Overview

Dataset statistics

Number of variables5
Number of observations3204
Missing cells1595
Missing cells (%)10.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory125.3 KiB
Average record size in memory40.0 B

Variable types

Categorical1
Text4

Dataset

Description청주시 미용업소 현황(일반미용, 피부미용, 네일, 메이크업)에 대한 데이터로 업종명, 업소명, 도로명주소, 지번주소, 소재지전화번호를 제공합니다.
Author공공데이터포털
URLhttps://www.data.go.kr/data/15045135/fileData.do

Alerts

도로명 주소 has 48 (1.5%) missing valuesMissing
소재지전화 has 1545 (48.2%) missing valuesMissing

Reproduction

Analysis started2024-04-17 18:36:07.679373
Analysis finished2024-04-17 18:36:08.592332
Duration0.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size25.2 KiB
일반미용업
1288 
미용업
651 
피부미용업
471 
네일미용업
307 
종합미용업
175 
Other values (11)
312 

Length

Max length23
Median length5
Mean length5.4694132
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 1288
40.2%
미용업 651
20.3%
피부미용업 471
 
14.7%
네일미용업 307
 
9.6%
종합미용업 175
 
5.5%
화장ㆍ분장 미용업 92
 
2.9%
피부미용업, 네일미용업 46
 
1.4%
네일미용업, 화장ㆍ분장 미용업 44
 
1.4%
일반미용업, 화장ㆍ분장 미용업 33
 
1.0%
피부미용업, 네일미용업, 화장ㆍ분장 미용업 26
 
0.8%
Other values (6) 71
 
2.2%

Length

2024-04-18T03:36:08.654926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 1369
36.9%
미용업 887
23.9%
피부미용업 587
15.8%
네일미용업 452
 
12.2%
화장ㆍ분장 236
 
6.4%
종합미용업 175
 
4.7%

업소명
Text

Distinct3031
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Memory size25.2 KiB
2024-04-18T03:36:08.897254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length28
Mean length6.5549313
Min length1

Characters and Unicode

Total characters21002
Distinct characters731
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2897 ?
Unique (%)90.4%

Sample

1st row올리
2nd row명랑미용실
3rd row베일미용실
4th row시대미용실
5th row정 미용실
ValueCountFrequency (%)
헤어 43
 
1.1%
hair 33
 
0.8%
nail 24
 
0.6%
미용실 21
 
0.5%
에스테틱 20
 
0.5%
네일 19
 
0.5%
뷰티 17
 
0.4%
더 15
 
0.4%
de 13
 
0.3%
salon 13
 
0.3%
Other values (3305) 3700
94.4%
2024-04-18T03:36:09.260489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 1334
 
6.4%
헤 1319
 
6.3%
714
 
3.4%
미 541
 
2.6%
스 437
 
2.1%
리 412
 
2.0%
) 393
 
1.9%
( 392
 
1.9%
이 377
 
1.8%
실 358
 
1.7%
Other values (721) 14725
70.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16497
78.5%
Lowercase Letter 1418
 
6.8%
Uppercase Letter 1230
 
5.9%
Space Separator 714
 
3.4%
Close Punctuation 395
 
1.9%
Open Punctuation 394
 
1.9%
Other Punctuation 213
 
1.0%
Decimal Number 119
 
0.6%
Dash Punctuation 15
 
0.1%
Connector Punctuation 3
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 1334
 
8.1%
헤 1319
 
8.0%
미 541
 
3.3%
스 437
 
2.6%
리 412
 
2.5%
이 377
 
2.3%
실 358
 
2.2%
일 356
 
2.2%
네 343
 
2.1%
용 329
 
2.0%
Other values (643) 10691
64.8%
Lowercase Letter
ValueCountFrequency (%)
a 192
13.5%
e 150
10.6%
i 145
10.2%
l 115
 
8.1%
o 114
 
8.0%
n 102
 
7.2%
r 93
 
6.6%
s 79
 
5.6%
h 79
 
5.6%
u 49
 
3.5%
Other values (15) 300
21.2%
Uppercase Letter
ValueCountFrequency (%)
A 115
 
9.3%
N 105
 
8.5%
O 97
 
7.9%
S 84
 
6.8%
H 83
 
6.7%
I 82
 
6.7%
L 81
 
6.6%
E 70
 
5.7%
M 70
 
5.7%
R 68
 
5.5%
Other values (15) 375
30.5%
Other Punctuation
ValueCountFrequency (%)
, 58
27.2%
& 54
25.4%
# 42
19.7%
. 40
18.8%
: 13
 
6.1%
? 2
 
0.9%
＃ 2
 
0.9%
% 1
 
0.5%
· 1
 
0.5%
Decimal Number
ValueCountFrequency (%)
1 35
29.4%
0 22
18.5%
8 14
 
11.8%
9 12
 
10.1%
3 11
 
9.2%
7 10
 
8.4%
2 9
 
7.6%
5 5
 
4.2%
6 1
 
0.8%
Close Punctuation
ValueCountFrequency (%)
) 393
99.5%
] 2
 
0.5%
Open Punctuation
ValueCountFrequency (%)
( 392
99.5%
[ 2
 
0.5%
Math Symbol
ValueCountFrequency (%)
+ 2
66.7%
~ 1
33.3%
Space Separator
ValueCountFrequency (%)
714
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16484
78.5%
Latin 2648
 
12.6%
Common 1857
 
8.8%
Han 13
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 1334
 
8.1%
헤 1319
 
8.0%
미 541
 
3.3%
스 437
 
2.7%
리 412
 
2.5%
이 377
 
2.3%
실 358
 
2.2%
일 356
 
2.2%
네 343
 
2.1%
용 329
 
2.0%
Other values (636) 10678
64.8%
Latin
ValueCountFrequency (%)
a 192
 
7.3%
e 150
 
5.7%
i 145
 
5.5%
A 115
 
4.3%
l 115
 
4.3%
o 114
 
4.3%
N 105
 
4.0%
n 102
 
3.9%
O 97
 
3.7%
r 93
 
3.5%
Other values (40) 1420
53.6%
Common
ValueCountFrequency (%)
714
38.4%
) 393
21.2%
( 392
21.1%
, 58
 
3.1%
& 54
 
2.9%
# 42
 
2.3%
. 40
 
2.2%
1 35
 
1.9%
0 22
 
1.2%
- 15
 
0.8%
Other values (18) 92
 
5.0%
Han
ValueCountFrequency (%)
美 7
53.8%
作 1
 
7.7%
金 1
 
7.7%
人 1
 
7.7%
祐 1
 
7.7%
男 1
 
7.7%
子 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16484
78.5%
ASCII 4502
 
21.4%
CJK 13
 
0.1%
None 3
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 1334
 
8.1%
헤 1319
 
8.0%
미 541
 
3.3%
스 437
 
2.7%
리 412
 
2.5%
이 377
 
2.3%
실 358
 
2.2%
일 356
 
2.2%
네 343
 
2.1%
용 329
 
2.0%
Other values (636) 10678
64.8%
ASCII
ValueCountFrequency (%)
714
 
15.9%
) 393
 
8.7%
( 392
 
8.7%
a 192
 
4.3%
e 150
 
3.3%
i 145
 
3.2%
A 115
 
2.6%
l 115
 
2.6%
o 114
 
2.5%
N 105
 
2.3%
Other values (66) 2067
45.9%
CJK
ValueCountFrequency (%)
美 7
53.8%
作 1
 
7.7%
金 1
 
7.7%
人 1
 
7.7%
祐 1
 
7.7%
男 1
 
7.7%
子 1
 
7.7%
None
ValueCountFrequency (%)
＃ 2
66.7%
· 1
33.3%

도로명 주소
Text

MISSING 

Distinct3088
Distinct (%)97.8%
Missing48
Missing (%)1.5%
Memory size25.2 KiB
2024-04-18T03:36:09.533562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length67
Median length58
Mean length35.937896
Min length23

Characters and Unicode

Total characters113420
Distinct characters386
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3022 ?
Unique (%)95.8%

Sample

1st row충청북도 청주시 상당구 영운로 144 (서운동)
2nd row충청북도 청주시 상당구 상당로25번길 11 (남문로1가)
3rd row충청북도 청주시 상당구 상당로59번길 4 (북문로1가)
4th row충청북도 청주시 상당구 용담로 58 (대성동)
5th row충청북도 청주시 상당구 사직대로350번길 75 (서문동)
ValueCountFrequency (%)
충청북도 3156
 
13.3%
청주시 3156
 
13.3%
1층 1687
 
7.1%
흥덕구 1035
 
4.4%
상당구 792
 
3.3%
서원구 685
 
2.9%
청원구 644
 
2.7%
2층 363
 
1.5%
복대동 273
 
1.2%
용암동 243
 
1.0%
Other values (2571) 11677
49.2%
2024-04-18T03:36:09.920805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20558
 
18.1%
청 7129
 
6.3%
1 6148
 
5.4%
주 3389
 
3.0%
북 3294
 
2.9%
동 3277
 
2.9%
충 3268
 
2.9%
구 3251
 
2.9%
시 3214
 
2.8%
로 3211
 
2.8%
Other values (376) 56681
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 64448
56.8%
Space Separator 20558
 
18.1%
Decimal Number 18043
 
15.9%
Close Punctuation 3179
 
2.8%
Open Punctuation 3178
 
2.8%
Other Punctuation 3151
 
2.8%
Dash Punctuation 705
 
0.6%
Uppercase Letter 114
 
0.1%
Lowercase Letter 35
 
< 0.1%
Math Symbol 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 7129
 
11.1%
주 3389
 
5.3%
북 3294
 
5.1%
동 3277
 
5.1%
충 3268
 
5.1%
구 3251
 
5.0%
시 3214
 
5.0%
로 3211
 
5.0%
도 3166
 
4.9%
층 2472
 
3.8%
Other values (327) 28777
44.7%
Uppercase Letter
ValueCountFrequency (%)
B 22
19.3%
A 20
17.5%
S 12
10.5%
L 10
8.8%
C 8
 
7.0%
H 7
 
6.1%
M 5
 
4.4%
K 5
 
4.4%
F 4
 
3.5%
I 3
 
2.6%
Other values (8) 18
15.8%
Decimal Number
ValueCountFrequency (%)
1 6148
34.1%
2 2763
15.3%
0 1868
 
10.4%
3 1691
 
9.4%
4 1216
 
6.7%
5 1067
 
5.9%
6 979
 
5.4%
7 883
 
4.9%
8 763
 
4.2%
9 665
 
3.7%
Lowercase Letter
ValueCountFrequency (%)
e 6
17.1%
s 5
14.3%
m 5
14.3%
j 5
14.3%
o 5
14.3%
y 5
14.3%
l 2
 
5.7%
a 1
 
2.9%
h 1
 
2.9%
Other Punctuation
ValueCountFrequency (%)
, 3138
99.6%
@ 9
 
0.3%
# 2
 
0.1%
. 1
 
< 0.1%
& 1
 
< 0.1%
Letter Number
ValueCountFrequency (%)
Ⅰ 2
50.0%
Ⅱ 2
50.0%
Space Separator
ValueCountFrequency (%)
20558
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3179
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3178
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 705
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 64448
56.8%
Common 48819
43.0%
Latin 153
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 7129
 
11.1%
주 3389
 
5.3%
북 3294
 
5.1%
동 3277
 
5.1%
충 3268
 
5.1%
구 3251
 
5.0%
시 3214
 
5.0%
로 3211
 
5.0%
도 3166
 
4.9%
층 2472
 
3.8%
Other values (327) 28777
44.7%
Latin
ValueCountFrequency (%)
B 22
14.4%
A 20
 
13.1%
S 12
 
7.8%
L 10
 
6.5%
C 8
 
5.2%
H 7
 
4.6%
e 6
 
3.9%
M 5
 
3.3%
s 5
 
3.3%
m 5
 
3.3%
Other values (19) 53
34.6%
Common
ValueCountFrequency (%)
20558
42.1%
1 6148
 
12.6%
) 3179
 
6.5%
( 3178
 
6.5%
, 3138
 
6.4%
2 2763
 
5.7%
0 1868
 
3.8%
3 1691
 
3.5%
4 1216
 
2.5%
5 1067
 
2.2%
Other values (10) 4013
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 64448
56.8%
ASCII 48968
43.2%
Number Forms 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20558
42.0%
1 6148
 
12.6%
) 3179
 
6.5%
( 3178
 
6.5%
, 3138
 
6.4%
2 2763
 
5.6%
0 1868
 
3.8%
3 1691
 
3.5%
4 1216
 
2.5%
5 1067
 
2.2%
Other values (37) 4162
 
8.5%
Hangul
ValueCountFrequency (%)
청 7129
 
11.1%
주 3389
 
5.3%
북 3294
 
5.1%
동 3277
 
5.1%
충 3268
 
5.1%
구 3251
 
5.0%
시 3214
 
5.0%
로 3211
 
5.0%
도 3166
 
4.9%
층 2472
 
3.8%
Other values (327) 28777
44.7%
Number Forms
ValueCountFrequency (%)
Ⅰ 2
50.0%
Ⅱ 2
50.0%

지번 주소
Text

Distinct2946
Distinct (%)92.0%
Missing2
Missing (%)0.1%
Memory size25.2 KiB
2024-04-18T03:36:10.217415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length47
Mean length27.377264
Min length19

Characters and Unicode

Total characters87662
Distinct characters375
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2769 ?
Unique (%)86.5%

Sample

1st row충청북도 청주시 상당구 서운동 22-3
2nd row충청북도 청주시 상당구 남문로1가 113-13
3rd row충청북도 청주시 상당구 북문로1가 45-1
4th row충청북도 청주시 상당구 대성동 164-10
5th row충청북도 청주시 상당구 서문동 95-1
ValueCountFrequency (%)
충청북도 3202
16.6%
청주시 3202
16.6%
흥덕구 1067
 
5.5%
1층 882
 
4.6%
상당구 808
 
4.2%
서원구 685
 
3.5%
청원구 642
 
3.3%
복대동 317
 
1.6%
용암동 268
 
1.4%
가경동 253
 
1.3%
Other values (3094) 7998
41.4%
2024-04-18T03:36:10.616330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18877
21.5%
청 7185
 
8.2%
1 4236
 
4.8%
주 3401
 
3.9%
북 3277
 
3.7%
충 3258
 
3.7%
시 3244
 
3.7%
구 3232
 
3.7%
도 3212
 
3.7%
동 3066
 
3.5%
Other values (365) 34674
39.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 50084
57.1%
Space Separator 18877
 
21.5%
Decimal Number 15984
 
18.2%
Dash Punctuation 1458
 
1.7%
Open Punctuation 512
 
0.6%
Close Punctuation 512
 
0.6%
Other Punctuation 101
 
0.1%
Uppercase Letter 93
 
0.1%
Lowercase Letter 36
 
< 0.1%
Letter Number 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 7185
14.3%
주 3401
 
6.8%
북 3277
 
6.5%
충 3258
 
6.5%
시 3244
 
6.5%
구 3232
 
6.5%
도 3212
 
6.4%
동 3066
 
6.1%
원 1396
 
2.8%
층 1201
 
2.4%
Other values (316) 17612
35.2%
Uppercase Letter
ValueCountFrequency (%)
B 16
17.2%
A 16
17.2%
S 11
11.8%
H 7
7.5%
L 6
 
6.5%
C 6
 
6.5%
M 5
 
5.4%
F 4
 
4.3%
D 3
 
3.2%
I 3
 
3.2%
Other values (8) 16
17.2%
Decimal Number
ValueCountFrequency (%)
1 4236
26.5%
2 2339
14.6%
0 1539
 
9.6%
3 1450
 
9.1%
4 1231
 
7.7%
6 1160
 
7.3%
5 1072
 
6.7%
7 1047
 
6.6%
8 1035
 
6.5%
9 875
 
5.5%
Lowercase Letter
ValueCountFrequency (%)
e 6
16.7%
m 5
13.9%
s 5
13.9%
y 5
13.9%
o 5
13.9%
j 5
13.9%
a 2
 
5.6%
l 2
 
5.6%
h 1
 
2.8%
Other Punctuation
ValueCountFrequency (%)
, 76
75.2%
@ 21
 
20.8%
# 2
 
2.0%
& 1
 
1.0%
. 1
 
1.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 2
66.7%
Ⅱ 1
33.3%
Space Separator
ValueCountFrequency (%)
18877
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1458
100.0%
Open Punctuation
ValueCountFrequency (%)
( 512
100.0%
Close Punctuation
ValueCountFrequency (%)
) 512
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 50084
57.1%
Common 37446
42.7%
Latin 132
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 7185
14.3%
주 3401
 
6.8%
북 3277
 
6.5%
충 3258
 
6.5%
시 3244
 
6.5%
구 3232
 
6.5%
도 3212
 
6.4%
동 3066
 
6.1%
원 1396
 
2.8%
층 1201
 
2.4%
Other values (316) 17612
35.2%
Latin
ValueCountFrequency (%)
B 16
 
12.1%
A 16
 
12.1%
S 11
 
8.3%
H 7
 
5.3%
L 6
 
4.5%
C 6
 
4.5%
e 6
 
4.5%
m 5
 
3.8%
s 5
 
3.8%
y 5
 
3.8%
Other values (19) 49
37.1%
Common
ValueCountFrequency (%)
18877
50.4%
1 4236
 
11.3%
2 2339
 
6.2%
0 1539
 
4.1%
- 1458
 
3.9%
3 1450
 
3.9%
4 1231
 
3.3%
6 1160
 
3.1%
5 1072
 
2.9%
7 1047
 
2.8%
Other values (10) 3037
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 50084
57.1%
ASCII 37575
42.9%
Number Forms 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18877
50.2%
1 4236
 
11.3%
2 2339
 
6.2%
0 1539
 
4.1%
- 1458
 
3.9%
3 1450
 
3.9%
4 1231
 
3.3%
6 1160
 
3.1%
5 1072
 
2.9%
7 1047
 
2.8%
Other values (37) 3166
 
8.4%
Hangul
ValueCountFrequency (%)
청 7185
14.3%
주 3401
 
6.8%
북 3277
 
6.5%
충 3258
 
6.5%
시 3244
 
6.5%
구 3232
 
6.5%
도 3212
 
6.4%
동 3066
 
6.1%
원 1396
 
2.8%
층 1201
 
2.4%
Other values (316) 17612
35.2%
Number Forms
ValueCountFrequency (%)
Ⅰ 2
66.7%
Ⅱ 1
33.3%

소재지전화
Text

MISSING 

Distinct1642
Distinct (%)99.0%
Missing1545
Missing (%)48.2%
Memory size25.2 KiB
2024-04-18T03:36:10.895418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length14.001206
Min length4

Characters and Unicode

Total characters23228
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1626 ?
Unique (%)98.0%

Sample

1st row 043- 256-1165
2nd row 043- 255-8043
3rd row 043- 223-1438
4th row 043- 297-5005
5th row 043- 298-7132
ValueCountFrequency (%)
043 1514
35.2%
288 57
 
1.3%
233 29
 
0.7%
223 29
 
0.7%
292 28
 
0.7%
287 28
 
0.7%
070 27
 
0.6%
232 27
 
0.6%
235 27
 
0.6%
221 26
 
0.6%
Other values (1642) 2504
58.3%
2024-04-18T03:36:11.272334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 3316
14.3%
3286
14.1%
3 2940
12.7%
2 2677
11.5%
0 2638
11.4%
4 2364
10.2%
1 1090
 
4.7%
8 1065
 
4.6%
5 1042
 
4.5%
6 1004
 
4.3%
Other values (2) 1806
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16626
71.6%
Dash Punctuation 3316
 
14.3%
Space Separator 3286
 
14.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2940
17.7%
2 2677
16.1%
0 2638
15.9%
4 2364
14.2%
1 1090
 
6.6%
8 1065
 
6.4%
5 1042
 
6.3%
6 1004
 
6.0%
7 998
 
6.0%
9 808
 
4.9%
Dash Punctuation
ValueCountFrequency (%)
- 3316
100.0%
Space Separator
ValueCountFrequency (%)
3286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23228
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 3316
14.3%
3286
14.1%
3 2940
12.7%
2 2677
11.5%
0 2638
11.4%
4 2364
10.2%
1 1090
 
4.7%
8 1065
 
4.6%
5 1042
 
4.5%
6 1004
 
4.3%
Other values (2) 1806
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 3316
14.3%
3286
14.1%
3 2940
12.7%
2 2677
11.5%
0 2638
11.4%
4 2364
10.2%
1 1090
 
4.7%
8 1065
 
4.6%
5 1042
 
4.5%
6 1004
 
4.3%
Other values (2) 1806
7.8%

Missing values

2024-04-18T03:36:08.403035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T03:36:08.473335image/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.
2024-04-18T03:36:08.542864image/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

업종명업소명도로명 주소지번 주소소재지전화
0미용업올리충청북도 청주시 상당구 영운로 144 (서운동)충청북도 청주시 상당구 서운동 22-3<NA>
1미용업명랑미용실충청북도 청주시 상당구 상당로25번길 11 (남문로1가)충청북도 청주시 상당구 남문로1가 113-13043- 256-1165
2미용업베일미용실충청북도 청주시 상당구 상당로59번길 4 (북문로1가)충청북도 청주시 상당구 북문로1가 45-1043- 255-8043
3미용업시대미용실충청북도 청주시 상당구 용담로 58 (대성동)충청북도 청주시 상당구 대성동 164-10<NA>
4미용업정 미용실충청북도 청주시 상당구 사직대로350번길 75 (서문동)충청북도 청주시 상당구 서문동 95-1043- 223-1438
5미용업가덕미장원충청북도 청주시 상당구 가덕면 단재로 1439-6충청북도 청주시 상당구 가덕면 병암리 185043- 297-5005
6미용업숙미용실충청북도 청주시 상당구 문의면 문의시내로 47충청북도 청주시 상당구 문의면 미천리 121-11043- 298-7132
7미용업청주미용실충청북도 청주시 상당구 미원면 미원시내2길 17-14, 1층충청북도 청주시 상당구 미원면 미원리 514-1043- 298-4989
8미용업꽃다리 미용실<NA>충청북도 청주시 상당구 석교동 117-3 외3필지(1층)<NA>
9미용업명아미용실충청북도 청주시 상당구 성안로 106 (석교동,(1층))충청북도 청주시 상당구 석교동 69-9 (1층)<NA>
업종명업소명도로명 주소지번 주소소재지전화
3194네일미용업, 화장ㆍ분장 미용업라이크유 네일충청북도 청주시 청원구 오창읍 구룡택지1로 15, 1층충청북도 청주시 청원구 오창읍 구룡리 425-2<NA>
3195네일미용업, 화장ㆍ분장 미용업네일153충청북도 청주시 청원구 율량로 135, 상가동 1층 118호 (주성동, 대원칸타빌3차아파트)충청북도 청주시 청원구 주성동 337 대원칸타빌3차아파트<NA>
3196네일미용업, 화장ㆍ분장 미용업글린트뷰티충청북도 청주시 청원구 율중로7번길 100, 지하1층 104호 (율량동, 제일풍경채)충청북도 청주시 청원구 율량동 2456 제일풍경채<NA>
3197네일미용업, 화장ㆍ분장 미용업라뷘트충청북도 청주시 청원구 오창읍 구룡4길 6-3, 4층 405호충청북도 청주시 청원구 오창읍 구룡리 426-4<NA>
3198네일미용업, 화장ㆍ분장 미용업오늘 듀 네일충청북도 청주시 청원구 율량로 4, 1층 (주중동)충청북도 청주시 청원구 주중동 940 1층<NA>
3199일반미용업, 피부미용업, 화장ㆍ분장 미용업HARA HILS(하라힐즈)충청북도 청주시 청원구 사뜸로36번길 4, 2층 (율량동)충청북도 청주시 청원구 율량동 1665 2층043- 212-6925
3200일반미용업, 네일미용업, 화장ㆍ분장 미용업브이아이피 메이크업박스충청북도 청주시 청원구 공항로97번길 7, 1층 (율량동)충청북도 청주시 청원구 율량동 1814 1층043 -213 -3523
3201피부미용업, 네일미용업, 화장ㆍ분장 미용업아름다운네일충청북도 청주시 청원구 율량로 103, 1층 103호 (주성동, 대원칸타빌1단지아파트)충청북도 청주시 청원구 주성동 335 대원칸타빌1단지아파트<NA>
3202피부미용업, 네일미용업, 화장ㆍ분장 미용업으누네일충청북도 청주시 청원구 율량로189번길 7, 1층 (율량동)충청북도 청주시 청원구 율량동 2117<NA>
3203피부미용업, 네일미용업, 화장ㆍ분장 미용업청담뷰티샵충청북도 청주시 청원구 오창읍 주성1길 18, 2층 202호충청북도 청주시 청원구 오창읍 주성리 615043 -221 -6910