Overview

Dataset statistics

Number of variables2
Number of observations438
Missing cells0
Missing cells (%)0.0%
Duplicate rows4
Duplicate rows (%)0.9%
Total size in memory7.0 KiB
Average record size in memory16.3 B

Variable types

Text1
Categorical1

Dataset

Description서울특별시 강남구에 위치한 400여개 의료기관에 대한 기관명, 기관분류에 대한 데이터를 제공합니다. 자세한 사항은 서울특별시 강남구 관관진흥과로 문의하여 주시기 바랍니다.
Author서울특별시 강남구
URLhttps://www.data.go.kr/data/15071686/fileData.do

Alerts

Dataset has 4 (0.9%) duplicate rowsDuplicates

Reproduction

Analysis started2024-04-20 15:37:22.316835
Analysis finished2024-04-20 15:37:22.745968
Duration0.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관명
Text

Distinct421
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2024-04-21T00:37:23.508359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length15
Mean length8.0182648
Min length3

Characters and Unicode

Total characters3512
Distinct characters364
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

Unique414 ?
Unique (%)94.5%

Sample

1st row타코성형외과
2nd row밝은성모안과
3rd row현대미학성형외과
4th row청담여신성형외과
5th row엠디클리닉 가슴성형센터
ValueCountFrequency (%)
성형외과의원 33
 
5.2%
성형외과 20
 
3.2%
치과의원 16
 
2.5%
피부과의원 13
 
2.1%
의원 12
 
1.9%
한의원 11
 
1.7%
안과의원 8
 
1.3%
강남세브란스병원 7
 
1.1%
삼성서울병원 6
 
0.9%
치과병원 5
 
0.8%
Other values (469) 501
79.3%
2024-04-21T00:37:24.836515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 291
 
8.3%
원 262
 
7.5%
213
 
6.1%
의 209
 
6.0%
성 158
 
4.5%
외 145
 
4.1%
형 143
 
4.1%
스 84
 
2.4%
이 75
 
2.1%
치 66
 
1.9%
Other values (354) 1866
53.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3185
90.7%
Space Separator 213
 
6.1%
Uppercase Letter 70
 
2.0%
Decimal Number 18
 
0.5%
Other Symbol 8
 
0.2%
Close Punctuation 6
 
0.2%
Open Punctuation 6
 
0.2%
Lowercase Letter 4
 
0.1%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 291
 
9.1%
원 262
 
8.2%
의 209
 
6.6%
성 158
 
5.0%
외 145
 
4.6%
형 143
 
4.5%
스 84
 
2.6%
이 75
 
2.4%
치 66
 
2.1%
병 53
 
1.7%
Other values (314) 1699
53.3%
Uppercase Letter
ValueCountFrequency (%)
K 8
11.4%
C 7
 
10.0%
P 6
 
8.6%
J 6
 
8.6%
B 6
 
8.6%
Y 5
 
7.1%
S 4
 
5.7%
I 3
 
4.3%
W 3
 
4.3%
A 3
 
4.3%
Other values (13) 19
27.1%
Decimal Number
ValueCountFrequency (%)
3 3
16.7%
1 3
16.7%
9 2
11.1%
8 2
11.1%
2 2
11.1%
6 2
11.1%
0 2
11.1%
4 1
 
5.6%
5 1
 
5.6%
Lowercase Letter
ValueCountFrequency (%)
m 3
75.0%
c 1
 
25.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
50.0%
& 1
50.0%
Space Separator
ValueCountFrequency (%)
213
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3193
90.9%
Common 245
 
7.0%
Latin 74
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 291
 
9.1%
원 262
 
8.2%
의 209
 
6.5%
성 158
 
4.9%
외 145
 
4.5%
형 143
 
4.5%
스 84
 
2.6%
이 75
 
2.3%
치 66
 
2.1%
병 53
 
1.7%
Other values (315) 1707
53.5%
Latin
ValueCountFrequency (%)
K 8
 
10.8%
C 7
 
9.5%
P 6
 
8.1%
J 6
 
8.1%
B 6
 
8.1%
Y 5
 
6.8%
S 4
 
5.4%
I 3
 
4.1%
W 3
 
4.1%
m 3
 
4.1%
Other values (15) 23
31.1%
Common
ValueCountFrequency (%)
213
86.9%
) 6
 
2.4%
( 6
 
2.4%
3 3
 
1.2%
1 3
 
1.2%
9 2
 
0.8%
8 2
 
0.8%
2 2
 
0.8%
6 2
 
0.8%
0 2
 
0.8%
Other values (4) 4
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3185
90.7%
ASCII 319
 
9.1%
None 8
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 291
 
9.1%
원 262
 
8.2%
의 209
 
6.6%
성 158
 
5.0%
외 145
 
4.6%
형 143
 
4.5%
스 84
 
2.6%
이 75
 
2.4%
치 66
 
2.1%
병 53
 
1.7%
Other values (314) 1699
53.3%
ASCII
ValueCountFrequency (%)
213
66.8%
K 8
 
2.5%
C 7
 
2.2%
P 6
 
1.9%
) 6
 
1.9%
J 6
 
1.9%
( 6
 
1.9%
B 6
 
1.9%
Y 5
 
1.6%
S 4
 
1.3%
Other values (29) 52
 
16.3%
None
ValueCountFrequency (%)
㈜ 8
100.0%

기관분류
Categorical

Distinct16
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
성형외과
148 
치과
63 
피부미용
45 
기타
27 
한방진료
23 
Other values (11)
132 

Length

Max length18
Median length4
Mean length4.2671233
Min length2

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row성형외과
2nd row안과
3rd row성형외과
4th row성형외과
5th row성형외과

Common Values

ValueCountFrequency (%)
성형외과 148
33.8%
치과 63
14.4%
피부미용 45
 
10.3%
기타 27
 
6.2%
한방진료 23
 
5.3%
스파, 쇼핑, 유치업체 / 기타 22
 
5.0%
안과 21
 
4.8%
종합검진 19
 
4.3%
척추/관절치료 18
 
4.1%
호텔 16
 
3.7%
Other values (6) 36
 
8.2%

Length

2024-04-21T00:37:25.082061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성형외과 148
28.1%
치과 63
12.0%
기타 49
 
9.3%
피부미용 45
 
8.6%
한방진료 23
 
4.4%
스파 22
 
4.2%
쇼핑 22
 
4.2%
유치업체 22
 
4.2%
22
 
4.2%
안과 21
 
4.0%
Other values (9) 89
16.9%

Missing values

2024-04-21T00:37:22.588217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T00:37:22.701239image/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

기관명기관분류
0타코성형외과성형외과
1밝은성모안과안과
2현대미학성형외과성형외과
3청담여신성형외과성형외과
4엠디클리닉 가슴성형센터성형외과
5바탕성형외과성형외과
6프리미어성형외과의원성형외과
7오페라성형외과의원성형외과
8소중치과치과
9포비성형외과성형외과
기관명기관분류
428㈜컨벤션헤리츠호텔포레힐지점호텔
429(주)강남패밀리호텔호텔
430호텔그라모스호텔
431호텔더디자이너스호텔
432트리아관광호텔호텔
433베스트웨스턴 프리미어 강남호텔호텔
434노보텔 앰배서더 강남호텔
435호텔리츠칼튼서울호텔
436제이비스관광호텔호텔
437오크우드프리미어코엑스센터호텔

Duplicate rows

Most frequently occurring

기관명기관분류# duplicates
0글로비성형외과성형외과2
1기쁨병원종합검진2
2이문원 한의원한방진료2
3하늘체한의원한방진료2