Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows73
Duplicate rows (%)0.7%
Total size in memory419.9 KiB
Average record size in memory43.0 B

Variable types

Text1
Numeric3

Dataset

Description- 월별 토요일의 렌터카 위치 정보 입니다. - 수집시각 기준 당일 05:00:00부터 익일 04:59:59까지의 데이터를 추출 - 기간: 2020년 1월부터 2021년 12월 까지
Author제주특별자치도 미래성장과
URLhttps://www.jejudatahub.net/data/view/data/1203

Alerts

Dataset has 73 (0.7%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-11 19:58:49.190631
Analysis finished2023-12-11 19:58:51.140064
Duration1.95 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct695
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T04:58:51.474439image/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

Unique67 ?
Unique (%)0.7%

Sample

1st row46101869
2nd row4610017e
3rd row4610105d
4th row461002d5
5th row461002d5
ValueCountFrequency (%)
4610017e 1273
 
12.7%
46100088 569
 
5.7%
461002e4 462
 
4.6%
46100156 380
 
3.8%
4610075d 328
 
3.3%
461002d5 317
 
3.2%
461000f6 282
 
2.8%
46100366 173
 
1.7%
46100108 164
 
1.6%
46101334 105
 
1.1%
Other values (685) 5947
59.5%
2023-12-12T04:58:52.081834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 19368
24.2%
1 16660
20.8%
6 12160
15.2%
4 11534
14.4%
7 2890
 
3.6%
8 2605
 
3.3%
e 2334
 
2.9%
2 2251
 
2.8%
5 2070
 
2.6%
3 1992
 
2.5%
Other values (6) 6136
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72442
90.6%
Lowercase Letter 7558
 
9.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19368
26.7%
1 16660
23.0%
6 12160
16.8%
4 11534
15.9%
7 2890
 
4.0%
8 2605
 
3.6%
2 2251
 
3.1%
5 2070
 
2.9%
3 1992
 
2.7%
9 912
 
1.3%
Lowercase Letter
ValueCountFrequency (%)
e 2334
30.9%
d 1434
19.0%
f 1393
18.4%
b 879
 
11.6%
c 836
 
11.1%
a 682
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72442
90.6%
Latin 7558
 
9.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19368
26.7%
1 16660
23.0%
6 12160
16.8%
4 11534
15.9%
7 2890
 
4.0%
8 2605
 
3.6%
2 2251
 
3.1%
5 2070
 
2.9%
3 1992
 
2.7%
9 912
 
1.3%
Latin
ValueCountFrequency (%)
e 2334
30.9%
d 1434
19.0%
f 1393
18.4%
b 879
 
11.6%
c 836
 
11.1%
a 682
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19368
24.2%
1 16660
20.8%
6 12160
15.2%
4 11534
14.4%
7 2890
 
3.6%
8 2605
 
3.3%
e 2334
 
2.9%
2 2251
 
2.8%
5 2070
 
2.6%
3 1992
 
2.5%
Other values (6) 6136
 
7.7%

collection_dt
Real number (ℝ)

Distinct9105
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0210605 × 1016
Minimum2.0210605 × 1016
Maximum2.0210605 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:58:52.409398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0210605 × 1016
5-th percentile2.0210605 × 1016
Q12.0210605 × 1016
median2.0210605 × 1016
Q32.0210605 × 1016
95-th percentile2.0210605 × 1016
Maximum2.0210605 × 1016
Range41101441
Interquartile range (IQR)12070064

Descriptive statistics

Standard deviation9596510.3
Coefficient of variation (CV)4.7482548 × 10-10
Kurtosis-0.028891326
Mean2.0210605 × 1016
Median Absolute Deviation (MAD)6200256
Skewness-0.85332196
Sum-8.0813403 × 1017
Variance9.209301 × 1013
MonotonicityNot monotonic
2023-12-12T04:58:52.641330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20210605085000186 4
 
< 0.1%
20210605090200582 4
 
< 0.1%
20210605090300310 4
 
< 0.1%
20210605085800128 4
 
< 0.1%
20210605081500833 4
 
< 0.1%
20210605085030769 4
 
< 0.1%
20210605090800135 4
 
< 0.1%
20210605090330315 4
 
< 0.1%
20210605085700354 4
 
< 0.1%
20210605085700635 4
 
< 0.1%
Other values (9095) 9960
99.6%
ValueCountFrequency (%)
20210605050030612 1
< 0.1%
20210605050030940 1
< 0.1%
20210605050100258 1
< 0.1%
20210605050100899 1
< 0.1%
20210605050130137 1
< 0.1%
20210605050130701 1
< 0.1%
20210605050130732 1
< 0.1%
20210605050200630 1
< 0.1%
20210605050230808 1
< 0.1%
20210605050300065 1
< 0.1%
ValueCountFrequency (%)
20210605091132053 1
 
< 0.1%
20210605091130975 2
< 0.1%
20210605091130959 1
 
< 0.1%
20210605091130912 1
 
< 0.1%
20210605091130897 1
 
< 0.1%
20210605091130803 2
< 0.1%
20210605091130787 1
 
< 0.1%
20210605091130772 3
< 0.1%
20210605091130741 1
 
< 0.1%
20210605091130725 1
 
< 0.1%

longitude
Real number (ℝ)

Distinct8196
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.44396
Minimum126.1818
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:58:52.882786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.1818
5-th percentile126.30767
Q1126.4374
median126.49778
Q3126.62577
95-th percentile180
Maximum180
Range53.818198
Interquartile range (IQR)0.1883725

Descriptive statistics

Standard deviation16.797068
Coefficient of variation (CV)0.12682396
Kurtosis4.1435643
Mean132.44396
Median Absolute Deviation (MAD)0.0906455
Skewness2.4783086
Sum1324439.6
Variance282.14148
MonotonicityNot monotonic
2023-12-12T04:58:53.607976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180.0000001 1109
 
11.1%
126.505667 13
 
0.1%
126.42565 12
 
0.1%
126.484824 8
 
0.1%
126.735859 7
 
0.1%
126.495434 6
 
0.1%
126.502454 6
 
0.1%
126.550099 5
 
0.1%
126.495427 5
 
0.1%
126.5501 5
 
0.1%
Other values (8186) 8824
88.2%
ValueCountFrequency (%)
126.181802 1
< 0.1%
126.198783 1
< 0.1%
126.199132 1
< 0.1%
126.201079 1
< 0.1%
126.211558 1
< 0.1%
126.213589 1
< 0.1%
126.213601 1
< 0.1%
126.217236 1
< 0.1%
126.217551 1
< 0.1%
126.218626 1
< 0.1%
ValueCountFrequency (%)
180.0000001 1109
11.1%
128.95062 1
 
< 0.1%
126.967225 1
 
< 0.1%
126.9648643 1
 
< 0.1%
126.964802 1
 
< 0.1%
126.9647805 1
 
< 0.1%
126.964517 1
 
< 0.1%
126.956351 1
 
< 0.1%
126.954175 1
 
< 0.1%
126.952292 1
 
< 0.1%

latitude
Real number (ℝ)

Distinct8038
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.697315
Minimum33.209622
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:58:53.821814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.209622
5-th percentile33.250399
Q133.378367
median33.468875
Q333.50467
95-th percentile90
Maximum90
Range56.790378
Interquartile range (IQR)0.1263025

Descriptive statistics

Standard deviation17.766771
Coefficient of variation (CV)0.447556
Kurtosis4.1442158
Mean39.697315
Median Absolute Deviation (MAD)0.046999
Skewness2.4785371
Sum396973.15
Variance315.65816
MonotonicityNot monotonic
2023-12-12T04:58:54.059335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.0000001 1109
 
11.1%
33.500723 13
 
0.1%
33.505449 13
 
0.1%
33.241612 12
 
0.1%
33.506051 9
 
0.1%
33.506054 8
 
0.1%
33.444608 7
 
0.1%
33.42188 7
 
0.1%
33.506053 7
 
0.1%
33.254299 6
 
0.1%
Other values (8028) 8809
88.1%
ValueCountFrequency (%)
33.209622 1
 
< 0.1%
33.209671 1
 
< 0.1%
33.209674 1
 
< 0.1%
33.209697 1
 
< 0.1%
33.209699 1
 
< 0.1%
33.209711 1
 
< 0.1%
33.209714 3
< 0.1%
33.209715 1
 
< 0.1%
33.209717 2
< 0.1%
33.212094 1
 
< 0.1%
ValueCountFrequency (%)
90.0000001 1109
11.1%
35.165832 1
 
< 0.1%
33.560419 1
 
< 0.1%
33.558374 1
 
< 0.1%
33.556791 1
 
< 0.1%
33.556325 1
 
< 0.1%
33.556128 1
 
< 0.1%
33.556035 1
 
< 0.1%
33.555952 1
 
< 0.1%
33.555803 1
 
< 0.1%

Interactions

2023-12-12T04:58:50.448769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:49.614413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:50.045838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:50.565140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:49.744782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:50.182661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:50.721757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:49.875632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:50.307271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T04:58:54.212950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.0000.5070.507
longitude0.5071.0001.000
latitude0.5071.0001.000
2023-12-12T04:58:54.359718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.000-0.309-0.090
longitude-0.3091.0000.474
latitude-0.0900.4741.000

Missing values

2023-12-12T04:58:50.945089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T04:58:51.075993image/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_dtlongitudelatitude
638784610186920210605083900812126.39868233.268456
328804610017e20210605074955089126.4942133.504321
8104610105d20210605053300026126.37762233.428286
70533461002d520210605084613563126.26309533.410247
59127461002d520210605083426883126.30296233.445052
791604610186220210605085600627126.2641433.426719
145524610186720210605065730119126.67557533.435107
31629461017f120210605074700044126.47635533.488676
69261461005ac20210605084444924126.50670733.489699
64430461002d520210605083933395126.27674333.431027
oidcollection_dtlongitudelatitude
307024610198620210605074430554126.53162933.493548
631774610120920210605083830027126.45472433.495379
370014610132f20210605080030040126.49297833.49606
537104610130520210605082900260126.45654433.49541
340674610017e20210605075319556126.4795633.495509
332784610035f20210605075100287126.86464933.43346
421804610130520210605080900844126.30498333.445802
769584610020020210605085330561126.37781733.329777
106414610195420210605063730095126.36115933.355865
699184610112f20210605084530540126.55680433.514789

Duplicate rows

Most frequently occurring

oidcollection_dtlongitudelatitude# duplicates
0461000f620210605083049245126.43410433.4457022
1461000f620210605083533220126.39050633.4086932
2461000f620210605084233124126.35486333.3318922
34610017e20210605065513442126.55014133.5103882
44610017e20210605072209098126.4942133.5066192
54610017e20210605072233836126.49411333.5065842
64610017e20210605073243089126.49568633.5057062
74610017e20210605073657486126.49400833.506612
84610017e20210605074235865126.49480933.5046692
94610017e20210605075729905126.47161233.481062