Overview

Dataset statistics

Number of variables7
Number of observations141
Missing cells284
Missing cells (%)28.8%
Duplicate rows5
Duplicate rows (%)3.5%
Total size in memory8.0 KiB
Average record size in memory57.9 B

Variable types

Unsupported5
Text2

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15052/S/1/datasetView.do

Alerts

Dataset has 5 (3.5%) duplicate rowsDuplicates
Unnamed: 0 has 141 (100.0%) missing valuesMissing
Unnamed: 3 has 2 (1.4%) missing valuesMissing
Unnamed: 5 has 4 (2.8%) missing valuesMissing
Unnamed: 6 has 135 (95.7%) missing valuesMissing
Unnamed: 0 is an unsupported type, check if it needs cleaning or further analysisUnsupported
경로당 현황(2024.기준) is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-13 12:57:42.642192
Analysis finished2024-03-13 12:57:43.205497
Duration0.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing141
Missing (%)100.0%
Memory size1.4 KiB

경로당 현황(2024.기준)
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.7%
Memory size1.2 KiB

Unnamed: 2
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.2 KiB

Unnamed: 3
Text

MISSING 

Distinct135
Distinct (%)97.1%
Missing2
Missing (%)1.4%
Memory size1.2 KiB
2024-03-13T21:57:43.445185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length32
Mean length24.230216
Min length3

Characters and Unicode

Total characters3368
Distinct characters138
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique131 ?
Unique (%)94.2%

Sample

1st row주 소
2nd row강동구 아리수로93가길 25(강일동, 강일리버파크1단지아파트)
3rd row강동구 아리수로97길 68(강일동, 강일리버파크2단지아파트)
4th row강동구 아리수로93길 40(강일동, 강일리버파크3단지아파트)
5th row강동구 아리수로97길 19(강일동, 강일리버파크4단지아파트)
ValueCountFrequency (%)
강동구 135
26.1%
고덕로 12
 
2.3%
천호대로 7
 
1.4%
상암로 7
 
1.4%
명일로 6
 
1.2%
풍성로 4
 
0.8%
진황도로 4
 
0.8%
현대아파트 3
 
0.6%
아리수로97길 3
 
0.6%
강동리버스트 3
 
0.6%
Other values (305) 334
64.5%
2024-03-13T21:57:43.961408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
388
 
11.5%
동 290
 
8.6%
강 175
 
5.2%
구 148
 
4.4%
로 133
 
3.9%
( 131
 
3.9%
) 131
 
3.9%
1 126
 
3.7%
길 101
 
3.0%
, 90
 
2.7%
Other values (128) 1655
49.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2023
60.1%
Decimal Number 583
 
17.3%
Space Separator 388
 
11.5%
Open Punctuation 131
 
3.9%
Close Punctuation 131
 
3.9%
Other Punctuation 91
 
2.7%
Dash Punctuation 17
 
0.5%
Uppercase Letter 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 290
 
14.3%
강 175
 
8.7%
구 148
 
7.3%
로 133
 
6.6%
길 101
 
5.0%
아 87
 
4.3%
파 86
 
4.3%
트 69
 
3.4%
일 67
 
3.3%
천 53
 
2.6%
Other values (109) 814
40.2%
Decimal Number
ValueCountFrequency (%)
1 126
21.6%
2 81
13.9%
3 61
10.5%
9 60
10.3%
5 46
 
7.9%
6 45
 
7.7%
7 45
 
7.7%
0 44
 
7.5%
4 41
 
7.0%
8 34
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
S 2
50.0%
I 1
25.0%
G 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 90
98.9%
. 1
 
1.1%
Space Separator
ValueCountFrequency (%)
388
100.0%
Open Punctuation
ValueCountFrequency (%)
( 131
100.0%
Close Punctuation
ValueCountFrequency (%)
) 131
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2023
60.1%
Common 1341
39.8%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 290
 
14.3%
강 175
 
8.7%
구 148
 
7.3%
로 133
 
6.6%
길 101
 
5.0%
아 87
 
4.3%
파 86
 
4.3%
트 69
 
3.4%
일 67
 
3.3%
천 53
 
2.6%
Other values (109) 814
40.2%
Common
ValueCountFrequency (%)
388
28.9%
( 131
 
9.8%
) 131
 
9.8%
1 126
 
9.4%
, 90
 
6.7%
2 81
 
6.0%
3 61
 
4.5%
9 60
 
4.5%
5 46
 
3.4%
6 45
 
3.4%
Other values (6) 182
13.6%
Latin
ValueCountFrequency (%)
S 2
50.0%
I 1
25.0%
G 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2023
60.1%
ASCII 1345
39.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
388
28.8%
( 131
 
9.7%
) 131
 
9.7%
1 126
 
9.4%
, 90
 
6.7%
2 81
 
6.0%
3 61
 
4.5%
9 60
 
4.5%
5 46
 
3.4%
6 45
 
3.3%
Other values (9) 186
13.8%
Hangul
ValueCountFrequency (%)
동 290
 
14.3%
강 175
 
8.7%
구 148
 
7.3%
로 133
 
6.6%
길 101
 
5.0%
아 87
 
4.3%
파 86
 
4.3%
트 69
 
3.4%
일 67
 
3.3%
천 53
 
2.6%
Other values (109) 814
40.2%

Unnamed: 4
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.7%
Memory size1.2 KiB

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4
Missing (%)2.8%
Memory size1.2 KiB

Unnamed: 6
Text

MISSING 

Distinct4
Distinct (%)66.7%
Missing135
Missing (%)95.7%
Memory size1.2 KiB
2024-03-13T21:57:44.153850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.3333333
Min length4

Characters and Unicode

Total characters38
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)33.3%

Sample

1st row비고(휴지)
2nd row휴지/2021
3rd row이름변경
4th row휴지/2023
5th row휴지/2021
ValueCountFrequency (%)
휴지/2021 2
33.3%
휴지/2023 2
33.3%
비고(휴지 1
16.7%
이름변경 1
16.7%
2024-03-13T21:57:44.493322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 8
21.1%
휴 5
13.2%
지 5
13.2%
/ 4
10.5%
0 4
10.5%
1 2
 
5.3%
3 2
 
5.3%
비 1
 
2.6%
고 1
 
2.6%
( 1
 
2.6%
Other values (5) 5
13.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16
42.1%
Other Letter 16
42.1%
Other Punctuation 4
 
10.5%
Open Punctuation 1
 
2.6%
Close Punctuation 1
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
휴 5
31.2%
지 5
31.2%
비 1
 
6.2%
고 1
 
6.2%
이 1
 
6.2%
름 1
 
6.2%
변 1
 
6.2%
경 1
 
6.2%
Decimal Number
ValueCountFrequency (%)
2 8
50.0%
0 4
25.0%
1 2
 
12.5%
3 2
 
12.5%
Other Punctuation
ValueCountFrequency (%)
/ 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22
57.9%
Hangul 16
42.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
휴 5
31.2%
지 5
31.2%
비 1
 
6.2%
고 1
 
6.2%
이 1
 
6.2%
름 1
 
6.2%
변 1
 
6.2%
경 1
 
6.2%
Common
ValueCountFrequency (%)
2 8
36.4%
/ 4
18.2%
0 4
18.2%
1 2
 
9.1%
3 2
 
9.1%
( 1
 
4.5%
) 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22
57.9%
Hangul 16
42.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 8
36.4%
/ 4
18.2%
0 4
18.2%
1 2
 
9.1%
3 2
 
9.1%
( 1
 
4.5%
) 1
 
4.5%
Hangul
ValueCountFrequency (%)
휴 5
31.2%
지 5
31.2%
비 1
 
6.2%
고 1
 
6.2%
이 1
 
6.2%
름 1
 
6.2%
변 1
 
6.2%
경 1
 
6.2%

Missing values

2024-03-13T21:57:42.826954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:57:42.982645image/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.
2024-03-13T21:57:43.121415image/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

Unnamed: 0경로당 현황(2024.기준)Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6
0<NA>NaN강동구<NA>NaNNaN<NA>
1<NA>연번경로당 명주 소소계경로당\n개설일비고(휴지)
2<NA>합계138<NA>4541NaN<NA>
3<NA>1강일리버파크1단지강동구 아리수로93가길 25(강일동, 강일리버파크1단지아파트)292009-08-12 00:00:00<NA>
4<NA>2강일리버파크2단지강동구 아리수로97길 68(강일동, 강일리버파크2단지아파트)262009-12-10 00:00:00<NA>
5<NA>3강일리버파크3단지강동구 아리수로93길 40(강일동, 강일리버파크3단지아파트)252009-08-28 00:00:00<NA>
6<NA>4강일리버파크4단지강동구 아리수로97길 19(강일동, 강일리버파크4단지아파트)292009-10-08 00:00:00<NA>
7<NA>5강일리버파크5단지강동구 아리수로97길 20(강일동, 강일리버파크5단지아파트)212010-04-29 00:00:00<NA>
8<NA>6강일리버파크6단지강동구 아리수로94길 19(강일동, 강일리버파크6단지아파트)302009-12-17 00:00:00<NA>
9<NA>7강일리버파크7단지강동구 아리수로98길 25(강일동, 강일리버파크7단지아파트)352009-11-25 00:00:00<NA>
Unnamed: 0경로당 현황(2024.기준)Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6
131<NA>129둔촌현대1차A재건축0NaN휴지/2021
132<NA>130둔촌현대3차A강동구 진황도로 211(둔촌동, 현대3차아파트)01989-07-01 00:00:00휴지/2023
133<NA>131둔촌현대4차A강동구 진황도로61길 7(둔촌동, 현대4차아파트)211996-12-21 00:00:00<NA>
134<NA>132신성둔촌미소지움1차A강동구 명일로 102(둔촌동, 신성둔촌미소지움)151998-08-06 00:00:00<NA>
135<NA>133신성둔촌미소지움2차A강동구 진황도로 212(둔촌동, 신성둔촌미소지움2차)192001-03-06 00:00:00<NA>
136<NA>134둔촌하이츠A강동구 명일로 113(둔촌동, 둔촌하이츠아파트)211998-10-13 00:00:00<NA>
137<NA>135둔촌동아A강동구 동남로49길 60-5(둔촌동, 동아아파트)191999-07-23 00:00:00<NA>
138<NA>136둔촌신동아A강동구 양재대로96길 79(둔촌동, 둔촌신동아아파트)142003-04-15 00:00:00<NA>
139<NA>137한솔솔파크강동구 천호대로198길 36(둔촌동, 둔촌한솔솔파크)212003-12-01 00:00:00<NA>
140<NA>138둔촌푸르지오A강동구 명일로 172(둔촌동, 둔촌푸르지오아파트)302010-12-08 00:00:00<NA>

Duplicate rows

Most frequently occurring

Unnamed: 3Unnamed: 6# duplicates
0강동구 고덕로 333(고덕동)<NA>2
1강동구 성안로25길 6(천호동, 삼성아파트)<NA>2
2강동구 아리수로50길 50(고덕동, 래미안힐스테이트)<NA>2
3강동구 천호대로 1055(천호동, 태영아파트)<NA>2
4<NA><NA>2