Overview

Dataset statistics

Number of variables16
Number of observations2332
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory319.0 KiB
Average record size in memory140.1 B

Variable types

Categorical3
Text1
Numeric12

Dataset

Description부산광역시_법정동별연료별차종별_자동차등록대수_20240229
Author부산광역시
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15063789

Alerts

시도 has constant value ""Constant
(관용)승합 is highly overall correlated with (관용)화물 and 2 other fieldsHigh correlation
(관용)화물 is highly overall correlated with (관용)승합 and 1 other fieldsHigh correlation
(관용)특수 is highly overall correlated with (관용)승합High correlation
(자가용)승용 is highly overall correlated with (영업용)승용High correlation
(자가용)승합 is highly overall correlated with (자가용)화물 and 3 other fieldsHigh correlation
(자가용)화물 is highly overall correlated with (자가용)승합 and 2 other fieldsHigh correlation
(자가용)특수 is highly overall correlated with (자가용)승합 and 3 other fieldsHigh correlation
(영업용)승용 is highly overall correlated with (자가용)승용High correlation
(영업용)승합 is highly overall correlated with (영업용)특수High correlation
(영업용)화물 is highly overall correlated with (자가용)승합 and 3 other fieldsHigh correlation
(영업용)특수 is highly overall correlated with (관용)승합 and 5 other fieldsHigh correlation
(관용)승합 is highly skewed (γ1 = 28.9928262)Skewed
(관용)특수 is highly skewed (γ1 = 20.82857446)Skewed
(영업용)승용 is highly skewed (γ1 = 31.41464063)Skewed
(영업용)특수 is highly skewed (γ1 = 21.48963772)Skewed
(관용)승용 has 2029 (87.0%) zerosZeros
(관용)승합 has 2230 (95.6%) zerosZeros
(관용)화물 has 2146 (92.0%) zerosZeros
(관용)특수 has 2297 (98.5%) zerosZeros
(자가용)승용 has 223 (9.6%) zerosZeros
(자가용)승합 has 1612 (69.1%) zerosZeros
(자가용)화물 has 1237 (53.0%) zerosZeros
(자가용)특수 has 2025 (86.8%) zerosZeros
(영업용)승용 has 1718 (73.7%) zerosZeros
(영업용)승합 has 2158 (92.5%) zerosZeros
(영업용)화물 has 1771 (75.9%) zerosZeros
(영업용)특수 has 2175 (93.3%) zerosZeros

Reproduction

Analysis started2024-03-23 07:05:40.223392
Analysis finished2024-03-23 07:06:41.906729
Duration1 minute and 1.68 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.3 KiB
부산광역시
2332 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 2332
100.0%

Length

2024-03-23T07:06:42.236059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T07:06:42.657861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 2332
100.0%

시군구
Categorical

Distinct16
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size18.3 KiB
기장군
573 
중구
269 
강서구
210 
서구
201 
영도구
177 
Other values (11)
902 

Length

Max length4
Median length3
Mean length2.8190395
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기장군 573
24.6%
중구 269
11.5%
강서구 210
 
9.0%
서구 201
 
8.6%
영도구 177
 
7.6%
부산진구 131
 
5.6%
금정구 129
 
5.5%
동래구 103
 
4.4%
사하구 95
 
4.1%
사상구 94
 
4.0%
Other values (6) 350
15.0%

Length

2024-03-23T07:06:43.030743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기장군 573
24.6%
중구 269
11.5%
강서구 210
 
9.0%
서구 201
 
8.6%
영도구 177
 
7.6%
부산진구 131
 
5.6%
금정구 129
 
5.5%
동래구 103
 
4.4%
사하구 95
 
4.1%
사상구 94
 
4.0%
Other values (6) 350
15.0%
Distinct270
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Memory size18.3 KiB
2024-03-23T07:06:43.767345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.1736707
Min length3

Characters and Unicode

Total characters12065
Distinct characters144
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.3%

Sample

1st row영주동
2nd row영주동
3rd row영주동
4th row영주동
5th row영주동
ValueCountFrequency (%)
기장읍 131
 
4.6%
장안읍 113
 
4.0%
일광읍 104
 
3.6%
정관읍 100
 
3.5%
철마면 87
 
3.0%
정관면 23
 
0.8%
송정동 22
 
0.8%
달산리 16
 
0.6%
용수리 15
 
0.5%
삼성리 15
 
0.5%
Other values (245) 2230
78.1%
2024-03-23T07:06:45.291589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2332
19.3%
1903
 
15.8%
599
 
5.0%
536
 
4.4%
460
 
3.8%
312
 
2.6%
221
 
1.8%
189
 
1.6%
1 185
 
1.5%
182
 
1.5%
Other values (134) 5146
42.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9123
75.6%
Space Separator 2332
 
19.3%
Decimal Number 610
 
5.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1903
20.9%
599
 
6.6%
536
 
5.9%
460
 
5.0%
312
 
3.4%
221
 
2.4%
189
 
2.1%
182
 
2.0%
164
 
1.8%
147
 
1.6%
Other values (126) 4410
48.3%
Decimal Number
ValueCountFrequency (%)
1 185
30.3%
2 177
29.0%
3 124
20.3%
4 72
 
11.8%
5 38
 
6.2%
6 13
 
2.1%
7 1
 
0.2%
Space Separator
ValueCountFrequency (%)
2332
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9123
75.6%
Common 2942
 
24.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1903
20.9%
599
 
6.6%
536
 
5.9%
460
 
5.0%
312
 
3.4%
221
 
2.4%
189
 
2.1%
182
 
2.0%
164
 
1.8%
147
 
1.6%
Other values (126) 4410
48.3%
Common
ValueCountFrequency (%)
2332
79.3%
1 185
 
6.3%
2 177
 
6.0%
3 124
 
4.2%
4 72
 
2.4%
5 38
 
1.3%
6 13
 
0.4%
7 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9123
75.6%
ASCII 2942
 
24.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2332
79.3%
1 185
 
6.3%
2 177
 
6.0%
3 124
 
4.2%
4 72
 
2.4%
5 38
 
1.3%
6 13
 
0.4%
7 1
 
< 0.1%
Hangul
ValueCountFrequency (%)
1903
20.9%
599
 
6.6%
536
 
5.9%
460
 
5.0%
312
 
3.4%
221
 
2.4%
189
 
2.1%
182
 
2.0%
164
 
1.8%
147
 
1.6%
Other values (126) 4410
48.3%

연료
Categorical

Distinct15
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size18.3 KiB
경유
266 
휘발유(무연)
262 
휘발유
260 
엘피지
252 
하이브리드(휘발유+전기)
247 
Other values (10)
1045 

Length

Max length13
Median length12
Mean length5.5141509
Min length2

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st row휘발유
2nd row경유
3rd row엘피지
4th row전기
5th row휘발유(유연)

Common Values

ValueCountFrequency (%)
경유 266
11.4%
휘발유(무연) 262
11.2%
휘발유 260
11.1%
엘피지 252
10.8%
하이브리드(휘발유+전기) 247
10.6%
전기 231
9.9%
기타연료 189
8.1%
수소 166
7.1%
하이브리드(경유+전기) 139
6.0%
휘발유(유연) 112
4.8%
Other values (5) 208
8.9%

Length

2024-03-23T07:06:45.998487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경유 266
11.4%
휘발유(무연 262
11.2%
휘발유 260
11.1%
엘피지 252
10.8%
하이브리드(휘발유+전기 247
10.6%
전기 231
9.9%
기타연료 189
8.1%
수소 166
7.1%
하이브리드(경유+전기 139
6.0%
휘발유(유연 112
4.8%
Other values (5) 208
8.9%

(관용)승용
Real number (ℝ)

ZEROS 

Distinct37
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.79459691
Minimum0
Maximum106
Zeros2029
Zeros (%)87.0%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2024-03-23T07:06:46.576936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum106
Range106
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.3939729
Coefficient of variation (CV)5.5298137
Kurtosis198.82172
Mean0.79459691
Median Absolute Deviation (MAD)0
Skewness11.823859
Sum1853
Variance19.306998
MonotonicityNot monotonic
2024-03-23T07:06:47.517501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 2029
87.0%
1 105
 
4.5%
2 59
 
2.5%
3 24
 
1.0%
4 21
 
0.9%
5 16
 
0.7%
6 14
 
0.6%
7 8
 
0.3%
12 5
 
0.2%
8 4
 
0.2%
Other values (27) 47
 
2.0%
ValueCountFrequency (%)
0 2029
87.0%
1 105
 
4.5%
2 59
 
2.5%
3 24
 
1.0%
4 21
 
0.9%
5 16
 
0.7%
6 14
 
0.6%
7 8
 
0.3%
8 4
 
0.2%
9 3
 
0.1%
ValueCountFrequency (%)
106 1
 
< 0.1%
66 1
 
< 0.1%
57 1
 
< 0.1%
50 1
 
< 0.1%
41 1
 
< 0.1%
40 2
0.1%
38 1
 
< 0.1%
34 1
 
< 0.1%
33 3
0.1%
31 1
 
< 0.1%

(관용)승합
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct28
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55188679
Minimum0
Maximum268
Zeros2230
Zeros (%)95.6%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2024-03-23T07:06:48.079439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum268
Range268
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.4370437
Coefficient of variation (CV)13.475669
Kurtosis950.16724
Mean0.55188679
Median Absolute Deviation (MAD)0
Skewness28.992826
Sum1287
Variance55.309619
MonotonicityNot monotonic
2024-03-23T07:06:48.548071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 2230
95.6%
1 34
 
1.5%
3 12
 
0.5%
2 10
 
0.4%
11 6
 
0.3%
6 6
 
0.3%
12 3
 
0.1%
14 3
 
0.1%
7 3
 
0.1%
18 2
 
0.1%
Other values (18) 23
 
1.0%
ValueCountFrequency (%)
0 2230
95.6%
1 34
 
1.5%
2 10
 
0.4%
3 12
 
0.5%
5 2
 
0.1%
6 6
 
0.3%
7 3
 
0.1%
9 1
 
< 0.1%
11 6
 
0.3%
12 3
 
0.1%
ValueCountFrequency (%)
268 1
< 0.1%
202 1
< 0.1%
52 1
< 0.1%
37 1
< 0.1%
33 2
0.1%
31 2
0.1%
30 1
< 0.1%
28 2
0.1%
25 1
< 0.1%
24 1
< 0.1%

(관용)화물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69168096
Minimum0
Maximum124
Zeros2146
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2024-03-23T07:06:48.936818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum124
Range124
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.1342057
Coefficient of variation (CV)7.4227946
Kurtosis213.87905
Mean0.69168096
Median Absolute Deviation (MAD)0
Skewness12.795418
Sum1613
Variance26.360068
MonotonicityNot monotonic
2024-03-23T07:06:49.469117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 2146
92.0%
1 76
 
3.3%
2 29
 
1.2%
3 17
 
0.7%
6 8
 
0.3%
5 6
 
0.3%
13 4
 
0.2%
4 4
 
0.2%
14 4
 
0.2%
19 3
 
0.1%
Other values (31) 35
 
1.5%
ValueCountFrequency (%)
0 2146
92.0%
1 76
 
3.3%
2 29
 
1.2%
3 17
 
0.7%
4 4
 
0.2%
5 6
 
0.3%
6 8
 
0.3%
7 2
 
0.1%
8 2
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
124 1
< 0.1%
73 1
< 0.1%
67 1
< 0.1%
65 1
< 0.1%
64 1
< 0.1%
55 1
< 0.1%
50 1
< 0.1%
48 1
< 0.1%
46 1
< 0.1%
43 1
< 0.1%

(관용)특수
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct13
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.067324185
Minimum0
Maximum27
Zeros2297
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2024-03-23T07:06:49.845067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum27
Range27
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.81660956
Coefficient of variation (CV)12.129513
Kurtosis570.25059
Mean0.067324185
Median Absolute Deviation (MAD)0
Skewness20.828574
Sum157
Variance0.66685118
MonotonicityNot monotonic
2024-03-23T07:06:50.217257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 2297
98.5%
1 11
 
0.5%
3 6
 
0.3%
2 4
 
0.2%
4 3
 
0.1%
7 3
 
0.1%
8 2
 
0.1%
13 1
 
< 0.1%
5 1
 
< 0.1%
11 1
 
< 0.1%
Other values (3) 3
 
0.1%
ValueCountFrequency (%)
0 2297
98.5%
1 11
 
0.5%
2 4
 
0.2%
3 6
 
0.3%
4 3
 
0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 3
 
0.1%
8 2
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
27 1
 
< 0.1%
13 1
 
< 0.1%
11 1
 
< 0.1%
9 1
 
< 0.1%
8 2
 
0.1%
7 3
0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
4 3
0.1%
3 6
0.3%

(자가용)승용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct678
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean501.75643
Minimum0
Maximum19425
Zeros223
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2024-03-23T07:06:50.679662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median16
Q3161
95-th percentile3051.5
Maximum19425
Range19425
Interquartile range (IQR)159

Descriptive statistics

Standard deviation1508.6142
Coefficient of variation (CV)3.0066664
Kurtosis37.130354
Mean501.75643
Median Absolute Deviation (MAD)16
Skewness5.3458799
Sum1170096
Variance2275916.8
MonotonicityNot monotonic
2024-03-23T07:06:51.513068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 233
 
10.0%
0 223
 
9.6%
2 133
 
5.7%
3 81
 
3.5%
4 73
 
3.1%
5 65
 
2.8%
6 63
 
2.7%
7 47
 
2.0%
9 39
 
1.7%
8 39
 
1.7%
Other values (668) 1336
57.3%
ValueCountFrequency (%)
0 223
9.6%
1 233
10.0%
2 133
5.7%
3 81
 
3.5%
4 73
 
3.1%
5 65
 
2.8%
6 63
 
2.7%
7 47
 
2.0%
8 39
 
1.7%
9 39
 
1.7%
ValueCountFrequency (%)
19425 1
< 0.1%
14270 1
< 0.1%
14228 1
< 0.1%
13857 1
< 0.1%
13034 1
< 0.1%
12985 1
< 0.1%
12899 1
< 0.1%
12302 1
< 0.1%
11818 1
< 0.1%
11167 1
< 0.1%

(자가용)승합
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct152
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.457118
Minimum0
Maximum909
Zeros1612
Zeros (%)69.1%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2024-03-23T07:06:51.959821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile56.45
Maximum909
Range909
Interquartile range (IQR)1

Descriptive statistics

Standard deviation55.659552
Coefficient of variation (CV)4.4680921
Kurtosis64.053418
Mean12.457118
Median Absolute Deviation (MAD)0
Skewness7.1243433
Sum29050
Variance3097.9857
MonotonicityNot monotonic
2024-03-23T07:06:52.449541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1612
69.1%
1 189
 
8.1%
2 82
 
3.5%
3 52
 
2.2%
4 37
 
1.6%
5 30
 
1.3%
6 18
 
0.8%
7 17
 
0.7%
8 15
 
0.6%
9 14
 
0.6%
Other values (142) 266
 
11.4%
ValueCountFrequency (%)
0 1612
69.1%
1 189
 
8.1%
2 82
 
3.5%
3 52
 
2.2%
4 37
 
1.6%
5 30
 
1.3%
6 18
 
0.8%
7 17
 
0.7%
8 15
 
0.6%
9 14
 
0.6%
ValueCountFrequency (%)
909 1
< 0.1%
545 1
< 0.1%
531 1
< 0.1%
524 1
< 0.1%
513 1
< 0.1%
495 1
< 0.1%
479 1
< 0.1%
477 1
< 0.1%
449 1
< 0.1%
433 1
< 0.1%

(자가용)화물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct235
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.624357
Minimum0
Maximum5180
Zeros1237
Zeros (%)53.0%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2024-03-23T07:06:52.832520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile170.9
Maximum5180
Range5180
Interquartile range (IQR)8

Descriptive statistics

Standard deviation299.63288
Coefficient of variation (CV)4.7094053
Kurtosis72.069752
Mean63.624357
Median Absolute Deviation (MAD)0
Skewness7.481796
Sum148372
Variance89779.866
MonotonicityNot monotonic
2024-03-23T07:06:53.546946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1237
53.0%
1 206
 
8.8%
2 107
 
4.6%
3 54
 
2.3%
4 50
 
2.1%
7 28
 
1.2%
6 28
 
1.2%
8 24
 
1.0%
11 22
 
0.9%
5 22
 
0.9%
Other values (225) 554
23.8%
ValueCountFrequency (%)
0 1237
53.0%
1 206
 
8.8%
2 107
 
4.6%
3 54
 
2.3%
4 50
 
2.1%
5 22
 
0.9%
6 28
 
1.2%
7 28
 
1.2%
8 24
 
1.0%
9 21
 
0.9%
ValueCountFrequency (%)
5180 1
< 0.1%
3220 1
< 0.1%
2878 1
< 0.1%
2828 1
< 0.1%
2618 1
< 0.1%
2566 1
< 0.1%
2422 1
< 0.1%
2345 1
< 0.1%
2338 1
< 0.1%
2335 1
< 0.1%

(자가용)특수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3507719
Minimum0
Maximum103
Zeros2025
Zeros (%)86.8%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2024-03-23T07:06:54.025016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum103
Range103
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.9041693
Coefficient of variation (CV)5.1112771
Kurtosis87.61621
Mean1.3507719
Median Absolute Deviation (MAD)0
Skewness8.414303
Sum3150
Variance47.667554
MonotonicityNot monotonic
2024-03-23T07:06:54.919615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2025
86.8%
1 103
 
4.4%
2 36
 
1.5%
3 20
 
0.9%
5 14
 
0.6%
4 13
 
0.6%
6 10
 
0.4%
10 9
 
0.4%
9 8
 
0.3%
12 7
 
0.3%
Other values (40) 87
 
3.7%
ValueCountFrequency (%)
0 2025
86.8%
1 103
 
4.4%
2 36
 
1.5%
3 20
 
0.9%
4 13
 
0.6%
5 14
 
0.6%
6 10
 
0.4%
7 7
 
0.3%
8 7
 
0.3%
9 8
 
0.3%
ValueCountFrequency (%)
103 2
0.1%
89 1
< 0.1%
85 1
< 0.1%
73 1
< 0.1%
71 1
< 0.1%
63 1
< 0.1%
62 1
< 0.1%
56 2
0.1%
55 1
< 0.1%
53 1
< 0.1%

(영업용)승용
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct169
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.186535
Minimum0
Maximum30862
Zeros1718
Zeros (%)73.7%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2024-03-23T07:06:55.377761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile71.9
Maximum30862
Range30862
Interquartile range (IQR)1

Descriptive statistics

Standard deviation768.6978
Coefficient of variation (CV)14.452865
Kurtosis1162.6631
Mean53.186535
Median Absolute Deviation (MAD)0
Skewness31.414641
Sum124031
Variance590896.31
MonotonicityNot monotonic
2024-03-23T07:06:55.978026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1718
73.7%
1 148
 
6.3%
2 46
 
2.0%
3 38
 
1.6%
4 20
 
0.9%
5 18
 
0.8%
8 17
 
0.7%
6 12
 
0.5%
10 12
 
0.5%
11 12
 
0.5%
Other values (159) 291
 
12.5%
ValueCountFrequency (%)
0 1718
73.7%
1 148
 
6.3%
2 46
 
2.0%
3 38
 
1.6%
4 20
 
0.9%
5 18
 
0.8%
6 12
 
0.5%
7 11
 
0.5%
8 17
 
0.7%
9 8
 
0.3%
ValueCountFrequency (%)
30862 1
< 0.1%
13711 1
< 0.1%
8832 1
< 0.1%
7741 1
< 0.1%
4373 1
< 0.1%
4190 1
< 0.1%
3625 1
< 0.1%
3383 1
< 0.1%
2865 1
< 0.1%
2813 1
< 0.1%

(영업용)승합
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct67
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6650943
Minimum0
Maximum515
Zeros2158
Zeros (%)92.5%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2024-03-23T07:06:56.662124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum515
Range515
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.351448
Coefficient of variation (CV)7.2610741
Kurtosis286.82995
Mean2.6650943
Median Absolute Deviation (MAD)0
Skewness14.395543
Sum6215
Variance374.47852
MonotonicityNot monotonic
2024-03-23T07:06:57.272781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2158
92.5%
1 28
 
1.2%
2 17
 
0.7%
3 8
 
0.3%
5 8
 
0.3%
16 7
 
0.3%
7 5
 
0.2%
13 4
 
0.2%
11 4
 
0.2%
39 4
 
0.2%
Other values (57) 89
 
3.8%
ValueCountFrequency (%)
0 2158
92.5%
1 28
 
1.2%
2 17
 
0.7%
3 8
 
0.3%
4 3
 
0.1%
5 8
 
0.3%
6 3
 
0.1%
7 5
 
0.2%
8 3
 
0.1%
9 3
 
0.1%
ValueCountFrequency (%)
515 1
< 0.1%
332 1
< 0.1%
272 1
< 0.1%
231 1
< 0.1%
215 1
< 0.1%
198 1
< 0.1%
129 1
< 0.1%
128 1
< 0.1%
121 1
< 0.1%
120 1
< 0.1%

(영업용)화물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct146
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.499142
Minimum0
Maximum3023
Zeros1771
Zeros (%)75.9%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2024-03-23T07:06:57.869973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile56.9
Maximum3023
Range3023
Interquartile range (IQR)0

Descriptive statistics

Standard deviation97.072173
Coefficient of variation (CV)6.2630674
Kurtosis434.64454
Mean15.499142
Median Absolute Deviation (MAD)0
Skewness17.013733
Sum36144
Variance9423.0069
MonotonicityNot monotonic
2024-03-23T07:06:58.514715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1771
75.9%
1 114
 
4.9%
2 49
 
2.1%
4 38
 
1.6%
3 37
 
1.6%
5 17
 
0.7%
12 15
 
0.6%
11 13
 
0.6%
10 13
 
0.6%
6 12
 
0.5%
Other values (136) 253
 
10.8%
ValueCountFrequency (%)
0 1771
75.9%
1 114
 
4.9%
2 49
 
2.1%
3 37
 
1.6%
4 38
 
1.6%
5 17
 
0.7%
6 12
 
0.5%
7 10
 
0.4%
8 7
 
0.3%
9 10
 
0.4%
ValueCountFrequency (%)
3023 1
< 0.1%
1359 1
< 0.1%
1146 1
< 0.1%
1061 1
< 0.1%
870 1
< 0.1%
856 1
< 0.1%
750 1
< 0.1%
715 1
< 0.1%
664 1
< 0.1%
602 1
< 0.1%

(영업용)특수
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct73
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8756432
Minimum0
Maximum1287
Zeros2175
Zeros (%)93.3%
Negative0
Negative (%)0.0%
Memory size20.6 KiB
2024-03-23T07:06:59.032420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum1287
Range1287
Interquartile range (IQR)0

Descriptive statistics

Standard deviation39.052077
Coefficient of variation (CV)10.076283
Kurtosis593.42764
Mean3.8756432
Median Absolute Deviation (MAD)0
Skewness21.489638
Sum9038
Variance1525.0648
MonotonicityNot monotonic
2024-03-23T07:06:59.701883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2175
93.3%
1 24
 
1.0%
3 13
 
0.6%
4 9
 
0.4%
2 8
 
0.3%
5 5
 
0.2%
16 4
 
0.2%
23 4
 
0.2%
9 3
 
0.1%
25 3
 
0.1%
Other values (63) 84
 
3.6%
ValueCountFrequency (%)
0 2175
93.3%
1 24
 
1.0%
2 8
 
0.3%
3 13
 
0.6%
4 9
 
0.4%
5 5
 
0.2%
6 2
 
0.1%
8 1
 
< 0.1%
9 3
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
1287 1
< 0.1%
784 1
< 0.1%
454 1
< 0.1%
426 1
< 0.1%
396 1
< 0.1%
383 1
< 0.1%
339 1
< 0.1%
275 1
< 0.1%
258 1
< 0.1%
177 1
< 0.1%

Interactions

2024-03-23T07:06:35.346831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:47.162903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:51.654085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:55.939543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:00.915500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:05.248579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:10.405872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:14.700502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:18.556660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:23.549892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:27.538646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:31.303907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:35.797285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:47.534775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:51.983960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:56.245950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:01.237456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:05.683510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:10.797756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:14.982344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:18.946594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:24.154866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:27.904763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:31.585726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:36.069900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:47.902578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:52.359569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:56.652724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:01.570672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:06.106139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:11.179632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:15.256801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:19.432564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:24.496177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:28.187711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:31.876815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:36.412821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:48.508268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:52.741796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:57.195110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:01.937503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:06.584290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:11.639873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:15.710281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:19.875192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:24.772904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:28.505709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:32.174547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:36.780436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:48.804058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:53.097066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:57.625531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:02.207198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:07.153852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:11.979242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:16.040263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:20.411467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:25.057382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:28.847927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:32.494888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:37.073461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:49.101583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:53.596181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:58.053858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:02.510737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:07.604227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:12.283758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:16.371435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:20.915903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:25.368841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:29.244581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:33.038792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:37.378845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:49.399416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:53.972248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:58.462139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:02.873849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:08.056991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:12.589273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:16.642480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:21.208748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:25.634908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:29.528283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:33.350059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:37.846473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:49.848192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:54.253063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:58.863422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:03.193735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:08.546478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:12.949578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:16.988417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:21.533779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:25.902240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:29.809956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:33.680490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:38.289058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:50.294427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:54.542507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:59.365331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:03.509727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:08.969543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:13.344434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:17.253564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:21.973757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:26.382507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:30.092890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:33.976689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:38.675862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:50.635394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:54.812944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:59.716135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:04.060573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:09.304786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:13.618938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:17.658116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:22.324012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:26.678205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:30.395271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:34.387510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:39.251012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:50.989686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:55.099130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:00.147394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:04.461795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:09.620089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:14.047697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:17.938562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:22.664155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:26.962365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:30.709507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:34.656243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:39.642426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:51.303292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:05:55.578222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:00.586582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:04.885318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:09.941395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:14.409232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:18.208749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:23.148295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:27.242676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:30.999868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:06:34.931516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T07:07:00.124539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구연료(관용)승용(관용)승합(관용)화물(관용)특수(자가용)승용(자가용)승합(자가용)화물(자가용)특수(영업용)승용(영업용)승합(영업용)화물(영업용)특수
시군구1.0000.1010.3730.3100.2100.1990.4000.2500.2960.2160.1830.2780.2140.202
연료0.1011.0000.1720.1390.2540.1670.2740.4030.4050.3570.0000.2330.1930.157
(관용)승용0.3730.1721.0000.8880.6370.6780.7500.6350.7900.5010.1820.1780.6670.449
(관용)승합0.3100.1390.8881.0000.7770.7800.5990.7540.9280.7860.1050.9040.7720.664
(관용)화물0.2100.2540.6370.7771.0000.7700.4560.8650.7200.6570.1760.3770.6620.500
(관용)특수0.1990.1670.6780.7800.7701.0000.5110.7070.7280.7240.6440.6860.8970.847
(자가용)승용0.4000.2740.7500.5990.4560.5111.0000.5960.5980.5460.3930.3830.5120.416
(자가용)승합0.2500.4030.6350.7540.8650.7070.5961.0000.8810.7890.2680.4810.6960.550
(자가용)화물0.2960.4050.7900.9280.7200.7280.5980.8811.0000.8310.2370.5970.7240.566
(자가용)특수0.2160.3570.5010.7860.6570.7240.5460.7890.8311.0000.4990.5690.6410.805
(영업용)승용0.1830.0000.1820.1050.1760.6440.3930.2680.2370.4991.0000.7040.1500.277
(영업용)승합0.2780.2330.1780.9040.3770.6860.3830.4810.5970.5690.7041.0000.2500.499
(영업용)화물0.2140.1930.6670.7720.6620.8970.5120.6960.7240.6410.1500.2501.0000.777
(영업용)특수0.2020.1570.4490.6640.5000.8470.4160.5500.5660.8050.2770.4990.7771.000
2024-03-23T07:07:00.583014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연료시군구
연료1.0000.034
시군구0.0341.000
2024-03-23T07:07:00.857989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
(관용)승용(관용)승합(관용)화물(관용)특수(자가용)승용(자가용)승합(자가용)화물(자가용)특수(영업용)승용(영업용)승합(영업용)화물(영업용)특수시군구연료
(관용)승용1.0000.2830.2130.3170.3960.1240.2210.0720.3690.1570.1640.1300.1390.074
(관용)승합0.2831.0000.5980.5200.2540.3930.3420.4210.2460.4580.3670.5520.1500.079
(관용)화물0.2130.5981.0000.4300.2220.4160.4230.4910.2130.4690.4750.6290.0970.117
(관용)특수0.3170.5200.4301.0000.1520.2070.2070.2790.1350.2790.2390.3690.0960.078
(자가용)승용0.3960.2540.2220.1521.0000.3910.3680.1110.5140.1820.1330.3120.1770.114
(자가용)승합0.1240.3930.4160.2070.3911.0000.7110.5730.3600.2960.5210.5050.1160.195
(자가용)화물0.2210.3420.4230.2070.3680.7111.0000.5450.4730.2870.7070.4490.1080.186
(자가용)특수0.0720.4210.4910.2790.1110.5730.5451.0000.1240.3680.6510.5800.0860.141
(영업용)승용0.3690.2460.2130.1350.5140.3600.4730.1241.0000.2200.3250.1400.0940.000
(영업용)승합0.1570.4580.4690.2790.1820.2960.2870.3680.2201.0000.3450.5080.1010.101
(영업용)화물0.1640.3670.4750.2390.1330.5210.7070.6510.3250.3451.0000.5180.1040.091
(영업용)특수0.1300.5520.6290.3690.3120.5050.4490.5800.1400.5080.5181.0000.0980.074
시군구0.1390.1500.0970.0960.1770.1160.1080.0860.0940.1010.1040.0981.0000.034
연료0.0740.0790.1170.0780.1140.1950.1860.1410.0000.1010.0910.0740.0341.000

Missing values

2024-03-23T07:06:40.185121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T07:06:41.279997image/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

시도시군구읍면동연료(관용)승용(관용)승합(관용)화물(관용)특수(자가용)승용(자가용)승합(자가용)화물(자가용)특수(영업용)승용(영업용)승합(영업용)화물(영업용)특수
0부산광역시중구영주동휘발유00004930100000
1부산광역시중구영주동경유001057366261410644
2부산광역시중구영주동엘피지00002331716038020
3부산광역시중구영주동전기00001401006050
4부산광역시중구영주동휘발유(유연)000030000000
5부산광역시중구영주동휘발유(무연)00009811000000
6부산광역시중구영주동하이브리드(휘발유+전기)00001000000000
7부산광역시중구영주동하이브리드(경유+전기)000010000000
8부산광역시중구영주동하이브리드(LPG+전기)000040000000
9부산광역시중구영주동수소000030000000
시도시군구읍면동연료(관용)승용(관용)승합(관용)화물(관용)특수(자가용)승용(자가용)승합(자가용)화물(자가용)특수(영업용)승용(영업용)승합(영업용)화물(영업용)특수
2322부산광역시기장군철마면 안평리수소000010000000
2323부산광역시기장군철마면 안평리기타연료000000100000
2324부산광역시기장군철마면 임기리휘발유0000310000000
2325부산광역시기장군철마면 임기리경유00005476600011
2326부산광역시기장군철마면 임기리엘피지0000170401000
2327부산광역시기장군철마면 임기리전기000040200000
2328부산광역시기장군철마면 임기리휘발유(무연)0000560000000
2329부산광역시기장군철마면 임기리하이브리드(휘발유+전기)000060000000
2330부산광역시기장군철마면 임기리수소000020000000
2331부산광역시기장군철마면 임기리기타연료000000100000