Overview

Dataset statistics

Number of variables6
Number of observations136
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.2 KiB
Average record size in memory54.0 B

Variable types

Numeric5
Categorical1

Dataset

Description2012년부터 2019년까지 지역별 및 차종별 도로부문 온실가스 배출량을 표준화하여 등록 (단위:천톤CO2eq)
Author한국교통안전공단
URLhttps://www.data.go.kr/data/15061155/fileData.do

Alerts

승용 is highly overall correlated with 승합 and 3 other fieldsHigh correlation
승합 is highly overall correlated with 승용 and 3 other fieldsHigh correlation
화물 is highly overall correlated with 승용 and 3 other fieldsHigh correlation
특수 is highly overall correlated with 승용 and 3 other fieldsHigh correlation
구 분 is highly overall correlated with 승용 and 3 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 18:06:02.541232
Analysis finished2023-12-12 18:06:05.698402
Duration3.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

Distinct8
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.5
Minimum2012
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T03:06:05.770652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12013.75
median2015.5
Q32017.25
95-th percentile2019
Maximum2019
Range7
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.2997584
Coefficient of variation (CV)0.0011410362
Kurtosis-1.2393991
Mean2015.5
Median Absolute Deviation (MAD)2
Skewness0
Sum274108
Variance5.2888889
MonotonicityIncreasing
2023-12-13T03:06:05.896662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2012 17
12.5%
2013 17
12.5%
2014 17
12.5%
2015 17
12.5%
2016 17
12.5%
2017 17
12.5%
2018 17
12.5%
2019 17
12.5%
ValueCountFrequency (%)
2012 17
12.5%
2013 17
12.5%
2014 17
12.5%
2015 17
12.5%
2016 17
12.5%
2017 17
12.5%
2018 17
12.5%
2019 17
12.5%
ValueCountFrequency (%)
2019 17
12.5%
2018 17
12.5%
2017 17
12.5%
2016 17
12.5%
2015 17
12.5%
2014 17
12.5%
2013 17
12.5%
2012 17
12.5%

구 분
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
서 울
 
8
부 산
 
8
대 구
 
8
인 천
 
8
광 주
 
8
Other values (13)
96 

Length

Max length3
Median length3
Mean length2.9926471
Min length2

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row서 울
2nd row부 산
3rd row대 구
4th row인 천
5th row광 주

Common Values

ValueCountFrequency (%)
서 울 8
 
5.9%
부 산 8
 
5.9%
대 구 8
 
5.9%
인 천 8
 
5.9%
광 주 8
 
5.9%
대 전 8
 
5.9%
울 산 8
 
5.9%
경 기 8
 
5.9%
강 원 8
 
5.9%
충 북 8
 
5.9%
Other values (8) 56
41.2%

Length

2023-12-13T03:06:06.050105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경 24
 
8.9%
남 24
 
8.9%
북 24
 
8.9%
전 24
 
8.9%
산 16
 
5.9%
대 16
 
5.9%
충 16
 
5.9%
주 16
 
5.9%
울 16
 
5.9%
강 8
 
3.0%
Other values (12) 87
32.1%

승용
Real number (ℝ)

HIGH CORRELATION 

Distinct133
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2410.1397
Minimum77
Maximum10471
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T03:06:06.218882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum77
5-th percentile275
Q11131
median1531
Q32737.75
95-th percentile8497.25
Maximum10471
Range10394
Interquartile range (IQR)1606.75

Descriptive statistics

Standard deviation2295.5336
Coefficient of variation (CV)0.95244835
Kurtosis3.2259196
Mean2410.1397
Median Absolute Deviation (MAD)531
Skewness2.0200535
Sum327779
Variance5269474.5
MonotonicityNot monotonic
2023-12-13T03:06:06.414881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 2
 
1.5%
1358 2
 
1.5%
1110 2
 
1.5%
2835 1
 
0.7%
3529 1
 
0.7%
1278 1
 
0.7%
1329 1
 
0.7%
205 1
 
0.7%
8570 1
 
0.7%
7858 1
 
0.7%
Other values (123) 123
90.4%
ValueCountFrequency (%)
77 1
0.7%
85 1
0.7%
120 1
0.7%
174 1
0.7%
205 1
0.7%
214 1
0.7%
254 1
0.7%
282 1
0.7%
529 1
0.7%
580 1
0.7%
ValueCountFrequency (%)
10471 1
0.7%
9953 1
0.7%
9397 1
0.7%
8784 1
0.7%
8768 1
0.7%
8658 1
0.7%
8570 1
0.7%
8473 1
0.7%
7858 1
0.7%
7689 1
0.7%

승합
Real number (ℝ)

HIGH CORRELATION 

Distinct128
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean577.75
Minimum19
Maximum3017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T03:06:06.575692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile37.75
Q1237
median389
Q3574
95-th percentile2229
Maximum3017
Range2998
Interquartile range (IQR)337

Descriptive statistics

Standard deviation645.65438
Coefficient of variation (CV)1.1175325
Kurtosis5.869726
Mean577.75
Median Absolute Deviation (MAD)167
Skewness2.5145465
Sum78574
Variance416869.58
MonotonicityNot monotonic
2023-12-13T03:06:06.776243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
286 2
 
1.5%
189 2
 
1.5%
574 2
 
1.5%
389 2
 
1.5%
329 2
 
1.5%
237 2
 
1.5%
177 2
 
1.5%
479 2
 
1.5%
34 1
 
0.7%
266 1
 
0.7%
Other values (118) 118
86.8%
ValueCountFrequency (%)
19 1
0.7%
23 1
0.7%
28 1
0.7%
30 1
0.7%
31 1
0.7%
33 1
0.7%
34 1
0.7%
39 1
0.7%
131 1
0.7%
135 1
0.7%
ValueCountFrequency (%)
3017 1
0.7%
2980 1
0.7%
2811 1
0.7%
2782 1
0.7%
2777 1
0.7%
2738 1
0.7%
2736 1
0.7%
2060 1
0.7%
1790 1
0.7%
1704 1
0.7%

화물
Real number (ℝ)

HIGH CORRELATION 

Distinct133
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1692.6324
Minimum114
Maximum7898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T03:06:06.960771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum114
5-th percentile136.75
Q1866
median1491
Q31881.5
95-th percentile6258.5
Maximum7898
Range7784
Interquartile range (IQR)1015.5

Descriptive statistics

Standard deviation1502.8127
Coefficient of variation (CV)0.88785534
Kurtosis6.5310582
Mean1692.6324
Median Absolute Deviation (MAD)546
Skewness2.458885
Sum230198
Variance2258446
MonotonicityNot monotonic
2023-12-13T03:06:07.143823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2367 2
 
1.5%
1568 2
 
1.5%
999 2
 
1.5%
6200 1
 
0.7%
1566 1
 
0.7%
869 1
 
0.7%
847 1
 
0.7%
590 1
 
0.7%
115 1
 
0.7%
3302 1
 
0.7%
Other values (123) 123
90.4%
ValueCountFrequency (%)
114 1
0.7%
115 1
0.7%
124 1
0.7%
126 1
0.7%
128 1
0.7%
129 1
0.7%
133 1
0.7%
138 1
0.7%
283 1
0.7%
305 1
0.7%
ValueCountFrequency (%)
7898 1
0.7%
7392 1
0.7%
7302 1
0.7%
6665 1
0.7%
6609 1
0.7%
6489 1
0.7%
6434 1
0.7%
6200 1
0.7%
3550 1
0.7%
3453 1
0.7%

특수
Real number (ℝ)

HIGH CORRELATION 

Distinct113
Distinct (%)83.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean253.72794
Minimum4
Maximum1149
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T03:06:07.307524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile7
Q1100
median166
Q3363
95-th percentile915.75
Maximum1149
Range1145
Interquartile range (IQR)263

Descriptive statistics

Standard deviation246.02491
Coefficient of variation (CV)0.96964061
Kurtosis3.6401832
Mean253.72794
Median Absolute Deviation (MAD)93
Skewness1.856899
Sum34507
Variance60528.259
MonotonicityNot monotonic
2023-12-13T03:06:07.495790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 3
 
2.2%
363 2
 
1.5%
358 2
 
1.5%
12 2
 
1.5%
152 2
 
1.5%
352 2
 
1.5%
145 2
 
1.5%
429 2
 
1.5%
95 2
 
1.5%
364 2
 
1.5%
Other values (103) 115
84.6%
ValueCountFrequency (%)
4 2
1.5%
5 1
 
0.7%
6 2
1.5%
7 3
2.2%
8 1
 
0.7%
9 1
 
0.7%
10 1
 
0.7%
11 2
1.5%
12 2
1.5%
14 1
 
0.7%
ValueCountFrequency (%)
1149 1
0.7%
1092 1
0.7%
1090 1
0.7%
1035 1
0.7%
996 1
0.7%
976 1
0.7%
975 1
0.7%
896 1
0.7%
624 1
0.7%
572 1
0.7%

Interactions

2023-12-13T03:06:04.988343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:02.742801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:03.199131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:03.985415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:04.482281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:05.083933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:02.833184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:03.293634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:04.075454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:04.601929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:05.170401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:02.926986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:03.367179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:04.170803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:04.691065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:05.272766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:03.019037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:03.462257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:04.255477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:04.790446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:05.384050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:03.116361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:03.567378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:04.364849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:06:04.897641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:06:07.628112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도구 분승용승합화물특수
년도1.0000.0000.0000.0000.0000.000
구 분0.0001.0000.9140.9110.8900.913
승용0.0000.9141.0000.8590.9800.695
승합0.0000.9110.8591.0000.8530.622
화물0.0000.8900.9800.8531.0000.683
특수0.0000.9130.6950.6220.6831.000
2023-12-13T03:06:07.736468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도승용승합화물특수구 분
년도1.0000.1050.008-0.031-0.0620.000
승용0.1051.0000.9170.9370.6840.676
승합0.0080.9171.0000.9400.7720.687
화물-0.0310.9370.9401.0000.7880.622
특수-0.0620.6840.7720.7881.0000.550
구 분0.0000.6760.6870.6220.5501.000

Missing values

2023-12-13T03:06:05.509827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:06:05.655030image/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

년도구 분승용승합화물특수
02012서 울785816703302157
12012부 산27826242010996
22012대 구21073811597109
32012인 천21365321652395
42012광 주995184783118
52012대 전131524788877
62012울 산901161660175
72012세종77191144
82012경 기876827366489546
92012강 원108031878964
년도구 분승용승합화물특수
1262019세 종282331297
1272019경 기865820606434429
1282019강 원111026877461
1292019충 북12672931027176
1302019충 남15803891361145
1312019전 북13412791273120
1322019전 남16274301486357
1332019경 북18973961835312
1342019경 남26445561953364
1352019제 주154717238611