Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows64
Duplicate rows (%)0.6%
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 64 (0.6%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-11 19:58:14.766315
Analysis finished2023-12-11 19:58:16.963092
Duration2.2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct624
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T04:58:17.243586image/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

Unique71 ?
Unique (%)0.7%

Sample

1st row4610020d
2nd row461011aa
3rd row46100204
4th row46100186
5th row461002db
ValueCountFrequency (%)
46100158 818
 
8.2%
46100103 681
 
6.8%
461006ed 614
 
6.1%
461002db 580
 
5.8%
46100734 570
 
5.7%
461000ec 285
 
2.9%
46100704 195
 
1.9%
4610078d 160
 
1.6%
461006da 121
 
1.2%
4610078f 91
 
0.9%
Other values (614) 5885
58.9%
2023-12-12T04:58:17.729862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 19768
24.7%
1 16059
20.1%
6 11986
15.0%
4 11961
15.0%
3 2822
 
3.5%
8 2253
 
2.8%
7 2250
 
2.8%
2 2217
 
2.8%
d 2176
 
2.7%
5 2023
 
2.5%
Other values (6) 6485
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72268
90.3%
Lowercase Letter 7732
 
9.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19768
27.4%
1 16059
22.2%
6 11986
16.6%
4 11961
16.6%
3 2822
 
3.9%
8 2253
 
3.1%
7 2250
 
3.1%
2 2217
 
3.1%
5 2023
 
2.8%
9 929
 
1.3%
Lowercase Letter
ValueCountFrequency (%)
d 2176
28.1%
e 1632
21.1%
b 1262
16.3%
c 921
11.9%
a 880
11.4%
f 861
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 72268
90.3%
Latin 7732
 
9.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19768
27.4%
1 16059
22.2%
6 11986
16.6%
4 11961
16.6%
3 2822
 
3.9%
8 2253
 
3.1%
7 2250
 
3.1%
2 2217
 
3.1%
5 2023
 
2.8%
9 929
 
1.3%
Latin
ValueCountFrequency (%)
d 2176
28.1%
e 1632
21.1%
b 1262
16.3%
c 921
11.9%
a 880
11.4%
f 861
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19768
24.7%
1 16059
20.1%
6 11986
15.0%
4 11961
15.0%
3 2822
 
3.5%
8 2253
 
2.8%
7 2250
 
2.8%
2 2217
 
2.8%
d 2176
 
2.7%
5 2023
 
2.5%
Other values (6) 6485
 
8.1%

collection_dt
Real number (ℝ)

Distinct9326
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0210102 × 1016
Minimum2.0210102 × 1016
Maximum2.0210102 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:58:17.930288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0210102 × 1016
5-th percentile2.0210102 × 1016
Q12.0210102 × 1016
median2.0210102 × 1016
Q32.0210102 × 1016
95-th percentile2.0210102 × 1016
Maximum2.0210102 × 1016
Range53730030
Interquartile range (IQR)18488660

Descriptive statistics

Standard deviation11744159
Coefficient of variation (CV)5.811034 × 10-10
Kurtosis0.0066693002
Mean2.0210102 × 1016
Median Absolute Deviation (MAD)8391016
Skewness-0.82828331
Sum-8.1316391 × 1017
Variance1.3792527 × 1014
MonotonicityNot monotonic
2023-12-12T04:58:18.177601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20210102100600072 4
 
< 0.1%
20210102101630500 4
 
< 0.1%
20210102095330150 3
 
< 0.1%
20210102102100679 3
 
< 0.1%
20210102092830265 3
 
< 0.1%
20210102102430474 3
 
< 0.1%
20210102100800485 3
 
< 0.1%
20210102100030503 3
 
< 0.1%
20210102103530656 3
 
< 0.1%
20210102091800850 3
 
< 0.1%
Other values (9316) 9968
99.7%
ValueCountFrequency (%)
20210102050000410 1
< 0.1%
20210102050030105 1
< 0.1%
20210102050030261 1
< 0.1%
20210102050100879 1
< 0.1%
20210102050130807 1
< 0.1%
20210102050200473 1
< 0.1%
20210102050200535 1
< 0.1%
20210102050200957 1
< 0.1%
20210102050400414 1
< 0.1%
20210102050500363 1
< 0.1%
ValueCountFrequency (%)
20210102103730440 1
 
< 0.1%
20210102103730409 1
 
< 0.1%
20210102103730362 1
 
< 0.1%
20210102103730315 1
 
< 0.1%
20210102103730221 1
 
< 0.1%
20210102103730205 1
 
< 0.1%
20210102103730190 1
 
< 0.1%
20210102103730159 1
 
< 0.1%
20210102103730143 1
 
< 0.1%
20210102103730127 3
< 0.1%

longitude
Real number (ℝ)

Distinct8682
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.78786
Minimum126.16743
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:58:18.346513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16743
5-th percentile126.30407
Q1126.45487
median126.50698
Q3126.69431
95-th percentile126.93376
Maximum180
Range53.832569
Interquartile range (IQR)0.23943175

Descriptive statistics

Standard deviation10.698169
Coefficient of variation (CV)0.083068147
Kurtosis18.966138
Mean128.78786
Median Absolute Deviation (MAD)0.103722
Skewness4.5777804
Sum1287878.6
Variance114.45082
MonotonicityNot monotonic
2023-12-12T04:58:18.556496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180.0000001 418
 
4.2%
126.422195 15
 
0.1%
126.549632 12
 
0.1%
126.801733 11
 
0.1%
126.491619 9
 
0.1%
126.422193 9
 
0.1%
126.49162 9
 
0.1%
126.501887 8
 
0.1%
126.801729 8
 
0.1%
126.801723 8
 
0.1%
Other values (8672) 9493
94.9%
ValueCountFrequency (%)
126.167431 1
< 0.1%
126.170613 1
< 0.1%
126.171731 1
< 0.1%
126.173643 1
< 0.1%
126.174308 1
< 0.1%
126.175789 1
< 0.1%
126.176948 1
< 0.1%
126.177341 1
< 0.1%
126.177597 1
< 0.1%
126.178505 1
< 0.1%
ValueCountFrequency (%)
180.0000001 418
4.2%
126.957402 1
 
< 0.1%
126.953969 1
 
< 0.1%
126.953476 1
 
< 0.1%
126.951761 1
 
< 0.1%
126.950865 1
 
< 0.1%
126.949286 1
 
< 0.1%
126.94877 1
 
< 0.1%
126.946487 1
 
< 0.1%
126.943629 1
 
< 0.1%

latitude
Real number (ℝ)

Distinct8533
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.800481
Minimum33.207811
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:58:19.249302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.207811
5-th percentile33.250597
Q133.405974
median33.469544
Q333.498554
95-th percentile33.557123
Maximum90
Range56.792189
Interquartile range (IQR)0.09258025

Descriptive statistics

Standard deviation11.321123
Coefficient of variation (CV)0.31622822
Kurtosis18.974684
Mean35.800481
Median Absolute Deviation (MAD)0.0354315
Skewness4.5792435
Sum358004.81
Variance128.16782
MonotonicityNot monotonic
2023-12-12T04:58:19.425575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.0000001 418
 
4.2%
33.459711 10
 
0.1%
33.496189 9
 
0.1%
33.496141 8
 
0.1%
33.485476 8
 
0.1%
33.557123 8
 
0.1%
33.496088 8
 
0.1%
33.433878 7
 
0.1%
33.496143 7
 
0.1%
33.423487 6
 
0.1%
Other values (8523) 9511
95.1%
ValueCountFrequency (%)
33.207811 1
< 0.1%
33.207828 1
< 0.1%
33.207933 1
< 0.1%
33.20805 1
< 0.1%
33.208502 1
< 0.1%
33.208515 1
< 0.1%
33.209591 1
< 0.1%
33.209708 2
< 0.1%
33.209709 2
< 0.1%
33.209711 2
< 0.1%
ValueCountFrequency (%)
90.0000001 418
4.2%
33.56423 1
 
< 0.1%
33.563969 1
 
< 0.1%
33.563787 1
 
< 0.1%
33.563558 1
 
< 0.1%
33.563548 1
 
< 0.1%
33.563425 1
 
< 0.1%
33.56335 1
 
< 0.1%
33.563179 1
 
< 0.1%
33.563126 1
 
< 0.1%

Interactions

2023-12-12T04:58:16.168284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:15.221605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:15.644417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:16.401520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:15.377150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:15.767148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:16.611308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:15.514127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:15.930841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T04:58:19.587615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.0000.0960.096
longitude0.0961.0001.000
latitude0.0961.0001.000
2023-12-12T04:58:19.781041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.000-0.020-0.031
longitude-0.0201.0000.357
latitude-0.0310.3571.000

Missing values

2023-12-12T04:58:16.795751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T04:58:16.910335image/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
341424610020d20210102085930735126.41273733.252284
34821461011aa20210102090100599126.5085633.492939
894394610020420210102103100145126.64374133.475987
25744610018620210102064500633126.8991733.411817
34924461002db20210102090121023126.54860733.511128
918984610134620210102103400049126.24931633.270432
33638461002db20210102085811480126.58540533.494895
399614610015820210102091306325126.83614633.436154
387164610184620210102090955220126.53590633.50078
52334610120720210102070930682126.64480733.527997
oidcollection_dtlongitudelatitude
578624610114a20210102094700876126.81263333.403057
69991461010ae20210102100430074126.49175133.502609
2958046100ffa20210102084500767126.31357933.463506
799304610083120210102101800420126.50529133.50019
646774610026920210102095730080126.3656733.271169
140904610010320210102074515913126.43107633.442723
88800461003c620210102103000729126.51692933.460442
492134610010b20210102093400466180.090.0
20273461002aa20210102080730262180.090.0
404704610015820210102091440076126.82143933.433371

Duplicate rows

Most frequently occurring

oidcollection_dtlongitudelatitude# duplicates
14610010320210102071834288126.36254533.3581533
21461002db20210102093954998126.60173233.486333
04610010320210102071715576126.35698933.343082
24610010320210102072113456126.37343633.38912
34610010320210102072126774126.37471233.3918262
44610010320210102072355556126.39842733.4140682
54610010320210102072553877126.41806533.4318242
64610010320210102072725758126.42219933.433882
74610010320210102074643765126.44193933.4551342
84610010320210102074723787126.44600233.459342