Overview

Dataset statistics

Number of variables5
Number of observations342
Missing cells3
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.8 KiB
Average record size in memory41.4 B

Variable types

Numeric1
Categorical2
Text2

Dataset

Description경상남도 사천시 경로당 현황 자료입니다.(연번, 읍면동, 경로당명, 도로명주소) 2023년 5월 30일 기준으로 총341건입니다.
Author경상남도 사천시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15114240

Alerts

데이터기준일자 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
읍면동 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 is highly overall correlated with 읍면동 and 1 other fieldsHigh correlation
데이터기준일자 is highly imbalanced (97.1%)Imbalance

Reproduction

Analysis started2023-12-11 00:27:32.898817
Analysis finished2023-12-11 00:27:33.465061
Duration0.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION 

Distinct341
Distinct (%)100.0%
Missing1
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean171
Minimum1
Maximum341
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-11T09:27:33.526408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q186
median171
Q3256
95-th percentile324
Maximum341
Range340
Interquartile range (IQR)170

Descriptive statistics

Standard deviation98.582453
Coefficient of variation (CV)0.57650557
Kurtosis-1.2
Mean171
Median Absolute Deviation (MAD)85
Skewness0
Sum58311
Variance9718.5
MonotonicityStrictly increasing
2023-12-11T09:27:33.735013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
226 1
 
0.3%
234 1
 
0.3%
233 1
 
0.3%
232 1
 
0.3%
231 1
 
0.3%
230 1
 
0.3%
229 1
 
0.3%
228 1
 
0.3%
227 1
 
0.3%
Other values (331) 331
96.8%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
341 1
0.3%
340 1
0.3%
339 1
0.3%
338 1
0.3%
337 1
0.3%
336 1
0.3%
335 1
0.3%
334 1
0.3%
333 1
0.3%
332 1
0.3%

읍면동
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
사천읍
37 
정동면
32 
곤양면
32 
사남면
31 
곤명면
28 
Other values (10)
182 

Length

Max length4
Median length3
Mean length3.0409357
Min length3

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row사천읍
2nd row사천읍
3rd row사천읍
4th row사천읍
5th row사천읍

Common Values

ValueCountFrequency (%)
사천읍 37
10.8%
정동면 32
9.4%
곤양면 32
9.4%
사남면 31
9.1%
곤명면 28
8.2%
남양동 28
8.2%
용현면 25
 
7.3%
서포면 23
 
6.7%
향촌동 23
 
6.7%
벌용동 20
 
5.8%
Other values (5) 63
18.4%

Length

2023-12-11T09:27:33.916308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
사천읍 37
10.8%
정동면 32
9.4%
곤양면 32
9.4%
사남면 31
9.1%
곤명면 28
8.2%
남양동 28
8.2%
용현면 25
 
7.3%
서포면 23
 
6.7%
향촌동 23
 
6.7%
벌용동 20
 
5.8%
Other values (5) 63
18.4%

경로당 명
Text

Distinct327
Distinct (%)95.9%
Missing1
Missing (%)0.3%
Memory size2.8 KiB
2023-12-11T09:27:34.168296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length5
Mean length5.6451613
Min length5

Characters and Unicode

Total characters1925
Distinct characters213
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique316 ?
Unique (%)92.7%

Sample

1st row선인1리경로당
2nd row선인2리경로당
3rd row선인3리경로당
4th row대경아파트경로당
5th row대경파미르경로당
ValueCountFrequency (%)
신기경로당 4
 
1.2%
중앙경로당 3
 
0.9%
중촌경로당 2
 
0.6%
사촌경로당 2
 
0.6%
용두경로당 2
 
0.6%
신송경로당 2
 
0.6%
대산경로당 2
 
0.6%
금곡경로당 2
 
0.6%
용산경로당 2
 
0.6%
신촌경로당 2
 
0.6%
Other values (317) 318
93.3%
2023-12-11T09:27:34.563862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
당 342
17.8%
경 336
17.5%
로 332
17.2%
리 35
 
1.8%
동 31
 
1.6%
신 23
 
1.2%
구 18
 
0.9%
아 18
 
0.9%
용 17
 
0.9%
파 17
 
0.9%
Other values (203) 756
39.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1884
97.9%
Decimal Number 36
 
1.9%
Uppercase Letter 3
 
0.2%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
당 342
18.2%
경 336
17.8%
로 332
17.6%
리 35
 
1.9%
동 31
 
1.6%
신 23
 
1.2%
구 18
 
1.0%
아 18
 
1.0%
용 17
 
0.9%
파 17
 
0.9%
Other values (193) 715
38.0%
Decimal Number
ValueCountFrequency (%)
2 11
30.6%
1 9
25.0%
3 7
19.4%
5 5
13.9%
6 3
 
8.3%
7 1
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
C 2
66.7%
K 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1884
97.9%
Common 38
 
2.0%
Latin 3
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
당 342
18.2%
경 336
17.8%
로 332
17.6%
리 35
 
1.9%
동 31
 
1.6%
신 23
 
1.2%
구 18
 
1.0%
아 18
 
1.0%
용 17
 
0.9%
파 17
 
0.9%
Other values (193) 715
38.0%
Common
ValueCountFrequency (%)
2 11
28.9%
1 9
23.7%
3 7
18.4%
5 5
13.2%
6 3
 
7.9%
7 1
 
2.6%
) 1
 
2.6%
( 1
 
2.6%
Latin
ValueCountFrequency (%)
C 2
66.7%
K 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1884
97.9%
ASCII 41
 
2.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
당 342
18.2%
경 336
17.8%
로 332
17.6%
리 35
 
1.9%
동 31
 
1.6%
신 23
 
1.2%
구 18
 
1.0%
아 18
 
1.0%
용 17
 
0.9%
파 17
 
0.9%
Other values (193) 715
38.0%
ASCII
ValueCountFrequency (%)
2 11
26.8%
1 9
22.0%
3 7
17.1%
5 5
12.2%
6 3
 
7.3%
C 2
 
4.9%
K 1
 
2.4%
7 1
 
2.4%
) 1
 
2.4%
( 1
 
2.4%

도로명주소
Text

Distinct339
Distinct (%)99.4%
Missing1
Missing (%)0.3%
Memory size2.8 KiB
2023-12-11T09:27:34.877870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length31
Mean length20.865103
Min length17

Characters and Unicode

Total characters7115
Distinct characters208
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique337 ?
Unique (%)98.8%

Sample

1st row경상남도 사천시 사천읍 서재농청길 73-9 2층
2nd row경상남도 사천시 사천읍 동구밖길 37-3 2층
3rd row경상남도 사천시 사천읍 읍내로 98, 가동 4호(신진아파트)
4th row경상남도 사천시 사천읍 서당길 1-5
5th row경상남도 사천시 사천읍 사천향교로 13
ValueCountFrequency (%)
경상남도 341
21.7%
사천시 339
21.5%
사천읍 37
 
2.4%
정동면 33
 
2.1%
곤양면 32
 
2.0%
사남면 31
 
2.0%
곤명면 28
 
1.8%
용현면 24
 
1.5%
서포면 22
 
1.4%
2층 16
 
1.0%
Other values (543) 671
42.6%
2023-12-11T09:27:35.362682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1259
17.7%
사 423
 
5.9%
천 395
 
5.6%
남 385
 
5.4%
도 350
 
4.9%
상 348
 
4.9%
시 346
 
4.9%
경 341
 
4.8%
길 285
 
4.0%
1 232
 
3.3%
Other values (198) 2751
38.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4561
64.1%
Space Separator 1259
 
17.7%
Decimal Number 975
 
13.7%
Open Punctuation 118
 
1.7%
Close Punctuation 118
 
1.7%
Dash Punctuation 75
 
1.1%
Math Symbol 7
 
0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 423
 
9.3%
천 395
 
8.7%
남 385
 
8.4%
도 350
 
7.7%
상 348
 
7.6%
시 346
 
7.6%
경 341
 
7.5%
길 285
 
6.2%
동 231
 
5.1%
면 188
 
4.1%
Other values (182) 1269
27.8%
Decimal Number
ValueCountFrequency (%)
1 232
23.8%
2 170
17.4%
3 100
10.3%
6 79
 
8.1%
5 78
 
8.0%
4 77
 
7.9%
8 65
 
6.7%
9 65
 
6.7%
7 57
 
5.8%
0 52
 
5.3%
Space Separator
ValueCountFrequency (%)
1259
100.0%
Open Punctuation
ValueCountFrequency (%)
( 118
100.0%
Close Punctuation
ValueCountFrequency (%)
) 118
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 75
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4561
64.1%
Common 2554
35.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 423
 
9.3%
천 395
 
8.7%
남 385
 
8.4%
도 350
 
7.7%
상 348
 
7.6%
시 346
 
7.6%
경 341
 
7.5%
길 285
 
6.2%
동 231
 
5.1%
면 188
 
4.1%
Other values (182) 1269
27.8%
Common
ValueCountFrequency (%)
1259
49.3%
1 232
 
9.1%
2 170
 
6.7%
( 118
 
4.6%
) 118
 
4.6%
3 100
 
3.9%
6 79
 
3.1%
5 78
 
3.1%
4 77
 
3.0%
- 75
 
2.9%
Other values (6) 248
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4561
64.1%
ASCII 2554
35.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1259
49.3%
1 232
 
9.1%
2 170
 
6.7%
( 118
 
4.6%
) 118
 
4.6%
3 100
 
3.9%
6 79
 
3.1%
5 78
 
3.1%
4 77
 
3.0%
- 75
 
2.9%
Other values (6) 248
 
9.7%
Hangul
ValueCountFrequency (%)
사 423
 
9.3%
천 395
 
8.7%
남 385
 
8.4%
도 350
 
7.7%
상 348
 
7.6%
시 346
 
7.6%
경 341
 
7.5%
길 285
 
6.2%
동 231
 
5.1%
면 188
 
4.1%
Other values (182) 1269
27.8%

데이터기준일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2023-10-31
341 
<NA>
 
1

Length

Max length10
Median length10
Mean length9.9824561
Min length4

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row2023-10-31
2nd row2023-10-31
3rd row2023-10-31
4th row2023-10-31
5th row2023-10-31

Common Values

ValueCountFrequency (%)
2023-10-31 341
99.7%
<NA> 1
 
0.3%

Length

2023-12-11T09:27:35.499284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:27:35.613992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-10-31 341
99.7%
na 1
 
0.3%

Interactions

2023-12-11T09:27:33.133739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:27:35.671531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번읍면동
연번1.0000.980
읍면동0.9801.000
2023-12-11T09:27:35.761853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터기준일자읍면동
데이터기준일자1.0001.000
읍면동1.0001.000
2023-12-11T09:27:35.852889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번읍면동데이터기준일자
연번1.0000.9091.000
읍면동0.9091.0001.000
데이터기준일자1.0001.0001.000

Missing values

2023-12-11T09:27:33.229158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:27:33.314615image/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.
2023-12-11T09:27:33.397061image/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

연번읍면동경로당 명도로명주소데이터기준일자
01사천읍선인1리경로당경상남도 사천시 사천읍 서재농청길 73-9 2층2023-10-31
12사천읍선인2리경로당경상남도 사천시 사천읍 동구밖길 37-3 2층2023-10-31
23사천읍선인3리경로당경상남도 사천시 사천읍 읍내로 98, 가동 4호(신진아파트)2023-10-31
34사천읍대경아파트경로당경상남도 사천시 사천읍 서당길 1-52023-10-31
45사천읍대경파미르경로당경상남도 사천시 사천읍 사천향교로 132023-10-31
56사천읍정의1리경로당경상남도 사천시 사천읍 시장1길 16-9 2층2023-10-31
67사천읍수양경로당경상남도 사천시 사천읍 정의길 822023-10-31
78사천읍정의3리경로당경상남도 사천시 사천읍 수양로 99-192023-10-31
89사천읍정의5리경로당경상남도 사천시 사천읍 수양로 1292023-10-31
910사천읍평화1리경로당경상남도 사천시 사천읍 평화길 192023-10-31
연번읍면동경로당 명도로명주소데이터기준일자
332333남양동신기경로당경상남도 사천시 죽계길84-10(죽림동)2023-10-31
333334남양동진동경로당경상남도 사천시 진동길51(죽림동)2023-10-31
334335남양동죽전경로당경상남도 사천시 죽전안길65(죽림동)2023-10-31
335336남양동죽계경로당경상남도 사천시 죽계길47(죽림동)2023-10-31
336337남양동임내경로당경상남도 사천시 임내길55(죽림동)2023-10-31
337338남양동남양경로당경상남도 사천시 숲안길7-8(죽림동)2023-10-31
338339남양동신우심포니경로당경상남도 사천시 임내길30(죽림동)2023-10-31
339340남양동문화경로당경상남도 사천시 문화안길153(죽림동)2023-10-31
340341남양동죽림아리안2단지경로당경상남도 사천시 죽계1길6(죽림동)2023-10-31
341<NA><NA><NA><NA><NA>