Overview

Dataset statistics

Number of variables6
Number of observations2343
Missing cells6
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory112.2 KiB
Average record size in memory49.1 B

Variable types

Categorical1
Text4
Numeric1

Dataset

Description공중위생업(숙박업,목욕장업,이용업,미용업,세탁업,건문위생관리업,위생처리업,세척제제조업,기타위생용품제조업) 업종명, 소재지, 전화번호
Author대구광역시 수성구
URLhttps://www.data.go.kr/data/15054677/fileData.do

Reproduction

Analysis started2023-12-12 13:08:32.497075
Analysis finished2023-12-12 13:08:33.725907
Duration1.23 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct21
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
일반미용업
1111 
피부미용업
273 
세탁업
206 
이용업
128 
네일미용업
127 
Other values (16)
498 

Length

Max length23
Median length5
Mean length5.7605634
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 1111
47.4%
피부미용업 273
 
11.7%
세탁업 206
 
8.8%
이용업 128
 
5.5%
네일미용업 127
 
5.4%
건물위생관리업 106
 
4.5%
종합미용업 71
 
3.0%
숙박업(일반) 54
 
2.3%
목욕장업 45
 
1.9%
네일미용업, 화장ㆍ분장 미용업 36
 
1.5%
Other values (11) 186
 
7.9%

Length

2023-12-12T22:08:33.807295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 1163
42.7%
피부미용업 388
 
14.3%
네일미용업 248
 
9.1%
세탁업 206
 
7.6%
미용업 159
 
5.8%
화장ㆍ분장 153
 
5.6%
이용업 128
 
4.7%
건물위생관리업 106
 
3.9%
종합미용업 71
 
2.6%
숙박업(일반 54
 
2.0%

업소명
Text

Distinct2229
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
2023-12-12T22:08:34.127930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length29
Mean length6.5480154
Min length1

Characters and Unicode

Total characters15342
Distinct characters674
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2142 ?
Unique (%)91.4%

Sample

1st row어리헤어살롱
2nd row미인미용실
3rd row영아미용소
4th row이재원미용실(수성점)
5th row앤미용실
ValueCountFrequency (%)
hair 13
 
0.5%
헤어 7
 
0.3%
nail 6
 
0.2%
미용실 6
 
0.2%
태후사랑 5
 
0.2%
by 5
 
0.2%
은헤어 5
 
0.2%
주 5
 
0.2%
주식회사 5
 
0.2%
수헤어 4
 
0.2%
Other values (2301) 2423
97.5%
2023-12-12T22:08:34.615490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 710
 
4.6%
헤 650
 
4.2%
이 391
 
2.5%
미 378
 
2.5%
) 369
 
2.4%
( 369
 
2.4%
스 368
 
2.4%
용 324
 
2.1%
소 298
 
1.9%
리 247
 
1.6%
Other values (664) 11238
73.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12271
80.0%
Lowercase Letter 1116
 
7.3%
Uppercase Letter 845
 
5.5%
Close Punctuation 369
 
2.4%
Open Punctuation 369
 
2.4%
Space Separator 160
 
1.0%
Decimal Number 108
 
0.7%
Other Punctuation 95
 
0.6%
Dash Punctuation 5
 
< 0.1%
Math Symbol 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 710
 
5.8%
헤 650
 
5.3%
이 391
 
3.2%
미 378
 
3.1%
스 368
 
3.0%
용 324
 
2.6%
소 298
 
2.4%
리 247
 
2.0%
티 214
 
1.7%
세 189
 
1.5%
Other values (588) 8502
69.3%
Lowercase Letter
ValueCountFrequency (%)
a 146
13.1%
i 121
10.8%
e 115
10.3%
n 87
 
7.8%
o 82
 
7.3%
r 72
 
6.5%
l 71
 
6.4%
h 61
 
5.5%
y 58
 
5.2%
s 52
 
4.7%
Other values (16) 251
22.5%
Uppercase Letter
ValueCountFrequency (%)
A 76
 
9.0%
S 60
 
7.1%
N 59
 
7.0%
H 58
 
6.9%
I 57
 
6.7%
L 53
 
6.3%
O 50
 
5.9%
T 44
 
5.2%
M 40
 
4.7%
E 40
 
4.7%
Other values (16) 308
36.4%
Decimal Number
ValueCountFrequency (%)
1 24
22.2%
2 21
19.4%
5 13
12.0%
3 12
11.1%
9 12
11.1%
6 7
 
6.5%
4 6
 
5.6%
8 5
 
4.6%
0 4
 
3.7%
7 4
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 28
29.5%
& 27
28.4%
# 15
15.8%
' 11
 
11.6%
, 7
 
7.4%
: 6
 
6.3%
% 1
 
1.1%
Close Punctuation
ValueCountFrequency (%)
) 369
100.0%
Open Punctuation
ValueCountFrequency (%)
( 369
100.0%
Space Separator
ValueCountFrequency (%)
160
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12262
79.9%
Latin 1961
 
12.8%
Common 1110
 
7.2%
Han 9
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 710
 
5.8%
헤 650
 
5.3%
이 391
 
3.2%
미 378
 
3.1%
스 368
 
3.0%
용 324
 
2.6%
소 298
 
2.4%
리 247
 
2.0%
티 214
 
1.7%
세 189
 
1.5%
Other values (584) 8493
69.3%
Latin
ValueCountFrequency (%)
a 146
 
7.4%
i 121
 
6.2%
e 115
 
5.9%
n 87
 
4.4%
o 82
 
4.2%
A 76
 
3.9%
r 72
 
3.7%
l 71
 
3.6%
h 61
 
3.1%
S 60
 
3.1%
Other values (42) 1070
54.6%
Common
ValueCountFrequency (%)
) 369
33.2%
( 369
33.2%
160
14.4%
. 28
 
2.5%
& 27
 
2.4%
1 24
 
2.2%
2 21
 
1.9%
# 15
 
1.4%
5 13
 
1.2%
3 12
 
1.1%
Other values (14) 72
 
6.5%
Han
ValueCountFrequency (%)
美 6
66.7%
壽 1
 
11.1%
挺 1
 
11.1%
愛 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12262
79.9%
ASCII 3070
 
20.0%
CJK 9
 
0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 710
 
5.8%
헤 650
 
5.3%
이 391
 
3.2%
미 378
 
3.1%
스 368
 
3.0%
용 324
 
2.6%
소 298
 
2.4%
리 247
 
2.0%
티 214
 
1.7%
세 189
 
1.5%
Other values (584) 8493
69.3%
ASCII
ValueCountFrequency (%)
) 369
 
12.0%
( 369
 
12.0%
160
 
5.2%
a 146
 
4.8%
i 121
 
3.9%
e 115
 
3.7%
n 87
 
2.8%
o 82
 
2.7%
A 76
 
2.5%
r 72
 
2.3%
Other values (65) 1473
48.0%
CJK
ValueCountFrequency (%)
美 6
66.7%
壽 1
 
11.1%
挺 1
 
11.1%
愛 1
 
11.1%
None
ValueCountFrequency (%)
° 1
100.0%

영업소 주소(도로명)
Text

Distinct2236
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
2023-12-12T22:08:34.931927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length62
Median length56
Mean length31.434913
Min length20

Characters and Unicode

Total characters73652
Distinct characters296
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2136 ?
Unique (%)91.2%

Sample

1st row대구광역시 수성구 들안로67길 30-1, 1층 (수성동4가)
2nd row대구광역시 수성구 지범로40길 11, 2동 203호 (범물동,서한화성아파트상가)
3rd row대구광역시 수성구 공경로 63 (만촌동)
4th row대구광역시 수성구 수성로 93, 2층 (상동)
5th row대구광역시 수성구 달구벌대로625길 6, 1층 (매호동)
ValueCountFrequency (%)
대구광역시 2343
 
16.3%
수성구 2343
 
16.3%
1층 781
 
5.4%
범어동 335
 
2.3%
지산동 263
 
1.8%
만촌동 239
 
1.7%
2층 183
 
1.3%
황금동 179
 
1.2%
두산동 162
 
1.1%
신매동 127
 
0.9%
Other values (1732) 7445
51.7%
2023-12-12T22:08:35.445050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12060
 
16.4%
구 5167
 
7.0%
1 3299
 
4.5%
수 3152
 
4.3%
동 3104
 
4.2%
성 3083
 
4.2%
대 2832
 
3.8%
시 2569
 
3.5%
) 2404
 
3.3%
( 2404
 
3.3%
Other values (286) 33578
45.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 41796
56.7%
Decimal Number 12457
 
16.9%
Space Separator 12060
 
16.4%
Close Punctuation 2404
 
3.3%
Open Punctuation 2404
 
3.3%
Other Punctuation 1979
 
2.7%
Dash Punctuation 417
 
0.6%
Uppercase Letter 127
 
0.2%
Lowercase Letter 5
 
< 0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 5167
 
12.4%
수 3152
 
7.5%
동 3104
 
7.4%
성 3083
 
7.4%
대 2832
 
6.8%
시 2569
 
6.1%
로 2354
 
5.6%
역 2348
 
5.6%
광 2344
 
5.6%
길 1321
 
3.2%
Other values (249) 13522
32.4%
Uppercase Letter
ValueCountFrequency (%)
B 30
23.6%
A 21
16.5%
S 19
15.0%
K 12
 
9.4%
T 7
 
5.5%
D 6
 
4.7%
L 5
 
3.9%
C 5
 
3.9%
N 5
 
3.9%
E 4
 
3.1%
Other values (7) 13
10.2%
Decimal Number
ValueCountFrequency (%)
1 3299
26.5%
2 1934
15.5%
3 1357
10.9%
0 1204
 
9.7%
4 1124
 
9.0%
5 933
 
7.5%
6 868
 
7.0%
7 611
 
4.9%
8 578
 
4.6%
9 549
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 1977
99.9%
/ 1
 
0.1%
. 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 4
80.0%
a 1
 
20.0%
Space Separator
ValueCountFrequency (%)
12060
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2404
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2404
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 417
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 41796
56.7%
Common 31724
43.1%
Latin 132
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 5167
 
12.4%
수 3152
 
7.5%
동 3104
 
7.4%
성 3083
 
7.4%
대 2832
 
6.8%
시 2569
 
6.1%
로 2354
 
5.6%
역 2348
 
5.6%
광 2344
 
5.6%
길 1321
 
3.2%
Other values (249) 13522
32.4%
Latin
ValueCountFrequency (%)
B 30
22.7%
A 21
15.9%
S 19
14.4%
K 12
 
9.1%
T 7
 
5.3%
D 6
 
4.5%
L 5
 
3.8%
C 5
 
3.8%
N 5
 
3.8%
E 4
 
3.0%
Other values (9) 18
13.6%
Common
ValueCountFrequency (%)
12060
38.0%
1 3299
 
10.4%
) 2404
 
7.6%
( 2404
 
7.6%
, 1977
 
6.2%
2 1934
 
6.1%
3 1357
 
4.3%
0 1204
 
3.8%
4 1124
 
3.5%
5 933
 
2.9%
Other values (8) 3028
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 41796
56.7%
ASCII 31856
43.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12060
37.9%
1 3299
 
10.4%
) 2404
 
7.5%
( 2404
 
7.5%
, 1977
 
6.2%
2 1934
 
6.1%
3 1357
 
4.3%
0 1204
 
3.8%
4 1124
 
3.5%
5 933
 
2.9%
Other values (27) 3160
 
9.9%
Hangul
ValueCountFrequency (%)
구 5167
 
12.4%
수 3152
 
7.5%
동 3104
 
7.4%
성 3083
 
7.4%
대 2832
 
6.8%
시 2569
 
6.1%
로 2354
 
5.6%
역 2348
 
5.6%
광 2344
 
5.6%
길 1321
 
3.2%
Other values (249) 13522
32.4%

우편번호(도로명)
Real number (ℝ)

Distinct262
Distinct (%)11.2%
Missing6
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean42151.528
Minimum42000
Maximum42288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2023-12-12T22:08:35.659160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42000
5-th percentile42013
Q142092
median42155
Q342209
95-th percentile42274
Maximum42288
Range288
Interquartile range (IQR)117

Descriptive statistics

Standard deviation79.23454
Coefficient of variation (CV)0.0018797548
Kurtosis-0.85958657
Mean42151.528
Median Absolute Deviation (MAD)58
Skewness-0.14560476
Sum98508122
Variance6278.1123
MonotonicityNot monotonic
2023-12-12T22:08:35.854895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42177 34
 
1.5%
42271 32
 
1.4%
42176 31
 
1.3%
42251 31
 
1.3%
42175 31
 
1.3%
42089 30
 
1.3%
42283 27
 
1.2%
42170 27
 
1.2%
42146 27
 
1.2%
42189 26
 
1.1%
Other values (252) 2041
87.1%
ValueCountFrequency (%)
42000 4
 
0.2%
42001 11
0.5%
42002 5
 
0.2%
42003 5
 
0.2%
42004 4
 
0.2%
42005 14
0.6%
42006 10
0.4%
42007 20
0.9%
42008 3
 
0.1%
42009 16
0.7%
ValueCountFrequency (%)
42288 2
 
0.1%
42287 11
0.5%
42286 7
 
0.3%
42285 3
 
0.1%
42284 2
 
0.1%
42283 27
1.2%
42282 4
 
0.2%
42281 6
 
0.3%
42279 12
0.5%
42278 2
 
0.1%

영업소 주소(지번)
Text

Distinct2229
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
2023-12-12T22:08:36.189328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length48
Mean length25.317542
Min length17

Characters and Unicode

Total characters59319
Distinct characters280
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2124 ?
Unique (%)90.7%

Sample

1st row대구광역시 수성구 수성동4가 1028-4 1층
2nd row대구광역시 수성구 범물동 1328 서한화성아파트상가 2동 203호
3rd row대구광역시 수성구 만촌동 445-25
4th row대구광역시 수성구 상동 282-2 2층
5th row대구광역시 수성구 매호동 1340-2 1층
ValueCountFrequency (%)
대구광역시 2343
19.6%
수성구 2343
19.6%
1층 694
 
5.8%
범어동 356
 
3.0%
지산동 293
 
2.4%
만촌동 262
 
2.2%
황금동 193
 
1.6%
두산동 171
 
1.4%
신매동 150
 
1.3%
2층 137
 
1.1%
Other values (2405) 5024
42.0%
2023-12-12T22:08:36.691785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11518
19.4%
구 4706
 
7.9%
1 3718
 
6.3%
성 2832
 
4.8%
동 2789
 
4.7%
수 2767
 
4.7%
시 2517
 
4.2%
대 2368
 
4.0%
역 2348
 
4.0%
광 2344
 
4.0%
Other values (270) 21412
36.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31970
53.9%
Decimal Number 13579
22.9%
Space Separator 11518
 
19.4%
Dash Punctuation 1890
 
3.2%
Uppercase Letter 111
 
0.2%
Other Punctuation 87
 
0.1%
Open Punctuation 77
 
0.1%
Close Punctuation 77
 
0.1%
Lowercase Letter 6
 
< 0.1%
Math Symbol 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 4706
14.7%
성 2832
 
8.9%
동 2789
 
8.7%
수 2767
 
8.7%
시 2517
 
7.9%
대 2368
 
7.4%
역 2348
 
7.3%
광 2344
 
7.3%
층 999
 
3.1%
호 653
 
2.0%
Other values (234) 7647
23.9%
Uppercase Letter
ValueCountFrequency (%)
B 29
26.1%
A 18
16.2%
S 17
15.3%
K 10
 
9.0%
T 5
 
4.5%
D 5
 
4.5%
E 4
 
3.6%
C 4
 
3.6%
N 4
 
3.6%
L 4
 
3.6%
Other values (7) 11
 
9.9%
Decimal Number
ValueCountFrequency (%)
1 3718
27.4%
2 1807
13.3%
0 1327
 
9.8%
3 1292
 
9.5%
4 1004
 
7.4%
5 1003
 
7.4%
7 923
 
6.8%
8 884
 
6.5%
6 842
 
6.2%
9 779
 
5.7%
Other Punctuation
ValueCountFrequency (%)
, 86
98.9%
/ 1
 
1.1%
Lowercase Letter
ValueCountFrequency (%)
e 5
83.3%
a 1
 
16.7%
Space Separator
ValueCountFrequency (%)
11518
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1890
100.0%
Open Punctuation
ValueCountFrequency (%)
( 77
100.0%
Close Punctuation
ValueCountFrequency (%)
) 77
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31970
53.9%
Common 27232
45.9%
Latin 117
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 4706
14.7%
성 2832
 
8.9%
동 2789
 
8.7%
수 2767
 
8.7%
시 2517
 
7.9%
대 2368
 
7.4%
역 2348
 
7.3%
광 2344
 
7.3%
층 999
 
3.1%
호 653
 
2.0%
Other values (234) 7647
23.9%
Latin
ValueCountFrequency (%)
B 29
24.8%
A 18
15.4%
S 17
14.5%
K 10
 
8.5%
T 5
 
4.3%
D 5
 
4.3%
e 5
 
4.3%
E 4
 
3.4%
C 4
 
3.4%
N 4
 
3.4%
Other values (9) 16
13.7%
Common
ValueCountFrequency (%)
11518
42.3%
1 3718
 
13.7%
- 1890
 
6.9%
2 1807
 
6.6%
0 1327
 
4.9%
3 1292
 
4.7%
4 1004
 
3.7%
5 1003
 
3.7%
7 923
 
3.4%
8 884
 
3.2%
Other values (7) 1866
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31970
53.9%
ASCII 27349
46.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11518
42.1%
1 3718
 
13.6%
- 1890
 
6.9%
2 1807
 
6.6%
0 1327
 
4.9%
3 1292
 
4.7%
4 1004
 
3.7%
5 1003
 
3.7%
7 923
 
3.4%
8 884
 
3.2%
Other values (26) 1983
 
7.3%
Hangul
ValueCountFrequency (%)
구 4706
14.7%
성 2832
 
8.9%
동 2789
 
8.7%
수 2767
 
8.7%
시 2517
 
7.9%
대 2368
 
7.4%
역 2348
 
7.3%
광 2344
 
7.3%
층 999
 
3.1%
호 653
 
2.0%
Other values (234) 7647
23.9%

소재지전화
Text

Distinct1396
Distinct (%)59.6%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
2023-12-12T22:08:36.966043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length10.021767
Min length7

Characters and Unicode

Total characters23481
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1385 ?
Unique (%)59.1%

Sample

1st row053-257-2674
2nd row053-783-2614
3rd row053-743-2555
4th row053-428-2585
5th row개인정보 포함
ValueCountFrequency (%)
개인정보 936
28.5%
포함 936
28.5%
053-742-0001 3
 
0.1%
053-763-7311 3
 
0.1%
053-247-8885 2
 
0.1%
053-765-4010 2
 
0.1%
053-767-0999 2
 
0.1%
053-784-7711 2
 
0.1%
053-782-2455 2
 
0.1%
053-762-1235 2
 
0.1%
Other values (1387) 1389
42.4%
2023-12-12T22:08:37.453334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2814
 
12.0%
5 2374
 
10.1%
3 2143
 
9.1%
0 2117
 
9.0%
7 1950
 
8.3%
6 1077
 
4.6%
2 993
 
4.2%
4 983
 
4.2%
인 936
 
4.0%
개 936
 
4.0%
Other values (8) 7158
30.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14115
60.1%
Other Letter 5616
 
23.9%
Dash Punctuation 2814
 
12.0%
Space Separator 936
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2374
16.8%
3 2143
15.2%
0 2117
15.0%
7 1950
13.8%
6 1077
7.6%
2 993
7.0%
4 983
7.0%
1 890
 
6.3%
8 848
 
6.0%
9 740
 
5.2%
Other Letter
ValueCountFrequency (%)
인 936
16.7%
개 936
16.7%
함 936
16.7%
포 936
16.7%
보 936
16.7%
정 936
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 2814
100.0%
Space Separator
ValueCountFrequency (%)
936
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17865
76.1%
Hangul 5616
 
23.9%

Most frequent character per script

Common
ValueCountFrequency (%)
- 2814
15.8%
5 2374
13.3%
3 2143
12.0%
0 2117
11.8%
7 1950
10.9%
6 1077
 
6.0%
2 993
 
5.6%
4 983
 
5.5%
936
 
5.2%
1 890
 
5.0%
Other values (2) 1588
8.9%
Hangul
ValueCountFrequency (%)
인 936
16.7%
개 936
16.7%
함 936
16.7%
포 936
16.7%
보 936
16.7%
정 936
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17865
76.1%
Hangul 5616
 
23.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 2814
15.8%
5 2374
13.3%
3 2143
12.0%
0 2117
11.8%
7 1950
10.9%
6 1077
 
6.0%
2 993
 
5.6%
4 983
 
5.5%
936
 
5.2%
1 890
 
5.0%
Other values (2) 1588
8.9%
Hangul
ValueCountFrequency (%)
인 936
16.7%
개 936
16.7%
함 936
16.7%
포 936
16.7%
보 936
16.7%
정 936
16.7%

Interactions

2023-12-12T22:08:33.420153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:08:37.572022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명우편번호(도로명)
업종명1.0000.112
우편번호(도로명)0.1121.000
2023-12-12T22:08:37.660462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호(도로명)업종명
우편번호(도로명)1.0000.041
업종명0.0411.000

Missing values

2023-12-12T22:08:33.559886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:08:33.677752image/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

업종명업소명영업소 주소(도로명)우편번호(도로명)영업소 주소(지번)소재지전화
0미용업어리헤어살롱대구광역시 수성구 들안로67길 30-1, 1층 (수성동4가)42005대구광역시 수성구 수성동4가 1028-4 1층053-257-2674
1미용업미인미용실대구광역시 수성구 지범로40길 11, 2동 203호 (범물동,서한화성아파트상가)42210대구광역시 수성구 범물동 1328 서한화성아파트상가 2동 203호053-783-2614
2미용업영아미용소대구광역시 수성구 공경로 63 (만촌동)42035대구광역시 수성구 만촌동 445-25053-743-2555
3미용업이재원미용실(수성점)대구광역시 수성구 수성로 93, 2층 (상동)42165대구광역시 수성구 상동 282-2 2층053-428-2585
4미용업앤미용실대구광역시 수성구 달구벌대로625길 6, 1층 (매호동)42268대구광역시 수성구 매호동 1340-2 1층개인정보 포함
5미용업에이(A)헤어샵대구광역시 수성구 달구벌대로 3294, 시지퍼스트 1동 1층 109호 (신매동)42279대구광역시 수성구 신매동 10-7 시지퍼스트, 1층 1동 109호070-7519-0665
6일반미용업쎄시미용실대구광역시 수성구 수성로 412, 118동 지하1층 3호 (수성동4가, 수성보성타운상가)42004대구광역시 수성구 수성동4가 1090-6053-476-6025
7일반미용업예지미용소대구광역시 수성구 수성로41길 54 (중동)42152대구광역시 수성구 중동 532-459053-766-1151
8일반미용업파동미용실대구광역시 수성구 파동로30길 2, 1층 (파동)42231대구광역시 수성구 파동 138-2 1층053-761-7736
9일반미용업미앤컬대구광역시 수성구 달구벌대로641길 27, 다동 105호 (매호동,상가)42269대구광역시 수성구 매호동 1082-1 상가 다동 105호053-792-3370
업종명업소명영업소 주소(도로명)우편번호(도로명)영업소 주소(지번)소재지전화
2333건물위생관리업푸레자(PUREZA)대구광역시 수성구 들안로81길 37, 1층 (수성동4가)42001대구광역시 수성구 수성동4가 1126-3 1층개인정보 포함
2334건물위생관리업상쾌한세상365대구광역시 수성구 달구벌대로 3086, 2층 (시지동)42252대구광역시 수성구 시지동 251-3 2층개인정보 포함
2335건물위생관리업넥스트플러스대구광역시 수성구 지범로 68, 2층 (지산동)42205대구광역시 수성구 지산동 1161-53 2층개인정보 포함
2336건물위생관리업구공청소대구광역시 수성구 달구벌대로627길 22-3, 1층 (매호동)42268대구광역시 수성구 매호동 1335-11 1층개인정보 포함
2337건물위생관리업해듬대구광역시 수성구 범안로64길 26-3, 1층 (삼덕동)42250대구광역시 수성구 삼덕동 466 1층개인정보 포함
2338건물위생관리업퓨로클린대구광역시 수성구 지범로47길 3-14, 1층 (범물동)42241대구광역시 수성구 범물동 1317-1 1층개인정보 포함
2339건물위생관리업주식회사지니토탈클린대구광역시 수성구 지범로 69-3, 2층 (지산동)42187대구광역시 수성구 지산동 1157-25 2층개인정보 포함
2340건물위생관리업주식회사태림에이앤씨대구광역시 수성구 희망로19길 34, 1-2층 (중동)42136대구광역시 수성구 중동 533-3 1-2층개인정보 포함
2341건물위생관리업대지환경대구광역시 수성구 상록로 16, 지하1층 (범어동)42019대구광역시 수성구 범어동 39-15 지하1층개인정보 포함
2342건물위생관리업대구정성클린대구광역시 수성구 들안로 165, A동 1층 (중동)42151대구광역시 수성구 중동 133-3 1층 A동053-611-7999