Overview

Dataset statistics

Number of variables8
Number of observations183
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.8 KiB
Average record size in memory71.7 B

Variable types

DateTime1
Numeric7

Dataset

Description전력시장 거래금액을 연료원별로 제공합니다.
Author한국전력거래소
URLhttps://www.data.go.kr/data/15069376/fileData.do

Alerts

원자력 is highly overall correlated with 유연탄 and 2 other fieldsHigh correlation
유연탄 is highly overall correlated with 원자력 and 4 other fieldsHigh correlation
무연탄 is highly overall correlated with 유연탄 and 3 other fieldsHigh correlation
유류 is highly overall correlated with LNGHigh correlation
LNG is highly overall correlated with 원자력 and 5 other fieldsHigh correlation
양수 is highly overall correlated with 유연탄 and 3 other fieldsHigh correlation
기타 is highly overall correlated with 원자력 and 4 other fieldsHigh correlation
기간 has unique valuesUnique
원자력 has unique valuesUnique
유연탄 has unique valuesUnique
무연탄 has unique valuesUnique
유류 has unique valuesUnique
LNG has unique valuesUnique
양수 has unique valuesUnique
기타 has unique valuesUnique

Reproduction

Analysis started2023-10-09 17:03:40.113972
Analysis finished2023-10-09 17:03:51.810307
Duration11.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기간
Date

UNIQUE 

Distinct183
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Minimum2002-01-01 00:00:00
Maximum2017-03-01 00:00:00
2023-10-10T02:03:52.015737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:52.371831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

원자력
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct183
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5168.7941
Minimum1463.9884
Maximum11513.075
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-10-10T02:03:52.701074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1463.9884
5-th percentile3353.8023
Q14192.6427
median4633.1061
Q35658.9199
95-th percentile8471.7162
Maximum11513.075
Range10049.087
Interquartile range (IQR)1466.2771

Descriptive statistics

Standard deviation1755.2257
Coefficient of variation (CV)0.33958128
Kurtosis2.2175923
Mean5168.7941
Median Absolute Deviation (MAD)639.26583
Skewness1.4684406
Sum945889.32
Variance3080817.3
MonotonicityNot monotonic
2023-10-10T02:03:53.138748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8272.544976 1
 
0.5%
4365.361247 1
 
0.5%
4067.746301 1
 
0.5%
4315.498023 1
 
0.5%
4475.118839 1
 
0.5%
4604.259092 1
 
0.5%
4055.149255 1
 
0.5%
4945.952221 1
 
0.5%
4482.764739 1
 
0.5%
4253.276281 1
 
0.5%
Other values (173) 173
94.5%
ValueCountFrequency (%)
1463.988359 1
0.5%
2179.221181 1
0.5%
2699.763676 1
0.5%
2902.944174 1
0.5%
2973.374191 1
0.5%
3206.563019 1
0.5%
3228.919166 1
0.5%
3267.403539 1
0.5%
3304.758728 1
0.5%
3353.020391 1
0.5%
ValueCountFrequency (%)
11513.07499 1
0.5%
11475.04652 1
0.5%
10828.52019 1
0.5%
10396.31838 1
0.5%
10097.23981 1
0.5%
9886.04962 1
0.5%
9818.876641 1
0.5%
9329.657924 1
0.5%
9210.059796 1
0.5%
8475.39982 1
0.5%

유연탄
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct183
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7815.7315
Minimum3281.0081
Maximum18801.463
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-10-10T02:03:53.450656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3281.0081
5-th percentile3719.0415
Q14354.6728
median8171.902
Q310346.365
95-th percentile13284.368
Maximum18801.463
Range15520.455
Interquartile range (IQR)5991.6925

Descriptive statistics

Standard deviation3498.5684
Coefficient of variation (CV)0.4476316
Kurtosis-0.27317138
Mean7815.7315
Median Absolute Deviation (MAD)3259.0893
Skewness0.54578777
Sum1430278.9
Variance12239981
MonotonicityNot monotonic
2023-10-10T02:03:53.765482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17080.07845 1
 
0.5%
3707.939938 1
 
0.5%
4687.104913 1
 
0.5%
4455.67539 1
 
0.5%
4350.816518 1
 
0.5%
4667.041843 1
 
0.5%
4225.323827 1
 
0.5%
5388.218671 1
 
0.5%
4499.448414 1
 
0.5%
3957.940612 1
 
0.5%
Other values (173) 173
94.5%
ValueCountFrequency (%)
3281.008111 1
0.5%
3488.766542 1
0.5%
3555.891137 1
0.5%
3572.793034 1
0.5%
3651.419792 1
0.5%
3657.476244 1
0.5%
3657.797318 1
0.5%
3702.039159 1
0.5%
3707.939938 1
0.5%
3718.656767 1
0.5%
ValueCountFrequency (%)
18801.46264 1
0.5%
18156.55823 1
0.5%
17080.07845 1
0.5%
16288.11669 1
0.5%
16135.08262 1
0.5%
15251.08045 1
0.5%
14542.05413 1
0.5%
13538.61238 1
0.5%
13339.46955 1
0.5%
13287.31138 1
0.5%

무연탄
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct183
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean475.53209
Minimum155.8538
Maximum1121.2726
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-10-10T02:03:54.154911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum155.8538
5-th percentile196.16386
Q1270.11537
median459.91604
Q3637.62856
95-th percentile858.79994
Maximum1121.2726
Range965.41876
Interquartile range (IQR)367.51319

Descriptive statistics

Standard deviation223.57599
Coefficient of variation (CV)0.47015962
Kurtosis-0.44220998
Mean475.53209
Median Absolute Deviation (MAD)187.16857
Skewness0.53803409
Sum87022.372
Variance49986.221
MonotonicityNot monotonic
2023-10-10T02:03:54.496915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
461.7101592 1
 
0.5%
212.7437857 1
 
0.5%
283.0759856 1
 
0.5%
277.7242605 1
 
0.5%
272.7474756 1
 
0.5%
230.6604829 1
 
0.5%
205.9302899 1
 
0.5%
341.1386513 1
 
0.5%
269.5964333 1
 
0.5%
211.3754726 1
 
0.5%
Other values (173) 173
94.5%
ValueCountFrequency (%)
155.8537966 1
0.5%
168.4827027 1
0.5%
172.5213242 1
0.5%
174.1058044 1
0.5%
179.2807327 1
0.5%
181.6458705 1
0.5%
190.214532 1
0.5%
193.6284671 1
0.5%
194.2296551 1
0.5%
195.5562475 1
0.5%
ValueCountFrequency (%)
1121.272558 1
0.5%
1120.618219 1
0.5%
1053.644634 1
0.5%
987.000329 1
0.5%
984.2712924 1
0.5%
945.1343231 1
0.5%
919.9258924 1
0.5%
883.796424 1
0.5%
864.0069003 1
0.5%
859.6387574 1
0.5%

유류
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct183
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1599.2455
Minimum536.17361
Maximum3961.8907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-10-10T02:03:54.827544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum536.17361
5-th percentile646.88255
Q1980.80785
median1369.6175
Q32053.9814
95-th percentile3220.9834
Maximum3961.8907
Range3425.717
Interquartile range (IQR)1073.1736

Descriptive statistics

Standard deviation792.68523
Coefficient of variation (CV)0.495662
Kurtosis0.14189106
Mean1599.2455
Median Absolute Deviation (MAD)461.89455
Skewness0.93780111
Sum292661.92
Variance628349.87
MonotonicityNot monotonic
2023-10-10T02:03:55.731961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
622.2088963 1
 
0.5%
880.9358152 1
 
0.5%
1371.949206 1
 
0.5%
1658.969101 1
 
0.5%
1324.714496 1
 
0.5%
2354.560948 1
 
0.5%
2038.289642 1
 
0.5%
2748.330939 1
 
0.5%
2823.232835 1
 
0.5%
2412.879937 1
 
0.5%
Other values (173) 173
94.5%
ValueCountFrequency (%)
536.1736082 1
0.5%
543.5182594 1
0.5%
606.7059078 1
0.5%
609.4219063 1
0.5%
613.3317478 1
0.5%
621.4069626 1
0.5%
621.5125251 1
0.5%
622.2088963 1
0.5%
640.5861805 1
0.5%
646.587881 1
0.5%
ValueCountFrequency (%)
3961.890657 1
0.5%
3726.024937 1
0.5%
3722.871566 1
0.5%
3585.611237 1
0.5%
3408.790421 1
0.5%
3390.881978 1
0.5%
3344.630266 1
0.5%
3266.492433 1
0.5%
3242.340909 1
0.5%
3222.767679 1
0.5%

LNG
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct183
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8250.3252
Minimum1451.9045
Maximum20492.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-10-10T02:03:56.142091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1451.9045
5-th percentile1874.6623
Q13760.7917
median7728.9361
Q311920.791
95-th percentile17045.879
Maximum20492.14
Range19040.235
Interquartile range (IQR)8159.9993

Descriptive statistics

Standard deviation4977.4941
Coefficient of variation (CV)0.60330883
Kurtosis-0.75622529
Mean8250.3252
Median Absolute Deviation (MAD)4034.9883
Skewness0.52520792
Sum1509809.5
Variance24775447
MonotonicityNot monotonic
2023-10-10T02:03:56.447669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12128.34363 1
 
0.5%
4377.636182 1
 
0.5%
5443.514432 1
 
0.5%
4980.648723 1
 
0.5%
5350.289152 1
 
0.5%
6238.387918 1
 
0.5%
5331.338435 1
 
0.5%
6089.735481 1
 
0.5%
6110.64568 1
 
0.5%
5189.015263 1
 
0.5%
Other values (173) 173
94.5%
ValueCountFrequency (%)
1451.904482 1
0.5%
1456.482467 1
0.5%
1657.936927 1
0.5%
1701.001879 1
0.5%
1741.249576 1
0.5%
1810.090539 1
0.5%
1810.5298 1
0.5%
1816.80625 1
0.5%
1850.892205 1
0.5%
1865.793692 1
0.5%
ValueCountFrequency (%)
20492.13978 1
0.5%
19483.87529 1
0.5%
19307.57448 1
0.5%
19169.33199 1
0.5%
18743.68961 1
0.5%
18643.35313 1
0.5%
18243.09929 1
0.5%
17600.91607 1
0.5%
17177.96094 1
0.5%
17075.22363 1
0.5%

양수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct183
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean362.80928
Minimum59.455777
Maximum958.93564
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-10-10T02:03:56.749207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum59.455777
5-th percentile96.621073
Q1155.23702
median290.61991
Q3543.04163
95-th percentile828.13867
Maximum958.93564
Range899.47986
Interquartile range (IQR)387.8046

Descriptive statistics

Standard deviation236.02969
Coefficient of variation (CV)0.65056133
Kurtosis-0.56123037
Mean362.80928
Median Absolute Deviation (MAD)161.78737
Skewness0.70671378
Sum66394.099
Variance55710.015
MonotonicityNot monotonic
2023-10-10T02:03:57.037340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
380.2661063 1
 
0.5%
260.4298329 1
 
0.5%
169.9189749 1
 
0.5%
115.943528 1
 
0.5%
78.76425461 1
 
0.5%
131.6856736 1
 
0.5%
121.1285569 1
 
0.5%
126.0782485 1
 
0.5%
142.532965 1
 
0.5%
124.0081457 1
 
0.5%
Other values (173) 173
94.5%
ValueCountFrequency (%)
59.45577747 1
0.5%
68.09447782 1
0.5%
70.50100957 1
0.5%
71.46170585 1
0.5%
78.76425461 1
0.5%
84.79664446 1
0.5%
87.99817573 1
0.5%
94.85931946 1
0.5%
95.73999256 1
0.5%
96.56408563 1
0.5%
ValueCountFrequency (%)
958.9356421 1
0.5%
931.0383112 1
0.5%
903.9897199 1
0.5%
899.0400582 1
0.5%
896.2590353 1
0.5%
893.4961993 1
0.5%
881.0576986 1
0.5%
869.2891787 1
0.5%
837.3679406 1
0.5%
828.9415214 1
0.5%

기타
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct183
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean826.98313
Minimum44.545172
Maximum3283.5221
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-10-10T02:03:57.308421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum44.545172
5-th percentile84.379014
Q1194.82263
median612.53722
Q31466.7077
95-th percentile1787.691
Maximum3283.5221
Range3238.977
Interquartile range (IQR)1271.8851

Descriptive statistics

Standard deviation659.31356
Coefficient of variation (CV)0.79725151
Kurtosis-0.56058257
Mean826.98313
Median Absolute Deviation (MAD)496.11796
Skewness0.55284319
Sum151337.91
Variance434694.36
MonotonicityNot monotonic
2023-10-10T02:03:57.599045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1752.976847 1
 
0.5%
293.0572048 1
 
0.5%
381.7940225 1
 
0.5%
370.2181756 1
 
0.5%
327.6529929 1
 
0.5%
278.2917902 1
 
0.5%
185.2231642 1
 
0.5%
227.2438065 1
 
0.5%
268.024733 1
 
0.5%
276.4869334 1
 
0.5%
Other values (173) 173
94.5%
ValueCountFrequency (%)
44.54517234 1
0.5%
46.10072854 1
0.5%
59.64753522 1
0.5%
68.16071676 1
0.5%
73.06884358 1
0.5%
74.41488889 1
0.5%
77.89466325 1
0.5%
80.285012 1
0.5%
82.5498533 1
0.5%
83.9874633 1
0.5%
ValueCountFrequency (%)
3283.522133 1
0.5%
2118.375407 1
0.5%
2117.633627 1
0.5%
2090.018579 1
0.5%
2012.767256 1
0.5%
1962.816919 1
0.5%
1889.654736 1
0.5%
1879.778283 1
0.5%
1794.547091 1
0.5%
1790.229589 1
0.5%

Interactions

2023-10-10T02:03:49.744110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:40.546636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:42.020796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:43.927684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:45.220755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:46.679736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:48.256765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:49.933965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:40.726229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:42.250988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:44.085405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:45.423418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:46.892538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:48.566208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:50.182153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:40.954387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:42.477687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:44.247625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:45.649990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:47.127582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:48.787428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:50.435779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:41.144683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:42.653860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:44.389195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:45.848651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:47.328167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:48.947256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:50.679929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:41.427207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:42.864484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:44.570920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:46.050158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:47.547433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:49.112470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:50.922747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:41.641551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:43.602906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:44.774719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:46.261752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:47.814017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:49.288717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:51.081153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:41.833491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:43.770421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:44.932610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:46.464996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:48.024294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-10T02:03:49.539567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-10T02:03:57.807669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
원자력유연탄무연탄유류LNG양수기타
원자력1.0000.8710.6450.5060.6490.5630.621
유연탄0.8711.0000.7520.3760.7710.7200.702
무연탄0.6450.7521.0000.5990.7390.6720.618
유류0.5060.3760.5991.0000.5870.6180.364
LNG0.6490.7710.7390.5871.0000.7170.650
양수0.5630.7200.6720.6180.7171.0000.579
기타0.6210.7020.6180.3640.6500.5791.000
2023-10-10T02:03:58.048042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
원자력유연탄무연탄유류LNG양수기타
원자력1.0000.7070.4650.0900.5400.3510.622
유연탄0.7071.0000.7800.2570.8240.6830.851
무연탄0.4650.7801.0000.3850.7530.7120.694
유류0.0900.2570.3851.0000.5130.2650.182
LNG0.5400.8240.7530.5131.0000.7640.869
양수0.3510.6830.7120.2650.7641.0000.793
기타0.6220.8510.6940.1820.8690.7931.000

Missing values

2023-10-10T02:03:51.368416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-10T02:03:51.689132image/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

기간원자력유연탄무연탄유류LNG양수기타
02017-03-018272.54497617080.07845461.710159622.20889612128.34363380.2661061752.976847
12017-02-018170.66729316288.11669580.8258651316.57917112044.93558430.1432631604.667645
22017-01-018430.52801118156.55823615.377491401.42693912742.42214457.0232531764.8436
32016-12-0111475.0465218801.46264639.0418451697.18378713280.16754430.9614251650.13253
42016-11-017793.22736113257.87723458.662752982.73620811543.80139286.2365611450.026726
52016-10-017848.28816911251.3812320.625125798.5842378129.432547247.8609011715.705857
62016-09-018086.49489711510.45559357.628676606.7059087728.936147356.2949181362.885027
72016-08-018438.5635813287.31138555.9368521024.1181979674.171329409.7746581593.932622
82016-07-017471.81630911934.9022523.762181195.0462668867.948017302.0031191631.174095
92016-06-016720.90539616135.08262436.632537758.8973047189.600347206.870013283.522133
기간원자력유연탄무연탄유류LNG양수기타
1732002-10-013827.7013783831.764505261.488699670.4200632199.65656105.32983373.068844
1742002-09-013440.0994613778.889442190.214532730.998341865.793692176.169583128.616021
1752002-08-014127.1861533931.453554306.759906536.1736081810.5298190.465728130.140965
1762002-07-013993.8402684095.688267309.885277694.4494552041.485821224.99790789.570589
1772002-06-013853.6021013798.368225237.916765646.5878811657.936927169.545473104.960571
1782002-05-013893.2433623572.793034234.1716351214.8055431456.482467151.67159289.904141
1792002-04-013353.0203913751.845699211.7577361369.6175241451.904482106.71227259.647535
1802002-03-013849.1009483657.797318174.1058041319.9887321816.80625144.06434544.545172
1812002-02-013531.4633833281.008111201.6323841139.8591811701.001879110.158146.100729
1822002-01-013743.8918023887.738231312.7365531576.7850652223.49337984.79664477.894663