Overview

Dataset statistics

Number of variables6
Number of observations42
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory57.1 B

Variable types

Numeric6

Dataset

Description한국광해광업공단은 석탄산업의 생산 기반 유지와 연탄의 안정적인 공급을 위해 석·연탄산업 지원 사업을 실시하고 있으며 이를 통해 자원안보와 서민생활보호 및 폐광지역 고용창출 등에 이바지하고 있습니다. 무연탄 소비현황을 성수기, 비수기, 월동기 등으로 구분하여 정보 제공합니다.
URLhttps://www.data.go.kr/data/15028081/fileData.do

Alerts

연 도 is highly overall correlated with 성수기 1-3월_10-12월(천톤) and 4 other fieldsHigh correlation
성수기 1-3월_10-12월(천톤) is highly overall correlated with 연 도 and 4 other fieldsHigh correlation
성수기 비율(퍼센트) is highly overall correlated with 연 도 and 4 other fieldsHigh correlation
비수기 4-9월(천톤) is highly overall correlated with 연 도 and 4 other fieldsHigh correlation
비수기 비율(퍼센트) is highly overall correlated with 연 도 and 4 other fieldsHigh correlation
월동기10월-12월_익년 1-3월(천톤) is highly overall correlated with 연 도 and 4 other fieldsHigh correlation
연 도 has unique valuesUnique
성수기 1-3월_10-12월(천톤) has unique valuesUnique
월동기10월-12월_익년 1-3월(천톤) has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:24:09.719233
Analysis finished2023-12-12 13:24:13.281060
Duration3.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연 도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001.5
Minimum1981
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T22:24:13.347204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1981
5-th percentile1983.05
Q11991.25
median2001.5
Q32011.75
95-th percentile2019.95
Maximum2022
Range41
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation12.267844
Coefficient of variation (CV)0.0061293251
Kurtosis-1.2
Mean2001.5
Median Absolute Deviation (MAD)10.5
Skewness0
Sum84063
Variance150.5
MonotonicityStrictly increasing
2023-12-12T22:24:13.464554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1981 1
 
2.4%
2013 1
 
2.4%
2005 1
 
2.4%
2006 1
 
2.4%
2007 1
 
2.4%
2008 1
 
2.4%
2009 1
 
2.4%
2010 1
 
2.4%
2011 1
 
2.4%
2012 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
1981 1
2.4%
1982 1
2.4%
1983 1
2.4%
1984 1
2.4%
1985 1
2.4%
1986 1
2.4%
1987 1
2.4%
1988 1
2.4%
1989 1
2.4%
1990 1
2.4%
ValueCountFrequency (%)
2022 1
2.4%
2021 1
2.4%
2020 1
2.4%
2019 1
2.4%
2018 1
2.4%
2017 1
2.4%
2016 1
2.4%
2015 1
2.4%
2014 1
2.4%
2013 1
2.4%

성수기 1-3월_10-12월(천톤)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5041.9524
Minimum342
Maximum17706
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T22:24:13.580320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum342
5-th percentile424.35
Q1935
median1508
Q310198
95-th percentile17169.85
Maximum17706
Range17364
Interquartile range (IQR)9263

Descriptive statistics

Standard deviation6151.4372
Coefficient of variation (CV)1.2200506
Kurtosis-0.47422929
Mean5041.9524
Median Absolute Deviation (MAD)625
Skewness1.1272216
Sum211762
Variance37840180
MonotonicityNot monotonic
2023-12-12T22:24:13.699441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
12350 1
 
2.4%
1510 1
 
2.4%
1553 1
 
2.4%
1648 1
 
2.4%
1800 1
 
2.4%
1944 1
 
2.4%
1506 1
 
2.4%
1404 1
 
2.4%
1424 1
 
2.4%
1519 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
342 1
2.4%
371 1
2.4%
419 1
2.4%
526 1
2.4%
753 1
2.4%
840 1
2.4%
881 1
2.4%
885 1
2.4%
908 1
2.4%
915 1
2.4%
ValueCountFrequency (%)
17706 1
2.4%
17325 1
2.4%
17185 1
2.4%
16882 1
2.4%
15919 1
2.4%
15288 1
2.4%
13951 1
2.4%
13608 1
2.4%
12350 1
2.4%
11856 1
2.4%

성수기 비율(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.440476
Minimum66.3
Maximum87.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T22:24:14.127509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum66.3
5-th percentile67.545
Q172.625
median75.7
Q381.3
95-th percentile84.815
Maximum87.7
Range21.4
Interquartile range (IQR)8.675

Descriptive statistics

Standard deviation5.3905997
Coefficient of variation (CV)0.070520227
Kurtosis-0.7153229
Mean76.440476
Median Absolute Deviation (MAD)4.3
Skewness0.091002115
Sum3210.5
Variance29.058566
MonotonicityNot monotonic
2023-12-12T22:24:14.265634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
81.6 2
 
4.8%
70.4 2
 
4.8%
75.0 2
 
4.8%
70.8 1
 
2.4%
86.1 1
 
2.4%
84.9 1
 
2.4%
77.6 1
 
2.4%
75.5 1
 
2.4%
78.2 1
 
2.4%
82.9 1
 
2.4%
Other values (29) 29
69.0%
ValueCountFrequency (%)
66.3 1
2.4%
66.6 1
2.4%
67.4 1
2.4%
70.3 1
2.4%
70.4 2
4.8%
70.7 1
2.4%
70.8 1
2.4%
71.4 1
2.4%
71.6 1
2.4%
72.5 1
2.4%
ValueCountFrequency (%)
87.7 1
2.4%
86.1 1
2.4%
84.9 1
2.4%
83.2 1
2.4%
83.0 1
2.4%
82.9 1
2.4%
82.6 1
2.4%
82.0 1
2.4%
81.7 1
2.4%
81.6 2
4.8%

비수기 4-9월(천톤)
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1843.9048
Minimum59
Maximum6705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T22:24:14.423774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum59
5-th percentile84.75
Q1277
median409
Q33816.25
95-th percentile6184.9
Maximum6705
Range6646
Interquartile range (IQR)3539.25

Descriptive statistics

Standard deviation2355.4499
Coefficient of variation (CV)1.2774249
Kurtosis-0.64432514
Mean1843.9048
Median Absolute Deviation (MAD)229.5
Skewness1.0658798
Sum77444
Variance5548144.1
MonotonicityNot monotonic
2023-12-12T22:24:14.609073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
277 2
 
4.8%
6193 1
 
2.4%
407 1
 
2.4%
679 1
 
2.4%
291 1
 
2.4%
345 1
 
2.4%
435 1
 
2.4%
455 1
 
2.4%
398 1
 
2.4%
314 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
59 1
2.4%
78 1
2.4%
83 1
2.4%
118 1
2.4%
160 1
2.4%
199 1
2.4%
227 1
2.4%
248 1
2.4%
250 1
2.4%
260 1
2.4%
ValueCountFrequency (%)
6705 1
2.4%
6544 1
2.4%
6193 1
2.4%
6031 1
2.4%
5775 1
2.4%
5741 1
2.4%
5397 1
2.4%
5171 1
2.4%
5009 1
2.4%
4766 1
2.4%

비수기 비율(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.585714
Minimum12.3
Maximum33.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T22:24:14.783895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12.3
5-th percentile15.185
Q118.7
median24.3
Q327.375
95-th percentile32.455
Maximum33.7
Range21.4
Interquartile range (IQR)8.675

Descriptive statistics

Standard deviation5.3605411
Coefficient of variation (CV)0.22727915
Kurtosis-0.68957932
Mean23.585714
Median Absolute Deviation (MAD)4.3
Skewness-0.088062543
Sum990.6
Variance28.735401
MonotonicityNot monotonic
2023-12-12T22:24:14.939262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
18.4 2
 
4.8%
29.6 2
 
4.8%
25.0 2
 
4.8%
29.2 1
 
2.4%
13.9 1
 
2.4%
15.1 1
 
2.4%
22.4 1
 
2.4%
24.5 1
 
2.4%
21.8 1
 
2.4%
17.1 1
 
2.4%
Other values (29) 29
69.0%
ValueCountFrequency (%)
12.3 1
2.4%
13.9 1
2.4%
15.1 1
2.4%
16.8 1
2.4%
17.1 1
2.4%
17.4 1
2.4%
18.0 1
2.4%
18.1 1
2.4%
18.3 1
2.4%
18.4 2
4.8%
ValueCountFrequency (%)
33.7 1
2.4%
33.4 1
2.4%
32.6 1
2.4%
29.7 1
2.4%
29.6 2
4.8%
29.3 1
2.4%
29.2 1
2.4%
28.6 1
2.4%
28.4 1
2.4%
27.5 1
2.4%

월동기10월-12월_익년 1-3월(천톤)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4855.7381
Minimum342
Maximum17733
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T22:24:15.091241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum342
5-th percentile401.05
Q1907
median1408
Q38609.5
95-th percentile17252.1
Maximum17733
Range17391
Interquartile range (IQR)7702.5

Descriptive statistics

Standard deviation6117.5986
Coefficient of variation (CV)1.25987
Kurtosis-0.30752663
Mean4855.7381
Median Absolute Deviation (MAD)566.5
Skewness1.1922868
Sum203941
Variance37425013
MonotonicityNot monotonic
2023-12-12T22:24:15.258326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
11182 1
 
2.4%
1403 1
 
2.4%
1707 1
 
2.4%
1702 1
 
2.4%
1884 1
 
2.4%
1620 1
 
2.4%
1451 1
 
2.4%
842 1
 
2.4%
550 1
 
2.4%
1413 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
342 1
2.4%
365 1
2.4%
397 1
2.4%
478 1
2.4%
550 1
2.4%
668 1
2.4%
841 1
2.4%
842 1
2.4%
862 1
2.4%
902 1
2.4%
ValueCountFrequency (%)
17733 1
2.4%
17422 1
2.4%
17301 1
2.4%
16323 1
2.4%
15958 1
2.4%
15150 1
2.4%
14584 1
2.4%
12933 1
2.4%
12538 1
2.4%
11182 1
2.4%

Interactions

2023-12-12T22:24:12.566510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:09.929172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:10.487325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:11.113883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:11.568779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:12.073199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:12.643717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:10.010318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:10.578307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:11.186891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:11.654476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:12.162714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:12.753872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:10.114815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:10.695403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:11.263278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:11.747112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:12.255766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:12.821564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:10.197734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:10.812195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:11.326608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:11.820125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:12.323722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:12.932494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:10.300117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:10.927214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:11.405113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:11.900265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:12.417275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:13.007175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:10.388443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:11.017122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:11.470194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:11.978069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:12.481609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:24:15.400587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연 도성수기 1-3월_10-12월(천톤)성수기 비율(퍼센트)비수기 4-9월(천톤)비수기 비율(퍼센트)월동기10월-12월_익년 1-3월(천톤)
연 도1.0000.5620.7960.7090.7960.578
성수기 1-3월_10-12월(천톤)0.5621.0000.4100.9780.4100.941
성수기 비율(퍼센트)0.7960.4101.0000.3101.0000.384
비수기 4-9월(천톤)0.7090.9780.3101.0000.3100.942
비수기 비율(퍼센트)0.7960.4101.0000.3101.0000.384
월동기10월-12월_익년 1-3월(천톤)0.5780.9410.3840.9420.3841.000
2023-12-12T22:24:15.543586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연 도성수기 1-3월_10-12월(천톤)성수기 비율(퍼센트)비수기 4-9월(천톤)비수기 비율(퍼센트)월동기10월-12월_익년 1-3월(천톤)
연 도1.000-0.7920.798-0.887-0.798-0.819
성수기 1-3월_10-12월(천톤)-0.7921.000-0.5350.9270.5330.960
성수기 비율(퍼센트)0.798-0.5351.000-0.772-1.000-0.537
비수기 4-9월(천톤)-0.8870.927-0.7721.0000.7710.890
비수기 비율(퍼센트)-0.7980.533-1.0000.7711.0000.535
월동기10월-12월_익년 1-3월(천톤)-0.8190.960-0.5370.8900.5351.000

Missing values

2023-12-12T22:24:13.121776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:24:13.232215image/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

연 도성수기 1-3월_10-12월(천톤)성수기 비율(퍼센트)비수기 4-9월(천톤)비수기 비율(퍼센트)월동기10월-12월_익년 1-3월(천톤)
019811235066.6619333.411182
119821185666.3603133.712538
219831395173.6500926.415150
319841591974.7539725.316323
419851732575.0577525.017733
519861770673.0654427.017422
619871688271.6670528.417301
719881718575.0574125.015958
819891528876.2476623.814584
919901360872.5517127.512933
연 도성수기 1-3월_10-12월(천톤)성수기 비율(퍼센트)비수기 4-9월(천톤)비수기 비율(퍼센트)월동기10월-12월_익년 1-3월(천톤)
322013151078.840721.21403
332014132981.630018.41326
342015122583.224816.81166
352016102982.022718.1960
36201788181.619918.4841
37201875383.016018.0668
38201952681.711818.3478
39202041987.75912.3397
40202137182.67817.4365
41202234280.48319.6342