Overview

Dataset statistics

Number of variables7
Number of observations23
Missing cells8
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory64.7 B

Variable types

Numeric3
Text3
Categorical1

Dataset

Description대구광역시 달성군_소독업체 현황_20240315
Author대구광역시 달성군
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15080724&dataSetDetailId=150807241811433d8cbf1&provdMethod=FILE

Alerts

데이터기준일자 has constant value ""Constant
전화번호 has 8 (34.8%) missing valuesMissing
순번 has unique valuesUnique
소독업소명칭 has unique valuesUnique
소재지 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2024-03-23 07:14:34.425159
Analysis finished2024-03-23 07:14:39.701666
Duration5.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:14:39.882003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.1
Q16.5
median12
Q317.5
95-th percentile21.9
Maximum23
Range22
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.78233
Coefficient of variation (CV)0.56519417
Kurtosis-1.2
Mean12
Median Absolute Deviation (MAD)6
Skewness0
Sum276
Variance46
MonotonicityStrictly increasing
2024-03-23T07:14:40.262486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 1
 
4.3%
2 1
 
4.3%
23 1
 
4.3%
22 1
 
4.3%
21 1
 
4.3%
20 1
 
4.3%
19 1
 
4.3%
18 1
 
4.3%
17 1
 
4.3%
16 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
1 1
4.3%
2 1
4.3%
3 1
4.3%
4 1
4.3%
5 1
4.3%
6 1
4.3%
7 1
4.3%
8 1
4.3%
9 1
4.3%
10 1
4.3%
ValueCountFrequency (%)
23 1
4.3%
22 1
4.3%
21 1
4.3%
20 1
4.3%
19 1
4.3%
18 1
4.3%
17 1
4.3%
16 1
4.3%
15 1
4.3%
14 1
4.3%

소독업소명칭
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2024-03-23T07:14:40.865501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length7.2608696
Min length2

Characters and Unicode

Total characters167
Distinct characters90
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row예원홈클린
2nd row더봄
3rd row클린터치
4th row거목시스템
5th row싹잡아 방역
ValueCountFrequency (%)
주식회사 4
 
12.5%
방역 2
 
6.2%
대구경북지사 2
 
6.2%
예원홈클린 1
 
3.1%
코뿔소환경 1
 
3.1%
주)나눔과행복 1
 
3.1%
성강건설(주 1
 
3.1%
비셀 1
 
3.1%
와이엔티 1
 
3.1%
방구방역 1
 
3.1%
Other values (17) 17
53.1%
2024-03-23T07:14:41.992165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 10
 
6.0%
9
 
5.4%
사 7
 
4.2%
( 6
 
3.6%
) 6
 
3.6%
방 6
 
3.6%
식 5
 
3.0%
역 5
 
3.0%
회 5
 
3.0%
경 4
 
2.4%
Other values (80) 104
62.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 139
83.2%
Space Separator 9
 
5.4%
Open Punctuation 6
 
3.6%
Close Punctuation 6
 
3.6%
Uppercase Letter 6
 
3.6%
Other Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 10
 
7.2%
사 7
 
5.0%
방 6
 
4.3%
식 5
 
3.6%
역 5
 
3.6%
회 5
 
3.6%
경 4
 
2.9%
린 4
 
2.9%
성 3
 
2.2%
행 3
 
2.2%
Other values (70) 87
62.6%
Uppercase Letter
ValueCountFrequency (%)
M 1
16.7%
L 1
16.7%
E 1
16.7%
O 1
16.7%
N 1
16.7%
G 1
16.7%
Space Separator
ValueCountFrequency (%)
9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 139
83.2%
Common 22
 
13.2%
Latin 6
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 10
 
7.2%
사 7
 
5.0%
방 6
 
4.3%
식 5
 
3.6%
역 5
 
3.6%
회 5
 
3.6%
경 4
 
2.9%
린 4
 
2.9%
성 3
 
2.2%
행 3
 
2.2%
Other values (70) 87
62.6%
Latin
ValueCountFrequency (%)
M 1
16.7%
L 1
16.7%
E 1
16.7%
O 1
16.7%
N 1
16.7%
G 1
16.7%
Common
ValueCountFrequency (%)
9
40.9%
( 6
27.3%
) 6
27.3%
& 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 139
83.2%
ASCII 28
 
16.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 10
 
7.2%
사 7
 
5.0%
방 6
 
4.3%
식 5
 
3.6%
역 5
 
3.6%
회 5
 
3.6%
경 4
 
2.9%
린 4
 
2.9%
성 3
 
2.2%
행 3
 
2.2%
Other values (70) 87
62.6%
ASCII
ValueCountFrequency (%)
9
32.1%
( 6
21.4%
) 6
21.4%
& 1
 
3.6%
M 1
 
3.6%
L 1
 
3.6%
E 1
 
3.6%
O 1
 
3.6%
N 1
 
3.6%
G 1
 
3.6%

소재지
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2024-03-23T07:14:42.627656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length28
Mean length24.695652
Min length19

Characters and Unicode

Total characters568
Distinct characters67
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

Unique23 ?
Unique (%)100.0%

Sample

1st row대구광역시 달성군 논공읍 북리2길 8
2nd row대구광역시 달성군 옥포읍 돌미로 85, 301호
3rd row대구광역시 달성군 옥포읍 비슬로 2170
4th row대구광역시 달성군 화원읍 성화로 9
5th row대구광역시 달성군 하빈면 하빈로 422
ValueCountFrequency (%)
대구광역시 23
18.1%
달성군 23
18.1%
화원읍 7
 
5.5%
다사읍 4
 
3.1%
현풍읍 3
 
2.4%
101호 2
 
1.6%
옥포읍 2
 
1.6%
유가읍 2
 
1.6%
논공읍 2
 
1.6%
비슬로 2
 
1.6%
Other values (53) 57
44.9%
2024-03-23T07:14:43.780985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
104
18.3%
성 26
 
4.6%
대 24
 
4.2%
달 24
 
4.2%
구 24
 
4.2%
시 23
 
4.0%
역 23
 
4.0%
광 23
 
4.0%
군 23
 
4.0%
1 22
 
3.9%
Other values (57) 252
44.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 348
61.3%
Space Separator 104
 
18.3%
Decimal Number 100
 
17.6%
Other Punctuation 10
 
1.8%
Dash Punctuation 6
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 26
 
7.5%
대 24
 
6.9%
달 24
 
6.9%
구 24
 
6.9%
시 23
 
6.6%
역 23
 
6.6%
광 23
 
6.6%
군 23
 
6.6%
읍 20
 
5.7%
로 18
 
5.2%
Other values (44) 120
34.5%
Decimal Number
ValueCountFrequency (%)
1 22
22.0%
2 18
18.0%
8 14
14.0%
5 9
9.0%
4 9
9.0%
0 8
 
8.0%
6 7
 
7.0%
3 5
 
5.0%
9 4
 
4.0%
7 4
 
4.0%
Space Separator
ValueCountFrequency (%)
104
100.0%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 348
61.3%
Common 220
38.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 26
 
7.5%
대 24
 
6.9%
달 24
 
6.9%
구 24
 
6.9%
시 23
 
6.6%
역 23
 
6.6%
광 23
 
6.6%
군 23
 
6.6%
읍 20
 
5.7%
로 18
 
5.2%
Other values (44) 120
34.5%
Common
ValueCountFrequency (%)
104
47.3%
1 22
 
10.0%
2 18
 
8.2%
8 14
 
6.4%
, 10
 
4.5%
5 9
 
4.1%
4 9
 
4.1%
0 8
 
3.6%
6 7
 
3.2%
- 6
 
2.7%
Other values (3) 13
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 348
61.3%
ASCII 220
38.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
104
47.3%
1 22
 
10.0%
2 18
 
8.2%
8 14
 
6.4%
, 10
 
4.5%
5 9
 
4.1%
4 9
 
4.1%
0 8
 
3.6%
6 7
 
3.2%
- 6
 
2.7%
Other values (3) 13
 
5.9%
Hangul
ValueCountFrequency (%)
성 26
 
7.5%
대 24
 
6.9%
달 24
 
6.9%
구 24
 
6.9%
시 23
 
6.6%
역 23
 
6.6%
광 23
 
6.6%
군 23
 
6.6%
읍 20
 
5.7%
로 18
 
5.2%
Other values (44) 120
34.5%

전화번호
Text

MISSING 

Distinct15
Distinct (%)100.0%
Missing8
Missing (%)34.8%
Memory size316.0 B
2024-03-23T07:14:44.379640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.4
Min length9

Characters and Unicode

Total characters171
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)100.0%

Sample

1st row053-654-3400
2nd row1688-6934
3rd row053-586-1011
4th row080-002-1119
5th row8678-2545
ValueCountFrequency (%)
053-654-3400 1
 
6.7%
1688-6934 1
 
6.7%
053-586-1011 1
 
6.7%
080-002-1119 1
 
6.7%
8678-2545 1
 
6.7%
053-588-9787 1
 
6.7%
053-241-4545 1
 
6.7%
053-643-7871 1
 
6.7%
053-943-1800 1
 
6.7%
053-617-9220 1
 
6.7%
Other values (5) 5
33.3%
2024-03-23T07:14:45.406681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 27
15.8%
0 25
14.6%
5 22
12.9%
3 18
10.5%
8 14
8.2%
4 13
7.6%
1 13
7.6%
2 11
6.4%
7 11
6.4%
6 10
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 144
84.2%
Dash Punctuation 27
 
15.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25
17.4%
5 22
15.3%
3 18
12.5%
8 14
9.7%
4 13
9.0%
1 13
9.0%
2 11
7.6%
7 11
7.6%
6 10
 
6.9%
9 7
 
4.9%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 171
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 27
15.8%
0 25
14.6%
5 22
12.9%
3 18
10.5%
8 14
8.2%
4 13
7.6%
1 13
7.6%
2 11
6.4%
7 11
6.4%
6 10
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 171
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 27
15.8%
0 25
14.6%
5 22
12.9%
3 18
10.5%
8 14
8.2%
4 13
7.6%
1 13
7.6%
2 11
6.4%
7 11
6.4%
6 10
 
5.8%

위도
Real number (ℝ)

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.791486
Minimum35.658223
Maximum35.901566
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:14:46.117477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.658223
5-th percentile35.6915
Q135.746166
median35.798758
Q335.833391
95-th percentile35.900087
Maximum35.901566
Range0.24334329
Interquartile range (IQR)0.08722496

Descriptive statistics

Standard deviation0.071030571
Coefficient of variation (CV)0.0019845661
Kurtosis-0.75890098
Mean35.791486
Median Absolute Deviation (MAD)0.06039237
Skewness-0.1651679
Sum823.20418
Variance0.005045342
MonotonicityNot monotonic
2024-03-23T07:14:46.528083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
35.72741404 1
 
4.3%
35.78136405 1
 
4.3%
35.80634603 1
 
4.3%
35.69706364 1
 
4.3%
35.7649178 1
 
4.3%
35.80163571 1
 
4.3%
35.79875803 1
 
4.3%
35.69991364 1
 
4.3%
35.8591504 1
 
4.3%
35.8012502 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
35.65822299 1
4.3%
35.69088723 1
4.3%
35.69701857 1
4.3%
35.69706364 1
4.3%
35.69991364 1
4.3%
35.72741404 1
4.3%
35.7649178 1
4.3%
35.78136405 1
4.3%
35.78755926 1
4.3%
35.79320511 1
4.3%
ValueCountFrequency (%)
35.90156628 1
4.3%
35.9015129 1
4.3%
35.88725559 1
4.3%
35.8727497 1
4.3%
35.87241172 1
4.3%
35.8591504 1
4.3%
35.80763136 1
4.3%
35.80634603 1
4.3%
35.80163571 1
4.3%
35.8012502 1
4.3%

경도
Real number (ℝ)

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.47697
Minimum128.42475
Maximum128.62677
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:14:47.078970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.42475
5-th percentile128.44325
Q1128.44751
median128.46425
Q3128.49495
95-th percentile128.50811
Maximum128.62677
Range0.2020264
Interquartile range (IQR)0.04743565

Descriptive statistics

Standard deviation0.040778005
Coefficient of variation (CV)0.00031739545
Kurtosis7.7759929
Mean128.47697
Median Absolute Deviation (MAD)0.0211611
Skewness2.2683354
Sum2954.9703
Variance0.0016628457
MonotonicityNot monotonic
2024-03-23T07:14:47.623739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
128.4515171 1
 
4.3%
128.4430844 1
 
4.3%
128.4945978 1
 
4.3%
128.4447708 1
 
4.3%
128.4247453 1
 
4.3%
128.5087971 1
 
4.3%
128.4986442 1
 
4.3%
128.4633731 1
 
4.3%
128.4642455 1
 
4.3%
128.4997331 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
128.4247453 1
4.3%
128.4430844 1
4.3%
128.4447708 1
4.3%
128.4452286 1
4.3%
128.4453441 1
4.3%
128.4454173 1
4.3%
128.4496104 1
4.3%
128.4515171 1
4.3%
128.4590597 1
4.3%
128.4594315 1
4.3%
ValueCountFrequency (%)
128.6267717 1
4.3%
128.5087971 1
4.3%
128.501964 1
4.3%
128.4997331 1
4.3%
128.4986442 1
4.3%
128.4953012 1
4.3%
128.4945978 1
4.3%
128.4939814 1
4.3%
128.4917301 1
4.3%
128.4856325 1
4.3%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
2024-03-15
23 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-03-15
2nd row2024-03-15
3rd row2024-03-15
4th row2024-03-15
5th row2024-03-15

Common Values

ValueCountFrequency (%)
2024-03-15 23
100.0%

Length

2024-03-23T07:14:48.229684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T07:14:48.597565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-03-15 23
100.0%

Interactions

2024-03-23T07:14:37.721722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:14:34.968340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:14:36.192644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:14:38.123809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:14:35.335164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:14:36.427656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:14:38.550074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:14:35.778972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:14:36.935492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T07:14:48.788800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번소독업소명칭소재지전화번호위도경도
순번1.0001.0001.0001.0000.2430.225
소독업소명칭1.0001.0001.0001.0001.0001.000
소재지1.0001.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.000
위도0.2431.0001.0001.0001.0000.000
경도0.2251.0001.0001.0000.0001.000
2024-03-23T07:14:49.130543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번위도경도
순번1.000-0.0060.204
위도-0.0061.0000.385
경도0.2040.3851.000

Missing values

2024-03-23T07:14:38.985269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T07:14:39.501273image/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.

Sample

순번소독업소명칭소재지전화번호위도경도데이터기준일자
01예원홈클린대구광역시 달성군 논공읍 북리2길 8<NA>35.727414128.4515172024-03-15
12더봄대구광역시 달성군 옥포읍 돌미로 85, 301호<NA>35.781364128.4430842024-03-15
23클린터치대구광역시 달성군 옥포읍 비슬로 2170<NA>35.787559128.459062024-03-15
34거목시스템대구광역시 달성군 화원읍 성화로 9053-654-340035.80067128.491732024-03-15
45싹잡아 방역대구광역시 달성군 하빈면 하빈로 422<NA>35.901513128.4452292024-03-15
56바른방역 대구경북지사대구광역시 달성군 현풍읍 비슬로 580, 202호1688-693435.690887128.4453442024-03-15
67(주)태광종합관리대구광역시 달성군 다사읍 세천남로2길 8-28, 3층053-586-101135.872412128.4773022024-03-15
78주식회사 페스콤대구광역시 달성군 화원읍 비슬로525길 37, 나동 101호080-002-111935.807631128.5019642024-03-15
89레몽(LEMONG)대구광역시 달성군 현풍읍 비슬로134길 189, 1층8678-254535.697019128.449612024-03-15
910(주)팔봉산업관리대구광역시 달성군 가창면 가창로 1011-1<NA>35.795678128.6267722024-03-15
순번소독업소명칭소재지전화번호위도경도데이터기준일자
1314크린톡대구광역시 달성군 화원읍 류목정길 48053-643-787135.793205128.4856332024-03-15
1415주식회사한미드론대구광역시 달성군 유가읍 도의길 86-38053-943-180035.658223128.4594312024-03-15
1516달성시니어 바이오&클린대구광역시 달성군 화원읍 비슬로512길 66, 2층053-617-922035.80125128.4997332024-03-15
1617(주)행복한동행대구광역시 달성군 다사읍 달구벌대로 861, 8층053-584-252035.85915128.4642462024-03-15
1718방구방역 대구경북지사대구광역시 달성군 유가읍 현풍로47길 15-5, 1층1522-092935.699914128.4633732024-03-15
1819주식회사 와이엔티대구광역시 달성군 화원읍 화암로 88, 2층 207호053-765-748435.798758128.4986442024-03-15
1920주식회사 비셀대구광역시 달성군 화원읍 성암로 16-14, 101호<NA>35.801636128.5087972024-03-15
2021성강건설(주)대구광역시 달성군 논공읍 노이2길 59053-637-010835.764918128.4247452024-03-15
2122(주)나눔과행복대구광역시 달성군 현풍읍 현풍중앙로16길 14<NA>35.697064128.4447712024-03-15
2223금창환경대구광역시 달성군 화원읍 성천로 122053-643-237735.806346128.4945982024-03-15