Overview

Dataset statistics

Number of variables14
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.4 KiB
Average record size in memory116.3 B

Variable types

Numeric3
Categorical3
Text5
DateTime3

Alerts

induty has constant value ""Constant
skey is highly overall correlated with lo and 1 other fieldsHigh correlation
la is highly overall correlated with lo and 1 other fieldsHigh correlation
lo is highly overall correlated with skey and 2 other fieldsHigh correlation
gugun_nm is highly overall correlated with skey and 2 other fieldsHigh correlation
skey has unique valuesUnique
bssh_nm has unique valuesUnique
locplc_rn has unique valuesUnique
locplc_lnm has unique valuesUnique
telno has unique valuesUnique
la has unique valuesUnique
lo has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:50:59.534634
Analysis finished2023-12-10 09:51:05.327618
Duration5.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.76
Minimum1
Maximum557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:05.580394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.95
Q127.75
median53.5
Q378.25
95-th percentile98.05
Maximum557
Range556
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation90.953326
Coefficient of variation (CV)1.3623925
Kurtosis23.89791
Mean66.76
Median Absolute Deviation (MAD)25.5
Skewness4.7580391
Sum6676
Variance8272.5075
MonotonicityNot monotonic
2023-12-10T18:51:05.865792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
9 1
1.0%
10 1
1.0%
11 1
1.0%
12 1
1.0%
ValueCountFrequency (%)
557 1
1.0%
556 1
1.0%
555 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%

induty
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
일반음식점
100 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반음식점 100
100.0%

Length

2023-12-10T18:51:06.118584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:06.304342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반음식점 100
100.0%

bizcnd
Categorical

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
한식
66 
일식
11 
식육(숯불구이)
 
6
경양식
 
5
회집
 
4
Other values (5)
8 

Length

Max length8
Median length2
Mean length2.53
Min length2

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row복어취급
2nd row한식
3rd row한식
4th row한식
5th row탕류(보신용)

Common Values

ValueCountFrequency (%)
한식 66
66.0%
일식 11
 
11.0%
식육(숯불구이) 6
 
6.0%
경양식 5
 
5.0%
회집 4
 
4.0%
복어취급 2
 
2.0%
뷔페식 2
 
2.0%
분식 2
 
2.0%
탕류(보신용) 1
 
1.0%
중국식 1
 
1.0%

Length

2023-12-10T18:51:06.519148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:06.795911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 66
66.0%
일식 11
 
11.0%
식육(숯불구이 6
 
6.0%
경양식 5
 
5.0%
회집 4
 
4.0%
복어취급 2
 
2.0%
뷔페식 2
 
2.0%
분식 2
 
2.0%
탕류(보신용 1
 
1.0%
중국식 1
 
1.0%

bssh_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:51:07.441673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length15
Mean length5.55
Min length2

Characters and Unicode

Total characters555
Distinct characters209
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
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 row초원복국
2nd row하얀집
3rd row기장식당
4th row남도
5th row남해돼지국밥
ValueCountFrequency (%)
초원복국 1
 
0.8%
예이제 1
 
0.8%
숟가락젓가락 1
 
0.8%
이재모피자 1
 
0.8%
장터식당 1
 
0.8%
섬진강재첩전문점 1
 
0.8%
원산면옥 1
 
0.8%
동화반점 1
 
0.8%
진주집 1
 
0.8%
바다횟집 1
 
0.8%
Other values (110) 110
91.7%
2023-12-10T18:51:08.525640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20
 
3.6%
집 16
 
2.9%
산 12
 
2.2%
식 11
 
2.0%
국 10
 
1.8%
원 10
 
1.8%
해 10
 
1.8%
부 9
 
1.6%
한 8
 
1.4%
점 8
 
1.4%
Other values (199) 441
79.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 522
94.1%
Space Separator 20
 
3.6%
Open Punctuation 3
 
0.5%
Close Punctuation 3
 
0.5%
Lowercase Letter 3
 
0.5%
Uppercase Letter 2
 
0.4%
Other Symbol 1
 
0.2%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
집 16
 
3.1%
산 12
 
2.3%
식 11
 
2.1%
국 10
 
1.9%
원 10
 
1.9%
해 10
 
1.9%
부 9
 
1.7%
한 8
 
1.5%
점 8
 
1.5%
대 8
 
1.5%
Other values (189) 420
80.5%
Lowercase Letter
ValueCountFrequency (%)
k 1
33.3%
r 1
33.3%
a 1
33.3%
Uppercase Letter
ValueCountFrequency (%)
P 1
50.0%
S 1
50.0%
Space Separator
ValueCountFrequency (%)
20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 523
94.2%
Common 27
 
4.9%
Latin 5
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
집 16
 
3.1%
산 12
 
2.3%
식 11
 
2.1%
국 10
 
1.9%
원 10
 
1.9%
해 10
 
1.9%
부 9
 
1.7%
한 8
 
1.5%
점 8
 
1.5%
대 8
 
1.5%
Other values (190) 421
80.5%
Latin
ValueCountFrequency (%)
k 1
20.0%
r 1
20.0%
a 1
20.0%
P 1
20.0%
S 1
20.0%
Common
ValueCountFrequency (%)
20
74.1%
( 3
 
11.1%
) 3
 
11.1%
2 1
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 522
94.1%
ASCII 32
 
5.8%
None 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20
62.5%
( 3
 
9.4%
) 3
 
9.4%
2 1
 
3.1%
k 1
 
3.1%
r 1
 
3.1%
a 1
 
3.1%
P 1
 
3.1%
S 1
 
3.1%
Hangul
ValueCountFrequency (%)
집 16
 
3.1%
산 12
 
2.3%
식 11
 
2.1%
국 10
 
1.9%
원 10
 
1.9%
해 10
 
1.9%
부 9
 
1.7%
한 8
 
1.5%
점 8
 
1.5%
대 8
 
1.5%
Other values (189) 420
80.5%
None
ValueCountFrequency (%)
㈜ 1
100.0%

locplc_rn
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:51:09.152995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length40
Mean length31.42
Min length21

Characters and Unicode

Total characters3142
Distinct characters145
Distinct categories10 ?
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 row부산광역시 해운대구 해운대해변로 329-2 (중동, 2,3층)
2nd row부산광역시 강서구 과학산단로334번다길 10 (지사동)
3rd row부산광역시 해운대구 중동2로 5 (중동, 1층)
4th row부산광역시 해운대구 센텀중앙로 97(재송동, 센텀스카이비즈 지하1층 C-B 107호
5th row부산광역시 해운대구 선수촌로 184 (반여동, 지상1층)
ValueCountFrequency (%)
부산광역시 100
 
17.0%
중구 43
 
7.3%
해운대구 36
 
6.1%
중동 18
 
3.1%
1층 18
 
3.1%
우동 9
 
1.5%
연제구 9
 
1.5%
영도구 9
 
1.5%
광복로 7
 
1.2%
3 6
 
1.0%
Other values (232) 332
56.6%
2023-12-10T18:51:10.206637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
487
 
15.5%
1 156
 
5.0%
동 119
 
3.8%
광 118
 
3.8%
부 115
 
3.7%
산 112
 
3.6%
구 106
 
3.4%
시 103
 
3.3%
( 103
 
3.3%
) 102
 
3.2%
Other values (135) 1621
51.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1775
56.5%
Decimal Number 534
 
17.0%
Space Separator 487
 
15.5%
Open Punctuation 103
 
3.3%
Close Punctuation 102
 
3.2%
Other Punctuation 99
 
3.2%
Dash Punctuation 32
 
1.0%
Math Symbol 5
 
0.2%
Uppercase Letter 4
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 119
 
6.7%
광 118
 
6.6%
부 115
 
6.5%
산 112
 
6.3%
구 106
 
6.0%
시 103
 
5.8%
역 100
 
5.6%
로 88
 
5.0%
중 87
 
4.9%
대 71
 
4.0%
Other values (115) 756
42.6%
Decimal Number
ValueCountFrequency (%)
1 156
29.2%
2 97
18.2%
3 64
12.0%
5 43
 
8.1%
6 38
 
7.1%
4 36
 
6.7%
0 35
 
6.6%
9 23
 
4.3%
7 22
 
4.1%
8 20
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
B 2
50.0%
C 1
25.0%
A 1
25.0%
Space Separator
ValueCountFrequency (%)
487
100.0%
Open Punctuation
ValueCountFrequency (%)
( 103
100.0%
Close Punctuation
ValueCountFrequency (%)
) 102
100.0%
Other Punctuation
ValueCountFrequency (%)
, 99
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1775
56.5%
Common 1362
43.3%
Latin 5
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 119
 
6.7%
광 118
 
6.6%
부 115
 
6.5%
산 112
 
6.3%
구 106
 
6.0%
시 103
 
5.8%
역 100
 
5.6%
로 88
 
5.0%
중 87
 
4.9%
대 71
 
4.0%
Other values (115) 756
42.6%
Common
ValueCountFrequency (%)
487
35.8%
1 156
 
11.5%
( 103
 
7.6%
) 102
 
7.5%
, 99
 
7.3%
2 97
 
7.1%
3 64
 
4.7%
5 43
 
3.2%
6 38
 
2.8%
4 36
 
2.6%
Other values (6) 137
 
10.1%
Latin
ValueCountFrequency (%)
B 2
40.0%
C 1
20.0%
e 1
20.0%
A 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1775
56.5%
ASCII 1367
43.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
487
35.6%
1 156
 
11.4%
( 103
 
7.5%
) 102
 
7.5%
, 99
 
7.2%
2 97
 
7.1%
3 64
 
4.7%
5 43
 
3.1%
6 38
 
2.8%
4 36
 
2.6%
Other values (10) 142
 
10.4%
Hangul
ValueCountFrequency (%)
동 119
 
6.7%
광 118
 
6.6%
부 115
 
6.5%
산 112
 
6.3%
구 106
 
6.0%
시 103
 
5.8%
역 100
 
5.6%
로 88
 
5.0%
중 87
 
4.9%
대 71
 
4.0%
Other values (115) 756
42.6%

locplc_lnm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:51:10.922189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length31
Mean length22.77
Min length17

Characters and Unicode

Total characters2277
Distinct characters84
Distinct categories8 ?
Distinct scripts2 ?
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 row부산광역시 해운대구 중동 1225-5
2nd row부산광역시 강서구 지사동 1191 - 6
3rd row부산광역시 해운대구 중동 1394-64
4th row부산광역시 해운대구 우동 1458
5th row부산광역시 해운대구 반여동 870-1
ValueCountFrequency (%)
부산광역시 100
18.7%
중구 43
 
8.0%
42
 
7.8%
해운대구 36
 
6.7%
중동 18
 
3.4%
1층 17
 
3.2%
1 10
 
1.9%
우동 10
 
1.9%
영도구 9
 
1.7%
연제구 9
 
1.7%
Other values (160) 242
45.1%
2023-12-10T18:51:12.060472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
437
19.2%
1 137
 
6.0%
부 110
 
4.8%
동 109
 
4.8%
산 109
 
4.8%
광 104
 
4.6%
시 102
 
4.5%
구 100
 
4.4%
역 100
 
4.4%
- 76
 
3.3%
Other values (74) 893
39.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1220
53.6%
Decimal Number 487
 
21.4%
Space Separator 437
 
19.2%
Dash Punctuation 76
 
3.3%
Open Punctuation 21
 
0.9%
Close Punctuation 21
 
0.9%
Other Punctuation 13
 
0.6%
Math Symbol 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 110
 
9.0%
동 109
 
8.9%
산 109
 
8.9%
광 104
 
8.5%
시 102
 
8.4%
구 100
 
8.2%
역 100
 
8.2%
중 72
 
5.9%
가 46
 
3.8%
대 39
 
3.2%
Other values (57) 329
27.0%
Decimal Number
ValueCountFrequency (%)
1 137
28.1%
2 71
14.6%
3 50
 
10.3%
4 43
 
8.8%
5 36
 
7.4%
6 33
 
6.8%
0 32
 
6.6%
7 29
 
6.0%
8 29
 
6.0%
9 27
 
5.5%
Other Punctuation
ValueCountFrequency (%)
, 12
92.3%
. 1
 
7.7%
Space Separator
ValueCountFrequency (%)
437
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 76
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1220
53.6%
Common 1057
46.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 110
 
9.0%
동 109
 
8.9%
산 109
 
8.9%
광 104
 
8.5%
시 102
 
8.4%
구 100
 
8.2%
역 100
 
8.2%
중 72
 
5.9%
가 46
 
3.8%
대 39
 
3.2%
Other values (57) 329
27.0%
Common
ValueCountFrequency (%)
437
41.3%
1 137
 
13.0%
- 76
 
7.2%
2 71
 
6.7%
3 50
 
4.7%
4 43
 
4.1%
5 36
 
3.4%
6 33
 
3.1%
0 32
 
3.0%
7 29
 
2.7%
Other values (7) 113
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1220
53.6%
ASCII 1057
46.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
437
41.3%
1 137
 
13.0%
- 76
 
7.2%
2 71
 
6.7%
3 50
 
4.7%
4 43
 
4.1%
5 36
 
3.4%
6 33
 
3.1%
0 32
 
3.0%
7 29
 
2.7%
Other values (7) 113
 
10.7%
Hangul
ValueCountFrequency (%)
부 110
 
9.0%
동 109
 
8.9%
산 109
 
8.9%
광 104
 
8.5%
시 102
 
8.4%
구 100
 
8.2%
역 100
 
8.2%
중 72
 
5.9%
가 46
 
3.8%
대 39
 
3.2%
Other values (57) 329
27.0%

menu
Text

Distinct66
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:51:12.689363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.15
Min length2

Characters and Unicode

Total characters315
Distinct characters107
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

Unique49 ?
Unique (%)49.0%

Sample

1st row복국
2nd row대구탕
3rd row가자미찌개
4th row고등어구이
5th row국밥류
ValueCountFrequency (%)
생선회 8
 
7.8%
복국 5
 
4.9%
보쌈 4
 
3.9%
한정식 4
 
3.9%
삼계탕 3
 
2.9%
아구찜 3
 
2.9%
소갈비 3
 
2.9%
족발 3
 
2.9%
밀면 3
 
2.9%
순두부 2
 
1.9%
Other values (55) 65
63.1%
2023-12-10T18:51:13.486768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
탕 13
 
4.1%
회 12
 
3.8%
밥 11
 
3.5%
국 11
 
3.5%
비 10
 
3.2%
구 10
 
3.2%
생 9
 
2.9%
갈 9
 
2.9%
선 8
 
2.5%
식 7
 
2.2%
Other values (97) 215
68.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 309
98.1%
Other Punctuation 3
 
1.0%
Space Separator 3
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 13
 
4.2%
회 12
 
3.9%
밥 11
 
3.6%
국 11
 
3.6%
비 10
 
3.2%
구 10
 
3.2%
생 9
 
2.9%
갈 9
 
2.9%
선 8
 
2.6%
식 7
 
2.3%
Other values (95) 209
67.6%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 309
98.1%
Common 6
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 13
 
4.2%
회 12
 
3.9%
밥 11
 
3.6%
국 11
 
3.6%
비 10
 
3.2%
구 10
 
3.2%
생 9
 
2.9%
갈 9
 
2.9%
선 8
 
2.6%
식 7
 
2.3%
Other values (95) 209
67.6%
Common
ValueCountFrequency (%)
, 3
50.0%
3
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 309
98.1%
ASCII 6
 
1.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
탕 13
 
4.2%
회 12
 
3.9%
밥 11
 
3.6%
국 11
 
3.6%
비 10
 
3.2%
구 10
 
3.2%
생 9
 
2.9%
갈 9
 
2.9%
선 8
 
2.6%
식 7
 
2.3%
Other values (95) 209
67.6%
ASCII
ValueCountFrequency (%)
, 3
50.0%
3
50.0%

telno
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:51:13.988139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.01
Min length12

Characters and Unicode

Total characters1201
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
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 row051-743-5291
2nd row051-973-7008
3rd row051-743-4944
4th row051-905-9292
5th row051-523-0468
ValueCountFrequency (%)
051-743-5291 1
 
1.0%
051-244-2146 1
 
1.0%
051-245-5534 1
 
1.0%
051-257-0701 1
 
1.0%
051-248-0135 1
 
1.0%
051-255-9494 1
 
1.0%
051-246-9789 1
 
1.0%
051-246-6471 1
 
1.0%
051-245-2310 1
 
1.0%
051-253-6661 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T18:51:14.854091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 200
16.7%
0 187
15.6%
5 185
15.4%
1 144
12.0%
4 99
8.2%
2 77
 
6.4%
7 75
 
6.2%
6 68
 
5.7%
3 66
 
5.5%
8 55
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1001
83.3%
Dash Punctuation 200
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 187
18.7%
5 185
18.5%
1 144
14.4%
4 99
9.9%
2 77
7.7%
7 75
7.5%
6 68
 
6.8%
3 66
 
6.6%
8 55
 
5.5%
9 45
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1201
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 200
16.7%
0 187
15.6%
5 185
15.4%
1 144
12.0%
4 99
8.2%
2 77
 
6.4%
7 75
 
6.2%
6 68
 
5.7%
3 66
 
5.5%
8 55
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1201
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 200
16.7%
0 187
15.6%
5 185
15.4%
1 144
12.0%
4 99
8.2%
2 77
 
6.4%
7 75
 
6.2%
6 68
 
5.7%
3 66
 
5.5%
8 55
 
4.6%

appn_de
Date

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2001-07-21 00:00:00
Maximum2020-11-26 00:00:00
2023-12-10T18:51:15.155519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:15.509290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)

re_appn_de
Date

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2019-12-30 00:00:00
Maximum2020-11-26 00:00:00
2023-12-10T18:51:15.703011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:15.859517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)

gugun_nm
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
부산광역시 중구
43 
부산광역시 해운대구
36 
부산광역시 영도구
9 
부산광역시 연제구
9 
부산광역시 강서구
 
3

Length

Max length10
Median length9
Mean length8.93
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 해운대구
2nd row부산광역시 강서구
3rd row부산광역시 해운대구
4th row부산광역시 해운대구
5th row부산광역시 해운대구

Common Values

ValueCountFrequency (%)
부산광역시 중구 43
43.0%
부산광역시 해운대구 36
36.0%
부산광역시 영도구 9
 
9.0%
부산광역시 연제구 9
 
9.0%
부산광역시 강서구 3
 
3.0%

Length

2023-12-10T18:51:16.123772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:16.377197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 100
50.0%
중구 43
21.5%
해운대구 36
 
18.0%
영도구 9
 
4.5%
연제구 9
 
4.5%
강서구 3
 
1.5%

data_stdr_de
Date

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2020-04-28 00:00:00
Maximum2021-01-21 00:00:00
2023-12-10T18:51:16.567966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:17.218511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)

la
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.130163
Minimum35.022835
Maximum35.208985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:17.471462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.022835
5-th percentile35.072974
Q135.099356
median35.105632
Q335.163251
95-th percentile35.185909
Maximum35.208985
Range0.18615045
Interquartile range (IQR)0.063895117

Descriptive statistics

Standard deviation0.039902378
Coefficient of variation (CV)0.0011358438
Kurtosis-1.1155256
Mean35.130163
Median Absolute Deviation (MAD)0.034862248
Skewness0.034141173
Sum3513.0163
Variance0.0015921997
MonotonicityNot monotonic
2023-12-10T18:51:17.829600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.16288958 1
 
1.0%
35.09713 1
 
1.0%
35.098281 1
 
1.0%
35.102315 1
 
1.0%
35.096524 1
 
1.0%
35.099889 1
 
1.0%
35.102102 1
 
1.0%
35.099669 1
 
1.0%
35.099768 1
 
1.0%
35.098979 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
35.0228346 1
1.0%
35.0622176752 1
1.0%
35.0699726953 1
1.0%
35.0705325681 1
1.0%
35.0710069356 1
1.0%
35.073078008 1
1.0%
35.0751554424 1
1.0%
35.0900524077 1
1.0%
35.0900591278 1
1.0%
35.0925887166 1
1.0%
ValueCountFrequency (%)
35.20898505 1
1.0%
35.196893 1
1.0%
35.19388093 1
1.0%
35.18989306 1
1.0%
35.185939 1
1.0%
35.185907 1
1.0%
35.18521 1
1.0%
35.185192 1
1.0%
35.184919 1
1.0%
35.184804 1
1.0%

lo
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.07894
Minimum128.8078
Maximum129.19112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:18.134302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.8078
5-th percentile129.02434
Q1129.0311
median129.06807
Q3129.15478
95-th percentile129.17501
Maximum129.19112
Range0.3833161
Interquartile range (IQR)0.12367615

Descriptive statistics

Standard deviation0.073291434
Coefficient of variation (CV)0.00056780319
Kurtosis1.8107078
Mean129.07894
Median Absolute Deviation (MAD)0.041045019
Skewness-0.72198401
Sum12907.894
Variance0.0053716343
MonotonicityNot monotonic
2023-12-10T18:51:18.402721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.1659385 1
 
1.0%
129.031195 1
 
1.0%
129.032613 1
 
1.0%
129.028891 1
 
1.0%
129.035651 1
 
1.0%
129.030078 1
 
1.0%
129.030578 1
 
1.0%
129.0273 1
 
1.0%
129.034749 1
 
1.0%
129.031054 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
128.807799 1
1.0%
128.833221 1
1.0%
128.868901 1
1.0%
129.021568 1
1.0%
129.02345 1
1.0%
129.024389 1
1.0%
129.024644 1
1.0%
129.024897 1
1.0%
129.025447 1
1.0%
129.025776 1
1.0%
ValueCountFrequency (%)
129.1911151 1
1.0%
129.1782872 1
1.0%
129.176784 1
1.0%
129.1757521 1
1.0%
129.1754645 1
1.0%
129.1749869 1
1.0%
129.1745194 1
1.0%
129.1739628 1
1.0%
129.1738099 1
1.0%
129.1736251 1
1.0%

Interactions

2023-12-10T18:51:03.714633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:01.245816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:02.514591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:03.972771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:01.674548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:03.107882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:04.196161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:02.192440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:03.366741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:51:18.581719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeybizcndbssh_nmlocplc_rnlocplc_lnmmenutelnoappn_dere_appn_degugun_nmdata_stdr_delalo
skey1.0000.2571.0001.0001.0000.8631.0000.9580.8170.8450.8450.7830.879
bizcnd0.2571.0001.0001.0001.0000.9241.0000.3820.3040.4650.4650.4200.325
bssh_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
locplc_rn1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
locplc_lnm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
menu0.8630.9241.0001.0001.0001.0001.0000.8000.7890.7780.7780.6040.742
telno1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
appn_de0.9580.3821.0001.0001.0000.8001.0001.0000.9930.9940.9940.9170.908
re_appn_de0.8170.3041.0001.0001.0000.7891.0000.9931.0001.0001.0000.9860.953
gugun_nm0.8450.4651.0001.0001.0000.7781.0000.9941.0001.0001.0000.9020.934
data_stdr_de0.8450.4651.0001.0001.0000.7781.0000.9941.0001.0001.0000.9020.934
la0.7830.4201.0001.0001.0000.6041.0000.9170.9860.9020.9021.0000.959
lo0.8790.3251.0001.0001.0000.7421.0000.9080.9530.9340.9340.9591.000
2023-12-10T18:51:18.787966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
gugun_nmbizcnd
gugun_nm1.0000.203
bizcnd0.2031.000
2023-12-10T18:51:18.936317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeylalobizcndgugun_nm
skey1.000-0.406-0.5730.1500.872
la-0.4061.0000.6230.2110.818
lo-0.5730.6231.0000.1700.899
bizcnd0.1500.2110.1701.0000.203
gugun_nm0.8720.8180.8990.2031.000

Missing values

2023-12-10T18:51:04.539744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:51:05.165346image/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

skeyindutybizcndbssh_nmlocplc_rnlocplc_lnmmenutelnoappn_dere_appn_degugun_nmdata_stdr_delalo
01일반음식점복어취급초원복국부산광역시 해운대구 해운대해변로 329-2 (중동, 2,3층)부산광역시 해운대구 중동 1225-5복국051-743-52912020-11-262020-11-26부산광역시 해운대구2020-04-2835.16289129.165939
1555일반음식점한식하얀집부산광역시 강서구 과학산단로334번다길 10 (지사동)부산광역시 강서구 지사동 1191 - 6대구탕051-973-70082016-10-262020-10-29부산광역시 강서구2021-01-2135.149506128.833221
23일반음식점한식기장식당부산광역시 해운대구 중동2로 5 (중동, 1층)부산광역시 해운대구 중동 1394-64가자미찌개051-743-49442020-11-262020-11-26부산광역시 해운대구2020-04-2835.16254129.164759
34일반음식점한식남도부산광역시 해운대구 센텀중앙로 97(재송동, 센텀스카이비즈 지하1층 C-B 107호부산광역시 해운대구 우동 1458고등어구이051-905-92922020-11-262020-11-26부산광역시 해운대구2020-04-2835.17513129.124875
45일반음식점탕류(보신용)남해돼지국밥부산광역시 해운대구 선수촌로 184 (반여동, 지상1층)부산광역시 해운대구 반여동 870-1국밥류051-523-04682020-11-262020-11-26부산광역시 해운대구2020-04-2835.208985129.124161
56일반음식점일식덴포라부산광역시 해운대구 좌동로10번길 21 (중동, 지상2층)부산광역시 해운대구 중동 1318-5바다가재051-742-06332020-11-262020-11-26부산광역시 해운대구2020-04-2835.167356129.170119
67일반음식점한식동백삼계탕부산광역시 해운대구 마린시티3로 23 (우동, 벽산e오렌지상가 320호 - 325호 )부산광역시 해운대구 우동 1435삼계탕051-900-99332020-11-262020-11-26부산광역시 해운대구2020-04-2835.156644129.146927
7556일반음식점한식부산축산농협 축산물종합프라자부산광역시 강서구 낙동남로 446-1, 1층,2층 (녹산동)부산광역시 강서구 녹산동 1215 - 17 1층 2층불고기051-831-32002016-10-262020-10-29부산광역시 강서구2021-01-2135.111525128.868901
89일반음식점한식신토불이보쌈부산광역시 해운대구 구남로29번길 35 (중동, 2층)부산광역시 해운대구 중동 1380-4보쌈051-731-14412020-11-262020-11-26부산광역시 해운대구2020-04-2835.162818129.162162
910일반음식점중국식얌차이나부산광역시 해운대구 센텀남대로 59 (우동, 롯데백화점센텀시티점6층)부산광역시 해운대구 우동 1496 롯데백화점센텀시티점중화요리051-730-39032020-11-262020-11-26부산광역시 해운대구2020-04-2835.169855129.131075
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9091일반음식점한식포항물회부산광역시 영도구 절영로 478 (동삼동)부산광역시 영도구 동삼동 570-1번지물회051-405-90772001-07-212020-10-30부산광역시 영도구2020-12-3135.070533129.068679
9192일반음식점한식대륙미가원부산광역시 연제구 월드컵대로120번길 12 (연산동,1층)부산광역시 연제구 연산동 746번지 3호 1층아구찜051-866-80082010-06-302020-10-30부산광역시 연제구2021-01-1235.18521129.08294
9293일반음식점한식청산민물장어부산광역시 연제구 월드컵대로145번길 42 (연산동)부산광역시 연제구 연산동 1288번지 22호장어구이051-853-88922010-06-302020-10-30부산광역시 연제구2021-01-1235.184804129.079586
9394일반음식점한식토곡식육식당부산광역시 연제구 토곡로 7 (연산동)부산광역시 연제구 연산동 490번지 30호소고기 모듬구이051-751-92802011-11-092020-10-30부산광역시 연제구2021-01-1235.18169129.101773
9495일반음식점일식팔미초밥부산광역시 연제구 월드컵대로119번길 8, 팔미빌딩 2층 (연산동)부산광역시 연제구 연산동 701번지 8호 팔미빌딩회초밥051-865-01602010-06-302020-10-30부산광역시 연제구2021-01-1235.184919129.08188
9596일반음식점한식할매보쌈부산광역시 연제구 월드컵대로145번길 11 (연산동)부산광역시 연제구 연산동 1242번지 6호보쌈051-853-80052010-06-302020-10-30부산광역시 연제구2021-01-1235.185939129.080347
9697일반음식점뷔페식해암뷔페부산광역시 연제구 중앙대로 1099 (연산동,,13)부산광역시 연제구 연산동 1242번지 8호 ,13뷔페051-867-76002010-06-302020-10-30부산광역시 연제구2021-01-1235.185907129.080791
9798일반음식점분식가야할매밀면부산광역시 연제구 월드컵대로145번길 32 (연산동,1층)부산광역시 연제구 연산동 1279번지 1호 1층밀면051-865-80172010-06-302020-10-30부산광역시 연제구2021-01-1235.185192129.07989
9899일반음식점한식경주박가국밥부산광역시 연제구 고분로 232 (연산동)부산광역시 연제구 연산동 480번지 2호돼지국밥051-759-82022015-11-032020-10-30부산광역시 연제구2021-01-1235.184365129.107464
99100일반음식점분식국제밀면부산광역시 연제구 중앙대로1235번길 23-6 (거제동)부산광역시 연제구 거제동 242번지 23호밀면051-501-55072010-06-302020-10-30부산광역시 연제구2021-01-1235.196893129.077884