Overview

Dataset statistics

Number of variables13
Number of observations1351
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory147.9 KiB
Average record size in memory112.1 B

Variable types

Numeric7
Text2
Categorical4

Dataset

Description대전광역시 서구 상권별 업종별 개폐업소수(률)(개방서비스 ID, 개방서비스명, 기준년, 상권코드, 상권명, 행정동코드, 행정동명, 전체업소수, 개업소수, 폐업소스 폐업률) 데이터를 제공합니다.
Author대전광역시 서구
URLhttps://www.data.go.kr/data/15109012/fileData.do

Alerts

기준년 has constant value ""Constant
데이터생성일자 has constant value ""Constant
순번 is highly overall correlated with 행정동코드 and 2 other fieldsHigh correlation
상권코드 is highly overall correlated with 상권명 and 1 other fieldsHigh correlation
행정동코드 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
전체업소수 is highly overall correlated with 개업소수 and 1 other fieldsHigh correlation
개업소수 is highly overall correlated with 전체업소수 and 1 other fieldsHigh correlation
폐업소수 is highly overall correlated with 전체업소수 and 2 other fieldsHigh correlation
폐업률 is highly overall correlated with 폐업소수High correlation
상권명 is highly overall correlated with 순번 and 3 other fieldsHigh correlation
행정동명 is highly overall correlated with 순번 and 3 other fieldsHigh correlation
순번 has unique valuesUnique
개업소수 has 1026 (75.9%) zerosZeros
폐업소수 has 1076 (79.6%) zerosZeros
폐업률 has 1076 (79.6%) zerosZeros

Reproduction

Analysis started2023-12-12 12:39:33.473113
Analysis finished2023-12-12 12:39:40.687336
Duration7.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1351
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean676
Minimum1
Maximum1351
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2023-12-12T21:39:40.767064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile68.5
Q1338.5
median676
Q31013.5
95-th percentile1283.5
Maximum1351
Range1350
Interquartile range (IQR)675

Descriptive statistics

Standard deviation390.14442
Coefficient of variation (CV)0.57713671
Kurtosis-1.2
Mean676
Median Absolute Deviation (MAD)338
Skewness0
Sum913276
Variance152212.67
MonotonicityStrictly increasing
2023-12-12T21:39:40.935549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
899 1
 
0.1%
907 1
 
0.1%
906 1
 
0.1%
905 1
 
0.1%
904 1
 
0.1%
903 1
 
0.1%
902 1
 
0.1%
901 1
 
0.1%
900 1
 
0.1%
Other values (1341) 1341
99.3%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1351 1
0.1%
1350 1
0.1%
1349 1
0.1%
1348 1
0.1%
1347 1
0.1%
1346 1
0.1%
1345 1
0.1%
1344 1
0.1%
1343 1
0.1%
1342 1
0.1%
Distinct111
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
2023-12-12T21:39:41.226472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters13510
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)1.0%

Sample

1st row04_17_01_P
2nd row07_22_19_P
3rd row11_46_01_P
4th row03_05_06_P
5th row07_22_10_P
ValueCountFrequency (%)
08_26_04_p 51
 
3.8%
08_26_03_p 36
 
2.7%
07_22_03_p 30
 
2.2%
07_24_04_p 28
 
2.1%
04_17_01_p 26
 
1.9%
01_02_03_p 26
 
1.9%
11_43_02_p 24
 
1.8%
01_01_02_p 23
 
1.7%
05_18_01_p 23
 
1.7%
03_12_01_p 22
 
1.6%
Other values (101) 1062
78.6%
2023-12-12T21:39:41.630693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 4053
30.0%
0 2984
22.1%
1 1452
 
10.7%
P 1351
 
10.0%
2 1192
 
8.8%
3 677
 
5.0%
4 420
 
3.1%
7 415
 
3.1%
5 296
 
2.2%
8 248
 
1.8%
Other values (2) 422
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8106
60.0%
Connector Punctuation 4053
30.0%
Uppercase Letter 1351
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2984
36.8%
1 1452
17.9%
2 1192
 
14.7%
3 677
 
8.4%
4 420
 
5.2%
7 415
 
5.1%
5 296
 
3.7%
8 248
 
3.1%
6 235
 
2.9%
9 187
 
2.3%
Connector Punctuation
ValueCountFrequency (%)
_ 4053
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 1351
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12159
90.0%
Latin 1351
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 4053
33.3%
0 2984
24.5%
1 1452
 
11.9%
2 1192
 
9.8%
3 677
 
5.6%
4 420
 
3.5%
7 415
 
3.4%
5 296
 
2.4%
8 248
 
2.0%
6 235
 
1.9%
Latin
ValueCountFrequency (%)
P 1351
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 4053
30.0%
0 2984
22.1%
1 1452
 
10.7%
P 1351
 
10.0%
2 1192
 
8.8%
3 677
 
5.0%
4 420
 
3.1%
7 415
 
3.1%
5 296
 
2.2%
8 248
 
1.8%
Other values (2) 422
 
3.1%
Distinct111
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
2023-12-12T21:39:41.851040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length6.199852
Min length2

Characters and Unicode

Total characters8376
Distinct characters160
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

Unique13 ?
Unique (%)1.0%

Sample

1st row출판사
2nd row즉석판매제조가공업
3rd row민방위급수시설
4th row일반게임제공업
5th row식품자동판매기업
ValueCountFrequency (%)
통신판매업 51
 
3.7%
전화권유판매업 36
 
2.6%
건강기능식품일반판매업 30
 
2.2%
일반음식점 28
 
2.0%
출판사 26
 
1.9%
의료기기판매(임대)업 26
 
1.9%
담배소매업 24
 
1.7%
의원 23
 
1.7%
미용업 23
 
1.7%
국내여행업 22
 
1.6%
Other values (102) 1083
78.9%
2023-12-12T21:39:42.190396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1062
 
12.7%
361
 
4.3%
354
 
4.2%
237
 
2.8%
215
 
2.6%
190
 
2.3%
187
 
2.2%
187
 
2.2%
156
 
1.9%
138
 
1.6%
Other values (150) 5289
63.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8206
98.0%
Close Punctuation 69
 
0.8%
Open Punctuation 69
 
0.8%
Space Separator 21
 
0.3%
Other Punctuation 11
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1062
 
12.9%
361
 
4.4%
354
 
4.3%
237
 
2.9%
215
 
2.6%
190
 
2.3%
187
 
2.3%
187
 
2.3%
156
 
1.9%
138
 
1.7%
Other values (146) 5119
62.4%
Close Punctuation
ValueCountFrequency (%)
) 69
100.0%
Open Punctuation
ValueCountFrequency (%)
( 69
100.0%
Space Separator
ValueCountFrequency (%)
21
100.0%
Other Punctuation
ValueCountFrequency (%)
. 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8206
98.0%
Common 170
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1062
 
12.9%
361
 
4.4%
354
 
4.3%
237
 
2.9%
215
 
2.6%
190
 
2.3%
187
 
2.3%
187
 
2.3%
156
 
1.9%
138
 
1.7%
Other values (146) 5119
62.4%
Common
ValueCountFrequency (%)
) 69
40.6%
( 69
40.6%
21
 
12.4%
. 11
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8206
98.0%
ASCII 170
 
2.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1062
 
12.9%
361
 
4.4%
354
 
4.3%
237
 
2.9%
215
 
2.6%
190
 
2.3%
187
 
2.3%
187
 
2.3%
156
 
1.9%
138
 
1.7%
Other values (146) 5119
62.4%
ASCII
ValueCountFrequency (%)
) 69
40.6%
( 69
40.6%
21
 
12.4%
. 11
 
6.5%

기준년
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
2021
1351 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 1351
100.0%

Length

2023-12-12T21:39:42.325293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:39:42.412350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 1351
100.0%

상권코드
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2612879
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2023-12-12T21:39:42.492577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13.5
median7
Q311
95-th percentile14
Maximum14
Range13
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation4.0988495
Coefficient of variation (CV)0.56447968
Kurtosis-1.3720686
Mean7.2612879
Median Absolute Deviation (MAD)4
Skewness0.14515398
Sum9810
Variance16.800567
MonotonicityNot monotonic
2023-12-12T21:39:42.598054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 155
11.5%
5 140
10.4%
12 132
9.8%
2 118
8.7%
13 113
8.4%
4 111
8.2%
9 100
 
7.4%
11 80
 
5.9%
6 77
 
5.7%
14 74
 
5.5%
Other values (4) 251
18.6%
ValueCountFrequency (%)
1 65
4.8%
2 118
8.7%
3 155
11.5%
4 111
8.2%
5 140
10.4%
6 77
5.7%
7 61
 
4.5%
8 54
 
4.0%
9 100
7.4%
10 71
5.3%
ValueCountFrequency (%)
14 74
5.5%
13 113
8.4%
12 132
9.8%
11 80
5.9%
10 71
5.3%
9 100
7.4%
8 54
 
4.0%
7 61
4.5%
6 77
5.7%
5 140
10.4%

상권명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
롯데백화점 주변 상권
155 
이마트 둔산점 주변 상권
140 
세이디에스탄방점 주변 상권
132 
한민시장 주변 상권
118 
대전시청역 주변 상권
113 
Other values (9)
693 

Length

Max length23
Median length19
Mean length13.185788
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사학연금회관 뒤편(토요코인호텔) 주변 상권
2nd row도마큰시장 주변 상권
3rd row도마큰시장 주변 상권
4th row도마큰시장 주변 상권
5th row도마큰시장 주변 상권

Common Values

ValueCountFrequency (%)
롯데백화점 주변 상권 155
11.5%
이마트 둔산점 주변 상권 140
10.4%
세이디에스탄방점 주변 상권 132
9.8%
한민시장 주변 상권 118
8.7%
대전시청역 주변 상권 113
8.4%
갤러리아백화점 주변 상권 111
8.2%
둔산3동상점가 주변 상권 100
 
7.4%
이마트 트레이더스 월평점 주변 상권 80
 
5.9%
사학연금회관 뒤편(토요코인호텔) 주변 상권 77
 
5.7%
서구 보건소 주변 상권 74
 
5.5%
Other values (4) 251
18.6%

Length

2023-12-12T21:39:42.743282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
상권 1351
30.0%
주변 1351
30.0%
이마트 220
 
4.9%
롯데백화점 155
 
3.4%
둔산점 140
 
3.1%
세이디에스탄방점 132
 
2.9%
한민시장 118
 
2.6%
대전시청역 113
 
2.5%
갤러리아백화점 111
 
2.5%
둔산3동상점가 100
 
2.2%
Other values (10) 713
15.8%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0170592 × 109
Minimum3.017051 × 109
Maximum3.017066 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2023-12-12T21:39:42.898395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.017051 × 109
5-th percentile3.017052 × 109
Q13.017056 × 109
median3.017059 × 109
Q33.017063 × 109
95-th percentile3.017065 × 109
Maximum3.017066 × 109
Range15000
Interquartile range (IQR)7000

Descriptive statistics

Standard deviation3645.5794
Coefficient of variation (CV)1.2083221 × 10-6
Kurtosis-1.1073603
Mean3.0170592 × 109
Median Absolute Deviation (MAD)3500
Skewness-0.1075092
Sum4.076047 × 1012
Variance13290250
MonotonicityIncreasing
2023-12-12T21:39:43.014692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
3017063000 426
31.5%
3017055500 196
14.5%
3017058600 137
 
10.1%
3017056000 127
 
9.4%
3017059600 118
 
8.7%
3017052000 70
 
5.2%
3017059000 69
 
5.1%
3017065000 67
 
5.0%
3017057000 46
 
3.4%
3017055000 41
 
3.0%
Other values (8) 54
 
4.0%
ValueCountFrequency (%)
3017051000 1
 
0.1%
3017052000 70
 
5.2%
3017053500 3
 
0.2%
3017054000 8
 
0.6%
3017055000 41
 
3.0%
3017055500 196
14.5%
3017056000 127
9.4%
3017057000 46
 
3.4%
3017057500 17
 
1.3%
3017058100 12
 
0.9%
ValueCountFrequency (%)
3017066000 9
 
0.7%
3017065000 67
 
5.0%
3017064000 2
 
0.1%
3017063000 426
31.5%
3017059600 118
 
8.7%
3017059000 69
 
5.1%
3017058800 2
 
0.1%
3017058600 137
 
10.1%
3017058100 12
 
0.9%
3017057500 17
 
1.3%

행정동명
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
둔산1동
426 
탄방동
196 
월평1동
137 
괴정동
127 
관저1동
118 
Other values (13)
347 

Length

Max length4
Median length4
Mean length3.6069578
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
둔산1동 426
31.5%
탄방동 196
14.5%
월평1동 137
 
10.1%
괴정동 127
 
9.4%
관저1동 118
 
8.7%
도마1동 70
 
5.2%
가수원동 69
 
5.1%
만년동 67
 
5.0%
가장동 46
 
3.4%
용문동 41
 
3.0%
Other values (8) 54
 
4.0%

Length

2023-12-12T21:39:43.166224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
둔산1동 426
31.5%
탄방동 196
14.5%
월평1동 137
 
10.1%
괴정동 127
 
9.4%
관저1동 118
 
8.7%
도마1동 70
 
5.2%
가수원동 69
 
5.1%
만년동 67
 
5.0%
가장동 46
 
3.4%
용문동 41
 
3.0%
Other values (8) 54
 
4.0%

전체업소수
Real number (ℝ)

HIGH CORRELATION 

Distinct127
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.57513
Minimum1
Maximum1185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2023-12-12T21:39:43.290074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q310
95-th percentile79.5
Maximum1185
Range1184
Interquartile range (IQR)9

Descriptive statistics

Standard deviation67.664587
Coefficient of variation (CV)3.456661
Kurtosis104.27826
Mean19.57513
Median Absolute Deviation (MAD)2
Skewness8.7580245
Sum26446
Variance4578.4964
MonotonicityNot monotonic
2023-12-12T21:39:43.416535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 403
29.8%
2 201
14.9%
3 104
 
7.7%
4 79
 
5.8%
5 67
 
5.0%
6 44
 
3.3%
8 36
 
2.7%
9 36
 
2.7%
7 28
 
2.1%
10 25
 
1.9%
Other values (117) 328
24.3%
ValueCountFrequency (%)
1 403
29.8%
2 201
14.9%
3 104
 
7.7%
4 79
 
5.8%
5 67
 
5.0%
6 44
 
3.3%
7 28
 
2.1%
8 36
 
2.7%
9 36
 
2.7%
10 25
 
1.9%
ValueCountFrequency (%)
1185 1
0.1%
789 1
0.1%
741 1
0.1%
666 1
0.1%
589 1
0.1%
494 1
0.1%
475 1
0.1%
427 1
0.1%
420 1
0.1%
411 1
0.1%

개업소수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.23094
Minimum0
Maximum114
Zeros1026
Zeros (%)75.9%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2023-12-12T21:39:43.540540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum114
Range114
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.7328131
Coefficient of variation (CV)4.6572643
Kurtosis169.31445
Mean1.23094
Median Absolute Deviation (MAD)0
Skewness11.24225
Sum1663
Variance32.865146
MonotonicityNot monotonic
2023-12-12T21:39:43.661163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 1026
75.9%
1 162
 
12.0%
2 46
 
3.4%
3 21
 
1.6%
4 16
 
1.2%
5 13
 
1.0%
7 8
 
0.6%
6 8
 
0.6%
8 6
 
0.4%
13 5
 
0.4%
Other values (22) 40
 
3.0%
ValueCountFrequency (%)
0 1026
75.9%
1 162
 
12.0%
2 46
 
3.4%
3 21
 
1.6%
4 16
 
1.2%
5 13
 
1.0%
6 8
 
0.6%
7 8
 
0.6%
8 6
 
0.4%
9 3
 
0.2%
ValueCountFrequency (%)
114 1
0.1%
80 1
0.1%
77 1
0.1%
48 1
0.1%
45 1
0.1%
42 1
0.1%
34 1
0.1%
31 1
0.1%
29 2
0.1%
28 1
0.1%

폐업소수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.77202073
Minimum0
Maximum108
Zeros1076
Zeros (%)79.6%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2023-12-12T21:39:43.784892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum108
Range108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.3298946
Coefficient of variation (CV)5.6085211
Kurtosis355.8837
Mean0.77202073
Median Absolute Deviation (MAD)0
Skewness16.640732
Sum1043
Variance18.747987
MonotonicityNot monotonic
2023-12-12T21:39:43.901107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1076
79.6%
1 148
 
11.0%
2 48
 
3.6%
3 16
 
1.2%
4 13
 
1.0%
6 9
 
0.7%
5 7
 
0.5%
7 6
 
0.4%
13 5
 
0.4%
10 4
 
0.3%
Other values (14) 19
 
1.4%
ValueCountFrequency (%)
0 1076
79.6%
1 148
 
11.0%
2 48
 
3.6%
3 16
 
1.2%
4 13
 
1.0%
5 7
 
0.5%
6 9
 
0.7%
7 6
 
0.4%
8 4
 
0.3%
9 3
 
0.2%
ValueCountFrequency (%)
108 1
0.1%
77 1
0.1%
32 1
0.1%
31 1
0.1%
29 1
0.1%
27 1
0.1%
22 1
0.1%
20 1
0.1%
17 1
0.1%
15 1
0.1%

폐업률
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct138
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1517543
Minimum0
Maximum100
Zeros1076
Zeros (%)79.6%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2023-12-12T21:39:44.055918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile15.79
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.980482
Coefficient of variation (CV)3.8012107
Kurtosis42.556444
Mean3.1517543
Median Absolute Deviation (MAD)0
Skewness6.149888
Sum4258.02
Variance143.53195
MonotonicityNot monotonic
2023-12-12T21:39:44.531034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1076
79.6%
11.11 14
 
1.0%
100.0 13
 
1.0%
50.0 11
 
0.8%
20.0 10
 
0.7%
16.67 9
 
0.7%
33.33 8
 
0.6%
9.09 7
 
0.5%
4.35 6
 
0.4%
25.0 6
 
0.4%
Other values (128) 191
 
14.1%
ValueCountFrequency (%)
0.0 1076
79.6%
0.46 1
 
0.1%
0.89 1
 
0.1%
1.03 1
 
0.1%
1.05 1
 
0.1%
1.08 1
 
0.1%
1.2 1
 
0.1%
1.25 1
 
0.1%
1.27 1
 
0.1%
1.35 1
 
0.1%
ValueCountFrequency (%)
100.0 13
1.0%
66.67 3
 
0.2%
50.0 11
0.8%
40.0 3
 
0.2%
33.33 8
0.6%
28.57 2
 
0.1%
25.0 6
0.4%
20.83 1
 
0.1%
20.0 10
0.7%
17.95 1
 
0.1%

데이터생성일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.7 KiB
2022-11-21
1351 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-11-21
2nd row2022-11-21
3rd row2022-11-21
4th row2022-11-21
5th row2022-11-21

Common Values

ValueCountFrequency (%)
2022-11-21 1351
100.0%

Length

2023-12-12T21:39:44.687146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:39:44.785170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-11-21 1351
100.0%

Interactions

2023-12-12T21:39:39.577888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:34.198699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:35.090449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:36.002122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:36.833930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:37.871310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:38.733704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:39.696804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:34.328975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:35.230827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:36.139924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:36.938006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:38.023360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:38.844159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:39.836329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:34.461008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:35.358213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:36.247542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:37.338008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:38.146878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:38.969821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:39.941224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:34.599648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:35.492957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:36.363378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:37.446765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:38.289019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:39.098031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:40.055651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:34.715948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:35.609872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:36.478395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:37.540554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:38.392133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:39.224177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:40.166322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:34.824653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:35.734650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:36.585193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:37.636389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:38.512629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:39.337036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:40.283032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:34.930296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:35.869539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:36.722533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:37.747721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:38.617471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:39:39.460225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:39:44.851441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번상권코드상권명행정동코드행정동명전체업소수개업소수폐업소수폐업률
순번1.0000.8660.8400.8790.9350.0180.0000.0000.143
상권코드0.8661.0001.0000.7640.8870.0000.0130.0830.079
상권명0.8401.0001.0000.8780.9210.0000.0000.0000.124
행정동코드0.8790.7640.8781.0001.0000.0000.0000.0000.000
행정동명0.9350.8870.9211.0001.0000.0000.0000.0000.000
전체업소수0.0180.0000.0000.0000.0001.0000.9370.8720.000
개업소수0.0000.0130.0000.0000.0000.9371.0000.8950.000
폐업소수0.0000.0830.0000.0000.0000.8720.8951.0000.072
폐업률0.1430.0790.1240.0000.0000.0000.0000.0721.000
2023-12-12T21:39:44.991076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명상권명
행정동명1.0000.633
상권명0.6331.000
2023-12-12T21:39:45.103138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번상권코드행정동코드전체업소수개업소수폐업소수폐업률상권명행정동명
순번1.0000.1990.9810.1010.0200.0540.0630.5420.726
상권코드0.1991.0000.2080.0460.0330.0020.0000.9990.606
행정동코드0.9810.2081.0000.0980.0320.0370.0420.6280.997
전체업소수0.1010.0460.0981.0000.5550.5360.4770.0000.000
개업소수0.0200.0330.0320.5551.0000.5280.4490.0000.000
폐업소수0.0540.0020.0370.5360.5281.0000.9810.0000.000
폐업률0.0630.0000.0420.4770.4490.9811.0000.0260.000
상권명0.5420.9990.6280.0000.0000.0000.0261.0000.633
행정동명0.7260.6060.9970.0000.0000.0000.0000.6331.000

Missing values

2023-12-12T21:39:40.431095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:39:40.617951image/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

순번개방서비스 아이디개방서비스명기준년상권코드상권명행정동코드행정동명전체업소수개업소수폐업소수폐업률데이터생성일자
0104_17_01_P출판사20216사학연금회관 뒤편(토요코인호텔) 주변 상권3017051000복수동1000.02022-11-21
1207_22_19_P즉석판매제조가공업202110도마큰시장 주변 상권3017052000도마1동123432.442022-11-21
2311_46_01_P민방위급수시설202110도마큰시장 주변 상권3017052000도마1동1000.02022-11-21
3403_05_06_P일반게임제공업202110도마큰시장 주변 상권3017052000도마1동18015.562022-11-21
4507_22_10_P식품자동판매기업202110도마큰시장 주변 상권3017052000도마1동44100.02022-11-21
5608_26_04_P통신판매업202110도마큰시장 주변 상권3017052000도마1동132821.522022-11-21
6705_19_01_P이용업202110도마큰시장 주변 상권3017052000도마1동13000.02022-11-21
7807_23_01_P단란주점영업202110도마큰시장 주변 상권3017052000도마1동4000.02022-11-21
8909_30_05_P건설폐기물처리업202110도마큰시장 주변 상권3017052000도마1동1000.02022-11-21
91009_28_08_P석유판매업202110도마큰시장 주변 상권3017052000도마1동5000.02022-11-21
순번개방서비스 아이디개방서비스명기준년상권코드상권명행정동코드행정동명전체업소수개업소수폐업소수폐업률데이터생성일자
1341134203_12_02_P국내외여행업202114서구 보건소 주변 상권3017065000만년동11000.02022-11-21
1342134305_18_01_P미용업202113대전시청역 주변 상권3017066000둔산3동1000.02022-11-21
1343134407_24_04_P일반음식점20216사학연금회관 뒤편(토요코인호텔) 주변 상권3017066000둔산3동1000.02022-11-21
1344134501_01_02_P의원20214갤러리아백화점 주변 상권3017066000둔산3동2000.02022-11-21
1345134601_02_03_P의료기기판매(임대)업202113대전시청역 주변 상권3017066000둔산3동1000.02022-11-21
1346134711_46_02_P민방위대피시설20214갤러리아백화점 주변 상권3017066000둔산3동2000.02022-11-21
1347134803_12_01_P국내여행업20215이마트 둔산점 주변 상권3017066000둔산3동1000.02022-11-21
1348134901_01_02_P의원20215이마트 둔산점 주변 상권3017066000둔산3동2000.02022-11-21
1349135011_46_02_P민방위대피시설20215이마트 둔산점 주변 상권3017066000둔산3동2000.02022-11-21
1350135103_12_01_P국내여행업20214갤러리아백화점 주변 상권3017066000둔산3동1000.02022-11-21