Overview

Dataset statistics

Number of variables6
Number of observations1140
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.0 KiB
Average record size in memory52.1 B

Variable types

Categorical2
Text1
Numeric3

Dataset

Description김해시에서 통계기반 도시현황 파악을 위해 개발한 통계지수 중 하나로서, 통계연도, 시도명, 시군구명, 폐기물재활용률(%), 총재활용량(톤/일), 생활계폐기물발생량(톤/일)으로 구성되어 있습니다. 김해시 중심의 통계지수로서, 데이터 수집, 가공 등의 어려움으로 김해시 외 지역의 정보는 누락될 수 있습니다.
Author경상남도 김해시
URLhttps://www.data.go.kr/data/15110177/fileData.do

Alerts

폐기물재활용률(퍼센트) is highly overall correlated with 총재활용량(톤_일)High correlation
총재활용량(톤_일) is highly overall correlated with 폐기물재활용률(퍼센트) and 1 other fieldsHigh correlation
생활계폐기물발생량(톤_일) is highly overall correlated with 총재활용량(톤_일)High correlation

Reproduction

Analysis started2023-12-12 06:04:34.360566
Analysis finished2023-12-12 06:04:35.926630
Duration1.57 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2016
228 
2017
228 
2018
228 
2019
228 
2020
228 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2016 228
20.0%
2017 228
20.0%
2018 228
20.0%
2019 228
20.0%
2020 228
20.0%

Length

2023-12-12T15:04:35.996607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:04:36.118037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 228
20.0%
2017 228
20.0%
2018 228
20.0%
2019 228
20.0%
2020 228
20.0%

시도명
Categorical

Distinct16
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
경기도
155 
서울특별시
125 
경상북도
115 
전라남도
110 
강원도
90 
Other values (11)
545 

Length

Max length7
Median length5
Mean length4.1359649
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 155
13.6%
서울특별시 125
11.0%
경상북도 115
10.1%
전라남도 110
9.6%
강원도 90
7.9%
경상남도 90
7.9%
부산광역시 80
7.0%
충청남도 75
6.6%
전라북도 70
 
6.1%
충청북도 55
 
4.8%
Other values (6) 175
15.4%

Length

2023-12-12T15:04:36.284155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 155
13.6%
서울특별시 125
11.0%
경상북도 115
10.1%
전라남도 110
9.6%
강원도 90
7.9%
경상남도 90
7.9%
부산광역시 80
7.0%
충청남도 75
6.6%
전라북도 70
 
6.1%
충청북도 55
 
4.8%
Other values (6) 175
15.4%

시군구명
Text

Distinct206
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2023-12-12T15:04:36.661323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9307018
Min length2

Characters and Unicode

Total characters3341
Distinct characters132
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
동구 30
 
2.6%
중구 30
 
2.6%
서구 25
 
2.2%
남구 22
 
1.9%
북구 20
 
1.8%
고성군 10
 
0.9%
강서구 10
 
0.9%
완주군 5
 
0.4%
무주군 5
 
0.4%
진안군 5
 
0.4%
Other values (196) 978
85.8%
2023-12-12T15:04:37.225453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 425
 
12.7%
시 390
 
11.7%
구 370
 
11.1%
천 110
 
3.3%
주 100
 
3.0%
성 90
 
2.7%
양 90
 
2.7%
동 85
 
2.5%
산 80
 
2.4%
서 65
 
1.9%
Other values (122) 1536
46.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3341
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 425
 
12.7%
시 390
 
11.7%
구 370
 
11.1%
천 110
 
3.3%
주 100
 
3.0%
성 90
 
2.7%
양 90
 
2.7%
동 85
 
2.5%
산 80
 
2.4%
서 65
 
1.9%
Other values (122) 1536
46.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3341
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 425
 
12.7%
시 390
 
11.7%
구 370
 
11.1%
천 110
 
3.3%
주 100
 
3.0%
성 90
 
2.7%
양 90
 
2.7%
동 85
 
2.5%
산 80
 
2.4%
서 65
 
1.9%
Other values (122) 1536
46.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3341
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 425
 
12.7%
시 390
 
11.7%
구 370
 
11.1%
천 110
 
3.3%
주 100
 
3.0%
성 90
 
2.7%
양 90
 
2.7%
동 85
 
2.5%
산 80
 
2.4%
서 65
 
1.9%
Other values (122) 1536
46.0%

폐기물재활용률(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct550
Distinct (%)48.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.367193
Minimum6.3
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-12T15:04:37.381106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.3
5-th percentile22.695
Q144.55
median56.2
Q367.8
95-th percentile83.7
Maximum100
Range93.7
Interquartile range (IQR)23.25

Descriptive statistics

Standard deviation17.739134
Coefficient of variation (CV)0.32039071
Kurtosis-0.16115036
Mean55.367193
Median Absolute Deviation (MAD)11.6
Skewness-0.27275319
Sum63118.6
Variance314.67689
MonotonicityNot monotonic
2023-12-12T15:04:37.540010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65.5 8
 
0.7%
55.5 7
 
0.6%
54.2 7
 
0.6%
50.7 6
 
0.5%
66.3 6
 
0.5%
56.6 6
 
0.5%
40.8 6
 
0.5%
47.4 6
 
0.5%
68.9 6
 
0.5%
70.4 6
 
0.5%
Other values (540) 1076
94.4%
ValueCountFrequency (%)
6.3 1
0.1%
6.5 1
0.1%
6.6 1
0.1%
8.9 1
0.1%
9.6 1
0.1%
9.9 1
0.1%
10.1 1
0.1%
10.3 1
0.1%
10.8 2
0.2%
11.0 1
0.1%
ValueCountFrequency (%)
100.0 5
0.4%
93.9 1
 
0.1%
93.6 1
 
0.1%
93.4 1
 
0.1%
92.6 1
 
0.1%
92.5 1
 
0.1%
92.4 1
 
0.1%
91.2 1
 
0.1%
91.1 1
 
0.1%
90.4 3
0.3%

총재활용량(톤_일)
Real number (ℝ)

HIGH CORRELATION 

Distinct930
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.10351
Minimum0.9
Maximum1105.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-12T15:04:37.671304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile8.5
Q129.9
median106.45
Q3223.4
95-th percentile449.315
Maximum1105.8
Range1104.9
Interquartile range (IQR)193.5

Descriptive statistics

Standard deviation152.53393
Coefficient of variation (CV)1.023007
Kurtosis3.9612919
Mean149.10351
Median Absolute Deviation (MAD)84.45
Skewness1.7239488
Sum169978
Variance23266.6
MonotonicityNot monotonic
2023-12-12T15:04:37.790240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44.9 4
 
0.4%
14.1 4
 
0.4%
16.0 4
 
0.4%
21.0 3
 
0.3%
6.2 3
 
0.3%
29.8 3
 
0.3%
22.2 3
 
0.3%
34.2 3
 
0.3%
22.0 3
 
0.3%
200.0 3
 
0.3%
Other values (920) 1107
97.1%
ValueCountFrequency (%)
0.9 1
 
0.1%
1.1 1
 
0.1%
2.0 1
 
0.1%
2.5 1
 
0.1%
2.6 1
 
0.1%
2.7 1
 
0.1%
3.0 1
 
0.1%
3.1 3
0.3%
3.3 1
 
0.1%
3.4 2
0.2%
ValueCountFrequency (%)
1105.8 1
0.1%
947.9 1
0.1%
857.6 1
0.1%
775.8 1
0.1%
743.6 1
0.1%
732.5 1
0.1%
721.4 1
0.1%
707.2 1
0.1%
696.6 1
0.1%
695.7 1
0.1%

생활계폐기물발생량(톤_일)
Real number (ℝ)

HIGH CORRELATION 

Distinct1015
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean246.20044
Minimum10.3
Maximum1526.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-12T15:04:37.932253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.3
5-th percentile27
Q166.175
median185.35
Q3352.775
95-th percentile708.715
Maximum1526.9
Range1516.6
Interquartile range (IQR)286.6

Descriptive statistics

Standard deviation235.16803
Coefficient of variation (CV)0.95518934
Kurtosis4.4274714
Mean246.20044
Median Absolute Deviation (MAD)129.15
Skewness1.8492637
Sum280668.5
Variance55304.004
MonotonicityNot monotonic
2023-12-12T15:04:38.056810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.0 3
 
0.3%
59.5 3
 
0.3%
89.2 3
 
0.3%
27.0 3
 
0.3%
39.0 3
 
0.3%
270.3 3
 
0.3%
46.2 3
 
0.3%
15.1 3
 
0.3%
29.1 3
 
0.3%
55.7 3
 
0.3%
Other values (1005) 1110
97.4%
ValueCountFrequency (%)
10.3 1
 
0.1%
10.7 1
 
0.1%
12.7 1
 
0.1%
13.0 1
 
0.1%
13.2 1
 
0.1%
14.4 1
 
0.1%
14.5 1
 
0.1%
14.8 2
0.2%
14.9 1
 
0.1%
15.1 3
0.3%
ValueCountFrequency (%)
1526.9 1
0.1%
1391.2 1
0.1%
1301.5 1
0.1%
1292.7 1
0.1%
1252.2 1
0.1%
1245.7 1
0.1%
1241.0 1
0.1%
1232.9 1
0.1%
1226.2 1
0.1%
1224.2 1
0.1%

Interactions

2023-12-12T15:04:35.307193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:34.665413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:34.975644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:35.426358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:34.772203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:35.074347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:35.555512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:34.874081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:04:35.181588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:04:38.142062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명폐기물재활용률(퍼센트)총재활용량(톤_일)생활계폐기물발생량(톤_일)
통계연도1.0000.0000.1310.0000.000
시도명0.0001.0000.6260.5620.576
폐기물재활용률(퍼센트)0.1310.6261.0000.5300.477
총재활용량(톤_일)0.0000.5620.5301.0000.958
생활계폐기물발생량(톤_일)0.0000.5760.4770.9581.000
2023-12-12T15:04:38.230498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명
통계연도1.0000.000
시도명0.0001.000
2023-12-12T15:04:38.573976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
폐기물재활용률(퍼센트)총재활용량(톤_일)생활계폐기물발생량(톤_일)통계연도시도명
폐기물재활용률(퍼센트)1.0000.6200.4240.0540.302
총재활용량(톤_일)0.6201.0000.9660.0000.258
생활계폐기물발생량(톤_일)0.4240.9661.0000.0000.267
통계연도0.0540.0000.0001.0000.000
시도명0.3020.2580.2670.0001.000

Missing values

2023-12-12T15:04:35.730667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:04:35.873445image/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

통계연도시도명시군구명폐기물재활용률(퍼센트)총재활용량(톤_일)생활계폐기물발생량(톤_일)
02016서울특별시종로구56.0227.2406.0
12016서울특별시중구62.5312.2499.2
22016서울특별시용산구63.7166.9262.1
32016서울특별시성동구68.5176.6257.8
42016서울특별시광진구73.8240.8326.3
52016서울특별시동대문구72.9311.0426.6
62016서울특별시중랑구73.1283.6387.9
72016서울특별시성북구70.8253.8358.3
82016서울특별시강북구67.3154.8230.0
92016서울특별시도봉구78.4247.0315.1
통계연도시도명시군구명폐기물재활용률(퍼센트)총재활용량(톤_일)생활계폐기물발생량(톤_일)
11302020경상남도창녕군60.341.468.6
11312020경상남도고성군41.312.630.5
11322020경상남도남해군70.044.663.7
11332020경상남도하동군58.023.941.2
11342020경상남도산청군27.111.040.6
11352020경상남도함양군65.644.067.1
11362020경상남도거창군51.952.2100.6
11372020경상남도합천군57.036.864.6
11382020제주특별자치도제주시63.3599.5947.6
11392020제주특별자치도서귀포시63.3235.9372.8