Overview

Dataset statistics

Number of variables9
Number of observations34
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory83.9 B

Variable types

Categorical1
Numeric7
DateTime1

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

합계 is highly overall correlated with 승용차 and 5 other fieldsHigh correlation
승용차 is highly overall correlated with 합계 and 5 other fieldsHigh correlation
승합차 is highly overall correlated with 합계 and 4 other fieldsHigh correlation
화물차 is highly overall correlated with 합계 and 5 other fieldsHigh correlation
특수차 is highly overall correlated with 합계 and 4 other fieldsHigh correlation
이륜자동차 is highly overall correlated with 합계 and 5 other fieldsHigh correlation
년도 is highly overall correlated with 합계 and 5 other fieldsHigh correlation
합계 has unique valuesUnique
승용차 has unique valuesUnique
기준일 has unique valuesUnique

Reproduction

Analysis started2024-01-09 22:33:59.819688
Analysis finished2024-01-09 22:34:04.606663
Duration4.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size404.0 B
2012
12 
2011
12 
2013
10 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2012 12
35.3%
2011 12
35.3%
2013 10
29.4%

Length

2024-01-10T07:34:04.674539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:34:04.771431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2012 12
35.3%
2011 12
35.3%
2013 10
29.4%

월
Real number (ℝ)

Distinct12
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2058824
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-10T07:34:04.856638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.25
median6
Q39
95-th percentile11.35
Maximum12
Range11
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation3.3735101
Coefficient of variation (CV)0.54359878
Kurtosis-1.1367273
Mean6.2058824
Median Absolute Deviation (MAD)3
Skewness0.062679405
Sum211
Variance11.38057
MonotonicityNot monotonic
2024-01-10T07:34:04.946271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 3
8.8%
9 3
8.8%
8 3
8.8%
7 3
8.8%
6 3
8.8%
5 3
8.8%
4 3
8.8%
3 3
8.8%
2 3
8.8%
1 3
8.8%
Other values (2) 4
11.8%
ValueCountFrequency (%)
1 3
8.8%
2 3
8.8%
3 3
8.8%
4 3
8.8%
5 3
8.8%
6 3
8.8%
7 3
8.8%
8 3
8.8%
9 3
8.8%
10 3
8.8%
ValueCountFrequency (%)
12 2
5.9%
11 2
5.9%
10 3
8.8%
9 3
8.8%
8 3
8.8%
7 3
8.8%
6 3
8.8%
5 3
8.8%
4 3
8.8%
3 3
8.8%

합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79590.559
Minimum75205
Maximum84519
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-10T07:34:05.047179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum75205
5-th percentile75646.7
Q177124.75
median78983.5
Q382229.75
95-th percentile84145.55
Maximum84519
Range9314
Interquartile range (IQR)5105

Descriptive statistics

Standard deviation2973.0985
Coefficient of variation (CV)0.037354914
Kurtosis-1.4039897
Mean79590.559
Median Absolute Deviation (MAD)2571.5
Skewness0.21288177
Sum2706079
Variance8839314.8
MonotonicityNot monotonic
2024-01-10T07:34:05.167684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
84519 1
 
2.9%
77005 1
 
2.9%
77600 1
 
2.9%
77769 1
 
2.9%
77575 1
 
2.9%
77619 1
 
2.9%
77622 1
 
2.9%
77484 1
 
2.9%
76861 1
 
2.9%
80099 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
75205 1
2.9%
75427 1
2.9%
75765 1
2.9%
76120 1
2.9%
76209 1
2.9%
76381 1
2.9%
76618 1
2.9%
76861 1
2.9%
77005 1
2.9%
77484 1
2.9%
ValueCountFrequency (%)
84519 1
2.9%
84245 1
2.9%
84092 1
2.9%
83826 1
2.9%
83477 1
2.9%
83182 1
2.9%
82938 1
2.9%
82556 1
2.9%
82275 1
2.9%
82094 1
2.9%

승용차
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48962.294
Minimum46054
Maximum52073
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-10T07:34:05.272715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46054
5-th percentile46351.95
Q147901.25
median48766
Q350263.5
95-th percentile51769.2
Maximum52073
Range6019
Interquartile range (IQR)2362.25

Descriptive statistics

Standard deviation1722.0591
Coefficient of variation (CV)0.035171128
Kurtosis-0.92560765
Mean48962.294
Median Absolute Deviation (MAD)1353
Skewness0.16576994
Sum1664718
Variance2965487.7
MonotonicityNot monotonic
2024-01-10T07:34:05.374823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
52073 1
 
2.9%
47448 1
 
2.9%
48298 1
 
2.9%
48178 1
 
2.9%
47992 1
 
2.9%
48021 1
 
2.9%
47980 1
 
2.9%
47875 1
 
2.9%
47322 1
 
2.9%
48549 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
46054 1
2.9%
46194 1
2.9%
46437 1
2.9%
46736 1
2.9%
46924 1
2.9%
47086 1
2.9%
47322 1
2.9%
47448 1
2.9%
47875 1
2.9%
47980 1
2.9%
ValueCountFrequency (%)
52073 1
2.9%
51868 1
2.9%
51716 1
2.9%
51494 1
2.9%
51209 1
2.9%
50993 1
2.9%
50832 1
2.9%
50530 1
2.9%
50300 1
2.9%
50154 1
2.9%

승합차
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3723.4118
Minimum3378
Maximum3840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-10T07:34:05.475875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3378
5-th percentile3573.05
Q13692.75
median3727
Q33789
95-th percentile3837.35
Maximum3840
Range462
Interquartile range (IQR)96.25

Descriptive statistics

Standard deviation100.73732
Coefficient of variation (CV)0.027055111
Kurtosis5.7057656
Mean3723.4118
Median Absolute Deviation (MAD)41
Skewness-2.0113435
Sum126596
Variance10148.007
MonotonicityNot monotonic
2024-01-10T07:34:05.574861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
3837 2
 
5.9%
3831 2
 
5.9%
3663 2
 
5.9%
3701 1
 
2.9%
3714 1
 
2.9%
3838 1
 
2.9%
3840 1
 
2.9%
3809 1
 
2.9%
3792 1
 
2.9%
3793 1
 
2.9%
Other values (21) 21
61.8%
ValueCountFrequency (%)
3378 1
2.9%
3406 1
2.9%
3663 2
5.9%
3683 1
2.9%
3684 1
2.9%
3685 1
2.9%
3687 1
2.9%
3692 1
2.9%
3695 1
2.9%
3701 1
2.9%
ValueCountFrequency (%)
3840 1
2.9%
3838 1
2.9%
3837 2
5.9%
3831 2
5.9%
3809 1
2.9%
3793 1
2.9%
3792 1
2.9%
3780 1
2.9%
3766 1
2.9%
3743 1
2.9%

화물차
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16148.088
Minimum11307
Maximum16808
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-10T07:34:05.668557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11307
5-th percentile15838.45
Q116094.75
median16245
Q316474
95-th percentile16737.05
Maximum16808
Range5501
Interquartile range (IQR)379.25

Descriptive statistics

Standard deviation894.80837
Coefficient of variation (CV)0.055412651
Kurtosis27.892613
Mean16148.088
Median Absolute Deviation (MAD)193.5
Skewness-5.0432632
Sum549035
Variance800682.02
MonotonicityNot monotonic
2024-01-10T07:34:05.763559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
16167 2
 
5.9%
16808 1
 
2.9%
16083 1
 
2.9%
16230 1
 
2.9%
16170 1
 
2.9%
16186 1
 
2.9%
16183 1
 
2.9%
16130 1
 
2.9%
16064 1
 
2.9%
16752 1
 
2.9%
Other values (23) 23
67.6%
ValueCountFrequency (%)
11307 1
2.9%
15791 1
2.9%
15864 1
2.9%
15929 1
2.9%
15954 1
2.9%
16021 1
2.9%
16039 1
2.9%
16064 1
2.9%
16083 1
2.9%
16130 1
2.9%
ValueCountFrequency (%)
16808 1
2.9%
16752 1
2.9%
16729 1
2.9%
16709 1
2.9%
16657 1
2.9%
16594 1
2.9%
16551 1
2.9%
16503 1
2.9%
16479 1
2.9%
16459 1
2.9%

특수차
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)67.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean277.08824
Minimum252
Maximum317
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-10T07:34:05.856922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum252
5-th percentile260.3
Q1265
median269.5
Q3281
95-th percentile315.35
Maximum317
Range65
Interquartile range (IQR)16

Descriptive statistics

Standard deviation18.421607
Coefficient of variation (CV)0.06648282
Kurtosis-0.037653549
Mean277.08824
Median Absolute Deviation (MAD)5
Skewness1.1311427
Sum9421
Variance339.35561
MonotonicityNot monotonic
2024-01-10T07:34:06.201213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
265 5
 
14.7%
272 3
 
8.8%
269 2
 
5.9%
281 2
 
5.9%
270 2
 
5.9%
268 2
 
5.9%
267 2
 
5.9%
252 1
 
2.9%
261 1
 
2.9%
277 1
 
2.9%
Other values (13) 13
38.2%
ValueCountFrequency (%)
252 1
 
2.9%
259 1
 
2.9%
261 1
 
2.9%
262 1
 
2.9%
263 1
 
2.9%
264 1
 
2.9%
265 5
14.7%
267 2
 
5.9%
268 2
 
5.9%
269 2
 
5.9%
ValueCountFrequency (%)
317 1
2.9%
316 1
2.9%
315 1
2.9%
310 1
2.9%
305 1
2.9%
302 1
2.9%
300 1
2.9%
296 1
2.9%
281 2
5.9%
277 1
2.9%

이륜자동차
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10479.676
Minimum9271
Maximum11622
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-10T07:34:06.322350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9271
5-th percentile9289.35
Q19406.75
median10583
Q311539.25
95-th percentile11593.75
Maximum11622
Range2351
Interquartile range (IQR)2132.5

Descriptive statistics

Standard deviation1049.1212
Coefficient of variation (CV)0.10011008
Kurtosis-2.0395506
Mean10479.676
Median Absolute Deviation (MAD)1002
Skewness-0.034658119
Sum356309
Variance1100655.4
MonotonicityNot monotonic
2024-01-10T07:34:06.421768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9397 2
 
5.9%
9409 2
 
5.9%
11622 1
 
2.9%
9863 1
 
2.9%
9271 1
 
2.9%
9277 1
 
2.9%
9296 1
 
2.9%
9320 1
 
2.9%
9330 1
 
2.9%
9410 1
 
2.9%
Other values (22) 22
64.7%
ValueCountFrequency (%)
9271 1
2.9%
9277 1
2.9%
9296 1
2.9%
9320 1
2.9%
9330 1
2.9%
9397 2
5.9%
9402 1
2.9%
9406 1
2.9%
9409 2
5.9%
9410 1
2.9%
ValueCountFrequency (%)
11622 1
2.9%
11597 1
2.9%
11592 1
2.9%
11578 1
2.9%
11572 1
2.9%
11567 1
2.9%
11560 1
2.9%
11541 1
2.9%
11540 1
2.9%
11537 1
2.9%

기준일
Date

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
Minimum2011-01-31 00:00:00
Maximum2013-10-31 00:00:00
2024-01-10T07:34:06.518655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:06.615587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)

Interactions

2024-01-10T07:34:03.857521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:00.042237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:00.815192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:01.519160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:02.057072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:02.605244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:03.162713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:03.960087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:00.120348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:00.881783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:01.605950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:02.137061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:02.674878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:03.269042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:04.050096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:00.182273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:00.941705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:01.679932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:02.214945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:02.743336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:03.360193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:04.147094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:00.269967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:01.023620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:01.758191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:02.308985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:02.830971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:03.463763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:04.214633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:00.349329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:01.301575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:01.832881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:02.383838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:02.908805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:03.557102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:04.282586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:00.436107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:01.368742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:01.913580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:02.462575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:02.983647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:03.665120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:04.355593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:00.754080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:01.447591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:01.988241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:02.538813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:03.082659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:34:03.765559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:34:06.698717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도월합계승용차승합차화물차특수차이륜자동차기준일
년도1.0000.0000.9060.8950.6590.5510.7460.7681.000
월0.0001.0000.2470.0000.0000.0000.5320.0001.000
합계0.9060.2471.0000.9610.8160.9890.7960.8691.000
승용차0.8950.0000.9611.0000.8940.9940.7030.5471.000
승합차0.6590.0000.8160.8941.0000.6560.4660.4991.000
화물차0.5510.0000.9890.9940.6561.0000.7180.8931.000
특수차0.7460.5320.7960.7030.4660.7181.0000.0001.000
이륜자동차0.7680.0000.8690.5470.4990.8930.0001.0001.000
기준일1.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-01-10T07:34:06.799610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월합계승용차승합차화물차특수차이륜자동차년도
월1.0000.1840.228-0.0790.1440.2310.2520.000
합계0.1841.0000.949-0.5930.9930.7490.9370.765
승용차0.2280.9491.000-0.6750.9240.7370.9870.746
승합차-0.079-0.593-0.6751.000-0.576-0.248-0.6630.624
화물차0.1440.9930.924-0.5761.0000.7340.9090.576
특수차0.2310.7490.737-0.2480.7341.0000.7600.584
이륜자동차0.2520.9370.987-0.6630.9090.7601.0000.663
년도0.0000.7650.7460.6240.5760.5840.6631.000

Missing values

2024-01-10T07:34:04.444690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:34:04.554514image/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

년도월합계승용차승합차화물차특수차이륜자동차기준일
02013108451952073370116808315116222013.10.31
1201398424551868371716752316115922013.09.30
2201388409251716373316729317115972013.08.31
3201378382651494374116709310115722013.07.31
4201368347751209372816657305115782013.06.30
5201358318250993372616594302115672013.05.31
6201348293850832369516551300115602013.04.30
7201338255650530368716503296115402013.03.31
8201328227550300368316479281115322013.02.28
9201318209450154366316459281115372013.01.31
년도월합계승용차승합차화물차특수차이륜자동차기준일
24201110776224798037801618327794022011.10.31
2520119774844787537931613027294142011.09.30
2620118770054744837921608327294102011.08.31
2720117768614732238091606426993972011.07.31
2820116766184708638311603926593972011.06.30
2920115763814692438371602126993302011.05.31
3020114761204673638401595427093202011.04.30
3120113757654643738381592926592962011.03.31
3220112754274619438311586426192772011.02.28
3320111752054605438371579125292712011.01.31