Overview

Dataset statistics

Number of variables10
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory94.5 B

Variable types

Text1
Numeric9

Dataset

Description서울특별시 강서구 재활용품 품목별 처리현황(2021년~2022년)입니다. 품목별 처리량 단위는 킬로그램입니다.
URLhttps://www.data.go.kr/data/15112748/fileData.do

Alerts

플라스틱 is highly overall correlated with 파지 and 1 other fieldsHigh correlation
캔 is highly overall correlated with 페트병High correlation
파지 is highly overall correlated with 플라스틱 and 2 other fieldsHigh correlation
잡철 is highly overall correlated with 파지High correlation
비철 is highly overall correlated with 기타High correlation
유리병 is highly overall correlated with 파지High correlation
페트병 is highly overall correlated with 플라스틱 and 1 other fieldsHigh correlation
기타 is highly overall correlated with 비철High correlation
연월 has unique valuesUnique
플라스틱 has unique valuesUnique
캔 has unique valuesUnique
파지 has unique valuesUnique
비철 has unique valuesUnique
유리병 has unique valuesUnique
페트병 has unique valuesUnique
기타 has 13 (54.2%) zerosZeros

Reproduction

Analysis started2023-12-12 09:47:25.510901
Analysis finished2023-12-12 09:47:34.115471
Duration8.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연월
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-12T18:47:34.516977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.25
Min length8

Characters and Unicode

Total characters198
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)100.0%

Sample

1st row2021년 1월
2nd row2021년 2월
3rd row2021년 3월
4th row2021년 4월
5th row2021년 5월
ValueCountFrequency (%)
2021년 12
25.0%
2022년 12
25.0%
1월 2
 
4.2%
2월 2
 
4.2%
3월 2
 
4.2%
4월 2
 
4.2%
5월 2
 
4.2%
6월 2
 
4.2%
7월 2
 
4.2%
8월 2
 
4.2%
Other values (4) 8
16.7%
2023-12-12T18:47:34.844588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 64
32.3%
0 26
13.1%
년 24
 
12.1%
24
 
12.1%
월 24
 
12.1%
1 22
 
11.1%
3 2
 
1.0%
4 2
 
1.0%
5 2
 
1.0%
6 2
 
1.0%
Other values (3) 6
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 126
63.6%
Other Letter 48
 
24.2%
Space Separator 24
 
12.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 64
50.8%
0 26
20.6%
1 22
 
17.5%
3 2
 
1.6%
4 2
 
1.6%
5 2
 
1.6%
6 2
 
1.6%
7 2
 
1.6%
8 2
 
1.6%
9 2
 
1.6%
Other Letter
ValueCountFrequency (%)
년 24
50.0%
월 24
50.0%
Space Separator
ValueCountFrequency (%)
24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 150
75.8%
Hangul 48
 
24.2%

Most frequent character per script

Common
ValueCountFrequency (%)
2 64
42.7%
0 26
17.3%
24
 
16.0%
1 22
 
14.7%
3 2
 
1.3%
4 2
 
1.3%
5 2
 
1.3%
6 2
 
1.3%
7 2
 
1.3%
8 2
 
1.3%
Hangul
ValueCountFrequency (%)
년 24
50.0%
월 24
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 150
75.8%
Hangul 48
 
24.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 64
42.7%
0 26
17.3%
24
 
16.0%
1 22
 
14.7%
3 2
 
1.3%
4 2
 
1.3%
5 2
 
1.3%
6 2
 
1.3%
7 2
 
1.3%
8 2
 
1.3%
Hangul
ValueCountFrequency (%)
년 24
50.0%
월 24
50.0%

플라스틱
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90300.833
Minimum65930
Maximum117140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:47:34.996197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum65930
5-th percentile70381
Q183520
median90335
Q398332.5
95-th percentile112167.5
Maximum117140
Range51210
Interquartile range (IQR)14812.5

Descriptive statistics

Standard deviation12613.68
Coefficient of variation (CV)0.13968509
Kurtosis0.09442066
Mean90300.833
Median Absolute Deviation (MAD)7900
Skewness0.15470998
Sum2167220
Variance1.5910492 × 108
MonotonicityNot monotonic
2023-12-12T18:47:35.125810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
90240 1
 
4.2%
82380 1
 
4.2%
90650 1
 
4.2%
94580 1
 
4.2%
86610 1
 
4.2%
88090 1
 
4.2%
73050 1
 
4.2%
89460 1
 
4.2%
69910 1
 
4.2%
80150 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
65930 1
4.2%
69910 1
4.2%
73050 1
4.2%
75770 1
4.2%
80150 1
4.2%
82380 1
4.2%
83900 1
4.2%
86610 1
4.2%
87610 1
4.2%
88090 1
4.2%
ValueCountFrequency (%)
117140 1
4.2%
113510 1
4.2%
104560 1
4.2%
104240 1
4.2%
100180 1
4.2%
98790 1
4.2%
98180 1
4.2%
94580 1
4.2%
91180 1
4.2%
90680 1
4.2%

캔
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29746.667
Minimum22640
Maximum38350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:47:35.229229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22640
5-th percentile23383
Q126225
median30005
Q331815
95-th percentile37480.5
Maximum38350
Range15710
Interquartile range (IQR)5590

Descriptive statistics

Standard deviation4495.5946
Coefficient of variation (CV)0.15112936
Kurtosis-0.56919189
Mean29746.667
Median Absolute Deviation (MAD)3280
Skewness0.30583309
Sum713920
Variance20210371
MonotonicityNot monotonic
2023-12-12T18:47:35.348802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
25700 1
 
4.2%
23740 1
 
4.2%
30170 1
 
4.2%
37030 1
 
4.2%
37560 1
 
4.2%
29840 1
 
4.2%
38350 1
 
4.2%
32040 1
 
4.2%
30230 1
 
4.2%
25300 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
22640 1
4.2%
23320 1
4.2%
23740 1
4.2%
24350 1
4.2%
25300 1
4.2%
25700 1
4.2%
26400 1
4.2%
27050 1
4.2%
29000 1
4.2%
29200 1
4.2%
ValueCountFrequency (%)
38350 1
4.2%
37560 1
4.2%
37030 1
4.2%
35100 1
4.2%
34700 1
4.2%
32040 1
4.2%
31740 1
4.2%
30430 1
4.2%
30380 1
4.2%
30330 1
4.2%

파지
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean211347.08
Minimum112050
Maximum309790
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:47:35.481729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum112050
5-th percentile122866
Q1177422.5
median213730
Q3247850
95-th percentile293124.5
Maximum309790
Range197740
Interquartile range (IQR)70427.5

Descriptive statistics

Standard deviation53899.341
Coefficient of variation (CV)0.25502761
Kurtosis-0.59237899
Mean211347.08
Median Absolute Deviation (MAD)36485
Skewness-0.10928717
Sum5072330
Variance2.905139 × 109
MonotonicityNot monotonic
2023-12-12T18:47:35.608796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
294350 1
 
4.2%
137350 1
 
4.2%
254930 1
 
4.2%
210230 1
 
4.2%
218030 1
 
4.2%
211930 1
 
4.2%
216270 1
 
4.2%
181960 1
 
4.2%
152090 1
 
4.2%
112050 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
112050 1
4.2%
120310 1
4.2%
137350 1
4.2%
147630 1
4.2%
152090 1
4.2%
163810 1
4.2%
181960 1
4.2%
188560 1
4.2%
206120 1
4.2%
206390 1
4.2%
ValueCountFrequency (%)
309790 1
4.2%
294350 1
4.2%
286180 1
4.2%
266730 1
4.2%
261180 1
4.2%
254930 1
4.2%
245490 1
4.2%
240840 1
4.2%
224580 1
4.2%
218030 1
4.2%

스티로폼
Real number (ℝ)

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25660
Minimum21720
Maximum37250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:47:35.732190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21720
5-th percentile22283.5
Q123547.5
median24960
Q327245
95-th percentile30156
Maximum37250
Range15530
Interquartile range (IQR)3697.5

Descriptive statistics

Standard deviation3336.8079
Coefficient of variation (CV)0.13003928
Kurtosis5.3864972
Mean25660
Median Absolute Deviation (MAD)2010
Skewness1.9246653
Sum615840
Variance11134287
MonotonicityNot monotonic
2023-12-12T18:47:35.901634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
24010 2
 
8.3%
24200 1
 
4.2%
23090 1
 
4.2%
25250 1
 
4.2%
25660 1
 
4.2%
27750 1
 
4.2%
30450 1
 
4.2%
28490 1
 
4.2%
27800 1
 
4.2%
21720 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
21720 1
4.2%
22240 1
4.2%
22530 1
4.2%
22660 1
4.2%
23090 1
4.2%
23510 1
4.2%
23560 1
4.2%
23840 1
4.2%
24010 2
8.3%
24200 1
4.2%
ValueCountFrequency (%)
37250 1
4.2%
30450 1
4.2%
28490 1
4.2%
27800 1
4.2%
27750 1
4.2%
27590 1
4.2%
27130 1
4.2%
27110 1
4.2%
25960 1
4.2%
25660 1
4.2%

잡철
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6921.25
Minimum3660
Maximum12040
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:47:36.054866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3660
5-th percentile3774.5
Q14840
median5610
Q39172.5
95-th percentile11725.5
Maximum12040
Range8380
Interquartile range (IQR)4332.5

Descriptive statistics

Standard deviation2881.1491
Coefficient of variation (CV)0.41627583
Kurtosis-1.080306
Mean6921.25
Median Absolute Deviation (MAD)1215
Skewness0.74192449
Sum166110
Variance8301020.1
MonotonicityNot monotonic
2023-12-12T18:47:36.175674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5360 2
 
8.3%
12040 1
 
4.2%
4410 1
 
4.2%
4860 1
 
4.2%
5130 1
 
4.2%
4780 1
 
4.2%
5160 1
 
4.2%
4870 1
 
4.2%
3710 1
 
4.2%
6840 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
3660 1
4.2%
3710 1
4.2%
4140 1
4.2%
4410 1
4.2%
4560 1
4.2%
4780 1
4.2%
4860 1
4.2%
4870 1
4.2%
5130 1
4.2%
5160 1
4.2%
ValueCountFrequency (%)
12040 1
4.2%
11820 1
4.2%
11190 1
4.2%
11120 1
4.2%
11030 1
4.2%
10800 1
4.2%
8630 1
4.2%
8580 1
4.2%
6840 1
4.2%
6120 1
4.2%

비철
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5889.5833
Minimum2670
Maximum11280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:47:36.301034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2670
5-th percentile2785
Q14767.5
median5675
Q36357.5
95-th percentile10677.5
Maximum11280
Range8610
Interquartile range (IQR)1590

Descriptive statistics

Standard deviation2317.5239
Coefficient of variation (CV)0.3934954
Kurtosis0.74482904
Mean5889.5833
Median Absolute Deviation (MAD)780
Skewness0.9084657
Sum141350
Variance5370917.2
MonotonicityNot monotonic
2023-12-12T18:47:36.419629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
5640 1
 
4.2%
4880 1
 
4.2%
6160 1
 
4.2%
6440 1
 
4.2%
7570 1
 
4.2%
5530 1
 
4.2%
2670 1
 
4.2%
5990 1
 
4.2%
2770 1
 
4.2%
3120 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
2670 1
4.2%
2770 1
4.2%
2870 1
4.2%
3120 1
4.2%
4030 1
4.2%
4430 1
4.2%
4880 1
4.2%
4980 1
4.2%
5070 1
4.2%
5320 1
4.2%
ValueCountFrequency (%)
11280 1
4.2%
10760 1
4.2%
10210 1
4.2%
7620 1
4.2%
7570 1
4.2%
6440 1
4.2%
6330 1
4.2%
6240 1
4.2%
6160 1
4.2%
5990 1
4.2%

유리병
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59645.417
Minimum28460
Maximum84180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:47:36.538537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28460
5-th percentile34726.5
Q149815
median61445
Q372427.5
95-th percentile83831
Maximum84180
Range55720
Interquartile range (IQR)22612.5

Descriptive statistics

Standard deviation16155.244
Coefficient of variation (CV)0.27085474
Kurtosis-0.76674079
Mean59645.417
Median Absolute Deviation (MAD)11535
Skewness-0.24850945
Sum1431490
Variance2.609919 × 108
MonotonicityNot monotonic
2023-12-12T18:47:36.673413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
77360 1
 
4.2%
58950 1
 
4.2%
67200 1
 
4.2%
72100 1
 
4.2%
50340 1
 
4.2%
62950 1
 
4.2%
28460 1
 
4.2%
38660 1
 
4.2%
53610 1
 
4.2%
48240 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
28460 1
4.2%
34470 1
4.2%
36180 1
4.2%
38660 1
4.2%
40810 1
4.2%
48240 1
4.2%
50340 1
4.2%
52010 1
4.2%
53610 1
4.2%
58950 1
4.2%
ValueCountFrequency (%)
84180 1
4.2%
84020 1
4.2%
82760 1
4.2%
77360 1
4.2%
75010 1
4.2%
73410 1
4.2%
72100 1
4.2%
67200 1
4.2%
64990 1
4.2%
62950 1
4.2%

페트병
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88701.25
Minimum68760
Maximum115620
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:47:36.795788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum68760
5-th percentile74210
Q181875
median86980
Q393025
95-th percentile106300
Maximum115620
Range46860
Interquartile range (IQR)11150

Descriptive statistics

Standard deviation10944.661
Coefficient of variation (CV)0.1233879
Kurtosis0.46402534
Mean88701.25
Median Absolute Deviation (MAD)5890
Skewness0.60426392
Sum2128830
Variance1.197856 × 108
MonotonicityNot monotonic
2023-12-12T18:47:36.929511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
80660 1
 
4.2%
74120 1
 
4.2%
81140 1
 
4.2%
115620 1
 
4.2%
105450 1
 
4.2%
84100 1
 
4.2%
101040 1
 
4.2%
85420 1
 
4.2%
83930 1
 
4.2%
80870 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
68760 1
4.2%
74120 1
4.2%
74720 1
4.2%
80660 1
4.2%
80870 1
4.2%
81140 1
4.2%
82120 1
4.2%
82510 1
4.2%
83930 1
4.2%
84100 1
4.2%
ValueCountFrequency (%)
115620 1
4.2%
106450 1
4.2%
105450 1
4.2%
101040 1
4.2%
97540 1
4.2%
93370 1
4.2%
92910 1
4.2%
92830 1
4.2%
92650 1
4.2%
89520 1
4.2%

기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean475.41667
Minimum0
Maximum1720
Zeros13
Zeros (%)54.2%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T18:47:37.053334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31020
95-th percentile1607.5
Maximum1720
Range1720
Interquartile range (IQR)1020

Descriptive statistics

Standard deviation618.22311
Coefficient of variation (CV)1.3003817
Kurtosis-0.78567773
Mean475.41667
Median Absolute Deviation (MAD)0
Skewness0.8932807
Sum11410
Variance382199.82
MonotonicityNot monotonic
2023-12-12T18:47:37.193493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 13
54.2%
1190 1
 
4.2%
970 1
 
4.2%
1290 1
 
4.2%
1480 1
 
4.2%
380 1
 
4.2%
1720 1
 
4.2%
370 1
 
4.2%
1630 1
 
4.2%
1170 1
 
4.2%
Other values (2) 2
 
8.3%
ValueCountFrequency (%)
0 13
54.2%
370 1
 
4.2%
380 1
 
4.2%
550 1
 
4.2%
660 1
 
4.2%
970 1
 
4.2%
1170 1
 
4.2%
1190 1
 
4.2%
1290 1
 
4.2%
1480 1
 
4.2%
ValueCountFrequency (%)
1720 1
4.2%
1630 1
4.2%
1480 1
4.2%
1290 1
4.2%
1190 1
4.2%
1170 1
4.2%
970 1
4.2%
660 1
4.2%
550 1
4.2%
380 1
4.2%

Interactions

2023-12-12T18:47:33.060874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:25.799723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:26.654223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:27.512234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:28.726122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:29.548023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:30.299144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:31.213223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:32.193973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:33.159004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:25.881085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:26.741776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:27.609589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:28.809300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:29.629622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:30.436067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:31.319444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:32.287926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:33.249815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:25.987231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:26.844266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:27.717809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:28.904709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:29.722794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:30.565916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:31.442849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:32.380775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:33.353137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:26.068022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:26.948949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:27.833111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:29.006000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:29.817611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:30.680756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:31.562836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:32.474607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:33.456578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:26.159243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:27.050159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:28.245468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:29.119039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:29.911344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:30.802038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:31.702002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:32.573408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:33.543454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:26.248402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:27.129876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:28.338552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:29.202368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:29.975476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:30.877555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:31.825683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:32.676248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:33.620212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:26.336095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:27.212816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:28.418546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:29.280563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:30.041546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:30.952188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:31.906155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:32.771265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:33.700355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:26.446706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:27.325400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:28.521051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:29.374155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:30.125245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:31.039840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:31.992337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:32.858162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:33.789285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:26.552852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:27.418017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:28.624161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:29.454863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:30.201885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:31.125999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:32.080815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:47:32.955837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:47:37.293652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연월플라스틱캔파지스티로폼잡철비철유리병페트병기타
연월1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
플라스틱1.0001.0000.0000.7650.3680.3750.4320.9030.6670.000
캔1.0000.0001.0000.6620.0000.5230.5200.5660.2860.000
파지1.0000.7650.6621.0000.0000.6720.5410.6710.0000.000
스티로폼1.0000.3680.0000.0001.0000.0000.0000.0000.2900.154
잡철1.0000.3750.5230.6720.0001.0000.3570.6330.4900.000
비철1.0000.4320.5200.5410.0000.3571.0000.5540.0000.671
유리병1.0000.9030.5660.6710.0000.6330.5541.0000.8990.000
페트병1.0000.6670.2860.0000.2900.4900.0000.8991.0000.000
기타1.0000.0000.0000.0000.1540.0000.6710.0000.0001.000
2023-12-12T18:47:37.418863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
플라스틱캔파지스티로폼잡철비철유리병페트병기타
플라스틱1.0000.4220.502-0.0870.3370.4630.3270.5490.226
캔0.4221.0000.3830.2960.0520.2370.0230.717-0.185
파지0.5020.3831.000-0.1360.5550.3900.5640.2200.306
스티로폼-0.0870.296-0.1361.000-0.239-0.091-0.2340.301-0.210
잡철0.3370.0520.555-0.2391.0000.2710.4390.1100.108
비철0.4630.2370.390-0.0910.2711.0000.4390.2370.540
유리병0.3270.0230.564-0.2340.4390.4391.000-0.0820.463
페트병0.5490.7170.2200.3010.1100.237-0.0821.0000.012
기타0.226-0.1850.306-0.2100.1080.5400.4630.0121.000

Missing values

2023-12-12T18:47:33.922771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:47:34.056411image/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

연월플라스틱캔파지스티로폼잡철비철유리병페트병기타
02021년 1월90240257002943502420012040564077360806601190
12021년 2월659302264026118023090586050706098068760970
22021년 3월1001803510030979022240118201076075010821200
32021년 4월98180290002667302266011030762084020929101290
42021년 5월11351034700286180235106120403084180928300
52021년 6월1042403038024549022530111901128061910933701480
62021년 7월1045603174024084037250536057306287097540380
72021년 8월1171403043022458027110111202870408101064500
82021년 9월904302932021553027130108001021082760926501720
92021년 10월987902705018856024670456062403618089520370
연월플라스틱캔파지스티로폼잡철비철유리병페트병기타
142022년 3월8390026400147630238406080498060020865600
152022년 4월8761023320120310240103660443034470825100
162022년 5월8015025300112050217206840312048240808700
172022년 6월6991030230152090240103710277053610839300
182022년 7월8946032040181960278004870599038660854200
192022년 8월73050383502162702849051602670284601010400
202022년 9월8809029840211930304504780553062950841000
212022년 10월86610375602180302775053607570503401054500
222022년 11월9458037030210230256605130644072100115620660
232022년 12월906503017025493025250486061606720081140550