Overview

Dataset statistics

Number of variables4
Number of observations519
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.9 KiB
Average record size in memory33.3 B

Variable types

Numeric1
DateTime1
Text2

Dataset

Description인천광역시에 소재한 방역 소독업체 현황에 대한 데이터로 업소명, 사무실소재지 정보를 제공합니다. (2023년 1월말)
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15069255&srcSe=7661IVAWM27C61E190

Alerts

연번 has unique valuesUnique

Reproduction

Analysis started2024-01-28 06:46:31.728279
Analysis finished2024-01-28 06:46:32.275580
Duration0.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct519
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean260
Minimum1
Maximum519
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-01-28T15:46:32.335264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile26.9
Q1130.5
median260
Q3389.5
95-th percentile493.1
Maximum519
Range518
Interquartile range (IQR)259

Descriptive statistics

Standard deviation149.96666
Coefficient of variation (CV)0.57679486
Kurtosis-1.2
Mean260
Median Absolute Deviation (MAD)130
Skewness0
Sum134940
Variance22490
MonotonicityStrictly increasing
2024-01-28T15:46:32.443672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
358 1
 
0.2%
356 1
 
0.2%
355 1
 
0.2%
354 1
 
0.2%
353 1
 
0.2%
352 1
 
0.2%
351 1
 
0.2%
350 1
 
0.2%
349 1
 
0.2%
Other values (509) 509
98.1%
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 (%)
519 1
0.2%
518 1
0.2%
517 1
0.2%
516 1
0.2%
515 1
0.2%
514 1
0.2%
513 1
0.2%
512 1
0.2%
511 1
0.2%
510 1
0.2%
Distinct468
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
Minimum1984-11-26 00:00:00
Maximum2023-01-10 00:00:00
2024-01-28T15:46:32.542417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:46:32.642090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct517
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2024-01-28T15:46:32.827797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length7.583815
Min length2

Characters and Unicode

Total characters3936
Distinct characters389
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

Unique515 ?
Unique (%)99.2%

Sample

1st row마름종합관리(주)
2nd row강화방송(주)
3rd rowClean H
4th row언제나클린
5th rowOK방역
ValueCountFrequency (%)
주식회사 66
 
10.1%
사회적협동조합 6
 
0.9%
주)세스코 5
 
0.8%
방역 4
 
0.6%
clean 3
 
0.5%
협동조합 3
 
0.5%
종합관리 2
 
0.3%
2
 
0.3%
페스탑 2
 
0.3%
그린f5 2
 
0.3%
Other values (555) 558
85.5%
2024-01-28T15:46:33.133451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
247
 
6.3%
( 180
 
4.6%
) 180
 
4.6%
134
 
3.4%
125
 
3.2%
118
 
3.0%
90
 
2.3%
88
 
2.2%
82
 
2.1%
80
 
2.0%
Other values (379) 2612
66.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3313
84.2%
Open Punctuation 180
 
4.6%
Close Punctuation 180
 
4.6%
Space Separator 134
 
3.4%
Uppercase Letter 53
 
1.3%
Lowercase Letter 39
 
1.0%
Decimal Number 30
 
0.8%
Other Punctuation 4
 
0.1%
Other Symbol 2
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
247
 
7.5%
125
 
3.8%
118
 
3.6%
90
 
2.7%
88
 
2.7%
82
 
2.5%
80
 
2.4%
73
 
2.2%
72
 
2.2%
69
 
2.1%
Other values (330) 2269
68.5%
Uppercase Letter
ValueCountFrequency (%)
C 9
17.0%
S 6
11.3%
K 5
9.4%
F 4
 
7.5%
T 4
 
7.5%
A 3
 
5.7%
M 3
 
5.7%
H 3
 
5.7%
B 3
 
5.7%
E 2
 
3.8%
Other values (8) 11
20.8%
Lowercase Letter
ValueCountFrequency (%)
l 8
20.5%
a 7
17.9%
e 6
15.4%
n 4
10.3%
i 3
 
7.7%
b 2
 
5.1%
o 2
 
5.1%
w 1
 
2.6%
c 1
 
2.6%
m 1
 
2.6%
Other values (4) 4
10.3%
Decimal Number
ValueCountFrequency (%)
1 10
33.3%
5 5
16.7%
6 3
 
10.0%
4 3
 
10.0%
9 3
 
10.0%
3 2
 
6.7%
2 2
 
6.7%
0 1
 
3.3%
8 1
 
3.3%
Other Punctuation
ValueCountFrequency (%)
& 2
50.0%
. 1
25.0%
1
25.0%
Open Punctuation
ValueCountFrequency (%)
( 180
100.0%
Close Punctuation
ValueCountFrequency (%)
) 180
100.0%
Space Separator
ValueCountFrequency (%)
134
100.0%
Other Symbol
ValueCountFrequency (%)
2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3315
84.2%
Common 529
 
13.4%
Latin 92
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
247
 
7.5%
125
 
3.8%
118
 
3.6%
90
 
2.7%
88
 
2.7%
82
 
2.5%
80
 
2.4%
73
 
2.2%
72
 
2.2%
69
 
2.1%
Other values (331) 2271
68.5%
Latin
ValueCountFrequency (%)
C 9
 
9.8%
l 8
 
8.7%
a 7
 
7.6%
S 6
 
6.5%
e 6
 
6.5%
K 5
 
5.4%
n 4
 
4.3%
F 4
 
4.3%
T 4
 
4.3%
i 3
 
3.3%
Other values (22) 36
39.1%
Common
ValueCountFrequency (%)
( 180
34.0%
) 180
34.0%
134
25.3%
1 10
 
1.9%
5 5
 
0.9%
6 3
 
0.6%
4 3
 
0.6%
9 3
 
0.6%
3 2
 
0.4%
2 2
 
0.4%
Other values (6) 7
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3313
84.2%
ASCII 620
 
15.8%
None 3
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
247
 
7.5%
125
 
3.8%
118
 
3.6%
90
 
2.7%
88
 
2.7%
82
 
2.5%
80
 
2.4%
73
 
2.2%
72
 
2.2%
69
 
2.1%
Other values (330) 2269
68.5%
ASCII
ValueCountFrequency (%)
( 180
29.0%
) 180
29.0%
134
21.6%
1 10
 
1.6%
C 9
 
1.5%
l 8
 
1.3%
a 7
 
1.1%
S 6
 
1.0%
e 6
 
1.0%
K 5
 
0.8%
Other values (37) 75
12.1%
None
ValueCountFrequency (%)
2
66.7%
1
33.3%
Distinct513
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2024-01-28T15:46:33.652722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length45
Mean length33.38343
Min length16

Characters and Unicode

Total characters17326
Distinct characters354
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique507 ?
Unique (%)97.7%

Sample

1st row인천광역시 강화군 강화읍 남문로 77-1, 노루표페인트 2층
2nd row인천광역시 강화군 강화읍 남문안길 16, 1층
3rd row인천광역시 강화군 선원면 중앙로 253, 세광하이퍼마켓 2층 204,205호
4th row인천광역시 강화군 강화읍 강화대로 198
5th row인천광역시 강화군 강화읍 강화대로 429, 중앙시장 A동 2층 가 4호
ValueCountFrequency (%)
인천광역시 458
 
13.6%
남동구 87
 
2.6%
서구 85
 
2.5%
1층 76
 
2.2%
2층 71
 
2.1%
미추홀구 70
 
2.1%
부평구 60
 
1.8%
계양구 58
 
1.7%
연수구 51
 
1.5%
중구 42
 
1.2%
Other values (1123) 2322
68.7%
2024-01-28T15:46:34.104654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2925
 
16.9%
729
 
4.2%
1 647
 
3.7%
542
 
3.1%
533
 
3.1%
507
 
2.9%
, 502
 
2.9%
) 493
 
2.8%
( 493
 
2.8%
490
 
2.8%
Other values (344) 9465
54.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9776
56.4%
Decimal Number 2962
 
17.1%
Space Separator 2925
 
16.9%
Other Punctuation 504
 
2.9%
Close Punctuation 493
 
2.8%
Open Punctuation 493
 
2.8%
Dash Punctuation 117
 
0.7%
Uppercase Letter 56
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
729
 
7.5%
542
 
5.5%
533
 
5.5%
507
 
5.2%
490
 
5.0%
474
 
4.8%
470
 
4.8%
459
 
4.7%
290
 
3.0%
256
 
2.6%
Other values (313) 5026
51.4%
Uppercase Letter
ValueCountFrequency (%)
B 23
41.1%
A 7
 
12.5%
T 5
 
8.9%
C 3
 
5.4%
D 3
 
5.4%
S 3
 
5.4%
R 2
 
3.6%
P 2
 
3.6%
G 2
 
3.6%
M 1
 
1.8%
Other values (5) 5
 
8.9%
Decimal Number
ValueCountFrequency (%)
1 647
21.8%
2 478
16.1%
3 352
11.9%
0 351
11.9%
4 257
 
8.7%
5 218
 
7.4%
6 199
 
6.7%
7 182
 
6.1%
8 145
 
4.9%
9 133
 
4.5%
Other Punctuation
ValueCountFrequency (%)
, 502
99.6%
& 2
 
0.4%
Space Separator
ValueCountFrequency (%)
2925
100.0%
Close Punctuation
ValueCountFrequency (%)
) 493
100.0%
Open Punctuation
ValueCountFrequency (%)
( 493
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 117
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9776
56.4%
Common 7494
43.3%
Latin 56
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
729
 
7.5%
542
 
5.5%
533
 
5.5%
507
 
5.2%
490
 
5.0%
474
 
4.8%
470
 
4.8%
459
 
4.7%
290
 
3.0%
256
 
2.6%
Other values (313) 5026
51.4%
Common
ValueCountFrequency (%)
2925
39.0%
1 647
 
8.6%
, 502
 
6.7%
) 493
 
6.6%
( 493
 
6.6%
2 478
 
6.4%
3 352
 
4.7%
0 351
 
4.7%
4 257
 
3.4%
5 218
 
2.9%
Other values (6) 778
 
10.4%
Latin
ValueCountFrequency (%)
B 23
41.1%
A 7
 
12.5%
T 5
 
8.9%
C 3
 
5.4%
D 3
 
5.4%
S 3
 
5.4%
R 2
 
3.6%
P 2
 
3.6%
G 2
 
3.6%
M 1
 
1.8%
Other values (5) 5
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9776
56.4%
ASCII 7550
43.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2925
38.7%
1 647
 
8.6%
, 502
 
6.6%
) 493
 
6.5%
( 493
 
6.5%
2 478
 
6.3%
3 352
 
4.7%
0 351
 
4.6%
4 257
 
3.4%
5 218
 
2.9%
Other values (21) 834
 
11.0%
Hangul
ValueCountFrequency (%)
729
 
7.5%
542
 
5.5%
533
 
5.5%
507
 
5.2%
490
 
5.0%
474
 
4.8%
470
 
4.8%
459
 
4.7%
290
 
3.0%
256
 
2.6%
Other values (313) 5026
51.4%

Interactions

2024-01-28T15:46:32.093403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2024-01-28T15:46:32.176456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T15:46:32.239305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/