Overview

Dataset statistics

Number of variables5
Number of observations3665
Missing cells1666
Missing cells (%)9.1%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory143.3 KiB
Average record size in memory40.0 B

Variable types

Categorical1
Text4

Dataset

Description부산광역시남구식품접객업소현황_20210527
Author부산광역시 남구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3081505

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
업종명 is highly imbalanced (55.8%)Imbalance
소재지전화 has 1636 (44.6%) missing valuesMissing

Reproduction

Analysis started2024-04-21 07:56:46.344028
Analysis finished2024-04-21 07:56:48.222500
Duration1.88 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
일반음식점
2751 
휴게음식점
673 
제과점영업
 
94
단란주점
 
70
유흥주점영업
 
46

Length

Max length6
Median length5
Mean length5.00191
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반음식점 2751
75.1%
휴게음식점 673
 
18.4%
제과점영업 94
 
2.6%
단란주점 70
 
1.9%
유흥주점영업 46
 
1.3%
위탁급식영업 31
 
0.8%

Length

2024-04-21T16:56:48.358273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T16:56:48.575916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반음식점 2751
75.1%
휴게음식점 673
 
18.4%
제과점영업 94
 
2.6%
단란주점 70
 
1.9%
유흥주점영업 46
 
1.3%
위탁급식영업 31
 
0.8%

업소명
Text

Distinct3528
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
2024-04-21T16:56:49.438102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length31
Mean length6.6687585
Min length1

Characters and Unicode

Total characters24441
Distinct characters904
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3425 ?
Unique (%)93.5%

Sample

1st row836숯불바베큐치킨(문현점)
2nd row중국성
3rd row웨이양
4th row진양갈비
5th row다리집
ValueCountFrequency (%)
대연점 75
 
1.5%
경성대점 51
 
1.0%
용호점 36
 
0.7%
문현점 28
 
0.6%
부경대점 18
 
0.4%
경성대부경대점 17
 
0.3%
카페 17
 
0.3%
gs25 16
 
0.3%
부산대연점 15
 
0.3%
씨유 13
 
0.3%
Other values (3949) 4697
94.3%
2024-04-21T16:56:50.558276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1320
 
5.4%
점 796
 
3.3%
대 535
 
2.2%
이 516
 
2.1%
스 373
 
1.5%
( 357
 
1.5%
) 357
 
1.5%
리 323
 
1.3%
부 299
 
1.2%
식 282
 
1.2%
Other values (894) 19283
78.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20320
83.1%
Space Separator 1320
 
5.4%
Uppercase Letter 811
 
3.3%
Lowercase Letter 709
 
2.9%
Decimal Number 458
 
1.9%
Open Punctuation 361
 
1.5%
Close Punctuation 361
 
1.5%
Other Punctuation 85
 
0.3%
Dash Punctuation 11
 
< 0.1%
Letter Number 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 796
 
3.9%
대 535
 
2.6%
이 516
 
2.5%
스 373
 
1.8%
리 323
 
1.6%
부 299
 
1.5%
식 282
 
1.4%
연 244
 
1.2%
경 241
 
1.2%
집 222
 
1.1%
Other values (814) 16489
81.1%
Lowercase Letter
ValueCountFrequency (%)
e 107
15.1%
o 71
 
10.0%
a 65
 
9.2%
i 48
 
6.8%
n 45
 
6.3%
c 37
 
5.2%
t 36
 
5.1%
l 35
 
4.9%
f 31
 
4.4%
r 30
 
4.2%
Other values (16) 204
28.8%
Uppercase Letter
ValueCountFrequency (%)
C 101
 
12.5%
S 74
 
9.1%
P 63
 
7.8%
E 54
 
6.7%
G 51
 
6.3%
O 46
 
5.7%
A 41
 
5.1%
F 38
 
4.7%
I 35
 
4.3%
B 33
 
4.1%
Other values (16) 275
33.9%
Decimal Number
ValueCountFrequency (%)
2 89
19.4%
1 77
16.8%
5 70
15.3%
0 61
13.3%
9 35
 
7.6%
3 35
 
7.6%
7 28
 
6.1%
4 25
 
5.5%
6 21
 
4.6%
8 17
 
3.7%
Other Punctuation
ValueCountFrequency (%)
& 31
36.5%
. 16
18.8%
, 16
18.8%
' 11
 
12.9%
: 6
 
7.1%
· 2
 
2.4%
! 1
 
1.2%
# 1
 
1.2%
/ 1
 
1.2%
Open Punctuation
ValueCountFrequency (%)
( 357
98.9%
[ 4
 
1.1%
Close Punctuation
ValueCountFrequency (%)
) 357
98.9%
] 4
 
1.1%
Letter Number
ValueCountFrequency (%)
Ⅱ 3
75.0%
Ⅲ 1
 
25.0%
Space Separator
ValueCountFrequency (%)
1320
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20295
83.0%
Common 2597
 
10.6%
Latin 1524
 
6.2%
Han 25
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 796
 
3.9%
대 535
 
2.6%
이 516
 
2.5%
스 373
 
1.8%
리 323
 
1.6%
부 299
 
1.5%
식 282
 
1.4%
연 244
 
1.2%
경 241
 
1.2%
집 222
 
1.1%
Other values (792) 16464
81.1%
Latin
ValueCountFrequency (%)
e 107
 
7.0%
C 101
 
6.6%
S 74
 
4.9%
o 71
 
4.7%
a 65
 
4.3%
P 63
 
4.1%
E 54
 
3.5%
G 51
 
3.3%
i 48
 
3.1%
O 46
 
3.0%
Other values (44) 844
55.4%
Common
ValueCountFrequency (%)
1320
50.8%
( 357
 
13.7%
) 357
 
13.7%
2 89
 
3.4%
1 77
 
3.0%
5 70
 
2.7%
0 61
 
2.3%
9 35
 
1.3%
3 35
 
1.3%
& 31
 
1.2%
Other values (16) 165
 
6.4%
Han
ValueCountFrequency (%)
人 4
 
16.0%
花 1
 
4.0%
生 1
 
4.0%
辣 1
 
4.0%
酒 1
 
4.0%
香 1
 
4.0%
二 1
 
4.0%
代 1
 
4.0%
麻 1
 
4.0%
食 1
 
4.0%
Other values (12) 12
48.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20295
83.0%
ASCII 4114
 
16.8%
CJK 23
 
0.1%
Number Forms 4
 
< 0.1%
None 3
 
< 0.1%
CJK Compat Ideographs 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1320
32.1%
( 357
 
8.7%
) 357
 
8.7%
e 107
 
2.6%
C 101
 
2.5%
2 89
 
2.2%
1 77
 
1.9%
S 74
 
1.8%
o 71
 
1.7%
5 70
 
1.7%
Other values (66) 1491
36.2%
Hangul
ValueCountFrequency (%)
점 796
 
3.9%
대 535
 
2.6%
이 516
 
2.5%
스 373
 
1.8%
리 323
 
1.6%
부 299
 
1.5%
식 282
 
1.4%
연 244
 
1.2%
경 241
 
1.2%
집 222
 
1.1%
Other values (792) 16464
81.1%
CJK
ValueCountFrequency (%)
人 4
 
17.4%
花 1
 
4.3%
生 1
 
4.3%
辣 1
 
4.3%
酒 1
 
4.3%
香 1
 
4.3%
二 1
 
4.3%
代 1
 
4.3%
麻 1
 
4.3%
食 1
 
4.3%
Other values (10) 10
43.5%
Number Forms
ValueCountFrequency (%)
Ⅱ 3
75.0%
Ⅲ 1
 
25.0%
None
ValueCountFrequency (%)
· 2
66.7%
° 1
33.3%
CJK Compat Ideographs
ValueCountFrequency (%)
見 1
50.0%
樂 1
50.0%

소재지(도로명)
Text

Distinct3162
Distinct (%)87.0%
Missing29
Missing (%)0.8%
Memory size28.8 KiB
2024-04-21T16:56:51.617639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length97
Median length65
Mean length29.681793
Min length20

Characters and Unicode

Total characters107923
Distinct characters350
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

Unique2805 ?
Unique (%)77.1%

Sample

1st row부산광역시 남구 황령대로74번길 60 (문현동)
2nd row부산광역시 남구 무민사로 41 (감만동)
3rd row부산광역시 남구 용소로7번길 78 (대연동)
4th row부산광역시 남구 홍곡로 359 (대연동)
5th row부산광역시 남구 신선로329번길 92 (용당동)
ValueCountFrequency (%)
부산광역시 3636
 
16.8%
남구 3636
 
16.8%
대연동 1825
 
8.4%
용호동 808
 
3.7%
1층 775
 
3.6%
문현동 503
 
2.3%
수영로 260
 
1.2%
2층 228
 
1.1%
감만동 166
 
0.8%
분포로 153
 
0.7%
Other values (1819) 9633
44.5%
2024-04-21T16:56:52.986033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17990
 
16.7%
1 5354
 
5.0%
동 4509
 
4.2%
부 3908
 
3.6%
) 3831
 
3.5%
( 3831
 
3.5%
남 3767
 
3.5%
시 3727
 
3.5%
산 3726
 
3.5%
로 3698
 
3.4%
Other values (340) 53582
49.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 60962
56.5%
Space Separator 17990
 
16.7%
Decimal Number 17756
 
16.5%
Close Punctuation 3831
 
3.5%
Open Punctuation 3831
 
3.5%
Other Punctuation 2539
 
2.4%
Dash Punctuation 612
 
0.6%
Uppercase Letter 344
 
0.3%
Lowercase Letter 38
 
< 0.1%
Math Symbol 20
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 4509
 
7.4%
부 3908
 
6.4%
남 3767
 
6.2%
시 3727
 
6.1%
산 3726
 
6.1%
로 3698
 
6.1%
광 3647
 
6.0%
구 3641
 
6.0%
역 3638
 
6.0%
대 2283
 
3.7%
Other values (289) 24418
40.1%
Uppercase Letter
ValueCountFrequency (%)
A 81
23.5%
B 64
18.6%
S 47
13.7%
G 34
9.9%
I 19
 
5.5%
C 19
 
5.5%
F 14
 
4.1%
K 14
 
4.1%
E 11
 
3.2%
W 9
 
2.6%
Other values (10) 32
 
9.3%
Decimal Number
ValueCountFrequency (%)
1 5354
30.2%
2 2426
13.7%
3 1883
 
10.6%
4 1544
 
8.7%
0 1489
 
8.4%
5 1254
 
7.1%
6 1148
 
6.5%
7 907
 
5.1%
9 905
 
5.1%
8 846
 
4.8%
Lowercase Letter
ValueCountFrequency (%)
l 13
34.2%
s 7
18.4%
i 7
18.4%
a 4
 
10.5%
n 2
 
5.3%
p 1
 
2.6%
c 1
 
2.6%
w 1
 
2.6%
o 1
 
2.6%
r 1
 
2.6%
Other Punctuation
ValueCountFrequency (%)
, 2521
99.3%
. 10
 
0.4%
/ 3
 
0.1%
: 2
 
0.1%
@ 2
 
0.1%
& 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
17990
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3831
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3831
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 612
100.0%
Math Symbol
ValueCountFrequency (%)
~ 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 60962
56.5%
Common 46579
43.2%
Latin 382
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 4509
 
7.4%
부 3908
 
6.4%
남 3767
 
6.2%
시 3727
 
6.1%
산 3726
 
6.1%
로 3698
 
6.1%
광 3647
 
6.0%
구 3641
 
6.0%
역 3638
 
6.0%
대 2283
 
3.7%
Other values (289) 24418
40.1%
Latin
ValueCountFrequency (%)
A 81
21.2%
B 64
16.8%
S 47
12.3%
G 34
8.9%
I 19
 
5.0%
C 19
 
5.0%
F 14
 
3.7%
K 14
 
3.7%
l 13
 
3.4%
E 11
 
2.9%
Other values (20) 66
17.3%
Common
ValueCountFrequency (%)
17990
38.6%
1 5354
 
11.5%
) 3831
 
8.2%
( 3831
 
8.2%
, 2521
 
5.4%
2 2426
 
5.2%
3 1883
 
4.0%
4 1544
 
3.3%
0 1489
 
3.2%
5 1254
 
2.7%
Other values (11) 4456
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 60962
56.5%
ASCII 46961
43.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17990
38.3%
1 5354
 
11.4%
) 3831
 
8.2%
( 3831
 
8.2%
, 2521
 
5.4%
2 2426
 
5.2%
3 1883
 
4.0%
4 1544
 
3.3%
0 1489
 
3.2%
5 1254
 
2.7%
Other values (41) 4838
 
10.3%
Hangul
ValueCountFrequency (%)
동 4509
 
7.4%
부 3908
 
6.4%
남 3767
 
6.2%
시 3727
 
6.1%
산 3726
 
6.1%
로 3698
 
6.1%
광 3647
 
6.0%
구 3641
 
6.0%
역 3638
 
6.0%
대 2283
 
3.7%
Other values (289) 24418
40.1%

소재지(지번)
Text

Distinct2724
Distinct (%)74.3%
Missing1
Missing (%)< 0.1%
Memory size28.8 KiB
2024-04-21T16:56:54.346773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length66
Median length59
Mean length22.101528
Min length15

Characters and Unicode

Total characters80980
Distinct characters404
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

Unique2165 ?
Unique (%)59.1%

Sample

1st row부산광역시 남구 문현동 529-2
2nd row부산광역시 남구 감만동 292-3
3rd row부산광역시 남구 대연동 34-21
4th row부산광역시 남구 대연동 1169-1
5th row부산광역시 남구 용당동 218-13
ValueCountFrequency (%)
부산광역시 3664
22.1%
남구 3664
22.1%
대연동 1933
 
11.7%
용호동 846
 
5.1%
문현동 544
 
3.3%
감만동 186
 
1.1%
1층 126
 
0.8%
용당동 90
 
0.5%
954 83
 
0.5%
더블유 81
 
0.5%
Other values (2877) 5357
32.3%
2024-04-21T16:56:55.962513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16336
20.2%
동 3917
 
4.8%
1 3837
 
4.7%
부 3808
 
4.7%
산 3774
 
4.7%
시 3743
 
4.6%
광 3676
 
4.5%
구 3674
 
4.5%
남 3671
 
4.5%
역 3665
 
4.5%
Other values (394) 30879
38.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42815
52.9%
Decimal Number 17699
21.9%
Space Separator 16336
 
20.2%
Dash Punctuation 3404
 
4.2%
Uppercase Letter 242
 
0.3%
Open Punctuation 158
 
0.2%
Close Punctuation 156
 
0.2%
Other Punctuation 107
 
0.1%
Lowercase Letter 53
 
0.1%
Math Symbol 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 3917
9.1%
부 3808
8.9%
산 3774
8.8%
시 3743
8.7%
광 3676
8.6%
구 3674
8.6%
남 3671
8.6%
역 3665
8.6%
대 2074
 
4.8%
연 2013
 
4.7%
Other values (342) 8800
20.6%
Uppercase Letter
ValueCountFrequency (%)
S 51
21.1%
A 37
15.3%
G 34
14.0%
B 24
9.9%
K 16
 
6.6%
E 11
 
4.5%
I 9
 
3.7%
T 8
 
3.3%
H 8
 
3.3%
W 7
 
2.9%
Other values (10) 37
15.3%
Lowercase Letter
ValueCountFrequency (%)
l 14
26.4%
i 9
17.0%
s 8
15.1%
a 5
 
9.4%
n 4
 
7.5%
c 3
 
5.7%
b 3
 
5.7%
r 3
 
5.7%
o 2
 
3.8%
h 1
 
1.9%
Decimal Number
ValueCountFrequency (%)
1 3837
21.7%
2 2085
11.8%
5 1993
11.3%
3 1815
10.3%
4 1615
9.1%
7 1443
 
8.2%
6 1382
 
7.8%
0 1220
 
6.9%
9 1200
 
6.8%
8 1109
 
6.3%
Other Punctuation
ValueCountFrequency (%)
, 86
80.4%
. 11
 
10.3%
/ 4
 
3.7%
: 2
 
1.9%
@ 2
 
1.9%
& 2
 
1.9%
Space Separator
ValueCountFrequency (%)
16336
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3404
100.0%
Open Punctuation
ValueCountFrequency (%)
( 158
100.0%
Close Punctuation
ValueCountFrequency (%)
) 156
100.0%
Math Symbol
ValueCountFrequency (%)
~ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42815
52.9%
Common 37870
46.8%
Latin 295
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 3917
9.1%
부 3808
8.9%
산 3774
8.8%
시 3743
8.7%
광 3676
8.6%
구 3674
8.6%
남 3671
8.6%
역 3665
8.6%
대 2074
 
4.8%
연 2013
 
4.7%
Other values (342) 8800
20.6%
Latin
ValueCountFrequency (%)
S 51
17.3%
A 37
12.5%
G 34
11.5%
B 24
 
8.1%
K 16
 
5.4%
l 14
 
4.7%
E 11
 
3.7%
I 9
 
3.1%
i 9
 
3.1%
T 8
 
2.7%
Other values (21) 82
27.8%
Common
ValueCountFrequency (%)
16336
43.1%
1 3837
 
10.1%
- 3404
 
9.0%
2 2085
 
5.5%
5 1993
 
5.3%
3 1815
 
4.8%
4 1615
 
4.3%
7 1443
 
3.8%
6 1382
 
3.6%
0 1220
 
3.2%
Other values (11) 2740
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42815
52.9%
ASCII 38165
47.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16336
42.8%
1 3837
 
10.1%
- 3404
 
8.9%
2 2085
 
5.5%
5 1993
 
5.2%
3 1815
 
4.8%
4 1615
 
4.2%
7 1443
 
3.8%
6 1382
 
3.6%
0 1220
 
3.2%
Other values (42) 3035
 
8.0%
Hangul
ValueCountFrequency (%)
동 3917
9.1%
부 3808
8.9%
산 3774
8.8%
시 3743
8.7%
광 3676
8.6%
구 3674
8.6%
남 3671
8.6%
역 3665
8.6%
대 2074
 
4.8%
연 2013
 
4.7%
Other values (342) 8800
20.6%

소재지전화
Text

MISSING 

Distinct1984
Distinct (%)97.8%
Missing1636
Missing (%)44.6%
Memory size28.8 KiB
2024-04-21T16:56:56.799948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.016757
Min length4

Characters and Unicode

Total characters24382
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

Unique1946 ?
Unique (%)95.9%

Sample

1st row051-646-2777
2nd row051-624-1182
3rd row051-624-0257
4th row051-645-1377
5th row051-626-8292
ValueCountFrequency (%)
051-629-5695 6
 
0.3%
051-626-6170 3
 
0.1%
051-805-0416 3
 
0.1%
051-632-2705 3
 
0.1%
051-636-5737 2
 
0.1%
051-609-1234 2
 
0.1%
051-636-1138 2
 
0.1%
051-627-4319 2
 
0.1%
051-611-2420 2
 
0.1%
051-609-1052 2
 
0.1%
Other values (1974) 2002
98.7%
2024-04-21T16:56:57.822411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 4054
16.6%
1 3448
14.1%
0 3166
13.0%
5 3079
12.6%
6 2769
11.4%
2 2298
9.4%
3 1429
 
5.9%
7 1111
 
4.6%
8 1071
 
4.4%
4 990
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20328
83.4%
Dash Punctuation 4054
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3448
17.0%
0 3166
15.6%
5 3079
15.1%
6 2769
13.6%
2 2298
11.3%
3 1429
7.0%
7 1111
 
5.5%
8 1071
 
5.3%
4 990
 
4.9%
9 967
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 4054
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24382
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 4054
16.6%
1 3448
14.1%
0 3166
13.0%
5 3079
12.6%
6 2769
11.4%
2 2298
9.4%
3 1429
 
5.9%
7 1111
 
4.6%
8 1071
 
4.4%
4 990
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24382
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 4054
16.6%
1 3448
14.1%
0 3166
13.0%
5 3079
12.6%
6 2769
11.4%
2 2298
9.4%
3 1429
 
5.9%
7 1111
 
4.6%
8 1071
 
4.4%
4 990
 
4.1%

Missing values

2024-04-21T16:56:47.799937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T16:56:47.968388image/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.
2024-04-21T16:56:48.130568image/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

업종명업소명소재지(도로명)소재지(지번)소재지전화
0일반음식점836숯불바베큐치킨(문현점)부산광역시 남구 황령대로74번길 60 (문현동)부산광역시 남구 문현동 529-2<NA>
1일반음식점중국성부산광역시 남구 무민사로 41 (감만동)부산광역시 남구 감만동 292-3051-646-2777
2일반음식점웨이양부산광역시 남구 용소로7번길 78 (대연동)부산광역시 남구 대연동 34-21<NA>
3일반음식점진양갈비부산광역시 남구 홍곡로 359 (대연동)부산광역시 남구 대연동 1169-1051-624-1182
4일반음식점다리집부산광역시 남구 신선로329번길 92 (용당동)부산광역시 남구 용당동 218-13051-624-0257
5일반음식점시골밥상부산광역시 남구 유엔로120번길 43 (대연동, 1층 일부분)부산광역시 남구 대연동 1090-17 1층 일부분<NA>
6일반음식점엄지척 닭도리탕부산광역시 남구 수영로334번길 5 (대연동)부산광역시 남구 대연동 39-21<NA>
7일반음식점우암식당<NA>부산광역시 남구 우암동 189<NA>
8일반음식점옥이집<NA>부산광역시 남구 용당동 193<NA>
9일반음식점우암집<NA>부산광역시 남구 우암동 189<NA>
업종명업소명소재지(도로명)소재지(지번)소재지전화
3655제과점영업우리동네호두과자부산광역시 남구 수영로 261, 402동 1층 101호 (대연동, 대연 SK VIEW Hills)부산광역시 남구 대연동 1903 대연 SK VIEW Hills<NA>
3656제과점영업에스키(Exquis)부산광역시 남구 분포로 145, 더블유 스퀘어동 1층 1016호 (용호동, 더블유)부산광역시 남구 용호동 954 더블유<NA>
3657제과점영업여미오븐부산광역시 남구 고동골로 29, 111동 2층 205호 (문현동)부산광역시 남구 문현동 1234<NA>
3658제과점영업미쉬-미쉬(MICHE-MICHE)부산광역시 남구 유엔로157번가길 118, 1층 일부호 (대연동, 글로리빌라)부산광역시 남구 대연동 548-3 글로리빌라<NA>
3659제과점영업보타닉아덴부산광역시 남구 전포대로 133, 지하1층 123,124,124A,124B,124C호 (문현동)부산광역시 남구 문현동 1227-2<NA>
3660제과점영업수수베이커리부산광역시 남구 진남로 54, 1층 102호 (대연동, 대동레미안3)부산광역시 남구 대연동 1463-5 대동레미안3<NA>
3661제과점영업아델라7부산광역시 남구 황령대로319번나길 30, 1(일부)층 (대연동)부산광역시 남구 대연동 245-68<NA>
3662제과점영업찰리베이커리부산광역시 남구 신선로 566, 401동 1층 117,118호 (용호동, GS하이츠자이)부산광역시 남구 용호동 197 GS하이츠자이 401동 117~8호<NA>
3663제과점영업하우스케이크(House cake)부산광역시 남구 분포로 145, 더블유스퀘어동 2층 2023호 (용호동, 더블유)부산광역시 남구 용호동 954 더블유<NA>
3664제과점영업아빠와소풍부산광역시 남구 분포로 145, 더블유스퀘어동 1층 1001호 (용호동, 더블유)부산광역시 남구 용호동 954 더블유051-627-0770

Duplicate rows

Most frequently occurring

업종명업소명소재지(도로명)소재지(지번)소재지전화# duplicates
0휴게음식점(주)이마트문현점부산광역시 남구 전포대로91번길 47 (문현동,이마트문현점 1층)부산광역시 남구 문현동 751 이마트문현점 1층051-609-10522