Overview

Dataset statistics

Number of variables9
Number of observations308
Missing cells84
Missing cells (%)3.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.4 KiB
Average record size in memory74.4 B

Variable types

Numeric2
Categorical1
Text3
Boolean3

Dataset

Description양산시 지능형홈시스템 운영되는 경로당 현황으로 주소 및 우편번호, 서비스 여부, 모니터링여부, 펫여부, 설치연도를 확인할 수 있습니다.
Author경상남도 양산시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15092009

Alerts

서비스여부 has constant value ""Constant
모니터링여부 has constant value ""Constant
펫여부 has constant value ""Constant
우편번호 is highly overall correlated with 읍면동High correlation
읍면동 is highly overall correlated with 우편번호High correlation
순번 has 77 (25.0%) missing valuesMissing

Reproduction

Analysis started2024-04-21 11:18:09.195267
Analysis finished2024-04-21 11:18:12.036120
Duration2.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

MISSING 

Distinct56
Distinct (%)24.2%
Missing77
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean16.532468
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-21T20:18:12.244222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median13
Q322.5
95-th percentile44.5
Maximum56
Range55
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation12.755818
Coefficient of variation (CV)0.77156167
Kurtosis0.8598968
Mean16.532468
Median Absolute Deviation (MAD)7
Skewness1.1469448
Sum3819
Variance162.7109
MonotonicityNot monotonic
2024-04-21T20:18:12.669922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 9
 
2.9%
2 9
 
2.9%
12 9
 
2.9%
11 9
 
2.9%
10 9
 
2.9%
9 9
 
2.9%
1 9
 
2.9%
7 9
 
2.9%
5 9
 
2.9%
4 9
 
2.9%
Other values (46) 141
45.8%
(Missing) 77
25.0%
ValueCountFrequency (%)
1 9
2.9%
2 9
2.9%
3 9
2.9%
4 9
2.9%
5 9
2.9%
6 9
2.9%
7 9
2.9%
8 9
2.9%
9 9
2.9%
10 9
2.9%
ValueCountFrequency (%)
56 1
0.3%
55 1
0.3%
54 1
0.3%
53 1
0.3%
52 1
0.3%
51 1
0.3%
50 1
0.3%
49 1
0.3%
48 1
0.3%
47 1
0.3%

읍면동
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
양산시(물금읍)
56 
양산시(상북면)
37 
양산시(동면)
26 
양산시(하북면)
25 
양산시(서창동)
25 
Other values (9)
139 

Length

Max length11
Median length8
Mean length7.9253247
Min length7

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row양산시(물금읍)
2nd row양산시(물금읍)
3rd row양산시(물금읍)
4th row양산시(물금읍)
5th row양산시(물금읍)

Common Values

ValueCountFrequency (%)
양산시(물금읍) 56
18.2%
양산시(상북면) 37
12.0%
양산시(동면) 26
8.4%
양산시(하북면) 25
8.1%
양산시(서창동) 25
8.1%
양산시(원동면) 24
7.8%
양산시(평산동) 21
 
6.8%
양산시(중앙동) 19
 
6.2%
양산시(삼성동) 18
 
5.8%
양산시(소주동) 17
 
5.5%
Other values (4) 40
13.0%

Length

2024-04-21T20:18:13.123169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
양산시(물금읍 56
18.2%
양산시(상북면 37
12.0%
양산시(동면 26
8.4%
양산시(하북면 25
8.1%
양산시(서창동 25
8.1%
양산시(원동면 24
7.8%
양산시(평산동 21
 
6.8%
양산시(중앙동 19
 
6.2%
양산시(삼성동 18
 
5.8%
양산시(소주동 17
 
5.5%
Other values (4) 40
13.0%

경로당명
Text

Distinct301
Distinct (%)98.0%
Missing1
Missing (%)0.3%
Memory size2.5 KiB
2024-04-21T20:18:14.047632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length7.7882736
Min length4

Characters and Unicode

Total characters2391
Distinct characters221
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

Unique296 ?
Unique (%)96.4%

Sample

1st row동부경로당
2nd row서부경로당
3rd row증산(상리)경로당
4th row남평경로당
5th row신기경로당
ValueCountFrequency (%)
서부경로당 3
 
1.0%
대성경로당 2
 
0.6%
평산경로당 2
 
0.6%
동부경로당 2
 
0.6%
남양산e 2
 
0.6%
중리경로당 2
 
0.6%
경로당 2
 
0.6%
대동2차경로당 1
 
0.3%
가촌(본리)경로당 1
 
0.3%
동신기경로당 1
 
0.3%
Other values (295) 295
94.2%
2024-04-21T20:18:15.431046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 310
 
13.0%
로 309
 
12.9%
당 306
 
12.8%
아 83
 
3.5%
트 78
 
3.3%
파 76
 
3.2%
동 57
 
2.4%
차 53
 
2.2%
대 30
 
1.3%
신 30
 
1.3%
Other values (211) 1059
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2248
94.0%
Decimal Number 81
 
3.4%
Open Punctuation 22
 
0.9%
Close Punctuation 22
 
0.9%
Space Separator 7
 
0.3%
Uppercase Letter 6
 
0.3%
Other Punctuation 3
 
0.1%
Lowercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 310
 
13.8%
로 309
 
13.7%
당 306
 
13.6%
아 83
 
3.7%
트 78
 
3.5%
파 76
 
3.4%
동 57
 
2.5%
차 53
 
2.4%
대 30
 
1.3%
신 30
 
1.3%
Other values (196) 916
40.7%
Decimal Number
ValueCountFrequency (%)
2 26
32.1%
1 19
23.5%
3 12
14.8%
5 7
 
8.6%
4 7
 
8.6%
6 4
 
4.9%
8 3
 
3.7%
7 3
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
L 3
50.0%
H 3
50.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Space Separator
ValueCountFrequency (%)
7
100.0%
Other Punctuation
ValueCountFrequency (%)
@ 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2248
94.0%
Common 135
 
5.6%
Latin 8
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 310
 
13.8%
로 309
 
13.7%
당 306
 
13.6%
아 83
 
3.7%
트 78
 
3.5%
파 76
 
3.4%
동 57
 
2.5%
차 53
 
2.4%
대 30
 
1.3%
신 30
 
1.3%
Other values (196) 916
40.7%
Common
ValueCountFrequency (%)
2 26
19.3%
( 22
16.3%
) 22
16.3%
1 19
14.1%
3 12
8.9%
5 7
 
5.2%
4 7
 
5.2%
7
 
5.2%
6 4
 
3.0%
@ 3
 
2.2%
Other values (2) 6
 
4.4%
Latin
ValueCountFrequency (%)
L 3
37.5%
H 3
37.5%
e 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2248
94.0%
ASCII 143
 
6.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 310
 
13.8%
로 309
 
13.7%
당 306
 
13.6%
아 83
 
3.7%
트 78
 
3.5%
파 76
 
3.4%
동 57
 
2.5%
차 53
 
2.4%
대 30
 
1.3%
신 30
 
1.3%
Other values (196) 916
40.7%
ASCII
ValueCountFrequency (%)
2 26
18.2%
( 22
15.4%
) 22
15.4%
1 19
13.3%
3 12
8.4%
5 7
 
4.9%
4 7
 
4.9%
7
 
4.9%
6 4
 
2.8%
@ 3
 
2.1%
Other values (5) 14
9.8%

경로당 주소
Text

Distinct306
Distinct (%)99.7%
Missing1
Missing (%)0.3%
Memory size2.5 KiB
2024-04-21T20:18:16.387680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length25
Mean length16.172638
Min length10

Characters and Unicode

Total characters4965
Distinct characters204
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

Unique305 ?
Unique (%)99.3%

Sample

1st row양산시 물금읍 학산2길 9-9
2nd row양산시 물금읍 화산길 51-15
3rd row양산시 물금읍 증산1길 31
4th row양산시 물금읍 남평길 6
5th row양산시 물금읍 가촌새터3길 7-27
ValueCountFrequency (%)
양산시 307
26.2%
물금읍 55
 
4.7%
상북면 37
 
3.2%
동면 26
 
2.2%
하북면 25
 
2.1%
원동면 24
 
2.1%
양주로 11
 
0.9%
11 8
 
0.7%
18 7
 
0.6%
7 7
 
0.6%
Other values (464) 663
56.7%
2024-04-21T20:18:17.799351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
876
 
17.6%
산 348
 
7.0%
양 326
 
6.6%
시 312
 
6.3%
1 259
 
5.2%
길 174
 
3.5%
로 135
 
2.7%
2 115
 
2.3%
면 112
 
2.3%
동 110
 
2.2%
Other values (194) 2198
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2964
59.7%
Space Separator 904
 
18.2%
Decimal Number 894
 
18.0%
Close Punctuation 79
 
1.6%
Open Punctuation 79
 
1.6%
Dash Punctuation 41
 
0.8%
Other Punctuation 3
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 348
 
11.7%
양 326
 
11.0%
시 312
 
10.5%
길 174
 
5.9%
로 135
 
4.6%
면 112
 
3.8%
동 110
 
3.7%
북 84
 
2.8%
금 73
 
2.5%
물 62
 
2.1%
Other values (177) 1228
41.4%
Decimal Number
ValueCountFrequency (%)
1 259
29.0%
2 115
12.9%
3 87
 
9.7%
5 83
 
9.3%
4 76
 
8.5%
7 68
 
7.6%
6 62
 
6.9%
8 53
 
5.9%
0 48
 
5.4%
9 43
 
4.8%
Space Separator
ValueCountFrequency (%)
876
96.9%
  28
 
3.1%
Close Punctuation
ValueCountFrequency (%)
) 79
100.0%
Open Punctuation
ValueCountFrequency (%)
( 79
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41
100.0%
Other Punctuation
ValueCountFrequency (%)
@ 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2964
59.7%
Common 2000
40.3%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 348
 
11.7%
양 326
 
11.0%
시 312
 
10.5%
길 174
 
5.9%
로 135
 
4.6%
면 112
 
3.8%
동 110
 
3.7%
북 84
 
2.8%
금 73
 
2.5%
물 62
 
2.1%
Other values (177) 1228
41.4%
Common
ValueCountFrequency (%)
876
43.8%
1 259
 
13.0%
2 115
 
5.8%
3 87
 
4.3%
5 83
 
4.2%
) 79
 
4.0%
( 79
 
4.0%
4 76
 
3.8%
7 68
 
3.4%
6 62
 
3.1%
Other values (6) 216
 
10.8%
Latin
ValueCountFrequency (%)
e 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2964
59.7%
ASCII 1973
39.7%
None 28
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
876
44.4%
1 259
 
13.1%
2 115
 
5.8%
3 87
 
4.4%
5 83
 
4.2%
) 79
 
4.0%
( 79
 
4.0%
4 76
 
3.9%
7 68
 
3.4%
6 62
 
3.1%
Other values (6) 189
 
9.6%
Hangul
ValueCountFrequency (%)
산 348
 
11.7%
양 326
 
11.0%
시 312
 
10.5%
길 174
 
5.9%
로 135
 
4.6%
면 112
 
3.8%
동 110
 
3.7%
북 84
 
2.8%
금 73
 
2.5%
물 62
 
2.1%
Other values (177) 1228
41.4%
None
ValueCountFrequency (%)
  28
100.0%

우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct139
Distinct (%)45.3%
Missing1
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean50577.691
Minimum50500
Maximum50658
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-04-21T20:18:18.200093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50500
5-th percentile50504.3
Q150541
median50580
Q350613.5
95-th percentile50652.7
Maximum50658
Range158
Interquartile range (IQR)72.5

Descriptive statistics

Standard deviation46.055934
Coefficient of variation (CV)0.0009105978
Kurtosis-1.0553703
Mean50577.691
Median Absolute Deviation (MAD)37
Skewness0.022836857
Sum15527351
Variance2121.149
MonotonicityNot monotonic
2024-04-21T20:18:18.655602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50590 8
 
2.6%
50557 7
 
2.3%
50589 6
 
1.9%
50603 5
 
1.6%
50585 5
 
1.6%
50580 5
 
1.6%
50508 5
 
1.6%
50653 5
 
1.6%
50536 4
 
1.3%
50622 4
 
1.3%
Other values (129) 253
82.1%
ValueCountFrequency (%)
50500 4
1.3%
50501 3
1.0%
50502 4
1.3%
50503 3
1.0%
50504 2
 
0.6%
50505 2
 
0.6%
50506 1
 
0.3%
50507 2
 
0.6%
50508 5
1.6%
50509 3
1.0%
ValueCountFrequency (%)
50658 2
 
0.6%
50656 4
1.3%
50655 3
1.0%
50654 2
 
0.6%
50653 5
1.6%
50652 3
1.0%
50651 3
1.0%
50650 2
 
0.6%
50649 2
 
0.6%
50647 3
1.0%

서비스여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing1
Missing (%)0.3%
Memory size744.0 B
True
307 
(Missing)
 
1
ValueCountFrequency (%)
True 307
99.7%
(Missing) 1
 
0.3%
2024-04-21T20:18:19.167851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

모니터링여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing1
Missing (%)0.3%
Memory size744.0 B
True
307 
(Missing)
 
1
ValueCountFrequency (%)
True 307
99.7%
(Missing) 1
 
0.3%
2024-04-21T20:18:19.297403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

펫여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing1
Missing (%)0.3%
Memory size744.0 B
True
307 
(Missing)
 
1
ValueCountFrequency (%)
True 307
99.7%
(Missing) 1
 
0.3%
2024-04-21T20:18:19.430310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

설치연도
Text

Distinct52
Distinct (%)16.9%
Missing1
Missing (%)0.3%
Memory size2.5 KiB
2024-04-21T20:18:20.013902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.1335505
Min length2

Characters and Unicode

Total characters1269
Distinct characters11
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

Unique13 ?
Unique (%)4.2%

Sample

1st row1989
2nd row1989
3rd row1989
4th row1989
5th row1989
ValueCountFrequency (%)
1989 26
 
8.5%
2008 14
 
4.6%
1998 14
 
4.6%
2000 12
 
3.9%
1997 12
 
3.9%
2003 12
 
3.9%
1994 12
 
3.9%
1989년 11
 
3.6%
2017 10
 
3.3%
2012 10
 
3.3%
Other values (42) 174
56.7%
2024-04-21T20:18:20.875205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 303
23.9%
0 286
22.5%
1 217
17.1%
2 190
15.0%
8 84
 
6.6%
년 47
 
3.7%
3 37
 
2.9%
7 32
 
2.5%
4 30
 
2.4%
5 25
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1222
96.3%
Other Letter 47
 
3.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 303
24.8%
0 286
23.4%
1 217
17.8%
2 190
15.5%
8 84
 
6.9%
3 37
 
3.0%
7 32
 
2.6%
4 30
 
2.5%
5 25
 
2.0%
6 18
 
1.5%
Other Letter
ValueCountFrequency (%)
년 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1222
96.3%
Hangul 47
 
3.7%

Most frequent character per script

Common
ValueCountFrequency (%)
9 303
24.8%
0 286
23.4%
1 217
17.8%
2 190
15.5%
8 84
 
6.9%
3 37
 
3.0%
7 32
 
2.6%
4 30
 
2.5%
5 25
 
2.0%
6 18
 
1.5%
Hangul
ValueCountFrequency (%)
년 47
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1222
96.3%
Hangul 47
 
3.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 303
24.8%
0 286
23.4%
1 217
17.8%
2 190
15.5%
8 84
 
6.9%
3 37
 
3.0%
7 32
 
2.6%
4 30
 
2.5%
5 25
 
2.0%
6 18
 
1.5%
Hangul
ValueCountFrequency (%)
년 47
100.0%

Interactions

2024-04-21T20:18:10.366379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:18:09.861578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:18:10.620682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:18:10.099742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T20:18:21.048358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번읍면동우편번호설치연도
순번1.0000.3770.3960.924
읍면동0.3771.0000.9310.806
우편번호0.3960.9311.0000.746
설치연도0.9240.8060.7461.000
2024-04-21T20:18:21.297993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번우편번호읍면동
순번1.0000.1590.186
우편번호0.1591.0000.723
읍면동0.1860.7231.000

Missing values

2024-04-21T20:18:10.997808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T20:18:11.444386image/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.
2024-04-21T20:18:11.809144image/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

순번읍면동경로당명경로당 주소우편번호서비스여부모니터링여부펫여부설치연도
01양산시(물금읍)동부경로당양산시 물금읍 학산2길 9-950603YYY1989
12양산시(물금읍)서부경로당양산시 물금읍 화산길 51-1550603YYY1989
23양산시(물금읍)증산(상리)경로당양산시 물금읍 증산1길 3150614YYY1989
34양산시(물금읍)남평경로당양산시 물금읍 남평길 650614YYY1989
45양산시(물금읍)신기경로당양산시 물금읍 가촌새터3길 7-2750603YYY1989
56양산시(물금읍)가촌(본리)경로당양산시 물금읍 가촌본리1길 950603YYY1993
67양산시(물금읍)서남경로당양산시 물금읍 서남4길 850599YYY1989
78양산시(물금읍)황전아파트경로당양산시 물금읍 오봉로 1550601YYY1995
89양산시(물금읍)덕산아파트경로당양산시 물금읍 오봉로 2950601YYY1994
910양산시(물금읍)동중경로당양산시 물금읍 동중5길 24-150597YYY1989
순번읍면동경로당명경로당 주소우편번호서비스여부모니터링여부펫여부설치연도
298<NA>양산시(덕계동)외산경로당양산시 웅상대로 86650538YYY1994년
299<NA>양산시(덕계동)대승1차경로당양산시 덕계북길 9 (대승하이아트2차아파트)50537YYY1994년
300<NA>양산시(덕계동)대승2차경로당양산시 매곡외산로 216 (매곡동)50540YYY1998년
301<NA>양산시(덕계동)매곡경로당양산시 덕계5길 14 (동일스위트2차아파트)50555YYY2003년
302<NA>양산시(덕계동)동일2차경로당양산시 덕계7길 6-77 (덕계동)50554YYY1989년
303<NA>양산시(덕계동)삼성경로당양산시 덕계2길 5-21 (세신주상복합형@)2동707호50556YYY2007
304<NA>양산시(덕계동)덕계시장세신상가아파트경로당양산시 덕계로 105 (웅상프라자)50553YYY2010
305<NA>양산시(덕계동)웅상쇼핑타운경로당양산시 덕계5길 7-6650554YYY2012
306<NA>양산시(덕계동)덕계2마을경로당양산시 신덕계로3450538YYY2014
307<NA>양산시(덕계동)경동스마트홈경로당<NA><NA><NA><NA><NA><NA>