Overview

Dataset statistics

Number of variables6
Number of observations42
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory54.1 B

Variable types

Categorical2
Text1
Numeric3

Dataset

Description한국지역난방공사의 일반폐기물 및 지정폐기물 구분에 따른 각 지사별 폐기물 배출 현황 정보(2019년~, 단위 : ton, 배출량 등)
URLhttps://www.data.go.kr/data/15117905/fileData.do

Alerts

단 위 has constant value ""Constant
2019 is highly overall correlated with 2020 and 1 other fieldsHigh correlation
2020 is highly overall correlated with 2019 and 1 other fieldsHigh correlation
2021 is highly overall correlated with 2019 and 1 other fieldsHigh correlation
2019 has 9 (21.4%) zerosZeros
2020 has 9 (21.4%) zerosZeros
2021 has 6 (14.3%) zerosZeros

Reproduction

Analysis started2023-12-12 18:35:36.251720
Analysis finished2023-12-12 18:35:38.426943
Duration2.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size468.0 B
일반폐기물
21 
지정폐기물
21 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반폐기물 21
50.0%
지정폐기물 21
50.0%

Length

2023-12-13T03:35:38.563643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:35:38.759290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반폐기물 21
50.0%
지정폐기물 21
50.0%

지사
Text

Distinct21
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size468.0 B
2023-12-13T03:35:39.055266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.4761905
Min length2

Characters and Unicode

Total characters104
Distinct characters40
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

Unique0 ?
Unique (%)0.0%

Sample

1st row본사
2nd row강남
3rd row고양
4th row광교
5th row광주전남
ValueCountFrequency (%)
본사 2
 
4.8%
수원 2
 
4.8%
평택 2
 
4.8%
판교 2
 
4.8%
파주 2
 
4.8%
청주 2
 
4.8%
중앙(중앙 2
 
4.8%
중앙(상암 2
 
4.8%
용인 2
 
4.8%
양산 2
 
4.8%
Other values (11) 22
52.4%
2023-12-13T03:35:39.962215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 6
 
5.8%
중 6
 
5.8%
앙 6
 
5.8%
남 4
 
3.8%
양 4
 
3.8%
광 4
 
3.8%
교 4
 
3.8%
( 4
 
3.8%
) 4
 
3.8%
본 2
 
1.9%
Other values (30) 60
57.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 96
92.3%
Open Punctuation 4
 
3.8%
Close Punctuation 4
 
3.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 6
 
6.2%
중 6
 
6.2%
앙 6
 
6.2%
남 4
 
4.2%
양 4
 
4.2%
광 4
 
4.2%
교 4
 
4.2%
본 2
 
2.1%
암 2
 
2.1%
용 2
 
2.1%
Other values (28) 56
58.3%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 96
92.3%
Common 8
 
7.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 6
 
6.2%
중 6
 
6.2%
앙 6
 
6.2%
남 4
 
4.2%
양 4
 
4.2%
광 4
 
4.2%
교 4
 
4.2%
본 2
 
2.1%
암 2
 
2.1%
용 2
 
2.1%
Other values (28) 56
58.3%
Common
ValueCountFrequency (%)
( 4
50.0%
) 4
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 96
92.3%
ASCII 8
 
7.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 6
 
6.2%
중 6
 
6.2%
앙 6
 
6.2%
남 4
 
4.2%
양 4
 
4.2%
광 4
 
4.2%
교 4
 
4.2%
본 2
 
2.1%
암 2
 
2.1%
용 2
 
2.1%
Other values (28) 56
58.3%
ASCII
ValueCountFrequency (%)
( 4
50.0%
) 4
50.0%

단 위
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size468.0 B
ton
42 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
ton 42
100.0%

Length

2023-12-13T03:35:40.160514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:35:40.311814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ton 42
100.0%

2019
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178.91741
Minimum0
Maximum6128.779
Zeros9
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-13T03:35:40.464295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.535
median19.8255
Q348.573375
95-th percentile149.8325
Maximum6128.779
Range6128.779
Interquartile range (IQR)47.038375

Descriptive statistics

Standard deviation941.41062
Coefficient of variation (CV)5.2617049
Kurtosis41.820863
Mean178.91741
Median Absolute Deviation (MAD)19.8255
Skewness6.4606416
Sum7514.5313
Variance886253.96
MonotonicityNot monotonic
2023-12-13T03:35:40.654433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0.0 9
 
21.4%
2.0 1
 
2.4%
86.05 1
 
2.4%
23.86 1
 
2.4%
1.7 1
 
2.4%
105.61 1
 
2.4%
6.5 1
 
2.4%
2.73 1
 
2.4%
67.155 1
 
2.4%
38.804 1
 
2.4%
Other values (24) 24
57.1%
ValueCountFrequency (%)
0.0 9
21.4%
0.98 1
 
2.4%
1.48 1
 
2.4%
1.7 1
 
2.4%
2.0 1
 
2.4%
2.73 1
 
2.4%
5.783 1
 
2.4%
6.5 1
 
2.4%
11.798 1
 
2.4%
12.89 1
 
2.4%
ValueCountFrequency (%)
6128.779 1
2.4%
168.6628 1
2.4%
151.18 1
2.4%
124.23 1
2.4%
105.61 1
2.4%
86.05 1
2.4%
81.3717 1
2.4%
67.155 1
2.4%
59.59 1
2.4%
51.06 1
2.4%

2020
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean286.92837
Minimum0
Maximum5621.21
Zeros9
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-13T03:35:40.960039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.3125
median14.61334
Q348.0145
95-th percentile486.06855
Maximum5621.21
Range5621.21
Interquartile range (IQR)47.702

Descriptive statistics

Standard deviation1145.4292
Coefficient of variation (CV)3.9920389
Kurtosis18.383972
Mean286.92837
Median Absolute Deviation (MAD)14.61334
Skewness4.397442
Sum12050.991
Variance1312008.1
MonotonicityNot monotonic
2023-12-13T03:35:41.235377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0.0 9
 
21.4%
2.11 1
 
2.4%
94.751 1
 
2.4%
3.87 1
 
2.4%
15.0 1
 
2.4%
2.3 1
 
2.4%
3.47 1
 
2.4%
0.35 1
 
2.4%
48.942 1
 
2.4%
33.96 1
 
2.4%
Other values (24) 24
57.1%
ValueCountFrequency (%)
0.0 9
21.4%
0.09 1
 
2.4%
0.3 1
 
2.4%
0.35 1
 
2.4%
0.67 1
 
2.4%
2.11 1
 
2.4%
2.3 1
 
2.4%
3.47 1
 
2.4%
3.5 1
 
2.4%
3.87 1
 
2.4%
ValueCountFrequency (%)
5621.21 1
2.4%
5034.305 1
2.4%
506.34 1
2.4%
100.911 1
2.4%
94.751 1
2.4%
92.3378 1
2.4%
65.72 1
2.4%
49.848 1
2.4%
48.942 1
2.4%
48.762 1
2.4%

2021
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean238.17918
Minimum0
Maximum4812.035
Zeros6
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-13T03:35:41.491148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.66
median15.935
Q340.89875
95-th percentile315.73673
Maximum4812.035
Range4812.035
Interquartile range (IQR)39.23875

Descriptive statistics

Standard deviation932.65169
Coefficient of variation (CV)3.9157566
Kurtosis19.349249
Mean238.17918
Median Absolute Deviation (MAD)15.585
Skewness4.4769391
Sum10003.525
Variance869839.17
MonotonicityNot monotonic
2023-12-13T03:35:41.775943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.0 6
 
14.3%
16.2 1
 
2.4%
1.2 1
 
2.4%
19.21 1
 
2.4%
0.8 1
 
2.4%
3.04 1
 
2.4%
1.09 1
 
2.4%
15.59 1
 
2.4%
6.525 1
 
2.4%
0.6 1
 
2.4%
Other values (27) 27
64.3%
ValueCountFrequency (%)
0.0 6
14.3%
0.6 1
 
2.4%
0.8 1
 
2.4%
1.09 1
 
2.4%
1.14 1
 
2.4%
1.2 1
 
2.4%
3.04 1
 
2.4%
3.18 1
 
2.4%
4.68 1
 
2.4%
6.525 1
 
2.4%
ValueCountFrequency (%)
4812.035 1
2.4%
3834.96 1
2.4%
324.229 1
2.4%
154.3836 1
2.4%
146.31 1
2.4%
93.704 1
2.4%
69.69 1
2.4%
55.66 1
2.4%
55.026 1
2.4%
53.496 1
2.4%

Interactions

2023-12-13T03:35:37.523111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:35:36.557602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:35:37.070425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:35:37.701200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:35:36.729837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:35:37.229883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:35:37.848782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:35:36.892129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:35:37.351227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:35:41.955208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분지사201920202021
구분1.0000.0000.0000.0300.030
지사0.0001.0000.1700.3050.305
20190.0000.1701.0001.0001.000
20200.0300.3051.0001.0001.000
20210.0300.3051.0001.0001.000
2023-12-13T03:35:42.132587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
201920202021구분
20191.0000.6950.7210.000
20200.6951.0000.6780.035
20210.7210.6781.0000.035
구분0.0000.0350.0351.000

Missing values

2023-12-13T03:35:38.051160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:35:38.351010image/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

구분지사단 위201920202021
0일반폐기물본사ton67.15548.94242.0
1일반폐기물강남ton81.3717100.91193.704
2일반폐기물고양ton42.71148.76253.496
3일반폐기물광교ton31.67533.3337.595
4일반폐기물광주전남ton36.3555034.3054812.035
5일반폐기물김해ton11.79812.4212.603
6일반폐기물대구ton6128.7795621.213834.96
7일반폐기물동탄ton168.662892.3378154.3836
8일반폐기물분당ton20.24121.02421.793
9일반폐기물삼송ton41.46230.86334.919
구분지사단 위201920202021
32지정폐기물수원ton19.410.671.14
33지정폐기물양산ton0.00.04.68
34지정폐기물용인ton1.4834.973.18
35지정폐기물중앙(상암)ton151.18506.3412.26
36지정폐기물중앙(중앙)ton0.980.30.0
37지정폐기물청주ton59.590.0324.229
38지정폐기물파주ton12.890.0915.67
39지정폐기물판교ton0.018.120.0
40지정폐기물평택ton0.00.00.0
41지정폐기물화성ton124.233.510.64