Overview

Dataset statistics

Number of variables9
Number of observations153
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.9 KiB
Average record size in memory72.9 B

Variable types

Categorical6
Text2
DateTime1

Dataset

Description전북특별자치도 정읍시에 위치한 분리수거대 현황중(시도, 시군구, 행정동, 위치명, 설치소재지, 상태, 관리부서, 관리부서연락처)등의 정보를 제공합니다.
Author전북특별자치도 정읍시
URLhttps://www.data.go.kr/data/15099835/fileData.do

Alerts

시도 has constant value ""Constant
시군구 has constant value ""Constant
관리부서 has constant value ""Constant
관리부서연락처 has constant value ""Constant
데이터기준일자 has constant value ""Constant
행정동 is highly overall correlated with 상태High correlation
상태 is highly overall correlated with 행정동High correlation

Reproduction

Analysis started2024-04-21 02:06:34.249649
Analysis finished2024-04-21 02:06:35.793834
Duration1.54 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
전북특별자치도
153 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전북특별자치도 153
100.0%

Length

2024-04-21T11:06:35.850287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:06:35.933738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전북특별자치도 153
100.0%

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
정읍시
153 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정읍시 153
100.0%

Length

2024-04-21T11:06:36.021240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:06:36.119815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정읍시 153
100.0%

행정동
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
내장상동
23 
초산동
15 
상교동
14 
옹동면
11 
태인면
10 
Other values (16)
80 

Length

Max length4
Median length3
Mean length3.1764706
Min length2

Unique

Unique3 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
내장상동 23
15.0%
초산동 15
9.8%
상교동 14
 
9.2%
옹동면 11
 
7.2%
태인면 10
 
6.5%
소성면 10
 
6.5%
칠보면 9
 
5.9%
신태인읍 9
 
5.9%
덕천면 8
 
5.2%
연지동 7
 
4.6%
Other values (11) 37
24.2%

Length

2024-04-21T11:06:36.225606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
내장상동 23
15.0%
초산동 15
9.8%
상교동 14
 
9.2%
옹동면 11
 
7.2%
태인면 10
 
6.5%
소성면 10
 
6.5%
칠보면 9
 
5.9%
신태인읍 9
 
5.9%
덕천면 8
 
5.2%
입암면 7
 
4.6%
Other values (11) 37
24.2%

위치명
Text

Distinct146
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2024-04-21T11:06:36.533299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length4.4052288
Min length2

Characters and Unicode

Total characters674
Distinct characters173
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique140 ?
Unique (%)91.5%

Sample

1st row마동마을
2nd row이암마을
3rd row농원마을
4th row태화마을
5th row연지마을
ValueCountFrequency (%)
민하 3
 
1.9%
과반마을 2
 
1.2%
내정마을 2
 
1.2%
미가아파트 2
 
1.2%
상동휴먼시아 2
 
1.2%
jk 2
 
1.2%
15통 2
 
1.2%
시기 2
 
1.2%
신정마을 2
 
1.2%
용계마을 1
 
0.6%
Other values (140) 140
87.5%
2024-04-21T11:06:36.997639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
마 74
 
11.0%
을 72
 
10.7%
1 28
 
4.2%
동 27
 
4.0%
산 20
 
3.0%
초 16
 
2.4%
아 13
 
1.9%
트 11
 
1.6%
파 11
 
1.6%
통 10
 
1.5%
Other values (163) 392
58.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 581
86.2%
Decimal Number 68
 
10.1%
Uppercase Letter 17
 
2.5%
Space Separator 8
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
마 74
 
12.7%
을 72
 
12.4%
동 27
 
4.6%
산 20
 
3.4%
초 16
 
2.8%
아 13
 
2.2%
트 11
 
1.9%
파 11
 
1.9%
통 10
 
1.7%
대 8
 
1.4%
Other values (145) 319
54.9%
Decimal Number
ValueCountFrequency (%)
1 28
41.2%
2 9
 
13.2%
0 8
 
11.8%
3 6
 
8.8%
5 5
 
7.4%
4 4
 
5.9%
9 2
 
2.9%
8 2
 
2.9%
7 2
 
2.9%
6 2
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
T 3
17.6%
P 3
17.6%
A 3
17.6%
J 2
11.8%
K 2
11.8%
H 2
11.8%
L 2
11.8%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 581
86.2%
Common 76
 
11.3%
Latin 17
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
마 74
 
12.7%
을 72
 
12.4%
동 27
 
4.6%
산 20
 
3.4%
초 16
 
2.8%
아 13
 
2.2%
트 11
 
1.9%
파 11
 
1.9%
통 10
 
1.7%
대 8
 
1.4%
Other values (145) 319
54.9%
Common
ValueCountFrequency (%)
1 28
36.8%
2 9
 
11.8%
0 8
 
10.5%
8
 
10.5%
3 6
 
7.9%
5 5
 
6.6%
4 4
 
5.3%
9 2
 
2.6%
8 2
 
2.6%
7 2
 
2.6%
Latin
ValueCountFrequency (%)
T 3
17.6%
P 3
17.6%
A 3
17.6%
J 2
11.8%
K 2
11.8%
H 2
11.8%
L 2
11.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 581
86.2%
ASCII 93
 
13.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
마 74
 
12.7%
을 72
 
12.4%
동 27
 
4.6%
산 20
 
3.4%
초 16
 
2.8%
아 13
 
2.2%
트 11
 
1.9%
파 11
 
1.9%
통 10
 
1.7%
대 8
 
1.4%
Other values (145) 319
54.9%
ASCII
ValueCountFrequency (%)
1 28
30.1%
2 9
 
9.7%
0 8
 
8.6%
8
 
8.6%
3 6
 
6.5%
5 5
 
5.4%
4 4
 
4.3%
T 3
 
3.2%
P 3
 
3.2%
A 3
 
3.2%
Other values (8) 16
17.2%

설치소재지
Text

Distinct148
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2024-04-21T11:06:37.360673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length27
Mean length23.660131
Min length19

Characters and Unicode

Total characters3620
Distinct characters138
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique145 ?
Unique (%)94.8%

Sample

1st row전북특별자치도 정읍시 신태인읍 마동길 18-3
2nd row전북특별자치도 정읍시 신태인읍 이암길 13-23
3rd row전북특별자치도 정읍시 신태인읍 농원길 13
4th row전북특별자치도 정읍시 신태인읍 태화2길 6
5th row전북특별자치도 정읍시 신태인읍 연정2길 3
ValueCountFrequency (%)
전북특별자치도 153
20.4%
정읍시 153
20.4%
내장상동 23
 
3.1%
초산동 15
 
2.0%
학산로 12
 
1.6%
옹동면 11
 
1.5%
태인면 10
 
1.3%
소성면 10
 
1.3%
칠보면 9
 
1.2%
신태인읍 9
 
1.2%
Other values (257) 346
46.1%
2024-04-21T11:06:37.834108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
611
 
16.9%
정 171
 
4.7%
읍 167
 
4.6%
북 163
 
4.5%
시 156
 
4.3%
전 154
 
4.3%
치 154
 
4.3%
도 153
 
4.2%
자 153
 
4.2%
별 153
 
4.2%
Other values (128) 1585
43.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2468
68.2%
Space Separator 611
 
16.9%
Decimal Number 483
 
13.3%
Dash Punctuation 58
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 171
 
6.9%
읍 167
 
6.8%
북 163
 
6.6%
시 156
 
6.3%
전 154
 
6.2%
치 154
 
6.2%
도 153
 
6.2%
자 153
 
6.2%
별 153
 
6.2%
특 153
 
6.2%
Other values (116) 891
36.1%
Decimal Number
ValueCountFrequency (%)
1 108
22.4%
2 60
12.4%
4 54
11.2%
3 51
10.6%
8 45
9.3%
5 43
 
8.9%
9 38
 
7.9%
7 31
 
6.4%
6 27
 
5.6%
0 26
 
5.4%
Space Separator
ValueCountFrequency (%)
611
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2468
68.2%
Common 1152
31.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 171
 
6.9%
읍 167
 
6.8%
북 163
 
6.6%
시 156
 
6.3%
전 154
 
6.2%
치 154
 
6.2%
도 153
 
6.2%
자 153
 
6.2%
별 153
 
6.2%
특 153
 
6.2%
Other values (116) 891
36.1%
Common
ValueCountFrequency (%)
611
53.0%
1 108
 
9.4%
2 60
 
5.2%
- 58
 
5.0%
4 54
 
4.7%
3 51
 
4.4%
8 45
 
3.9%
5 43
 
3.7%
9 38
 
3.3%
7 31
 
2.7%
Other values (2) 53
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2468
68.2%
ASCII 1152
31.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
611
53.0%
1 108
 
9.4%
2 60
 
5.2%
- 58
 
5.0%
4 54
 
4.7%
3 51
 
4.4%
8 45
 
3.9%
5 43
 
3.7%
9 38
 
3.3%
7 31
 
2.7%
Other values (2) 53
 
4.6%
Hangul
ValueCountFrequency (%)
정 171
 
6.9%
읍 167
 
6.8%
북 163
 
6.6%
시 156
 
6.3%
전 154
 
6.2%
치 154
 
6.2%
도 153
 
6.2%
자 153
 
6.2%
별 153
 
6.2%
특 153
 
6.2%
Other values (116) 891
36.1%

상태
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
양호
74 
보통
42 
불량
37 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
양호 74
48.4%
보통 42
27.5%
불량 37
24.2%

Length

2024-04-21T11:06:37.954115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:06:38.051837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
양호 74
48.4%
보통 42
27.5%
불량 37
24.2%

관리부서
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
정읍시 자원순환과
153 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row정읍시 자원순환과
2nd row정읍시 자원순환과
3rd row정읍시 자원순환과
4th row정읍시 자원순환과
5th row정읍시 자원순환과

Common Values

ValueCountFrequency (%)
정읍시 자원순환과 153
100.0%

Length

2024-04-21T11:06:38.153498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:06:38.235572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정읍시 153
50.0%
자원순환과 153
50.0%

관리부서연락처
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
063-539-8143
153 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row063-539-8143
2nd row063-539-8143
3rd row063-539-8143
4th row063-539-8143
5th row063-539-8143

Common Values

ValueCountFrequency (%)
063-539-8143 153
100.0%

Length

2024-04-21T11:06:38.340028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:06:38.437311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
063-539-8143 153
100.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2023-04-13 00:00:00
Maximum2023-04-13 00:00:00
2024-04-21T11:06:38.510058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:06:38.589387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Correlations

2024-04-21T11:06:38.652326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동상태
행정동1.0000.896
상태0.8961.000
2024-04-21T11:06:38.727426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동상태
행정동1.0000.629
상태0.6291.000
2024-04-21T11:06:38.799526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동상태
행정동1.0000.629
상태0.6291.000

Missing values

2024-04-21T11:06:35.561401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T11:06:35.735132image/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

시도시군구행정동위치명설치소재지상태관리부서관리부서연락처데이터기준일자
0전북특별자치도정읍시신태인읍마동마을전북특별자치도 정읍시 신태인읍 마동길 18-3보통정읍시 자원순환과063-539-81432023-04-13
1전북특별자치도정읍시신태인읍이암마을전북특별자치도 정읍시 신태인읍 이암길 13-23보통정읍시 자원순환과063-539-81432023-04-13
2전북특별자치도정읍시신태인읍농원마을전북특별자치도 정읍시 신태인읍 농원길 13보통정읍시 자원순환과063-539-81432023-04-13
3전북특별자치도정읍시신태인읍태화마을전북특별자치도 정읍시 신태인읍 태화2길 6보통정읍시 자원순환과063-539-81432023-04-13
4전북특별자치도정읍시신태인읍연지마을전북특별자치도 정읍시 신태인읍 연정2길 3보통정읍시 자원순환과063-539-81432023-04-13
5전북특별자치도정읍시신태인읍요마을전북특별자치도 정읍시 신태인읍 요동길 40-7보통정읍시 자원순환과063-539-81432023-04-13
6전북특별자치도정읍시신태인읍하신마을전북특별자치도 정읍시 신태인읍 하신양2길 37보통정읍시 자원순환과063-539-81432023-04-13
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