Overview

Dataset statistics

Number of variables5
Number of observations5195
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory218.3 KiB
Average record size in memory43.0 B

Variable types

Categorical2
Numeric2
Text1

Dataset

Description음식물쓰레기 종량제의 일환으로 시행중인 RFID기반 음식물쓰레기 종량제 시스템을 채택한 지자체의 음식물쓰레기 배출통계정보를 제공하는 서비스로 2017년7월~2020년7월까지의 지자체별 RFID음식물쓰레기 배출량
Author한국환경공단
URLhttps://www.data.go.kr/data/15065041/fileData.do

Reproduction

Analysis started2023-12-12 09:13:49.365706
Analysis finished2023-12-12 09:13:50.505702
Duration1.14 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

배출연도
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size40.7 KiB
2019
1787 
2018
1695 
2020
914 
2017
799 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2019 1787
34.4%
2018 1695
32.6%
2020 914
17.6%
2017 799
15.4%

Length

2023-12-12T18:13:50.574907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:13:50.688496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 1787
34.4%
2018 1695
32.6%
2020 914
17.6%
2017 799
15.4%

배출월
Real number (ℝ)

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6196343
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.8 KiB
2023-12-12T18:13:50.814242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3669896
Coefficient of variation (CV)0.50863679
Kurtosis-1.1198767
Mean6.6196343
Median Absolute Deviation (MAD)3
Skewness-0.044543207
Sum34389
Variance11.336619
MonotonicityNot monotonic
2023-12-12T18:13:50.973722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 574
11.0%
6 442
8.5%
5 440
8.5%
3 439
8.5%
4 439
8.5%
1 435
8.4%
12 433
8.3%
11 430
8.3%
10 429
8.3%
9 427
8.2%
Other values (2) 707
13.6%
ValueCountFrequency (%)
1 435
8.4%
2 284
5.5%
3 439
8.5%
4 439
8.5%
5 440
8.5%
6 442
8.5%
7 574
11.0%
8 423
8.1%
9 427
8.2%
10 429
8.3%
ValueCountFrequency (%)
12 433
8.3%
11 430
8.3%
10 429
8.3%
9 427
8.2%
8 423
8.1%
7 574
11.0%
6 442
8.5%
5 440
8.5%
4 439
8.5%
3 439
8.5%

광역시도
Categorical

Distinct16
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size40.7 KiB
경기도
949 
서울특별시
865 
부산광역시
576 
경상북도
389 
강원도
340 
Other values (11)
2076 

Length

Max length7
Median length5
Mean length4.2891242
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 949
18.3%
서울특별시 865
16.7%
부산광역시 576
11.1%
경상북도 389
7.5%
강원도 340
 
6.5%
인천광역시 324
 
6.2%
경상남도 304
 
5.9%
대구광역시 288
 
5.5%
전라북도 288
 
5.5%
전라남도 211
 
4.1%
Other values (6) 661
12.7%

Length

2023-12-12T18:13:51.126054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 949
18.3%
서울특별시 865
16.7%
부산광역시 576
11.1%
경상북도 389
7.5%
강원도 340
 
6.5%
인천광역시 324
 
6.2%
경상남도 304
 
5.9%
대구광역시 288
 
5.5%
전라북도 288
 
5.5%
전라남도 211
 
4.1%
Other values (6) 661
12.7%

기초지자체
Text

Distinct135
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size40.7 KiB
2023-12-12T18:13:51.485910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0207892
Min length2

Characters and Unicode

Total characters15693
Distinct characters109
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
중구 216
 
4.2%
동구 216
 
4.2%
서구 180
 
3.5%
북구 144
 
2.8%
남구 144
 
2.8%
강서구 72
 
1.4%
고성군 64
 
1.2%
청주시 36
 
0.7%
정읍시 36
 
0.7%
익산시 36
 
0.7%
Other values (125) 4051
78.0%
2023-12-12T18:13:52.014437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 2784
 
17.7%
시 2003
 
12.8%
군 842
 
5.4%
동 561
 
3.6%
천 440
 
2.8%
서 427
 
2.7%
주 422
 
2.7%
성 402
 
2.6%
남 341
 
2.2%
산 319
 
2.0%
Other values (99) 7152
45.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15693
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 2784
 
17.7%
시 2003
 
12.8%
군 842
 
5.4%
동 561
 
3.6%
천 440
 
2.8%
서 427
 
2.7%
주 422
 
2.7%
성 402
 
2.6%
남 341
 
2.2%
산 319
 
2.0%
Other values (99) 7152
45.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15693
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 2784
 
17.7%
시 2003
 
12.8%
군 842
 
5.4%
동 561
 
3.6%
천 440
 
2.8%
서 427
 
2.7%
주 422
 
2.7%
성 402
 
2.6%
남 341
 
2.2%
산 319
 
2.0%
Other values (99) 7152
45.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15693
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 2784
 
17.7%
시 2003
 
12.8%
군 842
 
5.4%
동 561
 
3.6%
천 440
 
2.8%
서 427
 
2.7%
주 422
 
2.7%
성 402
 
2.6%
남 341
 
2.2%
산 319
 
2.0%
Other values (99) 7152
45.6%

배출량(톤)
Real number (ℝ)

Distinct5171
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean459761.8
Minimum0
Maximum4065464
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size45.8 KiB
2023-12-12T18:13:52.209990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6684.6
Q180672
median267858
Q3631496.5
95-th percentile1544531.4
Maximum4065464
Range4065464
Interquartile range (IQR)550824.5

Descriptive statistics

Standard deviation572779.28
Coefficient of variation (CV)1.2458175
Kurtosis9.7817459
Mean459761.8
Median Absolute Deviation (MAD)228504
Skewness2.7035097
Sum2.3884625 × 109
Variance3.280761 × 1011
MonotonicityNot monotonic
2023-12-12T18:13:52.405861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17977 2
 
< 0.1%
369293 2
 
< 0.1%
88312 2
 
< 0.1%
382759 2
 
< 0.1%
63853 2
 
< 0.1%
14102 2
 
< 0.1%
390152 2
 
< 0.1%
13611 2
 
< 0.1%
11047 2
 
< 0.1%
7171 2
 
< 0.1%
Other values (5161) 5175
99.6%
ValueCountFrequency (%)
0 2
< 0.1%
2 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
13 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
ValueCountFrequency (%)
4065464 1
< 0.1%
4040229 1
< 0.1%
3920418 1
< 0.1%
3907487 1
< 0.1%
3884328 1
< 0.1%
3837391 1
< 0.1%
3813888 1
< 0.1%
3768402 1
< 0.1%
3708838 1
< 0.1%
3693866 1
< 0.1%

Interactions

2023-12-12T18:13:49.969240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:49.708215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:50.106045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:13:49.817620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:13:52.533562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출연도배출월광역시도배출량(톤)
배출연도1.0000.4790.0000.000
배출월0.4791.0000.0000.075
광역시도0.0000.0001.0000.516
배출량(톤)0.0000.0750.5161.000
2023-12-12T18:13:52.644066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출연도광역시도
배출연도1.0000.000
광역시도0.0001.000
2023-12-12T18:13:52.744434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출월배출량(톤)배출연도광역시도
배출월1.0000.0260.3060.000
배출량(톤)0.0261.0000.0000.231
배출연도0.3060.0001.0000.000
광역시도0.0000.2310.0001.000

Missing values

2023-12-12T18:13:50.310318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:13:50.452599image/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

배출연도배출월광역시도기초지자체배출량(톤)
020177서울특별시종로구87260
120177서울특별시중구189784
220177서울특별시용산구6679
320177서울특별시성동구296140
420177서울특별시광진구166759
520177서울특별시동대문구55791
620177서울특별시중랑구539912
720177서울특별시성북구194719
820177서울특별시도봉구2161188
920177서울특별시노원구1200885
배출연도배출월광역시도기초지자체배출량(톤)
518520207경상남도통영시44230
518620207경상남도김해시1929408
518720207경상남도거제시76463
518820207경상남도양산시779858
518920207경상남도의령군6031
519020207경상남도창녕군169073
519120207경상남도고성군39642
519220207경상남도하동군10051
519320207제주특별자치도제주시2202240
519420207제주특별자치도서귀포시803232