Overview

Dataset statistics

Number of variables6
Number of observations1709
Missing cells828
Missing cells (%)8.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory80.2 KiB
Average record size in memory48.1 B

Variable types

Categorical1
Text4
DateTime1

Dataset

Description광진구 관내 공중위생업(숙박업, 목욕장업, 이용업, 미용업, 세탁업, 건물위생관리업)에 대한 현황-업종명, 업소명, 소재지주소, 업소전화
Author서울특별시 광진구
URLhttps://www.data.go.kr/data/15006934/fileData.do

Alerts

데이터기준일 has constant value ""Constant
소재지전화 has 828 (48.4%) missing valuesMissing

Reproduction

Analysis started2024-03-14 17:05:29.913784
Analysis finished2024-03-14 17:05:31.592091
Duration1.68 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct21
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
일반미용업
408 
미용업
210 
종합미용업
179 
피부미용업
169 
세탁업
161 
Other values (16)
582 

Length

Max length23
Median length5
Mean length5.6278525
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row숙박업(일반)
2nd row숙박업(일반)
3rd row숙박업(일반)
4th row숙박업(일반)
5th row숙박업(일반)

Common Values

ValueCountFrequency (%)
일반미용업 408
23.9%
미용업 210
12.3%
종합미용업 179
10.5%
피부미용업 169
9.9%
세탁업 161
 
9.4%
네일미용업 115
 
6.7%
건물위생관리업 106
 
6.2%
이용업 98
 
5.7%
숙박업(일반) 67
 
3.9%
화장ㆍ분장 미용업 40
 
2.3%
Other values (11) 156
 
9.1%

Length

2024-03-15T02:05:31.774093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 467
23.3%
미용업 336
16.8%
피부미용업 229
11.4%
네일미용업 210
10.5%
종합미용업 179
 
8.9%
세탁업 161
 
8.0%
화장ㆍ분장 126
 
6.3%
건물위생관리업 106
 
5.3%
이용업 98
 
4.9%
숙박업(일반 67
 
3.3%
Other values (2) 25
 
1.2%

업소명
Text

Distinct1664
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
2024-03-15T02:05:33.081805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length29
Mean length6.9368051
Min length1

Characters and Unicode

Total characters11855
Distinct characters645
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1626 ?
Unique (%)95.1%

Sample

1st row워커힐
2nd row큐(Q)
3rd row그랜드모텔
4th row황금모텔
5th row신한모텔
ValueCountFrequency (%)
헤어 56
 
2.4%
hair 29
 
1.2%
주식회사 23
 
1.0%
nail 21
 
0.9%
네일 19
 
0.8%
미용실 16
 
0.7%
건대점 15
 
0.6%
에스테틱 13
 
0.6%
더 11
 
0.5%
스킨케어 9
 
0.4%
Other values (1918) 2127
90.9%
2024-03-15T02:05:34.635038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
630
 
5.3%
어 500
 
4.2%
헤 469
 
4.0%
) 309
 
2.6%
( 308
 
2.6%
이 294
 
2.5%
스 244
 
2.1%
일 199
 
1.7%
미 197
 
1.7%
네 172
 
1.5%
Other values (635) 8533
72.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8658
73.0%
Lowercase Letter 982
 
8.3%
Uppercase Letter 783
 
6.6%
Space Separator 630
 
5.3%
Close Punctuation 309
 
2.6%
Open Punctuation 308
 
2.6%
Decimal Number 95
 
0.8%
Other Punctuation 77
 
0.6%
Dash Punctuation 6
 
0.1%
Connector Punctuation 6
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 500
 
5.8%
헤 469
 
5.4%
이 294
 
3.4%
스 244
 
2.8%
일 199
 
2.3%
미 197
 
2.3%
네 172
 
2.0%
리 156
 
1.8%
용 143
 
1.7%
아 123
 
1.4%
Other values (560) 6161
71.2%
Lowercase Letter
ValueCountFrequency (%)
a 140
14.3%
i 97
9.9%
e 91
 
9.3%
o 83
 
8.5%
r 71
 
7.2%
n 71
 
7.2%
l 70
 
7.1%
h 47
 
4.8%
u 42
 
4.3%
s 39
 
4.0%
Other values (16) 231
23.5%
Uppercase Letter
ValueCountFrequency (%)
A 91
11.6%
H 61
 
7.8%
N 60
 
7.7%
I 59
 
7.5%
O 55
 
7.0%
L 52
 
6.6%
B 51
 
6.5%
R 47
 
6.0%
S 43
 
5.5%
E 38
 
4.9%
Other values (16) 226
28.9%
Decimal Number
ValueCountFrequency (%)
0 16
16.8%
1 15
15.8%
2 14
14.7%
9 12
12.6%
3 10
10.5%
5 9
9.5%
6 7
7.4%
8 5
 
5.3%
7 4
 
4.2%
4 3
 
3.2%
Other Punctuation
ValueCountFrequency (%)
& 17
22.1%
# 16
20.8%
, 15
19.5%
. 14
18.2%
' 8
10.4%
: 6
 
7.8%
· 1
 
1.3%
Space Separator
ValueCountFrequency (%)
630
100.0%
Close Punctuation
ValueCountFrequency (%)
) 309
100.0%
Open Punctuation
ValueCountFrequency (%)
( 308
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%
Other Symbol
ValueCountFrequency (%)
℃ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8651
73.0%
Latin 1765
 
14.9%
Common 1432
 
12.1%
Han 7
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 500
 
5.8%
헤 469
 
5.4%
이 294
 
3.4%
스 244
 
2.8%
일 199
 
2.3%
미 197
 
2.3%
네 172
 
2.0%
리 156
 
1.8%
용 143
 
1.7%
아 123
 
1.4%
Other values (554) 6154
71.1%
Latin
ValueCountFrequency (%)
a 140
 
7.9%
i 97
 
5.5%
A 91
 
5.2%
e 91
 
5.2%
o 83
 
4.7%
r 71
 
4.0%
n 71
 
4.0%
l 70
 
4.0%
H 61
 
3.5%
N 60
 
3.4%
Other values (42) 930
52.7%
Common
ValueCountFrequency (%)
630
44.0%
) 309
21.6%
( 308
21.5%
& 17
 
1.2%
# 16
 
1.1%
0 16
 
1.1%
1 15
 
1.0%
, 15
 
1.0%
. 14
 
1.0%
2 14
 
1.0%
Other values (13) 78
 
5.4%
Han
ValueCountFrequency (%)
美 2
28.6%
林 1
14.3%
月 1
14.3%
柒 1
14.3%
五 1
14.3%
州 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8651
73.0%
ASCII 3195
 
27.0%
CJK 7
 
0.1%
None 1
 
< 0.1%
Letterlike Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
630
19.7%
) 309
 
9.7%
( 308
 
9.6%
a 140
 
4.4%
i 97
 
3.0%
A 91
 
2.8%
e 91
 
2.8%
o 83
 
2.6%
r 71
 
2.2%
n 71
 
2.2%
Other values (63) 1304
40.8%
Hangul
ValueCountFrequency (%)
어 500
 
5.8%
헤 469
 
5.4%
이 294
 
3.4%
스 244
 
2.8%
일 199
 
2.3%
미 197
 
2.3%
네 172
 
2.0%
리 156
 
1.8%
용 143
 
1.7%
아 123
 
1.4%
Other values (554) 6154
71.1%
CJK
ValueCountFrequency (%)
美 2
28.6%
林 1
14.3%
月 1
14.3%
柒 1
14.3%
五 1
14.3%
州 1
14.3%
None
ValueCountFrequency (%)
· 1
100.0%
Letterlike Symbols
ValueCountFrequency (%)
℃ 1
100.0%

영업소 주소(도로명)
Text

Distinct1651
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
2024-03-15T02:05:35.849628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length61
Median length52
Mean length31.17086
Min length21

Characters and Unicode

Total characters53271
Distinct characters304
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1595 ?
Unique (%)93.3%

Sample

1st row서울특별시 광진구 워커힐로 177 (광장동)
2nd row서울특별시 광진구 동일로 168 (화양동)
3rd row서울특별시 광진구 동일로 148 (화양동)
4th row서울특별시 광진구 광나루로 517-5 (구의동)
5th row서울특별시 광진구 아차산로33길 67-4 (화양동)
ValueCountFrequency (%)
서울특별시 1709
 
16.0%
광진구 1709
 
16.0%
1층 730
 
6.8%
자양동 464
 
4.3%
중곡동 423
 
4.0%
구의동 346
 
3.2%
2층 196
 
1.8%
화양동 170
 
1.6%
군자동 106
 
1.0%
101호 100
 
0.9%
Other values (1316) 4734
44.3%
2024-03-15T02:05:37.658517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8978
 
16.9%
1 2550
 
4.8%
동 2211
 
4.2%
구 2147
 
4.0%
광 1980
 
3.7%
( 1759
 
3.3%
) 1759
 
3.3%
진 1742
 
3.3%
시 1721
 
3.2%
로 1721
 
3.2%
Other values (294) 26703
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30136
56.6%
Space Separator 8978
 
16.9%
Decimal Number 8815
 
16.5%
Open Punctuation 1759
 
3.3%
Close Punctuation 1759
 
3.3%
Other Punctuation 1519
 
2.9%
Dash Punctuation 191
 
0.4%
Uppercase Letter 91
 
0.2%
Lowercase Letter 19
 
< 0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2211
 
7.3%
구 2147
 
7.1%
광 1980
 
6.6%
진 1742
 
5.8%
시 1721
 
5.7%
로 1721
 
5.7%
서 1713
 
5.7%
울 1712
 
5.7%
특 1709
 
5.7%
별 1709
 
5.7%
Other values (243) 11771
39.1%
Uppercase Letter
ValueCountFrequency (%)
B 31
34.1%
A 13
14.3%
F 6
 
6.6%
C 4
 
4.4%
I 4
 
4.4%
S 4
 
4.4%
P 4
 
4.4%
D 4
 
4.4%
R 3
 
3.3%
H 3
 
3.3%
Other values (11) 15
16.5%
Lowercase Letter
ValueCountFrequency (%)
e 4
21.1%
i 3
15.8%
l 2
10.5%
s 2
10.5%
r 1
 
5.3%
a 1
 
5.3%
o 1
 
5.3%
m 1
 
5.3%
h 1
 
5.3%
c 1
 
5.3%
Other values (2) 2
10.5%
Decimal Number
ValueCountFrequency (%)
1 2550
28.9%
2 1228
13.9%
3 983
 
11.2%
0 835
 
9.5%
5 777
 
8.8%
4 638
 
7.2%
6 591
 
6.7%
7 450
 
5.1%
8 393
 
4.5%
9 370
 
4.2%
Other Punctuation
ValueCountFrequency (%)
, 1518
99.9%
/ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
8978
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1759
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1759
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 191
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30135
56.6%
Common 23024
43.2%
Latin 111
 
0.2%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2211
 
7.3%
구 2147
 
7.1%
광 1980
 
6.6%
진 1742
 
5.8%
시 1721
 
5.7%
로 1721
 
5.7%
서 1713
 
5.7%
울 1712
 
5.7%
특 1709
 
5.7%
별 1709
 
5.7%
Other values (242) 11770
39.1%
Latin
ValueCountFrequency (%)
B 31
27.9%
A 13
 
11.7%
F 6
 
5.4%
C 4
 
3.6%
I 4
 
3.6%
S 4
 
3.6%
P 4
 
3.6%
D 4
 
3.6%
e 4
 
3.6%
R 3
 
2.7%
Other values (24) 34
30.6%
Common
ValueCountFrequency (%)
8978
39.0%
1 2550
 
11.1%
( 1759
 
7.6%
) 1759
 
7.6%
, 1518
 
6.6%
2 1228
 
5.3%
3 983
 
4.3%
0 835
 
3.6%
5 777
 
3.4%
4 638
 
2.8%
Other values (7) 1999
 
8.7%
Han
ValueCountFrequency (%)
休 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30135
56.6%
ASCII 23134
43.4%
Number Forms 1
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8978
38.8%
1 2550
 
11.0%
( 1759
 
7.6%
) 1759
 
7.6%
, 1518
 
6.6%
2 1228
 
5.3%
3 983
 
4.2%
0 835
 
3.6%
5 777
 
3.4%
4 638
 
2.8%
Other values (40) 2109
 
9.1%
Hangul
ValueCountFrequency (%)
동 2211
 
7.3%
구 2147
 
7.1%
광 1980
 
6.6%
진 1742
 
5.8%
시 1721
 
5.7%
로 1721
 
5.7%
서 1713
 
5.7%
울 1712
 
5.7%
특 1709
 
5.7%
별 1709
 
5.7%
Other values (242) 11770
39.1%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%
CJK
ValueCountFrequency (%)
休 1
100.0%

영업소 주소(지번)
Text

Distinct1524
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
2024-03-15T02:05:38.799004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length45
Mean length23.078408
Min length16

Characters and Unicode

Total characters39441
Distinct characters282
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1388 ?
Unique (%)81.2%

Sample

1st row서울특별시 광진구 광장동 22-1
2nd row서울특별시 광진구 화양동 23-39
3rd row서울특별시 광진구 화양동 41-3
4th row서울특별시 광진구 구의동 67-21
5th row서울특별시 광진구 화양동 12-20
ValueCountFrequency (%)
서울특별시 1709
21.7%
광진구 1709
21.7%
자양동 485
 
6.2%
중곡동 436
 
5.5%
구의동 364
 
4.6%
1층 282
 
3.6%
화양동 176
 
2.2%
군자동 112
 
1.4%
광장동 86
 
1.1%
능동 54
 
0.7%
Other values (1746) 2473
31.4%
2024-03-15T02:05:40.676243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7759
19.7%
구 2095
 
5.3%
광 1829
 
4.6%
1 1798
 
4.6%
동 1767
 
4.5%
진 1740
 
4.4%
시 1721
 
4.4%
서 1713
 
4.3%
울 1712
 
4.3%
특 1709
 
4.3%
Other values (272) 15598
39.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21618
54.8%
Decimal Number 8271
 
21.0%
Space Separator 7759
 
19.7%
Dash Punctuation 1596
 
4.0%
Uppercase Letter 63
 
0.2%
Open Punctuation 47
 
0.1%
Close Punctuation 47
 
0.1%
Other Punctuation 20
 
0.1%
Lowercase Letter 19
 
< 0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 2095
9.7%
광 1829
 
8.5%
동 1767
 
8.2%
진 1740
 
8.0%
시 1721
 
8.0%
서 1713
 
7.9%
울 1712
 
7.9%
특 1709
 
7.9%
별 1709
 
7.9%
양 686
 
3.2%
Other values (222) 4937
22.8%
Uppercase Letter
ValueCountFrequency (%)
B 15
23.8%
A 9
14.3%
I 4
 
6.3%
S 4
 
6.3%
P 4
 
6.3%
R 3
 
4.8%
C 3
 
4.8%
H 3
 
4.8%
D 3
 
4.8%
Z 2
 
3.2%
Other values (11) 13
20.6%
Lowercase Letter
ValueCountFrequency (%)
e 4
21.1%
i 3
15.8%
l 2
10.5%
s 2
10.5%
c 1
 
5.3%
t 1
 
5.3%
n 1
 
5.3%
m 1
 
5.3%
r 1
 
5.3%
a 1
 
5.3%
Other values (2) 2
10.5%
Decimal Number
ValueCountFrequency (%)
1 1798
21.7%
2 1372
16.6%
3 833
10.1%
6 779
9.4%
5 777
9.4%
4 752
9.1%
0 558
 
6.7%
7 518
 
6.3%
8 461
 
5.6%
9 423
 
5.1%
Other Punctuation
ValueCountFrequency (%)
, 19
95.0%
/ 1
 
5.0%
Space Separator
ValueCountFrequency (%)
7759
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1596
100.0%
Open Punctuation
ValueCountFrequency (%)
( 47
100.0%
Close Punctuation
ValueCountFrequency (%)
) 47
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21617
54.8%
Common 17740
45.0%
Latin 83
 
0.2%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 2095
9.7%
광 1829
 
8.5%
동 1767
 
8.2%
진 1740
 
8.0%
시 1721
 
8.0%
서 1713
 
7.9%
울 1712
 
7.9%
특 1709
 
7.9%
별 1709
 
7.9%
양 686
 
3.2%
Other values (221) 4936
22.8%
Latin
ValueCountFrequency (%)
B 15
18.1%
A 9
 
10.8%
I 4
 
4.8%
e 4
 
4.8%
S 4
 
4.8%
P 4
 
4.8%
R 3
 
3.6%
C 3
 
3.6%
H 3
 
3.6%
D 3
 
3.6%
Other values (24) 31
37.3%
Common
ValueCountFrequency (%)
7759
43.7%
1 1798
 
10.1%
- 1596
 
9.0%
2 1372
 
7.7%
3 833
 
4.7%
6 779
 
4.4%
5 777
 
4.4%
4 752
 
4.2%
0 558
 
3.1%
7 518
 
2.9%
Other values (6) 998
 
5.6%
Han
ValueCountFrequency (%)
休 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21617
54.8%
ASCII 17822
45.2%
CJK 1
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7759
43.5%
1 1798
 
10.1%
- 1596
 
9.0%
2 1372
 
7.7%
3 833
 
4.7%
6 779
 
4.4%
5 777
 
4.4%
4 752
 
4.2%
0 558
 
3.1%
7 518
 
2.9%
Other values (39) 1080
 
6.1%
Hangul
ValueCountFrequency (%)
구 2095
9.7%
광 1829
 
8.5%
동 1767
 
8.2%
진 1740
 
8.0%
시 1721
 
8.0%
서 1713
 
7.9%
울 1712
 
7.9%
특 1709
 
7.9%
별 1709
 
7.9%
양 686
 
3.2%
Other values (221) 4936
22.8%
CJK
ValueCountFrequency (%)
休 1
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

소재지전화
Text

MISSING 

Distinct869
Distinct (%)98.6%
Missing828
Missing (%)48.4%
Memory size13.5 KiB
2024-03-15T02:05:41.697068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length11.205448
Min length9

Characters and Unicode

Total characters9872
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique857 ?
Unique (%)97.3%

Sample

1st row02-450-4425
2nd row02-463-1981
3rd row02-463-2043
4th row02-3436-0778
5th row02-497-7714
ValueCountFrequency (%)
02-3436-6022 2
 
0.2%
02-6084-7771 2
 
0.2%
02-450-4425 2
 
0.2%
02-462-0615 2
 
0.2%
02-455-1937 2
 
0.2%
02-452-1255 2
 
0.2%
02-453-7015 2
 
0.2%
02 2
 
0.2%
02-512-4852 2
 
0.2%
02-499-0099 2
 
0.2%
Other values (866) 869
97.8%
2024-03-15T02:05:43.252640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1762
17.8%
2 1392
14.1%
0 1388
14.1%
4 1331
13.5%
5 783
7.9%
6 702
 
7.1%
7 592
 
6.0%
3 550
 
5.6%
8 462
 
4.7%
1 458
 
4.6%
Other values (2) 452
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8100
82.1%
Dash Punctuation 1762
 
17.8%
Space Separator 10
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1392
17.2%
0 1388
17.1%
4 1331
16.4%
5 783
9.7%
6 702
8.7%
7 592
7.3%
3 550
 
6.8%
8 462
 
5.7%
1 458
 
5.7%
9 442
 
5.5%
Dash Punctuation
ValueCountFrequency (%)
- 1762
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9872
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1762
17.8%
2 1392
14.1%
0 1388
14.1%
4 1331
13.5%
5 783
7.9%
6 702
 
7.1%
7 592
 
6.0%
3 550
 
5.6%
8 462
 
4.7%
1 458
 
4.6%
Other values (2) 452
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9872
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1762
17.8%
2 1392
14.1%
0 1388
14.1%
4 1331
13.5%
5 783
7.9%
6 702
 
7.1%
7 592
 
6.0%
3 550
 
5.6%
8 462
 
4.7%
1 458
 
4.6%
Other values (2) 452
 
4.6%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
Minimum2024-02-07 00:00:00
Maximum2024-02-07 00:00:00
2024-03-15T02:05:43.542132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:05:43.848822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Missing values

2024-03-15T02:05:31.090902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T02:05:31.453589image/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)영업소 주소(지번)소재지전화데이터기준일
0숙박업(일반)워커힐서울특별시 광진구 워커힐로 177 (광장동)서울특별시 광진구 광장동 22-102-450-44252024-02-07
1숙박업(일반)큐(Q)서울특별시 광진구 동일로 168 (화양동)서울특별시 광진구 화양동 23-3902-463-19812024-02-07
2숙박업(일반)그랜드모텔서울특별시 광진구 동일로 148 (화양동)서울특별시 광진구 화양동 41-302-463-20432024-02-07
3숙박업(일반)황금모텔서울특별시 광진구 광나루로 517-5 (구의동)서울특별시 광진구 구의동 67-2102-3436-07782024-02-07
4숙박업(일반)신한모텔서울특별시 광진구 아차산로33길 67-4 (화양동)서울특별시 광진구 화양동 12-2002-497-77142024-02-07
5숙박업(일반)은하장여관서울특별시 광진구 능동로19길 53 (화양동)서울특별시 광진구 화양동 132-702-463-24592024-02-07
6숙박업(일반)호림장여관서울특별시 광진구 능동로17길 55 (화양동)서울특별시 광진구 화양동 132-6402-464-60442024-02-07
7숙박업(일반)부림장여관서울특별시 광진구 긴고랑로7길 7 (중곡동)서울특별시 광진구 중곡동 239-602-462-18282024-02-07
8숙박업(일반)중곡여관서울특별시 광진구 용마산로7길 6 (중곡동,2,3층)서울특별시 광진구 중곡동 125-7 2,3층02-454-34282024-02-07
9숙박업(일반)초원모텔서울특별시 광진구 동일로22길 28 (화양동)서울특별시 광진구 화양동 49-502-467-28582024-02-07
업종명업소명영업소 주소(도로명)영업소 주소(지번)소재지전화데이터기준일
1699일반미용업, 네일미용업, 화장ㆍ분장 미용업닥터모락건대점서울특별시 광진구 아차산로 226, 성용빌딩 6층 (자양동)서울특별시 광진구 자양동 7-17 성용빌딩<NA>2024-02-07
1700일반미용업, 네일미용업, 화장ㆍ분장 미용업셀렉트 헤어서울특별시 광진구 능동로13길 106, 1층 101호 (화양동)서울특별시 광진구 화양동 32-4 101호<NA>2024-02-07
1701피부미용업, 네일미용업, 화장ㆍ분장 미용업바이이연서울특별시 광진구 능동로 115, 지하1층 (화양동)서울특별시 광진구 화양동 5-46 지하1층02-464-31992024-02-07
1702피부미용업, 네일미용업, 화장ㆍ분장 미용업아이미(eye-mi)서울특별시 광진구 광나루로39길 11, 1층 137호 (구의동, 구의자이르네)서울특별시 광진구 구의동 671 구의자이르네<NA>2024-02-07
1703피부미용업, 네일미용업, 화장ㆍ분장 미용업마루(MARU)서울특별시 광진구 용마산로7길 5, 점포1호 (중곡동)서울특별시 광진구 중곡동 124-4<NA>2024-02-07
1704피부미용업, 네일미용업, 화장ㆍ분장 미용업네일조아(NAILZOA)서울특별시 광진구 아차산로 379, 송산빌딩 2층 201호 (구의동)서울특별시 광진구 구의동 246-104 송산빌딩<NA>2024-02-07
1705피부미용업, 네일미용업, 화장ㆍ분장 미용업끌림네일서울특별시 광진구 능동로 172, 빌리브 인테라스 1층 110호 (화양동)서울특별시 광진구 화양동 111-12 빌리브 인테라스<NA>2024-02-07
1706피부미용업, 네일미용업, 화장ㆍ분장 미용업루루앤아이래쉬건대점서울특별시 광진구 동일로22길 23, 1층 (화양동)서울특별시 광진구 화양동 44-26 1층<NA>2024-02-07
1707피부미용업, 네일미용업, 화장ㆍ분장 미용업차#네일아트서울특별시 광진구 면목로 172, 102호 (중곡동)서울특별시 광진구 중곡동 169-21<NA>2024-02-07
1708피부미용업, 네일미용업, 화장ㆍ분장 미용업네일드빈치(nail de vinci)서울특별시 광진구 천호대로111길 57, 경빈빌딩 1층 (중곡동)서울특별시 광진구 중곡동 141-2 경빈빌딩<NA>2024-02-07