Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows1677
Duplicate rows (%)16.8%
Total size in memory918.0 KiB
Average record size in memory94.0 B

Variable types

Categorical4
Numeric6

Dataset

Description가축분뇨전자인계관리시스템에서 관리하고있는 가축분뇨 중 액비의 마감된 전자인계 내역 정보로 등록된 데이터입니다.
Author한국환경공단
URLhttps://www.data.go.kr/data/15039577/fileData.do

Alerts

Dataset has 1677 (16.8%) duplicate rowsDuplicates
운반살포일자 is highly overall correlated with 마감처리일자 and 2 other fieldsHigh correlation
운반인수일자 is highly overall correlated with 마감처리일자 and 2 other fieldsHigh correlation
인계일자 is highly overall correlated with 마감처리일자 and 2 other fieldsHigh correlation
배출업체번호 is highly overall correlated with 운반업체번호High correlation
배출인계량(톤) is highly overall correlated with 운반인수량 and 1 other fieldsHigh correlation
운반업체번호 is highly overall correlated with 배출업체번호High correlation
운반인수량 is highly overall correlated with 배출인계량(톤) and 1 other fieldsHigh correlation
운반살포량 is highly overall correlated with 배출인계량(톤) and 1 other fieldsHigh correlation
마감처리일자 is highly overall correlated with 인계일자 and 2 other fieldsHigh correlation
운반살포량 has 170 (1.7%) zerosZeros

Reproduction

Analysis started2023-12-12 15:24:53.281133
Analysis finished2023-12-12 15:25:00.016498
Duration6.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

마감처리일자
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-03
3263 
2021-02
2579 
2021-04
2380 
2021-01
1606 
2021-06
 
97
Other values (4)
 
75

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2021-03
2nd row2021-03
3rd row2021-02
4th row2021-02
5th row2021-02

Common Values

ValueCountFrequency (%)
2021-03 3263
32.6%
2021-02 2579
25.8%
2021-04 2380
23.8%
2021-01 1606
16.1%
2021-06 97
 
1.0%
2021-09 36
 
0.4%
2021-07 22
 
0.2%
2021-05 16
 
0.2%
2021-08 1
 
< 0.1%

Length

2023-12-13T00:25:00.091462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:25:00.273802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-03 3263
32.6%
2021-02 2579
25.8%
2021-04 2380
23.8%
2021-01 1606
16.1%
2021-06 97
 
1.0%
2021-09 36
 
0.4%
2021-07 22
 
0.2%
2021-05 16
 
0.2%
2021-08 1
 
< 0.1%

배출업체번호
Real number (ℝ)

HIGH CORRELATION 

Distinct782
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.015636 × 109
Minimum2.0130001 × 109
Maximum2.0210002 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:25:00.440122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0130001 × 109
5-th percentile2.0130004 × 109
Q12.0150003 × 109
median2.016001 × 109
Q32.0160035 × 109
95-th percentile2.0170019 × 109
Maximum2.0210002 × 109
Range8000022
Interquartile range (IQR)1003250

Descriptive statistics

Standard deviation1352899.6
Coefficient of variation (CV)0.00067120236
Kurtosis1.1421521
Mean2.015636 × 109
Median Absolute Deviation (MAD)999437
Skewness0.058036517
Sum2.015636 × 1013
Variance1.8303374 × 1012
MonotonicityNot monotonic
2023-12-13T00:25:00.609914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2016000829 224
 
2.2%
2015000203 183
 
1.8%
2016003596 157
 
1.6%
2013000369 130
 
1.3%
2016003524 123
 
1.2%
2017000168 121
 
1.2%
2013000588 118
 
1.2%
2017000966 118
 
1.2%
2014000243 116
 
1.2%
2015000094 115
 
1.1%
Other values (772) 8595
86.0%
ValueCountFrequency (%)
2013000149 1
 
< 0.1%
2013000160 51
0.5%
2013000201 1
 
< 0.1%
2013000202 5
 
0.1%
2013000217 6
 
0.1%
2013000236 2
 
< 0.1%
2013000238 2
 
< 0.1%
2013000256 3
 
< 0.1%
2013000258 4
 
< 0.1%
2013000274 7
 
0.1%
ValueCountFrequency (%)
2021000171 1
 
< 0.1%
2021000157 2
 
< 0.1%
2021000154 1
 
< 0.1%
2021000137 3
 
< 0.1%
2021000117 1
 
< 0.1%
2021000014 3
 
< 0.1%
2020000753 9
0.1%
2020000740 1
 
< 0.1%
2020000708 6
0.1%
2020000635 7
0.1%

배출인계량(톤)
Real number (ℝ)

HIGH CORRELATION 

Distinct815
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.013328
Minimum2
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:25:00.768190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6.21
Q113.83
median16
Q323
95-th percentile25
Maximum300
Range298
Interquartile range (IQR)9.17

Descriptive statistics

Standard deviation10.932371
Coefficient of variation (CV)0.60690457
Kurtosis121.0005
Mean18.013328
Median Absolute Deviation (MAD)7
Skewness7.6440389
Sum180133.28
Variance119.51674
MonotonicityNot monotonic
2023-12-13T00:25:00.940269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.0 1301
 
13.0%
23.0 1115
 
11.2%
24.0 898
 
9.0%
8.0 481
 
4.8%
14.0 477
 
4.8%
22.0 440
 
4.4%
16.0 428
 
4.3%
20.0 428
 
4.3%
21.0 359
 
3.6%
7.0 321
 
3.2%
Other values (805) 3752
37.5%
ValueCountFrequency (%)
2.0 6
 
0.1%
2.4 1
 
< 0.1%
3.0 4
 
< 0.1%
3.8 1
 
< 0.1%
4.0 17
 
0.2%
4.3 2
 
< 0.1%
4.5 13
 
0.1%
5.0 170
1.7%
5.5 4
 
< 0.1%
5.6 1
 
< 0.1%
ValueCountFrequency (%)
300.0 1
 
< 0.1%
225.0 1
 
< 0.1%
200.0 4
< 0.1%
192.0 1
 
< 0.1%
175.0 1
 
< 0.1%
161.0 1
 
< 0.1%
154.0 1
 
< 0.1%
150.0 3
< 0.1%
128.0 1
 
< 0.1%
125.0 5
0.1%

인계일자
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-03
3293 
2021-02
2781 
2021-01
2099 
2021-04
1827 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-03
2nd row2021-03
3rd row2021-02
4th row2021-02
5th row2021-02

Common Values

ValueCountFrequency (%)
2021-03 3293
32.9%
2021-02 2781
27.8%
2021-01 2099
21.0%
2021-04 1827
18.3%

Length

2023-12-13T00:25:01.071028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:25:01.184786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-03 3293
32.9%
2021-02 2781
27.8%
2021-01 2099
21.0%
2021-04 1827
18.3%

관할관청
Real number (ℝ)

Distinct113
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1252.5946
Minimum201
Maximum1701
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:25:01.311797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201
5-th percentile829
Q11103
median1213
Q31505
95-th percentile1602
Maximum1701
Range1500
Interquartile range (IQR)402

Descriptive statistics

Standard deviation242.30764
Coefficient of variation (CV)0.19344458
Kurtosis-0.9328071
Mean1252.5946
Median Absolute Deviation (MAD)198
Skewness-0.062151817
Sum12525946
Variance58712.993
MonotonicityNot monotonic
2023-12-13T00:25:01.496110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1602 776
 
7.8%
1213 590
 
5.9%
1601 481
 
4.8%
1015 387
 
3.9%
1201 334
 
3.3%
912 298
 
3.0%
829 283
 
2.8%
1507 255
 
2.5%
1203 251
 
2.5%
1504 241
 
2.4%
Other values (103) 6104
61.0%
ValueCountFrequency (%)
201 2
 
< 0.1%
313 2
 
< 0.1%
511 1
 
< 0.1%
704 11
 
0.1%
802 1
 
< 0.1%
808 4
 
< 0.1%
816 4
 
< 0.1%
818 18
 
0.2%
820 177
1.8%
821 54
 
0.5%
ValueCountFrequency (%)
1701 118
 
1.2%
1602 776
7.8%
1601 481
4.8%
1515 145
 
1.5%
1514 25
 
0.2%
1513 52
 
0.5%
1512 45
 
0.4%
1511 207
 
2.1%
1510 184
 
1.8%
1509 74
 
0.7%

운반업체번호
Real number (ℝ)

HIGH CORRELATION 

Distinct339
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0156533 × 109
Minimum2.0130001 × 109
Maximum2.021 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:25:01.688590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0130001 × 109
5-th percentile2.0130004 × 109
Q12.0150003 × 109
median2.0160008 × 109
Q32.0160033 × 109
95-th percentile2.0180107 × 109
Maximum2.021 × 109
Range7999876
Interquartile range (IQR)1003068

Descriptive statistics

Standard deviation1574453.9
Coefficient of variation (CV)0.00078111344
Kurtosis2.4566767
Mean2.0156533 × 109
Median Absolute Deviation (MAD)1000405
Skewness0.90681193
Sum2.0156533 × 1013
Variance2.478905 × 1012
MonotonicityNot monotonic
2023-12-13T00:25:01.897856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2021000007 262
 
2.6%
2016000829 235
 
2.4%
2015000203 183
 
1.8%
2015000311 155
 
1.6%
2016000961 130
 
1.3%
2013000369 130
 
1.3%
2017000168 121
 
1.2%
2016001370 120
 
1.2%
2013000588 119
 
1.2%
2017000966 118
 
1.2%
Other values (329) 8427
84.3%
ValueCountFrequency (%)
2013000149 1
 
< 0.1%
2013000160 51
0.5%
2013000201 1
 
< 0.1%
2013000202 5
 
0.1%
2013000217 6
 
0.1%
2013000238 2
 
< 0.1%
2013000256 3
 
< 0.1%
2013000277 17
 
0.2%
2013000297 2
 
< 0.1%
2013000313 5
 
0.1%
ValueCountFrequency (%)
2021000025 5
 
0.1%
2021000007 262
2.6%
2020000737 13
 
0.1%
2020000645 3
 
< 0.1%
2020000642 11
 
0.1%
2020000618 4
 
< 0.1%
2020000530 2
 
< 0.1%
2020000527 3
 
< 0.1%
2020000476 6
 
0.1%
2020000413 23
 
0.2%

운반인수일자
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-03
3293 
2021-02
2781 
2021-01
2099 
2021-04
1827 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-03
2nd row2021-03
3rd row2021-02
4th row2021-02
5th row2021-02

Common Values

ValueCountFrequency (%)
2021-03 3293
32.9%
2021-02 2781
27.8%
2021-01 2099
21.0%
2021-04 1827
18.3%

Length

2023-12-13T00:25:02.079967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:25:02.209343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-03 3293
32.9%
2021-02 2781
27.8%
2021-01 2099
21.0%
2021-04 1827
18.3%

운반인수량
Real number (ℝ)

HIGH CORRELATION 

Distinct815
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.013328
Minimum2
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:25:02.360625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6.21
Q113.83
median16
Q323
95-th percentile25
Maximum300
Range298
Interquartile range (IQR)9.17

Descriptive statistics

Standard deviation10.932371
Coefficient of variation (CV)0.60690457
Kurtosis121.0005
Mean18.013328
Median Absolute Deviation (MAD)7
Skewness7.6440389
Sum180133.28
Variance119.51674
MonotonicityNot monotonic
2023-12-13T00:25:02.528816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.0 1301
 
13.0%
23.0 1115
 
11.2%
24.0 898
 
9.0%
8.0 481
 
4.8%
14.0 477
 
4.8%
22.0 440
 
4.4%
16.0 428
 
4.3%
20.0 428
 
4.3%
21.0 359
 
3.6%
7.0 321
 
3.2%
Other values (805) 3752
37.5%
ValueCountFrequency (%)
2.0 6
 
0.1%
2.4 1
 
< 0.1%
3.0 4
 
< 0.1%
3.8 1
 
< 0.1%
4.0 17
 
0.2%
4.3 2
 
< 0.1%
4.5 13
 
0.1%
5.0 170
1.7%
5.5 4
 
< 0.1%
5.6 1
 
< 0.1%
ValueCountFrequency (%)
300.0 1
 
< 0.1%
225.0 1
 
< 0.1%
200.0 4
< 0.1%
192.0 1
 
< 0.1%
175.0 1
 
< 0.1%
161.0 1
 
< 0.1%
154.0 1
 
< 0.1%
150.0 3
< 0.1%
128.0 1
 
< 0.1%
125.0 5
0.1%

운반살포일자
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-03
3261 
2021-02
2744 
2021-01
2053 
2021-04
1807 
<NA>
 
134

Length

Max length7
Median length7
Mean length6.9598
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2021-03
2nd row2021-03
3rd row2021-02
4th row2021-02
5th row2021-02

Common Values

ValueCountFrequency (%)
2021-03 3261
32.6%
2021-02 2744
27.4%
2021-01 2053
20.5%
2021-04 1807
18.1%
<NA> 134
 
1.3%
2021-09 1
 
< 0.1%

Length

2023-12-13T00:25:02.660931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:25:02.763079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-03 3261
32.6%
2021-02 2744
27.4%
2021-01 2053
20.5%
2021-04 1807
18.1%
na 134
 
1.3%
2021-09 1
 
< 0.1%

운반살포량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct816
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.719218
Minimum0
Maximum300
Zeros170
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:25:02.887551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q113
median16
Q323
95-th percentile25
Maximum300
Range300
Interquartile range (IQR)10

Descriptive statistics

Standard deviation11.118224
Coefficient of variation (CV)0.62746699
Kurtosis113.49309
Mean17.719218
Median Absolute Deviation (MAD)7
Skewness7.2377658
Sum177192.18
Variance123.61491
MonotonicityNot monotonic
2023-12-13T00:25:03.105014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.0 1253
 
12.5%
23.0 1108
 
11.1%
24.0 880
 
8.8%
8.0 478
 
4.8%
14.0 474
 
4.7%
16.0 427
 
4.3%
22.0 416
 
4.2%
20.0 412
 
4.1%
21.0 346
 
3.5%
7.0 321
 
3.2%
Other values (806) 3885
38.9%
ValueCountFrequency (%)
0.0 170
1.7%
2.0 6
 
0.1%
2.4 1
 
< 0.1%
3.0 4
 
< 0.1%
3.8 1
 
< 0.1%
4.0 16
 
0.2%
4.3 2
 
< 0.1%
4.5 13
 
0.1%
5.0 157
1.6%
5.5 1
 
< 0.1%
ValueCountFrequency (%)
300.0 1
 
< 0.1%
225.0 1
 
< 0.1%
200.0 4
< 0.1%
192.0 1
 
< 0.1%
175.0 1
 
< 0.1%
161.0 1
 
< 0.1%
154.0 1
 
< 0.1%
150.0 3
< 0.1%
128.0 1
 
< 0.1%
125.0 5
0.1%

Interactions

2023-12-13T00:24:58.751524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:54.937661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:55.767784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:56.537617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:57.262673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:57.983047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:58.865089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:55.092105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:55.901021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:56.659298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:57.389586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:58.096538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:58.984842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:55.211331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:56.032872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:56.763198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:57.502500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:58.215895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:59.117275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:55.352607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:56.163126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:56.910300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:57.648107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:58.363476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:59.217674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:55.483158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:56.313100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:57.049673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:57.760128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:58.485986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:59.329317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:55.609472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:56.425062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:57.154899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:57.871951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:58.634845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:25:03.242855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
마감처리일자배출업체번호배출인계량(톤)인계일자관할관청운반업체번호운반인수일자운반인수량운반살포일자운반살포량
마감처리일자1.0000.1990.0000.8900.2810.1820.8900.0000.8500.000
배출업체번호0.1991.0000.1000.1580.6730.9340.1580.1000.1910.102
배출인계량(톤)0.0000.1001.0000.0070.2530.1040.0071.0000.0001.000
인계일자0.8900.1580.0071.0000.1580.1511.0000.0070.9840.000
관할관청0.2810.6730.2530.1581.0000.7230.1580.2530.1520.246
운반업체번호0.1820.9340.1040.1510.7231.0000.1510.1040.1820.102
운반인수일자0.8900.1580.0071.0000.1580.1511.0000.0070.9840.000
운반인수량0.0000.1001.0000.0070.2530.1040.0071.0000.0001.000
운반살포일자0.8500.1910.0000.9840.1520.1820.9840.0001.0000.000
운반살포량0.0000.1021.0000.0000.2460.1020.0001.0000.0001.000
2023-12-13T00:25:03.425921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운반살포일자운반인수일자마감처리일자인계일자
운반살포일자1.0000.9990.7020.999
운반인수일자0.9991.0000.8051.000
마감처리일자0.7020.8051.0000.805
인계일자0.9991.0000.8051.000
2023-12-13T00:25:03.581100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출업체번호배출인계량(톤)관할관청운반업체번호운반인수량운반살포량마감처리일자인계일자운반인수일자운반살포일자
배출업체번호1.000-0.018-0.4430.774-0.018-0.0230.0910.0950.0950.080
배출인계량(톤)-0.0181.0000.052-0.0551.0000.9710.0000.0040.0040.000
관할관청-0.4430.0521.000-0.4180.0520.0550.0930.1010.1010.088
운반업체번호0.774-0.055-0.4181.000-0.055-0.0520.0840.0910.0910.077
운반인수량-0.0181.0000.052-0.0551.0000.9710.0000.0040.0040.000
운반살포량-0.0230.9710.055-0.0520.9711.0000.0000.0030.0030.000
마감처리일자0.0910.0000.0930.0840.0000.0001.0000.8050.8050.702
인계일자0.0950.0040.1010.0910.0040.0030.8051.0001.0000.999
운반인수일자0.0950.0040.1010.0910.0040.0030.8051.0001.0000.999
운반살포일자0.0800.0000.0880.0770.0000.0000.7020.9990.9991.000

Missing values

2023-12-13T00:24:59.472257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:24:59.947991image/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

마감처리일자배출업체번호배출인계량(톤)인계일자관할관청운반업체번호운반인수일자운반인수량운반살포일자운반살포량
641162021-03201300037114.02021-03160220130003712021-0314.02021-0314.0
610672021-03201600084022.02021-03150220160008402021-0322.02021-0322.0
401592021-02201801003125.02021-02120620190002572021-0225.02021-0225.0
392642021-02201700015112.02021-02151220190002522021-0212.02021-0212.0
276312021-022015000394150.02021-02130620150003942021-02150.02021-02150.0
360672021-0220160025628.02021-02101920160040942021-028.02021-028.0
243872021-02201300041023.02021-02160120130004102021-0223.02021-0223.0
406262021-02201500031814.02021-02120420150003182021-0214.02021-0214.0
326152021-02201500038112.02021-02110520150003812021-0212.02021-0212.0
345922021-02201700107523.162021-0290120170010742021-0223.162021-0223.16
마감처리일자배출업체번호배출인계량(톤)인계일자관할관청운반업체번호운반인수일자운반인수량운반살포일자운반살포량
776932021-04201600224123.02021-03131520160013342021-0323.02021-0423.0
931752021-0420150002268.02021-04150520150002262021-048.02021-048.0
850162021-04201600224123.02021-04131520160013342021-0423.02021-0423.0
460642021-03201500026717.02021-02151520170017502021-0217.02021-0217.0
361482021-02201600407121.02021-02102320160018812021-0221.02021-0221.0
259622021-02201700088660.02021-02140120170008862021-0260.02021-0260.0
268672021-02201500020321.682021-02151020150002032021-0221.682021-0221.68
310692021-02201600206921.02021-02130920170015512021-0221.02021-0221.0
639162021-0320170001358.02021-03130920170001282021-038.02021-038.0
931592021-04202000041321.02021-04151520200004132021-0421.02021-0421.0

Duplicate rows

Most frequently occurring

마감처리일자배출업체번호배출인계량(톤)인계일자관할관청운반업체번호운반인수일자운반인수량운반살포일자운반살포량# duplicates
7892021-03201400024315.02021-03121320140002432021-0315.02021-0315.042
8152021-03201400027815.02021-03120320140002782021-0315.02021-0315.035
7262021-03201300036916.02021-03160220130003692021-0316.02021-0316.034
2952021-02201300036916.02021-02160220130003692021-0216.02021-0216.030
8022021-03201400024714.02021-03121320140002472021-0314.02021-0314.029
11572021-03201700062415.02021-0382020170006242021-0315.02021-0315.029
7602021-03201300057215.02021-03160220130005722021-0315.02021-0315.027
3162021-02201300041023.02021-02160120130004102021-0223.02021-0223.026
3222021-02201300057215.02021-02160220130005722021-0215.02021-0215.026
12422021-04201300036916.02021-04160220130003692021-0416.02021-0416.026