Overview

Dataset statistics

Number of variables11
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory101.5 B

Variable types

Numeric8
Categorical3

Dataset

Description한국지역난방공사의 온실가스 배출 계수 정보(연도별, 구분, 단위, 수도권연계지사, 지사 구분 등 )를 제공합니다.
Author한국지역난방공사
URLhttps://www.data.go.kr/data/15090320/fileData.do

Alerts

구분 is highly overall correlated with 수도권연계지사 and 7 other fieldsHigh correlation
단위 is highly overall correlated with 수도권연계지사 and 7 other fieldsHigh correlation
수도권연계지사 is highly overall correlated with 청주지사 and 7 other fieldsHigh correlation
청주지사 is highly overall correlated with 수도권연계지사 and 7 other fieldsHigh correlation
세종지사 is highly overall correlated with 수도권연계지사 and 8 other fieldsHigh correlation
대구지사 is highly overall correlated with 수도권연계지사 and 7 other fieldsHigh correlation
양산지사 is highly overall correlated with 수도권연계지사 and 7 other fieldsHigh correlation
김해지사 is highly overall correlated with 수도권연계지사 and 7 other fieldsHigh correlation
광주전남지사 is highly overall correlated with 수도권연계지사 and 7 other fieldsHigh correlation
평택지사 is highly overall correlated with 세종지사High correlation
수도권연계지사 has unique valuesUnique
청주지사 has unique valuesUnique
세종지사 has unique valuesUnique
대구지사 has unique valuesUnique
양산지사 has unique valuesUnique
광주전남지사 has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:01:05.836496
Analysis finished2023-12-12 15:01:12.774500
Duration6.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct8
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.5
Minimum2015
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T00:01:12.835732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12016.75
median2018.5
Q32020.25
95-th percentile2022
Maximum2022
Range7
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.3405685
Coefficient of variation (CV)0.0011595583
Kurtosis-1.2422181
Mean2018.5
Median Absolute Deviation (MAD)2
Skewness0
Sum48444
Variance5.4782609
MonotonicityDecreasing
2023-12-13T00:01:12.952956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2022 3
12.5%
2021 3
12.5%
2020 3
12.5%
2019 3
12.5%
2018 3
12.5%
2017 3
12.5%
2016 3
12.5%
2015 3
12.5%
ValueCountFrequency (%)
2015 3
12.5%
2016 3
12.5%
2017 3
12.5%
2018 3
12.5%
2019 3
12.5%
2020 3
12.5%
2021 3
12.5%
2022 3
12.5%
ValueCountFrequency (%)
2022 3
12.5%
2021 3
12.5%
2020 3
12.5%
2019 3
12.5%
2018 3
12.5%
2017 3
12.5%
2016 3
12.5%
2015 3
12.5%

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
CO₂
8 
CH₄
8 
N₂O
8 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCO₂
2nd rowCH₄
3rd rowN₂O
4th rowCO₂
5th rowCH₄

Common Values

ValueCountFrequency (%)
CO₂ 8
33.3%
CH₄ 8
33.3%
N₂O 8
33.3%

Length

2023-12-13T00:01:13.128655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:01:13.246380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
co₂ 8
33.3%
ch₄ 8
33.3%
n₂o 8
33.3%

단위
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
kgCO₂/TJ
8 
kgCH₄/TJ
8 
kgN₂O/TJ
8 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkgCO₂/TJ
2nd rowkgCH₄/TJ
3rd rowkgN₂O/TJ
4th rowkgCO₂/TJ
5th rowkgCH₄/TJ

Common Values

ValueCountFrequency (%)
kgCO₂/TJ 8
33.3%
kgCH₄/TJ 8
33.3%
kgN₂O/TJ 8
33.3%

Length

2023-12-13T00:01:13.353667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:01:13.449324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kgco₂/tj 8
33.3%
kgch₄/tj 8
33.3%
kgn₂o/tj 8
33.3%

수도권연계지사
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12113.505
Minimum0.0638
Maximum37697
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T00:01:13.545849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0638
5-th percentile0.066115
Q10.08855
median0.67135
Q335399
95-th percentile37269.9
Maximum37697
Range37696.936
Interquartile range (IQR)35398.911

Descriptive statistics

Standard deviation17508.454
Coefficient of variation (CV)1.4453665
Kurtosis-1.5561572
Mean12113.505
Median Absolute Deviation (MAD)0.60185
Skewness0.75880818
Sum290724.12
Variance3.0654597 × 108
MonotonicityNot monotonic
2023-12-13T00:01:13.699428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
37317.0 1
 
4.2%
0.665 1
 
4.2%
0.0894 1
 
4.2%
0.7608 1
 
4.2%
37697.0 1
 
4.2%
0.0904 1
 
4.2%
0.7479 1
 
4.2%
36912.0 1
 
4.2%
0.086 1
 
4.2%
0.7323 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
0.0638 1
4.2%
0.0661 1
4.2%
0.0662 1
4.2%
0.0689 1
4.2%
0.0701 1
4.2%
0.086 1
4.2%
0.0894 1
4.2%
0.0904 1
4.2%
0.6316 1
4.2%
0.6466 1
4.2%
ValueCountFrequency (%)
37697.0 1
4.2%
37317.0 1
4.2%
37003.0 1
4.2%
36912.0 1
4.2%
35936.0 1
4.2%
35825.0 1
4.2%
35257.0 1
4.2%
34771.0 1
4.2%
0.7608 1
4.2%
0.7479 1
4.2%

청주지사
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19749.055
Minimum0.4197
Maximum67971
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T00:01:13.852036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4197
5-th percentile0.433395
Q10.50195
median2.286
Q354837.75
95-th percentile65500.1
Maximum67971
Range67970.58
Interquartile range (IQR)54837.248

Descriptive statistics

Standard deviation28690.332
Coefficient of variation (CV)1.4527446
Kurtosis-1.434975
Mean19749.055
Median Absolute Deviation (MAD)1.8288
Skewness0.79439107
Sum473977.32
Variance8.2313515 × 108
MonotonicityNot monotonic
2023-12-13T00:01:13.998990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
66173.0 1
 
4.2%
2.3097 1
 
4.2%
0.4197 1
 
4.2%
2.0984 1
 
4.2%
52870.0 1
 
4.2%
0.4329 1
 
4.2%
2.1647 1
 
4.2%
54734.0 1
 
4.2%
0.4362 1
 
4.2%
2.1812 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
0.4197 1
4.2%
0.4329 1
4.2%
0.4362 1
4.2%
0.4525 1
4.2%
0.4619 1
4.2%
0.4862 1
4.2%
0.5072 1
4.2%
0.5234 1
4.2%
2.0984 1
4.2%
2.1647 1
4.2%
ValueCountFrequency (%)
67971.0 1
4.2%
66173.0 1
4.2%
61687.0 1
4.2%
58322.0 1
4.2%
57049.0 1
4.2%
55149.0 1
4.2%
54734.0 1
4.2%
52870.0 1
4.2%
2.6169 1
4.2%
2.5362 1
4.2%

세종지사
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14504.745
Minimum0.0743
Maximum46025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T00:01:14.127527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0743
5-th percentile0.074815
Q10.081075
median0.78415
Q341796
95-th percentile45010.8
Maximum46025
Range46024.926
Interquartile range (IQR)41795.919

Descriptive statistics

Standard deviation20973.495
Coefficient of variation (CV)1.4459747
Kurtosis-1.5466593
Mean14504.745
Median Absolute Deviation (MAD)0.70575
Skewness0.76166732
Sum348113.88
Variance4.398875 × 108
MonotonicityNot monotonic
2023-12-13T00:01:14.544761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
42750.0 1
 
4.2%
0.7753 1
 
4.2%
0.0815 1
 
4.2%
0.8102 1
 
4.2%
45105.0 1
 
4.2%
0.0826 1
 
4.2%
0.8254 1
 
4.2%
46025.0 1
 
4.2%
0.0793 1
 
4.2%
0.793 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
0.0743 1
4.2%
0.0745 1
4.2%
0.0766 1
4.2%
0.0775 1
4.2%
0.0793 1
4.2%
0.0798 1
4.2%
0.0815 1
4.2%
0.0826 1
4.2%
0.7436 1
4.2%
0.7449 1
4.2%
ValueCountFrequency (%)
46025.0 1
4.2%
45105.0 1
4.2%
44477.0 1
4.2%
43811.0 1
4.2%
43105.0 1
4.2%
42750.0 1
4.2%
41478.0 1
4.2%
41356.0 1
4.2%
0.8254 1
4.2%
0.8102 1
4.2%

대구지사
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14960.624
Minimum0.832
Maximum57447
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T00:01:14.655175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.832
5-th percentile0.88758
Q11.07925
median6.30575
Q338495.5
95-th percentile52035.1
Maximum57447
Range57446.168
Interquartile range (IQR)38494.421

Descriptive statistics

Standard deviation21975.575
Coefficient of variation (CV)1.4688943
Kurtosis-1.1548821
Mean14960.624
Median Absolute Deviation (MAD)5.3398
Skewness0.87314503
Sum359054.97
Variance4.829259 × 108
MonotonicityNot monotonic
2023-12-13T00:01:14.834697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
57447.0 1
 
4.2%
5.8476 1
 
4.2%
1.1708 1
 
4.2%
8.1003 1
 
4.2%
37707.0 1
 
4.2%
1.0931 1
 
4.2%
7.4262 1
 
4.2%
39256.0 1
 
4.2%
0.9275 1
 
4.2%
6.1174 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
0.832 1
4.2%
0.8832 1
4.2%
0.9124 1
4.2%
0.9275 1
4.2%
1.0044 1
4.2%
1.0377 1
4.2%
1.0931 1
4.2%
1.1708 1
4.2%
5.0108 1
4.2%
5.4446 1
4.2%
ValueCountFrequency (%)
57447.0 1
4.2%
52792.0 1
4.2%
47746.0 1
4.2%
44563.0 1
4.2%
41243.0 1
4.2%
39256.0 1
4.2%
38242.0 1
4.2%
37707.0 1
4.2%
8.1003 1
4.2%
7.4262 1
4.2%

양산지사
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16173.775
Minimum0.0829
Maximum50642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T00:01:14.974847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0829
5-th percentile0.083635
Q10.088525
median0.8667
Q347100.75
95-th percentile49758.35
Maximum50642
Range50641.917
Interquartile range (IQR)47100.661

Descriptive statistics

Standard deviation23377.553
Coefficient of variation (CV)1.4453986
Kurtosis-1.5556753
Mean16173.775
Median Absolute Deviation (MAD)0.78
Skewness0.75894908
Sum388170.61
Variance5.4650999 × 108
MonotonicityNot monotonic
2023-12-13T00:01:15.127182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
50642.0 1
 
4.2%
0.8898 1
 
4.2%
0.0829 1
 
4.2%
0.8287 1
 
4.2%
46491.0 1
 
4.2%
0.0832 1
 
4.2%
0.8324 1
 
4.2%
46697.0 1
 
4.2%
0.0871 1
 
4.2%
0.871 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
0.0829 1
4.2%
0.0832 1
4.2%
0.0861 1
4.2%
0.0865 1
4.2%
0.0869 1
4.2%
0.0871 1
4.2%
0.089 1
4.2%
0.0903 1
4.2%
0.8287 1
4.2%
0.8324 1
4.2%
ValueCountFrequency (%)
50642.0 1
4.2%
49916.0 1
4.2%
48865.0 1
4.2%
48733.0 1
4.2%
48507.0 1
4.2%
48312.0 1
4.2%
46697.0 1
4.2%
46491.0 1
4.2%
0.9027 1
4.2%
0.8898 1
4.2%

김해지사
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11953.276
Minimum0.0617
Maximum37342
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T00:01:15.264601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0617
5-th percentile0.062365
Q10.06485
median0.6404
Q335037.5
95-th percentile36441.6
Maximum37342
Range37341.938
Interquartile range (IQR)35037.435

Descriptive statistics

Standard deviation17274.302
Coefficient of variation (CV)1.4451521
Kurtosis-1.5593918
Mean11953.276
Median Absolute Deviation (MAD)0.57635
Skewness0.75780695
Sum286878.62
Variance2.9840151 × 108
MonotonicityNot monotonic
2023-12-13T00:01:15.409877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.0644 2
 
8.3%
37342.0 1
 
4.2%
0.6656 1
 
4.2%
0.6445 1
 
4.2%
36156.0 1
 
4.2%
0.6442 1
 
4.2%
36140.0 1
 
4.2%
0.0637 1
 
4.2%
0.6366 1
 
4.2%
35713.0 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
0.0617 1
4.2%
0.0622 1
4.2%
0.0633 1
4.2%
0.0637 1
4.2%
0.0644 2
8.3%
0.065 1
4.2%
0.0666 1
4.2%
0.6174 1
4.2%
0.6217 1
4.2%
0.633 1
4.2%
ValueCountFrequency (%)
37342.0 1
4.2%
36492.0 1
4.2%
36156.0 1
4.2%
36140.0 1
4.2%
35713.0 1
4.2%
35513.0 1
4.2%
34879.0 1
4.2%
34638.0 1
4.2%
0.6656 1
4.2%
0.6505 1
4.2%

광주전남지사
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20395.807
Minimum0.1055
Maximum77793
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T00:01:15.538720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1055
5-th percentile0.10643
Q10.5554
median2.0544
Q357889
95-th percentile62601.5
Maximum77793
Range77792.894
Interquartile range (IQR)57888.445

Descriptive statistics

Standard deviation29737.153
Coefficient of variation (CV)1.4580032
Kurtosis-1.3143912
Mean20395.807
Median Absolute Deviation (MAD)1.9479
Skewness0.82407062
Sum489499.38
Variance8.8429829 × 108
MonotonicityNot monotonic
2023-12-13T00:01:15.672749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
52803.0 1
 
4.2%
1.0635 1
 
4.2%
0.5341 1
 
4.2%
2.8135 1
 
4.2%
77793.0 1
 
4.2%
0.1055 1
 
4.2%
1.055 1
 
4.2%
59188.0 1
 
4.2%
0.4136 1
 
4.2%
3.3508 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
0.1055 1
4.2%
0.1064 1
4.2%
0.1066 1
4.2%
0.1374 1
4.2%
0.4136 1
4.2%
0.5341 1
4.2%
0.5625 1
4.2%
0.5981 1
4.2%
1.055 1
4.2%
1.0635 1
4.2%
ValueCountFrequency (%)
77793.0 1
4.2%
63095.0 1
4.2%
59805.0 1
4.2%
59672.0 1
4.2%
59665.0 1
4.2%
59188.0 1
4.2%
57456.0 1
4.2%
52803.0 1
4.2%
4.7098 1
4.2%
4.4617 1
4.2%

평택지사
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
미집계
18 
14589
 
1
0.2601
 
1
0.026
 
1
25340
 
1
Other values (2)
2 

Length

Max length6
Median length3
Mean length3.625
Min length3

Unique

Unique6 ?
Unique (%)25.0%

Sample

1st row14589
2nd row0.2601
3rd row0.026
4th row25340
5th row0.4516

Common Values

ValueCountFrequency (%)
미집계 18
75.0%
14589 1
 
4.2%
0.2601 1
 
4.2%
0.026 1
 
4.2%
25340 1
 
4.2%
0.4516 1
 
4.2%
0.0451 1
 
4.2%

Length

2023-12-13T00:01:15.835631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:01:15.946526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미집계 18
75.0%
14589 1
 
4.2%
0.2601 1
 
4.2%
0.026 1
 
4.2%
25340 1
 
4.2%
0.4516 1
 
4.2%
0.0451 1
 
4.2%

Interactions

2023-12-13T00:01:11.857826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:06.255775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:07.044770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:07.762854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:08.833339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:09.612803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:10.386532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:11.119804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:11.955052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:06.342828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:07.131797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:07.852378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:08.939203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:09.717422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:10.480905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:11.208912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:12.039977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:06.441774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:07.251744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:07.937689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:09.047244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:09.814317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:10.576223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:11.300745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:12.120714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:06.542482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:07.339236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:08.025165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:09.140288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:09.914888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:10.652685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:11.418257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:12.188128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:06.624850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:07.417481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:08.096184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:09.210680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:10.004624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:10.724541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:11.499889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:12.265180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:06.729804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:07.501677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:08.493508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:09.305859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:10.098551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:10.845867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:11.588115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:12.344695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:06.811671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:07.593269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:08.576593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:09.407751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:10.181407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:10.959377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:11.667822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:12.441267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:06.896334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:07.682094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:08.739542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:09.515488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:10.286612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:11.038105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:11.768242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:01:16.045593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도구분단위수도권연계지사청주지사세종지사대구지사양산지사김해지사광주전남지사평택지사
연도1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.622
구분0.0001.0001.0001.0000.6340.9290.6621.0001.0000.6620.000
단위0.0001.0001.0001.0000.6340.9290.6621.0001.0000.6620.000
수도권연계지사0.0001.0001.0001.0001.0001.0001.0000.9880.9881.0000.000
청주지사0.0000.6340.6341.0001.0000.7120.8421.0001.0000.8640.000
세종지사0.0000.9290.9291.0000.7121.0000.7931.0001.0000.6920.744
대구지사0.0000.6620.6621.0000.8420.7931.0001.0001.0000.9230.256
양산지사0.0001.0001.0000.9881.0001.0001.0001.0000.9881.0000.000
김해지사0.0001.0001.0000.9881.0001.0001.0000.9881.0001.0000.000
광주전남지사0.0000.6620.6621.0000.8640.6920.9231.0001.0001.0000.416
평택지사0.6220.0000.0000.0000.0000.7440.2560.0000.0000.4161.000
2023-12-13T00:01:16.189435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분단위평택지사
구분1.0001.0000.000
단위1.0001.0000.000
평택지사0.0000.0001.000
2023-12-13T00:01:16.297532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도수도권연계지사청주지사세종지사대구지사양산지사김해지사광주전남지사구분단위평택지사
연도1.000-0.2020.315-0.276-0.0680.181-0.0300.0390.0000.0000.198
수도권연계지사-0.2021.0000.8220.9580.9060.8650.9570.8820.9770.9770.000
청주지사0.3150.8221.0000.7890.8660.9430.8830.8990.6360.6360.000
세종지사-0.2760.9580.7891.0000.9110.8490.9040.8680.6730.6730.600
대구지사-0.0680.9060.8660.9111.0000.8530.8740.8220.5980.5980.110
양산지사0.1810.8650.9430.8490.8531.0000.9250.8930.9770.9770.000
김해지사-0.0300.9570.8830.9040.8740.9251.0000.8910.9770.9770.000
광주전남지사0.0390.8820.8990.8680.8220.8930.8911.0000.5980.5980.243
구분0.0000.9770.6360.6730.5980.9770.9770.5981.0001.0000.000
단위0.0000.9770.6360.6730.5980.9770.9770.5981.0001.0000.000
평택지사0.1980.0000.0000.6000.1100.0000.0000.2430.0000.0001.000

Missing values

2023-12-13T00:01:12.564086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:01:12.706718image/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

연도구분단위수도권연계지사청주지사세종지사대구지사양산지사김해지사광주전남지사평택지사
02022CO₂kgCO₂/TJ37317.066173.042750.057447.050642.037342.052803.014589
12022CH₄kgCH₄/TJ0.67772.53620.7665.01080.90270.66564.70980.2601
22022N₂OkgN₂O/TJ0.06890.50720.07660.8320.09030.06660.59810.026
32021CO₂kgCO₂/TJ35936.067971.041356.052792.048312.035513.057456.025340
42021CH₄kgCH₄/TJ0.65342.61690.74365.44460.86120.6334.46170.4516
52021N₂OkgN₂O/TJ0.06610.52340.07430.88320.08610.06330.56250.0451
62020CO₂kgCO₂/TJ34771.061687.041478.047746.048507.034879.059672.0미집계
72020CH₄kgCH₄/TJ0.63162.43120.74496.66480.86470.62171.2953미집계
82020N₂OkgN₂O/TJ0.06380.48620.07451.03770.08650.06220.1374미집계
92019CO₂kgCO₂/TJ35257.057049.044477.041243.048733.034638.059805.0미집계
연도구분단위수도권연계지사청주지사세종지사대구지사양산지사김해지사광주전남지사평택지사
142018N₂OkgN₂O/TJ0.07010.46190.07750.91240.0890.0650.1064미집계
152017CO₂kgCO₂/TJ37003.055149.043811.038242.048865.035713.063095.0미집계
162017CH₄kgCH₄/TJ0.73232.18120.7936.11740.8710.63663.3508미집계
172017N₂OkgN₂O/TJ0.0860.43620.07930.92750.08710.06370.4136미집계
182016CO₂kgCO₂/TJ36912.054734.046025.039256.046697.036140.059188.0미집계
192016CH₄kgCH₄/TJ0.74792.16470.82547.42620.83240.64421.055미집계
202016N₂OkgN₂O/TJ0.09040.43290.08261.09310.08320.06440.1055미집계
212015CO₂kgCO₂/TJ37697.052870.045105.037707.046491.036156.077793.0미집계
222015CH₄kgCH₄/TJ0.76082.09840.81028.10030.82870.64452.8135미집계
232015N₂OkgN₂O/TJ0.08940.41970.08151.17080.08290.06440.5341미집계