Overview

Dataset statistics

Number of variables6
Number of observations57
Missing cells1
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory51.3 B

Variable types

Categorical2
Text3
Numeric1

Dataset

Description부산광역시_동래구_노인복지시설현황_20221013
Author부산광역시 동래구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3079126

Alerts

구분 is highly overall correlated with 정원 and 1 other fieldsHigh correlation
비고 is highly overall correlated with 정원 and 1 other fieldsHigh correlation
정원 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
구분 is highly imbalanced (58.4%)Imbalance
비고 is highly imbalanced (58.4%)Imbalance
정원 has 1 (1.8%) missing valuesMissing
소재지 has unique valuesUnique
정원 has 38 (66.7%) zerosZeros

Reproduction

Analysis started2023-12-10 16:19:13.702396
Analysis finished2023-12-10 16:19:14.209543
Duration0.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size588.0 B
재가노인 복지시설
47 
요양시설
8 
양로시설
 
1
노인복지관
 
1

Length

Max length9
Median length9
Mean length8.1403509
Min length4

Unique

Unique2 ?
Unique (%)3.5%

Sample

1st row양로시설
2nd row요양시설
3rd row요양시설
4th row요양시설
5th row요양시설

Common Values

ValueCountFrequency (%)
재가노인 복지시설 47
82.5%
요양시설 8
 
14.0%
양로시설 1
 
1.8%
노인복지관 1
 
1.8%

Length

2023-12-11T01:19:14.268422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:19:14.360740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재가노인 47
45.2%
복지시설 47
45.2%
요양시설 8
 
7.7%
양로시설 1
 
1.0%
노인복지관 1
 
1.0%

시설명
Text

Distinct56
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size588.0 B
2023-12-11T01:19:14.563856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length9.6491228
Min length5

Characters and Unicode

Total characters550
Distinct characters121
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)96.5%

Sample

1st row황전양로원
2nd row황전요양원
3rd row무량수노인요양원
4th row성지노인요양원
5th row안심노인종합복지센터
ValueCountFrequency (%)
노인복지센터 3
 
4.2%
안심노인종합복지센터 2
 
2.8%
재가복지센터 2
 
2.8%
재가노인복지센터 2
 
2.8%
복지센터 2
 
2.8%
동래구노인복지관 2
 
2.8%
황금빛동행데이케어 1
 
1.4%
맘편한 1
 
1.4%
삐삐재가복지센터 1
 
1.4%
한울재가복지센터 1
 
1.4%
Other values (55) 55
76.4%
2023-12-11T01:19:14.952109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
센 47
 
8.5%
터 47
 
8.5%
지 40
 
7.3%
복 39
 
7.1%
재 30
 
5.5%
가 28
 
5.1%
노 20
 
3.6%
인 20
 
3.6%
15
 
2.7%
양 12
 
2.2%
Other values (111) 252
45.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 524
95.3%
Space Separator 15
 
2.7%
Decimal Number 6
 
1.1%
Close Punctuation 2
 
0.4%
Open Punctuation 2
 
0.4%
Uppercase Letter 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
센 47
 
9.0%
터 47
 
9.0%
지 40
 
7.6%
복 39
 
7.4%
재 30
 
5.7%
가 28
 
5.3%
노 20
 
3.8%
인 20
 
3.8%
양 12
 
2.3%
요 11
 
2.1%
Other values (102) 230
43.9%
Decimal Number
ValueCountFrequency (%)
0 2
33.3%
3 1
16.7%
1 1
16.7%
5 1
16.7%
6 1
16.7%
Space Separator
ValueCountFrequency (%)
15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 524
95.3%
Common 25
 
4.5%
Latin 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
센 47
 
9.0%
터 47
 
9.0%
지 40
 
7.6%
복 39
 
7.4%
재 30
 
5.7%
가 28
 
5.3%
노 20
 
3.8%
인 20
 
3.8%
양 12
 
2.3%
요 11
 
2.1%
Other values (102) 230
43.9%
Common
ValueCountFrequency (%)
15
60.0%
0 2
 
8.0%
) 2
 
8.0%
( 2
 
8.0%
3 1
 
4.0%
1 1
 
4.0%
5 1
 
4.0%
6 1
 
4.0%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 524
95.3%
ASCII 26
 
4.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
센 47
 
9.0%
터 47
 
9.0%
지 40
 
7.6%
복 39
 
7.4%
재 30
 
5.7%
가 28
 
5.3%
노 20
 
3.8%
인 20
 
3.8%
양 12
 
2.3%
요 11
 
2.1%
Other values (102) 230
43.9%
ASCII
ValueCountFrequency (%)
15
57.7%
0 2
 
7.7%
) 2
 
7.7%
( 2
 
7.7%
3 1
 
3.8%
A 1
 
3.8%
1 1
 
3.8%
5 1
 
3.8%
6 1
 
3.8%

소재지
Text

UNIQUE 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size588.0 B
2023-12-11T01:19:15.247787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length36
Mean length29.842105
Min length23

Characters and Unicode

Total characters1701
Distinct characters89
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)100.0%

Sample

1st row부산광역시 동래구 구만덕로172 (온천동)
2nd row부산광역시 동래구 구만덕로172 (온천동)
3rd row부산광역시 동래구 금정마을로59-6 (온천동)
4th row부산광역시 동래구 석사북로40-1 (사직동)
5th row부산광역시 동래구 충렬대로86번길5 (온천동)
ValueCountFrequency (%)
부산광역시 57
17.4%
동래구 57
17.4%
안락동 17
 
5.2%
온천동 14
 
4.3%
1층 10
 
3.1%
2층 8
 
2.4%
3층 8
 
2.4%
명장동 7
 
2.1%
명륜동 5
 
1.5%
사직동 5
 
1.5%
Other values (114) 139
42.5%
2023-12-11T01:19:15.680479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
271
 
15.9%
동 120
 
7.1%
시 63
 
3.7%
래 60
 
3.5%
구 59
 
3.5%
부 57
 
3.4%
로 57
 
3.4%
산 57
 
3.4%
( 57
 
3.4%
) 57
 
3.4%
Other values (79) 843
49.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 985
57.9%
Decimal Number 283
 
16.6%
Space Separator 271
 
15.9%
Open Punctuation 57
 
3.4%
Close Punctuation 57
 
3.4%
Other Punctuation 40
 
2.4%
Dash Punctuation 8
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 120
 
12.2%
시 63
 
6.4%
래 60
 
6.1%
구 59
 
6.0%
부 57
 
5.8%
로 57
 
5.8%
산 57
 
5.8%
역 57
 
5.8%
광 57
 
5.8%
길 36
 
3.7%
Other values (64) 362
36.8%
Decimal Number
ValueCountFrequency (%)
1 54
19.1%
2 44
15.5%
3 40
14.1%
0 27
9.5%
6 26
9.2%
4 21
 
7.4%
5 21
 
7.4%
9 18
 
6.4%
7 18
 
6.4%
8 14
 
4.9%
Space Separator
ValueCountFrequency (%)
271
100.0%
Open Punctuation
ValueCountFrequency (%)
( 57
100.0%
Close Punctuation
ValueCountFrequency (%)
) 57
100.0%
Other Punctuation
ValueCountFrequency (%)
, 40
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 985
57.9%
Common 716
42.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 120
 
12.2%
시 63
 
6.4%
래 60
 
6.1%
구 59
 
6.0%
부 57
 
5.8%
로 57
 
5.8%
산 57
 
5.8%
역 57
 
5.8%
광 57
 
5.8%
길 36
 
3.7%
Other values (64) 362
36.8%
Common
ValueCountFrequency (%)
271
37.8%
( 57
 
8.0%
) 57
 
8.0%
1 54
 
7.5%
2 44
 
6.1%
3 40
 
5.6%
, 40
 
5.6%
0 27
 
3.8%
6 26
 
3.6%
4 21
 
2.9%
Other values (5) 79
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 985
57.9%
ASCII 716
42.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
271
37.8%
( 57
 
8.0%
) 57
 
8.0%
1 54
 
7.5%
2 44
 
6.1%
3 40
 
5.6%
, 40
 
5.6%
0 27
 
3.8%
6 26
 
3.6%
4 21
 
2.9%
Other values (5) 79
 
11.0%
Hangul
ValueCountFrequency (%)
동 120
 
12.2%
시 63
 
6.4%
래 60
 
6.1%
구 59
 
6.0%
부 57
 
5.8%
로 57
 
5.8%
산 57
 
5.8%
역 57
 
5.8%
광 57
 
5.8%
길 36
 
3.7%
Other values (64) 362
36.8%

정원
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct17
Distinct (%)30.4%
Missing1
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean15.035714
Minimum0
Maximum120
Zeros38
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size645.0 B
2023-12-11T01:19:15.827740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q322.75
95-th percentile81
Maximum120
Range120
Interquartile range (IQR)22.75

Descriptive statistics

Standard deviation27.669454
Coefficient of variation (CV)1.8402487
Kurtosis3.9077936
Mean15.035714
Median Absolute Deviation (MAD)0
Skewness2.0639464
Sum842
Variance765.5987
MonotonicityNot monotonic
2023-12-11T01:19:15.960944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 38
66.7%
49 2
 
3.5%
22 2
 
3.5%
88 1
 
1.8%
43 1
 
1.8%
46 1
 
1.8%
27 1
 
1.8%
28 1
 
1.8%
68 1
 
1.8%
39 1
 
1.8%
Other values (7) 7
 
12.3%
ValueCountFrequency (%)
0 38
66.7%
9 1
 
1.8%
13 1
 
1.8%
22 2
 
3.5%
25 1
 
1.8%
27 1
 
1.8%
28 1
 
1.8%
30 1
 
1.8%
39 1
 
1.8%
43 1
 
1.8%
ValueCountFrequency (%)
120 1
1.8%
88 1
1.8%
84 1
1.8%
80 1
1.8%
68 1
1.8%
49 2
3.5%
46 1
1.8%
43 1
1.8%
39 1
1.8%
30 1
1.8%

전화번호
Text

Distinct55
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Memory size588.0 B
2023-12-11T01:19:16.237789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters684
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)93.0%

Sample

1st row051-556-3373
2nd row051-554-6661
3rd row051-552-7900
4th row051-506-0033
5th row051-503-0002
ValueCountFrequency (%)
051-503-0002 2
 
3.5%
051-554-6252 2
 
3.5%
051-918-5473 1
 
1.8%
051-557-4919 1
 
1.8%
051-521-3666 1
 
1.8%
051-552-3813 1
 
1.8%
051-556-3373 1
 
1.8%
051-524-0425 1
 
1.8%
051-333-3346 1
 
1.8%
051-717-2429 1
 
1.8%
Other values (45) 45
78.9%
2023-12-11T01:19:16.653368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 161
23.5%
- 114
16.7%
0 99
14.5%
1 78
11.4%
2 45
 
6.6%
7 42
 
6.1%
3 40
 
5.8%
4 29
 
4.2%
8 29
 
4.2%
9 26
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 570
83.3%
Dash Punctuation 114
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 161
28.2%
0 99
17.4%
1 78
13.7%
2 45
 
7.9%
7 42
 
7.4%
3 40
 
7.0%
4 29
 
5.1%
8 29
 
5.1%
9 26
 
4.6%
6 21
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 684
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 161
23.5%
- 114
16.7%
0 99
14.5%
1 78
11.4%
2 45
 
6.6%
7 42
 
6.1%
3 40
 
5.8%
4 29
 
4.2%
8 29
 
4.2%
9 26
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 161
23.5%
- 114
16.7%
0 99
14.5%
1 78
11.4%
2 45
 
6.6%
7 42
 
6.1%
3 40
 
5.8%
4 29
 
4.2%
8 29
 
4.2%
9 26
 
3.8%

비고
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size588.0 B
재가노인복지
47 
노인의료복지
8 
노인주거복지
 
1
노인여가복지
 
1

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique2 ?
Unique (%)3.5%

Sample

1st row노인주거복지
2nd row노인의료복지
3rd row노인의료복지
4th row노인의료복지
5th row노인의료복지

Common Values

ValueCountFrequency (%)
재가노인복지 47
82.5%
노인의료복지 8
 
14.0%
노인주거복지 1
 
1.8%
노인여가복지 1
 
1.8%

Length

2023-12-11T01:19:16.808537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:19:16.919137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재가노인복지 47
82.5%
노인의료복지 8
 
14.0%
노인주거복지 1
 
1.8%
노인여가복지 1
 
1.8%

Interactions

2023-12-11T01:19:13.970637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:19:16.995492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분시설명소재지정원전화번호비고
구분1.0000.8311.0000.9920.0001.000
시설명0.8311.0001.0000.0001.0000.831
소재지1.0001.0001.0001.0001.0001.000
정원0.9920.0001.0001.0000.0000.992
전화번호0.0001.0001.0000.0001.0000.000
비고1.0000.8311.0000.9920.0001.000
2023-12-11T01:19:17.097641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분비고
구분1.0001.000
비고1.0001.000
2023-12-11T01:19:17.202121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정원구분비고
정원1.0000.8400.840
구분0.8401.0001.000
비고0.8401.0001.000

Missing values

2023-12-11T01:19:14.074902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:19:14.172723image/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.

Sample

구분시설명소재지정원전화번호비고
0양로시설황전양로원부산광역시 동래구 구만덕로172 (온천동)88051-556-3373노인주거복지
1요양시설황전요양원부산광역시 동래구 구만덕로172 (온천동)84051-554-6661노인의료복지
2요양시설무량수노인요양원부산광역시 동래구 금정마을로59-6 (온천동)120051-552-7900노인의료복지
3요양시설성지노인요양원부산광역시 동래구 석사북로40-1 (사직동)9051-506-0033노인의료복지
4요양시설안심노인종합복지센터부산광역시 동래구 충렬대로86번길5 (온천동)13051-503-0002노인의료복지
5요양시설원광효마을요양원부산광역시 동래구 명륜로207번길 6(명륜동)49051-554-4438노인의료복지
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