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 20:06:09.763139
Analysis finished2023-12-11 20:06:13.746180
Duration3.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct1788
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T05:06:14.075923image/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

Unique126 ?
Unique (%)1.3%

Sample

1st row4610052e
2nd row46100fbe
3rd row46101061
4th row46100422
5th row461005c4
ValueCountFrequency (%)
4610077b 15
 
0.1%
46100136 15
 
0.1%
4610049e 14
 
0.1%
46100193 14
 
0.1%
46100307 14
 
0.1%
461002a5 13
 
0.1%
46100343 13
 
0.1%
46100777 13
 
0.1%
4610059d 13
 
0.1%
46101124 13
 
0.1%
Other values (1778) 9863
98.6%
2023-12-12T05:06:14.579869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 20670
25.8%
1 14920
18.6%
4 12251
15.3%
6 12009
15.0%
3 2332
 
2.9%
5 2239
 
2.8%
7 2033
 
2.5%
2 1996
 
2.5%
d 1776
 
2.2%
f 1705
 
2.1%
Other values (6) 8069
 
10.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70927
88.7%
Lowercase Letter 9073
 
11.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20670
29.1%
1 14920
21.0%
4 12251
17.3%
6 12009
16.9%
3 2332
 
3.3%
5 2239
 
3.2%
7 2033
 
2.9%
2 1996
 
2.8%
8 1280
 
1.8%
9 1197
 
1.7%
Lowercase Letter
ValueCountFrequency (%)
d 1776
19.6%
f 1705
18.8%
c 1607
17.7%
b 1444
15.9%
a 1317
14.5%
e 1224
13.5%

Most occurring scripts

ValueCountFrequency (%)
Common 70927
88.7%
Latin 9073
 
11.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20670
29.1%
1 14920
21.0%
4 12251
17.3%
6 12009
16.9%
3 2332
 
3.3%
5 2239
 
3.2%
7 2033
 
2.9%
2 1996
 
2.8%
8 1280
 
1.8%
9 1197
 
1.7%
Latin
ValueCountFrequency (%)
d 1776
19.6%
f 1705
18.8%
c 1607
17.7%
b 1444
15.9%
a 1317
14.5%
e 1224
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20670
25.8%
1 14920
18.6%
4 12251
15.3%
6 12009
15.0%
3 2332
 
2.9%
5 2239
 
2.8%
7 2033
 
2.5%
2 1996
 
2.5%
d 1776
 
2.2%
f 1705
 
2.1%
Other values (6) 8069
 
10.1%

collection_dt
Real number (ℝ)

Distinct7000
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0200714 × 1013
Minimum2.0200704 × 1013
Maximum2.0200726 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:06:14.762728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0200704 × 1013
5-th percentile2.0200704 × 1013
Q12.0200711 × 1013
median2.0200711 × 1013
Q32.0200718 × 1013
95-th percentile2.0200725 × 1013
Maximum2.0200726 × 1013
Range21990932
Interquartile range (IQR)7127655

Descriptive statistics

Standard deviation7619736.4
Coefficient of variation (CV)3.7720133 × 10-7
Kurtosis-1.2924052
Mean2.0200714 × 1013
Median Absolute Deviation (MAD)6975470
Skewness0.028151618
Sum2.0200714 × 1017
Variance5.8060383 × 1013
MonotonicityNot monotonic
2023-12-12T05:06:14.951627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200704181500 7
 
0.1%
20200711104600 7
 
0.1%
20200725133200 7
 
0.1%
20200725172800 6
 
0.1%
20200718183530 6
 
0.1%
20200704110930 6
 
0.1%
20200718152100 6
 
0.1%
20200725111100 6
 
0.1%
20200718120000 6
 
0.1%
20200704131030 6
 
0.1%
Other values (6990) 9937
99.4%
ValueCountFrequency (%)
20200704051200 1
< 0.1%
20200704051400 1
< 0.1%
20200704051617 1
< 0.1%
20200704054127 1
< 0.1%
20200704054130 1
< 0.1%
20200704054630 1
< 0.1%
20200704054900 1
< 0.1%
20200704055230 1
< 0.1%
20200704055820 1
< 0.1%
20200704061300 1
< 0.1%
ValueCountFrequency (%)
20200726042132 1
< 0.1%
20200726012530 1
< 0.1%
20200726010330 1
< 0.1%
20200726010230 1
< 0.1%
20200726004400 1
< 0.1%
20200725235330 1
< 0.1%
20200725233830 1
< 0.1%
20200725233630 1
< 0.1%
20200725233300 1
< 0.1%
20200725232950 1
< 0.1%

longitude
Real number (ℝ)

Distinct9824
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.53278
Minimum126.16331
Maximum126.96931
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:06:15.361757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16331
5-th percentile126.24913
Q1126.40719
median126.50256
Q3126.65888
95-th percentile126.91795
Maximum126.96931
Range0.805999
Interquartile range (IQR)0.25169003

Descriptive statistics

Standard deviation0.19316851
Coefficient of variation (CV)0.0015266282
Kurtosis-0.45530632
Mean126.53278
Median Absolute Deviation (MAD)0.1198095
Skewness0.49055317
Sum1265327.8
Variance0.037314075
MonotonicityNot monotonic
2023-12-12T05:06:15.574237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.498418 3
 
< 0.1%
126.498505 3
 
< 0.1%
126.492583 3
 
< 0.1%
126.4864 3
 
< 0.1%
126.486259 3
 
< 0.1%
126.52615 3
 
< 0.1%
126.414501 2
 
< 0.1%
126.669658 2
 
< 0.1%
126.637607 2
 
< 0.1%
126.485246 2
 
< 0.1%
Other values (9814) 9974
99.7%
ValueCountFrequency (%)
126.163306 1
< 0.1%
126.163572 1
< 0.1%
126.164104 1
< 0.1%
126.164308 1
< 0.1%
126.16439 1
< 0.1%
126.164514 1
< 0.1%
126.164545 1
< 0.1%
126.164694 1
< 0.1%
126.164702 1
< 0.1%
126.164766 1
< 0.1%
ValueCountFrequency (%)
126.969305 1
< 0.1%
126.969213 1
< 0.1%
126.969206 1
< 0.1%
126.96912 1
< 0.1%
126.968889 1
< 0.1%
126.968879 1
< 0.1%
126.968679 1
< 0.1%
126.967494 1
< 0.1%
126.967277 1
< 0.1%
126.967218 1
< 0.1%

latitude
Real number (ℝ)

Distinct9716
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.411959
Minimum33.200128
Maximum33.563368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:06:15.729773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.200128
5-th percentile33.241219
Q133.301729
median33.45952
Q333.498083
95-th percentile33.541269
Maximum33.563368
Range0.36324
Interquartile range (IQR)0.19635412

Descriptive statistics

Standard deviation0.1066474
Coefficient of variation (CV)0.0031918931
Kurtosis-1.2743871
Mean33.411959
Median Absolute Deviation (MAD)0.056111
Skewness-0.53391135
Sum334119.59
Variance0.011373669
MonotonicityNot monotonic
2023-12-12T05:06:15.897211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.51137 4
 
< 0.1%
33.496486 3
 
< 0.1%
33.498017 3
 
< 0.1%
33.493257 3
 
< 0.1%
33.497646 3
 
< 0.1%
33.512018 3
 
< 0.1%
33.472737 3
 
< 0.1%
33.503037 3
 
< 0.1%
33.512134 3
 
< 0.1%
33.516171 3
 
< 0.1%
Other values (9706) 9969
99.7%
ValueCountFrequency (%)
33.200128 2
< 0.1%
33.200204 1
< 0.1%
33.200219 1
< 0.1%
33.204741 1
< 0.1%
33.205174 1
< 0.1%
33.205194 1
< 0.1%
33.205291 1
< 0.1%
33.205825 1
< 0.1%
33.205856 1
< 0.1%
33.206021 1
< 0.1%
ValueCountFrequency (%)
33.563368 1
< 0.1%
33.563196 1
< 0.1%
33.561416 1
< 0.1%
33.561218 1
< 0.1%
33.561125 1
< 0.1%
33.560563 1
< 0.1%
33.560515 1
< 0.1%
33.56039 1
< 0.1%
33.56006 1
< 0.1%
33.559584 1
< 0.1%

time
Date

Distinct7000
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-07-04 05:12:00
Maximum2020-07-26 04:21:32
2023-12-12T05:06:16.089442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:16.259584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Diff
Real number (ℝ)

Distinct5545
Distinct (%)55.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86901.348
Minimum1200
Maximum1225522
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:06:16.432262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile1452.9
Q12531
median4140
Q39096
95-th percentile576508.1
Maximum1225522
Range1224322
Interquartile range (IQR)6565

Descriptive statistics

Standard deviation204829.54
Coefficient of variation (CV)2.3570353
Kurtosis3.9378079
Mean86901.348
Median Absolute Deviation (MAD)2070
Skewness2.2590327
Sum8.6901348 × 108
Variance4.1955142 × 1010
MonotonicityNot monotonic
2023-12-12T05:06:16.631833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2670 40
 
0.4%
1290 37
 
0.4%
1230 35
 
0.4%
1590 33
 
0.3%
2610 32
 
0.3%
2190 32
 
0.3%
1890 32
 
0.3%
1350 31
 
0.3%
2250 31
 
0.3%
1410 31
 
0.3%
Other values (5535) 9666
96.7%
ValueCountFrequency (%)
1200 18
0.2%
1201 1
 
< 0.1%
1202 1
 
< 0.1%
1204 1
 
< 0.1%
1207 1
 
< 0.1%
1208 1
 
< 0.1%
1209 1
 
< 0.1%
1210 2
 
< 0.1%
1212 2
 
< 0.1%
1213 1
 
< 0.1%
ValueCountFrequency (%)
1225522 1
< 0.1%
1224742 1
< 0.1%
1214416 1
< 0.1%
1199923 1
< 0.1%
1198536 1
< 0.1%
1198004 1
< 0.1%
1194282 1
< 0.1%
1192299 1
< 0.1%
1192170 1
< 0.1%
1190980 1
< 0.1%

Interactions

2023-12-12T05:06:12.961024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:11.034906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:11.820163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:12.405790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:13.084587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:11.347077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:11.965778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:12.534043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:13.227233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:11.526439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:12.112498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:12.692228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:13.362841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:11.680004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:12.262889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:12.846200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:06:16.751940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.0000.0450.0410.314
longitude0.0451.0000.8330.037
latitude0.0410.8331.0000.041
Diff0.3140.0370.0411.000
2023-12-12T05:06:16.876071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.000-0.014-0.026-0.058
longitude-0.0141.0000.3070.000
latitude-0.0260.3071.0000.015
Diff-0.0580.0000.0151.000

Missing values

2023-12-12T05:06:13.543729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:06:13.681985image/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
115434610052e20200711115900126.47007433.5078872020-07-11 11:59:003804
2173446100fbe20200711184400126.51022433.2511672020-07-11 18:44:003060
244714610106120200725174000126.93573433.462382020-07-25 17:40:004620
85274610042220200718154409126.73663333.4356032020-07-18 15:44:092226
13213461005c420200725143830126.36850133.2908942020-07-25 14:38:305249
41444610024d20200718115154126.42645133.2389262020-07-18 11:51:541586
241414610104b20200725123430126.41536233.2442982020-07-25 12:34:303752
178524610078b20200725152300126.49181333.489352020-07-25 15:23:001410
14034610013820200725192830126.4878933.5181592020-07-25 19:28:301830
230034610100420200711202730126.56218333.2501112020-07-11 20:27:301500
oidcollection_dtlongitudelatitudetimeDiff
2177846100fc220200704180300126.66511633.2765352020-07-04 18:03:00578460
335461000c320200718210700126.52885433.512832020-07-18 21:07:002490
109461000b420200704105230126.9110533.4967712020-07-04 10:52:3011142
19974610016a20200704161249126.52984733.4994972020-07-04 16:12:496173
4837461002a820200725134130126.89969233.4932972020-07-25 13:41:303649
164074610070a20200718180030126.50782833.2485152020-07-18 18:00:302308
174894610077320200725110420126.57131433.2453892020-07-25 11:04:201359
59614610032220200725110600126.56249433.2499432020-07-25 11:06:002970
12864461005a320200704145600126.78112433.4468872020-07-04 14:56:001214
237524610103120200711121130126.30657633.4509562020-07-11 12:11:301500