Overview

Dataset statistics

Number of variables18
Number of observations197
Missing cells95
Missing cells (%)2.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.1 KiB
Average record size in memory156.6 B

Variable types

Text4
DateTime1
Categorical9
Numeric4

Dataset

Description전북특별자치도 전주시 내 소독업을 제공하며 사업장명, 인허가일자, 영업상태, 전화번호, 주소 등을 제공합니다.항목 : 사업장명, 인허가일자, 영업상태명, 소재지전화번호, 도로명주소 등제공부서 : 보건소 감염병관리과
Author전북특별자치도 전주시
URLhttps://www.data.go.kr/data/3069067/fileData.do

Alerts

영업상태명 has constant value ""Constant
보호안경수 is highly overall correlated with 동력분무기수 and 3 other fieldsHigh correlation
진공청소기수 is highly overall correlated with 휴대용소독기수 and 4 other fieldsHigh correlation
동력분무기수 is highly overall correlated with 휴대용소독기수 and 5 other fieldsHigh correlation
방독면수 is highly overall correlated with 동력분무기수 and 3 other fieldsHigh correlation
보호용의복수 is highly overall correlated with 휴대용소독기수 and 4 other fieldsHigh correlation
휴대용소독기수 is highly overall correlated with 동력분무기수 and 2 other fieldsHigh correlation
수동식분무기수 is highly overall correlated with 동력분무기수 and 2 other fieldsHigh correlation
초미립자살포기수 is highly imbalanced (80.3%)Imbalance
휴대용소독기수 is highly imbalanced (91.8%)Imbalance
방독면수 is highly imbalanced (95.4%)Imbalance
보호안경수 is highly imbalanced (95.4%)Imbalance
보호용의복수 is highly imbalanced (94.2%)Imbalance
진공청소기수 is highly imbalanced (88.1%)Imbalance
소재지전화 has 89 (45.2%) missing valuesMissing
사무실면적 has 3 (1.5%) missing valuesMissing
소독차량차고면적 has 3 (1.5%) missing valuesMissing
사업장명 has unique valuesUnique

Reproduction

Analysis started2024-03-14 16:26:15.332097
Analysis finished2024-03-14 16:26:23.046002
Duration7.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업장명
Text

UNIQUE 

Distinct197
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-03-15T01:26:23.875412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length7.2994924
Min length2

Characters and Unicode

Total characters1438
Distinct characters272
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

Unique197 ?
Unique (%)100.0%

Sample

1st row(유)가온종합관리
2nd row(유)개미환경위생
3rd row(유)깨끗한 세상
4th row(유)대양환경
5th row(유)대한종합관리
ValueCountFrequency (%)
유한회사 11
 
4.5%
주식회사 8
 
3.3%
주)세스코 3
 
1.2%
방역 3
 
1.2%
그린라인 2
 
0.8%
환경 2
 
0.8%
뿌려주세요 1
 
0.4%
더 1
 
0.4%
우창환경방역공사 1
 
0.4%
위더스 1
 
0.4%
Other values (212) 212
86.5%
2024-03-15T01:26:25.301510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 81
 
5.6%
( 80
 
5.6%
유 62
 
4.3%
48
 
3.3%
주 46
 
3.2%
경 40
 
2.8%
환 38
 
2.6%
사 37
 
2.6%
스 32
 
2.2%
방 29
 
2.0%
Other values (262) 945
65.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1172
81.5%
Close Punctuation 81
 
5.6%
Open Punctuation 80
 
5.6%
Space Separator 48
 
3.3%
Uppercase Letter 28
 
1.9%
Lowercase Letter 20
 
1.4%
Decimal Number 5
 
0.3%
Other Punctuation 4
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
유 62
 
5.3%
주 46
 
3.9%
경 40
 
3.4%
환 38
 
3.2%
사 37
 
3.2%
스 32
 
2.7%
방 29
 
2.5%
역 25
 
2.1%
리 25
 
2.1%
이 24
 
2.0%
Other values (226) 814
69.5%
Uppercase Letter
ValueCountFrequency (%)
K 3
10.7%
E 3
10.7%
F 3
10.7%
N 2
 
7.1%
H 2
 
7.1%
W 2
 
7.1%
C 2
 
7.1%
O 2
 
7.1%
B 1
 
3.6%
J 1
 
3.6%
Other values (7) 7
25.0%
Lowercase Letter
ValueCountFrequency (%)
e 4
20.0%
u 3
15.0%
r 3
15.0%
d 2
10.0%
a 2
10.0%
l 2
10.0%
t 1
 
5.0%
y 1
 
5.0%
o 1
 
5.0%
n 1
 
5.0%
Decimal Number
ValueCountFrequency (%)
5 2
40.0%
1 2
40.0%
9 1
20.0%
Other Punctuation
ValueCountFrequency (%)
& 2
50.0%
! 1
25.0%
. 1
25.0%
Close Punctuation
ValueCountFrequency (%)
) 81
100.0%
Open Punctuation
ValueCountFrequency (%)
( 80
100.0%
Space Separator
ValueCountFrequency (%)
48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1172
81.5%
Common 218
 
15.2%
Latin 48
 
3.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
유 62
 
5.3%
주 46
 
3.9%
경 40
 
3.4%
환 38
 
3.2%
사 37
 
3.2%
스 32
 
2.7%
방 29
 
2.5%
역 25
 
2.1%
리 25
 
2.1%
이 24
 
2.0%
Other values (226) 814
69.5%
Latin
ValueCountFrequency (%)
e 4
 
8.3%
K 3
 
6.2%
E 3
 
6.2%
u 3
 
6.2%
r 3
 
6.2%
F 3
 
6.2%
N 2
 
4.2%
H 2
 
4.2%
d 2
 
4.2%
W 2
 
4.2%
Other values (17) 21
43.8%
Common
ValueCountFrequency (%)
) 81
37.2%
( 80
36.7%
48
22.0%
5 2
 
0.9%
& 2
 
0.9%
1 2
 
0.9%
! 1
 
0.5%
9 1
 
0.5%
. 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1172
81.5%
ASCII 266
 
18.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 81
30.5%
( 80
30.1%
48
18.0%
e 4
 
1.5%
K 3
 
1.1%
E 3
 
1.1%
u 3
 
1.1%
r 3
 
1.1%
F 3
 
1.1%
N 2
 
0.8%
Other values (26) 36
13.5%
Hangul
ValueCountFrequency (%)
유 62
 
5.3%
주 46
 
3.9%
경 40
 
3.4%
환 38
 
3.2%
사 37
 
3.2%
스 32
 
2.7%
방 29
 
2.5%
역 25
 
2.1%
리 25
 
2.1%
이 24
 
2.0%
Other values (226) 814
69.5%

인허가일자
Date

Distinct190
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Minimum1985-01-22 00:00:00
Maximum2022-05-23 00:00:00
2024-03-15T01:26:25.653484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:26:26.087522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

영업상태명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
영업/정상
197 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영업/정상
2nd row영업/정상
3rd row영업/정상
4th row영업/정상
5th row영업/정상

Common Values

ValueCountFrequency (%)
영업/정상 197
100.0%

Length

2024-03-15T01:26:26.496886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:26:26.804981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업/정상 197
100.0%

소재지전화
Text

MISSING 

Distinct106
Distinct (%)98.1%
Missing89
Missing (%)45.2%
Memory size1.7 KiB
2024-03-15T01:26:27.761675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length11.962963
Min length9

Characters and Unicode

Total characters1292
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique104 ?
Unique (%)96.3%

Sample

1st row063-222-1318
2nd row063-253-0762
3rd row063-227-0820
4th row063-224-5283
5th row063-224-8222
ValueCountFrequency (%)
063-272-6353 2
 
1.9%
063-274-6060 2
 
1.9%
063-222-2507 1
 
0.9%
063-236-7774 1
 
0.9%
063-222-1318 1
 
0.9%
063-905-9110 1
 
0.9%
063-904-5598 1
 
0.9%
063-229-5589 1
 
0.9%
063-278-2255 1
 
0.9%
063-244-0849 1
 
0.9%
Other values (96) 96
88.9%
2024-03-15T01:26:28.936841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 214
16.6%
2 190
14.7%
3 173
13.4%
0 171
13.2%
6 169
13.1%
1 73
 
5.7%
5 65
 
5.0%
7 63
 
4.9%
4 63
 
4.9%
8 60
 
4.6%
Other values (2) 51
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1077
83.4%
Dash Punctuation 214
 
16.6%
Math Symbol 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 190
17.6%
3 173
16.1%
0 171
15.9%
6 169
15.7%
1 73
 
6.8%
5 65
 
6.0%
7 63
 
5.8%
4 63
 
5.8%
8 60
 
5.6%
9 50
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 214
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1292
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 214
16.6%
2 190
14.7%
3 173
13.4%
0 171
13.2%
6 169
13.1%
1 73
 
5.7%
5 65
 
5.0%
7 63
 
4.9%
4 63
 
4.9%
8 60
 
4.6%
Other values (2) 51
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1292
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 214
16.6%
2 190
14.7%
3 173
13.4%
0 171
13.2%
6 169
13.1%
1 73
 
5.7%
5 65
 
5.0%
7 63
 
4.9%
4 63
 
4.9%
8 60
 
4.6%
Other values (2) 51
 
3.9%

도로명주소
Text

Distinct186
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-03-15T01:26:30.608895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length49
Mean length35.583756
Min length28

Characters and Unicode

Total characters7010
Distinct characters210
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

Unique176 ?
Unique (%)89.3%

Sample

1st row전북특별자치도 전주시 완산구 백제대로 289, 5층 (중화산동2가)
2nd row전북특별자치도 전주시 완산구 서신천변15길 15-9 (서신동)
3rd row전북특별자치도 전주시 완산구 천잠로 205 (효자동2가)
4th row전북특별자치도 전주시 완산구 용머리로 73 (효자동1가,효자프라자 지하2층 203호)
5th row전북특별자치도 전주시 완산구 강변로 220-16, 1호 (효자동1가, 삼호아파트)
ValueCountFrequency (%)
전북특별자치도 197
 
14.9%
전주시 197
 
14.9%
완산구 109
 
8.2%
덕진구 88
 
6.6%
1층 28
 
2.1%
효자동1가 19
 
1.4%
삼천동1가 19
 
1.4%
기린대로 15
 
1.1%
인후동1가 15
 
1.1%
2층 15
 
1.1%
Other values (390) 622
47.0%
2024-03-15T01:26:32.722668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1128
 
16.1%
전 407
 
5.8%
1 275
 
3.9%
자 241
 
3.4%
동 215
 
3.1%
주 209
 
3.0%
북 202
 
2.9%
도 200
 
2.9%
시 199
 
2.8%
구 198
 
2.8%
Other values (200) 3736
53.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4340
61.9%
Space Separator 1128
 
16.1%
Decimal Number 961
 
13.7%
Close Punctuation 197
 
2.8%
Open Punctuation 197
 
2.8%
Other Punctuation 118
 
1.7%
Dash Punctuation 69
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 407
 
9.4%
자 241
 
5.6%
동 215
 
5.0%
주 209
 
4.8%
북 202
 
4.7%
도 200
 
4.6%
시 199
 
4.6%
구 198
 
4.6%
치 198
 
4.6%
특 197
 
4.5%
Other values (184) 2074
47.8%
Decimal Number
ValueCountFrequency (%)
1 275
28.6%
2 198
20.6%
3 117
12.2%
0 73
 
7.6%
4 70
 
7.3%
5 65
 
6.8%
6 46
 
4.8%
8 45
 
4.7%
7 37
 
3.9%
9 35
 
3.6%
Other Punctuation
ValueCountFrequency (%)
, 117
99.2%
. 1
 
0.8%
Space Separator
ValueCountFrequency (%)
1128
100.0%
Close Punctuation
ValueCountFrequency (%)
) 197
100.0%
Open Punctuation
ValueCountFrequency (%)
( 197
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 69
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4340
61.9%
Common 2670
38.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 407
 
9.4%
자 241
 
5.6%
동 215
 
5.0%
주 209
 
4.8%
북 202
 
4.7%
도 200
 
4.6%
시 199
 
4.6%
구 198
 
4.6%
치 198
 
4.6%
특 197
 
4.5%
Other values (184) 2074
47.8%
Common
ValueCountFrequency (%)
1128
42.2%
1 275
 
10.3%
2 198
 
7.4%
) 197
 
7.4%
( 197
 
7.4%
3 117
 
4.4%
, 117
 
4.4%
0 73
 
2.7%
4 70
 
2.6%
- 69
 
2.6%
Other values (6) 229
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4340
61.9%
ASCII 2670
38.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1128
42.2%
1 275
 
10.3%
2 198
 
7.4%
) 197
 
7.4%
( 197
 
7.4%
3 117
 
4.4%
, 117
 
4.4%
0 73
 
2.7%
4 70
 
2.6%
- 69
 
2.6%
Other values (6) 229
 
8.6%
Hangul
ValueCountFrequency (%)
전 407
 
9.4%
자 241
 
5.6%
동 215
 
5.0%
주 209
 
4.8%
북 202
 
4.7%
도 200
 
4.6%
시 199
 
4.6%
구 198
 
4.6%
치 198
 
4.6%
특 197
 
4.5%
Other values (184) 2074
47.8%

지번주소
Text

Distinct175
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-03-15T01:26:34.229119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length29
Mean length27.035533
Min length22

Characters and Unicode

Total characters5326
Distinct characters66
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

Unique155 ?
Unique (%)78.7%

Sample

1st row전북특별자치도 전주시 완산구 중화산동2가 595-1
2nd row전북특별자치도 전주시 완산구 서신동 799-16
3rd row전북특별자치도 전주시 완산구 효자동2가 709
4th row전북특별자치도 전주시 완산구 효자동1가 296-175
5th row전북특별자치도 전주시 완산구 효자동1가 370-2
ValueCountFrequency (%)
전북특별자치도 197
20.0%
전주시 197
20.0%
완산구 109
 
11.1%
덕진구 88
 
8.9%
효자동1가 20
 
2.0%
삼천동1가 20
 
2.0%
인후동1가 16
 
1.6%
중화산동2가 14
 
1.4%
효자동3가 13
 
1.3%
금암동 9
 
0.9%
Other values (212) 303
30.7%
2024-03-15T01:26:36.278030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
789
 
14.8%
전 397
 
7.5%
자 235
 
4.4%
1 235
 
4.4%
북 200
 
3.8%
동 199
 
3.7%
주 197
 
3.7%
구 197
 
3.7%
시 197
 
3.7%
도 197
 
3.7%
Other values (56) 2483
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3327
62.5%
Decimal Number 1027
 
19.3%
Space Separator 789
 
14.8%
Dash Punctuation 183
 
3.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 397
 
11.9%
자 235
 
7.1%
북 200
 
6.0%
동 199
 
6.0%
주 197
 
5.9%
구 197
 
5.9%
시 197
 
5.9%
도 197
 
5.9%
치 197
 
5.9%
별 197
 
5.9%
Other values (44) 1114
33.5%
Decimal Number
ValueCountFrequency (%)
1 235
22.9%
2 155
15.1%
3 110
10.7%
5 100
9.7%
4 81
 
7.9%
9 78
 
7.6%
6 76
 
7.4%
7 70
 
6.8%
0 61
 
5.9%
8 61
 
5.9%
Space Separator
ValueCountFrequency (%)
789
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 183
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3327
62.5%
Common 1999
37.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 397
 
11.9%
자 235
 
7.1%
북 200
 
6.0%
동 199
 
6.0%
주 197
 
5.9%
구 197
 
5.9%
시 197
 
5.9%
도 197
 
5.9%
치 197
 
5.9%
별 197
 
5.9%
Other values (44) 1114
33.5%
Common
ValueCountFrequency (%)
789
39.5%
1 235
 
11.8%
- 183
 
9.2%
2 155
 
7.8%
3 110
 
5.5%
5 100
 
5.0%
4 81
 
4.1%
9 78
 
3.9%
6 76
 
3.8%
7 70
 
3.5%
Other values (2) 122
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3327
62.5%
ASCII 1999
37.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
789
39.5%
1 235
 
11.8%
- 183
 
9.2%
2 155
 
7.8%
3 110
 
5.5%
5 100
 
5.0%
4 81
 
4.1%
9 78
 
3.9%
6 76
 
3.8%
7 70
 
3.5%
Other values (2) 122
 
6.1%
Hangul
ValueCountFrequency (%)
전 397
 
11.9%
자 235
 
7.1%
북 200
 
6.0%
동 199
 
6.0%
주 197
 
5.9%
구 197
 
5.9%
시 197
 
5.9%
도 197
 
5.9%
치 197
 
5.9%
별 197
 
5.9%
Other values (44) 1114
33.5%

위도
Real number (ℝ)

Distinct175
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.827166
Minimum35.767077
Maximum35.890212
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-03-15T01:26:36.706426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.767077
5-th percentile35.794684
Q135.809521
median35.826053
Q335.842917
95-th percentile35.864332
Maximum35.890212
Range0.12313488
Interquartile range (IQR)0.03339539

Descriptive statistics

Standard deviation0.022958665
Coefficient of variation (CV)0.00064081723
Kurtosis-0.20968036
Mean35.827166
Median Absolute Deviation (MAD)0.01657665
Skewness0.18934004
Sum7057.9517
Variance0.00052710031
MonotonicityNot monotonic
2024-03-15T01:26:37.179054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.84618191 3
 
1.5%
35.8122059 3
 
1.5%
35.86053852 2
 
1.0%
35.85786898 2
 
1.0%
35.89021195 2
 
1.0%
35.84498809 2
 
1.0%
35.82605304 2
 
1.0%
35.81569854 2
 
1.0%
35.86432859 2
 
1.0%
35.81171767 2
 
1.0%
Other values (165) 175
88.8%
ValueCountFrequency (%)
35.76707707 2
1.0%
35.78697871 1
0.5%
35.78902361 1
0.5%
35.79031846 1
0.5%
35.7926887 1
0.5%
35.79341369 1
0.5%
35.79385276 1
0.5%
35.79414523 1
0.5%
35.79448704 1
0.5%
35.79473361 1
0.5%
ValueCountFrequency (%)
35.89021195 2
1.0%
35.87690846 1
0.5%
35.87661174 1
0.5%
35.87422161 1
0.5%
35.87375013 1
0.5%
35.8735992 1
0.5%
35.87302379 1
0.5%
35.86576713 1
0.5%
35.86434378 1
0.5%
35.86432859 2
1.0%

경도
Real number (ℝ)

Distinct175
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.12604
Minimum127.05799
Maximum127.17876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-03-15T01:26:37.602605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.05799
5-th percentile127.0821
Q1127.1145
median127.12245
Q3127.14589
95-th percentile127.16309
Maximum127.17876
Range0.1207749
Interquartile range (IQR)0.0313901

Descriptive statistics

Standard deviation0.024039619
Coefficient of variation (CV)0.00018910066
Kurtosis-0.15390358
Mean127.12604
Median Absolute Deviation (MAD)0.0166103
Skewness-0.2462734
Sum25043.83
Variance0.00057790327
MonotonicityNot monotonic
2024-03-15T01:26:37.979322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.120745 3
 
1.5%
127.1184804 3
 
1.5%
127.1133768 2
 
1.0%
127.1248513 2
 
1.0%
127.1291917 2
 
1.0%
127.1627139 2
 
1.0%
127.164431 2
 
1.0%
127.1144952 2
 
1.0%
127.0820954 2
 
1.0%
127.118129 2
 
1.0%
Other values (165) 175
88.8%
ValueCountFrequency (%)
127.0579855 1
0.5%
127.0670573 1
0.5%
127.0679175 1
0.5%
127.0704807 1
0.5%
127.0734696 1
0.5%
127.0739178 1
0.5%
127.0768963 1
0.5%
127.0785536 1
0.5%
127.0789414 1
0.5%
127.0820954 2
1.0%
ValueCountFrequency (%)
127.1787604 1
0.5%
127.1736921 1
0.5%
127.1720412 2
1.0%
127.164431 2
1.0%
127.1636093 1
0.5%
127.1635795 1
0.5%
127.1634243 1
0.5%
127.1632916 1
0.5%
127.163036 1
0.5%
127.1629396 1
0.5%

사무실면적
Real number (ℝ)

MISSING 

Distinct177
Distinct (%)91.2%
Missing3
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean43.965722
Minimum4
Maximum695.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-03-15T01:26:38.377676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8.5755
Q119.85
median30.235
Q345.275
95-th percentile112.24
Maximum695.2
Range691.2
Interquartile range (IQR)25.425

Descriptive statistics

Standard deviation60.924297
Coefficient of variation (CV)1.3857227
Kurtosis70.141838
Mean43.965722
Median Absolute Deviation (MAD)12.72
Skewness7.2601232
Sum8529.35
Variance3711.77
MonotonicityNot monotonic
2024-03-15T01:26:38.833018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.0 3
 
1.5%
25.2 3
 
1.5%
11.0 2
 
1.0%
27.0 2
 
1.0%
14.5 2
 
1.0%
18.4 2
 
1.0%
29.0 2
 
1.0%
12.8 2
 
1.0%
12.0 2
 
1.0%
65.0 2
 
1.0%
Other values (167) 172
87.3%
(Missing) 3
 
1.5%
ValueCountFrequency (%)
4.0 1
0.5%
5.1 1
0.5%
5.17 1
0.5%
5.22 1
0.5%
7.1 1
0.5%
7.6 1
0.5%
7.68 1
0.5%
7.8 1
0.5%
8.37 1
0.5%
8.53 1
0.5%
ValueCountFrequency (%)
695.2 1
0.5%
327.3 1
0.5%
220.5 1
0.5%
193.44 2
1.0%
140.21 1
0.5%
132.6 1
0.5%
120.0 1
0.5%
119.04 1
0.5%
112.5 1
0.5%
112.1 1
0.5%

소독차량차고면적
Real number (ℝ)

MISSING 

Distinct174
Distinct (%)89.7%
Missing3
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean25.274948
Minimum1.55
Maximum363
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-03-15T01:26:39.257054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.55
5-th percentile3.339
Q18.19
median13.79
Q323.3925
95-th percentile83.195
Maximum363
Range361.45
Interquartile range (IQR)15.2025

Descriptive statistics

Standard deviation43.236913
Coefficient of variation (CV)1.7106628
Kurtosis33.105611
Mean25.274948
Median Absolute Deviation (MAD)7.14
Skewness5.2396083
Sum4903.34
Variance1869.4307
MonotonicityNot monotonic
2024-03-15T01:26:39.699949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.0 4
 
2.0%
6.0 3
 
1.5%
4.32 3
 
1.5%
23.1 2
 
1.0%
5.0 2
 
1.0%
6.65 2
 
1.0%
30.0 2
 
1.0%
8.4 2
 
1.0%
13.3 2
 
1.0%
12.09 2
 
1.0%
Other values (164) 170
86.3%
(Missing) 3
 
1.5%
ValueCountFrequency (%)
1.55 1
0.5%
1.6 1
0.5%
1.65 1
0.5%
1.9 1
0.5%
2.0 1
0.5%
2.2 1
0.5%
2.3 1
0.5%
3.0 1
0.5%
3.1 1
0.5%
3.3 1
0.5%
ValueCountFrequency (%)
363.0 1
0.5%
327.3 1
0.5%
220.5 1
0.5%
162.7 1
0.5%
130.72 1
0.5%
126.04 1
0.5%
125.49 1
0.5%
115.0 1
0.5%
110.0 1
0.5%
92.1 1
0.5%

초미립자살포기수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1
191 
2
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 191
97.0%
2 6
 
3.0%

Length

2024-03-15T01:26:40.109794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:26:40.421500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 191
97.0%
2 6
 
3.0%

휴대용소독기수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2
195 
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 195
99.0%
3 2
 
1.0%

Length

2024-03-15T01:26:40.744224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:26:41.053999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 195
99.0%
3 2
 
1.0%

동력분무기수
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1
99 
0
97 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 99
50.3%
0 97
49.2%
2 1
 
0.5%

Length

2024-03-15T01:26:41.377045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:26:41.693381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 99
50.3%
0 97
49.2%
2 1
 
0.5%

수동식분무기수
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
5
98 
3
97 
6
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row3

Common Values

ValueCountFrequency (%)
5 98
49.7%
3 97
49.2%
6 2
 
1.0%

Length

2024-03-15T01:26:42.107284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:26:42.435380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 98
49.7%
3 97
49.2%
6 2
 
1.0%

방독면수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
5
196 
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 196
99.5%
6 1
 
0.5%

Length

2024-03-15T01:26:43.098885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:26:43.424598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 196
99.5%
6 1
 
0.5%

보호안경수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
5
196 
10
 
1

Length

Max length2
Median length1
Mean length1.0050761
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 196
99.5%
10 1
 
0.5%

Length

2024-03-15T01:26:44.050631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:26:44.390570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 196
99.5%
10 1
 
0.5%

보호용의복수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
5
195 
10
 
1
7
 
1

Length

Max length2
Median length1
Mean length1.0050761
Min length1

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 195
99.0%
10 1
 
0.5%
7 1
 
0.5%

Length

2024-03-15T01:26:44.754106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:26:45.127917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 195
99.0%
10 1
 
0.5%
7 1
 
0.5%

진공청소기수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1
192 
2
 
4
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 192
97.5%
2 4
 
2.0%
5 1
 
0.5%

Length

2024-03-15T01:26:45.445154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:26:45.779603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 192
97.5%
2 4
 
2.0%
5 1
 
0.5%

Interactions

2024-03-15T01:26:20.207023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:26:16.867824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:26:17.906157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:26:19.053374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:26:20.457025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:26:17.112962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:26:18.157865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:26:19.378215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:26:20.753504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:26:17.371258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:26:18.476894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:26:19.664682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:26:21.032175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:26:17.635110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:26:18.781347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:26:19.932710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T01:26:46.005513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도사무실면적소독차량차고면적초미립자살포기수휴대용소독기수동력분무기수수동식분무기수방독면수보호안경수보호용의복수진공청소기수
위도1.0000.7840.5660.6380.1430.0000.0000.0000.0000.0000.0000.000
경도0.7841.0000.5260.3320.4300.1710.3680.2920.3650.3650.3000.427
사무실면적0.5660.5261.0000.8070.3280.0000.0000.0000.0000.0000.0000.000
소독차량차고면적0.6380.3320.8071.0000.0000.2380.4000.2660.3850.3850.3650.367
초미립자살포기수0.1430.4300.3280.0001.0000.0000.0000.0000.0000.0000.0000.234
휴대용소독기수0.0000.1710.0000.2380.0001.0000.4540.3040.5120.5120.4530.453
동력분무기수0.0000.3680.0000.4000.0000.4541.0000.9881.0001.0000.9420.942
수동식분무기수0.0000.2920.0000.2660.0000.3040.9881.0000.4530.4530.8220.822
방독면수0.0000.3650.0000.3850.0000.5121.0000.4531.0000.7001.0001.000
보호안경수0.0000.3650.0000.3850.0000.5121.0000.4530.7001.0001.0001.000
보호용의복수0.0000.3000.0000.3650.0000.4530.9420.8221.0001.0001.0000.941
진공청소기수0.0000.4270.0000.3670.2340.4530.9420.8221.0001.0000.9411.000
2024-03-15T01:26:46.326936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
초미립자살포기수보호안경수수동식분무기수진공청소기수동력분무기수방독면수휴대용소독기수보호용의복수
초미립자살포기수1.0000.0000.0000.3810.0000.0000.0000.000
보호안경수0.0001.0000.7000.9970.9970.4940.3430.997
수동식분무기수0.0000.7001.0000.4940.8630.7000.4880.494
진공청소기수0.3810.9970.4941.0000.7050.9970.7000.703
동력분무기수0.0000.9970.8630.7051.0000.9970.7020.705
방독면수0.0000.4940.7000.9970.9971.0000.3430.997
휴대용소독기수0.0000.3430.4880.7000.7020.3431.0000.700
보호용의복수0.0000.9970.4940.7030.7050.9970.7001.000
2024-03-15T01:26:46.574204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도사무실면적소독차량차고면적초미립자살포기수휴대용소독기수동력분무기수수동식분무기수방독면수보호안경수보호용의복수진공청소기수
위도1.000-0.0410.1650.0530.1060.0000.0000.0000.0000.0000.0000.000
경도-0.0411.000-0.115-0.0070.3230.1270.2330.1790.2740.2740.1840.279
사무실면적0.165-0.1151.0000.1750.2340.0000.0000.0000.0000.0000.0000.000
소독차량차고면적0.053-0.0070.1751.0000.0000.2510.2910.1820.4070.4070.2610.263
초미립자살포기수0.1060.3230.2340.0001.0000.0000.0000.0000.0000.0000.0000.381
휴대용소독기수0.0000.1270.0000.2510.0001.0000.7020.4880.3430.3430.7000.700
동력분무기수0.0000.2330.0000.2910.0000.7021.0000.8630.9970.9970.7050.705
수동식분무기수0.0000.1790.0000.1820.0000.4880.8631.0000.7000.7000.4940.494
방독면수0.0000.2740.0000.4070.0000.3430.9970.7001.0000.4940.9970.997
보호안경수0.0000.2740.0000.4070.0000.3430.9970.7000.4941.0000.9970.997
보호용의복수0.0000.1840.0000.2610.0000.7000.7050.4940.9970.9971.0000.703
진공청소기수0.0000.2790.0000.2630.3810.7000.7050.4940.9970.9970.7031.000

Missing values

2024-03-15T01:26:21.463848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T01:26:22.210291image/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.
2024-03-15T01:26:22.901603image/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

사업장명인허가일자영업상태명소재지전화도로명주소지번주소위도경도사무실면적소독차량차고면적초미립자살포기수휴대용소독기수동력분무기수수동식분무기수방독면수보호안경수보호용의복수진공청소기수
0(유)가온종합관리2008-12-03영업/정상063-222-1318전북특별자치도 전주시 완산구 백제대로 289, 5층 (중화산동2가)전북특별자치도 전주시 완산구 중화산동2가 595-135.818852127.1224487.651.4112155551
1(유)개미환경위생1998-07-09영업/정상063-253-0762전북특별자치도 전주시 완산구 서신천변15길 15-9 (서신동)전북특별자치도 전주시 완산구 서신동 799-1635.831236127.11270538.723.312155551
2(유)깨끗한 세상2006-06-01영업/정상063-227-0820전북특별자치도 전주시 완산구 천잠로 205 (효자동2가)전북특별자치도 전주시 완산구 효자동2가 70935.805284127.09601163.39.812155551
3(유)대양환경1992-06-12영업/정상063-224-5283전북특별자치도 전주시 완산구 용머리로 73 (효자동1가,효자프라자 지하2층 203호)전북특별자치도 전주시 완산구 효자동1가 296-17535.807046127.118163119.0442.012155551
4(유)대한종합관리2020-11-10영업/정상<NA>전북특별자치도 전주시 완산구 강변로 220-16, 1호 (효자동1가, 삼호아파트)전북특별자치도 전주시 완산구 효자동1가 370-235.809521127.11393830.070.012035551
5(유)대호개발2007-03-12영업/정상063-224-8222전북특별자치도 전주시 완산구 인정4길 12-8 (중화산동2가)전북특별자치도 전주시 완산구 중화산동2가 582-335.820006127.12131321.016.012155551
6(유)동감2019-06-20영업/정상<NA>전북특별자치도 전주시 덕진구 오공로 138, 401호 (만성동)전북특별자치도 전주시 덕진구 만성동 1167-235.835057127.07048141.8812.1112035551
7(유)동산실업2005-11-21영업/정상063-236-5533전북특별자치도 전주시 완산구 거마중앙로 25 (삼천동1가)전북특별자치도 전주시 완산구 삼천동1가 631-1335.796652127.11951867.7614.0812155551
8(유)두승산업2002-03-09영업/정상063-225-2939전북특별자치도 전주시 완산구 거마평로 189-11 (효자동1가, 효자동미송아파트상가301호)전북특별자치도 전주시 완산구 효자동1가 43135.808559127.11467716.031.912155551
9(유)드림환경산업2017-01-11영업/정상063-223-6881전북특별자치도 전주시 완산구 솟대1길 34 (삼천동1가)전북특별자치도 전주시 완산구 삼천동1가 685-535.793853127.12279411.212.812035551
사업장명인허가일자영업상태명소재지전화도로명주소지번주소위도경도사무실면적소독차량차고면적초미립자살포기수휴대용소독기수동력분무기수수동식분무기수방독면수보호안경수보호용의복수진공청소기수
187한마음환경위생협동조합2013-10-17영업/정상<NA>전북특별자치도 전주시 완산구 안행로 110 (효자동1가)전북특별자치도 전주시 완산구 효자동1가 51-335.806131127.1343857.841.612155551
188해방 방역2020-12-10영업/정상063-222-0275전북특별자치도 전주시 완산구 솟대1길 41, 1층 (삼천동1가)전북특별자치도 전주시 완산구 삼천동1가 700-635.793414127.12179639.04.012035551
189해충방역 명장2021-08-12영업/정상<NA>전북특별자치도 전주시 완산구 관선5길 6(중노송동)전북특별자치도 전주시 완산구 중노송동 232-2835.822773127.1571722.1916.0512035551
190핸드앤클린(H&C)2020-08-20영업/정상<NA>전북특별자치도 전주시 완산구 홍산중앙로 47, 510호 (효자동3가)전북특별자치도 전주시 완산구 효자동3가 1527-1035.817897127.10877212.012.012035551
191험터스2012-08-17영업/정상<NA>전북특별자치도 전주시 덕진구 솔내7길 19 (송천동1가)전북특별자치도 전주시 덕진구 송천동1가 121-1635.859813127.12333328.48.0312155551
192현대환경방역공사1996-06-03영업/정상063-227-1188전북특별자치도 전주시 완산구 신촌3길 27-18 (중화산동2가, 동원맨션)전북특별자치도 전주시 완산구 중화산동2가 485-12535.815699127.11449521.232.012155551
193현무산업1997-05-15영업/정상<NA>전북특별자치도 전주시 덕진구 안골4길 16-5, 1층 (인후동1가, 장원빌딩)전북특별자치도 전주시 덕진구 인후동1가 753-535.841133127.15681520.645.812155551
194현성테크2021-11-30영업/정상<NA>전북특별자치도 전주시 덕진구 백동5길 23(인후동2가)전북특별자치도 전주시 덕진구 인후동2가 1566-1135.848022127.1497610.359.812035551
195호남종합개발1996-11-12영업/정상063-213-7676전북특별자치도 전주시 완산구 용머리로 6 (효자동1가)전북특별자치도 전주시 완산구 효자동1가 410-235.804865127.11115223.9423.3712155551
196환경개발(주)2003-01-15영업/정상063-231-1600전북특별자치도 전주시 완산구 현무2길 13-7 (경원동3가)전북특별자치도 전주시 완산구 경원동3가 86-935.819895127.14927527.029.212155551