Overview

Dataset statistics

Number of variables7
Number of observations246
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.5 KiB
Average record size in memory60.5 B

Variable types

Numeric3
Text1
Categorical2
Boolean1

Dataset

Description폐기물처분부담금관리시스템 내 등록된 데이터로 폐기물처분부담금 시ㆍ군ㆍ구 정보 등록 조회 하는 데이터 자료 입니다.
Author한국환경공단
URLhttps://www.data.go.kr/data/15092765/fileData.do

Alerts

사용여부 has constant value ""Constant
시군구코드 is highly overall correlated with 시도코드 and 1 other fieldsHigh correlation
시도코드 is highly overall correlated with 시군구코드 and 1 other fieldsHigh correlation
시도명 is highly overall correlated with 시군구코드 and 1 other fieldsHigh correlation
레벨 is highly imbalanced (63.7%)Imbalance
시군구코드 has unique valuesUnique

Reproduction

Analysis started2023-12-12 04:13:33.864626
Analysis finished2023-12-12 04:13:35.781347
Duration1.92 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct246
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7598634 × 109
Minimum1.1 × 109
Maximum5.013 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-12T13:13:35.903170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1 × 109
5-th percentile1.13875 × 109
Q12.87125 × 109
median4.2475 × 109
Q34.67175 × 109
95-th percentile4.86225 × 109
Maximum5.013 × 109
Range3.913 × 109
Interquartile range (IQR)1.8005 × 109

Descriptive statistics

Standard deviation1.1600503 × 109
Coefficient of variation (CV)0.30853523
Kurtosis0.11255251
Mean3.7598634 × 109
Median Absolute Deviation (MAD)4.725 × 108
Skewness-1.1286265
Sum9.249264 × 1011
Variance1.3457167 × 1018
MonotonicityStrictly increasing
2023-12-12T13:13:36.097627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1100000000 1
 
0.4%
4471000000 1
 
0.4%
4477000000 1
 
0.4%
4479000000 1
 
0.4%
4480000000 1
 
0.4%
4481000000 1
 
0.4%
4482500000 1
 
0.4%
4500000000 1
 
0.4%
4511000000 1
 
0.4%
4513000000 1
 
0.4%
Other values (236) 236
95.9%
ValueCountFrequency (%)
1100000000 1
0.4%
1111000000 1
0.4%
1114000000 1
0.4%
1117000000 1
0.4%
1120000000 1
0.4%
1121500000 1
0.4%
1123000000 1
0.4%
1126000000 1
0.4%
1129000000 1
0.4%
1130500000 1
0.4%
ValueCountFrequency (%)
5013000000 1
0.4%
5011000000 1
0.4%
5000000000 1
0.4%
4889000000 1
0.4%
4888000000 1
0.4%
4887000000 1
0.4%
4886000000 1
0.4%
4885000000 1
0.4%
4884000000 1
0.4%
4882000000 1
0.4%

시군구명
Text

Distinct223
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-12T13:13:36.525668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0691057
Min length2

Characters and Unicode

Total characters755
Distinct characters139
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique215 ?
Unique (%)87.4%

Sample

1st row서울특별시
2nd row종로구
3rd row중구
4th row용산구
5th row성동구
ValueCountFrequency (%)
중구 6
 
2.4%
동구 6
 
2.4%
서구 5
 
2.0%
북구 4
 
1.6%
남구 4
 
1.6%
강서구 2
 
0.8%
세종특별자치시 2
 
0.8%
고성군 2
 
0.8%
남원시 1
 
0.4%
완주군 1
 
0.4%
Other values (213) 213
86.6%
2023-12-12T13:13:37.109735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 87
 
11.5%
군 85
 
11.3%
구 75
 
9.9%
천 23
 
3.0%
주 22
 
2.9%
성 18
 
2.4%
산 18
 
2.4%
양 18
 
2.4%
동 17
 
2.3%
남 15
 
2.0%
Other values (129) 377
49.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 755
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 87
 
11.5%
군 85
 
11.3%
구 75
 
9.9%
천 23
 
3.0%
주 22
 
2.9%
성 18
 
2.4%
산 18
 
2.4%
양 18
 
2.4%
동 17
 
2.3%
남 15
 
2.0%
Other values (129) 377
49.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 755
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 87
 
11.5%
군 85
 
11.3%
구 75
 
9.9%
천 23
 
3.0%
주 22
 
2.9%
성 18
 
2.4%
산 18
 
2.4%
양 18
 
2.4%
동 17
 
2.3%
남 15
 
2.0%
Other values (129) 377
49.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 755
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 87
 
11.5%
군 85
 
11.3%
구 75
 
9.9%
천 23
 
3.0%
주 22
 
2.9%
성 18
 
2.4%
산 18
 
2.4%
양 18
 
2.4%
동 17
 
2.3%
남 15
 
2.0%
Other values (129) 377
49.9%

레벨
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
1
229 
0
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 229
93.1%
0 17
 
6.9%

Length

2023-12-12T13:13:37.284521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:13:37.380989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 229
93.1%
0 17
 
6.9%

시도명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
경기도
32 
서울특별시
26 
경상북도
24 
전라남도
23 
강원도
19 
Other values (12)
122 

Length

Max length7
Median length5
Mean length4.1829268
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 32
13.0%
서울특별시 26
10.6%
경상북도 24
9.8%
전라남도 23
9.3%
강원도 19
7.7%
경상남도 19
7.7%
부산광역시 17
 
6.9%
충청남도 16
 
6.5%
전라북도 15
 
6.1%
충청북도 12
 
4.9%
Other values (7) 43
17.5%

Length

2023-12-12T13:13:37.510633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 32
13.0%
서울특별시 26
10.6%
경상북도 24
9.8%
전라남도 23
9.3%
강원도 19
7.7%
경상남도 19
7.7%
부산광역시 17
 
6.9%
충청남도 16
 
6.5%
전라북도 15
 
6.1%
충청북도 12
 
4.9%
Other values (7) 43
17.5%

시도코드
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7170732 × 109
Minimum1.1 × 109
Maximum5 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-12T13:13:37.646914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1 × 109
5-th percentile1.1 × 109
Q12.8 × 109
median4.2 × 109
Q34.6 × 109
95-th percentile4.8 × 109
Maximum5 × 109
Range3.9 × 109
Interquartile range (IQR)1.8 × 109

Descriptive statistics

Standard deviation1.1525452 × 109
Coefficient of variation (CV)0.31006793
Kurtosis0.16669702
Mean3.7170732 × 109
Median Absolute Deviation (MAD)5 × 108
Skewness-1.1474185
Sum9.144 × 1011
Variance1.3283604 × 1018
MonotonicityIncreasing
2023-12-12T13:13:37.795018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
4100000000 32
13.0%
1100000000 26
10.6%
4700000000 24
9.8%
4600000000 23
9.3%
4800000000 19
7.7%
4200000000 19
7.7%
2600000000 17
 
6.9%
4400000000 16
 
6.5%
4500000000 15
 
6.1%
4300000000 12
 
4.9%
Other values (7) 43
17.5%
ValueCountFrequency (%)
1100000000 26
10.6%
2600000000 17
6.9%
2700000000 9
 
3.7%
2800000000 11
 
4.5%
2900000000 6
 
2.4%
3000000000 6
 
2.4%
3100000000 6
 
2.4%
3600000000 2
 
0.8%
4100000000 32
13.0%
4200000000 19
7.7%
ValueCountFrequency (%)
5000000000 3
 
1.2%
4800000000 19
7.7%
4700000000 24
9.8%
4600000000 23
9.3%
4500000000 15
6.1%
4400000000 16
6.5%
4300000000 12
 
4.9%
4200000000 19
7.7%
4100000000 32
13.0%
3600000000 2
 
0.8%

정렬
Real number (ℝ)

Distinct32
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.182927
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-12T13:13:37.976711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median9
Q315
95-th percentile23.75
Maximum32
Range31
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.1573477
Coefficient of variation (CV)0.70287726
Kurtosis-0.084471917
Mean10.182927
Median Absolute Deviation (MAD)5
Skewness0.75577962
Sum2505
Variance51.227626
MonotonicityNot monotonic
2023-12-12T13:13:38.151772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 17
 
6.9%
2 17
 
6.9%
3 16
 
6.5%
4 15
 
6.1%
5 15
 
6.1%
6 15
 
6.1%
7 12
 
4.9%
8 12
 
4.9%
9 12
 
4.9%
10 11
 
4.5%
Other values (22) 104
42.3%
ValueCountFrequency (%)
1 17
6.9%
2 17
6.9%
3 16
6.5%
4 15
6.1%
5 15
6.1%
6 15
6.1%
7 12
4.9%
8 12
4.9%
9 12
4.9%
10 11
4.5%
ValueCountFrequency (%)
32 1
 
0.4%
31 1
 
0.4%
30 1
 
0.4%
29 1
 
0.4%
28 1
 
0.4%
27 1
 
0.4%
26 2
0.8%
25 2
0.8%
24 3
1.2%
23 4
1.6%

사용여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size378.0 B
True
246 
ValueCountFrequency (%)
True 246
100.0%
2023-12-12T13:13:38.290054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-12-12T13:13:35.094795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:34.221441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:34.673855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:35.233498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:34.384929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:34.824724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:35.363796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:34.534189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:34.965585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:13:38.354249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드레벨시도명시도코드정렬
시군구코드1.0000.1760.9930.9980.315
레벨0.1761.0000.0000.1030.545
시도명0.9930.0001.0001.0000.056
시도코드0.9980.1031.0001.0000.236
정렬0.3150.5450.0560.2361.000
2023-12-12T13:13:38.476953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
레벨시도명
레벨1.0000.000
시도명0.0001.000
2023-12-12T13:13:38.608543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드시도코드정렬레벨시도명
시군구코드1.0000.9960.1150.0930.950
시도코드0.9961.0000.0460.0990.981
정렬0.1150.0461.0000.4130.014
레벨0.0930.0990.4131.0000.000
시도명0.9500.9810.0140.0001.000

Missing values

2023-12-12T13:13:35.557805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:13:35.720691image/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

시군구코드시군구명레벨시도명시도코드정렬사용여부
01100000000서울특별시0서울특별시11000000001Y
11111000000종로구1서울특별시11000000002Y
21114000000중구1서울특별시11000000003Y
31117000000용산구1서울특별시11000000004Y
41120000000성동구1서울특별시11000000005Y
51121500000광진구1서울특별시11000000006Y
61123000000동대문구1서울특별시11000000007Y
71126000000중랑구1서울특별시11000000008Y
81129000000성북구1서울특별시11000000009Y
91130500000강북구1서울특별시110000000010Y
시군구코드시군구명레벨시도명시도코드정렬사용여부
2364882000000고성군1경상남도480000000013Y
2374884000000남해군1경상남도480000000014Y
2384885000000하동군1경상남도480000000015Y
2394886000000산청군1경상남도480000000016Y
2404887000000함양군1경상남도480000000017Y
2414888000000거창군1경상남도480000000018Y
2424889000000합천군1경상남도480000000019Y
2435000000000제주특별자치도0제주특별자치도50000000001Y
2445011000000제주시1제주특별자치도50000000002Y
2455013000000서귀포시1제주특별자치도50000000003Y