Overview

Dataset statistics

Number of variables12
Number of observations956
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory96.3 KiB
Average record size in memory103.1 B

Variable types

Numeric6
Text2
Categorical4

Dataset

Description대전광역시 서구 상권별 업종별 연차별 생존율현황(개방서비스ID, 개방서비스명, 기준년, 상권코드, 상권명, 행정동코드, 해정동명, 신생업소수, 1년내 폐업 및 생존율) 등데이터를 제공합니다.
Author대전광역시 서구
URLhttps://www.data.go.kr/data/15109018/fileData.do

Alerts

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
1년내폐업소수 is highly overall correlated with 1년내생존률High correlation
1년내생존률 is highly overall correlated with 1년내폐업소수High 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
1년내폐업소수 has 792 (82.8%) zerosZeros
1년내생존률 has 19 (2.0%) zerosZeros

Reproduction

Analysis started2023-12-12 10:44:27.216178
Analysis finished2023-12-12 10:44:33.362884
Duration6.15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct956
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean478.5
Minimum1
Maximum956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2023-12-12T19:44:33.473355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile48.75
Q1239.75
median478.5
Q3717.25
95-th percentile908.25
Maximum956
Range955
Interquartile range (IQR)477.5

Descriptive statistics

Standard deviation276.11773
Coefficient of variation (CV)0.57704854
Kurtosis-1.2
Mean478.5
Median Absolute Deviation (MAD)239
Skewness0
Sum457446
Variance76241
MonotonicityNot monotonic
2023-12-12T19:44:33.684686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
644 1
 
0.1%
632 1
 
0.1%
633 1
 
0.1%
634 1
 
0.1%
635 1
 
0.1%
636 1
 
0.1%
637 1
 
0.1%
638 1
 
0.1%
639 1
 
0.1%
Other values (946) 946
99.0%
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 (%)
956 1
0.1%
955 1
0.1%
954 1
0.1%
953 1
0.1%
952 1
0.1%
951 1
0.1%
950 1
0.1%
949 1
0.1%
948 1
0.1%
947 1
0.1%

개방서비스아이디
Text

Distinct77
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
2023-12-12T19:44:33.987127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters9560
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

Unique14 ?
Unique (%)1.5%

Sample

1st row09_30_12_P
2nd row07_22_19_P
3rd row11_43_02_P
4th row03_13_05_P
5th row07_22_03_P
ValueCountFrequency (%)
08_26_04_p 64
 
6.7%
07_22_03_p 62
 
6.5%
07_24_04_p 61
 
6.4%
07_24_05_p 59
 
6.2%
01_02_03_p 54
 
5.6%
07_22_19_p 53
 
5.5%
05_18_01_p 49
 
5.1%
11_43_02_p 42
 
4.4%
01_01_05_p 29
 
3.0%
07_22_10_p 27
 
2.8%
Other values (67) 456
47.7%
2023-12-12T19:44:34.422311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 2868
30.0%
0 2048
21.4%
2 1003
 
10.5%
P 956
 
10.0%
1 828
 
8.7%
4 399
 
4.2%
7 396
 
4.1%
3 376
 
3.9%
5 225
 
2.4%
8 191
 
2.0%
Other values (2) 270
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5736
60.0%
Connector Punctuation 2868
30.0%
Uppercase Letter 956
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2048
35.7%
2 1003
17.5%
1 828
14.4%
4 399
 
7.0%
7 396
 
6.9%
3 376
 
6.6%
5 225
 
3.9%
8 191
 
3.3%
6 148
 
2.6%
9 122
 
2.1%
Connector Punctuation
ValueCountFrequency (%)
_ 2868
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 956
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8604
90.0%
Latin 956
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 2868
33.3%
0 2048
23.8%
2 1003
 
11.7%
1 828
 
9.6%
4 399
 
4.6%
7 396
 
4.6%
3 376
 
4.4%
5 225
 
2.6%
8 191
 
2.2%
6 148
 
1.7%
Latin
ValueCountFrequency (%)
P 956
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 2868
30.0%
0 2048
21.4%
2 1003
 
10.5%
P 956
 
10.0%
1 828
 
8.7%
4 399
 
4.2%
7 396
 
4.1%
3 376
 
3.9%
5 225
 
2.4%
8 191
 
2.0%
Other values (2) 270
 
2.8%

개방서비스명
Text

Distinct77
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
2023-12-12T19:44:34.770054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length6.6307531
Min length2

Characters and Unicode

Total characters6339
Distinct characters136
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

Unique14 ?
Unique (%)1.5%

Sample

1st row수질오염원설치시설(기타)
2nd row즉석판매제조가공업
3rd row담배소매업
4th row영화제작업
5th row건강기능식품일반판매업
ValueCountFrequency (%)
통신판매업 64
 
6.5%
건강기능식품일반판매업 62
 
6.3%
일반음식점 61
 
6.2%
휴게음식점 59
 
6.0%
의료기기판매(임대)업 54
 
5.5%
즉석판매제조가공업 53
 
5.4%
미용업 49
 
5.0%
담배소매업 42
 
4.3%
안전상비의약품 29
 
2.9%
판매업소 29
 
2.9%
Other values (68) 483
49.0%
2023-12-12T19:44:35.280155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
업 717
 
11.3%
매 424
 
6.7%
판 398
 
6.3%
식 254
 
4.0%
기 244
 
3.8%
품 145
 
2.3%
점 138
 
2.2%
음 136
 
2.1%
반 135
 
2.1%
제 129
 
2.0%
Other values (126) 3619
57.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6154
97.1%
Open Punctuation 74
 
1.2%
Close Punctuation 74
 
1.2%
Space Separator 29
 
0.5%
Other Punctuation 8
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 717
 
11.7%
매 424
 
6.9%
판 398
 
6.5%
식 254
 
4.1%
기 244
 
4.0%
품 145
 
2.4%
점 138
 
2.2%
음 136
 
2.2%
반 135
 
2.2%
제 129
 
2.1%
Other values (122) 3434
55.8%
Open Punctuation
ValueCountFrequency (%)
( 74
100.0%
Close Punctuation
ValueCountFrequency (%)
) 74
100.0%
Space Separator
ValueCountFrequency (%)
29
100.0%
Other Punctuation
ValueCountFrequency (%)
. 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6154
97.1%
Common 185
 
2.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 717
 
11.7%
매 424
 
6.9%
판 398
 
6.5%
식 254
 
4.1%
기 244
 
4.0%
품 145
 
2.4%
점 138
 
2.2%
음 136
 
2.2%
반 135
 
2.2%
제 129
 
2.1%
Other values (122) 3434
55.8%
Common
ValueCountFrequency (%)
( 74
40.0%
) 74
40.0%
29
 
15.7%
. 8
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6154
97.1%
ASCII 185
 
2.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
업 717
 
11.7%
매 424
 
6.9%
판 398
 
6.5%
식 254
 
4.1%
기 244
 
4.0%
품 145
 
2.4%
점 138
 
2.2%
음 136
 
2.2%
반 135
 
2.2%
제 129
 
2.1%
Other values (122) 3434
55.8%
ASCII
ValueCountFrequency (%)
( 74
40.0%
) 74
40.0%
29
 
15.7%
. 8
 
4.3%

기준년
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
2021
325 
2019
325 
2020
306 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2019
4th row2020
5th row2019

Common Values

ValueCountFrequency (%)
2021 325
34.0%
2019 325
34.0%
2020 306
32.0%

Length

2023-12-12T19:44:35.439741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:44:35.545659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 325
34.0%
2019 325
34.0%
2020 306
32.0%

상권코드
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3702929
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2023-12-12T19:44:35.676280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q311
95-th percentile14
Maximum14
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.1208087
Coefficient of variation (CV)0.55911057
Kurtosis-1.3835417
Mean7.3702929
Median Absolute Deviation (MAD)4
Skewness0.081011801
Sum7046
Variance16.981064
MonotonicityNot monotonic
2023-12-12T19:44:35.808259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 114
11.9%
13 93
9.7%
12 88
9.2%
5 83
8.7%
4 81
8.5%
11 69
 
7.2%
2 68
 
7.1%
8 60
 
6.3%
9 59
 
6.2%
1 55
 
5.8%
Other values (4) 186
19.5%
ValueCountFrequency (%)
1 55
5.8%
2 68
7.1%
3 114
11.9%
4 81
8.5%
5 83
8.7%
6 38
 
4.0%
7 55
5.8%
8 60
6.3%
9 59
6.2%
10 44
 
4.6%
ValueCountFrequency (%)
14 49
5.1%
13 93
9.7%
12 88
9.2%
11 69
7.2%
10 44
4.6%
9 59
6.2%
8 60
6.3%
7 55
5.8%
6 38
4.0%
5 83
8.7%

상권명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
롯데백화점 주변 상권
114 
대전시청역 주변 상권
93 
세이디에스탄방점 주변 상권
88 
이마트 둔산점 주변 상권
83 
갤러리아백화점 주변 상권
81 
Other values (9)
497 

Length

Max length23
Median length19
Mean length13.148536
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row도마큰시장 주변 상권
2nd row도마큰시장 주변 상권
3rd row도마큰시장 주변 상권
4th row도마큰시장 주변 상권
5th row도마큰시장 주변 상권

Common Values

ValueCountFrequency (%)
롯데백화점 주변 상권 114
11.9%
대전시청역 주변 상권 93
9.7%
세이디에스탄방점 주변 상권 88
9.2%
이마트 둔산점 주변 상권 83
8.7%
갤러리아백화점 주변 상권 81
8.5%
이마트 트레이더스 월평점 주변 상권 69
 
7.2%
한민시장 주변 상권 68
 
7.1%
마치광장골목형상점가 주변 상권 60
 
6.3%
둔산3동상점가 주변 상권 59
 
6.2%
가수원상점가 주변 상권 55
 
5.8%
Other values (4) 186
19.5%

Length

2023-12-12T19:44:35.952829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
주변 956
30.1%
상권 956
30.1%
이마트 152
 
4.8%
롯데백화점 114
 
3.6%
대전시청역 93
 
2.9%
세이디에스탄방점 88
 
2.8%
둔산점 83
 
2.6%
갤러리아백화점 81
 
2.6%
트레이더스 69
 
2.2%
월평점 69
 
2.2%
Other values (10) 515
16.2%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0170593 × 109
Minimum3.017052 × 109
Maximum3.017065 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2023-12-12T19:44:36.078618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.017052 × 109
5-th percentile3.017055 × 109
Q13.017056 × 109
median3.017059 × 109
Q33.017063 × 109
95-th percentile3.017065 × 109
Maximum3.017065 × 109
Range13000
Interquartile range (IQR)7000

Descriptive statistics

Standard deviation3531.371
Coefficient of variation (CV)1.1704679 × 10-6
Kurtosis-1.0693042
Mean3.0170593 × 109
Median Absolute Deviation (MAD)3500
Skewness-0.14681319
Sum2.8843087 × 1012
Variance12470581
MonotonicityNot monotonic
2023-12-12T19:44:36.224566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3017063000 301
31.5%
3017055500 150
15.7%
3017059600 115
 
12.0%
3017058600 98
 
10.3%
3017056000 96
 
10.0%
3017059000 56
 
5.9%
3017065000 49
 
5.1%
3017052000 45
 
4.7%
3017057000 23
 
2.4%
3017055000 20
 
2.1%
Other values (3) 3
 
0.3%
ValueCountFrequency (%)
3017052000 45
 
4.7%
3017054000 1
 
0.1%
3017055000 20
 
2.1%
3017055500 150
15.7%
3017056000 96
10.0%
3017057000 23
 
2.4%
3017057500 1
 
0.1%
3017058100 1
 
0.1%
3017058600 98
10.3%
3017059000 56
 
5.9%
ValueCountFrequency (%)
3017065000 49
 
5.1%
3017063000 301
31.5%
3017059600 115
 
12.0%
3017059000 56
 
5.9%
3017058600 98
 
10.3%
3017058100 1
 
0.1%
3017057500 1
 
0.1%
3017057000 23
 
2.4%
3017056000 96
 
10.0%
3017055500 150
15.7%

행정동명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
둔산1동
301 
탄방동
150 
관저1동
115 
월평1동
98 
괴정동
96 
Other values (8)
196 

Length

Max length4
Median length4
Mean length3.6422594
Min length2

Unique

Unique3 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
둔산1동 301
31.5%
탄방동 150
15.7%
관저1동 115
 
12.0%
월평1동 98
 
10.3%
괴정동 96
 
10.0%
가수원동 56
 
5.9%
만년동 49
 
5.1%
도마1동 45
 
4.7%
가장동 23
 
2.4%
용문동 20
 
2.1%
Other values (3) 3
 
0.3%

Length

2023-12-12T19:44:36.404491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
둔산1동 301
31.5%
탄방동 150
15.7%
관저1동 115
 
12.0%
월평1동 98
 
10.3%
괴정동 96
 
10.0%
가수원동 56
 
5.9%
만년동 49
 
5.1%
도마1동 45
 
4.7%
가장동 23
 
2.4%
용문동 20
 
2.1%
Other values (3) 3
 
0.3%

신생업소수
Real number (ℝ)

Distinct47
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1276151
Minimum1
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2023-12-12T19:44:36.589706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile20.5
Maximum124
Range123
Interquartile range (IQR)3

Descriptive statistics

Standard deviation11.117574
Coefficient of variation (CV)2.1681764
Kurtosis43.419537
Mean5.1276151
Median Absolute Deviation (MAD)0
Skewness5.8102425
Sum4902
Variance123.60045
MonotonicityNot monotonic
2023-12-12T19:44:36.763114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1 479
50.1%
2 137
 
14.3%
3 68
 
7.1%
4 48
 
5.0%
5 36
 
3.8%
6 25
 
2.6%
7 16
 
1.7%
8 16
 
1.7%
10 13
 
1.4%
12 13
 
1.4%
Other values (37) 105
 
11.0%
ValueCountFrequency (%)
1 479
50.1%
2 137
 
14.3%
3 68
 
7.1%
4 48
 
5.0%
5 36
 
3.8%
6 25
 
2.6%
7 16
 
1.7%
8 16
 
1.7%
9 7
 
0.7%
10 13
 
1.4%
ValueCountFrequency (%)
124 1
0.1%
114 1
0.1%
100 1
0.1%
99 1
0.1%
82 1
0.1%
80 1
0.1%
77 2
0.2%
65 1
0.1%
58 1
0.1%
51 2
0.2%

1년내폐업소수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1380753
Minimum0
Maximum115
Zeros792
Zeros (%)82.8%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2023-12-12T19:44:36.899598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum115
Range115
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.1049254
Coefficient of variation (CV)7.1216072
Kurtosis108.89558
Mean1.1380753
Median Absolute Deviation (MAD)0
Skewness10.092704
Sum1088
Variance65.689816
MonotonicityNot monotonic
2023-12-12T19:44:37.030405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 792
82.8%
1 111
 
11.6%
2 21
 
2.2%
3 6
 
0.6%
4 3
 
0.3%
9 3
 
0.3%
8 2
 
0.2%
69 2
 
0.2%
10 2
 
0.2%
73 1
 
0.1%
Other values (13) 13
 
1.4%
ValueCountFrequency (%)
0 792
82.8%
1 111
 
11.6%
2 21
 
2.2%
3 6
 
0.6%
4 3
 
0.3%
5 1
 
0.1%
6 1
 
0.1%
7 1
 
0.1%
8 2
 
0.2%
9 3
 
0.3%
ValueCountFrequency (%)
115 1
0.1%
97 1
0.1%
92 1
0.1%
82 1
0.1%
73 1
0.1%
69 2
0.2%
64 1
0.1%
48 1
0.1%
46 1
0.1%
30 1
0.1%

1년내생존률
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.904707
Minimum0
Maximum100
Zeros19
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2023-12-12T19:44:37.194798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.128538
Coefficient of variation (CV)0.20370159
Kurtosis14.563558
Mean93.904707
Median Absolute Deviation (MAD)0
Skewness-3.8401317
Sum89772.9
Variance365.90098
MonotonicityNot monotonic
2023-12-12T19:44:37.357474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 792
82.8%
0.0 19
 
2.0%
66.67 16
 
1.7%
75.0 13
 
1.4%
50.0 11
 
1.2%
80.0 8
 
0.8%
83.33 7
 
0.7%
87.5 6
 
0.6%
93.75 5
 
0.5%
90.0 4
 
0.4%
Other values (53) 75
 
7.8%
ValueCountFrequency (%)
0.0 19
2.0%
1.54 1
 
0.1%
5.19 1
 
0.1%
5.88 1
 
0.1%
6.25 1
 
0.1%
7.26 1
 
0.1%
8.0 1
 
0.1%
9.68 1
 
0.1%
9.8 1
 
0.1%
10.39 1
 
0.1%
ValueCountFrequency (%)
100.0 792
82.8%
97.06 1
 
0.1%
96.97 1
 
0.1%
96.55 2
 
0.2%
96.3 1
 
0.1%
96.15 1
 
0.1%
96.0 1
 
0.1%
95.83 1
 
0.1%
95.65 1
 
0.1%
95.56 1
 
0.1%

데이터생성일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.6 KiB
2022-11-17
956 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-11-17
2nd row2022-11-17
3rd row2022-11-17
4th row2022-11-17
5th row2022-11-17

Common Values

ValueCountFrequency (%)
2022-11-17 956
100.0%

Length

2023-12-12T19:44:37.506822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:44:37.616513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-11-17 956
100.0%

Interactions

2023-12-12T19:44:31.698040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:27.823627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:28.574997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:29.319701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:30.100532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:30.914615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:31.811846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:27.927591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:28.690077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:29.441589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:30.229652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:31.038345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:32.296350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:28.058075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:28.816782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:29.569396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:30.369426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:31.196071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:32.521216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:28.191699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:28.933663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:29.692296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:30.520151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:31.318533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:32.660441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:28.304436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:29.053748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:29.815109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:30.645515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:31.448441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:32.810404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:28.449397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:29.182064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:29.963181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:30.791612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:44:31.576174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:44:37.709928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번개방서비스아이디개방서비스명기준년상권코드상권명행정동코드행정동명신생업소수1년내폐업소수1년내생존률
순번1.0000.0000.0000.2940.8940.8640.8620.9060.0000.0000.050
개방서비스아이디0.0001.0001.0000.2490.0000.0000.0000.0000.0000.0000.000
개방서비스명0.0001.0001.0000.2490.0000.0000.0000.0000.0000.0000.000
기준년0.2940.2490.2491.0000.0000.0000.0000.0000.0000.0000.000
상권코드0.8940.0000.0000.0001.0001.0000.7850.8910.1300.0000.138
상권명0.8640.0000.0000.0001.0001.0000.8970.9360.0000.0000.162
행정동코드0.8620.0000.0000.0000.7850.8971.0001.0000.0000.0000.142
행정동명0.9060.0000.0000.0000.8910.9361.0001.0000.0000.0000.113
신생업소수0.0000.0000.0000.0000.1300.0000.0000.0001.0000.8870.578
1년내폐업소수0.0000.0000.0000.0000.0000.0000.0000.0000.8871.0000.757
1년내생존률0.0500.0000.0000.0000.1380.1620.1420.1130.5780.7571.000
2023-12-12T19:44:37.867118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년행정동명상권명
기준년1.0000.0000.000
행정동명0.0001.0000.707
상권명0.0000.7071.000
2023-12-12T19:44:37.981461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번상권코드행정동코드신생업소수1년내폐업소수1년내생존률기준년상권명행정동명
순번1.0000.2100.9800.0370.049-0.0430.1830.5840.684
상권코드0.2101.0000.206-0.043-0.0330.0320.0000.9980.651
행정동코드0.9800.2061.0000.0180.032-0.0250.0000.7460.997
신생업소수0.037-0.0430.0181.0000.494-0.4540.0000.0000.000
1년내폐업소수0.049-0.0330.0320.4941.000-0.9920.0000.0000.000
1년내생존률-0.0430.032-0.025-0.454-0.9921.0000.0000.0680.040
기준년0.1830.0000.0000.0000.0000.0001.0000.0000.000
상권명0.5840.9980.7460.0000.0000.0680.0001.0000.707
행정동명0.6840.6510.9970.0000.0000.0400.0000.7071.000

Missing values

2023-12-12T19:44:33.033942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:44:33.274616image/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

순번개방서비스아이디개방서비스명기준년상권코드상권명행정동코드행정동명신생업소수1년내폐업소수1년내생존률데이터생성일자
0109_30_12_P수질오염원설치시설(기타)202110도마큰시장 주변 상권3017052000도마1동40100.02022-11-17
1207_22_19_P즉석판매제조가공업202110도마큰시장 주변 상권3017052000도마1동4175.02022-11-17
2311_43_02_P담배소매업201910도마큰시장 주변 상권3017052000도마1동20100.02022-11-17
3403_13_05_P영화제작업202010도마큰시장 주변 상권3017052000도마1동10100.02022-11-17
4507_22_03_P건강기능식품일반판매업201910도마큰시장 주변 상권3017052000도마1동50100.02022-11-17
5602_03_06_P동물생산업202110도마큰시장 주변 상권3017052000도마1동20100.02022-11-17
6707_24_04_P일반음식점201910도마큰시장 주변 상권3017052000도마1동100100.02022-11-17
7808_26_04_P통신판매업202110도마큰시장 주변 상권3017052000도마1동80100.02022-11-17
8901_02_03_P의료기기판매(임대)업201910도마큰시장 주변 상권3017052000도마1동20100.02022-11-17
91007_22_19_P즉석판매제조가공업201910도마큰시장 주변 상권3017052000도마1동10100.02022-11-17
순번개방서비스아이디개방서비스명기준년상권코드상권명행정동코드행정동명신생업소수1년내폐업소수1년내생존률데이터생성일자
94694711_43_02_P담배소매업202014서구 보건소 주변 상권3017065000만년동10100.02022-11-17
94794804_16_01_P인쇄사202114서구 보건소 주변 상권3017065000만년동10100.02022-11-17
94894904_16_01_P인쇄사202014서구 보건소 주변 상권3017065000만년동10100.02022-11-17
94995007_22_10_P식품자동판매기업202114서구 보건소 주변 상권3017065000만년동20100.02022-11-17
95095107_22_19_P즉석판매제조가공업202114서구 보건소 주변 상권3017065000만년동10100.02022-11-17
95195201_02_03_P의료기기판매(임대)업201914서구 보건소 주변 상권3017065000만년동6183.332022-11-17
95295304_15_01_P옥외광고업201914서구 보건소 주변 상권3017065000만년동10100.02022-11-17
95395407_22_03_P건강기능식품일반판매업201914서구 보건소 주변 상권3017065000만년동30100.02022-11-17
95495507_22_08_P식품소분업201914서구 보건소 주변 상권3017065000만년동10100.02022-11-17
95595607_22_19_P즉석판매제조가공업201914서구 보건소 주변 상권3017065000만년동4325.02022-11-17