Overview

Dataset statistics

Number of variables3
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory26.3 B

Variable types

Numeric1
Text2

Alerts

lsr_cd has unique valuesUnique
lsr_nm has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:38:54.982659
Analysis finished2023-12-10 09:39:01.377156
Duration6.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

lsr_cd
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17803.15
Minimum19
Maximum53980
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:39:01.502361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile140.95
Q111623.75
median20456
Q323984.75
95-th percentile24655.2
Maximum53980
Range53961
Interquartile range (IQR)12361

Descriptive statistics

Standard deviation10712.989
Coefficient of variation (CV)0.60174683
Kurtosis2.3285001
Mean17803.15
Median Absolute Deviation (MAD)3944.5
Skewness0.44170278
Sum1780315
Variance1.1476813 × 108
MonotonicityNot monotonic
2023-12-10T18:39:01.719824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 1
 
1.0%
23038 1
 
1.0%
23865 1
 
1.0%
23838 1
 
1.0%
23276 1
 
1.0%
23239 1
 
1.0%
23203 1
 
1.0%
23187 1
 
1.0%
23186 1
 
1.0%
23130 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
19 1
1.0%
31 1
1.0%
108 1
1.0%
124 1
1.0%
140 1
1.0%
141 1
1.0%
197 1
1.0%
268 1
1.0%
744 1
1.0%
796 1
1.0%
ValueCountFrequency (%)
53980 1
1.0%
53565 1
1.0%
53563 1
1.0%
24661 1
1.0%
24659 1
1.0%
24655 1
1.0%
24640 1
1.0%
24609 1
1.0%
24560 1
1.0%
24555 1
1.0%

lsr_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:39:02.158355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length26
Mean length20.05
Min length6

Characters and Unicode

Total characters2005
Distinct characters280
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row[전북 전주]말순이 : 한옥마을 한복대여권
2nd row[경기 일산] 아쿠아플라넷 일산 주말입장권
3rd row[전북 전주] 플라이코리아 패러글라이딩
4th row[경기 여주] 쎈토이 뮤지엄 입장권(키덜트)
5th row[서울 종로] 리브레 : 수제비누 & 향초 만들기 체험
ValueCountFrequency (%)
28
 
6.1%
서울 27
 
5.8%
전북 25
 
5.4%
경기 14
 
3.0%
패러글라이딩 10
 
2.2%
전주 9
 
1.9%
한복대여 9
 
1.9%
경북 6
 
1.3%
강원 6
 
1.3%
일산 6
 
1.3%
Other values (241) 322
69.7%
2023-12-10T18:39:02.797370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
364
 
18.2%
[ 98
 
4.9%
] 98
 
4.9%
전 51
 
2.5%
이 42
 
2.1%
북 36
 
1.8%
패 31
 
1.5%
한 30
 
1.5%
서 29
 
1.4%
대 28
 
1.4%
Other values (270) 1198
59.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1381
68.9%
Space Separator 364
 
18.2%
Open Punctuation 99
 
4.9%
Close Punctuation 99
 
4.9%
Other Punctuation 31
 
1.5%
Uppercase Letter 19
 
0.9%
Math Symbol 7
 
0.3%
Decimal Number 5
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 51
 
3.7%
이 42
 
3.0%
북 36
 
2.6%
패 31
 
2.2%
한 30
 
2.2%
서 29
 
2.1%
대 28
 
2.0%
스 28
 
2.0%
울 28
 
2.0%
경 27
 
2.0%
Other values (244) 1051
76.1%
Uppercase Letter
ValueCountFrequency (%)
T 3
15.8%
V 3
15.8%
R 2
10.5%
B 2
10.5%
A 2
10.5%
I 1
 
5.3%
U 1
 
5.3%
E 1
 
5.3%
S 1
 
5.3%
P 1
 
5.3%
Other values (2) 2
10.5%
Other Punctuation
ValueCountFrequency (%)
: 16
51.6%
& 8
25.8%
/ 5
 
16.1%
, 2
 
6.5%
Decimal Number
ValueCountFrequency (%)
3 2
40.0%
5 1
20.0%
9 1
20.0%
4 1
20.0%
Open Punctuation
ValueCountFrequency (%)
[ 98
99.0%
( 1
 
1.0%
Close Punctuation
ValueCountFrequency (%)
] 98
99.0%
) 1
 
1.0%
Space Separator
ValueCountFrequency (%)
364
100.0%
Math Symbol
ValueCountFrequency (%)
+ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1381
68.9%
Common 605
30.2%
Latin 19
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 51
 
3.7%
이 42
 
3.0%
북 36
 
2.6%
패 31
 
2.2%
한 30
 
2.2%
서 29
 
2.1%
대 28
 
2.0%
스 28
 
2.0%
울 28
 
2.0%
경 27
 
2.0%
Other values (244) 1051
76.1%
Common
ValueCountFrequency (%)
364
60.2%
[ 98
 
16.2%
] 98
 
16.2%
: 16
 
2.6%
& 8
 
1.3%
+ 7
 
1.2%
/ 5
 
0.8%
, 2
 
0.3%
3 2
 
0.3%
( 1
 
0.2%
Other values (4) 4
 
0.7%
Latin
ValueCountFrequency (%)
T 3
15.8%
V 3
15.8%
R 2
10.5%
B 2
10.5%
A 2
10.5%
I 1
 
5.3%
U 1
 
5.3%
E 1
 
5.3%
S 1
 
5.3%
P 1
 
5.3%
Other values (2) 2
10.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1381
68.9%
ASCII 624
31.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
364
58.3%
[ 98
 
15.7%
] 98
 
15.7%
: 16
 
2.6%
& 8
 
1.3%
+ 7
 
1.1%
/ 5
 
0.8%
T 3
 
0.5%
V 3
 
0.5%
R 2
 
0.3%
Other values (16) 20
 
3.2%
Hangul
ValueCountFrequency (%)
전 51
 
3.7%
이 42
 
3.0%
북 36
 
2.6%
패 31
 
2.2%
한 30
 
2.2%
서 29
 
2.1%
대 28
 
2.0%
스 28
 
2.0%
울 28
 
2.0%
경 27
 
2.0%
Other values (244) 1051
76.1%

lsr_load_addr
Text

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:39:03.745778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length17.98
Min length13

Characters and Unicode

Total characters1798
Distinct characters167
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87 ?
Unique (%)87.0%

Sample

1st row전북 전주시 완산구 은행로 54-1
2nd row경기 고양시 일산서구 한류월드로 282
3rd row전라북도 완주군 구이면 덕평로 171
4th row경기 여주시 명품1로 42-9
5th row서울 종로구 인사동길 44
ValueCountFrequency (%)
서울 29
 
6.3%
전북 22
 
4.8%
완산구 12
 
2.6%
경기 12
 
2.6%
전주시 12
 
2.6%
마포구 7
 
1.5%
경북 6
 
1.3%
강원 6
 
1.3%
종로구 5
 
1.1%
영등포구 5
 
1.1%
Other values (271) 342
74.7%
2023-12-10T18:39:04.614031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
358
 
19.9%
로 62
 
3.4%
1 62
 
3.4%
구 57
 
3.2%
2 53
 
2.9%
전 46
 
2.6%
시 44
 
2.4%
- 44
 
2.4%
산 39
 
2.2%
북 37
 
2.1%
Other values (157) 996
55.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1054
58.6%
Space Separator 358
 
19.9%
Decimal Number 342
 
19.0%
Dash Punctuation 44
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 62
 
5.9%
구 57
 
5.4%
전 46
 
4.4%
시 44
 
4.2%
산 39
 
3.7%
북 37
 
3.5%
서 35
 
3.3%
울 30
 
2.8%
동 29
 
2.8%
길 26
 
2.5%
Other values (145) 649
61.6%
Decimal Number
ValueCountFrequency (%)
1 62
18.1%
2 53
15.5%
3 37
10.8%
5 31
9.1%
4 31
9.1%
6 30
8.8%
0 28
8.2%
7 24
 
7.0%
9 23
 
6.7%
8 23
 
6.7%
Space Separator
ValueCountFrequency (%)
358
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1054
58.6%
Common 744
41.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 62
 
5.9%
구 57
 
5.4%
전 46
 
4.4%
시 44
 
4.2%
산 39
 
3.7%
북 37
 
3.5%
서 35
 
3.3%
울 30
 
2.8%
동 29
 
2.8%
길 26
 
2.5%
Other values (145) 649
61.6%
Common
ValueCountFrequency (%)
358
48.1%
1 62
 
8.3%
2 53
 
7.1%
- 44
 
5.9%
3 37
 
5.0%
5 31
 
4.2%
4 31
 
4.2%
6 30
 
4.0%
0 28
 
3.8%
7 24
 
3.2%
Other values (2) 46
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1054
58.6%
ASCII 744
41.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
358
48.1%
1 62
 
8.3%
2 53
 
7.1%
- 44
 
5.9%
3 37
 
5.0%
5 31
 
4.2%
4 31
 
4.2%
6 30
 
4.0%
0 28
 
3.8%
7 24
 
3.2%
Other values (2) 46
 
6.2%
Hangul
ValueCountFrequency (%)
로 62
 
5.9%
구 57
 
5.4%
전 46
 
4.4%
시 44
 
4.2%
산 39
 
3.7%
북 37
 
3.5%
서 35
 
3.3%
울 30
 
2.8%
동 29
 
2.8%
길 26
 
2.5%
Other values (145) 649
61.6%

Interactions

2023-12-10T18:39:00.910211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:39:04.820056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
lsr_cdlsr_nmlsr_load_addr
lsr_cd1.0001.0000.887
lsr_nm1.0001.0001.000
lsr_load_addr0.8871.0001.000

Missing values

2023-12-10T18:39:01.204726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:39:01.327652image/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

lsr_cdlsr_nmlsr_load_addr
019[전북 전주]말순이 : 한옥마을 한복대여권전북 전주시 완산구 은행로 54-1
153563[경기 일산] 아쿠아플라넷 일산 주말입장권경기 고양시 일산서구 한류월드로 282
231[전북 전주] 플라이코리아 패러글라이딩전라북도 완주군 구이면 덕평로 171
3108[경기 여주] 쎈토이 뮤지엄 입장권(키덜트)경기 여주시 명품1로 42-9
4124[서울 종로] 리브레 : 수제비누 & 향초 만들기 체험서울 종로구 인사동길 44
5140[경기 시흥] 창조자연사 박물관 입장권경기도 시흥시 신천동 184-1
6141[충북 단양] 클레이사격충북 단양군 단양읍 노동장현로 207-17
753565[경기 일산] 아쿠아플라넷 일산 평일입장권경기 고양시 일산서구 한류월드로 282
8197[서울 강남] 클라임이모션서울 강남구 논현로76길 27
9268[충남 당진] 삽교호 카트월드 이용권충남 당진시 신평면 삽교천길 71
lsr_cdlsr_nmlsr_load_addr
9024464[경기 파주] 헤이리 팝트릭아트 93뮤지엄경기 파주시 탄현면 헤이리마을길 59-58
9124477[전북 무주] 무주투어패스전북 무주군 무주읍 한풍루로 326-17
9224478[전북 장수] 장수투어패스전북 장수군 장수읍 한누리로 393
9324555[경기 포천] 산사원 : 전통주 갤러리경기 포천시 화현면 화동로432번길 25
9424560[전북 임실] 임실투어패스전북 임실군 청웅면 청운로 168-46
9524609[서울 홍대] 포트링 : 도자기만들기 체험서울 마포구 월드컵북로6길 57-6
9624640[전북 전주] 한옥마을 왕발통 & 전동 바이크전북 전주시 완산구 교동 80-9
9724655[충북 단양] 스카이 패러글라이딩충북 단양군 단양읍 수변로 61
9824659[부산 김해]렛츠런파크 일루미아빛 테마파크경남 김해시 수가동 1333
9924661[전북 임실] 임실치즈테마파크 치즈체험 + 임실투어패스전북 임실군 성수면 도인2길 50