Overview

Dataset statistics

Number of variables8
Number of observations41
Missing cells69
Missing cells (%)21.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory70.2 B

Variable types

Numeric3
DateTime2
Categorical1
Text2

Dataset

Description2021년 4월 13일 기준 광주광역시 남구에 소재지를 둔 방역업체 명단입니다.
Author광주광역시 남구
URLhttps://www.data.go.kr/data/15080555/fileData.do

Alerts

영업구분 has constant value ""Constant
데이터기준일자 has constant value ""Constant
사무실면적 is highly overall correlated with 방역차고면적High correlation
방역차고면적 is highly overall correlated with 사무실면적High correlation
사무실면적 has 34 (82.9%) missing valuesMissing
방역차고면적 has 35 (85.4%) missing valuesMissing
순번 has unique valuesUnique
신고일자 has unique valuesUnique
소독업소명칭 has unique valuesUnique

Reproduction

Analysis started2023-12-12 12:42:18.354603
Analysis finished2023-12-12 12:42:19.971236
Duration1.62 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21
Minimum1
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T21:42:20.313462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median21
Q331
95-th percentile39
Maximum41
Range40
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.979149
Coefficient of variation (CV)0.57043565
Kurtosis-1.2
Mean21
Median Absolute Deviation (MAD)10
Skewness0
Sum861
Variance143.5
MonotonicityStrictly increasing
2023-12-12T21:42:20.486358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1 1
 
2.4%
32 1
 
2.4%
24 1
 
2.4%
25 1
 
2.4%
26 1
 
2.4%
27 1
 
2.4%
28 1
 
2.4%
29 1
 
2.4%
30 1
 
2.4%
31 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
1 1
2.4%
2 1
2.4%
3 1
2.4%
4 1
2.4%
5 1
2.4%
6 1
2.4%
7 1
2.4%
8 1
2.4%
9 1
2.4%
10 1
2.4%
ValueCountFrequency (%)
41 1
2.4%
40 1
2.4%
39 1
2.4%
38 1
2.4%
37 1
2.4%
36 1
2.4%
35 1
2.4%
34 1
2.4%
33 1
2.4%
32 1
2.4%

신고일자
Date

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
Minimum1998-08-10 00:00:00
Maximum2022-03-02 00:00:00
2023-12-12T21:42:20.647872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:42:20.806238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)

영업구분
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
영업중
41 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영업중 41
100.0%

Length

2023-12-12T21:42:20.969827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:42:21.097828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업중 41
100.0%

소독업소명칭
Text

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-12T21:42:21.331511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length6.902439
Min length3

Characters and Unicode

Total characters283
Distinct characters112
Distinct categories6 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)100.0%

Sample

1st row(유)세인주택개발
2nd row이에이
3rd row주식회사 에코플랜
4th row프로케이방역 협동조합
5th row그린365
ValueCountFrequency (%)
주식회사 5
 
9.3%
더크린 2
 
3.7%
유한회사 2
 
3.7%
유)세인주택개발 1
 
1.9%
주)케이티앤에이 1
 
1.9%
주)케이에스종합관리 1
 
1.9%
크린환경협동조합 1
 
1.9%
에스에이치종합관리 1
 
1.9%
주)서현 1
 
1.9%
cms 1
 
1.9%
Other values (38) 38
70.4%
2023-12-12T21:42:21.731182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 17
 
6.0%
13
 
4.6%
( 12
 
4.2%
) 12
 
4.2%
스 11
 
3.9%
이 10
 
3.5%
사 10
 
3.5%
에 8
 
2.8%
회 7
 
2.5%
한 6
 
2.1%
Other values (102) 177
62.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 240
84.8%
Space Separator 13
 
4.6%
Open Punctuation 12
 
4.2%
Close Punctuation 12
 
4.2%
Lowercase Letter 3
 
1.1%
Decimal Number 3
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 17
 
7.1%
스 11
 
4.6%
이 10
 
4.2%
사 10
 
4.2%
에 8
 
3.3%
회 7
 
2.9%
한 6
 
2.5%
린 6
 
2.5%
개 5
 
2.1%
식 5
 
2.1%
Other values (93) 155
64.6%
Lowercase Letter
ValueCountFrequency (%)
c 1
33.3%
m 1
33.3%
s 1
33.3%
Decimal Number
ValueCountFrequency (%)
5 1
33.3%
6 1
33.3%
3 1
33.3%
Space Separator
ValueCountFrequency (%)
13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 237
83.7%
Common 40
 
14.1%
Latin 3
 
1.1%
Han 3
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 17
 
7.2%
스 11
 
4.6%
이 10
 
4.2%
사 10
 
4.2%
에 8
 
3.4%
회 7
 
3.0%
한 6
 
2.5%
린 6
 
2.5%
개 5
 
2.1%
식 5
 
2.1%
Other values (90) 152
64.1%
Common
ValueCountFrequency (%)
13
32.5%
( 12
30.0%
) 12
30.0%
5 1
 
2.5%
6 1
 
2.5%
3 1
 
2.5%
Latin
ValueCountFrequency (%)
c 1
33.3%
m 1
33.3%
s 1
33.3%
Han
ValueCountFrequency (%)
學 1
33.3%
株 1
33.3%
敎 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 237
83.7%
ASCII 43
 
15.2%
CJK 3
 
1.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 17
 
7.2%
스 11
 
4.6%
이 10
 
4.2%
사 10
 
4.2%
에 8
 
3.4%
회 7
 
3.0%
한 6
 
2.5%
린 6
 
2.5%
개 5
 
2.1%
식 5
 
2.1%
Other values (90) 152
64.1%
ASCII
ValueCountFrequency (%)
13
30.2%
( 12
27.9%
) 12
27.9%
c 1
 
2.3%
m 1
 
2.3%
s 1
 
2.3%
5 1
 
2.3%
6 1
 
2.3%
3 1
 
2.3%
CJK
ValueCountFrequency (%)
學 1
33.3%
株 1
33.3%
敎 1
33.3%

사무실소재지(도로명)
Text

Distinct40
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-12T21:42:22.055596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length39
Mean length31.195122
Min length21

Characters and Unicode

Total characters1279
Distinct characters102
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

Unique39 ?
Unique (%)95.1%

Sample

1st row광주광역시 남구 천변좌로370번길 9-2(사동)
2nd row광주광역시 남구 수박등로5번길 1, 1층 (월산동)
3rd row광주광역시 남구 제중로 91, 2층 203호 (서동, 아남프라자아파트)
4th row광주광역시 남구 백운로34번길 3, 1층 (백운동)
5th row광주광역시 남구 군분로148번길 3, 상가동 26호 (월산동, 남부아파트)
ValueCountFrequency (%)
광주광역시 41
 
15.7%
남구 41
 
15.7%
주월동 12
 
4.6%
월산동 12
 
4.6%
1층 9
 
3.4%
대남대로 6
 
2.3%
2층 5
 
1.9%
4층 4
 
1.5%
백운동 4
 
1.5%
상가동 4
 
1.5%
Other values (96) 123
47.1%
2023-12-12T21:42:22.576549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
220
 
17.2%
광 85
 
6.6%
주 57
 
4.5%
남 55
 
4.3%
1 52
 
4.1%
동 51
 
4.0%
( 41
 
3.2%
역 41
 
3.2%
시 41
 
3.2%
구 41
 
3.2%
Other values (92) 595
46.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 727
56.8%
Space Separator 220
 
17.2%
Decimal Number 203
 
15.9%
Open Punctuation 41
 
3.2%
Close Punctuation 41
 
3.2%
Other Punctuation 37
 
2.9%
Dash Punctuation 10
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 85
 
11.7%
주 57
 
7.8%
남 55
 
7.6%
동 51
 
7.0%
역 41
 
5.6%
시 41
 
5.6%
구 41
 
5.6%
로 38
 
5.2%
월 32
 
4.4%
대 24
 
3.3%
Other values (77) 262
36.0%
Decimal Number
ValueCountFrequency (%)
1 52
25.6%
2 40
19.7%
3 24
11.8%
4 24
11.8%
0 17
 
8.4%
9 13
 
6.4%
5 10
 
4.9%
7 8
 
3.9%
8 8
 
3.9%
6 7
 
3.4%
Space Separator
ValueCountFrequency (%)
220
100.0%
Open Punctuation
ValueCountFrequency (%)
( 41
100.0%
Close Punctuation
ValueCountFrequency (%)
) 41
100.0%
Other Punctuation
ValueCountFrequency (%)
, 37
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 727
56.8%
Common 552
43.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 85
 
11.7%
주 57
 
7.8%
남 55
 
7.6%
동 51
 
7.0%
역 41
 
5.6%
시 41
 
5.6%
구 41
 
5.6%
로 38
 
5.2%
월 32
 
4.4%
대 24
 
3.3%
Other values (77) 262
36.0%
Common
ValueCountFrequency (%)
220
39.9%
1 52
 
9.4%
( 41
 
7.4%
) 41
 
7.4%
2 40
 
7.2%
, 37
 
6.7%
3 24
 
4.3%
4 24
 
4.3%
0 17
 
3.1%
9 13
 
2.4%
Other values (5) 43
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 727
56.8%
ASCII 552
43.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
220
39.9%
1 52
 
9.4%
( 41
 
7.4%
) 41
 
7.4%
2 40
 
7.2%
, 37
 
6.7%
3 24
 
4.3%
4 24
 
4.3%
0 17
 
3.1%
9 13
 
2.4%
Other values (5) 43
 
7.8%
Hangul
ValueCountFrequency (%)
광 85
 
11.7%
주 57
 
7.8%
남 55
 
7.6%
동 51
 
7.0%
역 41
 
5.6%
시 41
 
5.6%
구 41
 
5.6%
로 38
 
5.2%
월 32
 
4.4%
대 24
 
3.3%
Other values (77) 262
36.0%

사무실면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)100.0%
Missing34
Missing (%)82.9%
Infinite0
Infinite (%)0.0%
Mean53.071429
Minimum20
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T21:42:22.731904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile27.266
Q144.99
median50
Q367.26
95-th percentile75.8
Maximum77
Range57
Interquartile range (IQR)22.27

Descriptive statistics

Standard deviation19.476741
Coefficient of variation (CV)0.36699109
Kurtosis0.099970762
Mean53.071429
Median Absolute Deviation (MAD)11.52
Skewness-0.47578891
Sum371.5
Variance379.34345
MonotonicityNot monotonic
2023-12-12T21:42:22.863037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
45.76 1
 
2.4%
50.0 1
 
2.4%
61.52 1
 
2.4%
73.0 1
 
2.4%
77.0 1
 
2.4%
44.22 1
 
2.4%
20.0 1
 
2.4%
(Missing) 34
82.9%
ValueCountFrequency (%)
20.0 1
2.4%
44.22 1
2.4%
45.76 1
2.4%
50.0 1
2.4%
61.52 1
2.4%
73.0 1
2.4%
77.0 1
2.4%
ValueCountFrequency (%)
77.0 1
2.4%
73.0 1
2.4%
61.52 1
2.4%
50.0 1
2.4%
45.76 1
2.4%
44.22 1
2.4%
20.0 1
2.4%

방역차고면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)100.0%
Missing35
Missing (%)85.4%
Infinite0
Infinite (%)0.0%
Mean33.47
Minimum6.24
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T21:42:22.989067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.24
5-th percentile9.43
Q119.25
median24.79
Q344.145
95-th percentile70
Maximum77
Range70.76
Interquartile range (IQR)24.895

Descriptive statistics

Standard deviation25.626036
Coefficient of variation (CV)0.76564195
Kurtosis0.68989585
Mean33.47
Median Absolute Deviation (MAD)12.17
Skewness1.0786357
Sum200.82
Variance656.69372
MonotonicityNot monotonic
2023-12-12T21:42:23.129790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
20.0 1
 
2.4%
19.0 1
 
2.4%
49.0 1
 
2.4%
77.0 1
 
2.4%
6.24 1
 
2.4%
29.58 1
 
2.4%
(Missing) 35
85.4%
ValueCountFrequency (%)
6.24 1
2.4%
19.0 1
2.4%
20.0 1
2.4%
29.58 1
2.4%
49.0 1
2.4%
77.0 1
2.4%
ValueCountFrequency (%)
77.0 1
2.4%
49.0 1
2.4%
29.58 1
2.4%
20.0 1
2.4%
19.0 1
2.4%
6.24 1
2.4%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
Minimum2022-04-11 00:00:00
Maximum2022-04-11 00:00:00
2023-12-12T21:42:23.303415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:42:23.446862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T21:42:19.284465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:42:18.649211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:42:18.991752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:42:19.380784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:42:18.756682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:42:19.089316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:42:19.522615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:42:18.861715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:42:19.186679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:42:23.572459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번신고일자소독업소명칭사무실소재지(도로명)사무실면적방역차고면적
순번1.0001.0001.0000.9450.7491.000
신고일자1.0001.0001.0001.0001.0001.000
소독업소명칭1.0001.0001.0001.0001.0001.000
사무실소재지(도로명)0.9451.0001.0001.0001.0001.000
사무실면적0.7491.0001.0001.0001.0000.859
방역차고면적1.0001.0001.0001.0000.8591.000
2023-12-12T21:42:23.694832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번사무실면적방역차고면적
순번1.000-0.2860.086
사무실면적-0.2861.0000.600
방역차고면적0.0860.6001.000

Missing values

2023-12-12T21:42:19.664240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:42:19.783764image/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.
2023-12-12T21:42:19.912090image/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

순번신고일자영업구분소독업소명칭사무실소재지(도로명)사무실면적방역차고면적데이터기준일자
012022-03-02영업중(유)세인주택개발광주광역시 남구 천변좌로370번길 9-2(사동)<NA><NA>2022-04-11
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