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/1208

Alerts

longitude is highly skewed (γ1 = 97.45057218)Skewed
latitude is highly skewed (γ1 = 77.71887836)Skewed

Reproduction

Analysis started2023-12-11 20:03:30.294802
Analysis finished2023-12-11 20:03:32.353647
Duration2.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct1689
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T05:03:32.570075image/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

Unique53 ?
Unique (%)0.5%

Sample

1st row46101009
2nd row46101079
3rd row461010ad
4th row461010c0
5th row4610065a
ValueCountFrequency (%)
4610133f 16
 
0.2%
461011f5 16
 
0.2%
46101126 15
 
0.1%
4610031b 15
 
0.1%
461018e5 15
 
0.1%
46101322 14
 
0.1%
461011fd 14
 
0.1%
4610114e 14
 
0.1%
461017f6 13
 
0.1%
46100474 13
 
0.1%
Other values (1679) 9855
98.6%
2023-12-12T05:03:32.968277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 18032
22.5%
0 17121
21.4%
4 11989
15.0%
6 11779
14.7%
2 2566
 
3.2%
7 2552
 
3.2%
3 2272
 
2.8%
8 2271
 
2.8%
5 1960
 
2.5%
9 1681
 
2.1%
Other values (6) 7777
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72223
90.3%
Lowercase Letter 7777
 
9.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 18032
25.0%
0 17121
23.7%
4 11989
16.6%
6 11779
16.3%
2 2566
 
3.6%
7 2552
 
3.5%
3 2272
 
3.1%
8 2271
 
3.1%
5 1960
 
2.7%
9 1681
 
2.3%
Lowercase Letter
ValueCountFrequency (%)
f 1577
20.3%
b 1327
17.1%
a 1258
16.2%
d 1254
16.1%
e 1205
15.5%
c 1156
14.9%

Most occurring scripts

ValueCountFrequency (%)
Common 72223
90.3%
Latin 7777
 
9.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 18032
25.0%
0 17121
23.7%
4 11989
16.6%
6 11779
16.3%
2 2566
 
3.6%
7 2552
 
3.5%
3 2272
 
3.1%
8 2271
 
3.1%
5 1960
 
2.7%
9 1681
 
2.3%
Latin
ValueCountFrequency (%)
f 1577
20.3%
b 1327
17.1%
a 1258
16.2%
d 1254
16.1%
e 1205
15.5%
c 1156
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 18032
22.5%
0 17121
21.4%
4 11989
15.0%
6 11779
14.7%
2 2566
 
3.2%
7 2552
 
3.2%
3 2272
 
2.8%
8 2271
 
2.8%
5 1960
 
2.5%
9 1681
 
2.1%
Other values (6) 7777
9.7%

collection_dt
Real number (ℝ)

Distinct6286
Distinct (%)62.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0211016 × 1013
Minimum2.0211002 × 1013
Maximum2.0211031 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:03:33.119788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0211002 × 1013
5-th percentile2.0211002 × 1013
Q12.0211009 × 1013
median2.0211016 × 1013
Q32.0211023 × 1013
95-th percentile2.021103 × 1013
Maximum2.0211031 × 1013
Range28959600
Interquartile range (IQR)14046848

Descriptive statistics

Standard deviation9684312.1
Coefficient of variation (CV)4.7916009 × 10-7
Kurtosis-1.2679682
Mean2.0211016 × 1013
Median Absolute Deviation (MAD)7021650
Skewness0.049931708
Sum2.0211016 × 1017
Variance9.3785901 × 1013
MonotonicityNot monotonic
2023-12-12T05:03:33.257403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20211023162700 7
 
0.1%
20211023110430 7
 
0.1%
20211023120130 7
 
0.1%
20211030172000 7
 
0.1%
20211016130500 6
 
0.1%
20211023162400 6
 
0.1%
20211009161300 6
 
0.1%
20211016155330 6
 
0.1%
20211002105200 6
 
0.1%
20211016115730 6
 
0.1%
Other values (6276) 9936
99.4%
ValueCountFrequency (%)
20211002050500 1
 
< 0.1%
20211002053100 1
 
< 0.1%
20211002053600 1
 
< 0.1%
20211002054500 1
 
< 0.1%
20211002055230 1
 
< 0.1%
20211002061130 1
 
< 0.1%
20211002061400 1
 
< 0.1%
20211002061930 3
< 0.1%
20211002063000 1
 
< 0.1%
20211002063530 1
 
< 0.1%
ValueCountFrequency (%)
20211031010100 1
< 0.1%
20211030233830 1
< 0.1%
20211030224200 1
< 0.1%
20211030220830 1
< 0.1%
20211030215830 1
< 0.1%
20211030215000 1
< 0.1%
20211030214544 1
< 0.1%
20211030214130 1
< 0.1%
20211030213630 1
< 0.1%
20211030213400 1
< 0.1%

longitude
Real number (ℝ)

SKEWED 

Distinct9749
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.54792
Minimum126.16358
Maximum295.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:03:33.411039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16358
5-th percentile126.24959
Q1126.38162
median126.4978
Q3126.63743
95-th percentile126.92036
Maximum295.8
Range169.63642
Interquartile range (IQR)0.25580725

Descriptive statistics

Standard deviation1.7073595
Coefficient of variation (CV)0.013491803
Kurtosis9660.6361
Mean126.54792
Median Absolute Deviation (MAD)0.1245425
Skewness97.450572
Sum1265479.2
Variance2.9150765
MonotonicityNot monotonic
2023-12-12T05:03:33.542364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.562341 3
 
< 0.1%
126.48337 3
 
< 0.1%
126.573771 3
 
< 0.1%
126.918245 3
 
< 0.1%
126.486363 3
 
< 0.1%
126.309816 3
 
< 0.1%
126.486172 3
 
< 0.1%
126.497723 3
 
< 0.1%
126.465754 3
 
< 0.1%
126.486339 3
 
< 0.1%
Other values (9739) 9970
99.7%
ValueCountFrequency (%)
126.163584 1
< 0.1%
126.16377 1
< 0.1%
126.163805 1
< 0.1%
126.163883 1
< 0.1%
126.164073 1
< 0.1%
126.164108 1
< 0.1%
126.164366 1
< 0.1%
126.164376 1
< 0.1%
126.164394 1
< 0.1%
126.164578 1
< 0.1%
ValueCountFrequency (%)
295.8 1
< 0.1%
129.228428 1
< 0.1%
129.21459 1
< 0.1%
129.175069 1
< 0.1%
129.172891 1
< 0.1%
129.160523 1
< 0.1%
129.159087 1
< 0.1%
129.113287 1
< 0.1%
129.082605 1
< 0.1%
129.043703 1
< 0.1%

latitude
Real number (ℝ)

SKEWED 

Distinct9622
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.414501
Minimum33.19944
Maximum67.499303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:03:33.664515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.19944
5-th percentile33.2396
Q133.289831
median33.449494
Q333.496494
95-th percentile33.536485
Maximum67.499303
Range34.299863
Interquartile range (IQR)0.20666275

Descriptive statistics

Standard deviation0.37164062
Coefficient of variation (CV)0.011122136
Kurtosis7082.4038
Mean33.414501
Median Absolute Deviation (MAD)0.0628405
Skewness77.718878
Sum334145.01
Variance0.13811675
MonotonicityNot monotonic
2023-12-12T05:03:33.783031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.250133 3
 
< 0.1%
33.435008 3
 
< 0.1%
33.512169 3
 
< 0.1%
33.49627 3
 
< 0.1%
33.485428 3
 
< 0.1%
33.498041 3
 
< 0.1%
33.502719 3
 
< 0.1%
33.510751 3
 
< 0.1%
33.462722 3
 
< 0.1%
33.502802 3
 
< 0.1%
Other values (9612) 9970
99.7%
ValueCountFrequency (%)
33.19944 1
< 0.1%
33.200019 1
< 0.1%
33.200064 1
< 0.1%
33.201124 1
< 0.1%
33.204135 1
< 0.1%
33.204475 1
< 0.1%
33.204616 1
< 0.1%
33.205145 1
< 0.1%
33.205534 1
< 0.1%
33.205965 1
< 0.1%
ValueCountFrequency (%)
67.499303 1
< 0.1%
37.770673 1
< 0.1%
37.740541 1
< 0.1%
36.037823 1
< 0.1%
35.8595381 1
< 0.1%
35.833163 1
< 0.1%
35.37046 1
< 0.1%
35.199059 1
< 0.1%
35.167927 1
< 0.1%
35.161146 1
< 0.1%

time
Date

Distinct6286
Distinct (%)62.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-10-02 05:05:00
Maximum2021-10-31 01:01:00
2023-12-12T05:03:33.909305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:34.258512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Diff
Real number (ℝ)

Distinct6283
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95602.446
Minimum1200
Maximum1816350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:03:34.392445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile1470
Q12490
median3970.5
Q38700
95-th percentile574994.25
Maximum1816350
Range1815150
Interquartile range (IQR)6210

Descriptive statistics

Standard deviation213306.65
Coefficient of variation (CV)2.2311841
Kurtosis3.6204778
Mean95602.446
Median Absolute Deviation (MAD)1897.5
Skewness2.1036655
Sum9.5602446 × 108
Variance4.5499728 × 1010
MonotonicityNot monotonic
2023-12-12T05:03:34.591022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 23
 
0.2%
2790 19
 
0.2%
3210 19
 
0.2%
2370 18
 
0.2%
2610 17
 
0.2%
1410 17
 
0.2%
2970 17
 
0.2%
2550 15
 
0.1%
3330 15
 
0.1%
2850 15
 
0.1%
Other values (6273) 9825
98.2%
ValueCountFrequency (%)
1200 7
0.1%
1201 3
< 0.1%
1202 2
 
< 0.1%
1203 3
< 0.1%
1204 2
 
< 0.1%
1205 2
 
< 0.1%
1207 1
 
< 0.1%
1208 1
 
< 0.1%
1209 1
 
< 0.1%
1210 3
< 0.1%
ValueCountFrequency (%)
1816350 1
< 0.1%
1805976 1
< 0.1%
1783718 1
< 0.1%
1210549 1
< 0.1%
1207428 1
< 0.1%
1200400 1
< 0.1%
1190908 1
< 0.1%
1190732 1
< 0.1%
1190126 1
< 0.1%
1186770 1
< 0.1%

Interactions

2023-12-12T05:03:31.863856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:30.809792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:31.149274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:31.513785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:31.945021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:30.890162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:31.234710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:31.600239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:32.045585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:30.975051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:31.320439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:31.686727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:32.131672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:31.058177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:31.419304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:31.766226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:03:34.725232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.0000.0000.0340.172
longitude0.0001.0001.0000.010
latitude0.0341.0001.0000.000
Diff0.1720.0100.0001.000
2023-12-12T05:03:34.812151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.0000.0030.006-0.040
longitude0.0031.0000.3070.014
latitude0.0060.3071.000-0.008
Diff-0.0400.014-0.0081.000

Missing values

2023-12-12T05:03:32.230953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:03:32.315511image/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
191664610100920211016163000126.23604933.3818572021-10-16 16:30:005707
210864610107920211030102930126.30864933.2330582021-10-30 10:29:303557
21913461010ad20211002094900126.62895133.4401612021-10-02 09:49:0010687
22278461010c020211002142500126.50079133.5028132021-10-02 14:25:001950
139014610065a20211016220030126.54699733.4454212021-10-16 22:00:30555388
203874610104a20211002141130126.6333533.3083832021-10-02 14:11:305865
318054610133520211023132230126.81188833.4912622021-10-23 13:22:304669
86764610048c20211002142830126.93105433.460682021-10-02 14:28:304296
4726461002eb20211030144230126.24701733.399342021-10-30 14:42:301290
30504461012d120211016151900126.50264533.5124142021-10-16 15:19:00587422
oidcollection_dtlongitudelatitudetimeDiff
21988461010b120211016085330126.50047133.5030212021-10-16 08:53:301572
1897446100fff20211016163930126.22081433.3728362021-10-16 16:39:302439
397404610192a20211002150800126.56259433.250082021-10-02 15:08:005250
2305461001cd20211002205700126.5678233.5212432021-10-02 20:57:001186770
26670461011ba20211009103930126.82914333.3319942021-10-09 10:39:302235
38373461018ce20211016171830126.52538733.5108662021-10-16 17:18:302811
34201461017d220211016130430126.73367833.4338022021-10-16 13:04:304830
143744610069120211009140430126.40781833.2581382021-10-09 14:04:302441
402164610194520211023114418126.36364833.3636742021-10-23 11:44:184621
245104610114c20211016194130126.17750833.269562021-10-16 19:41:30566763