Overview

Dataset statistics

Number of variables9
Number of observations23
Missing cells43
Missing cells (%)20.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory86.7 B

Variable types

Numeric7
Categorical2

Dataset

Description연도별 건설(유류, 장비 등) 수송실적 입니다.
Author한국철도공사
URLhttps://www.data.go.kr/data/15068405/fileData.do

Alerts

건설1(톤) is highly overall correlated with 연도 and 1 other fieldsHigh correlation
건설8(톤) is highly overall correlated with 연도 and 1 other fieldsHigh correlation
연도 is highly overall correlated with 1장비(톤) and 4 other fieldsHigh correlation
1장비(톤) is highly overall correlated with 연도 and 2 other fieldsHigh correlation
1기타(톤) is highly overall correlated with 8유류(톤) and 2 other fieldsHigh correlation
8유류(톤) is highly overall correlated with 연도 and 4 other fieldsHigh correlation
8장비(톤) is highly overall correlated with 연도 and 4 other fieldsHigh correlation
8기타(톤) is highly overall correlated with 1기타(톤) and 2 other fieldsHigh correlation
건설1(톤) is highly imbalanced (56.3%)Imbalance
건설8(톤) is highly imbalanced (56.3%)Imbalance
1유류(톤) has 5 (21.7%) missing valuesMissing
1장비(톤) has 4 (17.4%) missing valuesMissing
1기타(톤) has 4 (17.4%) missing valuesMissing
8유류(톤) has 17 (73.9%) missing valuesMissing
8장비(톤) has 8 (34.8%) missing valuesMissing
8기타(톤) has 5 (21.7%) missing valuesMissing
연도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 14:31:42.427660
Analysis finished2023-12-12 14:31:48.090685
Duration5.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007
Minimum1996
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T23:31:48.166964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1996
5-th percentile1997.1
Q12001.5
median2007
Q32012.5
95-th percentile2016.9
Maximum2018
Range22
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.78233
Coefficient of variation (CV)0.0033793373
Kurtosis-1.2
Mean2007
Median Absolute Deviation (MAD)6
Skewness0
Sum46161
Variance46
MonotonicityStrictly increasing
2023-12-12T23:31:48.288657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1996 1
 
4.3%
1997 1
 
4.3%
2018 1
 
4.3%
2017 1
 
4.3%
2016 1
 
4.3%
2015 1
 
4.3%
2014 1
 
4.3%
2013 1
 
4.3%
2012 1
 
4.3%
2011 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
1996 1
4.3%
1997 1
4.3%
1998 1
4.3%
1999 1
4.3%
2000 1
4.3%
2001 1
4.3%
2002 1
4.3%
2003 1
4.3%
2004 1
4.3%
2005 1
4.3%
ValueCountFrequency (%)
2018 1
4.3%
2017 1
4.3%
2016 1
4.3%
2015 1
4.3%
2014 1
4.3%
2013 1
4.3%
2012 1
4.3%
2011 1
4.3%
2010 1
4.3%
2009 1
4.3%

1유류(톤)
Real number (ℝ)

MISSING 

Distinct18
Distinct (%)100.0%
Missing5
Missing (%)21.7%
Infinite0
Infinite (%)0.0%
Mean21315.667
Minimum1670
Maximum38534
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T23:31:48.398124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1670
5-th percentile11615.85
Q117609.75
median19697.5
Q326193.5
95-th percentile33486.7
Maximum38534
Range36864
Interquartile range (IQR)8583.75

Descriptive statistics

Standard deviation8043.1651
Coefficient of variation (CV)0.37733584
Kurtosis1.6530464
Mean21315.667
Median Absolute Deviation (MAD)4013.5
Skewness-0.15742253
Sum383682
Variance64692505
MonotonicityNot monotonic
2023-12-12T23:31:48.499244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
25769 1
 
4.3%
1670 1
 
4.3%
22234 1
 
4.3%
38534 1
 
4.3%
17678 1
 
4.3%
17254 1
 
4.3%
18074 1
 
4.3%
13371 1
 
4.3%
32596 1
 
4.3%
19318 1
 
4.3%
Other values (8) 8
34.8%
(Missing) 5
21.7%
ValueCountFrequency (%)
1670 1
4.3%
13371 1
4.3%
15713 1
4.3%
17254 1
4.3%
17587 1
4.3%
17678 1
4.3%
18074 1
4.3%
18681 1
4.3%
19318 1
4.3%
20077 1
4.3%
ValueCountFrequency (%)
38534 1
4.3%
32596 1
4.3%
27768 1
4.3%
27283 1
4.3%
26335 1
4.3%
25769 1
4.3%
23740 1
4.3%
22234 1
4.3%
20077 1
4.3%
19318 1
4.3%

1장비(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)100.0%
Missing4
Missing (%)17.4%
Infinite0
Infinite (%)0.0%
Mean165652.47
Minimum49144
Maximum287136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T23:31:48.611422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49144
5-th percentile50165.5
Q1128847.5
median175661
Q3209182
95-th percentile258444
Maximum287136
Range237992
Interquartile range (IQR)80334.5

Descriptive statistics

Standard deviation68736.012
Coefficient of variation (CV)0.41494105
Kurtosis-0.57362469
Mean165652.47
Median Absolute Deviation (MAD)40516
Skewness-0.3366477
Sum3147397
Variance4.7246394 × 109
MonotonicityNot monotonic
2023-12-12T23:31:48.732323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
287136 1
 
4.3%
50279 1
 
4.3%
49144 1
 
4.3%
61449 1
 
4.3%
81937 1
 
4.3%
202187 1
 
4.3%
157269 1
 
4.3%
109961 1
 
4.3%
147734 1
 
4.3%
234506 1
 
4.3%
Other values (9) 9
39.1%
(Missing) 4
17.4%
ValueCountFrequency (%)
49144 1
4.3%
50279 1
4.3%
61449 1
4.3%
81937 1
4.3%
109961 1
4.3%
147734 1
4.3%
157269 1
4.3%
160210 1
4.3%
163595 1
4.3%
175661 1
4.3%
ValueCountFrequency (%)
287136 1
4.3%
255256 1
4.3%
234506 1
4.3%
221038 1
4.3%
216177 1
4.3%
202187 1
4.3%
201308 1
4.3%
191747 1
4.3%
180803 1
4.3%
175661 1
4.3%

1기타(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)100.0%
Missing4
Missing (%)17.4%
Infinite0
Infinite (%)0.0%
Mean56687.579
Minimum26519
Maximum128180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T23:31:48.846820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26519
5-th percentile34358
Q139314.5
median46088
Q363665.5
95-th percentile117274.7
Maximum128180
Range101661
Interquartile range (IQR)24351

Descriptive statistics

Standard deviation28128.063
Coefficient of variation (CV)0.49619447
Kurtosis1.6289036
Mean56687.579
Median Absolute Deviation (MAD)8149
Skewness1.5513693
Sum1077064
Variance7.9118793 × 108
MonotonicityNot monotonic
2023-12-12T23:31:48.951840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
51436 1
 
4.3%
91299 1
 
4.3%
77468 1
 
4.3%
128180 1
 
4.3%
116063 1
 
4.3%
26519 1
 
4.3%
46802 1
 
4.3%
41279 1
 
4.3%
36797 1
 
4.3%
74496 1
 
4.3%
Other values (9) 9
39.1%
(Missing) 4
17.4%
ValueCountFrequency (%)
26519 1
4.3%
35229 1
4.3%
36797 1
4.3%
37208 1
4.3%
37939 1
4.3%
40690 1
4.3%
41279 1
4.3%
42447 1
4.3%
43280 1
4.3%
46088 1
4.3%
ValueCountFrequency (%)
128180 1
4.3%
116063 1
4.3%
91299 1
4.3%
77468 1
4.3%
74496 1
4.3%
52835 1
4.3%
51436 1
4.3%
51009 1
4.3%
46802 1
4.3%
46088 1
4.3%

8유류(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)100.0%
Missing17
Missing (%)73.9%
Infinite0
Infinite (%)0.0%
Mean52028.667
Minimum5427
Maximum91268
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T23:31:49.386683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5427
5-th percentile11263.25
Q130487.25
median50155.5
Q380965
95-th percentile90049.5
Maximum91268
Range85841
Interquartile range (IQR)50477.75

Descriptive statistics

Standard deviation34240.657
Coefficient of variation (CV)0.65811136
Kurtosis-1.7318389
Mean52028.667
Median Absolute Deviation (MAD)28811
Skewness-0.11333178
Sum312172
Variance1.1724226 × 109
MonotonicityStrictly decreasing
2023-12-12T23:31:49.484011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
91268 1
 
4.3%
86394 1
 
4.3%
64678 1
 
4.3%
35633 1
 
4.3%
28772 1
 
4.3%
5427 1
 
4.3%
(Missing) 17
73.9%
ValueCountFrequency (%)
5427 1
4.3%
28772 1
4.3%
35633 1
4.3%
64678 1
4.3%
86394 1
4.3%
91268 1
4.3%
ValueCountFrequency (%)
91268 1
4.3%
86394 1
4.3%
64678 1
4.3%
35633 1
4.3%
28772 1
4.3%
5427 1
4.3%

8장비(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)100.0%
Missing8
Missing (%)34.8%
Infinite0
Infinite (%)0.0%
Mean53722
Minimum5725
Maximum102335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T23:31:49.638380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5725
5-th percentile10169.3
Q130983
median46858
Q380316
95-th percentile96767.2
Maximum102335
Range96610
Interquartile range (IQR)49333

Descriptive statistics

Standard deviation30762.536
Coefficient of variation (CV)0.57262454
Kurtosis-1.2468613
Mean53722
Median Absolute Deviation (MAD)25751
Skewness0.11544853
Sum805830
Variance9.463336 × 108
MonotonicityNot monotonic
2023-12-12T23:31:49.800817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
94381 1
 
4.3%
86099 1
 
4.3%
54464 1
 
4.3%
74533 1
 
4.3%
72609 1
 
4.3%
46858 1
 
4.3%
102335 1
 
4.3%
40383 1
 
4.3%
87989 1
 
4.3%
39443 1
 
4.3%
Other values (5) 5
21.7%
(Missing) 8
34.8%
ValueCountFrequency (%)
5725 1
4.3%
12074 1
4.3%
26971 1
4.3%
29868 1
4.3%
32098 1
4.3%
39443 1
4.3%
40383 1
4.3%
46858 1
4.3%
54464 1
4.3%
72609 1
4.3%
ValueCountFrequency (%)
102335 1
4.3%
94381 1
4.3%
87989 1
4.3%
86099 1
4.3%
74533 1
4.3%
72609 1
4.3%
54464 1
4.3%
46858 1
4.3%
40383 1
4.3%
39443 1
4.3%

8기타(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)100.0%
Missing5
Missing (%)21.7%
Infinite0
Infinite (%)0.0%
Mean18012.389
Minimum1035
Maximum66560
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T23:31:49.913238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1035
5-th percentile1117.45
Q14012.75
median9687
Q327529.75
95-th percentile62451.1
Maximum66560
Range65525
Interquartile range (IQR)23517

Descriptive statistics

Standard deviation20157.537
Coefficient of variation (CV)1.1190929
Kurtosis1.3793229
Mean18012.389
Median Absolute Deviation (MAD)6978
Skewness1.4996101
Sum324223
Variance4.063263 × 108
MonotonicityNot monotonic
2023-12-12T23:31:50.038245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1914 1
 
4.3%
11157 1
 
4.3%
13867 1
 
4.3%
37223 1
 
4.3%
34227 1
 
4.3%
2341 1
 
4.3%
1132 1
 
4.3%
1035 1
 
4.3%
66560 1
 
4.3%
61726 1
 
4.3%
Other values (8) 8
34.8%
(Missing) 5
21.7%
ValueCountFrequency (%)
1035 1
4.3%
1132 1
4.3%
1914 1
4.3%
2341 1
4.3%
3428 1
4.3%
5767 1
4.3%
8406 1
4.3%
8495 1
4.3%
8510 1
4.3%
10864 1
4.3%
ValueCountFrequency (%)
66560 1
4.3%
61726 1
4.3%
37223 1
4.3%
34227 1
4.3%
31274 1
4.3%
16297 1
4.3%
13867 1
4.3%
11157 1
4.3%
10864 1
4.3%
8510 1
4.3%

건설1(톤)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
19 
103336
 
1
88659
 
1
73517
 
1
84909
 
1

Length

Max length6
Median length4
Mean length4.2173913
Min length4

Unique

Unique4 ?
Unique (%)17.4%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 19
82.6%
103336 1
 
4.3%
88659 1
 
4.3%
73517 1
 
4.3%
84909 1
 
4.3%

Length

2023-12-12T23:31:50.228514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:31:50.369697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
82.6%
103336 1
 
4.3%
88659 1
 
4.3%
73517 1
 
4.3%
84909 1
 
4.3%

건설8(톤)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
19 
11176
 
1
20079
 
1
33247
 
1
29687
 
1

Length

Max length5
Median length4
Mean length4.173913
Min length4

Unique

Unique4 ?
Unique (%)17.4%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 19
82.6%
11176 1
 
4.3%
20079 1
 
4.3%
33247 1
 
4.3%
29687 1
 
4.3%

Length

2023-12-12T23:31:50.528456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:31:50.659471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
82.6%
11176 1
 
4.3%
20079 1
 
4.3%
33247 1
 
4.3%
29687 1
 
4.3%

Interactions

2023-12-12T23:31:46.992565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:43.034416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:43.669129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:44.357052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:45.075288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:45.762210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:46.412217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:47.092393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:43.115682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:43.779325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:44.459506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:45.172711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:45.856547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:46.508610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:47.189954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:43.222729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:43.881326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:44.554540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:45.265991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:45.980947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:46.608549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:47.283429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:43.319179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:43.971589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:44.649545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:45.368932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:46.076838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:46.688802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:47.364408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:43.415518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:44.057664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:44.744935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:45.461949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:46.180625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:46.761345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:47.439278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:43.489479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:44.141490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:44.847175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:45.566300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:46.259276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:46.831687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:47.535171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:43.577080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:44.242785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:44.951189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:45.657959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:46.338026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:31:46.906464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:31:50.743648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도1유류(톤)1장비(톤)1기타(톤)8유류(톤)8장비(톤)8기타(톤)건설1(톤)건설8(톤)
연도1.0000.2190.6340.8441.0000.7440.2771.0001.000
1유류(톤)0.2191.0000.0000.7030.9130.4750.000NaNNaN
1장비(톤)0.6340.0001.0000.5700.9130.7030.499NaNNaN
1기타(톤)0.8440.7030.5701.0000.9130.8600.788NaNNaN
8유류(톤)1.0000.9130.9130.9131.0001.0001.000NaNNaN
8장비(톤)0.7440.4750.7030.8601.0001.0000.782NaNNaN
8기타(톤)0.2770.0000.4990.7881.0000.7821.000NaNNaN
건설1(톤)1.000NaNNaNNaNNaNNaNNaN1.0001.000
건설8(톤)1.000NaNNaNNaNNaNNaNNaN1.0001.000
2023-12-12T23:31:50.865671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건설1(톤)건설8(톤)
건설1(톤)1.0001.000
건설8(톤)1.0001.000
2023-12-12T23:31:50.947603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도1유류(톤)1장비(톤)1기타(톤)8유류(톤)8장비(톤)8기타(톤)건설1(톤)건설8(톤)
연도1.000-0.383-0.7890.293-1.000-0.804-0.2881.0001.000
1유류(톤)-0.3831.0000.2730.2630.2570.4750.3900.0000.000
1장비(톤)-0.7890.2731.000-0.2050.7140.5960.1330.0000.000
1기타(톤)0.2930.263-0.2051.0000.7710.5210.5850.0000.000
8유류(톤)-1.0000.2570.7140.7711.0000.8291.0000.0000.000
8장비(톤)-0.8040.4750.5960.5210.8291.0000.7490.0000.000
8기타(톤)-0.2880.3900.1330.5851.0000.7491.0000.0000.000
건설1(톤)1.0000.0000.0000.0000.0000.0000.0001.0001.000
건설8(톤)1.0000.0000.0000.0000.0000.0000.0001.0001.000

Missing values

2023-12-12T23:31:47.696072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:31:47.831403image/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.
2023-12-12T23:31:47.993875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연도1유류(톤)1장비(톤)1기타(톤)8유류(톤)8장비(톤)8기타(톤)건설1(톤)건설8(톤)
019963259623450674496912689438166560<NA><NA>
119971931828713651436863948609961726<NA><NA>
219982728322103851009646785446431274<NA><NA>
319991758716021035229356337453316297<NA><NA>
420002776818080337208287727260910864<NA><NA>
5200126335191747406905427468588406<NA><NA>
620022374020130843280<NA>1023355767<NA><NA>
720031571321617742447<NA>403838495<NA><NA>
820042007725525652835<NA>879898510<NA><NA>
920051868117566146088<NA>394433428<NA><NA>
연도1유류(톤)1장비(톤)1기타(톤)8유류(톤)8장비(톤)8기타(톤)건설1(톤)건설8(톤)
1320091725415726946802<NA>298681132<NA><NA>
1420101767820218726519<NA>57252341<NA><NA>
1520113853481937116063<NA><NA>34227<NA><NA>
1620122223461449128180<NA><NA>37223<NA><NA>
17201316704914477468<NA><NA>13867<NA><NA>
182014<NA>5027991299<NA><NA>11157<NA><NA>
192015<NA><NA><NA><NA><NA><NA>10333611176
202016<NA><NA><NA><NA><NA><NA>8865920079
212017<NA><NA><NA><NA><NA><NA>7351733247
222018<NA><NA><NA><NA><NA><NA>8490929687