Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows898
Duplicate rows (%)9.0%
Total size in memory1.1 MiB
Average record size in memory111.0 B

Variable types

Categorical5
Numeric7

Dataset

Description가축분뇨전자인계관리시스템에서 관리하고있는 가축분뇨 중 분뇨의 마감된 전자인계 내역 정보로 등록된 데이터입니다.
Author한국환경공단
URLhttps://www.data.go.kr/data/15039578/fileData.do

Alerts

Dataset has 898 (9.0%) duplicate rowsDuplicates
운반일수일자 is highly overall correlated with 마감처리일자 and 3 other fieldsHigh correlation
인계일자 is highly overall correlated with 마감처리일자 and 3 other fieldsHigh correlation
운반인계일자 is highly overall correlated with 마감처리일자 and 3 other fieldsHigh correlation
처리인수일자 is highly overall correlated with 마감처리일자 and 3 other fieldsHigh correlation
배출업체번호 is highly overall correlated with 운반업체번호 and 1 other fieldsHigh correlation
배출인계량(톤) is highly overall correlated with 운반인수량 and 1 other fieldsHigh correlation
운반업체번호 is highly overall correlated with 배출업체번호 and 1 other fieldsHigh correlation
운반인수량 is highly overall correlated with 배출인계량(톤) and 1 other fieldsHigh correlation
처리업체번호 is highly overall correlated with 배출업체번호 and 1 other fieldsHigh correlation
처리인수량 is highly overall correlated with 배출인계량(톤) and 1 other fieldsHigh correlation
마감처리일자 is highly overall correlated with 인계일자 and 3 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 13:15:57.138394
Analysis finished2023-12-12 13:16:05.871006
Duration8.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

마감처리일자
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-03
3674 
2021-02
3204 
2021-01
2801 
2021-04
 
275
2021-05
 
32
Other values (3)
 
14

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
2021-03 3674
36.7%
2021-02 3204
32.0%
2021-01 2801
28.0%
2021-04 275
 
2.8%
2021-05 32
 
0.3%
2021-06 9
 
0.1%
2022-03 4
 
< 0.1%
2022-10 1
 
< 0.1%

Length

2023-12-12T22:16:05.942114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:16:06.056966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-03 3674
36.7%
2021-02 3204
32.0%
2021-01 2801
28.0%
2021-04 275
 
2.8%
2021-05 32
 
0.3%
2021-06 9
 
0.1%
2022-03 4
 
< 0.1%
2022-10 1
 
< 0.1%

배출업체번호
Real number (ℝ)

HIGH CORRELATION 

Distinct2704
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0161967 × 109
Minimum2.0130001 × 109
Maximum2.0210002 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:16:06.190414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0130001 × 109
5-th percentile2.0130005 × 109
Q12.0160005 × 109
median2.0160025 × 109
Q32.0170002 × 109
95-th percentile2.0190003 × 109
Maximum2.0210002 × 109
Range8000071
Interquartile range (IQR)999729

Descriptive statistics

Standard deviation1458677.3
Coefficient of variation (CV)0.00072347964
Kurtosis1.6507815
Mean2.0161967 × 109
Median Absolute Deviation (MAD)2245
Skewness0.41672823
Sum2.0161967 × 1013
Variance2.1277394 × 1012
MonotonicityNot monotonic
2023-12-12T22:16:06.358105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2016004003 86
 
0.9%
2015000106 32
 
0.3%
2014000090 29
 
0.3%
2016002680 29
 
0.3%
2017000802 28
 
0.3%
2016002002 27
 
0.3%
2016003207 27
 
0.3%
2017000801 26
 
0.3%
2016004130 23
 
0.2%
2020000214 23
 
0.2%
Other values (2694) 9670
96.7%
ValueCountFrequency (%)
2013000118 2
 
< 0.1%
2013000135 3
 
< 0.1%
2013000136 2
 
< 0.1%
2013000142 4
< 0.1%
2013000151 2
 
< 0.1%
2013000154 2
 
< 0.1%
2013000156 9
0.1%
2013000159 5
0.1%
2013000168 1
 
< 0.1%
2013000177 4
< 0.1%
ValueCountFrequency (%)
2021000189 1
 
< 0.1%
2021000173 1
 
< 0.1%
2021000172 2
 
< 0.1%
2021000162 3
< 0.1%
2021000150 1
 
< 0.1%
2021000141 1
 
< 0.1%
2021000126 1
 
< 0.1%
2021000119 6
0.1%
2021000107 1
 
< 0.1%
2021000105 1
 
< 0.1%

배출인계량(톤)
Real number (ℝ)

HIGH CORRELATION 

Distinct1854
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.482889
Minimum0.5
Maximum270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:16:06.539102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile5
Q17.57
median15
Q322.91
95-th percentile25
Maximum270
Range269.5
Interquartile range (IQR)15.34

Descriptive statistics

Standard deviation8.937964
Coefficient of variation (CV)0.57728013
Kurtosis98.991619
Mean15.482889
Median Absolute Deviation (MAD)7.54
Skewness4.7484379
Sum154828.89
Variance79.887201
MonotonicityNot monotonic
2023-12-12T22:16:06.724338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.0 515
 
5.1%
15.0 465
 
4.7%
24.0 380
 
3.8%
20.0 350
 
3.5%
5.0 318
 
3.2%
25.0 318
 
3.2%
8.0 265
 
2.6%
6.0 217
 
2.2%
21.0 169
 
1.7%
7.0 139
 
1.4%
Other values (1844) 6864
68.6%
ValueCountFrequency (%)
0.5 9
 
0.1%
1.0 56
0.6%
1.18 1
 
< 0.1%
1.42 1
 
< 0.1%
1.5 8
 
0.1%
1.7 1
 
< 0.1%
1.84 1
 
< 0.1%
1.95 1
 
< 0.1%
2.0 35
0.4%
2.04 1
 
< 0.1%
ValueCountFrequency (%)
270.0 1
 
< 0.1%
210.0 1
 
< 0.1%
135.0 1
 
< 0.1%
110.0 1
 
< 0.1%
99.0 2
 
< 0.1%
90.0 2
 
< 0.1%
87.0 9
0.1%
85.0 1
 
< 0.1%
82.0 1
 
< 0.1%
73.0 1
 
< 0.1%

인계일자
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-03
3368 
2021-01
3342 
2021-02
3290 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2021-03 3368
33.7%
2021-01 3342
33.4%
2021-02 3290
32.9%

Length

2023-12-12T22:16:06.885785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:16:06.999949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-03 3368
33.7%
2021-01 3342
33.4%
2021-02 3290
32.9%

관할관청
Real number (ℝ)

Distinct117
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1215.0825
Minimum203
Maximum1701
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:16:07.121240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum203
5-th percentile820
Q11015
median1209
Q31412
95-th percentile1602
Maximum1701
Range1498
Interquartile range (IQR)397

Descriptive statistics

Standard deviation247.52012
Coefficient of variation (CV)0.20370643
Kurtosis-0.66295664
Mean1215.0825
Median Absolute Deviation (MAD)199
Skewness-0.16079049
Sum12150825
Variance61266.209
MonotonicityNot monotonic
2023-12-12T22:16:07.297366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1602 564
 
5.6%
1209 533
 
5.3%
829 484
 
4.8%
1213 346
 
3.5%
1504 333
 
3.3%
1203 323
 
3.2%
1515 294
 
2.9%
1511 256
 
2.6%
1306 219
 
2.2%
1502 200
 
2.0%
Other values (107) 6448
64.5%
ValueCountFrequency (%)
203 12
 
0.1%
210 3
 
< 0.1%
511 5
 
0.1%
603 1
 
< 0.1%
703 7
 
0.1%
704 14
 
0.1%
801 46
0.5%
802 15
 
0.1%
805 2
 
< 0.1%
816 84
0.8%
ValueCountFrequency (%)
1701 53
 
0.5%
1602 564
5.6%
1601 196
 
2.0%
1515 294
2.9%
1513 68
 
0.7%
1512 39
 
0.4%
1511 256
2.6%
1510 104
 
1.0%
1509 29
 
0.3%
1508 32
 
0.3%

운반업체번호
Real number (ℝ)

HIGH CORRELATION 

Distinct449
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0156485 × 109
Minimum2.0130004 × 109
Maximum2.0210002 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:16:07.479691image/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 percentile2.0180107 × 109
Maximum2.0210002 × 109
Range7999793
Interquartile range (IQR)1002127

Descriptive statistics

Standard deviation1259648.8
Coefficient of variation (CV)0.00062493476
Kurtosis1.6382626
Mean2.0156485 × 109
Median Absolute Deviation (MAD)999701
Skewness0.29699949
Sum2.0156485 × 1013
Variance1.5867152 × 1012
MonotonicityNot monotonic
2023-12-12T22:16:07.655937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2015000322 457
 
4.6%
2017000168 153
 
1.5%
2019000255 143
 
1.4%
2016002219 132
 
1.3%
2016002849 130
 
1.3%
2018010804 128
 
1.3%
2016001736 117
 
1.2%
2013000369 108
 
1.1%
2017000803 103
 
1.0%
2013000588 93
 
0.9%
Other values (439) 8436
84.4%
ValueCountFrequency (%)
2013000369 108
1.1%
2013000371 24
 
0.2%
2013000374 12
 
0.1%
2013000377 8
 
0.1%
2013000378 9
 
0.1%
2013000379 11
 
0.1%
2013000380 18
 
0.2%
2013000384 29
 
0.3%
2013000385 43
 
0.4%
2013000406 66
0.7%
ValueCountFrequency (%)
2021000162 3
 
< 0.1%
2021000110 2
 
< 0.1%
2020000750 8
 
0.1%
2020000737 14
0.1%
2020000601 22
0.2%
2020000531 2
 
< 0.1%
2020000413 17
0.2%
2020000294 15
0.1%
2020000183 3
 
< 0.1%
2020000105 1
 
< 0.1%

운반일수일자
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-03
3368 
2021-01
3342 
2021-02
3290 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2021-03 3368
33.7%
2021-01 3342
33.4%
2021-02 3290
32.9%

Length

2023-12-12T22:16:07.842777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:16:07.954914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-03 3368
33.7%
2021-01 3342
33.4%
2021-02 3290
32.9%

운반인수량
Real number (ℝ)

HIGH CORRELATION 

Distinct1854
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.482889
Minimum0.5
Maximum270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:16:08.117964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile5
Q17.57
median15
Q322.91
95-th percentile25
Maximum270
Range269.5
Interquartile range (IQR)15.34

Descriptive statistics

Standard deviation8.937964
Coefficient of variation (CV)0.57728013
Kurtosis98.991619
Mean15.482889
Median Absolute Deviation (MAD)7.54
Skewness4.7484379
Sum154828.89
Variance79.887201
MonotonicityNot monotonic
2023-12-12T22:16:08.277729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.0 515
 
5.1%
15.0 465
 
4.7%
24.0 380
 
3.8%
20.0 350
 
3.5%
5.0 318
 
3.2%
25.0 318
 
3.2%
8.0 265
 
2.6%
6.0 217
 
2.2%
21.0 169
 
1.7%
7.0 139
 
1.4%
Other values (1844) 6864
68.6%
ValueCountFrequency (%)
0.5 9
 
0.1%
1.0 56
0.6%
1.18 1
 
< 0.1%
1.42 1
 
< 0.1%
1.5 8
 
0.1%
1.7 1
 
< 0.1%
1.84 1
 
< 0.1%
1.95 1
 
< 0.1%
2.0 35
0.4%
2.04 1
 
< 0.1%
ValueCountFrequency (%)
270.0 1
 
< 0.1%
210.0 1
 
< 0.1%
135.0 1
 
< 0.1%
110.0 1
 
< 0.1%
99.0 2
 
< 0.1%
90.0 2
 
< 0.1%
87.0 9
0.1%
85.0 1
 
< 0.1%
82.0 1
 
< 0.1%
73.0 1
 
< 0.1%

운반인계일자
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-03
3366 
2021-01
3341 
2021-02
3289 
2021-04
 
4

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2021-03 3366
33.7%
2021-01 3341
33.4%
2021-02 3289
32.9%
2021-04 4
 
< 0.1%

Length

2023-12-12T22:16:08.426411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:16:08.565403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-03 3366
33.7%
2021-01 3341
33.4%
2021-02 3289
32.9%
2021-04 4
 
< 0.1%

처리업체번호
Real number (ℝ)

HIGH CORRELATION 

Distinct285
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0157571 × 109
Minimum2.0130004 × 109
Maximum2.0210002 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:16:08.718395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0130004 × 109
5-th percentile2.0130004 × 109
Q12.0150003 × 109
median2.0160008 × 109
Q32.016003 × 109
95-th percentile2.0180108 × 109
Maximum2.0210002 × 109
Range7999793
Interquartile range (IQR)1002664

Descriptive statistics

Standard deviation1323405.6
Coefficient of variation (CV)0.00065653027
Kurtosis1.8520264
Mean2.0157571 × 109
Median Absolute Deviation (MAD)3306
Skewness0.38948894
Sum2.0157571 × 1013
Variance1.7514023 × 1012
MonotonicityNot monotonic
2023-12-12T22:16:08.903043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2015000331 457
 
4.6%
2014000327 167
 
1.7%
2015000437 158
 
1.6%
2017000168 153
 
1.5%
2019000255 143
 
1.4%
2016002334 132
 
1.3%
2018010804 128
 
1.3%
2016000829 120
 
1.2%
2013000369 108
 
1.1%
2016000212 106
 
1.1%
Other values (275) 8328
83.3%
ValueCountFrequency (%)
2013000369 108
1.1%
2013000371 24
 
0.2%
2013000374 12
 
0.1%
2013000377 1
 
< 0.1%
2013000378 9
 
0.1%
2013000379 11
 
0.1%
2013000380 18
 
0.2%
2013000384 29
 
0.3%
2013000385 43
 
0.4%
2013000386 81
0.8%
ValueCountFrequency (%)
2021000162 3
 
< 0.1%
2021000160 4
 
< 0.1%
2020000724 2
 
< 0.1%
2020000623 21
0.2%
2020000617 22
0.2%
2020000587 4
 
< 0.1%
2020000523 2
 
< 0.1%
2020000424 5
 
0.1%
2020000413 17
0.2%
2020000295 3
 
< 0.1%

처리인수일자
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-03
3372 
2021-01
3318 
2021-02
3285 
2021-04
 
14
2021-05
 
11

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2021-03 3372
33.7%
2021-01 3318
33.2%
2021-02 3285
32.9%
2021-04 14
 
0.1%
2021-05 11
 
0.1%

Length

2023-12-12T22:16:09.072437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:16:09.521778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-03 3372
33.7%
2021-01 3318
33.2%
2021-02 3285
32.9%
2021-04 14
 
0.1%
2021-05 11
 
0.1%

처리인수량
Real number (ℝ)

HIGH CORRELATION 

Distinct1854
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.482889
Minimum0.5
Maximum270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:16:09.677559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile5
Q17.57
median15
Q322.91
95-th percentile25
Maximum270
Range269.5
Interquartile range (IQR)15.34

Descriptive statistics

Standard deviation8.937964
Coefficient of variation (CV)0.57728013
Kurtosis98.991619
Mean15.482889
Median Absolute Deviation (MAD)7.54
Skewness4.7484379
Sum154828.89
Variance79.887201
MonotonicityNot monotonic
2023-12-12T22:16:09.824846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.0 515
 
5.1%
15.0 465
 
4.7%
24.0 380
 
3.8%
20.0 350
 
3.5%
5.0 318
 
3.2%
25.0 318
 
3.2%
8.0 265
 
2.6%
6.0 217
 
2.2%
21.0 169
 
1.7%
7.0 139
 
1.4%
Other values (1844) 6864
68.6%
ValueCountFrequency (%)
0.5 9
 
0.1%
1.0 56
0.6%
1.18 1
 
< 0.1%
1.42 1
 
< 0.1%
1.5 8
 
0.1%
1.7 1
 
< 0.1%
1.84 1
 
< 0.1%
1.95 1
 
< 0.1%
2.0 35
0.4%
2.04 1
 
< 0.1%
ValueCountFrequency (%)
270.0 1
 
< 0.1%
210.0 1
 
< 0.1%
135.0 1
 
< 0.1%
110.0 1
 
< 0.1%
99.0 2
 
< 0.1%
90.0 2
 
< 0.1%
87.0 9
0.1%
85.0 1
 
< 0.1%
82.0 1
 
< 0.1%
73.0 1
 
< 0.1%

Interactions

2023-12-12T22:16:04.693013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:15:59.268950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:00.290248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:01.133223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:01.898207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:02.843447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:03.930731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:04.829332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:15:59.413921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:00.407184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:01.243552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:02.068561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:02.954816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:04.056242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:04.930413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:15:59.570594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:00.509743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:01.339854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:02.218816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:03.047741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:04.161513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:05.057097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:15:59.710234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:00.639197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:01.439912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:02.354025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:03.505148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:04.267049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:05.215230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:15:59.871568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:00.777008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:01.550873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:02.467378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:03.613334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:04.386450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:05.341009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:15:59.993301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:00.901524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:01.645788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:02.603580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:03.714615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:04.484017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:05.450140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:00.153990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:01.029329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:01.778841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:02.741344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:03.823301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:16:04.589049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:16:09.924479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
마감처리일자배출업체번호배출인계량(톤)인계일자관할관청운반업체번호운반일수일자운반인수량운반인계일자처리업체번호처리인수일자처리인수량
마감처리일자1.0000.1420.2620.8770.0660.1750.8770.2620.9450.2050.8120.262
배출업체번호0.1421.0000.1300.0440.6480.8720.0440.1300.0270.8670.1650.130
배출인계량(톤)0.2620.1301.0000.0000.0720.1710.0001.0000.0000.1440.0001.000
인계일자0.8770.0440.0001.0000.0370.0001.0000.0000.9630.0400.9530.000
관할관청0.0660.6480.0720.0371.0000.7260.0370.0720.0230.7110.0680.072
운반업체번호0.1750.8720.1710.0000.7261.0000.0000.1710.0110.9750.1140.171
운반일수일자0.8770.0440.0001.0000.0370.0001.0000.0000.9630.0400.9530.000
운반인수량0.2620.1301.0000.0000.0720.1710.0001.0000.0000.1440.0001.000
운반인계일자0.9450.0270.0000.9630.0230.0110.9630.0001.0000.0410.8380.000
처리업체번호0.2050.8670.1440.0400.7110.9750.0400.1440.0411.0000.1330.144
처리인수일자0.8120.1650.0000.9530.0680.1140.9530.0000.8380.1331.0000.000
처리인수량0.2620.1301.0000.0000.0720.1710.0001.0000.0000.1440.0001.000
2023-12-12T22:16:10.096097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운반일수일자인계일자운반인계일자마감처리일자처리인수일자
운반일수일자1.0001.0000.9990.8450.994
인계일자1.0001.0000.9990.8450.994
운반인계일자0.9990.9991.0000.6930.811
마감처리일자0.8450.8450.6931.0000.677
처리인수일자0.9940.9940.8110.6771.000
2023-12-12T22:16:10.212844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출업체번호배출인계량(톤)관할관청운반업체번호운반인수량처리업체번호처리인수량마감처리일자인계일자운반일수일자운반인계일자처리인수일자
배출업체번호1.000-0.170-0.2250.579-0.1700.589-0.1700.0680.0260.0260.0160.069
배출인계량(톤)-0.1701.0000.158-0.1741.000-0.2041.0000.1430.0000.0000.0000.000
관할관청-0.2250.1581.000-0.2780.158-0.3000.1580.0330.0200.0200.0170.040
운반업체번호0.579-0.174-0.2781.000-0.1740.825-0.1740.0840.0000.0000.0060.048
운반인수량-0.1701.0000.158-0.1741.000-0.2041.0000.1430.0000.0000.0000.000
처리업체번호0.589-0.204-0.3000.825-0.2041.000-0.2040.0990.0230.0230.0240.055
처리인수량-0.1701.0000.158-0.1741.000-0.2041.0000.1430.0000.0000.0000.000
마감처리일자0.0680.1430.0330.0840.1430.0990.1431.0000.8450.8450.6930.677
인계일자0.0260.0000.0200.0000.0000.0230.0000.8451.0001.0000.9990.994
운반일수일자0.0260.0000.0200.0000.0000.0230.0000.8451.0001.0000.9990.994
운반인계일자0.0160.0000.0170.0060.0000.0240.0000.6930.9990.9991.0000.811
처리인수일자0.0690.0000.0400.0480.0000.0550.0000.6770.9940.9940.8111.000

Missing values

2023-12-12T22:16:05.585984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:16:05.787810image/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)인계일자관할관청운반업체번호운반일수일자운반인수량운반인계일자처리업체번호처리인수일자처리인수량
608962021-03201600249715.622021-02100120160024492021-0215.622021-0220160025162021-0215.62
621242021-0320160026716.952021-02101120160022192021-026.952021-0220160023342021-026.95
780232021-03201600112215.122021-0381820160007452021-0315.122021-0320160007482021-0315.12
473112021-02201300032621.52021-02160220130005892021-0221.52021-0220130003862021-0221.5
359262021-02201600230524.02021-02132120160013702021-0224.02021-0220160013702021-0224.0
386912021-0220160028308.042021-02102220160023602021-028.042021-0220160028582021-028.04
435122021-02201600361525.062021-02101520160020142021-0225.062021-0220170011332021-0225.06
639012021-03201500025423.02021-03151120150002302021-0323.02021-0320150004372021-0323.0
565062021-02201600203721.02021-02130920160009732021-0221.02021-0220160009732021-0221.0
285862021-02201300027013.02021-01160220130005892021-0113.02021-0120130003862021-0113.0
마감처리일자배출업체번호배출인계량(톤)인계일자관할관청운반업체번호운반일수일자운반인수량운반인계일자처리업체번호처리인수일자처리인수량
117782021-01201600078422.02021-0191220190004092021-0122.02021-0120160008162021-0122.0
427592021-0220160003677.02021-02111520160031812021-027.02021-0220150003862021-027.0
689442021-03201500027823.362021-03151120150002272021-0323.362021-0320150002272021-0323.36
909762021-03201801068123.592021-03150220160008432021-0323.592021-0320160008392021-0323.59
151182021-01201500017911.952021-01120320140002622021-0111.952021-0120200006232021-0111.95
26872021-01201700160224.042021-01151320180104362021-0124.042021-0120150003012021-0124.04
581202021-03201600202923.52021-02130920160009732021-0223.52021-0220160009732021-0223.5
449862021-0220160004417.12021-02110920160004412021-027.12021-0220150003682021-027.1
673392021-0320200003228.062021-03150420160008292021-038.062021-0320160008292021-038.06
436492021-02201400000523.02021-02160220130004102021-0223.02021-0220130004102021-0223.0

Duplicate rows

Most frequently occurring

마감처리일자배출업체번호배출인계량(톤)인계일자관할관청운반업체번호운반일수일자운반인수량운반인계일자처리업체번호처리인수일자처리인수량# duplicates
472021-01201500010717.02021-01120620150000042021-0117.02021-0120150000042021-0117.012
2162021-01201801052225.02021-01130920160004272021-0125.02021-0120160004272021-0125.011
6802021-0320160017698.02021-03151120160017692021-038.02021-0320150004372021-038.011
8172021-03201700080120.02021-0382920170008032021-0320.02021-0320170008032021-0320.011
8182021-03201700080220.02021-0382920170008032021-0320.02021-0320170008032021-0320.010
1132021-0120160020026.02021-01130520160016802021-016.02021-0120160016802021-016.09
1972021-0120170007997.02021-0182920170008032021-017.02021-0120170008032021-017.09
572021-01201500045424.02021-01120420150003182021-0124.02021-0120150003182021-0124.08
1992021-01201700080220.02021-0182920170008032021-0120.02021-0120170008032021-0120.08
2642021-02201300033923.02021-02160220130005722021-0223.02021-0220130005722021-0223.08