Overview

Dataset statistics

Number of variables10
Number of observations33
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory86.0 B

Variable types

Categorical2
Text5
Numeric2
DateTime1

Dataset

Description대구광역시 동구에 위치한 음식점, 세탁업, 미용업 등의 착한가격업소 현황 데이터입니다. 업소명, 주소, 취급품목, 전화번호 등의 항목을 포함하고 있습니다.
URLhttps://www.data.go.kr/data/15059765/fileData.do

Alerts

데이터기준일 has constant value ""Constant
위도 is highly overall correlated with 법정동명High correlation
경도 is highly overall correlated with 법정동명High correlation
법정동명 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
업소명 has unique valuesUnique
소재지 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 14:23:17.073540
Analysis finished2023-12-12 14:23:18.347510
Duration1.27 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종
Categorical

Distinct6
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size396.0 B
한식
25 
중식
3 
일식
 
2
세탁업
 
1
기타양식(제과점)
 
1

Length

Max length9
Median length2
Mean length2.2727273
Min length2

Unique

Unique3 ?
Unique (%)9.1%

Sample

1st row한식
2nd row한식
3rd row중식
4th row한식
5th row한식

Common Values

ValueCountFrequency (%)
한식 25
75.8%
중식 3
 
9.1%
일식 2
 
6.1%
세탁업 1
 
3.0%
기타양식(제과점) 1
 
3.0%
미용업 1
 
3.0%

Length

2023-12-12T23:23:18.433773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:23:18.590109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 25
75.8%
중식 3
 
9.1%
일식 2
 
6.1%
세탁업 1
 
3.0%
기타양식(제과점 1
 
3.0%
미용업 1
 
3.0%

업소명
Text

UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-12T23:23:18.849594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length7
Mean length5.2424242
Min length2

Characters and Unicode

Total characters173
Distinct characters97
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)100.0%

Sample

1st row대구삼계탕
2nd row두곡동숯불갈비
3rd row영남루반점
4th row유정갈비
5th row태종대
ValueCountFrequency (%)
대구삼계탕 1
 
2.9%
미소띤하루 1
 
2.9%
희망나눔장수 1
 
2.9%
잔치국수 1
 
2.9%
소반 1
 
2.9%
면사랑칼국수 1
 
2.9%
부산복해물칼국수 1
 
2.9%
태왕세탁소 1
 
2.9%
신산홍 1
 
2.9%
두곡동숯불갈비 1
 
2.9%
Other values (24) 24
70.6%
2023-12-12T23:23:19.221570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 7
 
4.0%
국 6
 
3.5%
식 6
 
3.5%
갈 5
 
2.9%
비 5
 
2.9%
칼 5
 
2.9%
당 5
 
2.9%
어 4
 
2.3%
숯 4
 
2.3%
불 4
 
2.3%
Other values (87) 122
70.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 172
99.4%
Space Separator 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 7
 
4.1%
국 6
 
3.5%
식 6
 
3.5%
갈 5
 
2.9%
비 5
 
2.9%
칼 5
 
2.9%
당 5
 
2.9%
어 4
 
2.3%
숯 4
 
2.3%
불 4
 
2.3%
Other values (86) 121
70.3%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 172
99.4%
Common 1
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 7
 
4.1%
국 6
 
3.5%
식 6
 
3.5%
갈 5
 
2.9%
비 5
 
2.9%
칼 5
 
2.9%
당 5
 
2.9%
어 4
 
2.3%
숯 4
 
2.3%
불 4
 
2.3%
Other values (86) 121
70.3%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 172
99.4%
ASCII 1
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 7
 
4.1%
국 6
 
3.5%
식 6
 
3.5%
갈 5
 
2.9%
비 5
 
2.9%
칼 5
 
2.9%
당 5
 
2.9%
어 4
 
2.3%
숯 4
 
2.3%
불 4
 
2.3%
Other values (86) 121
70.3%
ASCII
ValueCountFrequency (%)
1
100.0%

소재지
Text

UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-12T23:23:19.527960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length32
Mean length21.030303
Min length16

Characters and Unicode

Total characters694
Distinct characters82
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

Unique33 ?
Unique (%)100.0%

Sample

1st row대구 동구 대현로 118-7(신암동)
2nd row대구 동구 아양로49길 6(신암동)
3rd row대구 동구 해동로 18(지저동)
4th row대구 동구 아양로 6(신암동)
5th row대구 동구 팔공로24길 19-10(불로동)
ValueCountFrequency (%)
대구 33
23.2%
동구 33
23.2%
아양로 4
 
2.8%
동촌로 3
 
2.1%
6(신암동 2
 
1.4%
장등로 2
 
1.4%
이노밸리로 1
 
0.7%
12 1
 
0.7%
아양로7길 1
 
0.7%
안심빌딩 1
 
0.7%
Other values (61) 61
43.0%
2023-12-12T23:23:19.986839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
109
15.7%
동 75
 
10.8%
구 66
 
9.5%
로 36
 
5.2%
대 34
 
4.9%
( 33
 
4.8%
) 33
 
4.8%
1 29
 
4.2%
길 17
 
2.4%
0 15
 
2.2%
Other values (72) 247
35.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 375
54.0%
Decimal Number 126
 
18.2%
Space Separator 109
 
15.7%
Open Punctuation 33
 
4.8%
Close Punctuation 33
 
4.8%
Dash Punctuation 11
 
1.6%
Other Punctuation 7
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 75
20.0%
구 66
17.6%
로 36
 
9.6%
대 34
 
9.1%
길 17
 
4.5%
신 13
 
3.5%
암 10
 
2.7%
양 9
 
2.4%
아 9
 
2.4%
효 7
 
1.9%
Other values (57) 99
26.4%
Decimal Number
ValueCountFrequency (%)
1 29
23.0%
0 15
11.9%
3 15
11.9%
2 14
11.1%
8 12
9.5%
5 10
 
7.9%
6 10
 
7.9%
7 7
 
5.6%
9 7
 
5.6%
4 7
 
5.6%
Space Separator
ValueCountFrequency (%)
109
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 375
54.0%
Common 319
46.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 75
20.0%
구 66
17.6%
로 36
 
9.6%
대 34
 
9.1%
길 17
 
4.5%
신 13
 
3.5%
암 10
 
2.7%
양 9
 
2.4%
아 9
 
2.4%
효 7
 
1.9%
Other values (57) 99
26.4%
Common
ValueCountFrequency (%)
109
34.2%
( 33
 
10.3%
) 33
 
10.3%
1 29
 
9.1%
0 15
 
4.7%
3 15
 
4.7%
2 14
 
4.4%
8 12
 
3.8%
- 11
 
3.4%
5 10
 
3.1%
Other values (5) 38
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 375
54.0%
ASCII 319
46.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
109
34.2%
( 33
 
10.3%
) 33
 
10.3%
1 29
 
9.1%
0 15
 
4.7%
3 15
 
4.7%
2 14
 
4.4%
8 12
 
3.8%
- 11
 
3.4%
5 10
 
3.1%
Other values (5) 38
 
11.9%
Hangul
ValueCountFrequency (%)
동 75
20.0%
구 66
17.6%
로 36
 
9.6%
대 34
 
9.1%
길 17
 
4.5%
신 13
 
3.5%
암 10
 
2.7%
양 9
 
2.4%
아 9
 
2.4%
효 7
 
1.9%
Other values (57) 99
26.4%

취급품목(원산지)
Text

Distinct28
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-12T23:23:20.198019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length4.8181818
Min length2

Characters and Unicode

Total characters159
Distinct characters74
Distinct categories7 ?
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 (%)69.7%

Sample

1st row삼계탕
2nd row돼지왕갈비(국내산)
3rd row자장면
4th row왕갈비(국내산)
5th row삼겹살(국내산)
ValueCountFrequency (%)
삼계탕 2
 
6.1%
복어탕 2
 
6.1%
회덮밥 2
 
6.1%
잔치국수 2
 
6.1%
칼국수 2
 
6.1%
추어탕 1
 
3.0%
얼큰냉면 1
 
3.0%
간짜장 1
 
3.0%
헤어컷(여성 1
 
3.0%
왕갈비 1
 
3.0%
Other values (18) 18
54.5%
2023-12-12T23:23:20.553910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
국 11
 
6.9%
탕 7
 
4.4%
비 7
 
4.4%
갈 6
 
3.8%
) 6
 
3.8%
수 6
 
3.8%
산 6
 
3.8%
( 6
 
3.8%
삼 4
 
2.5%
내 4
 
2.5%
Other values (64) 96
60.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 141
88.7%
Close Punctuation 6
 
3.8%
Open Punctuation 6
 
3.8%
Other Punctuation 2
 
1.3%
Math Symbol 2
 
1.3%
Space Separator 1
 
0.6%
Uppercase Letter 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 11
 
7.8%
탕 7
 
5.0%
비 7
 
5.0%
갈 6
 
4.3%
수 6
 
4.3%
산 6
 
4.3%
삼 4
 
2.8%
내 4
 
2.8%
어 4
 
2.8%
칼 4
 
2.8%
Other values (58) 82
58.2%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 141
88.7%
Common 17
 
10.7%
Latin 1
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 11
 
7.8%
탕 7
 
5.0%
비 7
 
5.0%
갈 6
 
4.3%
수 6
 
4.3%
산 6
 
4.3%
삼 4
 
2.8%
내 4
 
2.8%
어 4
 
2.8%
칼 4
 
2.8%
Other values (58) 82
58.2%
Common
ValueCountFrequency (%)
) 6
35.3%
( 6
35.3%
, 2
 
11.8%
+ 2
 
11.8%
1
 
5.9%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 141
88.7%
ASCII 18
 
11.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
국 11
 
7.8%
탕 7
 
5.0%
비 7
 
5.0%
갈 6
 
4.3%
수 6
 
4.3%
산 6
 
4.3%
삼 4
 
2.8%
내 4
 
2.8%
어 4
 
2.8%
칼 4
 
2.8%
Other values (58) 82
58.2%
ASCII
ValueCountFrequency (%)
) 6
33.3%
( 6
33.3%
, 2
 
11.1%
+ 2
 
11.1%
1
 
5.6%
A 1
 
5.6%

전화번호
Text

Distinct32
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-12T23:23:20.754037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters396
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

Unique31 ?
Unique (%)93.9%

Sample

1st row053-955-7848
2nd row053-942-8495
3rd row053-981-9881
4th row053-943-6616
5th row053-983-3477
ValueCountFrequency (%)
053-000-0000 2
 
6.1%
053-955-7848 1
 
3.0%
053-942-8495 1
 
3.0%
053-962-5444 1
 
3.0%
053-983-9233 1
 
3.0%
053-814-0640 1
 
3.0%
053-982-7524 1
 
3.0%
053-944-5650 1
 
3.0%
053-965-9794 1
 
3.0%
053-751-1121 1
 
3.0%
Other values (22) 22
66.7%
2023-12-12T23:23:21.138945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 66
16.7%
0 60
15.2%
5 56
14.1%
3 47
11.9%
9 46
11.6%
4 30
7.6%
8 23
 
5.8%
2 22
 
5.6%
6 18
 
4.5%
1 16
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 330
83.3%
Dash Punctuation 66
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 60
18.2%
5 56
17.0%
3 47
14.2%
9 46
13.9%
4 30
9.1%
8 23
 
7.0%
2 22
 
6.7%
6 18
 
5.5%
1 16
 
4.8%
7 12
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 396
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 66
16.7%
0 60
15.2%
5 56
14.1%
3 47
11.9%
9 46
11.6%
4 30
7.6%
8 23
 
5.8%
2 22
 
5.6%
6 18
 
4.5%
1 16
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 396
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 66
16.7%
0 60
15.2%
5 56
14.1%
3 47
11.9%
9 46
11.6%
4 30
7.6%
8 23
 
5.8%
2 22
 
5.6%
6 18
 
4.5%
1 16
 
4.0%

행정동명
Text

Distinct18
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-12T23:23:21.338554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.8484848
Min length3

Characters and Unicode

Total characters127
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)27.3%

Sample

1st row신암2동
2nd row신암5동
3rd row지저동
4th row신암3동
5th row불로.봉무동
ValueCountFrequency (%)
공산동 4
12.1%
효목1동 3
 
9.1%
신암5동 3
 
9.1%
불로.봉무동 3
 
9.1%
동촌동 3
 
9.1%
효목2동 2
 
6.1%
신암3동 2
 
6.1%
신천3동 2
 
6.1%
신암1동 2
 
6.1%
안심2동 1
 
3.0%
Other values (8) 8
24.2%
2023-12-12T23:23:21.646555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 36
28.3%
신 13
 
10.2%
암 9
 
7.1%
효 5
 
3.9%
목 5
 
3.9%
1 5
 
3.9%
3 5
 
3.9%
공 4
 
3.1%
촌 4
 
3.1%
2 4
 
3.1%
Other values (16) 37
29.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 105
82.7%
Decimal Number 19
 
15.0%
Other Punctuation 3
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 36
34.3%
신 13
 
12.4%
암 9
 
8.6%
효 5
 
4.8%
목 5
 
4.8%
공 4
 
3.8%
촌 4
 
3.8%
산 4
 
3.8%
무 3
 
2.9%
안 3
 
2.9%
Other values (10) 19
18.1%
Decimal Number
ValueCountFrequency (%)
1 5
26.3%
3 5
26.3%
2 4
21.1%
5 3
15.8%
4 2
 
10.5%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 105
82.7%
Common 22
 
17.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 36
34.3%
신 13
 
12.4%
암 9
 
8.6%
효 5
 
4.8%
목 5
 
4.8%
공 4
 
3.8%
촌 4
 
3.8%
산 4
 
3.8%
무 3
 
2.9%
안 3
 
2.9%
Other values (10) 19
18.1%
Common
ValueCountFrequency (%)
1 5
22.7%
3 5
22.7%
2 4
18.2%
. 3
13.6%
5 3
13.6%
4 2
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 105
82.7%
ASCII 22
 
17.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 36
34.3%
신 13
 
12.4%
암 9
 
8.6%
효 5
 
4.8%
목 5
 
4.8%
공 4
 
3.8%
촌 4
 
3.8%
산 4
 
3.8%
무 3
 
2.9%
안 3
 
2.9%
Other values (10) 19
18.1%
ASCII
ValueCountFrequency (%)
1 5
22.7%
3 5
22.7%
2 4
18.2%
. 3
13.6%
5 3
13.6%
4 2
 
9.1%

법정동명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Memory size396.0 B
신암동
9 
효목동
5 
불로동
3 
신천동
3 
검사동
2 
Other values (9)
11 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique7 ?
Unique (%)21.2%

Sample

1st row신암동
2nd row신암동
3rd row지저동
4th row신암동
5th row불로동

Common Values

ValueCountFrequency (%)
신암동 9
27.3%
효목동 5
15.2%
불로동 3
 
9.1%
신천동 3
 
9.1%
검사동 2
 
6.1%
방촌동 2
 
6.1%
지묘동 2
 
6.1%
지저동 1
 
3.0%
입석동 1
 
3.0%
각산동 1
 
3.0%
Other values (4) 4
12.1%

Length

2023-12-12T23:23:21.791572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신암동 9
27.3%
효목동 5
15.2%
불로동 3
 
9.1%
신천동 3
 
9.1%
검사동 2
 
6.1%
방촌동 2
 
6.1%
지묘동 2
 
6.1%
지저동 1
 
3.0%
입석동 1
 
3.0%
각산동 1
 
3.0%
Other values (4) 4
12.1%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.893371
Minimum35.870624
Maximum35.990634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T23:23:21.923361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.870624
5-th percentile35.871803
Q135.879387
median35.884158
Q335.890762
95-th percentile35.946256
Maximum35.990634
Range0.12001001
Interquartile range (IQR)0.01137458

Descriptive statistics

Standard deviation0.026743147
Coefficient of variation (CV)0.00074507203
Kurtosis5.1000183
Mean35.893371
Median Absolute Deviation (MAD)0.00555955
Skewness2.2330535
Sum1184.4813
Variance0.00071519592
MonotonicityNot monotonic
2023-12-12T23:23:22.051791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
35.88113773 1
 
3.0%
35.88233377 1
 
3.0%
35.87162715 1
 
3.0%
35.87491435 1
 
3.0%
35.88727613 1
 
3.0%
35.91045056 1
 
3.0%
35.87938708 1
 
3.0%
35.87844487 1
 
3.0%
35.95393177 1
 
3.0%
35.88730699 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
35.87062366 1
3.0%
35.87162715 1
3.0%
35.87192 1
3.0%
35.87412326 1
3.0%
35.874705 1
3.0%
35.87491435 1
3.0%
35.87844487 1
3.0%
35.87888572 1
3.0%
35.87938708 1
3.0%
35.88031131 1
3.0%
ValueCountFrequency (%)
35.99063367 1
3.0%
35.95393177 1
3.0%
35.94113913 1
3.0%
35.93869953 1
3.0%
35.91135273 1
3.0%
35.91045056 1
3.0%
35.90986622 1
3.0%
35.89662303 1
3.0%
35.89076166 1
3.0%
35.889718 1
3.0%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.64659
Minimum128.61189
Maximum128.73336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-12T23:23:22.180928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.61189
5-th percentile128.61497
Q1128.63076
median128.63934
Q3128.65076
95-th percentile128.70435
Maximum128.73336
Range0.1214779
Interquartile range (IQR)0.0200038

Descriptive statistics

Standard deviation0.028746696
Coefficient of variation (CV)0.00022345478
Kurtosis2.1014538
Mean128.64659
Median Absolute Deviation (MAD)0.0097293
Skewness1.5007062
Sum4245.3375
Variance0.00082637254
MonotonicityNot monotonic
2023-12-12T23:23:22.341519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
128.6118851 1
 
3.0%
128.6176867 1
 
3.0%
128.6296075 1
 
3.0%
128.6232105 1
 
3.0%
128.6411178 1
 
3.0%
128.6425017 1
 
3.0%
128.6399023 1
 
3.0%
128.7156793 1
 
3.0%
128.6938467 1
 
3.0%
128.6387145 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
128.6118851 1
3.0%
128.6148524 1
3.0%
128.6150491 1
3.0%
128.6176867 1
3.0%
128.6185413 1
3.0%
128.6232105 1
3.0%
128.6232875 1
3.0%
128.6296075 1
3.0%
128.6307591 1
3.0%
128.632626 1
3.0%
ValueCountFrequency (%)
128.733363 1
3.0%
128.7156793 1
3.0%
128.696797 1
3.0%
128.6938467 1
3.0%
128.679282 1
3.0%
128.668727 1
3.0%
128.660897 1
3.0%
128.660874 1
3.0%
128.6507629 1
3.0%
128.6466288 1
3.0%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
Minimum2023-06-05 00:00:00
Maximum2023-06-05 00:00:00
2023-12-12T23:23:22.475516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:23:22.591223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T23:23:17.793126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:23:17.583273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:23:17.903880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:23:17.698288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:23:22.688469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종업소명소재지취급품목(원산지)전화번호행정동명법정동명위도경도
업종1.0001.0001.0001.0000.0000.8410.8280.0000.747
업소명1.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지1.0001.0001.0001.0001.0001.0001.0001.0001.000
취급품목(원산지)1.0001.0001.0001.0000.9650.4130.7370.7470.358
전화번호0.0001.0001.0000.9651.0000.9370.9010.9760.000
행정동명0.8411.0001.0000.4130.9371.0000.9490.8010.930
법정동명0.8281.0001.0000.7370.9010.9491.0000.9970.937
위도0.0001.0001.0000.7470.9760.8010.9971.0000.475
경도0.7471.0001.0000.3580.0000.9300.9370.4751.000
2023-12-12T23:23:22.819188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동명업종
법정동명1.0000.493
업종0.4931.000
2023-12-12T23:23:22.908992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도업종법정동명
위도1.0000.1850.0000.780
경도0.1851.0000.4620.688
업종0.0000.4621.0000.493
법정동명0.7800.6880.4931.000

Missing values

2023-12-12T23:23:18.069726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:23:18.279789image/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

업종업소명소재지취급품목(원산지)전화번호행정동명법정동명위도경도데이터기준일
0한식대구삼계탕대구 동구 대현로 118-7(신암동)삼계탕053-955-7848신암2동신암동35.881138128.6118852023-06-05
1한식두곡동숯불갈비대구 동구 아양로49길 6(신암동)돼지왕갈비(국내산)053-942-8495신암5동신암동35.887307128.6387142023-06-05
2중식영남루반점대구 동구 해동로 18(지저동)자장면053-981-9881지저동지저동35.896623128.6376932023-06-05
3한식유정갈비대구 동구 아양로 6(신암동)왕갈비(국내산)053-943-6616신암3동신암동35.881032128.6148522023-06-05
4한식태종대대구 동구 팔공로24길 19-10(불로동)삼겹살(국내산)053-983-3477불로.봉무동불로동35.909866128.642222023-06-05
5한식팔공식당대구 동구 송라로32길 5(신암동)돌솥비빔밥053-941-1289신암3동신암동35.88068128.6150492023-06-05
6한식흥부고을숯불갈비대구 동구 동촌로 80-14(검사동)돼지갈비(독일,칠레산)053-986-0092동촌동검사동35.887026128.6507632023-06-05
7한식고향손칼국수대구 동구 장등로 35(신천동)잔치국수053-752-8894신천3동신천동35.870624128.6232882023-06-05
8한식미진손칼국수대구 동구 아양로50길 119-1(효목동)손칼국수053-941-4664효목1동효목동35.882463128.6428292023-06-05
9한식이가숯불갈비대구 동구 아양로 37-4(신암동)돼지갈비(국내산,미국산)053-958-7353신암1동신암동35.882555128.6185412023-06-05
업종업소명소재지취급품목(원산지)전화번호행정동명법정동명위도경도데이터기준일
23세탁업태왕세탁소대구 동구 동북로 500, 603-115(효목동, 태왕메트로시티)드라이053-751-1121효목2동효목동35.879387128.6399022023-06-05
24기타양식(제과점)미소띤하루대구 동구 이노밸리로 168, 103호(각산동, 안심빌딩)소보로빵053-965-9794혁신동각산동35.878445128.7156792023-06-05
25한식신산홍대구 동구 아양로7길 12, 가상가동 지하1층 108호(신암동, 신암뜨란채)닭목살053-944-5650신암1동신암동35.882334128.6176872023-06-05
26한식만보칼국수대구 동구 팔공로209길 15(백안동)칼국수053-982-7524공산동백안동35.953932128.6938472023-06-05
27한식가원식당대구 동구 팔공산로199길 6-3(용수동)능이닭백숙053-814-0640공산동용수동35.990634128.6967972023-06-05
28한식공산숯불갈비대구 동구 파계로22길 40(지묘동)왕갈비053-983-9233공산동지묘동35.941139128.6393372023-06-05
29미용업김정아헤어샵대구 동구 아양로37길 89(신암동)헤어컷(여성)053-000-0000신암5동신암동35.889718128.6326262023-06-05
30일식도심속바닷가식당대구 동구 화랑로 508(용계동)회덮밥053-962-5444안심2동용계동35.874705128.6792822023-06-05
31중식영동중화요리대구 동구 방촌로15길 3(검사동)간짜장053-962-6999동촌동검사동35.885334128.6608972023-06-05
32중식원데이대구 동구 동내로 6(괴전동)쟁반짜장053-000-0000안심3동괴전동35.87192128.7333632023-06-05