Overview

Dataset statistics

Number of variables12
Number of observations58
Missing cells56
Missing cells (%)8.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.0 KiB
Average record size in memory105.3 B

Variable types

Categorical2
Text3
Numeric7

Dataset

Description중증장애인요양시설 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=71GZEBB4UFDA00RQO5L613873553&infSeq=1

Alerts

영업상태명 has constant value ""Constant
인허가일자 is highly overall correlated with 입소정원(명) and 1 other fieldsHigh correlation
입소정원(명) is highly overall correlated with 인허가일자 and 1 other fieldsHigh correlation
총인원수(명) is highly overall correlated with 인허가일자 and 1 other fieldsHigh 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
입소정원(명) has 1 (1.7%) missing valuesMissing
자격소유인원수(명) has 7 (12.1%) missing valuesMissing
총인원수(명) has 5 (8.6%) missing valuesMissing
소재지도로명주소 has 29 (50.0%) missing valuesMissing
소재지우편번호 has 14 (24.1%) missing valuesMissing
입소정원(명) has 1 (1.7%) zerosZeros
자격소유인원수(명) has 8 (13.8%) zerosZeros
총인원수(명) has 2 (3.4%) zerosZeros

Reproduction

Analysis started2023-12-10 21:30:02.818610
Analysis finished2023-12-10 21:30:08.196077
Duration5.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)36.2%
Missing0
Missing (%)0.0%
Memory size596.0 B
이천시
7 
양평군
7 
화성시
4 
남양주시
4 
시흥시
 
3
Other values (16)
33 

Length

Max length4
Median length3
Mean length3.0689655
Min length3

Unique

Unique4 ?
Unique (%)6.9%

Sample

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

Common Values

ValueCountFrequency (%)
이천시 7
 
12.1%
양평군 7
 
12.1%
화성시 4
 
6.9%
남양주시 4
 
6.9%
시흥시 3
 
5.2%
파주시 3
 
5.2%
용인시 3
 
5.2%
안산시 3
 
5.2%
가평군 3
 
5.2%
부천시 3
 
5.2%
Other values (11) 18
31.0%

Length

2023-12-11T06:30:08.263494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
이천시 7
 
12.1%
양평군 7
 
12.1%
화성시 4
 
6.9%
남양주시 4
 
6.9%
시흥시 3
 
5.2%
파주시 3
 
5.2%
용인시 3
 
5.2%
안산시 3
 
5.2%
가평군 3
 
5.2%
부천시 3
 
5.2%
Other values (11) 18
31.0%

사업장명
Text

Distinct57
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size596.0 B
2023-12-11T06:30:08.696491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length5.362069
Min length2

Characters and Unicode

Total characters311
Distinct characters119
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

Unique56 ?
Unique (%)96.6%

Sample

1st row성가정의 집
2nd row가평꽃동네 희망의집
3rd row작은예수회현리요셉의집
4th row해밀
5th row홀트일산요양원
ValueCountFrequency (%)
집 6
 
8.7%
비젼하우스 2
 
2.9%
향기로운집(실비 1
 
1.4%
베데스다 1
 
1.4%
창인요양원 1
 
1.4%
창인홈 1
 
1.4%
동트는 1
 
1.4%
마을 1
 
1.4%
가람 1
 
1.4%
성심요양원 1
 
1.4%
Other values (53) 53
76.8%
2023-12-11T06:30:09.050094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
집 21
 
6.8%
의 20
 
6.4%
원 14
 
4.5%
양 12
 
3.9%
11
 
3.5%
요 9
 
2.9%
지 7
 
2.3%
리 7
 
2.3%
복 6
 
1.9%
마 6
 
1.9%
Other values (109) 198
63.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 298
95.8%
Space Separator 11
 
3.5%
Open Punctuation 1
 
0.3%
Close Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
집 21
 
7.0%
의 20
 
6.7%
원 14
 
4.7%
양 12
 
4.0%
요 9
 
3.0%
지 7
 
2.3%
리 7
 
2.3%
복 6
 
2.0%
마 6
 
2.0%
아 6
 
2.0%
Other values (106) 190
63.8%
Space Separator
ValueCountFrequency (%)
11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 298
95.8%
Common 13
 
4.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
집 21
 
7.0%
의 20
 
6.7%
원 14
 
4.7%
양 12
 
4.0%
요 9
 
3.0%
지 7
 
2.3%
리 7
 
2.3%
복 6
 
2.0%
마 6
 
2.0%
아 6
 
2.0%
Other values (106) 190
63.8%
Common
ValueCountFrequency (%)
11
84.6%
( 1
 
7.7%
) 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 298
95.8%
ASCII 13
 
4.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
집 21
 
7.0%
의 20
 
6.7%
원 14
 
4.7%
양 12
 
4.0%
요 9
 
3.0%
지 7
 
2.3%
리 7
 
2.3%
복 6
 
2.0%
마 6
 
2.0%
아 6
 
2.0%
Other values (106) 190
63.8%
ASCII
ValueCountFrequency (%)
11
84.6%
( 1
 
7.7%
) 1
 
7.7%

인허가일자
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20039824
Minimum19840401
Maximum20160628
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size654.0 B
2023-12-11T06:30:09.192759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19840401
5-th percentile19890643
Q119990383
median20060708
Q320091230
95-th percentile20150772
Maximum20160628
Range320227
Interquartile range (IQR)100846.25

Descriptive statistics

Standard deviation80133.053
Coefficient of variation (CV)0.0039986905
Kurtosis-0.44067151
Mean20039824
Median Absolute Deviation (MAD)44986.5
Skewness-0.66874481
Sum1.1623098 × 109
Variance6.4213062 × 109
MonotonicityNot monotonic
2023-12-11T06:30:09.324601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20120925 2
 
3.4%
20080201 2
 
3.4%
20090701 2
 
3.4%
20080804 1
 
1.7%
19991230 1
 
1.7%
20100311 1
 
1.7%
20040326 1
 
1.7%
20090303 1
 
1.7%
19940516 1
 
1.7%
20151001 1
 
1.7%
Other values (45) 45
77.6%
ValueCountFrequency (%)
19840401 1
1.7%
19890201 1
1.7%
19890202 1
1.7%
19890721 1
1.7%
19900419 1
1.7%
19910412 1
1.7%
19910618 1
1.7%
19920601 1
1.7%
19931227 1
1.7%
19940516 1
1.7%
ValueCountFrequency (%)
20160628 1
1.7%
20160223 1
1.7%
20151001 1
1.7%
20150731 1
1.7%
20140811 1
1.7%
20131011 1
1.7%
20120925 2
3.4%
20111219 1
1.7%
20110420 1
1.7%
20101202 1
1.7%

영업상태명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size596.0 B
운영중
58 

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 58
100.0%

Length

2023-12-11T06:30:09.571907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:30:09.716624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 58
100.0%

입소정원(명)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct26
Distinct (%)45.6%
Missing1
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean51.035088
Minimum0
Maximum330
Zeros1
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size654.0 B
2023-12-11T06:30:09.818522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.4
Q130
median40
Q360
95-th percentile110
Maximum330
Range330
Interquartile range (IQR)30

Descriptive statistics

Standard deviation46.993147
Coefficient of variation (CV)0.92080075
Kurtosis22.304713
Mean51.035088
Median Absolute Deviation (MAD)11
Skewness4.1212521
Sum2909
Variance2208.3559
MonotonicityNot monotonic
2023-12-11T06:30:09.930278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
30 12
20.7%
40 8
13.8%
29 4
 
6.9%
70 3
 
5.2%
35 3
 
5.2%
50 3
 
5.2%
60 3
 
5.2%
10 2
 
3.4%
80 2
 
3.4%
53 1
 
1.7%
Other values (16) 16
27.6%
ValueCountFrequency (%)
0 1
 
1.7%
10 2
 
3.4%
13 1
 
1.7%
15 1
 
1.7%
25 1
 
1.7%
27 1
 
1.7%
29 4
 
6.9%
30 12
20.7%
31 1
 
1.7%
35 3
 
5.2%
ValueCountFrequency (%)
330 1
 
1.7%
155 1
 
1.7%
130 1
 
1.7%
105 1
 
1.7%
100 1
 
1.7%
81 1
 
1.7%
80 2
3.4%
70 3
5.2%
64 1
 
1.7%
60 3
5.2%

자격소유인원수(명)
Real number (ℝ)

MISSING  ZEROS 

Distinct25
Distinct (%)49.0%
Missing7
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean9.745098
Minimum0
Maximum39
Zeros8
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size654.0 B
2023-12-11T06:30:10.046660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q315
95-th percentile28
Maximum39
Range39
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.7362069
Coefficient of variation (CV)0.99908763
Kurtosis0.37840835
Mean9.745098
Median Absolute Deviation (MAD)6
Skewness1.0188875
Sum497
Variance94.793725
MonotonicityNot monotonic
2023-12-11T06:30:10.171544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 8
13.8%
1 6
 
10.3%
15 4
 
6.9%
6 4
 
6.9%
8 3
 
5.2%
2 3
 
5.2%
17 2
 
3.4%
3 2
 
3.4%
7 2
 
3.4%
9 2
 
3.4%
Other values (15) 15
25.9%
(Missing) 7
12.1%
ValueCountFrequency (%)
0 8
13.8%
1 6
10.3%
2 3
 
5.2%
3 2
 
3.4%
4 1
 
1.7%
5 1
 
1.7%
6 4
6.9%
7 2
 
3.4%
8 3
 
5.2%
9 2
 
3.4%
ValueCountFrequency (%)
39 1
1.7%
30 1
1.7%
29 1
1.7%
27 1
1.7%
25 1
1.7%
24 1
1.7%
23 1
1.7%
21 1
1.7%
20 1
1.7%
19 1
1.7%

총인원수(명)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct35
Distinct (%)66.0%
Missing5
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean25.698113
Minimum0
Maximum113
Zeros2
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size654.0 B
2023-12-11T06:30:10.340163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.6
Q114
median23
Q329
95-th percentile48.6
Maximum113
Range113
Interquartile range (IQR)15

Descriptive statistics

Standard deviation20.435249
Coefficient of variation (CV)0.79520427
Kurtosis7.6827929
Mean25.698113
Median Absolute Deviation (MAD)9
Skewness2.2424095
Sum1362
Variance417.59942
MonotonicityNot monotonic
2023-12-11T06:30:10.467517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
27 4
 
6.9%
29 3
 
5.2%
28 3
 
5.2%
21 3
 
5.2%
0 2
 
3.4%
3 2
 
3.4%
19 2
 
3.4%
20 2
 
3.4%
23 2
 
3.4%
4 2
 
3.4%
Other values (25) 28
48.3%
(Missing) 5
 
8.6%
ValueCountFrequency (%)
0 2
3.4%
2 1
1.7%
3 2
3.4%
4 2
3.4%
6 1
1.7%
8 1
1.7%
9 2
3.4%
11 1
1.7%
12 1
1.7%
14 1
1.7%
ValueCountFrequency (%)
113 1
1.7%
97 1
1.7%
54 1
1.7%
45 1
1.7%
44 1
1.7%
42 1
1.7%
41 1
1.7%
40 1
1.7%
39 1
1.7%
38 1
1.7%

소재지도로명주소
Text

MISSING 

Distinct27
Distinct (%)93.1%
Missing29
Missing (%)50.0%
Memory size596.0 B
2023-12-11T06:30:10.720616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length23
Mean length22.310345
Min length15

Characters and Unicode

Total characters647
Distinct characters94
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

Unique25 ?
Unique (%)86.2%

Sample

1st row경기도 가평군 조종면 연인산로474번길 139-37
2nd row경기도 가평군 조종면 연인산로474번길 140-57
3rd row경기도 고양시 일산동구 견달산로225번길 21-83
4th row경기도 고양시 일산서구 탄현로 42
5th row경기도 김포시 월곶면 용강로37번길 76-25
ValueCountFrequency (%)
경기도 29
 
20.7%
단원구 3
 
2.1%
부천시 3
 
2.1%
화성시 3
 
2.1%
안산시 3
 
2.1%
이천시 3
 
2.1%
조종면 2
 
1.4%
포천시 2
 
1.4%
수동면 2
 
1.4%
남양주시 2
 
1.4%
Other values (76) 88
62.9%
2023-12-11T06:30:11.090053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
111
 
17.2%
경 30
 
4.6%
기 30
 
4.6%
도 29
 
4.5%
시 28
 
4.3%
로 24
 
3.7%
3 23
 
3.6%
2 23
 
3.6%
1 20
 
3.1%
길 19
 
2.9%
Other values (84) 310
47.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 377
58.3%
Decimal Number 143
 
22.1%
Space Separator 111
 
17.2%
Dash Punctuation 16
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 30
 
8.0%
기 30
 
8.0%
도 29
 
7.7%
시 28
 
7.4%
로 24
 
6.4%
길 19
 
5.0%
번 14
 
3.7%
면 11
 
2.9%
산 11
 
2.9%
천 10
 
2.7%
Other values (72) 171
45.4%
Decimal Number
ValueCountFrequency (%)
3 23
16.1%
2 23
16.1%
1 20
14.0%
4 16
11.2%
7 15
10.5%
6 12
8.4%
8 11
7.7%
9 9
 
6.3%
5 7
 
4.9%
0 7
 
4.9%
Space Separator
ValueCountFrequency (%)
111
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 377
58.3%
Common 270
41.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 30
 
8.0%
기 30
 
8.0%
도 29
 
7.7%
시 28
 
7.4%
로 24
 
6.4%
길 19
 
5.0%
번 14
 
3.7%
면 11
 
2.9%
산 11
 
2.9%
천 10
 
2.7%
Other values (72) 171
45.4%
Common
ValueCountFrequency (%)
111
41.1%
3 23
 
8.5%
2 23
 
8.5%
1 20
 
7.4%
4 16
 
5.9%
- 16
 
5.9%
7 15
 
5.6%
6 12
 
4.4%
8 11
 
4.1%
9 9
 
3.3%
Other values (2) 14
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 377
58.3%
ASCII 270
41.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
111
41.1%
3 23
 
8.5%
2 23
 
8.5%
1 20
 
7.4%
4 16
 
5.9%
- 16
 
5.9%
7 15
 
5.6%
6 12
 
4.4%
8 11
 
4.1%
9 9
 
3.3%
Other values (2) 14
 
5.2%
Hangul
ValueCountFrequency (%)
경 30
 
8.0%
기 30
 
8.0%
도 29
 
7.7%
시 28
 
7.4%
로 24
 
6.4%
길 19
 
5.0%
번 14
 
3.7%
면 11
 
2.9%
산 11
 
2.9%
천 10
 
2.7%
Other values (72) 171
45.4%

소재지지번주소
Text

Distinct53
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Memory size596.0 B
2023-12-11T06:30:11.437041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length24
Mean length18.741379
Min length11

Characters and Unicode

Total characters1087
Distinct characters121
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

Unique48 ?
Unique (%)82.8%

Sample

1st row경기도 가평군 하면 마일리 296-14번지
2nd row경기도 가평군 하면 하판리 가평꽃동네 희망의 집
3rd row경기도 가평군 하면 마일리 102-2번지
4th row경기도 고양시 일산동구 식사동 290-9번지
5th row경기도 고양시 일산서구 탄현동 41-1번지
ValueCountFrequency (%)
경기도 58
 
22.7%
이천시 7
 
2.7%
양평군 7
 
2.7%
수동면 4
 
1.6%
남양주시 4
 
1.6%
화성시 4
 
1.6%
용인시 3
 
1.2%
하면 3
 
1.2%
가평군 3
 
1.2%
시흥시 3
 
1.2%
Other values (124) 160
62.5%
2023-12-11T06:30:11.918115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
198
 
18.2%
경 58
 
5.3%
도 58
 
5.3%
기 58
 
5.3%
시 49
 
4.5%
리 36
 
3.3%
지 36
 
3.3%
번 31
 
2.9%
면 30
 
2.8%
동 25
 
2.3%
Other values (111) 508
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 748
68.8%
Space Separator 198
 
18.2%
Decimal Number 119
 
10.9%
Dash Punctuation 22
 
2.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 58
 
7.8%
도 58
 
7.8%
기 58
 
7.8%
시 49
 
6.6%
리 36
 
4.8%
지 36
 
4.8%
번 31
 
4.1%
면 30
 
4.0%
동 25
 
3.3%
양 24
 
3.2%
Other values (99) 343
45.9%
Decimal Number
ValueCountFrequency (%)
1 24
20.2%
2 17
14.3%
5 15
12.6%
4 12
10.1%
3 11
9.2%
9 10
8.4%
0 8
 
6.7%
8 8
 
6.7%
7 7
 
5.9%
6 7
 
5.9%
Space Separator
ValueCountFrequency (%)
198
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 748
68.8%
Common 339
31.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 58
 
7.8%
도 58
 
7.8%
기 58
 
7.8%
시 49
 
6.6%
리 36
 
4.8%
지 36
 
4.8%
번 31
 
4.1%
면 30
 
4.0%
동 25
 
3.3%
양 24
 
3.2%
Other values (99) 343
45.9%
Common
ValueCountFrequency (%)
198
58.4%
1 24
 
7.1%
- 22
 
6.5%
2 17
 
5.0%
5 15
 
4.4%
4 12
 
3.5%
3 11
 
3.2%
9 10
 
2.9%
0 8
 
2.4%
8 8
 
2.4%
Other values (2) 14
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 748
68.8%
ASCII 339
31.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
198
58.4%
1 24
 
7.1%
- 22
 
6.5%
2 17
 
5.0%
5 15
 
4.4%
4 12
 
3.5%
3 11
 
3.2%
9 10
 
2.9%
0 8
 
2.4%
8 8
 
2.4%
Other values (2) 14
 
4.1%
Hangul
ValueCountFrequency (%)
경 58
 
7.8%
도 58
 
7.8%
기 58
 
7.8%
시 49
 
6.6%
리 36
 
4.8%
지 36
 
4.8%
번 31
 
4.1%
면 30
 
4.0%
동 25
 
3.3%
양 24
 
3.2%
Other values (99) 343
45.9%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)81.8%
Missing14
Missing (%)24.1%
Infinite0
Infinite (%)0.0%
Mean14260.068
Minimum10021
Maximum18556
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size654.0 B
2023-12-11T06:30:12.059733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10021
5-th percentile10390.35
Q112027.75
median13937
Q317403.25
95-th percentile18232.05
Maximum18556
Range8535
Interquartile range (IQR)5375.5

Descriptive statistics

Standard deviation2817.2536
Coefficient of variation (CV)0.19756242
Kurtosis-1.5226945
Mean14260.068
Median Absolute Deviation (MAD)2608.5
Skewness0.15123245
Sum627443
Variance7936918.1
MonotonicityNot monotonic
2023-12-11T06:30:12.174380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
15646 2
 
3.4%
10806 2
 
3.4%
17407 2
 
3.4%
18102 2
 
3.4%
12528 2
 
3.4%
12540 2
 
3.4%
11502 2
 
3.4%
12433 2
 
3.4%
14753 1
 
1.7%
14746 1
 
1.7%
Other values (26) 26
44.8%
(Missing) 14
24.1%
ValueCountFrequency (%)
10021 1
1.7%
10249 1
1.7%
10317 1
1.7%
10806 2
3.4%
11015 1
1.7%
11137 1
1.7%
11155 1
1.7%
11502 2
3.4%
12024 1
1.7%
12029 1
1.7%
ValueCountFrequency (%)
18556 1
1.7%
18547 1
1.7%
18255 1
1.7%
18102 2
3.4%
17814 1
1.7%
17509 1
1.7%
17413 1
1.7%
17408 1
1.7%
17407 2
3.4%
17402 1
1.7%

WGS84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.478809
Minimum37.007874
Maximum38.11913
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size654.0 B
2023-12-11T06:30:12.328616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.007874
5-th percentile37.124298
Q137.236413
median37.439835
Q337.723072
95-th percentile37.893691
Maximum38.11913
Range1.111256
Interquartile range (IQR)0.48665925

Descriptive statistics

Standard deviation0.28425687
Coefficient of variation (CV)0.0075844693
Kurtosis-1.0291211
Mean37.478809
Median Absolute Deviation (MAD)0.24932391
Skewness0.3269623
Sum2173.7709
Variance0.08080197
MonotonicityNot monotonic
2023-12-11T06:30:12.472747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5556347776 2
 
3.4%
37.248817738 2
 
3.4%
37.2446144405 2
 
3.4%
37.8831034982 2
 
3.4%
37.4279248249 2
 
3.4%
37.8443452194 1
 
1.7%
37.173078847 1
 
1.7%
38.0134307675 1
 
1.7%
38.1191304561 1
 
1.7%
37.176994872 1
 
1.7%
Other values (43) 43
74.1%
ValueCountFrequency (%)
37.0078744535 1
1.7%
37.0503620792 1
1.7%
37.0985222772 1
1.7%
37.1288461627 1
1.7%
37.1399391539 1
1.7%
37.1460578326 1
1.7%
37.1600949349 1
1.7%
37.173078847 1
1.7%
37.176994872 1
1.7%
37.1793710565 1
1.7%
ValueCountFrequency (%)
38.1191304561 1
1.7%
38.0134307675 1
1.7%
37.9536881093 1
1.7%
37.8831034982 2
3.4%
37.8662537833 1
1.7%
37.8655601834 1
1.7%
37.8443452194 1
1.7%
37.8423129876 1
1.7%
37.8143517636 1
1.7%
37.8128923958 1
1.7%

WGS84경도
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.13331
Minimum126.55276
Maximum127.765
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size654.0 B
2023-12-11T06:30:12.635362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.55276
5-th percentile126.57081
Q1126.81695
median127.15138
Q3127.38733
95-th percentile127.71505
Maximum127.765
Range1.2122439
Interquartile range (IQR)0.57037999

Descriptive statistics

Standard deviation0.35226228
Coefficient of variation (CV)0.0027708102
Kurtosis-1.0917104
Mean127.13331
Median Absolute Deviation (MAD)0.3012562
Skewness0.10555927
Sum7373.7322
Variance0.12408872
MonotonicityNot monotonic
2023-12-11T06:30:12.782777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.684444936 2
 
3.4%
126.5696536523 2
 
3.4%
127.2893559968 2
 
3.4%
126.8754678161 2
 
3.4%
127.7650018852 2
 
3.4%
127.3873433409 1
 
1.7%
127.4868983582 1
 
1.7%
127.1026908778 1
 
1.7%
127.0695816252 1
 
1.7%
127.0165296837 1
 
1.7%
Other values (43) 43
74.1%
ValueCountFrequency (%)
126.5527579848 1
1.7%
126.5696536523 2
3.4%
126.5710183967 1
1.7%
126.6194132674 1
1.7%
126.6941806937 1
1.7%
126.7330934517 1
1.7%
126.7512117839 1
1.7%
126.7708219333 1
1.7%
126.772847555 1
1.7%
126.7816330615 1
1.7%
ValueCountFrequency (%)
127.7650018852 2
3.4%
127.7637380958 1
1.7%
127.7064534593 1
1.7%
127.684444936 2
3.4%
127.5551036075 1
1.7%
127.5437605454 1
1.7%
127.5143157983 1
1.7%
127.4945569375 1
1.7%
127.4868983582 1
1.7%
127.468155607 1
1.7%

Interactions

2023-12-11T06:30:07.251690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:03.584043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:04.248915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:04.824630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:05.444186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:06.075101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:06.692060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:07.335703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:03.697726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:04.334743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:04.922543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:05.527893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:06.154970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:06.779767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:07.420782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:03.793816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:04.414955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:04.997963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:05.630945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:06.246364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:06.866770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:07.524364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:03.885958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:04.508392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:05.098000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:05.715767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:06.352365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:06.948715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:07.597215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:03.967700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:04.587525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:05.179794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:05.831690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:06.429115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:07.026009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:07.661386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:04.063098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:04.663100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:05.262502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:05.917800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:06.498934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:07.099066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:07.732024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:04.162574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:04.742179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:05.355372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:05.998159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:06.591935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:07.174417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:30:12.882287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명사업장명인허가일자입소정원(명)자격소유인원수(명)총인원수(명)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
시군명1.0001.0000.5140.5320.5770.7641.0001.0001.0000.9580.948
사업장명1.0001.0001.0001.0001.0001.0001.0000.9901.0001.0001.000
인허가일자0.5141.0001.0000.6370.5810.5050.7350.5640.0000.0000.370
입소정원(명)0.5321.0000.6371.0000.7550.8340.8640.3940.1150.2690.000
자격소유인원수(명)0.5771.0000.5810.7551.0000.7270.8760.6770.4830.0000.000
총인원수(명)0.7641.0000.5050.8340.7271.0000.9370.9670.6750.0000.000
소재지도로명주소1.0001.0000.7350.8640.8760.9371.0001.0001.0001.0001.000
소재지지번주소1.0000.9900.5640.3940.6770.9671.0001.0001.0001.0001.000
소재지우편번호1.0001.0000.0000.1150.4830.6751.0001.0001.0000.6670.900
WGS84위도0.9581.0000.0000.2690.0000.0001.0001.0000.6671.0000.803
WGS84경도0.9481.0000.3700.0000.0000.0001.0001.0000.9000.8031.000
2023-12-11T06:30:13.009772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자입소정원(명)자격소유인원수(명)총인원수(명)소재지우편번호WGS84위도WGS84경도시군명
인허가일자1.000-0.664-0.134-0.5770.225-0.142-0.2070.324
입소정원(명)-0.6641.0000.1810.724-0.1210.0030.1560.214
자격소유인원수(명)-0.1340.1811.0000.3330.011-0.002-0.0560.214
총인원수(명)-0.5770.7240.3331.000-0.3850.2590.2240.324
소재지우편번호0.225-0.1210.011-0.3851.000-0.9200.0160.850
WGS84위도-0.1420.003-0.0020.259-0.9201.0000.0030.687
WGS84경도-0.2070.156-0.0560.2240.0160.0031.0000.657
시군명0.3240.2140.2140.3240.8500.6870.6571.000

Missing values

2023-12-11T06:30:07.863297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:30:08.028243image/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.
2023-12-11T06:30:08.135046image/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
0가평군성가정의 집20080804운영중29614경기도 가평군 조종면 연인산로474번길 139-37경기도 가평군 하면 마일리 296-14번지1243337.844345127.387343
1가평군가평꽃동네 희망의집19950327운영중3300113<NA>경기도 가평군 하면 하판리 가평꽃동네 희망의 집1243237.86556127.368168
2가평군작은예수회현리요셉의집19900419운영중5518경기도 가평군 조종면 연인산로474번길 140-57경기도 가평군 하면 마일리 102-2번지1243337.842313127.39165
3고양시해밀20091228운영중30311경기도 고양시 일산동구 견달산로225번길 21-83경기도 고양시 일산동구 식사동 290-9번지1031737.685768126.814677
4고양시홀트일산요양원19990101운영중1553997경기도 고양시 일산서구 탄현로 42경기도 고양시 일산서구 탄현동 41-1번지1024937.695677126.772848
5광주시품안의 집19931227운영중812339<NA>경기도 광주시 곤지암읍<NA>37.366681127.383211
6군포시양지의집20031027운영중31922<NA>경기도 군포시 당정동<NA>37.357625126.956807
7김포시가연마을20080215운영중702044경기도 김포시 월곶면 용강로37번길 76-25경기도 김포시 월곶면 고막리 382번지1002137.723783126.552758
8김포시소망의 집20111219운영중1516<NA>경기도 김포시 양촌읍 구래리<NA>37.649986126.619413
9남양주시참누리요양원19890201운영중<NA><NA><NA><NA>경기도 남양주시 수동면<NA>37.738844127.278033
시군명사업장명인허가일자영업상태명입소정원(명)자격소유인원수(명)총인원수(명)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
48파주시큰나무20100422운영중30<NA><NA>경기도 파주시 법원읍 술이홀로1333번길 69경기도 파주시 법원읍 금곡리 428-1번지1080637.883103126.875468
49파주시아름다운누리19920601운영중64642경기도 파주시 법원읍 술이홀로1333번길 69경기도 파주시 법원읍 금곡리 428-1번지1080637.883103126.875468
50파주시금빛사랑의집20140811운영중1024<NA>경기도 파주시 조리읍 금빛사랑의집<NA>37.753517126.823777
51평택시아나율의집20131011운영중30<NA>23경기도 평택시 포승읍 절골길 43-1경기도 평택시 포승읍 석정리 1-6번지1781437.007874126.897134
52포천시생수의집20051212운영중561937경기도 포천시 군내면 청군로 2813-18경기도 포천시 군내면 직두리 155-5번지1115537.866254127.250515
53포천시노아의집19971226운영중70028경기도 포천시 신북면 청신로 1095경기도 포천시 신북면 갈월리 441번지1113737.953688127.141056
54화성시브니엘복지원20051229운영중1313<NA>경기도 화성시 팔탄면 덕천리<NA>37.146058126.875904
55화성시화성아름마을20030806운영중351420경기도 화성시 송산면 공룡로264번길 16경기도 화성시 송산면 쌍정리 1000번지1854737.233679126.733093
56화성시불이원20090218운영중482715경기도 화성시 남양읍 현대기아로 648경기도 화성시 북양동 620번지1825537.205412126.854796
57화성시둘다섯해누리20080908운영중80716경기도 화성시 서신면 밸미길 87-31경기도 화성시 서신면 백미리 588-1번지1855637.139939126.694181