Overview

Dataset statistics

Number of variables6
Number of observations1151
Missing cells1515
Missing cells (%)21.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory56.3 KiB
Average record size in memory50.1 B

Variable types

Categorical1
Text3
Numeric1
Unsupported1

Dataset

Description경기도 광명시 내 공중위생관리업소 현황에 대한 정보 데이터로 업종, 업소명, 주소지, 업소전화번호 등의 항목을 제공합니다.
Author경기도 광명시
URLhttps://www.data.go.kr/data/15006976/fileData.do

Alerts

소재지전화 has 359 (31.2%) missing valuesMissing
Unnamed: 5 has 1151 (100.0%) missing valuesMissing
Unnamed: 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 05:00:46.441073
Analysis finished2023-12-12 05:00:47.414948
Duration0.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct21
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
일반미용업
513 
피부미용업
117 
건물위생관리업
102 
세탁업
94 
네일미용업
76 
Other values (16)
249 

Length

Max length23
Median length5
Mean length5.8705474
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 513
44.6%
피부미용업 117
 
10.2%
건물위생관리업 102
 
8.9%
세탁업 94
 
8.2%
네일미용업 76
 
6.6%
숙박업(일반) 57
 
5.0%
이용업 55
 
4.8%
종합미용업 21
 
1.8%
피부미용업, 네일미용업 21
 
1.8%
화장ㆍ분장 미용업 20
 
1.7%
Other values (11) 75
 
6.5%

Length

2023-12-12T14:00:47.498211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 539
40.7%
피부미용업 164
 
12.4%
네일미용업 133
 
10.0%
건물위생관리업 102
 
7.7%
세탁업 94
 
7.1%
미용업 76
 
5.7%
화장ㆍ분장 73
 
5.5%
숙박업(일반 57
 
4.3%
이용업 55
 
4.2%
종합미용업 21
 
1.6%

업소명
Text

Distinct1118
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
2023-12-12T14:00:47.803967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length5.3301477
Min length1

Characters and Unicode

Total characters6135
Distinct characters523
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

Unique1093 ?
Unique (%)95.0%

Sample

1st row프라임호텔
2nd row행운장여관
3rd row뉴스타
4th row램파트
5th row제이모텔
ValueCountFrequency (%)
미용실 16
 
1.3%
주식회사 8
 
0.7%
세탁소 6
 
0.5%
헤어샵 4
 
0.3%
수헤어 4
 
0.3%
태후사랑 3
 
0.2%
헤어아트 3
 
0.2%
머리사랑 3
 
0.2%
백양세탁 3
 
0.2%
봄네일 3
 
0.2%
Other values (1129) 1162
95.6%
2023-12-12T14:00:48.351910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 360
 
5.9%
헤 336
 
5.5%
이 179
 
2.9%
스 153
 
2.5%
미 133
 
2.2%
리 132
 
2.2%
일 118
 
1.9%
네 99
 
1.6%
용 88
 
1.4%
세 85
 
1.4%
Other values (513) 4452
72.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5913
96.4%
Space Separator 64
 
1.0%
Open Punctuation 44
 
0.7%
Close Punctuation 44
 
0.7%
Decimal Number 40
 
0.7%
Lowercase Letter 16
 
0.3%
Uppercase Letter 11
 
0.2%
Other Punctuation 2
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 360
 
6.1%
헤 336
 
5.7%
이 179
 
3.0%
스 153
 
2.6%
미 133
 
2.2%
리 132
 
2.2%
일 118
 
2.0%
네 99
 
1.7%
용 88
 
1.5%
세 85
 
1.4%
Other values (482) 4230
71.5%
Lowercase Letter
ValueCountFrequency (%)
o 3
18.8%
d 3
18.8%
e 2
12.5%
a 2
12.5%
g 1
 
6.2%
y 1
 
6.2%
r 1
 
6.2%
h 1
 
6.2%
l 1
 
6.2%
i 1
 
6.2%
Decimal Number
ValueCountFrequency (%)
1 11
27.5%
2 10
25.0%
9 4
 
10.0%
4 4
 
10.0%
7 3
 
7.5%
8 3
 
7.5%
0 2
 
5.0%
5 2
 
5.0%
3 1
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
K 3
27.3%
A 2
18.2%
G 2
18.2%
V 1
 
9.1%
N 1
 
9.1%
X 1
 
9.1%
T 1
 
9.1%
Space Separator
ValueCountFrequency (%)
64
100.0%
Open Punctuation
ValueCountFrequency (%)
( 44
100.0%
Close Punctuation
ValueCountFrequency (%)
) 44
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5913
96.4%
Common 195
 
3.2%
Latin 27
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 360
 
6.1%
헤 336
 
5.7%
이 179
 
3.0%
스 153
 
2.6%
미 133
 
2.2%
리 132
 
2.2%
일 118
 
2.0%
네 99
 
1.7%
용 88
 
1.5%
세 85
 
1.4%
Other values (482) 4230
71.5%
Latin
ValueCountFrequency (%)
o 3
11.1%
d 3
11.1%
K 3
11.1%
A 2
 
7.4%
G 2
 
7.4%
e 2
 
7.4%
a 2
 
7.4%
g 1
 
3.7%
y 1
 
3.7%
r 1
 
3.7%
Other values (7) 7
25.9%
Common
ValueCountFrequency (%)
64
32.8%
( 44
22.6%
) 44
22.6%
1 11
 
5.6%
2 10
 
5.1%
9 4
 
2.1%
4 4
 
2.1%
7 3
 
1.5%
8 3
 
1.5%
0 2
 
1.0%
Other values (4) 6
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5913
96.4%
ASCII 222
 
3.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 360
 
6.1%
헤 336
 
5.7%
이 179
 
3.0%
스 153
 
2.6%
미 133
 
2.2%
리 132
 
2.2%
일 118
 
2.0%
네 99
 
1.7%
용 88
 
1.5%
세 85
 
1.4%
Other values (482) 4230
71.5%
ASCII
ValueCountFrequency (%)
64
28.8%
( 44
19.8%
) 44
19.8%
1 11
 
5.0%
2 10
 
4.5%
9 4
 
1.8%
4 4
 
1.8%
7 3
 
1.4%
o 3
 
1.4%
d 3
 
1.4%
Other values (21) 32
14.4%

영업소 주소(도로명)
Text

Distinct1134
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
2023-12-12T14:00:48.698364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length46
Mean length33.835795
Min length19

Characters and Unicode

Total characters38945
Distinct characters308
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

Unique1118 ?
Unique (%)97.1%

Sample

1st row경기도 광명시 범안로 1000 (하안동, 3층-10층)
2nd row경기도 광명시 목감로268번길 19 (광명동)
3rd row경기도 광명시 기아로6번길 8 (소하동,2-5층)
4th row경기도 광명시 범안로 986 (하안동)
5th row경기도 광명시 범안로 1002 (하안동)
ValueCountFrequency (%)
경기도 1151
 
14.3%
광명시 1151
 
14.3%
광명동 334
 
4.1%
1층 318
 
3.9%
철산동 185
 
2.3%
하안동 172
 
2.1%
소하동 163
 
2.0%
일직동 145
 
1.8%
2층 138
 
1.7%
오리로 111
 
1.4%
Other values (1301) 4204
52.1%
2023-12-12T14:00:49.311800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6934
 
17.8%
광 1872
 
4.8%
명 1794
 
4.6%
1 1770
 
4.5%
동 1368
 
3.5%
, 1291
 
3.3%
도 1239
 
3.2%
시 1234
 
3.2%
기 1184
 
3.0%
) 1176
 
3.0%
Other values (298) 19083
49.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21115
54.2%
Space Separator 6934
 
17.8%
Decimal Number 6733
 
17.3%
Other Punctuation 1300
 
3.3%
Close Punctuation 1176
 
3.0%
Open Punctuation 1176
 
3.0%
Dash Punctuation 230
 
0.6%
Uppercase Letter 185
 
0.5%
Lowercase Letter 89
 
0.2%
Math Symbol 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 1872
 
8.9%
명 1794
 
8.5%
동 1368
 
6.5%
도 1239
 
5.9%
시 1234
 
5.8%
기 1184
 
5.6%
로 1167
 
5.5%
경 1159
 
5.5%
호 862
 
4.1%
층 711
 
3.4%
Other values (257) 8525
40.4%
Uppercase Letter
ValueCountFrequency (%)
B 71
38.4%
A 22
 
11.9%
T 16
 
8.6%
C 15
 
8.1%
F 14
 
7.6%
S 9
 
4.9%
D 9
 
4.9%
I 6
 
3.2%
K 6
 
3.2%
E 4
 
2.2%
Other values (5) 13
 
7.0%
Decimal Number
ValueCountFrequency (%)
1 1770
26.3%
2 1109
16.5%
0 799
11.9%
3 637
 
9.5%
8 472
 
7.0%
4 458
 
6.8%
9 428
 
6.4%
5 408
 
6.1%
6 346
 
5.1%
7 306
 
4.5%
Lowercase Letter
ValueCountFrequency (%)
e 16
18.0%
s 15
16.9%
t 14
15.7%
h 14
15.7%
r 14
15.7%
i 14
15.7%
b 2
 
2.2%
Other Punctuation
ValueCountFrequency (%)
, 1291
99.3%
& 4
 
0.3%
/ 3
 
0.2%
. 2
 
0.2%
Space Separator
ValueCountFrequency (%)
6934
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1176
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1176
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 230
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21115
54.2%
Common 17556
45.1%
Latin 274
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 1872
 
8.9%
명 1794
 
8.5%
동 1368
 
6.5%
도 1239
 
5.9%
시 1234
 
5.8%
기 1184
 
5.6%
로 1167
 
5.5%
경 1159
 
5.5%
호 862
 
4.1%
층 711
 
3.4%
Other values (257) 8525
40.4%
Latin
ValueCountFrequency (%)
B 71
25.9%
A 22
 
8.0%
T 16
 
5.8%
e 16
 
5.8%
C 15
 
5.5%
s 15
 
5.5%
t 14
 
5.1%
h 14
 
5.1%
r 14
 
5.1%
F 14
 
5.1%
Other values (12) 63
23.0%
Common
ValueCountFrequency (%)
6934
39.5%
1 1770
 
10.1%
, 1291
 
7.4%
) 1176
 
6.7%
( 1176
 
6.7%
2 1109
 
6.3%
0 799
 
4.6%
3 637
 
3.6%
8 472
 
2.7%
4 458
 
2.6%
Other values (9) 1734
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21115
54.2%
ASCII 17830
45.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6934
38.9%
1 1770
 
9.9%
, 1291
 
7.2%
) 1176
 
6.6%
( 1176
 
6.6%
2 1109
 
6.2%
0 799
 
4.5%
3 637
 
3.6%
8 472
 
2.6%
4 458
 
2.6%
Other values (31) 2008
 
11.3%
Hangul
ValueCountFrequency (%)
광 1872
 
8.9%
명 1794
 
8.5%
동 1368
 
6.5%
도 1239
 
5.9%
시 1234
 
5.8%
기 1184
 
5.6%
로 1167
 
5.5%
경 1159
 
5.5%
호 862
 
4.1%
층 711
 
3.4%
Other values (257) 8525
40.4%

우편번호(도로명)
Real number (ℝ)

Distinct119
Distinct (%)10.4%
Missing5
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean14280.138
Minimum14200
Maximum14354
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.2 KiB
2023-12-12T14:00:49.755641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14200
5-th percentile14214
Q114245
median14279
Q314316
95-th percentile14345
Maximum14354
Range154
Interquartile range (IQR)71

Descriptive statistics

Standard deviation43.696274
Coefficient of variation (CV)0.0030599336
Kurtosis-1.1666591
Mean14280.138
Median Absolute Deviation (MAD)37
Skewness-0.025618372
Sum16365038
Variance1909.3644
MonotonicityNot monotonic
2023-12-12T14:00:49.958642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14345 51
 
4.4%
14344 42
 
3.6%
14333 39
 
3.4%
14306 38
 
3.3%
14316 31
 
2.7%
14303 28
 
2.4%
14305 28
 
2.4%
14202 25
 
2.2%
14248 24
 
2.1%
14221 24
 
2.1%
Other values (109) 816
70.9%
ValueCountFrequency (%)
14200 3
 
0.3%
14201 12
1.0%
14202 25
2.2%
14203 3
 
0.3%
14207 2
 
0.2%
14209 4
 
0.3%
14211 7
 
0.6%
14214 5
 
0.4%
14215 13
1.1%
14216 8
 
0.7%
ValueCountFrequency (%)
14354 4
 
0.3%
14353 4
 
0.3%
14351 14
 
1.2%
14349 13
 
1.1%
14348 13
 
1.1%
14347 1
 
0.1%
14346 3
 
0.3%
14345 51
4.4%
14344 42
3.6%
14341 2
 
0.2%

소재지전화
Text

MISSING 

Distinct787
Distinct (%)99.4%
Missing359
Missing (%)31.2%
Memory size9.1 KiB
2023-12-12T14:00:50.273833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length13.842172
Min length10

Characters and Unicode

Total characters10963
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

Unique783 ?
Unique (%)98.9%

Sample

1st row 02- 897-6781
2nd row 02-2612-9695
3rd row02- 897-3697
4th row 02- 897-8510
5th row 02- 898-4721
ValueCountFrequency (%)
02 272
 
24.5%
02-899 8
 
0.7%
02-898 7
 
0.6%
02-897 7
 
0.6%
031 4
 
0.4%
070 4
 
0.4%
897-7512 3
 
0.3%
02-2684-2326 2
 
0.2%
02-2613-9020 2
 
0.2%
893-8855 2
 
0.2%
Other values (796) 797
71.9%
2023-12-12T14:00:50.774619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1753
16.0%
2 1605
14.6%
- 1584
14.4%
0 1345
12.3%
8 1008
9.2%
6 838
7.6%
9 632
 
5.8%
1 601
 
5.5%
7 500
 
4.6%
5 398
 
3.6%
Other values (2) 699
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7626
69.6%
Space Separator 1753
 
16.0%
Dash Punctuation 1584
 
14.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1605
21.0%
0 1345
17.6%
8 1008
13.2%
6 838
11.0%
9 632
 
8.3%
1 601
 
7.9%
7 500
 
6.6%
5 398
 
5.2%
3 362
 
4.7%
4 337
 
4.4%
Space Separator
ValueCountFrequency (%)
1753
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1584
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10963
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1753
16.0%
2 1605
14.6%
- 1584
14.4%
0 1345
12.3%
8 1008
9.2%
6 838
7.6%
9 632
 
5.8%
1 601
 
5.5%
7 500
 
4.6%
5 398
 
3.6%
Other values (2) 699
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10963
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1753
16.0%
2 1605
14.6%
- 1584
14.4%
0 1345
12.3%
8 1008
9.2%
6 838
7.6%
9 632
 
5.8%
1 601
 
5.5%
7 500
 
4.6%
5 398
 
3.6%
Other values (2) 699
 
6.4%

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1151
Missing (%)100.0%
Memory size10.2 KiB

Interactions

2023-12-12T14:00:47.001299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:00:50.930067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명우편번호(도로명)
업종명1.0000.305
우편번호(도로명)0.3051.000
2023-12-12T14:00:51.041302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호(도로명)업종명
우편번호(도로명)1.0000.118
업종명0.1181.000

Missing values

2023-12-12T14:00:47.145434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:00:47.273092image/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.
2023-12-12T14:00:47.367534image/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

업종명업소명영업소 주소(도로명)우편번호(도로명)소재지전화Unnamed: 5
0숙박업(일반)프라임호텔경기도 광명시 범안로 1000 (하안동, 3층-10층)1430302- 897-6781<NA>
1숙박업(일반)행운장여관경기도 광명시 목감로268번길 19 (광명동)1421102-2612-9695<NA>
2숙박업(일반)뉴스타경기도 광명시 기아로6번길 8 (소하동,2-5층)1432702- 897-3697<NA>
3숙박업(일반)램파트경기도 광명시 범안로 986 (하안동)1430302- 897-8510<NA>
4숙박업(일반)제이모텔경기도 광명시 범안로 1002 (하안동)1430302- 898-4721<NA>
5숙박업(일반)라마다모텔경기도 광명시 새터로121번길 21 (광명동)1426302-2612-2526<NA>
6숙박업(일반)몰디브모텔경기도 광명시 오리로 1023 (광명동)1428502-2616-1688<NA>
7숙박업(일반)동원여관경기도 광명시 광명로831번길 5 (광명동)1429402-2686-9741<NA>
8숙박업(일반)대영장경기도 광명시 광명로888번길 8 (광명동)1426702-2688-6088<NA>
9숙박업(일반)에이치모텔경기도 광명시 새터로121번길 29 (광명동)1426202-2615-7347<NA>
업종명업소명영업소 주소(도로명)우편번호(도로명)소재지전화Unnamed: 5
1141일반미용업, 네일미용업, 화장ㆍ분장 미용업제이니헤어경기도 광명시 광명로 894-1, 3층 전체호 (광명동)14262<NA><NA>
1142피부미용업, 네일미용업, 화장ㆍ분장 미용업광명탈렌트경기도 광명시 광명로 898, 광명빌딩 3층 304호 (광명동)1426202-2618-1616<NA>
1143피부미용업, 네일미용업, 화장ㆍ분장 미용업런던네일경기도 광명시 디지털로 29, 2층 210~213호 (철산동, 정인코아빌딩)1423902-2618-6058<NA>
1144피부미용업, 네일미용업, 화장ㆍ분장 미용업포쉬네일세이브존광명점경기도 광명시 철망산로 87, 세이브존 2층 일부호 (철산동)1424302-2686-4145<NA>
1145피부미용업, 네일미용업, 화장ㆍ분장 미용업손끝꽃경기도 광명시 디지털로33번길 5, 그랜드프라자 502호 (철산동)14239<NA><NA>
1146피부미용업, 네일미용업, 화장ㆍ분장 미용업네일그리다경기도 광명시 광명역로 26, 1층 106호 (일직동, 광명역파크자이)14349<NA><NA>
1147피부미용업, 네일미용업, 화장ㆍ분장 미용업슈르르네일경기도 광명시 성채안로 26, 가상가동 1층 106호 (소하동, 광명역세권휴먼시아)14325<NA><NA>
1148피부미용업, 네일미용업, 화장ㆍ분장 미용업벨라비스경기도 광명시 소하로 94, 선우퓨처마크 2층 201호 (소하동)1431602- 898-6385<NA>
1149피부미용업, 네일미용업, 화장ㆍ분장 미용업뷰티데이경기도 광명시 도덕로38번길 3, 3층 2호 (광명동)14279<NA><NA>
1150피부미용업, 네일미용업, 화장ㆍ분장 미용업반디인하우스광명경기도 광명시 양지로 16, B1층 1,2호 (일직동, 광명역 써밋플레이스)1434602-2039-0007<NA>