Overview

Dataset statistics

Number of variables10
Number of observations1000
Missing cells1105
Missing cells (%)11.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory84.1 KiB
Average record size in memory86.1 B

Variable types

Categorical2
Numeric4
Text3
Unsupported1

Alerts

SUBWAY_LT has constant value ""Constant
RSTRNT_LA is highly overall correlated with RSTRNT_LO and 1 other fieldsHigh correlation
RSTRNT_LO is highly overall correlated with RSTRNT_LA and 1 other fieldsHigh correlation
CITY_NM is highly overall correlated with RSTRNT_LA and 1 other fieldsHigh correlation
RSTRNT_GIBUN_ADDR has 24 (2.4%) missing valuesMissing
RSTRNT_ROAD_ADDR has 55 (5.5%) missing valuesMissing
RSTRNT_TEL_NO has 26 (2.6%) missing valuesMissing
SUBWAY_NM has 1000 (100.0%) missing valuesMissing
SUBWAY_NM is an unsupported type, check if it needs cleaning or further analysisUnsupported
RSTRNT_LA has 420 (42.0%) zerosZeros
RSTRNT_LO has 420 (42.0%) zerosZeros

Reproduction

Analysis started2023-12-10 09:55:44.428276
Analysis finished2023-12-10 09:55:50.972107
Duration6.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

CITY_NM
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
jeju
506 
busan
494 

Length

Max length5
Median length4
Mean length4.494
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
jeju 506
50.6%
busan 494
49.4%

Length

2023-12-10T18:55:51.110855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:55:51.327811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
jeju 506
50.6%
busan 494
49.4%

RSTRNT_ID
Real number (ℝ)

Distinct673
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean314.982
Minimum1
Maximum811
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-10T18:55:51.625834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile26.95
Q1140
median289.5
Q3443.25
95-th percentile738.05
Maximum811
Range810
Interquartile range (IQR)303.25

Descriptive statistics

Standard deviation210.87735
Coefficient of variation (CV)0.66949016
Kurtosis-0.50528381
Mean314.982
Median Absolute Deviation (MAD)151.5
Skewness0.54288386
Sum314982
Variance44469.257
MonotonicityNot monotonic
2023-12-10T18:55:52.013895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2
 
0.2%
273 2
 
0.2%
286 2
 
0.2%
284 2
 
0.2%
283 2
 
0.2%
282 2
 
0.2%
281 2
 
0.2%
280 2
 
0.2%
278 2
 
0.2%
277 2
 
0.2%
Other values (663) 980
98.0%
ValueCountFrequency (%)
1 2
0.2%
2 2
0.2%
3 2
0.2%
4 2
0.2%
5 2
0.2%
6 2
0.2%
7 2
0.2%
8 2
0.2%
9 2
0.2%
10 2
0.2%
ValueCountFrequency (%)
811 1
0.1%
810 1
0.1%
808 1
0.1%
806 1
0.1%
805 1
0.1%
804 1
0.1%
802 1
0.1%
800 1
0.1%
799 1
0.1%
798 1
0.1%

RSTRNT_NM
Text

Distinct985
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-12-10T18:55:52.751554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length22
Mean length6.632
Min length2

Characters and Unicode

Total characters6632
Distinct characters579
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique970 ?
Unique (%)97.0%

Sample

1st row라마앤바바나
2nd row사이공
3rd row충무횟집
4th row대륙훠궈
5th row동백섬횟집
ValueCountFrequency (%)
부산 18
 
1.5%
파리바게뜨 17
 
1.4%
서면점 11
 
0.9%
해운대점 11
 
0.9%
뚜레쥬르 9
 
0.7%
서귀포점 6
 
0.5%
본죽 6
 
0.5%
20190806 5
 
0.4%
서면본점 5
 
0.4%
제주점 5
 
0.4%
Other values (1046) 1142
92.5%
2023-12-10T18:55:53.813476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 278
 
4.2%
236
 
3.6%
( 112
 
1.7%
) 112
 
1.7%
제 99
 
1.5%
스 94
 
1.4%
산 91
 
1.4%
주 90
 
1.4%
비 88
 
1.3%
서 83
 
1.3%
Other values (569) 5349
80.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5929
89.4%
Space Separator 236
 
3.6%
Decimal Number 168
 
2.5%
Open Punctuation 122
 
1.8%
Close Punctuation 122
 
1.8%
Uppercase Letter 29
 
0.4%
Lowercase Letter 16
 
0.2%
Other Punctuation 9
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 278
 
4.7%
제 99
 
1.7%
스 94
 
1.6%
산 91
 
1.5%
주 90
 
1.5%
비 88
 
1.5%
서 83
 
1.4%
이 83
 
1.4%
부 80
 
1.3%
갈 77
 
1.3%
Other values (527) 4866
82.1%
Uppercase Letter
ValueCountFrequency (%)
A 4
13.8%
L 3
10.3%
D 3
10.3%
O 3
10.3%
M 3
10.3%
T 2
6.9%
E 2
6.9%
C 2
6.9%
N 2
6.9%
G 1
 
3.4%
Other values (4) 4
13.8%
Decimal Number
ValueCountFrequency (%)
1 44
26.2%
0 41
24.4%
2 22
13.1%
9 15
 
8.9%
8 13
 
7.7%
3 11
 
6.5%
6 8
 
4.8%
5 7
 
4.2%
4 4
 
2.4%
7 3
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
t 4
25.0%
e 3
18.8%
s 2
12.5%
r 2
12.5%
a 2
12.5%
h 1
 
6.2%
n 1
 
6.2%
u 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 4
44.4%
, 3
33.3%
… 1
 
11.1%
& 1
 
11.1%
Open Punctuation
ValueCountFrequency (%)
( 112
91.8%
[ 10
 
8.2%
Close Punctuation
ValueCountFrequency (%)
) 112
91.8%
] 10
 
8.2%
Space Separator
ValueCountFrequency (%)
236
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5929
89.4%
Common 658
 
9.9%
Latin 45
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 278
 
4.7%
제 99
 
1.7%
스 94
 
1.6%
산 91
 
1.5%
주 90
 
1.5%
비 88
 
1.5%
서 83
 
1.4%
이 83
 
1.4%
부 80
 
1.3%
갈 77
 
1.3%
Other values (527) 4866
82.1%
Latin
ValueCountFrequency (%)
t 4
 
8.9%
A 4
 
8.9%
L 3
 
6.7%
D 3
 
6.7%
O 3
 
6.7%
e 3
 
6.7%
M 3
 
6.7%
T 2
 
4.4%
E 2
 
4.4%
s 2
 
4.4%
Other values (12) 16
35.6%
Common
ValueCountFrequency (%)
236
35.9%
( 112
17.0%
) 112
17.0%
1 44
 
6.7%
0 41
 
6.2%
2 22
 
3.3%
9 15
 
2.3%
8 13
 
2.0%
3 11
 
1.7%
] 10
 
1.5%
Other values (10) 42
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5929
89.4%
ASCII 702
 
10.6%
Punctuation 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 278
 
4.7%
제 99
 
1.7%
스 94
 
1.6%
산 91
 
1.5%
주 90
 
1.5%
비 88
 
1.5%
서 83
 
1.4%
이 83
 
1.4%
부 80
 
1.3%
갈 77
 
1.3%
Other values (527) 4866
82.1%
ASCII
ValueCountFrequency (%)
236
33.6%
( 112
16.0%
) 112
16.0%
1 44
 
6.3%
0 41
 
5.8%
2 22
 
3.1%
9 15
 
2.1%
8 13
 
1.9%
3 11
 
1.6%
] 10
 
1.4%
Other values (31) 86
 
12.3%
Punctuation
ValueCountFrequency (%)
… 1
100.0%

RSTRNT_GIBUN_ADDR
Text

MISSING 

Distinct926
Distinct (%)94.9%
Missing24
Missing (%)2.4%
Memory size7.9 KiB
2023-12-10T18:55:54.500360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length44
Mean length23.335041
Min length15

Characters and Unicode

Total characters22775
Distinct characters327
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

Unique892 ?
Unique (%)91.4%

Sample

1st row부산광역시 해운대구 중동 1417-2 씨스타빌딩 6층
2nd row부산광역시 수영구 남천동 3-29
3rd row부산광역시 중구 남포동6가 9-1
4th row부산광역시 수영구 민락동 181-36
5th row부산광역시 해운대구 우동 655-7
ValueCountFrequency (%)
제주특별자치도 500
 
11.4%
부산광역시 475
 
10.8%
제주시 356
 
8.1%
부산진구 157
 
3.6%
서귀포시 143
 
3.3%
부전동 130
 
3.0%
중구 117
 
2.7%
해운대구 84
 
1.9%
연동 66
 
1.5%
1층 48
 
1.1%
Other values (1313) 2305
52.6%
2023-12-10T18:55:55.731842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3460
 
15.2%
1 1110
 
4.9%
시 1013
 
4.4%
동 974
 
4.3%
주 872
 
3.8%
- 869
 
3.8%
제 868
 
3.8%
부 808
 
3.5%
2 735
 
3.2%
도 670
 
2.9%
Other values (317) 11396
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13725
60.3%
Decimal Number 4677
 
20.5%
Space Separator 3460
 
15.2%
Dash Punctuation 869
 
3.8%
Other Punctuation 21
 
0.1%
Uppercase Letter 21
 
0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 1013
 
7.4%
동 974
 
7.1%
주 872
 
6.4%
제 868
 
6.3%
부 808
 
5.9%
도 670
 
4.9%
산 662
 
4.8%
자 531
 
3.9%
치 527
 
3.8%
별 501
 
3.7%
Other values (287) 6299
45.9%
Uppercase Letter
ValueCountFrequency (%)
S 3
14.3%
B 2
9.5%
T 2
9.5%
M 2
9.5%
N 2
9.5%
E 2
9.5%
A 1
 
4.8%
K 1
 
4.8%
U 1
 
4.8%
L 1
 
4.8%
Other values (4) 4
19.0%
Decimal Number
ValueCountFrequency (%)
1 1110
23.7%
2 735
15.7%
3 483
10.3%
5 417
 
8.9%
4 416
 
8.9%
7 349
 
7.5%
6 317
 
6.8%
0 295
 
6.3%
9 284
 
6.1%
8 271
 
5.8%
Other Punctuation
ValueCountFrequency (%)
, 19
90.5%
. 2
 
9.5%
Space Separator
ValueCountFrequency (%)
3460
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 869
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13725
60.3%
Common 9029
39.6%
Latin 21
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 1013
 
7.4%
동 974
 
7.1%
주 872
 
6.4%
제 868
 
6.3%
부 808
 
5.9%
도 670
 
4.9%
산 662
 
4.8%
자 531
 
3.9%
치 527
 
3.8%
별 501
 
3.7%
Other values (287) 6299
45.9%
Common
ValueCountFrequency (%)
3460
38.3%
1 1110
 
12.3%
- 869
 
9.6%
2 735
 
8.1%
3 483
 
5.3%
5 417
 
4.6%
4 416
 
4.6%
7 349
 
3.9%
6 317
 
3.5%
0 295
 
3.3%
Other values (6) 578
 
6.4%
Latin
ValueCountFrequency (%)
S 3
14.3%
B 2
9.5%
T 2
9.5%
M 2
9.5%
N 2
9.5%
E 2
9.5%
A 1
 
4.8%
K 1
 
4.8%
U 1
 
4.8%
L 1
 
4.8%
Other values (4) 4
19.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13725
60.3%
ASCII 9050
39.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3460
38.2%
1 1110
 
12.3%
- 869
 
9.6%
2 735
 
8.1%
3 483
 
5.3%
5 417
 
4.6%
4 416
 
4.6%
7 349
 
3.9%
6 317
 
3.5%
0 295
 
3.3%
Other values (20) 599
 
6.6%
Hangul
ValueCountFrequency (%)
시 1013
 
7.4%
동 974
 
7.1%
주 872
 
6.4%
제 868
 
6.3%
부 808
 
5.9%
도 670
 
4.9%
산 662
 
4.8%
자 531
 
3.9%
치 527
 
3.8%
별 501
 
3.7%
Other values (287) 6299
45.9%

RSTRNT_ROAD_ADDR
Text

MISSING 

Distinct888
Distinct (%)94.0%
Missing55
Missing (%)5.5%
Memory size7.9 KiB
2023-12-10T18:55:56.363973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length37
Mean length21.220106
Min length14

Characters and Unicode

Total characters20053
Distinct characters265
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

Unique847 ?
Unique (%)89.6%

Sample

1st row부산광역시 해운대구 해운대해변로 271
2nd row부산광역시 수영구 남천바다로 36
3rd row부산광역시 중구 자갈치해안로 29
4th row부산광역시 수영구 광안해변로 285
5th row부산광역시 해운대구 해운대해변로209번나길 17
ValueCountFrequency (%)
부산광역시 472
 
11.6%
제주특별자치도 471
 
11.6%
제주시 337
 
8.3%
부산진구 160
 
3.9%
서귀포시 134
 
3.3%
중구 117
 
2.9%
해운대구 82
 
2.0%
1층 42
 
1.0%
52 38
 
0.9%
자갈치해안로 36
 
0.9%
Other values (958) 2167
53.4%
2023-12-10T18:55:57.403469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3180
 
15.9%
시 959
 
4.8%
로 824
 
4.1%
주 823
 
4.1%
제 821
 
4.1%
1 691
 
3.4%
산 668
 
3.3%
부 667
 
3.3%
광 556
 
2.8%
자 521
 
2.6%
Other values (255) 10343
51.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13360
66.6%
Decimal Number 3271
 
16.3%
Space Separator 3180
 
15.9%
Dash Punctuation 196
 
1.0%
Uppercase Letter 26
 
0.1%
Other Punctuation 18
 
0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 959
 
7.2%
로 824
 
6.2%
주 823
 
6.2%
제 821
 
6.1%
산 668
 
5.0%
부 667
 
5.0%
광 556
 
4.2%
자 521
 
3.9%
치 517
 
3.9%
구 506
 
3.8%
Other values (225) 6498
48.6%
Uppercase Letter
ValueCountFrequency (%)
E 5
19.2%
S 4
15.4%
T 3
11.5%
B 2
 
7.7%
M 2
 
7.7%
N 2
 
7.7%
R 1
 
3.8%
A 1
 
3.8%
U 1
 
3.8%
I 1
 
3.8%
Other values (4) 4
15.4%
Decimal Number
ValueCountFrequency (%)
1 691
21.1%
2 514
15.7%
3 326
10.0%
4 307
9.4%
5 289
8.8%
6 285
8.7%
8 241
 
7.4%
7 210
 
6.4%
9 209
 
6.4%
0 199
 
6.1%
Other Punctuation
ValueCountFrequency (%)
, 16
88.9%
. 2
 
11.1%
Space Separator
ValueCountFrequency (%)
3180
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 196
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13360
66.6%
Common 6667
33.2%
Latin 26
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 959
 
7.2%
로 824
 
6.2%
주 823
 
6.2%
제 821
 
6.1%
산 668
 
5.0%
부 667
 
5.0%
광 556
 
4.2%
자 521
 
3.9%
치 517
 
3.9%
구 506
 
3.8%
Other values (225) 6498
48.6%
Common
ValueCountFrequency (%)
3180
47.7%
1 691
 
10.4%
2 514
 
7.7%
3 326
 
4.9%
4 307
 
4.6%
5 289
 
4.3%
6 285
 
4.3%
8 241
 
3.6%
7 210
 
3.1%
9 209
 
3.1%
Other values (6) 415
 
6.2%
Latin
ValueCountFrequency (%)
E 5
19.2%
S 4
15.4%
T 3
11.5%
B 2
 
7.7%
M 2
 
7.7%
N 2
 
7.7%
R 1
 
3.8%
A 1
 
3.8%
U 1
 
3.8%
I 1
 
3.8%
Other values (4) 4
15.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13360
66.6%
ASCII 6693
33.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3180
47.5%
1 691
 
10.3%
2 514
 
7.7%
3 326
 
4.9%
4 307
 
4.6%
5 289
 
4.3%
6 285
 
4.3%
8 241
 
3.6%
7 210
 
3.1%
9 209
 
3.1%
Other values (20) 441
 
6.6%
Hangul
ValueCountFrequency (%)
시 959
 
7.2%
로 824
 
6.2%
주 823
 
6.2%
제 821
 
6.1%
산 668
 
5.0%
부 667
 
5.0%
광 556
 
4.2%
자 521
 
3.9%
치 517
 
3.9%
구 506
 
3.8%
Other values (225) 6498
48.6%

RSTRNT_TEL_NO
Real number (ℝ)

MISSING 

Distinct954
Distinct (%)97.9%
Missing26
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean9.2328668 × 108
Minimum15442998
Maximum5.0714121 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-10T18:55:57.711006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15442998
5-th percentile5.1245795 × 108
Q15.1795037 × 108
median6.4724473 × 108
Q36.4755954 × 108
95-th percentile1.0661099 × 109
Maximum5.0714121 × 1010
Range5.0698678 × 1010
Interquartile range (IQR)1.2960917 × 108

Descriptive statistics

Standard deviation2.9272819 × 109
Coefficient of variation (CV)3.1705016
Kurtosis254.79445
Mean9.2328668 × 108
Median Absolute Deviation (MAD)717515.5
Skewness15.306941
Sum8.9928122 × 1011
Variance8.568979 × 1018
MonotonicityNot monotonic
2023-12-10T18:55:57.967373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
518180519 3
 
0.3%
518183559 2
 
0.2%
518198809 2
 
0.2%
517477484 2
 
0.2%
512450077 2
 
0.2%
518022655 2
 
0.2%
518068669 2
 
0.2%
516466295 2
 
0.2%
518026282 2
 
0.2%
1094599389 2
 
0.2%
Other values (944) 953
95.3%
(Missing) 26
 
2.6%
ValueCountFrequency (%)
15442998 1
0.1%
15779820 1
0.1%
16000749 1
0.1%
18996837 1
0.1%
18998637 1
0.1%
269647955 1
0.1%
512077592 1
0.1%
512082880 1
0.1%
512311850 1
0.1%
512312015 1
0.1%
ValueCountFrequency (%)
50714120672 1
0.1%
50713085692 1
0.1%
50381217006 1
0.1%
7088397591 1
0.1%
7088180410 1
0.1%
7088102671 1
0.1%
7082301116 1
0.1%
7081115539 2
0.2%
7077977799 1
0.1%
7077962347 1
0.1%

SUBWAY_NM
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1000
Missing (%)100.0%
Memory size8.9 KiB

SUBWAY_LT
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1000
100.0%

Length

2023-12-10T18:55:58.248471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:55:58.458741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1000
100.0%

RSTRNT_LA
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct557
Distinct (%)55.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.51312
Minimum0
Maximum35.229153
Zeros420
Zeros (%)42.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-10T18:55:58.683161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median33.252148
Q333.496716
95-th percentile35.154717
Maximum35.229153
Range35.229153
Interquartile range (IQR)33.496716

Descriptive statistics

Standard deviation16.61935
Coefficient of variation (CV)0.85170134
Kurtosis-1.8966374
Mean19.51312
Median Absolute Deviation (MAD)0.29566192
Skewness-0.32237808
Sum19513.12
Variance276.2028
MonotonicityNot monotonic
2023-12-10T18:55:58.970688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 420
42.0%
32.52205276 4
 
0.4%
33.24520111 3
 
0.3%
33.49353409 2
 
0.2%
33.24774933 2
 
0.2%
33.4878006 2
 
0.2%
33.48828506 2
 
0.2%
33.24433899 2
 
0.2%
33.49833679 2
 
0.2%
33.25199127 2
 
0.2%
Other values (547) 559
55.9%
ValueCountFrequency (%)
0.0 420
42.0%
32.52205276 4
 
0.4%
33.11967087 1
 
0.1%
33.20982742 1
 
0.1%
33.21943283 1
 
0.1%
33.2201767 1
 
0.1%
33.22152328 1
 
0.1%
33.22169495 1
 
0.1%
33.22180176 1
 
0.1%
33.2233889 1
 
0.1%
ValueCountFrequency (%)
35.22915268 1
0.1%
35.1611203 1
0.1%
35.15980148 1
0.1%
35.1572403 1
0.1%
35.1569669 1
0.1%
35.1566287 1
0.1%
35.1565719 1
0.1%
35.1564459 1
0.1%
35.1563066 1
0.1%
35.156058 1
0.1%

RSTRNT_LO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct559
Distinct (%)55.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.560936
Minimum0
Maximum129.161
Zeros420
Zeros (%)42.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-10T18:55:59.311853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median126.46836
Q3126.53236
95-th percentile129.05668
Maximum129.161
Range129.161
Interquartile range (IQR)126.53236

Descriptive statistics

Standard deviation62.632628
Coefficient of variation (CV)0.8514387
Kurtosis-1.898133
Mean73.560936
Median Absolute Deviation (MAD)0.48258972
Skewness-0.3243036
Sum73560.936
Variance3922.8461
MonotonicityNot monotonic
2023-12-10T18:55:59.727445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 420
42.0%
123.74176788 4
 
0.4%
126.49259186 2
 
0.2%
126.53878021 2
 
0.2%
126.49034119 2
 
0.2%
126.41633606 2
 
0.2%
126.49111176 2
 
0.2%
126.43070984 2
 
0.2%
126.56909943 2
 
0.2%
126.52445984 2
 
0.2%
Other values (549) 560
56.0%
ValueCountFrequency (%)
0.0 420
42.0%
123.74176788 4
 
0.4%
126.21947479 1
 
0.1%
126.23979187 1
 
0.1%
126.25141907 1
 
0.1%
126.25177002 1
 
0.1%
126.2519455 1
 
0.1%
126.25319672 1
 
0.1%
126.25339508 1
 
0.1%
126.2543023 1
 
0.1%
ValueCountFrequency (%)
129.16099548 1
0.1%
129.1589893 1
0.1%
129.11721802 1
0.1%
129.08384705 1
0.1%
129.0638007 1
0.1%
129.0627883 1
0.1%
129.0621496 1
0.1%
129.0620229 1
0.1%
129.0618285 1
0.1%
129.0616026 1
0.1%

Interactions

2023-12-10T18:55:49.288502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:45.923455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:47.269488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:48.309141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:49.554299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:46.170098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:47.484756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:48.556247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:49.763887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:46.378907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:47.832112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:48.851803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:49.974693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:46.625794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:48.094976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:55:49.062931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:55:59.928742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CITY_NMRSTRNT_IDRSTRNT_TEL_NORSTRNT_LARSTRNT_LO
CITY_NM1.0000.5560.0740.9760.976
RSTRNT_ID0.5561.0000.0000.5150.515
RSTRNT_TEL_NO0.0740.0001.0000.0600.060
RSTRNT_LA0.9760.5150.0601.0001.000
RSTRNT_LO0.9760.5150.0601.0001.000
2023-12-10T18:56:00.165230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RSTRNT_IDRSTRNT_TEL_NORSTRNT_LARSTRNT_LOCITY_NM
RSTRNT_ID1.0000.2390.2570.2410.427
RSTRNT_TEL_NO0.2391.0000.3180.3750.122
RSTRNT_LA0.2570.3181.0000.8740.859
RSTRNT_LO0.2410.3750.8741.0000.859
CITY_NM0.4270.1220.8590.8591.000

Missing values

2023-12-10T18:55:50.280604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:55:50.608823image/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.
2023-12-10T18:55:50.851736image/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

CITY_NMRSTRNT_IDRSTRNT_NMRSTRNT_GIBUN_ADDRRSTRNT_ROAD_ADDRRSTRNT_TEL_NOSUBWAY_NMSUBWAY_LTRSTRNT_LARSTRNT_LO
0busan1라마앤바바나부산광역시 해운대구 중동 1417-2 씨스타빌딩 6층부산광역시 해운대구 해운대해변로 271517465549<NA>035.159801129.160995
1busan2사이공부산광역시 수영구 남천동 3-29부산광역시 수영구 남천바다로 36517554205<NA>00.00.0
2busan3충무횟집부산광역시 중구 남포동6가 9-1부산광역시 중구 자갈치해안로 29512468563<NA>00.00.0
3busan4대륙훠궈부산광역시 수영구 민락동 181-36부산광역시 수영구 광안해변로 285517524849<NA>00.00.0
4busan5동백섬횟집부산광역시 해운대구 우동 655-7부산광역시 해운대구 해운대해변로209번나길 17517413888<NA>00.00.0
5busan6안가부산광역시 해운대구 중동 1276-1 숯불구이부산광역시 해운대구 좌동순환로 494-1517427852<NA>00.00.0
6busan7발리우드부산광역시 수영구 광안동 194-7부산광역시 수영구 광남로130번길 9519879673<NA>035.153103129.117218
7busan8춘하추동밀면부산광역시 해운대구 우동 1359부산광역시 해운대구 해운대해변로265번길 13517468658<NA>00.00.0
8busan9대길고추불고기부산광역시 금정구 장전로 47 대길고추불고기부산광역시 금정구 장전동 360-5515164707<NA>035.229153129.083847
9busan10인근주민 (남포동점)부산광역시 중구 광복동3가 1-3부산광역시 중구 광복중앙로 22-1512542777<NA>00.00.0
CITY_NMRSTRNT_IDRSTRNT_NMRSTRNT_GIBUN_ADDRRSTRNT_ROAD_ADDRRSTRNT_TEL_NOSUBWAY_NMSUBWAY_LTRSTRNT_LARSTRNT_LO
990jeju798배터지는생돈까스서귀포점제주특별자치도 서귀포시 동홍동 430-38<NA>647637477<NA>033.251991126.569191
991jeju799배터지는생돈까스제주특별자치도 제주시 연동 1399 대림아파트제주특별자치도 제주시 국기로 14647110014<NA>033.480549126.488617
992jeju800솔향가든제주특별자치도 서귀포시 토평동 2263-3제주특별자치도 서귀포시 516로 145647637353<NA>033.266193126.584961
993jeju802비석거리가든제주특별자치도 서귀포시 토평동 862-1 비석거리가든제주특별자치도 서귀포시 일주동로 8508647638838<NA>033.255875126.579094
994jeju804돼지공화국제주특별자치도 서귀포시 동홍동 1099-2제주특별자치도 서귀포시 중산간동로 7915647638892<NA>033.2654126.570656
995jeju805돈키동키제주본점제주특별자치도 제주시 노형동 758-10제주특별자치도 제주시 진군4길 18647481676<NA>033.476807126.478989
996jeju806신흥갈비일번지제주특별자치도 서귀포시 남원읍 신흥리 161-1제주특별자치도 서귀포시 남원읍 일주동로 6611647640011<NA>033.301334126.768272
997jeju808한우원제주특별자치도 서귀포시 남원읍 남원리 216-4제주특별자치도 서귀포시 남원읍 남조로 30647642759<NA>033.282391126.720833
998jeju810대덕가든제주특별자치도 서귀포시 남원읍 태흥리 1 1089제주특별자치도 서귀포시 남원읍 태신해안로 181 1089647642872<NA>033.281239126.740532
999jeju811돈까스마을제주특별자치도 제주시 이도일동 1688-12제주특별자치도 제주시 서광로29길 36647271771<NA>033.503361126.526321