Overview

Dataset statistics

Number of variables9
Number of observations202
Missing cells168
Missing cells (%)9.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.7 KiB
Average record size in memory74.6 B

Variable types

Numeric2
Categorical3
Text4

Dataset

Description인천광역시 부평, 계양 고시원 현황 데이터입니다.목록으로는 연번, 관할서, 상호, 지번주소, 도로명주소, 영업장 면적, 업종, 증명발급일, 지하/지상 등이 있습니다.고시원 현황 자료는 각 지역 소방서에서 자료를 받아 데이터 업로드 하였습니다.
Author인천광역시
URLhttps://www.data.go.kr/data/15126429/fileData.do

Alerts

업종 has constant value ""Constant
연번 is highly overall correlated with 관할서High correlation
영업장면적(제곱미터) is highly overall correlated with 관할서High correlation
관할서 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
영업장면적(제곱미터) has 84 (41.6%) missing valuesMissing
증명발급일 has 84 (41.6%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-14 14:26:13.045214
Analysis finished2024-03-14 14:26:15.887393
Duration2.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct202
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.5
Minimum1
Maximum202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-03-14T23:26:16.117421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.05
Q151.25
median101.5
Q3151.75
95-th percentile191.95
Maximum202
Range201
Interquartile range (IQR)100.5

Descriptive statistics

Standard deviation58.456537
Coefficient of variation (CV)0.57592647
Kurtosis-1.2
Mean101.5
Median Absolute Deviation (MAD)50.5
Skewness0
Sum20503
Variance3417.1667
MonotonicityStrictly increasing
2024-03-14T23:26:16.527767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
140 1
 
0.5%
130 1
 
0.5%
131 1
 
0.5%
132 1
 
0.5%
133 1
 
0.5%
134 1
 
0.5%
135 1
 
0.5%
136 1
 
0.5%
137 1
 
0.5%
Other values (192) 192
95.0%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
202 1
0.5%
201 1
0.5%
200 1
0.5%
199 1
0.5%
198 1
0.5%
197 1
0.5%
196 1
0.5%
195 1
0.5%
194 1
0.5%
193 1
0.5%

관할서
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
부평소방서
118 
계양소방서
84 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부평소방서 118
58.4%
계양소방서 84
41.6%

Length

2024-03-14T23:26:16.765138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:26:17.212152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부평소방서 118
58.4%
계양소방서 84
41.6%

상호
Text

Distinct196
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-03-14T23:26:18.326880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length29
Mean length7.0594059
Min length1

Characters and Unicode

Total characters1426
Distinct characters245
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique190 ?
Unique (%)94.1%

Sample

1st row청솔홈타운
2nd row푸른솔고시텔(서철모)
3rd row창빌딩고시텔
4th row비존고시텔
5th row일현
ValueCountFrequency (%)
고시텔 3
 
1.2%
오픈하우스 3
 
1.2%
고시원 3
 
1.2%
본가원룸텔 2
 
0.8%
계양점 2
 
0.8%
스테이벅스 2
 
0.8%
원룸텔(구 2
 
0.8%
헤르만하우스 2
 
0.8%
윤스테이 2
 
0.8%
망고스테이 2
 
0.8%
Other values (226) 231
90.9%
2024-03-14T23:26:19.655093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 88
 
6.2%
시 87
 
6.1%
고 84
 
5.9%
원 69
 
4.8%
텔 66
 
4.6%
54
 
3.8%
하 49
 
3.4%
우 49
 
3.4%
) 33
 
2.3%
이 33
 
2.3%
Other values (235) 814
57.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1232
86.4%
Space Separator 54
 
3.8%
Close Punctuation 36
 
2.5%
Open Punctuation 32
 
2.2%
Uppercase Letter 29
 
2.0%
Decimal Number 24
 
1.7%
Other Punctuation 15
 
1.1%
Dash Punctuation 3
 
0.2%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 88
 
7.1%
시 87
 
7.1%
고 84
 
6.8%
원 69
 
5.6%
텔 66
 
5.4%
하 49
 
4.0%
우 49
 
4.0%
이 33
 
2.7%
리 29
 
2.4%
룸 19
 
1.5%
Other values (207) 659
53.5%
Uppercase Letter
ValueCountFrequency (%)
B 5
17.2%
A 4
13.8%
L 3
10.3%
J 3
10.3%
K 3
10.3%
S 3
10.3%
C 2
 
6.9%
O 2
 
6.9%
M 2
 
6.9%
T 1
 
3.4%
Decimal Number
ValueCountFrequency (%)
0 6
25.0%
4 5
20.8%
2 3
12.5%
3 3
12.5%
5 3
12.5%
7 2
 
8.3%
1 2
 
8.3%
Other Punctuation
ValueCountFrequency (%)
. 11
73.3%
, 2
 
13.3%
* 2
 
13.3%
Close Punctuation
ValueCountFrequency (%)
) 33
91.7%
] 3
 
8.3%
Open Punctuation
ValueCountFrequency (%)
( 29
90.6%
[ 3
 
9.4%
Space Separator
ValueCountFrequency (%)
54
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1232
86.4%
Common 164
 
11.5%
Latin 30
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 88
 
7.1%
시 87
 
7.1%
고 84
 
6.8%
원 69
 
5.6%
텔 66
 
5.4%
하 49
 
4.0%
우 49
 
4.0%
이 33
 
2.7%
리 29
 
2.4%
룸 19
 
1.5%
Other values (207) 659
53.5%
Common
ValueCountFrequency (%)
54
32.9%
) 33
20.1%
( 29
17.7%
. 11
 
6.7%
0 6
 
3.7%
4 5
 
3.0%
2 3
 
1.8%
[ 3
 
1.8%
- 3
 
1.8%
3 3
 
1.8%
Other values (6) 14
 
8.5%
Latin
ValueCountFrequency (%)
B 5
16.7%
A 4
13.3%
L 3
10.0%
J 3
10.0%
K 3
10.0%
S 3
10.0%
C 2
 
6.7%
O 2
 
6.7%
M 2
 
6.7%
e 1
 
3.3%
Other values (2) 2
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1232
86.4%
ASCII 194
 
13.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 88
 
7.1%
시 87
 
7.1%
고 84
 
6.8%
원 69
 
5.6%
텔 66
 
5.4%
하 49
 
4.0%
우 49
 
4.0%
이 33
 
2.7%
리 29
 
2.4%
룸 19
 
1.5%
Other values (207) 659
53.5%
ASCII
ValueCountFrequency (%)
54
27.8%
) 33
17.0%
( 29
14.9%
. 11
 
5.7%
0 6
 
3.1%
4 5
 
2.6%
B 5
 
2.6%
A 4
 
2.1%
L 3
 
1.5%
2 3
 
1.5%
Other values (18) 41
21.1%

지번주소
Text

Distinct198
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-03-14T23:26:20.847866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length35
Mean length23.663366
Min length20

Characters and Unicode

Total characters4780
Distinct characters65
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique194 ?
Unique (%)96.0%

Sample

1st row인천광역시 부평구 청천동 9-9번지
2nd row인천광역시 부평구 청천동 9-72번지
3rd row인천광역시 부평구 청천동 178-41번지
4th row인천광역시 부평구 청천동 177-17번지
5th row인천광역시 부평구 청천동 177-16번지
ValueCountFrequency (%)
인천광역시 200
23.5%
부평구 118
13.8%
계양구 84
 
9.9%
부평동 65
 
7.6%
계산동 49
 
5.8%
십정동 16
 
1.9%
작전동 14
 
1.6%
부개동 10
 
1.2%
산곡동 10
 
1.2%
갈산동 9
 
1.1%
Other values (241) 277
32.5%
2024-03-14T23:26:22.283695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
820
 
17.2%
지 216
 
4.5%
천 210
 
4.4%
인 204
 
4.3%
광 204
 
4.3%
역 204
 
4.3%
시 204
 
4.3%
동 203
 
4.2%
번 202
 
4.2%
- 202
 
4.2%
Other values (55) 2111
44.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2717
56.8%
Decimal Number 1016
 
21.3%
Space Separator 820
 
17.2%
Dash Punctuation 202
 
4.2%
Other Punctuation 17
 
0.4%
Math Symbol 8
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 216
 
7.9%
천 210
 
7.7%
인 204
 
7.5%
광 204
 
7.5%
역 204
 
7.5%
시 204
 
7.5%
동 203
 
7.5%
번 202
 
7.4%
구 202
 
7.4%
부 195
 
7.2%
Other values (41) 673
24.8%
Decimal Number
ValueCountFrequency (%)
1 192
18.9%
4 129
12.7%
2 124
12.2%
0 92
9.1%
3 86
8.5%
9 86
8.5%
5 83
8.2%
7 80
7.9%
6 72
 
7.1%
8 72
 
7.1%
Space Separator
ValueCountFrequency (%)
820
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 202
100.0%
Other Punctuation
ValueCountFrequency (%)
, 17
100.0%
Math Symbol
ValueCountFrequency (%)
~ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2717
56.8%
Common 2063
43.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 216
 
7.9%
천 210
 
7.7%
인 204
 
7.5%
광 204
 
7.5%
역 204
 
7.5%
시 204
 
7.5%
동 203
 
7.5%
번 202
 
7.4%
구 202
 
7.4%
부 195
 
7.2%
Other values (41) 673
24.8%
Common
ValueCountFrequency (%)
820
39.7%
- 202
 
9.8%
1 192
 
9.3%
4 129
 
6.3%
2 124
 
6.0%
0 92
 
4.5%
3 86
 
4.2%
9 86
 
4.2%
5 83
 
4.0%
7 80
 
3.9%
Other values (4) 169
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2717
56.8%
ASCII 2063
43.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
820
39.7%
- 202
 
9.8%
1 192
 
9.3%
4 129
 
6.3%
2 124
 
6.0%
0 92
 
4.5%
3 86
 
4.2%
9 86
 
4.2%
5 83
 
4.0%
7 80
 
3.9%
Other values (4) 169
 
8.2%
Hangul
ValueCountFrequency (%)
지 216
 
7.9%
천 210
 
7.7%
인 204
 
7.5%
광 204
 
7.5%
역 204
 
7.5%
시 204
 
7.5%
동 203
 
7.5%
번 202
 
7.4%
구 202
 
7.4%
부 195
 
7.2%
Other values (41) 673
24.8%

도로명 주소
Text

Distinct195
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-03-14T23:26:23.460821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length23
Mean length16.371287
Min length9

Characters and Unicode

Total characters3307
Distinct characters183
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique188 ?
Unique (%)93.1%

Sample

1st row마장로468번길 18-0 2,3,4층
2nd row마장로 468-0
3rd row세월천로40번길 63-0
4th row마장로410번길 21-0
5th row마장로410번길 21-0
ValueCountFrequency (%)
2,3,4층 8
 
1.5%
4-0 7
 
1.3%
주부토로 6
 
1.2%
계양문화로 6
 
1.2%
마장로 5
 
1.0%
18-0 5
 
1.0%
6-0 5
 
1.0%
효서로 5
 
1.0%
10-0 5
 
1.0%
장제로 5
 
1.0%
Other values (329) 463
89.0%
2024-03-14T23:26:25.216498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
626
18.9%
0 208
 
6.3%
- 201
 
6.1%
로 198
 
6.0%
1 174
 
5.3%
2 141
 
4.3%
길 127
 
3.8%
번 119
 
3.6%
3 117
 
3.5%
4 97
 
2.9%
Other values (173) 1299
39.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1390
42.0%
Decimal Number 1018
30.8%
Space Separator 626
18.9%
Dash Punctuation 201
 
6.1%
Other Punctuation 55
 
1.7%
Uppercase Letter 8
 
0.2%
Math Symbol 7
 
0.2%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 198
 
14.2%
길 127
 
9.1%
번 119
 
8.6%
장 41
 
2.9%
층 40
 
2.9%
부 37
 
2.7%
대 37
 
2.7%
계 33
 
2.4%
산 31
 
2.2%
경 29
 
2.1%
Other values (148) 698
50.2%
Decimal Number
ValueCountFrequency (%)
0 208
20.4%
1 174
17.1%
2 141
13.9%
3 117
11.5%
4 97
9.5%
5 65
 
6.4%
7 63
 
6.2%
6 60
 
5.9%
9 50
 
4.9%
8 43
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
H 1
12.5%
N 1
12.5%
O 1
12.5%
Z 1
12.5%
I 1
12.5%
E 1
12.5%
A 1
12.5%
B 1
12.5%
Other Punctuation
ValueCountFrequency (%)
, 54
98.2%
& 1
 
1.8%
Space Separator
ValueCountFrequency (%)
626
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 201
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1909
57.7%
Hangul 1388
42.0%
Latin 8
 
0.2%
Han 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 198
 
14.3%
길 127
 
9.1%
번 119
 
8.6%
장 41
 
3.0%
층 40
 
2.9%
부 37
 
2.7%
대 37
 
2.7%
계 33
 
2.4%
산 31
 
2.2%
경 29
 
2.1%
Other values (146) 696
50.1%
Common
ValueCountFrequency (%)
626
32.8%
0 208
 
10.9%
- 201
 
10.5%
1 174
 
9.1%
2 141
 
7.4%
3 117
 
6.1%
4 97
 
5.1%
5 65
 
3.4%
7 63
 
3.3%
6 60
 
3.1%
Other values (7) 157
 
8.2%
Latin
ValueCountFrequency (%)
H 1
12.5%
N 1
12.5%
O 1
12.5%
Z 1
12.5%
I 1
12.5%
E 1
12.5%
A 1
12.5%
B 1
12.5%
Han
ValueCountFrequency (%)
美 1
50.0%
德 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1917
58.0%
Hangul 1388
42.0%
CJK 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
626
32.7%
0 208
 
10.9%
- 201
 
10.5%
1 174
 
9.1%
2 141
 
7.4%
3 117
 
6.1%
4 97
 
5.1%
5 65
 
3.4%
7 63
 
3.3%
6 60
 
3.1%
Other values (15) 165
 
8.6%
Hangul
ValueCountFrequency (%)
로 198
 
14.3%
길 127
 
9.1%
번 119
 
8.6%
장 41
 
3.0%
층 40
 
2.9%
부 37
 
2.7%
대 37
 
2.7%
계 33
 
2.4%
산 31
 
2.2%
경 29
 
2.1%
Other values (146) 696
50.1%
CJK
ValueCountFrequency (%)
美 1
50.0%
德 1
50.0%

영업장면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct115
Distinct (%)97.5%
Missing84
Missing (%)41.6%
Infinite0
Infinite (%)0.0%
Mean489.49653
Minimum84.73
Maximum1789.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-03-14T23:26:25.460339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum84.73
5-th percentile122.85
Q1308.85
median455.795
Q3610.8
95-th percentile957.543
Maximum1789.81
Range1705.08
Interquartile range (IQR)301.95

Descriptive statistics

Standard deviation288.25587
Coefficient of variation (CV)0.58888235
Kurtosis5.8047817
Mean489.49653
Median Absolute Deviation (MAD)154.845
Skewness1.8358142
Sum57760.59
Variance83091.444
MonotonicityNot monotonic
2024-03-14T23:26:25.719968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
845.0 2
 
1.0%
719.61 2
 
1.0%
447.99 2
 
1.0%
252.91 1
 
0.5%
556.02 1
 
0.5%
839.0 1
 
0.5%
486.94 1
 
0.5%
534.2 1
 
0.5%
431.46 1
 
0.5%
513.59 1
 
0.5%
Other values (105) 105
52.0%
(Missing) 84
41.6%
ValueCountFrequency (%)
84.73 1
0.5%
86.62 1
0.5%
103.0 1
0.5%
115.24 1
0.5%
117.23 1
0.5%
118.94 1
0.5%
123.54 1
0.5%
137.17 1
0.5%
144.78 1
0.5%
147.69 1
0.5%
ValueCountFrequency (%)
1789.81 1
0.5%
1706.4 1
0.5%
1518.55 1
0.5%
976.2 1
0.5%
969.6 1
0.5%
958.07 1
0.5%
957.45 1
0.5%
883.06 1
0.5%
857.2 1
0.5%
850.56 1
0.5%

업종
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
고시원업
202 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고시원업 202
100.0%

Length

2024-03-14T23:26:26.047376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:26:26.214858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고시원업 202
100.0%

증명발급일
Text

MISSING 

Distinct99
Distinct (%)83.9%
Missing84
Missing (%)41.6%
Memory size1.7 KiB
2024-03-14T23:26:27.239725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1180
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)74.6%

Sample

1st row2017-02-23
2nd row2019-12-20
3rd row2013-06-14
4th row2012-02-14
5th row2012-02-27
ValueCountFrequency (%)
2011-08-23 7
 
5.9%
2018-06-19 4
 
3.4%
2019-12-20 3
 
2.5%
2016-10-25 2
 
1.7%
2012-04-19 2
 
1.7%
2019-12-18 2
 
1.7%
2019-12-11 2
 
1.7%
2012-07-27 2
 
1.7%
3011-11-11 2
 
1.7%
2010-12-06 2
 
1.7%
Other values (89) 90
76.3%
2024-03-14T23:26:28.526058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 265
22.5%
1 238
20.2%
- 236
20.0%
2 215
18.2%
3 46
 
3.9%
9 38
 
3.2%
8 35
 
3.0%
6 33
 
2.8%
7 28
 
2.4%
4 26
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 944
80.0%
Dash Punctuation 236
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 265
28.1%
1 238
25.2%
2 215
22.8%
3 46
 
4.9%
9 38
 
4.0%
8 35
 
3.7%
6 33
 
3.5%
7 28
 
3.0%
4 26
 
2.8%
5 20
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 236
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1180
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 265
22.5%
1 238
20.2%
- 236
20.0%
2 215
18.2%
3 46
 
3.9%
9 38
 
3.2%
8 35
 
3.0%
6 33
 
2.8%
7 28
 
2.4%
4 26
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 265
22.5%
1 238
20.2%
- 236
20.0%
2 215
18.2%
3 46
 
3.9%
9 38
 
3.2%
8 35
 
3.0%
6 33
 
2.8%
7 28
 
2.4%
4 26
 
2.2%

지하_지상
Categorical

Distinct37
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
지상 2,3,4
35 
지상 2,3,4,5
17 
지상 4
15 
지상 3
12 
지상 2
12 
Other values (32)
111 

Length

Max length20
Median length18
Mean length7.8168317
Min length4

Unique

Unique13 ?
Unique (%)6.4%

Sample

1st row지상 2,3,4
2nd row지상 2
3rd row지상 2,3,4
4th row지상 2,3,4,5,6,7
5th row지상 2,3,4,5,6

Common Values

ValueCountFrequency (%)
지상 2,3,4 35
17.3%
지상 2,3,4,5 17
 
8.4%
지상 4 15
 
7.4%
지상 3 12
 
5.9%
지상 2 12
 
5.9%
지상 2,3,4,5,6 11
 
5.4%
지상 3,4 11
 
5.4%
지상 2,3 11
 
5.4%
지상 1,2,3,4 9
 
4.5%
지상 6 7
 
3.5%
Other values (27) 62
30.7%

Length

2024-03-14T23:26:28.786595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지상 200
49.4%
2,3,4 35
 
8.6%
2,3,4,5 17
 
4.2%
4 15
 
3.7%
3 12
 
3.0%
2 12
 
3.0%
2,3,4,5,6 11
 
2.7%
3,4 11
 
2.7%
2,3 11
 
2.7%
1,2,3,4 9
 
2.2%
Other values (29) 72
 
17.8%

Interactions

2024-03-14T23:26:14.371351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:13.845414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:14.625834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:14.113930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T23:26:28.946816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번관할서영업장면적(제곱미터)증명발급일지하_지상
연번1.0000.9990.1840.8550.522
관할서0.9991.000NaNNaN0.477
영업장면적(제곱미터)0.184NaN1.0000.9200.759
증명발급일0.855NaN0.9201.0000.929
지하_지상0.5220.4770.7590.9291.000
2024-03-14T23:26:29.125124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지하_지상관할서
지하_지상1.0000.366
관할서0.3661.000
2024-03-14T23:26:29.264020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번영업장면적(제곱미터)관할서지하_지상
연번1.000-0.0460.9530.192
영업장면적(제곱미터)-0.0461.0001.0000.362
관할서0.9531.0001.0000.366
지하_지상0.1920.3620.3661.000

Missing values

2024-03-14T23:26:14.985570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T23:26:15.433482image/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.
2024-03-14T23:26:15.743850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연번관할서상호지번주소도로명 주소영업장면적(제곱미터)업종증명발급일지하_지상
01부평소방서청솔홈타운인천광역시 부평구 청천동 9-9번지마장로468번길 18-0 2,3,4층496.8고시원업2017-02-23지상 2,3,4
12부평소방서푸른솔고시텔(서철모)인천광역시 부평구 청천동 9-72번지마장로 468-0194.54고시원업2019-12-20지상 2
23부평소방서창빌딩고시텔인천광역시 부평구 청천동 178-41번지세월천로40번길 63-0418.23고시원업2013-06-14지상 2,3,4
34부평소방서비존고시텔인천광역시 부평구 청천동 177-17번지마장로410번길 21-0883.06고시원업2012-02-14지상 2,3,4,5,6,7
45부평소방서일현인천광역시 부평구 청천동 177-16번지마장로410번길 21-0668.5고시원업2012-02-27지상 2,3,4,5,6
56부평소방서영풍하우스인천광역시 부평구 청천동 174-10번지청중로 72-0489.6고시원업2015-07-24지상 2,3,4
67부평소방서원휴먼하우스인천광역시 부평구 십정동 521-12번지백범로422번길 31-3329.99고시원업2020-02-18지상 1,2,3,4
78부평소방서세종빌인천광역시 부평구 십정동 510-4번지 외1필지 5,6,7,8층동암남로 29-0 세종빌딩857.2고시원업2011-09-30지상 5,6,7,8
89부평소방서모듬채인천광역시 부평구 십정동 494-63번지아트센터로74번길 11-0 모둠채84.73고시원업2012-03-14지상 1,2,3
910부평소방서오픈하우스 간석오거리역점인천광역시 부평구 십정동 481-9번지경인로667번길 4-0 경원빌딩 3,4층490.0고시원업2012-04-19지상 3,4
연번관할서상호지번주소도로명 주소영업장면적(제곱미터)업종증명발급일지하_지상
192193계양소방서하늘스테이(구. 이삭고시텔)인천광역시 계양구 계산동 928-29번지 지상4층경명대로 1071-0<NA>고시원업<NA>지상 4
193194계양소방서하숙고시원인천광역시 계양구 계산동 986-0번지계산로 66-0<NA>고시원업<NA>지상 3,4
194195계양소방서하이탑고시원인천광역시 계양구 임학동 28-0번지계양산로 182-0 지상3층, 4층<NA>고시원업<NA>지상 3,4
195196계양소방서한샘고시텔인천광역시 계양구 작전동 852-73번지계양대로119번길 4-1<NA>고시원업<NA>지상 3
196197계양소방서해피하우스인천광역시 계양구 작전동 853-6번지계양대로119번길 7-0 해피하우스<NA>고시원업<NA>지하 1/지상 1,2
197198계양소방서현)이지하우스계산점 구)유정고시원인천광역시 계양구 계산동 1074-2번지계양문화로59번길 14-0<NA>고시원업<NA>지상 6
198199계양소방서현대원룸고시텔인천광역시 계양구 계산동 987-30번지계양대로139번길 4-0<NA>고시원업<NA>지상 2,3
199200계양소방서화진아이빌(2013)인천광역시 계양구 계산동 954-8번지계산시장길 28-3<NA>고시원업<NA>지상 2,3,4
200201계양소방서황제하우스인천광역시 계양구 계산동 926-26번지하느재로20번길 6-0<NA>고시원업<NA>지상 2,3,4,5
201202계양소방서힐하우스인천광역시 계양구 계산동 897-130번지경명대로1029번길 31-5<NA>고시원업<NA>지상 2,3,4