Overview

Dataset statistics

Number of variables12
Number of observations53
Missing cells72
Missing cells (%)11.3%
Duplicate rows1
Duplicate rows (%)1.9%
Total size in memory5.4 KiB
Average record size in memory104.5 B

Variable types

Categorical3
Text3
Numeric6

Dataset

Description특수장소의약품취급업소(휴게소등) 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=Y7G89G6QNH3MRKZKT2FO14096681&infSeq=1

Alerts

인허가구분명 has constant value ""Constant
Dataset has 1 (1.9%) duplicate rowsDuplicates
인허가일자 is highly overall correlated with 지정일자 and 2 other fieldsHigh correlation
폐업일자 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
지정일자 is highly overall correlated with 인허가일자 and 2 other fieldsHigh correlation
소재지우편번호 is highly overall correlated with 인허가일자 and 2 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
시군명 is highly overall correlated with 폐업일자 and 4 other fieldsHigh correlation
영업상태명 is highly overall correlated with 인허가일자 and 5 other fieldsHigh correlation
폐업일자 has 36 (67.9%) missing valuesMissing
소재지도로명주소 has 9 (17.0%) missing valuesMissing
소재지지번주소 has 4 (7.5%) missing valuesMissing
소재지우편번호 has 3 (5.7%) missing valuesMissing
WGS84위도 has 10 (18.9%) missing valuesMissing
WGS84경도 has 10 (18.9%) missing valuesMissing

Reproduction

Analysis started2023-12-10 21:42:23.640314
Analysis finished2023-12-10 21:42:27.484403
Duration3.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Memory size556.0 B
연천군
10 
파주시
7 
안성시
5 
양평군
5 
가평군
4 
Other values (10)
22 

Length

Max length4
Median length3
Mean length3.0566038
Min length3

Unique

Unique2 ?
Unique (%)3.8%

Sample

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

Common Values

ValueCountFrequency (%)
연천군 10
18.9%
파주시 7
13.2%
안성시 5
9.4%
양평군 5
9.4%
가평군 4
 
7.5%
용인시 3
 
5.7%
이천시 3
 
5.7%
포천시 3
 
5.7%
하남시 3
 
5.7%
동두천시 2
 
3.8%
Other values (5) 8
15.1%

Length

2023-12-11T06:42:27.545743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
연천군 10
18.9%
파주시 7
13.2%
안성시 5
9.4%
양평군 5
9.4%
가평군 4
 
7.5%
용인시 3
 
5.7%
이천시 3
 
5.7%
포천시 3
 
5.7%
하남시 3
 
5.7%
동두천시 2
 
3.8%
Other values (5) 8
15.1%

사업장명
Text

Distinct44
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Memory size556.0 B
2023-12-11T06:42:27.730477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length8.0754717
Min length3

Characters and Unicode

Total characters428
Distinct characters100
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

Unique40 ?
Unique (%)75.5%

Sample

1st row연하리지정소
2nd row대성리지정소
3rd row가평휴게소(서울방향)
4th row가평휴게소(춘천방향)
5th row외방리 지정소
ValueCountFrequency (%)
제25보병사단 4
 
6.2%
양평프라자약국 4
 
6.2%
2136부대 3
 
4.6%
서울약국 2
 
3.1%
육군 2
 
3.1%
1570부대 1
 
1.5%
대보유통(주)화성휴게소(상 1
 
1.5%
남사약국 1
 
1.5%
대학약국 1
 
1.5%
사단장 1
 
1.5%
Other values (45) 45
69.2%
2023-12-11T06:42:28.063374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
소 22
 
5.1%
대 17
 
4.0%
국 16
 
3.7%
약 16
 
3.7%
5 15
 
3.5%
( 14
 
3.3%
) 14
 
3.3%
12
 
2.8%
1 12
 
2.8%
정 12
 
2.8%
Other values (90) 278
65.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 335
78.3%
Decimal Number 53
 
12.4%
Open Punctuation 14
 
3.3%
Close Punctuation 14
 
3.3%
Space Separator 12
 
2.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
소 22
 
6.6%
대 17
 
5.1%
국 16
 
4.8%
약 16
 
4.8%
정 12
 
3.6%
리 11
 
3.3%
지 10
 
3.0%
게 10
 
3.0%
부 10
 
3.0%
휴 10
 
3.0%
Other values (80) 201
60.0%
Decimal Number
ValueCountFrequency (%)
5 15
28.3%
1 12
22.6%
2 8
15.1%
7 7
13.2%
3 4
 
7.5%
0 4
 
7.5%
6 3
 
5.7%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 335
78.3%
Common 93
 
21.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
소 22
 
6.6%
대 17
 
5.1%
국 16
 
4.8%
약 16
 
4.8%
정 12
 
3.6%
리 11
 
3.3%
지 10
 
3.0%
게 10
 
3.0%
부 10
 
3.0%
휴 10
 
3.0%
Other values (80) 201
60.0%
Common
ValueCountFrequency (%)
5 15
16.1%
( 14
15.1%
) 14
15.1%
12
12.9%
1 12
12.9%
2 8
8.6%
7 7
7.5%
3 4
 
4.3%
0 4
 
4.3%
6 3
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 335
78.3%
ASCII 93
 
21.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
소 22
 
6.6%
대 17
 
5.1%
국 16
 
4.8%
약 16
 
4.8%
정 12
 
3.6%
리 11
 
3.3%
지 10
 
3.0%
게 10
 
3.0%
부 10
 
3.0%
휴 10
 
3.0%
Other values (80) 201
60.0%
ASCII
ValueCountFrequency (%)
5 15
16.1%
( 14
15.1%
) 14
15.1%
12
12.9%
1 12
12.9%
2 8
8.6%
7 7
7.5%
3 4
 
4.3%
0 4
 
4.3%
6 3
 
3.2%

인허가일자
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20063150
Minimum19790625
Maximum20180323
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-11T06:42:28.195071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19790625
5-th percentile19862527
Q119980416
median20090921
Q320170406
95-th percentile20171113
Maximum20180323
Range389698
Interquartile range (IQR)189990

Descriptive statistics

Standard deviation109709.91
Coefficient of variation (CV)0.0054682297
Kurtosis-0.36349327
Mean20063150
Median Absolute Deviation (MAD)79485
Skewness-0.83718521
Sum1.0633469 × 109
Variance1.2036265 × 1010
MonotonicityNot monotonic
2023-12-11T06:42:28.325530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
20170406 3
 
5.7%
20170921 2
 
3.8%
20120731 2
 
3.8%
20090706 2
 
3.8%
20070514 2
 
3.8%
20171113 2
 
3.8%
20121112 2
 
3.8%
19950202 1
 
1.9%
20020914 1
 
1.9%
20130306 1
 
1.9%
Other values (35) 35
66.0%
ValueCountFrequency (%)
19790625 1
1.9%
19811130 1
1.9%
19851127 1
1.9%
19870127 1
1.9%
19880414 1
1.9%
19900411 1
1.9%
19910927 1
1.9%
19921028 1
1.9%
19930807 1
1.9%
19950202 1
1.9%
ValueCountFrequency (%)
20180323 1
 
1.9%
20180322 1
 
1.9%
20171113 2
3.8%
20171101 1
 
1.9%
20171031 1
 
1.9%
20171024 1
 
1.9%
20170921 2
3.8%
20170525 1
 
1.9%
20170414 1
 
1.9%
20170406 3
5.7%

영업상태명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size556.0 B
운영중
27 
폐업 등
26 

Length

Max length4
Median length3
Mean length3.490566
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
운영중 27
50.9%
폐업 등 26
49.1%

Length

2023-12-11T06:42:28.474287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:42:28.569912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 27
34.2%
폐업 26
32.9%
등 26
32.9%

폐업일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)82.4%
Missing36
Missing (%)67.9%
Infinite0
Infinite (%)0.0%
Mean20130642
Minimum20100326
Maximum20161213
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-11T06:42:28.651246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20100326
5-th percentile20116388
Q120121109
median20130515
Q320130816
95-th percentile20152805
Maximum20161213
Range60887
Interquartile range (IQR)9707

Descriptive statistics

Standard deviation13342.674
Coefficient of variation (CV)0.00066280417
Kurtosis1.913474
Mean20130642
Median Absolute Deviation (MAD)9406
Skewness0.20457365
Sum3.4222092 × 108
Variance1.7802694 × 108
MonotonicityNot monotonic
2023-12-11T06:42:28.960955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
20130429 2
 
3.8%
20130515 2
 
3.8%
20120703 2
 
3.8%
20121109 1
 
1.9%
20130508 1
 
1.9%
20120403 1
 
1.9%
20130607 1
 
1.9%
20140617 1
 
1.9%
20130711 1
 
1.9%
20150703 1
 
1.9%
Other values (4) 4
 
7.5%
(Missing) 36
67.9%
ValueCountFrequency (%)
20100326 1
1.9%
20120403 1
1.9%
20120703 2
3.8%
20121109 1
1.9%
20130429 2
3.8%
20130508 1
1.9%
20130515 2
3.8%
20130607 1
1.9%
20130711 1
1.9%
20130816 1
1.9%
ValueCountFrequency (%)
20161213 1
1.9%
20150703 1
1.9%
20140617 1
1.9%
20140611 1
1.9%
20130816 1
1.9%
20130711 1
1.9%
20130607 1
1.9%
20130515 2
3.8%
20130508 1
1.9%
20130429 2
3.8%

인허가구분명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size556.0 B
특수장소의의약품취급자
53 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
특수장소의의약품취급자 53
100.0%

Length

2023-12-11T06:42:29.065624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:42:29.146562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
특수장소의의약품취급자 53
100.0%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20063150
Minimum19790625
Maximum20180323
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-11T06:42:29.243399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19790625
5-th percentile19862527
Q119980416
median20090921
Q320170406
95-th percentile20171113
Maximum20180323
Range389698
Interquartile range (IQR)189990

Descriptive statistics

Standard deviation109709.91
Coefficient of variation (CV)0.0054682297
Kurtosis-0.36349327
Mean20063150
Median Absolute Deviation (MAD)79485
Skewness-0.83718521
Sum1.0633469 × 109
Variance1.2036265 × 1010
MonotonicityNot monotonic
2023-12-11T06:42:29.367835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
20170406 3
 
5.7%
20170921 2
 
3.8%
20120731 2
 
3.8%
20090706 2
 
3.8%
20070514 2
 
3.8%
20171113 2
 
3.8%
20121112 2
 
3.8%
19950202 1
 
1.9%
20020914 1
 
1.9%
20130306 1
 
1.9%
Other values (35) 35
66.0%
ValueCountFrequency (%)
19790625 1
1.9%
19811130 1
1.9%
19851127 1
1.9%
19870127 1
1.9%
19880414 1
1.9%
19900411 1
1.9%
19910927 1
1.9%
19921028 1
1.9%
19930807 1
1.9%
19950202 1
1.9%
ValueCountFrequency (%)
20180323 1
 
1.9%
20180322 1
 
1.9%
20171113 2
3.8%
20171101 1
 
1.9%
20171031 1
 
1.9%
20171024 1
 
1.9%
20170921 2
3.8%
20170525 1
 
1.9%
20170414 1
 
1.9%
20170406 3
5.7%

소재지도로명주소
Text

MISSING 

Distinct37
Distinct (%)84.1%
Missing9
Missing (%)17.0%
Memory size556.0 B
2023-12-11T06:42:29.583580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length32
Mean length23.113636
Min length17

Characters and Unicode

Total characters1017
Distinct characters117
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

Unique34 ?
Unique (%)77.3%

Sample

1st row경기도 가평군 상면 기와집길 14-5
2nd row경기도 가평군 청평면 모꼬지로 61
3rd row경기도 가평군 설악면 미사리로540번길 51
4th row경기도 가평군 설악면 미사리로540번길 51 (설악휴게소)
5th row경기도 남양주시 수동면 비룡로 1057
ValueCountFrequency (%)
경기도 44
 
18.4%
연천군 10
 
4.2%
파주시 7
 
2.9%
등원로 5
 
2.1%
조리읍 5
 
2.1%
450 5
 
2.1%
가평군 4
 
1.7%
1 4
 
1.7%
신장동 3
 
1.3%
용인시 3
 
1.3%
Other values (111) 149
62.3%
2023-12-11T06:42:29.911542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
196
 
19.3%
도 47
 
4.6%
기 46
 
4.5%
1 45
 
4.4%
경 44
 
4.3%
로 43
 
4.2%
시 30
 
2.9%
5 25
 
2.5%
면 23
 
2.3%
0 20
 
2.0%
Other values (107) 498
49.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 610
60.0%
Space Separator 196
 
19.3%
Decimal Number 169
 
16.6%
Close Punctuation 16
 
1.6%
Open Punctuation 16
 
1.6%
Dash Punctuation 9
 
0.9%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 47
 
7.7%
기 46
 
7.5%
경 44
 
7.2%
로 43
 
7.0%
시 30
 
4.9%
면 23
 
3.8%
군 20
 
3.3%
천 18
 
3.0%
동 17
 
2.8%
평 16
 
2.6%
Other values (92) 306
50.2%
Decimal Number
ValueCountFrequency (%)
1 45
26.6%
5 25
14.8%
0 20
11.8%
4 19
11.2%
3 14
 
8.3%
2 12
 
7.1%
7 10
 
5.9%
9 10
 
5.9%
8 8
 
4.7%
6 6
 
3.6%
Space Separator
ValueCountFrequency (%)
196
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 610
60.0%
Common 407
40.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 47
 
7.7%
기 46
 
7.5%
경 44
 
7.2%
로 43
 
7.0%
시 30
 
4.9%
면 23
 
3.8%
군 20
 
3.3%
천 18
 
3.0%
동 17
 
2.8%
평 16
 
2.6%
Other values (92) 306
50.2%
Common
ValueCountFrequency (%)
196
48.2%
1 45
 
11.1%
5 25
 
6.1%
0 20
 
4.9%
4 19
 
4.7%
) 16
 
3.9%
( 16
 
3.9%
3 14
 
3.4%
2 12
 
2.9%
7 10
 
2.5%
Other values (5) 34
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 610
60.0%
ASCII 407
40.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
196
48.2%
1 45
 
11.1%
5 25
 
6.1%
0 20
 
4.9%
4 19
 
4.7%
) 16
 
3.9%
( 16
 
3.9%
3 14
 
3.4%
2 12
 
2.9%
7 10
 
2.5%
Other values (5) 34
 
8.4%
Hangul
ValueCountFrequency (%)
도 47
 
7.7%
기 46
 
7.5%
경 44
 
7.2%
로 43
 
7.0%
시 30
 
4.9%
면 23
 
3.8%
군 20
 
3.3%
천 18
 
3.0%
동 17
 
2.8%
평 16
 
2.6%
Other values (92) 306
50.2%

소재지지번주소
Text

MISSING 

Distinct43
Distinct (%)87.8%
Missing4
Missing (%)7.5%
Memory size556.0 B
2023-12-11T06:42:30.197520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length34
Mean length24.020408
Min length15

Characters and Unicode

Total characters1177
Distinct characters114
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)77.6%

Sample

1st row경기도 가평군 상면 연하리 187번지
2nd row경기도 가평군 청평면 대성리 595번지
3rd row경기도 가평군 설악면 미사리 145번지
4th row경기도 가평군 설악면 미사리 149번지
5th row경기도 남양주시 수동면 외방리 525번지
ValueCountFrequency (%)
경기도 49
 
17.0%
연천군 8
 
2.8%
4호 8
 
2.8%
1호 7
 
2.4%
파주시 7
 
2.4%
산 5
 
1.7%
안성시 5
 
1.7%
양평군 5
 
1.7%
오산리 5
 
1.7%
조리읍 5
 
1.7%
Other values (130) 184
63.9%
2023-12-11T06:42:30.596800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
239
20.3%
1 54
 
4.6%
지 51
 
4.3%
도 50
 
4.2%
기 50
 
4.2%
경 49
 
4.2%
번 48
 
4.1%
리 44
 
3.7%
시 32
 
2.7%
면 31
 
2.6%
Other values (104) 529
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 729
61.9%
Space Separator 239
 
20.3%
Decimal Number 199
 
16.9%
Dash Punctuation 8
 
0.7%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 51
 
7.0%
도 50
 
6.9%
기 50
 
6.9%
경 49
 
6.7%
번 48
 
6.6%
리 44
 
6.0%
시 32
 
4.4%
면 31
 
4.3%
호 30
 
4.1%
군 19
 
2.6%
Other values (90) 325
44.6%
Decimal Number
ValueCountFrequency (%)
1 54
27.1%
2 30
15.1%
4 24
12.1%
9 17
 
8.5%
0 16
 
8.0%
8 13
 
6.5%
7 13
 
6.5%
5 12
 
6.0%
3 11
 
5.5%
6 9
 
4.5%
Space Separator
ValueCountFrequency (%)
239
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 729
61.9%
Common 448
38.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 51
 
7.0%
도 50
 
6.9%
기 50
 
6.9%
경 49
 
6.7%
번 48
 
6.6%
리 44
 
6.0%
시 32
 
4.4%
면 31
 
4.3%
호 30
 
4.1%
군 19
 
2.6%
Other values (90) 325
44.6%
Common
ValueCountFrequency (%)
239
53.3%
1 54
 
12.1%
2 30
 
6.7%
4 24
 
5.4%
9 17
 
3.8%
0 16
 
3.6%
8 13
 
2.9%
7 13
 
2.9%
5 12
 
2.7%
3 11
 
2.5%
Other values (4) 19
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 729
61.9%
ASCII 448
38.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
239
53.3%
1 54
 
12.1%
2 30
 
6.7%
4 24
 
5.4%
9 17
 
3.8%
0 16
 
3.6%
8 13
 
2.9%
7 13
 
2.9%
5 12
 
2.7%
3 11
 
2.5%
Other values (4) 19
 
4.2%
Hangul
ValueCountFrequency (%)
지 51
 
7.0%
도 50
 
6.9%
기 50
 
6.9%
경 49
 
6.7%
번 48
 
6.6%
리 44
 
6.0%
시 32
 
4.4%
면 31
 
4.3%
호 30
 
4.1%
군 19
 
2.6%
Other values (90) 325
44.6%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct35
Distinct (%)70.0%
Missing3
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean322864.8
Minimum10806
Maximum487896
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-11T06:42:30.718357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10806
5-th percentile10949
Q111355.25
median456832.5
Q3476802
95-th percentile486863.9
Maximum487896
Range477090
Interquartile range (IQR)465446.75

Descriptive statistics

Standard deviation216241.1
Coefficient of variation (CV)0.66975744
Kurtosis-1.4265611
Mean322864.8
Median Absolute Deviation (MAD)21005.5
Skewness-0.78690877
Sum16143240
Variance4.6760214 × 1010
MonotonicityNot monotonic
2023-12-11T06:42:30.835493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
10949 5
 
9.4%
476802 4
 
7.5%
456812 2
 
3.8%
445919 2
 
3.8%
10806 2
 
3.8%
486832 2
 
3.8%
486822 2
 
3.8%
11021 2
 
3.8%
456210 2
 
3.8%
11340 2
 
3.8%
Other values (25) 25
47.2%
(Missing) 3
 
5.7%
ValueCountFrequency (%)
10806 2
 
3.8%
10949 5
9.4%
11021 2
 
3.8%
11049 1
 
1.9%
11117 1
 
1.9%
11340 2
 
3.8%
11401 1
 
1.9%
12462 1
 
1.9%
12616 1
 
1.9%
445919 2
 
3.8%
ValueCountFrequency (%)
487896 1
1.9%
487886 1
1.9%
486890 1
1.9%
486832 2
3.8%
486822 2
3.8%
482140 1
1.9%
477853 1
1.9%
477823 1
1.9%
477812 1
1.9%
476863 1
1.9%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)83.7%
Missing10
Missing (%)18.9%
Infinite0
Infinite (%)0.0%
Mean37.588041
Minimum36.968668
Maximum38.213632
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-11T06:42:30.949252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.968668
5-th percentile37.007273
Q137.256541
median37.701936
Q337.90102
95-th percentile38.119554
Maximum38.213632
Range1.2449633
Interquartile range (IQR)0.64447928

Descriptive statistics

Standard deviation0.37415553
Coefficient of variation (CV)0.0099541109
Kurtosis-1.2259034
Mean37.588041
Median Absolute Deviation (MAD)0.28629808
Skewness-0.18311022
Sum1616.2858
Variance0.13999236
MonotonicityNot monotonic
2023-12-11T06:42:31.066535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
37.7768981823 5
 
9.4%
37.4904154203 2
 
3.8%
37.7019355153 2
 
3.8%
37.0072733118 2
 
3.8%
37.2939596621 1
 
1.9%
37.2413199999 1
 
1.9%
37.2645883019 1
 
1.9%
37.9036316228 1
 
1.9%
37.8994447012 1
 
1.9%
37.0461598914 1
 
1.9%
Other values (26) 26
49.1%
(Missing) 10
 
18.9%
ValueCountFrequency (%)
36.9686684925 1
1.9%
37.0070366427 1
1.9%
37.0072733118 2
3.8%
37.0461598914 1
1.9%
37.0761240011 1
1.9%
37.1141159695 1
1.9%
37.1421239135 1
1.9%
37.145440849 1
1.9%
37.2413199999 1
1.9%
37.2484932671 1
1.9%
ValueCountFrequency (%)
38.2136317792 1
1.9%
38.1789643093 1
1.9%
38.1233673809 1
1.9%
38.0852349807 1
1.9%
38.0660294614 1
1.9%
38.0020303602 1
1.9%
37.9882335969 1
1.9%
37.9577057422 1
1.9%
37.9094456886 1
1.9%
37.9036316228 1
1.9%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)83.7%
Missing10
Missing (%)18.9%
Infinite0
Infinite (%)0.0%
Mean127.16132
Minimum126.84392
Maximum127.71326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-11T06:42:31.181161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.84392
5-th percentile126.84392
Q1126.94136
median127.15012
Q3127.31739
95-th percentile127.54162
Maximum127.71326
Range0.86933288
Interquartile range (IQR)0.37603098

Descriptive statistics

Standard deviation0.23175533
Coefficient of variation (CV)0.00182253
Kurtosis-0.73118921
Mean127.16132
Median Absolute Deviation (MAD)0.18551782
Skewness0.25896425
Sum5467.9369
Variance0.053710532
MonotonicityNot monotonic
2023-12-11T06:42:31.283409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
126.8439247278 5
 
9.4%
127.4950340444 2
 
3.8%
127.5467944736 2
 
3.8%
127.269669138 2
 
3.8%
127.3783627805 1
 
1.9%
127.3898123224 1
 
1.9%
127.40366186 1
 
1.9%
126.8890312807 1
 
1.9%
126.8893934166 1
 
1.9%
127.0969184925 1
 
1.9%
Other values (26) 26
49.1%
(Missing) 10
 
18.9%
ValueCountFrequency (%)
126.8439247278 5
9.4%
126.8794777026 1
 
1.9%
126.880407662 1
 
1.9%
126.8848205222 1
 
1.9%
126.8890312807 1
 
1.9%
126.8893934166 1
 
1.9%
126.9181122331 1
 
1.9%
126.9646068794 1
 
1.9%
127.017173949 1
 
1.9%
127.019635282 1
 
1.9%
ValueCountFrequency (%)
127.7132576046 1
1.9%
127.5467944736 2
3.8%
127.4950340444 2
3.8%
127.40366186 1
1.9%
127.3898123224 1
1.9%
127.3783627805 1
1.9%
127.3739196571 1
1.9%
127.3551382347 1
1.9%
127.3180261346 1
1.9%
127.3167549407 1
1.9%

Interactions

2023-12-11T06:42:26.632490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:24.242888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:24.693062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:25.168784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:25.675987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:26.165537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:26.705441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:24.319797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:24.768533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:25.249781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:25.756475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:26.249334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:26.794219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:24.398002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:24.844206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:25.325941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:25.846041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:26.326811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:26.889128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:24.480062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:24.932111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:25.424319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:25.942573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:26.410159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:26.963378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:24.551972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:25.009564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:25.504092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:26.025667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:26.481021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:27.033619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:24.627587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:25.078568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:25.602659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:26.101581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:42:26.560227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:42:31.361682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명사업장명인허가일자영업상태명폐업일자지정일자소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
시군명1.0000.9900.8000.7080.8360.8001.0001.0000.7270.9350.970
사업장명0.9901.0001.0000.7950.9211.0000.9900.9731.0000.9730.969
인허가일자0.8001.0001.0000.7080.8401.0001.0000.9900.9630.6200.767
영업상태명0.7080.7950.7081.000NaN0.7080.8991.0000.3780.7390.967
폐업일자0.8360.9210.840NaN1.0000.8401.0000.9650.7250.9040.729
지정일자0.8001.0001.0000.7080.8401.0001.0000.9900.9630.6200.767
소재지도로명주소1.0000.9901.0000.8991.0001.0001.0001.0001.0001.0001.000
소재지지번주소1.0000.9730.9901.0000.9650.9901.0001.0001.0001.0000.989
소재지우편번호0.7271.0000.9630.3780.7250.9631.0001.0001.0000.5980.675
WGS84위도0.9350.9730.6200.7390.9040.6201.0001.0000.5981.0000.724
WGS84경도0.9700.9690.7670.9670.7290.7671.0000.9890.6750.7241.000
2023-12-11T06:42:31.488695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업상태명시군명
영업상태명1.0000.566
시군명0.5661.000
2023-12-11T06:42:31.555749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자폐업일자지정일자소재지우편번호WGS84위도WGS84경도시군명영업상태명
인허가일자1.0000.0461.000-0.6860.079-0.4320.3930.520
폐업일자0.0461.0000.046-0.4420.113-0.2960.6791.000
지정일자1.0000.0461.000-0.6860.079-0.4320.3930.520
소재지우편번호-0.686-0.442-0.6861.0000.1290.4480.6110.298
WGS84위도0.0790.1130.0790.1291.000-0.3320.6730.686
WGS84경도-0.432-0.296-0.4320.448-0.3321.0000.7470.756
시군명0.3930.6790.3930.6110.6730.7471.0000.566
영업상태명0.5201.0000.5200.2980.6860.7560.5661.000

Missing values

2023-12-11T06:42:27.132058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:42:27.289304image/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.
2023-12-11T06:42:27.414324image/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

시군명사업장명인허가일자영업상태명폐업일자인허가구분명지정일자소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0가평군연하리지정소19880414운영중<NA>특수장소의의약품취급자19880414경기도 가평군 상면 기와집길 14-5경기도 가평군 상면 연하리 187번지47782337.805499127.355138
1가평군대성리지정소19811130폐업 등20121109특수장소의의약품취급자19811130경기도 가평군 청평면 모꼬지로 61경기도 가평군 청평면 대성리 595번지47781237.683012127.37392
2가평군가평휴게소(서울방향)20090706폐업 등20130429특수장소의의약품취급자20090706경기도 가평군 설악면 미사리로540번길 51경기도 가평군 설악면 미사리 145번지47785337.701936127.546794
3가평군가평휴게소(춘천방향)20090706폐업 등20130508특수장소의의약품취급자20090706경기도 가평군 설악면 미사리로540번길 51 (설악휴게소)경기도 가평군 설악면 미사리 149번지1246237.701936127.546794
4남양주시외방리 지정소19970205운영중<NA>특수장소의의약품취급자19970205경기도 남양주시 수동면 비룡로 1057경기도 남양주시 수동면 외방리 525번지47285337.723455127.307231
5동두천시제1575부대20170414운영중<NA>특수장소의의약품취급자20170414경기도 동두천시 벌마들로39번길 9-19 (상패동)경기도 동두천시 상패동 93-12번지1134037.902595127.043665
6동두천시1575부대장20170525운영중<NA>특수장소의의약품취급자20170525경기도 동두천시 벌마들로 39 (상패동, 사서함 92-1)<NA>11340<NA><NA>
7안성시안성맞춤(평택방향) 휴게소20090920폐업 등20130515특수장소의의약품취급자20090920<NA>경기도 안성시 서운면 신능리 82번지 1호45685336.968668127.265657
8안성시안성맞춤(충주방향)휴게소20090921폐업 등20130515특수장소의의약품취급자20090921경기도 안성시 중앙로382번길 19경기도 안성시 서인동 82번지 1호45621037.007037127.26849
9안성시(주) 영풍 안성휴게소20030618폐업 등20120403특수장소의의약품취급자20030618<NA>경기도 안성시 원곡면 산하리 산 68번지 7호45681237.076124127.132802
시군명사업장명인허가일자영업상태명폐업일자인허가구분명지정일자소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
43평택시공군제5315부대20170322운영중<NA>특수장소의의약품취급자20170322경기도 평택시 신장로 00 (신장동)<NA><NA><NA><NA>
44평택시성신약국 취급소20020314폐업 등20140611특수장소의의약품취급자20020314경기도 평택시 도일로 3 (가재동)경기도 평택시 가재동 4번지 1호45906037.04616127.096918
45포천시신진약국영평리지정소19870127운영중<NA>특수장소의의약품취급자19870127<NA>경기도 포천시 영중면 영평리 272번지487886<NA><NA>
46포천시민생약국산정리지정소19980416운영중<NA>특수장소의의약품취급자19980416경기도 포천시 영북면 산정호수로411번길 13경기도 포천시 영북면 산정리 451번지 4호48789638.066029127.316755
47포천시광장약국노곡리지정소20041123폐업 등20161213특수장소의의약품취급자20041123경기도 포천시 일동면 화동로 1058-1경기도 포천시 일동면 기산리 90번지 120호1111737.957706127.318026
48하남시상록수약국19851127폐업 등<NA>특수장소의의약품취급자19851127경기도 하남시 신평로 57 (신장동)경기도 하남시 신장동 427번지 227호46582037.538874127.204984
49하남시갑오사약국19790625폐업 등<NA>특수장소의의약품취급자19790625경기도 하남시 신평로 68 (신장동)경기도 하남시 신장1동 427번지 127호46503137.539444127.205785
50하남시대학약국19921028폐업 등20130816특수장소의의약품취급자19921028경기도 하남시 신장로 74 (천현동)경기도 하남시 천현동 205번지 4호46581637.536001127.209455
51화성시대보유통(주)화성휴게소(상)20070514운영중<NA>특수장소의의약품취급자20070514<NA>경기도 화성시 팔탄면 덕천리 산 19번지 12호44591937.145441126.880408
52화성시대보유통(주)화성휴게소(하)20070514운영중<NA>특수장소의의약품취급자20070514경기도 화성시 팔탄면 서해안고속도로 301-1경기도 화성시 팔탄면 덕천리 산 102번지 1호 화성(하)휴게소44591937.142124126.879478

Duplicate rows

Most frequently occurring

시군명사업장명인허가일자영업상태명폐업일자인허가구분명지정일자소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도# duplicates
0연천군2136부대20170921폐업 등<NA>특수장소의의약품취급자20170921경기도 연천군 청산면 궁평로 1경기도 연천군 청산면 궁평리 0번지 청산면 사서함 106-1711021<NA><NA>2