Overview

Dataset statistics

Number of variables7
Number of observations122
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 KiB
Average record size in memory58.1 B

Variable types

Numeric1
Categorical2
Text3
DateTime1

Dataset

Description광주광역시 동구 경로당 현황입니다.동명, 경로당명, 형태, 당 장, 도로명주소, 비고 등으로 구성되어있습니다.
Author광주광역시 동구
URLhttps://www.data.go.kr/data/15106974/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 동명High correlation
동명 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
경로당명 has unique valuesUnique
당 장 has unique valuesUnique

Reproduction

Analysis started2023-12-12 03:37:57.496093
Analysis finished2023-12-12 03:37:58.245350
Duration0.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct122
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.5
Minimum1
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T12:37:58.330863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.05
Q131.25
median61.5
Q391.75
95-th percentile115.95
Maximum122
Range121
Interquartile range (IQR)60.5

Descriptive statistics

Standard deviation35.362409
Coefficient of variation (CV)0.57499853
Kurtosis-1.2
Mean61.5
Median Absolute Deviation (MAD)30.5
Skewness0
Sum7503
Variance1250.5
MonotonicityStrictly increasing
2023-12-12T12:37:58.536237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
93 1
 
0.8%
91 1
 
0.8%
90 1
 
0.8%
89 1
 
0.8%
88 1
 
0.8%
87 1
 
0.8%
86 1
 
0.8%
85 1
 
0.8%
84 1
 
0.8%
Other values (112) 112
91.8%
ValueCountFrequency (%)
1 1
0.8%
2 1
0.8%
3 1
0.8%
4 1
0.8%
5 1
0.8%
6 1
0.8%
7 1
0.8%
8 1
0.8%
9 1
0.8%
10 1
0.8%
ValueCountFrequency (%)
122 1
0.8%
121 1
0.8%
120 1
0.8%
119 1
0.8%
118 1
0.8%
117 1
0.8%
116 1
0.8%
115 1
0.8%
114 1
0.8%
113 1
0.8%

동명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
지원2동
28 
학운동
19 
산수2동
13 
계림2동
10 
지원1동
10 
Other values (8)
42 

Length

Max length4
Median length4
Mean length3.6393443
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지원2동 28
23.0%
학운동 19
15.6%
산수2동 13
10.7%
계림2동 10
 
8.2%
지원1동 10
 
8.2%
지산2동 8
 
6.6%
충장동 7
 
5.7%
계림1동 6
 
4.9%
산수1동 5
 
4.1%
학동 5
 
4.1%
Other values (3) 11
 
9.0%

Length

2023-12-12T12:37:58.731755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지원2동 28
23.0%
학운동 19
15.6%
산수2동 13
10.7%
계림2동 10
 
8.2%
지원1동 10
 
8.2%
지산2동 8
 
6.6%
충장동 7
 
5.7%
계림1동 6
 
4.9%
산수1동 5
 
4.1%
학동 5
 
4.1%
Other values (3) 11
 
9.0%

경로당명
Text

UNIQUE 

Distinct122
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-12T12:37:59.087386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length4.0491803
Min length2

Characters and Unicode

Total characters494
Distinct characters161
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

Unique122 ?
Unique (%)100.0%

Sample

1st row상이군경회
2nd row충수
3rd row충의
4th row수영
5th row삼성
ValueCountFrequency (%)
무등산골드클래스 2
 
1.6%
산수2동 2
 
1.6%
라인광명 1
 
0.8%
세연 1
 
0.8%
대아부녀 1
 
0.8%
모아미래도 1
 
0.8%
석천 1
 
0.8%
백조부녀 1
 
0.8%
태봉 1
 
0.8%
원머리 1
 
0.8%
Other values (113) 113
90.4%
2023-12-12T12:37:59.625476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 24
 
4.9%
부 18
 
3.6%
녀 18
 
3.6%
아 16
 
3.2%
동 15
 
3.0%
수 12
 
2.4%
파 12
 
2.4%
무 10
 
2.0%
차 10
 
2.0%
등 9
 
1.8%
Other values (151) 350
70.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 463
93.7%
Decimal Number 16
 
3.2%
Space Separator 6
 
1.2%
Uppercase Letter 5
 
1.0%
Close Punctuation 2
 
0.4%
Open Punctuation 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 24
 
5.2%
부 18
 
3.9%
녀 18
 
3.9%
아 16
 
3.5%
동 15
 
3.2%
수 12
 
2.6%
파 12
 
2.6%
무 10
 
2.2%
차 10
 
2.2%
등 9
 
1.9%
Other values (141) 319
68.9%
Uppercase Letter
ValueCountFrequency (%)
L 1
20.0%
H 1
20.0%
A 1
20.0%
K 1
20.0%
S 1
20.0%
Decimal Number
ValueCountFrequency (%)
2 8
50.0%
1 8
50.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 463
93.7%
Common 26
 
5.3%
Latin 5
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 24
 
5.2%
부 18
 
3.9%
녀 18
 
3.9%
아 16
 
3.5%
동 15
 
3.2%
수 12
 
2.6%
파 12
 
2.6%
무 10
 
2.2%
차 10
 
2.2%
등 9
 
1.9%
Other values (141) 319
68.9%
Common
ValueCountFrequency (%)
2 8
30.8%
1 8
30.8%
6
23.1%
) 2
 
7.7%
( 2
 
7.7%
Latin
ValueCountFrequency (%)
L 1
20.0%
H 1
20.0%
A 1
20.0%
K 1
20.0%
S 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 463
93.7%
ASCII 31
 
6.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 24
 
5.2%
부 18
 
3.9%
녀 18
 
3.9%
아 16
 
3.5%
동 15
 
3.2%
수 12
 
2.6%
파 12
 
2.6%
무 10
 
2.2%
차 10
 
2.2%
등 9
 
1.9%
Other values (141) 319
68.9%
ASCII
ValueCountFrequency (%)
2 8
25.8%
1 8
25.8%
6
19.4%
) 2
 
6.5%
( 2
 
6.5%
L 1
 
3.2%
H 1
 
3.2%
A 1
 
3.2%
K 1
 
3.2%
S 1
 
3.2%

형태
Categorical

Distinct3
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
혼합
71 
여
35 
남
16 

Length

Max length2
Median length2
Mean length1.5819672
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남
2nd row혼합
3rd row혼합
4th row혼합
5th row혼합

Common Values

ValueCountFrequency (%)
혼합 71
58.2%
여 35
28.7%
남 16
 
13.1%

Length

2023-12-12T12:37:59.834353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:37:59.993230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
혼합 71
58.2%
여 35
28.7%
남 16
 
13.1%

당 장
Text

UNIQUE 

Distinct122
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-12T12:38:00.466183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3
Min length2

Characters and Unicode

Total characters366
Distinct characters116
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

Unique122 ?
Unique (%)100.0%

Sample

1st row윤평현
2nd row윤주식
3rd row송양진
4th row김승길
5th row황경순
ValueCountFrequency (%)
윤평현 1
 
0.8%
장삼례 1
 
0.8%
조영자 1
 
0.8%
박영학 1
 
0.8%
김경일 1
 
0.8%
장금옥 1
 
0.8%
장명희 1
 
0.8%
조홍기 1
 
0.8%
양해승 1
 
0.8%
정은제 1
 
0.8%
Other values (112) 112
91.8%
2023-12-12T12:38:01.163438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 27
 
7.4%
정 16
 
4.4%
이 13
 
3.6%
자 11
 
3.0%
순 10
 
2.7%
박 9
 
2.5%
영 9
 
2.5%
남 8
 
2.2%
수 8
 
2.2%
장 7
 
1.9%
Other values (106) 248
67.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 365
99.7%
Space Separator 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 27
 
7.4%
정 16
 
4.4%
이 13
 
3.6%
자 11
 
3.0%
순 10
 
2.7%
박 9
 
2.5%
영 9
 
2.5%
남 8
 
2.2%
수 8
 
2.2%
장 7
 
1.9%
Other values (105) 247
67.7%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 365
99.7%
Common 1
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 27
 
7.4%
정 16
 
4.4%
이 13
 
3.6%
자 11
 
3.0%
순 10
 
2.7%
박 9
 
2.5%
영 9
 
2.5%
남 8
 
2.2%
수 8
 
2.2%
장 7
 
1.9%
Other values (105) 247
67.7%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 365
99.7%
ASCII 1
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 27
 
7.4%
정 16
 
4.4%
이 13
 
3.6%
자 11
 
3.0%
순 10
 
2.7%
박 9
 
2.5%
영 9
 
2.5%
남 8
 
2.2%
수 8
 
2.2%
장 7
 
1.9%
Other values (105) 247
67.7%
ASCII
ValueCountFrequency (%)
1
100.0%

도로명주소
Text

Distinct119
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-12T12:38:01.555770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length22.5
Mean length17.631148
Min length13

Characters and Unicode

Total characters2151
Distinct characters80
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique116 ?
Unique (%)95.1%

Sample

1st row광주광역시 동구 구성로204번길 6-1
2nd row광주광역시 동구 충장로 14-3
3rd row광주광역시 동구 예술길 19-8
4th row광주광역시 동구 서석로7번길 12-14
5th row광주광역시 동구 중앙로148번길 11-23
ValueCountFrequency (%)
광주광역시 122
30.7%
동구 122
30.7%
1층 7
 
1.8%
2층 7
 
1.8%
백서로224번길 2
 
0.5%
밤실로92번길3 2
 
0.5%
필문대로145번길1-4 2
 
0.5%
백서로125번길32-7 2
 
0.5%
필문대로187번길 2
 
0.5%
6 2
 
0.5%
Other values (121) 128
32.2%
2023-12-12T12:38:02.276920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
276
 
12.8%
광 244
 
11.3%
동 130
 
6.0%
구 123
 
5.7%
주 123
 
5.7%
역 122
 
5.7%
시 122
 
5.7%
로 97
 
4.5%
1 91
 
4.2%
길 79
 
3.7%
Other values (70) 744
34.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1366
63.5%
Decimal Number 450
 
20.9%
Space Separator 276
 
12.8%
Dash Punctuation 43
 
2.0%
Other Punctuation 16
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 244
17.9%
동 130
9.5%
구 123
9.0%
주 123
9.0%
역 122
8.9%
시 122
8.9%
로 97
 
7.1%
길 79
 
5.8%
번 54
 
4.0%
문 20
 
1.5%
Other values (57) 252
18.4%
Decimal Number
ValueCountFrequency (%)
1 91
20.2%
2 67
14.9%
3 59
13.1%
7 37
8.2%
8 37
8.2%
4 35
 
7.8%
5 35
 
7.8%
6 34
 
7.6%
0 32
 
7.1%
9 23
 
5.1%
Space Separator
ValueCountFrequency (%)
276
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%
Other Punctuation
ValueCountFrequency (%)
, 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1366
63.5%
Common 785
36.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 244
17.9%
동 130
9.5%
구 123
9.0%
주 123
9.0%
역 122
8.9%
시 122
8.9%
로 97
 
7.1%
길 79
 
5.8%
번 54
 
4.0%
문 20
 
1.5%
Other values (57) 252
18.4%
Common
ValueCountFrequency (%)
276
35.2%
1 91
 
11.6%
2 67
 
8.5%
3 59
 
7.5%
- 43
 
5.5%
7 37
 
4.7%
8 37
 
4.7%
4 35
 
4.5%
5 35
 
4.5%
6 34
 
4.3%
Other values (3) 71
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1366
63.5%
ASCII 785
36.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
276
35.2%
1 91
 
11.6%
2 67
 
8.5%
3 59
 
7.5%
- 43
 
5.5%
7 37
 
4.7%
8 37
 
4.7%
4 35
 
4.5%
5 35
 
4.5%
6 34
 
4.3%
Other values (3) 71
 
9.0%
Hangul
ValueCountFrequency (%)
광 244
17.9%
동 130
9.5%
구 123
9.0%
주 123
9.0%
역 122
8.9%
시 122
8.9%
로 97
 
7.1%
길 79
 
5.8%
번 54
 
4.0%
문 20
 
1.5%
Other values (57) 252
18.4%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum2023-10-31 00:00:00
Maximum2023-10-31 00:00:00
2023-12-12T12:38:02.533640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:38:02.702532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T12:37:57.886089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:38:02.806340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번동명형태
연번1.0000.9320.495
동명0.9321.0000.535
형태0.4950.5351.000
2023-12-12T12:38:02.926167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동명형태
동명1.0000.342
형태0.3421.000
2023-12-12T12:38:03.049514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번동명형태
연번1.0000.7420.332
동명0.7421.0000.342
형태0.3320.3421.000

Missing values

2023-12-12T12:37:58.024025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:37:58.186603image/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

연번동명경로당명형태당 장도로명주소데이터기준일자
01충장동상이군경회남윤평현광주광역시 동구 구성로204번길 6-12023-10-31
12충장동충수혼합윤주식광주광역시 동구 충장로 14-32023-10-31
23충장동충의혼합송양진광주광역시 동구 예술길 19-82023-10-31
34충장동수영혼합김승길광주광역시 동구 서석로7번길 12-142023-10-31
45충장동삼성혼합황경순광주광역시 동구 중앙로148번길 11-232023-10-31
56충장동파월남오시열광주광역시 동구 동계천로 74, 5층2023-10-31
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