Overview

Dataset statistics

Number of variables11
Number of observations164
Missing cells263
Missing cells (%)14.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.2 KiB
Average record size in memory94.8 B

Variable types

Categorical2
Text3
Numeric6

Alerts

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
영업상태명 is highly imbalanced (83.5%)Imbalance
자격소유인원수(명) has 20 (12.2%) missing valuesMissing
총인원수(명) has 17 (10.4%) missing valuesMissing
소재지도로명주소 has 68 (41.5%) missing valuesMissing
소재지우편번호 has 42 (25.6%) missing valuesMissing
WGS84위도 has 58 (35.4%) missing valuesMissing
WGS84경도 has 58 (35.4%) missing valuesMissing
자격소유인원수(명) has 13 (7.9%) zerosZeros
총인원수(명) has 7 (4.3%) zerosZeros

Reproduction

Analysis started2023-12-10 22:26:25.417981
Analysis finished2023-12-10 22:26:29.866300
Duration4.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
화성시
19 
포천시
19 
안산시
13 
안성시
12 
가평군
11 
Other values (24)
90 

Length

Max length4
Median length3
Mean length3.0609756
Min length3

Unique

Unique8 ?
Unique (%)4.9%

Sample

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

Common Values

ValueCountFrequency (%)
화성시 19
 
11.6%
포천시 19
 
11.6%
안산시 13
 
7.9%
안성시 12
 
7.3%
가평군 11
 
6.7%
의왕시 9
 
5.5%
여주시 8
 
4.9%
평택시 8
 
4.9%
고양시 8
 
4.9%
양평군 7
 
4.3%
Other values (19) 50
30.5%

Length

2023-12-11T07:26:29.926406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
화성시 19
 
11.6%
포천시 19
 
11.6%
안산시 13
 
7.9%
안성시 12
 
7.3%
가평군 11
 
6.7%
의왕시 9
 
5.5%
여주시 8
 
4.9%
평택시 8
 
4.9%
고양시 8
 
4.9%
양평군 7
 
4.3%
Other values (19) 50
30.5%

사업장명
Text

Distinct154
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-11T07:26:30.194411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length13
Mean length6.195122
Min length1

Characters and Unicode

Total characters1016
Distinct characters218
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

Unique145 ?
Unique (%)88.4%

Sample

1st row영인시니어타운
2nd row172 하느님 사랑방 사람들
3rd row열린효경원
4th row한울
5th row아름다운 집
ValueCountFrequency (%)
집 11
 
5.2%
양로원 9
 
4.3%
천사마을 3
 
1.4%
효누림실버타운 3
 
1.4%
사랑의 3
 
1.4%
실버홈 2
 
0.9%
밝은집 2
 
0.9%
아름다운 2
 
0.9%
실버타운 2
 
0.9%
보은의집 2
 
0.9%
Other values (165) 172
81.5%
2023-12-11T07:26:30.588961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 61
 
6.0%
의 53
 
5.2%
집 48
 
4.7%
47
 
4.6%
양 44
 
4.3%
로 44
 
4.3%
사 30
 
3.0%
운 25
 
2.5%
실 22
 
2.2%
버 21
 
2.1%
Other values (208) 621
61.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 956
94.1%
Space Separator 47
 
4.6%
Decimal Number 7
 
0.7%
Uppercase Letter 3
 
0.3%
Close Punctuation 2
 
0.2%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 61
 
6.4%
의 53
 
5.5%
집 48
 
5.0%
양 44
 
4.6%
로 44
 
4.6%
사 30
 
3.1%
운 25
 
2.6%
실 22
 
2.3%
버 21
 
2.2%
타 20
 
2.1%
Other values (199) 588
61.5%
Decimal Number
ValueCountFrequency (%)
2 4
57.1%
1 2
28.6%
7 1
 
14.3%
Uppercase Letter
ValueCountFrequency (%)
P 1
33.3%
I 1
33.3%
V 1
33.3%
Space Separator
ValueCountFrequency (%)
47
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 956
94.1%
Common 57
 
5.6%
Latin 3
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 61
 
6.4%
의 53
 
5.5%
집 48
 
5.0%
양 44
 
4.6%
로 44
 
4.6%
사 30
 
3.1%
운 25
 
2.6%
실 22
 
2.3%
버 21
 
2.2%
타 20
 
2.1%
Other values (199) 588
61.5%
Common
ValueCountFrequency (%)
47
82.5%
2 4
 
7.0%
) 2
 
3.5%
1 2
 
3.5%
( 1
 
1.8%
7 1
 
1.8%
Latin
ValueCountFrequency (%)
P 1
33.3%
I 1
33.3%
V 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 956
94.1%
ASCII 60
 
5.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 61
 
6.4%
의 53
 
5.5%
집 48
 
5.0%
양 44
 
4.6%
로 44
 
4.6%
사 30
 
3.1%
운 25
 
2.6%
실 22
 
2.3%
버 21
 
2.2%
타 20
 
2.1%
Other values (199) 588
61.5%
ASCII
ValueCountFrequency (%)
47
78.3%
2 4
 
6.7%
) 2
 
3.3%
1 2
 
3.3%
P 1
 
1.7%
I 1
 
1.7%
V 1
 
1.7%
( 1
 
1.7%
7 1
 
1.7%

인허가일자
Real number (ℝ)

Distinct160
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20068071
Minimum19521222
Maximum20180601
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T07:26:30.724316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19521222
5-th percentile19933914
Q120050724
median20070866
Q320113440
95-th percentile20150942
Maximum20180601
Range659379
Interquartile range (IQR)62716.75

Descriptive statistics

Standard deviation78941.775
Coefficient of variation (CV)0.0039337003
Kurtosis16.110307
Mean20068071
Median Absolute Deviation (MAD)30088.5
Skewness-3.0258473
Sum3.2911636 × 109
Variance6.2318039 × 109
MonotonicityNot monotonic
2023-12-11T07:26:30.897198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20071105 2
 
1.2%
20091230 2
 
1.2%
20050718 2
 
1.2%
20091222 2
 
1.2%
20100830 1
 
0.6%
20140612 1
 
0.6%
20150610 1
 
0.6%
20050513 1
 
0.6%
20130910 1
 
0.6%
20110112 1
 
0.6%
Other values (150) 150
91.5%
ValueCountFrequency (%)
19521222 1
0.6%
19701028 1
0.6%
19840721 1
0.6%
19880701 1
0.6%
19891107 1
0.6%
19910907 1
0.6%
19920123 1
0.6%
19920327 1
0.6%
19931004 1
0.6%
19950403 1
0.6%
ValueCountFrequency (%)
20180601 1
0.6%
20180515 1
0.6%
20180320 1
0.6%
20180305 1
0.6%
20170303 1
0.6%
20160825 1
0.6%
20160701 1
0.6%
20151127 1
0.6%
20151001 1
0.6%
20150610 1
0.6%

영업상태명
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
운영중
160 
휴업 등
 
4

Length

Max length4
Median length3
Mean length3.0243902
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
운영중 160
97.6%
휴업 등 4
 
2.4%

Length

2023-12-11T07:26:31.033222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:26:31.156855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 160
95.2%
휴업 4
 
2.4%
등 4
 
2.4%

자격소유인원수(명)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct16
Distinct (%)11.1%
Missing20
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean2.9513889
Minimum0
Maximum19
Zeros13
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T07:26:31.240051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile9.85
Maximum19
Range19
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.976199
Coefficient of variation (CV)1.0084062
Kurtosis7.5931001
Mean2.9513889
Median Absolute Deviation (MAD)1
Skewness2.4010232
Sum425
Variance8.8577603
MonotonicityNot monotonic
2023-12-11T07:26:31.341764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 38
23.2%
2 34
20.7%
3 18
11.0%
4 17
10.4%
0 13
 
7.9%
5 6
 
3.7%
6 5
 
3.0%
7 3
 
1.8%
10 3
 
1.8%
14 1
 
0.6%
Other values (6) 6
 
3.7%
(Missing) 20
12.2%
ValueCountFrequency (%)
0 13
 
7.9%
1 38
23.2%
2 34
20.7%
3 18
11.0%
4 17
10.4%
5 6
 
3.7%
6 5
 
3.0%
7 3
 
1.8%
8 1
 
0.6%
9 1
 
0.6%
ValueCountFrequency (%)
19 1
 
0.6%
14 1
 
0.6%
13 1
 
0.6%
12 1
 
0.6%
11 1
 
0.6%
10 3
1.8%
9 1
 
0.6%
8 1
 
0.6%
7 3
1.8%
6 5
3.0%

총인원수(명)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct18
Distinct (%)12.2%
Missing17
Missing (%)10.4%
Infinite0
Infinite (%)0.0%
Mean6.0204082
Minimum0
Maximum52
Zeros7
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T07:26:31.473300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q37
95-th percentile13
Maximum52
Range52
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.5424506
Coefficient of variation (CV)0.92061044
Kurtosis33.143367
Mean6.0204082
Median Absolute Deviation (MAD)2
Skewness4.6020956
Sum885
Variance30.718759
MonotonicityNot monotonic
2023-12-11T07:26:31.622116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
6 25
15.2%
5 20
12.2%
2 19
11.6%
4 18
11.0%
3 12
7.3%
7 11
6.7%
9 8
 
4.9%
8 8
 
4.9%
0 7
 
4.3%
12 4
 
2.4%
Other values (8) 15
9.1%
(Missing) 17
10.4%
ValueCountFrequency (%)
0 7
 
4.3%
1 3
 
1.8%
2 19
11.6%
3 12
7.3%
4 18
11.0%
5 20
12.2%
6 25
15.2%
7 11
6.7%
8 8
 
4.9%
9 8
 
4.9%
ValueCountFrequency (%)
52 1
 
0.6%
27 1
 
0.6%
19 1
 
0.6%
18 3
 
1.8%
14 1
 
0.6%
13 2
 
1.2%
12 4
2.4%
11 3
 
1.8%
9 8
4.9%
8 8
4.9%

소재지도로명주소
Text

MISSING 

Distinct90
Distinct (%)93.8%
Missing68
Missing (%)41.5%
Memory size1.4 KiB
2023-12-11T07:26:31.884080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length24
Mean length20.46875
Min length14

Characters and Unicode

Total characters1965
Distinct characters160
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

Unique85 ?
Unique (%)88.5%

Sample

1st row경기도 가평군 청평면 양진길 154
2nd row경기도 가평군 설악면 유명로 1906-26
3rd row경기도 가평군 상면 솔안길 26
4th row경기도 가평군 가평읍 복장포길 50
5th row경기도 가평군 조종면 명지산로 655-25
ValueCountFrequency (%)
경기도 96
 
20.9%
화성시 16
 
3.5%
포천시 13
 
2.8%
가평군 10
 
2.2%
안산시 9
 
2.0%
상록구 6
 
1.3%
의왕시 6
 
1.3%
시흥시 4
 
0.9%
양평군 4
 
0.9%
일동면 4
 
0.9%
Other values (237) 292
63.5%
2023-12-11T07:26:32.376685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
364
 
18.5%
도 101
 
5.1%
기 99
 
5.0%
경 98
 
5.0%
시 86
 
4.4%
1 78
 
4.0%
2 59
 
3.0%
길 58
 
3.0%
로 57
 
2.9%
- 45
 
2.3%
Other values (150) 920
46.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1182
60.2%
Decimal Number 374
 
19.0%
Space Separator 364
 
18.5%
Dash Punctuation 45
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 101
 
8.5%
기 99
 
8.4%
경 98
 
8.3%
시 86
 
7.3%
길 58
 
4.9%
로 57
 
4.8%
면 45
 
3.8%
성 30
 
2.5%
안 25
 
2.1%
평 25
 
2.1%
Other values (138) 558
47.2%
Decimal Number
ValueCountFrequency (%)
1 78
20.9%
2 59
15.8%
3 39
10.4%
5 38
10.2%
6 38
10.2%
4 29
 
7.8%
9 27
 
7.2%
7 25
 
6.7%
8 24
 
6.4%
0 17
 
4.5%
Space Separator
ValueCountFrequency (%)
364
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1182
60.2%
Common 783
39.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 101
 
8.5%
기 99
 
8.4%
경 98
 
8.3%
시 86
 
7.3%
길 58
 
4.9%
로 57
 
4.8%
면 45
 
3.8%
성 30
 
2.5%
안 25
 
2.1%
평 25
 
2.1%
Other values (138) 558
47.2%
Common
ValueCountFrequency (%)
364
46.5%
1 78
 
10.0%
2 59
 
7.5%
- 45
 
5.7%
3 39
 
5.0%
5 38
 
4.9%
6 38
 
4.9%
4 29
 
3.7%
9 27
 
3.4%
7 25
 
3.2%
Other values (2) 41
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1182
60.2%
ASCII 783
39.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
364
46.5%
1 78
 
10.0%
2 59
 
7.5%
- 45
 
5.7%
3 39
 
5.0%
5 38
 
4.9%
6 38
 
4.9%
4 29
 
3.7%
9 27
 
3.4%
7 25
 
3.2%
Other values (2) 41
 
5.2%
Hangul
ValueCountFrequency (%)
도 101
 
8.5%
기 99
 
8.4%
경 98
 
8.3%
시 86
 
7.3%
길 58
 
4.9%
로 57
 
4.8%
면 45
 
3.8%
성 30
 
2.5%
안 25
 
2.1%
평 25
 
2.1%
Other values (138) 558
47.2%

소재지지번주소
Text

Distinct154
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-11T07:26:32.681532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length26
Mean length19.182927
Min length10

Characters and Unicode

Total characters3146
Distinct characters172
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique144 ?
Unique (%)87.8%

Sample

1st row경기도 가평군 청평면 고성리 638-10번지
2nd row경기도 가평군 하면
3rd row경기도 가평군 설악면 회곡리 4-14번지
4th row경기도 가평군 상면 율길리 77-1번지
5th row경기도 가평군 가평읍 복장리 243번지
ValueCountFrequency (%)
경기도 164
 
22.6%
화성시 19
 
2.6%
포천시 19
 
2.6%
안산시 13
 
1.8%
안성시 12
 
1.7%
가평군 11
 
1.5%
의왕시 9
 
1.2%
상록구 9
 
1.2%
평택시 8
 
1.1%
고양시 8
 
1.1%
Other values (321) 454
62.5%
2023-12-11T07:26:33.128246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
562
 
17.9%
도 174
 
5.5%
기 167
 
5.3%
경 164
 
5.2%
시 151
 
4.8%
지 115
 
3.7%
번 108
 
3.4%
리 90
 
2.9%
1 82
 
2.6%
- 81
 
2.6%
Other values (162) 1452
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2068
65.7%
Space Separator 562
 
17.9%
Decimal Number 424
 
13.5%
Dash Punctuation 81
 
2.6%
Other Punctuation 7
 
0.2%
Open Punctuation 2
 
0.1%
Close Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 174
 
8.4%
기 167
 
8.1%
경 164
 
7.9%
시 151
 
7.3%
지 115
 
5.6%
번 108
 
5.2%
리 90
 
4.4%
동 81
 
3.9%
면 78
 
3.8%
성 42
 
2.0%
Other values (147) 898
43.4%
Decimal Number
ValueCountFrequency (%)
1 82
19.3%
2 54
12.7%
4 53
12.5%
3 50
11.8%
5 47
11.1%
6 37
8.7%
9 31
 
7.3%
8 27
 
6.4%
7 22
 
5.2%
0 21
 
5.0%
Space Separator
ValueCountFrequency (%)
562
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 81
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2068
65.7%
Common 1078
34.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 174
 
8.4%
기 167
 
8.1%
경 164
 
7.9%
시 151
 
7.3%
지 115
 
5.6%
번 108
 
5.2%
리 90
 
4.4%
동 81
 
3.9%
면 78
 
3.8%
성 42
 
2.0%
Other values (147) 898
43.4%
Common
ValueCountFrequency (%)
562
52.1%
1 82
 
7.6%
- 81
 
7.5%
2 54
 
5.0%
4 53
 
4.9%
3 50
 
4.6%
5 47
 
4.4%
6 37
 
3.4%
9 31
 
2.9%
8 27
 
2.5%
Other values (5) 54
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2068
65.7%
ASCII 1078
34.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
562
52.1%
1 82
 
7.6%
- 81
 
7.5%
2 54
 
5.0%
4 53
 
4.9%
3 50
 
4.6%
5 47
 
4.4%
6 37
 
3.4%
9 31
 
2.9%
8 27
 
2.5%
Other values (5) 54
 
5.0%
Hangul
ValueCountFrequency (%)
도 174
 
8.4%
기 167
 
8.1%
경 164
 
7.9%
시 151
 
7.3%
지 115
 
5.6%
번 108
 
5.2%
리 90
 
4.4%
동 81
 
3.9%
면 78
 
3.8%
성 42
 
2.0%
Other values (147) 898
43.4%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct103
Distinct (%)84.4%
Missing42
Missing (%)25.6%
Infinite0
Infinite (%)0.0%
Mean14442.344
Minimum10038
Maximum18589
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T07:26:33.310256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10038
5-th percentile10952.3
Q111942
median14274.5
Q317500
95-th percentile18569.3
Maximum18589
Range8551
Interquartile range (IQR)5558

Descriptive statistics

Standard deviation2812.4768
Coefficient of variation (CV)0.19473825
Kurtosis-1.5473295
Mean14442.344
Median Absolute Deviation (MAD)2763
Skewness0.16592796
Sum1761966
Variance7910025.8
MonotonicityNot monotonic
2023-12-11T07:26:33.507260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11115 4
 
2.4%
11188 3
 
1.8%
11103 3
 
1.8%
15321 2
 
1.2%
18556 2
 
1.2%
18589 2
 
1.2%
18335 2
 
1.2%
10867 2
 
1.2%
16108 2
 
1.2%
16066 2
 
1.2%
Other values (93) 98
59.8%
(Missing) 42
25.6%
ValueCountFrequency (%)
10038 1
 
0.6%
10481 1
 
0.6%
10503 1
 
0.6%
10867 2
1.2%
10940 1
 
0.6%
10949 1
 
0.6%
11015 1
 
0.6%
11103 3
1.8%
11115 4
2.4%
11133 1
 
0.6%
ValueCountFrequency (%)
18589 2
1.2%
18586 1
0.6%
18583 1
0.6%
18574 1
0.6%
18573 1
0.6%
18570 1
0.6%
18556 2
1.2%
18545 1
0.6%
18541 1
0.6%
18522 1
0.6%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct100
Distinct (%)94.3%
Missing58
Missing (%)35.4%
Infinite0
Infinite (%)0.0%
Mean37.482695
Minimum37.009283
Maximum38.109321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T07:26:33.745408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.009283
5-th percentile37.057561
Q137.242271
median37.425007
Q337.741526
95-th percentile38.016143
Maximum38.109321
Range1.1000372
Interquartile range (IQR)0.49925493

Descriptive statistics

Standard deviation0.29797492
Coefficient of variation (CV)0.0079496663
Kurtosis-1.0664188
Mean37.482695
Median Absolute Deviation (MAD)0.2599319
Skewness0.25869985
Sum3973.1657
Variance0.088789054
MonotonicityNot monotonic
2023-12-11T07:26:33.916498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.7945497914 3
 
1.8%
37.3451938013 2
 
1.2%
37.3146981334 2
 
1.2%
38.0161607096 2
 
1.2%
37.3159108193 2
 
1.2%
37.7555576978 1
 
0.6%
38.0684434884 1
 
0.6%
37.0584644421 1
 
0.6%
37.0551303727 1
 
0.6%
37.0803022524 1
 
0.6%
Other values (90) 90
54.9%
(Missing) 58
35.4%
ValueCountFrequency (%)
37.0092834362 1
0.6%
37.0331866509 1
0.6%
37.0418132631 1
0.6%
37.0421267696 1
0.6%
37.0551303727 1
0.6%
37.0572592808 1
0.6%
37.0584644421 1
0.6%
37.066882376 1
0.6%
37.0710565593 1
0.6%
37.0803022524 1
0.6%
ValueCountFrequency (%)
38.109320683 1
0.6%
38.0684434884 1
0.6%
38.0384850625 1
0.6%
38.0161882058 1
0.6%
38.0161607096 2
1.2%
38.016089363 1
0.6%
37.9461540625 1
0.6%
37.9319580327 1
0.6%
37.9236123101 1
0.6%
37.8843960964 1
0.6%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct100
Distinct (%)94.3%
Missing58
Missing (%)35.4%
Infinite0
Infinite (%)0.0%
Mean127.09401
Minimum126.59188
Maximum127.68602
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T07:26:34.069923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.59188
5-th percentile126.74777
Q1126.86113
median127.05012
Q3127.29467
95-th percentile127.56049
Maximum127.68602
Range1.0941357
Interquartile range (IQR)0.43353395

Descriptive statistics

Standard deviation0.26403531
Coefficient of variation (CV)0.0020774803
Kurtosis-0.77491758
Mean127.09401
Median Absolute Deviation (MAD)0.20602065
Skewness0.32420693
Sum13471.965
Variance0.069714644
MonotonicityNot monotonic
2023-12-11T07:26:34.224116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.2487443007 3
 
1.8%
126.9814985354 2
 
1.2%
126.9433824247 2
 
1.2%
127.2944704144 2
 
1.2%
126.8605140591 2
 
1.2%
126.8371435503 1
 
0.6%
127.3101936561 1
 
0.6%
127.1243985947 1
 
0.6%
127.0927541105 1
 
0.6%
127.1232108187 1
 
0.6%
Other values (90) 90
54.9%
(Missing) 58
35.4%
ValueCountFrequency (%)
126.5918803721 1
0.6%
126.5962497405 1
0.6%
126.6707765339 1
0.6%
126.6921160509 1
0.6%
126.6944695737 1
0.6%
126.7463163742 1
0.6%
126.7521202539 1
0.6%
126.7537192292 1
0.6%
126.7786699254 1
0.6%
126.7900043073 1
0.6%
ValueCountFrequency (%)
127.6860161141 1
0.6%
127.6450929595 1
0.6%
127.6431226307 1
0.6%
127.6205240208 1
0.6%
127.5822950244 1
0.6%
127.5704526815 1
0.6%
127.5306164909 1
0.6%
127.5295997265 1
0.6%
127.5081878353 1
0.6%
127.5050736008 1
0.6%

Interactions

2023-12-11T07:26:28.932939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:25.964535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:26.500810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:27.048890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:27.563524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:28.361850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:29.028421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:26.062242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:26.590700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:27.146335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:27.654155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:28.470019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:29.130852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:26.151928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:26.679301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:27.236474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:27.742556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:28.562704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:29.216605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:26.238839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:26.782019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:27.308836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:27.828994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:28.649659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:29.297256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:26.335435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:26.870751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:27.385518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:27.921102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:28.754896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:29.373720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:26.418578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:26.960142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:27.471956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:28.257650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:26:28.842052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:26:34.654676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명인허가일자영업상태명자격소유인원수(명)총인원수(명)소재지도로명주소소재지우편번호WGS84위도WGS84경도
시군명1.0000.6580.5710.6950.6741.0000.9930.9160.923
인허가일자0.6581.0000.1100.5110.4800.9370.3130.2400.357
영업상태명0.5710.1101.0000.0000.0001.0000.0000.3080.236
자격소유인원수(명)0.6950.5110.0001.0000.8530.9840.0000.1800.438
총인원수(명)0.6740.4800.0000.8531.0000.8020.2010.3440.276
소재지도로명주소1.0000.9371.0000.9840.8021.0001.0001.0001.000
소재지우편번호0.9930.3130.0000.0000.2011.0001.0000.8950.822
WGS84위도0.9160.2400.3080.1800.3441.0000.8951.0000.757
WGS84경도0.9230.3570.2360.4380.2761.0000.8220.7571.000
2023-12-11T07:26:34.794068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업상태명시군명
영업상태명1.0000.449
시군명0.4491.000
2023-12-11T07:26:34.912579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자자격소유인원수(명)총인원수(명)소재지우편번호WGS84위도WGS84경도시군명영업상태명
인허가일자1.0000.091-0.165-0.1250.096-0.0400.4370.086
자격소유인원수(명)0.0911.0000.5890.021-0.0350.0430.3170.000
총인원수(명)-0.1650.5891.0000.036-0.0190.0400.3410.000
소재지우편번호-0.1250.0210.0361.000-0.932-0.3620.8770.000
WGS84위도0.096-0.035-0.019-0.9321.0000.4310.5790.225
WGS84경도-0.0400.0430.040-0.3620.4311.0000.5970.171
시군명0.4370.3170.3410.8770.5790.5971.0000.449
영업상태명0.0860.0000.0000.0000.2250.1710.4491.000

Missing values

2023-12-11T07:26:29.489498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:26:29.657182image/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.
2023-12-11T07:26:29.777825image/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
0가평군영인시니어타운20100830운영중16경기도 가평군 청평면 양진길 154경기도 가평군 청평면 고성리 638-10번지1245637.727452127.488811
1가평군172 하느님 사랑방 사람들20120629운영중711<NA>경기도 가평군 하면<NA><NA><NA>
2가평군열린효경원20071109운영중47경기도 가평군 설악면 유명로 1906-26경기도 가평군 설악면 회곡리 4-14번지1245937.680724127.472033
3가평군한울20071128운영중14경기도 가평군 상면 솔안길 26경기도 가평군 상면 율길리 77-1번지1244137.826868127.304292
4가평군아름다운 집20070207운영중37경기도 가평군 가평읍 복장포길 50경기도 가평군 가평읍 복장리 243번지1243037.742315127.505074
5가평군가평시니어하우스20031201운영중13경기도 가평군 조종면 명지산로 655-25경기도 가평군 하면 상판리 17번지1243137.923612127.393831
6가평군원방의 집20061113운영중15경기도 가평군 가평읍 호반로 2135경기도 가평군 가평읍 이화리 118번지1242737.780982127.508188
7가평군성나실버타운19950403운영중312경기도 가평군 상면 물골길 262-23경기도 가평군 상면 봉수리 49-4번지1244037.840847127.304209
8가평군원방의집20020809운영중00경기도 가평군 청평면 초옥길 127경기도 가평군 외서면 상천리 524번지1244937.786958127.454731
9가평군행복이가득한집20021203운영중1419경기도 가평군 청평면 북한강로1604번길 64-27경기도 가평군 청평면 삼회리 500번지1245837.664297127.388204
시군명사업장명인허가일자영업상태명자격소유인원수(명)총인원수(명)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
154화성시하늘정원 실버타운20090309운영중00경기도 화성시 송산면 고포길 56경기도 화성시 송산면 고포리 182-1번지1854537.236088126.670777
155화성시화성한마음양로원20120203운영중24<NA>경기도 화성시 장안면 독정리<NA><NA><NA>
156화성시화성양로원20131129운영중<NA><NA><NA>경기도 화성시 정남면 문학리<NA><NA><NA>
157화성시성녀루이제의집19920327운영중1014경기도 화성시 정남면 서봉로921번길 45경기도 화성시 정남면 문학리 586-2번지1852237.15669126.960298
158화성시보은의집20041230운영중18<NA>경기도 화성시 봉담읍 마하리 145번지18335<NA><NA>
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