Overview

Dataset statistics

Number of variables5
Number of observations244
Missing cells10
Missing cells (%)0.8%
Duplicate rows2
Duplicate rows (%)0.8%
Total size in memory9.7 KiB
Average record size in memory40.5 B

Variable types

Unsupported1
Categorical1
Text3

Alerts

Dataset has 2 (0.8%) duplicate rowsDuplicates
Unnamed: 4 has 7 (2.9%) missing valuesMissing
대구광역시 북구 관내 경로당 현황 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-21 16:33:48.587050
Analysis finished2024-04-21 16:33:49.612037
Duration1.02 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대구광역시 북구 관내 경로당 현황
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.4%
Memory size2.0 KiB

Unnamed: 1
Categorical

Distinct25
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
동천동
22 
구암동
21 
읍내동
20 
복현2동
18 
무태조야동
17 
Other values (20)
146 

Length

Max length5
Median length4
Mean length3.5655738
Min length2

Unique

Unique2 ?
Unique (%)0.8%

Sample

1st row<NA>
2nd row동명
3rd row고성동
4th row고성동
5th row고성동

Common Values

ValueCountFrequency (%)
동천동 22
 
9.0%
구암동 21
 
8.6%
읍내동 20
 
8.2%
복현2동 18
 
7.4%
무태조야동 17
 
7.0%
태전1동 16
 
6.6%
침산3동 15
 
6.1%
관문동 13
 
5.3%
태전2동 13
 
5.3%
침산2동 12
 
4.9%
Other values (15) 77
31.6%

Length

2024-04-22T01:33:49.737093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
동천동 22
 
9.0%
구암동 21
 
8.6%
읍내동 20
 
8.2%
복현2동 18
 
7.4%
무태조야동 17
 
7.0%
태전1동 16
 
6.6%
침산3동 15
 
6.1%
관문동 13
 
5.3%
태전2동 13
 
5.3%
침산2동 12
 
4.9%
Other values (15) 77
31.6%

Unnamed: 2
Text

Distinct238
Distinct (%)97.9%
Missing1
Missing (%)0.4%
Memory size2.0 KiB
2024-04-22T01:33:50.517643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length8.7654321
Min length4

Characters and Unicode

Total characters2130
Distinct characters201
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique233 ?
Unique (%)95.9%

Sample

1st row경로당명
2nd row고성1가 경로당
3rd row고성2·3가 경로당
4th row고성노우회 경로당
5th row고성A 경로당
ValueCountFrequency (%)
경로당 239
49.1%
침산 3
 
0.6%
동화타운 2
 
0.4%
칠곡네스빌 2
 
0.4%
영남네오빌아트 2
 
0.4%
삼성a 2
 
0.4%
칠곡7단지부영a 2
 
0.4%
태전협화a 1
 
0.2%
에덴팔공 1
 
0.2%
새마을 1
 
0.2%
Other values (232) 232
47.6%
2024-04-22T01:33:51.548418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
244
 
11.5%
경 244
 
11.5%
당 244
 
11.5%
로 243
 
11.4%
A 54
 
2.5%
동 40
 
1.9%
산 34
 
1.6%
성 33
 
1.5%
2 33
 
1.5%
1 30
 
1.4%
Other values (191) 931
43.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1725
81.0%
Space Separator 244
 
11.5%
Decimal Number 96
 
4.5%
Uppercase Letter 55
 
2.6%
Close Punctuation 3
 
0.1%
Open Punctuation 3
 
0.1%
Other Punctuation 3
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 244
 
14.1%
당 244
 
14.1%
로 243
 
14.1%
동 40
 
2.3%
산 34
 
2.0%
성 33
 
1.9%
칠 28
 
1.6%
곡 28
 
1.6%
타 27
 
1.6%
운 26
 
1.5%
Other values (175) 778
45.1%
Decimal Number
ValueCountFrequency (%)
2 33
34.4%
1 30
31.2%
3 20
20.8%
7 3
 
3.1%
8 3
 
3.1%
6 2
 
2.1%
5 2
 
2.1%
0 2
 
2.1%
4 1
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
A 54
98.2%
U 1
 
1.8%
Space Separator
ValueCountFrequency (%)
244
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Other Punctuation
ValueCountFrequency (%)
· 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1725
81.0%
Common 349
 
16.4%
Latin 56
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 244
 
14.1%
당 244
 
14.1%
로 243
 
14.1%
동 40
 
2.3%
산 34
 
2.0%
성 33
 
1.9%
칠 28
 
1.6%
곡 28
 
1.6%
타 27
 
1.6%
운 26
 
1.5%
Other values (175) 778
45.1%
Common
ValueCountFrequency (%)
244
69.9%
2 33
 
9.5%
1 30
 
8.6%
3 20
 
5.7%
7 3
 
0.9%
) 3
 
0.9%
8 3
 
0.9%
( 3
 
0.9%
· 3
 
0.9%
6 2
 
0.6%
Other values (3) 5
 
1.4%
Latin
ValueCountFrequency (%)
A 54
96.4%
e 1
 
1.8%
U 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1725
81.0%
ASCII 402
 
18.9%
None 3
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
244
60.7%
A 54
 
13.4%
2 33
 
8.2%
1 30
 
7.5%
3 20
 
5.0%
7 3
 
0.7%
) 3
 
0.7%
8 3
 
0.7%
( 3
 
0.7%
6 2
 
0.5%
Other values (5) 7
 
1.7%
Hangul
ValueCountFrequency (%)
경 244
 
14.1%
당 244
 
14.1%
로 243
 
14.1%
동 40
 
2.3%
산 34
 
2.0%
성 33
 
1.9%
칠 28
 
1.6%
곡 28
 
1.6%
타 27
 
1.6%
운 26
 
1.5%
Other values (175) 778
45.1%
None
ValueCountFrequency (%)
· 3
100.0%

Unnamed: 3
Text

Distinct236
Distinct (%)97.1%
Missing1
Missing (%)0.4%
Memory size2.0 KiB
2024-04-22T01:33:52.834415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length9.0288066
Min length3

Characters and Unicode

Total characters2194
Distinct characters60
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique229 ?
Unique (%)94.2%

Sample

1st row소재지
2nd row고성1가 157-13
3rd row고성3가 78-6
4th row고성3가 6-262
5th row고성2가 112-3
ValueCountFrequency (%)
태전동 26
 
5.3%
구암동 21
 
4.3%
읍내동 20
 
4.1%
복현2동 18
 
3.7%
침산3동 15
 
3.1%
침산2동 12
 
2.5%
동천동 11
 
2.3%
서변동 10
 
2.1%
칠성2가 7
 
1.4%
관음동 7
 
1.4%
Other values (264) 339
69.8%
2024-04-22T01:33:54.342074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
243
 
11.1%
동 239
 
10.9%
1 233
 
10.6%
2 165
 
7.5%
- 153
 
7.0%
3 137
 
6.2%
7 86
 
3.9%
6 83
 
3.8%
5 81
 
3.7%
9 80
 
3.6%
Other values (50) 694
31.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1065
48.5%
Other Letter 733
33.4%
Space Separator 243
 
11.1%
Dash Punctuation 153
 
7.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 239
32.6%
산 56
 
7.6%
침 33
 
4.5%
태 29
 
4.0%
현 29
 
4.0%
전 28
 
3.8%
가 22
 
3.0%
복 22
 
3.0%
암 21
 
2.9%
구 21
 
2.9%
Other values (38) 233
31.8%
Decimal Number
ValueCountFrequency (%)
1 233
21.9%
2 165
15.5%
3 137
12.9%
7 86
 
8.1%
6 83
 
7.8%
5 81
 
7.6%
9 80
 
7.5%
4 72
 
6.8%
0 66
 
6.2%
8 62
 
5.8%
Space Separator
ValueCountFrequency (%)
243
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 153
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1461
66.6%
Hangul 733
33.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 239
32.6%
산 56
 
7.6%
침 33
 
4.5%
태 29
 
4.0%
현 29
 
4.0%
전 28
 
3.8%
가 22
 
3.0%
복 22
 
3.0%
암 21
 
2.9%
구 21
 
2.9%
Other values (38) 233
31.8%
Common
ValueCountFrequency (%)
243
16.6%
1 233
15.9%
2 165
11.3%
- 153
10.5%
3 137
9.4%
7 86
 
5.9%
6 83
 
5.7%
5 81
 
5.5%
9 80
 
5.5%
4 72
 
4.9%
Other values (2) 128
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1461
66.6%
Hangul 733
33.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
243
16.6%
1 233
15.9%
2 165
11.3%
- 153
10.5%
3 137
9.4%
7 86
 
5.9%
6 83
 
5.7%
5 81
 
5.5%
9 80
 
5.5%
4 72
 
4.9%
Other values (2) 128
8.8%
Hangul
ValueCountFrequency (%)
동 239
32.6%
산 56
 
7.6%
침 33
 
4.5%
태 29
 
4.0%
현 29
 
4.0%
전 28
 
3.8%
가 22
 
3.0%
복 22
 
3.0%
암 21
 
2.9%
구 21
 
2.9%
Other values (38) 233
31.8%

Unnamed: 4
Text

MISSING 

Distinct233
Distinct (%)98.3%
Missing7
Missing (%)2.9%
Memory size2.0 KiB
2024-04-22T01:33:55.440362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.9873418
Min length4

Characters and Unicode

Total characters1893
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique229 ?
Unique (%)96.6%

Sample

1st row전화번호
2nd row354-9108
3rd row357-7265
4th row356-0038
5th row356-3868
ValueCountFrequency (%)
326-2223 2
 
0.8%
358-2819 2
 
0.8%
381-8365 2
 
0.8%
324-9156 2
 
0.8%
323-7444 1
 
0.4%
311-5890 1
 
0.4%
311-9760 1
 
0.4%
312-4743 1
 
0.4%
312-5047 1
 
0.4%
322-1284 1
 
0.4%
Other values (223) 223
94.1%
2024-04-22T01:33:56.743602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 320
16.9%
- 236
12.5%
2 227
12.0%
5 192
10.1%
1 169
8.9%
4 155
8.2%
9 147
7.8%
8 129
6.8%
6 124
 
6.6%
7 99
 
5.2%
Other values (5) 95
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1653
87.3%
Dash Punctuation 236
 
12.5%
Other Letter 4
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 320
19.4%
2 227
13.7%
5 192
11.6%
1 169
10.2%
4 155
9.4%
9 147
8.9%
8 129
7.8%
6 124
 
7.5%
7 99
 
6.0%
0 91
 
5.5%
Other Letter
ValueCountFrequency (%)
전 1
25.0%
화 1
25.0%
번 1
25.0%
호 1
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 236
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1889
99.8%
Hangul 4
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
3 320
16.9%
- 236
12.5%
2 227
12.0%
5 192
10.2%
1 169
8.9%
4 155
8.2%
9 147
7.8%
8 129
6.8%
6 124
 
6.6%
7 99
 
5.2%
Hangul
ValueCountFrequency (%)
전 1
25.0%
화 1
25.0%
번 1
25.0%
호 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1889
99.8%
Hangul 4
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 320
16.9%
- 236
12.5%
2 227
12.0%
5 192
10.2%
1 169
8.9%
4 155
8.2%
9 147
7.8%
8 129
6.8%
6 124
 
6.6%
7 99
 
5.2%
Hangul
ValueCountFrequency (%)
전 1
25.0%
화 1
25.0%
번 1
25.0%
호 1
25.0%

Missing values

2024-04-22T01:33:48.939838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-22T01:33:49.106919image/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.
2024-04-22T01:33:49.491332image/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

대구광역시 북구 관내 경로당 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4
0NaN<NA><NA><NA><NA>
1연번동명경로당명소재지전화번호
21고성동고성1가 경로당고성1가 157-13354-9108
32고성동고성2·3가 경로당고성3가 78-6357-7265
43고성동고성노우회 경로당고성3가 6-262356-0038
54고성동고성A 경로당고성2가 112-3356-3868
65고성동광명할머니 경로당고성3가 5-139355-6852
76고성동한마음 경로당고성1가 96-22359-2770
87칠성동북성 경로당칠성2가 144-19351-2196
98칠성동삼성A 경로당칠성2가 378-4356-7475
대구광역시 북구 관내 경로당 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4
234233동천동화성3차 경로당동천동 915325-5693
235234동천동부영e그린타운 경로당동천동 914326-4067
236235동천동영남네오빌아트 경로당동청동 913326-2223
237236동천동영남네오빌아트 경로당동청동 913326-2223
238237동천동학정청아람 경로당학정동 925<NA>
239238국우동국우 경로당국우동 1037-1321-4735
240239국우동그린빌1단지 경로당국우동 1110322-4535
241240국우동그린빌5단지 경로당국우동 1080323-0495
242241국우동부영1단지A 경로당국우동 1079325-8775
243242국우동칠곡현대A 경로당국우동 1110-3324-0173

Duplicate rows

Most frequently occurring

Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4# duplicates
0동천동영남네오빌아트 경로당동청동 913326-22232
1동천동칠곡네스빌 경로당동천동 890324-91562