Overview

Dataset statistics

Number of variables8
Number of observations141
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.0 KiB
Average record size in memory72.9 B

Variable types

Numeric8

Dataset

Description충청남도 서산시에 등록된 자동차에 대한 등록현행자료로 년별, 월별, 승용차, 화물차, 특수차, 이륜자동차 등록 통계를 낸 자료
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=451&beforeMenuCd=DOM_000000201001001000&publicdatapk=15000832

Alerts

년도 is highly overall correlated with 합계 and 5 other fieldsHigh correlation
합계 is highly overall correlated with 년도 and 5 other fieldsHigh correlation
승용차 is highly overall correlated with 년도 and 5 other fieldsHigh correlation
승합차 is highly overall correlated with 년도 and 5 other fieldsHigh correlation
화물차 is highly overall correlated with 년도 and 5 other fieldsHigh correlation
특수차 is highly overall correlated with 년도 and 5 other fieldsHigh correlation
이륜자동차 is highly overall correlated with 년도 and 5 other fieldsHigh correlation

Reproduction

Analysis started2024-01-09 22:34:24.564717
Analysis finished2024-01-09 22:34:30.206174
Duration5.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.383
Minimum2011
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-01-10T07:34:30.253340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011
Q12013
median2016
Q32019
95-th percentile2022
Maximum2022
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4051633
Coefficient of variation (CV)0.0016887483
Kurtosis-1.2010266
Mean2016.383
Median Absolute Deviation (MAD)3
Skewness0.013027381
Sum284310
Variance11.595137
MonotonicityDecreasing
2024-01-10T07:34:30.342878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2021 12
8.5%
2020 12
8.5%
2019 12
8.5%
2018 12
8.5%
2017 12
8.5%
2016 12
8.5%
2015 12
8.5%
2014 12
8.5%
2013 12
8.5%
2012 12
8.5%
Other values (2) 21
14.9%
ValueCountFrequency (%)
2011 12
8.5%
2012 12
8.5%
2013 12
8.5%
2014 12
8.5%
2015 12
8.5%
2016 12
8.5%
2017 12
8.5%
2018 12
8.5%
2019 12
8.5%
2020 12
8.5%
ValueCountFrequency (%)
2022 9
6.4%
2021 12
8.5%
2020 12
8.5%
2019 12
8.5%
2018 12
8.5%
2017 12
8.5%
2016 12
8.5%
2015 12
8.5%
2014 12
8.5%
2013 12
8.5%

월
Real number (ℝ)

Distinct12
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4042553
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-01-10T07:34:30.435384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4350686
Coefficient of variation (CV)0.53637283
Kurtosis-1.1930292
Mean6.4042553
Median Absolute Deviation (MAD)3
Skewness0.031148403
Sum903
Variance11.799696
MonotonicityNot monotonic
2024-01-10T07:34:30.534704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9 12
8.5%
8 12
8.5%
7 12
8.5%
6 12
8.5%
5 12
8.5%
4 12
8.5%
3 12
8.5%
2 12
8.5%
1 12
8.5%
12 11
7.8%
Other values (2) 22
15.6%
ValueCountFrequency (%)
1 12
8.5%
2 12
8.5%
3 12
8.5%
4 12
8.5%
5 12
8.5%
6 12
8.5%
7 12
8.5%
8 12
8.5%
9 12
8.5%
10 11
7.8%
ValueCountFrequency (%)
12 11
7.8%
11 11
7.8%
10 11
7.8%
9 12
8.5%
8 12
8.5%
7 12
8.5%
6 12
8.5%
5 12
8.5%
4 12
8.5%
3 12
8.5%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct140
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96078.738
Minimum75205
Maximum115269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-01-10T07:34:30.656728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum75205
5-th percentile76861
Q184705
median96586
Q3107037
95-th percentile113678
Maximum115269
Range40064
Interquartile range (IQR)22332

Descriptive statistics

Standard deviation12360.794
Coefficient of variation (CV)0.12865275
Kurtosis-1.3027123
Mean96078.738
Median Absolute Deviation (MAD)11243
Skewness-0.10635259
Sum13547102
Variance1.5278922 × 108
MonotonicityNot monotonic
2024-01-10T07:34:30.779641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94944 2
 
1.4%
115269 1
 
0.7%
87451 1
 
0.7%
89014 1
 
0.7%
88586 1
 
0.7%
88395 1
 
0.7%
88241 1
 
0.7%
87986 1
 
0.7%
87733 1
 
0.7%
86993 1
 
0.7%
Other values (130) 130
92.2%
ValueCountFrequency (%)
75205 1
0.7%
75427 1
0.7%
75765 1
0.7%
76120 1
0.7%
76209 1
0.7%
76381 1
0.7%
76618 1
0.7%
76861 1
0.7%
77005 1
0.7%
77484 1
0.7%
ValueCountFrequency (%)
115269 1
0.7%
115050 1
0.7%
114756 1
0.7%
114387 1
0.7%
114241 1
0.7%
114090 1
0.7%
113965 1
0.7%
113678 1
0.7%
113505 1
0.7%
113260 1
0.7%

승용차
Real number (ℝ)

HIGH CORRELATION 

Distinct140
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62118.34
Minimum46054
Maximum78617
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-01-10T07:34:30.905672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46054
5-th percentile47448
Q152241
median62246
Q370960
95-th percentile77273
Maximum78617
Range32563
Interquartile range (IQR)18719

Descriptive statistics

Standard deviation10145.898
Coefficient of variation (CV)0.16333177
Kurtosis-1.3552458
Mean62118.34
Median Absolute Deviation (MAD)9485
Skewness0.0035759554
Sum8758686
Variance1.0293925 × 108
MonotonicityNot monotonic
2024-01-10T07:34:31.250563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60812 2
 
1.4%
78617 1
 
0.7%
54569 1
 
0.7%
55865 1
 
0.7%
55506 1
 
0.7%
55369 1
 
0.7%
55194 1
 
0.7%
55006 1
 
0.7%
54804 1
 
0.7%
54148 1
 
0.7%
Other values (130) 130
92.2%
ValueCountFrequency (%)
46054 1
0.7%
46194 1
0.7%
46437 1
0.7%
46736 1
0.7%
46924 1
0.7%
47086 1
0.7%
47322 1
0.7%
47448 1
0.7%
47875 1
0.7%
47980 1
0.7%
ValueCountFrequency (%)
78617 1
0.7%
78459 1
0.7%
78200 1
0.7%
77936 1
0.7%
77790 1
0.7%
77621 1
0.7%
77462 1
0.7%
77273 1
0.7%
77182 1
0.7%
77044 1
0.7%

승합차
Real number (ℝ)

HIGH CORRELATION 

Distinct121
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3554.7305
Minimum3230
Maximum3840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-01-10T07:34:31.365730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3230
5-th percentile3327
Q13468
median3541
Q33673
95-th percentile3793
Maximum3840
Range610
Interquartile range (IQR)205

Descriptive statistics

Standard deviation146.5916
Coefficient of variation (CV)0.041238458
Kurtosis-0.74342302
Mean3554.7305
Median Absolute Deviation (MAD)113
Skewness-0.046508151
Sum501217
Variance21489.098
MonotonicityNot monotonic
2024-01-10T07:34:31.484146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3596 2
 
1.4%
3349 2
 
1.4%
3474 2
 
1.4%
3481 2
 
1.4%
3502 2
 
1.4%
3482 2
 
1.4%
3663 2
 
1.4%
3532 2
 
1.4%
3533 2
 
1.4%
3452 2
 
1.4%
Other values (111) 121
85.8%
ValueCountFrequency (%)
3230 1
0.7%
3260 1
0.7%
3276 1
0.7%
3278 1
0.7%
3290 1
0.7%
3315 1
0.7%
3327 2
1.4%
3329 1
0.7%
3331 1
0.7%
3332 1
0.7%
ValueCountFrequency (%)
3840 1
0.7%
3838 1
0.7%
3837 2
1.4%
3831 2
1.4%
3809 1
0.7%
3793 1
0.7%
3792 1
0.7%
3780 1
0.7%
3766 1
0.7%
3743 1
0.7%

화물차
Real number (ℝ)

HIGH CORRELATION 

Distinct136
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18215.759
Minimum11307
Maximum20309
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-01-10T07:34:31.606842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11307
5-th percentile16064
Q116853
median18419
Q319626
95-th percentile20139
Maximum20309
Range9002
Interquartile range (IQR)2773

Descriptive statistics

Standard deviation1520.8472
Coefficient of variation (CV)0.08349074
Kurtosis1.3244177
Mean18215.759
Median Absolute Deviation (MAD)1297
Skewness-0.79706623
Sum2568422
Variance2312976.2
MonotonicityNot monotonic
2024-01-10T07:34:31.747298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19800 2
 
1.4%
18287 2
 
1.4%
19879 2
 
1.4%
19767 2
 
1.4%
16167 2
 
1.4%
17237 1
 
0.7%
17357 1
 
0.7%
17338 1
 
0.7%
17293 1
 
0.7%
17263 1
 
0.7%
Other values (126) 126
89.4%
ValueCountFrequency (%)
11307 1
0.7%
15791 1
0.7%
15864 1
0.7%
15929 1
0.7%
15954 1
0.7%
16021 1
0.7%
16039 1
0.7%
16064 1
0.7%
16083 1
0.7%
16130 1
0.7%
ValueCountFrequency (%)
20309 1
0.7%
20275 1
0.7%
20224 1
0.7%
20192 1
0.7%
20148 1
0.7%
20144 1
0.7%
20140 1
0.7%
20139 1
0.7%
20079 1
0.7%
19995 1
0.7%

특수차
Real number (ℝ)

HIGH CORRELATION 

Distinct114
Distinct (%)80.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean440.15603
Minimum252
Maximum761
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-01-10T07:34:31.886072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum252
5-th percentile265
Q1320
median432
Q3505
95-th percentile708
Maximum761
Range509
Interquartile range (IQR)185

Descriptive statistics

Standard deviation136.68788
Coefficient of variation (CV)0.31054414
Kurtosis-0.47516881
Mean440.15603
Median Absolute Deviation (MAD)96
Skewness0.53161316
Sum62062
Variance18683.575
MonotonicityNot monotonic
2024-01-10T07:34:32.038200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
265 5
 
3.5%
495 3
 
2.1%
505 3
 
2.1%
412 3
 
2.1%
272 3
 
2.1%
492 3
 
2.1%
432 2
 
1.4%
269 2
 
1.4%
501 2
 
1.4%
434 2
 
1.4%
Other values (104) 113
80.1%
ValueCountFrequency (%)
252 1
 
0.7%
259 1
 
0.7%
261 1
 
0.7%
262 1
 
0.7%
263 1
 
0.7%
264 1
 
0.7%
265 5
3.5%
267 2
 
1.4%
268 2
 
1.4%
269 2
 
1.4%
ValueCountFrequency (%)
761 1
0.7%
753 1
0.7%
747 1
0.7%
740 1
0.7%
730 1
0.7%
720 1
0.7%
714 1
0.7%
708 1
0.7%
705 1
0.7%
700 1
0.7%

이륜자동차
Real number (ℝ)

HIGH CORRELATION 

Distinct131
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11749.688
Minimum9271
Maximum12894
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-01-10T07:34:32.176157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9271
5-th percentile9402
Q111639
median11917
Q312305
95-th percentile12703
Maximum12894
Range3623
Interquartile range (IQR)666

Descriptive statistics

Standard deviation933.68383
Coefficient of variation (CV)0.079464564
Kurtosis1.931861
Mean11749.688
Median Absolute Deviation (MAD)374
Skewness-1.6445366
Sum1656706
Variance871765.5
MonotonicityNot monotonic
2024-01-10T07:34:32.295721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11639 3
 
2.1%
9397 2
 
1.4%
9409 2
 
1.4%
11821 2
 
1.4%
11824 2
 
1.4%
11842 2
 
1.4%
11959 2
 
1.4%
11917 2
 
1.4%
11916 2
 
1.4%
11870 1
 
0.7%
Other values (121) 121
85.8%
ValueCountFrequency (%)
9271 1
0.7%
9277 1
0.7%
9296 1
0.7%
9320 1
0.7%
9330 1
0.7%
9397 2
1.4%
9402 1
0.7%
9406 1
0.7%
9409 2
1.4%
9410 1
0.7%
ValueCountFrequency (%)
12894 1
0.7%
12877 1
0.7%
12872 1
0.7%
12835 1
0.7%
12825 1
0.7%
12753 1
0.7%
12706 1
0.7%
12703 1
0.7%
12697 1
0.7%
12687 1
0.7%

Interactions

2024-01-10T07:34:29.397976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:24.786344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:25.383134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:25.983259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:26.875644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:27.609994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:28.230909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:28.798815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:29.478307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:24.857864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:25.457869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:26.078021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:26.978202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:27.679915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:28.304060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:28.879204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:29.556405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:24.927967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:25.531035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:26.416029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:27.073469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:27.750286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:28.369940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:28.952973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:29.649096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:25.004829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:25.608271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:26.496190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:27.180730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:27.828526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:28.452299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:29.033319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:29.728318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:25.089998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:25.680224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:26.571939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:27.278061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:27.930866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:28.522650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:29.112114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:29.810060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:25.170190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:25.752038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:26.648669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:27.375385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:28.004967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:28.596792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:29.185770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:29.879622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:25.240513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:25.820663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:26.715985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:27.454254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:28.075558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:28.659351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:29.253432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:29.958906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:25.304756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:25.891797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:26.788058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:27.529770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:28.145163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:28.724827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:29.317565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:34:32.386338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도월합계승용차승합차화물차특수차이륜자동차
년도1.0000.0000.9770.9950.8790.9960.9610.815
월0.0001.0000.0000.0000.0000.0000.0000.000
합계0.9770.0001.0000.9730.9520.9940.9740.871
승용차0.9950.0000.9731.0000.9540.9960.9640.864
승합차0.8790.0000.9520.9541.0000.8280.9640.806
화물차0.9960.0000.9940.9960.8281.0000.9840.829
특수차0.9610.0000.9740.9640.9640.9841.0000.846
이륜자동차0.8150.0000.8710.8640.8060.8290.8461.000
2024-01-10T07:34:32.493321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도월합계승용차승합차화물차특수차이륜자동차
년도1.000-0.0450.9960.996-0.9420.9930.9920.950
월-0.0451.0000.0360.0390.0060.0300.0360.057
합계0.9960.0361.0000.999-0.9410.9970.9960.952
승용차0.9960.0390.9991.000-0.9420.9960.9960.953
승합차-0.9420.006-0.941-0.9421.000-0.937-0.930-0.891
화물차0.9930.0300.9970.996-0.9371.0000.9940.950
특수차0.9920.0360.9960.996-0.9300.9941.0000.949
이륜자동차0.9500.0570.9520.953-0.8910.9500.9491.000

Missing values

2024-01-10T07:34:30.066256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:34:30.166321image/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

년도월합계승용차승합차화물차특수차이륜자동차
0202291152697861732302030976112352
1202281150507845932602027575312303
2202271147567820032762022474712309
3202261143877793632782013974012294
4202251142417779032902014073012291
5202241140907762133152014472012290
6202231139657746233362019271412252
7202221136787727333312014870812218
8202211135057718233322007970512207
92021121132607704433341999570012187
년도월합계승용차승합차화물차특수차이륜자동차
13120111077622479803780161832779402
1322011977484478753793161302729414
1332011877005474483792160832729410
1342011776861473223809160642699397
1352011676618470863831160392659397
1362011576381469243837160212699330
1372011476120467363840159542709320
1382011375765464373838159292659296
1392011275427461943831158642619277
1402011175205460543837157912529271