Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells88
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory947.3 KiB
Average record size in memory97.0 B

Variable types

Numeric6
Categorical4

Dataset

Description가축분뇨 전자인계관리시스템에서 관리하고 있는 데이터 중에 가축분뇨 중 분뇨 배출자 관리대장에 등록된 정보 입니다.
Author한국환경공단
URLhttps://www.data.go.kr/data/15041909/fileData.do

Alerts

배출업체번호 is highly overall correlated with 운반업체번호 and 1 other fieldsHigh correlation
운반업체번호 is highly overall correlated with 배출업체번호 and 2 other fieldsHigh correlation
처리업체번호 is highly overall correlated with 배출업체번호 and 2 other fieldsHigh correlation
배출량 is highly overall correlated with 처리방법High correlation
처리구분 is highly overall correlated with 대장등록타입High correlation
처리방법 is highly overall correlated with 배출량High correlation
대장등록타입 is highly overall correlated with 운반업체번호 and 2 other fieldsHigh correlation
축종 is highly imbalanced (98.4%)Imbalance
처리구분 is highly imbalanced (93.7%)Imbalance
대장등록타입 is highly imbalanced (95.9%)Imbalance
배출량 is highly skewed (γ1 = 98.70854855)Skewed
관리대장번호 has unique valuesUnique
배출량 has 326 (3.3%) zerosZeros

Reproduction

Analysis started2023-12-12 17:36:44.400681
Analysis finished2023-12-12 17:36:51.362182
Duration6.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리대장번호
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1469292.1
Minimum1366804
Maximum3684085
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:36:51.468941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1366804
5-th percentile1385828.2
Q11415674.5
median1445207
Q31473647.2
95-th percentile1543831.2
Maximum3684085
Range2317281
Interquartile range (IQR)57972.75

Descriptive statistics

Standard deviation167460.58
Coefficient of variation (CV)0.11397365
Kurtosis59.556575
Mean1469292.1
Median Absolute Deviation (MAD)28982.5
Skewness7.0940114
Sum1.4692921 × 1010
Variance2.8043046 × 1010
MonotonicityNot monotonic
2023-12-13T02:36:51.684669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1462276 1
 
< 0.1%
1419943 1
 
< 0.1%
1366906 1
 
< 0.1%
1384939 1
 
< 0.1%
1542168 1
 
< 0.1%
1442331 1
 
< 0.1%
1485063 1
 
< 0.1%
1455687 1
 
< 0.1%
1421546 1
 
< 0.1%
1394186 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1366804 1
< 0.1%
1366813 1
< 0.1%
1366814 1
< 0.1%
1366818 1
< 0.1%
1366838 1
< 0.1%
1366852 1
< 0.1%
1366853 1
< 0.1%
1366860 1
< 0.1%
1366883 1
< 0.1%
1366906 1
< 0.1%
ValueCountFrequency (%)
3684085 1
< 0.1%
3684078 1
< 0.1%
3684071 1
< 0.1%
3684056 1
< 0.1%
3684055 1
< 0.1%
3684029 1
< 0.1%
3684017 1
< 0.1%
3684016 1
< 0.1%
3684012 1
< 0.1%
3684008 1
< 0.1%

배출업체번호
Real number (ℝ)

HIGH CORRELATION 

Distinct2764
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0161805 × 109
Minimum2.0130001 × 109
Maximum2.0210002 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:36:51.888172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0130001 × 109
5-th percentile2.0130005 × 109
Q12.0160004 × 109
median2.0160025 × 109
Q32.0170002 × 109
95-th percentile2.0190002 × 109
Maximum2.0210002 × 109
Range8000074
Interquartile range (IQR)999755.25

Descriptive statistics

Standard deviation1428836.4
Coefficient of variation (CV)0.00070868473
Kurtosis1.7362998
Mean2.0161805 × 109
Median Absolute Deviation (MAD)2146
Skewness0.31488349
Sum2.0161805 × 1013
Variance2.0415733 × 1012
MonotonicityNot monotonic
2023-12-13T02:36:52.119714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2016002439 24
 
0.2%
2016000215 22
 
0.2%
2015000278 21
 
0.2%
2018010788 21
 
0.2%
2015000106 20
 
0.2%
2016004003 20
 
0.2%
2017000223 20
 
0.2%
2016002851 20
 
0.2%
2019000016 18
 
0.2%
2016000777 17
 
0.2%
Other values (2754) 9797
98.0%
ValueCountFrequency (%)
2013000135 5
0.1%
2013000136 6
0.1%
2013000142 2
 
< 0.1%
2013000149 2
 
< 0.1%
2013000151 1
 
< 0.1%
2013000154 5
0.1%
2013000156 12
0.1%
2013000159 6
0.1%
2013000164 1
 
< 0.1%
2013000174 3
 
< 0.1%
ValueCountFrequency (%)
2021000209 2
 
< 0.1%
2021000207 1
 
< 0.1%
2021000203 1
 
< 0.1%
2021000189 1
 
< 0.1%
2021000169 1
 
< 0.1%
2021000162 5
0.1%
2021000161 1
 
< 0.1%
2021000151 1
 
< 0.1%
2021000141 2
 
< 0.1%
2021000139 2
 
< 0.1%

운반업체번호
Real number (ℝ)

HIGH CORRELATION 

Distinct455
Distinct (%)4.6%
Missing44
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean2.0156623 × 109
Minimum2.0130004 × 109
Maximum2.0210002 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:36:52.340495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0130004 × 109
5-th percentile2.0130006 × 109
Q12.0150003 × 109
median2.0160008 × 109
Q32.0160024 × 109
95-th percentile2.0180108 × 109
Maximum2.0210002 × 109
Range7999851
Interquartile range (IQR)1002097

Descriptive statistics

Standard deviation1321740.8
Coefficient of variation (CV)0.00065573523
Kurtosis1.6520478
Mean2.0156623 × 109
Median Absolute Deviation (MAD)1000340
Skewness0.47832557
Sum2.0067934 × 1013
Variance1.7469986 × 1012
MonotonicityNot monotonic
2023-12-13T02:36:52.541792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2015000322 502
 
5.0%
2018010804 194
 
1.9%
2016001736 141
 
1.4%
2019000255 138
 
1.4%
2017000168 133
 
1.3%
2013000588 127
 
1.3%
2016002849 112
 
1.1%
2014000273 105
 
1.1%
2016000843 98
 
1.0%
2013000589 97
 
1.0%
Other values (445) 8309
83.1%
ValueCountFrequency (%)
2013000369 34
0.3%
2013000371 18
0.2%
2013000373 2
 
< 0.1%
2013000374 10
 
0.1%
2013000377 10
 
0.1%
2013000378 6
 
0.1%
2013000379 15
0.1%
2013000380 8
 
0.1%
2013000384 34
0.3%
2013000385 28
0.3%
ValueCountFrequency (%)
2021000220 1
 
< 0.1%
2021000162 5
 
0.1%
2021000110 2
 
< 0.1%
2020000751 9
 
0.1%
2020000750 6
 
0.1%
2020000737 30
 
0.3%
2020000601 75
0.8%
2020000531 3
 
< 0.1%
2020000451 1
 
< 0.1%
2020000413 1
 
< 0.1%

처리업체번호
Real number (ℝ)

HIGH CORRELATION 

Distinct289
Distinct (%)2.9%
Missing44
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean2.0157484 × 109
Minimum2.0130004 × 109
Maximum2.0210002 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:36:52.730486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0130004 × 109
5-th percentile2.0130004 × 109
Q12.0150003 × 109
median2.0160008 × 109
Q32.0160026 × 109
95-th percentile2.0180108 × 109
Maximum2.0210002 × 109
Range7999793
Interquartile range (IQR)1002228

Descriptive statistics

Standard deviation1327913.5
Coefficient of variation (CV)0.00065876946
Kurtosis2.0939739
Mean2.0157484 × 109
Median Absolute Deviation (MAD)3077
Skewness0.43785168
Sum2.0068791 × 1013
Variance1.7633543 × 1012
MonotonicityNot monotonic
2023-12-13T02:36:52.905491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2015000331 502
 
5.0%
2018010804 194
 
1.9%
2015000437 165
 
1.7%
2016002515 149
 
1.5%
2016000212 140
 
1.4%
2016000587 140
 
1.4%
2016001790 138
 
1.4%
2019000255 138
 
1.4%
2014000271 134
 
1.3%
2017000168 133
 
1.3%
Other values (279) 8123
81.2%
ValueCountFrequency (%)
2013000369 34
0.3%
2013000371 18
0.2%
2013000373 2
 
< 0.1%
2013000374 10
 
0.1%
2013000377 2
 
< 0.1%
2013000378 6
 
0.1%
2013000379 15
0.1%
2013000380 8
 
0.1%
2013000384 34
0.3%
2013000385 28
0.3%
ValueCountFrequency (%)
2021000162 5
 
0.1%
2021000160 23
 
0.2%
2020000623 21
 
0.2%
2020000617 75
0.8%
2020000451 1
 
< 0.1%
2020000424 7
 
0.1%
2020000413 1
 
< 0.1%
2020000295 2
 
< 0.1%
2020000290 2
 
< 0.1%
2020000272 6
 
0.1%

축종
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9968 
3
 
17
9
 
7
5
 
4
4
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 9968
99.7%
3 17
 
0.2%
9 7
 
0.1%
5 4
 
< 0.1%
4 4
 
< 0.1%

Length

2023-12-13T02:36:53.046783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:36:53.148129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9968
99.7%
3 17
 
0.2%
9 7
 
0.1%
5 4
 
< 0.1%
4 4
 
< 0.1%

축분
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9008
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:36:53.241459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q33
95-th percentile3
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.37413013
Coefficient of variation (CV)0.12897481
Kurtosis9.7788306
Mean2.9008
Median Absolute Deviation (MAD)0
Skewness-2.1579068
Sum29008
Variance0.13997336
MonotonicityNot monotonic
2023-12-13T02:36:53.354027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 8876
88.8%
2 900
 
9.0%
1 110
 
1.1%
4 101
 
1.0%
5 12
 
0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
1 110
 
1.1%
2 900
 
9.0%
3 8876
88.8%
4 101
 
1.0%
5 12
 
0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 12
 
0.1%
4 101
 
1.0%
3 8876
88.8%
2 900
 
9.0%
1 110
 
1.1%

처리구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
C
9926 
S
 
74

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
C 9926
99.3%
S 74
 
0.7%

Length

2023-12-13T02:36:53.476953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:36:53.589181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c 9926
99.3%
s 74
 
0.7%

처리방법
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5450 
1
3139 
4
1096 
3
 
159
2
 
156

Length

Max length4
Median length4
Mean length2.635
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
<NA> 5450
54.5%
1 3139
31.4%
4 1096
 
11.0%
3 159
 
1.6%
2 156
 
1.6%

Length

2023-12-13T02:36:53.696678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:36:53.813168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5450
54.5%
1 3139
31.4%
4 1096
 
11.0%
3 159
 
1.6%
2 156
 
1.6%

배출량
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2562
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.906122
Minimum0
Maximum23783.5
Zeros326
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:36:53.945729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.5
Q112.6
median20.855
Q324.37
95-th percentile64
Maximum23783.5
Range23783.5
Interquartile range (IQR)11.77

Descriptive statistics

Standard deviation238.63385
Coefficient of variation (CV)9.2114847
Kurtosis9827.729
Mean25.906122
Median Absolute Deviation (MAD)6.145
Skewness98.708549
Sum259061.22
Variance56946.115
MonotonicityNot monotonic
2023-12-13T02:36:54.118220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.0 331
 
3.3%
0.0 326
 
3.3%
15.0 272
 
2.7%
24.0 258
 
2.6%
20.0 247
 
2.5%
25.0 179
 
1.8%
8.0 135
 
1.4%
22.0 134
 
1.3%
16.0 134
 
1.3%
5.0 127
 
1.3%
Other values (2552) 7857
78.6%
ValueCountFrequency (%)
0.0 326
3.3%
0.5 18
 
0.2%
0.7 1
 
< 0.1%
1.0 85
 
0.9%
1.11 1
 
< 0.1%
1.5 12
 
0.1%
1.65 1
 
< 0.1%
2.0 50
 
0.5%
2.5 12
 
0.1%
2.63 1
 
< 0.1%
ValueCountFrequency (%)
23783.5 1
< 0.1%
450.0 1
< 0.1%
295.0 1
< 0.1%
270.0 1
< 0.1%
240.0 2
< 0.1%
231.0 1
< 0.1%
220.0 1
< 0.1%
216.0 1
< 0.1%
210.0 1
< 0.1%
204.0 1
< 0.1%

대장등록타입
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
5
9956 
1
 
44

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 9956
99.6%
1 44
 
0.4%

Length

2023-12-13T02:36:54.253334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:36:54.378033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 9956
99.6%
1 44
 
0.4%

Interactions

2023-12-13T02:36:50.139637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:46.013379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:46.821284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:47.463690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:48.506511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:49.344891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:50.249242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:46.115492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:46.930233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:47.571951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:48.663559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:49.470882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:50.377833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:46.272121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:47.049943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:47.684531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:48.823001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:49.613477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:50.530364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:46.386163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:47.162541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:47.804195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:48.949665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:49.756028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:50.676834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:46.572275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:47.270076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:47.933116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:49.106848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:49.894056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:50.780097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:46.712729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:47.369948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:48.391103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:49.221732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:36:50.027560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:36:54.493546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리대장번호배출업체번호운반업체번호처리업체번호축종축분처리구분처리방법배출량대장등록타입
관리대장번호1.0000.3930.4790.4650.0000.0290.0000.2190.0000.000
배출업체번호0.3931.0000.8740.8760.2700.3270.1960.3810.0000.210
운반업체번호0.4790.8741.0000.9740.0280.3070.4200.5080.000NaN
처리업체번호0.4650.8760.9741.0000.0000.2770.2520.5070.000NaN
축종0.0000.2700.0280.0001.0000.3460.0000.2330.0000.000
축분0.0290.3270.3070.2770.3461.0000.0820.5610.0000.011
처리구분0.0000.1960.4200.2520.0000.0821.0000.2040.0000.930
처리방법0.2190.3810.5080.5070.2330.5610.2041.000NaN0.261
배출량0.0000.0000.0000.0000.0000.0000.000NaN1.0000.000
대장등록타입0.0000.210NaNNaN0.0000.0110.9300.2610.0001.000
2023-12-13T02:36:55.005288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대장등록타입처리구분축종처리방법
대장등록타입1.0000.7610.0000.174
처리구분0.7611.0000.0000.136
축종0.0000.0001.0000.222
처리방법0.1740.1360.2221.000
2023-12-13T02:36:55.136266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리대장번호배출업체번호운반업체번호처리업체번호축분배출량축종처리구분처리방법대장등록타입
관리대장번호1.0000.0320.0530.050-0.012-0.0180.0000.0000.1520.000
배출업체번호0.0321.0000.5720.596-0.181-0.1720.1150.1500.2370.161
운반업체번호0.0530.5721.0000.831-0.168-0.1560.0120.3220.3291.000
처리업체번호0.0500.5960.8311.000-0.161-0.1500.0000.1930.3281.000
축분-0.012-0.181-0.168-0.1611.0000.1360.2430.0590.3960.008
배출량-0.018-0.172-0.156-0.1500.1361.0000.0000.0001.0000.000
축종0.0000.1150.0120.0000.2430.0001.0000.0000.2220.000
처리구분0.0000.1500.3220.1930.0590.0000.0001.0000.1360.761
처리방법0.1520.2370.3290.3280.3961.0000.2220.1361.0000.174
대장등록타입0.0000.1611.0001.0000.0080.0000.0000.7610.1741.000

Missing values

2023-12-13T02:36:50.912738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:36:51.105294image/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.
2023-12-13T02:36:51.277282image/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

관리대장번호배출업체번호운반업체번호처리업체번호축종축분처리구분처리방법배출량대장등록타입
64094146227620160000062017000197201500038213C<NA>24.55
20357141013820160015242016000742201600132913C<NA>24.75
94441154691820160011642016000841201600084113C124.845
17233140359420180104592016000427201600042714C1150.05
85129148479320160011142016000745201600074813C16.85
61160146005620170005312016000783201600078313C115.05
89125151525820160018292016000835201600083213C<NA>22.625
4852314436382017001752<NA><NA>13S440.01
22557141397820160025842016002446201600251513C<NA>21.095
15774140130520160017492016000760201600075813C<NA>14.915
관리대장번호배출업체번호운반업체번호처리업체번호축종축분처리구분처리방법배출량대장등록타입
52620144897020160018452016000834201600083213C<NA>22.435
6058138662620160000312015000359201600002513C<NA>24.655
18195140426320170004632016002849201700015813C<NA>0.05
77931147691920160003352015000323201600032313C<NA>5.025
86835150356920170006382016002849201600364013C<NA>14.385
90908153468120160033922016000843201600084013C<NA>23.545
66445146595720160035272016002448201600251613C<NA>16.375
64366146356520160013942016000841201600084113C146.825
85776148907320160001322015000346201500034613C120.565
36990142849720160033282016002848201600284813C123.05