Overview

Dataset statistics

Number of variables9
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory85.5 B

Variable types

Text1
Numeric5
Categorical3

Dataset

Description서울특별시 강서구 행정동별 연간 쓰레기 배출량 데이터입니다. 2019년도와 2020년도 데이터이고, 일반주택/공동주택/소형음식점/사업장으로 구분되어 있습니다.
Author서울특별시 강서구
URLhttps://www.data.go.kr/data/15091531/fileData.do

Alerts

소형음식점 2019 has constant value ""Constant
사업장 2019 is highly overall correlated with 공동주택 2020 and 1 other fieldsHigh correlation
사업장 2020 is highly overall correlated with 공동주택 2020 and 1 other fieldsHigh correlation
일반주택 2019 is highly overall correlated with 일반주택 2020High correlation
일반주택 2020 is highly overall correlated with 일반주택 2019High correlation
공동주택 2019 is highly overall correlated with 공동주택 2020High correlation
공동주택 2020 is highly overall correlated with 공동주택 2019 and 2 other fieldsHigh correlation
사업장 2019 is highly imbalanced (68.6%)Imbalance
사업장 2020 is highly imbalanced (68.6%)Imbalance
행정동 has unique valuesUnique
일반주택 2019 has unique valuesUnique
일반주택 2020 has unique valuesUnique
일반주택 2020 has 1 (4.2%) zerosZeros
공동주택 2019 has 14 (58.3%) zerosZeros
공동주택 2020 has 13 (54.2%) zerosZeros
소형음식점 2020 has 19 (79.2%) zerosZeros

Reproduction

Analysis started2023-12-12 12:21:32.816831
Analysis finished2023-12-12 12:21:36.427520
Duration3.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-12T21:21:36.596025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.9166667
Min length3

Characters and Unicode

Total characters94
Distinct characters33
Distinct categories2 ?
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 row가양1동
2nd row가양2동
3rd row가양3동
4th row공항동
5th row김포공항내
ValueCountFrequency (%)
가양1동 1
 
4.2%
가양2동 1
 
4.2%
화곡본동 1
 
4.2%
화곡8동 1
 
4.2%
화곡6동 1
 
4.2%
화곡4동 1
 
4.2%
화곡3동 1
 
4.2%
화곡2동 1
 
4.2%
화곡1동 1
 
4.2%
우장산동 1
 
4.2%
Other values (14) 14
58.3%
2023-12-12T21:21:37.064245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 21
22.3%
화 10
 
10.6%
곡 9
 
9.6%
1 5
 
5.3%
2 4
 
4.3%
3 4
 
4.3%
가 3
 
3.2%
촌 3
 
3.2%
방 3
 
3.2%
양 3
 
3.2%
Other values (23) 29
30.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78
83.0%
Decimal Number 16
 
17.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 21
26.9%
화 10
12.8%
곡 9
11.5%
가 3
 
3.8%
촌 3
 
3.8%
방 3
 
3.8%
양 3
 
3.8%
등 3
 
3.8%
마 2
 
2.6%
산 2
 
2.6%
Other values (17) 19
24.4%
Decimal Number
ValueCountFrequency (%)
1 5
31.2%
2 4
25.0%
3 4
25.0%
8 1
 
6.2%
6 1
 
6.2%
4 1
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78
83.0%
Common 16
 
17.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 21
26.9%
화 10
12.8%
곡 9
11.5%
가 3
 
3.8%
촌 3
 
3.8%
방 3
 
3.8%
양 3
 
3.8%
등 3
 
3.8%
마 2
 
2.6%
산 2
 
2.6%
Other values (17) 19
24.4%
Common
ValueCountFrequency (%)
1 5
31.2%
2 4
25.0%
3 4
25.0%
8 1
 
6.2%
6 1
 
6.2%
4 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78
83.0%
ASCII 16
 
17.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 21
26.9%
화 10
12.8%
곡 9
11.5%
가 3
 
3.8%
촌 3
 
3.8%
방 3
 
3.8%
양 3
 
3.8%
등 3
 
3.8%
마 2
 
2.6%
산 2
 
2.6%
Other values (17) 19
24.4%
ASCII
ValueCountFrequency (%)
1 5
31.2%
2 4
25.0%
3 4
25.0%
8 1
 
6.2%
6 1
 
6.2%
4 1
 
6.2%

일반주택 2019
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean319805.42
Minimum1570
Maximum1088770
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T21:21:37.227580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1570
5-th percentile5817.5
Q126937.5
median278200
Q3511540
95-th percentile849556
Maximum1088770
Range1087200
Interquartile range (IQR)484602.5

Descriptive statistics

Standard deviation317222.32
Coefficient of variation (CV)0.9919229
Kurtosis-0.18023215
Mean319805.42
Median Absolute Deviation (MAD)253505
Skewness0.78965223
Sum7675330
Variance1.0063 × 1011
MonotonicityNot monotonic
2023-12-12T21:21:37.413158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
399610 1
 
4.2%
218430 1
 
4.2%
337970 1
 
4.2%
861220 1
 
4.2%
195140 1
 
4.2%
459490 1
 
4.2%
378630 1
 
4.2%
404790 1
 
4.2%
579110 1
 
4.2%
1088770 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1570 1
4.2%
4820 1
4.2%
11470 1
4.2%
13110 1
4.2%
13980 1
4.2%
20210 1
4.2%
29180 1
4.2%
35760 1
4.2%
44250 1
4.2%
70840 1
4.2%
ValueCountFrequency (%)
1088770 1
4.2%
861220 1
4.2%
783460 1
4.2%
662920 1
4.2%
579110 1
4.2%
567820 1
4.2%
492780 1
4.2%
459490 1
4.2%
404790 1
4.2%
399610 1
4.2%

일반주택 2020
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1027805.4
Minimum0
Maximum5893150
Zeros1
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T21:21:37.625557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9111
Q140135
median828195
Q31430865
95-th percentile2742046.5
Maximum5893150
Range5893150
Interquartile range (IQR)1390730

Descriptive statistics

Standard deviation1334043.4
Coefficient of variation (CV)1.2979532
Kurtosis7.0138318
Mean1027805.4
Median Absolute Deviation (MAD)785800
Skewness2.3130062
Sum24667330
Variance1.7796717 × 1012
MonotonicityNot monotonic
2023-12-12T21:21:37.787730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
925380 1
 
4.2%
444500 1
 
4.2%
2618890 1
 
4.2%
2763780 1
 
4.2%
739440 1
 
4.2%
1676460 1
 
4.2%
1370680 1
 
4.2%
994380 1
 
4.2%
1862250 1
 
4.2%
5893150 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
0 1
4.2%
6060 1
4.2%
26400 1
4.2%
28630 1
4.2%
31240 1
4.2%
35890 1
4.2%
41550 1
4.2%
43240 1
4.2%
76840 1
4.2%
181270 1
4.2%
ValueCountFrequency (%)
5893150 1
4.2%
2763780 1
4.2%
2618890 1
4.2%
1862250 1
4.2%
1676460 1
4.2%
1532670 1
4.2%
1396930 1
4.2%
1370680 1
4.2%
1060750 1
4.2%
994380 1
4.2%

공동주택 2019
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62965
Minimum0
Maximum297520
Zeros14
Zeros (%)58.3%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T21:21:37.943485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q393200
95-th percentile247871.5
Maximum297520
Range297520
Interquartile range (IQR)93200

Descriptive statistics

Standard deviation97640.845
Coefficient of variation (CV)1.5507162
Kurtosis0.2977745
Mean62965
Median Absolute Deviation (MAD)0
Skewness1.3314726
Sum1511160
Variance9.5337347 × 109
MonotonicityNot monotonic
2023-12-12T21:21:38.104953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 14
58.3%
220170 1
 
4.2%
297520 1
 
4.2%
252760 1
 
4.2%
50280 1
 
4.2%
207310 1
 
4.2%
129950 1
 
4.2%
80950 1
 
4.2%
203860 1
 
4.2%
52950 1
 
4.2%
ValueCountFrequency (%)
0 14
58.3%
15410 1
 
4.2%
50280 1
 
4.2%
52950 1
 
4.2%
80950 1
 
4.2%
129950 1
 
4.2%
203860 1
 
4.2%
207310 1
 
4.2%
220170 1
 
4.2%
252760 1
 
4.2%
ValueCountFrequency (%)
297520 1
4.2%
252760 1
4.2%
220170 1
4.2%
207310 1
4.2%
203860 1
4.2%
129950 1
4.2%
80950 1
4.2%
52950 1
4.2%
50280 1
4.2%
15410 1
4.2%

공동주택 2020
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101796.67
Minimum0
Maximum790170
Zeros13
Zeros (%)54.2%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T21:21:38.255692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q377570
95-th percentile548913
Maximum790170
Range790170
Interquartile range (IQR)77570

Descriptive statistics

Standard deviation207029.42
Coefficient of variation (CV)2.0337544
Kurtosis5.3517857
Mean101796.67
Median Absolute Deviation (MAD)0
Skewness2.4055701
Sum2443120
Variance4.2861181 × 1010
MonotonicityNot monotonic
2023-12-12T21:21:38.388361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 13
54.2%
422790 1
 
4.2%
571170 1
 
4.2%
5020 1
 
4.2%
63060 1
 
4.2%
5290 1
 
4.2%
790170 1
 
4.2%
254880 1
 
4.2%
140230 1
 
4.2%
108380 1
 
4.2%
Other values (2) 2
 
8.3%
ValueCountFrequency (%)
0 13
54.2%
5020 1
 
4.2%
5290 1
 
4.2%
14830 1
 
4.2%
63060 1
 
4.2%
67300 1
 
4.2%
108380 1
 
4.2%
140230 1
 
4.2%
254880 1
 
4.2%
422790 1
 
4.2%
ValueCountFrequency (%)
790170 1
4.2%
571170 1
4.2%
422790 1
4.2%
254880 1
4.2%
140230 1
4.2%
108380 1
4.2%
67300 1
4.2%
63060 1
4.2%
14830 1
4.2%
5290 1
4.2%

소형음식점 2019
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
0
24 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 24
100.0%

Length

2023-12-12T21:21:38.542700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:21:38.661943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 24
100.0%

소형음식점 2020
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1656.6667
Minimum0
Maximum14450
Zeros19
Zeros (%)79.2%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T21:21:38.760542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9644
Maximum14450
Range14450
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3866.8285
Coefficient of variation (CV)2.3341017
Kurtosis5.152481
Mean1656.6667
Median Absolute Deviation (MAD)0
Skewness2.4150645
Sum39760
Variance14952362
MonotonicityNot monotonic
2023-12-12T21:21:38.887168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 19
79.2%
9800 1
 
4.2%
8760 1
 
4.2%
5020 1
 
4.2%
14450 1
 
4.2%
1730 1
 
4.2%
ValueCountFrequency (%)
0 19
79.2%
1730 1
 
4.2%
5020 1
 
4.2%
8760 1
 
4.2%
9800 1
 
4.2%
14450 1
 
4.2%
ValueCountFrequency (%)
14450 1
 
4.2%
9800 1
 
4.2%
8760 1
 
4.2%
5020 1
 
4.2%
1730 1
 
4.2%
0 19
79.2%

사업장 2019
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
0
22 
28990
 
1
227690
 
1

Length

Max length6
Median length1
Mean length1.375
Min length1

Unique

Unique2 ?
Unique (%)8.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row28990

Common Values

ValueCountFrequency (%)
0 22
91.7%
28990 1
 
4.2%
227690 1
 
4.2%

Length

2023-12-12T21:21:39.040728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:21:39.150096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22
91.7%
28990 1
 
4.2%
227690 1
 
4.2%

사업장 2020
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
0
22 
1170
 
1
497200
 
1

Length

Max length6
Median length1
Mean length1.3333333
Min length1

Unique

Unique2 ?
Unique (%)8.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1170

Common Values

ValueCountFrequency (%)
0 22
91.7%
1170 1
 
4.2%
497200 1
 
4.2%

Length

2023-12-12T21:21:39.276587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:21:39.390255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22
91.7%
1170 1
 
4.2%
497200 1
 
4.2%

Interactions

2023-12-12T21:21:35.572488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:33.196306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:33.803031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:34.383642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:34.955419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:35.663391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:33.313303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:33.906156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:34.488351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:35.088035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:35.769931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:33.433082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:34.009623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:34.598007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:35.206240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:35.872116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:33.561910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:34.126972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:34.731488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:35.318249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:35.999364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:33.679943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:34.268591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:34.849880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:21:35.454105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:21:39.474511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동일반주택 2019일반주택 2020공동주택 2019공동주택 2020소형음식점 2020사업장 2019사업장 2020
행정동1.0001.0001.0001.0001.0001.0001.0001.000
일반주택 20191.0001.0000.8440.5870.7360.3060.0000.000
일반주택 20201.0000.8441.0000.0000.0000.4840.0000.000
공동주택 20191.0000.5870.0001.0000.9550.0000.0000.000
공동주택 20201.0000.7360.0000.9551.0000.0000.8950.895
소형음식점 20201.0000.3060.4840.0000.0001.0000.0000.000
사업장 20191.0000.0000.0000.0000.8950.0001.0001.000
사업장 20201.0000.0000.0000.0000.8950.0001.0001.000
2023-12-12T21:21:40.022259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업장 2019사업장 2020
사업장 20191.0001.000
사업장 20201.0001.000
2023-12-12T21:21:40.129329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일반주택 2019일반주택 2020공동주택 2019공동주택 2020소형음식점 2020사업장 2019사업장 2020
일반주택 20191.0000.8030.0130.056-0.3260.0000.000
일반주택 20200.8031.000-0.050-0.017-0.1630.0000.000
공동주택 20190.013-0.0501.0000.835-0.1770.0000.000
공동주택 20200.056-0.0170.8351.000-0.2940.5630.563
소형음식점 2020-0.326-0.163-0.177-0.2941.0000.0000.000
사업장 20190.0000.0000.0000.5630.0001.0001.000
사업장 20200.0000.0000.0000.5630.0001.0001.000

Missing values

2023-12-12T21:21:36.154971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:21:36.350635image/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

행정동일반주택 2019일반주택 2020공동주택 2019공동주택 2020소형음식점 2019소형음식점 2020사업장 2019사업장 2020
0가양1동3996109253802201704227900000
1가양2동1570312402975205711700000
2가양3동4820181270050200000
3공항동1398035890000980000
4김포공항내3576060600000289901170
5등촌1동2021028630000000
6등촌2동1311043240000876000
7등촌3동1147041550252760630600502000
8마곡동7084026400000000
9마곡지구4425005028052900000
행정동일반주택 2019일반주택 2020공동주택 2019공동주택 2020소형음식점 2019소형음식점 2020사업장 2019사업장 2020
14염창동29180768400001445000
15우장산동49278015326705295000000
16화곡1동10887705893150000173000
17화곡2동5791101862250000000
18화곡3동4047909943800673000000
19화곡4동3786301370680000000
20화곡6동4594901676460000000
21화곡8동195140739440000000
22화곡본동8612202763780000000
23업체혼합337970261889015410148300000