Overview

Dataset statistics

Number of variables12
Number of observations103
Missing cells108
Missing cells (%)8.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.5 KiB
Average record size in memory104.3 B

Variable types

Categorical2
Text3
Numeric7

Alerts

인허가일자 is highly overall correlated with 폐업일자 and 1 other fieldsHigh correlation
폐업일자 is highly overall correlated with 인허가일자 and 4 other fieldsHigh correlation
의료인수(명) is highly overall correlated with 시군명High correlation
소재지우편번호 is highly overall correlated with 시군명High correlation
WGS84위도 is highly overall correlated with 폐업일자High correlation
WGS84경도 is highly overall correlated with 폐업일자High correlation
시군명 is highly overall correlated with 인허가일자 and 3 other fieldsHigh correlation
영업상태명 is highly overall correlated with 폐업일자High correlation
영업상태명 is highly imbalanced (64.2%)Imbalance
폐업일자 has 96 (93.2%) missing valuesMissing
WGS84위도 has 5 (4.9%) missing valuesMissing
WGS84경도 has 5 (4.9%) missing valuesMissing
사업장명 has unique valuesUnique
소재지지번주소 has unique valuesUnique
연면적(㎡) has 7 (6.8%) zerosZeros

Reproduction

Analysis started2024-05-10 20:58:42.327259
Analysis finished2024-05-10 20:59:01.043414
Duration18.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Memory size956.0 B
화성시
16 
남양주시
11 
평택시
9 
여주시
9 
이천시
8 
Other values (14)
50 

Length

Max length4
Median length3
Mean length3.1165049
Min length3

Unique

Unique4 ?
Unique (%)3.9%

Sample

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

Common Values

ValueCountFrequency (%)
화성시 16
15.5%
남양주시 11
10.7%
평택시 9
8.7%
여주시 9
8.7%
이천시 8
 
7.8%
연천군 7
 
6.8%
용인시 7
 
6.8%
김포시 6
 
5.8%
가평군 5
 
4.9%
포천시 5
 
4.9%
Other values (9) 20
19.4%

Length

2024-05-10T20:59:01.271023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
화성시 16
15.5%
남양주시 11
10.7%
평택시 9
8.7%
여주시 9
8.7%
이천시 8
 
7.8%
연천군 7
 
6.8%
용인시 7
 
6.8%
김포시 6
 
5.8%
파주시 5
 
4.9%
포천시 5
 
4.9%
Other values (9) 20
19.4%

사업장명
Text

UNIQUE 

Distinct103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size956.0 B
2024-05-10T20:59:01.793335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length6
Mean length7
Min length6

Characters and Unicode

Total characters721
Distinct characters109
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)100.0%

Sample

1st row설악면보건지소
2nd row청평면보건지소
3rd row상면보건지소
4th row북면보건지소
5th row조종면보건지소
ValueCountFrequency (%)
설악면보건지소 1
 
1.0%
남양주시와부보건지소 1
 
1.0%
서탄보건지소 1
 
1.0%
현덕보건지소 1
 
1.0%
오성보건지소 1
 
1.0%
문산보건지소 1
 
1.0%
파주시파평면보건지소 1
 
1.0%
파주시탄현면보건지소 1
 
1.0%
파주시적성면보건지소 1
 
1.0%
파주시월롱면보건지소 1
 
1.0%
Other values (94) 94
90.4%
2024-05-10T20:59:02.739330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
건 108
15.0%
보 105
14.6%
지 104
14.4%
소 104
14.4%
남 17
 
2.4%
시 17
 
2.4%
면 13
 
1.8%
주 12
 
1.7%
양 11
 
1.5%
부 10
 
1.4%
Other values (99) 220
30.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 720
99.9%
Space Separator 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
건 108
15.0%
보 105
14.6%
지 104
14.4%
소 104
14.4%
남 17
 
2.4%
시 17
 
2.4%
면 13
 
1.8%
주 12
 
1.7%
양 11
 
1.5%
부 10
 
1.4%
Other values (98) 219
30.4%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 720
99.9%
Common 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
건 108
15.0%
보 105
14.6%
지 104
14.4%
소 104
14.4%
남 17
 
2.4%
시 17
 
2.4%
면 13
 
1.8%
주 12
 
1.7%
양 11
 
1.5%
부 10
 
1.4%
Other values (98) 219
30.4%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 720
99.9%
ASCII 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
건 108
15.0%
보 105
14.6%
지 104
14.4%
소 104
14.4%
남 17
 
2.4%
시 17
 
2.4%
면 13
 
1.8%
주 12
 
1.7%
양 11
 
1.5%
부 10
 
1.4%
Other values (98) 219
30.4%
ASCII
ValueCountFrequency (%)
1
100.0%

인허가일자
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)60.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19898773
Minimum19681231
Maximum20180329
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-05-10T20:59:03.162290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19681231
5-th percentile19681231
Q119830101
median19880101
Q319996170
95-th percentile20118387
Maximum20180329
Range499098
Interquartile range (IQR)166069

Descriptive statistics

Standard deviation132636.53
Coefficient of variation (CV)0.0066655631
Kurtosis-0.68311993
Mean19898773
Median Absolute Deviation (MAD)70417
Skewness0.03115669
Sum2.0495736 × 109
Variance1.7592448 × 1010
MonotonicityNot monotonic
2024-05-10T20:59:03.649538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19681231 11
 
10.7%
19830101 9
 
8.7%
19880101 6
 
5.8%
19950510 5
 
4.9%
19930501 5
 
4.9%
19710101 5
 
4.9%
19850125 4
 
3.9%
19860624 3
 
2.9%
19851228 2
 
1.9%
20180329 1
 
1.0%
Other values (52) 52
50.5%
ValueCountFrequency (%)
19681231 11
10.7%
19701201 1
 
1.0%
19710101 5
4.9%
19711120 1
 
1.0%
19801222 1
 
1.0%
19811111 1
 
1.0%
19820531 1
 
1.0%
19821227 1
 
1.0%
19830101 9
8.7%
19840215 1
 
1.0%
ValueCountFrequency (%)
20180329 1
1.0%
20170524 1
1.0%
20151021 1
1.0%
20140519 1
1.0%
20131216 1
1.0%
20120330 1
1.0%
20100901 1
1.0%
20091128 1
1.0%
20081103 1
1.0%
20080829 1
1.0%

영업상태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size956.0 B
영업중
96 
폐업
 
7

Length

Max length3
Median length3
Mean length2.9320388
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영업중 96
93.2%
폐업 7
 
6.8%

Length

2024-05-10T20:59:04.110725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T20:59:04.466422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업중 96
93.2%
폐업 7
 
6.8%

폐업일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)85.7%
Missing96
Missing (%)93.2%
Infinite0
Infinite (%)0.0%
Mean20137772
Minimum20110415
Maximum20170206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-05-10T20:59:04.718476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20110415
5-th percentile20116474
Q120130912
median20131213
Q320145374
95-th percentile20164300
Maximum20170206
Range59791
Interquartile range (IQR)14461.5

Descriptive statistics

Standard deviation18736.014
Coefficient of variation (CV)0.00093039157
Kurtosis1.0360797
Mean20137772
Median Absolute Deviation (MAD)9014
Skewness0.52503331
Sum1.409644 × 108
Variance3.510382 × 108
MonotonicityNot monotonic
2024-05-10T20:59:05.075815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
20131213 2
 
1.9%
20150520 1
 
1.0%
20170206 1
 
1.0%
20130611 1
 
1.0%
20110415 1
 
1.0%
20140227 1
 
1.0%
(Missing) 96
93.2%
ValueCountFrequency (%)
20110415 1
1.0%
20130611 1
1.0%
20131213 2
1.9%
20140227 1
1.0%
20150520 1
1.0%
20170206 1
1.0%
ValueCountFrequency (%)
20170206 1
1.0%
20150520 1
1.0%
20140227 1
1.0%
20131213 2
1.9%
20130611 1
1.0%
20110415 1
1.0%

의료인수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.407767
Minimum0
Maximum28
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-05-10T20:59:05.346368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile4.9
Maximum28
Range28
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.8334033
Coefficient of variation (CV)1.1767764
Kurtosis66.350419
Mean2.407767
Median Absolute Deviation (MAD)1
Skewness7.4682143
Sum248
Variance8.0281744
MonotonicityNot monotonic
2024-05-10T20:59:05.562426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 46
44.7%
1 29
28.2%
3 16
 
15.5%
4 5
 
4.9%
6 2
 
1.9%
7 2
 
1.9%
0 1
 
1.0%
28 1
 
1.0%
5 1
 
1.0%
ValueCountFrequency (%)
0 1
 
1.0%
1 29
28.2%
2 46
44.7%
3 16
 
15.5%
4 5
 
4.9%
5 1
 
1.0%
6 2
 
1.9%
7 2
 
1.9%
28 1
 
1.0%
ValueCountFrequency (%)
28 1
 
1.0%
7 2
 
1.9%
6 2
 
1.9%
5 1
 
1.0%
4 5
 
4.9%
3 16
 
15.5%
2 46
44.7%
1 29
28.2%
0 1
 
1.0%

연면적(㎡)
Real number (ℝ)

ZEROS 

Distinct90
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean697.67981
Minimum0
Maximum5232
Zeros7
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-05-10T20:59:05.916440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1169.65
median331.76
Q3723.33
95-th percentile2648.65
Maximum5232
Range5232
Interquartile range (IQR)553.68

Descriptive statistics

Standard deviation1005.796
Coefficient of variation (CV)1.4416298
Kurtosis7.4594782
Mean697.67981
Median Absolute Deviation (MAD)181.28
Skewness2.6580507
Sum71861.02
Variance1011625.5
MonotonicityNot monotonic
2024-05-10T20:59:06.341477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7
 
6.8%
198.0 3
 
2.9%
120.2 2
 
1.9%
148.92 2
 
1.9%
100.0 2
 
1.9%
200.0 2
 
1.9%
154.0 2
 
1.9%
4996.53 1
 
1.0%
1996.0 1
 
1.0%
173.0 1
 
1.0%
Other values (80) 80
77.7%
ValueCountFrequency (%)
0.0 7
6.8%
72.0 1
 
1.0%
100.0 2
 
1.9%
120.2 2
 
1.9%
142.51 1
 
1.0%
148.88 1
 
1.0%
148.92 2
 
1.9%
149.29 1
 
1.0%
150.48 1
 
1.0%
154.0 2
 
1.9%
ValueCountFrequency (%)
5232.0 1
1.0%
4996.53 1
1.0%
4061.99 1
1.0%
3189.0 1
1.0%
3100.0 1
1.0%
2649.5 1
1.0%
2641.0 1
1.0%
2596.0 1
1.0%
2536.0 1
1.0%
2203.0 1
1.0%

소재지도로명주소
Text

Distinct102
Distinct (%)100.0%
Missing1
Missing (%)1.0%
Memory size956.0 B
2024-05-10T20:59:06.803362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length34
Mean length23.666667
Min length17

Characters and Unicode

Total characters2414
Distinct characters196
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique102 ?
Unique (%)100.0%

Sample

1st row경기도 가평군 설악면 한서로 6
2nd row경기도 가평군 청평면 은고개로 21
3rd row경기도 가평군 상면 청군로 1099
4th row경기도 가평군 북면 화악산로 23
5th row경기도 가평군 하면 조종내길 75
ValueCountFrequency (%)
경기도 102
 
18.6%
화성시 16
 
2.9%
남양주시 10
 
1.8%
여주시 9
 
1.6%
평택시 9
 
1.6%
이천시 8
 
1.5%
연천군 7
 
1.3%
처인구 7
 
1.3%
용인시 7
 
1.3%
김포시 6
 
1.1%
Other values (332) 367
67.0%
2024-05-10T20:59:07.709613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
447
 
18.5%
도 108
 
4.5%
경 107
 
4.4%
기 105
 
4.3%
시 94
 
3.9%
로 87
 
3.6%
1 83
 
3.4%
면 72
 
3.0%
2 55
 
2.3%
3 39
 
1.6%
Other values (186) 1217
50.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1518
62.9%
Space Separator 447
 
18.5%
Decimal Number 352
 
14.6%
Dash Punctuation 29
 
1.2%
Close Punctuation 26
 
1.1%
Open Punctuation 26
 
1.1%
Other Punctuation 15
 
0.6%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 108
 
7.1%
경 107
 
7.0%
기 105
 
6.9%
시 94
 
6.2%
로 87
 
5.7%
면 72
 
4.7%
길 34
 
2.2%
주 32
 
2.1%
천 27
 
1.8%
양 27
 
1.8%
Other values (170) 825
54.3%
Decimal Number
ValueCountFrequency (%)
1 83
23.6%
2 55
15.6%
3 39
11.1%
0 33
 
9.4%
4 28
 
8.0%
9 28
 
8.0%
5 26
 
7.4%
6 25
 
7.1%
7 24
 
6.8%
8 11
 
3.1%
Space Separator
ValueCountFrequency (%)
447
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 26
100.0%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Other Punctuation
ValueCountFrequency (%)
, 15
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1518
62.9%
Common 896
37.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 108
 
7.1%
경 107
 
7.0%
기 105
 
6.9%
시 94
 
6.2%
로 87
 
5.7%
면 72
 
4.7%
길 34
 
2.2%
주 32
 
2.1%
천 27
 
1.8%
양 27
 
1.8%
Other values (170) 825
54.3%
Common
ValueCountFrequency (%)
447
49.9%
1 83
 
9.3%
2 55
 
6.1%
3 39
 
4.4%
0 33
 
3.7%
- 29
 
3.2%
4 28
 
3.1%
9 28
 
3.1%
) 26
 
2.9%
( 26
 
2.9%
Other values (6) 102
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1518
62.9%
ASCII 896
37.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
447
49.9%
1 83
 
9.3%
2 55
 
6.1%
3 39
 
4.4%
0 33
 
3.7%
- 29
 
3.2%
4 28
 
3.1%
9 28
 
3.1%
) 26
 
2.9%
( 26
 
2.9%
Other values (6) 102
 
11.4%
Hangul
ValueCountFrequency (%)
도 108
 
7.1%
경 107
 
7.0%
기 105
 
6.9%
시 94
 
6.2%
로 87
 
5.7%
면 72
 
4.7%
길 34
 
2.2%
주 32
 
2.1%
천 27
 
1.8%
양 27
 
1.8%
Other values (170) 825
54.3%

소재지지번주소
Text

UNIQUE 

Distinct103
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size956.0 B
2024-05-10T20:59:08.205979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length32
Mean length22.728155
Min length15

Characters and Unicode

Total characters2341
Distinct characters168
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)100.0%

Sample

1st row경기도 가평군 설악면 신천리 156번지 1호
2nd row경기도 가평군 청평면 청평리 333번지 1호
3rd row경기도 가평군 상면 연하리 171번지 1호
4th row경기도 가평군 북면 목동리 883번지 13호
5th row경기도 가평군 하면 현리 567번지 7호
ValueCountFrequency (%)
경기도 103
 
18.0%
화성시 16
 
2.8%
1호 15
 
2.6%
3호 11
 
1.9%
남양주시 11
 
1.9%
평택시 9
 
1.6%
여주시 9
 
1.6%
2호 8
 
1.4%
이천시 8
 
1.4%
처인구 7
 
1.2%
Other values (318) 374
65.5%
2024-05-10T20:59:09.387101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
470
20.1%
도 109
 
4.7%
경 103
 
4.4%
기 103
 
4.4%
시 92
 
3.9%
지 89
 
3.8%
리 88
 
3.8%
번 77
 
3.3%
1 69
 
2.9%
면 68
 
2.9%
Other values (158) 1073
45.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1502
64.2%
Space Separator 470
 
20.1%
Decimal Number 353
 
15.1%
Dash Punctuation 14
 
0.6%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 109
 
7.3%
경 103
 
6.9%
기 103
 
6.9%
시 92
 
6.1%
지 89
 
5.9%
리 88
 
5.9%
번 77
 
5.1%
면 68
 
4.5%
호 56
 
3.7%
주 31
 
2.1%
Other values (145) 686
45.7%
Decimal Number
ValueCountFrequency (%)
1 69
19.5%
3 53
15.0%
5 39
11.0%
8 38
10.8%
2 36
10.2%
6 31
8.8%
7 30
8.5%
4 22
 
6.2%
9 19
 
5.4%
0 16
 
4.5%
Space Separator
ValueCountFrequency (%)
470
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1502
64.2%
Common 839
35.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 109
 
7.3%
경 103
 
6.9%
기 103
 
6.9%
시 92
 
6.1%
지 89
 
5.9%
리 88
 
5.9%
번 77
 
5.1%
면 68
 
4.5%
호 56
 
3.7%
주 31
 
2.1%
Other values (145) 686
45.7%
Common
ValueCountFrequency (%)
470
56.0%
1 69
 
8.2%
3 53
 
6.3%
5 39
 
4.6%
8 38
 
4.5%
2 36
 
4.3%
6 31
 
3.7%
7 30
 
3.6%
4 22
 
2.6%
9 19
 
2.3%
Other values (3) 32
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1502
64.2%
ASCII 839
35.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
470
56.0%
1 69
 
8.2%
3 53
 
6.3%
5 39
 
4.6%
8 38
 
4.5%
2 36
 
4.3%
6 31
 
3.7%
7 30
 
3.6%
4 22
 
2.6%
9 19
 
2.3%
Other values (3) 32
 
3.8%
Hangul
ValueCountFrequency (%)
도 109
 
7.3%
경 103
 
6.9%
기 103
 
6.9%
시 92
 
6.1%
지 89
 
5.9%
리 88
 
5.9%
번 77
 
5.1%
면 68
 
4.5%
호 56
 
3.7%
주 31
 
2.1%
Other values (145) 686
45.7%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct102
Distinct (%)100.0%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean299359.95
Minimum10011
Maximum487936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-05-10T20:59:09.832031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10011
5-th percentile10862.4
Q115552
median445904
Q3469845.5
95-th percentile486880.45
Maximum487936
Range477925
Interquartile range (IQR)454293.5

Descriptive statistics

Standard deviation217012.97
Coefficient of variation (CV)0.72492319
Kurtosis-1.6939825
Mean299359.95
Median Absolute Deviation (MAD)33073
Skewness-0.56472693
Sum30534715
Variance4.709463 × 1010
MonotonicityNot monotonic
2024-05-10T20:59:10.282166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
477853 1
 
1.0%
17408 1
 
1.0%
451803 1
 
1.0%
17704 1
 
1.0%
451813 1
 
1.0%
451873 1
 
1.0%
413904 1
 
1.0%
10801 1
 
1.0%
10858 1
 
1.0%
10803 1
 
1.0%
Other values (92) 92
89.3%
ValueCountFrequency (%)
10011 1
1.0%
10019 1
1.0%
10125 1
1.0%
10801 1
1.0%
10803 1
1.0%
10858 1
1.0%
10946 1
1.0%
11124 1
1.0%
12031 1
1.0%
12127 1
1.0%
ValueCountFrequency (%)
487936 1
1.0%
487883 1
1.0%
487831 1
1.0%
487814 1
1.0%
486890 1
1.0%
486881 1
1.0%
486870 1
1.0%
486851 1
1.0%
486832 1
1.0%
486822 1
1.0%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct98
Distinct (%)100.0%
Missing5
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean37.465877
Minimum36.957641
Maximum38.18413
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-05-10T20:59:10.744055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.957641
5-th percentile37.035509
Q137.205545
median37.364315
Q337.717673
95-th percentile38.03631
Maximum38.18413
Range1.2264885
Interquartile range (IQR)0.51212767

Descriptive statistics

Standard deviation0.32898456
Coefficient of variation (CV)0.0087809118
Kurtosis-0.95284954
Mean37.465877
Median Absolute Deviation (MAD)0.2472847
Skewness0.43781729
Sum3671.6559
Variance0.10823084
MonotonicityNot monotonic
2024-05-10T20:59:11.300872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.9828194755 1
 
1.0%
37.1082410673 1
 
1.0%
36.9672154134 1
 
1.0%
37.0096557403 1
 
1.0%
37.8527551537 1
 
1.0%
37.9222299248 1
 
1.0%
37.8021630386 1
 
1.0%
37.9542690134 1
 
1.0%
37.7959905038 1
 
1.0%
37.3062693206 1
 
1.0%
Other values (88) 88
85.4%
(Missing) 5
 
4.9%
ValueCountFrequency (%)
36.9576412931 1
1.0%
36.9672154134 1
1.0%
36.9828194755 1
1.0%
36.9857568173 1
1.0%
37.0096557403 1
1.0%
37.0400707535 1
1.0%
37.0423309148 1
1.0%
37.067512737 1
1.0%
37.0818092615 1
1.0%
37.0893026652 1
1.0%
ValueCountFrequency (%)
38.1841297682 1
1.0%
38.1566896835 1
1.0%
38.1349712089 1
1.0%
38.0851875866 1
1.0%
38.058691347 1
1.0%
38.0323608112 1
1.0%
38.0312198199 1
1.0%
38.0060596967 1
1.0%
37.9821124485 1
1.0%
37.9542690134 1
1.0%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct98
Distinct (%)100.0%
Missing5
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean127.1277
Minimum126.55219
Maximum127.70964
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-05-10T20:59:11.749025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.55219
5-th percentile126.69759
Q1126.91483
median127.10736
Q3127.32985
95-th percentile127.57397
Maximum127.70964
Range1.15745
Interquartile range (IQR)0.41502028

Descriptive statistics

Standard deviation0.28613033
Coefficient of variation (CV)0.0022507316
Kurtosis-0.80925249
Mean127.1277
Median Absolute Deviation (MAD)0.20523298
Skewness0.055289316
Sum12458.515
Variance0.081870568
MonotonicityNot monotonic
2024-05-10T20:59:12.232833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.8853133358 1
 
1.0%
127.0353407653 1
 
1.0%
126.9211291947 1
 
1.0%
126.9859335473 1
 
1.0%
126.7848955148 1
 
1.0%
126.8378347947 1
 
1.0%
126.7178873741 1
 
1.0%
126.9176241334 1
 
1.0%
126.7907246514 1
 
1.0%
127.4040600868 1
 
1.0%
Other values (88) 88
85.4%
(Missing) 5
 
4.9%
ValueCountFrequency (%)
126.5521888049 1
1.0%
126.5825369936 1
1.0%
126.5856069357 1
1.0%
126.5973003937 1
1.0%
126.631336059 1
1.0%
126.7092835951 1
1.0%
126.7178873741 1
1.0%
126.7324609814 1
1.0%
126.7401474512 1
1.0%
126.7696371409 1
1.0%
ValueCountFrequency (%)
127.7096387566 1
1.0%
127.6780761491 1
1.0%
127.6624147091 1
1.0%
127.638405802 1
1.0%
127.5872385706 1
1.0%
127.5716247943 1
1.0%
127.5502664741 1
1.0%
127.5470108399 1
1.0%
127.5448724152 1
1.0%
127.5431545906 1
1.0%

Interactions

2024-05-10T20:58:57.908466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:46.964483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:48.615111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:50.349486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:51.916942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:53.757781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:55.557793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:58.200617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:47.249841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:48.882474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:50.551851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:52.172086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:54.023242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:55.909283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:58.456143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:47.506374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:49.140832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:50.787399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:52.434645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:54.351576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:56.267232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:58.734670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:47.752579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:49.403870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:50.974589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:52.678482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:54.599856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:56.789835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:58.987308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:47.953453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:49.686973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:51.177564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:52.908671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:54.803800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:57.083056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:59.268490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:48.155256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:49.881592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:51.421993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:53.148755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:55.046649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:57.317684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:59.549161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:48.365364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:50.154354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:51.671615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:53.513933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:55.306228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:58:57.624832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T20:59:12.699176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명인허가일자영업상태명폐업일자의료인수(명)연면적(㎡)소재지우편번호WGS84위도WGS84경도
시군명1.0000.8920.4451.0000.8720.5490.8000.8480.850
인허가일자0.8921.0000.1900.9260.5810.6100.6060.5900.517
영업상태명0.4450.1901.000NaN0.2540.0810.0150.2830.505
폐업일자1.0000.926NaN1.0000.0001.000NaN1.000NaN
의료인수(명)0.8720.5810.2540.0001.0000.5410.1270.0000.000
연면적(㎡)0.5490.6100.0811.0000.5411.0000.0000.0000.000
소재지우편번호0.8000.6060.015NaN0.1270.0001.0000.2400.453
WGS84위도0.8480.5900.2831.0000.0000.0000.2401.0000.459
WGS84경도0.8500.5170.505NaN0.0000.0000.4530.4591.000
2024-05-10T20:59:13.014075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업상태명시군명
영업상태명1.0000.359
시군명0.3591.000
2024-05-10T20:59:13.269846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자폐업일자의료인수(명)연면적(㎡)소재지우편번호WGS84위도WGS84경도시군명영업상태명
인허가일자1.0000.5230.2950.290-0.0710.018-0.0140.5710.142
폐업일자0.5231.0000.324-0.018-0.0900.900-0.6000.7071.000
의료인수(명)0.2950.3241.0000.442-0.0500.079-0.1570.6360.166
연면적(㎡)0.290-0.0180.4421.000-0.290-0.128-0.1750.2330.073
소재지우편번호-0.071-0.090-0.050-0.2901.0000.1740.1690.5480.035
WGS84위도0.0180.9000.079-0.1280.1741.0000.0180.4970.205
WGS84경도-0.014-0.600-0.157-0.1750.1690.0181.0000.4990.371
시군명0.5710.7070.6360.2330.5480.4970.4991.0000.359
영업상태명0.1421.0000.1660.0730.0350.2050.3710.3591.000

Missing values

2024-05-10T20:58:59.904570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T20:59:00.451125image/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.
2024-05-10T20:59:00.845483image/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

시군명사업장명인허가일자영업상태명폐업일자의료인수(명)연면적(㎡)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0가평군설악면보건지소19701201영업중<NA>2352.65경기도 가평군 설악면 한서로 6경기도 가평군 설악면 신천리 156번지 1호47785337.675851127.494307
1가평군청평면보건지소19841106영업중<NA>3442.24경기도 가평군 청평면 은고개로 21경기도 가평군 청평면 청평리 333번지 1호47781537.740011127.424534
2가평군상면보건지소19711120영업중<NA>2149.29경기도 가평군 상면 청군로 1099경기도 가평군 상면 연하리 171번지 1호47782337.805007127.357672
3가평군북면보건지소19801222영업중<NA>1166.3경기도 가평군 북면 화악산로 23경기도 가평군 북면 목동리 883번지 13호47784237.887381127.550266
4가평군조종면보건지소19821227영업중<NA>2337.89경기도 가평군 하면 조종내길 75경기도 가평군 하면 현리 567번지 7호47783437.817926127.353251
5광주시퇴촌남종통합보건지소19970101영업중<NA>1616.0경기도 광주시 퇴촌면 산수로 1347경기도 광주시 퇴촌면 오리 73-146484137.474771127.305858
6광주시도척보건지소19940101영업중<NA>2233.0경기도 광주시 도척면 노곡로 20경기도 광주시 도척면 노곡리 58-1446488237.304818127.331166
7광주시초월보건지소19910101영업중<NA>0198.0경기도 광주시 초월읍 경충대로 1009-40경기도 광주시 초월읍 쌍동리 163번지 7호46486137.368845127.302766
8군포시군포시산본보건지소20151021영업중<NA>282649.5경기도 군포시 산본천로 101 (산본동, 군포시산본보건지소)경기도 군포시 산본동 1096번지 군포시산본보건지소1581837.364298126.933169
9김포시하성면보건지소19710101영업중<NA>2154.0경기도 김포시 하성면 애기봉로 845 (하성면사무소)경기도 김포시 하성면 마곡리 623번지 3호1001137.720178126.631336
시군명사업장명인허가일자영업상태명폐업일자의료인수(명)연면적(㎡)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
93화성시남양보건지소19681231영업중<NA>2563.13경기도 화성시 남양시장로 25-2 (남양동)경기도 화성시 남양동 581번지44501037.208751126.811833
94화성시우정보건지소19681231영업중<NA>2361.24경기도 화성시 우정읍 쌍봉로 109-12 (우정읍사무소)경기도 화성시 우정읍 화수리 산 61번지 1호1856337.089303126.815829
95화성시화성시보건소동부보건지소20051214영업중<NA>71022.0경기도 화성시 떡전골로 72-3, 2,3층 (병점동, 리치프라자)경기도 화성시 병점동 400번지 6호 리치프라자 2,3층44536037.205285127.034904
96화성시봉담주민건강지원센터20091128영업중<NA>54061.99경기도 화성시 봉담읍 동화새터길 109경기도 화성시 봉담읍 동화리 614번지44589337.21706126.96112
97화성시정남보건지소19681231영업중<NA>2830.0경기도 화성시 정남면 서봉로 998 (정남면사무소)경기도 화성시 정남면 발산리 729번지 7호1851937.160272126.971119
98화성시팔탄보건지소19681231영업중<NA>2201.39경기도 화성시 팔탄면 서촌길1번길 15경기도 화성시 팔탄면 구장리 536번지 2호44591537.160957126.902784
99화성시장안보건지소19681231영업중<NA>21024.6경기도 화성시 장안면 포승장안로 1219경기도 화성시 장안면 독정리 933번지 1호44594437.067513126.84733
100화성시양감보건지소19681231영업중<NA>2154.7경기도 화성시 양감면 은행나무로 265경기도 화성시 양감면 신왕리 678번지 1호44593437.081809126.944696
101화성시동탄보건지소20081103영업중<NA>35232.0경기도 화성시 노작로 226-9 (석우동)경기도 화성시 석우동 60번지44517037.207026127.078563
102화성시동탄면보건지소19681231폐업201402271165.0경기도 화성시 동탄면 동부대로925번길 39-5경기도 화성시 동탄면 오산리 858번지445813<NA><NA>