Overview

Dataset statistics

Number of variables16
Number of observations85
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.0 KiB
Average record size in memory144.6 B

Variable types

Categorical2
Numeric14

Dataset

Description17개 시도, 업종별(버스, 고속버스, 전세버스, 마을버스, 택시, 화물 등) 교통사고통계정보를 항목별로 제공합니다.
URLhttps://www.data.go.kr/data/3048010/fileData.do

Alerts

버스 is highly overall correlated with 버스계 and 10 other fieldsHigh correlation
버스계 is highly overall correlated with 버스 and 10 other fieldsHigh correlation
전세버스 is highly overall correlated with 버스 and 9 other fieldsHigh correlation
마을버스 is highly overall correlated with 버스 and 7 other fieldsHigh correlation
특수여객자동차 is highly overall correlated with 일반화물 and 4 other fieldsHigh correlation
개인택시 is highly overall correlated with 버스 and 11 other fieldsHigh correlation
일반택시 is highly overall correlated with 버스 and 10 other fieldsHigh correlation
택시계 is highly overall correlated with 버스 and 10 other fieldsHigh correlation
일반화물 is highly overall correlated with 버스 and 12 other fieldsHigh correlation
용달화물 is highly overall correlated with 버스 and 10 other fieldsHigh correlation
개별화물 is highly overall correlated with 버스 and 11 other fieldsHigh correlation
화물계 is highly overall correlated with 버스 and 12 other fieldsHigh correlation
합계 is highly overall correlated with 버스 and 11 other fieldsHigh correlation
시도 is highly overall correlated with 개인택시 and 3 other fieldsHigh correlation
고속버스 has 61 (71.8%) zerosZeros
마을버스 has 51 (60.0%) zerosZeros
특수여객자동차 has 35 (41.2%) zerosZeros
용달화물 has 5 (5.9%) zerosZeros
개별화물 has 5 (5.9%) zerosZeros

Reproduction

Analysis started2023-12-12 22:32:40.537183
Analysis finished2023-12-12 22:33:01.205759
Duration20.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Categorical

Distinct5
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size812.0 B
2018
17 
2019
17 
2020
17 
2021
17 
2022
17 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 17
20.0%
2019 17
20.0%
2020 17
20.0%
2021 17
20.0%
2022 17
20.0%

Length

2023-12-13T07:33:01.271025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:33:01.406506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 17
20.0%
2019 17
20.0%
2020 17
20.0%
2021 17
20.0%
2022 17
20.0%

시도
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size812.0 B
서울특별시청
 
5
부산광역시청
 
5
대구광역시청
 
5
인천광역시청
 
5
광주광역시청
 
5
Other values (12)
60 

Length

Max length8
Median length7
Mean length5.5882353
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시청
2nd row부산광역시청
3rd row대구광역시청
4th row인천광역시청
5th row광주광역시청

Common Values

ValueCountFrequency (%)
서울특별시청 5
 
5.9%
부산광역시청 5
 
5.9%
대구광역시청 5
 
5.9%
인천광역시청 5
 
5.9%
광주광역시청 5
 
5.9%
대전광역시청 5
 
5.9%
울산광역시청 5
 
5.9%
세종특별자치시 5
 
5.9%
경기도청 5
 
5.9%
강원도청 5
 
5.9%
Other values (7) 35
41.2%

Length

2023-12-13T07:33:01.524804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울특별시청 5
 
5.9%
강원도청 5
 
5.9%
경상남도청 5
 
5.9%
경상북도청 5
 
5.9%
전라남도청 5
 
5.9%
전라북도청 5
 
5.9%
충청남도청 5
 
5.9%
충청북도청 5
 
5.9%
경기도청 5
 
5.9%
부산광역시청 5
 
5.9%
Other values (7) 35
41.2%

버스
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean256.58824
Minimum14
Maximum2284
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-13T07:33:01.648907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile34
Q188
median135
Q3190
95-th percentile1227.6
Maximum2284
Range2270
Interquartile range (IQR)102

Descriptive statistics

Standard deviation415.01467
Coefficient of variation (CV)1.6174345
Kurtosis13.300399
Mean256.58824
Median Absolute Deviation (MAD)49
Skewness3.5644254
Sum21810
Variance172237.17
MonotonicityNot monotonic
2023-12-13T07:33:01.786062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133 3
 
3.5%
64 3
 
3.5%
141 3
 
3.5%
152 2
 
2.4%
106 2
 
2.4%
132 2
 
2.4%
125 2
 
2.4%
158 2
 
2.4%
86 2
 
2.4%
107 2
 
2.4%
Other values (61) 62
72.9%
ValueCountFrequency (%)
14 1
 
1.2%
15 1
 
1.2%
16 1
 
1.2%
17 1
 
1.2%
29 1
 
1.2%
54 1
 
1.2%
58 1
 
1.2%
60 1
 
1.2%
64 3
3.5%
66 1
 
1.2%
ValueCountFrequency (%)
2284 1
1.2%
2254 1
1.2%
1462 1
1.2%
1366 1
1.2%
1337 1
1.2%
790 1
1.2%
783 1
1.2%
657 1
1.2%
640 1
1.2%
638 1
1.2%

고속버스
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4941176
Minimum0
Maximum17
Zeros61
Zeros (%)71.8%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-13T07:33:01.891582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.4073573
Coefficient of variation (CV)2.2805148
Kurtosis8.0853741
Mean1.4941176
Median Absolute Deviation (MAD)0
Skewness2.8556488
Sum127
Variance11.610084
MonotonicityNot monotonic
2023-12-13T07:33:01.987768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 61
71.8%
2 7
 
8.2%
4 3
 
3.5%
1 3
 
3.5%
10 3
 
3.5%
3 2
 
2.4%
5 2
 
2.4%
17 1
 
1.2%
8 1
 
1.2%
13 1
 
1.2%
ValueCountFrequency (%)
0 61
71.8%
1 3
 
3.5%
2 7
 
8.2%
3 2
 
2.4%
4 3
 
3.5%
5 2
 
2.4%
8 1
 
1.2%
10 3
 
3.5%
13 1
 
1.2%
14 1
 
1.2%
ValueCountFrequency (%)
17 1
 
1.2%
14 1
 
1.2%
13 1
 
1.2%
10 3
3.5%
8 1
 
1.2%
5 2
 
2.4%
4 3
3.5%
3 2
 
2.4%
2 7
8.2%
1 3
3.5%

버스계
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258.08235
Minimum14
Maximum2297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-13T07:33:02.115105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile34
Q189
median136
Q3190
95-th percentile1235.6
Maximum2297
Range2283
Interquartile range (IQR)101

Descriptive statistics

Standard deviation417.32824
Coefficient of variation (CV)1.6170352
Kurtosis13.362107
Mean258.08235
Median Absolute Deviation (MAD)49
Skewness3.5721559
Sum21937
Variance174162.86
MonotonicityNot monotonic
2023-12-13T07:33:02.257858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143 3
 
3.5%
64 3
 
3.5%
89 2
 
2.4%
133 2
 
2.4%
138 2
 
2.4%
214 2
 
2.4%
97 2
 
2.4%
168 2
 
2.4%
107 2
 
2.4%
135 2
 
2.4%
Other values (61) 63
74.1%
ValueCountFrequency (%)
14 1
 
1.2%
15 1
 
1.2%
16 1
 
1.2%
17 1
 
1.2%
29 1
 
1.2%
54 1
 
1.2%
58 1
 
1.2%
60 1
 
1.2%
64 3
3.5%
66 1
 
1.2%
ValueCountFrequency (%)
2297 1
1.2%
2271 1
1.2%
1472 1
1.2%
1370 1
1.2%
1347 1
1.2%
790 1
1.2%
783 1
1.2%
657 1
1.2%
640 1
1.2%
638 1
1.2%

전세버스
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.211765
Minimum1
Maximum581
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-13T07:33:02.394602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.2
Q131
median49
Q365
95-th percentile298
Maximum581
Range580
Interquartile range (IQR)34

Descriptive statistics

Standard deviation109.12554
Coefficient of variation (CV)1.4905465
Kurtosis13.831979
Mean73.211765
Median Absolute Deviation (MAD)17
Skewness3.7698596
Sum6223
Variance11908.383
MonotonicityNot monotonic
2023-12-13T07:33:02.564894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44 3
 
3.5%
64 3
 
3.5%
49 3
 
3.5%
48 3
 
3.5%
38 3
 
3.5%
59 2
 
2.4%
20 2
 
2.4%
31 2
 
2.4%
60 2
 
2.4%
75 2
 
2.4%
Other values (50) 60
70.6%
ValueCountFrequency (%)
1 1
1.2%
3 1
1.2%
4 1
1.2%
5 1
1.2%
6 1
1.2%
12 1
1.2%
14 1
1.2%
18 1
1.2%
20 2
2.4%
21 1
1.2%
ValueCountFrequency (%)
581 1
1.2%
557 1
1.2%
536 1
1.2%
423 1
1.2%
346 1
1.2%
106 1
1.2%
104 1
1.2%
102 1
1.2%
99 1
1.2%
83 1
1.2%

마을버스
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.635294
Minimum0
Maximum673
Zeros51
Zeros (%)60.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-13T07:33:02.739594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile448.2
Maximum673
Range673
Interquartile range (IQR)5

Descriptive statistics

Standard deviation144.5408
Coefficient of variation (CV)2.7460814
Kurtosis8.1944255
Mean52.635294
Median Absolute Deviation (MAD)0
Skewness2.988622
Sum4474
Variance20892.044
MonotonicityNot monotonic
2023-12-13T07:33:02.876373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 51
60.0%
1 5
 
5.9%
2 4
 
4.7%
5 2
 
2.4%
9 2
 
2.4%
4 2
 
2.4%
305 1
 
1.2%
525 1
 
1.2%
476 1
 
1.2%
8 1
 
1.2%
Other values (15) 15
 
17.6%
ValueCountFrequency (%)
0 51
60.0%
1 5
 
5.9%
2 4
 
4.7%
3 1
 
1.2%
4 2
 
2.4%
5 2
 
2.4%
6 1
 
1.2%
8 1
 
1.2%
9 2
 
2.4%
10 1
 
1.2%
ValueCountFrequency (%)
673 1
1.2%
618 1
1.2%
525 1
1.2%
493 1
1.2%
476 1
1.2%
337 1
1.2%
305 1
1.2%
261 1
1.2%
258 1
1.2%
244 1
1.2%

특수여객자동차
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2941176
Minimum0
Maximum10
Zeros35
Zeros (%)41.2%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-13T07:33:03.026972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.907526
Coefficient of variation (CV)1.4739974
Kurtosis8.3589433
Mean1.2941176
Median Absolute Deviation (MAD)1
Skewness2.654345
Sum110
Variance3.6386555
MonotonicityNot monotonic
2023-12-13T07:33:03.163324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 35
41.2%
1 27
31.8%
2 11
 
12.9%
4 5
 
5.9%
3 3
 
3.5%
8 1
 
1.2%
9 1
 
1.2%
10 1
 
1.2%
5 1
 
1.2%
ValueCountFrequency (%)
0 35
41.2%
1 27
31.8%
2 11
 
12.9%
3 3
 
3.5%
4 5
 
5.9%
5 1
 
1.2%
8 1
 
1.2%
9 1
 
1.2%
10 1
 
1.2%
ValueCountFrequency (%)
10 1
 
1.2%
9 1
 
1.2%
8 1
 
1.2%
5 1
 
1.2%
4 5
 
5.9%
3 3
 
3.5%
2 11
 
12.9%
1 27
31.8%
0 35
41.2%

개인택시
Real number (ℝ)

HIGH CORRELATION 

Distinct74
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean338.36471
Minimum3
Maximum2176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-13T07:33:03.312111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile22.6
Q1128
median182
Q3267
95-th percentile1545.2
Maximum2176
Range2173
Interquartile range (IQR)139

Descriptive statistics

Standard deviation448.77502
Coefficient of variation (CV)1.3263057
Kurtosis7.2526486
Mean338.36471
Median Absolute Deviation (MAD)61
Skewness2.7517834
Sum28761
Variance201399.02
MonotonicityNot monotonic
2023-12-13T07:33:03.486296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151 3
 
3.5%
6 3
 
3.5%
111 2
 
2.4%
476 2
 
2.4%
126 2
 
2.4%
171 2
 
2.4%
169 2
 
2.4%
182 2
 
2.4%
199 2
 
2.4%
5 1
 
1.2%
Other values (64) 64
75.3%
ValueCountFrequency (%)
3 1
 
1.2%
5 1
 
1.2%
6 3
3.5%
89 1
 
1.2%
94 1
 
1.2%
95 1
 
1.2%
96 1
 
1.2%
99 1
 
1.2%
103 1
 
1.2%
107 1
 
1.2%
ValueCountFrequency (%)
2176 1
1.2%
2038 1
1.2%
1880 1
1.2%
1687 1
1.2%
1670 1
1.2%
1046 1
1.2%
1011 1
1.2%
999 1
1.2%
931 1
1.2%
919 1
1.2%

일반택시
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean585.50588
Minimum4
Maximum6756
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-13T07:33:03.630220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile28
Q1178
median337
Q3521
95-th percentile1894.4
Maximum6756
Range6752
Interquartile range (IQR)343

Descriptive statistics

Standard deviation950.65845
Coefficient of variation (CV)1.6236531
Kurtosis24.348286
Mean585.50588
Median Absolute Deviation (MAD)171
Skewness4.5533265
Sum49768
Variance903751.49
MonotonicityNot monotonic
2023-12-13T07:33:03.758428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 2
 
2.4%
486 2
 
2.4%
459 2
 
2.4%
161 1
 
1.2%
331 1
 
1.2%
134 1
 
1.2%
197 1
 
1.2%
138 1
 
1.2%
820 1
 
1.2%
103 1
 
1.2%
Other values (72) 72
84.7%
ValueCountFrequency (%)
4 1
1.2%
7 1
1.2%
8 2
2.4%
13 1
1.2%
88 1
1.2%
103 1
1.2%
113 1
1.2%
134 1
1.2%
136 1
1.2%
138 1
1.2%
ValueCountFrequency (%)
6756 1
1.2%
4511 1
1.2%
3238 1
1.2%
2393 1
1.2%
2025 1
1.2%
1372 1
1.2%
1332 1
1.2%
1089 1
1.2%
966 1
1.2%
961 1
1.2%

택시계
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean923.87059
Minimum10
Maximum8794
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-13T07:33:03.886473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile53.4
Q1312
median508
Q3808
95-th percentile3600.6
Maximum8794
Range8784
Interquartile range (IQR)496

Descriptive statistics

Standard deviation1365.0182
Coefficient of variation (CV)1.4774993
Kurtosis16.73924
Mean923.87059
Median Absolute Deviation (MAD)235
Skewness3.819644
Sum78529
Variance1863274.6
MonotonicityNot monotonic
2023-12-13T07:33:04.013604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
304 2
 
2.4%
13 2
 
2.4%
344 2
 
2.4%
8794 1
 
1.2%
690 1
 
1.2%
229 1
 
1.2%
325 1
 
1.2%
256 1
 
1.2%
1751 1
 
1.2%
192 1
 
1.2%
Other values (72) 72
84.7%
ValueCountFrequency (%)
10 1
1.2%
11 1
1.2%
13 2
2.4%
19 1
1.2%
191 1
1.2%
192 1
1.2%
209 1
1.2%
229 1
1.2%
243 1
1.2%
249 1
1.2%
ValueCountFrequency (%)
8794 1
1.2%
6687 1
1.2%
4908 1
1.2%
4080 1
1.2%
3905 1
1.2%
2383 1
1.2%
2378 1
1.2%
1885 1
1.2%
1803 1
1.2%
1751 1
1.2%

일반화물
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean276.34118
Minimum14
Maximum1403
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-13T07:33:04.159728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile26.8
Q1141
median181
Q3314
95-th percentile1179.4
Maximum1403
Range1389
Interquartile range (IQR)173

Descriptive statistics

Standard deviation300.53304
Coefficient of variation (CV)1.0875435
Kurtosis8.2885742
Mean276.34118
Median Absolute Deviation (MAD)93
Skewness2.8775399
Sum23489
Variance90320.108
MonotonicityNot monotonic
2023-12-13T07:33:04.310865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
164 3
 
3.5%
165 3
 
3.5%
162 2
 
2.4%
180 2
 
2.4%
15 2
 
2.4%
175 2
 
2.4%
55 2
 
2.4%
291 2
 
2.4%
322 2
 
2.4%
329 2
 
2.4%
Other values (62) 63
74.1%
ValueCountFrequency (%)
14 1
1.2%
15 2
2.4%
16 1
1.2%
21 1
1.2%
50 1
1.2%
55 2
2.4%
57 1
1.2%
63 1
1.2%
84 1
1.2%
87 1
1.2%
ValueCountFrequency (%)
1403 1
1.2%
1375 1
1.2%
1373 1
1.2%
1368 1
1.2%
1338 1
1.2%
545 1
1.2%
528 1
1.2%
525 1
1.2%
501 1
1.2%
474 1
1.2%

용달화물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.752941
Minimum0
Maximum678
Zeros5
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-13T07:33:04.443137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.8
Q122
median46
Q374
95-th percentile489
Maximum678
Range678
Interquartile range (IQR)52

Descriptive statistics

Standard deviation152.88355
Coefficient of variation (CV)1.5801437
Kurtosis5.1662096
Mean96.752941
Median Absolute Deviation (MAD)27
Skewness2.5074985
Sum8224
Variance23373.379
MonotonicityNot monotonic
2023-12-13T07:33:04.591323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
5.9%
46 3
 
3.5%
58 3
 
3.5%
18 3
 
3.5%
40 3
 
3.5%
37 3
 
3.5%
14 3
 
3.5%
36 3
 
3.5%
59 2
 
2.4%
45 2
 
2.4%
Other values (49) 55
64.7%
ValueCountFrequency (%)
0 5
5.9%
9 1
 
1.2%
10 1
 
1.2%
11 1
 
1.2%
12 1
 
1.2%
13 2
 
2.4%
14 3
3.5%
15 1
 
1.2%
16 1
 
1.2%
18 3
3.5%
ValueCountFrequency (%)
678 1
1.2%
625 1
1.2%
546 1
1.2%
533 1
1.2%
491 1
1.2%
481 1
1.2%
429 1
1.2%
412 1
1.2%
393 1
1.2%
390 1
1.2%

개별화물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)64.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.188235
Minimum0
Maximum364
Zeros5
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-13T07:33:05.030766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.4
Q123
median37
Q352
95-th percentile264.2
Maximum364
Range364
Interquartile range (IQR)29

Descriptive statistics

Standard deviation74.170892
Coefficient of variation (CV)1.3200431
Kurtosis9.3075564
Mean56.188235
Median Absolute Deviation (MAD)14
Skewness3.0916818
Sum4776
Variance5501.3213
MonotonicityNot monotonic
2023-12-13T07:33:05.164383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 5
 
5.9%
0 5
 
5.9%
40 3
 
3.5%
28 3
 
3.5%
18 3
 
3.5%
31 3
 
3.5%
24 3
 
3.5%
62 3
 
3.5%
54 2
 
2.4%
26 2
 
2.4%
Other values (45) 53
62.4%
ValueCountFrequency (%)
0 5
5.9%
7 2
 
2.4%
8 1
 
1.2%
10 1
 
1.2%
11 1
 
1.2%
14 1
 
1.2%
16 1
 
1.2%
17 1
 
1.2%
18 3
3.5%
19 1
 
1.2%
ValueCountFrequency (%)
364 1
1.2%
341 1
1.2%
324 1
1.2%
323 1
1.2%
298 1
1.2%
129 1
1.2%
128 1
1.2%
122 1
1.2%
112 1
1.2%
108 1
1.2%

화물계
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean649.43529
Minimum28
Maximum3431
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-13T07:33:05.301575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile56.2
Q1304
median418
Q3680
95-th percentile2891
Maximum3431
Range3403
Interquartile range (IQR)376

Descriptive statistics

Standard deviation741.80264
Coefficient of variation (CV)1.1422272
Kurtosis7.7614575
Mean649.43529
Median Absolute Deviation (MAD)202
Skewness2.8168124
Sum55202
Variance550271.15
MonotonicityNot monotonic
2023-12-13T07:33:05.443850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
237 2
 
2.4%
370 2
 
2.4%
636 2
 
2.4%
30 2
 
2.4%
704 1
 
1.2%
554 1
 
1.2%
344 1
 
1.2%
477 1
 
1.2%
387 1
 
1.2%
3431 1
 
1.2%
Other values (71) 71
83.5%
ValueCountFrequency (%)
28 1
1.2%
30 2
2.4%
32 1
1.2%
42 1
1.2%
113 1
1.2%
121 1
1.2%
123 1
1.2%
128 1
1.2%
138 1
1.2%
183 1
1.2%
ValueCountFrequency (%)
3431 1
1.2%
3354 1
1.2%
3283 1
1.2%
3282 1
1.2%
3227 1
1.2%
1547 1
1.2%
1519 1
1.2%
1462 1
1.2%
1395 1
1.2%
1338 1
1.2%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct84
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1738.3765
Minimum45
Maximum10977
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2023-12-13T07:33:05.556685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile129.6
Q1719
median1018
Q31372
95-th percentile6876.8
Maximum10977
Range10932
Interquartile range (IQR)653

Descriptive statistics

Standard deviation2213.4498
Coefficient of variation (CV)1.2732857
Kurtosis5.6292869
Mean1738.3765
Median Absolute Deviation (MAD)339
Skewness2.5149913
Sum147762
Variance4899360.2
MonotonicityNot monotonic
2023-12-13T07:33:05.659405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1009 2
 
2.4%
10977 1
 
1.2%
1138 1
 
1.2%
737 1
 
1.2%
719 1
 
1.2%
519 1
 
1.2%
6410 1
 
1.2%
50 1
 
1.2%
392 1
 
1.2%
952 1
 
1.2%
Other values (74) 74
87.1%
ValueCountFrequency (%)
45 1
1.2%
47 1
1.2%
50 1
1.2%
51 1
1.2%
64 1
1.2%
392 1
1.2%
402 1
1.2%
440 1
1.2%
448 1
1.2%
451 1
1.2%
ValueCountFrequency (%)
10977 1
1.2%
9070 1
1.2%
8168 1
1.2%
7985 1
1.2%
6960 1
1.2%
6544 1
1.2%
6460 1
1.2%
6410 1
1.2%
6092 1
1.2%
5902 1
1.2%

Interactions

2023-12-13T07:32:59.342759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:41.132150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:42.696986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:44.056062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:45.418729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:46.789991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:48.373854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:49.607078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:50.883634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:52.236360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:53.502217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:55.207064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:56.499959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:57.775560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:59.434763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:41.233910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:42.778631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:44.143407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:45.507915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:46.869369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:48.451505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:49.677038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:50.984340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:52.307454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:53.584501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:55.302008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:56.590498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:57.883052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:59.533688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:41.340904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:42.865344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:44.238697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:45.618475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:46.992345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:48.537968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:49.758466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:51.082613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:52.414703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:53.675921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:55.407036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:56.684489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:57.993799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:59.613243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:41.441781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:42.941614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:44.313453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:45.703861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:47.086055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:48.610598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:49.827770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:51.186681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:52.506489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:53.769854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:55.482192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:56.772429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:58.099500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:59.693238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:41.545305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:43.050498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:44.409134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:45.814948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:47.164101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:48.713392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:49.902442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:51.296847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:52.617321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:53.862720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:55.568405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:56.877197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:58.256248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:59.780997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:41.643719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:43.166808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:44.524771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:45.922879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:47.246933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:48.794426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:49.997277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:51.396227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:52.708577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:53.955209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:55.654555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:56.965187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:58.372002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:59.866955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:42.047341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:43.246985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:44.615166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:46.038920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:47.323224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:48.871431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:50.073780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:51.494151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:52.818865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:54.414950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:55.745044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:57.045173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:58.467456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:33:00.271613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:42.114012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:43.341831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:44.710499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:46.124800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:47.411878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:48.942239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:50.153645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:51.585499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:52.905593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:54.511716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:55.834326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:57.128690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:58.554497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:33:00.371455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:42.204623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:43.449792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:44.807450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:46.245925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:47.504197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:49.049361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:50.244344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:51.673519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:53.013766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:54.619279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:55.941852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:57.213172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:58.654887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:33:00.449795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:42.273266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:43.569825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:44.906125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:46.332209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:47.589713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:49.131580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:50.330595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:51.764533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:53.097796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:54.704402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:56.026786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:57.305173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:58.755312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:33:00.536284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:42.377423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:43.679747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:45.004804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:46.435465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:47.698674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:49.232727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:50.422428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:51.867567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:53.177447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:54.804921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:56.124688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:57.403265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:58.876640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:33:00.628797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:42.464365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:43.771210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:45.116327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:46.528933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:47.783836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:49.335110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:50.539634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:51.975549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:53.257185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:54.913957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:56.208941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:57.492978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:58.996567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:33:00.718551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:42.549830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:43.862178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:45.227284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:46.619182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:48.190720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:49.439621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:50.662162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:52.061822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:53.337372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:55.017431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:56.303613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:57.581019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:59.121879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:33:00.806609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:42.627333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:43.952033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:45.330049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:46.716157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:48.283064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:49.530625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:50.779482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:52.160935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:53.429729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:55.119039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:56.408900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:57.671617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:32:59.234727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:33:05.741181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도시도버스고속버스버스계전세버스마을버스특수여객자동차개인택시일반택시택시계일반화물용달화물개별화물화물계합계
년도1.0000.0000.0000.0980.0000.4370.0000.2170.0000.0000.0000.0000.0000.0000.0000.141
시도0.0001.0000.7740.6220.7740.6910.7790.5550.8650.5600.7960.9770.7910.9040.9590.728
버스0.0000.7741.0000.6881.0000.8570.9430.7510.9570.9250.9530.8060.9650.8770.8220.877
고속버스0.0980.6220.6881.0000.6880.7210.7030.0000.3800.3500.3330.5610.7040.6010.4860.753
버스계0.0000.7741.0000.6881.0000.8570.9430.7510.9570.9250.9530.8060.9650.8770.8220.877
전세버스0.4370.6910.8570.7210.8571.0000.7160.2530.6360.3980.6560.8840.7210.7400.6850.682
마을버스0.0000.7790.9430.7030.9430.7161.0000.7770.8770.9050.9350.7970.8860.9480.9740.965
특수여객자동차0.2170.5550.7510.0000.7510.2530.7771.0000.8550.8520.8020.6590.7340.7010.7010.862
개인택시0.0000.8650.9570.3800.9570.6360.8770.8551.0000.9600.9760.8170.9570.8320.8710.867
일반택시0.0000.5600.9250.3500.9250.3980.9050.8520.9601.0000.9930.6740.9340.7120.8180.916
택시계0.0000.7960.9530.3330.9530.6560.9350.8020.9760.9931.0000.7870.9410.7720.8990.940
일반화물0.0000.9770.8060.5610.8060.8840.7970.6590.8170.6740.7871.0000.7880.8350.9050.761
용달화물0.0000.7910.9650.7040.9650.7210.8860.7340.9570.9340.9410.7881.0000.8750.8110.840
개별화물0.0000.9040.8770.6010.8770.7400.9480.7010.8320.7120.7720.8350.8751.0000.9540.810
화물계0.0000.9590.8220.4860.8220.6850.9740.7010.8710.8180.8990.9050.8110.9541.0000.872
합계0.1410.7280.8770.7530.8770.6820.9650.8620.8670.9160.9400.7610.8400.8100.8721.000
2023-12-13T07:33:05.858764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도시도
년도1.0000.000
시도0.0001.000
2023-12-13T07:33:05.945672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
버스고속버스버스계전세버스마을버스특수여객자동차개인택시일반택시택시계일반화물용달화물개별화물화물계합계년도시도
버스1.0000.2670.9990.7220.5420.4480.8600.7580.7900.7910.8020.7420.8170.8950.0000.457
고속버스0.2671.0000.2920.4330.2200.0810.048-0.045-0.0230.3770.2050.2010.3590.0870.0490.295
버스계0.9990.2921.0000.7250.5420.4450.8510.7470.7790.7950.7980.7350.8200.8880.0000.457
전세버스0.7220.4330.7251.0000.4020.3380.5610.5350.5440.7740.6670.7230.7750.6790.1820.392
마을버스0.5420.2200.5420.4021.0000.4150.6000.6460.6450.6240.4340.4820.6080.6530.0000.472
특수여객자동차0.4480.0810.4450.3380.4151.0000.4840.4710.4870.5250.5280.5290.5150.5170.0000.139
개인택시0.8600.0480.8510.5610.6000.4841.0000.9070.9480.6670.7390.7370.7020.9440.0000.585
일반택시0.758-0.0450.7470.5350.6460.4710.9071.0000.9900.6100.5610.6410.6210.9340.0000.269
택시계0.790-0.0230.7790.5440.6450.4870.9480.9901.0000.6340.6210.6770.6550.9530.0000.483
일반화물0.7910.3770.7950.7740.6240.5250.6670.6100.6341.0000.7700.7360.9910.7870.0000.864
용달화물0.8020.2050.7980.6670.4340.5280.7390.5610.6210.7701.0000.8960.8260.7640.0000.477
개별화물0.7420.2010.7350.7230.4820.5290.7370.6410.6770.7360.8961.0000.7760.7710.0000.661
화물계0.8170.3590.8200.7750.6080.5150.7020.6210.6550.9910.8260.7761.0000.8110.0000.790
합계0.8950.0870.8880.6790.6530.5170.9440.9340.9530.7870.7640.7710.8111.0000.0790.385
년도0.0000.0490.0000.1820.0000.0000.0000.0000.0000.0000.0000.0000.0000.0791.0000.000
시도0.4570.2950.4570.3920.4720.1390.5850.2690.4830.8640.4770.6610.7900.3850.0001.000

Missing values

2023-12-13T07:33:00.934267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:33:01.136641image/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

년도시도버스고속버스버스계전세버스마을버스특수여객자동차개인택시일반택시택시계일반화물용달화물개별화물화물계합계
02018서울특별시청79007901043058203867568794474390112133810977
12018부산광역시청191019172460492961145332953497112193
22018대구광역시청192019264014671089155616471873992135
32018인천광역시청2520252610120845966724358475441329
42018광주광역시청1230123563119977297116518183481355
52018대전광역시청1870187280025351877114440303281200
62018울산광역시청81081260011116027184157183484
72018세종특별자치시29029600671316003264
82018경기도청22541722715576184101113722383137348129832277985
92018강원도청11621184502171233404872825202709
년도시도버스고속버스버스계전세버스마을버스특수여객자동차개인택시일반택시택시계일반화물용달화물개별화물화물계합계
752022세종특별자치시16016500641014002845
762022경기도청13664137053647629998041803133867834133546544
772022강원도청952974300143142285884633222592
782022충청북도청8618758011521523041803637396703
792022충청남도청10621086900961132091905845438679
802022전라북도청113011340011582534111412221304749
812022전라남도청106511148111261692953224024684842
822022경상북도청1320132600218431249624272515561055
832022경상남도청152415648411882053932605862578982
842022제주특별자치도청640643100113136249551823128440