Overview

Dataset statistics

Number of variables13
Number of observations145
Missing cells77
Missing cells (%)4.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.6 KiB
Average record size in memory109.9 B

Variable types

Categorical4
Text3
Numeric5
Boolean1

Alerts

다중이용업소여부 has constant value ""Constant
위생업태명 is highly overall correlated with 위생업종명High correlation
위생업종명 is highly overall correlated with 인허가일자 and 7 other fieldsHigh correlation
시군명 is highly overall correlated with 소재지우편번호 and 3 other fieldsHigh correlation
영업상태명 is highly overall correlated with 폐업일자 and 1 other fieldsHigh correlation
인허가일자 is highly overall correlated with 위생업종명High correlation
폐업일자 is highly overall correlated with 영업상태명 and 1 other fieldsHigh correlation
소재지우편번호 is highly overall correlated with WGS84경도 and 2 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
위생업종명 is highly imbalanced (78.4%)Imbalance
위생업태명 is highly imbalanced (82.6%)Imbalance
폐업일자 has 52 (35.9%) missing valuesMissing
다중이용업소여부 has 5 (3.4%) missing valuesMissing
소재지도로명주소 has 12 (8.3%) missing valuesMissing
WGS84위도 has 4 (2.8%) missing valuesMissing
WGS84경도 has 4 (2.8%) missing valuesMissing

Reproduction

Analysis started2023-12-10 22:03:30.430916
Analysis finished2023-12-10 22:03:33.672851
Duration3.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
안산시
21 
고양시
15 
수원시
12 
김포시
10 
용인시
9 
Other values (23)
78 

Length

Max length4
Median length3
Mean length3.062069
Min length3

Unique

Unique8 ?
Unique (%)5.5%

Sample

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

Common Values

ValueCountFrequency (%)
안산시 21
14.5%
고양시 15
 
10.3%
수원시 12
 
8.3%
김포시 10
 
6.9%
용인시 9
 
6.2%
성남시 7
 
4.8%
파주시 7
 
4.8%
평택시 7
 
4.8%
안양시 7
 
4.8%
부천시 7
 
4.8%
Other values (18) 43
29.7%

Length

2023-12-11T07:03:33.736401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
안산시 21
14.5%
고양시 15
 
10.3%
수원시 12
 
8.3%
김포시 10
 
6.9%
용인시 9
 
6.2%
성남시 7
 
4.8%
파주시 7
 
4.8%
평택시 7
 
4.8%
안양시 7
 
4.8%
부천시 7
 
4.8%
Other values (18) 43
29.7%

사업장명
Text

Distinct134
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-11T07:03:33.949332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length11
Mean length5.1724138
Min length2

Characters and Unicode

Total characters750
Distinct characters138
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

Unique125 ?
Unique (%)86.2%

Sample

1st row태화기업
2nd row서경
3rd row해동기업
4th row고양산업
5th row예광물산
ValueCountFrequency (%)
중앙위생물수건 3
 
2.0%
동아산업 3
 
2.0%
국제위생물수건 2
 
1.3%
대성물산 2
 
1.3%
대진산업 2
 
1.3%
송탄위생물수건 2
 
1.3%
명성실업 2
 
1.3%
대영실업 2
 
1.3%
위생물수건 2
 
1.3%
한일위생 2
 
1.3%
Other values (127) 127
85.2%
2023-12-11T07:03:34.305322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
위 54
 
7.2%
생 54
 
7.2%
수 54
 
7.2%
물 51
 
6.8%
업 49
 
6.5%
건 47
 
6.3%
산 39
 
5.2%
사 15
 
2.0%
성 15
 
2.0%
일 15
 
2.0%
Other values (128) 357
47.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 726
96.8%
Open Punctuation 10
 
1.3%
Close Punctuation 10
 
1.3%
Space Separator 4
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
위 54
 
7.4%
생 54
 
7.4%
수 54
 
7.4%
물 51
 
7.0%
업 49
 
6.7%
건 47
 
6.5%
산 39
 
5.4%
사 15
 
2.1%
성 15
 
2.1%
일 15
 
2.1%
Other values (125) 333
45.9%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 726
96.8%
Common 24
 
3.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
위 54
 
7.4%
생 54
 
7.4%
수 54
 
7.4%
물 51
 
7.0%
업 49
 
6.7%
건 47
 
6.5%
산 39
 
5.4%
사 15
 
2.1%
성 15
 
2.1%
일 15
 
2.1%
Other values (125) 333
45.9%
Common
ValueCountFrequency (%)
( 10
41.7%
) 10
41.7%
4
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 726
96.8%
ASCII 24
 
3.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
위 54
 
7.4%
생 54
 
7.4%
수 54
 
7.4%
물 51
 
7.0%
업 49
 
6.7%
건 47
 
6.5%
산 39
 
5.4%
사 15
 
2.1%
성 15
 
2.1%
일 15
 
2.1%
Other values (125) 333
45.9%
ASCII
ValueCountFrequency (%)
( 10
41.7%
) 10
41.7%
4
 
16.7%

인허가일자
Real number (ℝ)

HIGH CORRELATION 

Distinct140
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19989591
Minimum19850221
Maximum20180105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T07:03:34.452986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19850221
5-th percentile19870426
Q119930310
median20000109
Q320031210
95-th percentile20108608
Maximum20180105
Range329884
Interquartile range (IQR)100900

Descriptive statistics

Standard deviation72812.283
Coefficient of variation (CV)0.0036425099
Kurtosis-0.37385153
Mean19989591
Median Absolute Deviation (MAD)49183
Skewness0.050629492
Sum2.8984907 × 109
Variance5.3016285 × 109
MonotonicityNot monotonic
2023-12-11T07:03:34.586607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19990728 2
 
1.4%
20021114 2
 
1.4%
19930304 2
 
1.4%
20020812 2
 
1.4%
19860310 2
 
1.4%
19970501 1
 
0.7%
19991018 1
 
0.7%
20030324 1
 
0.7%
20000407 1
 
0.7%
19911025 1
 
0.7%
Other values (130) 130
89.7%
ValueCountFrequency (%)
19850221 1
0.7%
19850323 1
0.7%
19850803 1
0.7%
19851030 1
0.7%
19860310 2
1.4%
19860528 1
0.7%
19870404 1
0.7%
19870513 1
0.7%
19870716 1
0.7%
19871231 1
0.7%
ValueCountFrequency (%)
20180105 1
0.7%
20160407 1
0.7%
20160404 1
0.7%
20141017 1
0.7%
20140919 1
0.7%
20111007 1
0.7%
20110926 1
0.7%
20110728 1
0.7%
20100126 1
0.7%
20091102 1
0.7%

영업상태명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
폐업 등
93 
운영중
52 

Length

Max length4
Median length4
Mean length3.6413793
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 등 93
64.1%
운영중 52
35.9%

Length

2023-12-11T07:03:34.714709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:03:34.822409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 93
39.1%
등 93
39.1%
운영중 52
21.8%

폐업일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct92
Distinct (%)98.9%
Missing52
Missing (%)35.9%
Infinite0
Infinite (%)0.0%
Mean20090665
Minimum19941126
Maximum20180321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T07:03:34.937895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19941126
5-th percentile19980375
Q120040821
median20101216
Q320141215
95-th percentile20170910
Maximum20180321
Range239195
Interquartile range (IQR)100394

Descriptive statistics

Standard deviation60246.11
Coefficient of variation (CV)0.0029987117
Kurtosis-0.6099709
Mean20090665
Median Absolute Deviation (MAD)49291
Skewness-0.43954176
Sum1.8684318 × 109
Variance3.6295938 × 109
MonotonicityNot monotonic
2023-12-11T07:03:35.105432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20180321 2
 
1.4%
20170202 1
 
0.7%
20000105 1
 
0.7%
20130313 1
 
0.7%
20170915 1
 
0.7%
20101221 1
 
0.7%
20070913 1
 
0.7%
20180205 1
 
0.7%
20170612 1
 
0.7%
20040624 1
 
0.7%
Other values (82) 82
56.6%
(Missing) 52
35.9%
ValueCountFrequency (%)
19941126 1
0.7%
19950816 1
0.7%
19960228 1
0.7%
19971001 1
0.7%
19980313 1
0.7%
19980417 1
0.7%
19990712 1
0.7%
20000105 1
0.7%
20010220 1
0.7%
20011102 1
0.7%
ValueCountFrequency (%)
20180321 2
1.4%
20180205 1
0.7%
20171214 1
0.7%
20170915 1
0.7%
20170906 1
0.7%
20170612 1
0.7%
20170410 1
0.7%
20170316 1
0.7%
20170302 1
0.7%
20170202 1
0.7%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.7%
Missing5
Missing (%)3.4%
Memory size422.0 B
False
140 
(Missing)
 
5
ValueCountFrequency (%)
False 140
96.6%
(Missing) 5
 
3.4%
2023-12-11T07:03:35.215935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

위생업종명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
위생처리업
140 
<NA>
 
5

Length

Max length5
Median length5
Mean length4.9655172
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
위생처리업 140
96.6%
<NA> 5
 
3.4%

Length

2023-12-11T07:03:35.311260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:03:35.448321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
위생처리업 140
96.6%
na 5
 
3.4%

위생업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
물수건위생처리업
139 
<NA>
 
5
물수건위생처리업 기타
 
1

Length

Max length11
Median length8
Mean length7.8827586
Min length4

Unique

Unique1 ?
Unique (%)0.7%

Sample

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

Common Values

ValueCountFrequency (%)
물수건위생처리업 139
95.9%
<NA> 5
 
3.4%
물수건위생처리업 기타 1
 
0.7%

Length

2023-12-11T07:03:35.572725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:03:35.674448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
물수건위생처리업 140
95.9%
na 5
 
3.4%
기타 1
 
0.7%

소재지도로명주소
Text

MISSING 

Distinct131
Distinct (%)98.5%
Missing12
Missing (%)8.3%
Memory size1.3 KiB
2023-12-11T07:03:35.965615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length32
Mean length24.481203
Min length14

Characters and Unicode

Total characters3256
Distinct characters187
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique129 ?
Unique (%)97.0%

Sample

1st row경기도 가평군 가평읍 중촌로 13
2nd row경기도 고양시 덕양구 행당로11번길 25-18 (토당동,(지하1층))
3rd row경기도 고양시 일산동구 지영로194번길 40-43, 1층 전부호 (지영동)
4th row경기도 고양시 일산서구 산현로 124
5th row경기도 고양시 덕양구 호국로812번길 63
ValueCountFrequency (%)
경기도 133
 
18.7%
안산시 20
 
2.8%
상록구 15
 
2.1%
고양시 14
 
2.0%
수원시 10
 
1.4%
용인시 9
 
1.3%
평택시 7
 
1.0%
성남시 7
 
1.0%
부천시 7
 
1.0%
덕양구 6
 
0.8%
Other values (348) 484
68.0%
2023-12-11T07:03:36.430792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
579
 
17.8%
기 141
 
4.3%
시 139
 
4.3%
경 136
 
4.2%
도 133
 
4.1%
1 117
 
3.6%
로 112
 
3.4%
길 93
 
2.9%
동 88
 
2.7%
2 82
 
2.5%
Other values (177) 1636
50.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1930
59.3%
Space Separator 579
 
17.8%
Decimal Number 538
 
16.5%
Close Punctuation 70
 
2.1%
Open Punctuation 70
 
2.1%
Dash Punctuation 38
 
1.2%
Other Punctuation 30
 
0.9%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 141
 
7.3%
시 139
 
7.2%
경 136
 
7.0%
도 133
 
6.9%
로 112
 
5.8%
길 93
 
4.8%
동 88
 
4.6%
구 66
 
3.4%
번 66
 
3.4%
안 47
 
2.4%
Other values (160) 909
47.1%
Decimal Number
ValueCountFrequency (%)
1 117
21.7%
2 82
15.2%
3 61
11.3%
5 51
9.5%
4 47
8.7%
0 42
 
7.8%
7 41
 
7.6%
8 36
 
6.7%
6 36
 
6.7%
9 25
 
4.6%
Other Punctuation
ValueCountFrequency (%)
, 29
96.7%
. 1
 
3.3%
Space Separator
ValueCountFrequency (%)
579
100.0%
Close Punctuation
ValueCountFrequency (%)
) 70
100.0%
Open Punctuation
ValueCountFrequency (%)
( 70
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 38
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1930
59.3%
Common 1325
40.7%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 141
 
7.3%
시 139
 
7.2%
경 136
 
7.0%
도 133
 
6.9%
로 112
 
5.8%
길 93
 
4.8%
동 88
 
4.6%
구 66
 
3.4%
번 66
 
3.4%
안 47
 
2.4%
Other values (160) 909
47.1%
Common
ValueCountFrequency (%)
579
43.7%
1 117
 
8.8%
2 82
 
6.2%
) 70
 
5.3%
( 70
 
5.3%
3 61
 
4.6%
5 51
 
3.8%
4 47
 
3.5%
0 42
 
3.2%
7 41
 
3.1%
Other values (6) 165
 
12.5%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1930
59.3%
ASCII 1326
40.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
579
43.7%
1 117
 
8.8%
2 82
 
6.2%
) 70
 
5.3%
( 70
 
5.3%
3 61
 
4.6%
5 51
 
3.8%
4 47
 
3.5%
0 42
 
3.2%
7 41
 
3.1%
Other values (7) 166
 
12.5%
Hangul
ValueCountFrequency (%)
기 141
 
7.3%
시 139
 
7.2%
경 136
 
7.0%
도 133
 
6.9%
로 112
 
5.8%
길 93
 
4.8%
동 88
 
4.6%
구 66
 
3.4%
번 66
 
3.4%
안 47
 
2.4%
Other values (160) 909
47.1%

소재지지번주소
Text

Distinct139
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-11T07:03:36.743252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length31
Mean length23.296552
Min length15

Characters and Unicode

Total characters3378
Distinct characters161
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique134 ?
Unique (%)92.4%

Sample

1st row경기도 가평군 가평읍 읍내리 639-1번지
2nd row경기도 고양시 덕양구 토당동 872-7번지 (지하1층)
3rd row경기도 고양시 일산동구 지영동 313-14번지 1층 전부호
4th row경기도 고양시 일산서구 일산동 1726번지 번지
5th row경기도 고양시 덕양구 성사동 488-47번지
ValueCountFrequency (%)
경기도 145
 
19.9%
안산시 21
 
2.9%
고양시 15
 
2.1%
상록구 15
 
2.1%
지하1층 13
 
1.8%
수원시 12
 
1.6%
김포시 10
 
1.4%
사동 9
 
1.2%
용인시 9
 
1.2%
1층 8
 
1.1%
Other values (316) 472
64.7%
2023-12-11T07:03:37.233911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
584
 
17.3%
지 179
 
5.3%
번 150
 
4.4%
기 149
 
4.4%
시 149
 
4.4%
도 146
 
4.3%
경 145
 
4.3%
1 139
 
4.1%
동 138
 
4.1%
- 123
 
3.6%
Other values (151) 1476
43.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1998
59.1%
Decimal Number 656
 
19.4%
Space Separator 584
 
17.3%
Dash Punctuation 123
 
3.6%
Other Punctuation 6
 
0.2%
Close Punctuation 5
 
0.1%
Open Punctuation 5
 
0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 179
 
9.0%
번 150
 
7.5%
기 149
 
7.5%
시 149
 
7.5%
도 146
 
7.3%
경 145
 
7.3%
동 138
 
6.9%
구 73
 
3.7%
안 42
 
2.1%
양 41
 
2.1%
Other values (134) 786
39.3%
Decimal Number
ValueCountFrequency (%)
1 139
21.2%
3 83
12.7%
2 79
12.0%
5 61
9.3%
7 57
8.7%
8 56
8.5%
9 54
 
8.2%
4 49
 
7.5%
6 46
 
7.0%
0 32
 
4.9%
Other Punctuation
ValueCountFrequency (%)
, 5
83.3%
. 1
 
16.7%
Space Separator
ValueCountFrequency (%)
584
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 123
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1998
59.1%
Common 1379
40.8%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 179
 
9.0%
번 150
 
7.5%
기 149
 
7.5%
시 149
 
7.5%
도 146
 
7.3%
경 145
 
7.3%
동 138
 
6.9%
구 73
 
3.7%
안 42
 
2.1%
양 41
 
2.1%
Other values (134) 786
39.3%
Common
ValueCountFrequency (%)
584
42.3%
1 139
 
10.1%
- 123
 
8.9%
3 83
 
6.0%
2 79
 
5.7%
5 61
 
4.4%
7 57
 
4.1%
8 56
 
4.1%
9 54
 
3.9%
4 49
 
3.6%
Other values (6) 94
 
6.8%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1998
59.1%
ASCII 1380
40.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
584
42.3%
1 139
 
10.1%
- 123
 
8.9%
3 83
 
6.0%
2 79
 
5.7%
5 61
 
4.4%
7 57
 
4.1%
8 56
 
4.1%
9 54
 
3.9%
4 49
 
3.6%
Other values (7) 95
 
6.9%
Hangul
ValueCountFrequency (%)
지 179
 
9.0%
번 150
 
7.5%
기 149
 
7.5%
시 149
 
7.5%
도 146
 
7.3%
경 145
 
7.3%
동 138
 
6.9%
구 73
 
3.7%
안 42
 
2.1%
양 41
 
2.1%
Other values (134) 786
39.3%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct124
Distinct (%)85.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean416717.72
Minimum14405
Maximum487877
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T07:03:37.381295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14405
5-th percentile96320.8
Q1415809
median430849
Q3456360
95-th percentile480836
Maximum487877
Range473472
Interquartile range (IQR)40551

Descriptive statistics

Standard deviation99835.481
Coefficient of variation (CV)0.2395758
Kurtosis12.162332
Mean416717.72
Median Absolute Deviation (MAD)16996
Skewness-3.6209362
Sum60424070
Variance9.9671233 × 109
MonotonicityNot monotonic
2023-12-11T07:03:37.521719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
426825 6
 
4.1%
480836 3
 
2.1%
415808 3
 
2.1%
426807 2
 
1.4%
411440 2
 
1.4%
446595 2
 
1.4%
447150 2
 
1.4%
472861 2
 
1.4%
443807 2
 
1.4%
459812 2
 
1.4%
Other values (114) 119
82.1%
ValueCountFrequency (%)
14405 1
0.7%
14437 1
0.7%
14445 1
0.7%
14447 1
0.7%
14492 1
0.7%
14572 1
0.7%
14734 1
0.7%
17811 1
0.7%
410360 1
0.7%
410530 1
0.7%
ValueCountFrequency (%)
487877 1
 
0.7%
487871 1
 
0.7%
487861 1
 
0.7%
487836 1
 
0.7%
483800 1
 
0.7%
482010 1
 
0.7%
480836 3
2.1%
480815 1
 
0.7%
477801 1
 
0.7%
476802 1
 
0.7%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct133
Distinct (%)94.3%
Missing4
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean37.439688
Minimum36.989298
Maximum38.016311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T07:03:37.655875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.989298
5-th percentile37.060171
Q137.281248
median37.404364
Q337.638099
95-th percentile37.836666
Maximum38.016311
Range1.0270127
Interquartile range (IQR)0.35685162

Descriptive statistics

Standard deviation0.23123985
Coefficient of variation (CV)0.0061763295
Kurtosis-0.63882314
Mean37.439688
Median Absolute Deviation (MAD)0.13659114
Skewness0.19297008
Sum5278.996
Variance0.053471868
MonotonicityNot monotonic
2023-12-11T07:03:38.094330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.736360607 3
 
2.1%
37.2823606127 2
 
1.4%
37.0601714119 2
 
1.4%
37.2810326251 2
 
1.4%
37.1432526343 2
 
1.4%
37.3296288092 2
 
1.4%
37.711955672 2
 
1.4%
37.2873387024 1
 
0.7%
37.3933562476 1
 
0.7%
37.2924950995 1
 
0.7%
Other values (123) 123
84.8%
(Missing) 4
 
2.8%
ValueCountFrequency (%)
36.9892981851 1
0.7%
36.9935905359 1
0.7%
37.0028805675 1
0.7%
37.0059404997 1
0.7%
37.0150265126 1
0.7%
37.0294577325 1
0.7%
37.0432644736 1
0.7%
37.0601714119 2
1.4%
37.0695253203 1
0.7%
37.0703810165 1
0.7%
ValueCountFrequency (%)
38.0163109264 1
0.7%
37.9107494385 1
0.7%
37.8832382155 1
0.7%
37.8723949308 1
0.7%
37.8649684755 1
0.7%
37.8545337422 1
0.7%
37.8533207974 1
0.7%
37.8366663753 1
0.7%
37.8333307172 1
0.7%
37.8249977825 1
0.7%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct133
Distinct (%)94.3%
Missing4
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean126.98159
Minimum126.56382
Maximum127.6491
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T07:03:38.244225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.56382
5-th percentile126.70444
Q1126.82472
median126.94109
Q3127.109
95-th percentile127.35989
Maximum127.6491
Range1.0852789
Interquartile range (IQR)0.2842815

Descriptive statistics

Standard deviation0.21713253
Coefficient of variation (CV)0.0017099528
Kurtosis0.51293947
Mean126.98159
Median Absolute Deviation (MAD)0.1448612
Skewness0.77501513
Sum17904.404
Variance0.047146536
MonotonicityNot monotonic
2023-12-11T07:03:38.364186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.085953659 3
 
2.1%
126.8504424246 2
 
1.4%
127.0573872303 2
 
1.4%
126.8482654336 2
 
1.4%
127.0615202227 2
 
1.4%
126.8603300794 2
 
1.4%
126.8282289001 2
 
1.4%
126.8641741999 1
 
0.7%
126.9770554166 1
 
0.7%
127.6339665124 1
 
0.7%
Other values (123) 123
84.8%
(Missing) 4
 
2.8%
ValueCountFrequency (%)
126.5638230368 1
0.7%
126.5851365433 1
0.7%
126.6269386973 1
0.7%
126.6743070073 1
0.7%
126.690761048 1
0.7%
126.6979823972 1
0.7%
126.699723283 1
0.7%
126.7044374036 1
0.7%
126.7111597947 1
0.7%
126.7187740617 1
0.7%
ValueCountFrequency (%)
127.6491019651 1
0.7%
127.6339665124 1
0.7%
127.6029708709 1
0.7%
127.5087845556 1
0.7%
127.4971426938 1
0.7%
127.4963575305 1
0.7%
127.4335558838 1
0.7%
127.3598850365 1
0.7%
127.2750570363 1
0.7%
127.2711617969 1
0.7%

Interactions

2023-12-11T07:03:32.782151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:31.003678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:31.594050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:31.975645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:32.390884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:32.875161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:31.078829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:31.667593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:32.066827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:32.467535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:32.956177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:31.152937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:31.738129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:32.157427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:32.538671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:33.030594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:31.231672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:31.812445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:32.246057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:32.620369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:33.121849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:31.512890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:31.893925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:32.314833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:32.700622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:03:38.450637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명인허가일자영업상태명폐업일자위생업태명소재지우편번호WGS84위도WGS84경도
시군명1.0000.2470.3560.0000.3190.9860.9510.952
인허가일자0.2471.0000.0000.3850.3610.3060.4450.342
영업상태명0.3560.0001.000NaN0.0000.1320.3230.456
폐업일자0.0000.385NaN1.0000.0000.0000.3520.000
위생업태명0.3190.3610.0000.0001.0000.0000.2640.137
소재지우편번호0.9860.3060.1320.0000.0001.0000.6790.801
WGS84위도0.9510.4450.3230.3520.2640.6791.0000.846
WGS84경도0.9520.3420.4560.0000.1370.8010.8461.000
2023-12-11T07:03:38.554118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위생업태명위생업종명시군명영업상태명
위생업태명1.0001.0000.2250.000
위생업종명1.0001.0001.0001.000
시군명0.2251.0001.0000.254
영업상태명0.0001.0000.2541.000
2023-12-11T07:03:38.642689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자폐업일자소재지우편번호WGS84위도WGS84경도시군명영업상태명위생업종명위생업태명
인허가일자1.0000.242-0.0220.022-0.0070.0830.0001.0000.269
폐업일자0.2421.0000.0570.0520.0860.0001.0001.0000.000
소재지우편번호-0.0220.0571.000-0.1990.8770.8740.1981.0000.000
WGS84위도0.0220.052-0.1991.000-0.3180.6940.2401.0000.195
WGS84경도-0.0070.0860.877-0.3181.0000.7000.3401.0000.100
시군명0.0830.0000.8740.6940.7001.0000.2541.0000.225
영업상태명0.0001.0000.1980.2400.3400.2541.0001.0000.000
위생업종명1.0001.0001.0001.0001.0001.0001.0001.0001.000
위생업태명0.2690.0000.0000.1950.1000.2250.0001.0001.000

Missing values

2023-12-11T07:03:33.245207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:03:33.430823image/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.
2023-12-11T07:03:33.571822image/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

시군명사업장명인허가일자영업상태명폐업일자다중이용업소여부위생업종명위생업태명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0가평군태화기업19970501폐업 등20150804N위생처리업물수건위생처리업경기도 가평군 가평읍 중촌로 13경기도 가평군 가평읍 읍내리 639-1번지47780137.833331127.508785
1고양시서경20080916운영중<NA>N위생처리업물수건위생처리업경기도 고양시 덕양구 행당로11번길 25-18 (토당동,(지하1층))경기도 고양시 덕양구 토당동 872-7번지 (지하1층)41282137.621212126.825443
2고양시해동기업20141017운영중<NA>N위생처리업물수건위생처리업경기도 고양시 일산동구 지영로194번길 40-43, 1층 전부호 (지영동)경기도 고양시 일산동구 지영동 313-14번지 1층 전부호41054037.711956126.828229
3고양시고양산업19931028폐업 등20041231N위생처리업물수건위생처리업경기도 고양시 일산서구 산현로 124경기도 고양시 일산서구 일산동 1726번지 번지41185937.691909126.774772
4고양시예광물산19930205폐업 등19980417N위생처리업물수건위생처리업경기도 고양시 덕양구 호국로812번길 63경기도 고양시 덕양구 성사동 488-47번지41280637.656615126.841063
5고양시백석산업20011105폐업 등20031107N위생처리업물수건위생처리업경기도 고양시 일산동구 호수로446번길 27경기도 고양시 일산동구 백석동 1455-5번지 번지 1층41036037.645102126.778979
6고양시송원물수건20050826폐업 등20150605N위생처리업물수건위생처리업경기도 고양시 덕양구 안진동길 39 (지축동)경기도 고양시 덕양구 지축동 471번지41281537.64583126.917119
7고양시(주)춘파20140919폐업 등20150731N위생처리업물수건위생처리업경기도 고양시 일산동구 공릉천로 88, 2층 전부호 (사리현동)경기도 고양시 일산동구 사리현동 188-19번지 2층 전부호41053037.700901126.84674
8고양시대영산업20080411폐업 등20130925N위생처리업물수건위생처리업경기도 고양시 일산동구 지영로194번길 40-43 (지영동)경기도 고양시 일산동구 지영동 313-14번지41054037.711956126.828229
9고양시화정산업19981125폐업 등20030503N위생처리업물수건위생처리업경기도 고양시 덕양구 행신로 358-1경기도 고양시 덕양구 행신동 913-1번지41283637.625935126.84462
시군명사업장명인허가일자영업상태명폐업일자다중이용업소여부위생업종명위생업태명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
135평택시국제위생물수건19990206폐업 등20091201N위생처리업물수건위생처리업경기도 평택시 송탄로 287경기도 평택시 서정동 791-2번지45981337.069525127.059031
136평택시안중물수건19890323폐업 등20040607N위생처리업물수건위생처리업경기도 평택시 청북읍 신포길 80경기도 평택시 청북읍 현곡리 299-3번지1781137.043264126.933259
137평택시송탄위생물수건19871231폐업 등20060112N위생처리업물수건위생처리업경기도 평택시 서두물로18번길 30경기도 평택시 서정동 239-6번지45981237.060171127.057387
138포천시포천위생물수건19961129운영중<NA>N위생처리업물수건위생처리업경기도 포천시 군내면 청군로 3204-1경기도 포천시 군내면 구읍리 63-3번지48787137.883238127.224255
139포천시하얀나라 위생물수건20041221폐업 등20161010N위생처리업물수건위생처리업경기도 포천시 내촌면 오림포길 30경기도 포천시 내촌면 진목리 474번지48783637.802254127.215901
140포천시유앤미20111007폐업 등20150507N위생처리업물수건위생처리업 기타경기도 포천시 군내면 용두로 96경기도 포천시 군내면 직두리 775-8번지48787737.872395127.224012
141포천시태양위생물수건19910711폐업 등20061016N위생처리업물수건위생처리업<NA>경기도 포천시 이동면 노곡리 51-10번지48786138.016311127.359885
142하남시하남샤론위생20000728운영중<NA>N위생처리업물수건위생처리업경기도 하남시 하남대로784번안길 12 (신장동)경기도 하남시 신장동 387-19번지46581037.540955127.211858
143하남시하남산업19971122폐업 등20050407N위생처리업물수건위생처리업경기도 하남시 하남대로784번안길 10경기도 하남시 신장동 387-17번지46581037.540892127.211734
144화성시화성위생산업19960909운영중<NA>N위생처리업물수건위생처리업경기도 화성시 정남면 덕절제기길 6경기도 화성시 정남면 덕절리 381-6번지44596437.130752127.017933