Overview

Dataset statistics

Number of variables6
Number of observations136
Missing cells4
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 KiB
Average record size in memory50.0 B

Variable types

Categorical3
Text3

Dataset

Description경상남도 밀양시 폐형광등 및 폐건전지 수거함 현황에 대한 자료로, 배출품목, 수거장소 명칭, 소재지, 수량, 관리주체, 데이터기준일자에 대한 정보를 제공합니다.
Author경상남도 밀양시
URLhttps://www.data.go.kr/data/15127480/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
수량 is highly imbalanced (59.5%)Imbalance
수거장소 명칭 has 4 (2.9%) missing valuesMissing

Reproduction

Analysis started2024-04-06 08:14:16.499627
Analysis finished2024-04-06 08:14:17.623370
Duration1.12 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

배출품목
Categorical

Distinct5
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
폐건전지
65 
폐형광등, 폐건전지
34 
폐형광등
32 
폐건전지
 
3
폐형광등
 
2

Length

Max length10
Median length4
Mean length5.5367647
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row폐형광등, 폐건전지
2nd row폐형광등, 폐건전지
3rd row폐형광등
4th row폐형광등, 폐건전지
5th row폐건전지

Common Values

ValueCountFrequency (%)
폐건전지 65
47.8%
폐형광등, 폐건전지 34
25.0%
폐형광등 32
23.5%
폐건전지 3
 
2.2%
폐형광등 2
 
1.5%

Length

2024-04-06T17:14:17.765513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:14:17.997126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐건전지 102
60.0%
폐형광등 68
40.0%

수거장소 명칭
Text

MISSING 

Distinct107
Distinct (%)81.1%
Missing4
Missing (%)2.9%
Memory size1.2 KiB
2024-04-06T17:14:18.376823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length7.3181818
Min length3

Characters and Unicode

Total characters966
Distinct characters165
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)62.9%

Sample

1st row삼랑진읍 행정복지센터
2nd row수마트
3rd row외송 집하장
4th row삼랑진 중고등학교
5th row삼랑진역
ValueCountFrequency (%)
행정복지센터 6
 
3.5%
거점수거장소 6
 
3.5%
어린이집 4
 
2.3%
복지회관 4
 
2.3%
분리수거장 4
 
2.3%
삼우아파트 3
 
1.7%
쌍용아파트 2
 
1.2%
밀양삼문지엘리베라움 2
 
1.2%
초동면사무소 2
 
1.2%
이편한밀양강아파트 2
 
1.2%
Other values (116) 137
79.7%
2024-04-06T17:14:18.994055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 41
 
4.2%
트 41
 
4.2%
40
 
4.1%
파 39
 
4.0%
지 29
 
3.0%
복 22
 
2.3%
회 21
 
2.2%
관 20
 
2.1%
거 20
 
2.1%
정 19
 
2.0%
Other values (155) 674
69.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 905
93.7%
Space Separator 40
 
4.1%
Decimal Number 10
 
1.0%
Uppercase Letter 4
 
0.4%
Close Punctuation 3
 
0.3%
Open Punctuation 3
 
0.3%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 41
 
4.5%
트 41
 
4.5%
파 39
 
4.3%
지 29
 
3.2%
복 22
 
2.4%
회 21
 
2.3%
관 20
 
2.2%
거 20
 
2.2%
정 19
 
2.1%
마 18
 
2.0%
Other values (146) 635
70.2%
Decimal Number
ValueCountFrequency (%)
1 5
50.0%
2 4
40.0%
3 1
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
L 2
50.0%
H 2
50.0%
Space Separator
ValueCountFrequency (%)
40
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 905
93.7%
Common 56
 
5.8%
Latin 5
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 41
 
4.5%
트 41
 
4.5%
파 39
 
4.3%
지 29
 
3.2%
복 22
 
2.4%
회 21
 
2.3%
관 20
 
2.2%
거 20
 
2.2%
정 19
 
2.1%
마 18
 
2.0%
Other values (146) 635
70.2%
Common
ValueCountFrequency (%)
40
71.4%
1 5
 
8.9%
2 4
 
7.1%
) 3
 
5.4%
( 3
 
5.4%
3 1
 
1.8%
Latin
ValueCountFrequency (%)
L 2
40.0%
H 2
40.0%
e 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 905
93.7%
ASCII 61
 
6.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 41
 
4.5%
트 41
 
4.5%
파 39
 
4.3%
지 29
 
3.2%
복 22
 
2.4%
회 21
 
2.3%
관 20
 
2.2%
거 20
 
2.2%
정 19
 
2.1%
마 18
 
2.0%
Other values (146) 635
70.2%
ASCII
ValueCountFrequency (%)
40
65.6%
1 5
 
8.2%
2 4
 
6.6%
) 3
 
4.9%
( 3
 
4.9%
L 2
 
3.3%
H 2
 
3.3%
3 1
 
1.6%
e 1
 
1.6%

소재지
Text

Distinct112
Distinct (%)82.4%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-04-06T17:14:19.699376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length18.602941
Min length14

Characters and Unicode

Total characters2530
Distinct characters96
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

Unique88 ?
Unique (%)64.7%

Sample

1st row경상남도 밀양시 외송중앙길 6
2nd row경상남도 밀양시 삼랑진로 199
3rd row경상남도 밀양시 송지리 141-1
4th row경상남도 밀양시 만어로 16
5th row경상남도 밀양시 삼랑진로 201
ValueCountFrequency (%)
경상남도 136
22.9%
밀양시 136
22.9%
하남읍 21
 
3.5%
상동면 11
 
1.9%
중앙로 10
 
1.7%
창밀로 9
 
1.5%
산내면 7
 
1.2%
다죽리 6
 
1.0%
점필재로 6
 
1.0%
무안면 6
 
1.0%
Other values (180) 245
41.3%
2024-04-06T17:14:20.733601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
457
18.1%
남 165
 
6.5%
상 154
 
6.1%
밀 147
 
5.8%
양 144
 
5.7%
도 141
 
5.6%
시 138
 
5.5%
경 136
 
5.4%
1 95
 
3.8%
로 74
 
2.9%
Other values (86) 879
34.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1592
62.9%
Space Separator 457
 
18.1%
Decimal Number 436
 
17.2%
Dash Punctuation 45
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 165
10.4%
상 154
 
9.7%
밀 147
 
9.2%
양 144
 
9.0%
도 141
 
8.9%
시 138
 
8.7%
경 136
 
8.5%
로 74
 
4.6%
길 50
 
3.1%
면 33
 
2.1%
Other values (74) 410
25.8%
Decimal Number
ValueCountFrequency (%)
1 95
21.8%
3 68
15.6%
2 51
11.7%
4 46
10.6%
5 38
 
8.7%
6 38
 
8.7%
8 32
 
7.3%
0 25
 
5.7%
7 23
 
5.3%
9 20
 
4.6%
Space Separator
ValueCountFrequency (%)
457
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1592
62.9%
Common 938
37.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 165
10.4%
상 154
 
9.7%
밀 147
 
9.2%
양 144
 
9.0%
도 141
 
8.9%
시 138
 
8.7%
경 136
 
8.5%
로 74
 
4.6%
길 50
 
3.1%
면 33
 
2.1%
Other values (74) 410
25.8%
Common
ValueCountFrequency (%)
457
48.7%
1 95
 
10.1%
3 68
 
7.2%
2 51
 
5.4%
4 46
 
4.9%
- 45
 
4.8%
5 38
 
4.1%
6 38
 
4.1%
8 32
 
3.4%
0 25
 
2.7%
Other values (2) 43
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1592
62.9%
ASCII 938
37.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
457
48.7%
1 95
 
10.1%
3 68
 
7.2%
2 51
 
5.4%
4 46
 
4.9%
- 45
 
4.8%
5 38
 
4.1%
6 38
 
4.1%
8 32
 
3.4%
0 25
 
2.7%
Other values (2) 43
 
4.6%
Hangul
ValueCountFrequency (%)
남 165
10.4%
상 154
 
9.7%
밀 147
 
9.2%
양 144
 
9.0%
도 141
 
8.9%
시 138
 
8.7%
경 136
 
8.5%
로 74
 
4.6%
길 50
 
3.1%
면 33
 
2.1%
Other values (74) 410
25.8%

수량
Categorical

IMBALANCE 

Distinct5
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
1
112 
4
12 
2
 
9
3
 
2
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 112
82.4%
4 12
 
8.8%
2 9
 
6.6%
3 2
 
1.5%
6 1
 
0.7%

Length

2024-04-06T17:14:20.987154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:14:21.194292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 112
82.4%
4 12
 
8.8%
2 9
 
6.6%
3 2
 
1.5%
6 1
 
0.7%

관리주체
Text

Distinct58
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-04-06T17:14:21.596143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length4.8088235
Min length2

Characters and Unicode

Total characters654
Distinct characters117
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

Unique31 ?
Unique (%)22.8%

Sample

1st row삼랑진읍
2nd row삼랑진읍
3rd row삼랑진읍
4th row삼랑진읍
5th row삼랑진읍
ValueCountFrequency (%)
하남읍 21
 
14.3%
삼랑진읍 11
 
7.5%
상동면 11
 
7.5%
내일동 7
 
4.8%
산외면 6
 
4.1%
산내면 5
 
3.4%
관리실 4
 
2.7%
부북면 4
 
2.7%
롯데인벤스아파트 2
 
1.4%
쌍용아파트 2
 
1.4%
Other values (55) 74
50.3%
2024-04-06T17:14:22.322298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
면 36
 
5.5%
아 36
 
5.5%
파 34
 
5.2%
트 33
 
5.0%
읍 32
 
4.9%
남 25
 
3.8%
동 24
 
3.7%
하 22
 
3.4%
삼 19
 
2.9%
내 18
 
2.8%
Other values (107) 375
57.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 634
96.9%
Space Separator 11
 
1.7%
Decimal Number 4
 
0.6%
Uppercase Letter 4
 
0.6%
Lowercase Letter 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 36
 
5.7%
아 36
 
5.7%
파 34
 
5.4%
트 33
 
5.2%
읍 32
 
5.0%
남 25
 
3.9%
동 24
 
3.8%
하 22
 
3.5%
삼 19
 
3.0%
내 18
 
2.8%
Other values (101) 355
56.0%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
1 2
50.0%
Uppercase Letter
ValueCountFrequency (%)
H 2
50.0%
L 2
50.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 634
96.9%
Common 15
 
2.3%
Latin 5
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 36
 
5.7%
아 36
 
5.7%
파 34
 
5.4%
트 33
 
5.2%
읍 32
 
5.0%
남 25
 
3.9%
동 24
 
3.8%
하 22
 
3.5%
삼 19
 
3.0%
내 18
 
2.8%
Other values (101) 355
56.0%
Common
ValueCountFrequency (%)
11
73.3%
2 2
 
13.3%
1 2
 
13.3%
Latin
ValueCountFrequency (%)
H 2
40.0%
L 2
40.0%
e 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 634
96.9%
ASCII 20
 
3.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
면 36
 
5.7%
아 36
 
5.7%
파 34
 
5.4%
트 33
 
5.2%
읍 32
 
5.0%
남 25
 
3.9%
동 24
 
3.8%
하 22
 
3.5%
삼 19
 
3.0%
내 18
 
2.8%
Other values (101) 355
56.0%
ASCII
ValueCountFrequency (%)
11
55.0%
2 2
 
10.0%
1 2
 
10.0%
H 2
 
10.0%
L 2
 
10.0%
e 1
 
5.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-04-04
136 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-04-04
2nd row2024-04-04
3rd row2024-04-04
4th row2024-04-04
5th row2024-04-04

Common Values

ValueCountFrequency (%)
2024-04-04 136
100.0%

Length

2024-04-06T17:14:22.609660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:14:22.986462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-04-04 136
100.0%

Correlations

2024-04-06T17:14:23.111388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출품목수량관리주체
배출품목1.0000.1080.564
수량0.1081.0000.863
관리주체0.5640.8631.000
2024-04-06T17:14:23.302115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수량배출품목
수량1.0000.037
배출품목0.0371.000
2024-04-06T17:14:23.471225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출품목수량
배출품목1.0000.037
수량0.0371.000

Missing values

2024-04-06T17:14:17.181598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:14:17.497314image/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

배출품목수거장소 명칭소재지수량관리주체데이터기준일자
0폐형광등, 폐건전지삼랑진읍 행정복지센터경상남도 밀양시 외송중앙길 62삼랑진읍2024-04-04
1폐형광등, 폐건전지수마트경상남도 밀양시 삼랑진로 1992삼랑진읍2024-04-04
2폐형광등외송 집하장경상남도 밀양시 송지리 141-11삼랑진읍2024-04-04
3폐형광등, 폐건전지삼랑진 중고등학교경상남도 밀양시 만어로 162삼랑진읍2024-04-04
4폐건전지삼랑진역경상남도 밀양시 삼랑진로 2011삼랑진읍2024-04-04
5폐건전지삼랑진초등학교경상남도 밀양시 만어로111삼랑진읍2024-04-04
6폐건전지송진초등학교경상남도 밀양시 삼랑진로 2061삼랑진읍2024-04-04
7폐건전지숭진초등학교경상남도 밀양시 삼랑진로 12861삼랑진읍2024-04-04
8폐건전지아이마트경상남도 밀양시 천태로 13-21삼랑진읍2024-04-04
9폐건전지삼랑진홈마트경상남도 밀양시 송지리 359-121삼랑진읍2024-04-04
배출품목수거장소 명칭소재지수량관리주체데이터기준일자
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