Overview

Dataset statistics

Number of variables14
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 KiB
Average record size in memory134.0 B

Variable types

Numeric12
Categorical2

Dataset

Description연도별 상용자가발전설비의 업종별 설비용량
Author한국전력거래소
URLhttps://www.data.go.kr/data/15046163/fileData.do

Alerts

연도 is highly overall correlated with 음식료 and 8 other fieldsHigh correlation
음식료 is highly overall correlated with 연도 and 9 other fieldsHigh correlation
섬유 is highly overall correlated with 연도 and 11 other fieldsHigh correlation
제재/목재 is highly overall correlated with 연도 and 10 other fieldsHigh correlation
제지/펄프 is highly overall correlated with 연도 and 9 other fieldsHigh correlation
정유 is highly overall correlated with 연도 and 10 other fieldsHigh correlation
화학 is highly overall correlated with 연도 and 9 other fieldsHigh correlation
비금속 is highly overall correlated with 연도 and 9 other fieldsHigh correlation
1차금속(철강) is highly overall correlated with 비철금속High correlation
기계류 is highly overall correlated with 연도 and 9 other fieldsHigh correlation
에너지 is highly overall correlated with 섬유 and 3 other fieldsHigh correlation
서비스/기타 is highly overall correlated with 연도 and 9 other fieldsHigh correlation
비철금속 is highly overall correlated with 음식료 and 6 other fieldsHigh correlation
전자 is highly overall correlated with 섬유 and 4 other fieldsHigh correlation
전자 is highly imbalanced (54.6%)Imbalance
정유 has unique valuesUnique
서비스/기타 has unique valuesUnique
섬유 has 4 (19.0%) zerosZeros
기계류 has 7 (33.3%) zerosZeros

Reproduction

Analysis started2023-12-12 20:24:58.382268
Analysis finished2023-12-12 20:25:14.174019
Duration15.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.256429
Minimum5
Maximum78.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T05:25:14.235345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q115.8
median38.3
Q374.13
95-th percentile78.7
Maximum78.7
Range73.7
Interquartile range (IQR)58.33

Descriptive statistics

Standard deviation27.292368
Coefficient of variation (CV)0.67796297
Kurtosis-1.2417543
Mean40.256429
Median Absolute Deviation (MAD)33.3
Skewness0.14412033
Sum845.385
Variance744.87335
MonotonicityNot monotonic
2023-12-13T05:25:14.340128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5.0 5
23.8%
37.5 3
14.3%
38.3 3
14.3%
78.7 3
14.3%
74.2 2
 
9.5%
29.9 1
 
4.8%
15.8 1
 
4.8%
40.855 1
 
4.8%
47.8 1
 
4.8%
74.13 1
 
4.8%
ValueCountFrequency (%)
5.0 5
23.8%
15.8 1
 
4.8%
29.9 1
 
4.8%
37.5 3
14.3%
38.3 3
14.3%
40.855 1
 
4.8%
47.8 1
 
4.8%
74.13 1
 
4.8%
74.2 2
 
9.5%
78.7 3
14.3%
ValueCountFrequency (%)
78.7 3
14.3%
74.2 2
 
9.5%
74.13 1
 
4.8%
47.8 1
 
4.8%
40.855 1
 
4.8%
38.3 3
14.3%
37.5 3
14.3%
29.9 1
 
4.8%
15.8 1
 
4.8%
5.0 5
23.8%

음식료
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.793333
Minimum18.45
Maximum269.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T05:25:14.443869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18.45
5-th percentile18.45
Q141.2
median59.18
Q3193.55
95-th percentile193.65
Maximum269.95
Range251.5
Interquartile range (IQR)152.35

Descriptive statistics

Standard deviation77.473692
Coefficient of variation (CV)0.77634136
Kurtosis-0.81594863
Mean99.793333
Median Absolute Deviation (MAD)40.73
Skewness0.72178236
Sum2095.66
Variance6002.173
MonotonicityNot monotonic
2023-12-13T05:25:14.541777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
43.1 3
14.3%
193.65 3
14.3%
26.55 2
9.5%
18.45 2
9.5%
193.55 2
9.5%
40.95 1
 
4.8%
49.05 1
 
4.8%
41.2 1
 
4.8%
59.18 1
 
4.8%
75.73 1
 
4.8%
Other values (4) 4
19.0%
ValueCountFrequency (%)
18.45 2
9.5%
26.55 2
9.5%
40.95 1
 
4.8%
41.2 1
 
4.8%
43.1 3
14.3%
49.05 1
 
4.8%
59.18 1
 
4.8%
75.73 1
 
4.8%
122.76 1
 
4.8%
123.08 1
 
4.8%
ValueCountFrequency (%)
269.95 1
 
4.8%
193.65 3
14.3%
193.55 2
9.5%
126.41 1
 
4.8%
123.08 1
 
4.8%
122.76 1
 
4.8%
75.73 1
 
4.8%
59.18 1
 
4.8%
49.05 1
 
4.8%
43.1 3
14.3%

섬유
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2280952
Minimum0
Maximum13.885
Zeros4
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T05:25:14.629949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.665
median3.665
Q39.165
95-th percentile13.885
Maximum13.885
Range13.885
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation5.0524104
Coefficient of variation (CV)0.81122883
Kurtosis-1.1666634
Mean6.2280952
Median Absolute Deviation (MAD)3.665
Skewness0.46436089
Sum130.79
Variance25.526851
MonotonicityNot monotonic
2023-12-13T05:25:14.720493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3.665 8
38.1%
0.0 4
19.0%
13.885 4
19.0%
9.165 1
 
4.8%
6.4 1
 
4.8%
8.815 1
 
4.8%
8.365 1
 
4.8%
13.185 1
 
4.8%
ValueCountFrequency (%)
0.0 4
19.0%
3.665 8
38.1%
6.4 1
 
4.8%
8.365 1
 
4.8%
8.815 1
 
4.8%
9.165 1
 
4.8%
13.185 1
 
4.8%
13.885 4
19.0%
ValueCountFrequency (%)
13.885 4
19.0%
13.185 1
 
4.8%
9.165 1
 
4.8%
8.815 1
 
4.8%
8.365 1
 
4.8%
6.4 1
 
4.8%
3.665 8
38.1%
0.0 4
19.0%

제재/목재
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.752381
Minimum31.18
Maximum127.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T05:25:14.813363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31.18
5-th percentile36.43
Q137.316
median63.432
Q3127.53
95-th percentile127.53
Maximum127.53
Range96.35
Interquartile range (IQR)90.214

Descriptive statistics

Standard deviation39.476126
Coefficient of variation (CV)0.47703916
Kurtosis-1.8767535
Mean82.752381
Median Absolute Deviation (MAD)31.8
Skewness-0.00034386729
Sum1737.8
Variance1558.3646
MonotonicityNot monotonic
2023-12-13T05:25:14.907208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
127.53 6
28.6%
37.316 3
14.3%
36.43 2
 
9.5%
63.387 2
 
9.5%
63.432 2
 
9.5%
55.622 1
 
4.8%
31.18 1
 
4.8%
95.232 1
 
4.8%
119.23 1
 
4.8%
117.38 1
 
4.8%
ValueCountFrequency (%)
31.18 1
 
4.8%
36.43 2
9.5%
37.316 3
14.3%
55.622 1
 
4.8%
63.387 2
9.5%
63.432 2
9.5%
95.232 1
 
4.8%
115.53 1
 
4.8%
117.38 1
 
4.8%
119.23 1
 
4.8%
ValueCountFrequency (%)
127.53 6
28.6%
119.23 1
 
4.8%
117.38 1
 
4.8%
115.53 1
 
4.8%
95.232 1
 
4.8%
63.432 2
 
9.5%
63.387 2
 
9.5%
55.622 1
 
4.8%
37.316 3
14.3%
36.43 2
 
9.5%

제지/펄프
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean297.93957
Minimum155.6
Maximum397.192
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T05:25:15.013301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum155.6
5-th percentile178.319
Q1254.78
median282.225
Q3373.225
95-th percentile376.975
Maximum397.192
Range241.592
Interquartile range (IQR)118.445

Descriptive statistics

Standard deviation68.731618
Coefficient of variation (CV)0.23068979
Kurtosis-0.60152906
Mean297.93957
Median Absolute Deviation (MAD)39.755
Skewness-0.24488438
Sum6256.731
Variance4724.0353
MonotonicityNot monotonic
2023-12-13T05:25:15.129594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
282.225 3
14.3%
376.975 3
14.3%
254.78 2
 
9.5%
248.71 2
 
9.5%
321.98 1
 
4.8%
239.37 1
 
4.8%
292.78 1
 
4.8%
293.18 1
 
4.8%
155.6 1
 
4.8%
178.319 1
 
4.8%
Other values (5) 5
23.8%
ValueCountFrequency (%)
155.6 1
 
4.8%
178.319 1
 
4.8%
239.37 1
 
4.8%
248.71 2
9.5%
254.78 2
9.5%
272.225 1
 
4.8%
282.225 3
14.3%
292.78 1
 
4.8%
293.18 1
 
4.8%
321.98 1
 
4.8%
ValueCountFrequency (%)
397.192 1
 
4.8%
376.975 3
14.3%
376.015 1
 
4.8%
373.225 1
 
4.8%
372.265 1
 
4.8%
321.98 1
 
4.8%
293.18 1
 
4.8%
292.78 1
 
4.8%
282.225 3
14.3%
272.225 1
 
4.8%

정유
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean516.70519
Minimum230.838
Maximum848.454
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T05:25:15.231734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum230.838
5-th percentile269.064
Q1378.99
median454.034
Q3730.494
95-th percentile807.319
Maximum848.454
Range617.616
Interquartile range (IQR)351.504

Descriptive statistics

Standard deviation200.94145
Coefficient of variation (CV)0.38888994
Kurtosis-1.3228213
Mean516.70519
Median Absolute Deviation (MAD)180.07
Skewness0.29360462
Sum10850.809
Variance40377.466
MonotonicityNot monotonic
2023-12-13T05:25:15.339327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
269.064 1
 
4.8%
273.964 1
 
4.8%
751.681 1
 
4.8%
732.694 1
 
4.8%
730.494 1
 
4.8%
804.019 1
 
4.8%
807.319 1
 
4.8%
848.454 1
 
4.8%
619.729 1
 
4.8%
657.582 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
230.838 1
4.8%
269.064 1
4.8%
270.869 1
4.8%
273.964 1
4.8%
377.619 1
4.8%
378.99 1
4.8%
397.019 1
4.8%
398.519 1
4.8%
407.619 1
4.8%
451.784 1
4.8%
ValueCountFrequency (%)
848.454 1
4.8%
807.319 1
4.8%
804.019 1
4.8%
751.681 1
4.8%
732.694 1
4.8%
730.494 1
4.8%
657.582 1
4.8%
619.729 1
4.8%
528.884 1
4.8%
459.634 1
4.8%

화학
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.72381
Minimum9.1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T05:25:15.450553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.1
5-th percentile9.1
Q132
median32
Q359
95-th percentile98.8
Maximum100
Range90.9
Interquartile range (IQR)27

Descriptive statistics

Standard deviation29.589181
Coefficient of variation (CV)0.67672926
Kurtosis-0.3466545
Mean43.72381
Median Absolute Deviation (MAD)22.9
Skewness0.66528187
Sum918.2
Variance875.51965
MonotonicityNot monotonic
2023-12-13T05:25:15.556873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
32.0 7
33.3%
9.1 5
23.8%
59.0 2
 
9.5%
98.8 1
 
4.8%
98.5 1
 
4.8%
100.0 1
 
4.8%
56.5 1
 
4.8%
55.2 1
 
4.8%
59.25 1
 
4.8%
62.45 1
 
4.8%
ValueCountFrequency (%)
9.1 5
23.8%
32.0 7
33.3%
55.2 1
 
4.8%
56.5 1
 
4.8%
59.0 2
 
9.5%
59.25 1
 
4.8%
62.45 1
 
4.8%
98.5 1
 
4.8%
98.8 1
 
4.8%
100.0 1
 
4.8%
ValueCountFrequency (%)
100.0 1
 
4.8%
98.8 1
 
4.8%
98.5 1
 
4.8%
62.45 1
 
4.8%
59.25 1
 
4.8%
59.0 2
 
9.5%
56.5 1
 
4.8%
55.2 1
 
4.8%
32.0 7
33.3%
9.1 5
23.8%

비금속
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2768.5544
Minimum2057.49
Maximum3557.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T05:25:15.782511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2057.49
5-th percentile2405
Q12456.85
median2738.6
Q32983.19
95-th percentile3557.19
Maximum3557.69
Range1500.2
Interquartile range (IQR)526.34

Descriptive statistics

Standard deviation369.23795
Coefficient of variation (CV)0.1333685
Kurtosis0.52905809
Mean2768.5544
Median Absolute Deviation (MAD)248.39
Skewness0.52233264
Sum58139.642
Variance136336.66
MonotonicityNot monotonic
2023-12-13T05:25:15.948405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2986.99 2
 
9.5%
2983.19 2
 
9.5%
2456.85 2
 
9.5%
3557.69 1
 
4.8%
2756.276 1
 
4.8%
2057.49 1
 
4.8%
2405.0 1
 
4.8%
2438.79 1
 
4.8%
2541.6 1
 
4.8%
2445.0 1
 
4.8%
Other values (8) 8
38.1%
ValueCountFrequency (%)
2057.49 1
4.8%
2405.0 1
4.8%
2438.79 1
4.8%
2445.0 1
4.8%
2456.85 2
9.5%
2497.276 1
4.8%
2541.6 1
4.8%
2693.5 1
4.8%
2716.1 1
4.8%
2738.6 1
4.8%
ValueCountFrequency (%)
3557.69 1
4.8%
3557.19 1
4.8%
2991.19 1
4.8%
2986.99 2
9.5%
2983.19 2
9.5%
2981.19 1
4.8%
2908.69 1
4.8%
2756.276 1
4.8%
2738.6 1
4.8%
2716.1 1
4.8%

1차금속(철강)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.551238
Minimum34.1
Maximum124.146
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T05:25:16.081152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.1
5-th percentile34.104
Q134.104
median82.616
Q3108.537
95-th percentile124.146
Maximum124.146
Range90.046
Interquartile range (IQR)74.433

Descriptive statistics

Standard deviation35.455569
Coefficient of variation (CV)0.46316128
Kurtosis-1.5908671
Mean76.551238
Median Absolute Deviation (MAD)37.963
Skewness0.0010474229
Sum1607.576
Variance1257.0974
MonotonicityNot monotonic
2023-12-13T05:25:16.196541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
34.104 5
23.8%
124.146 2
 
9.5%
71.502 2
 
9.5%
34.1 1
 
4.8%
38.154 1
 
4.8%
118.497 1
 
4.8%
120.579 1
 
4.8%
101.296 1
 
4.8%
108.537 1
 
4.8%
122.499 1
 
4.8%
Other values (5) 5
23.8%
ValueCountFrequency (%)
34.1 1
 
4.8%
34.104 5
23.8%
38.154 1
 
4.8%
56.502 1
 
4.8%
71.502 2
 
9.5%
82.616 1
 
4.8%
83.866 1
 
4.8%
89.332 1
 
4.8%
89.782 1
 
4.8%
101.296 1
 
4.8%
ValueCountFrequency (%)
124.146 2
9.5%
122.499 1
4.8%
120.579 1
4.8%
118.497 1
4.8%
108.537 1
4.8%
101.296 1
4.8%
89.782 1
4.8%
89.332 1
4.8%
83.866 1
4.8%
82.616 1
4.8%

비철금속
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Memory size336.0 B
0.0
12 
262.84
250.0
261.84
65.0
 
1

Length

Max length6
Median length3
Mean length4.0952381
Min length3

Unique

Unique1 ?
Unique (%)4.8%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 12
57.1%
262.84 4
 
19.0%
250.0 2
 
9.5%
261.84 2
 
9.5%
65.0 1
 
4.8%

Length

2023-12-13T05:25:16.325817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:25:16.468213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 12
57.1%
262.84 4
 
19.0%
250.0 2
 
9.5%
261.84 2
 
9.5%
65.0 1
 
4.8%

전자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size336.0 B
0.0
19 
8.028

Length

Max length5
Median length3
Mean length3.1904762
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.028
2nd row8.028
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 19
90.5%
8.028 2
 
9.5%

Length

2023-12-13T05:25:16.604802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:25:16.736338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 19
90.5%
8.028 2
 
9.5%

기계류
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean375.04648
Minimum0
Maximum1306.451
Zeros7
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T05:25:16.828941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q3829.7
95-th percentile1306.451
Maximum1306.451
Range1306.451
Interquartile range (IQR)829.7

Descriptive statistics

Standard deviation521.51857
Coefficient of variation (CV)1.3905439
Kurtosis-0.69192052
Mean375.04648
Median Absolute Deviation (MAD)4
Skewness1.0337816
Sum7875.976
Variance271981.62
MonotonicityNot monotonic
2023-12-13T05:25:16.947276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.0 7
33.3%
4.0 2
 
9.5%
1306.451 2
 
9.5%
2.8 1
 
4.8%
2.0 1
 
4.8%
452.74 1
 
4.8%
169.008 1
 
4.8%
195.388 1
 
4.8%
185.968 1
 
4.8%
894.064 1
 
4.8%
Other values (3) 3
14.3%
ValueCountFrequency (%)
0.0 7
33.3%
2.0 1
 
4.8%
2.8 1
 
4.8%
4.0 2
 
9.5%
169.008 1
 
4.8%
185.968 1
 
4.8%
195.388 1
 
4.8%
452.74 1
 
4.8%
829.7 1
 
4.8%
894.064 1
 
4.8%
ValueCountFrequency (%)
1306.451 2
9.5%
1293.151 1
4.8%
1230.255 1
4.8%
894.064 1
4.8%
829.7 1
4.8%
452.74 1
4.8%
195.388 1
4.8%
185.968 1
4.8%
169.008 1
4.8%
4.0 2
9.5%

에너지
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.68319
Minimum72.938
Maximum259.349
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T05:25:17.087971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum72.938
5-th percentile87.367
Q1109.589
median132.773
Q3157.11
95-th percentile197.838
Maximum259.349
Range186.411
Interquartile range (IQR)47.521

Descriptive statistics

Standard deviation46.54
Coefficient of variation (CV)0.33081422
Kurtosis0.54438718
Mean140.68319
Median Absolute Deviation (MAD)24.337
Skewness0.87837784
Sum2954.347
Variance2165.9716
MonotonicityNot monotonic
2023-12-13T05:25:17.226192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
196.338 2
 
9.5%
197.838 2
 
9.5%
101.607 1
 
4.8%
109.589 1
 
4.8%
72.938 1
 
4.8%
259.349 1
 
4.8%
126.241 1
 
4.8%
157.11 1
 
4.8%
117.273 1
 
4.8%
92.355 1
 
4.8%
Other values (9) 9
42.9%
ValueCountFrequency (%)
72.938 1
4.8%
87.367 1
4.8%
92.355 1
4.8%
97.396 1
4.8%
101.607 1
4.8%
109.589 1
4.8%
110.174 1
4.8%
117.273 1
4.8%
126.241 1
4.8%
129.087 1
4.8%
ValueCountFrequency (%)
259.349 1
4.8%
197.838 2
9.5%
196.338 2
9.5%
157.11 1
4.8%
151.83 1
4.8%
146.417 1
4.8%
141.677 1
4.8%
132.812 1
4.8%
132.773 1
4.8%
129.087 1
4.8%

서비스/기타
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4552.1807
Minimum3709.47
Maximum5980.294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-13T05:25:17.433567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3709.47
5-th percentile3821.215
Q13940.825
median4222.771
Q35230.846
95-th percentile5813.255
Maximum5980.294
Range2270.824
Interquartile range (IQR)1290.021

Descriptive statistics

Standard deviation768.16788
Coefficient of variation (CV)0.16874723
Kurtosis-0.96323284
Mean4552.1807
Median Absolute Deviation (MAD)301.492
Skewness0.80996462
Sum95595.794
Variance590081.9
MonotonicityNot monotonic
2023-12-13T05:25:17.547992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4464.139 1
 
4.8%
4410.161 1
 
4.8%
5230.846 1
 
4.8%
5761.365 1
 
4.8%
5813.255 1
 
4.8%
5980.294 1
 
4.8%
5427.943 1
 
4.8%
5704.439 1
 
4.8%
4319.5 1
 
4.8%
4855.762 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
3709.47 1
4.8%
3821.215 1
4.8%
3905.605 1
4.8%
3921.279 1
4.8%
3923.734 1
4.8%
3940.825 1
4.8%
3941.468 1
4.8%
4043.517 1
4.8%
4089.658 1
4.8%
4108.548 1
4.8%
ValueCountFrequency (%)
5980.294 1
4.8%
5813.255 1
4.8%
5761.365 1
4.8%
5704.439 1
4.8%
5427.943 1
4.8%
5230.846 1
4.8%
4855.762 1
4.8%
4464.139 1
4.8%
4410.161 1
4.8%
4319.5 1
4.8%

Interactions

2023-12-13T05:25:12.459872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:24:58.865909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:00.120518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:01.494348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:02.595541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:03.649794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:04.579970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:05.828961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:07.318778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:08.624519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:09.805436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:11.038867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:12.571684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:24:58.971827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:00.222376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:01.590018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:02.697337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:03.729336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:04.664578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:05.906736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:07.435194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:08.715892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:09.905069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:11.169311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:12.666667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:24:59.072480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:00.623576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:01.677034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:02.794566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:03.800821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:04.754863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:05.977624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:07.537666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:08.796008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:09.984129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:11.273136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:12.764030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:24:59.179629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:00.711917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:01.789313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:02.894889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:03.878638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:04.849137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:06.077474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:07.653047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:08.910419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:10.086758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:11.375094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:12.839517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:24:59.270425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:00.791889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:01.871031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:02.966523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:03.950695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:04.927802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:06.174593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:07.760724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:08.988605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:10.167257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:11.483753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:12.943375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:24:59.358449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:00.871015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:01.967201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:03.048342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:04.023920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:05.016658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:06.269675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:07.886292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:09.069626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:10.265214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:11.594137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:13.055710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:24:59.476553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:00.961246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:02.076254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:03.126422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:04.105242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:05.112951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:06.369999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:08.013234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:09.176740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:10.362570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:11.725993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:13.140558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:24:59.575643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:01.045573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:02.172377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:03.229555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:04.175124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:05.216184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:06.454540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:08.105742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:09.297778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:10.452816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:11.818964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:13.241808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:24:59.683452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:01.148848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:02.271573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:03.329056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:04.272676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:05.355784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:06.899380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:08.218945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:09.422992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:10.595827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:11.951169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:13.324772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:24:59.818518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:01.247434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:02.344914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:03.405394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:04.349438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:05.465302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:06.993363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:08.310759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:09.513194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:10.700107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:12.067763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:13.710481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:24:59.922711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:01.343302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:02.424614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:03.489037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:04.427206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:05.577166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:07.122546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:08.425309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:09.615409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:10.810809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:12.192084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:13.785933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:00.030986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:01.426539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:02.509438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:03.573932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:04.509937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:05.730234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:07.220085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:08.536769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:09.715117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:10.928799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:25:12.329520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:25:17.646751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도음식료섬유제재/목재제지/펄프정유화학비금속1차금속(철강)비철금속전자기계류에너지서비스/기타
연도1.0000.7900.7030.9640.5230.6750.7990.7320.6900.5990.4980.5670.7180.581
음식료0.7901.0000.6810.9530.5960.7460.7180.8030.8360.8010.0000.8980.6230.518
섬유0.7030.6811.0000.7350.5270.7630.8140.6790.7940.9170.4630.9110.8100.858
제재/목재0.9640.9530.7351.0000.6750.7860.7330.7770.8280.7190.2520.8290.6830.634
제지/펄프0.5230.5960.5270.6751.0000.7450.5900.3590.5670.5140.6370.7690.0000.742
정유0.6750.7460.7630.7860.7451.0000.8800.7270.8380.9330.6370.8430.7560.812
화학0.7990.7180.8140.7330.5900.8801.0000.8860.9120.5700.9270.6580.6000.851
비금속0.7320.8030.6790.7770.3590.7270.8861.0000.6470.3671.0000.5660.7180.826
1차금속(철강)0.6900.8360.7940.8280.5670.8380.9120.6471.0000.9000.0000.8620.7920.778
비철금속0.5990.8010.9170.7190.5140.9330.5700.3670.9001.0000.0000.9310.7100.744
전자0.4980.0000.4630.2520.6370.6370.9271.0000.0000.0001.0000.0000.2501.000
기계류0.5670.8980.9110.8290.7690.8430.6580.5660.8620.9310.0001.0000.7440.883
에너지0.7180.6230.8100.6830.0000.7560.6000.7180.7920.7100.2500.7441.0000.813
서비스/기타0.5810.5180.8580.6340.7420.8120.8510.8260.7780.7441.0000.8830.8131.000
2023-12-13T05:25:17.807311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비철금속전자
비철금속1.0000.000
전자0.0001.000
2023-12-13T05:25:17.948190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도음식료섬유제재/목재제지/펄프정유화학비금속1차금속(철강)기계류에너지서비스/기타비철금속전자
연도1.0000.8880.9300.9530.6410.912-0.911-0.9580.4130.8490.5000.7230.4310.302
음식료0.8881.0000.8070.9080.5960.883-0.810-0.8530.3010.8850.4830.7760.6610.000
섬유0.9300.8071.0000.8870.6520.865-0.894-0.9050.3040.7790.5150.6830.5950.510
제재/목재0.9530.9080.8871.0000.6810.930-0.850-0.9040.4170.8870.5130.7830.5590.118
제지/펄프0.6410.5960.6520.6811.0000.640-0.613-0.5290.0050.6650.3290.6740.3440.591
정유0.9120.8830.8650.9300.6401.000-0.895-0.8680.4080.8290.5290.7310.8480.394
화학-0.911-0.810-0.894-0.850-0.613-0.8951.0000.910-0.338-0.817-0.412-0.5970.4730.712
비금속-0.958-0.853-0.905-0.904-0.529-0.8680.9101.000-0.384-0.782-0.363-0.5830.1880.858
1차금속(철강)0.4130.3010.3040.4170.0050.408-0.338-0.3841.0000.1790.4410.0260.7410.000
기계류0.8490.8850.7790.8870.6650.829-0.817-0.7820.1791.0000.3910.8800.6280.000
에너지0.5000.4830.5150.5130.3290.529-0.412-0.3630.4410.3911.0000.4320.5130.197
서비스/기타0.7230.7760.6830.7830.6740.731-0.597-0.5830.0260.8800.4321.0000.4810.795
비철금속0.4310.6610.5950.5590.3440.8480.4730.1880.7410.6280.5130.4811.0000.000
전자0.3020.0000.5100.1180.5910.3940.7120.8580.0000.0000.1970.7950.0001.000

Missing values

2023-12-13T05:25:13.888527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:25:14.083013image/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

연도음식료섬유제재/목재제지/펄프정유화학비금속1차금속(철강)비철금속전자기계류에너지서비스/기타
20205.026.550.037.316321.98269.06498.83557.6934.1040.08.0284.0101.6074464.139
20195.043.10.037.316239.37273.96498.53557.1934.1040.08.0284.0109.5894410.161
20185.043.10.037.316254.78230.838100.02986.9934.1040.00.00.0129.0873821.215
20175.043.10.036.43254.78407.61956.52986.9934.1040.00.00.0110.1743940.825
20165.026.553.66536.43292.78377.61959.02991.1934.1040.00.00.097.3963923.734
201529.918.453.66555.622293.18378.9959.02981.1934.10.00.00.087.3673941.468
201415.818.453.66531.18155.6270.86955.22983.1938.1540.00.00.0132.8123709.47
201337.540.953.66563.387248.71397.01959.252983.19118.4970.00.00.0151.834108.548
201237.549.053.66563.387248.71398.51962.452908.69120.5790.00.00.0146.4174043.517
201137.541.23.66563.432282.225454.03432.02738.6124.1460.00.02.8141.6773921.279
연도음식료섬유제재/목재제지/펄프정유화학비금속1차금속(철강)비철금속전자기계류에너지서비스/기타
200938.375.733.66595.232178.319459.63432.02693.5101.2960.00.0452.7492.3554222.771
200838.3122.769.165119.23282.225528.88432.02497.276108.53765.00.0169.008117.2734089.658
200740.855123.086.4117.38397.192657.58232.02756.276122.499250.00.0195.388157.114855.762
200647.8126.418.815115.53272.225619.72932.02445.089.782250.00.0185.968126.2414319.5
200574.13193.558.365127.53373.225848.45432.02541.689.332262.840.0894.064259.3495704.439
200474.2193.5513.885127.53372.265807.3199.12456.8583.866261.840.0829.7197.8385427.943
200374.2269.9513.885127.53376.015804.0199.12456.8582.616261.840.01306.451197.8385980.294
200278.7193.6513.885127.53376.975730.4949.12438.7971.502262.840.01306.451196.3385813.255
200178.7193.6513.885127.53376.975732.6949.12405.071.502262.840.01293.151196.3385761.365
200078.7193.6513.185127.53376.975751.6819.12057.4956.502262.840.01230.25572.9385230.846