Overview

Dataset statistics

Number of variables9
Number of observations32
Missing cells27
Missing cells (%)9.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 KiB
Average record size in memory85.1 B

Variable types

Numeric8
Categorical1

Dataset

Description연도별 증기, 고속철도 등 열차키로 입니다.
Author한국철도공사
URLhttps://www.data.go.kr/data/15068506/fileData.do

Alerts

연도 is highly overall correlated with 고속철도 and 5 other fieldsHigh correlation
고속철도 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
디젤 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
전기 is highly overall correlated with 연도 and 6 other fieldsHigh correlation
동차 is highly overall correlated with 증기High correlation
새마을동차 is highly overall correlated with 고속철도 and 3 other fieldsHigh correlation
수도권전동차 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
전기동차 is highly overall correlated with 연도 and 6 other fieldsHigh correlation
증기 is highly overall correlated with 연도 and 6 other fieldsHigh correlation
증기 is highly imbalanced (70.1%)Imbalance
고속철도 has 17 (53.1%) missing valuesMissing
새마을동차 has 3 (9.4%) missing valuesMissing
전기동차 has 7 (21.9%) missing valuesMissing
연도 has unique valuesUnique
디젤 has unique valuesUnique
전기 has unique valuesUnique
동차 has unique valuesUnique
수도권전동차 has unique valuesUnique

Reproduction

Analysis started2023-12-12 00:18:40.401941
Analysis finished2023-12-12 00:18:47.509683
Duration7.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2002.5
Minimum1987
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T09:18:47.616796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1987
5-th percentile1988.55
Q11994.75
median2002.5
Q32010.25
95-th percentile2016.45
Maximum2018
Range31
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation9.3808315
Coefficient of variation (CV)0.0046845601
Kurtosis-1.2
Mean2002.5
Median Absolute Deviation (MAD)8
Skewness0
Sum64080
Variance88
MonotonicityStrictly increasing
2023-12-12T09:18:47.772998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1987 1
 
3.1%
2004 1
 
3.1%
2018 1
 
3.1%
2017 1
 
3.1%
2016 1
 
3.1%
2015 1
 
3.1%
2014 1
 
3.1%
2013 1
 
3.1%
2012 1
 
3.1%
2011 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
1987 1
3.1%
1988 1
3.1%
1989 1
3.1%
1990 1
3.1%
1991 1
3.1%
1992 1
3.1%
1993 1
3.1%
1994 1
3.1%
1995 1
3.1%
1996 1
3.1%
ValueCountFrequency (%)
2018 1
3.1%
2017 1
3.1%
2016 1
3.1%
2015 1
3.1%
2014 1
3.1%
2013 1
3.1%
2012 1
3.1%
2011 1
3.1%
2010 1
3.1%
2009 1
3.1%

증기
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
<NA>
29 
1910.0
 
1
3359.3
 
1
1726.7
 
1

Length

Max length6
Median length4
Mean length4.1875
Min length4

Unique

Unique3 ?
Unique (%)9.4%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 29
90.6%
1910.0 1
 
3.1%
3359.3 1
 
3.1%
1726.7 1
 
3.1%

Length

2023-12-12T09:18:47.933820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:18:48.072296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 29
90.6%
1910.0 1
 
3.1%
3359.3 1
 
3.1%
1726.7 1
 
3.1%

고속철도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)100.0%
Missing17
Missing (%)53.1%
Infinite0
Infinite (%)0.0%
Mean27975049
Minimum13971073
Maximum41310129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T09:18:48.178318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13971073
5-th percentile18003474
Q121617663
median27703707
Q334597045
95-th percentile39033900
Maximum41310129
Range27339055
Interquartile range (IQR)12979382

Descriptive statistics

Standard deviation8258311
Coefficient of variation (CV)0.29520273
Kurtosis-1.198874
Mean27975049
Median Absolute Deviation (MAD)6288829.4
Skewness0.098471729
Sum4.1962574 × 108
Variance6.81997 × 1013
MonotonicityNot monotonic
2023-12-12T09:18:48.306257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
13971073.3 1
 
3.1%
19731646.3 1
 
3.1%
20618228.9 1
 
3.1%
21414877.5 1
 
3.1%
21820447.9 1
 
3.1%
22032842.8 1
 
3.1%
23343585.5 1
 
3.1%
27703706.9 1
 
3.1%
29596898.5 1
 
3.1%
33037367.2 1
 
3.1%
Other values (5) 5
 
15.6%
(Missing) 17
53.1%
ValueCountFrequency (%)
13971073.3 1
3.1%
19731646.3 1
3.1%
20618228.9 1
3.1%
21414877.5 1
3.1%
21820447.9 1
3.1%
22032842.8 1
3.1%
23343585.5 1
3.1%
27703706.9 1
3.1%
29596898.5 1
3.1%
32820630.2 1
3.1%
ValueCountFrequency (%)
41310128.7 1
3.1%
38058373.2 1
3.1%
38009212.7 1
3.1%
36156722.4 1
3.1%
33037367.2 1
3.1%
32820630.2 1
3.1%
29596898.5 1
3.1%
27703706.9 1
3.1%
23343585.5 1
3.1%
22032842.8 1
3.1%

디젤
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50659347
Minimum22016933
Maximum68055313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T09:18:48.468119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22016933
5-th percentile22631142
Q134977163
median60863864
Q363740269
95-th percentile66741619
Maximum68055313
Range46038380
Interquartile range (IQR)28763106

Descriptive statistics

Standard deviation16109527
Coefficient of variation (CV)0.31799713
Kurtosis-1.2572259
Mean50659347
Median Absolute Deviation (MAD)6104093.5
Skewness-0.60758167
Sum1.6210991 × 109
Variance2.5951685 × 1014
MonotonicityNot monotonic
2023-12-12T09:18:48.616856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
63410869.9 1
 
3.1%
55503419.7 1
 
3.1%
22341538.6 1
 
3.1%
22016932.7 1
 
3.1%
22868089.5 1
 
3.1%
24847259.2 1
 
3.1%
28496127.8 1
 
3.1%
34789101.9 1
 
3.1%
33987711.6 1
 
3.1%
33129406.7 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
22016932.7 1
3.1%
22341538.6 1
3.1%
22868089.5 1
3.1%
24847259.2 1
3.1%
28496127.8 1
3.1%
33129406.7 1
3.1%
33987711.6 1
3.1%
34789101.9 1
3.1%
35039850.6 1
3.1%
37619893.3 1
3.1%
ValueCountFrequency (%)
68055312.7 1
3.1%
67711606.8 1
3.1%
65947993.3 1
3.1%
65533541.7 1
3.1%
64587960.3 1
3.1%
64261050.7 1
3.1%
64156911.8 1
3.1%
64061378.4 1
3.1%
63633233.0 1
3.1%
63492400.6 1
3.1%

전기
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11646033
Minimum6717073.9
Maximum21488107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T09:18:48.784558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6717073.9
5-th percentile6948554.3
Q17262781.5
median7596533.8
Q317731958
95-th percentile20744424
Maximum21488107
Range14771033
Interquartile range (IQR)10469177

Descriptive statistics

Standard deviation5721298.1
Coefficient of variation (CV)0.49126583
Kurtosis-1.4459577
Mean11646033
Median Absolute Deviation (MAD)624018.85
Skewness0.6748654
Sum3.7267306 × 108
Variance3.2733252 × 1013
MonotonicityNot monotonic
2023-12-12T09:18:48.971383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
7680651.9 1
 
3.1%
7162992.7 1
 
3.1%
19541347.0 1
 
3.1%
20011312.5 1
 
3.1%
19715817.2 1
 
3.1%
21488106.7 1
 
3.1%
21153679.8 1
 
3.1%
20409578.7 1
 
3.1%
18979211.8 1
 
3.1%
18643496.2 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
6717073.9 1
3.1%
6938055.6 1
3.1%
6957144.2 1
3.1%
6987885.7 1
3.1%
7088328.3 1
3.1%
7107659.5 1
3.1%
7121017.2 1
3.1%
7162992.7 1
3.1%
7296044.4 1
3.1%
7308476.1 1
3.1%
ValueCountFrequency (%)
21488106.7 1
3.1%
21153679.8 1
3.1%
20409578.7 1
3.1%
20011312.5 1
3.1%
19715817.2 1
3.1%
19541347.0 1
3.1%
18979211.8 1
3.1%
18643496.2 1
3.1%
17428112.2 1
3.1%
16938431.7 1
3.1%

동차
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3529629.3
Minimum1622502.1
Maximum5231472.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T09:18:49.149804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1622502.1
5-th percentile1882306.2
Q12576179
median3739168.1
Q34336811.1
95-th percentile4968563
Maximum5231472.5
Range3608970.4
Interquartile range (IQR)1760632.1

Descriptive statistics

Standard deviation1080818
Coefficient of variation (CV)0.30621289
Kurtosis-1.2343712
Mean3529629.3
Median Absolute Deviation (MAD)986857.65
Skewness-0.18004553
Sum1.1294814 × 108
Variance1.1681675 × 1012
MonotonicityNot monotonic
2023-12-12T09:18:49.328719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
5231472.5 1
 
3.1%
4418453.0 1
 
3.1%
2519204.0 1
 
3.1%
2286394.5 1
 
3.1%
2833520.7 1
 
3.1%
3729033.1 1
 
3.1%
3842304.3 1
 
3.1%
4070823.2 1
 
3.1%
4047999.8 1
 
3.1%
4034124.7 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
1622502.1 1
3.1%
1652944.5 1
3.1%
2069965.7 1
3.1%
2166628.4 1
3.1%
2212288.3 1
3.1%
2286394.5 1
3.1%
2461522.9 1
3.1%
2519204.0 1
3.1%
2595170.6 1
3.1%
2670901.9 1
3.1%
ValueCountFrequency (%)
5231472.5 1
3.1%
4990995.9 1
3.1%
4950208.8 1
3.1%
4949004.2 1
3.1%
4866882.8 1
3.1%
4789541.5 1
3.1%
4418453.0 1
3.1%
4362933.5 1
3.1%
4328103.6 1
3.1%
4306209.1 1
3.1%

새마을동차
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)100.0%
Missing3
Missing (%)9.4%
Infinite0
Infinite (%)0.0%
Mean6660182.6
Minimum64.1
Maximum10369145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T09:18:49.507457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum64.1
5-th percentile13836.76
Q13183643.7
median8338146.4
Q39577233.7
95-th percentile10271064
Maximum10369145
Range10369081
Interquartile range (IQR)6393590

Descriptive statistics

Standard deviation3668968.1
Coefficient of variation (CV)0.550881
Kurtosis-0.96513999
Mean6660182.6
Median Absolute Deviation (MAD)1543711
Skewness-0.79360004
Sum1.9314529 × 108
Variance1.3461327 × 1013
MonotonicityNot monotonic
2023-12-12T09:18:49.669555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1474923.3 1
 
3.1%
110.6 1
 
3.1%
64.1 1
 
3.1%
34426.0 1
 
3.1%
5381712.8 1
 
3.1%
7168270.3 1
 
3.1%
8128729.6 1
 
3.1%
8368617.4 1
 
3.1%
9261679.3 1
 
3.1%
8919676.4 1
 
3.1%
Other values (19) 19
59.4%
(Missing) 3
 
9.4%
ValueCountFrequency (%)
64.1 1
3.1%
110.6 1
3.1%
34426.0 1
3.1%
628401.2 1
3.1%
1474923.3 1
3.1%
2900582.2 1
3.1%
2927035.3 1
3.1%
3183643.7 1
3.1%
3711174.0 1
3.1%
5381712.8 1
3.1%
ValueCountFrequency (%)
10369145.1 1
3.1%
10293930.3 1
3.1%
10236763.8 1
3.1%
10053137.7 1
3.1%
9881857.4 1
3.1%
9733992.2 1
3.1%
9732831.9 1
3.1%
9577233.7 1
3.1%
9402307.8 1
3.1%
9261679.3 1
3.1%

수도권전동차
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24077766
Minimum8125493.6
Maximum41861414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T09:18:49.833813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8125493.6
5-th percentile9106395.9
Q115045809
median22449716
Q333886886
95-th percentile39611408
Maximum41861414
Range33735920
Interquartile range (IQR)18841077

Descriptive statistics

Standard deviation10797946
Coefficient of variation (CV)0.44846129
Kurtosis-1.2518351
Mean24077766
Median Absolute Deviation (MAD)9793593.2
Skewness0.078774046
Sum7.7048852 × 108
Variance1.1659564 × 1014
MonotonicityNot monotonic
2023-12-12T09:18:50.275095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
8125493.6 1
 
3.1%
23768108.7 1
 
3.1%
41356981.1 1
 
3.1%
41861413.8 1
 
3.1%
38151178.1 1
 
3.1%
38183212.4 1
 
3.1%
37281783.1 1
 
3.1%
37489362.6 1
 
3.1%
36174380.5 1
 
3.1%
36703703.2 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
8125493.6 1
3.1%
8526598.2 1
3.1%
9580775.8 1
3.1%
9581071.8 1
3.1%
10246144.6 1
3.1%
10776095.8 1
3.1%
11063490.4 1
3.1%
13919776.7 1
3.1%
15421152.5 1
3.1%
17531912.0 1
3.1%
ValueCountFrequency (%)
41861413.8 1
3.1%
41356981.1 1
3.1%
38183212.4 1
3.1%
38151178.1 1
3.1%
37489362.6 1
3.1%
37281783.1 1
3.1%
36703703.2 1
3.1%
36174380.5 1
3.1%
33124387.9 1
3.1%
31362230.5 1
3.1%

전기동차
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)100.0%
Missing7
Missing (%)21.9%
Infinite0
Infinite (%)0.0%
Mean2127509.8
Minimum12930
Maximum9496843.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T09:18:50.383368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12930
5-th percentile197060.66
Q1231841.8
median250358.9
Q32919662.5
95-th percentile8938043.6
Maximum9496843.5
Range9483913.5
Interquartile range (IQR)2687820.7

Descriptive statistics

Standard deviation3238627.1
Coefficient of variation (CV)1.5222619
Kurtosis0.64366726
Mean2127509.8
Median Absolute Deviation (MAD)41979.6
Skewness1.4954243
Sum53187745
Variance1.0488706 × 1013
MonotonicityNot monotonic
2023-12-12T09:18:50.506703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
248563.9 1
 
3.1%
9153567.2 1
 
3.1%
9496843.5 1
 
3.1%
8047521.4 1
 
3.1%
8075949.3 1
 
3.1%
6158324.3 1
 
3.1%
3300667.2 1
 
3.1%
2919662.5 1
 
3.1%
1216610.9 1
 
3.1%
1053875.4 1
 
3.1%
Other values (15) 15
46.9%
(Missing) 7
21.9%
ValueCountFrequency (%)
12930.0 1
3.1%
194231.0 1
3.1%
208379.3 1
3.1%
213429.7 1
3.1%
226468.6 1
3.1%
231381.0 1
3.1%
231841.8 1
3.1%
235891.6 1
3.1%
235932.2 1
3.1%
243195.1 1
3.1%
ValueCountFrequency (%)
9496843.5 1
3.1%
9153567.2 1
3.1%
8075949.3 1
3.1%
8047521.4 1
3.1%
6158324.3 1
3.1%
3300667.2 1
3.1%
2919662.5 1
3.1%
1216610.9 1
3.1%
1053875.4 1
3.1%
458632.5 1
3.1%

Interactions

2023-12-12T09:18:46.323322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:40.695385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:41.509104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:42.325043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:43.094869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:44.115765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:44.825155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:45.519807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:46.418677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:40.820824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:41.610482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:42.423377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:43.200806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:44.188758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:44.904550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:45.613097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:46.501968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:40.947292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:41.709389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:42.515782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:43.292076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:44.260546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:44.974203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:45.701475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:46.595771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:41.053818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:41.792411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:42.599076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:43.393363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:44.348899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:45.047004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:45.797416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:46.698288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:41.147718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:41.891186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:42.695819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:43.503864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:44.456845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:45.139976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:45.916910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:46.797161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:41.257478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:41.996267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:42.808816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:43.609144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:44.551297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:45.229866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:46.019955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:46.896125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:41.346683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:42.090219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:42.901193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:43.695700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:44.651261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:45.307684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:46.105890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:47.004780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:41.429471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:42.228283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:43.003166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:44.046525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:44.747145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:45.395769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:46.218544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:18:50.605050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도증기고속철도디젤전기동차새마을동차수도권전동차전기동차
연도1.0001.0000.6850.9410.5980.6990.6150.9420.764
증기1.0001.000NaN1.000NaN1.000NaN1.000NaN
고속철도0.685NaN1.0000.2610.6180.6460.9200.7800.858
디젤0.9411.0000.2611.0000.8520.2560.2840.8510.959
전기0.598NaN0.6180.8521.0000.0000.7400.8530.631
동차0.6991.0000.6460.2560.0001.0000.0000.6440.000
새마을동차0.615NaN0.9200.2840.7400.0001.0000.8120.515
수도권전동차0.9421.0000.7800.8510.8530.6440.8121.0000.718
전기동차0.764NaN0.8580.9590.6310.0000.5150.7181.000
2023-12-12T09:18:50.726678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도고속철도디젤전기동차새마을동차수도권전동차전기동차증기
연도1.0000.996-0.9130.652-0.110-0.0680.9980.5821.000
고속철도0.9961.000-0.9710.886-0.254-0.8390.9890.9640.000
디젤-0.913-0.9711.000-0.6330.1890.117-0.912-0.6541.000
전기0.6520.886-0.6331.000-0.188-0.6940.6550.9221.000
동차-0.110-0.2540.189-0.1881.0000.045-0.109-0.1891.000
새마을동차-0.068-0.8390.117-0.6940.0451.000-0.065-0.7981.000
수도권전동차0.9980.989-0.9120.655-0.109-0.0651.0000.5821.000
전기동차0.5820.964-0.6540.922-0.189-0.7980.5821.0001.000
증기1.0000.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-12T09:18:47.114985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:18:47.277785image/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.
2023-12-12T09:18:47.431536image/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

연도증기고속철도디젤전기동차새마을동차수도권전동차전기동차
01987<NA><NA>63410869.97680651.95231472.5628401.28125493.6258091.9
11988<NA><NA>64587960.37886261.74362933.51474923.38526598.2248563.9
21989<NA><NA>64261050.77420866.23749303.12900582.29580775.8269487.4
31990<NA><NA>65947993.37296044.43446665.33711174.09581071.8235932.2
41991<NA><NA>67711606.87615339.43012697.32927035.310246144.6243195.1
51992<NA><NA>68055312.77466985.32595170.63183643.710776095.8250358.9
61993<NA><NA>65533541.77577728.22670901.97154619.511063490.4245908.3
71994<NA><NA>62555189.47107659.52166628.49122893.313919776.7235891.6
81995<NA><NA>63382916.46987885.71652944.59577233.715421152.5231381.0
91996<NA><NA>62762430.67121017.22069965.79881857.417531912.0231841.8
연도증기고속철도디젤전기동차새마을동차수도권전동차전기동차
222009<NA>22032842.837619893.316938431.72461522.98368617.431362230.5458632.5
232010<NA>23343585.535039850.617428112.24328103.68128729.633124387.91053875.4
242011<NA>27703706.933129406.718643496.24034124.77168270.336703703.21216610.9
252012<NA>29596898.533987711.618979211.84047999.85381712.836174380.52919662.5
262013<NA>33037367.234789101.920409578.74070823.234426.037489362.63300667.2
272014<NA>32820630.228496127.821153679.83842304.364.137281783.16158324.3
282015<NA>36156722.424847259.221488106.73729033.1110.638183212.48075949.3
292016<NA>38009212.722868089.519715817.22833520.7<NA>38151178.18047521.4
302017<NA>38058373.222016932.720011312.52286394.5<NA>41861413.89496843.5
312018<NA>41310128.722341538.619541347.02519204.0<NA>41356981.19153567.2