Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows36
Duplicate rows (%)0.4%
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 05:00:00 부터 익일 04:59:59까지의 데이터를 추축 - 기간: 2020년 1월부터 2021년 12월 까지
Author제주특별자치도 미래성장과
URLhttps://www.jejudatahub.net/data/view/data/1202

Alerts

Dataset has 36 (0.4%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-11 19:43:42.376181
Analysis finished2023-12-11 19:43:44.392133
Duration2.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct807
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T04:43:44.725506image/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

Unique103 ?
Unique (%)1.0%

Sample

1st row461006e5
2nd row46100133
3rd row46100152
4th row461002d5
5th row46100108
ValueCountFrequency (%)
46100156 487
 
4.9%
461002e4 405
 
4.0%
461002de 355
 
3.5%
461006e5 351
 
3.5%
46100108 328
 
3.3%
46100152 319
 
3.2%
46100734 256
 
2.6%
461002e1 253
 
2.5%
461002d5 231
 
2.3%
46100159 214
 
2.1%
Other values (797) 6801
68.0%
2023-12-12T04:43:45.565935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22015
27.5%
1 14124
17.7%
6 12132
15.2%
4 12020
15.0%
2 3012
 
3.8%
5 2581
 
3.2%
e 2532
 
3.2%
d 2217
 
2.8%
7 1835
 
2.3%
3 1756
 
2.2%
Other values (6) 5776
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71517
89.4%
Lowercase Letter 8483
 
10.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22015
30.8%
1 14124
19.7%
6 12132
17.0%
4 12020
16.8%
2 3012
 
4.2%
5 2581
 
3.6%
7 1835
 
2.6%
3 1756
 
2.5%
8 1233
 
1.7%
9 809
 
1.1%
Lowercase Letter
ValueCountFrequency (%)
e 2532
29.8%
d 2217
26.1%
f 1417
16.7%
b 936
 
11.0%
c 781
 
9.2%
a 600
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Common 71517
89.4%
Latin 8483
 
10.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22015
30.8%
1 14124
19.7%
6 12132
17.0%
4 12020
16.8%
2 3012
 
4.2%
5 2581
 
3.6%
7 1835
 
2.6%
3 1756
 
2.5%
8 1233
 
1.7%
9 809
 
1.1%
Latin
ValueCountFrequency (%)
e 2532
29.8%
d 2217
26.1%
f 1417
16.7%
b 936
 
11.0%
c 781
 
9.2%
a 600
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22015
27.5%
1 14124
17.7%
6 12132
15.2%
4 12020
15.0%
2 3012
 
3.8%
5 2581
 
3.2%
e 2532
 
3.2%
d 2217
 
2.8%
7 1835
 
2.3%
3 1756
 
2.2%
Other values (6) 5776
 
7.2%

collection_dt
Real number (ℝ)

Distinct8733
Distinct (%)87.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0200704 × 1016
Minimum2.0200704 × 1016
Maximum2.0200704 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:43:45.882973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0200704 × 1016
5-th percentile2.0200704 × 1016
Q12.0200704 × 1016
median2.0200704 × 1016
Q32.0200704 × 1016
95-th percentile2.0200704 × 1016
Maximum2.0200704 × 1016
Range10430064
Interquartile range (IQR)7315032

Descriptive statistics

Standard deviation3644065.8
Coefficient of variation (CV)1.8039301 × 10-10
Kurtosis-1.5715553
Mean2.0200704 × 1016
Median Absolute Deviation (MAD)2569924
Skewness0.34943502
Sum-9.0714325 × 1017
Variance1.3279216 × 1013
MonotonicityNot monotonic
2023-12-12T04:43:46.163839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200704161530900 6
 
0.1%
20200704154830203 6
 
0.1%
20200704152230989 5
 
0.1%
20200704152800386 5
 
0.1%
20200704161800874 5
 
0.1%
20200704161530884 5
 
0.1%
20200704155530635 5
 
0.1%
20200704152830880 5
 
0.1%
20200704155800236 4
 
< 0.1%
20200704151830795 4
 
< 0.1%
Other values (8723) 9950
99.5%
ValueCountFrequency (%)
20200704151700373 1
 
< 0.1%
20200704151700388 1
 
< 0.1%
20200704151700466 1
 
< 0.1%
20200704151700498 1
 
< 0.1%
20200704151700529 1
 
< 0.1%
20200704151700560 1
 
< 0.1%
20200704151700576 2
< 0.1%
20200704151700638 3
< 0.1%
20200704151700654 1
 
< 0.1%
20200704151700669 2
< 0.1%
ValueCountFrequency (%)
20200704162130437 1
< 0.1%
20200704162130421 1
< 0.1%
20200704162130343 1
< 0.1%
20200704162130265 1
< 0.1%
20200704162130250 1
< 0.1%
20200704162130218 1
< 0.1%
20200704162130203 1
< 0.1%
20200704162130171 2
< 0.1%
20200704162130124 1
< 0.1%
20200704162130109 2
< 0.1%

longitude
Real number (ℝ)

Distinct8931
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.14837
Minimum126.16303
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:43:46.421018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16303
5-th percentile126.24379
Q1126.36718
median126.50333
Q3126.66852
95-th percentile126.91515
Maximum180
Range53.83697
Interquartile range (IQR)0.3013335

Descriptive statistics

Standard deviation9.1992178
Coefficient of variation (CV)0.071785679
Kurtosis27.807173
Mean128.14837
Median Absolute Deviation (MAD)0.147002
Skewness5.457795
Sum1281483.7
Variance84.625607
MonotonicityNot monotonic
2023-12-12T04:43:46.697657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180.0000001 305
 
3.0%
126.622809 16
 
0.2%
126.622812 12
 
0.1%
126.622806 12
 
0.1%
126.622807 12
 
0.1%
126.674455 11
 
0.1%
126.622801 10
 
0.1%
126.622813 9
 
0.1%
126.278426 9
 
0.1%
126.736778 9
 
0.1%
Other values (8921) 9595
96.0%
ValueCountFrequency (%)
126.16303 1
< 0.1%
126.16434 1
< 0.1%
126.1643686 1
< 0.1%
126.164932 1
< 0.1%
126.165449 1
< 0.1%
126.165451 2
< 0.1%
126.165453 2
< 0.1%
126.165819 1
< 0.1%
126.166136 1
< 0.1%
126.166636 1
< 0.1%
ValueCountFrequency (%)
180.0000001 305
3.0%
126.969561 1
 
< 0.1%
126.969554 1
 
< 0.1%
126.968974 1
 
< 0.1%
126.967237 1
 
< 0.1%
126.9672 1
 
< 0.1%
126.965512 1
 
< 0.1%
126.964815 1
 
< 0.1%
126.9627663 1
 
< 0.1%
126.95948 1
 
< 0.1%

latitude
Real number (ℝ)

Distinct8799
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.138797
Minimum33.203335
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:43:46.938381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.203335
5-th percentile33.249831
Q133.348368
median33.443191
Q333.492528
95-th percentile33.555359
Maximum90
Range56.796665
Interquartile range (IQR)0.14415925

Descriptive statistics

Standard deviation9.7315564
Coefficient of variation (CV)0.2769462
Kurtosis27.82741
Mean35.138797
Median Absolute Deviation (MAD)0.055428
Skewness5.460663
Sum351387.97
Variance94.703191
MonotonicityNot monotonic
2023-12-12T04:43:47.208167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.0000001 305
 
3.0%
33.494834 18
 
0.2%
33.494833 18
 
0.2%
33.494844 12
 
0.1%
33.494855 9
 
0.1%
33.494841 9
 
0.1%
33.544191 9
 
0.1%
33.49484 8
 
0.1%
33.435733 8
 
0.1%
33.466159 8
 
0.1%
Other values (8789) 9596
96.0%
ValueCountFrequency (%)
33.203335 1
< 0.1%
33.203695 1
< 0.1%
33.204351 1
< 0.1%
33.2043718 1
< 0.1%
33.206279 1
< 0.1%
33.206367 1
< 0.1%
33.206564 1
< 0.1%
33.208124 1
< 0.1%
33.209211 1
< 0.1%
33.212452 1
< 0.1%
ValueCountFrequency (%)
90.0000001 305
3.0%
33.564618 1
 
< 0.1%
33.56456 1
 
< 0.1%
33.564518 1
 
< 0.1%
33.564437 1
 
< 0.1%
33.564348 1
 
< 0.1%
33.564185 1
 
< 0.1%
33.564155 1
 
< 0.1%
33.564011 1
 
< 0.1%
33.563972 1
 
< 0.1%

Interactions

2023-12-12T04:43:43.701634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:43:42.870212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:43:43.265841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:43:43.842982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:43:42.988955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:43:43.431896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:43:44.004344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:43:43.126065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:43:43.563562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T04:43:47.370186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.0000.0900.090
longitude0.0901.0001.000
latitude0.0901.0001.000
2023-12-12T04:43:47.535506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.000-0.053-0.022
longitude-0.0531.0000.440
latitude-0.0220.4401.000

Missing values

2023-12-12T04:43:44.189948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T04:43:44.328545image/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
34576461006e520200704154107350126.50715633.249785
532924610013320200704155439440126.49280333.494482
732924610015220200704160731772126.62281233.494838
78573461002d520200704161056176126.30616333.447006
559944610010820200704155659309126.6422733.428131
59475461004e320200704155930035126.54492433.29048
7318461005a320200704152200244126.77288533.457887
608584610014e20200704160017603180.090.0
540354610073420200704155518168126.73280433.437834
73190461004fb20200704160730741126.36894833.291048
oidcollection_dtlongitudelatitude
3797461004e720200704151930645126.56175533.283678
6520146100fae20200704160300063126.50485833.507454
171834610072c20200704152830599126.49685633.505384
92494610010320200704152313997126.68284433.40257
3592446100fce20200704154200717126.63641933.264295
641364610073420200704160216228126.67756733.468883
138854610070420200704152606139126.91616133.448656
17527461002e420200704152847987126.54702233.462427
2211846100feb20200704153200579126.38126233.4758
4574610010320200704151716339126.69724833.352072

Duplicate rows

Most frequently occurring

oidcollection_dtlongitudelatitude# duplicates
04610010020200704152805869126.35871333.2781792
14610013720200704160100687126.73635633.4359552
24610015620200704153558634126.370233.3776712
34610015620200704154215449126.42002433.4333692
44610015620200704154628864126.44907433.4614012
5461002d520200704160110673126.27084533.4061972
6461002de20200704154905182126.23935433.3927792
7461002de20200704161307287126.24118633.3945482
8461002df20200704161821082126.59796333.4885762
9461002e120200704155154311126.24035533.3935382