Overview

Dataset statistics

Number of variables11
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory46.5 KiB
Average record size in memory95.3 B

Variable types

Numeric7
Text2
Categorical2

Alerts

rstrnt_addr is highly overall correlated with rstrnt_la and 2 other fieldsHigh correlation
rstrnt_nm is highly overall correlated with rstrnt_la and 2 other fieldsHigh correlation
rstrnt_la is highly overall correlated with rstrnt_lo and 2 other fieldsHigh correlation
rstrnt_lo is highly overall correlated with rstrnt_la and 2 other fieldsHigh correlation

Reproduction

Analysis started2023-12-10 10:07:40.213017
Analysis finished2023-12-10 10:07:51.060829
Duration10.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ldgs_cd
Real number (ℝ)

Distinct69
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66064488
Minimum23197
Maximum1.0001104 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T19:07:51.185796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23197
5-th percentile25586
Q11019681
median3001038
Q33008549
95-th percentile1.0001041 × 109
Maximum1.0001104 × 109
Range1.0000872 × 109
Interquartile range (IQR)1988868

Descriptive statistics

Standard deviation2.4448854 × 108
Coefficient of variation (CV)3.7007558
Kurtosis10.812515
Mean66064488
Median Absolute Deviation (MAD)9345
Skewness3.5733555
Sum3.3032244 × 1010
Variance5.9774646 × 1016
MonotonicityNot monotonic
2023-12-10T19:07:51.406079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26275 14
 
2.8%
27140 14
 
2.8%
27583 14
 
2.8%
1019101 14
 
2.8%
1019681 14
 
2.8%
25586 14
 
2.8%
3014573 14
 
2.8%
3000975 14
 
2.8%
3001688 14
 
2.8%
23197 13
 
2.6%
Other values (59) 361
72.2%
ValueCountFrequency (%)
23197 13
2.6%
23207 2
 
0.4%
23589 1
 
0.2%
24424 5
 
1.0%
25405 1
 
0.2%
25586 14
2.8%
26275 14
2.8%
26429 4
 
0.8%
27140 14
2.8%
27279 4
 
0.8%
ValueCountFrequency (%)
1000110398 13
2.6%
1000105637 3
 
0.6%
1000104143 13
2.6%
1000102477 1
 
0.2%
1000099522 2
 
0.4%
3018553 10
2.0%
3017934 9
1.8%
3017844 10
2.0%
3016815 4
 
0.8%
3015586 5
 
1.0%
Distinct67
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T19:07:51.785199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length10.112
Min length4

Characters and Unicode

Total characters5056
Distinct characters156
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

Unique8 ?
Unique (%)1.6%

Sample

1st row강남 렉시
2nd row역삼(강남역) 마레
3rd row강남 시애틀
4th row강남 밀라노
5th row강남 648호텔
ValueCountFrequency (%)
강남 264
 
19.4%
호텔 171
 
12.6%
역삼 72
 
5.3%
서울 35
 
2.6%
648호텔 27
 
2.0%
25
 
1.8%
hotel 23
 
1.7%
앰배서더 23
 
1.7%
게스트하우스 22
 
1.6%
디자이너스 20
 
1.5%
Other values (86) 676
49.8%
2023-12-10T19:07:52.481655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
858
 
17.0%
307
 
6.1%
307
 
6.1%
245
 
4.8%
243
 
4.8%
235
 
4.6%
114
 
2.3%
109
 
2.2%
97
 
1.9%
77
 
1.5%
Other values (146) 2464
48.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3509
69.4%
Space Separator 858
 
17.0%
Lowercase Letter 302
 
6.0%
Uppercase Letter 207
 
4.1%
Decimal Number 90
 
1.8%
Open Punctuation 38
 
0.8%
Close Punctuation 38
 
0.8%
Dash Punctuation 11
 
0.2%
Other Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
307
 
8.7%
307
 
8.7%
245
 
7.0%
243
 
6.9%
235
 
6.7%
114
 
3.2%
109
 
3.1%
97
 
2.8%
77
 
2.2%
62
 
1.8%
Other values (102) 1713
48.8%
Uppercase Letter
ValueCountFrequency (%)
N 49
23.7%
H 35
16.9%
A 27
13.0%
L 20
9.7%
B 14
 
6.8%
T 11
 
5.3%
K 9
 
4.3%
I 6
 
2.9%
O 6
 
2.9%
E 6
 
2.9%
Other values (9) 24
11.6%
Lowercase Letter
ValueCountFrequency (%)
e 66
21.9%
t 41
13.6%
o 33
10.9%
l 31
10.3%
n 23
 
7.6%
s 19
 
6.3%
u 18
 
6.0%
i 15
 
5.0%
a 13
 
4.3%
v 11
 
3.6%
Other values (6) 32
10.6%
Decimal Number
ValueCountFrequency (%)
4 27
30.0%
8 27
30.0%
6 27
30.0%
1 9
 
10.0%
Space Separator
ValueCountFrequency (%)
858
100.0%
Open Punctuation
ValueCountFrequency (%)
( 38
100.0%
Close Punctuation
ValueCountFrequency (%)
) 38
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Other Punctuation
ValueCountFrequency (%)
& 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3509
69.4%
Common 1038
 
20.5%
Latin 509
 
10.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
307
 
8.7%
307
 
8.7%
245
 
7.0%
243
 
6.9%
235
 
6.7%
114
 
3.2%
109
 
3.1%
97
 
2.8%
77
 
2.2%
62
 
1.8%
Other values (102) 1713
48.8%
Latin
ValueCountFrequency (%)
e 66
 
13.0%
N 49
 
9.6%
t 41
 
8.1%
H 35
 
6.9%
o 33
 
6.5%
l 31
 
6.1%
A 27
 
5.3%
n 23
 
4.5%
L 20
 
3.9%
s 19
 
3.7%
Other values (25) 165
32.4%
Common
ValueCountFrequency (%)
858
82.7%
( 38
 
3.7%
) 38
 
3.7%
4 27
 
2.6%
8 27
 
2.6%
6 27
 
2.6%
- 11
 
1.1%
1 9
 
0.9%
& 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3509
69.4%
ASCII 1547
30.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
858
55.5%
e 66
 
4.3%
N 49
 
3.2%
t 41
 
2.7%
( 38
 
2.5%
) 38
 
2.5%
H 35
 
2.3%
o 33
 
2.1%
l 31
 
2.0%
4 27
 
1.7%
Other values (34) 331
 
21.4%
Hangul
ValueCountFrequency (%)
307
 
8.7%
307
 
8.7%
245
 
7.0%
243
 
6.9%
235
 
6.7%
114
 
3.2%
109
 
3.1%
97
 
2.8%
77
 
2.2%
62
 
1.8%
Other values (102) 1713
48.8%
Distinct69
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T19:07:53.058361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length28
Mean length21.662
Min length17

Characters and Unicode

Total characters10831
Distinct characters54
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)2.0%

Sample

1st row서울특별시 강남구 테헤란로16길 11
2nd row서울 강남구 테헤란로2길 33 (역삼동)
3rd row서울 강남구 테헤란로2길 37 (역삼동)
4th row서울 강남구 테헤란로2길 35 (역삼동)
5th row서울특별시 강남구 강남대로94길 56-4 (역삼동)
ValueCountFrequency (%)
강남구 435
19.8%
서울특별시 415
18.9%
역삼동 151
 
6.9%
봉은사로 92
 
4.2%
서울 85
 
3.9%
테헤란로2길 83
 
3.8%
서초구 65
 
3.0%
11 32
 
1.5%
논현동 29
 
1.3%
13 28
 
1.3%
Other values (84) 786
35.7%
2023-12-10T19:07:53.822901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1701
 
15.7%
596
 
5.5%
509
 
4.7%
509
 
4.7%
500
 
4.6%
500
 
4.6%
500
 
4.6%
1 469
 
4.3%
415
 
3.8%
415
 
3.8%
Other values (44) 4717
43.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6708
61.9%
Decimal Number 1878
 
17.3%
Space Separator 1701
 
15.7%
Close Punctuation 213
 
2.0%
Open Punctuation 213
 
2.0%
Dash Punctuation 108
 
1.0%
Other Punctuation 10
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
596
 
8.9%
509
 
7.6%
509
 
7.6%
500
 
7.5%
500
 
7.5%
500
 
7.5%
415
 
6.2%
415
 
6.2%
415
 
6.2%
358
 
5.3%
Other values (29) 1991
29.7%
Decimal Number
ValueCountFrequency (%)
1 469
25.0%
3 338
18.0%
2 241
12.8%
5 220
11.7%
7 124
 
6.6%
4 123
 
6.5%
0 114
 
6.1%
8 85
 
4.5%
6 82
 
4.4%
9 82
 
4.4%
Space Separator
ValueCountFrequency (%)
1701
100.0%
Close Punctuation
ValueCountFrequency (%)
) 213
100.0%
Open Punctuation
ValueCountFrequency (%)
( 213
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 108
100.0%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6708
61.9%
Common 4123
38.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
596
 
8.9%
509
 
7.6%
509
 
7.6%
500
 
7.5%
500
 
7.5%
500
 
7.5%
415
 
6.2%
415
 
6.2%
415
 
6.2%
358
 
5.3%
Other values (29) 1991
29.7%
Common
ValueCountFrequency (%)
1701
41.3%
1 469
 
11.4%
3 338
 
8.2%
2 241
 
5.8%
5 220
 
5.3%
) 213
 
5.2%
( 213
 
5.2%
7 124
 
3.0%
4 123
 
3.0%
0 114
 
2.8%
Other values (5) 367
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6708
61.9%
ASCII 4123
38.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1701
41.3%
1 469
 
11.4%
3 338
 
8.2%
2 241
 
5.8%
5 220
 
5.3%
) 213
 
5.2%
( 213
 
5.2%
7 124
 
3.0%
4 123
 
3.0%
0 114
 
2.8%
Other values (5) 367
 
8.9%
Hangul
ValueCountFrequency (%)
596
 
8.9%
509
 
7.6%
509
 
7.6%
500
 
7.5%
500
 
7.5%
500
 
7.5%
415
 
6.2%
415
 
6.2%
415
 
6.2%
358
 
5.3%
Other values (29) 1991
29.7%

ldgs_la
Real number (ℝ)

Distinct69
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.500796
Minimum37.489359
Maximum37.512042
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T19:07:54.203823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.489359
5-th percentile37.494461
Q137.497314
median37.50087
Q337.504066
95-th percentile37.50634
Maximum37.512042
Range0.022683
Interquartile range (IQR)0.006752

Descriptive statistics

Standard deviation0.0040961272
Coefficient of variation (CV)0.00010922774
Kurtosis-0.51995345
Mean37.500796
Median Absolute Deviation (MAD)0.0035555
Skewness-0.27803679
Sum18750.398
Variance1.6778258 × 10-5
MonotonicityNot monotonic
2023-12-10T19:07:54.451633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.4958567 14
 
2.8%
37.4959547 14
 
2.8%
37.5000015 14
 
2.8%
37.4973142 14
 
2.8%
37.5040662 14
 
2.8%
37.4961027 14
 
2.8%
37.4962133 14
 
2.8%
37.499008 14
 
2.8%
37.49872 14
 
2.8%
37.4987923 13
 
2.6%
Other values (59) 361
72.2%
ValueCountFrequency (%)
37.489359 4
 
0.8%
37.491632 9
1.8%
37.493376 10
2.0%
37.494461 3
 
0.6%
37.4944613 3
 
0.6%
37.4946337 2
 
0.4%
37.49479 8
1.6%
37.4958567 14
2.8%
37.4959547 14
2.8%
37.4961027 14
2.8%
ValueCountFrequency (%)
37.512042 2
 
0.4%
37.5105165 1
 
0.2%
37.508377 2
 
0.4%
37.506801 9
1.8%
37.506432 10
2.0%
37.50634 10
2.0%
37.505795 10
2.0%
37.505607 10
2.0%
37.5055177 10
2.0%
37.5054808 9
1.8%

ldgs_lo
Real number (ℝ)

Distinct69
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.03122
Minimum127.01787
Maximum127.04114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T19:07:54.769155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.01787
5-th percentile127.02205
Q1127.02851
median127.02999
Q3127.03541
95-th percentile127.0392
Maximum127.04114
Range0.0232695
Interquartile range (IQR)0.00689575

Descriptive statistics

Standard deviation0.0050680735
Coefficient of variation (CV)3.9896283 × 10-5
Kurtosis-0.31032669
Mean127.03122
Median Absolute Deviation (MAD)0.0038896
Skewness-0.14439843
Sum63515.61
Variance2.5685369 × 10-5
MonotonicityNot monotonic
2023-12-10T19:07:55.491911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0299851 14
 
2.8%
127.0298425 14
 
2.8%
127.0318757 14
 
2.8%
127.0291977 14
 
2.8%
127.0273281 14
 
2.8%
127.0298563 14
 
2.8%
127.02980739999998 14
 
2.8%
127.028512 14
 
2.8%
127.032518 14
 
2.8%
127.03395580000006 13
 
2.6%
Other values (59) 361
72.2%
ValueCountFrequency (%)
127.0178707 3
 
0.6%
127.017871 3
 
0.6%
127.01844630000004 1
 
0.2%
127.020469 12
2.4%
127.02205170000002 10
2.0%
127.023661 2
 
0.4%
127.023759 13
2.6%
127.0252206 9
1.8%
127.025842 10
2.0%
127.0258797 10
2.0%
ValueCountFrequency (%)
127.04114019999996 1
0.2%
127.041135 1
0.2%
127.0411349 1
0.2%
127.041064 1
0.2%
127.040503 1
0.2%
127.0404898 1
0.2%
127.0403744 1
0.2%
127.0402482 2
0.4%
127.04008299999998 2
0.4%
127.0399368 1
0.2%

ldgs_tel_no
Real number (ℝ)

Distinct66
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9668407 × 1010
Minimum25086247
Maximum5.0350522 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T19:07:55.755800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25086247
5-th percentile25534737
Q125619442
median2.328811 × 108
Q35.0350502 × 1010
95-th percentile5.0350521 × 1010
Maximum5.0350522 × 1010
Range5.0325435 × 1010
Interquartile range (IQR)5.0324882 × 1010

Descriptive statistics

Standard deviation2.4464481 × 1010
Coefficient of variation (CV)1.2438466
Kurtosis-1.7941313
Mean1.9668407 × 1010
Median Absolute Deviation (MAD)2.0734555 × 108
Skewness0.45878575
Sum9.8342037 × 1012
Variance5.9851084 × 1020
MonotonicityNot monotonic
2023-12-10T19:07:56.284451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25534737 27
 
5.4%
232881100 14
 
2.8%
50350521643 14
 
2.8%
50350501678 14
 
2.8%
50350514359 14
 
2.8%
50350514576 14
 
2.8%
50350509644 14
 
2.8%
50350500920 14
 
2.8%
25554204 14
 
2.8%
218999994 13
 
2.6%
Other values (56) 348
69.6%
ValueCountFrequency (%)
25086247 4
 
0.8%
25088366 1
 
0.2%
25111884 2
 
0.4%
25485489 10
 
2.0%
25529711 4
 
0.8%
25534737 27
5.4%
25535551 8
 
1.6%
25544250 13
2.6%
25554204 14
2.8%
25571221 10
 
2.0%
ValueCountFrequency (%)
50350521643 14
2.8%
50350521505 8
1.6%
50350521494 3
 
0.6%
50350521013 2
 
0.4%
50350520176 1
 
0.2%
50350519739 2
 
0.4%
50350518877 9
1.8%
50350518818 10
2.0%
50350515953 10
2.0%
50350515100 2
 
0.4%

rstrnt_nm
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
육장현
53 
유정원
50 
서래
45 
떡도리탕
43 
블랙스테이크 강남점
36 
Other values (9)
273 

Length

Max length12
Median length10
Mean length5.56
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
육장현 53
10.6%
유정원 50
10.0%
서래 45
9.0%
떡도리탕 43
8.6%
블랙스테이크 강남점 36
 
7.2%
하루하루 샤브샤브 36
 
7.2%
프리모바치오바치 강남점 35
 
7.0%
씨블루 35
 
7.0%
골목집 33
 
6.6%
북촌수라간 30
 
6.0%
Other values (4) 104
20.8%

Length

2023-12-10T19:07:56.646482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남점 71
 
11.2%
육장현 53
 
8.4%
유정원 50
 
7.9%
서래 45
 
7.1%
떡도리탕 43
 
6.8%
블랙스테이크 36
 
5.7%
하루하루 36
 
5.7%
샤브샤브 36
 
5.7%
씨블루 35
 
5.5%
프리모바치오바치 35
 
5.5%
Other values (7) 194
30.6%

rstrnt_addr
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
서울특별시 강남구 테헤란로5길 20 1층
53 
서울특별시 강남구 테헤란로8길 41
50 
서울특별시 강남구 강남대로94길 10 K스퀘어 3층
45 
서울특별시 강남구 테헤란로1길 28-4
43 
서울특별시 강남구 강남대로106길 23
36 
Other values (9)
273 

Length

Max length28
Median length24
Mean length21.92
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시 서초구 강남대로 365 대우도씨에빛
2nd row서울특별시 서초구 강남대로 365 대우도씨에빛
3rd row서울특별시 서초구 강남대로 365 대우도씨에빛
4th row서울특별시 서초구 강남대로 365 대우도씨에빛
5th row서울특별시 서초구 강남대로 365 대우도씨에빛

Common Values

ValueCountFrequency (%)
서울특별시 강남구 테헤란로5길 20 1층 53
10.6%
서울특별시 강남구 테헤란로8길 41 50
10.0%
서울특별시 강남구 강남대로94길 10 K스퀘어 3층 45
9.0%
서울특별시 강남구 테헤란로1길 28-4 43
8.6%
서울특별시 강남구 강남대로106길 23 36
 
7.2%
서울특별시 서초구 서초대로77길 3 아라타워 36
 
7.2%
서울특별시 강남구 강남대로 416 창림빌딩 35
 
7.0%
서울특별시 강남구 봉은사로4길 17 1층 35
 
7.0%
서울특별시 강남구 봉은사로4길 40 33
 
6.6%
서울특별시 강남구 테헤란로2길 28 30
 
6.0%
Other values (4) 104
20.8%

Length

2023-12-10T19:07:57.005363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울특별시 500
21.7%
강남구 360
15.6%
서초구 140
 
6.1%
1층 88
 
3.8%
봉은사로4길 68
 
3.0%
강남대로 59
 
2.6%
테헤란로5길 53
 
2.3%
20 53
 
2.3%
테헤란로8길 50
 
2.2%
41 50
 
2.2%
Other values (26) 881
38.3%

rstrnt_la
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.499666
Minimum37.495108
Maximum37.503727
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T19:07:57.221298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.495108
5-th percentile37.495332
Q137.496275
median37.499978
Q337.502357
95-th percentile37.503727
Maximum37.503727
Range0.0086192
Interquartile range (IQR)0.0060816

Descriptive statistics

Standard deviation0.0027499169
Coefficient of variation (CV)7.3331769 × 10-5
Kurtosis-1.1595351
Mean37.499666
Median Absolute Deviation (MAD)0.0023793
Skewness-0.12367765
Sum18749.833
Variance7.5620427 × 10-6
MonotonicityNot monotonic
2023-12-10T19:07:57.478021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
37.4999776 53
10.6%
37.4962753 50
10.0%
37.4991963 45
9.0%
37.5001917 43
8.6%
37.5033174 36
 
7.2%
37.4980112 36
 
7.2%
37.499835 35
 
7.0%
37.5037272 35
 
7.0%
37.5023569 33
 
6.6%
37.4962536 30
 
6.0%
Other values (4) 104
20.8%
ValueCountFrequency (%)
37.495108 24
4.8%
37.4953321 24
4.8%
37.4962536 30
6.0%
37.4962753 50
10.0%
37.4980112 36
7.2%
37.4991963 45
9.0%
37.499835 35
7.0%
37.4999776 53
10.6%
37.5001917 43
8.6%
37.5018201 29
5.8%
ValueCountFrequency (%)
37.5037272 35
7.0%
37.5033174 36
7.2%
37.5032281 27
5.4%
37.5023569 33
6.6%
37.5018201 29
5.8%
37.5001917 43
8.6%
37.4999776 53
10.6%
37.499835 35
7.0%
37.4991963 45
9.0%
37.4980112 36
7.2%

rstrnt_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.0278
Minimum127.02358
Maximum127.03209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T19:07:57.748847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.02358
5-th percentile127.02358
Q1127.02658
median127.02779
Q3127.02907
95-th percentile127.03209
Maximum127.03209
Range0.0085148
Interquartile range (IQR)0.0024871

Descriptive statistics

Standard deviation0.0020204216
Coefficient of variation (CV)1.590535 × 10-5
Kurtosis0.52555177
Mean127.0278
Median Absolute Deviation (MAD)0.0012111
Skewness0.25301603
Sum63513.9
Variance4.0821034 × 10-6
MonotonicityNot monotonic
2023-12-10T19:07:57.992390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
127.0290682 53
10.6%
127.032093 50
10.0%
127.0282272 45
9.0%
127.0277922 43
8.6%
127.0274054 36
 
7.2%
127.0265811 36
 
7.2%
127.027083 35
 
7.0%
127.0264689 35
 
7.0%
127.0269408 33
 
6.6%
127.0293785 30
 
6.0%
Other values (4) 104
20.8%
ValueCountFrequency (%)
127.0235782 27
5.4%
127.0246093 29
5.8%
127.0264689 35
7.0%
127.0265811 36
7.2%
127.0269408 33
6.6%
127.027083 35
7.0%
127.0274054 36
7.2%
127.0277922 43
8.6%
127.0279346 24
4.8%
127.0282272 45
9.0%
ValueCountFrequency (%)
127.032093 50
10.0%
127.0293785 30
6.0%
127.0290682 53
10.6%
127.0283363 24
4.8%
127.0282272 45
9.0%
127.0279346 24
4.8%
127.0277922 43
8.6%
127.0274054 36
7.2%
127.027083 35
7.0%
127.0269408 33
6.6%

ldgs_rstrnt_btwn_dstnc_co
Real number (ℝ)

Distinct362
Distinct (%)72.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.650222
Minimum0.029
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T19:07:58.437909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.029
5-th percentile0.193
Q10.44875
median0.6935
Q30.86925
95-th percentile0.988
Maximum1
Range0.971
Interquartile range (IQR)0.4205

Descriptive statistics

Standard deviation0.25623038
Coefficient of variation (CV)0.394066
Kurtosis-0.83578351
Mean0.650222
Median Absolute Deviation (MAD)0.1995
Skewness-0.47047002
Sum325.111
Variance0.065654009
MonotonicityNot monotonic
2023-12-10T19:07:58.796461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.999 6
 
1.2%
0.928 5
 
1.0%
0.77 4
 
0.8%
0.788 4
 
0.8%
0.202 4
 
0.8%
0.636 4
 
0.8%
0.361 3
 
0.6%
0.976 3
 
0.6%
0.989 3
 
0.6%
0.644 3
 
0.6%
Other values (352) 461
92.2%
ValueCountFrequency (%)
0.029 1
0.2%
0.033 1
0.2%
0.038 1
0.2%
0.045 1
0.2%
0.053 1
0.2%
0.069 1
0.2%
0.084 1
0.2%
0.085 1
0.2%
0.105 1
0.2%
0.118 1
0.2%
ValueCountFrequency (%)
1.0 3
0.6%
0.999 6
1.2%
0.998 2
 
0.4%
0.997 3
0.6%
0.996 1
 
0.2%
0.994 2
 
0.4%
0.993 1
 
0.2%
0.991 2
 
0.4%
0.99 1
 
0.2%
0.989 3
0.6%

Interactions

2023-12-10T19:07:49.368738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:41.513180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:43.145087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:44.226783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:45.330767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:46.549408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:47.903466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:49.565350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:41.711664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:43.297874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:44.398671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:45.481417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:46.728795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:48.197060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:49.743772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:41.881405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:43.445251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:44.568907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:45.632260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:46.965741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:48.346460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:49.928245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:42.097867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:43.602470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:44.725044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:45.803358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:47.165541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:48.530432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:50.103218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:42.656179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:43.765144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:44.879423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:45.985346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:47.328599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:48.710387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:50.259243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:42.815818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:43.903753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:45.025032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:46.180460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:47.488245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:48.919152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:50.445633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:42.968498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:44.058484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:45.160523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:46.356753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:47.638292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:49.178247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:07:59.000388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ldgs_cdldgs_nmldgs_addrldgs_laldgs_loldgs_tel_norstrnt_nmrstrnt_addrrstrnt_larstrnt_loldgs_rstrnt_btwn_dstnc_co
ldgs_cd1.0000.9591.0000.4650.5220.0510.0000.0000.0000.0000.210
ldgs_nm0.9591.0001.0001.0001.0001.0000.0000.0000.0000.0000.444
ldgs_addr1.0001.0001.0001.0001.0001.0000.0000.0000.0000.0000.414
ldgs_la0.4651.0001.0001.0000.8930.5940.3050.3050.3000.3100.579
ldgs_lo0.5221.0001.0000.8931.0000.5260.3310.3310.3130.3590.662
ldgs_tel_no0.0511.0001.0000.5940.5261.0000.0000.0000.0000.0000.289
rstrnt_nm0.0000.0000.0000.3050.3310.0001.0001.0001.0001.0000.358
rstrnt_addr0.0000.0000.0000.3050.3310.0001.0001.0001.0001.0000.358
rstrnt_la0.0000.0000.0000.3000.3130.0001.0001.0001.0000.9060.288
rstrnt_lo0.0000.0000.0000.3100.3590.0001.0001.0000.9061.0000.234
ldgs_rstrnt_btwn_dstnc_co0.2100.4440.4140.5790.6620.2890.3580.3580.2880.2341.000
2023-12-10T19:07:59.306552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
rstrnt_addrrstrnt_nm
rstrnt_addr1.0001.000
rstrnt_nm1.0001.000
2023-12-10T19:07:59.569438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ldgs_cdldgs_laldgs_loldgs_tel_norstrnt_larstrnt_loldgs_rstrnt_btwn_dstnc_corstrnt_nmrstrnt_addr
ldgs_cd1.0000.206-0.074-0.0970.043-0.0300.0180.0000.000
ldgs_la0.2061.000-0.0710.0050.301-0.1870.0820.1270.127
ldgs_lo-0.074-0.0711.000-0.160-0.1520.3420.4790.1390.139
ldgs_tel_no-0.0970.005-0.1601.0000.0200.016-0.0100.0000.000
rstrnt_la0.0430.301-0.1520.0201.000-0.652-0.0460.9940.994
rstrnt_lo-0.030-0.1870.3420.016-0.6521.0000.0600.9930.993
ldgs_rstrnt_btwn_dstnc_co0.0180.0820.479-0.010-0.0460.0601.0000.1520.152
rstrnt_nm0.0000.1270.1390.0000.9940.9930.1521.0001.000
rstrnt_addr0.0000.1270.1390.0000.9940.9930.1521.0001.000

Missing values

2023-12-10T19:07:50.691435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:07:50.953450image/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

ldgs_cdldgs_nmldgs_addrldgs_laldgs_loldgs_tel_norstrnt_nmrstrnt_addrrstrnt_larstrnt_loldgs_rstrnt_btwn_dstnc_co
023197강남 렉시서울특별시 강남구 테헤란로16길 1137.498792127.03395625544250다미선서울특별시 서초구 강남대로 365 대우도씨에빛37.495332127.0283360.628
125586역삼(강남역) 마레서울 강남구 테헤란로2길 33 (역삼동)37.496103127.02985650350500920다미선서울특별시 서초구 강남대로 365 대우도씨에빛37.495332127.0283360.159
226275강남 시애틀서울 강남구 테헤란로2길 37 (역삼동)37.495857127.02998550350521643다미선서울특별시 서초구 강남대로 365 대우도씨에빛37.495332127.0283360.157
327140강남 밀라노서울 강남구 테헤란로2길 35 (역삼동)37.495955127.02984350350501678다미선서울특별시 서초구 강남대로 365 대우도씨에빛37.495332127.0283360.15
427583강남 648호텔서울특별시 강남구 강남대로94길 56-4 (역삼동)37.500002127.03187625534737다미선서울특별시 서초구 강남대로 365 대우도씨에빛37.495332127.0283360.606
51019101강남 캠퍼스서울 강남구 테헤란로2길 13 (역삼동)37.497314127.02919850350514359다미선서울특별시 서초구 강남대로 365 대우도씨에빛37.495332127.0283360.233
61019681서울 빅존스 플레이스 게스트하우스서울특별시 강남구 강남대로110길 31-1 (역삼동)37.504066127.02732850350514576다미선서울특별시 서초구 강남대로 365 대우도씨에빛37.495332127.0283360.975
73000585머큐어 앰배서더 강남 쏘도베서울특별시 강남구 테헤란로25길 1037.50122127.03546220506000다미선서울특별시 서초구 강남대로 365 대우도씨에빛37.495332127.0283360.908
83000638강남아르누보씨티 호텔서울특별시 서초구 서초대로74길 4937.493376127.02812525807500다미선서울특별시 서초구 강남대로 365 대우도씨에빛37.495332127.0283360.218
93000697호텔 아르누보 서초서울특별시 서초구 서초대로 35737.496129127.02046925607101다미선서울특별시 서초구 강남대로 365 대우도씨에빛37.495332127.0283360.7
ldgs_cdldgs_nmldgs_addrldgs_laldgs_loldgs_tel_norstrnt_nmrstrnt_addrrstrnt_larstrnt_loldgs_rstrnt_btwn_dstnc_co
4903001437호텔 더 디자이너스 리즈 강남서울특별시 강남구 봉은사로 11337.50517127.02584225674000젊은조개구이바강남역점서울특별시 서초구 강남대로69길 8 서울빌딩37.50182127.0246090.388
4913001516카푸치노 호텔서울특별시 강남구 봉은사로 15537.506801127.031485220389500젊은조개구이바강남역점서울특별시 서초구 강남대로69길 8 서울빌딩37.50182127.0246090.821
4923001688호텔 소울하다서울특별시 강남구 테헤란로10길 537.49872127.032518232881100젊은조개구이바강남역점서울특별시 서초구 강남대로69길 8 서울빌딩37.50182127.0246090.778
4933001747스테이 호텔 강남서울특별시 강남구 논현로87길 15-437.499343127.03540325686200젊은조개구이바강남역점서울특별시 서초구 강남대로69길 8 서울빌딩37.50182127.0246090.991
4943008549도미인 서울 강남서울특별시 강남구 봉은사로 13437.505607127.02944825485489젊은조개구이바강남역점서울특별시 서초구 강남대로69길 8 서울빌딩37.50182127.0246090.6
4953010383논현 왈츠서울특별시 강남구 강남대로112길 20 (논현동)37.505518127.02609550350515953젊은조개구이바강남역점서울특별시 서초구 강남대로69길 8 서울빌딩37.50182127.0246090.432
4963013608호텔 세느 강남서울특별시 강남구 논현로 53337.503903127.035093220520551젊은조개구이바강남역점서울특별시 서초구 강남대로69길 8 서울빌딩37.50182127.0246090.953
4973014573강남 BNN서울특별시 강남구 테헤란로2길 31 (역삼동)37.496213127.02980750350509644젊은조개구이바강남역점서울특별시 서초구 강남대로69길 8 서울빌딩37.50182127.0246090.774
4983015356르 메르디앙 서울서울특별시 강남구 봉은사로 12037.504847127.027142234518000젊은조개구이바강남역점서울특별시 서초구 강남대로69길 8 서울빌딩37.50182127.0246090.404
4993017844강남 AN 게스트하우스(L stay)서울특별시 서초구 사평대로53길 8-3 (반포동, 세종빌리지)37.504854127.02205250350518818젊은조개구이바강남역점서울특별시 서초구 강남대로69길 8 서울빌딩37.50182127.0246090.406