Overview

Dataset statistics

Number of variables17
Number of observations230
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.0 KiB
Average record size in memory151.6 B

Variable types

Categorical1
Text1
Numeric15

Dataset

Description국내에 등록되어 도로를 운행 중인 자동차의 용도별, 차종별, 시군구별 일평균 자동차 주행거리 현황입니다. 단위 : (km/대)
Author한국교통안전공단
URLhttps://www.data.go.kr/data/15088483/fileData.do

Alerts

전체_계 is highly overall correlated with 전체_승용차 and 3 other fieldsHigh correlation
전체_승용차 is highly overall correlated with 전체_계 and 3 other fieldsHigh correlation
전체_승합차 is highly overall correlated with 사업용_승합차High correlation
전체_화물차 is highly overall correlated with 비사업용_승용차 and 1 other fieldsHigh correlation
전체_특수차 is highly overall correlated with 전체_계 and 2 other fieldsHigh correlation
비사업용_계 is highly overall correlated with 전체_계 and 3 other fieldsHigh correlation
비사업용_승용차 is highly overall correlated with 전체_승용차 and 4 other fieldsHigh correlation
비사업용_승합차 is highly overall correlated with 전체_승용차 and 2 other fieldsHigh correlation
비사업용_화물차 is highly overall correlated with 전체_화물차 and 1 other fieldsHigh correlation
사업용_계 is highly overall correlated with 사업용_화물차High correlation
사업용_승합차 is highly overall correlated with 전체_승합차High correlation
사업용_화물차 is highly overall correlated with 전체_계 and 3 other fieldsHigh correlation
사업용_특수차 is highly overall correlated with 전체_특수차 and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 20:39:08.174524
Analysis finished2023-12-12 20:39:35.007084
Duration26.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

Distinct16
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
경기도
31 
서울특별시
25 
경상북도
23 
전라남도
22 
강원도
18 
Other values (11)
111 

Length

Max length7
Median length5
Mean length4.1347826
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 31
13.5%
서울특별시 25
10.9%
경상북도 23
10.0%
전라남도 22
9.6%
강원도 18
7.8%
경상남도 18
7.8%
부산광역시 16
7.0%
충청남도 16
7.0%
전라북도 14
 
6.1%
충청북도 12
 
5.2%
Other values (6) 35
15.2%

Length

2023-12-13T05:39:35.072529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 31
13.5%
서울특별시 25
10.9%
경상북도 23
10.0%
전라남도 22
9.6%
강원도 18
7.8%
경상남도 18
7.8%
부산광역시 16
7.0%
충청남도 16
7.0%
전라북도 14
 
6.1%
충청북도 12
 
5.2%
Other values (6) 35
15.2%
Distinct208
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-13T05:39:35.322076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0434783
Min length3

Characters and Unicode

Total characters700
Distinct characters133
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique201 ?
Unique (%)87.4%

Sample

1st row종로구
2nd row중 구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
25
 
9.8%
6
 
2.4%
6
 
2.4%
5
 
2.0%
4
 
1.6%
4
 
1.6%
고성군 2
 
0.8%
강서구 2
 
0.8%
정읍시 1
 
0.4%
화순군 1
 
0.4%
Other values (199) 199
78.0%
2023-12-13T05:39:35.680075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
87
 
12.4%
78
 
11.1%
74
 
10.6%
25
 
3.6%
22
 
3.1%
20
 
2.9%
18
 
2.6%
18
 
2.6%
17
 
2.4%
16
 
2.3%
Other values (123) 325
46.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 675
96.4%
Space Separator 25
 
3.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
87
 
12.9%
78
 
11.6%
74
 
11.0%
22
 
3.3%
20
 
3.0%
18
 
2.7%
18
 
2.7%
17
 
2.5%
16
 
2.4%
13
 
1.9%
Other values (122) 312
46.2%
Space Separator
ValueCountFrequency (%)
25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 675
96.4%
Common 25
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
87
 
12.9%
78
 
11.6%
74
 
11.0%
22
 
3.3%
20
 
3.0%
18
 
2.7%
18
 
2.7%
17
 
2.5%
16
 
2.4%
13
 
1.9%
Other values (122) 312
46.2%
Common
ValueCountFrequency (%)
25
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 675
96.4%
ASCII 25
 
3.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
87
 
12.9%
78
 
11.6%
74
 
11.0%
22
 
3.3%
20
 
3.0%
18
 
2.7%
18
 
2.7%
17
 
2.5%
16
 
2.4%
13
 
1.9%
Other values (122) 312
46.2%
ASCII
ValueCountFrequency (%)
25
100.0%

전체_계
Real number (ℝ)

HIGH CORRELATION 

Distinct105
Distinct (%)45.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.343043
Minimum0
Maximum77.5
Zeros2
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:39:35.799592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.545
Q136.3
median38
Q339.975
95-th percentile45.575
Maximum77.5
Range77.5
Interquartile range (IQR)3.675

Descriptive statistics

Standard deviation5.8527804
Coefficient of variation (CV)0.15264256
Kurtosis24.120949
Mean38.343043
Median Absolute Deviation (MAD)1.8
Skewness-0.73626544
Sum8818.9
Variance34.255039
MonotonicityNot monotonic
2023-12-13T05:39:35.931299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.2 9
 
3.9%
38.0 7
 
3.0%
37.7 7
 
3.0%
38.2 6
 
2.6%
36.3 6
 
2.6%
37.5 6
 
2.6%
38.4 6
 
2.6%
39.3 5
 
2.2%
37.0 5
 
2.2%
37.4 4
 
1.7%
Other values (95) 169
73.5%
ValueCountFrequency (%)
0.0 2
0.9%
25.2 1
0.4%
26.8 1
0.4%
31.4 1
0.4%
31.7 1
0.4%
32.1 1
0.4%
32.7 1
0.4%
32.9 2
0.9%
33.0 1
0.4%
33.5 1
0.4%
ValueCountFrequency (%)
77.5 1
0.4%
57.8 1
0.4%
55.0 1
0.4%
51.8 1
0.4%
51.4 1
0.4%
50.9 1
0.4%
49.8 1
0.4%
49.1 1
0.4%
48.2 1
0.4%
47.9 1
0.4%

전체_승용차
Real number (ℝ)

HIGH CORRELATION 

Distinct102
Distinct (%)44.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.623913
Minimum0
Maximum56.6
Zeros2
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:39:36.050436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30.845
Q133.7
median35.6
Q338
95-th percentile40
Maximum56.6
Range56.6
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation4.9311586
Coefficient of variation (CV)0.13842271
Kurtosis24.613641
Mean35.623913
Median Absolute Deviation (MAD)2.25
Skewness-2.5391619
Sum8193.5
Variance24.316325
MonotonicityNot monotonic
2023-12-13T05:39:36.166332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.8 7
 
3.0%
33.2 6
 
2.6%
35.6 6
 
2.6%
38.6 6
 
2.6%
33.7 5
 
2.2%
37.9 5
 
2.2%
36.2 5
 
2.2%
35.7 5
 
2.2%
34.1 5
 
2.2%
34.6 4
 
1.7%
Other values (92) 176
76.5%
ValueCountFrequency (%)
0.0 2
0.9%
27.7 1
0.4%
29.3 1
0.4%
29.5 1
0.4%
29.6 1
0.4%
29.7 1
0.4%
29.8 1
0.4%
30.1 1
0.4%
30.5 1
0.4%
30.7 1
0.4%
ValueCountFrequency (%)
56.6 1
0.4%
52.3 1
0.4%
51.8 1
0.4%
49.6 1
0.4%
46.4 1
0.4%
44.3 1
0.4%
40.9 1
0.4%
40.7 1
0.4%
40.6 1
0.4%
40.5 1
0.4%

전체_승합차
Real number (ℝ)

HIGH CORRELATION 

Distinct165
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.553043
Minimum0
Maximum239.9
Zeros2
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:39:36.280848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41.445
Q148.6
median52.6
Q361
95-th percentile79.63
Maximum239.9
Range239.9
Interquartile range (IQR)12.4

Descriptive statistics

Standard deviation18.462751
Coefficient of variation (CV)0.32646785
Kurtosis43.466015
Mean56.553043
Median Absolute Deviation (MAD)5.3
Skewness4.6448264
Sum13007.2
Variance340.87316
MonotonicityNot monotonic
2023-12-13T05:39:36.396721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.5 5
 
2.2%
48.0 4
 
1.7%
49.7 3
 
1.3%
49.6 3
 
1.3%
55.2 3
 
1.3%
51.6 3
 
1.3%
46.3 3
 
1.3%
50.8 3
 
1.3%
56.1 3
 
1.3%
50.4 3
 
1.3%
Other values (155) 197
85.7%
ValueCountFrequency (%)
0.0 2
0.9%
26.9 1
0.4%
28.5 1
0.4%
33.8 1
0.4%
34.9 1
0.4%
36.3 1
0.4%
37.5 1
0.4%
38.3 1
0.4%
38.6 1
0.4%
40.1 1
0.4%
ValueCountFrequency (%)
239.9 1
0.4%
130.0 1
0.4%
110.8 1
0.4%
102.1 1
0.4%
94.5 1
0.4%
93.3 1
0.4%
86.9 1
0.4%
85.1 1
0.4%
85.0 1
0.4%
82.5 1
0.4%

전체_화물차
Real number (ℝ)

HIGH CORRELATION 

Distinct169
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.098696
Minimum0
Maximum101.4
Zeros2
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:39:36.796288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31.69
Q136.125
median46.45
Q354.075
95-th percentile65.765
Maximum101.4
Range101.4
Interquartile range (IQR)17.95

Descriptive statistics

Standard deviation12.311192
Coefficient of variation (CV)0.26706162
Kurtosis2.4270173
Mean46.098696
Median Absolute Deviation (MAD)8.95
Skewness0.2269119
Sum10602.7
Variance151.56546
MonotonicityNot monotonic
2023-12-13T05:39:36.927381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.8 4
 
1.7%
48.7 3
 
1.3%
43.0 3
 
1.3%
38.9 3
 
1.3%
55.6 3
 
1.3%
34.9 3
 
1.3%
52.1 3
 
1.3%
33.7 3
 
1.3%
34.7 3
 
1.3%
59.4 3
 
1.3%
Other values (159) 199
86.5%
ValueCountFrequency (%)
0.0 2
0.9%
17.7 1
0.4%
20.4 1
0.4%
28.9 1
0.4%
30.1 1
0.4%
30.5 1
0.4%
30.7 1
0.4%
30.8 1
0.4%
30.9 2
0.9%
31.6 1
0.4%
ValueCountFrequency (%)
101.4 1
0.4%
81.6 1
0.4%
77.4 1
0.4%
77.1 1
0.4%
71.8 1
0.4%
71.1 1
0.4%
69.4 1
0.4%
69.2 1
0.4%
69.0 1
0.4%
68.1 1
0.4%

전체_특수차
Real number (ℝ)

HIGH CORRELATION 

Distinct218
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.430435
Minimum0
Maximum287
Zeros2
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:39:37.066811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28.405
Q148.45
median81.2
Q3123.525
95-th percentile184.75
Maximum287
Range287
Interquartile range (IQR)75.075

Descriptive statistics

Standard deviation52.210308
Coefficient of variation (CV)0.57103861
Kurtosis0.57503779
Mean91.430435
Median Absolute Deviation (MAD)35.65
Skewness0.86373035
Sum21029
Variance2725.9163
MonotonicityNot monotonic
2023-12-13T05:39:37.294338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56.6 2
 
0.9%
0.0 2
 
0.9%
123.0 2
 
0.9%
70.3 2
 
0.9%
95.8 2
 
0.9%
40.1 2
 
0.9%
66.6 2
 
0.9%
80.1 2
 
0.9%
41.3 2
 
0.9%
39.6 2
 
0.9%
Other values (208) 210
91.3%
ValueCountFrequency (%)
0.0 2
0.9%
8.2 1
0.4%
19.0 1
0.4%
20.4 1
0.4%
22.1 1
0.4%
23.9 1
0.4%
24.9 1
0.4%
25.1 1
0.4%
25.2 1
0.4%
25.8 1
0.4%
ValueCountFrequency (%)
287.0 1
0.4%
257.6 1
0.4%
244.8 1
0.4%
235.8 1
0.4%
223.5 1
0.4%
205.8 1
0.4%
202.4 1
0.4%
197.1 1
0.4%
193.2 1
0.4%
190.2 1
0.4%

비사업용_계
Real number (ℝ)

HIGH CORRELATION 

Distinct84
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.31
Minimum0
Maximum39.6
Zeros2
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:39:37.475353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30.4
Q133.025
median34.9
Q336.475
95-th percentile37.755
Maximum39.6
Range39.6
Interquartile range (IQR)3.45

Descriptive statistics

Standard deviation4.0174331
Coefficient of variation (CV)0.11709219
Kurtosis45.15695
Mean34.31
Median Absolute Deviation (MAD)1.7
Skewness-5.5862944
Sum7891.3
Variance16.139769
MonotonicityNot monotonic
2023-12-13T05:39:37.704725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.8 7
 
3.0%
34.4 7
 
3.0%
35.6 7
 
3.0%
35.3 6
 
2.6%
36.5 6
 
2.6%
36.6 6
 
2.6%
34.6 5
 
2.2%
34.5 5
 
2.2%
33.1 5
 
2.2%
36.0 5
 
2.2%
Other values (74) 171
74.3%
ValueCountFrequency (%)
0.0 2
0.9%
23.8 1
 
0.4%
26.3 1
 
0.4%
29.3 1
 
0.4%
29.7 3
1.3%
29.9 1
 
0.4%
30.2 1
 
0.4%
30.3 1
 
0.4%
30.4 4
1.7%
30.5 2
0.9%
ValueCountFrequency (%)
39.6 1
 
0.4%
38.6 2
 
0.9%
38.2 1
 
0.4%
38.1 4
1.7%
38.0 2
 
0.9%
37.9 1
 
0.4%
37.8 1
 
0.4%
37.7 3
1.3%
37.6 3
1.3%
37.5 5
2.2%

비사업용_승용차
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)46.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.216522
Minimum0
Maximum40.7
Zeros2
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:39:37.892983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.1
Q131.925
median34.7
Q337.3
95-th percentile39.055
Maximum40.7
Range40.7
Interquartile range (IQR)5.375

Descriptive statistics

Standard deviation4.5264758
Coefficient of variation (CV)0.13228919
Kurtosis26.739015
Mean34.216522
Median Absolute Deviation (MAD)2.7
Skewness-3.7806344
Sum7869.8
Variance20.488983
MonotonicityNot monotonic
2023-12-13T05:39:38.078549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.3 7
 
3.0%
33.0 6
 
2.6%
37.6 5
 
2.2%
35.4 5
 
2.2%
30.5 5
 
2.2%
38.6 5
 
2.2%
37.2 4
 
1.7%
37.1 4
 
1.7%
35.9 4
 
1.7%
36.3 4
 
1.7%
Other values (96) 181
78.7%
ValueCountFrequency (%)
0.0 2
0.9%
26.3 1
0.4%
28.1 1
0.4%
28.4 1
0.4%
28.5 1
0.4%
28.8 2
0.9%
28.9 1
0.4%
29.0 2
0.9%
29.1 2
0.9%
29.2 2
0.9%
ValueCountFrequency (%)
40.7 1
 
0.4%
40.3 1
 
0.4%
39.8 3
1.3%
39.7 1
 
0.4%
39.5 2
0.9%
39.4 1
 
0.4%
39.1 3
1.3%
39.0 1
 
0.4%
38.9 2
0.9%
38.8 1
 
0.4%

비사업용_승합차
Real number (ℝ)

HIGH CORRELATION 

Distinct114
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.41087
Minimum0
Maximum46.9
Zeros2
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:39:38.264090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32.825
Q136.625
median39
Q341.1
95-th percentile43.655
Maximum46.9
Range46.9
Interquartile range (IQR)4.475

Descriptive statistics

Standard deviation5.0375459
Coefficient of variation (CV)0.13114897
Kurtosis28.730966
Mean38.41087
Median Absolute Deviation (MAD)2.2
Skewness-4.1644808
Sum8834.5
Variance25.376868
MonotonicityNot monotonic
2023-12-13T05:39:38.427077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.9 5
 
2.2%
37.9 5
 
2.2%
38.6 5
 
2.2%
38.0 5
 
2.2%
37.5 5
 
2.2%
40.7 5
 
2.2%
41.5 4
 
1.7%
39.5 4
 
1.7%
39.3 4
 
1.7%
39.0 4
 
1.7%
Other values (104) 184
80.0%
ValueCountFrequency (%)
0.0 2
0.9%
20.4 1
0.4%
24.7 1
0.4%
28.5 1
0.4%
29.6 1
0.4%
30.3 1
0.4%
31.5 1
0.4%
31.9 2
0.9%
32.3 1
0.4%
32.6 1
0.4%
ValueCountFrequency (%)
46.9 1
 
0.4%
45.8 1
 
0.4%
45.3 1
 
0.4%
45.1 1
 
0.4%
44.7 1
 
0.4%
44.4 1
 
0.4%
44.3 1
 
0.4%
44.2 3
1.3%
43.8 1
 
0.4%
43.7 1
 
0.4%

비사업용_화물차
Real number (ℝ)

HIGH CORRELATION 

Distinct139
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.007826
Minimum0
Maximum53
Zeros2
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:39:38.658526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.645
Q132.925
median36.85
Q341.75
95-th percentile45.3
Maximum53
Range53
Interquartile range (IQR)8.825

Descriptive statistics

Standard deviation6.4338305
Coefficient of variation (CV)0.17385054
Kurtosis8.3343882
Mean37.007826
Median Absolute Deviation (MAD)4.35
Skewness-1.616286
Sum8511.8
Variance41.394174
MonotonicityNot monotonic
2023-12-13T05:39:38.838701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.0 5
 
2.2%
33.3 5
 
2.2%
41.4 4
 
1.7%
33.6 4
 
1.7%
32.6 4
 
1.7%
31.6 3
 
1.3%
30.8 3
 
1.3%
41.9 3
 
1.3%
31.0 3
 
1.3%
40.3 3
 
1.3%
Other values (129) 193
83.9%
ValueCountFrequency (%)
0.0 2
0.9%
17.5 1
0.4%
19.9 1
0.4%
27.0 1
0.4%
28.0 1
0.4%
29.1 1
0.4%
29.3 1
0.4%
29.4 2
0.9%
29.5 1
0.4%
29.6 1
0.4%
ValueCountFrequency (%)
53.0 1
0.4%
50.0 1
0.4%
49.8 1
0.4%
47.0 1
0.4%
46.9 1
0.4%
46.7 2
0.9%
46.0 1
0.4%
45.7 1
0.4%
45.6 1
0.4%
45.5 1
0.4%

비사업용_특수차
Real number (ℝ)

Distinct163
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.673478
Minimum0
Maximum88.6
Zeros2
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:39:39.005236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.57
Q128.125
median33
Q339.25
95-th percentile51.055
Maximum88.6
Range88.6
Interquartile range (IQR)11.125

Descriptive statistics

Standard deviation10.990597
Coefficient of variation (CV)0.32638734
Kurtosis4.8569088
Mean33.673478
Median Absolute Deviation (MAD)5.65
Skewness0.88140736
Sum7744.9
Variance120.79322
MonotonicityNot monotonic
2023-12-13T05:39:39.161368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.4 4
 
1.7%
29.1 4
 
1.7%
34.4 4
 
1.7%
29.9 4
 
1.7%
25.9 3
 
1.3%
32.0 3
 
1.3%
27.2 3
 
1.3%
35.5 3
 
1.3%
24.0 3
 
1.3%
41.9 3
 
1.3%
Other values (153) 196
85.2%
ValueCountFrequency (%)
0.0 2
0.9%
8.2 1
0.4%
10.4 1
0.4%
11.2 1
0.4%
11.5 1
0.4%
12.0 1
0.4%
15.4 1
0.4%
15.5 1
0.4%
15.6 1
0.4%
15.9 1
0.4%
ValueCountFrequency (%)
88.6 1
0.4%
87.5 1
0.4%
63.3 1
0.4%
59.7 1
0.4%
53.8 1
0.4%
53.5 2
0.9%
52.8 1
0.4%
52.6 1
0.4%
52.2 1
0.4%
51.7 1
0.4%

사업용_계
Real number (ℝ)

HIGH CORRELATION 

Distinct207
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.47304
Minimum0
Maximum358.3
Zeros2
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:39:39.293393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile66.84
Q1103.425
median121.7
Q3139.55
95-th percentile180.14
Maximum358.3
Range358.3
Interquartile range (IQR)36.125

Descriptive statistics

Standard deviation36.404019
Coefficient of variation (CV)0.29724108
Kurtosis7.8227873
Mean122.47304
Median Absolute Deviation (MAD)18.1
Skewness0.95149105
Sum28168.8
Variance1325.2526
MonotonicityNot monotonic
2023-12-13T05:39:39.445439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121.3 2
 
0.9%
122.8 2
 
0.9%
139.4 2
 
0.9%
152.8 2
 
0.9%
124.7 2
 
0.9%
123.0 2
 
0.9%
112.2 2
 
0.9%
84.4 2
 
0.9%
134.2 2
 
0.9%
88.8 2
 
0.9%
Other values (197) 210
91.3%
ValueCountFrequency (%)
0.0 2
0.9%
29.9 1
0.4%
41.7 1
0.4%
46.7 1
0.4%
59.5 1
0.4%
62.6 1
0.4%
63.1 1
0.4%
63.6 1
0.4%
64.7 1
0.4%
65.3 1
0.4%
ValueCountFrequency (%)
358.3 1
0.4%
214.4 1
0.4%
208.6 1
0.4%
200.6 1
0.4%
198.3 1
0.4%
189.3 1
0.4%
187.9 1
0.4%
187.2 1
0.4%
187.1 1
0.4%
181.6 1
0.4%

사업용_승용차
Real number (ℝ)

Distinct178
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.796087
Minimum0
Maximum109.1
Zeros2
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:39:39.635946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile59.4
Q172.6
median82.65
Q392.225
95-th percentile103.42
Maximum109.1
Range109.1
Interquartile range (IQR)19.625

Descriptive statistics

Standard deviation15.42535
Coefficient of variation (CV)0.18858298
Kurtosis5.4758647
Mean81.796087
Median Absolute Deviation (MAD)10.05
Skewness-1.3386428
Sum18813.1
Variance237.94143
MonotonicityNot monotonic
2023-12-13T05:39:39.813815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.3 4
 
1.7%
79.2 3
 
1.3%
80.7 3
 
1.3%
74.7 3
 
1.3%
60.0 3
 
1.3%
91.3 3
 
1.3%
64.9 3
 
1.3%
80.0 3
 
1.3%
78.3 2
 
0.9%
93.5 2
 
0.9%
Other values (168) 201
87.4%
ValueCountFrequency (%)
0.0 2
0.9%
43.0 1
0.4%
50.2 1
0.4%
52.3 1
0.4%
56.1 1
0.4%
57.2 1
0.4%
58.8 1
0.4%
59.0 2
0.9%
59.3 1
0.4%
59.4 2
0.9%
ValueCountFrequency (%)
109.1 1
0.4%
108.2 1
0.4%
108.1 1
0.4%
107.6 1
0.4%
106.7 1
0.4%
106.6 1
0.4%
105.7 1
0.4%
105.1 2
0.9%
104.4 1
0.4%
103.9 1
0.4%

사업용_승합차
Real number (ℝ)

HIGH CORRELATION 

Distinct216
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176.79391
Minimum0
Maximum500
Zeros2
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:39:40.030184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile90.405
Q1143.7
median169.55
Q3201.275
95-th percentile286.295
Maximum500
Range500
Interquartile range (IQR)57.575

Descriptive statistics

Standard deviation60.938082
Coefficient of variation (CV)0.34468428
Kurtosis4.0044634
Mean176.79391
Median Absolute Deviation (MAD)28.65
Skewness1.0275223
Sum40662.6
Variance3713.4498
MonotonicityNot monotonic
2023-12-13T05:39:40.189856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178.1 2
 
0.9%
0.0 2
 
0.9%
207.9 2
 
0.9%
141.1 2
 
0.9%
140.4 2
 
0.9%
121.1 2
 
0.9%
164.4 2
 
0.9%
95.6 2
 
0.9%
184.3 2
 
0.9%
193.1 2
 
0.9%
Other values (206) 210
91.3%
ValueCountFrequency (%)
0.0 2
0.9%
40.4 1
0.4%
50.9 1
0.4%
67.9 1
0.4%
74.1 1
0.4%
79.4 1
0.4%
87.1 1
0.4%
87.9 1
0.4%
88.0 1
0.4%
89.1 1
0.4%
ValueCountFrequency (%)
500.0 1
0.4%
365.6 1
0.4%
360.2 1
0.4%
347.5 1
0.4%
332.5 1
0.4%
327.3 1
0.4%
323.5 1
0.4%
313.9 1
0.4%
307.8 1
0.4%
300.8 1
0.4%

사업용_화물차
Real number (ℝ)

HIGH CORRELATION 

Distinct203
Distinct (%)88.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.28478
Minimum0
Maximum208
Zeros2
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:39:40.357524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75.94
Q1113.075
median134.15
Q3155.15
95-th percentile180.575
Maximum208
Range208
Interquartile range (IQR)42.075

Descriptive statistics

Standard deviation34.468487
Coefficient of variation (CV)0.26056275
Kurtosis1.7643684
Mean132.28478
Median Absolute Deviation (MAD)21.25
Skewness-0.86746305
Sum30425.5
Variance1188.0766
MonotonicityNot monotonic
2023-12-13T05:39:40.553363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95.6 2
 
0.9%
132.5 2
 
0.9%
120.4 2
 
0.9%
165.6 2
 
0.9%
135.4 2
 
0.9%
132.9 2
 
0.9%
169.3 2
 
0.9%
145.4 2
 
0.9%
122.3 2
 
0.9%
158.2 2
 
0.9%
Other values (193) 210
91.3%
ValueCountFrequency (%)
0.0 2
0.9%
21.2 1
0.4%
28.1 1
0.4%
30.2 1
0.4%
45.1 1
0.4%
55.1 1
0.4%
59.7 1
0.4%
61.3 1
0.4%
65.5 1
0.4%
72.2 1
0.4%
ValueCountFrequency (%)
208.0 1
0.4%
206.8 1
0.4%
204.9 1
0.4%
192.7 1
0.4%
192.1 1
0.4%
190.8 1
0.4%
186.0 1
0.4%
184.5 1
0.4%
182.6 1
0.4%
182.3 1
0.4%

사업용_특수차
Real number (ℝ)

HIGH CORRELATION 

Distinct216
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.06913
Minimum0
Maximum316.8
Zeros2
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:39:40.739737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31.075
Q170.35
median129.1
Q3179.35
95-th percentile229
Maximum316.8
Range316.8
Interquartile range (IQR)109

Descriptive statistics

Standard deviation65.460246
Coefficient of variation (CV)0.51113212
Kurtosis-0.72207246
Mean128.06913
Median Absolute Deviation (MAD)56
Skewness0.18210516
Sum29455.9
Variance4285.0438
MonotonicityNot monotonic
2023-12-13T05:39:40.918679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.3 2
 
0.9%
185.7 2
 
0.9%
192.8 2
 
0.9%
212.5 2
 
0.9%
51.0 2
 
0.9%
73.1 2
 
0.9%
155.7 2
 
0.9%
204.9 2
 
0.9%
125.0 2
 
0.9%
0.0 2
 
0.9%
Other values (206) 210
91.3%
ValueCountFrequency (%)
0.0 2
0.9%
8.7 1
0.4%
10.1 1
0.4%
18.6 1
0.4%
19.7 1
0.4%
23.7 1
0.4%
25.1 1
0.4%
26.2 1
0.4%
28.7 1
0.4%
29.9 1
0.4%
ValueCountFrequency (%)
316.8 1
0.4%
296.3 1
0.4%
266.1 1
0.4%
256.4 1
0.4%
254.4 1
0.4%
248.8 1
0.4%
244.0 1
0.4%
243.2 1
0.4%
239.8 1
0.4%
232.5 1
0.4%

Interactions

2023-12-13T05:39:33.064178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:09.427453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:11.280288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:12.836931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:14.315199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:15.833871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:17.479528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:19.040879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:20.473352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:21.806294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:23.924200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:25.697905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:27.479943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:29.084715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:31.308435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:33.198272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:09.575815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:11.400377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:12.946454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:14.454192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:15.957572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:17.583831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:19.141908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:20.565693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:22.305064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:24.049031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:25.834813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:27.594777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:29.200670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:31.445052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:33.322817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:09.695379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:11.519140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:13.041271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:14.569873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:16.044699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:17.685761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:19.235996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:20.665256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:22.477124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:24.139690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:25.953979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:27.692498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:29.296808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:31.567989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:33.445584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:09.827442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:11.632199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:13.141572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:14.660223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:16.454577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:17.780106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:19.323900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:20.769132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:22.567250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:24.244392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:26.067381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:27.793382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:29.418288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:31.675030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:33.570411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:09.950141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:11.731113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:13.230885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:14.742060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:16.529377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:17.879743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:19.413263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:20.860749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:22.652545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:24.339964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:26.187273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:27.876264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:29.537508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:31.770449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:33.713404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:10.064859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:11.832628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:13.319069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:14.849719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:16.604986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:17.982209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:19.513670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:20.942343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:22.758891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:24.448451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:26.324457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:27.977570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:29.657520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:31.880451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:33.856130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:10.206559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:11.946180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:13.426723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:14.964465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:16.692717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:18.094337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:19.621183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:21.024318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:22.877325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:24.609883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:26.444547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:28.069788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:30.174733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:32.025589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:34.003509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:10.328349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:12.057815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:13.527985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:15.086394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:16.787333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:18.199835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:19.732354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:21.127643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:22.983053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:24.736877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:26.576874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:28.188869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:30.305545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:32.154381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:34.098179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:10.442103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:12.143189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:13.614473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:15.171427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:16.863767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:18.286869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:19.825253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:21.220557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:23.088420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:24.845351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:26.695436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:28.289673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:30.439111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:32.266016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:34.186266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:10.574965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:12.237823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:13.697474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:15.262808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:16.953723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:18.397735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:19.927306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:21.300284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:23.193012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:24.993076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:26.823192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:28.410302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:30.556949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:32.395790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:34.286799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:10.678151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:12.348666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:13.791428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:15.356106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:17.050096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:18.493817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:20.011225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:21.375852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:23.309541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:25.125667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:26.920724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:28.507134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:30.684476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:32.506221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:34.383423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:10.793995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:12.469178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:13.905176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:15.465967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:17.142508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:18.594683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:20.101110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:21.465863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:23.444015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:25.246586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:27.054519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:28.638326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:30.828879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:32.638708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:34.464015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:10.895253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:12.566743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:14.006571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:15.558506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:17.222529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:18.716431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:20.200427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:21.555926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:23.563985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:25.355905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:27.150587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:28.739965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:30.947769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:32.751211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:34.536854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:11.015377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:12.651909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:14.108203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:15.641810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:17.303972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:18.829070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:20.292185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:21.637864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:23.696040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:25.470925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:27.266797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:28.875821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:31.058667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:32.855771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:34.611392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:11.148115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:12.743507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:14.207859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:15.749310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:17.387130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:18.935752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:20.380211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:21.715417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:23.824069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:25.579835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:27.367306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:28.978333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:31.170951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:39:32.956733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:39:41.035767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도전체_계전체_승용차전체_승합차전체_화물차전체_특수차비사업용_계비사업용_승용차비사업용_승합차비사업용_화물차비사업용_특수차사업용_계사업용_승용차사업용_승합차사업용_화물차사업용_특수차
시도1.0000.3300.5950.0000.4660.4190.6470.6470.5630.5980.2790.4070.3050.4430.4200.549
전체_계0.3301.0000.9170.8370.8930.5770.8400.7040.9260.9210.6050.8660.7640.6570.7250.406
전체_승용차0.5950.9171.0000.8020.8050.2860.8030.8020.9370.8870.6190.8320.7990.6950.7270.378
전체_승합차0.0000.8370.8021.0000.6450.2050.6620.6560.8150.8580.5260.9020.6240.8590.6430.254
전체_화물차0.4660.8930.8050.6451.0000.6850.9340.9190.8350.8360.6390.6240.8110.6100.8310.441
전체_특수차0.4190.5770.2860.2050.6851.0000.5510.4360.3720.3760.4510.4700.3580.3510.6560.912
비사업용_계0.6470.8400.8030.6620.9340.5511.0000.9500.8700.8340.6740.6300.8070.6570.8850.499
비사업용_승용차0.6470.7040.8020.6560.9190.4360.9501.0000.8160.7450.6680.6080.7430.6350.8610.497
비사업용_승합차0.5630.9260.9370.8150.8350.3720.8700.8161.0000.9260.6760.7860.8330.6570.7370.316
비사업용_화물차0.5980.9210.8870.8580.8360.3760.8340.7450.9261.0000.6120.7810.7630.6390.7030.334
비사업용_특수차0.2790.6050.6190.5260.6390.4510.6740.6680.6760.6121.0000.4470.6430.7640.6010.352
사업용_계0.4070.8660.8320.9020.6240.4700.6300.6080.7860.7810.4471.0000.6190.7890.7000.503
사업용_승용차0.3050.7640.7990.6240.8110.3580.8070.7430.8330.7630.6430.6191.0000.6380.7080.345
사업용_승합차0.4430.6570.6950.8590.6100.3510.6570.6350.6570.6390.7640.7890.6381.0000.5680.375
사업용_화물차0.4200.7250.7270.6430.8310.6560.8850.8610.7370.7030.6010.7000.7080.5681.0000.635
사업용_특수차0.5490.4060.3780.2540.4410.9120.4990.4970.3160.3340.3520.5030.3450.3750.6351.000
2023-12-13T05:39:41.568757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전체_계전체_승용차전체_승합차전체_화물차전체_특수차비사업용_계비사업용_승용차비사업용_승합차비사업용_화물차비사업용_특수차사업용_계사업용_승용차사업용_승합차사업용_화물차사업용_특수차시도
전체_계1.0000.5880.4420.2740.5020.6540.4730.4820.1130.1800.497-0.1940.1490.5700.4850.153
전체_승용차0.5881.0000.105-0.4680.0720.8160.9060.758-0.478-0.0890.217-0.099-0.0330.2000.2270.316
전체_승합차0.4420.1051.0000.2710.2010.2190.0730.1760.2670.2250.306-0.0510.6450.1550.1170.000
전체_화물차0.274-0.4680.2711.0000.352-0.197-0.502-0.2980.8640.3140.123-0.1220.0910.3690.1590.199
전체_특수차0.5020.0720.2010.3521.0000.1270.0470.0220.1530.3420.461-0.0250.0790.5470.9170.175
비사업용_계0.6540.8160.219-0.1970.1271.0000.8930.838-0.1630.0960.381-0.1110.1630.2780.2210.389
비사업용_승용차0.4730.9060.073-0.5020.0470.8931.0000.846-0.521-0.0710.3750.0010.0920.2160.2210.388
비사업용_승합차0.4820.7580.176-0.2980.0220.8380.8461.000-0.2990.0060.328-0.0440.1250.2140.1510.292
비사업용_화물차0.113-0.4780.2670.8640.153-0.163-0.521-0.2991.0000.372-0.128-0.1440.1450.076-0.0420.318
비사업용_특수차0.180-0.0890.2250.3140.3420.096-0.0710.0060.3721.0000.059-0.0180.1850.1940.1590.115
사업용_계0.4970.2170.3060.1230.4610.3810.3750.328-0.1280.0591.0000.3350.4320.6330.4760.194
사업용_승용차-0.194-0.099-0.051-0.122-0.025-0.1110.001-0.044-0.144-0.0180.3351.0000.217-0.101-0.0510.108
사업용_승합차0.149-0.0330.6450.0910.0790.1630.0920.1250.1450.1850.4320.2171.0000.0480.0400.196
사업용_화물차0.5700.2000.1550.3690.5470.2780.2160.2140.0760.1940.633-0.1010.0481.0000.5490.175
사업용_특수차0.4850.2270.1170.1590.9170.2210.2210.151-0.0420.1590.476-0.0510.0400.5491.0000.246
시도0.1530.3160.0000.1990.1750.3890.3880.2920.3180.1150.1940.1080.1960.1750.2461.000

Missing values

2023-12-13T05:39:34.740125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:39:34.932172image/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

시도시군구전체_계전체_승용차전체_승합차전체_화물차전체_특수차비사업용_계비사업용_승용차비사업용_승합차비사업용_화물차비사업용_특수차사업용_계사업용_승용차사업용_승합차사업용_화물차사업용_특수차
0서울특별시종로구32.729.641.546.673.729.729.028.537.111.5121.379.2193.5113.6215.4
1서울특별시중 구37.932.646.959.4155.732.430.040.743.823.081.259.0100.4114.5256.4
2서울특별시용산구32.129.836.350.125.830.229.033.141.420.991.878.2137.0101.981.2
3서울특별시성동구37.533.076.955.642.031.029.134.245.533.396.977.8158.1121.448.4
4서울특별시광진구34.831.849.249.852.931.730.136.341.434.494.078.5155.2111.856.1
5서울특별시동대문구34.130.938.352.142.130.528.934.840.533.4100.691.0143.4112.948.5
6서울특별시중랑구37.133.352.454.243.032.130.136.543.335.2103.496.6175.7105.645.6
7서울특별시성북구33.029.762.849.134.129.328.133.840.132.8115.898.0182.5102.935.1
8서울특별시강북구39.931.361.277.136.030.328.833.740.034.7135.693.7193.0153.136.7
9서울특별시도봉구35.332.251.751.938.330.729.135.541.831.9105.4100.3176.6102.841.3
시도시군구전체_계전체_승용차전체_승합차전체_화물차전체_특수차비사업용_계비사업용_승용차비사업용_승합차비사업용_화물차비사업용_특수차사업용_계사업용_승용차사업용_승합차사업용_화물차사업용_특수차
220경상남도창녕군37.037.747.133.666.635.737.239.031.633.291.665.1168.8104.1125.0
221경상남도고성군37.737.947.335.176.536.237.239.433.145.5102.478.7140.9129.8107.0
222경상남도남해군36.337.070.430.541.834.336.340.829.429.0129.888.3243.386.765.6
223경상남도하동군37.439.148.432.0149.136.138.742.930.636.6131.997.4189.1108.1218.8
224경상남도산청군41.540.050.740.3171.037.739.744.433.727.2181.6100.1148.5186.0229.0
225경상남도함양군37.238.163.532.965.135.637.641.331.632.1119.774.7290.197.8125.0
226경상남도거창군35.534.164.232.7140.833.133.838.031.032.0163.492.3251.8120.4189.9
227경상남도합천군37.037.549.233.9125.435.337.238.031.535.5121.394.4174.6100.8209.5
228제주특별자치도제주시45.346.459.234.940.431.030.532.632.626.964.765.087.945.152.2
229제주특별자치도서귀포시32.933.242.130.744.431.732.433.529.729.1101.793.7203.586.768.6