Overview

Dataset statistics

Number of variables11
Number of observations169
Missing cells31
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.3 KiB
Average record size in memory92.8 B

Variable types

Numeric3
Categorical4
Text4

Dataset

Description대전광역시 서구_행정동별 업종별 착한가격업소현황(기준년, 업소명, 대표자명, 대상품목, 지정가격, 행정동코드, 행정동명, 표준산업분류코드, 전화번호 등 )데이터를 제공합니다.
Author대전광역시 서구
URLhttps://www.data.go.kr/data/15109031/fileData.do

Alerts

기준년 has constant value ""Constant
데이터생성일자 has constant value ""Constant
순번 is highly overall correlated with 행정동코드 and 1 other fieldsHigh correlation
행정동코드 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
행정동명 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
전화번호 has 31 (18.3%) missing valuesMissing
순번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 18:21:45.138848
Analysis finished2023-12-12 18:21:47.068146
Duration1.93 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct169
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85
Minimum1
Maximum169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T03:21:47.150444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.4
Q143
median85
Q3127
95-th percentile160.6
Maximum169
Range168
Interquartile range (IQR)84

Descriptive statistics

Standard deviation48.930222
Coefficient of variation (CV)0.57564968
Kurtosis-1.2
Mean85
Median Absolute Deviation (MAD)42
Skewness0
Sum14365
Variance2394.1667
MonotonicityStrictly increasing
2023-12-13T03:21:47.299625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
117 1
 
0.6%
109 1
 
0.6%
110 1
 
0.6%
111 1
 
0.6%
112 1
 
0.6%
113 1
 
0.6%
114 1
 
0.6%
115 1
 
0.6%
116 1
 
0.6%
Other values (159) 159
94.1%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
169 1
0.6%
168 1
0.6%
167 1
0.6%
166 1
0.6%
165 1
0.6%
164 1
0.6%
163 1
0.6%
162 1
0.6%
161 1
0.6%
160 1
0.6%

기준년
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2022
169 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 169
100.0%

Length

2023-12-13T03:21:47.436737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:21:47.536860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 169
100.0%

업소명
Text

Distinct128
Distinct (%)75.7%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-13T03:21:47.778936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length10
Mean length5.3076923
Min length2

Characters and Unicode

Total characters897
Distinct characters247
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95 ?
Unique (%)56.2%

Sample

1st row뜰건너
2nd row뜰건너
3rd row복수칼국수
4th row순대군
5th row진칼국수
ValueCountFrequency (%)
라면타임 4
 
2.2%
중경손세차장 4
 
2.2%
거북집 3
 
1.7%
대명반점 3
 
1.7%
미소김밥 3
 
1.7%
도솔왕돈까스와 3
 
1.7%
국수 3
 
1.7%
수정미용실 2
 
1.1%
뜰건너 2
 
1.1%
김밥천국 2
 
1.1%
Other values (124) 149
83.7%
2023-12-13T03:21:48.194776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
용 29
 
3.2%
미 23
 
2.6%
국 22
 
2.5%
수 21
 
2.3%
이 21
 
2.3%
원 18
 
2.0%
어 17
 
1.9%
밥 17
 
1.9%
헤 15
 
1.7%
실 15
 
1.7%
Other values (237) 699
77.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 879
98.0%
Space Separator 9
 
1.0%
Decimal Number 7
 
0.8%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
용 29
 
3.3%
미 23
 
2.6%
국 22
 
2.5%
수 21
 
2.4%
이 21
 
2.4%
원 18
 
2.0%
어 17
 
1.9%
밥 17
 
1.9%
헤 15
 
1.7%
실 15
 
1.7%
Other values (228) 681
77.5%
Decimal Number
ValueCountFrequency (%)
9 2
28.6%
4 1
14.3%
2 1
14.3%
3 1
14.3%
0 1
14.3%
1 1
14.3%
Space Separator
ValueCountFrequency (%)
9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 879
98.0%
Common 18
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
용 29
 
3.3%
미 23
 
2.6%
국 22
 
2.5%
수 21
 
2.4%
이 21
 
2.4%
원 18
 
2.0%
어 17
 
1.9%
밥 17
 
1.9%
헤 15
 
1.7%
실 15
 
1.7%
Other values (228) 681
77.5%
Common
ValueCountFrequency (%)
9
50.0%
9 2
 
11.1%
4 1
 
5.6%
2 1
 
5.6%
3 1
 
5.6%
0 1
 
5.6%
1 1
 
5.6%
) 1
 
5.6%
( 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 877
97.8%
ASCII 18
 
2.0%
Compat Jamo 2
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
용 29
 
3.3%
미 23
 
2.6%
국 22
 
2.5%
수 21
 
2.4%
이 21
 
2.4%
원 18
 
2.1%
어 17
 
1.9%
밥 17
 
1.9%
헤 15
 
1.7%
실 15
 
1.7%
Other values (226) 679
77.4%
ASCII
ValueCountFrequency (%)
9
50.0%
9 2
 
11.1%
4 1
 
5.6%
2 1
 
5.6%
3 1
 
5.6%
0 1
 
5.6%
1 1
 
5.6%
) 1
 
5.6%
( 1
 
5.6%
Compat Jamo
ValueCountFrequency (%)
ㅊ 1
50.0%
ㅁ 1
50.0%

대표자명
Text

Distinct129
Distinct (%)76.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-13T03:21:48.517230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.0177515
Min length2

Characters and Unicode

Total characters510
Distinct characters107
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

Unique95 ?
Unique (%)56.2%

Sample

1st row전명순
2nd row전명순
3rd row신미숙
4th row조용환
5th row송재희
ValueCountFrequency (%)
윤중경 4
 
2.4%
최재진 3
 
1.8%
김명희 3
 
1.8%
안점숙 3
 
1.8%
김환 3
 
1.8%
민경숙 2
 
1.2%
박금숙 2
 
1.2%
전명순 2
 
1.2%
유덕순 2
 
1.2%
김응진 2
 
1.2%
Other values (119) 143
84.6%
2023-12-13T03:21:49.004186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 31
 
6.1%
이 28
 
5.5%
숙 24
 
4.7%
순 20
 
3.9%
경 15
 
2.9%
진 14
 
2.7%
박 13
 
2.5%
미 13
 
2.5%
최 12
 
2.4%
영 12
 
2.4%
Other values (97) 328
64.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 507
99.4%
Decimal Number 3
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 31
 
6.1%
이 28
 
5.5%
숙 24
 
4.7%
순 20
 
3.9%
경 15
 
3.0%
진 14
 
2.8%
박 13
 
2.6%
미 13
 
2.6%
최 12
 
2.4%
영 12
 
2.4%
Other values (96) 325
64.1%
Decimal Number
ValueCountFrequency (%)
1 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 507
99.4%
Common 3
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 31
 
6.1%
이 28
 
5.5%
숙 24
 
4.7%
순 20
 
3.9%
경 15
 
3.0%
진 14
 
2.8%
박 13
 
2.6%
미 13
 
2.6%
최 12
 
2.4%
영 12
 
2.4%
Other values (96) 325
64.1%
Common
ValueCountFrequency (%)
1 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 507
99.4%
ASCII 3
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 31
 
6.1%
이 28
 
5.5%
숙 24
 
4.7%
순 20
 
3.9%
경 15
 
3.0%
진 14
 
2.8%
박 13
 
2.6%
미 13
 
2.6%
최 12
 
2.4%
영 12
 
2.4%
Other values (96) 325
64.1%
ASCII
ValueCountFrequency (%)
1 3
100.0%

대상품목
Text

Distinct65
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-13T03:21:49.242860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length3.1242604
Min length2

Characters and Unicode

Total characters528
Distinct characters124
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

Unique39 ?
Unique (%)23.1%

Sample

1st row김치찌개
2nd row된장찌개
3rd row칼국수
4th row순대국밥
5th row칼국수
ValueCountFrequency (%)
커트 27
 
15.9%
삼겹살 13
 
7.6%
퍼머 11
 
6.5%
칼국수 7
 
4.1%
된장찌개 6
 
3.5%
김치찌개 5
 
2.9%
이용료 5
 
2.9%
짬뽕 5
 
2.9%
짜장면 5
 
2.9%
냉면 4
 
2.4%
Other values (56) 82
48.2%
2023-12-13T03:21:49.634349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
커 29
 
5.5%
트 28
 
5.3%
장 23
 
4.4%
국 23
 
4.4%
삼 19
 
3.6%
면 15
 
2.8%
밥 15
 
2.8%
겹 14
 
2.7%
살 14
 
2.7%
수 14
 
2.7%
Other values (114) 334
63.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 503
95.3%
Decimal Number 11
 
2.1%
Close Punctuation 4
 
0.8%
Open Punctuation 4
 
0.8%
Uppercase Letter 3
 
0.6%
Lowercase Letter 2
 
0.4%
Space Separator 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
커 29
 
5.8%
트 28
 
5.6%
장 23
 
4.6%
국 23
 
4.6%
삼 19
 
3.8%
면 15
 
3.0%
밥 15
 
3.0%
겹 14
 
2.8%
살 14
 
2.8%
수 14
 
2.8%
Other values (102) 309
61.4%
Decimal Number
ValueCountFrequency (%)
1 5
45.5%
0 3
27.3%
6 1
 
9.1%
2 1
 
9.1%
5 1
 
9.1%
Uppercase Letter
ValueCountFrequency (%)
S 1
33.3%
U 1
33.3%
V 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Lowercase Letter
ValueCountFrequency (%)
g 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 503
95.3%
Common 20
 
3.8%
Latin 5
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
커 29
 
5.8%
트 28
 
5.6%
장 23
 
4.6%
국 23
 
4.6%
삼 19
 
3.8%
면 15
 
3.0%
밥 15
 
3.0%
겹 14
 
2.8%
살 14
 
2.8%
수 14
 
2.8%
Other values (102) 309
61.4%
Common
ValueCountFrequency (%)
1 5
25.0%
) 4
20.0%
( 4
20.0%
0 3
15.0%
1
 
5.0%
6 1
 
5.0%
2 1
 
5.0%
5 1
 
5.0%
Latin
ValueCountFrequency (%)
g 2
40.0%
S 1
20.0%
U 1
20.0%
V 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 503
95.3%
ASCII 25
 
4.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
커 29
 
5.8%
트 28
 
5.6%
장 23
 
4.6%
국 23
 
4.6%
삼 19
 
3.8%
면 15
 
3.0%
밥 15
 
3.0%
겹 14
 
2.8%
살 14
 
2.8%
수 14
 
2.8%
Other values (102) 309
61.4%
ASCII
ValueCountFrequency (%)
1 5
20.0%
) 4
16.0%
( 4
16.0%
0 3
12.0%
g 2
 
8.0%
1
 
4.0%
6 1
 
4.0%
2 1
 
4.0%
5 1
 
4.0%
S 1
 
4.0%
Other values (2) 2
 
8.0%

지정가격
Real number (ℝ)

Distinct27
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8932.5444
Minimum1200
Maximum30000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T03:21:49.790614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile2500
Q15000
median7000
Q311000
95-th percentile25000
Maximum30000
Range28800
Interquartile range (IQR)6000

Descriptive statistics

Standard deviation6249.0337
Coefficient of variation (CV)0.69958049
Kurtosis3.8657826
Mean8932.5444
Median Absolute Deviation (MAD)3000
Skewness1.9328088
Sum1509600
Variance39050423
MonotonicityNot monotonic
2023-12-13T03:21:49.967484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
6000 26
15.4%
5000 21
12.4%
10000 19
11.2%
12000 13
 
7.7%
7000 11
 
6.5%
4000 9
 
5.3%
13000 8
 
4.7%
9000 8
 
4.7%
2000 7
 
4.1%
11000 6
 
3.6%
Other values (17) 41
24.3%
ValueCountFrequency (%)
1200 1
 
0.6%
2000 7
 
4.1%
2500 3
 
1.8%
3000 5
 
3.0%
3500 1
 
0.6%
4000 9
5.3%
4500 2
 
1.2%
4800 1
 
0.6%
5000 21
12.4%
5500 4
 
2.4%
ValueCountFrequency (%)
30000 6
3.6%
29000 1
 
0.6%
25000 4
 
2.4%
20000 3
 
1.8%
16000 1
 
0.6%
15000 2
 
1.2%
13000 8
4.7%
12000 13
7.7%
11900 1
 
0.6%
11000 6
3.6%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.017058 × 109
Minimum3.017051 × 109
Maximum3.017066 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T03:21:50.089255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.017051 × 109
5-th percentile3.017052 × 109
Q13.017055 × 109
median3.0170582 × 109
Q33.0170597 × 109
95-th percentile3.0170656 × 109
Maximum3.017066 × 109
Range15000
Interquartile range (IQR)4700

Descriptive statistics

Standard deviation3957.0594
Coefficient of variation (CV)1.3115622 × 10-6
Kurtosis-0.4988091
Mean3.017058 × 109
Median Absolute Deviation (MAD)2200
Skewness0.3155298
Sum5.0988281 × 1011
Variance15658319
MonotonicityIncreasing
2023-12-13T03:21:50.223837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3017058100 13
 
7.7%
3017059000 12
 
7.1%
3017064000 11
 
6.5%
3017052000 10
 
5.9%
3017059700 9
 
5.3%
3017066000 9
 
5.3%
3017058200 8
 
4.7%
3017058600 8
 
4.7%
3017055500 8
 
4.7%
3017054000 8
 
4.7%
Other values (14) 73
43.2%
ValueCountFrequency (%)
3017051000 4
 
2.4%
3017052000 10
5.9%
3017053000 8
4.7%
3017053500 8
4.7%
3017054000 8
4.7%
3017055000 7
4.1%
3017055500 8
4.7%
3017056000 4
 
2.4%
3017057000 6
3.6%
3017057500 7
4.1%
ValueCountFrequency (%)
3017066000 9
5.3%
3017065000 6
3.6%
3017064000 11
6.5%
3017063000 4
 
2.4%
3017060000 6
3.6%
3017059700 9
5.3%
3017059600 4
 
2.4%
3017059300 1
 
0.6%
3017059000 12
7.1%
3017058800 6
3.6%

행정동명
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
갈마1동
13 
가수원동
12 
둔산2동
 
11
도마1동
 
10
둔산3동
 
9
Other values (19)
114 

Length

Max length4
Median length4
Mean length3.5266272
Min length2

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row복수동
2nd row복수동
3rd row복수동
4th row복수동
5th row도마1동

Common Values

ValueCountFrequency (%)
갈마1동 13
 
7.7%
가수원동 12
 
7.1%
둔산2동 11
 
6.5%
도마1동 10
 
5.9%
둔산3동 9
 
5.3%
관저2동 9
 
5.3%
갈마2동 8
 
4.7%
월평1동 8
 
4.7%
탄방동 8
 
4.7%
변동 8
 
4.7%
Other values (14) 73
43.2%

Length

2023-12-13T03:21:50.382585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
갈마1동 13
 
7.7%
가수원동 12
 
7.1%
둔산2동 11
 
6.5%
도마1동 10
 
5.9%
둔산3동 9
 
5.3%
관저2동 9
 
5.3%
갈마2동 8
 
4.7%
월평1동 8
 
4.7%
탄방동 8
 
4.7%
변동 8
 
4.7%
Other values (14) 73
43.2%

표준산업분류코드
Categorical

Distinct10
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
I5611
91 
S96112
33 
I56121
15 
S96111
11 
S9691
 
7
Other values (5)
12 

Length

Max length6
Median length5
Mean length5.4023669
Min length5

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowI5611
2nd rowI5611
3rd rowI5611
4th rowI5611
5th rowI5611

Common Values

ValueCountFrequency (%)
I5611 91
53.8%
S96112 33
 
19.5%
I56121 15
 
8.9%
S96111 11
 
6.5%
S9691 7
 
4.1%
S95213 4
 
2.4%
C1061 3
 
1.8%
I56221 2
 
1.2%
I56193 2
 
1.2%
I56123 1
 
0.6%

Length

2023-12-13T03:21:50.546831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:21:50.662363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i5611 91
53.8%
s96112 33
 
19.5%
i56121 15
 
8.9%
s96111 11
 
6.5%
s9691 7
 
4.1%
s95213 4
 
2.4%
c1061 3
 
1.8%
i56221 2
 
1.2%
i56193 2
 
1.2%
i56123 1
 
0.6%

전화번호
Text

MISSING 

Distinct102
Distinct (%)73.9%
Missing31
Missing (%)18.3%
Memory size1.4 KiB
2023-12-13T03:21:50.905555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1656
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

Unique72 ?
Unique (%)52.2%

Sample

1st row042-587-3337
2nd row042-587-3337
3rd row042-582-5234
4th row042-586-0110
5th row042-522-1751
ValueCountFrequency (%)
042-538-8834 4
 
2.9%
042-531-1816 3
 
2.2%
042-534-2156 3
 
2.2%
042-582-0317 3
 
2.2%
042-471-3337 3
 
2.2%
042-483-8898 2
 
1.4%
042-537-8989 2
 
1.4%
042-541-6581 2
 
1.4%
042-527-0600 2
 
1.4%
042-526-3578 2
 
1.4%
Other values (92) 112
81.2%
2023-12-13T03:21:51.281005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 276
16.7%
4 261
15.8%
2 237
14.3%
0 175
10.6%
5 165
10.0%
3 140
8.5%
8 136
8.2%
6 73
 
4.4%
9 65
 
3.9%
7 64
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1380
83.3%
Dash Punctuation 276
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 261
18.9%
2 237
17.2%
0 175
12.7%
5 165
12.0%
3 140
10.1%
8 136
9.9%
6 73
 
5.3%
9 65
 
4.7%
7 64
 
4.6%
1 64
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 276
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1656
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 276
16.7%
4 261
15.8%
2 237
14.3%
0 175
10.6%
5 165
10.0%
3 140
8.5%
8 136
8.2%
6 73
 
4.4%
9 65
 
3.9%
7 64
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 276
16.7%
4 261
15.8%
2 237
14.3%
0 175
10.6%
5 165
10.0%
3 140
8.5%
8 136
8.2%
6 73
 
4.4%
9 65
 
3.9%
7 64
 
3.9%

데이터생성일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2022-11-23
169 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-11-23
2nd row2022-11-23
3rd row2022-11-23
4th row2022-11-23
5th row2022-11-23

Common Values

ValueCountFrequency (%)
2022-11-23 169
100.0%

Length

2023-12-13T03:21:51.401488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:21:51.494253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-11-23 169
100.0%

Interactions

2023-12-13T03:21:46.263117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:45.700080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:46.012335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:46.353756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:45.801733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:46.101046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:46.452152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:45.907643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:46.181087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:21:51.555861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번대상품목지정가격행정동코드행정동명표준산업분류코드
순번1.0000.4500.2670.9310.9850.599
대상품목0.4501.0000.8860.2910.0000.997
지정가격0.2670.8861.0000.0000.1850.644
행정동코드0.9310.2910.0001.0001.0000.439
행정동명0.9850.0000.1851.0001.0000.673
표준산업분류코드0.5990.9970.6440.4390.6731.000
2023-12-13T03:21:51.649873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
표준산업분류코드행정동명
표준산업분류코드1.0000.304
행정동명0.3041.000
2023-12-13T03:21:51.752690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번지정가격행정동코드행정동명표준산업분류코드
순번1.0000.1520.9990.8620.227
지정가격0.1521.0000.1420.0640.357
행정동코드0.9990.1421.0000.9520.220
행정동명0.8620.0640.9521.0000.304
표준산업분류코드0.2270.3570.2200.3041.000

Missing values

2023-12-13T03:21:46.862305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:21:47.012491image/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

순번기준년업소명대표자명대상품목지정가격행정동코드행정동명표준산업분류코드전화번호데이터생성일자
012022뜰건너전명순김치찌개60003017051000복수동I5611042-587-33372022-11-23
122022뜰건너전명순된장찌개60003017051000복수동I5611042-587-33372022-11-23
232022복수칼국수신미숙칼국수50003017051000복수동I5611042-582-52342022-11-23
342022순대군조용환순대국밥70003017051000복수동I5611042-586-01102022-11-23
452022진칼국수송재희칼국수40003017052000도마1동I5611042-522-17512022-11-23
562022한울해물칼국수박한성칼국수55003017052000도마1동I5611042-526-81462022-11-23
672022고향소머리국밥오혜경갈비탕100003017052000도마1동I5611042-536-23342022-11-23
782022고구려강태구삼겹살110003017052000도마1동I5611042-536-92862022-11-23
892022소문난칼국수김창현칼국수60003017052000도마1동I5611042-533-08882022-11-23
9102022복돼지구이임소정삼겹살120003017052000도마1동I5611042-522-33922022-11-23
순번기준년업소명대표자명대상품목지정가격행정동코드행정동명표준산업분류코드전화번호데이터생성일자
1591602022먹고을최은주삼겹살130003017065000만년동I5611<NA>2022-11-23
1601612022서울식당이미숙김치찌개50003017066000둔산3동I5611042-488-82812022-11-23
1611622022명랑분식박복남청국장60003017066000둔산3동I5611042-472-53452022-11-23
1621632022행복한분식윤복임손수제비60003017066000둔산3동I5611042-484-20802022-11-23
1631642022거북집김환잔치국수45003017066000둔산3동I5611042-471-33372022-11-23
1641652022거북집김환돈까스60003017066000둔산3동I5611042-471-33372022-11-23
1651662022거북집김환비빔밥55003017066000둔산3동I5611042-471-33372022-11-23
1661672022이수자헤어갤러리이수자커트120003017066000둔산3동S96112<NA>2022-11-23
1671682022김영숙미용실김영숙퍼머250003017066000둔산3동S96112042-486-46112022-11-23
1681692022동키치킨이성순치킨119003017066000둔산3동I56193042-485-98482022-11-23