Overview

Dataset statistics

Number of variables7
Number of observations348
Missing cells69
Missing cells (%)2.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.9 KiB
Average record size in memory61.4 B

Variable types

Categorical3
Numeric4

Dataset

Description양천구 구역별쓰레기배출량 데이터입니다.기준연월별 구역별 일반생화, 재활용품, 음식물류 쓰레기 배출량에 대한 정보를 제공합니다.(공동주택 자체계약에 따른 재활용 배출량, 사업장 자체계약에 따른 쓰레기 배출량 등 제외됩니다.)
Author서울특별시 양천구
URLhttps://www.data.go.kr/data/15093944/fileData.do

Alerts

구역 is highly overall correlated with 일반생활(톤) and 3 other fieldsHigh correlation
행정동 is highly overall correlated with 일반생활(톤) and 3 other fieldsHigh correlation
일반생활(톤) is highly overall correlated with 음식물류(톤) and 3 other fieldsHigh correlation
음식물류(톤) is highly overall correlated with 일반생활(톤) and 3 other fieldsHigh correlation
재활용품(톤) is highly overall correlated with 일반생활(톤) and 3 other fieldsHigh correlation
재활용품(톤) has 69 (19.8%) missing valuesMissing

Reproduction

Analysis started2023-12-12 20:34:13.019135
Analysis finished2023-12-12 20:34:15.541058
Duration2.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년도
Categorical

Distinct5
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2019
72 
2020
72 
2021
72 
2022
72 
2023
60 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 72
20.7%
2020 72
20.7%
2021 72
20.7%
2022 72
20.7%
2023 60
17.2%

Length

2023-12-13T05:34:15.633996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:34:15.780615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 72
20.7%
2020 72
20.7%
2021 72
20.7%
2022 72
20.7%
2023 60
17.2%

기준월
Real number (ℝ)

Distinct12
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3275862
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-13T05:34:15.908945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3852771
Coefficient of variation (CV)0.53500293
Kurtosis-1.1762929
Mean6.3275862
Median Absolute Deviation (MAD)3
Skewness0.038377681
Sum2202
Variance11.460101
MonotonicityNot monotonic
2023-12-13T05:34:16.033232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 30
8.6%
2 30
8.6%
3 30
8.6%
4 30
8.6%
5 30
8.6%
6 30
8.6%
7 30
8.6%
8 30
8.6%
9 30
8.6%
10 30
8.6%
Other values (2) 48
13.8%
ValueCountFrequency (%)
1 30
8.6%
2 30
8.6%
3 30
8.6%
4 30
8.6%
5 30
8.6%
6 30
8.6%
7 30
8.6%
8 30
8.6%
9 30
8.6%
10 30
8.6%
ValueCountFrequency (%)
12 24
6.9%
11 24
6.9%
10 30
8.6%
9 30
8.6%
8 30
8.6%
7 30
8.6%
6 30
8.6%
5 30
8.6%
4 30
8.6%
3 30
8.6%

구역
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
1구역
58 
2구역
58 
3구역
58 
4구역
58 
5구역
58 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1구역
2nd row2구역
3rd row3구역
4th row4구역
5th row5구역

Common Values

ValueCountFrequency (%)
1구역 58
16.7%
2구역 58
16.7%
3구역 58
16.7%
4구역 58
16.7%
5구역 58
16.7%
6구역 58
16.7%

Length

2023-12-13T05:34:16.164418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:34:16.597375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1구역 58
16.7%
2구역 58
16.7%
3구역 58
16.7%
4구역 58
16.7%
5구역 58
16.7%
6구역 58
16.7%

행정동
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
신월1·2·3·5동
58 
신월4·6·7동, 신정3동, 신정1동 1031,1032
58 
목1동, 신정2동, 신정4동(943~1013, 1302)
58 
신정1동(1031,1032 제외), 신정6·7동
58 
목2·3·4동, 신정4동(872~942, 1305, 1308)
58 

Length

Max length34
Median length28
Mean length22.333333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row신월1·2·3·5동
2nd row신월4·6·7동, 신정3동, 신정1동 1031,1032
3rd row목1동, 신정2동, 신정4동(943~1013, 1302)
4th row신정1동(1031,1032 제외), 신정6·7동
5th row목2·3·4동, 신정4동(872~942, 1305, 1308)

Common Values

ValueCountFrequency (%)
신월1·2·3·5동 58
16.7%
신월4·6·7동, 신정3동, 신정1동 1031,1032 58
16.7%
목1동, 신정2동, 신정4동(943~1013, 1302) 58
16.7%
신정1동(1031,1032 제외), 신정6·7동 58
16.7%
목2·3·4동, 신정4동(872~942, 1305, 1308) 58
16.7%
목5동 58
16.7%

Length

2023-12-13T05:34:16.760083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:34:16.910772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신월1·2·3·5동 58
 
5.9%
신정1동(1031,1032 58
 
5.9%
1308 58
 
5.9%
1305 58
 
5.9%
신정4동(872~942 58
 
5.9%
목2·3·4동 58
 
5.9%
신정6·7동 58
 
5.9%
제외 58
 
5.9%
1302 58
 
5.9%
신월4·6·7동 58
 
5.9%
Other values (7) 406
41.2%

일반생활(톤)
Real number (ℝ)

HIGH CORRELATION 

Distinct223
Distinct (%)64.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean515.90517
Minimum38
Maximum854
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-13T05:34:17.070572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38
5-th percentile224.7
Q1463
median547.5
Q3623.25
95-th percentile690
Maximum854
Range816
Interquartile range (IQR)160.25

Descriptive statistics

Standard deviation147.4852
Coefficient of variation (CV)0.28587657
Kurtosis0.073519193
Mean515.90517
Median Absolute Deviation (MAD)78.5
Skewness-0.94312641
Sum179535
Variance21751.884
MonotonicityNot monotonic
2023-12-13T05:34:17.267352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
583 5
 
1.4%
581 4
 
1.1%
224 4
 
1.1%
623 4
 
1.1%
646 4
 
1.1%
608 4
 
1.1%
595 4
 
1.1%
485 4
 
1.1%
243 4
 
1.1%
672 3
 
0.9%
Other values (213) 308
88.5%
ValueCountFrequency (%)
38 1
0.3%
158 1
0.3%
171 1
0.3%
182 1
0.3%
186 1
0.3%
193 1
0.3%
204 1
0.3%
208 1
0.3%
209 1
0.3%
218 1
0.3%
ValueCountFrequency (%)
854 1
0.3%
743 1
0.3%
727 1
0.3%
724 1
0.3%
721 1
0.3%
711 1
0.3%
710 1
0.3%
706 2
0.6%
703 1
0.3%
702 1
0.3%

음식물류(톤)
Real number (ℝ)

HIGH CORRELATION 

Distinct214
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean398.5977
Minimum168
Maximum615
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-13T05:34:17.443299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum168
5-th percentile199
Q1350.75
median416
Q3476
95-th percentile536.65
Maximum615
Range447
Interquartile range (IQR)125.25

Descriptive statistics

Standard deviation103.82977
Coefficient of variation (CV)0.26048763
Kurtosis-0.43006862
Mean398.5977
Median Absolute Deviation (MAD)62
Skewness-0.60953246
Sum138712
Variance10780.622
MonotonicityNot monotonic
2023-12-13T05:34:17.604989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
484 5
 
1.4%
448 4
 
1.1%
376 4
 
1.1%
365 4
 
1.1%
512 4
 
1.1%
383 4
 
1.1%
413 4
 
1.1%
200 4
 
1.1%
382 4
 
1.1%
429 3
 
0.9%
Other values (204) 308
88.5%
ValueCountFrequency (%)
168 1
0.3%
181 1
0.3%
183 1
0.3%
184 1
0.3%
186 1
0.3%
187 1
0.3%
189 1
0.3%
190 2
0.6%
191 1
0.3%
193 2
0.6%
ValueCountFrequency (%)
615 1
0.3%
582 1
0.3%
581 1
0.3%
578 1
0.3%
570 1
0.3%
569 1
0.3%
559 1
0.3%
556 1
0.3%
555 1
0.3%
554 1
0.3%

재활용품(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct145
Distinct (%)52.0%
Missing69
Missing (%)19.8%
Infinite0
Infinite (%)0.0%
Mean194.81362
Minimum37
Maximum497
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-13T05:34:17.792523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile40
Q1146
median163
Q3242.5
95-th percentile403.1
Maximum497
Range460
Interquartile range (IQR)96.5

Descriptive statistics

Standard deviation115.55002
Coefficient of variation (CV)0.59313111
Kurtosis-0.42802774
Mean194.81362
Median Absolute Deviation (MAD)69
Skewness0.56230502
Sum54353
Variance13351.807
MonotonicityNot monotonic
2023-12-13T05:34:17.971859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 12
 
3.4%
155 9
 
2.6%
41 8
 
2.3%
152 8
 
2.3%
42 7
 
2.0%
154 6
 
1.7%
156 6
 
1.7%
159 6
 
1.7%
43 5
 
1.4%
164 5
 
1.4%
Other values (135) 207
59.5%
(Missing) 69
 
19.8%
ValueCountFrequency (%)
37 4
 
1.1%
38 2
 
0.6%
39 3
 
0.9%
40 12
3.4%
41 8
2.3%
42 7
2.0%
43 5
1.4%
44 4
 
1.1%
45 4
 
1.1%
46 1
 
0.3%
ValueCountFrequency (%)
497 1
0.3%
482 1
0.3%
453 1
0.3%
449 1
0.3%
446 1
0.3%
445 1
0.3%
440 1
0.3%
434 1
0.3%
428 1
0.3%
419 1
0.3%

Interactions

2023-12-13T05:34:14.867971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:34:13.459338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:34:13.943241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:34:14.385952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:34:14.969335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:34:13.596980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:34:14.047488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:34:14.503372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:34:15.074306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:34:13.723109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:34:14.169253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:34:14.637820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:34:15.201227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:34:13.842483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:34:14.273274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:34:14.753508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:34:18.086083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도기준월구역행정동일반생활(톤)음식물류(톤)재활용품(톤)
기준년도1.0000.0000.0000.0000.3350.0000.393
기준월0.0001.0000.0000.0000.0000.3510.000
구역0.0000.0001.0001.0000.8270.8630.987
행정동0.0000.0001.0001.0000.8270.8630.987
일반생활(톤)0.3350.0000.8270.8271.0000.7510.620
음식물류(톤)0.0000.3510.8630.8630.7511.0000.666
재활용품(톤)0.3930.0000.9870.9870.6200.6661.000
2023-12-13T05:34:18.232199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도구역행정동
기준년도1.0000.0000.000
구역0.0001.0001.000
행정동0.0001.0001.000
2023-12-13T05:34:18.339376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준월일반생활(톤)음식물류(톤)재활용품(톤)기준년도구역행정동
기준월1.0000.1460.1310.0260.0000.0000.000
일반생활(톤)0.1461.0000.8970.5080.1440.6230.623
음식물류(톤)0.1310.8971.0000.5260.0000.6420.642
재활용품(톤)0.0260.5080.5261.0000.1720.8270.827
기준년도0.0000.1440.0000.1721.0000.0000.000
구역0.0000.6230.6420.8270.0001.0001.000
행정동0.0000.6230.6420.8270.0001.0001.000

Missing values

2023-12-13T05:34:15.329661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:34:15.491949image/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

기준년도기준월구역행정동일반생활(톤)음식물류(톤)재활용품(톤)
0201911구역신월1·2·3·5동524427<NA>
1201912구역신월4·6·7동, 신정3동, 신정1동 1031,1032591484<NA>
2201913구역목1동, 신정2동, 신정4동(943~1013, 1302)552500<NA>
3201914구역신정1동(1031,1032 제외), 신정6·7동432368<NA>
4201915구역목2·3·4동, 신정4동(872~942, 1305, 1308)601544<NA>
5201916구역목5동250206<NA>
6201921구역신월1·2·3·5동496383<NA>
7201922구역신월4·6·7동, 신정3동, 신정1동 1031,1032533451<NA>
8201923구역목1동, 신정2동, 신정4동(943~1013, 1302)506442<NA>
9201924구역신정1동(1031,1032 제외), 신정6·7동406340<NA>
기준년도기준월구역행정동일반생활(톤)음식물류(톤)재활용품(톤)
338202393구역목1동, 신정2동, 신정4동(943~1013, 1302)532389143
339202394구역신정1동(1031,1032 제외), 신정6·7동43632040
340202395구역목2·3·4동, 신정4동(872~942, 1305, 1308)568453415
341202396구역목5동218191<NA>
3422023101구역신월1·2·3·5동450390253
3432023102구역신월4·6·7동, 신정3동, 신정1동 1031,1032672484170
3442023103구역목1동, 신정2동, 신정4동(943~1013, 1302)608441156
3452023104구역신정1동(1031,1032 제외), 신정6·7동50536543
3462023105구역목2·3·4동, 신정4동(872~942, 1305, 1308)689530449
3472023106구역목5동276223<NA>