Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory585.9 KiB
Average record size in memory60.0 B

Variable types

Text1
Numeric4
DateTime1

Dataset

Description- 월별 토요일의 렌터카 체류 거점 위치정보 입니다. - 체류 거점은 수집시간 전로그와 후로그의 시각차이가 20분 이상인 경우, 전로그의 위치를 의미합니다. - 기간: 2020년 1월부터 2021년 12월 까지
Author제주특별자치도 미래성장과
URLhttps://www.jejudatahub.net/data/view/data/1206

Reproduction

Analysis started2023-12-11 20:06:18.690386
Analysis finished2023-12-11 20:06:20.858970
Duration2.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct1781
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T05:06:21.090227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters80000
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)0.8%

Sample

1st row461017b5
2nd row461012d7
3rd row46101146
4th row461003b0
5th row46101086
ValueCountFrequency (%)
4610032d 16
 
0.2%
461010ab 15
 
0.1%
46100590 15
 
0.1%
46100740 15
 
0.1%
46100528 14
 
0.1%
46100597 14
 
0.1%
461005c4 14
 
0.1%
46101061 14
 
0.1%
4610117d 13
 
0.1%
46100ff7 13
 
0.1%
Other values (1771) 9857
98.6%
2023-12-12T05:06:21.597100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18945
23.7%
1 17102
21.4%
4 12000
15.0%
6 11907
14.9%
7 2528
 
3.2%
3 2092
 
2.6%
5 1993
 
2.5%
2 1977
 
2.5%
f 1785
 
2.2%
8 1676
 
2.1%
Other values (6) 7995
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71652
89.6%
Lowercase Letter 8348
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18945
26.4%
1 17102
23.9%
4 12000
16.7%
6 11907
16.6%
7 2528
 
3.5%
3 2092
 
2.9%
5 1993
 
2.8%
2 1977
 
2.8%
8 1676
 
2.3%
9 1432
 
2.0%
Lowercase Letter
ValueCountFrequency (%)
f 1785
21.4%
d 1348
16.1%
b 1332
16.0%
e 1326
15.9%
a 1297
15.5%
c 1260
15.1%

Most occurring scripts

ValueCountFrequency (%)
Common 71652
89.6%
Latin 8348
 
10.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18945
26.4%
1 17102
23.9%
4 12000
16.7%
6 11907
16.6%
7 2528
 
3.5%
3 2092
 
2.9%
5 1993
 
2.8%
2 1977
 
2.8%
8 1676
 
2.3%
9 1432
 
2.0%
Latin
ValueCountFrequency (%)
f 1785
21.4%
d 1348
16.1%
b 1332
16.0%
e 1326
15.9%
a 1297
15.5%
c 1260
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18945
23.7%
1 17102
21.4%
4 12000
15.0%
6 11907
14.9%
7 2528
 
3.2%
3 2092
 
2.6%
5 1993
 
2.5%
2 1977
 
2.5%
f 1785
 
2.2%
8 1676
 
2.1%
Other values (6) 7995
10.0%

collection_dt
Real number (ℝ)

Distinct6660
Distinct (%)66.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0200814 × 1013
Minimum2.0200801 × 1013
Maximum2.020083 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:06:21.805376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0200801 × 1013
5-th percentile2.0200801 × 1013
Q12.0200801 × 1013
median2.0200808 × 1013
Q32.0200822 × 1013
95-th percentile2.0200829 × 1013
Maximum2.020083 × 1013
Range28971430
Interquartile range (IQR)20959389

Descriptive statistics

Standard deviation10566910
Coefficient of variation (CV)5.2309328 × 10-7
Kurtosis-1.5319757
Mean2.0200814 × 1013
Median Absolute Deviation (MAD)7063570
Skewness0.21569245
Sum2.0200814 × 1017
Variance1.1165958 × 1014
MonotonicityNot monotonic
2023-12-12T05:06:22.005426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200808100230 8
 
0.1%
20200808122330 8
 
0.1%
20200801161830 7
 
0.1%
20200808135630 6
 
0.1%
20200808115800 6
 
0.1%
20200801161200 6
 
0.1%
20200822143830 6
 
0.1%
20200801133200 6
 
0.1%
20200808162330 6
 
0.1%
20200808150330 6
 
0.1%
Other values (6650) 9935
99.4%
ValueCountFrequency (%)
20200801050300 1
< 0.1%
20200801052300 1
< 0.1%
20200801054630 1
< 0.1%
20200801061730 1
< 0.1%
20200801062357 1
< 0.1%
20200801064600 1
< 0.1%
20200801064819 1
< 0.1%
20200801064830 1
< 0.1%
20200801064900 1
< 0.1%
20200801065600 1
< 0.1%
ValueCountFrequency (%)
20200830021730 1
< 0.1%
20200830010830 1
< 0.1%
20200829235330 1
< 0.1%
20200829230900 1
< 0.1%
20200829230800 1
< 0.1%
20200829224730 1
< 0.1%
20200829224430 1
< 0.1%
20200829224200 1
< 0.1%
20200829223000 1
< 0.1%
20200829222930 1
< 0.1%

longitude
Real number (ℝ)

Distinct9816
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.53034
Minimum126.16399
Maximum126.96951
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:06:22.181777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16399
5-th percentile126.24995
Q1126.40186
median126.50051
Q3126.6448
95-th percentile126.91704
Maximum126.96951
Range0.805522
Interquartile range (IQR)0.24294075

Descriptive statistics

Standard deviation0.19317486
Coefficient of variation (CV)0.0015267079
Kurtosis-0.42488519
Mean126.53034
Median Absolute Deviation (MAD)0.11966095
Skewness0.53676122
Sum1265303.4
Variance0.037316527
MonotonicityNot monotonic
2023-12-12T05:06:22.424263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.528175 3
 
< 0.1%
126.486262 3
 
< 0.1%
126.502905 3
 
< 0.1%
126.500573 3
 
< 0.1%
126.500514 3
 
< 0.1%
126.312069 3
 
< 0.1%
126.238145 2
 
< 0.1%
126.91715 2
 
< 0.1%
126.483446 2
 
< 0.1%
126.910318 2
 
< 0.1%
Other values (9806) 9974
99.7%
ValueCountFrequency (%)
126.16399 1
< 0.1%
126.164134 1
< 0.1%
126.1643596 1
< 0.1%
126.164448 1
< 0.1%
126.164524 1
< 0.1%
126.164549 1
< 0.1%
126.164677 1
< 0.1%
126.164696 1
< 0.1%
126.164735 1
< 0.1%
126.164777 1
< 0.1%
ValueCountFrequency (%)
126.969512 1
< 0.1%
126.969377 1
< 0.1%
126.969282 1
< 0.1%
126.96913 1
< 0.1%
126.968063 1
< 0.1%
126.967522 1
< 0.1%
126.967355 1
< 0.1%
126.967278 1
< 0.1%
126.9671411 1
< 0.1%
126.966912 1
< 0.1%

latitude
Real number (ℝ)

Distinct9706
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.409588
Minimum33.200026
Maximum33.563799
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:06:22.614273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.200026
5-th percentile33.240751
Q133.290981
median33.453671
Q333.497696
95-th percentile33.54027
Maximum33.563799
Range0.363773
Interquartile range (IQR)0.20671425

Descriptive statistics

Standard deviation0.1074753
Coefficient of variation (CV)0.0032168998
Kurtosis-1.3140234
Mean33.409588
Median Absolute Deviation (MAD)0.05933
Skewness-0.50678326
Sum334095.88
Variance0.01155094
MonotonicityNot monotonic
2023-12-12T05:06:22.787895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.450136 3
 
< 0.1%
33.492798 3
 
< 0.1%
33.515472 3
 
< 0.1%
33.39893 3
 
< 0.1%
33.472152 3
 
< 0.1%
33.49779 3
 
< 0.1%
33.505593 3
 
< 0.1%
33.470911 3
 
< 0.1%
33.249837 3
 
< 0.1%
33.497714 3
 
< 0.1%
Other values (9696) 9970
99.7%
ValueCountFrequency (%)
33.200026 1
< 0.1%
33.200118 1
< 0.1%
33.205272 1
< 0.1%
33.205342 1
< 0.1%
33.206003 1
< 0.1%
33.206041 1
< 0.1%
33.206065 1
< 0.1%
33.206079 1
< 0.1%
33.206096 1
< 0.1%
33.206127 1
< 0.1%
ValueCountFrequency (%)
33.563799 1
< 0.1%
33.563788 1
< 0.1%
33.563699 1
< 0.1%
33.563679 1
< 0.1%
33.563635 1
< 0.1%
33.5635195 1
< 0.1%
33.563498 1
< 0.1%
33.561523 1
< 0.1%
33.5614 1
< 0.1%
33.561397 1
< 0.1%

time
Date

Distinct6660
Distinct (%)66.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-08-01 05:03:00
Maximum2020-08-30 02:17:30
2023-12-12T05:06:23.257268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:23.434307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Diff
Real number (ℝ)

Distinct4680
Distinct (%)46.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125909.03
Minimum1200
Maximum2397110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:06:23.612781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile1470
Q12559.75
median4230
Q310147.5
95-th percentile1159953
Maximum2397110
Range2395910
Interquartile range (IQR)7587.75

Descriptive statistics

Standard deviation310950.23
Coefficient of variation (CV)2.469642
Kurtosis6.6129763
Mean125909.03
Median Absolute Deviation (MAD)2179
Skewness2.6754785
Sum1.2590903 × 109
Variance9.6690047 × 1010
MonotonicityNot monotonic
2023-12-12T05:06:23.799897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1410 48
 
0.5%
1470 48
 
0.5%
1920 47
 
0.5%
2250 47
 
0.5%
2910 46
 
0.5%
1350 45
 
0.4%
2460 44
 
0.4%
3600 44
 
0.4%
1980 44
 
0.4%
2370 43
 
0.4%
Other values (4670) 9544
95.4%
ValueCountFrequency (%)
1200 32
0.3%
1207 3
 
< 0.1%
1209 2
 
< 0.1%
1210 2
 
< 0.1%
1211 1
 
< 0.1%
1212 1
 
< 0.1%
1214 1
 
< 0.1%
1216 1
 
< 0.1%
1221 1
 
< 0.1%
1222 1
 
< 0.1%
ValueCountFrequency (%)
2397110 1
< 0.1%
1813470 1
< 0.1%
1809997 1
< 0.1%
1809791 1
< 0.1%
1809302 1
< 0.1%
1808904 1
< 0.1%
1806103 1
< 0.1%
1805610 1
< 0.1%
1799037 1
< 0.1%
1797972 1
< 0.1%

Interactions

2023-12-12T05:06:20.333574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:19.226397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:19.610771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:19.972881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:20.426380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:19.330865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:19.701693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:20.061499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:20.522096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:19.434766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:19.793282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:20.157621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:20.611783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:19.530021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:19.883137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:20.248539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:06:23.903585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.0000.0800.0610.402
longitude0.0801.0000.8320.066
latitude0.0610.8321.0000.065
Diff0.4020.0660.0651.000
2023-12-12T05:06:24.021767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.0000.008-0.016-0.073
longitude0.0081.0000.2900.008
latitude-0.0160.2901.0000.003
Diff-0.0730.0080.0031.000

Missing values

2023-12-12T05:06:20.716980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:06:20.810824image/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

oidcollection_dtlongitudelatitudetimeDiff
27331461017b520200822124230126.23927333.3930222020-08-22 12:42:308970
26283461012d720200829154830126.62257233.2538832020-08-29 15:48:302190
234104610114620200822161730126.6227433.2518712020-08-22 16:17:304170
6907461003b020200801122600126.88397433.3846152020-08-01 12:26:001314
207424610108620200808163400126.86167233.524722020-08-08 16:34:001230
246954610119320200808174230126.46352633.4791042020-08-08 17:42:304590
25919461011e220200808142130126.35862733.3482012020-08-08 14:21:302430
196074610103920200801130830126.38197133.4855462020-08-01 13:08:304350
27833461017d720200808150930126.2884633.3043772020-08-08 15:09:305100
162604610078b20200822154800126.45543333.4932042020-08-22 15:48:005564
oidcollection_dtlongitudelatitudetimeDiff
55084610032420200801100730126.84278333.3812962020-08-01 10:07:303930
59364610034720200808123441126.62216133.2540262020-08-08 12:34:419279
39234610025620200801151333126.83760133.5307422020-08-01 15:13:336351
155844610074420200829190710126.79974733.5550592020-08-29 19:07:102172
289314610181520200801120300126.44652433.2433582020-08-01 12:03:005280
111604610056f20200801190030126.94489533.4942582020-08-01 19:00:30577767
1788546100fcd20200822143130126.61722133.2567692020-08-22 14:31:303060
39354610025720200808194051126.74911833.2890072020-08-08 19:40:511180362
288004610180e20200808090230126.75625433.4536612020-08-08 09:02:304050
135994610066a20200822142500126.73747333.4360142020-08-22 14:25:001710