Overview

Dataset statistics

Number of variables6
Number of observations698
Missing cells21
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.5 KiB
Average record size in memory49.2 B

Variable types

Text3
Categorical2
Numeric1

Dataset

Description키,상호,행정시,행정구,행정동,객실수
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-13072/S/1/datasetView.do

Alerts

행정시 has constant value ""Constant
객실수 has 21 (3.0%) missing valuesMissing
키 has unique valuesUnique

Reproduction

Analysis started2024-04-14 04:45:59.429232
Analysis finished2024-04-14 04:46:02.764625
Duration3.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

키
Text

UNIQUE 

Distinct698
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-04-14T13:46:03.471216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters9772
Distinct characters18
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique698 ?
Unique (%)100.0%

Sample

1st rowBE_LiST20-0022
2nd rowBE_LiST20-0023
3rd rowBE_LiST20-0024
4th rowBE_LiST20-0025
5th rowBE_LiST20-0026
ValueCountFrequency (%)
be_list20-0022 1
 
0.1%
be_list20-0657 1
 
0.1%
be_list20-0690 1
 
0.1%
be_list20-0660 1
 
0.1%
be_list20-0651 1
 
0.1%
be_list20-0652 1
 
0.1%
be_list20-0653 1
 
0.1%
be_list20-0654 1
 
0.1%
be_list20-0655 1
 
0.1%
be_list20-0656 1
 
0.1%
Other values (688) 688
98.6%
2024-04-14T13:46:04.709832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1633
16.7%
2 938
9.6%
B 698
7.1%
T 698
7.1%
E 698
7.1%
- 698
7.1%
S 698
7.1%
i 698
7.1%
L 698
7.1%
_ 698
7.1%
Other values (8) 1617
16.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4188
42.9%
Uppercase Letter 3490
35.7%
Dash Punctuation 698
 
7.1%
Lowercase Letter 698
 
7.1%
Connector Punctuation 698
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1633
39.0%
2 938
22.4%
3 240
 
5.7%
1 240
 
5.7%
4 240
 
5.7%
5 240
 
5.7%
6 239
 
5.7%
7 140
 
3.3%
8 140
 
3.3%
9 138
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
B 698
20.0%
T 698
20.0%
E 698
20.0%
S 698
20.0%
L 698
20.0%
Dash Punctuation
ValueCountFrequency (%)
- 698
100.0%
Lowercase Letter
ValueCountFrequency (%)
i 698
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 698
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5584
57.1%
Latin 4188
42.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1633
29.2%
2 938
16.8%
- 698
12.5%
_ 698
12.5%
3 240
 
4.3%
1 240
 
4.3%
4 240
 
4.3%
5 240
 
4.3%
6 239
 
4.3%
7 140
 
2.5%
Other values (2) 278
 
5.0%
Latin
ValueCountFrequency (%)
B 698
16.7%
T 698
16.7%
E 698
16.7%
S 698
16.7%
i 698
16.7%
L 698
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9772
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1633
16.7%
2 938
9.6%
B 698
7.1%
T 698
7.1%
E 698
7.1%
- 698
7.1%
S 698
7.1%
i 698
7.1%
L 698
7.1%
_ 698
7.1%
Other values (8) 1617
16.5%

상호
Text

Distinct683
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-04-14T13:46:06.116191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length31
Mean length14.528653
Min length2

Characters and Unicode

Total characters10141
Distinct characters74
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique671 ?
Unique (%)96.1%

Sample

1st rowYEHADOYE Guesthouse
2nd rowB-House
3rd rowHide & Seek
4th rowYein House
5th rowSeoul Guesthouse
ValueCountFrequency (%)
guesthouse 223
 
13.9%
house 166
 
10.4%
homestay 45
 
2.8%
seoul 39
 
2.4%
guest 28
 
1.8%
the 23
 
1.4%
home 18
 
1.1%
2 18
 
1.1%
stay 17
 
1.1%
hongdae 12
 
0.8%
Other values (689) 1010
63.2%
2024-04-14T13:46:08.012530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1173
 
11.6%
925
 
9.1%
s 881
 
8.7%
o 874
 
8.6%
u 814
 
8.0%
a 504
 
5.0%
t 481
 
4.7%
n 390
 
3.8%
h 327
 
3.2%
H 325
 
3.2%
Other values (64) 3447
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7153
70.5%
Uppercase Letter 1866
 
18.4%
Space Separator 925
 
9.1%
Other Punctuation 92
 
0.9%
Decimal Number 85
 
0.8%
Dash Punctuation 14
 
0.1%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Other Symbol 1
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1173
16.4%
s 881
12.3%
o 874
12.2%
u 814
11.4%
a 504
7.0%
t 481
6.7%
n 390
 
5.5%
h 327
 
4.6%
i 298
 
4.2%
l 217
 
3.0%
Other values (16) 1194
16.7%
Uppercase Letter
ValueCountFrequency (%)
H 325
17.4%
G 317
17.0%
S 188
 
10.1%
A 74
 
4.0%
J 69
 
3.7%
O 69
 
3.7%
E 68
 
3.6%
B 65
 
3.5%
M 64
 
3.4%
T 63
 
3.4%
Other values (16) 564
30.2%
Decimal Number
ValueCountFrequency (%)
2 29
34.1%
4 12
14.1%
8 10
 
11.8%
1 9
 
10.6%
0 7
 
8.2%
9 6
 
7.1%
7 4
 
4.7%
3 4
 
4.7%
5 3
 
3.5%
6 1
 
1.2%
Other Punctuation
ValueCountFrequency (%)
' 50
54.3%
& 15
 
16.3%
. 11
 
12.0%
? 11
 
12.0%
@ 3
 
3.3%
; 2
 
2.2%
Space Separator
ValueCountFrequency (%)
925
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Other Symbol
ValueCountFrequency (%)
☆ 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9019
88.9%
Common 1122
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1173
 
13.0%
s 881
 
9.8%
o 874
 
9.7%
u 814
 
9.0%
a 504
 
5.6%
t 481
 
5.3%
n 390
 
4.3%
h 327
 
3.6%
H 325
 
3.6%
G 317
 
3.5%
Other values (42) 2933
32.5%
Common
ValueCountFrequency (%)
925
82.4%
' 50
 
4.5%
2 29
 
2.6%
& 15
 
1.3%
- 14
 
1.2%
4 12
 
1.1%
. 11
 
1.0%
? 11
 
1.0%
8 10
 
0.9%
1 9
 
0.8%
Other values (12) 36
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10140
> 99.9%
Misc Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1173
 
11.6%
925
 
9.1%
s 881
 
8.7%
o 874
 
8.6%
u 814
 
8.0%
a 504
 
5.0%
t 481
 
4.7%
n 390
 
3.8%
h 327
 
3.2%
H 325
 
3.2%
Other values (63) 3446
34.0%
Misc Symbols
ValueCountFrequency (%)
☆ 1
100.0%

행정시
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Seoul
698 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Seoul 698
100.0%

Length

2024-04-14T13:46:08.418185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T13:46:08.730744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
seoul 698
100.0%

행정구
Categorical

Distinct25
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Mapo-gu
209 
Jung-gu
76 
Yongsan-gu
63 
Gangnam-gu
49 
Jongno-gu
44 
Other values (20)
257 

Length

Max length15
Median length13
Mean length8.9455587
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJongno-gu
2nd rowJongno-gu
3rd rowJongno-gu
4th rowJongno-gu
5th rowJongno-gu

Common Values

ValueCountFrequency (%)
Mapo-gu 209
29.9%
Jung-gu 76
 
10.9%
Yongsan-gu 63
 
9.0%
Gangnam-gu 49
 
7.0%
Jongno-gu 44
 
6.3%
Songpa-gu 41
 
5.9%
Seodaemun-gu 27
 
3.9%
Seocho-gu 25
 
3.6%
Gwanak-gu 20
 
2.9%
Eunpyeong-gu 19
 
2.7%
Other values (15) 125
17.9%

Length

2024-04-14T13:46:09.016598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mapo-gu 209
29.9%
jung-gu 76
 
10.9%
yongsan-gu 63
 
9.0%
gangnam-gu 49
 
7.0%
jongno-gu 44
 
6.3%
songpa-gu 41
 
5.9%
seodaemun-gu 27
 
3.9%
seocho-gu 25
 
3.6%
gwanak-gu 20
 
2.9%
eunpyeong-gu 19
 
2.7%
Other values (15) 125
17.9%

행정동
Text

Distinct207
Distinct (%)29.7%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-04-14T13:46:09.749015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length12.137536
Min length8

Characters and Unicode

Total characters8472
Distinct characters48
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)13.8%

Sample

1st rowJongno1.2.3.4ga-dong
2nd rowChangsin2-dong
3rd rowSajik-dong
4th rowCheongunhyoja-dong
5th rowJongno1.2.3.4ga-dong
ValueCountFrequency (%)
seogyo-dong 108
 
15.5%
yeonnam-dong 61
 
8.7%
myeong-dong 22
 
3.2%
hoehyeon-dong 20
 
2.9%
itaewon1-dong 18
 
2.6%
pil-dong 13
 
1.9%
hannam-dong 12
 
1.7%
sinsa-dong 11
 
1.6%
sajik-dong 9
 
1.3%
hyehwa-dong 9
 
1.3%
Other values (197) 415
59.5%
2024-04-14T13:46:10.715231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 1387
16.4%
o 1376
16.2%
g 1132
13.4%
d 722
8.5%
- 698
8.2%
e 474
 
5.6%
a 459
 
5.4%
S 234
 
2.8%
y 221
 
2.6%
h 181
 
2.1%
Other values (38) 1588
18.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6817
80.5%
Dash Punctuation 698
 
8.2%
Uppercase Letter 698
 
8.2%
Decimal Number 250
 
3.0%
Other Punctuation 9
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1387
20.3%
o 1376
20.2%
g 1132
16.6%
d 722
10.6%
e 474
 
7.0%
a 459
 
6.7%
y 221
 
3.2%
h 181
 
2.7%
m 167
 
2.4%
i 133
 
2.0%
Other values (11) 565
8.3%
Uppercase Letter
ValueCountFrequency (%)
S 234
33.5%
Y 92
 
13.2%
H 63
 
9.0%
J 55
 
7.9%
M 43
 
6.2%
D 36
 
5.2%
C 34
 
4.9%
I 34
 
4.9%
B 26
 
3.7%
G 25
 
3.6%
Other values (7) 56
 
8.0%
Decimal Number
ValueCountFrequency (%)
1 107
42.8%
2 85
34.0%
3 29
 
11.6%
4 15
 
6.0%
5 6
 
2.4%
7 4
 
1.6%
6 3
 
1.2%
8 1
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
- 698
100.0%
Other Punctuation
ValueCountFrequency (%)
. 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7515
88.7%
Common 957
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1387
18.5%
o 1376
18.3%
g 1132
15.1%
d 722
9.6%
e 474
 
6.3%
a 459
 
6.1%
S 234
 
3.1%
y 221
 
2.9%
h 181
 
2.4%
m 167
 
2.2%
Other values (28) 1162
15.5%
Common
ValueCountFrequency (%)
- 698
72.9%
1 107
 
11.2%
2 85
 
8.9%
3 29
 
3.0%
4 15
 
1.6%
. 9
 
0.9%
5 6
 
0.6%
7 4
 
0.4%
6 3
 
0.3%
8 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1387
16.4%
o 1376
16.2%
g 1132
13.4%
d 722
8.5%
- 698
8.2%
e 474
 
5.6%
a 459
 
5.4%
S 234
 
2.8%
y 221
 
2.6%
h 181
 
2.1%
Other values (38) 1588
18.7%

객실수
Real number (ℝ)

MISSING 

Distinct17
Distinct (%)2.5%
Missing21
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean3.2614476
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2024-04-14T13:46:10.927165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile8
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.5497197
Coefficient of variation (CV)0.78177548
Kurtosis6.9123379
Mean3.2614476
Median Absolute Deviation (MAD)1
Skewness2.0917911
Sum2208
Variance6.5010707
MonotonicityNot monotonic
2024-04-14T13:46:11.136692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 199
28.5%
1 158
22.6%
3 108
15.5%
4 54
 
7.7%
5 50
 
7.2%
6 31
 
4.4%
7 28
 
4.0%
8 20
 
2.9%
9 12
 
1.7%
10 6
 
0.9%
Other values (7) 11
 
1.6%
(Missing) 21
 
3.0%
ValueCountFrequency (%)
1 158
22.6%
2 199
28.5%
3 108
15.5%
4 54
 
7.7%
5 50
 
7.2%
6 31
 
4.4%
7 28
 
4.0%
8 20
 
2.9%
9 12
 
1.7%
10 6
 
0.9%
ValueCountFrequency (%)
21 1
 
0.1%
18 1
 
0.1%
17 1
 
0.1%
14 1
 
0.1%
13 2
 
0.3%
12 1
 
0.1%
11 4
 
0.6%
10 6
 
0.9%
9 12
1.7%
8 20
2.9%

Interactions

2024-04-14T13:46:01.851051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-14T13:46:11.291936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구객실수
행정구1.0000.247
객실수0.2471.000
2024-04-14T13:46:11.425867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
객실수행정구
객실수1.0000.000
행정구0.0001.000

Missing values

2024-04-14T13:46:02.219533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-14T13:46:02.593886image/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

키상호행정시행정구행정동객실수
0BE_LiST20-0022YEHADOYE GuesthouseSeoulJongno-guJongno1.2.3.4ga-dong7
1BE_LiST20-0023B-HouseSeoulJongno-guChangsin2-dong1
2BE_LiST20-0024Hide & SeekSeoulJongno-guSajik-dong5
3BE_LiST20-0025Yein HouseSeoulJongno-guCheongunhyoja-dong8
4BE_LiST20-0026Seoul GuesthouseSeoulJongno-guJongno1.2.3.4ga-dong4
5BE_LiST20-0027Windroad & Flower GuesthouseSeoulJongno-guHyehwa-dong1
6BE_LiST20-0028White Rabbit GuesthouseSeoulJongno-guHyehwa-dong3
7BE_LiST20-0029Bong BackpackersSeoulJongno-guHyehwa-dong5
8BE_LiST20-0030The Present GuesthouseSeoulJongno-guHyehwa-dong3
9BE_LiST20-0031Lydia Craft GuesthouseSeoulJongno-guSajik-dong2
키상호행정시행정구행정동객실수
688BE_LiST20-0441Two Two HouseSeoulMapo-guYeonnam-dong3
689BE_LiST20-0442Min's HouseSeoulMapo-guMangwon2-dong2
690BE_LiST20-0443K GuesthouseSeoulMapo-guSeogang-dong1
691BE_LiST20-0444JeongstaySeoulMapo-guSeogyo-dong2
692BE_LiST20-04451970 GuesthouseSeoulMapo-guYeonnam-dong6
693BE_LiST20-0446Mono HouseSeoulMapo-guSeogyo-dong8
694BE_LiST20-044788 House HongdaeSeoulMapo-guSeogyo-dong2
695BE_LiST20-0448Seoul Sweet STyleSeoulMapo-guYeonnam-dong4
696BE_LiST20-0449House In SeoulSeoulMapo-guSeogyo-dong2
697BE_LiST20-0450Triangel GuesthouseSeoulMapo-guSeogyo-dong3