Overview

Dataset statistics

Number of variables15
Number of observations164
Missing cells198
Missing cells (%)8.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.3 KiB
Average record size in memory126.8 B

Variable types

Categorical5
Text3
Numeric6
Boolean1

Dataset

Description공중이용시설 현황(공중이용시설기타)
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=EXP9CF75GWL6M188RS4D1616953&infSeq=1

Alerts

다중이용업소여부 has constant value ""Constant
시군명 is highly overall correlated with 인허가일자 and 8 other fieldsHigh correlation
위생업종명 is highly overall correlated with 인허가일자 and 9 other fieldsHigh correlation
영업상태명 is highly overall correlated with 인허가일자 and 6 other fieldsHigh correlation
위생업태명 is highly overall correlated with 인허가일자 and 9 other fieldsHigh correlation
건물소유구분명 is highly overall correlated with 인허가일자 and 8 other fieldsHigh correlation
인허가일자 is highly overall correlated with 년도 and 5 other fieldsHigh correlation
폐업일자 is highly overall correlated with 시군명 and 3 other fieldsHigh correlation
년도 is highly overall correlated with 인허가일자 and 5 other fieldsHigh correlation
소재지우편번호 is highly overall correlated with WGS84경도 and 4 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 년도 and 4 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 소재지우편번호 and 4 other fieldsHigh correlation
시군명 is highly imbalanced (51.9%)Imbalance
영업상태명 is highly imbalanced (69.3%)Imbalance
위생업종명 is highly imbalanced (69.3%)Imbalance
위생업태명 is highly imbalanced (69.3%)Imbalance
폐업일자 has 155 (94.5%) missing valuesMissing
년도 has 9 (5.5%) missing valuesMissing
다중이용업소여부 has 9 (5.5%) missing valuesMissing
소재지도로명주소 has 13 (7.9%) missing valuesMissing
WGS84위도 has 6 (3.7%) missing valuesMissing
WGS84경도 has 6 (3.7%) missing valuesMissing

Reproduction

Analysis started2023-12-10 22:02:42.436429
Analysis finished2023-12-10 22:02:47.998542
Duration5.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
수원시
120 
용인시
 
10
부천시
 
9
하남시
 
8
안양시
 
6
Other values (5)
 
11

Length

Max length4
Median length3
Mean length3.0060976
Min length3

Unique

Unique2 ?
Unique (%)1.2%

Sample

1st row동두천시
2nd row부천시
3rd row부천시
4th row부천시
5th row부천시

Common Values

ValueCountFrequency (%)
수원시 120
73.2%
용인시 10
 
6.1%
부천시 9
 
5.5%
하남시 8
 
4.9%
안양시 6
 
3.7%
시흥시 4
 
2.4%
안산시 3
 
1.8%
성남시 2
 
1.2%
동두천시 1
 
0.6%
양평군 1
 
0.6%

Length

2023-12-11T07:02:48.097702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:02:48.211756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수원시 120
73.2%
용인시 10
 
6.1%
부천시 9
 
5.5%
하남시 8
 
4.9%
안양시 6
 
3.7%
시흥시 4
 
2.4%
안산시 3
 
1.8%
성남시 2
 
1.2%
동두천시 1
 
0.6%
양평군 1
 
0.6%

사업장명
Text

Distinct161
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-11T07:02:48.460603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length6.7256098
Min length2

Characters and Unicode

Total characters1103
Distinct characters258
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique158 ?
Unique (%)96.3%

Sample

1st row세아프라자
2nd row현대백화점부천점
3rd row투나
4th row홈플러스테스코(주)중동점
5th row(주)신세계이마트중동점
ValueCountFrequency (%)
현대프라자 2
 
1.1%
삼성에버랜드(주)종합연수원 2
 
1.1%
현대홈타운아파트(상가 2
 
1.1%
센타프라자 2
 
1.1%
백송상가 1
 
0.6%
키라레스토아 1
 
0.6%
경희유니빌 1
 
0.6%
아이텐텐빌딩 1
 
0.6%
매직프라자 1
 
0.6%
월드프라자 1
 
0.6%
Other values (166) 166
92.2%
2023-12-11T07:02:48.859465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
라 47
 
4.3%
자 45
 
4.1%
프 42
 
3.8%
스 33
 
3.0%
아 30
 
2.7%
상 27
 
2.4%
가 23
 
2.1%
트 20
 
1.8%
빌 18
 
1.6%
주 18
 
1.6%
Other values (248) 800
72.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1015
92.0%
Uppercase Letter 21
 
1.9%
Open Punctuation 18
 
1.6%
Close Punctuation 18
 
1.6%
Space Separator 16
 
1.5%
Decimal Number 8
 
0.7%
Lowercase Letter 2
 
0.2%
Other Punctuation 2
 
0.2%
Dash Punctuation 2
 
0.2%
Other Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
라 47
 
4.6%
자 45
 
4.4%
프 42
 
4.1%
스 33
 
3.3%
아 30
 
3.0%
상 27
 
2.7%
가 23
 
2.3%
트 20
 
2.0%
빌 18
 
1.8%
주 18
 
1.8%
Other values (221) 712
70.1%
Uppercase Letter
ValueCountFrequency (%)
L 3
14.3%
G 2
 
9.5%
D 2
 
9.5%
W 2
 
9.5%
A 2
 
9.5%
M 1
 
4.8%
Z 1
 
4.8%
P 1
 
4.8%
R 1
 
4.8%
O 1
 
4.8%
Other values (5) 5
23.8%
Decimal Number
ValueCountFrequency (%)
2 3
37.5%
1 2
25.0%
6 1
 
12.5%
5 1
 
12.5%
4 1
 
12.5%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Space Separator
ValueCountFrequency (%)
16
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1016
92.1%
Common 64
 
5.8%
Latin 23
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
라 47
 
4.6%
자 45
 
4.4%
프 42
 
4.1%
스 33
 
3.2%
아 30
 
3.0%
상 27
 
2.7%
가 23
 
2.3%
트 20
 
2.0%
빌 18
 
1.8%
주 18
 
1.8%
Other values (222) 713
70.2%
Latin
ValueCountFrequency (%)
L 3
13.0%
e 2
 
8.7%
G 2
 
8.7%
D 2
 
8.7%
W 2
 
8.7%
A 2
 
8.7%
M 1
 
4.3%
Z 1
 
4.3%
P 1
 
4.3%
R 1
 
4.3%
Other values (6) 6
26.1%
Common
ValueCountFrequency (%)
( 18
28.1%
) 18
28.1%
16
25.0%
2 3
 
4.7%
, 2
 
3.1%
- 2
 
3.1%
1 2
 
3.1%
6 1
 
1.6%
5 1
 
1.6%
4 1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1015
92.0%
ASCII 87
 
7.9%
None 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
라 47
 
4.6%
자 45
 
4.4%
프 42
 
4.1%
스 33
 
3.3%
아 30
 
3.0%
상 27
 
2.7%
가 23
 
2.3%
트 20
 
2.0%
빌 18
 
1.8%
주 18
 
1.8%
Other values (221) 712
70.1%
ASCII
ValueCountFrequency (%)
( 18
20.7%
) 18
20.7%
16
18.4%
L 3
 
3.4%
2 3
 
3.4%
e 2
 
2.3%
, 2
 
2.3%
- 2
 
2.3%
G 2
 
2.3%
D 2
 
2.3%
Other values (16) 19
21.8%
None
ValueCountFrequency (%)
㈜ 1
100.0%

인허가일자
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20050183
Minimum19890718
Maximum20100928
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T07:02:49.017577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19890718
5-th percentile19930938
Q120071029
median20071029
Q320071029
95-th percentile20097868
Maximum20100928
Range210210
Interquartile range (IQR)0

Descriptive statistics

Standard deviation49038.934
Coefficient of variation (CV)0.0024458098
Kurtosis1.7685563
Mean20050183
Median Absolute Deviation (MAD)0
Skewness-1.7728257
Sum3.28823 × 109
Variance2.404817 × 109
MonotonicityNot monotonic
2023-12-11T07:02:49.133937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
20071029 104
63.4%
20100928 9
 
5.5%
20031001 4
 
2.4%
20071217 3
 
1.8%
20080103 3
 
1.8%
20070312 3
 
1.8%
20040803 3
 
1.8%
19941129 2
 
1.2%
20040706 2
 
1.2%
20080211 2
 
1.2%
Other values (28) 29
 
17.7%
ValueCountFrequency (%)
19890718 1
0.6%
19910110 1
0.6%
19910813 1
0.6%
19921226 1
0.6%
19930429 1
0.6%
19930722 1
0.6%
19930823 1
0.6%
19930907 1
0.6%
19930909 1
0.6%
19931102 1
0.6%
ValueCountFrequency (%)
20100928 9
 
5.5%
20080529 1
 
0.6%
20080211 2
 
1.2%
20080103 3
 
1.8%
20071217 3
 
1.8%
20071213 2
 
1.2%
20071029 104
63.4%
20070312 3
 
1.8%
20040803 3
 
1.8%
20040706 2
 
1.2%

영업상태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
운영중
155 
폐업 등
 
9

Length

Max length4
Median length3
Mean length3.054878
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
운영중 155
94.5%
폐업 등 9
 
5.5%

Length

2023-12-11T07:02:49.279577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:02:49.700869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 155
89.6%
폐업 9
 
5.2%
등 9
 
5.2%

폐업일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)66.7%
Missing155
Missing (%)94.5%
Infinite0
Infinite (%)0.0%
Mean20076077
Minimum20021231
Maximum20110224
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T07:02:49.790908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20021231
5-th percentile20040861
Q120070319
median20070319
Q320100423
95-th percentile20106627
Maximum20110224
Range88993
Interquartile range (IQR)30104

Descriptive statistics

Standard deviation26375.858
Coefficient of variation (CV)0.0013137954
Kurtosis1.6079347
Mean20076077
Median Absolute Deviation (MAD)14
Skewness-0.83880786
Sum1.8068469 × 108
Variance6.9568587 × 108
MonotonicityNot monotonic
2023-12-11T07:02:49.911696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
20070319 4
 
2.4%
20101231 1
 
0.6%
20070305 1
 
0.6%
20100423 1
 
0.6%
20110224 1
 
0.6%
20021231 1
 
0.6%
(Missing) 155
94.5%
ValueCountFrequency (%)
20021231 1
 
0.6%
20070305 1
 
0.6%
20070319 4
2.4%
20100423 1
 
0.6%
20101231 1
 
0.6%
20110224 1
 
0.6%
ValueCountFrequency (%)
20110224 1
 
0.6%
20101231 1
 
0.6%
20100423 1
 
0.6%
20070319 4
2.4%
20070305 1
 
0.6%
20021231 1
 
0.6%

건물소유구분명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
자가
91 
<NA>
73 

Length

Max length4
Median length2
Mean length2.8902439
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row자가
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
자가 91
55.5%
<NA> 73
44.5%

Length

2023-12-11T07:02:50.056803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:02:50.172741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자가 91
55.5%
na 73
44.5%

년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)9.0%
Missing9
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean2004.6323
Minimum1989
Maximum2008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T07:02:50.262620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1989
5-th percentile1993
Q12007
median2007
Q32007
95-th percentile2007
Maximum2008
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.8750882
Coefficient of variation (CV)0.0024319115
Kurtosis1.6112797
Mean2004.6323
Median Absolute Deviation (MAD)0
Skewness-1.7837868
Sum310718
Variance23.766485
MonotonicityNot monotonic
2023-12-11T07:02:50.383761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2007 112
68.3%
1993 6
 
3.7%
2008 6
 
3.7%
2004 6
 
3.7%
1994 5
 
3.0%
1997 4
 
2.4%
1995 4
 
2.4%
2003 4
 
2.4%
1991 2
 
1.2%
1998 2
 
1.2%
Other values (4) 4
 
2.4%
(Missing) 9
 
5.5%
ValueCountFrequency (%)
1989 1
 
0.6%
1991 2
 
1.2%
1992 1
 
0.6%
1993 6
3.7%
1994 5
3.0%
1995 4
2.4%
1996 1
 
0.6%
1997 4
2.4%
1998 2
 
1.2%
1999 1
 
0.6%
ValueCountFrequency (%)
2008 6
 
3.7%
2007 112
68.3%
2004 6
 
3.7%
2003 4
 
2.4%
1999 1
 
0.6%
1998 2
 
1.2%
1997 4
 
2.4%
1996 1
 
0.6%
1995 4
 
2.4%
1994 5
 
3.0%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.6%
Missing9
Missing (%)5.5%
Memory size460.0 B
False
155 
(Missing)
 
9
ValueCountFrequency (%)
False 155
94.5%
(Missing) 9
 
5.5%
2023-12-11T07:02:50.505888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

위생업종명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
공중이용시설
155 
<NA>
 
9

Length

Max length6
Median length6
Mean length5.8902439
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공중이용시설
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
공중이용시설 155
94.5%
<NA> 9
 
5.5%

Length

2023-12-11T07:02:50.622156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:02:50.735487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공중이용시설 155
94.5%
na 9
 
5.5%

위생업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
공중이용시설 기타
155 
<NA>
 
9

Length

Max length9
Median length9
Mean length8.7256098
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공중이용시설 기타
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
공중이용시설 기타 155
94.5%
<NA> 9
 
5.5%

Length

2023-12-11T07:02:50.845727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:02:50.966047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공중이용시설 155
48.6%
기타 155
48.6%
na 9
 
2.8%

소재지도로명주소
Text

MISSING 

Distinct146
Distinct (%)96.7%
Missing13
Missing (%)7.9%
Memory size1.4 KiB
2023-12-11T07:02:51.147693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length34
Mean length26.331126
Min length14

Characters and Unicode

Total characters3976
Distinct characters139
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

Unique141 ?
Unique (%)93.4%

Sample

1st row경기도 동두천시 큰시장로 64 (생연동)
2nd row경기도 부천시 길주로 180 (중동)
3rd row경기도 부천시 부일로 223 (상동)
4th row경기도 부천시 길주로 297 (중동)
5th row경기도 부천시 석천로 188 (중동)
ValueCountFrequency (%)
경기도 151
 
17.0%
수원시 113
 
12.7%
영통구 103
 
11.6%
영통동 51
 
5.7%
매탄동 30
 
3.4%
망포동 13
 
1.5%
봉영로 10
 
1.1%
원천동 9
 
1.0%
부천시 9
 
1.0%
장안구 8
 
0.9%
Other values (246) 391
44.0%
2023-12-11T07:02:51.502999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
737
18.5%
영 198
 
5.0%
통 164
 
4.1%
동 155
 
3.9%
시 155
 
3.9%
기 155
 
3.9%
경 152
 
3.8%
도 151
 
3.8%
로 151
 
3.8%
) 142
 
3.6%
Other values (129) 1816
45.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2385
60.0%
Space Separator 737
 
18.5%
Decimal Number 552
 
13.9%
Close Punctuation 142
 
3.6%
Open Punctuation 142
 
3.6%
Other Punctuation 12
 
0.3%
Dash Punctuation 6
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 198
 
8.3%
통 164
 
6.9%
동 155
 
6.5%
시 155
 
6.5%
기 155
 
6.5%
경 152
 
6.4%
도 151
 
6.3%
로 151
 
6.3%
원 139
 
5.8%
구 130
 
5.5%
Other values (114) 835
35.0%
Decimal Number
ValueCountFrequency (%)
1 125
22.6%
2 68
12.3%
3 62
11.2%
0 49
 
8.9%
6 48
 
8.7%
8 43
 
7.8%
4 43
 
7.8%
5 42
 
7.6%
7 37
 
6.7%
9 35
 
6.3%
Space Separator
ValueCountFrequency (%)
737
100.0%
Close Punctuation
ValueCountFrequency (%)
) 142
100.0%
Open Punctuation
ValueCountFrequency (%)
( 142
100.0%
Other Punctuation
ValueCountFrequency (%)
, 12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2385
60.0%
Common 1591
40.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 198
 
8.3%
통 164
 
6.9%
동 155
 
6.5%
시 155
 
6.5%
기 155
 
6.5%
경 152
 
6.4%
도 151
 
6.3%
로 151
 
6.3%
원 139
 
5.8%
구 130
 
5.5%
Other values (114) 835
35.0%
Common
ValueCountFrequency (%)
737
46.3%
) 142
 
8.9%
( 142
 
8.9%
1 125
 
7.9%
2 68
 
4.3%
3 62
 
3.9%
0 49
 
3.1%
6 48
 
3.0%
8 43
 
2.7%
4 43
 
2.7%
Other values (5) 132
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2385
60.0%
ASCII 1591
40.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
737
46.3%
) 142
 
8.9%
( 142
 
8.9%
1 125
 
7.9%
2 68
 
4.3%
3 62
 
3.9%
0 49
 
3.1%
6 48
 
3.0%
8 43
 
2.7%
4 43
 
2.7%
Other values (5) 132
 
8.3%
Hangul
ValueCountFrequency (%)
영 198
 
8.3%
통 164
 
6.9%
동 155
 
6.5%
시 155
 
6.5%
기 155
 
6.5%
경 152
 
6.4%
도 151
 
6.3%
로 151
 
6.3%
원 139
 
5.8%
구 130
 
5.5%
Other values (114) 835
35.0%

소재지지번주소
Text

Distinct159
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-11T07:02:51.761813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length29
Mean length22.731707
Min length16

Characters and Unicode

Total characters3728
Distinct characters106
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

Unique154 ?
Unique (%)93.9%

Sample

1st row경기도 동두천시 생연동 809번지
2nd row경기도 부천시 중동 1164번지
3rd row경기도 부천시 상동 461번지
4th row경기도 부천시 중동 1059번지
5th row경기도 부천시 중동 1157번지
ValueCountFrequency (%)
경기도 164
20.2%
수원시 120
14.8%
영통구 108
 
13.3%
영통동 51
 
6.3%
매탄동 30
 
3.7%
망포동 13
 
1.6%
원천동 12
 
1.5%
장안구 10
 
1.2%
용인시 10
 
1.2%
부천시 9
 
1.1%
Other values (218) 284
35.0%
2023-12-11T07:02:52.206532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
647
17.4%
시 167
 
4.5%
기 166
 
4.5%
동 166
 
4.5%
경 164
 
4.4%
도 164
 
4.4%
지 164
 
4.4%
번 164
 
4.4%
영 162
 
4.3%
통 159
 
4.3%
Other values (96) 1605
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2266
60.8%
Decimal Number 685
 
18.4%
Space Separator 647
 
17.4%
Dash Punctuation 126
 
3.4%
Other Punctuation 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 167
 
7.4%
기 166
 
7.3%
동 166
 
7.3%
경 164
 
7.2%
도 164
 
7.2%
지 164
 
7.2%
번 164
 
7.2%
영 162
 
7.1%
통 159
 
7.0%
구 142
 
6.3%
Other values (82) 648
28.6%
Decimal Number
ValueCountFrequency (%)
1 157
22.9%
2 93
13.6%
9 76
11.1%
0 63
9.2%
5 56
 
8.2%
4 53
 
7.7%
3 52
 
7.6%
6 51
 
7.4%
7 42
 
6.1%
8 42
 
6.1%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
, 2
50.0%
Space Separator
ValueCountFrequency (%)
647
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2266
60.8%
Common 1462
39.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 167
 
7.4%
기 166
 
7.3%
동 166
 
7.3%
경 164
 
7.2%
도 164
 
7.2%
지 164
 
7.2%
번 164
 
7.2%
영 162
 
7.1%
통 159
 
7.0%
구 142
 
6.3%
Other values (82) 648
28.6%
Common
ValueCountFrequency (%)
647
44.3%
1 157
 
10.7%
- 126
 
8.6%
2 93
 
6.4%
9 76
 
5.2%
0 63
 
4.3%
5 56
 
3.8%
4 53
 
3.6%
3 52
 
3.6%
6 51
 
3.5%
Other values (4) 88
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2266
60.8%
ASCII 1462
39.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
647
44.3%
1 157
 
10.7%
- 126
 
8.6%
2 93
 
6.4%
9 76
 
5.2%
0 63
 
4.3%
5 56
 
3.8%
4 53
 
3.6%
3 52
 
3.6%
6 51
 
3.5%
Other values (4) 88
 
6.0%
Hangul
ValueCountFrequency (%)
시 167
 
7.4%
기 166
 
7.3%
동 166
 
7.3%
경 164
 
7.2%
도 164
 
7.2%
지 164
 
7.2%
번 164
 
7.2%
영 162
 
7.1%
통 159
 
7.0%
구 142
 
6.3%
Other values (82) 648
28.6%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)44.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean420957.56
Minimum14527
Maximum483030
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T07:02:52.372076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14527
5-th percentile76451.95
Q1443400
median443813.5
Q3443822
95-th percentile465621.5
Maximum483030
Range468503
Interquartile range (IQR)422

Descriptive statistics

Standard deviation98542.952
Coefficient of variation (CV)0.23409237
Kurtosis13.522971
Mean420957.56
Median Absolute Deviation (MAD)34.5
Skewness-3.9022136
Sum69037040
Variance9.7107133 × 109
MonotonicityNot monotonic
2023-12-11T07:02:52.531057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
443814 31
18.9%
443848 17
 
10.4%
443400 13
 
7.9%
443810 7
 
4.3%
443812 6
 
3.7%
443822 6
 
3.7%
443821 4
 
2.4%
443809 4
 
2.4%
443807 3
 
1.8%
443370 3
 
1.8%
Other values (63) 70
42.7%
ValueCountFrequency (%)
14527 1
0.6%
14539 1
0.6%
14542 1
0.6%
14545 2
1.2%
14546 1
0.6%
14547 1
0.6%
14548 1
0.6%
14623 1
0.6%
426816 1
0.6%
426837 1
0.6%
ValueCountFrequency (%)
483030 1
0.6%
476842 1
0.6%
465821 1
0.6%
465812 2
1.2%
465810 2
1.2%
465711 1
0.6%
465710 1
0.6%
465120 1
0.6%
462807 1
0.6%
461805 1
0.6%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct153
Distinct (%)96.8%
Missing6
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean37.301384
Minimum37.122127
Maximum37.900457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T07:02:52.659648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.122127
5-th percentile37.237914
Q137.251601
median37.259463
Q337.292362
95-th percentile37.536904
Maximum37.900457
Range0.77833036
Interquartile range (IQR)0.040760401

Descriptive statistics

Standard deviation0.10123429
Coefficient of variation (CV)0.0027139554
Kurtosis7.8693361
Mean37.301384
Median Absolute Deviation (MAD)0.011697047
Skewness2.4548581
Sum5893.6186
Variance0.010248382
MonotonicityNot monotonic
2023-12-11T07:02:52.839716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.2484226663 2
 
1.2%
37.2597361227 2
 
1.2%
37.2479960304 2
 
1.2%
37.2529264232 2
 
1.2%
37.2655925945 2
 
1.2%
37.2516329244 1
 
0.6%
37.2524736337 1
 
0.6%
37.2559872171 1
 
0.6%
37.264882274 1
 
0.6%
37.2949628124 1
 
0.6%
Other values (143) 143
87.2%
(Missing) 6
 
3.7%
ValueCountFrequency (%)
37.1221270912 1
0.6%
37.2314895326 1
0.6%
37.2349700407 1
0.6%
37.235167894 1
0.6%
37.2373925781 1
0.6%
37.23741092 1
0.6%
37.237780592 1
0.6%
37.2378359355 1
0.6%
37.2379280569 1
0.6%
37.2387443419 1
0.6%
ValueCountFrequency (%)
37.9004574509 1
0.6%
37.5414449492 1
0.6%
37.5411002724 1
0.6%
37.5405814486 1
0.6%
37.5395656015 1
0.6%
37.5395542388 1
0.6%
37.5379046466 1
0.6%
37.5373318426 1
0.6%
37.5368279734 1
0.6%
37.5064035555 1
0.6%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct153
Distinct (%)96.8%
Missing6
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean127.04362
Minimum126.72908
Maximum127.57909
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T07:02:53.003715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.72908
5-th percentile126.76367
Q1127.04327
median127.05807
Q3127.0756
95-th percentile127.21471
Maximum127.57909
Range0.85001602
Interquartile range (IQR)0.032324435

Descriptive statistics

Standard deviation0.11428083
Coefficient of variation (CV)0.00089954013
Kurtosis4.4639974
Mean127.04362
Median Absolute Deviation (MAD)0.017061229
Skewness-0.32992733
Sum20072.892
Variance0.013060109
MonotonicityNot monotonic
2023-12-11T07:02:53.191880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0772161958 2
 
1.2%
127.0434787628 2
 
1.2%
127.0789099225 2
 
1.2%
127.0763114748 2
 
1.2%
127.0643194229 2
 
1.2%
127.0750557879 1
 
0.6%
127.0757276434 1
 
0.6%
127.0390583288 1
 
0.6%
127.04326725 1
 
0.6%
127.0252137967 1
 
0.6%
Other values (143) 143
87.2%
(Missing) 6
 
3.7%
ValueCountFrequency (%)
126.7290764796 1
0.6%
126.7364226758 1
0.6%
126.747612858 1
0.6%
126.754145423 1
0.6%
126.7550485645 1
0.6%
126.7552924952 1
0.6%
126.7566905848 1
0.6%
126.7620745903 1
0.6%
126.7639551478 1
0.6%
126.7752346544 1
0.6%
ValueCountFrequency (%)
127.5790924947 1
0.6%
127.3648146702 1
0.6%
127.225407546 1
0.6%
127.2245342645 1
0.6%
127.2236683657 1
0.6%
127.218790909 1
0.6%
127.2178061617 1
0.6%
127.2160118288 1
0.6%
127.2144834225 1
0.6%
127.2139196743 1
0.6%

Interactions

2023-12-11T07:02:46.856142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:43.098509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:44.257523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:44.973244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:45.583362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:46.249881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:46.947958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:43.175254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:44.529173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:45.093917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:45.683261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:46.339502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:47.037002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:43.275417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:44.683708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:45.186736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:45.789404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:46.420692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:47.125831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:43.535379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:44.762134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:45.284727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:45.909138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:46.531628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:47.203441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:43.711412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:44.830497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:45.371470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:46.001839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:46.625217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:47.307641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:43.978787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:44.903380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:45.480640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:46.128854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:02:46.763847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:02:53.300438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명인허가일자영업상태명폐업일자년도소재지우편번호WGS84위도WGS84경도
시군명1.0000.8450.9330.7890.7541.0000.9310.939
인허가일자0.8451.0000.6990.7890.9120.9310.7610.733
영업상태명0.9330.6991.000NaN0.4970.2540.3320.643
폐업일자0.7890.789NaN1.0000.7890.4190.0001.000
년도0.7540.9120.4970.7891.0000.4830.8430.648
소재지우편번호1.0000.9310.2540.4190.4831.0000.8230.860
WGS84위도0.9310.7610.3320.0000.8430.8231.0000.876
WGS84경도0.9390.7330.6431.0000.6480.8600.8761.000
2023-12-11T07:02:53.419028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명위생업종명영업상태명위생업태명건물소유구분명
시군명1.0001.0000.7621.0001.000
위생업종명1.0001.0001.0001.0001.000
영업상태명0.7621.0001.0001.0001.000
위생업태명1.0001.0001.0001.0001.000
건물소유구분명1.0001.0001.0001.0001.000
2023-12-11T07:02:53.517775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자폐업일자년도소재지우편번호WGS84위도WGS84경도시군명영업상태명건물소유구분명위생업종명위생업태명
인허가일자1.0000.4360.9440.165-0.3780.1710.5950.6941.0001.0001.000
폐업일자0.4361.0000.393-0.331-0.322-0.0170.7831.000NaN1.0001.000
년도0.9440.3931.0000.333-0.5860.3290.4830.5181.0001.0001.000
소재지우편번호0.165-0.3310.3331.000-0.1550.6600.9780.4121.0001.0001.000
WGS84위도-0.378-0.322-0.586-0.1551.000-0.3700.8150.3501.0001.0001.000
WGS84경도0.171-0.0170.3290.660-0.3701.0000.8110.4781.0001.0001.000
시군명0.5950.7830.4830.9780.8150.8111.0000.7621.0001.0001.000
영업상태명0.6941.0000.5180.4120.3500.4780.7621.0001.0001.0001.000
건물소유구분명1.000NaN1.0001.0001.0001.0001.0001.0001.0001.0001.000
위생업종명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위생업태명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-11T07:02:47.446177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:02:47.715472image/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.
2023-12-11T07:02:47.873959image/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

시군명사업장명인허가일자영업상태명폐업일자건물소유구분명년도다중이용업소여부위생업종명위생업태명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0동두천시세아프라자19930429운영중<NA>자가1993N공중이용시설공중이용시설 기타경기도 동두천시 큰시장로 64 (생연동)경기도 동두천시 생연동 809번지48303037.900457127.050196
1부천시현대백화점부천점20100928운영중<NA><NA><NA><NA><NA><NA>경기도 부천시 길주로 180 (중동)경기도 부천시 중동 1164번지1454637.504317126.762075
2부천시투나20100928운영중<NA><NA><NA><NA><NA><NA>경기도 부천시 부일로 223 (상동)경기도 부천시 상동 461번지1462337.48888126.755292
3부천시홈플러스테스코(주)중동점20100928운영중<NA><NA><NA><NA><NA><NA>경기도 부천시 길주로 297 (중동)경기도 부천시 중동 1059번지1453937.50411126.775235
4부천시(주)신세계이마트중동점20100928운영중<NA><NA><NA><NA><NA><NA>경기도 부천시 석천로 188 (중동)경기도 부천시 중동 1157번지1454737.504068126.763955
5부천시뉴코아부천점20100928운영중<NA><NA><NA><NA><NA><NA>경기도 부천시 송내대로 239 (상동)경기도 부천시 상동 539-1번지1454537.504175126.756691
6부천시광성상가20100928운영중<NA><NA><NA><NA><NA><NA>경기도 부천시 옥산로138번길 30 (춘의동)경기도 부천시 춘의동 127번지1452737.505503126.78193
7부천시(주)세이브존아이앤씨부천상동점20100928운영중<NA><NA><NA><NA><NA><NA>경기도 부천시 길주로 105 (상동,,6,7)경기도 부천시 상동 535-5번지 ,6,71454237.506404126.754145
8부천시삼성테스코(주)홈플러스상동점20100928운영중<NA><NA><NA><NA><NA><NA>경기도 부천시 길주로 118 (상동)경기도 부천시 상동 540-1번지1454537.504327126.755049
9부천시GS스퀘어부천점20100928운영중<NA><NA><NA><NA><NA><NA>경기도 부천시 길주로 300 (중동)경기도 부천시 중동 1140번지1454837.502552126.775374
시군명사업장명인허가일자영업상태명폐업일자건물소유구분명년도다중이용업소여부위생업종명위생업태명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
154용인시(주)유한양행 기흥연구소20080211운영중<NA>자가2008N공중이용시설공중이용시설 기타경기도 용인시 기흥구 탑실로35번길 25 (공세동)경기도 용인시 기흥구 공세동 416-1번지44690237.241262127.110268
155용인시서울시립백암정신병원20071213운영중<NA><NA>2007N공중이용시설공중이용시설 기타경기도 용인시 처인구 백암면 용천로71번길 30경기도 용인시 처인구 백암면 용천리 264-5번지44986437.122127127.364815
156하남시부영아파트상가19950420운영중<NA>자가1995N공중이용시설공중이용시설 기타경기도 하남시 대청로 119 (창우동, 부영아파트 상가)경기도 하남시 창우동 518번지 부영아파트 상가46571037.541445127.224534
157하남시(상호미정)오피스텔, 김삼환20040706운영중<NA>자가2004N공중이용시설공중이용시설 기타경기도 하남시 신장1로3번길 16 (신장동)경기도 하남시 신장동 440-3번지46582137.537332127.208307
158하남시신안상가19950613운영중<NA>자가1995N공중이용시설공중이용시설 기타경기도 하남시 대청로116번길 59-1 (창우동, 꿈동산 신안아파트 상가)경기도 하남시 창우동 521번지 꿈동산 신안아파트 상가46571137.536828127.223668
159하남시백조 현대상가19941129운영중<NA>자가1994N공중이용시설공중이용시설 기타경기도 하남시 대청로 62 (신장동, 백조현대상가)경기도 하남시 신장동 528번지 백조현대상가46581237.539566127.218791
160하남시한신 백송상가19941129운영중<NA>자가1994N공중이용시설공중이용시설 기타경기도 하남시 대청로 50 (신장동, 백송한신상가)경기도 하남시 신장동 528-5번지 백송한신상가46581237.539554127.217806
161하남시(상호미정) 동양트레빌 임두섭20040706운영중<NA>자가2004N공중이용시설공중이용시설 기타경기도 하남시 대청로 15 (신장동)경기도 하남시 신장동 519번지46581037.540581127.21392
162하남시대한예수교감리회 주님의교회20040304운영중<NA>자가2004N공중이용시설공중이용시설 기타경기도 하남시 대청로116번길 21 (창우동)경기도 하남시 창우동 521-3번지46512037.537905127.225408
163하남시(주)현대베스코아19980911운영중<NA>자가1998N공중이용시설공중이용시설 기타경기도 하남시 대청로 33 (신장동)경기도 하남시 신장동 523-1번지46581037.5411127.216012