Overview

Dataset statistics

Number of variables9
Number of observations1703
Missing cells5520
Missing cells (%)36.0%
Duplicate rows1
Duplicate rows (%)0.1%
Total size in memory124.9 KiB
Average record size in memory75.1 B

Variable types

Categorical3
Text3
Numeric3

Dataset

Description경상남도 양신시 관내 공중위생업소(목욕장업, 피부미용업, 일반미용업, 네일미용업, 화장, 세탁업, 숙박업 등) 업소소재지, 전화번호 정보 등
Author경상남도 양산시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15006926

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 1 (0.1%) duplicate rowsDuplicates
객실수 is highly overall correlated with 양실수 and 1 other fieldsHigh correlation
한실수 is highly overall correlated with 양실수 and 1 other fieldsHigh correlation
양실수 is highly overall correlated with 객실수 and 2 other fieldsHigh correlation
업종명 is highly overall correlated with 객실수 and 3 other fieldsHigh correlation
업태명 is highly overall correlated with 업종명High correlation
소재지전화 has 827 (48.6%) missing valuesMissing
객실수 has 1564 (91.8%) missing valuesMissing
한실수 has 1565 (91.9%) missing valuesMissing
양실수 has 1564 (91.8%) missing valuesMissing
한실수 has 50 (2.9%) zerosZeros

Reproduction

Analysis started2023-12-10 23:26:31.139627
Analysis finished2023-12-10 23:26:33.101053
Duration1.96 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
일반미용업
511 
미용업
225 
피부미용업
150 
숙박업(일반)
139 
네일미용업
123 
Other values (17)
555 

Length

Max length23
Median length19
Mean length5.6294774
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 511
30.0%
미용업 225
13.2%
피부미용업 150
 
8.8%
숙박업(일반) 139
 
8.2%
네일미용업 123
 
7.2%
세탁업 103
 
6.0%
이용업 94
 
5.5%
건물위생관리업 77
 
4.5%
종합미용업 59
 
3.5%
목욕장업 58
 
3.4%
Other values (12) 164
 
9.6%

Length

2023-12-11T08:26:33.172162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 567
28.8%
미용업 341
17.3%
피부미용업 206
 
10.5%
네일미용업 204
 
10.4%
숙박업(일반 139
 
7.1%
화장ㆍ분장 116
 
5.9%
세탁업 103
 
5.2%
이용업 94
 
4.8%
건물위생관리업 77
 
3.9%
종합미용업 59
 
3.0%
Other values (2) 65
 
3.3%

업소명
Text

Distinct1656
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
2023-12-11T08:26:33.424432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length5.870229
Min length1

Characters and Unicode

Total characters9997
Distinct characters624
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

Unique1613 ?
Unique (%)94.7%

Sample

1st row경남여관
2nd row산장여관
3rd row송원장여관
4th row꼭지모텔
5th row티파니
ValueCountFrequency (%)
헤어 18
 
0.9%
hair 12
 
0.6%
네일 11
 
0.6%
주식회사 10
 
0.5%
미용실 10
 
0.5%
에스테틱 8
 
0.4%
양산점 8
 
0.4%
더 7
 
0.4%
모텔 6
 
0.3%
nail 6
 
0.3%
Other values (1773) 1894
95.2%
2023-12-11T08:26:33.794081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 562
 
5.6%
헤 529
 
5.3%
287
 
2.9%
이 273
 
2.7%
스 213
 
2.1%
미 178
 
1.8%
일 175
 
1.8%
네 166
 
1.7%
리 158
 
1.6%
) 147
 
1.5%
Other values (614) 7309
73.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8645
86.5%
Lowercase Letter 366
 
3.7%
Space Separator 287
 
2.9%
Uppercase Letter 275
 
2.8%
Close Punctuation 147
 
1.5%
Open Punctuation 147
 
1.5%
Other Punctuation 66
 
0.7%
Decimal Number 57
 
0.6%
Dash Punctuation 3
 
< 0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 562
 
6.5%
헤 529
 
6.1%
이 273
 
3.2%
스 213
 
2.5%
미 178
 
2.1%
일 175
 
2.0%
네 166
 
1.9%
리 158
 
1.8%
용 136
 
1.6%
라 120
 
1.4%
Other values (544) 6135
71.0%
Lowercase Letter
ValueCountFrequency (%)
e 42
11.5%
a 40
10.9%
i 40
10.9%
l 27
 
7.4%
n 26
 
7.1%
o 24
 
6.6%
r 24
 
6.6%
h 24
 
6.6%
s 18
 
4.9%
t 18
 
4.9%
Other values (13) 83
22.7%
Uppercase Letter
ValueCountFrequency (%)
A 31
 
11.3%
N 24
 
8.7%
J 21
 
7.6%
S 20
 
7.3%
I 19
 
6.9%
L 18
 
6.5%
H 18
 
6.5%
E 15
 
5.5%
B 14
 
5.1%
R 14
 
5.1%
Other values (13) 81
29.5%
Decimal Number
ValueCountFrequency (%)
2 14
24.6%
1 10
17.5%
3 7
12.3%
0 6
10.5%
6 6
10.5%
9 4
 
7.0%
8 3
 
5.3%
7 3
 
5.3%
5 3
 
5.3%
4 1
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 19
28.8%
, 14
21.2%
& 13
19.7%
# 10
15.2%
' 5
 
7.6%
: 3
 
4.5%
· 1
 
1.5%
@ 1
 
1.5%
Space Separator
ValueCountFrequency (%)
287
100.0%
Close Punctuation
ValueCountFrequency (%)
) 147
100.0%
Open Punctuation
ValueCountFrequency (%)
( 147
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8643
86.5%
Common 711
 
7.1%
Latin 641
 
6.4%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 562
 
6.5%
헤 529
 
6.1%
이 273
 
3.2%
스 213
 
2.5%
미 178
 
2.1%
일 175
 
2.0%
네 166
 
1.9%
리 158
 
1.8%
용 136
 
1.6%
라 120
 
1.4%
Other values (543) 6133
71.0%
Latin
ValueCountFrequency (%)
e 42
 
6.6%
a 40
 
6.2%
i 40
 
6.2%
A 31
 
4.8%
l 27
 
4.2%
n 26
 
4.1%
o 24
 
3.7%
r 24
 
3.7%
h 24
 
3.7%
N 24
 
3.7%
Other values (36) 339
52.9%
Common
ValueCountFrequency (%)
287
40.4%
) 147
20.7%
( 147
20.7%
. 19
 
2.7%
, 14
 
2.0%
2 14
 
2.0%
& 13
 
1.8%
# 10
 
1.4%
1 10
 
1.4%
3 7
 
1.0%
Other values (14) 43
 
6.0%
Han
ValueCountFrequency (%)
美 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8643
86.5%
ASCII 1351
 
13.5%
CJK 2
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 562
 
6.5%
헤 529
 
6.1%
이 273
 
3.2%
스 213
 
2.5%
미 178
 
2.1%
일 175
 
2.0%
네 166
 
1.9%
리 158
 
1.8%
용 136
 
1.6%
라 120
 
1.4%
Other values (543) 6133
71.0%
ASCII
ValueCountFrequency (%)
287
21.2%
) 147
 
10.9%
( 147
 
10.9%
e 42
 
3.1%
a 40
 
3.0%
i 40
 
3.0%
A 31
 
2.3%
l 27
 
2.0%
n 26
 
1.9%
o 24
 
1.8%
Other values (59) 540
40.0%
CJK
ValueCountFrequency (%)
美 2
100.0%
None
ValueCountFrequency (%)
· 1
100.0%

영업소 주소(도로명)
Text

Distinct1674
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
2023-12-11T08:26:34.069141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length49
Mean length30.311803
Min length18

Characters and Unicode

Total characters51621
Distinct characters325
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

Unique1648 ?
Unique (%)96.8%

Sample

1st row경상남도 양산시 북안남4길 8-1 (북부동)
2nd row경상남도 양산시 하북면 신평강변로 84
3rd row경상남도 양산시 장터3길 16 (중부동)
4th row경상남도 양산시 북안남3길 9 (북부동)
5th row경상남도 양산시 장터2길 14 (중부동)
ValueCountFrequency (%)
경상남도 1703
 
15.2%
양산시 1703
 
15.2%
물금읍 529
 
4.7%
1층 493
 
4.4%
삼호동 164
 
1.5%
중부동 155
 
1.4%
2층 149
 
1.3%
동면 138
 
1.2%
상가동 111
 
1.0%
평산동 106
 
0.9%
Other values (1572) 5928
53.0%
2023-12-11T08:26:34.511208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9521
 
18.4%
1 2788
 
5.4%
산 2179
 
4.2%
상 2045
 
4.0%
양 2001
 
3.9%
남 1858
 
3.6%
시 1823
 
3.5%
도 1776
 
3.4%
경 1716
 
3.3%
동 1571
 
3.0%
Other values (315) 24343
47.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29299
56.8%
Space Separator 9521
 
18.4%
Decimal Number 8653
 
16.8%
Other Punctuation 1396
 
2.7%
Open Punctuation 1122
 
2.2%
Close Punctuation 1122
 
2.2%
Dash Punctuation 386
 
0.7%
Uppercase Letter 85
 
0.2%
Math Symbol 24
 
< 0.1%
Lowercase Letter 13
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 2179
 
7.4%
상 2045
 
7.0%
양 2001
 
6.8%
남 1858
 
6.3%
시 1823
 
6.2%
도 1776
 
6.1%
경 1716
 
5.9%
동 1571
 
5.4%
호 1140
 
3.9%
로 885
 
3.0%
Other values (278) 12305
42.0%
Uppercase Letter
ValueCountFrequency (%)
B 25
29.4%
A 21
24.7%
N 8
 
9.4%
E 5
 
5.9%
C 5
 
5.9%
D 3
 
3.5%
I 3
 
3.5%
L 3
 
3.5%
T 3
 
3.5%
P 3
 
3.5%
Other values (3) 6
 
7.1%
Decimal Number
ValueCountFrequency (%)
1 2788
32.2%
2 1273
14.7%
0 970
 
11.2%
3 819
 
9.5%
4 591
 
6.8%
5 581
 
6.7%
7 472
 
5.5%
6 471
 
5.4%
8 380
 
4.4%
9 308
 
3.6%
Lowercase Letter
ValueCountFrequency (%)
e 8
61.5%
c 2
 
15.4%
t 1
 
7.7%
i 1
 
7.7%
y 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
, 1385
99.2%
. 7
 
0.5%
/ 2
 
0.1%
@ 2
 
0.1%
Space Separator
ValueCountFrequency (%)
9521
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1122
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1122
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 386
100.0%
Math Symbol
ValueCountFrequency (%)
~ 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29299
56.8%
Common 22224
43.1%
Latin 98
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 2179
 
7.4%
상 2045
 
7.0%
양 2001
 
6.8%
남 1858
 
6.3%
시 1823
 
6.2%
도 1776
 
6.1%
경 1716
 
5.9%
동 1571
 
5.4%
호 1140
 
3.9%
로 885
 
3.0%
Other values (278) 12305
42.0%
Common
ValueCountFrequency (%)
9521
42.8%
1 2788
 
12.5%
, 1385
 
6.2%
2 1273
 
5.7%
( 1122
 
5.0%
) 1122
 
5.0%
0 970
 
4.4%
3 819
 
3.7%
4 591
 
2.7%
5 581
 
2.6%
Other values (9) 2052
 
9.2%
Latin
ValueCountFrequency (%)
B 25
25.5%
A 21
21.4%
e 8
 
8.2%
N 8
 
8.2%
E 5
 
5.1%
C 5
 
5.1%
D 3
 
3.1%
I 3
 
3.1%
L 3
 
3.1%
T 3
 
3.1%
Other values (8) 14
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29299
56.8%
ASCII 22322
43.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9521
42.7%
1 2788
 
12.5%
, 1385
 
6.2%
2 1273
 
5.7%
( 1122
 
5.0%
) 1122
 
5.0%
0 970
 
4.3%
3 819
 
3.7%
4 591
 
2.6%
5 581
 
2.6%
Other values (27) 2150
 
9.6%
Hangul
ValueCountFrequency (%)
산 2179
 
7.4%
상 2045
 
7.0%
양 2001
 
6.8%
남 1858
 
6.3%
시 1823
 
6.2%
도 1776
 
6.1%
경 1716
 
5.9%
동 1571
 
5.4%
호 1140
 
3.9%
로 885
 
3.0%
Other values (278) 12305
42.0%

소재지전화
Text

MISSING 

Distinct864
Distinct (%)98.6%
Missing827
Missing (%)48.6%
Memory size13.4 KiB
2023-12-11T08:26:34.784096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.022831
Min length9

Characters and Unicode

Total characters10532
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

Unique852 ?
Unique (%)97.3%

Sample

1st row055-386-2678
2nd row055-382-6723
3rd row055-385-9333
4th row055-382-8321
5th row055-382-4849
ValueCountFrequency (%)
055-374-0100 2
 
0.2%
055-383-0671 2
 
0.2%
055-382-0035 2
 
0.2%
055-362-8891 2
 
0.2%
055-366-4468 2
 
0.2%
055-365-7615 2
 
0.2%
055-382-8321 2
 
0.2%
055-384-0061 2
 
0.2%
055-384-0925 2
 
0.2%
055-388-3882 2
 
0.2%
Other values (854) 856
97.7%
2023-12-11T08:26:35.224674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 2188
20.8%
- 1751
16.6%
0 1358
12.9%
3 1323
12.6%
8 903
8.6%
6 756
 
7.2%
7 537
 
5.1%
2 502
 
4.8%
1 454
 
4.3%
4 401
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8781
83.4%
Dash Punctuation 1751
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2188
24.9%
0 1358
15.5%
3 1323
15.1%
8 903
10.3%
6 756
 
8.6%
7 537
 
6.1%
2 502
 
5.7%
1 454
 
5.2%
4 401
 
4.6%
9 359
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 1751
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10532
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 2188
20.8%
- 1751
16.6%
0 1358
12.9%
3 1323
12.6%
8 903
8.6%
6 756
 
7.2%
7 537
 
5.1%
2 502
 
4.8%
1 454
 
4.3%
4 401
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10532
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 2188
20.8%
- 1751
16.6%
0 1358
12.9%
3 1323
12.6%
8 903
8.6%
6 756
 
7.2%
7 537
 
5.1%
2 502
 
4.8%
1 454
 
4.3%
4 401
 
3.8%

업태명
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
일반미용업
814 
피부미용업
176 
네일아트업
161 
여관업
118 
일반이용업
93 
Other values (16)
341 

Length

Max length14
Median length5
Mean length4.9665297
Min length2

Unique

Unique3 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 814
47.8%
피부미용업 176
 
10.3%
네일아트업 161
 
9.5%
여관업 118
 
6.9%
일반이용업 93
 
5.5%
일반세탁업 92
 
5.4%
건물위생관리업 76
 
4.5%
메이크업업 55
 
3.2%
공동탕업 46
 
2.7%
기타 18
 
1.1%
Other values (11) 54
 
3.2%

Length

2023-12-11T08:26:35.388759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 814
47.3%
피부미용업 176
 
10.2%
네일아트업 161
 
9.3%
여관업 118
 
6.9%
일반이용업 93
 
5.4%
일반세탁업 92
 
5.3%
건물위생관리업 77
 
4.5%
메이크업업 55
 
3.2%
공동탕업 46
 
2.7%
기타 37
 
2.1%
Other values (10) 53
 
3.1%

객실수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct37
Distinct (%)26.6%
Missing1564
Missing (%)91.8%
Infinite0
Infinite (%)0.0%
Mean27.992806
Minimum10
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2023-12-11T08:26:35.712162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile12
Q119
median28
Q335
95-th percentile45
Maximum82
Range72
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.084325
Coefficient of variation (CV)0.3959705
Kurtosis3.1894887
Mean27.992806
Median Absolute Deviation (MAD)8
Skewness1.082699
Sum3891
Variance122.86227
MonotonicityNot monotonic
2023-12-11T08:26:36.214705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
19 9
 
0.5%
32 8
 
0.5%
24 8
 
0.5%
18 8
 
0.5%
15 7
 
0.4%
35 7
 
0.4%
29 7
 
0.4%
17 6
 
0.4%
28 5
 
0.3%
36 5
 
0.3%
Other values (27) 69
 
4.1%
(Missing) 1564
91.8%
ValueCountFrequency (%)
10 3
 
0.2%
11 2
 
0.1%
12 3
 
0.2%
14 1
 
0.1%
15 7
0.4%
16 1
 
0.1%
17 6
0.4%
18 8
0.5%
19 9
0.5%
20 4
0.2%
ValueCountFrequency (%)
82 1
 
0.1%
61 1
 
0.1%
52 2
0.1%
48 1
 
0.1%
46 1
 
0.1%
45 3
0.2%
42 3
0.2%
41 3
0.2%
40 3
0.2%
39 4
0.2%

한실수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct26
Distinct (%)18.8%
Missing1565
Missing (%)91.9%
Infinite0
Infinite (%)0.0%
Mean6.3478261
Minimum0
Maximum42
Zeros50
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2023-12-11T08:26:36.549727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q310
95-th percentile20.15
Maximum42
Range42
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.7286576
Coefficient of variation (CV)1.2175283
Kurtosis4.408229
Mean6.3478261
Median Absolute Deviation (MAD)4
Skewness1.8078015
Sum876
Variance59.732149
MonotonicityNot monotonic
2023-12-11T08:26:36.724257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 50
 
2.9%
2 9
 
0.5%
12 7
 
0.4%
9 7
 
0.4%
7 7
 
0.4%
5 6
 
0.4%
3 5
 
0.3%
8 5
 
0.3%
10 5
 
0.3%
11 5
 
0.3%
Other values (16) 32
 
1.9%
(Missing) 1565
91.9%
ValueCountFrequency (%)
0 50
2.9%
1 3
 
0.2%
2 9
 
0.5%
3 5
 
0.3%
4 4
 
0.2%
5 6
 
0.4%
6 3
 
0.2%
7 7
 
0.4%
8 5
 
0.3%
9 7
 
0.4%
ValueCountFrequency (%)
42 1
0.1%
36 1
0.1%
30 2
0.1%
26 1
0.1%
22 1
0.1%
21 1
0.1%
20 1
0.1%
18 2
0.1%
17 1
0.1%
16 2
0.1%

양실수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct46
Distinct (%)33.1%
Missing1564
Missing (%)91.8%
Infinite0
Infinite (%)0.0%
Mean21.690647
Minimum0
Maximum80
Zeros5
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2023-12-11T08:26:36.875963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.8
Q112
median19
Q331
95-th percentile41.1
Maximum80
Range80
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.097654
Coefficient of variation (CV)0.60383877
Kurtosis1.5179507
Mean21.690647
Median Absolute Deviation (MAD)10
Skewness0.77246444
Sum3015
Variance171.54854
MonotonicityNot monotonic
2023-12-11T08:26:37.016349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
14 8
 
0.5%
12 8
 
0.5%
15 7
 
0.4%
9 7
 
0.4%
19 6
 
0.4%
32 5
 
0.3%
0 5
 
0.3%
10 4
 
0.2%
16 4
 
0.2%
37 4
 
0.2%
Other values (36) 81
 
4.8%
(Missing) 1564
91.8%
ValueCountFrequency (%)
0 5
0.3%
1 1
 
0.1%
2 1
 
0.1%
4 1
 
0.1%
5 3
0.2%
6 2
 
0.1%
7 2
 
0.1%
8 4
0.2%
9 7
0.4%
10 4
0.2%
ValueCountFrequency (%)
80 1
 
0.1%
52 1
 
0.1%
46 2
0.1%
45 1
 
0.1%
42 2
0.1%
41 2
0.1%
40 2
0.1%
39 2
0.1%
38 4
0.2%
37 4
0.2%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
2022-03-24
1703 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-03-24
2nd row2022-03-24
3rd row2022-03-24
4th row2022-03-24
5th row2022-03-24

Common Values

ValueCountFrequency (%)
2022-03-24 1703
100.0%

Length

2023-12-11T08:26:37.193114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:26:37.294771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-03-24 1703
100.0%

Interactions

2023-12-11T08:26:32.336964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:31.825050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:32.104466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:32.429197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:31.921661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:32.199254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:32.513701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:31.995552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:26:32.264992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:26:37.377107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업태명객실수한실수양실수
업종명1.0000.953NaNNaNNaN
업태명0.9531.0000.7960.2900.814
객실수NaN0.7961.0000.6870.928
한실수NaN0.2900.6871.0000.493
양실수NaN0.8140.9280.4931.000
2023-12-11T08:26:37.492522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업태명
업종명1.0000.655
업태명0.6551.000
2023-12-11T08:26:37.582578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
객실수한실수양실수업종명업태명
객실수1.000-0.1300.8001.0000.455
한실수-0.1301.000-0.6421.0000.199
양실수0.800-0.6421.0001.0000.487
업종명1.0001.0001.0001.0000.655
업태명0.4550.1990.4870.6551.000

Missing values

2023-12-11T08:26:32.855152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:26:32.961769image/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.
2023-12-11T08:26:33.050726image/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)소재지전화업태명객실수한실수양실수데이터기준일자
0숙박업(일반)경남여관경상남도 양산시 북안남4길 8-1 (북부동)055-386-2678여관업101002022-03-24
1숙박업(일반)산장여관경상남도 양산시 하북면 신평강변로 84055-382-6723여관업17892022-03-24
2숙박업(일반)송원장여관경상남도 양산시 장터3길 16 (중부동)055-385-9333여관업111012022-03-24
3숙박업(일반)꼭지모텔경상남도 양산시 북안남3길 9 (북부동)<NA>여관업2814142022-03-24
4숙박업(일반)티파니경상남도 양산시 장터2길 14 (중부동)055-382-8321여관업2311122022-03-24
5숙박업(일반)제일장여관경상남도 양산시 북안남4길 15 (북부동)055-382-4849여관업12842022-03-24
6숙박업(일반)향촌장여관경상남도 양산시 북안남4길 13 (북부동)055-382-3208여관업10552022-03-24
7숙박업(일반)동원장여관경상남도 양산시 서창서1길 8-10 (삼호동)055-365-0218여관업199102022-03-24
8숙박업(일반)보미모텔경상남도 양산시 장터3길 13 (중부동)055-385-6547여관업172152022-03-24
9숙박업(일반)연호장여관경상남도 양산시 연호2길 9-8 (삼호동)055-366-2217여관업15782022-03-24
업종명업소명영업소 주소(도로명)소재지전화업태명객실수한실수양실수데이터기준일자
1693피부미용업, 네일미용업, 화장ㆍ분장 미용업속눈썹휘날리며경상남도 양산시 동면 금오2길 75, 104호<NA>메이크업업<NA><NA><NA>2022-03-24
1694피부미용업, 네일미용업, 화장ㆍ분장 미용업뷰티풀라인(beautiful line)경상남도 양산시 평산6길 25 (평산동)<NA>피부미용업<NA><NA><NA>2022-03-24
1695피부미용업, 네일미용업, 화장ㆍ분장 미용업네일에 담다경상남도 양산시 물금읍 범구로 11, 국보프라자 108호<NA>네일아트업<NA><NA><NA>2022-03-24
1696피부미용업, 네일미용업, 화장ㆍ분장 미용업뮤즈뷰티경상남도 양산시 물금읍 증산역로 177, 라피에스타 4층 4-073호<NA>피부미용업<NA><NA><NA>2022-03-24
1697피부미용업, 네일미용업, 화장ㆍ분장 미용업안녕, 소중한 네일경상남도 양산시 물금읍 화합길 37, 나래월드빌딩 111호<NA>네일아트업<NA><NA><NA>2022-03-24
1698피부미용업, 네일미용업, 화장ㆍ분장 미용업제시아카데미경상남도 양산시 물금읍 서들로 148, 채움 307호<NA>피부미용업<NA><NA><NA>2022-03-24
1699피부미용업, 네일미용업, 화장ㆍ분장 미용업글램뷰티경상남도 양산시 물금읍 백호로 155, 411동 129호 (양산신도시4차동원로얄듀크비스타)<NA>네일아트업<NA><NA><NA>2022-03-24
1700피부미용업, 네일미용업, 화장ㆍ분장 미용업피우다,네일경상남도 양산시 동면 금오로 253, 101호<NA>네일아트업<NA><NA><NA>2022-03-24
1701피부미용업, 네일미용업, 화장ㆍ분장 미용업루비&네일경상남도 양산시 동면 금오7길 37-12, 1층일부<NA>네일아트업<NA><NA><NA>2022-03-24
1702피부미용업, 네일미용업, 화장ㆍ분장 미용업로얄라인(Royal Line)경상남도 양산시 물금읍 야리1길 22, 미래타워 201호<NA>피부미용업<NA><NA><NA>2022-03-24

Duplicate rows

Most frequently occurring

업종명업소명영업소 주소(도로명)소재지전화업태명객실수한실수양실수데이터기준일자# duplicates
0이용업젠틀맨경상남도 양산시 서창서1길 5-2 (삼호동)<NA>일반이용업<NA><NA><NA>2022-03-242