Overview

Dataset statistics

Number of variables4
Number of observations141
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory32.9 B

Variable types

Categorical2
Text2

Dataset

Description대구광역시 수성구_실내공기질관리대상시설_20231212
Author대구광역시 수성구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15073526&dataSetDetailId=150735261a4543a068395&provdMethod=FILE

Alerts

데이터기준일자 has constant value ""Constant

Reproduction

Analysis started2023-12-16 05:02:18.329754
Analysis finished2023-12-16 05:02:20.624637
Duration2.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설군
Categorical

Distinct15
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
어린이집
33 
의료기관
27 
실내주차장
18 
인터넷컴퓨터게임시설제공업
15 
지하역사
10 
Other values (10)
38 

Length

Max length13
Median length4
Mean length5.106383
Min length2

Unique

Unique4 ?
Unique (%)2.8%

Sample

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

Common Values

ValueCountFrequency (%)
어린이집 33
23.4%
의료기관 27
19.1%
실내주차장 18
12.8%
인터넷컴퓨터게임시설제공업 15
10.6%
지하역사 10
 
7.1%
학원 10
 
7.1%
노인요양시설 7
 
5.0%
산후조리원 7
 
5.0%
대규모점포 5
 
3.5%
도서관 3
 
2.1%
Other values (5) 6
 
4.3%

Length

2023-12-16T05:02:21.405761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
어린이집 33
23.4%
의료기관 27
19.1%
실내주차장 18
12.8%
인터넷컴퓨터게임시설제공업 15
10.6%
지하역사 10
 
7.1%
학원 10
 
7.1%
노인요양시설 7
 
5.0%
산후조리원 7
 
5.0%
대규모점포 5
 
3.5%
도서관 3
 
2.1%
Other values (5) 6
 
4.3%

시설명
Text

Distinct138
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-16T05:02:22.592180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length17
Mean length7.787234
Min length3

Characters and Unicode

Total characters1098
Distinct characters247
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

Unique135 ?
Unique (%)95.7%

Sample

1st row대구은행역
2nd row범어역
3rd row수성구청역
4th row만촌역
5th row담티역
ValueCountFrequency (%)
수성점 5
 
3.0%
산후조리원 4
 
2.4%
동아백화점 3
 
1.8%
삼성홈플러스 2
 
1.2%
수성구립 2
 
1.2%
pc방 2
 
1.2%
쇼핑몰 2
 
1.2%
서우마트[수성하이츠 2
 
1.2%
송효의료재단효산요양병원 1
 
0.6%
참좋은요양병원 1
 
0.6%
Other values (142) 142
85.5%
2023-12-16T05:02:24.887825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 55
 
5.0%
성 44
 
4.0%
어 42
 
3.8%
이 41
 
3.7%
린 33
 
3.0%
집 32
 
2.9%
수 27
 
2.5%
26
 
2.4%
병 26
 
2.4%
대 25
 
2.3%
Other values (237) 747
68.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 985
89.7%
Uppercase Letter 45
 
4.1%
Space Separator 26
 
2.4%
Lowercase Letter 18
 
1.6%
Open Punctuation 9
 
0.8%
Close Punctuation 9
 
0.8%
Decimal Number 3
 
0.3%
Other Symbol 2
 
0.2%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 55
 
5.6%
성 44
 
4.5%
어 42
 
4.3%
이 41
 
4.2%
린 33
 
3.4%
집 32
 
3.2%
수 27
 
2.7%
병 26
 
2.6%
대 25
 
2.5%
구 20
 
2.0%
Other values (204) 640
65.0%
Lowercase Letter
ValueCountFrequency (%)
n 3
16.7%
o 3
16.7%
b 2
11.1%
u 2
11.1%
g 1
 
5.6%
i 1
 
5.6%
z 1
 
5.6%
m 1
 
5.6%
k 1
 
5.6%
e 1
 
5.6%
Other values (2) 2
11.1%
Uppercase Letter
ValueCountFrequency (%)
C 14
31.1%
P 12
26.7%
B 5
 
11.1%
G 3
 
6.7%
D 2
 
4.4%
W 2
 
4.4%
V 2
 
4.4%
O 2
 
4.4%
X 1
 
2.2%
K 1
 
2.2%
Decimal Number
ValueCountFrequency (%)
3 1
33.3%
2 1
33.3%
1 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 7
77.8%
[ 2
 
22.2%
Close Punctuation
ValueCountFrequency (%)
) 7
77.8%
] 2
 
22.2%
Space Separator
ValueCountFrequency (%)
26
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 987
89.9%
Latin 63
 
5.7%
Common 48
 
4.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 55
 
5.6%
성 44
 
4.5%
어 42
 
4.3%
이 41
 
4.2%
린 33
 
3.3%
집 32
 
3.2%
수 27
 
2.7%
병 26
 
2.6%
대 25
 
2.5%
구 20
 
2.0%
Other values (205) 642
65.0%
Latin
ValueCountFrequency (%)
C 14
22.2%
P 12
19.0%
B 5
 
7.9%
G 3
 
4.8%
n 3
 
4.8%
o 3
 
4.8%
D 2
 
3.2%
W 2
 
3.2%
b 2
 
3.2%
u 2
 
3.2%
Other values (13) 15
23.8%
Common
ValueCountFrequency (%)
26
54.2%
( 7
 
14.6%
) 7
 
14.6%
[ 2
 
4.2%
] 2
 
4.2%
3 1
 
2.1%
2 1
 
2.1%
- 1
 
2.1%
1 1
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 985
89.7%
ASCII 111
 
10.1%
None 2
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 55
 
5.6%
성 44
 
4.5%
어 42
 
4.3%
이 41
 
4.2%
린 33
 
3.4%
집 32
 
3.2%
수 27
 
2.7%
병 26
 
2.6%
대 25
 
2.5%
구 20
 
2.0%
Other values (204) 640
65.0%
ASCII
ValueCountFrequency (%)
26
23.4%
C 14
12.6%
P 12
10.8%
( 7
 
6.3%
) 7
 
6.3%
B 5
 
4.5%
G 3
 
2.7%
n 3
 
2.7%
o 3
 
2.7%
D 2
 
1.8%
Other values (22) 29
26.1%
None
ValueCountFrequency (%)
㈜ 2
100.0%

소재지
Text

Distinct137
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-16T05:02:26.101300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length29
Mean length17.312057
Min length10

Characters and Unicode

Total characters2441
Distinct characters146
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

Unique134 ?
Unique (%)95.0%

Sample

1st row달구벌대로 2300(수성4가)
2nd row달구벌대로 2396(범어동)
3rd row달구벌대로 2500(범어동)
4th row달구벌대로 2584(만촌동)
5th row달구벌대로 2672(만촌동)
ValueCountFrequency (%)
달구벌대로 34
 
8.6%
동대구로 16
 
4.1%
범어동 9
 
2.3%
지범로 8
 
2.0%
지산동 8
 
2.0%
수성로 7
 
1.8%
중동 5
 
1.3%
신매동 5
 
1.3%
191(범물동 4
 
1.0%
4
 
1.0%
Other values (256) 295
74.7%
2023-12-16T05:02:28.449374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
261
 
10.7%
동 158
 
6.5%
) 143
 
5.9%
( 143
 
5.9%
로 141
 
5.8%
1 124
 
5.1%
2 95
 
3.9%
3 69
 
2.8%
대 64
 
2.6%
구 63
 
2.6%
Other values (136) 1180
48.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1199
49.1%
Decimal Number 591
24.2%
Space Separator 261
 
10.7%
Close Punctuation 143
 
5.9%
Open Punctuation 143
 
5.9%
Other Punctuation 59
 
2.4%
Dash Punctuation 20
 
0.8%
Math Symbol 13
 
0.5%
Uppercase Letter 12
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 158
 
13.2%
로 141
 
11.8%
대 64
 
5.3%
구 63
 
5.3%
범 50
 
4.2%
지 42
 
3.5%
벌 41
 
3.4%
달 41
 
3.4%
길 36
 
3.0%
층 36
 
3.0%
Other values (111) 527
44.0%
Decimal Number
ValueCountFrequency (%)
1 124
21.0%
2 95
16.1%
3 69
11.7%
4 59
10.0%
5 55
9.3%
6 53
9.0%
9 39
 
6.6%
0 34
 
5.8%
8 33
 
5.6%
7 30
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
T 2
16.7%
P 2
16.7%
A 2
16.7%
S 2
16.7%
K 2
16.7%
G 1
8.3%
B 1
8.3%
Other Punctuation
ValueCountFrequency (%)
, 58
98.3%
/ 1
 
1.7%
Math Symbol
ValueCountFrequency (%)
~ 12
92.3%
→ 1
 
7.7%
Space Separator
ValueCountFrequency (%)
261
100.0%
Close Punctuation
ValueCountFrequency (%)
) 143
100.0%
Open Punctuation
ValueCountFrequency (%)
( 143
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1230
50.4%
Hangul 1199
49.1%
Latin 12
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 158
 
13.2%
로 141
 
11.8%
대 64
 
5.3%
구 63
 
5.3%
범 50
 
4.2%
지 42
 
3.5%
벌 41
 
3.4%
달 41
 
3.4%
길 36
 
3.0%
층 36
 
3.0%
Other values (111) 527
44.0%
Common
ValueCountFrequency (%)
261
21.2%
) 143
11.6%
( 143
11.6%
1 124
10.1%
2 95
 
7.7%
3 69
 
5.6%
4 59
 
4.8%
, 58
 
4.7%
5 55
 
4.5%
6 53
 
4.3%
Other values (8) 170
13.8%
Latin
ValueCountFrequency (%)
T 2
16.7%
P 2
16.7%
A 2
16.7%
S 2
16.7%
K 2
16.7%
G 1
8.3%
B 1
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1241
50.8%
Hangul 1199
49.1%
Arrows 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
261
21.0%
) 143
11.5%
( 143
11.5%
1 124
10.0%
2 95
 
7.7%
3 69
 
5.6%
4 59
 
4.8%
, 58
 
4.7%
5 55
 
4.4%
6 53
 
4.3%
Other values (14) 181
14.6%
Hangul
ValueCountFrequency (%)
동 158
 
13.2%
로 141
 
11.8%
대 64
 
5.3%
구 63
 
5.3%
범 50
 
4.2%
지 42
 
3.5%
벌 41
 
3.4%
달 41
 
3.4%
길 36
 
3.0%
층 36
 
3.0%
Other values (111) 527
44.0%
Arrows
ValueCountFrequency (%)
→ 1
100.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-12
141 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-12-12
2nd row2023-12-12
3rd row2023-12-12
4th row2023-12-12
5th row2023-12-12

Common Values

ValueCountFrequency (%)
2023-12-12 141
100.0%

Length

2023-12-16T05:02:29.321831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T05:02:29.966572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-12-12 141
100.0%

Missing values

2023-12-16T05:02:19.220059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T05:02:20.382133image/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

시설군시설명소재지데이터기준일자
0지하역사대구은행역달구벌대로 2300(수성4가)2023-12-12
1지하역사범어역달구벌대로 2396(범어동)2023-12-12
2지하역사수성구청역달구벌대로 2500(범어동)2023-12-12
3지하역사만촌역달구벌대로 2584(만촌동)2023-12-12
4지하역사담티역달구벌대로 2672(만촌동)2023-12-12
5지하역사연호역달구벌대로 2870(연호동)2023-12-12
6지하역사대공원역달구벌대로 2950(연호동)2023-12-12
7지하역사고산역달구벌대로 3080(시지동)2023-12-12
8지하역사신매역달구벌대로 3180(매호동)2023-12-12
9지하역사사월역달구벌대로 3290(신매동)2023-12-12
시설군시설명소재지데이터기준일자
131노인요양시설대구재활요양원만촌로 93(만촌동)2023-12-12
132노인요양시설더행복요양원천을로 199(매호동)2023-12-12
133노인요양시설수성윤정실버타운희망로 93-2(중동)2023-12-12
134산후조리원여성메디파크 산후조리원동대구로 271(범어동)2023-12-12
135산후조리원지온산후조리원청수로 166(지산동)2023-12-12
136산후조리원김혜정산후조리원명덕로 432(수성3가)2023-12-12
137산후조리원엘리자벳산후조리원무학로 25(상동)2023-12-12
138산후조리원본느마망 신관 산후조리원수성로38길 33(중동)2023-12-12
139산후조리원시엘 산후조리원달구벌대로 3316(신매동)2023-12-12
140산후조리원W 산후조리원화랑로 58(만촌동) 5,6층2023-12-12