Overview

Dataset statistics

Number of variables12
Number of observations8802
Missing cells8838
Missing cells (%)8.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory859.7 KiB
Average record size in memory100.0 B

Variable types

Numeric4
Text3
Categorical4
DateTime1

Dataset

Description업소일련번호,업소명,동명,주소,면적(㎡),전화번호,업종,품목코드,품목,가격(원),점검일자,구명
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-1169/S/1/datasetView.do

Alerts

동명 is highly overall correlated with 구명High correlation
구명 is highly overall correlated with 동명High correlation
품목 is highly overall correlated with 면적(㎡) and 2 other fieldsHigh correlation
업종 is highly overall correlated with 품목High correlation
업소일련번호 is highly overall correlated with 면적(㎡)High correlation
면적(㎡) is highly overall correlated with 업소일련번호 and 1 other fieldsHigh correlation
품목코드 is highly overall correlated with 품목High correlation
면적(㎡) has 7390 (84.0%) missing valuesMissing
전화번호 has 386 (4.4%) missing valuesMissing
가격(원) has 1062 (12.1%) missing valuesMissing
면적(㎡) has 481 (5.5%) zerosZeros

Reproduction

Analysis started2024-05-11 01:36:10.910549
Analysis finished2024-05-11 01:36:31.017666
Duration20.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업소일련번호
Real number (ℝ)

HIGH CORRELATION 

Distinct4086
Distinct (%)46.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4671362 × 1012
Minimum1.4188906 × 1012
Maximum1.7127945 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.5 KiB
2024-05-11T01:36:31.325162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4188906 × 1012
5-th percentile1.4188906 × 1012
Q11.4188907 × 1012
median1.4188907 × 1012
Q31.5020826 × 1012
95-th percentile1.6541446 × 1012
Maximum1.7127945 × 1012
Range2.9390388 × 1011
Interquartile range (IQR)8.3191939 × 1010

Descriptive statistics

Standard deviation8.502314 × 1010
Coefficient of variation (CV)0.057951769
Kurtosis0.62087466
Mean1.4671362 × 1012
Median Absolute Deviation (MAD)64561.5
Skewness1.4774569
Sum1.2913733 × 1016
Variance7.2289343 × 1021
MonotonicityNot monotonic
2024-05-11T01:36:32.101936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1418890676147 17
 
0.2%
1418890656533 16
 
0.2%
1418890620381 16
 
0.2%
1418890650962 16
 
0.2%
1418890709186 15
 
0.2%
1418890651601 14
 
0.2%
1536900840375 14
 
0.2%
1418890650090 14
 
0.2%
1505380539885 12
 
0.1%
1502082484104 12
 
0.1%
Other values (4076) 8656
98.3%
ValueCountFrequency (%)
1418890614412 2
< 0.1%
1418890614413 2
< 0.1%
1418890614418 2
< 0.1%
1418890614422 1
< 0.1%
1418890614427 2
< 0.1%
1418890614428 2
< 0.1%
1418890614429 2
< 0.1%
1418890614431 2
< 0.1%
1418890614432 2
< 0.1%
1418890614496 1
< 0.1%
ValueCountFrequency (%)
1712794493809 1
 
< 0.1%
1712283973383 7
0.1%
1712283274955 1
 
< 0.1%
1712282495324 1
 
< 0.1%
1712282427985 2
 
< 0.1%
1712276899269 1
 
< 0.1%
1712212831795 2
 
< 0.1%
1712210638109 3
< 0.1%
1712203128433 1
 
< 0.1%
1712194584196 1
 
< 0.1%

업소명
Text

Distinct3650
Distinct (%)41.5%
Missing0
Missing (%)0.0%
Memory size68.9 KiB
2024-05-11T01:36:33.059920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length26
Mean length5.4401272
Min length1

Characters and Unicode

Total characters47884
Distinct characters815
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

Unique1303 ?
Unique (%)14.8%

Sample

1st row이수오미용실
2nd row이수오미용실
3rd row남성이발관
4th row현대세탁
5th row명진이발관
ValueCountFrequency (%)
김밥천국 88
 
0.9%
김밥나라 40
 
0.4%
김밥 37
 
0.4%
크린토피아 36
 
0.4%
헤어 34
 
0.4%
hair 24
 
0.2%
미용실 24
 
0.2%
피자스쿨 22
 
0.2%
멸치국수 22
 
0.2%
메가커피 20
 
0.2%
Other values (3841) 9333
96.4%
2024-05-11T01:36:34.480977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 1254
 
2.6%
헤 1207
 
2.5%
이 902
 
1.9%
미 882
 
1.8%
881
 
1.8%
리 861
 
1.8%
세 670
 
1.4%
밥 618
 
1.3%
탁 613
 
1.3%
김 589
 
1.2%
Other values (805) 39407
82.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 43150
90.1%
Uppercase Letter 1067
 
2.2%
Lowercase Letter 1048
 
2.2%
Space Separator 881
 
1.8%
Decimal Number 718
 
1.5%
Other Punctuation 708
 
1.5%
Open Punctuation 151
 
0.3%
Close Punctuation 151
 
0.3%
Dash Punctuation 8
 
< 0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 1254
 
2.9%
헤 1207
 
2.8%
이 902
 
2.1%
미 882
 
2.0%
리 861
 
2.0%
세 670
 
1.6%
밥 618
 
1.4%
탁 613
 
1.4%
김 589
 
1.4%
용 574
 
1.3%
Other values (729) 34980
81.1%
Uppercase Letter
ValueCountFrequency (%)
C 110
 
10.3%
B 80
 
7.5%
O 64
 
6.0%
E 61
 
5.7%
S 58
 
5.4%
H 58
 
5.4%
A 58
 
5.4%
M 56
 
5.2%
P 54
 
5.1%
K 52
 
4.9%
Other values (16) 416
39.0%
Lowercase Letter
ValueCountFrequency (%)
a 188
17.9%
p 142
13.5%
m 136
13.0%
e 100
9.5%
o 64
 
6.1%
r 46
 
4.4%
i 43
 
4.1%
s 40
 
3.8%
y 34
 
3.2%
f 34
 
3.2%
Other values (14) 221
21.1%
Other Punctuation
ValueCountFrequency (%)
; 267
37.7%
& 215
30.4%
# 154
21.8%
. 40
 
5.6%
, 20
 
2.8%
? 3
 
0.4%
! 3
 
0.4%
/ 3
 
0.4%
＆ 2
 
0.3%
% 1
 
0.1%
Decimal Number
ValueCountFrequency (%)
4 175
24.4%
1 150
20.9%
0 132
18.4%
2 98
13.6%
5 41
 
5.7%
9 41
 
5.7%
3 30
 
4.2%
8 21
 
2.9%
6 16
 
2.2%
7 14
 
1.9%
Space Separator
ValueCountFrequency (%)
881
100.0%
Open Punctuation
ValueCountFrequency (%)
( 151
100.0%
Close Punctuation
ValueCountFrequency (%)
) 151
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%
Math Symbol
ValueCountFrequency (%)
= 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 43140
90.1%
Common 2619
 
5.5%
Latin 2115
 
4.4%
Han 10
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 1254
 
2.9%
헤 1207
 
2.8%
이 902
 
2.1%
미 882
 
2.0%
리 861
 
2.0%
세 670
 
1.6%
밥 618
 
1.4%
탁 613
 
1.4%
김 589
 
1.4%
용 574
 
1.3%
Other values (724) 34970
81.1%
Latin
ValueCountFrequency (%)
a 188
 
8.9%
p 142
 
6.7%
m 136
 
6.4%
C 110
 
5.2%
e 100
 
4.7%
B 80
 
3.8%
o 64
 
3.0%
O 64
 
3.0%
E 61
 
2.9%
S 58
 
2.7%
Other values (40) 1112
52.6%
Common
ValueCountFrequency (%)
881
33.6%
; 267
 
10.2%
& 215
 
8.2%
4 175
 
6.7%
# 154
 
5.9%
( 151
 
5.8%
) 151
 
5.8%
1 150
 
5.7%
0 132
 
5.0%
2 98
 
3.7%
Other values (16) 245
 
9.4%
Han
ValueCountFrequency (%)
日 2
20.0%
月 2
20.0%
李 2
20.0%
家 2
20.0%
美 2
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 43140
90.1%
ASCII 4731
 
9.9%
CJK 8
 
< 0.1%
None 3
 
< 0.1%
CJK Compat Ideographs 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 1254
 
2.9%
헤 1207
 
2.8%
이 902
 
2.1%
미 882
 
2.0%
리 861
 
2.0%
세 670
 
1.6%
밥 618
 
1.4%
탁 613
 
1.4%
김 589
 
1.4%
용 574
 
1.3%
Other values (724) 34970
81.1%
ASCII
ValueCountFrequency (%)
881
 
18.6%
; 267
 
5.6%
& 215
 
4.5%
a 188
 
4.0%
4 175
 
3.7%
# 154
 
3.3%
( 151
 
3.2%
) 151
 
3.2%
1 150
 
3.2%
p 142
 
3.0%
Other values (64) 2257
47.7%
None
ValueCountFrequency (%)
＆ 2
66.7%
° 1
33.3%
CJK
ValueCountFrequency (%)
日 2
25.0%
月 2
25.0%
家 2
25.0%
美 2
25.0%
CJK Compat Ideographs
ValueCountFrequency (%)
李 2
100.0%

동명
Categorical

HIGH CORRELATION 

Distinct46
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size68.9 KiB
미아동
1251 
수유동
1021 
봉천동
819 
번동
649 
신림동
644 
Other values (41)
4418 

Length

Max length4
Median length3
Mean length2.8581004
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row중화동
2nd row중화동
3rd row풍납동
4th row풍납동
5th row풍납동

Common Values

ValueCountFrequency (%)
미아동 1251
14.2%
수유동 1021
 
11.6%
봉천동 819
 
9.3%
번동 649
 
7.4%
신림동 644
 
7.3%
면목동 531
 
6.0%
창동 394
 
4.5%
묵동 325
 
3.7%
쌍문동 275
 
3.1%
방학동 246
 
2.8%
Other values (36) 2647
30.1%

Length

2024-05-11T01:36:35.036954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미아동 1251
14.2%
수유동 1021
 
11.6%
봉천동 819
 
9.3%
번동 649
 
7.4%
신림동 644
 
7.3%
면목동 531
 
6.0%
창동 394
 
4.5%
묵동 325
 
3.7%
쌍문동 275
 
3.1%
방학동 246
 
2.8%
Other values (36) 2647
30.1%

주소
Text

Distinct3424
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Memory size68.9 KiB
2024-05-11T01:36:36.291169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length51
Mean length26.273347
Min length16

Characters and Unicode

Total characters231258
Distinct characters439
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1082 ?
Unique (%)12.3%

Sample

1st row서울특별시 중랑구 동일로 752 (중화동, 중화한신아파트)
2nd row서울특별시 중랑구 동일로 752 (중화동, 중화한신아파트)
3rd row서울특별시 송파구 올림픽로47길 15 (풍납동)
4th row서울특별시 송파구 풍성로16길 8-1 (풍납동)
5th row서울특별시 송파구 풍성로14길 7 (풍납동)
ValueCountFrequency (%)
서울특별시 8799
 
18.9%
강북구 3059
 
6.6%
관악구 1463
 
3.1%
마포구 1403
 
3.0%
미아동 1243
 
2.7%
도봉구 1091
 
2.3%
수유동 1010
 
2.2%
중랑구 886
 
1.9%
봉천동 731
 
1.6%
번동 645
 
1.4%
Other values (2425) 26166
56.3%
2024-05-11T01:36:38.072560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37725
 
16.3%
동 9342
 
4.0%
서 8987
 
3.9%
시 8936
 
3.9%
구 8856
 
3.8%
울 8832
 
3.8%
( 8832
 
3.8%
) 8825
 
3.8%
특 8799
 
3.8%
별 8799
 
3.8%
Other values (429) 113325
49.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 140183
60.6%
Space Separator 37725
 
16.3%
Decimal Number 31228
 
13.5%
Open Punctuation 8836
 
3.8%
Close Punctuation 8829
 
3.8%
Other Punctuation 3236
 
1.4%
Dash Punctuation 779
 
0.3%
Lowercase Letter 223
 
0.1%
Uppercase Letter 215
 
0.1%
Letter Number 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 9342
 
6.7%
서 8987
 
6.4%
시 8936
 
6.4%
구 8856
 
6.3%
울 8832
 
6.3%
특 8799
 
6.3%
별 8799
 
6.3%
로 8322
 
5.9%
길 4929
 
3.5%
봉 3638
 
2.6%
Other values (368) 60743
43.3%
Uppercase Letter
ValueCountFrequency (%)
B 35
16.3%
C 23
10.7%
K 21
9.8%
I 21
9.8%
A 20
9.3%
T 17
7.9%
G 16
7.4%
S 13
 
6.0%
D 12
 
5.6%
M 12
 
5.6%
Other values (9) 25
11.6%
Lowercase Letter
ValueCountFrequency (%)
e 35
15.7%
t 33
14.8%
a 32
14.3%
m 30
13.5%
p 30
13.5%
l 15
6.7%
n 13
 
5.8%
r 13
 
5.8%
g 9
 
4.0%
d 5
 
2.2%
Other values (4) 8
 
3.6%
Decimal Number
ValueCountFrequency (%)
1 6536
20.9%
2 4487
14.4%
3 3711
11.9%
4 3250
10.4%
5 2801
9.0%
7 2238
 
7.2%
6 2191
 
7.0%
0 2107
 
6.7%
9 2018
 
6.5%
8 1889
 
6.0%
Other Punctuation
ValueCountFrequency (%)
, 2197
67.9%
; 346
 
10.7%
& 316
 
9.8%
# 298
 
9.2%
. 45
 
1.4%
: 29
 
0.9%
？ 2
 
0.1%
@ 2
 
0.1%
/ 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 8832
> 99.9%
[ 3
 
< 0.1%
{ 1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 8825
> 99.9%
] 3
 
< 0.1%
} 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
37725
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 779
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 140183
60.6%
Common 90633
39.2%
Latin 442
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 9342
 
6.7%
서 8987
 
6.4%
시 8936
 
6.4%
구 8856
 
6.3%
울 8832
 
6.3%
특 8799
 
6.3%
별 8799
 
6.3%
로 8322
 
5.9%
길 4929
 
3.5%
봉 3638
 
2.6%
Other values (368) 60743
43.3%
Latin
ValueCountFrequency (%)
B 35
 
7.9%
e 35
 
7.9%
t 33
 
7.5%
a 32
 
7.2%
m 30
 
6.8%
p 30
 
6.8%
C 23
 
5.2%
K 21
 
4.8%
I 21
 
4.8%
A 20
 
4.5%
Other values (24) 162
36.7%
Common
ValueCountFrequency (%)
37725
41.6%
( 8832
 
9.7%
) 8825
 
9.7%
1 6536
 
7.2%
2 4487
 
5.0%
3 3711
 
4.1%
4 3250
 
3.6%
5 2801
 
3.1%
7 2238
 
2.5%
, 2197
 
2.4%
Other values (17) 10031
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 140183
60.6%
ASCII 91069
39.4%
Number Forms 4
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37725
41.4%
( 8832
 
9.7%
) 8825
 
9.7%
1 6536
 
7.2%
2 4487
 
4.9%
3 3711
 
4.1%
4 3250
 
3.6%
5 2801
 
3.1%
7 2238
 
2.5%
, 2197
 
2.4%
Other values (49) 10467
 
11.5%
Hangul
ValueCountFrequency (%)
동 9342
 
6.7%
서 8987
 
6.4%
시 8936
 
6.4%
구 8856
 
6.3%
울 8832
 
6.3%
특 8799
 
6.3%
별 8799
 
6.3%
로 8322
 
5.9%
길 4929
 
3.5%
봉 3638
 
2.6%
Other values (368) 60743
43.3%
Number Forms
ValueCountFrequency (%)
Ⅱ 4
100.0%
None
ValueCountFrequency (%)
？ 2
100.0%

면적(㎡)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct158
Distinct (%)11.2%
Missing7390
Missing (%)84.0%
Infinite0
Infinite (%)0.0%
Mean39.246565
Minimum0
Maximum3788
Zeros481
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size77.5 KiB
2024-05-11T01:36:38.648143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q333
95-th percentile114
Maximum3788
Range3788
Interquartile range (IQR)33

Descriptive statistics

Standard deviation163.244
Coefficient of variation (CV)4.1594468
Kurtosis400.58916
Mean39.246565
Median Absolute Deviation (MAD)20
Skewness18.382483
Sum55416.15
Variance26648.604
MonotonicityNot monotonic
2024-05-11T01:36:39.248043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 481
 
5.5%
30.0 119
 
1.4%
20.0 60
 
0.7%
33.0 46
 
0.5%
15.0 45
 
0.5%
26.0 32
 
0.4%
18.0 28
 
0.3%
24.0 26
 
0.3%
21.0 25
 
0.3%
40.0 23
 
0.3%
Other values (148) 527
 
6.0%
(Missing) 7390
84.0%
ValueCountFrequency (%)
0.0 481
5.5%
3.0 2
 
< 0.1%
4.0 3
 
< 0.1%
5.0 2
 
< 0.1%
6.0 3
 
< 0.1%
7.0 2
 
< 0.1%
8.0 5
 
0.1%
9.0 3
 
< 0.1%
10.0 23
 
0.3%
11.0 9
 
0.1%
ValueCountFrequency (%)
3788.0 2
< 0.1%
1296.0 1
< 0.1%
1268.0 1
< 0.1%
1057.0 1
< 0.1%
931.0 1
< 0.1%
900.0 2
< 0.1%
495.0 1
< 0.1%
452.0 1
< 0.1%
336.6 1
< 0.1%
330.0 1
< 0.1%

전화번호
Text

MISSING 

Distinct3729
Distinct (%)44.3%
Missing386
Missing (%)4.4%
Memory size68.9 KiB
2024-05-11T01:36:40.033834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length11.182391
Min length2

Characters and Unicode

Total characters94111
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1390 ?
Unique (%)16.5%

Sample

1st row02-482-9064
2nd row02-488-8375
3rd row02-475-3158
4th row02-482-6568
5th row02-482-6568
ValueCountFrequency (%)
02-0000-0000 143
 
1.7%
02-988-6039 17
 
0.2%
02-988-4005 16
 
0.2%
02-900-9760 16
 
0.2%
02-990-1911 16
 
0.2%
02-987-7066 16
 
0.2%
02-985-6048 15
 
0.2%
02-989-5905 14
 
0.2%
02-981-1708 14
 
0.2%
02-989-3392 14
 
0.2%
Other values (3719) 8135
96.7%
2024-05-11T01:36:41.631555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 16820
17.9%
0 16116
17.1%
2 13041
13.9%
9 9465
10.1%
8 7515
8.0%
3 6292
 
6.7%
7 5835
 
6.2%
5 5146
 
5.5%
4 5062
 
5.4%
1 4733
 
5.0%
Other values (3) 4086
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 77287
82.1%
Dash Punctuation 16820
 
17.9%
Other Letter 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16116
20.9%
2 13041
16.9%
9 9465
12.2%
8 7515
9.7%
3 6292
 
8.1%
7 5835
 
7.5%
5 5146
 
6.7%
4 5062
 
6.5%
1 4733
 
6.1%
6 4082
 
5.3%
Other Letter
ValueCountFrequency (%)
없 2
50.0%
음 2
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 16820
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 94107
> 99.9%
Hangul 4
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
- 16820
17.9%
0 16116
17.1%
2 13041
13.9%
9 9465
10.1%
8 7515
8.0%
3 6292
 
6.7%
7 5835
 
6.2%
5 5146
 
5.5%
4 5062
 
5.4%
1 4733
 
5.0%
Hangul
ValueCountFrequency (%)
없 2
50.0%
음 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94107
> 99.9%
Hangul 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 16820
17.9%
0 16116
17.1%
2 13041
13.9%
9 9465
10.1%
8 7515
8.0%
3 6292
 
6.7%
7 5835
 
6.2%
5 5146
 
5.5%
4 5062
 
5.4%
1 4733
 
5.0%
Hangul
ValueCountFrequency (%)
없 2
50.0%
음 2
50.0%

업종
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size68.9 KiB
한식
2860 
미용업
2136 
기타서비스
977 
다방업
619 
세탁업
524 
Other values (9)
1686 

Length

Max length5
Median length3
Mean length2.9401272
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
한식 2860
32.5%
미용업 2136
24.3%
기타서비스 977
 
11.1%
다방업 619
 
7.0%
세탁업 524
 
6.0%
기타음식업 489
 
5.6%
중식 483
 
5.5%
이용업 186
 
2.1%
경양식 176
 
2.0%
숙박업 165
 
1.9%
Other values (4) 187
 
2.1%

Length

2024-05-11T01:36:42.259433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 2860
32.5%
미용업 2136
24.3%
기타서비스 977
 
11.1%
다방업 619
 
7.0%
세탁업 524
 
6.0%
기타음식업 489
 
5.6%
중식 483
 
5.5%
이용업 186
 
2.1%
경양식 176
 
2.0%
숙박업 165
 
1.9%
Other values (4) 187
 
2.1%

품목코드
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4188938 × 1012
Minimum1.4186228 × 1012
Maximum1.5325825 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.5 KiB
2024-05-11T01:36:43.027989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.4186228 × 1012
5-th percentile1.4186228 × 1012
Q11.4186228 × 1012
median1.4186228 × 1012
Q31.4186228 × 1012
95-th percentile1.4186228 × 1012
Maximum1.5325825 × 1012
Range1.139597 × 1011
Interquartile range (IQR)19

Descriptive statistics

Standard deviation4.1450172 × 109
Coefficient of variation (CV)0.0029213018
Kurtosis287.61612
Mean1.4188938 × 1012
Median Absolute Deviation (MAD)11
Skewness16.244856
Sum1.2489104 × 1016
Variance1.7181168 × 1019
MonotonicityNot monotonic
2024-05-11T01:36:43.679611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1418622761403 1072
 
12.2%
1418622761402 1064
 
12.1%
1418622761392 487
 
5.5%
1418622761397 427
 
4.9%
1418622761384 403
 
4.6%
1418622761381 394
 
4.5%
1418622761373 389
 
4.4%
1418622761411 381
 
4.3%
1418622761385 356
 
4.0%
1418622761372 351
 
4.0%
Other values (38) 3478
39.5%
ValueCountFrequency (%)
1418622761370 121
 
1.4%
1418622761371 79
 
0.9%
1418622761372 351
4.0%
1418622761373 389
4.4%
1418622761374 181
2.1%
1418622761375 163
1.9%
1418622761376 162
1.8%
1418622761378 29
 
0.3%
1418622761379 57
 
0.6%
1418622761381 394
4.5%
ValueCountFrequency (%)
1532582458411 2
 
< 0.1%
1476951899274 37
 
0.4%
1418622761427 1
 
< 0.1%
1418622761426 39
 
0.4%
1418622761425 12
 
0.1%
1418622761423 2
 
< 0.1%
1418622761422 105
1.2%
1418622761421 28
 
0.3%
1418622761420 176
2.0%
1418622761419 158
1.8%

품목
Categorical

HIGH CORRELATION 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size68.9 KiB
미용료 (커트)
1072 
미용료 (파마)
1064 
양복 세탁료
 
487
의복수선료
 
431
커피(외식)
 
427
Other values (42)
5321 

Length

Max length10
Median length8
Mean length5.7359691
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row미용료 (파마)
2nd row미용료 (커트)
3rd row이용료(커트)
4th row양복 세탁료
5th row이용료(커트)

Common Values

ValueCountFrequency (%)
미용료 (커트) 1072
 
12.2%
미용료 (파마) 1064
 
12.1%
양복 세탁료 487
 
5.5%
의복수선료 431
 
4.9%
커피(외식) 427
 
4.9%
김치찌개 백반 403
 
4.6%
삼겹살 389
 
4.4%
냉면(물) 381
 
4.3%
치킨 356
 
4.0%
된장찌개 백반 351
 
4.0%
Other values (37) 3441
39.1%

Length

2024-05-11T01:36:44.285530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용료 2136
 
16.3%
커트 1072
 
8.2%
파마 1064
 
8.1%
백반 754
 
5.8%
양복 487
 
3.7%
세탁료 487
 
3.7%
의복수선료 431
 
3.3%
커피(외식 427
 
3.3%
이용료 412
 
3.1%
김치찌개 403
 
3.1%
Other values (47) 5432
41.4%

가격(원)
Real number (ℝ)

MISSING 

Distinct220
Distinct (%)2.8%
Missing1062
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean16132.522
Minimum0
Maximum250000
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size77.5 KiB
2024-05-11T01:36:44.789034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3000
Q17000
median10000
Q318000
95-th percentile45000
Maximum250000
Range250000
Interquartile range (IQR)11000

Descriptive statistics

Standard deviation19379.433
Coefficient of variation (CV)1.2012649
Kurtosis31.329789
Mean16132.522
Median Absolute Deviation (MAD)5000
Skewness4.6004239
Sum1.2486572 × 108
Variance3.7556241 × 108
MonotonicityNot monotonic
2024-05-11T01:36:45.276292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8000 789
 
9.0%
7000 594
 
6.7%
15000 426
 
4.8%
10000 401
 
4.6%
9000 385
 
4.4%
4000 365
 
4.1%
30000 335
 
3.8%
6000 281
 
3.2%
12000 277
 
3.1%
20000 257
 
2.9%
Other values (210) 3630
41.2%
(Missing) 1062
 
12.1%
ValueCountFrequency (%)
0 5
 
0.1%
300 4
 
< 0.1%
350 1
 
< 0.1%
400 3
 
< 0.1%
450 1
 
< 0.1%
500 24
0.3%
573 1
 
< 0.1%
600 5
 
0.1%
610 1
 
< 0.1%
615 1
 
< 0.1%
ValueCountFrequency (%)
250000 1
 
< 0.1%
230000 1
 
< 0.1%
225634 1
 
< 0.1%
222743 1
 
< 0.1%
200000 5
0.1%
199757 1
 
< 0.1%
192351 1
 
< 0.1%
190000 2
 
< 0.1%
184266 1
 
< 0.1%
180412 1
 
< 0.1%

점검일자
Date

Distinct2703
Distinct (%)30.7%
Missing0
Missing (%)0.0%
Memory size68.9 KiB
Minimum2024-04-11 00:00:00
Maximum2024-05-10 14:00:24
2024-05-11T01:36:45.718526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:36:46.535326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

구명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size68.9 KiB
강북구
3059 
관악구
1463 
마포구
1403 
도봉구
1091 
중랑구
886 
Other values (2)
900 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중랑구
2nd row중랑구
3rd row송파구
4th row송파구
5th row송파구

Common Values

ValueCountFrequency (%)
강북구 3059
34.8%
관악구 1463
16.6%
마포구 1403
15.9%
도봉구 1091
 
12.4%
중랑구 886
 
10.1%
노원구 483
 
5.5%
송파구 417
 
4.7%

Length

2024-05-11T01:36:46.989349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:36:47.447770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강북구 3059
34.8%
관악구 1463
16.6%
마포구 1403
15.9%
도봉구 1091
 
12.4%
중랑구 886
 
10.1%
노원구 483
 
5.5%
송파구 417
 
4.7%

Interactions

2024-05-11T01:36:27.942873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:36:23.272353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:36:24.834714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:36:26.514001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:36:28.265327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:36:23.676254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:36:25.250162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:36:26.889594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:36:28.593608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:36:24.096509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:36:25.670473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:36:27.276670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:36:28.892205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:36:24.502801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:36:26.107688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:36:27.596230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T01:36:47.742171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업소일련번호동명면적(㎡)업종품목코드품목가격(원)구명
업소일련번호1.0000.6650.0000.2580.1510.3220.1310.537
동명0.6651.0000.0000.3580.1200.3720.5191.000
면적(㎡)0.0000.0001.0000.4170.0000.8900.3240.000
업종0.2580.3580.4171.0000.3911.0000.4360.333
품목코드0.1510.1200.0000.3911.0000.9040.0000.068
품목0.3220.3720.8901.0000.9041.0000.7650.383
가격(원)0.1310.5190.3240.4360.0000.7651.0000.140
구명0.5371.0000.0000.3330.0680.3830.1401.000
2024-05-11T01:36:48.111153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동명구명품목업종
동명1.0000.9980.0730.112
구명0.9981.0000.1640.129
품목0.0730.1641.0000.994
업종0.1120.1290.9941.000
2024-05-11T01:36:48.469939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업소일련번호면적(㎡)품목코드가격(원)동명업종품목구명
업소일련번호1.000-0.6660.043-0.0150.2950.1070.1160.308
면적(㎡)-0.6661.000-0.0610.0770.0000.2410.6090.000
품목코드0.043-0.0611.0000.0890.0580.2370.7320.045
가격(원)-0.0150.0770.0891.0000.2050.1920.3830.071
동명0.2950.0000.0580.2051.0000.1120.0730.998
업종0.1070.2410.2370.1920.1121.0000.9940.129
품목0.1160.6090.7320.3830.0730.9941.0000.164
구명0.3080.0000.0450.0710.9980.1290.1641.000

Missing values

2024-05-11T01:36:29.345336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T01:36:30.336475image/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.
2024-05-11T01:36:30.839904image/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

업소일련번호업소명동명주소면적(㎡)전화번호업종품목코드품목가격(원)점검일자구명
01617926393629이수오미용실중화동서울특별시 중랑구 동일로 752 (중화동, 중화한신아파트)<NA><NA>미용업1418622761402미용료 (파마)450002024-05-10 14:00:24.0중랑구
11617926393629이수오미용실중화동서울특별시 중랑구 동일로 752 (중화동, 중화한신아파트)<NA><NA>미용업1418622761403미용료 (커트)140002024-05-10 14:00:24.0중랑구
21418890619560남성이발관풍납동서울특별시 송파구 올림픽로47길 15 (풍납동)<NA>02-482-9064이용업1418622761401이용료(커트)100002024-05-10 09:35:05.0송파구
31418890732538현대세탁풍납동서울특별시 송파구 풍성로16길 8-1 (풍납동)<NA>02-488-8375세탁업1418622761392양복 세탁료100002024-05-10 09:25:53.0송파구
41418890759507명진이발관풍납동서울특별시 송파구 풍성로14길 7 (풍납동)<NA>02-475-3158이용업1418622761401이용료(커트)100002024-05-10 09:25:00.0송파구
51418890674401크로바건강랜드풍납동서울특별시 송파구 풍성로 52 (풍납동, 대아아파트)<NA>02-482-6568목욕업1418622761404목욕료 (성인)100002024-05-10 09:23:09.0송파구
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