Overview

Dataset statistics

Number of variables5
Number of observations463
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.7 KiB
Average record size in memory41.3 B

Variable types

Numeric1
Categorical1
Text2
DateTime1

Dataset

Description인천광역시 중구 음식물 다량배출사업장 상호, 주소, 구분 현황에 대한 파일입니다.파일명 인천광역시_중구_음식물류 다량배출사업장 현황
Author인천광역시 중구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15034356&srcSe=7661IVAWM27C61E190

Alerts

데이터기준일자 has constant value ""Constant
순번 is highly overall correlated with 업종High correlation
업종 is highly overall correlated with 순번High correlation
순번 has unique valuesUnique
다량배출사업장명 has unique valuesUnique

Reproduction

Analysis started2024-01-28 07:45:36.563507
Analysis finished2024-01-28 07:45:37.164998
Duration0.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct463
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean232
Minimum1
Maximum463
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2024-01-28T16:45:37.225531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile24.1
Q1116.5
median232
Q3347.5
95-th percentile439.9
Maximum463
Range462
Interquartile range (IQR)231

Descriptive statistics

Standard deviation133.80085
Coefficient of variation (CV)0.57672779
Kurtosis-1.2
Mean232
Median Absolute Deviation (MAD)116
Skewness0
Sum107416
Variance17902.667
MonotonicityStrictly increasing
2024-01-28T16:45:37.346601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
306 1
 
0.2%
318 1
 
0.2%
317 1
 
0.2%
316 1
 
0.2%
315 1
 
0.2%
314 1
 
0.2%
313 1
 
0.2%
312 1
 
0.2%
311 1
 
0.2%
Other values (453) 453
97.8%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
463 1
0.2%
462 1
0.2%
461 1
0.2%
460 1
0.2%
459 1
0.2%
458 1
0.2%
457 1
0.2%
456 1
0.2%
455 1
0.2%
454 1
0.2%

업종
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
일반음식점
248 
집단급식소
145 
관광숙박시설
64 
휴게음식점
 
4
농수산물시장
 
1

Length

Max length6
Median length5
Mean length5.1403888
Min length5

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
일반음식점 248
53.6%
집단급식소 145
31.3%
관광숙박시설 64
 
13.8%
휴게음식점 4
 
0.9%
농수산물시장 1
 
0.2%
대규모점포 1
 
0.2%

Length

2024-01-28T16:45:37.461504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T16:45:37.553928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반음식점 248
53.6%
집단급식소 145
31.3%
관광숙박시설 64
 
13.8%
휴게음식점 4
 
0.9%
농수산물시장 1
 
0.2%
대규모점포 1
 
0.2%

다량배출사업장명
Text

UNIQUE 

Distinct463
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2024-01-28T16:45:37.772617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length24
Mean length10.274298
Min length2

Characters and Unicode

Total characters4757
Distinct characters463
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique463 ?
Unique (%)100.0%

Sample

1st row(주)호텔쉘브르
2nd row세비아관광호텔
3rd row씨월드관광호텔
4th row을왕관광호텔
5th row주식회사 에코리아
ValueCountFrequency (%)
주)아워홈 17
 
2.5%
주)동원홈푸드 13
 
1.9%
인천공항 11
 
1.6%
주)신세계푸드 7
 
1.0%
인천공항점 7
 
1.0%
주식회사 6
 
0.9%
주)풀무원푸드앤컬쳐 5
 
0.7%
씨제이프레시웨이(주 4
 
0.6%
주)풀무원푸드앤컬처 4
 
0.6%
영종점 4
 
0.6%
Other values (557) 591
88.3%
2024-01-28T16:45:38.122629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
206
 
4.3%
( 177
 
3.7%
) 177
 
3.7%
주 160
 
3.4%
인 128
 
2.7%
이 115
 
2.4%
천 108
 
2.3%
스 104
 
2.2%
점 95
 
2.0%
드 78
 
1.6%
Other values (453) 3409
71.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4010
84.3%
Space Separator 206
 
4.3%
Open Punctuation 191
 
4.0%
Close Punctuation 191
 
4.0%
Uppercase Letter 71
 
1.5%
Decimal Number 49
 
1.0%
Dash Punctuation 15
 
0.3%
Lowercase Letter 9
 
0.2%
Other Symbol 7
 
0.1%
Other Punctuation 7
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 160
 
4.0%
인 128
 
3.2%
이 115
 
2.9%
천 108
 
2.7%
스 104
 
2.6%
점 95
 
2.4%
드 78
 
1.9%
공 72
 
1.8%
항 67
 
1.7%
푸 64
 
1.6%
Other values (405) 3019
75.3%
Uppercase Letter
ValueCountFrequency (%)
S 14
19.7%
C 10
14.1%
A 8
11.3%
T 7
9.9%
K 7
9.9%
G 5
 
7.0%
J 3
 
4.2%
B 3
 
4.2%
I 2
 
2.8%
D 2
 
2.8%
Other values (9) 10
14.1%
Decimal Number
ValueCountFrequency (%)
2 20
40.8%
1 11
22.4%
7 5
 
10.2%
3 3
 
6.1%
9 2
 
4.1%
8 2
 
4.1%
5 2
 
4.1%
4 2
 
4.1%
6 1
 
2.0%
0 1
 
2.0%
Lowercase Letter
ValueCountFrequency (%)
a 3
33.3%
e 2
22.2%
n 1
 
11.1%
u 1
 
11.1%
y 1
 
11.1%
f 1
 
11.1%
Open Punctuation
ValueCountFrequency (%)
( 177
92.7%
[ 12
 
6.3%
［ 2
 
1.0%
Close Punctuation
ValueCountFrequency (%)
) 177
92.7%
] 12
 
6.3%
］ 2
 
1.0%
Other Punctuation
ValueCountFrequency (%)
& 3
42.9%
/ 3
42.9%
. 1
 
14.3%
Space Separator
ValueCountFrequency (%)
206
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 7
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4017
84.4%
Common 660
 
13.9%
Latin 80
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 160
 
4.0%
인 128
 
3.2%
이 115
 
2.9%
천 108
 
2.7%
스 104
 
2.6%
점 95
 
2.4%
드 78
 
1.9%
공 72
 
1.8%
항 67
 
1.7%
푸 64
 
1.6%
Other values (406) 3026
75.3%
Latin
ValueCountFrequency (%)
S 14
17.5%
C 10
12.5%
A 8
10.0%
T 7
 
8.8%
K 7
 
8.8%
G 5
 
6.2%
J 3
 
3.8%
a 3
 
3.8%
B 3
 
3.8%
I 2
 
2.5%
Other values (15) 18
22.5%
Common
ValueCountFrequency (%)
206
31.2%
( 177
26.8%
) 177
26.8%
2 20
 
3.0%
- 15
 
2.3%
] 12
 
1.8%
[ 12
 
1.8%
1 11
 
1.7%
7 5
 
0.8%
& 3
 
0.5%
Other values (12) 22
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4010
84.3%
ASCII 736
 
15.5%
None 11
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
206
28.0%
( 177
24.0%
) 177
24.0%
2 20
 
2.7%
- 15
 
2.0%
S 14
 
1.9%
] 12
 
1.6%
[ 12
 
1.6%
1 11
 
1.5%
C 10
 
1.4%
Other values (35) 82
 
11.1%
Hangul
ValueCountFrequency (%)
주 160
 
4.0%
인 128
 
3.2%
이 115
 
2.9%
천 108
 
2.7%
스 104
 
2.6%
점 95
 
2.4%
드 78
 
1.9%
공 72
 
1.8%
항 67
 
1.7%
푸 64
 
1.6%
Other values (405) 3019
75.3%
None
ValueCountFrequency (%)
㈜ 7
63.6%
［ 2
 
18.2%
］ 2
 
18.2%

소재지
Text

Distinct434
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2024-01-28T16:45:38.353801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length61
Median length49
Mean length31.265659
Min length18

Characters and Unicode

Total characters14476
Distinct characters299
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique414 ?
Unique (%)89.4%

Sample

1st row인천광역시 중구 용유서로 283 (을왕동)
2nd row인천광역시 중구 신도시남로150번길 8 (운서동)
3rd row인천광역시 중구 용유서로479번길 50-7 (을왕동)
4th row인천광역시 중구 용유서로304번길 12 (을왕동)
5th row인천광역시 중구 월미로218번길 6_ 풀문호텔 (북성동1가)
ValueCountFrequency (%)
인천광역시 462
 
16.8%
중구 462
 
16.8%
운서동 143
 
5.2%
을왕동 65
 
2.4%
공항로 50
 
1.8%
중산동 50
 
1.8%
272 47
 
1.7%
북성동1가 32
 
1.2%
항동7가 30
 
1.1%
1층 29
 
1.1%
Other values (644) 1379
50.2%
2024-01-28T16:45:38.739029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2294
 
15.8%
인 543
 
3.8%
동 537
 
3.7%
중 535
 
3.7%
천 526
 
3.6%
시 496
 
3.4%
광 474
 
3.3%
역 472
 
3.3%
) 469
 
3.2%
( 468
 
3.2%
Other values (289) 7662
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8573
59.2%
Space Separator 2294
 
15.8%
Decimal Number 2252
 
15.6%
Close Punctuation 469
 
3.2%
Open Punctuation 468
 
3.2%
Connector Punctuation 276
 
1.9%
Dash Punctuation 75
 
0.5%
Uppercase Letter 50
 
0.3%
Math Symbol 15
 
0.1%
Other Punctuation 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 543
 
6.3%
동 537
 
6.3%
중 535
 
6.2%
천 526
 
6.1%
시 496
 
5.8%
광 474
 
5.5%
역 472
 
5.5%
구 464
 
5.4%
로 458
 
5.3%
서 226
 
2.6%
Other values (252) 3842
44.8%
Uppercase Letter
ValueCountFrequency (%)
B 11
22.0%
A 7
14.0%
F 5
10.0%
M 4
 
8.0%
P 4
 
8.0%
N 3
 
6.0%
C 3
 
6.0%
I 2
 
4.0%
W 2
 
4.0%
X 1
 
2.0%
Other values (8) 8
16.0%
Decimal Number
ValueCountFrequency (%)
2 458
20.3%
1 433
19.2%
4 248
11.0%
3 243
10.8%
7 197
8.7%
0 197
8.7%
5 138
 
6.1%
9 120
 
5.3%
6 120
 
5.3%
8 98
 
4.4%
Other Punctuation
ValueCountFrequency (%)
· 2
50.0%
/ 1
25.0%
& 1
25.0%
Space Separator
ValueCountFrequency (%)
2294
100.0%
Close Punctuation
ValueCountFrequency (%)
) 469
100.0%
Open Punctuation
ValueCountFrequency (%)
( 468
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 276
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 75
100.0%
Math Symbol
ValueCountFrequency (%)
~ 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8573
59.2%
Common 5853
40.4%
Latin 50
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 543
 
6.3%
동 537
 
6.3%
중 535
 
6.2%
천 526
 
6.1%
시 496
 
5.8%
광 474
 
5.5%
역 472
 
5.5%
구 464
 
5.4%
로 458
 
5.3%
서 226
 
2.6%
Other values (252) 3842
44.8%
Common
ValueCountFrequency (%)
2294
39.2%
) 469
 
8.0%
( 468
 
8.0%
2 458
 
7.8%
1 433
 
7.4%
_ 276
 
4.7%
4 248
 
4.2%
3 243
 
4.2%
7 197
 
3.4%
0 197
 
3.4%
Other values (9) 570
 
9.7%
Latin
ValueCountFrequency (%)
B 11
22.0%
A 7
14.0%
F 5
10.0%
M 4
 
8.0%
P 4
 
8.0%
N 3
 
6.0%
C 3
 
6.0%
I 2
 
4.0%
W 2
 
4.0%
X 1
 
2.0%
Other values (8) 8
16.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8573
59.2%
ASCII 5901
40.8%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2294
38.9%
) 469
 
7.9%
( 468
 
7.9%
2 458
 
7.8%
1 433
 
7.3%
_ 276
 
4.7%
4 248
 
4.2%
3 243
 
4.1%
7 197
 
3.3%
0 197
 
3.3%
Other values (26) 618
 
10.5%
Hangul
ValueCountFrequency (%)
인 543
 
6.3%
동 537
 
6.3%
중 535
 
6.2%
천 526
 
6.1%
시 496
 
5.8%
광 474
 
5.5%
역 472
 
5.5%
구 464
 
5.4%
로 458
 
5.3%
서 226
 
2.6%
Other values (252) 3842
44.8%
None
ValueCountFrequency (%)
· 2
100.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
Minimum2023-08-21 00:00:00
Maximum2023-08-21 00:00:00
2024-01-28T16:45:38.851554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:45:38.943117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-28T16:45:36.939342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T16:45:39.012708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번업종
순번1.0000.802
업종0.8021.000
2024-01-28T16:45:39.087534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번업종
순번1.0000.588
업종0.5881.000

Missing values

2024-01-28T16:45:37.043732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T16:45:37.130765image/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

순번업종다량배출사업장명소재지데이터기준일자
01관광숙박시설(주)호텔쉘브르인천광역시 중구 용유서로 283 (을왕동)2023-08-21
12관광숙박시설세비아관광호텔인천광역시 중구 신도시남로150번길 8 (운서동)2023-08-21
23관광숙박시설씨월드관광호텔인천광역시 중구 용유서로479번길 50-7 (을왕동)2023-08-21
34관광숙박시설을왕관광호텔인천광역시 중구 용유서로304번길 12 (을왕동)2023-08-21
45관광숙박시설주식회사 에코리아인천광역시 중구 월미로218번길 6_ 풀문호텔 (북성동1가)2023-08-21
56관광숙박시설하나호스텔인천광역시 중구 용유서로304번길 6 (을왕동)2023-08-21
67관광숙박시설태종대펜션인천광역시 중구 용유서로302번길 21-15 (을왕동)2023-08-21
78관광숙박시설노을바다펜션인천광역시 중구 용유서로302번길 18 (을왕동)2023-08-21
89관광숙박시설엘림비치존인천광역시 중구 용유서로423번길 31-1_ 엘림비치존2 (을왕동)2023-08-21
910관광숙박시설바이드302펜션인천광역시 중구 용유서로302번길 11 (을왕동)2023-08-21
순번업종다량배출사업장명소재지데이터기준일자
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