Overview

Dataset statistics

Number of variables5
Number of observations440
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.7 KiB
Average record size in memory41.3 B

Variable types

Numeric1
Categorical2
Text2

Dataset

Description경상남도 밀양시 소재 경로당 데이터입니다.
Author경상남도 밀양시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15007433

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 읍면동High correlation
읍면동 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2024-04-17 14:46:19.357183
Analysis finished2024-04-17 14:46:19.994434
Duration0.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct440
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean220.5
Minimum1
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-04-17T23:46:20.048576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile22.95
Q1110.75
median220.5
Q3330.25
95-th percentile418.05
Maximum440
Range439
Interquartile range (IQR)219.5

Descriptive statistics

Standard deviation127.16131
Coefficient of variation (CV)0.57669531
Kurtosis-1.2
Mean220.5
Median Absolute Deviation (MAD)110
Skewness0
Sum97020
Variance16170
MonotonicityStrictly increasing
2024-04-17T23:46:20.150043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
291 1
 
0.2%
302 1
 
0.2%
301 1
 
0.2%
300 1
 
0.2%
299 1
 
0.2%
298 1
 
0.2%
297 1
 
0.2%
296 1
 
0.2%
295 1
 
0.2%
Other values (430) 430
97.7%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
440 1
0.2%
439 1
0.2%
438 1
0.2%
437 1
0.2%
436 1
0.2%
435 1
0.2%
434 1
0.2%
433 1
0.2%
432 1
0.2%
431 1
0.2%

읍면동
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
무안면
52 
삼랑진읍
38 
상남면
38 
하남읍
36 
부북면
34 
Other values (11)
242 

Length

Max length4
Median length3
Mean length3.0659091
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
무안면 52
11.8%
삼랑진읍 38
 
8.6%
상남면 38
 
8.6%
하남읍 36
 
8.2%
부북면 34
 
7.7%
산내면 33
 
7.5%
단장면 32
 
7.3%
초동면 31
 
7.0%
상동면 25
 
5.7%
산외면 22
 
5.0%
Other values (6) 99
22.5%

Length

2024-04-17T23:46:20.250760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
무안면 52
11.8%
삼랑진읍 38
 
8.6%
상남면 38
 
8.6%
하남읍 36
 
8.2%
부북면 34
 
7.7%
산내면 33
 
7.5%
단장면 32
 
7.3%
초동면 31
 
7.0%
상동면 25
 
5.7%
산외면 22
 
5.0%
Other values (6) 99
22.5%

경로당명
Text

Distinct422
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2024-04-17T23:46:20.459112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length5
Mean length5.7727273
Min length5

Characters and Unicode

Total characters2540
Distinct characters232
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique405 ?
Unique (%)92.0%

Sample

1st row임천분회경로당
2nd row금곡경로당
3rd row숭진경로당
4th row금호경로당
5th row청학경로당
ValueCountFrequency (%)
동촌경로당 3
 
0.7%
금곡경로당 2
 
0.5%
평지경로당 2
 
0.5%
평리경로당 2
 
0.5%
동산경로당 2
 
0.5%
서편경로당 2
 
0.5%
우곡경로당 2
 
0.5%
대성경로당 2
 
0.5%
인산경로당 2
 
0.5%
구기경로당 2
 
0.5%
Other values (412) 419
95.2%
2024-04-17T23:46:20.778077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
당 447
17.6%
경 443
17.4%
로 443
17.4%
동 53
 
2.1%
곡 36
 
1.4%
촌 31
 
1.2%
산 29
 
1.1%
성 28
 
1.1%
대 25
 
1.0%
부 24
 
0.9%
Other values (222) 981
38.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2491
98.1%
Decimal Number 41
 
1.6%
Other Punctuation 4
 
0.2%
Uppercase Letter 2
 
0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
당 447
17.9%
경 443
17.8%
로 443
17.8%
동 53
 
2.1%
곡 36
 
1.4%
촌 31
 
1.2%
산 29
 
1.2%
성 28
 
1.1%
대 25
 
1.0%
부 24
 
1.0%
Other values (208) 932
37.4%
Decimal Number
ValueCountFrequency (%)
1 14
34.1%
2 14
34.1%
3 5
 
12.2%
4 3
 
7.3%
5 2
 
4.9%
7 2
 
4.9%
8 1
 
2.4%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
· 1
25.0%
, 1
25.0%
Uppercase Letter
ValueCountFrequency (%)
L 1
50.0%
H 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2491
98.1%
Common 47
 
1.9%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
당 447
17.9%
경 443
17.8%
로 443
17.8%
동 53
 
2.1%
곡 36
 
1.4%
촌 31
 
1.2%
산 29
 
1.2%
성 28
 
1.1%
대 25
 
1.0%
부 24
 
1.0%
Other values (208) 932
37.4%
Common
ValueCountFrequency (%)
1 14
29.8%
2 14
29.8%
3 5
 
10.6%
4 3
 
6.4%
5 2
 
4.3%
. 2
 
4.3%
7 2
 
4.3%
8 1
 
2.1%
· 1
 
2.1%
, 1
 
2.1%
Other values (2) 2
 
4.3%
Latin
ValueCountFrequency (%)
L 1
50.0%
H 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2491
98.1%
ASCII 48
 
1.9%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
당 447
17.9%
경 443
17.8%
로 443
17.8%
동 53
 
2.1%
곡 36
 
1.4%
촌 31
 
1.2%
산 29
 
1.2%
성 28
 
1.1%
대 25
 
1.0%
부 24
 
1.0%
Other values (208) 932
37.4%
ASCII
ValueCountFrequency (%)
1 14
29.2%
2 14
29.2%
3 5
 
10.4%
4 3
 
6.2%
5 2
 
4.2%
. 2
 
4.2%
7 2
 
4.2%
L 1
 
2.1%
H 1
 
2.1%
8 1
 
2.1%
Other values (3) 3
 
6.2%
None
ValueCountFrequency (%)
· 1
100.0%

경로당 주소
Text

Distinct431
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2024-04-17T23:46:21.026251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length27
Mean length16.102273
Min length12

Characters and Unicode

Total characters7085
Distinct characters199
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

Unique422 ?
Unique (%)95.9%

Sample

1st row밀양시 삼랑진읍 임천3길 10
2nd row밀양시 삼랑진읍 임천5길 33
3rd row밀양시 삼랑진읍 숭진석탑길 86
4th row밀양시 삼랑진읍 숭진길 56
5th row밀양시 삼랑진읍 청학1길 77
ValueCountFrequency (%)
밀양시 438
25.9%
무안면 52
 
3.1%
상남면 38
 
2.2%
삼랑진읍 38
 
2.2%
하남읍 36
 
2.1%
부북면 34
 
2.0%
산내면 33
 
2.0%
단장면 32
 
1.9%
초동면 31
 
1.8%
상동면 25
 
1.5%
Other values (662) 934
55.2%
2024-04-17T23:46:21.380213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1256
17.7%
양 459
 
6.5%
밀 450
 
6.4%
시 443
 
6.3%
길 343
 
4.8%
1 335
 
4.7%
면 288
 
4.1%
2 221
 
3.1%
동 173
 
2.4%
3 170
 
2.4%
Other values (189) 2947
41.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4190
59.1%
Decimal Number 1345
 
19.0%
Space Separator 1256
 
17.7%
Dash Punctuation 127
 
1.8%
Open Punctuation 77
 
1.1%
Close Punctuation 77
 
1.1%
Other Punctuation 12
 
0.2%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
양 459
 
11.0%
밀 450
 
10.7%
시 443
 
10.6%
길 343
 
8.2%
면 288
 
6.9%
동 173
 
4.1%
로 114
 
2.7%
산 101
 
2.4%
남 97
 
2.3%
안 83
 
2.0%
Other values (173) 1639
39.1%
Decimal Number
ValueCountFrequency (%)
1 335
24.9%
2 221
16.4%
3 170
12.6%
4 142
10.6%
5 100
 
7.4%
6 85
 
6.3%
7 83
 
6.2%
8 80
 
5.9%
9 66
 
4.9%
0 63
 
4.7%
Space Separator
ValueCountFrequency (%)
1256
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 127
100.0%
Open Punctuation
ValueCountFrequency (%)
( 77
100.0%
Close Punctuation
ValueCountFrequency (%)
) 77
100.0%
Other Punctuation
ValueCountFrequency (%)
, 12
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4190
59.1%
Common 2894
40.8%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
양 459
 
11.0%
밀 450
 
10.7%
시 443
 
10.6%
길 343
 
8.2%
면 288
 
6.9%
동 173
 
4.1%
로 114
 
2.7%
산 101
 
2.4%
남 97
 
2.3%
안 83
 
2.0%
Other values (173) 1639
39.1%
Common
ValueCountFrequency (%)
1256
43.4%
1 335
 
11.6%
2 221
 
7.6%
3 170
 
5.9%
4 142
 
4.9%
- 127
 
4.4%
5 100
 
3.5%
6 85
 
2.9%
7 83
 
2.9%
8 80
 
2.8%
Other values (5) 295
 
10.2%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4190
59.1%
ASCII 2895
40.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1256
43.4%
1 335
 
11.6%
2 221
 
7.6%
3 170
 
5.9%
4 142
 
4.9%
- 127
 
4.4%
5 100
 
3.5%
6 85
 
2.9%
7 83
 
2.9%
8 80
 
2.8%
Other values (6) 296
 
10.2%
Hangul
ValueCountFrequency (%)
양 459
 
11.0%
밀 450
 
10.7%
시 443
 
10.6%
길 343
 
8.2%
면 288
 
6.9%
동 173
 
4.1%
로 114
 
2.7%
산 101
 
2.4%
남 97
 
2.3%
안 83
 
2.0%
Other values (173) 1639
39.1%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2023-07-13
440 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-07-13
2nd row2023-07-13
3rd row2023-07-13
4th row2023-07-13
5th row2023-07-13

Common Values

ValueCountFrequency (%)
2023-07-13 440
100.0%

Length

2024-04-17T23:46:21.485704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T23:46:21.559316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-07-13 440
100.0%

Interactions

2024-04-17T23:46:19.588202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T23:46:21.607061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번읍면동
연번1.0000.972
읍면동0.9721.000
2024-04-17T23:46:21.672491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번읍면동
연번1.0000.865
읍면동0.8651.000

Missing values

2024-04-17T23:46:19.666125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T23:46:19.965374image/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

연번읍면동경로당명경로당 주소데이터기준일자
01삼랑진읍임천분회경로당밀양시 삼랑진읍 임천3길 102023-07-13
12삼랑진읍금곡경로당밀양시 삼랑진읍 임천5길 332023-07-13
23삼랑진읍숭진경로당밀양시 삼랑진읍 숭진석탑길 862023-07-13
34삼랑진읍금호경로당밀양시 삼랑진읍 숭진길 562023-07-13
45삼랑진읍청학경로당밀양시 삼랑진읍 청학1길 772023-07-13
56삼랑진읍용성경로당밀양시 삼랑진읍 청룡2길 302023-07-13
67삼랑진읍청용경로당밀양시 삼랑진읍 청룡1길 442023-07-13
78삼랑진읍인전경로당밀양시 삼랑진읍 칠성2길 102023-07-13
89삼랑진읍칠성경로당밀양시 삼랑진읍 칠성길 682023-07-13
910삼랑진읍용전경로당밀양시 삼랑진읍 사기점길 1082023-07-13
연번읍면동경로당명경로당 주소데이터기준일자
430431가곡동우영타워경로당밀양시 가곡벚꽃길 63(가곡동)2023-07-13
431432가곡동경남아파트경로당밀양시 가곡7길 23(가곡동)2023-07-13
432433가곡동대승아파트경로당밀양시 가곡4길 19(가곡동)2023-07-13
433434가곡동가곡11통경로당밀양시 역앞광장로 10-1(가곡동)2023-07-13
434435가곡동대송파크경로당밀양시 중앙로 38 (가곡동)2023-07-13
435436가곡동주공2단지경로당밀양시 중앙로 47(가곡동)2023-07-13
436437가곡동가곡주공경로당밀양시 중앙로 47(가곡동)2023-07-13
437438가곡동밀주경로당밀양시 가곡9안길 8(가곡동)2023-07-13
438439가곡동밀양강푸르지오경로당밀양시 역앞광장로 15(가곡동)2023-07-13
439440가곡동용두경로당밀양시 가곡15안길 9, 1동 101호2023-07-13