Overview

Dataset statistics

Number of variables13
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory115.3 B

Variable types

Numeric6
Categorical6
Boolean1

Dataset

Description환경기초시설에서 발생되는 폐기물 운영․관리의 업무 효율성 증대를 위하여 DB화를 통하여 안정적인 데이터 관리 및 다양한 추이분석을 통하여 체계적인 폐기물(지정 데이터)관리
Author광주도시관리공사
URLhttps://www.data.go.kr/data/15101376/fileData.do

Alerts

일자 has constant value ""Constant
수정일 has constant value ""Constant
삭제여부 has constant value ""Constant
폐기물 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
처리단가 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
구분 is highly overall correlated with 순번 and 3 other fieldsHigh correlation
순번 is highly overall correlated with 발생량 and 5 other fieldsHigh correlation
발생량 is highly overall correlated with 순번 and 4 other fieldsHigh correlation
처리비 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
운반비 is highly overall correlated with 순번 and 3 other fieldsHigh correlation
합계 is highly overall correlated with 발생량 and 3 other fieldsHigh correlation
등록일 is highly overall correlated with 발생량 and 2 other fieldsHigh correlation
등록일 is highly imbalanced (79.4%)Imbalance
순번 has unique valuesUnique
처리비 has 10 (32.3%) zerosZeros

Reproduction

Analysis started2023-12-12 12:48:19.778333
Analysis finished2023-12-12 12:48:23.940148
Duration4.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28
Minimum13
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T21:48:24.001516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile14.5
Q120.5
median28
Q335.5
95-th percentile41.5
Maximum43
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.0921211
Coefficient of variation (CV)0.32471861
Kurtosis-1.2
Mean28
Median Absolute Deviation (MAD)8
Skewness0
Sum868
Variance82.666667
MonotonicityStrictly increasing
2023-12-12T21:48:24.164716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
13 1
 
3.2%
14 1
 
3.2%
43 1
 
3.2%
42 1
 
3.2%
41 1
 
3.2%
40 1
 
3.2%
39 1
 
3.2%
38 1
 
3.2%
37 1
 
3.2%
36 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
13 1
3.2%
14 1
3.2%
15 1
3.2%
16 1
3.2%
17 1
3.2%
18 1
3.2%
19 1
3.2%
20 1
3.2%
21 1
3.2%
22 1
3.2%
ValueCountFrequency (%)
43 1
3.2%
42 1
3.2%
41 1
3.2%
40 1
3.2%
39 1
3.2%
38 1
3.2%
37 1
3.2%
36 1
3.2%
35 1
3.2%
34 1
3.2%

일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
2022-03-22 AM 12:00:00
31 

Length

Max length22
Median length22
Mean length22
Min length22

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-03-22 AM 12:00:00
2nd row2022-03-22 AM 12:00:00
3rd row2022-03-22 AM 12:00:00
4th row2022-03-22 AM 12:00:00
5th row2022-03-22 AM 12:00:00

Common Values

ValueCountFrequency (%)
2022-03-22 AM 12:00:00 31
100.0%

Length

2023-12-12T21:48:24.321059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:48:24.443159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-03-22 31
33.3%
am 31
33.3%
12:00:00 31
33.3%

처리장
Real number (ℝ)

Distinct10
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7419355
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T21:48:24.553945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q37
95-th percentile9.5
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8515229
Coefficient of variation (CV)0.60134156
Kurtosis-1.0264871
Mean4.7419355
Median Absolute Deviation (MAD)2
Skewness0.29719193
Sum147
Variance8.1311828
MonotonicityNot monotonic
2023-12-12T21:48:24.674489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 5
16.1%
2 4
12.9%
5 4
12.9%
3 3
9.7%
6 3
9.7%
4 3
9.7%
7 3
9.7%
8 2
 
6.5%
9 2
 
6.5%
10 2
 
6.5%
ValueCountFrequency (%)
1 5
16.1%
2 4
12.9%
3 3
9.7%
4 3
9.7%
5 4
12.9%
6 3
9.7%
7 3
9.7%
8 2
 
6.5%
9 2
 
6.5%
10 2
 
6.5%
ValueCountFrequency (%)
10 2
 
6.5%
9 2
 
6.5%
8 2
 
6.5%
7 3
9.7%
6 3
9.7%
5 4
12.9%
4 3
9.7%
3 3
9.7%
2 4
12.9%
1 5
16.1%

폐기물
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size380.0 B
폐기계유폐동작유
10 
폐광물유
10 
폐유성페인트
7 
폐오일필터
2 
폐락카
2 

Length

Max length8
Median length6
Mean length5.7419355
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐기계유폐동작유 10
32.3%
폐광물유 10
32.3%
폐유성페인트 7
22.6%
폐오일필터 2
 
6.5%
폐락카 2
 
6.5%

Length

2023-12-12T21:48:24.805862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:48:24.958855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐기계유폐동작유 10
32.3%
폐광물유 10
32.3%
폐유성페인트 7
22.6%
폐오일필터 2
 
6.5%
폐락카 2
 
6.5%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size380.0 B
고상
21 
액상
10 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고상 21
67.7%
액상 10
32.3%

Length

2023-12-12T21:48:25.115693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:48:25.228915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고상 21
67.7%
액상 10
32.3%

발생량
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.03226
Minimum5
Maximum1200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T21:48:25.337317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7.5
Q120
median40
Q3145
95-th percentile405
Maximum1200
Range1195
Interquartile range (IQR)125

Descriptive statistics

Standard deviation230.855
Coefficient of variation (CV)1.7891263
Kurtosis15.800239
Mean129.03226
Median Absolute Deviation (MAD)30
Skewness3.6588272
Sum4000
Variance53294.032
MonotonicityNot monotonic
2023-12-12T21:48:25.499263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
20 7
22.6%
10 5
16.1%
70 2
 
6.5%
5 2
 
6.5%
40 2
 
6.5%
60 2
 
6.5%
90 2
 
6.5%
230 1
 
3.2%
220 1
 
3.2%
1200 1
 
3.2%
Other values (6) 6
19.4%
ValueCountFrequency (%)
5 2
 
6.5%
10 5
16.1%
20 7
22.6%
40 2
 
6.5%
60 2
 
6.5%
70 2
 
6.5%
90 2
 
6.5%
140 1
 
3.2%
150 1
 
3.2%
160 1
 
3.2%
ValueCountFrequency (%)
1200 1
3.2%
430 1
3.2%
380 1
3.2%
370 1
3.2%
230 1
3.2%
220 1
3.2%
160 1
3.2%
150 1
3.2%
140 1
3.2%
90 2
6.5%

처리단가
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size380.0 B
900
12 
0
10 
1100
9 

Length

Max length4
Median length3
Mean length2.6451613
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
900 12
38.7%
0 10
32.3%
1100 9
29.0%

Length

2023-12-12T21:48:25.667251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:48:25.802228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
900 12
38.7%
0 10
32.3%
1100 9
29.0%

처리비
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29903.226
Minimum0
Maximum207000
Zeros10
Zeros (%)32.3%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T21:48:25.906701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11000
Q329000
95-th percentile139500
Maximum207000
Range207000
Interquartile range (IQR)29000

Descriptive statistics

Standard deviation51060.817
Coefficient of variation (CV)1.7075354
Kurtosis7.4686832
Mean29903.226
Median Absolute Deviation (MAD)11000
Skewness2.7288078
Sum927000
Variance2.607207 × 109
MonotonicityNot monotonic
2023-12-12T21:48:26.020817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 10
32.3%
18000 4
 
12.9%
11000 3
 
9.7%
9000 2
 
6.5%
22000 2
 
6.5%
5500 2
 
6.5%
198000 1
 
3.2%
81000 1
 
3.2%
54000 1
 
3.2%
207000 1
 
3.2%
Other values (4) 4
 
12.9%
ValueCountFrequency (%)
0 10
32.3%
5500 2
 
6.5%
9000 2
 
6.5%
11000 3
 
9.7%
18000 4
 
12.9%
22000 2
 
6.5%
36000 1
 
3.2%
44000 1
 
3.2%
54000 1
 
3.2%
63000 1
 
3.2%
ValueCountFrequency (%)
207000 1
 
3.2%
198000 1
 
3.2%
81000 1
 
3.2%
66000 1
 
3.2%
63000 1
 
3.2%
54000 1
 
3.2%
44000 1
 
3.2%
36000 1
 
3.2%
22000 2
6.5%
18000 4
12.9%

운반비
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22580.645
Minimum1768
Maximum139535
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T21:48:26.119544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1768
5-th percentile2047
Q15303
median10465
Q322980
95-th percentile79545.5
Maximum139535
Range137767
Interquartile range (IQR)17677

Descriptive statistics

Standard deviation30010.066
Coefficient of variation (CV)1.3290172
Kurtosis7.2620612
Mean22580.645
Median Absolute Deviation (MAD)6930
Skewness2.5323956
Sum700000
Variance9.0060404 × 108
MonotonicityNot monotonic
2023-12-12T21:48:26.542500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
7071 6
19.4%
3535 5
16.1%
1768 2
 
6.5%
14141 2
 
6.5%
21212 2
 
6.5%
50000 1
 
3.2%
24748 1
 
3.2%
81313 1
 
3.2%
31818 1
 
3.2%
77778 1
 
3.2%
Other values (9) 9
29.0%
ValueCountFrequency (%)
1768 2
 
6.5%
2326 1
 
3.2%
3535 5
16.1%
7071 6
19.4%
8139 1
 
3.2%
10465 1
 
3.2%
14141 2
 
6.5%
16279 1
 
3.2%
17442 1
 
3.2%
18605 1
 
3.2%
ValueCountFrequency (%)
139535 1
3.2%
81313 1
3.2%
77778 1
3.2%
50000 1
3.2%
44186 1
3.2%
43023 1
3.2%
31818 1
3.2%
24748 1
3.2%
21212 2
6.5%
18605 1
3.2%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52483.871
Minimum2326
Maximum288313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T21:48:26.696908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2326
5-th percentile7268
Q114535
median25071
Q354141
95-th percentile207656.5
Maximum288313
Range285987
Interquartile range (IQR)39606

Descriptive statistics

Standard deviation69641.852
Coefficient of variation (CV)1.3269191
Kurtosis6.5315392
Mean52483.871
Median Absolute Deviation (MAD)16932
Skewness2.5554424
Sum1627000
Variance4.8499876 × 109
MonotonicityNot monotonic
2023-12-12T21:48:26.816904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
25071 4
 
12.9%
14535 3
 
9.7%
7268 2
 
6.5%
29071 2
 
6.5%
12535 2
 
6.5%
139535 1
 
3.2%
112818 1
 
3.2%
58141 1
 
3.2%
87212 1
 
3.2%
87748 1
 
3.2%
Other values (13) 13
41.9%
ValueCountFrequency (%)
2326 1
 
3.2%
7268 2
6.5%
8139 1
 
3.2%
10465 1
 
3.2%
12535 2
6.5%
14535 3
9.7%
16279 1
 
3.2%
17442 1
 
3.2%
18605 1
 
3.2%
25071 4
12.9%
ValueCountFrequency (%)
288313 1
3.2%
275778 1
3.2%
139535 1
3.2%
112818 1
3.2%
87748 1
3.2%
87212 1
3.2%
75212 1
3.2%
58141 1
3.2%
50141 1
3.2%
50000 1
3.2%

등록일
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size380.0 B
2022-05-10 PM 10:23:31
30 
2022-05-10 PM 10:23:30
 
1

Length

Max length22
Median length22
Mean length22
Min length22

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row2022-05-10 PM 10:23:30
2nd row2022-05-10 PM 10:23:31
3rd row2022-05-10 PM 10:23:31
4th row2022-05-10 PM 10:23:31
5th row2022-05-10 PM 10:23:31

Common Values

ValueCountFrequency (%)
2022-05-10 PM 10:23:31 30
96.8%
2022-05-10 PM 10:23:30 1
 
3.2%

Length

2023-12-12T21:48:26.974456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:48:27.085984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-05-10 31
33.3%
pm 31
33.3%
10:23:31 30
32.3%
10:23:30 1
 
1.1%

수정일
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
2022-05-10 PM 10:25:45
31 

Length

Max length22
Median length22
Mean length22
Min length22

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-05-10 PM 10:25:45
2nd row2022-05-10 PM 10:25:45
3rd row2022-05-10 PM 10:25:45
4th row2022-05-10 PM 10:25:45
5th row2022-05-10 PM 10:25:45

Common Values

ValueCountFrequency (%)
2022-05-10 PM 10:25:45 31
100.0%

Length

2023-12-12T21:48:27.199295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:48:27.305869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-05-10 31
33.3%
pm 31
33.3%
10:25:45 31
33.3%

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size163.0 B
False
31 
ValueCountFrequency (%)
False 31
100.0%
2023-12-12T21:48:27.381966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-12-12T21:48:23.111795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:20.271541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:20.905503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:21.473730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:22.050342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:22.561590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:23.187979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:20.374583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:20.999678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:21.568802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:22.141598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:22.651204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:23.269800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:20.474577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:21.096753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:21.658971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:22.218848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:22.741129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:23.353177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:20.591324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:21.198419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:21.741212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:22.309900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:22.835458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:23.431786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:20.684335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:21.289848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:21.836694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:22.388836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:22.924793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:23.532874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:20.794962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:21.392834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:21.944650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:22.473057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:48:23.023624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:48:27.464015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번처리장폐기물구분발생량처리단가처리비운반비합계등록일
순번1.0000.0000.9880.9930.5350.9580.0000.0000.2280.223
처리장0.0001.0000.0000.0000.0000.0000.6080.0000.0000.000
폐기물0.9880.0001.0001.0000.2431.0000.1690.0000.0000.000
구분0.9930.0001.0001.0000.8231.0000.2740.5870.4010.000
발생량0.5350.0000.2430.8231.0000.4040.2090.9660.8801.000
처리단가0.9580.0001.0001.0000.4041.0000.2750.4600.3440.046
처리비0.0000.6080.1690.2740.2090.2751.0000.6130.7640.000
운반비0.0000.0000.0000.5870.9660.4600.6131.0000.9771.000
합계0.2280.0000.0000.4010.8800.3440.7640.9771.0001.000
등록일0.2230.0000.0000.0001.0000.0460.0001.0001.0001.000
2023-12-12T21:48:27.609081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
폐기물등록일처리단가구분
폐기물1.0000.0000.9640.947
등록일0.0001.0000.0590.000
처리단가0.9640.0591.0000.983
구분0.9470.0000.9831.000
2023-12-12T21:48:27.710060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번처리장발생량처리비운반비합계폐기물구분처리단가등록일
순번1.000-0.026-0.7460.559-0.567-0.1230.7540.8510.8660.000
처리장-0.0261.000-0.007-0.118-0.115-0.2320.0000.0000.0000.000
발생량-0.746-0.0071.000-0.1540.9410.6180.1840.5930.3840.965
처리비0.559-0.118-0.1541.0000.1410.6010.0000.3110.1970.000
운반비-0.567-0.1150.9410.1411.0000.8270.0000.4180.2550.928
합계-0.123-0.2320.6180.6010.8271.0000.0000.2280.0970.928
폐기물0.7540.0000.1840.0000.0000.0001.0000.9470.9640.000
구분0.8510.0000.5930.3110.4180.2280.9471.0000.9830.000
처리단가0.8660.0000.3840.1970.2550.0970.9640.9831.0000.059
등록일0.0000.0000.9650.0000.9280.9280.0000.0000.0591.000

Missing values

2023-12-12T21:48:23.685400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:48:23.858908image/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

순번일자처리장폐기물구분발생량처리단가처리비운반비합계등록일수정일삭제여부
0132022-03-22 AM 12:00:001폐기계유폐동작유액상1200001395351395352022-05-10 PM 10:23:302022-05-10 PM 10:25:45N
1142022-03-22 AM 12:00:002폐기계유폐동작유액상4300050000500002022-05-10 PM 10:23:312022-05-10 PM 10:25:45N
2152022-03-22 AM 12:00:005폐기계유폐동작유액상1400016279162792022-05-10 PM 10:23:312022-05-10 PM 10:25:45N
3162022-03-22 AM 12:00:003폐기계유폐동작유액상3700043023430232022-05-10 PM 10:23:312022-05-10 PM 10:25:45N
4172022-03-22 AM 12:00:006폐기계유폐동작유액상1500017442174422022-05-10 PM 10:23:312022-05-10 PM 10:25:45N
5182022-03-22 AM 12:00:004폐기계유폐동작유액상3800044186441862022-05-10 PM 10:23:312022-05-10 PM 10:25:45N
6192022-03-22 AM 12:00:008폐기계유폐동작유액상1600018605186052022-05-10 PM 10:23:312022-05-10 PM 10:25:45N
7202022-03-22 AM 12:00:009폐기계유폐동작유액상900010465104652022-05-10 PM 10:23:312022-05-10 PM 10:25:45N
8212022-03-22 AM 12:00:007폐기계유폐동작유액상2000232623262022-05-10 PM 10:23:312022-05-10 PM 10:25:45N
9222022-03-22 AM 12:00:0010폐기계유폐동작유액상7000813981392022-05-10 PM 10:23:312022-05-10 PM 10:25:45N
순번일자처리장폐기물구분발생량처리단가처리비운반비합계등록일수정일삭제여부
21342022-03-22 AM 12:00:0010폐광물유고상20900180007071250712022-05-10 PM 10:23:312022-05-10 PM 10:25:45N
22352022-03-22 AM 12:00:001폐유성페인트고상101100110003535145352022-05-10 PM 10:23:312022-05-10 PM 10:25:45N
23362022-03-22 AM 12:00:002폐유성페인트고상6011006600021212872122022-05-10 PM 10:23:312022-05-10 PM 10:25:45N
24372022-03-22 AM 12:00:005폐유성페인트고상4011004400014141581412022-05-10 PM 10:23:312022-05-10 PM 10:25:45N
25382022-03-22 AM 12:00:003폐유성페인트고상101100110003535145352022-05-10 PM 10:23:312022-05-10 PM 10:25:45N
26392022-03-22 AM 12:00:006폐유성페인트고상101100110003535145352022-05-10 PM 10:23:312022-05-10 PM 10:25:45N
27402022-03-22 AM 12:00:004폐유성페인트고상201100220007071290712022-05-10 PM 10:23:312022-05-10 PM 10:25:45N
28412022-03-22 AM 12:00:007폐유성페인트고상201100220007071290712022-05-10 PM 10:23:312022-05-10 PM 10:25:45N
29422022-03-22 AM 12:00:001폐락카고상511005500176872682022-05-10 PM 10:23:312022-05-10 PM 10:25:45N
30432022-03-22 AM 12:00:002폐락카고상511005500176872682022-05-10 PM 10:23:312022-05-10 PM 10:25:45N