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 20:06:25.760674
Analysis finished2023-12-11 20:06:28.649328
Duration2.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct1706
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T05:06:28.962185image/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

Unique87 ?
Unique (%)0.9%

Sample

1st row46100594
2nd row461000bf
3rd row46100fb1
4th row4610106c
5th row461011b8
ValueCountFrequency (%)
46100122 16
 
0.2%
46101035 15
 
0.1%
46100fff 15
 
0.1%
46100204 15
 
0.1%
461010d4 14
 
0.1%
46100654 14
 
0.1%
46100395 14
 
0.1%
46100467 14
 
0.1%
4610013f 14
 
0.1%
46101192 14
 
0.1%
Other values (1696) 9855
98.6%
2023-12-12T05:06:29.506436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18560
23.2%
1 17274
21.6%
4 11927
14.9%
6 11875
14.8%
7 2478
 
3.1%
3 2148
 
2.7%
8 2059
 
2.6%
5 1994
 
2.5%
2 1899
 
2.4%
f 1671
 
2.1%
Other values (6) 8115
10.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71727
89.7%
Lowercase Letter 8273
 
10.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18560
25.9%
1 17274
24.1%
4 11927
16.6%
6 11875
16.6%
7 2478
 
3.5%
3 2148
 
3.0%
8 2059
 
2.9%
5 1994
 
2.8%
2 1899
 
2.6%
9 1513
 
2.1%
Lowercase Letter
ValueCountFrequency (%)
f 1671
20.2%
a 1419
17.2%
e 1323
16.0%
c 1294
15.6%
b 1292
15.6%
d 1274
15.4%

Most occurring scripts

ValueCountFrequency (%)
Common 71727
89.7%
Latin 8273
 
10.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18560
25.9%
1 17274
24.1%
4 11927
16.6%
6 11875
16.6%
7 2478
 
3.5%
3 2148
 
3.0%
8 2059
 
2.9%
5 1994
 
2.8%
2 1899
 
2.6%
9 1513
 
2.1%
Latin
ValueCountFrequency (%)
f 1671
20.2%
a 1419
17.2%
e 1323
16.0%
c 1294
15.6%
b 1292
15.6%
d 1274
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18560
23.2%
1 17274
21.6%
4 11927
14.9%
6 11875
14.8%
7 2478
 
3.1%
3 2148
 
2.7%
8 2059
 
2.6%
5 1994
 
2.5%
2 1899
 
2.4%
f 1671
 
2.1%
Other values (6) 8115
10.1%

collection_dt
Real number (ℝ)

Distinct6523
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0200916 × 1013
Minimum2.0200905 × 1013
Maximum2.0200927 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:06:29.753816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0200905 × 1013
5-th percentile2.0200905 × 1013
Q12.0200912 × 1013
median2.0200919 × 1013
Q32.020092 × 1013
95-th percentile2.0200926 × 1013
Maximum2.0200927 × 1013
Range21981321
Interquartile range (IQR)7905264.2

Descriptive statistics

Standard deviation7329313.3
Coefficient of variation (CV)3.6282083 × 10-7
Kurtosis-1.1749899
Mean2.0200916 × 1013
Median Absolute Deviation (MAD)6981435
Skewness-0.13549806
Sum2.0200916 × 1017
Variance5.3718834 × 1013
MonotonicityNot monotonic
2023-12-12T05:06:29.990442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200926165530 7
 
0.1%
20200912123500 7
 
0.1%
20200912181400 7
 
0.1%
20200919162300 6
 
0.1%
20200919132130 6
 
0.1%
20200912135130 6
 
0.1%
20200919180300 6
 
0.1%
20200926152900 6
 
0.1%
20200919160330 6
 
0.1%
20200926155930 6
 
0.1%
Other values (6513) 9937
99.4%
ValueCountFrequency (%)
20200905051830 1
< 0.1%
20200905054020 1
< 0.1%
20200905060030 1
< 0.1%
20200905060400 1
< 0.1%
20200905062130 1
< 0.1%
20200905063000 1
< 0.1%
20200905063330 1
< 0.1%
20200905063400 1
< 0.1%
20200905064200 1
< 0.1%
20200905064847 1
< 0.1%
ValueCountFrequency (%)
20200927033151 1
< 0.1%
20200927022608 1
< 0.1%
20200927005841 1
< 0.1%
20200927005144 1
< 0.1%
20200927002900 1
< 0.1%
20200927001400 1
< 0.1%
20200926235438 1
< 0.1%
20200926234938 1
< 0.1%
20200926233900 1
< 0.1%
20200926231607 1
< 0.1%

longitude
Real number (ℝ)

Distinct9827
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.53253
Minimum126.16301
Maximum126.96928
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:06:30.189666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16301
5-th percentile126.24821
Q1126.40844
median126.50184
Q3126.6587
95-th percentile126.91608
Maximum126.96928
Range0.806274
Interquartile range (IQR)0.2502566

Descriptive statistics

Standard deviation0.19307349
Coefficient of variation (CV)0.0015258803
Kurtosis-0.44968315
Mean126.53253
Median Absolute Deviation (MAD)0.1173478
Skewness0.49715031
Sum1265325.3
Variance0.037277373
MonotonicityNot monotonic
2023-12-12T05:06:30.444967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.528284 4
 
< 0.1%
126.502618 3
 
< 0.1%
126.486225 3
 
< 0.1%
126.669696 2
 
< 0.1%
126.562313 2
 
< 0.1%
126.503135 2
 
< 0.1%
126.430443 2
 
< 0.1%
126.472371 2
 
< 0.1%
126.49296 2
 
< 0.1%
126.482715 2
 
< 0.1%
Other values (9817) 9976
99.8%
ValueCountFrequency (%)
126.163007 1
< 0.1%
126.1639928 1
< 0.1%
126.164018 1
< 0.1%
126.164023 1
< 0.1%
126.164057 1
< 0.1%
126.164101 1
< 0.1%
126.164109 1
< 0.1%
126.164232 1
< 0.1%
126.164386 1
< 0.1%
126.164543 1
< 0.1%
ValueCountFrequency (%)
126.969281 1
< 0.1%
126.969157 1
< 0.1%
126.969144 1
< 0.1%
126.968872 1
< 0.1%
126.968863 1
< 0.1%
126.968611 1
< 0.1%
126.96861 1
< 0.1%
126.968576 1
< 0.1%
126.968532 1
< 0.1%
126.968501 1
< 0.1%

latitude
Real number (ℝ)

Distinct9705
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.414814
Minimum33.199336
Maximum33.563896
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:06:30.650892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.199336
5-th percentile33.241174
Q133.305404
median33.461416
Q333.497975
95-th percentile33.541689
Maximum33.563896
Range0.36456
Interquartile range (IQR)0.192571

Descriptive statistics

Standard deviation0.10558796
Coefficient of variation (CV)0.0031599145
Kurtosis-1.1811863
Mean33.414814
Median Absolute Deviation (MAD)0.0540315
Skewness-0.58883958
Sum334148.14
Variance0.011148816
MonotonicityNot monotonic
2023-12-12T05:06:30.868540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.496406 4
 
< 0.1%
33.50618 3
 
< 0.1%
33.485259 3
 
< 0.1%
33.423483 3
 
< 0.1%
33.394855 3
 
< 0.1%
33.512187 3
 
< 0.1%
33.250353 3
 
< 0.1%
33.497802 3
 
< 0.1%
33.496558 3
 
< 0.1%
33.496463 3
 
< 0.1%
Other values (9695) 9969
99.7%
ValueCountFrequency (%)
33.199336 1
< 0.1%
33.199539 1
< 0.1%
33.200082 1
< 0.1%
33.200178 1
< 0.1%
33.204167 1
< 0.1%
33.204463 1
< 0.1%
33.204478 1
< 0.1%
33.204492 1
< 0.1%
33.204569 1
< 0.1%
33.204598 1
< 0.1%
ValueCountFrequency (%)
33.563896 1
< 0.1%
33.563865 1
< 0.1%
33.5635426 1
< 0.1%
33.563434 1
< 0.1%
33.562434 1
< 0.1%
33.562028 1
< 0.1%
33.561284 1
< 0.1%
33.560347 1
< 0.1%
33.560174 1
< 0.1%
33.559699 1
< 0.1%

time
Date

Distinct6523
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-09-05 05:18:30
Maximum2020-09-27 03:31:51
2023-12-12T05:06:31.061142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:31.254236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Diff
Real number (ℝ)

Distinct4085
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93436.673
Minimum1200
Maximum1789650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:06:31.714712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile1440
Q12430
median4088.5
Q39660
95-th percentile578710.05
Maximum1789650
Range1788450
Interquartile range (IQR)7230

Descriptive statistics

Standard deviation217472.95
Coefficient of variation (CV)2.3274903
Kurtosis5.743884
Mean93436.673
Median Absolute Deviation (MAD)2138.5
Skewness2.3929103
Sum9.3436673 × 108
Variance4.7294484 × 1010
MonotonicityNot monotonic
2023-12-12T05:06:31.888693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1710 61
 
0.6%
1290 56
 
0.6%
1350 55
 
0.5%
2160 55
 
0.5%
1500 54
 
0.5%
2820 54
 
0.5%
1230 53
 
0.5%
2040 53
 
0.5%
1560 52
 
0.5%
1470 52
 
0.5%
Other values (4075) 9455
94.5%
ValueCountFrequency (%)
1200 38
0.4%
1207 1
 
< 0.1%
1210 1
 
< 0.1%
1214 1
 
< 0.1%
1215 2
 
< 0.1%
1216 1
 
< 0.1%
1220 1
 
< 0.1%
1223 1
 
< 0.1%
1224 1
 
< 0.1%
1227 2
 
< 0.1%
ValueCountFrequency (%)
1789650 1
< 0.1%
1788030 1
< 0.1%
1787490 1
< 0.1%
1780982 1
< 0.1%
1767090 1
< 0.1%
1759590 1
< 0.1%
1213560 1
< 0.1%
1213380 1
< 0.1%
1204500 1
< 0.1%
1202040 1
< 0.1%

Interactions

2023-12-12T05:06:27.914183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:26.392617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:26.839564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:27.363512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:28.020813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:26.487088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:26.979820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:27.490048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:28.135073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:26.625153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:27.098688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:27.635636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:28.247492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:26.735616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:27.225840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:27.757901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:06:31.991564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.0000.0710.0630.178
longitude0.0711.0000.8310.076
latitude0.0630.8311.0000.089
Diff0.1780.0760.0891.000
2023-12-12T05:06:32.088063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.000-0.007-0.028-0.080
longitude-0.0071.0000.3030.026
latitude-0.0280.3031.0000.031
Diff-0.0800.0260.0311.000

Missing values

2023-12-12T05:06:28.433853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:06:28.584981image/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
103494610059420200919151917126.47165433.4803732020-09-19 15:19:179824
257461000bf20200919112510126.83127933.3332022020-09-19 11:25:101770
1521246100fb120200912173900126.56400533.2862012020-09-12 17:39:00571350
173124610106c20200905111830126.932633.4721992020-09-05 11:18:3023400
21324461011b820200919120230126.50060533.5031792020-09-19 12:02:302490
1520746100faf20200926130730126.46777433.4791662020-09-26 13:07:301770
208694610119220200905173100126.34862733.4559942020-09-05 17:31:003270
206384610118320200926194830126.51975233.5116232020-09-26 19:48:301890
120944610065c20200919123400126.24214233.3959122020-09-19 12:34:002130
11124610012920200905134542126.72066533.2842422020-09-05 13:45:421351
oidcollection_dtlongitudelatitudetimeDiff
224004610177b20200919113330126.30712633.4519082020-09-19 11:33:301620
1593346100ff520200912145830126.24075833.394732020-09-12 14:58:308160
12804461006c320200912122730126.51982833.5112332020-09-12 12:27:307498
98954610056120200926122525126.27107533.4064592020-09-26 12:25:254106
10484461005a320200912122900126.62220533.2541162020-09-12 12:29:0012620
10710461005be20200912162722126.49620833.4935642020-09-12 16:27:22576081
26418461018ec20200926130600126.28953733.2064792020-09-26 13:06:0010770
59024610038020200912175830126.89776533.5127762020-09-12 17:58:304770
6190461003af20200926113930126.83657833.46462020-09-26 11:39:304614
9065461004f320200919152100126.41551733.2451442020-09-19 15:21:006990