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 20:06:50.919359
Analysis finished2023-12-11 20:06:53.315767
Duration2.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct1953
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T05:06:53.562793image/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

Unique123 ?
Unique (%)1.2%

Sample

1st row46100fc5
2nd row4610047e
3rd row4610122f
4th row461004a1
5th row4610126a
ValueCountFrequency (%)
461000b5 17
 
0.2%
46101176 16
 
0.2%
46100635 14
 
0.1%
46100fcd 13
 
0.1%
46100780 13
 
0.1%
46100599 13
 
0.1%
4610183f 13
 
0.1%
46100ffa 13
 
0.1%
46100499 13
 
0.1%
461005a8 13
 
0.1%
Other values (1943) 9862
98.6%
2023-12-12T05:06:54.045410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 17696
22.1%
1 17252
21.6%
4 11923
14.9%
6 11869
14.8%
2 2760
 
3.5%
7 2421
 
3.0%
8 2262
 
2.8%
3 2193
 
2.7%
5 2034
 
2.5%
9 1629
 
2.0%
Other values (6) 7961
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72039
90.0%
Lowercase Letter 7961
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17696
24.6%
1 17252
23.9%
4 11923
16.6%
6 11869
16.5%
2 2760
 
3.8%
7 2421
 
3.4%
8 2262
 
3.1%
3 2193
 
3.0%
5 2034
 
2.8%
9 1629
 
2.3%
Lowercase Letter
ValueCountFrequency (%)
f 1584
19.9%
a 1390
17.5%
b 1387
17.4%
c 1249
15.7%
d 1217
15.3%
e 1134
14.2%

Most occurring scripts

ValueCountFrequency (%)
Common 72039
90.0%
Latin 7961
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17696
24.6%
1 17252
23.9%
4 11923
16.6%
6 11869
16.5%
2 2760
 
3.8%
7 2421
 
3.4%
8 2262
 
3.1%
3 2193
 
3.0%
5 2034
 
2.8%
9 1629
 
2.3%
Latin
ValueCountFrequency (%)
f 1584
19.9%
a 1390
17.5%
b 1387
17.4%
c 1249
15.7%
d 1217
15.3%
e 1134
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17696
22.1%
1 17252
21.6%
4 11923
14.9%
6 11869
14.8%
2 2760
 
3.5%
7 2421
 
3.0%
8 2262
 
2.8%
3 2193
 
2.7%
5 2034
 
2.5%
9 1629
 
2.0%
Other values (6) 7961
10.0%

collection_dt
Real number (ℝ)

Distinct3822
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0201215 × 1013
Minimum2.0201212 × 1013
Maximum2.020122 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:06:54.220885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0201212 × 1013
5-th percentile2.0201212 × 1013
Q12.0201212 × 1013
median2.0201212 × 1013
Q32.0201219 × 1013
95-th percentile2.0201219 × 1013
Maximum2.020122 × 1013
Range7989888
Interquartile range (IQR)6987501.5

Descriptive statistics

Standard deviation3375542
Coefficient of variation (CV)1.6709599 × 10-7
Kurtosis-1.7153161
Mean2.0201215 × 1013
Median Absolute Deviation (MAD)68792.5
Skewness0.53231502
Sum2.0201215 × 1017
Variance1.1394284 × 1013
MonotonicityNot monotonic
2023-12-12T05:06:54.404600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20201212143230 12
 
0.1%
20201212134600 11
 
0.1%
20201212142830 10
 
0.1%
20201212182030 10
 
0.1%
20201212145330 10
 
0.1%
20201212112530 10
 
0.1%
20201212124130 10
 
0.1%
20201212111400 10
 
0.1%
20201219122800 10
 
0.1%
20201212164930 10
 
0.1%
Other values (3812) 9897
99.0%
ValueCountFrequency (%)
20201212050230 1
< 0.1%
20201212050300 1
< 0.1%
20201212050530 1
< 0.1%
20201212051800 1
< 0.1%
20201212052030 1
< 0.1%
20201212054100 1
< 0.1%
20201212055800 1
< 0.1%
20201212060030 1
< 0.1%
20201212060130 1
< 0.1%
20201212060300 1
< 0.1%
ValueCountFrequency (%)
20201220040118 1
< 0.1%
20201220015130 1
< 0.1%
20201220012400 1
< 0.1%
20201220001821 1
< 0.1%
20201219234107 1
< 0.1%
20201219232130 1
< 0.1%
20201219230800 1
< 0.1%
20201219230710 1
< 0.1%
20201219223400 1
< 0.1%
20201219222700 1
< 0.1%

longitude
Real number (ℝ)

Distinct9749
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.52789
Minimum126.16416
Maximum130.95289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:06:54.579329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16416
5-th percentile126.25785
Q1126.40837
median126.4979
Q3126.62221
95-th percentile126.91622
Maximum130.95289
Range4.788732
Interquartile range (IQR)0.213847

Descriptive statistics

Standard deviation0.19073566
Coefficient of variation (CV)0.0015074594
Kurtosis28.67843
Mean126.52789
Median Absolute Deviation (MAD)0.102349
Skewness1.8180322
Sum1265278.9
Variance0.03638009
MonotonicityNot monotonic
2023-12-12T05:06:54.738251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.311209 3
 
< 0.1%
126.50325 3
 
< 0.1%
126.497676 3
 
< 0.1%
126.678887 3
 
< 0.1%
126.497767 3
 
< 0.1%
126.483388 3
 
< 0.1%
126.455986 3
 
< 0.1%
126.486259 3
 
< 0.1%
126.497678 3
 
< 0.1%
126.497677 3
 
< 0.1%
Other values (9739) 9970
99.7%
ValueCountFrequency (%)
126.164159 1
< 0.1%
126.164352 1
< 0.1%
126.164371 1
< 0.1%
126.164517 1
< 0.1%
126.164582 1
< 0.1%
126.164752 1
< 0.1%
126.164787 1
< 0.1%
126.164798 1
< 0.1%
126.165122 1
< 0.1%
126.165428 1
< 0.1%
ValueCountFrequency (%)
130.952891 1
< 0.1%
127.5110966 1
< 0.1%
127.5094125 1
< 0.1%
127.508867 1
< 0.1%
126.9888811 1
< 0.1%
126.969617 1
< 0.1%
126.9688983 1
< 0.1%
126.968829 1
< 0.1%
126.967814 1
< 0.1%
126.967039 1
< 0.1%

latitude
Real number (ℝ)

Distinct9616
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.411369
Minimum33.200066
Maximum37.633589
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:06:54.904460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.200066
5-th percentile33.239355
Q133.291806
median33.45489
Q333.497373
95-th percentile33.53445
Maximum37.633589
Range4.433523
Interquartile range (IQR)0.20556725

Descriptive statistics

Standard deviation0.13342597
Coefficient of variation (CV)0.0039934302
Kurtosis268.86784
Mean33.411369
Median Absolute Deviation (MAD)0.058552
Skewness9.4792945
Sum334113.69
Variance0.017802489
MonotonicityNot monotonic
2023-12-12T05:06:55.054830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.244428 4
 
< 0.1%
33.496521 3
 
< 0.1%
33.51683 3
 
< 0.1%
33.489704 3
 
< 0.1%
33.492624 3
 
< 0.1%
33.502217 3
 
< 0.1%
33.480148 3
 
< 0.1%
33.517979 3
 
< 0.1%
33.241234 3
 
< 0.1%
33.51729 3
 
< 0.1%
Other values (9606) 9969
99.7%
ValueCountFrequency (%)
33.200066 1
< 0.1%
33.204004 1
< 0.1%
33.204054 1
< 0.1%
33.204069 1
< 0.1%
33.204197 1
< 0.1%
33.20443 1
< 0.1%
33.204565 1
< 0.1%
33.204598 1
< 0.1%
33.204694 1
< 0.1%
33.205096 1
< 0.1%
ValueCountFrequency (%)
37.633589 1
< 0.1%
37.257413 1
< 0.1%
36.6642035 1
< 0.1%
36.6133396 1
< 0.1%
36.6129543 1
< 0.1%
33.564786 1
< 0.1%
33.560881 1
< 0.1%
33.560792 1
< 0.1%
33.560198 1
< 0.1%
33.559564 1
< 0.1%

time
Date

Distinct3822
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-12-12 05:02:30
Maximum2020-12-20 04:01:18
2023-12-12T05:06:55.217813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:55.345058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Diff
Real number (ℝ)

Distinct5969
Distinct (%)59.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60652.991
Minimum1200
Maximum630027
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:06:55.468701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile1416.95
Q12304.75
median3660
Q37297.75
95-th percentile573031.05
Maximum630027
Range628827
Interquartile range (IQR)4993

Descriptive statistics

Standard deviation169121.11
Coefficient of variation (CV)2.7883391
Kurtosis5.3698473
Mean60652.991
Median Absolute Deviation (MAD)1707
Skewness2.7114446
Sum6.0652992 × 108
Variance2.8601948 × 1010
MonotonicityNot monotonic
2023-12-12T05:06:55.599895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2490 26
 
0.3%
1650 21
 
0.2%
2130 20
 
0.2%
1770 20
 
0.2%
3630 19
 
0.2%
2370 18
 
0.2%
1530 18
 
0.2%
2610 17
 
0.2%
2850 17
 
0.2%
1410 17
 
0.2%
Other values (5959) 9807
98.1%
ValueCountFrequency (%)
1200 14
0.1%
1202 2
 
< 0.1%
1203 1
 
< 0.1%
1204 2
 
< 0.1%
1205 2
 
< 0.1%
1206 4
 
< 0.1%
1207 3
 
< 0.1%
1208 3
 
< 0.1%
1209 4
 
< 0.1%
1210 2
 
< 0.1%
ValueCountFrequency (%)
630027 1
< 0.1%
624347 1
< 0.1%
622723 1
< 0.1%
617424 1
< 0.1%
616988 1
< 0.1%
616628 1
< 0.1%
616445 1
< 0.1%
616163 1
< 0.1%
615937 1
< 0.1%
613992 1
< 0.1%

Interactions

2023-12-12T05:06:52.604998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:51.375241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:51.962389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:52.287464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:52.736757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:51.466678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:52.067106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:52.373422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:52.855580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:51.552962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:52.146934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:52.448816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:52.945957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:51.856953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:52.214842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:52.524911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:06:55.694437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.0000.0000.0000.291
longitude0.0001.0000.8030.045
latitude0.0000.8031.0000.082
Diff0.2910.0450.0821.000
2023-12-12T05:06:55.808501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.000-0.007-0.035-0.056
longitude-0.0071.0000.262-0.020
latitude-0.0350.2621.0000.036
Diff-0.056-0.0200.0361.000

Missing values

2023-12-12T05:06:53.096293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:06:53.261734image/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
693246100fc520201212231600126.48894133.2341382020-12-12 23:16:00544677
32054610047e20201219080400126.55046733.4221092020-12-19 08:04:0025477
98784610122f20201212143900126.47748433.4952322020-12-12 14:39:003952
3436461004a120201212141200126.79540133.5560762020-12-12 14:12:004774
101444610126a20201212114000126.49655933.3480622020-12-12 11:40:004774
65914610081720201212181130126.42357333.2410742020-12-12 18:11:302123
8307461010df20201212195230126.62537733.263892020-12-12 19:52:306412
124374610185320201212143400126.9351233.4611512020-12-12 14:34:002316
700146100fcf20201219093130126.84340933.3254412020-12-19 09:31:302489
4576461005b920201219122400126.22903633.2383562020-12-19 12:24:003360
oidcollection_dtlongitudelatitudetimeDiff
4525461005a820201219132530126.28954333.206382020-12-19 13:25:301680
76774610106520201219123430126.68907633.4350812020-12-19 12:34:301556
4841461005fb20201219093600126.50555433.4895652020-12-19 09:36:001879
5619461006da20201212221805126.3182633.3045912020-12-12 22:18:05562790
8313461010df20201219190800126.674133.5441052020-12-19 19:08:002593
1772461002d520201212103717126.93030333.4234192020-12-12 10:37:173060
99154610123620201212110400126.24358633.3968482020-12-12 11:04:002255
72724610100b20201219132800126.30962433.4627222020-12-19 13:28:005155
51874610066a20201219114430126.83225633.3266822020-12-19 11:44:3011221
693846100fc620201212171530126.22673333.3855592020-12-12 17:15:304699