Overview

Dataset statistics

Number of variables3
Number of observations298
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.7 KiB
Average record size in memory26.4 B

Variable types

Numeric1
Categorical2

Dataset

Description가축분뇨 전자인계관리시스템에서 관리하고 있는 데이터 중 액비배출자 배출계획(업체번호, 배출물코드, 신고허가관리번호 등) 으로 등록된 정보 입니다.
Author한국환경공단
URLhttps://www.data.go.kr/data/15041916/fileData.do

Alerts

배출물 has constant value ""Constant

Reproduction

Analysis started2023-12-12 06:16:02.315880
Analysis finished2023-12-12 06:16:02.607888
Duration0.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업체(개인)번호
Real number (ℝ)

Distinct290
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0165081 × 109
Minimum2.0130002 × 109
Maximum2.0220002 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-12T15:16:02.706973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0130002 × 109
5-th percentile2.0140002 × 109
Q12.0160008 × 109
median2.0160034 × 109
Q32.017001 × 109
95-th percentile2.0200005 × 109
Maximum2.0220002 × 109
Range8999997
Interquartile range (IQR)1000256.5

Descriptive statistics

Standard deviation1561914.8
Coefficient of variation (CV)0.00077456408
Kurtosis2.4019173
Mean2.0165081 × 109
Median Absolute Deviation (MAD)997118.5
Skewness0.96002692
Sum6.0091942 × 1011
Variance2.4395777 × 1012
MonotonicityNot monotonic
2023-12-12T15:16:02.842597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014000237 3
 
1.0%
2016000840 2
 
0.7%
2016000732 2
 
0.7%
2017001690 2
 
0.7%
2016002980 2
 
0.7%
2017001891 2
 
0.7%
2013000460 2
 
0.7%
2017001945 1
 
0.3%
2017001201 1
 
0.3%
2016003958 1
 
0.3%
Other values (280) 280
94.0%
ValueCountFrequency (%)
2013000238 1
0.3%
2013000256 1
0.3%
2013000258 1
0.3%
2013000411 1
0.3%
2013000453 1
0.3%
2013000460 2
0.7%
2013000588 1
0.3%
2014000032 1
0.3%
2014000037 1
0.3%
2014000058 1
0.3%
ValueCountFrequency (%)
2022000235 1
0.3%
2022000119 1
0.3%
2022000011 1
0.3%
2021000417 1
0.3%
2021000416 1
0.3%
2021000406 1
0.3%
2021000385 1
0.3%
2021000367 1
0.3%
2021000235 1
0.3%
2021000008 1
0.3%

배출물
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
90
298 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row90
2nd row90
3rd row90
4th row90
5th row90

Common Values

ValueCountFrequency (%)
90 298
100.0%

Length

2023-12-12T15:16:02.965265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:16:03.048684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
90 298
100.0%

신고허가관리번호
Categorical

Distinct9
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
P03
160 
P04
41 
P05
30 
P02
21 
P06
18 
Other values (4)
28 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP03
2nd rowP03
3rd rowP04
4th rowP04
5th rowP03

Common Values

ValueCountFrequency (%)
P03 160
53.7%
P04 41
 
13.8%
P05 30
 
10.1%
P02 21
 
7.0%
P06 18
 
6.0%
P07 15
 
5.0%
P08 7
 
2.3%
P01 4
 
1.3%
P09 2
 
0.7%

Length

2023-12-12T15:16:03.143597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:16:03.307137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
p03 160
53.7%
p04 41
 
13.8%
p05 30
 
10.1%
p02 21
 
7.0%
p06 18
 
6.0%
p07 15
 
5.0%
p08 7
 
2.3%
p01 4
 
1.3%
p09 2
 
0.7%

Interactions

2023-12-12T15:16:02.388719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:16:03.417947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업체(개인)번호신고허가관리번호
업체(개인)번호1.0000.372
신고허가관리번호0.3721.000
2023-12-12T15:16:03.522662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업체(개인)번호신고허가관리번호
업체(개인)번호1.0000.176
신고허가관리번호0.1761.000

Missing values

2023-12-12T15:16:02.486598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:16:02.568293image/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

업체(개인)번호배출물신고허가관리번호
0201300023890P03
1201300025690P03
2201300025890P04
3201300046090P04
4201300046090P03
5201300045390P05
6201300058890P05
7201300041190P05
8201400003290P06
9201400003790P05
업체(개인)번호배출물신고허가관리번호
288201900014990P01
289202000011090P03
290202200001190P04
291201801056190P03
292201900023590P03
293202000045790P01
294202200023590P03
295202000053890P02
296201900029290P03
297201801070490P03