Overview

Dataset statistics

Number of variables4
Number of observations30
Missing cells44
Missing cells (%)36.7%
Duplicate rows1
Duplicate rows (%)3.3%
Total size in memory1.1 KiB
Average record size in memory36.4 B

Variable types

Text4

Alerts

Dataset has 1 (3.3%) duplicate rowsDuplicates
가정폭력상담소현황 has 24 (80.0%) missing valuesMissing
Unnamed: 1 has 1 (3.3%) missing valuesMissing
Unnamed: 2 has 15 (50.0%) missing valuesMissing
Unnamed: 3 has 4 (13.3%) missing valuesMissing

Reproduction

Analysis started2024-03-14 02:40:01.141037
Analysis finished2024-03-14 02:40:01.541009
Duration0.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

가정폭력상담소현황
Text

MISSING 

Distinct6
Distinct (%)100.0%
Missing24
Missing (%)80.0%
Memory size372.0 B
2024-03-14T11:40:01.631653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.3333333
Min length3

Characters and Unicode

Total characters20
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)100.0%

Sample

1st row시군명
2nd row전주시
3rd row군산시
4th row익산시
5th row정읍시
ValueCountFrequency (%)
시군명 1
12.5%
전주시 1
12.5%
군산시 1
12.5%
익산시 1
12.5%
정읍시 1
12.5%
남 1
12.5%
원 1
12.5%
시 1
12.5%
2024-03-14T11:40:01.929984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 6
30.0%
군 2
 
10.0%
산 2
 
10.0%
2
 
10.0%
명 1
 
5.0%
전 1
 
5.0%
주 1
 
5.0%
익 1
 
5.0%
정 1
 
5.0%
읍 1
 
5.0%
Other values (2) 2
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18
90.0%
Space Separator 2
 
10.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 6
33.3%
군 2
 
11.1%
산 2
 
11.1%
명 1
 
5.6%
전 1
 
5.6%
주 1
 
5.6%
익 1
 
5.6%
정 1
 
5.6%
읍 1
 
5.6%
남 1
 
5.6%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18
90.0%
Common 2
 
10.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 6
33.3%
군 2
 
11.1%
산 2
 
11.1%
명 1
 
5.6%
전 1
 
5.6%
주 1
 
5.6%
익 1
 
5.6%
정 1
 
5.6%
읍 1
 
5.6%
남 1
 
5.6%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18
90.0%
ASCII 2
 
10.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 6
33.3%
군 2
 
11.1%
산 2
 
11.1%
명 1
 
5.6%
전 1
 
5.6%
주 1
 
5.6%
익 1
 
5.6%
정 1
 
5.6%
읍 1
 
5.6%
남 1
 
5.6%
ASCII
ValueCountFrequency (%)
2
100.0%

Unnamed: 1
Text

MISSING 

Distinct18
Distinct (%)62.1%
Missing1
Missing (%)3.3%
Memory size372.0 B
2024-03-14T11:40:02.151941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length6.3793103
Min length1

Characters and Unicode

Total characters185
Distinct characters39
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)51.7%

Sample

1st row시 설 명
2nd row계
3rd row전주가정폭력
4th row상담소
5th row전주여성의전화부설
ValueCountFrequency (%)
가정폭력상담소 10
31.2%
상담소 2
 
6.2%
한국가정법률상담소 2
 
6.2%
익산 1
 
3.1%
명 1
 
3.1%
설 1
 
3.1%
시 1
 
3.1%
한국가정폭력 1
 
3.1%
ywca부설 1
 
3.1%
남원 1
 
3.1%
Other values (11) 11
34.4%
2024-03-14T11:40:02.407653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
정 17
 
9.2%
가 16
 
8.6%
상 16
 
8.6%
담 16
 
8.6%
소 16
 
8.6%
폭 13
 
7.0%
력 13
 
7.0%
부 8
 
4.3%
설 7
 
3.8%
전 5
 
2.7%
Other values (29) 58
31.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 177
95.7%
Space Separator 4
 
2.2%
Uppercase Letter 4
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 17
 
9.6%
가 16
 
9.0%
상 16
 
9.0%
담 16
 
9.0%
소 16
 
9.0%
폭 13
 
7.3%
력 13
 
7.3%
부 8
 
4.5%
설 7
 
4.0%
전 5
 
2.8%
Other values (24) 50
28.2%
Uppercase Letter
ValueCountFrequency (%)
C 1
25.0%
W 1
25.0%
Y 1
25.0%
A 1
25.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 177
95.7%
Common 4
 
2.2%
Latin 4
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 17
 
9.6%
가 16
 
9.0%
상 16
 
9.0%
담 16
 
9.0%
소 16
 
9.0%
폭 13
 
7.3%
력 13
 
7.3%
부 8
 
4.5%
설 7
 
4.0%
전 5
 
2.8%
Other values (24) 50
28.2%
Latin
ValueCountFrequency (%)
C 1
25.0%
W 1
25.0%
Y 1
25.0%
A 1
25.0%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 177
95.7%
ASCII 8
 
4.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
정 17
 
9.6%
가 16
 
9.0%
상 16
 
9.0%
담 16
 
9.0%
소 16
 
9.0%
폭 13
 
7.3%
력 13
 
7.3%
부 8
 
4.5%
설 7
 
4.0%
전 5
 
2.8%
Other values (24) 50
28.2%
ASCII
ValueCountFrequency (%)
4
50.0%
C 1
 
12.5%
W 1
 
12.5%
Y 1
 
12.5%
A 1
 
12.5%

Unnamed: 2
Text

MISSING 

Distinct15
Distinct (%)100.0%
Missing15
Missing (%)50.0%
Memory size372.0 B
2024-03-14T11:40:02.566780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0666667
Min length3

Characters and Unicode

Total characters46
Distinct characters33
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)100.0%

Sample

1st row시설장
2nd row13개소
3rd row김영수
4th row한선미
5th row서방선
ValueCountFrequency (%)
시설장 1
 
6.7%
13개소 1
 
6.7%
김영수 1
 
6.7%
한선미 1
 
6.7%
서방선 1
 
6.7%
이민규 1
 
6.7%
이현숙 1
 
6.7%
민인순 1
 
6.7%
김수기 1
 
6.7%
황정금 1
 
6.7%
Other values (5) 5
33.3%
2024-03-14T11:40:02.845409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 5
 
10.9%
숙 3
 
6.5%
민 2
 
4.3%
김 2
 
4.3%
미 2
 
4.3%
선 2
 
4.3%
수 2
 
4.3%
진 2
 
4.3%
인 2
 
4.3%
병 1
 
2.2%
Other values (23) 23
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 44
95.7%
Decimal Number 2
 
4.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 5
 
11.4%
숙 3
 
6.8%
민 2
 
4.5%
김 2
 
4.5%
미 2
 
4.5%
선 2
 
4.5%
수 2
 
4.5%
진 2
 
4.5%
인 2
 
4.5%
병 1
 
2.3%
Other values (21) 21
47.7%
Decimal Number
ValueCountFrequency (%)
3 1
50.0%
1 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 44
95.7%
Common 2
 
4.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 5
 
11.4%
숙 3
 
6.8%
민 2
 
4.5%
김 2
 
4.5%
미 2
 
4.5%
선 2
 
4.5%
수 2
 
4.5%
진 2
 
4.5%
인 2
 
4.5%
병 1
 
2.3%
Other values (21) 21
47.7%
Common
ValueCountFrequency (%)
3 1
50.0%
1 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 44
95.7%
ASCII 2
 
4.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 5
 
11.4%
숙 3
 
6.8%
민 2
 
4.5%
김 2
 
4.5%
미 2
 
4.5%
선 2
 
4.5%
수 2
 
4.5%
진 2
 
4.5%
인 2
 
4.5%
병 1
 
2.3%
Other values (21) 21
47.7%
ASCII
ValueCountFrequency (%)
3 1
50.0%
1 1
50.0%

Unnamed: 3
Text

MISSING 

Distinct26
Distinct (%)100.0%
Missing4
Missing (%)13.3%
Memory size372.0 B
2024-03-14T11:40:03.023978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length16
Mean length12
Min length5

Characters and Unicode

Total characters312
Distinct characters55
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st row소 재 지
2nd row(전화번호)
3rd row전주시 완산구 노송광장로 7
4th row( 244-0227 )
5th row전주시 완산구 동문길26 2층
ValueCountFrequency (%)
전주시 4
 
6.3%
군산시 3
 
4.8%
3
 
4.8%
완산구 3
 
4.8%
익산시 3
 
4.8%
남원시 2
 
3.2%
인북로 2
 
3.2%
7 2
 
3.2%
445-2285 1
 
1.6%
19 1
 
1.6%
Other values (39) 39
61.9%
2024-03-14T11:40:03.312482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37
 
11.9%
2 20
 
6.4%
1 19
 
6.1%
5 15
 
4.8%
- 15
 
4.8%
시 14
 
4.5%
) 14
 
4.5%
( 14
 
4.5%
3 13
 
4.2%
4 13
 
4.2%
Other values (45) 138
44.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 128
41.0%
Other Letter 104
33.3%
Space Separator 37
 
11.9%
Dash Punctuation 15
 
4.8%
Close Punctuation 14
 
4.5%
Open Punctuation 14
 
4.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 14
 
13.5%
로 10
 
9.6%
산 10
 
9.6%
전 5
 
4.8%
구 5
 
4.8%
익 4
 
3.8%
주 4
 
3.8%
길 4
 
3.8%
대 3
 
2.9%
원 3
 
2.9%
Other values (31) 42
40.4%
Decimal Number
ValueCountFrequency (%)
2 20
15.6%
1 19
14.8%
5 15
11.7%
3 13
10.2%
4 13
10.2%
8 12
9.4%
6 11
8.6%
7 10
7.8%
0 9
7.0%
9 6
 
4.7%
Space Separator
ValueCountFrequency (%)
37
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 208
66.7%
Hangul 104
33.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 14
 
13.5%
로 10
 
9.6%
산 10
 
9.6%
전 5
 
4.8%
구 5
 
4.8%
익 4
 
3.8%
주 4
 
3.8%
길 4
 
3.8%
대 3
 
2.9%
원 3
 
2.9%
Other values (31) 42
40.4%
Common
ValueCountFrequency (%)
37
17.8%
2 20
9.6%
1 19
9.1%
5 15
7.2%
- 15
7.2%
) 14
 
6.7%
( 14
 
6.7%
3 13
 
6.2%
4 13
 
6.2%
8 12
 
5.8%
Other values (4) 36
17.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 208
66.7%
Hangul 104
33.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37
17.8%
2 20
9.6%
1 19
9.1%
5 15
7.2%
- 15
7.2%
) 14
 
6.7%
( 14
 
6.7%
3 13
 
6.2%
4 13
 
6.2%
8 12
 
5.8%
Other values (4) 36
17.3%
Hangul
ValueCountFrequency (%)
시 14
 
13.5%
로 10
 
9.6%
산 10
 
9.6%
전 5
 
4.8%
구 5
 
4.8%
익 4
 
3.8%
주 4
 
3.8%
길 4
 
3.8%
대 3
 
2.9%
원 3
 
2.9%
Other values (31) 42
40.4%

Correlations

2024-03-14T11:40:03.388027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가정폭력상담소현황Unnamed: 1Unnamed: 2Unnamed: 3
가정폭력상담소현황1.0001.0001.0001.000
Unnamed: 11.0001.0001.0001.000
Unnamed: 21.0001.0001.0001.000
Unnamed: 31.0001.0001.0001.000

Missing values

2024-03-14T11:40:01.329769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:40:01.408686image/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.
2024-03-14T11:40:01.483531image/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

가정폭력상담소현황Unnamed: 1Unnamed: 2Unnamed: 3
0시군명시 설 명시설장소 재 지
1<NA><NA><NA>(전화번호)
2<NA>계13개소<NA>
3전주시전주가정폭력김영수전주시 완산구 노송광장로 7
4<NA>상담소<NA>( 244-0227 )
5<NA>전주여성의전화부설한선미전주시 완산구 동문길26 2층
6<NA>가정폭력상담소<NA>( 287-7324)
7<NA>새벽이슬서방선전주시 완산구 백제대로 167
8<NA>가정폭력상담소<NA>(223-3014)
9<NA>호남이민규전주시 덕진구 송천2길 7
가정폭력상담소현황Unnamed: 1Unnamed: 2Unnamed: 3
20<NA>익산여성의전화부설손인숙익산시 익산대로 172-1
21<NA>가정폭력상담소<NA>(858-9191)
22<NA>익산이병진익산시 인북로 48길 19
23<NA>가정폭력상담소<NA>(853-0909)
24정읍시한국가정법률상담소송미경정읍시 충정로 348 (535-8223)
25<NA>정읍지부<NA><NA>
26<NA>가정폭력상담소<NA><NA>
27남 원 시남원 YWCA부설이남진남원시 시청로 65
28<NA>가정폭력상담소<NA>(625-1318)
29<NA>남원가정폭력상담소이효숙남원시 요천로 1415(635-0712)

Duplicate rows

Most frequently occurring

가정폭력상담소현황Unnamed: 1Unnamed: 2Unnamed: 3# duplicates
0<NA>가정폭력상담소<NA><NA>2