Overview

Dataset statistics

Number of variables10
Number of observations25
Missing cells95
Missing cells (%)38.0%
Duplicate rows1
Duplicate rows (%)4.0%
Total size in memory2.1 KiB
Average record size in memory87.3 B

Variable types

Categorical5
Text2
Numeric2
DateTime1

Dataset

Description충청남도 부여군에서 설치하고 관리중인 가로휴지통(쓰레기통) 설치 현황(휴지통 종류, 설치위치, 위도, 경도, 관리기관 등)
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=333&beforeMenuCd=DOM_000000201001001000&publicdatapk=15087103

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 1 (4.0%) duplicate rowsDuplicates
관리기관 전화번호 is highly overall correlated with 설치위치 위도 and 5 other fieldsHigh correlation
시군구명 is highly overall correlated with 설치위치 위도 and 5 other fieldsHigh correlation
쓰레기통 종류 is highly overall correlated with 설치위치 위도 and 5 other fieldsHigh correlation
관리기관명 is highly overall correlated with 설치위치 위도 and 5 other fieldsHigh correlation
시도명 is highly overall correlated with 설치위치 위도 and 5 other fieldsHigh correlation
설치위치 위도 is highly overall correlated with 시도명 and 4 other fieldsHigh correlation
설치위치 경도 is highly overall correlated with 시도명 and 4 other fieldsHigh correlation
설치지점 도로명 has 19 (76.0%) missing valuesMissing
설치지점 has 19 (76.0%) missing valuesMissing
설치위치 위도 has 19 (76.0%) missing valuesMissing
설치위치 경도 has 19 (76.0%) missing valuesMissing
데이터기준일자 has 19 (76.0%) missing valuesMissing

Reproduction

Analysis started2024-01-09 20:40:34.078749
Analysis finished2024-01-09 20:40:35.097080
Duration1.02 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
<NA>
19 
충청남도
6 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 19
76.0%
충청남도 6
 
24.0%

Length

2024-01-10T05:40:35.157755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:40:35.243694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
76.0%
충청남도 6
 
24.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
<NA>
19 
부여군
6 

Length

Max length4
Median length4
Mean length3.76
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 19
76.0%
부여군 6
 
24.0%

Length

2024-01-10T05:40:35.333884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:40:35.436239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
76.0%
부여군 6
 
24.0%

쓰레기통 종류
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
<NA>
19 
원형 스텐레스
6 

Length

Max length7
Median length4
Mean length4.72
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row원형 스텐레스
2nd row원형 스텐레스
3rd row원형 스텐레스
4th row원형 스텐레스
5th row원형 스텐레스

Common Values

ValueCountFrequency (%)
<NA> 19
76.0%
원형 스텐레스 6
 
24.0%

Length

2024-01-10T05:40:35.539377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:40:35.633744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
61.3%
원형 6
 
19.4%
스텐레스 6
 
19.4%

설치지점 도로명
Text

MISSING 

Distinct4
Distinct (%)66.7%
Missing19
Missing (%)76.0%
Memory size332.0 B
2024-01-10T05:40:35.757596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length16
Mean length16.833333
Min length16

Characters and Unicode

Total characters101
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)33.3%

Sample

1st row충청남도 부여군 부여읍 석탑로
2nd row충청남도 부여군 부여읍 석탑로
3rd row충청남도 부여군 부여읍 사비로
4th row충청남도 부여군 부여읍 사비로
5th row충청남도 부여군 부여읍 금성로
ValueCountFrequency (%)
충청남도 6
25.0%
부여군 6
25.0%
부여읍 6
25.0%
석탑로 2
 
8.3%
사비로 2
 
8.3%
금성로 1
 
4.2%
성왕로352번길 1
 
4.2%
2024-01-10T05:40:36.003744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18
17.8%
부 12
11.9%
여 12
11.9%
충 6
 
5.9%
군 6
 
5.9%
청 6
 
5.9%
읍 6
 
5.9%
로 6
 
5.9%
도 6
 
5.9%
남 6
 
5.9%
Other values (12) 17
16.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 80
79.2%
Space Separator 18
 
17.8%
Decimal Number 3
 
3.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 12
15.0%
여 12
15.0%
충 6
7.5%
군 6
7.5%
청 6
7.5%
읍 6
7.5%
로 6
7.5%
도 6
7.5%
남 6
7.5%
석 2
 
2.5%
Other values (8) 12
15.0%
Decimal Number
ValueCountFrequency (%)
3 1
33.3%
5 1
33.3%
2 1
33.3%
Space Separator
ValueCountFrequency (%)
18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 80
79.2%
Common 21
 
20.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 12
15.0%
여 12
15.0%
충 6
7.5%
군 6
7.5%
청 6
7.5%
읍 6
7.5%
로 6
7.5%
도 6
7.5%
남 6
7.5%
석 2
 
2.5%
Other values (8) 12
15.0%
Common
ValueCountFrequency (%)
18
85.7%
3 1
 
4.8%
5 1
 
4.8%
2 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 80
79.2%
ASCII 21
 
20.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18
85.7%
3 1
 
4.8%
5 1
 
4.8%
2 1
 
4.8%
Hangul
ValueCountFrequency (%)
부 12
15.0%
여 12
15.0%
충 6
7.5%
군 6
7.5%
청 6
7.5%
읍 6
7.5%
로 6
7.5%
도 6
7.5%
남 6
7.5%
석 2
 
2.5%
Other values (8) 12
15.0%

설치지점
Text

MISSING 

Distinct5
Distinct (%)83.3%
Missing19
Missing (%)76.0%
Memory size332.0 B
2024-01-10T05:40:36.124995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6
Min length4

Characters and Unicode

Total characters36
Distinct characters23
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

Unique4 ?
Unique (%)66.7%

Sample

1st row정림사지
2nd row정림사지
3rd row버스터미널 앞
4th row우영프라자 앞
5th row동남아파트 앞
ValueCountFrequency (%)
앞 4
40.0%
정림사지 2
20.0%
버스터미널 1
 
10.0%
우영프라자 1
 
10.0%
동남아파트 1
 
10.0%
왕궁아파트 1
 
10.0%
2024-01-10T05:40:36.346953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4
 
11.1%
앞 4
 
11.1%
정 2
 
5.6%
사 2
 
5.6%
지 2
 
5.6%
트 2
 
5.6%
파 2
 
5.6%
아 2
 
5.6%
림 2
 
5.6%
라 1
 
2.8%
Other values (13) 13
36.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32
88.9%
Space Separator 4
 
11.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 4
 
12.5%
정 2
 
6.2%
사 2
 
6.2%
지 2
 
6.2%
트 2
 
6.2%
파 2
 
6.2%
아 2
 
6.2%
림 2
 
6.2%
라 1
 
3.1%
왕 1
 
3.1%
Other values (12) 12
37.5%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32
88.9%
Common 4
 
11.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 4
 
12.5%
정 2
 
6.2%
사 2
 
6.2%
지 2
 
6.2%
트 2
 
6.2%
파 2
 
6.2%
아 2
 
6.2%
림 2
 
6.2%
라 1
 
3.1%
왕 1
 
3.1%
Other values (12) 12
37.5%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32
88.9%
ASCII 4
 
11.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4
100.0%
Hangul
ValueCountFrequency (%)
앞 4
 
12.5%
정 2
 
6.2%
사 2
 
6.2%
지 2
 
6.2%
트 2
 
6.2%
파 2
 
6.2%
아 2
 
6.2%
림 2
 
6.2%
라 1
 
3.1%
왕 1
 
3.1%
Other values (12) 12
37.5%

설치위치 위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)100.0%
Missing19
Missing (%)76.0%
Infinite0
Infinite (%)0.0%
Mean36.279198
Minimum36.274351
Maximum36.281771
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-01-10T05:40:36.443537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.274351
5-th percentile36.275479
Q136.279114
median36.279991
Q336.280193
95-th percentile36.281383
Maximum36.281771
Range0.00742
Interquartile range (IQR)0.00108

Descriptive statistics

Standard deviation0.0025518751
Coefficient of variation (CV)7.03399 × 10-5
Kurtosis3.6152453
Mean36.279198
Median Absolute Deviation (MAD)0.00068
Skewness-1.7097328
Sum217.67519
Variance6.5120667 × 10-6
MonotonicityNot monotonic
2024-01-10T05:40:36.540782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
36.279871 1
 
4.0%
36.278861 1
 
4.0%
36.280111 1
 
4.0%
36.280221 1
 
4.0%
36.274351 1
 
4.0%
36.281771 1
 
4.0%
(Missing) 19
76.0%
ValueCountFrequency (%)
36.274351 1
4.0%
36.278861 1
4.0%
36.279871 1
4.0%
36.280111 1
4.0%
36.280221 1
4.0%
36.281771 1
4.0%
ValueCountFrequency (%)
36.281771 1
4.0%
36.280221 1
4.0%
36.280111 1
4.0%
36.279871 1
4.0%
36.278861 1
4.0%
36.274351 1
4.0%

설치위치 경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)100.0%
Missing19
Missing (%)76.0%
Infinite0
Infinite (%)0.0%
Mean126.91523
Minimum126.91046
Maximum126.9265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-01-10T05:40:36.642369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.91046
5-th percentile126.91049
Q1126.91113
median126.91283
Q3126.91687
95-th percentile126.92443
Maximum126.9265
Range0.01604
Interquartile range (IQR)0.0057325

Descriptive statistics

Standard deviation0.0061956417
Coefficient of variation (CV)4.8817164 × 10-5
Kurtosis1.9492285
Mean126.91523
Median Absolute Deviation (MAD)0.00231
Skewness1.5381746
Sum761.4914
Variance3.8385977 × 10-5
MonotonicityNot monotonic
2024-01-10T05:40:36.735521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
126.912821 1
 
4.0%
126.912831 1
 
4.0%
126.910461 1
 
4.0%
126.910571 1
 
4.0%
126.918211 1
 
4.0%
126.926501 1
 
4.0%
(Missing) 19
76.0%
ValueCountFrequency (%)
126.910461 1
4.0%
126.910571 1
4.0%
126.912821 1
4.0%
126.912831 1
4.0%
126.918211 1
4.0%
126.926501 1
4.0%
ValueCountFrequency (%)
126.926501 1
4.0%
126.918211 1
4.0%
126.912831 1
4.0%
126.912821 1
4.0%
126.910571 1
4.0%
126.910461 1
4.0%

관리기관명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
<NA>
19 
부여군청
6 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 19
76.0%
부여군청 6
 
24.0%

Length

2024-01-10T05:40:36.854559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:40:36.936574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
76.0%
부여군청 6
 
24.0%

관리기관 전화번호
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
<NA>
19 
041-830-2306
6 

Length

Max length12
Median length4
Mean length5.92
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row041-830-2306
2nd row041-830-2306
3rd row041-830-2306
4th row041-830-2306
5th row041-830-2306

Common Values

ValueCountFrequency (%)
<NA> 19
76.0%
041-830-2306 6
 
24.0%

Length

2024-01-10T05:40:37.037362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:40:37.132216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
76.0%
041-830-2306 6
 
24.0%

데이터기준일자
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)16.7%
Missing19
Missing (%)76.0%
Memory size332.0 B
Minimum2021-08-31 00:00:00
Maximum2021-08-31 00:00:00
2024-01-10T05:40:37.206806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:40:37.291405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-10T05:40:34.534962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:40:34.334483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:40:34.628925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:40:34.425429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:40:37.357365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치지점 도로명설치지점설치위치 위도설치위치 경도
설치지점 도로명1.0001.0000.9641.000
설치지점1.0001.0000.6471.000
설치위치 위도0.9640.6471.0000.964
설치위치 경도1.0001.0000.9641.000
2024-01-10T05:40:37.445809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리기관 전화번호시군구명쓰레기통 종류관리기관명시도명
관리기관 전화번호1.0001.0001.0001.0001.000
시군구명1.0001.0001.0001.0001.000
쓰레기통 종류1.0001.0001.0001.0001.000
관리기관명1.0001.0001.0001.0001.000
시도명1.0001.0001.0001.0001.000
2024-01-10T05:40:37.531909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치위치 위도설치위치 경도시도명시군구명쓰레기통 종류관리기관명관리기관 전화번호
설치위치 위도1.000-0.0861.0001.0001.0001.0001.000
설치위치 경도-0.0861.0001.0001.0001.0001.0001.000
시도명1.0001.0001.0001.0001.0001.0001.000
시군구명1.0001.0001.0001.0001.0001.0001.000
쓰레기통 종류1.0001.0001.0001.0001.0001.0001.000
관리기관명1.0001.0001.0001.0001.0001.0001.000
관리기관 전화번호1.0001.0001.0001.0001.0001.0001.000

Missing values

2024-01-10T05:40:34.740140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:40:34.863024image/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.
2024-01-10T05:40:34.991440image/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

시도명시군구명쓰레기통 종류설치지점 도로명설치지점설치위치 위도설치위치 경도관리기관명관리기관 전화번호데이터기준일자
0충청남도부여군원형 스텐레스충청남도 부여군 부여읍 석탑로정림사지36.279871126.912821부여군청041-830-23062021-08-31
1충청남도부여군원형 스텐레스충청남도 부여군 부여읍 석탑로정림사지36.278861126.912831부여군청041-830-23062021-08-31
2충청남도부여군원형 스텐레스충청남도 부여군 부여읍 사비로버스터미널 앞36.280111126.910461부여군청041-830-23062021-08-31
3충청남도부여군원형 스텐레스충청남도 부여군 부여읍 사비로우영프라자 앞36.280221126.910571부여군청041-830-23062021-08-31
4충청남도부여군원형 스텐레스충청남도 부여군 부여읍 금성로동남아파트 앞36.274351126.918211부여군청041-830-23062021-08-31
5충청남도부여군원형 스텐레스충청남도 부여군 부여읍 성왕로352번길왕궁아파트 앞36.281771126.926501부여군청041-830-23062021-08-31
6<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
7<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
8<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
9<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
시도명시군구명쓰레기통 종류설치지점 도로명설치지점설치위치 위도설치위치 경도관리기관명관리기관 전화번호데이터기준일자
15<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
16<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
17<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
18<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
19<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
20<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
21<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
22<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
23<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
24<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

시도명시군구명쓰레기통 종류설치지점 도로명설치지점설치위치 위도설치위치 경도관리기관명관리기관 전화번호데이터기준일자# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>19