Overview

Dataset statistics

Number of variables7
Number of observations286
Missing cells314
Missing cells (%)15.7%
Duplicate rows6
Duplicate rows (%)2.1%
Total size in memory15.8 KiB
Average record size in memory56.5 B

Variable types

Unsupported6
Text1

Dataset

Description경상남도 남해군 행정리 인구현황에 대한 데이터로서 마을별, 세대별, 남자인원수,여자인원수 , 65세이상 고령자 수 등의 항목을 제공합니다.
Author경상남도 남해군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15033270

Alerts

Dataset has 6 (2.1%) duplicate rowsDuplicates
연 별 및 마을별 has 33 (11.5%) missing valuesMissing
Unnamed: 1 has 64 (22.4%) missing valuesMissing
세 대 No. of Households has 48 (16.8%) missing valuesMissing
인 구 Population has 42 (14.7%) missing valuesMissing
Unnamed: 4 has 41 (14.3%) missing valuesMissing
Unnamed: 5 has 38 (13.3%) missing valuesMissing
65세 이상 고령자 Person 65 years old and over has 48 (16.8%) missing valuesMissing
연 별 및 마을별 is an unsupported type, check if it needs cleaning or further analysisUnsupported
세 대 No. of Households is an unsupported type, check if it needs cleaning or further analysisUnsupported
인 구 Population 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
65세 이상 고령자 Person 65 years old and over is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 23:56:32.341540
Analysis finished2023-12-10 23:56:32.782060
Duration0.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연 별 및 마을별
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing33
Missing (%)11.5%
Memory size2.4 KiB

Unnamed: 1
Text

MISSING 

Distinct217
Distinct (%)97.7%
Missing64
Missing (%)22.4%
Memory size2.4 KiB
2023-12-11T08:56:33.026002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length6.7567568
Min length3

Characters and Unicode

Total characters1500
Distinct characters43
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique212 ?
Unique (%)95.5%

Sample

1st rowBukbyeon 1
2nd rowBukbyeon 2
3rd rowYurim 1
4th rowYurim 2
5th rowHyeondae
ValueCountFrequency (%)
jijok 4
 
1.7%
1 3
 
1.3%
2 3
 
1.3%
danghang 2
 
0.9%
yangji 2
 
0.9%
yurim 2
 
0.9%
bukbyeon 2
 
0.9%
dosan 2
 
0.9%
daegok 2
 
0.9%
nogu 2
 
0.9%
Other values (205) 205
89.5%
2023-12-11T08:56:33.469128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 203
13.5%
n 200
13.3%
g 133
 
8.9%
a 133
 
8.9%
e 132
 
8.8%
u 69
 
4.6%
h 58
 
3.9%
m 51
 
3.4%
y 41
 
2.7%
k 38
 
2.5%
Other values (33) 442
29.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1264
84.3%
Uppercase Letter 214
 
14.3%
Decimal Number 14
 
0.9%
Space Separator 8
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 203
16.1%
n 200
15.8%
g 133
10.5%
a 133
10.5%
e 132
10.4%
u 69
 
5.5%
h 58
 
4.6%
m 51
 
4.0%
y 41
 
3.2%
k 38
 
3.0%
Other values (11) 206
16.3%
Uppercase Letter
ValueCountFrequency (%)
S 38
17.8%
D 33
15.4%
G 29
13.6%
J 19
8.9%
N 17
7.9%
Y 16
7.5%
B 14
 
6.5%
H 9
 
4.2%
M 8
 
3.7%
P 6
 
2.8%
Other values (8) 25
11.7%
Decimal Number
ValueCountFrequency (%)
1 7
50.0%
2 6
42.9%
3 1
 
7.1%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1478
98.5%
Common 22
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 203
13.7%
n 200
13.5%
g 133
 
9.0%
a 133
 
9.0%
e 132
 
8.9%
u 69
 
4.7%
h 58
 
3.9%
m 51
 
3.5%
y 41
 
2.8%
k 38
 
2.6%
Other values (29) 420
28.4%
Common
ValueCountFrequency (%)
8
36.4%
1 7
31.8%
2 6
27.3%
3 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 203
13.5%
n 200
13.3%
g 133
 
8.9%
a 133
 
8.9%
e 132
 
8.8%
u 69
 
4.6%
h 58
 
3.9%
m 51
 
3.4%
y 41
 
2.7%
k 38
 
2.5%
Other values (33) 442
29.5%

세 대 No. of Households
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing48
Missing (%)16.8%
Memory size2.4 KiB

인 구 Population
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing42
Missing (%)14.7%
Memory size2.4 KiB

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing41
Missing (%)14.3%
Memory size2.4 KiB

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38
Missing (%)13.3%
Memory size2.4 KiB

65세 이상 고령자 Person 65 years old and over
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing48
Missing (%)16.8%
Memory size2.4 KiB

Missing values

2023-12-11T08:56:32.449419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:56:32.561721image/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.
2023-12-11T08:56:32.692675image/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

연 별 및 마을별Unnamed: 1세 대 No. of Households인 구 PopulationUnnamed: 4Unnamed: 565세 이상 고령자 Person 65 years old and over
0NaN<NA>NaN총수\nTotalNaNNaNNaN
1NaN<NA>NaNNaN남여NaN
2NaN<NA>NaNNaNMaleFemaleNaN
32012<NA>2247748223229862523715308
4NaN<NA>NaNNaNNaNNaNNaN
5남해읍\nNamhae-Eup<NA>568413816673270842406
6북변1Bukbyeon 1277651306345111
7북변2Bukbyeon 224347724723099
8유림1Yurim 14771215593618193
9유림2Yurim 2383990485505172
연 별 및 마을별Unnamed: 1세 대 No. of Households인 구 PopulationUnnamed: 4Unnamed: 565세 이상 고령자 Person 65 years old and over
276부 윤2Buyun267145707545
277오 용Oyong58102475556
278연 곡Yeongok961878510276
279식 포Sikpo2347272025
280언 포Eonpo3767323530
281고 두Godu4895435237
282가 인Gain75152708260
283적 량Jeokrang11722410811699
284대 곡Daegok4082384434
285장 포Jangpo161322149173118

Duplicate rows

Most frequently occurring

Unnamed: 1# duplicates
5<NA>64
0Daegok2
1Danghang2
2Dosan2
3Nogu2
4Yangji2