Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows39
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 추출 - 기간: 2020년 1월부터 2021년 12월 까지
Author제주특별자치도 미래성장과
URLhttps://www.jejudatahub.net/data/view/data/1201

Alerts

Dataset has 39 (0.4%) duplicate rowsDuplicates
longitude is highly overall correlated with latitudeHigh correlation
latitude is highly overall correlated with longitudeHigh correlation

Reproduction

Analysis started2023-12-11 19:51:33.397707
Analysis finished2023-12-11 19:51:35.360529
Duration1.96 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct737
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T04:51:35.710863image/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

Unique95 ?
Unique (%)0.9%

Sample

1st row461002df
2nd row46100159
3rd row461000f6
4th row46100108
5th row461002d5
ValueCountFrequency (%)
46100734 438
 
4.4%
461002e2 430
 
4.3%
461006ba 361
 
3.6%
461002e4 354
 
3.5%
461006da 351
 
3.5%
46100108 349
 
3.5%
461002d5 337
 
3.4%
461000ec 318
 
3.2%
461000f6 311
 
3.1%
46100704 296
 
3.0%
Other values (727) 6455
64.5%
2023-12-12T04:51:36.364108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22330
27.9%
4 12418
15.5%
1 12414
15.5%
6 12350
15.4%
2 3079
 
3.8%
7 2200
 
2.8%
e 2151
 
2.7%
d 2051
 
2.6%
5 1963
 
2.5%
3 1728
 
2.2%
Other values (6) 7316
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70494
88.1%
Lowercase Letter 9506
 
11.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22330
31.7%
4 12418
17.6%
1 12414
17.6%
6 12350
17.5%
2 3079
 
4.4%
7 2200
 
3.1%
5 1963
 
2.8%
3 1728
 
2.5%
8 1289
 
1.8%
9 723
 
1.0%
Lowercase Letter
ValueCountFrequency (%)
e 2151
22.6%
d 2051
21.6%
c 1574
16.6%
b 1326
13.9%
a 1273
13.4%
f 1131
11.9%

Most occurring scripts

ValueCountFrequency (%)
Common 70494
88.1%
Latin 9506
 
11.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22330
31.7%
4 12418
17.6%
1 12414
17.6%
6 12350
17.5%
2 3079
 
4.4%
7 2200
 
3.1%
5 1963
 
2.8%
3 1728
 
2.5%
8 1289
 
1.8%
9 723
 
1.0%
Latin
ValueCountFrequency (%)
e 2151
22.6%
d 2051
21.6%
c 1574
16.6%
b 1326
13.9%
a 1273
13.4%
f 1131
11.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22330
27.9%
4 12418
15.5%
1 12414
15.5%
6 12350
15.4%
2 3079
 
3.8%
7 2200
 
2.8%
e 2151
 
2.7%
d 2051
 
2.6%
5 1963
 
2.5%
3 1728
 
2.2%
Other values (6) 7316
 
9.1%

collection_dt
Real number (ℝ)

Distinct8971
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.020053 × 1016
Minimum2.020053 × 1016
Maximum2.020053 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:51:36.642944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.020053 × 1016
5-th percentile2.020053 × 1016
Q12.020053 × 1016
median2.020053 × 1016
Q32.020053 × 1016
95-th percentile2.020053 × 1016
Maximum2.020053 × 1016
Range10785042
Interquartile range (IQR)7333896

Descriptive statistics

Standard deviation3635432.5
Coefficient of variation (CV)1.7996718 × 10-10
Kurtosis-1.466815
Mean2.020053 × 1016
Median Absolute Deviation (MAD)2478312
Skewness0.40789596
Sum-9.0888315 × 1017
Variance1.321637 × 1013
MonotonicityNot monotonic
2023-12-12T04:51:36.934387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200530162500990 7
 
0.1%
20200530165630873 5
 
0.1%
20200530170200738 5
 
0.1%
20200530161900958 5
 
0.1%
20200530171400131 4
 
< 0.1%
20200530162300370 4
 
< 0.1%
20200530164000526 4
 
< 0.1%
20200530162500365 4
 
< 0.1%
20200530162200880 4
 
< 0.1%
20200530161600660 4
 
< 0.1%
Other values (8961) 9954
99.5%
ValueCountFrequency (%)
20200530161315564 1
< 0.1%
20200530161315939 1
< 0.1%
20200530161316626 1
< 0.1%
20200530161316938 1
< 0.1%
20200530161317813 1
< 0.1%
20200530161320282 1
< 0.1%
20200530161321609 1
< 0.1%
20200530161321703 1
< 0.1%
20200530161323328 1
< 0.1%
20200530161325718 1
< 0.1%
ValueCountFrequency (%)
20200530172100606 2
< 0.1%
20200530172100575 1
< 0.1%
20200530172100450 1
< 0.1%
20200530172100435 1
< 0.1%
20200530172100403 1
< 0.1%
20200530172100356 1
< 0.1%
20200530172100263 1
< 0.1%
20200530172100247 1
< 0.1%
20200530172100200 1
< 0.1%
20200530172100185 1
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct8982
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.70464
Minimum126.1633
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:51:37.299679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.1633
5-th percentile126.28655
Q1126.40875
median126.49383
Q3126.66602
95-th percentile126.88975
Maximum180
Range53.836697
Interquartile range (IQR)0.25727625

Descriptive statistics

Standard deviation7.8456549
Coefficient of variation (CV)0.06143594
Kurtosis40.45692
Mean127.70464
Median Absolute Deviation (MAD)0.118141
Skewness6.5136007
Sum1277046.4
Variance61.5543
MonotonicityNot monotonic
2023-12-12T04:51:37.555145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180.0000001 220
 
2.2%
126.736482 12
 
0.1%
126.503009 11
 
0.1%
126.661413 10
 
0.1%
126.810923 9
 
0.1%
126.929466 8
 
0.1%
126.35818 8
 
0.1%
126.371141 8
 
0.1%
126.308464 7
 
0.1%
126.831227 7
 
0.1%
Other values (8972) 9700
97.0%
ValueCountFrequency (%)
126.163303 1
< 0.1%
126.163334 1
< 0.1%
126.163788 1
< 0.1%
126.164569 1
< 0.1%
126.165896 2
< 0.1%
126.165899 1
< 0.1%
126.168621 1
< 0.1%
126.16866 1
< 0.1%
126.168664 1
< 0.1%
126.168666 1
< 0.1%
ValueCountFrequency (%)
180.0000001 220
2.2%
126.961121 1
 
< 0.1%
126.958635 1
 
< 0.1%
126.957988 1
 
< 0.1%
126.956144 1
 
< 0.1%
126.955059 1
 
< 0.1%
126.95505 1
 
< 0.1%
126.953096 1
 
< 0.1%
126.952049 1
 
< 0.1%
126.951416 1
 
< 0.1%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct8846
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.662941
Minimum33.200237
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:51:37.779467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.200237
5-th percentile33.247264
Q133.317715
median33.463304
Q333.500148
95-th percentile33.549232
Maximum90
Range56.799763
Interquartile range (IQR)0.18243225

Descriptive statistics

Standard deviation8.3006531
Coefficient of variation (CV)0.23946765
Kurtosis40.484864
Mean34.662941
Median Absolute Deviation (MAD)0.054805
Skewness6.5168845
Sum346629.41
Variance68.900842
MonotonicityNot monotonic
2023-12-12T04:51:37.996226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.0000001 220
 
2.2%
33.435737 13
 
0.1%
33.469846 11
 
0.1%
33.541071 10
 
0.1%
33.503746 9
 
0.1%
33.494081 8
 
0.1%
33.54107 8
 
0.1%
33.528105 8
 
0.1%
33.494083 7
 
0.1%
33.435488 7
 
0.1%
Other values (8836) 9699
97.0%
ValueCountFrequency (%)
33.200237 1
< 0.1%
33.200337 1
< 0.1%
33.203751 1
< 0.1%
33.204812 1
< 0.1%
33.204922 1
< 0.1%
33.20589 1
< 0.1%
33.206142 1
< 0.1%
33.20644 1
< 0.1%
33.206521 1
< 0.1%
33.206528 1
< 0.1%
ValueCountFrequency (%)
90.0000001 220
2.2%
33.564729 1
 
< 0.1%
33.564445 1
 
< 0.1%
33.564413 1
 
< 0.1%
33.564351 1
 
< 0.1%
33.564279 1
 
< 0.1%
33.5642 1
 
< 0.1%
33.564163 1
 
< 0.1%
33.564112 1
 
< 0.1%
33.564081 1
 
< 0.1%

Interactions

2023-12-12T04:51:34.702003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:33.871228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:34.271864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:34.886259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:34.009820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:34.415180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:35.021643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:34.150718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:34.551385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T04:51:38.145009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.0000.0850.085
longitude0.0851.0001.000
latitude0.0851.0001.000
2023-12-12T04:51:38.290945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.000-0.025-0.015
longitude-0.0251.0000.533
latitude-0.0150.5331.000

Missing values

2023-12-12T04:51:35.193597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T04:51:35.304003image/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
76415461002df20200530170846919126.38770133.306651
163734610015920200530162657159126.44134933.25177
41949461000f620200530164606292126.3532633.327489
326654610010820200530163910972126.48878533.485194
81309461002d520200530171148763126.71230133.450266
404564610033520200530164500553126.52091633.51767
27847461000c820200530163530587126.44389833.26347
559346100dbe20200530161830478126.56100133.252471
110414610073420200530162300354126.51655133.508663
436494610036a20200530164730215126.2900333.251958
oidcollection_dtlongitudelatitude
617846100ca220200530161900614126.46805333.47848
75858461006dd20200530170830204126.70553833.4338
901764610028420200530171730564180.090.0
46392461004eb20200530164930507126.56199833.246254
78893461002e220200530171015029126.68643533.53104
26648461006a620200530163430848126.29601733.41295
9556461000ec20200530162150600126.51668533.511212
11149461000bd20200530162300682126.91968533.449091
569224610079e20200530165630748126.89721933.407054
273954610073420200530163507669126.48472133.489186

Duplicate rows

Most frequently occurring

oidcollection_dtlongitudelatitude# duplicates
254610073420200530163406024126.48787433.490863
0461000ec20200530162622383126.49726733.5053512
1461000ec20200530162701189126.49623433.5054472
2461000ec20200530163544680126.45412933.4953922
34610010020200530170333519126.4911433.4930912
44610015820200530164501381126.38399633.4762342
54610022620200530165030591126.50406633.2500152
6461002d520200530171919561126.66141333.4698462
7461002e220200530162329068126.73557333.4354882
8461002e320200530165632247126.40908833.2502192