422 1
 
4.3%
37.4140482207 1
 
4.3%
37.3461878431 1
 
4.3%
37.7207673314 1
 
4.3%
37.768646612 1
 
4.3%
37.9074152109 1
 
4.3%
37.44351654 1
 
4.3%
37.3079538108 1
 
4.3%
37.3365144934 1
 
4.3%
37.3971690271 1
 
4.3%
Other values (4) 4
17.4%
(Missing) 9
39.1%
ValueCountFrequency (%)
37.3079538108 1
4.3%
37.3365144934 1
4.3%
37.3461878431 1
4.3%
37.3849363728 1
4.3%
37.3971690271 1
4.3%
37.4140482207 1
4.3%
37.44351654 1
4.3%
37.4716850422 1
4.3%
37.5454738542 1
4.3%
37.7207673314 1
4.3%
ValueCountFrequency (%)
38.1198310604 1
4.3%
37.9074152109 1
4.3%
37.768646612 1
4.3%
37.7546127379 1
4.3%
37.7207673314 1
4.3%
37.5454738542 1
4.3%
37.4716850422 1
4.3%
37.44351654 1
4.3%
37.4140482207 1
4.3%
37.3971690271 1
4.3%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)100.0%
Missing9
Missing (%)39.1%
Infinite0
Infinite (%)0.0%
Mean127.06792
Minimum126.78348
Maximum127.68261
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T07:27:12.054226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.78348
5-th percentile126.82749
Q1126.87476
median126.9951
Q3127.21421
95-th percentile127.4441
Maximum127.68261
Range0.8991286
Interquartile range (IQR)0.33945151

Descriptive statistics

Standard deviation0.24825967
Coefficient of variation (CV)0.0019537556
Kurtosis1.4319433
Mean127.06792
Median Absolute Deviation (MAD)0.14319007
Skewness1.1946596
Sum1778.9509
Variance0.061632864
MonotonicityNot monotonic
2023-12-11T07:27:12.147373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
126.851186463 1
 
4.3%
127.2256230217 1
 
4.3%
126.9424161911 1
 
4.3%
127.3156788087 1
 
4.3%
127.2906610907 1
 
4.3%
127.0477763271 1
 
4.3%
127.1799678595 1
 
4.3%
126.8526259109 1
 
4.3%
126.8585106482 1
 
4.3%
126.9234989265 1
 
4.3%
Other values (4) 4
17.4%
(Missing) 9
39.1%
ValueCountFrequency (%)
126.78348002 1
4.3%
126.851186463 1
4.3%
126.8526259109 1
4.3%
126.8585106482 1
4.3%
126.9234989265 1
4.3%
126.926992701 1
4.3%
126.9424161911 1
4.3%
127.0477763271 1
4.3%
127.0698907577 1
4.3%
127.1799678595 1
4.3%
ValueCountFrequency (%)
127.6826086153 1
4.3%
127.3156788087 1
4.3%
127.2906610907 1
4.3%
127.2256230217 1
4.3%
127.1799678595 1
4.3%
127.0698907577 1
4.3%
127.0477763271 1
4.3%
126.9424161911 1
4.3%
126.926992701 1
4.3%
126.9234989265 1
4.3%

Interactions

2023-12-11T07:27:07.406895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:02.942708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:03.621317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:04.394576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:05.035125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:06.039021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:06.744853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:07.486557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:03.046233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:03.712858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:04.506169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:05.130263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:06.146470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:06.846101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:07.603356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:03.139713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:03.842851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:04.602701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:05.223413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:06.260881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:06.952078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:07.710424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:03.247866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:03.954639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:04.680348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:05.326033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:06.342830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:07.042967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:07.798372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:03.358898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:04.097917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:04.762836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:05.426013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:06.439745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:07.139107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:07.872698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:03.441982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:04.191509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:04.850534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:05.555790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:06.538223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:07.223973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:07.963839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:03.536502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:04.294598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:04.958940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:05.671868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:06.659857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:27:07.325636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:27:12.224450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명사업장명인허가일자입소정원(명)자격소유인원수(명)총인원수(명)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
시군명1.0001.0000.6270.6790.8490.0001.0001.0000.9090.9611.000
사업장명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
인허가일자0.6271.0001.0000.7630.8130.0001.0001.0000.7620.0000.000
입소정원(명)0.6791.0000.7631.0000.6910.4001.0001.0000.4240.0000.000
자격소유인원수(명)0.8491.0000.8130.6911.0000.8631.0001.0000.6790.0000.692
총인원수(명)0.0001.0000.0000.4000.8631.0001.0001.0000.6790.0000.000
소재지도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지우편번호0.9091.0000.7620.4240.6790.6791.0001.0001.0000.6560.894
WGS84위도0.9611.0000.0000.0000.0000.0001.0001.0000.6561.0000.873
WGS84경도1.0001.0000.0000.0000.6920.0001.0001.0000.8940.8731.000
2023-12-11T07:27:12.561158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자입소정원(명)자격소유인원수(명)총인원수(명)소재지우편번호WGS84위도WGS84경도
인허가일자1.000-0.3060.169-0.1960.143-0.213-0.662
입소정원(명)-0.3061.0000.0440.1810.249-0.2050.141
자격소유인원수(명)0.1690.0441.0000.4690.295-0.1960.071
총인원수(명)-0.1960.1810.4691.0000.442-0.329-0.060
소재지우편번호0.1430.2490.2950.4421.000-0.945-0.297
WGS84위도-0.213-0.205-0.196-0.329-0.9451.0000.380
WGS84경도-0.6620.1410.071-0.060-0.2970.3801.000

Missing values

2023-12-11T07:27:08.072673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:27:08.224768image/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.
2023-12-11T07:27:08.343404image/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
0고양시고양시장애인주간보호센터 부설 단기보호시설20120227운영중10<NA><NA><NA>경기도 고양시 일산서구 일산동<NA><NA><NA>
1고양시나너우리센터20071231운영중1044<NA>경기도 고양시 일산동구 설문동<NA><NA><NA>
2고양시우림누리20020501운영중1033<NA>경기도 고양시 일산동구 성석동<NA><NA><NA>
3광명시라마의 집20081219운영중1024<NA>경기도 광명시 광명동 374-80번지<NA>37.471685126.851186
4광주시광주시 장애인 단기보호시설(북부권)20050126운영중1033경기도 광주시 사기막길95번길 59경기도 광주시 탄벌동 674-1번지1274837.414048127.225623
5광주시광주시장애인단기보호시설20120102운영중1024<NA>경기도 광주시 곤지암읍 연곡리<NA><NA><NA>
6군포시가온누리단기보호20110719운영중1044경기도 군포시 군포로 444경기도 군포시 당동 서일빌딩1587537.346188126.942416
7남양주시신망애단기보호센터20040707운영중1044<NA>경기도 남양주시 수동면 입석리 522번지1202937.720767127.315679
8남양주시맑은누리20051111운영중3044경기도 남양주시 수동면 비룡로1742번길 95경기도 남양주시 수동면 내방리 377-3번지1202437.768647127.290661
9동두천시동두천시 장애인단기보호센터20041011운영중1233경기도 동두천시 상패로 64경기도 동두천시 상패동 54번지1133837.907415127.047776
시군명사업장명인허가일자영업상태명입소정원(명)자격소유인원수(명)총인원수(명)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
13안산시안산시상록장애인단기보호시설20131220운영중1044<NA>경기도 안산시 상록구 사동<NA><NA><NA>
14안양시베데스다장애인단기보호시설20041026운영중1046경기도 안양시 만안구 장내로140번길 14경기도 안양시 만안구 안양동 622-205번지1399637.397169126.923499
15안양시안양시수리장애인단기보호시설20031105운영중2069경기도 안양시 만안구 냉천로 39경기도 안양시 만안구 안양동 477-1번지1409137.384936126.926993
16양평군창인단기보호시설19970115운영중1313경기도 양평군 단월면 백동길 92경기도 양평군 단월면 덕수리 213-2번지1252837.545474127.682609
17연천군햇살마당20060218운영중1034경기도 연천군 연천읍 연신로305번길 88-69경기도 연천군 연천읍 현가리 321번지1101538.119831127.069891
18이천시엘리엘동산 단기보호센터20121123운영중10<NA>2<NA>경기도 이천시 마장면<NA><NA><NA>
19이천시이천시장애인단기보호센터(효양동산)20121101운영중12<NA><NA><NA>경기도 이천시 부발읍<NA><NA><NA>
20파주시사랑의빛20100427운영중1022<NA>경기도 파주시 금릉동<NA><NA><NA>
21파주시푸른솔둥지20001226운영중1014<NA>경기도 파주시 법원읍<NA><NA><NA>
22파주시사랑의빛20100427운영중1022경기도 파주시 독암길 14경기도 파주시 금능동 199번지 흰돌마을아파트 102동 102호1093237.754613126.78348