Overview

Dataset statistics

Number of variables3
Number of observations180
Missing cells18
Missing cells (%)3.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.7 KiB
Average record size in memory26.7 B

Variable types

Numeric2
Categorical1

Dataset

Description경기도 산업분야별 온실가스 배출량
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=Z5XU1QOR7868FS29MUCZ31537692&infSeq=1

Alerts

전체온실가스배출량 has 18 (10.0%) missing valuesMissing

Reproduction

Analysis started2023-12-10 22:31:34.546531
Analysis finished2023-12-10 22:31:35.403366
Duration0.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

Distinct9
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015
Minimum2011
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T07:31:35.482681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011
Q12013
median2015
Q32017
95-th percentile2019
Maximum2019
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5891911
Coefficient of variation (CV)0.0012849584
Kurtosis-1.2307741
Mean2015
Median Absolute Deviation (MAD)2
Skewness0
Sum362700
Variance6.7039106
MonotonicityDecreasing
2023-12-11T07:31:35.597045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2019 20
11.1%
2018 20
11.1%
2017 20
11.1%
2016 20
11.1%
2015 20
11.1%
2014 20
11.1%
2013 20
11.1%
2012 20
11.1%
2011 20
11.1%
ValueCountFrequency (%)
2011 20
11.1%
2012 20
11.1%
2013 20
11.1%
2014 20
11.1%
2015 20
11.1%
2016 20
11.1%
2017 20
11.1%
2018 20
11.1%
2019 20
11.1%
ValueCountFrequency (%)
2019 20
11.1%
2018 20
11.1%
2017 20
11.1%
2016 20
11.1%
2015 20
11.1%
2014 20
11.1%
2013 20
11.1%
2012 20
11.1%
2011 20
11.1%

산업구분
Categorical

Distinct20
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
건물
 
9
교통
 
9
기계
 
9
반도체.디스플레이.전기전자
 
9
목재
 
9
Other values (15)
135 

Length

Max length15
Median length14
Mean length4.5
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row건물
2nd row교통
3rd row기계
4th row반도체.디스플레이.전기전자
5th row목재

Common Values

ValueCountFrequency (%)
건물 9
 
5.0%
교통 9
 
5.0%
기계 9
 
5.0%
반도체.디스플레이.전기전자 9
 
5.0%
목재 9
 
5.0%
에너지 9
 
5.0%
비철금속 9
 
5.0%
산업단지 9
 
5.0%
석유화학 9
 
5.0%
섬유 9
 
5.0%
Other values (10) 90
50.0%

Length

2023-12-11T07:31:35.773723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
건물 9
 
3.7%
자동차 9
 
3.7%
지정외 9
 
3.7%
처리업 9
 
3.7%
분뇨 9
 
3.7%
및 9
 
3.7%
폐수 9
 
3.7%
하수 9
 
3.7%
폐기물 9
 
3.7%
통신 9
 
3.7%
Other values (17) 153
63.0%

전체온실가스배출량
Real number (ℝ)

MISSING 

Distinct162
Distinct (%)100.0%
Missing18
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean2622741.7
Minimum18819
Maximum24271363
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T07:31:35.938065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18819
5-th percentile34628.3
Q1198645
median470262.5
Q32146481.2
95-th percentile16239511
Maximum24271363
Range24252544
Interquartile range (IQR)1947836.2

Descriptive statistics

Standard deviation5239070.3
Coefficient of variation (CV)1.9975548
Kurtosis6.5185859
Mean2622741.7
Median Absolute Deviation (MAD)406960
Skewness2.7277366
Sum4.2488415 × 108
Variance2.7447857 × 1013
MonotonicityNot monotonic
2023-12-11T07:31:36.129187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
779207 1
 
0.6%
408361 1
 
0.6%
233621 1
 
0.6%
411750 1
 
0.6%
2060511 1
 
0.6%
462494 1
 
0.6%
77619 1
 
0.6%
1170405 1
 
0.6%
4130479 1
 
0.6%
24982 1
 
0.6%
Other values (152) 152
84.4%
(Missing) 18
 
10.0%
ValueCountFrequency (%)
18819 1
0.6%
20283 1
0.6%
24296 1
0.6%
24982 1
0.6%
25612 1
0.6%
29059 1
0.6%
29757 1
0.6%
34216 1
0.6%
34512 1
0.6%
36838 1
0.6%
ValueCountFrequency (%)
24271363 1
0.6%
23278255 1
0.6%
22429049 1
0.6%
21982021 1
0.6%
19145895 1
0.6%
18596538 1
0.6%
17833675 1
0.6%
16618488 1
0.6%
16242148 1
0.6%
16189417 1
0.6%

Interactions

2023-12-11T07:31:34.931996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:34.654810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:35.063078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:31:34.795504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:31:36.246123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도산업구분전체온실가스배출량
년도1.0000.0000.076
산업구분0.0001.0000.812
전체온실가스배출량0.0760.8121.000
2023-12-11T07:31:36.352793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도전체온실가스배출량산업구분
년도1.0000.0310.000
전체온실가스배출량0.0311.0000.500
산업구분0.0000.5001.000

Missing values

2023-12-11T07:31:35.224851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:31:35.356507image/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

년도산업구분전체온실가스배출량
02019건물153846
12019교통1194370
22019기계142764
32019반도체.디스플레이.전기전자22429049
42019목재<NA>
52019에너지23278255
62019비철금속643896
72019산업단지2157706
82019석유화학2936806
92019섬유141057
년도산업구분전체온실가스배출량
1702011유리 · 요업194370
1712011식음료제조85406
1722011자동차273802
1732011제지2015866
1742011조선508018
1752011철강<NA>
1762011통신1173863
1772011폐기물1328351
1782011하수. 폐수 및 분뇨 처리업<NA>
1792011지정외 폐기물처리업<NA>