Overview

Dataset statistics

Number of variables5
Number of observations1028
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory41.3 KiB
Average record size in memory41.1 B

Variable types

Numeric1
Categorical1
Text3

Dataset

Description창원시 소재 노인여가복지시설(경로당) 현황입니다.
Author경상남도 창원시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3077748

Alerts

연번 is highly overall correlated with 구청명High correlation
구청명 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:26:07.873630
Analysis finished2023-12-11 00:26:08.539902
Duration0.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1028
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean514.5
Minimum1
Maximum1028
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.2 KiB
2023-12-11T09:26:08.613960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile52.35
Q1257.75
median514.5
Q3771.25
95-th percentile976.65
Maximum1028
Range1027
Interquartile range (IQR)513.5

Descriptive statistics

Standard deviation296.90234
Coefficient of variation (CV)0.57706966
Kurtosis-1.2
Mean514.5
Median Absolute Deviation (MAD)257
Skewness0
Sum528906
Variance88151
MonotonicityStrictly increasing
2023-12-11T09:26:08.744160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
645 1
 
0.1%
679 1
 
0.1%
680 1
 
0.1%
681 1
 
0.1%
682 1
 
0.1%
683 1
 
0.1%
684 1
 
0.1%
685 1
 
0.1%
686 1
 
0.1%
Other values (1018) 1018
99.0%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1028 1
0.1%
1027 1
0.1%
1026 1
0.1%
1025 1
0.1%
1024 1
0.1%
1023 1
0.1%
1022 1
0.1%
1021 1
0.1%
1020 1
0.1%
1019 1
0.1%

구청명
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
진해구
193 
의창구
179 
마산회원구
153 
마산합포구
151 
마산합포구
147 
Other values (4)
205 

Length

Max length7
Median length5
Mean length5.1595331
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 의창구
2nd row 의창구
3rd row 의창구
4th row 의창구
5th row 의창구

Common Values

ValueCountFrequency (%)
진해구 193
18.8%
의창구 179
17.4%
마산회원구 153
14.9%
마산합포구 151
14.7%
마산합포구 147
14.3%
성산구 74
 
7.2%
의창구 66
 
6.4%
성산구 43
 
4.2%
마산회원구 22
 
2.1%

Length

2023-12-11T09:26:08.880013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:26:09.037500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
마산합포구 298
29.0%
의창구 245
23.8%
진해구 193
18.8%
마산회원구 175
17.0%
성산구 117
 
11.4%

읍면동명
Text

Distinct57
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
2023-12-11T09:26:09.347449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.0914397
Min length2

Characters and Unicode

Total characters4206
Distinct characters69
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

Unique0 ?
Unique (%)0.0%

Sample

1st row 동읍
2nd row 동읍
3rd row 동읍
4th row 동읍
5th row 동읍
ValueCountFrequency (%)
북면 70
 
6.8%
동읍 68
 
6.6%
내서읍 63
 
6.1%
대산면 51
 
5.0%
진전면 45
 
4.4%
월영동 35
 
3.4%
진동면 33
 
3.2%
구산면 30
 
2.9%
진북면 30
 
2.9%
웅동2동 25
 
2.4%
Other values (45) 578
56.2%
2023-12-11T09:26:09.754676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1124
26.7%
동 801
19.0%
면 259
 
6.2%
읍 131
 
3.1%
산 113
 
2.7%
진 108
 
2.6%
북 100
 
2.4%
웅 78
 
1.9%
2 72
 
1.7%
서 63
 
1.5%
Other values (59) 1357
32.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2963
70.4%
Space Separator 1124
 
26.7%
Decimal Number 119
 
2.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 801
27.0%
면 259
 
8.7%
읍 131
 
4.4%
산 113
 
3.8%
진 108
 
3.6%
북 100
 
3.4%
웅 78
 
2.6%
서 63
 
2.1%
내 63
 
2.1%
전 62
 
2.1%
Other values (56) 1185
40.0%
Decimal Number
ValueCountFrequency (%)
2 72
60.5%
1 47
39.5%
Space Separator
ValueCountFrequency (%)
1124
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2963
70.4%
Common 1243
29.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 801
27.0%
면 259
 
8.7%
읍 131
 
4.4%
산 113
 
3.8%
진 108
 
3.6%
북 100
 
3.4%
웅 78
 
2.6%
서 63
 
2.1%
내 63
 
2.1%
전 62
 
2.1%
Other values (56) 1185
40.0%
Common
ValueCountFrequency (%)
1124
90.4%
2 72
 
5.8%
1 47
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2963
70.4%
ASCII 1243
29.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1124
90.4%
2 72
 
5.8%
1 47
 
3.8%
Hangul
ValueCountFrequency (%)
동 801
27.0%
면 259
 
8.7%
읍 131
 
4.4%
산 113
 
3.8%
진 108
 
3.6%
북 100
 
3.4%
웅 78
 
2.6%
서 63
 
2.1%
내 63
 
2.1%
전 62
 
2.1%
Other values (56) 1185
40.0%

경로당명
Text

Distinct1008
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
2023-12-11T09:26:10.296962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length19
Mean length8.4912451
Min length5

Characters and Unicode

Total characters8729
Distinct characters336
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique991 ?
Unique (%)96.4%

Sample

1st row 고양 경로당
2nd row 곡목 경로당
3rd row 금동 경로당
4th row 금산 경로당
5th row 기로회 경로당
ValueCountFrequency (%)
경로당 82
 
7.2%
신촌경로당 3
 
0.3%
성원아파트경로당 3
 
0.3%
신기경로당 3
 
0.3%
중앙경로당 3
 
0.3%
중촌경로당 3
 
0.3%
무학경로당 3
 
0.3%
연동경로당 3
 
0.3%
대방경로당 3
 
0.3%
구암1동 2
 
0.2%
Other values (993) 1030
90.5%
2023-12-11T09:26:10.735808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 1043
 
11.9%
로 1038
 
11.9%
당 1029
 
11.8%
921
 
10.6%
성 256
 
2.9%
아 236
 
2.7%
파 223
 
2.6%
동 220
 
2.5%
트 215
 
2.5%
남 120
 
1.4%
Other values (326) 3428
39.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7488
85.8%
Space Separator 921
 
10.6%
Decimal Number 209
 
2.4%
Uppercase Letter 45
 
0.5%
Close Punctuation 25
 
0.3%
Open Punctuation 25
 
0.3%
Lowercase Letter 8
 
0.1%
Other Punctuation 7
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 1043
 
13.9%
로 1038
 
13.9%
당 1029
 
13.7%
성 256
 
3.4%
아 236
 
3.2%
파 223
 
3.0%
동 220
 
2.9%
트 215
 
2.9%
남 120
 
1.6%
여 114
 
1.5%
Other values (294) 2994
40.0%
Uppercase Letter
ValueCountFrequency (%)
A 17
37.8%
T 6
 
13.3%
P 4
 
8.9%
S 4
 
8.9%
H 4
 
8.9%
L 3
 
6.7%
C 2
 
4.4%
N 1
 
2.2%
F 1
 
2.2%
B 1
 
2.2%
Other values (2) 2
 
4.4%
Decimal Number
ValueCountFrequency (%)
1 83
39.7%
2 76
36.4%
3 29
 
13.9%
4 8
 
3.8%
5 6
 
2.9%
6 4
 
1.9%
0 1
 
0.5%
9 1
 
0.5%
7 1
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
e 4
50.0%
h 1
 
12.5%
i 1
 
12.5%
t 1
 
12.5%
y 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
. 5
71.4%
@ 2
 
28.6%
Space Separator
ValueCountFrequency (%)
921
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7488
85.8%
Common 1188
 
13.6%
Latin 53
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 1043
 
13.9%
로 1038
 
13.9%
당 1029
 
13.7%
성 256
 
3.4%
아 236
 
3.2%
파 223
 
3.0%
동 220
 
2.9%
트 215
 
2.9%
남 120
 
1.6%
여 114
 
1.5%
Other values (294) 2994
40.0%
Latin
ValueCountFrequency (%)
A 17
32.1%
T 6
 
11.3%
P 4
 
7.5%
S 4
 
7.5%
H 4
 
7.5%
e 4
 
7.5%
L 3
 
5.7%
C 2
 
3.8%
N 1
 
1.9%
F 1
 
1.9%
Other values (7) 7
13.2%
Common
ValueCountFrequency (%)
921
77.5%
1 83
 
7.0%
2 76
 
6.4%
3 29
 
2.4%
) 25
 
2.1%
( 25
 
2.1%
4 8
 
0.7%
5 6
 
0.5%
. 5
 
0.4%
6 4
 
0.3%
Other values (5) 6
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7488
85.8%
ASCII 1241
 
14.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 1043
 
13.9%
로 1038
 
13.9%
당 1029
 
13.7%
성 256
 
3.4%
아 236
 
3.2%
파 223
 
3.0%
동 220
 
2.9%
트 215
 
2.9%
남 120
 
1.6%
여 114
 
1.5%
Other values (294) 2994
40.0%
ASCII
ValueCountFrequency (%)
921
74.2%
1 83
 
6.7%
2 76
 
6.1%
3 29
 
2.3%
) 25
 
2.0%
( 25
 
2.0%
A 17
 
1.4%
4 8
 
0.6%
5 6
 
0.5%
T 6
 
0.5%
Other values (22) 45
 
3.6%

도로명주소
Text

Distinct884
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
2023-12-11T09:26:11.112254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length35
Mean length22.799611
Min length10

Characters and Unicode

Total characters23438
Distinct characters322
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

Unique743 ?
Unique (%)72.3%

Sample

1st row 의창구 동읍 백월로 540
2nd row 의창구 동읍 동읍로 359번길 43
3rd row 의창구 동읍 동읍로 835번길 7
4th row 의창구 동읍 동읍로 670번길 6
5th row 의창구 동읍 용잠로 45-5
ValueCountFrequency (%)
마산합포구 300
 
7.7%
의창구 259
 
6.6%
진해구 193
 
4.9%
마산회원구 172
 
4.4%
성산구 119
 
3.0%
북면 71
 
1.8%
동읍 64
 
1.6%
내서읍 63
 
1.6%
대산면 51
 
1.3%
진전면 44
 
1.1%
Other values (1551) 2577
65.9%
2023-12-11T09:26:11.715734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4464
 
19.0%
구 1152
 
4.9%
동 948
 
4.0%
1 908
 
3.9%
산 882
 
3.8%
길 705
 
3.0%
) 684
 
2.9%
( 684
 
2.9%
로 634
 
2.7%
2 542
 
2.3%
Other values (312) 11835
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13322
56.8%
Space Separator 4464
 
19.0%
Decimal Number 3754
 
16.0%
Close Punctuation 684
 
2.9%
Open Punctuation 684
 
2.9%
Other Punctuation 300
 
1.3%
Dash Punctuation 218
 
0.9%
Uppercase Letter 12
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 1152
 
8.6%
동 948
 
7.1%
산 882
 
6.6%
길 705
 
5.3%
로 634
 
4.8%
마 511
 
3.8%
진 377
 
2.8%
면 368
 
2.8%
포 347
 
2.6%
합 331
 
2.5%
Other values (289) 7067
53.0%
Decimal Number
ValueCountFrequency (%)
1 908
24.2%
2 542
14.4%
3 394
10.5%
5 352
 
9.4%
4 313
 
8.3%
8 264
 
7.0%
6 253
 
6.7%
0 244
 
6.5%
7 243
 
6.5%
9 241
 
6.4%
Uppercase Letter
ValueCountFrequency (%)
S 4
33.3%
H 2
16.7%
L 2
16.7%
D 1
 
8.3%
K 1
 
8.3%
T 1
 
8.3%
X 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
, 299
99.7%
. 1
 
0.3%
Space Separator
ValueCountFrequency (%)
4464
100.0%
Close Punctuation
ValueCountFrequency (%)
) 684
100.0%
Open Punctuation
ValueCountFrequency (%)
( 684
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 218
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13322
56.8%
Common 10104
43.1%
Latin 12
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 1152
 
8.6%
동 948
 
7.1%
산 882
 
6.6%
길 705
 
5.3%
로 634
 
4.8%
마 511
 
3.8%
진 377
 
2.8%
면 368
 
2.8%
포 347
 
2.6%
합 331
 
2.5%
Other values (289) 7067
53.0%
Common
ValueCountFrequency (%)
4464
44.2%
1 908
 
9.0%
) 684
 
6.8%
( 684
 
6.8%
2 542
 
5.4%
3 394
 
3.9%
5 352
 
3.5%
4 313
 
3.1%
, 299
 
3.0%
8 264
 
2.6%
Other values (6) 1200
 
11.9%
Latin
ValueCountFrequency (%)
S 4
33.3%
H 2
16.7%
L 2
16.7%
D 1
 
8.3%
K 1
 
8.3%
T 1
 
8.3%
X 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13322
56.8%
ASCII 10116
43.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4464
44.1%
1 908
 
9.0%
) 684
 
6.8%
( 684
 
6.8%
2 542
 
5.4%
3 394
 
3.9%
5 352
 
3.5%
4 313
 
3.1%
, 299
 
3.0%
8 264
 
2.6%
Other values (13) 1212
 
12.0%
Hangul
ValueCountFrequency (%)
구 1152
 
8.6%
동 948
 
7.1%
산 882
 
6.6%
길 705
 
5.3%
로 634
 
4.8%
마 511
 
3.8%
진 377
 
2.8%
면 368
 
2.8%
포 347
 
2.6%
합 331
 
2.5%
Other values (289) 7067
53.0%

Interactions

2023-12-11T09:26:08.298107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:26:11.825272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구청명읍면동명
연번1.0000.8960.995
구청명0.8961.0001.000
읍면동명0.9951.0001.000
2023-12-11T09:26:11.927116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구청명
연번1.0000.692
구청명0.6921.000

Missing values

2023-12-11T09:26:08.413610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:26:08.502838image/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

연번구청명읍면동명경로당명도로명주소
01의창구동읍고양 경로당의창구 동읍 백월로 540
12의창구동읍곡목 경로당의창구 동읍 동읍로 359번길 43
23의창구동읍금동 경로당의창구 동읍 동읍로 835번길 7
34의창구동읍금산 경로당의창구 동읍 동읍로 670번길 6
45의창구동읍기로회 경로당의창구 동읍 용잠로 45-5
56의창구동읍남산경로당의창구 동읍 용남길 10번길 74
67의창구동읍내단 경로당의창구 동읍 자여로 118번길 7
78의창구동읍노옥 경로당의창구 동읍노연로 143번길 12
89의창구동읍다호 경로당의창구 동읍 의창구 동읍으로 267번길 35
910의창구동읍단산경로당의창구 동읍 자여로 78번길 5
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