Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells71
Missing cells (%)0.2%
Duplicate rows5
Duplicate rows (%)0.1%
Total size in memory390.6 KiB
Average record size in memory40.0 B

Variable types

Text4

Dataset

Description전국 시장에 전자상품권가맹점에 대한 데이터로 점포가 속해있는 시장명, 가맹 점포명, 취급품목, 주소 등을 항목으로 제공합니다.
Author소상공인시장진흥공단
URLhttps://www.data.go.kr/data/15091259/fileData.do

Alerts

Dataset has 5 (0.1%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-12 04:28:22.793912
Analysis finished2023-12-12 04:28:24.695927
Duration1.9 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시장명
Text

Distinct1317
Distinct (%)13.2%
Missing9
Missing (%)0.1%
Memory size156.2 KiB
2023-12-12T13:28:24.847370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length22
Mean length7.174457
Min length3

Characters and Unicode

Total characters71680
Distinct characters400
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

Unique247 ?
Unique (%)2.5%

Sample

1st row충무데파트
2nd row새마을시장
3rd row일산서문상점가
4th row연수구옥련시장
5th row구천동공구시장
ValueCountFrequency (%)
남대문시장 194
 
1.8%
포항죽도시장 106
 
1.0%
평화시장 81
 
0.8%
르네시떼시장 76
 
0.7%
광장시장 74
 
0.7%
광명시장 74
 
0.7%
부산진시장 73
 
0.7%
삼익패션타운 64
 
0.6%
충주자유시장 58
 
0.5%
부산평화시장 57
 
0.5%
Other values (1369) 9926
92.1%
2023-12-12T13:28:25.231364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 9016
 
12.6%
시 8851
 
12.3%
상 2247
 
3.1%
가 2142
 
3.0%
동 1489
 
2.1%
산 1461
 
2.0%
중 1362
 
1.9%
전 1245
 
1.7%
부 1129
 
1.6%
앙 1125
 
1.6%
Other values (390) 41613
58.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68157
95.1%
Open Punctuation 982
 
1.4%
Close Punctuation 982
 
1.4%
Space Separator 845
 
1.2%
Decimal Number 494
 
0.7%
Other Punctuation 131
 
0.2%
Control 29
 
< 0.1%
Uppercase Letter 28
 
< 0.1%
Math Symbol 18
 
< 0.1%
Lowercase Letter 14
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 9016
 
13.2%
시 8851
 
13.0%
상 2247
 
3.3%
가 2142
 
3.1%
동 1489
 
2.2%
산 1461
 
2.1%
중 1362
 
2.0%
전 1245
 
1.8%
부 1129
 
1.7%
앙 1125
 
1.7%
Other values (367) 38090
55.9%
Decimal Number
ValueCountFrequency (%)
1 157
31.8%
2 85
17.2%
5 85
17.2%
3 73
14.8%
4 57
 
11.5%
7 24
 
4.9%
6 6
 
1.2%
9 6
 
1.2%
0 1
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
D 12
42.9%
A 10
35.7%
C 3
 
10.7%
B 2
 
7.1%
S 1
 
3.6%
Other Punctuation
ValueCountFrequency (%)
, 127
96.9%
! 4
 
3.1%
Lowercase Letter
ValueCountFrequency (%)
k 7
50.0%
s 7
50.0%
Open Punctuation
ValueCountFrequency (%)
( 982
100.0%
Close Punctuation
ValueCountFrequency (%)
) 982
100.0%
Space Separator
ValueCountFrequency (%)
845
100.0%
Control
ValueCountFrequency (%)
29
100.0%
Math Symbol
ValueCountFrequency (%)
~ 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 68157
95.1%
Common 3481
 
4.9%
Latin 42
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 9016
 
13.2%
시 8851
 
13.0%
상 2247
 
3.3%
가 2142
 
3.1%
동 1489
 
2.2%
산 1461
 
2.1%
중 1362
 
2.0%
전 1245
 
1.8%
부 1129
 
1.7%
앙 1125
 
1.7%
Other values (367) 38090
55.9%
Common
ValueCountFrequency (%)
( 982
28.2%
) 982
28.2%
845
24.3%
1 157
 
4.5%
, 127
 
3.6%
2 85
 
2.4%
5 85
 
2.4%
3 73
 
2.1%
4 57
 
1.6%
29
 
0.8%
Other values (6) 59
 
1.7%
Latin
ValueCountFrequency (%)
D 12
28.6%
A 10
23.8%
k 7
16.7%
s 7
16.7%
C 3
 
7.1%
B 2
 
4.8%
S 1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 68157
95.1%
ASCII 3523
 
4.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 9016
 
13.2%
시 8851
 
13.0%
상 2247
 
3.3%
가 2142
 
3.1%
동 1489
 
2.2%
산 1461
 
2.1%
중 1362
 
2.0%
전 1245
 
1.8%
부 1129
 
1.7%
앙 1125
 
1.7%
Other values (367) 38090
55.9%
ASCII
ValueCountFrequency (%)
( 982
27.9%
) 982
27.9%
845
24.0%
1 157
 
4.5%
, 127
 
3.6%
2 85
 
2.4%
5 85
 
2.4%
3 73
 
2.1%
4 57
 
1.6%
29
 
0.8%
Other values (13) 101
 
2.9%

점포명
Text

Distinct9164
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T13:28:25.585749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length4.8117
Min length1

Characters and Unicode

Total characters48117
Distinct characters1020
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8613 ?
Unique (%)86.1%

Sample

1st row엔느
2nd row공가네한방족발
3rd row귀빈
4th row옥련족발
5th row매일공구
ValueCountFrequency (%)
낙원떡집 14
 
0.1%
형제상회 11
 
0.1%
대성상회 11
 
0.1%
올리브 11
 
0.1%
오렌지 8
 
0.1%
크로커다일 8
 
0.1%
삼성상회 7
 
0.1%
경북상회 7
 
0.1%
제일상회 7
 
0.1%
대흥상회 7
 
0.1%
Other values (9300) 10133
99.1%
2023-12-12T13:28:26.041595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
상 1106
 
2.3%
회 926
 
1.9%
산 821
 
1.7%
이 812
 
1.7%
사 700
 
1.5%
점 695
 
1.4%
식 642
 
1.3%
리 630
 
1.3%
수 608
 
1.3%
대 595
 
1.2%
Other values (1010) 40582
84.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 46251
96.1%
Uppercase Letter 521
 
1.1%
Decimal Number 363
 
0.8%
Lowercase Letter 245
 
0.5%
Space Separator 234
 
0.5%
Open Punctuation 182
 
0.4%
Close Punctuation 149
 
0.3%
Other Punctuation 115
 
0.2%
Other Symbol 43
 
0.1%
Dash Punctuation 12
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 1106
 
2.4%
회 926
 
2.0%
산 821
 
1.8%
이 812
 
1.8%
사 700
 
1.5%
점 695
 
1.5%
식 642
 
1.4%
리 630
 
1.4%
수 608
 
1.3%
대 595
 
1.3%
Other values (930) 38716
83.7%
Uppercase Letter
ValueCountFrequency (%)
C 55
 
10.6%
B 38
 
7.3%
S 34
 
6.5%
A 29
 
5.6%
Y 28
 
5.4%
M 28
 
5.4%
O 27
 
5.2%
N 25
 
4.8%
D 25
 
4.8%
P 24
 
4.6%
Other values (19) 208
39.9%
Lowercase Letter
ValueCountFrequency (%)
e 33
13.5%
o 26
10.6%
a 20
 
8.2%
s 19
 
7.8%
r 17
 
6.9%
t 16
 
6.5%
i 15
 
6.1%
l 13
 
5.3%
m 12
 
4.9%
h 11
 
4.5%
Other values (12) 63
25.7%
Decimal Number
ValueCountFrequency (%)
2 80
22.0%
1 51
14.0%
3 46
12.7%
0 40
11.0%
5 37
10.2%
4 30
 
8.3%
7 25
 
6.9%
8 22
 
6.1%
9 16
 
4.4%
6 15
 
4.1%
Other Punctuation
ValueCountFrequency (%)
. 60
52.2%
& 18
 
15.7%
, 16
 
13.9%
？ 6
 
5.2%
/ 5
 
4.3%
' 3
 
2.6%
． 2
 
1.7%
? 2
 
1.7%
# 2
 
1.7%
; 1
 
0.9%
Open Punctuation
ValueCountFrequency (%)
( 180
98.9%
[ 2
 
1.1%
Space Separator
ValueCountFrequency (%)
234
100.0%
Close Punctuation
ValueCountFrequency (%)
) 149
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 43
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Modifier Symbol
ValueCountFrequency (%)
˚ 1
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 46283
96.2%
Common 1056
 
2.2%
Latin 767
 
1.6%
Han 11
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 1106
 
2.4%
회 926
 
2.0%
산 821
 
1.8%
이 812
 
1.8%
사 700
 
1.5%
점 695
 
1.5%
식 642
 
1.4%
리 630
 
1.4%
수 608
 
1.3%
대 595
 
1.3%
Other values (921) 38748
83.7%
Latin
ValueCountFrequency (%)
C 55
 
7.2%
B 38
 
5.0%
S 34
 
4.4%
e 33
 
4.3%
A 29
 
3.8%
Y 28
 
3.7%
M 28
 
3.7%
O 27
 
3.5%
o 26
 
3.4%
N 25
 
3.3%
Other values (42) 444
57.9%
Common
ValueCountFrequency (%)
234
22.2%
( 180
17.0%
) 149
14.1%
2 80
 
7.6%
. 60
 
5.7%
1 51
 
4.8%
3 46
 
4.4%
0 40
 
3.8%
5 37
 
3.5%
4 30
 
2.8%
Other values (17) 149
14.1%
Han
ValueCountFrequency (%)
秀 2
18.2%
會 1
9.1%
商 1
9.1%
燁 1
9.1%
政 1
9.1%
樣 1
9.1%
孝 1
9.1%
狗 1
9.1%
孃 1
9.1%
安 1
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 46237
96.1%
ASCII 1809
 
3.8%
None 55
 
0.1%
CJK 11
 
< 0.1%
Compat Jamo 3
 
< 0.1%
Modifier Letters 1
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
상 1106
 
2.4%
회 926
 
2.0%
산 821
 
1.8%
이 812
 
1.8%
사 700
 
1.5%
점 695
 
1.5%
식 642
 
1.4%
리 630
 
1.4%
수 608
 
1.3%
대 595
 
1.3%
Other values (919) 38702
83.7%
ASCII
ValueCountFrequency (%)
234
 
12.9%
( 180
 
10.0%
) 149
 
8.2%
2 80
 
4.4%
. 60
 
3.3%
C 55
 
3.0%
1 51
 
2.8%
3 46
 
2.5%
0 40
 
2.2%
B 38
 
2.1%
Other values (61) 876
48.4%
None
ValueCountFrequency (%)
㈜ 43
78.2%
？ 6
 
10.9%
． 2
 
3.6%
５ 1
 
1.8%
Ｂ 1
 
1.8%
Ｙ 1
 
1.8%
Ｃ 1
 
1.8%
Compat Jamo
ValueCountFrequency (%)
ㅋ 3
100.0%
CJK
ValueCountFrequency (%)
秀 2
18.2%
會 1
9.1%
商 1
9.1%
燁 1
9.1%
政 1
9.1%
樣 1
9.1%
孝 1
9.1%
狗 1
9.1%
孃 1
9.1%
安 1
9.1%
Modifier Letters
ValueCountFrequency (%)
˚ 1
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

취급품목
Text

Distinct1548
Distinct (%)15.6%
Missing62
Missing (%)0.6%
Memory size156.2 KiB
2023-12-12T13:28:26.321957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length27
Mean length4.4868183
Min length1

Characters and Unicode

Total characters44590
Distinct characters410
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1030 ?
Unique (%)10.4%

Sample

1st row의류
2nd row음식점
3rd row소매(한복,이불)
4th row일반음식점업
5th row기계공구
ValueCountFrequency (%)
농축수산품 628
 
6.3%
일반한식 529
 
5.3%
의류 423
 
4.3%
정장 361
 
3.6%
정장(여성 317
 
3.2%
정육점 295
 
3.0%
기타음료식품 180
 
1.8%
한식 170
 
1.7%
제과점 168
 
1.7%
슈퍼마켓 161
 
1.6%
Other values (1533) 6706
67.5%
2023-12-12T13:28:26.728947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
식 2501
 
5.6%
( 1944
 
4.4%
) 1931
 
4.3%
품 1568
 
3.5%
점 1426
 
3.2%
매 1297
 
2.9%
소 1148
 
2.6%
의 1124
 
2.5%
정 1107
 
2.5%
수 1054
 
2.4%
Other values (400) 29490
66.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 39548
88.7%
Open Punctuation 1944
 
4.4%
Close Punctuation 1932
 
4.3%
Other Punctuation 778
 
1.7%
Dash Punctuation 364
 
0.8%
Uppercase Letter 14
 
< 0.1%
Decimal Number 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
식 2501
 
6.3%
품 1568
 
4.0%
점 1426
 
3.6%
매 1297
 
3.3%
소 1148
 
2.9%
의 1124
 
2.8%
정 1107
 
2.8%
수 1054
 
2.7%
기 1045
 
2.6%
음 1032
 
2.6%
Other values (384) 26246
66.4%
Uppercase Letter
ValueCountFrequency (%)
C 6
42.9%
P 3
21.4%
A 1
 
7.1%
O 1
 
7.1%
R 1
 
7.1%
V 1
 
7.1%
T 1
 
7.1%
Other Punctuation
ValueCountFrequency (%)
, 735
94.5%
/ 33
 
4.2%
. 8
 
1.0%
？ 2
 
0.3%
Close Punctuation
ValueCountFrequency (%)
) 1931
99.9%
} 1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 1944
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 364
100.0%
Decimal Number
ValueCountFrequency (%)
4 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 39548
88.7%
Common 5028
 
11.3%
Latin 14
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
식 2501
 
6.3%
품 1568
 
4.0%
점 1426
 
3.6%
매 1297
 
3.3%
소 1148
 
2.9%
의 1124
 
2.8%
정 1107
 
2.8%
수 1054
 
2.7%
기 1045
 
2.6%
음 1032
 
2.6%
Other values (384) 26246
66.4%
Common
ValueCountFrequency (%)
( 1944
38.7%
) 1931
38.4%
, 735
 
14.6%
- 364
 
7.2%
/ 33
 
0.7%
4 10
 
0.2%
. 8
 
0.2%
？ 2
 
< 0.1%
} 1
 
< 0.1%
Latin
ValueCountFrequency (%)
C 6
42.9%
P 3
21.4%
A 1
 
7.1%
O 1
 
7.1%
R 1
 
7.1%
V 1
 
7.1%
T 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 39548
88.7%
ASCII 5040
 
11.3%
None 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
식 2501
 
6.3%
품 1568
 
4.0%
점 1426
 
3.6%
매 1297
 
3.3%
소 1148
 
2.9%
의 1124
 
2.8%
정 1107
 
2.8%
수 1054
 
2.7%
기 1045
 
2.6%
음 1032
 
2.6%
Other values (384) 26246
66.4%
ASCII
ValueCountFrequency (%)
( 1944
38.6%
) 1931
38.3%
, 735
 
14.6%
- 364
 
7.2%
/ 33
 
0.7%
4 10
 
0.2%
. 8
 
0.2%
C 6
 
0.1%
P 3
 
0.1%
} 1
 
< 0.1%
Other values (5) 5
 
0.1%
None
ValueCountFrequency (%)
？ 2
100.0%

주소
Text

Distinct9654
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T13:28:27.191348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length50
Mean length19.4978
Min length1

Characters and Unicode

Total characters194978
Distinct characters532
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9419 ?
Unique (%)94.2%

Sample

1st row경남 통영시 중앙로 160 (충무데파트, 124호)
2nd row서울 송파구 잠실동 212-31
3rd row고양시 일산서구 일산동 635-8
4th row인천 연수구 한진로25번길 23, 1층 103호(옥련동)
5th row경기수원시팔달구구천동98
ValueCountFrequency (%)
경기도 558
 
2.0%
중구 451
 
1.6%
서울 440
 
1.6%
경기 342
 
1.2%
서울시 295
 
1.1%
강원도 251
 
0.9%
1층 250
 
0.9%
대구 215
 
0.8%
경북 200
 
0.7%
충남 197
 
0.7%
Other values (11030) 24427
88.4%
2023-12-12T13:28:27.650974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17760
 
9.1%
1 11706
 
6.0%
동 8234
 
4.2%
구 7348
 
3.8%
2 7179
 
3.7%
시 6803
 
3.5%
- 6182
 
3.2%
3 5391
 
2.8%
4 4312
 
2.2%
5 4299
 
2.2%
Other values (522) 115764
59.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 117938
60.5%
Decimal Number 48940
25.1%
Space Separator 17760
 
9.1%
Dash Punctuation 6183
 
3.2%
Other Punctuation 1694
 
0.9%
Open Punctuation 887
 
0.5%
Close Punctuation 886
 
0.5%
Uppercase Letter 618
 
0.3%
Math Symbol 59
 
< 0.1%
Lowercase Letter 13
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 8234
 
7.0%
구 7348
 
6.2%
시 6803
 
5.8%
로 3260
 
2.8%
경 3133
 
2.7%
지 3048
 
2.6%
서 3004
 
2.5%
남 2970
 
2.5%
산 2957
 
2.5%
번 2897
 
2.5%
Other values (467) 74284
63.0%
Uppercase Letter
ValueCountFrequency (%)
B 153
24.8%
A 134
21.7%
C 127
20.6%
D 85
13.8%
E 52
 
8.4%
F 18
 
2.9%
H 10
 
1.6%
L 7
 
1.1%
K 7
 
1.1%
I 6
 
1.0%
Other values (8) 19
 
3.1%
Decimal Number
ValueCountFrequency (%)
1 11706
23.9%
2 7179
14.7%
3 5391
11.0%
4 4312
 
8.8%
5 4299
 
8.8%
6 3517
 
7.2%
0 3455
 
7.1%
7 3432
 
7.0%
8 2911
 
5.9%
9 2733
 
5.6%
Other values (5) 5
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
, 1354
79.9%
. 212
 
12.5%
/ 91
 
5.4%
· 21
 
1.2%
* 12
 
0.7%
？ 2
 
0.1%
@ 2
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
a 3
23.1%
e 2
15.4%
r 2
15.4%
d 2
15.4%
f 2
15.4%
b 1
 
7.7%
c 1
 
7.7%
Dash Punctuation
ValueCountFrequency (%)
- 6182
> 99.9%
－ 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 886
99.9%
[ 1
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 885
99.9%
] 1
 
0.1%
Space Separator
ValueCountFrequency (%)
17760
100.0%
Math Symbol
ValueCountFrequency (%)
~ 59
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 117938
60.5%
Common 76409
39.2%
Latin 631
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 8234
 
7.0%
구 7348
 
6.2%
시 6803
 
5.8%
로 3260
 
2.8%
경 3133
 
2.7%
지 3048
 
2.6%
서 3004
 
2.5%
남 2970
 
2.5%
산 2957
 
2.5%
번 2897
 
2.5%
Other values (467) 74284
63.0%
Common
ValueCountFrequency (%)
17760
23.2%
1 11706
15.3%
2 7179
9.4%
- 6182
 
8.1%
3 5391
 
7.1%
4 4312
 
5.6%
5 4299
 
5.6%
6 3517
 
4.6%
0 3455
 
4.5%
7 3432
 
4.5%
Other values (20) 9176
12.0%
Latin
ValueCountFrequency (%)
B 153
24.2%
A 134
21.2%
C 127
20.1%
D 85
13.5%
E 52
 
8.2%
F 18
 
2.9%
H 10
 
1.6%
L 7
 
1.1%
K 7
 
1.1%
I 6
 
1.0%
Other values (15) 32
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 117935
60.5%
ASCII 77011
39.5%
None 29
 
< 0.1%
Compat Jamo 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17760
23.1%
1 11706
15.2%
2 7179
9.3%
- 6182
 
8.0%
3 5391
 
7.0%
4 4312
 
5.6%
5 4299
 
5.6%
6 3517
 
4.6%
0 3455
 
4.5%
7 3432
 
4.5%
Other values (37) 9778
12.7%
Hangul
ValueCountFrequency (%)
동 8234
 
7.0%
구 7348
 
6.2%
시 6803
 
5.8%
로 3260
 
2.8%
경 3133
 
2.7%
지 3048
 
2.6%
서 3004
 
2.5%
남 2970
 
2.5%
산 2957
 
2.5%
번 2897
 
2.5%
Other values (464) 74281
63.0%
None
ValueCountFrequency (%)
· 21
72.4%
？ 2
 
6.9%
７ 1
 
3.4%
９ 1
 
3.4%
４ 1
 
3.4%
１ 1
 
3.4%
－ 1
 
3.4%
２ 1
 
3.4%
Compat Jamo
ValueCountFrequency (%)
ㅠ 1
33.3%
ㅁ 1
33.3%
ㅣ 1
33.3%

Missing values

2023-12-12T13:28:24.397393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:28:24.527386image/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.
2023-12-12T13:28:24.638111image/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

시장명점포명취급품목주소
82442충무데파트엔느의류경남 통영시 중앙로 160 (충무데파트, 124호)
92436새마을시장공가네한방족발음식점서울 송파구 잠실동 212-31
96703일산서문상점가귀빈소매(한복,이불)고양시 일산서구 일산동 635-8
63002연수구옥련시장옥련족발일반음식점업인천 연수구 한진로25번길 23, 1층 103호(옥련동)
23704구천동공구시장매일공구기계공구경기수원시팔달구구천동98
92417문정동로데오상점가슈나이더짐휘트니스서울 송파구 문정동 29-24
28134남광주시장완도황제전복농축수산품광주동구학동81-22번지
4801동대문상가B동한국화점신발서울종로구창신1동436-78번지(동대문상가B동1가-6)
11312양양전통시장보생정육점정육점강원양양군양양읍남문리시장상가47호(4/3)
39666서문시장4지구종합대체상가(베네시움)윙윙정장대구중구대신동115-377번지서문시장4지구3층347-1호
시장명점포명취급품목주소
52125고척근린시장굿모닝청과도소매(과일)서울특별시 구로구 고척로32길 3(고척동)
38378진주중앙시장누네띠네화장품경남진주시대안동4-3번지
34702르네시떼시장향수기타유통업부산사상구괘법동529-1번지르네시떼2661호
75296여수진남시장비비상회의류전남 여수시 학동1길 6-4
38877산청시장지산지업페인트사카페트,커텐,천막,지물경남산청군산청읍옥산리447번지
6806고분다리전통시장대성상회기타음료식품서울강동구천호동397-58
4944영천시장충남상회미곡상서울서대문구영천동283번지26통2반
30555부산진시장우리상회옷감직물부산동구범일2동290진시장1층하포341
77841부안상설시장동진식당음식(한식)전북 부안군 부안읍 시장길 8-2
55271홍도동상점가새마을기름집식용유대전광역시 동구 한남로7번길 98

Duplicate rows

Most frequently occurring

시장명점포명취급품목주소# duplicates
0금산수삼센타금산수삼센타홍삼제품충남금산군금산읍중도리242
1목사랑시장(구 목4동시장)완도울엄마건어물건어물서울 양천구 목동 727-312
2송현자유시장로얄스포츠스포츠레져용품인천동구송현동100번지2
3울산서동시장더월마트슈퍼마켓울산시 중구 동천4길 542
4청주가경터미널시장인디안가죽수제화신발충북 청주시 흥덕구 풍년로160번길 362