Overview

Dataset statistics

Number of variables5
Number of observations246
Missing cells40
Missing cells (%)3.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.7 KiB
Average record size in memory40.5 B

Variable types

Categorical2
Text3

Dataset

Description경기도 성남시 안경업소 현황에 대한 데이터로, 구별,안경업소명칭,소재지,전화번호 등의 항목을 제공하는 데이터입니다.
Author경기도 성남시
URLhttps://www.data.go.kr/data/15004910/fileData.do

Alerts

데이터기준일자 is highly overall correlated with 구별High correlation
구별 is highly overall correlated with 데이터기준일자High correlation
데이터기준일자 is highly imbalanced (96.2%)Imbalance
사업장전화번호 has 40 (16.3%) missing valuesMissing

Reproduction

Analysis started2024-03-15 01:39:58.999115
Analysis finished2024-03-15 01:39:59.868460
Duration0.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구별
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
분당구
139 
수정구
62 
중원구
45 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
분당구 139
56.5%
수정구 62
25.2%
중원구 45
 
18.3%

Length

2024-03-15T10:40:00.020105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:40:00.335727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
분당구 139
56.5%
수정구 62
25.2%
중원구 45
 
18.3%

안경업소명칭
Text

Distinct243
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2024-03-15T10:40:01.359048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length20
Mean length7.3780488
Min length3

Characters and Unicode

Total characters1815
Distinct characters268
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

Unique240 ?
Unique (%)97.6%

Sample

1st row으뜸50안경
2nd row으뜸50안경 성남 태평로점
3rd row새로봄안경
4th row스튜디오옵티크
5th row프랫 판교점
ValueCountFrequency (%)
안경원 8
 
2.4%
판교점 8
 
2.4%
안경 6
 
1.8%
으뜸50안경 6
 
1.8%
오렌즈 6
 
1.8%
분당점 5
 
1.5%
야탑점 4
 
1.2%
안경마을 4
 
1.2%
미금점 3
 
0.9%
판교 3
 
0.9%
Other values (263) 280
84.1%
2024-03-15T10:40:02.685543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
안 194
 
10.7%
경 190
 
10.5%
87
 
4.8%
원 64
 
3.5%
점 62
 
3.4%
이 43
 
2.4%
스 43
 
2.4%
아 38
 
2.1%
트 30
 
1.7%
리 26
 
1.4%
Other values (258) 1038
57.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1535
84.6%
Uppercase Letter 111
 
6.1%
Space Separator 87
 
4.8%
Open Punctuation 21
 
1.2%
Close Punctuation 21
 
1.2%
Decimal Number 16
 
0.9%
Lowercase Letter 14
 
0.8%
Other Punctuation 9
 
0.5%
Connector Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
안 194
 
12.6%
경 190
 
12.4%
원 64
 
4.2%
점 62
 
4.0%
이 43
 
2.8%
스 43
 
2.8%
아 38
 
2.5%
트 30
 
2.0%
리 26
 
1.7%
라 23
 
1.5%
Other values (216) 822
53.6%
Uppercase Letter
ValueCountFrequency (%)
E 14
12.6%
A 13
11.7%
O 10
 
9.0%
L 9
 
8.1%
D 7
 
6.3%
T 6
 
5.4%
Y 6
 
5.4%
I 5
 
4.5%
P 5
 
4.5%
C 5
 
4.5%
Other values (12) 31
27.9%
Lowercase Letter
ValueCountFrequency (%)
e 4
28.6%
s 2
14.3%
y 1
 
7.1%
d 1
 
7.1%
i 1
 
7.1%
k 1
 
7.1%
n 1
 
7.1%
a 1
 
7.1%
r 1
 
7.1%
t 1
 
7.1%
Other Punctuation
ValueCountFrequency (%)
. 6
66.7%
& 1
 
11.1%
, 1
 
11.1%
· 1
 
11.1%
Decimal Number
ValueCountFrequency (%)
0 8
50.0%
5 8
50.0%
Space Separator
ValueCountFrequency (%)
87
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1535
84.6%
Common 155
 
8.5%
Latin 125
 
6.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
안 194
 
12.6%
경 190
 
12.4%
원 64
 
4.2%
점 62
 
4.0%
이 43
 
2.8%
스 43
 
2.8%
아 38
 
2.5%
트 30
 
2.0%
리 26
 
1.7%
라 23
 
1.5%
Other values (216) 822
53.6%
Latin
ValueCountFrequency (%)
E 14
 
11.2%
A 13
 
10.4%
O 10
 
8.0%
L 9
 
7.2%
D 7
 
5.6%
T 6
 
4.8%
Y 6
 
4.8%
I 5
 
4.0%
P 5
 
4.0%
C 5
 
4.0%
Other values (22) 45
36.0%
Common
ValueCountFrequency (%)
87
56.1%
( 21
 
13.5%
) 21
 
13.5%
0 8
 
5.2%
5 8
 
5.2%
. 6
 
3.9%
_ 1
 
0.6%
& 1
 
0.6%
, 1
 
0.6%
· 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1535
84.6%
ASCII 279
 
15.4%
None 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
안 194
 
12.6%
경 190
 
12.4%
원 64
 
4.2%
점 62
 
4.0%
이 43
 
2.8%
스 43
 
2.8%
아 38
 
2.5%
트 30
 
2.0%
리 26
 
1.7%
라 23
 
1.5%
Other values (216) 822
53.6%
ASCII
ValueCountFrequency (%)
87
31.2%
( 21
 
7.5%
) 21
 
7.5%
E 14
 
5.0%
A 13
 
4.7%
O 10
 
3.6%
L 9
 
3.2%
0 8
 
2.9%
5 8
 
2.9%
D 7
 
2.5%
Other values (31) 81
29.0%
None
ValueCountFrequency (%)
· 1
100.0%

사업장소재지(도로명)
Text

Distinct245
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2024-03-15T10:40:03.816084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length49
Mean length38.268293
Min length24

Characters and Unicode

Total characters9414
Distinct characters270
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

Unique244 ?
Unique (%)99.2%

Sample

1st row경기도 성남시 수정구 위례광장로 104, 위례 한화 오벨리스크 센트럴스퀘어 (창곡동)
2nd row경기도 성남시 수정구 수정로 115, 만홍빌딩 3층 (태평동)
3rd row경기도 성남시 수정구 산성대로 267(신흥동)
4th row경기도 성남시 수정구 수정로 291, 산성역포레스티아아파트 B103호 (신흥동)
5th row경기도 성남시 수정구 창업로 18, 파미에스몰 108호 (시흥동)
ValueCountFrequency (%)
경기도 246
 
12.6%
성남시 243
 
12.4%
분당구 136
 
7.0%
1층 67
 
3.4%
수정구 62
 
3.2%
중원구 45
 
2.3%
서현동 26
 
1.3%
야탑동 22
 
1.1%
산성대로 21
 
1.1%
성남대로 19
 
1.0%
Other values (538) 1066
54.6%
2024-03-15T10:40:05.349778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1707
 
18.1%
1 470
 
5.0%
성 331
 
3.5%
남 300
 
3.2%
, 290
 
3.1%
동 272
 
2.9%
구 258
 
2.7%
도 255
 
2.7%
시 254
 
2.7%
로 251
 
2.7%
Other values (260) 5026
53.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5383
57.2%
Space Separator 1707
 
18.1%
Decimal Number 1458
 
15.5%
Other Punctuation 297
 
3.2%
Close Punctuation 248
 
2.6%
Open Punctuation 248
 
2.6%
Uppercase Letter 34
 
0.4%
Dash Punctuation 33
 
0.4%
Math Symbol 4
 
< 0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 331
 
6.1%
남 300
 
5.6%
동 272
 
5.1%
구 258
 
4.8%
도 255
 
4.7%
시 254
 
4.7%
로 251
 
4.7%
경 246
 
4.6%
기 246
 
4.6%
호 169
 
3.1%
Other values (228) 2801
52.0%
Uppercase Letter
ValueCountFrequency (%)
B 12
35.3%
A 9
26.5%
K 2
 
5.9%
C 2
 
5.9%
Y 2
 
5.9%
S 1
 
2.9%
W 1
 
2.9%
I 1
 
2.9%
T 1
 
2.9%
D 1
 
2.9%
Other values (2) 2
 
5.9%
Decimal Number
ValueCountFrequency (%)
1 470
32.2%
0 195
13.4%
2 175
 
12.0%
3 152
 
10.4%
4 101
 
6.9%
6 101
 
6.9%
5 87
 
6.0%
7 68
 
4.7%
8 58
 
4.0%
9 51
 
3.5%
Other Punctuation
ValueCountFrequency (%)
, 290
97.6%
. 6
 
2.0%
? 1
 
0.3%
Space Separator
ValueCountFrequency (%)
1707
100.0%
Close Punctuation
ValueCountFrequency (%)
) 248
100.0%
Open Punctuation
ValueCountFrequency (%)
( 248
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 33
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%
Letter Number
ValueCountFrequency (%)
Ⅲ 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
a 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5383
57.2%
Common 3995
42.4%
Latin 36
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 331
 
6.1%
남 300
 
5.6%
동 272
 
5.1%
구 258
 
4.8%
도 255
 
4.7%
시 254
 
4.7%
로 251
 
4.7%
경 246
 
4.6%
기 246
 
4.6%
호 169
 
3.1%
Other values (228) 2801
52.0%
Common
ValueCountFrequency (%)
1707
42.7%
1 470
 
11.8%
, 290
 
7.3%
) 248
 
6.2%
( 248
 
6.2%
0 195
 
4.9%
2 175
 
4.4%
3 152
 
3.8%
4 101
 
2.5%
6 101
 
2.5%
Other values (8) 308
 
7.7%
Latin
ValueCountFrequency (%)
B 12
33.3%
A 9
25.0%
K 2
 
5.6%
C 2
 
5.6%
Y 2
 
5.6%
S 1
 
2.8%
W 1
 
2.8%
I 1
 
2.8%
T 1
 
2.8%
D 1
 
2.8%
Other values (4) 4
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5383
57.2%
ASCII 4030
42.8%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1707
42.4%
1 470
 
11.7%
, 290
 
7.2%
) 248
 
6.2%
( 248
 
6.2%
0 195
 
4.8%
2 175
 
4.3%
3 152
 
3.8%
4 101
 
2.5%
6 101
 
2.5%
Other values (21) 343
 
8.5%
Hangul
ValueCountFrequency (%)
성 331
 
6.1%
남 300
 
5.6%
동 272
 
5.1%
구 258
 
4.8%
도 255
 
4.7%
시 254
 
4.7%
로 251
 
4.7%
경 246
 
4.6%
기 246
 
4.6%
호 169
 
3.1%
Other values (228) 2801
52.0%
Number Forms
ValueCountFrequency (%)
Ⅲ 1
100.0%

사업장전화번호
Text

MISSING 

Distinct203
Distinct (%)98.5%
Missing40
Missing (%)16.3%
Memory size2.0 KiB
2024-03-15T10:40:06.323458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.033981
Min length9

Characters and Unicode

Total characters2479
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

Unique200 ?
Unique (%)97.1%

Sample

1st row031-722-1005
2nd row031-722-1202
3rd row02-752-0101
4th row031-751-0099
5th row031-721-9666
ValueCountFrequency (%)
031-756-1272 2
 
1.0%
031-758-8001 2
 
1.0%
031-721-9666 2
 
1.0%
031-702-5009 1
 
0.5%
031-709-1204 1
 
0.5%
031-603-0102 1
 
0.5%
031-725-1001 1
 
0.5%
031-711-6262 1
 
0.5%
031-781-6006 1
 
0.5%
031-705-6505 1
 
0.5%
Other values (193) 193
93.7%
2024-03-15T10:40:07.744877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 439
17.7%
- 410
16.5%
1 400
16.1%
3 291
11.7%
7 262
10.6%
5 145
 
5.8%
2 126
 
5.1%
6 121
 
4.9%
8 112
 
4.5%
9 88
 
3.5%
Other values (2) 85
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2068
83.4%
Dash Punctuation 410
 
16.5%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 439
21.2%
1 400
19.3%
3 291
14.1%
7 262
12.7%
5 145
 
7.0%
2 126
 
6.1%
6 121
 
5.9%
8 112
 
5.4%
9 88
 
4.3%
4 84
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 410
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2479
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 439
17.7%
- 410
16.5%
1 400
16.1%
3 291
11.7%
7 262
10.6%
5 145
 
5.8%
2 126
 
5.1%
6 121
 
4.9%
8 112
 
4.5%
9 88
 
3.5%
Other values (2) 85
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2479
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 439
17.7%
- 410
16.5%
1 400
16.1%
3 291
11.7%
7 262
10.6%
5 145
 
5.8%
2 126
 
5.1%
6 121
 
4.9%
8 112
 
4.5%
9 88
 
3.5%
Other values (2) 85
 
3.4%

데이터기준일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2024-02-28
245 
<NA>
 
1

Length

Max length10
Median length10
Mean length9.9756098
Min length4

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row2024-02-28
2nd row2024-02-28
3rd row2024-02-28
4th row2024-02-28
5th row2024-02-28

Common Values

ValueCountFrequency (%)
2024-02-28 245
99.6%
<NA> 1
 
0.4%

Length

2024-03-15T10:40:08.179002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:40:08.540402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-02-28 245
99.6%
na 1
 
0.4%

Correlations

2024-03-15T10:40:08.722155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구별
구별1.000
2024-03-15T10:40:08.939701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터기준일자구별
데이터기준일자1.0001.000
구별1.0001.000
2024-03-15T10:40:09.172386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구별데이터기준일자
구별1.0001.000
데이터기준일자1.0001.000

Missing values

2024-03-15T10:39:59.609156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T10:39:59.799558image/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

구별안경업소명칭사업장소재지(도로명)사업장전화번호데이터기준일자
0수정구으뜸50안경경기도 성남시 수정구 위례광장로 104, 위례 한화 오벨리스크 센트럴스퀘어 (창곡동)<NA>2024-02-28
1수정구으뜸50안경 성남 태평로점경기도 성남시 수정구 수정로 115, 만홍빌딩 3층 (태평동)<NA>2024-02-28
2수정구새로봄안경경기도 성남시 수정구 산성대로 267(신흥동)<NA>2024-02-28
3수정구스튜디오옵티크경기도 성남시 수정구 수정로 291, 산성역포레스티아아파트 B103호 (신흥동)<NA>2024-02-28
4수정구프랫 판교점경기도 성남시 수정구 창업로 18, 파미에스몰 108호 (시흥동)<NA>2024-02-28
5수정구다비치안경 위례신도시점경기도 성남시 수정구 위례광장로 328, 우성위례타워 101~103호 (창곡동)031-722-10052024-02-28
6수정구조안경경기도 성남시 수정구 산성대로 91, 성호빌딩 2층 (수진동)<NA>2024-02-28
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