Overview

Dataset statistics

Number of variables6
Number of observations170
Missing cells1
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.9 KiB
Average record size in memory53.8 B

Variable types

Numeric5
Categorical1

Dataset

Description광역시도별 온실가스 배출량
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=OYUMJUDK201TN8I8CM9Y31515663&infSeq=1

Alerts

전체온실가스배출량(tonCO2-eq) is highly overall correlated with 1인당인구배출량(tonCO2-eq) and 3 other fieldsHigh correlation
1인당인구배출량(tonCO2-eq) is highly overall correlated with 전체온실가스배출량(tonCO2-eq) and 3 other fieldsHigh correlation
관리업체1개당 온실가스배출량(tonCO2-eq) is highly overall correlated with 전체온실가스배출량(tonCO2-eq) and 3 other fieldsHigh correlation
사업장1개당온실가스 배출량(tonCO2-eq) is highly overall correlated with 전체온실가스배출량(tonCO2-eq) and 3 other fieldsHigh correlation
광역시도명 is highly overall correlated with 전체온실가스배출량(tonCO2-eq) and 3 other fieldsHigh correlation
전체온실가스배출량(tonCO2-eq) has unique valuesUnique
관리업체1개당 온실가스배출량(tonCO2-eq) has unique valuesUnique
사업장1개당온실가스 배출량(tonCO2-eq) has unique valuesUnique

Reproduction

Analysis started2023-12-10 22:28:43.387471
Analysis finished2023-12-10 22:28:46.650570
Duration3.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

Distinct10
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.5
Minimum2011
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T07:28:46.727850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011
Q12013
median2015.5
Q32018
95-th percentile2020
Maximum2020
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8807667
Coefficient of variation (CV)0.0014293062
Kurtosis-1.2248853
Mean2015.5
Median Absolute Deviation (MAD)2.5
Skewness0
Sum342635
Variance8.2988166
MonotonicityDecreasing
2023-12-11T07:28:46.836301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2020 17
10.0%
2019 17
10.0%
2018 17
10.0%
2017 17
10.0%
2016 17
10.0%
2015 17
10.0%
2014 17
10.0%
2013 17
10.0%
2012 17
10.0%
2011 17
10.0%
ValueCountFrequency (%)
2011 17
10.0%
2012 17
10.0%
2013 17
10.0%
2014 17
10.0%
2015 17
10.0%
2016 17
10.0%
2017 17
10.0%
2018 17
10.0%
2019 17
10.0%
2020 17
10.0%
ValueCountFrequency (%)
2020 17
10.0%
2019 17
10.0%
2018 17
10.0%
2017 17
10.0%
2016 17
10.0%
2015 17
10.0%
2014 17
10.0%
2013 17
10.0%
2012 17
10.0%
2011 17
10.0%

광역시도명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
서울특별시
 
10
부산광역시
 
10
대구광역시
 
10
인천광역시
 
10
광주광역시
 
10
Other values (12)
120 

Length

Max length7
Median length5
Mean length4.6470588
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시
2nd row부산광역시
3rd row대구광역시
4th row인천광역시
5th row광주광역시

Common Values

ValueCountFrequency (%)
서울특별시 10
 
5.9%
부산광역시 10
 
5.9%
대구광역시 10
 
5.9%
인천광역시 10
 
5.9%
광주광역시 10
 
5.9%
대전광역시 10
 
5.9%
울산광역시 10
 
5.9%
세종특별자치시 10
 
5.9%
경기도 10
 
5.9%
강원도 10
 
5.9%
Other values (7) 70
41.2%

Length

2023-12-11T07:28:47.015164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울특별시 10
 
5.9%
강원도 10
 
5.9%
경상남도 10
 
5.9%
경상북도 10
 
5.9%
전라남도 10
 
5.9%
전라북도 10
 
5.9%
충청남도 10
 
5.9%
충청북도 10
 
5.9%
경기도 10
 
5.9%
부산광역시 10
 
5.9%
Other values (7) 70
41.2%

전체온실가스배출량(tonCO2-eq)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct170
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35548595
Minimum246362
Maximum1.5973122 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T07:28:47.221341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum246362
5-th percentile1685813
Q14351168.2
median21863018
Q351614270
95-th percentile1.3527155 × 108
Maximum1.5973122 × 108
Range1.5948486 × 108
Interquartile range (IQR)47263102

Descriptive statistics

Standard deviation37719910
Coefficient of variation (CV)1.0610802
Kurtosis1.9359661
Mean35548595
Median Absolute Deviation (MAD)19980580
Skewness1.4649251
Sum6.0432611 × 109
Variance1.4227916 × 1015
MonotonicityNot monotonic
2023-12-11T07:28:47.429440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12324706 1
 
0.6%
57120950 1
 
0.6%
1884917 1
 
0.6%
45770551 1
 
0.6%
38470120 1
 
0.6%
22082483 1
 
0.6%
144348565 1
 
0.6%
14856645 1
 
0.6%
93448429 1
 
0.6%
55090702 1
 
0.6%
Other values (160) 160
94.1%
ValueCountFrequency (%)
246362 1
0.6%
362902 1
0.6%
1082797 1
0.6%
1443049 1
0.6%
1549902 1
0.6%
1557749 1
0.6%
1584366 1
0.6%
1630268 1
0.6%
1676939 1
0.6%
1696659 1
0.6%
ValueCountFrequency (%)
159731217 1
0.6%
157622104 1
0.6%
151149802 1
0.6%
144348565 1
0.6%
141911607 1
0.6%
140923975 1
0.6%
139577573 1
0.6%
138932268 1
0.6%
135905423 1
0.6%
134496827 1
0.6%

1인당인구배출량(tonCO2-eq)
Real number (ℝ)

HIGH CORRELATION 

Distinct167
Distinct (%)98.8%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean18.71484
Minimum1.061
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T07:28:47.882671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.061
5-th percentile1.1888
Q12.708
median8.993
Q324.058
95-th percentile66.7496
Maximum144
Range142.939
Interquartile range (IQR)21.35

Descriptive statistics

Standard deviation23.05238
Coefficient of variation (CV)1.2317701
Kurtosis6.1253106
Mean18.71484
Median Absolute Deviation (MAD)7.409
Skewness2.1680296
Sum3162.808
Variance531.41224
MonotonicityNot monotonic
2023-12-11T07:28:48.099052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.0 2
 
1.2%
16.0 2
 
1.2%
3.228 1
 
0.6%
3.704 1
 
0.6%
24.909 1
 
0.6%
13.986 1
 
0.6%
69.995 1
 
0.6%
7.938 1
 
0.6%
49.034 1
 
0.6%
20.398 1
 
0.6%
Other values (157) 157
92.4%
ValueCountFrequency (%)
1.061 1
0.6%
1.07 1
0.6%
1.137 1
0.6%
1.138 1
0.6%
1.159 1
0.6%
1.168 1
0.6%
1.175 1
0.6%
1.177 1
0.6%
1.188 1
0.6%
1.19 1
0.6%
ValueCountFrequency (%)
144.0 1
0.6%
104.0 1
0.6%
99.0 1
0.6%
75.46 1
0.6%
74.13 1
0.6%
71.173 1
0.6%
69.995 1
0.6%
68.823 1
0.6%
66.87 1
0.6%
66.569 1
0.6%

관리업체1개당 온실가스배출량(tonCO2-eq)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct170
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean227571.23
Minimum6713
Maximum957080.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T07:28:48.289778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6713
5-th percentile25303.848
Q139395.636
median139211.47
Q3334050.08
95-th percentile764449.18
Maximum957080.44
Range950367.44
Interquartile range (IQR)294654.45

Descriptive statistics

Standard deviation238401.78
Coefficient of variation (CV)1.0475919
Kurtosis1.1068101
Mean227571.23
Median Absolute Deviation (MAD)109216.25
Skewness1.3760267
Sum38687109
Variance5.683541 × 1010
MonotonicityNot monotonic
2023-12-11T07:28:48.458779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37348.0 1
 
0.6%
326405.429 1
 
0.6%
60803.774 1
 
0.6%
144843.516 1
 
0.6%
337457.193 1
 
0.6%
147216.553 1
 
0.6%
885574.018 1
 
0.6%
97741.086 1
 
0.6%
724406.426 1
 
0.6%
301042.087 1
 
0.6%
Other values (160) 160
94.1%
ValueCountFrequency (%)
6713.0 1
0.6%
14010.0 1
0.6%
14695.745 1
0.6%
19183.276 1
0.6%
21102.687 1
0.6%
21850.182 1
0.6%
22324.461 1
0.6%
24887.84 1
0.6%
25212.344 1
0.6%
25415.687 1
0.6%
ValueCountFrequency (%)
957080.444 1
0.6%
886314.308 1
0.6%
885574.018 1
0.6%
879064.229 1
0.6%
868104.44 1
0.6%
852011.373 1
0.6%
826977.786 1
0.6%
816243.117 1
0.6%
767257.878 1
0.6%
761016.319 1
0.6%

사업장1개당온실가스 배출량(tonCO2-eq)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct170
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60374.443
Minimum2997.134
Maximum358589.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T07:28:48.613038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2997.134
5-th percentile3989.9008
Q19929.1012
median29220.029
Q381197.364
95-th percentile216208.44
Maximum358589.51
Range355592.37
Interquartile range (IQR)71268.263

Descriptive statistics

Standard deviation69225.492
Coefficient of variation (CV)1.1466026
Kurtosis2.6203938
Mean60374.443
Median Absolute Deviation (MAD)22872.039
Skewness1.6841118
Sum10263655
Variance4.7921687 × 109
MonotonicityNot monotonic
2023-12-11T07:28:48.821711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3582.0 1
 
0.6%
67122.15 1
 
0.6%
31947.746 1
 
0.6%
15685.59 1
 
0.6%
74990.487 1
 
0.6%
36865.581 1
 
0.6%
261501.024 1
 
0.6%
29130.676 1
 
0.6%
171465.007 1
 
0.6%
70089.952 1
 
0.6%
Other values (160) 160
94.1%
ValueCountFrequency (%)
2997.134 1
0.6%
3182.047 1
0.6%
3305.208 1
0.6%
3386.465 1
0.6%
3499.893 1
0.6%
3520.561 1
0.6%
3582.0 1
0.6%
3668.731 1
0.6%
3754.76 1
0.6%
4277.295 1
0.6%
ValueCountFrequency (%)
358589.507 1
0.6%
276864.391 1
0.6%
261501.024 1
0.6%
257656.757 1
0.6%
250591.58 1
0.6%
242016.995 1
0.6%
232896.459 1
0.6%
224446.313 1
0.6%
219807.202 1
0.6%
211809.961 1
0.6%

Interactions

2023-12-11T07:28:45.933049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:43.641893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:44.222071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:44.837733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:45.401819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:46.019524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:43.742014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:44.343918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:44.924882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:45.495268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:46.129099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:43.855780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:44.464571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:45.031210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:45.601678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:46.234999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:43.974162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:44.585779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:45.152812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:45.706013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:46.322492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:44.104890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:44.704809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:45.261752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:45.832156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:28:48.946639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도광역시도명전체온실가스배출량(tonCO2-eq)1인당인구배출량(tonCO2-eq)관리업체1개당 온실가스배출량(tonCO2-eq)사업장1개당온실가스 배출량(tonCO2-eq)
년도1.0000.0000.0000.0000.0000.000
광역시도명0.0001.0000.9260.8810.9130.900
전체온실가스배출량(tonCO2-eq)0.0000.9261.0000.9380.8620.918
1인당인구배출량(tonCO2-eq)0.0000.8810.9381.0000.8900.956
관리업체1개당 온실가스배출량(tonCO2-eq)0.0000.9130.8620.8901.0000.888
사업장1개당온실가스 배출량(tonCO2-eq)0.0000.9000.9180.9560.8881.000
2023-12-11T07:28:49.093084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도전체온실가스배출량(tonCO2-eq)1인당인구배출량(tonCO2-eq)관리업체1개당 온실가스배출량(tonCO2-eq)사업장1개당온실가스 배출량(tonCO2-eq)광역시도명
년도1.0000.0230.083-0.058-0.0770.000
전체온실가스배출량(tonCO2-eq)0.0231.0000.7830.9150.8030.695
1인당인구배출량(tonCO2-eq)0.0830.7831.0000.8960.9590.595
관리업체1개당 온실가스배출량(tonCO2-eq)-0.0580.9150.8961.0000.9370.660
사업장1개당온실가스 배출량(tonCO2-eq)-0.0770.8030.9590.9371.0000.634
광역시도명0.0000.6950.5950.6600.6341.000

Missing values

2023-12-11T07:28:46.456096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:28:46.603012image/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

년도광역시도명전체온실가스배출량(tonCO2-eq)1인당인구배출량(tonCO2-eq)관리업체1개당 온실가스배출량(tonCO2-eq)사업장1개당온실가스 배출량(tonCO2-eq)
02020서울특별시123247063.037348.03582.0
12020부산광역시67870074.048827.09086.0
22020대구광역시36976023.030813.07395.0
32020인천광역시4512683236.0324653.070954.0
42020광주광역시14430492.014010.04454.0
52020대전광역시38677996.030455.08771.0
62020울산광역시4720369599.0284360.0124878.0
72020세종특별자치시224589516.054778.024680.0
82020경기도6289614911.0173268.019299.0
92020강원도4420619660.0340048.081261.0
년도광역시도명전체온실가스배출량(tonCO2-eq)1인당인구배출량(tonCO2-eq)관리업체1개당 온실가스배출량(tonCO2-eq)사업장1개당온실가스 배출량(tonCO2-eq)
1602011세종특별자치시246362<NA>30795.2530795.25
1612011경기도362614113.038138402.33217308.549
1622011강원도3930569225.582401078.49104259.13
1632011충청북도1954161612.503145832.95557475.341
1642011충청남도13590542364.677957080.444358589.507
1652011전라북도138792457.406104355.22638553.458
1662011전라남도8599484444.921761016.319211809.961
1672011경상북도5262013919.495330944.2799283.281
1682011경상남도5975299118.059383031.99492928.446
1692011제주특별자치도21846863.79244585.42910762.0