Overview

Dataset statistics

Number of variables9
Number of observations2547
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory189.2 KiB
Average record size in memory76.1 B

Variable types

Numeric3
Text2
Categorical3
DateTime1

Dataset

Description대전광역시 서구 행정동별 업종별 인허가업소 현황(개방서비스 ID, 개방서비스명, 행정동코드, 행정동명, 영업상태코드, 영업상태명, 인허가업소 수등) 데이터를 제공합니다.
Author대전광역시 서구
URLhttps://www.data.go.kr/data/15109040/fileData.do

Alerts

데이터생성일자 has constant value ""Constant
영업상태코드 is highly overall correlated with 영업상태명High correlation
영업상태명 is highly overall correlated with 영업상태코드High correlation
행정동코드 is highly overall correlated with 행정동명High correlation
행정동명 is highly overall correlated with 행정동코드High correlation
순번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 12:10:52.998473
Analysis finished2023-12-12 12:10:55.379359
Duration2.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct2547
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1274
Minimum1
Maximum2547
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-12-12T21:10:55.480908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile128.3
Q1637.5
median1274
Q31910.5
95-th percentile2419.7
Maximum2547
Range2546
Interquartile range (IQR)1273

Descriptive statistics

Standard deviation735.39989
Coefficient of variation (CV)0.57723696
Kurtosis-1.2
Mean1274
Median Absolute Deviation (MAD)637
Skewness0
Sum3244878
Variance540813
MonotonicityNot monotonic
2023-12-12T21:10:55.666083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
1703 1
 
< 0.1%
1696 1
 
< 0.1%
1697 1
 
< 0.1%
1698 1
 
< 0.1%
1699 1
 
< 0.1%
1700 1
 
< 0.1%
1701 1
 
< 0.1%
1702 1
 
< 0.1%
1704 1
 
< 0.1%
Other values (2537) 2537
99.6%
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 (%)
2547 1
< 0.1%
2546 1
< 0.1%
2545 1
< 0.1%
2544 1
< 0.1%
2543 1
< 0.1%
2542 1
< 0.1%
2541 1
< 0.1%
2540 1
< 0.1%
2539 1
< 0.1%
2538 1
< 0.1%

개방서비스아이디
Text

Distinct133
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size20.0 KiB
2023-12-12T21:10:56.032227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters25470
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

Unique15 ?
Unique (%)0.6%

Sample

1st row03_10_02_P
2nd row11_46_02_P
3rd row03_12_01_P
4th row02_03_03_P
5th row03_11_04_P
ValueCountFrequency (%)
08_26_04_p 63
 
2.5%
11_43_02_p 56
 
2.2%
08_26_02_p 50
 
2.0%
07_22_04_p 48
 
1.9%
09_30_12_p 48
 
1.9%
03_05_05_p 45
 
1.8%
08_26_03_p 45
 
1.8%
01_02_03_p 42
 
1.6%
09_30_11_p 40
 
1.6%
09_28_08_p 39
 
1.5%
Other values (123) 2071
81.3%
2023-12-12T21:10:56.585563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 7641
30.0%
0 5623
22.1%
1 2717
 
10.7%
P 2547
 
10.0%
2 2265
 
8.9%
3 1307
 
5.1%
7 748
 
2.9%
4 736
 
2.9%
5 571
 
2.2%
8 458
 
1.8%
Other values (2) 857
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15282
60.0%
Connector Punctuation 7641
30.0%
Uppercase Letter 2547
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5623
36.8%
1 2717
17.8%
2 2265
14.8%
3 1307
 
8.6%
7 748
 
4.9%
4 736
 
4.8%
5 571
 
3.7%
8 458
 
3.0%
9 451
 
3.0%
6 406
 
2.7%
Connector Punctuation
ValueCountFrequency (%)
_ 7641
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 2547
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22923
90.0%
Latin 2547
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 7641
33.3%
0 5623
24.5%
1 2717
 
11.9%
2 2265
 
9.9%
3 1307
 
5.7%
7 748
 
3.3%
4 736
 
3.2%
5 571
 
2.5%
8 458
 
2.0%
9 451
 
2.0%
Latin
ValueCountFrequency (%)
P 2547
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 7641
30.0%
0 5623
22.1%
1 2717
 
10.7%
P 2547
 
10.0%
2 2265
 
8.9%
3 1307
 
5.1%
7 748
 
2.9%
4 736
 
2.9%
5 571
 
2.2%
8 458
 
1.8%
Other values (2) 857
 
3.4%

개방서비스명
Text

Distinct133
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size20.0 KiB
2023-12-12T21:10:56.852189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length6.3227326
Min length2

Characters and Unicode

Total characters16104
Distinct characters178
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

Unique15 ?
Unique (%)0.6%

Sample

1st row비디오물배급업
2nd row민방위대피시설
3rd row국내여행업
4th row동물용의료용구판매업
5th row외국인관광도시민박업
ValueCountFrequency (%)
통신판매업 63
 
2.4%
담배소매업 56
 
2.2%
방문판매업 50
 
1.9%
축산판매업 48
 
1.9%
수질오염원설치시설(기타 48
 
1.9%
인터넷컴퓨터게임시설제공업 45
 
1.7%
전화권유판매업 45
 
1.7%
의료기기판매(임대)업 42
 
1.6%
소독업 40
 
1.6%
석유판매업 39
 
1.5%
Other values (124) 2103
81.5%
2023-12-12T21:10:57.299598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
업 2059
 
12.8%
매 642
 
4.0%
판 598
 
3.7%
식 422
 
2.6%
기 416
 
2.6%
소 399
 
2.5%
물 388
 
2.4%
제 330
 
2.0%
설 318
 
2.0%
장 292
 
1.8%
Other values (168) 10240
63.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15766
97.9%
Close Punctuation 144
 
0.9%
Open Punctuation 144
 
0.9%
Space Separator 32
 
0.2%
Other Punctuation 18
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 2059
 
13.1%
매 642
 
4.1%
판 598
 
3.8%
식 422
 
2.7%
기 416
 
2.6%
소 399
 
2.5%
물 388
 
2.5%
제 330
 
2.1%
설 318
 
2.0%
장 292
 
1.9%
Other values (162) 9902
62.8%
Other Punctuation
ValueCountFrequency (%)
. 14
77.8%
· 2
 
11.1%
/ 2
 
11.1%
Close Punctuation
ValueCountFrequency (%)
) 144
100.0%
Open Punctuation
ValueCountFrequency (%)
( 144
100.0%
Space Separator
ValueCountFrequency (%)
32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15766
97.9%
Common 338
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 2059
 
13.1%
매 642
 
4.1%
판 598
 
3.8%
식 422
 
2.7%
기 416
 
2.6%
소 399
 
2.5%
물 388
 
2.5%
제 330
 
2.1%
설 318
 
2.0%
장 292
 
1.9%
Other values (162) 9902
62.8%
Common
ValueCountFrequency (%)
) 144
42.6%
( 144
42.6%
32
 
9.5%
. 14
 
4.1%
· 2
 
0.6%
/ 2
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15766
97.9%
ASCII 336
 
2.1%
None 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
업 2059
 
13.1%
매 642
 
4.1%
판 598
 
3.8%
식 422
 
2.7%
기 416
 
2.6%
소 399
 
2.5%
물 388
 
2.5%
제 330
 
2.1%
설 318
 
2.0%
장 292
 
1.9%
Other values (162) 9902
62.8%
ASCII
ValueCountFrequency (%)
) 144
42.9%
( 144
42.9%
32
 
9.5%
. 14
 
4.2%
/ 2
 
0.6%
None
ValueCountFrequency (%)
· 2
100.0%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0170574 × 109
Minimum3.017051 × 109
Maximum3.017066 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-12-12T21:10:57.455834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.017051 × 109
5-th percentile3.017051 × 109
Q13.017055 × 109
median3.0170575 × 109
Q33.0170596 × 109
95-th percentile3.017065 × 109
Maximum3.017066 × 109
Range15000
Interquartile range (IQR)4600

Descriptive statistics

Standard deviation3695.9205
Coefficient of variation (CV)1.2250084 × 10-6
Kurtosis-0.41641584
Mean3.0170574 × 109
Median Absolute Deviation (MAD)2100
Skewness0.31896096
Sum7.6844452 × 1012
Variance13659828
MonotonicityNot monotonic
2023-12-12T21:10:57.586701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
3017063000 218
 
8.6%
3017058600 202
 
7.9%
3017056000 189
 
7.4%
3017055500 187
 
7.3%
3017058100 178
 
7.0%
3017052000 173
 
6.8%
3017059000 168
 
6.6%
3017059600 156
 
6.1%
3017055000 149
 
5.9%
3017065000 148
 
5.8%
Other values (13) 779
30.6%
ValueCountFrequency (%)
3017051000 130
5.1%
3017052000 173
6.8%
3017053000 1
 
< 0.1%
3017053500 131
5.1%
3017054000 142
5.6%
3017055000 149
5.9%
3017055500 187
7.3%
3017056000 189
7.4%
3017057000 125
4.9%
3017057500 129
5.1%
ValueCountFrequency (%)
3017066000 13
 
0.5%
3017065000 148
5.8%
3017064000 6
 
0.2%
3017063000 218
8.6%
3017060000 96
3.8%
3017059700 1
 
< 0.1%
3017059600 156
6.1%
3017059000 168
6.6%
3017058800 1
 
< 0.1%
3017058700 1
 
< 0.1%

행정동명
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size20.0 KiB
둔산1동
218 
월평1동
202 
괴정동
189 
탄방동
187 
갈마1동
178 
Other values (18)
1573 

Length

Max length4
Median length3
Mean length3.333726
Min length2

Unique

Unique4 ?
Unique (%)0.2%

Sample

1st row괴정동
2nd row용문동
3rd row월평1동
4th row관저1동
5th row월평1동

Common Values

ValueCountFrequency (%)
둔산1동 218
 
8.6%
월평1동 202
 
7.9%
괴정동 189
 
7.4%
탄방동 187
 
7.3%
갈마1동 178
 
7.0%
도마1동 173
 
6.8%
가수원동 168
 
6.6%
관저1동 156
 
6.1%
용문동 149
 
5.9%
만년동 148
 
5.8%
Other values (13) 779
30.6%

Length

2023-12-12T21:10:57.766643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
둔산1동 218
 
8.6%
월평1동 202
 
7.9%
괴정동 189
 
7.4%
탄방동 187
 
7.3%
갈마1동 178
 
7.0%
도마1동 173
 
6.8%
가수원동 168
 
6.6%
관저1동 156
 
6.1%
용문동 149
 
5.9%
만년동 148
 
5.8%
Other values (13) 779
30.6%

영업상태코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size20.0 KiB
1
1175 
3
1051 
4
268 
2
 
53

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row4
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1175
46.1%
3 1051
41.3%
4 268
 
10.5%
2 53
 
2.1%

Length

2023-12-12T21:10:57.950698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:10:58.072659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1175
46.1%
3 1051
41.3%
4 268
 
10.5%
2 53
 
2.1%

영업상태명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size20.0 KiB
영업/정상
1175 
폐업
1051 
취소/말소/만료/정지/중지
268 
휴업
 
53

Length

Max length14
Median length5
Mean length4.6466431
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row폐업
2nd row취소/말소/만료/정지/중지
3rd row취소/말소/만료/정지/중지
4th row영업/정상
5th row영업/정상

Common Values

ValueCountFrequency (%)
영업/정상 1175
46.1%
폐업 1051
41.3%
취소/말소/만료/정지/중지 268
 
10.5%
휴업 53
 
2.1%

Length

2023-12-12T21:10:58.210676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:10:58.344900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업/정상 1175
46.1%
폐업 1051
41.3%
취소/말소/만료/정지/중지 268
 
10.5%
휴업 53
 
2.1%

인허가업소수
Real number (ℝ)

Distinct225
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.969376
Minimum1
Maximum2891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2023-12-12T21:10:58.480151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q316
95-th percentile129
Maximum2891
Range2890
Interquartile range (IQR)14

Descriptive statistics

Standard deviation132.31355
Coefficient of variation (CV)4.0132259
Kurtosis157.56707
Mean32.969376
Median Absolute Deviation (MAD)4
Skewness10.727041
Sum83973
Variance17506.877
MonotonicityNot monotonic
2023-12-12T21:10:58.642562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 606
23.8%
2 277
 
10.9%
3 184
 
7.2%
4 161
 
6.3%
5 113
 
4.4%
7 97
 
3.8%
6 95
 
3.7%
8 66
 
2.6%
9 54
 
2.1%
11 51
 
2.0%
Other values (215) 843
33.1%
ValueCountFrequency (%)
1 606
23.8%
2 277
10.9%
3 184
 
7.2%
4 161
 
6.3%
5 113
 
4.4%
6 95
 
3.7%
7 97
 
3.8%
8 66
 
2.6%
9 54
 
2.1%
10 50
 
2.0%
ValueCountFrequency (%)
2891 1
< 0.1%
2142 1
< 0.1%
1931 1
< 0.1%
1452 1
< 0.1%
1420 1
< 0.1%
1317 1
< 0.1%
1315 1
< 0.1%
1295 1
< 0.1%
1129 1
< 0.1%
1090 1
< 0.1%

데이터생성일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.0 KiB
Minimum2022-11-15 00:00:00
Maximum2022-11-15 00:00:00
2023-12-12T21:10:58.763267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:10:58.853342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T21:10:54.729503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:10:53.481926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:10:54.094712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:10:54.855669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:10:53.599320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:10:54.364838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:10:54.980613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:10:53.815969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:10:54.582516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:10:58.929570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번행정동코드행정동명영업상태코드영업상태명인허가업소수
순번1.0000.0320.0000.0000.0000.028
행정동코드0.0321.0001.0000.0000.0000.038
행정동명0.0001.0001.0000.0000.0000.000
영업상태코드0.0000.0000.0001.0001.0000.000
영업상태명0.0000.0000.0001.0001.0000.000
인허가업소수0.0280.0380.0000.0000.0001.000
2023-12-12T21:10:59.057666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업상태코드영업상태명행정동명
영업상태코드1.0001.0000.000
영업상태명1.0001.0000.000
행정동명0.0000.0001.000
2023-12-12T21:10:59.190964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번행정동코드인허가업소수행정동명영업상태코드영업상태명
순번1.000-0.0320.0160.0000.0000.000
행정동코드-0.0321.0000.0140.9970.0000.000
인허가업소수0.0160.0141.0000.0000.0000.000
행정동명0.0000.9970.0001.0000.0000.000
영업상태코드0.0000.0000.0000.0001.0001.000
영업상태명0.0000.0000.0000.0001.0001.000

Missing values

2023-12-12T21:10:55.120433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:10:55.306532image/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

순번개방서비스아이디개방서비스명행정동코드행정동명영업상태코드영업상태명인허가업소수데이터생성일자
0103_10_02_P비디오물배급업3017056000괴정동3폐업12022-11-15
1211_46_02_P민방위대피시설3017055000용문동4취소/말소/만료/정지/중지232022-11-15
2303_12_01_P국내여행업3017058600월평1동4취소/말소/만료/정지/중지12022-11-15
3402_03_03_P동물용의료용구판매업3017059600관저1동1영업/정상32022-11-15
4503_11_04_P외국인관광도시민박업3017058600월평1동1영업/정상12022-11-15
5607_22_08_P식품소분업3017060000기성동3폐업32022-11-15
6707_24_05_P휴게음식점3017055500탄방동3폐업2722022-11-15
7802_03_10_P동물위탁관리업3017058100갈마1동1영업/정상62022-11-15
8908_25_01_P대규모점포3017052000도마1동1영업/정상22022-11-15
91009_30_11_P소독업3017057500내동1영업/정상52022-11-15
순번개방서비스아이디개방서비스명행정동코드행정동명영업상태코드영업상태명인허가업소수데이터생성일자
2537253809_30_11_P소독업3017065000만년동3폐업32022-11-15
2538253901_02_01_P안경업3017058600월평1동1영업/정상102022-11-15
2539254004_17_01_P출판사3017058600월평1동3폐업182022-11-15
2540254103_12_01_P국내여행업3017055500탄방동3폐업262022-11-15
2541254211_46_02_P민방위대피시설3017059000가수원동1영업/정상92022-11-15
2542254303_13_05_P영화제작업3017056000괴정동1영업/정상22022-11-15
2543254408_26_03_P전화권유판매업3017059000가수원동4취소/말소/만료/정지/중지32022-11-15
2544254502_03_12_P동물운송업3017054000변동1영업/정상22022-11-15
2545254610_42_01_P체력단련장업3017052000도마1동3폐업42022-11-15
2546254703_11_03_P숙박업3017057000가장동1영업/정상12022-11-15