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/1208

Reproduction

Analysis started2023-12-11 20:03:21.892117
Analysis finished2023-12-11 20:03:24.554816
Duration2.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct1702
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T05:03:24.819641image/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

Unique82 ?
Unique (%)0.8%

Sample

1st row46101072
2nd row4610015a
3rd row461000f6
4th row461004d7
5th row46101234
ValueCountFrequency (%)
46100684 15
 
0.1%
46101136 15
 
0.1%
4610103b 14
 
0.1%
461005c4 14
 
0.1%
4610030b 14
 
0.1%
461017e0 14
 
0.1%
4610110d 13
 
0.1%
46101953 13
 
0.1%
4610110e 13
 
0.1%
46101189 13
 
0.1%
Other values (1692) 9862
98.6%
2023-12-12T05:03:25.270707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 18004
22.5%
0 17275
21.6%
4 11965
15.0%
6 11680
14.6%
2 2577
 
3.2%
7 2454
 
3.1%
8 2296
 
2.9%
3 2210
 
2.8%
5 1969
 
2.5%
9 1706
 
2.1%
Other values (6) 7864
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72136
90.2%
Lowercase Letter 7864
 
9.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 18004
25.0%
0 17275
23.9%
4 11965
16.6%
6 11680
16.2%
2 2577
 
3.6%
7 2454
 
3.4%
8 2296
 
3.2%
3 2210
 
3.1%
5 1969
 
2.7%
9 1706
 
2.4%
Lowercase Letter
ValueCountFrequency (%)
f 1496
19.0%
b 1355
17.2%
a 1326
16.9%
e 1273
16.2%
d 1271
16.2%
c 1143
14.5%

Most occurring scripts

ValueCountFrequency (%)
Common 72136
90.2%
Latin 7864
 
9.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1 18004
25.0%
0 17275
23.9%
4 11965
16.6%
6 11680
16.2%
2 2577
 
3.6%
7 2454
 
3.4%
8 2296
 
3.2%
3 2210
 
3.1%
5 1969
 
2.7%
9 1706
 
2.4%
Latin
ValueCountFrequency (%)
f 1496
19.0%
b 1355
17.2%
a 1326
16.9%
e 1273
16.2%
d 1271
16.2%
c 1143
14.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 18004
22.5%
0 17275
21.6%
4 11965
15.0%
6 11680
14.6%
2 2577
 
3.2%
7 2454
 
3.1%
8 2296
 
2.9%
3 2210
 
2.8%
5 1969
 
2.5%
9 1706
 
2.1%
Other values (6) 7864
9.8%

collection_dt
Real number (ℝ)

Distinct5745
Distinct (%)57.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0210915 × 1013
Minimum2.0210904 × 1013
Maximum2.0210926 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:03:25.465540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0210904 × 1013
5-th percentile2.0210904 × 1013
Q12.0210905 × 1013
median2.0210911 × 1013
Q32.0210919 × 1013
95-th percentile2.0210925 × 1013
Maximum2.0210926 × 1013
Range21989100
Interquartile range (IQR)13980050

Descriptive statistics

Standard deviation7815654.5
Coefficient of variation (CV)3.8670465 × 10-7
Kurtosis-1.357509
Mean2.0210915 × 1013
Median Absolute Deviation (MAD)7000335
Skewness0.031666589
Sum2.0210915 × 1017
Variance6.1084456 × 1013
MonotonicityNot monotonic
2023-12-12T05:03:25.657825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20210925122430 8
 
0.1%
20210925133600 7
 
0.1%
20210925111930 7
 
0.1%
20210904143730 7
 
0.1%
20210904161500 7
 
0.1%
20210918175800 7
 
0.1%
20210911112930 7
 
0.1%
20210904184300 7
 
0.1%
20210925132230 7
 
0.1%
20210918152830 6
 
0.1%
Other values (5735) 9930
99.3%
ValueCountFrequency (%)
20210904053500 1
< 0.1%
20210904053830 1
< 0.1%
20210904055330 1
< 0.1%
20210904055530 1
< 0.1%
20210904055730 1
< 0.1%
20210904055900 1
< 0.1%
20210904060030 1
< 0.1%
20210904060930 1
< 0.1%
20210904061927 1
< 0.1%
20210904061930 1
< 0.1%
ValueCountFrequency (%)
20210926042600 1
< 0.1%
20210925234800 1
< 0.1%
20210925232200 1
< 0.1%
20210925231130 1
< 0.1%
20210925230500 1
< 0.1%
20210925225830 1
< 0.1%
20210925224200 1
< 0.1%
20210925223700 1
< 0.1%
20210925223500 1
< 0.1%
20210925222700 1
< 0.1%

longitude
Real number (ℝ)

Distinct9775
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.52732
Minimum126.16393
Maximum129.24465
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:03:25.819954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16393
5-th percentile126.24189
Q1126.37632
median126.49764
Q3126.62595
95-th percentile126.9184
Maximum129.24465
Range3.080712
Interquartile range (IQR)0.2496265

Descriptive statistics

Standard deviation0.23102438
Coefficient of variation (CV)0.0018258853
Kurtosis31.242541
Mean126.52732
Median Absolute Deviation (MAD)0.1246905
Skewness3.3418987
Sum1265273.2
Variance0.053372263
MonotonicityNot monotonic
2023-12-12T05:03:25.970772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.502418 3
 
< 0.1%
126.482681 3
 
< 0.1%
126.308871 3
 
< 0.1%
126.48258 3
 
< 0.1%
126.465639 3
 
< 0.1%
126.486111 3
 
< 0.1%
126.526228 3
 
< 0.1%
126.50282 3
 
< 0.1%
126.526074 2
 
< 0.1%
126.239259 2
 
< 0.1%
Other values (9765) 9972
99.7%
ValueCountFrequency (%)
126.163934 1
< 0.1%
126.163935 1
< 0.1%
126.163955 1
< 0.1%
126.164326 1
< 0.1%
126.16438 1
< 0.1%
126.164401 1
< 0.1%
126.164426 1
< 0.1%
126.164621 1
< 0.1%
126.164725 1
< 0.1%
126.164731 1
< 0.1%
ValueCountFrequency (%)
129.244646 1
< 0.1%
129.244075 1
< 0.1%
129.220659 1
< 0.1%
129.182869 1
< 0.1%
129.134041 1
< 0.1%
129.091034 1
< 0.1%
129.090884 1
< 0.1%
129.063589 1
< 0.1%
129.058373 1
< 0.1%
129.027705 1
< 0.1%

latitude
Real number (ℝ)

Distinct9618
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.417148
Minimum33.199742
Maximum35.267247
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:03:26.091683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.199742
5-th percentile33.238683
Q133.305252
median33.457774
Q333.49802
95-th percentile33.541425
Maximum35.267247
Range2.067505
Interquartile range (IQR)0.19276875

Descriptive statistics

Standard deviation0.1371748
Coefficient of variation (CV)0.0041049223
Kurtosis60.368876
Mean33.417148
Median Absolute Deviation (MAD)0.05781565
Skewness4.6611593
Sum334171.48
Variance0.018816925
MonotonicityNot monotonic
2023-12-12T05:03:26.209734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.489688 4
 
< 0.1%
33.503061 4
 
< 0.1%
33.462458 3
 
< 0.1%
33.502985 3
 
< 0.1%
33.483531 3
 
< 0.1%
33.515552 3
 
< 0.1%
33.396258 3
 
< 0.1%
33.496218 3
 
< 0.1%
33.238209 3
 
< 0.1%
33.512019 3
 
< 0.1%
Other values (9608) 9968
99.7%
ValueCountFrequency (%)
33.199742 1
< 0.1%
33.199822 1
< 0.1%
33.199989 1
< 0.1%
33.200026 1
< 0.1%
33.200045 1
< 0.1%
33.200054 1
< 0.1%
33.200065 1
< 0.1%
33.200076 1
< 0.1%
33.20027 1
< 0.1%
33.204304 1
< 0.1%
ValueCountFrequency (%)
35.267247 1
< 0.1%
35.258624 1
< 0.1%
35.252409 1
< 0.1%
35.233207 1
< 0.1%
35.231186 1
< 0.1%
35.231115 1
< 0.1%
35.216921 1
< 0.1%
35.210866 1
< 0.1%
35.189043 1
< 0.1%
35.171087 1
< 0.1%

time
Date

Distinct5745
Distinct (%)57.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-09-04 05:35:00
Maximum2021-09-26 04:26:00
2023-12-12T05:03:26.328652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:26.661575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Diff
Real number (ℝ)

Distinct6125
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91983.74
Minimum1200
Maximum1806588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:03:26.795703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile1461
Q12430
median3858.5
Q38498.75
95-th percentile577130.5
Maximum1806588
Range1805388
Interquartile range (IQR)6068.75

Descriptive statistics

Standard deviation221107.08
Coefficient of variation (CV)2.4037627
Kurtosis6.9685837
Mean91983.74
Median Absolute Deviation (MAD)1848.5
Skewness2.5688829
Sum9.198374 × 108
Variance4.8888341 × 1010
MonotonicityNot monotonic
2023-12-12T05:03:26.928234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 20
 
0.2%
2610 20
 
0.2%
1410 20
 
0.2%
2250 19
 
0.2%
1890 19
 
0.2%
2070 18
 
0.2%
2850 18
 
0.2%
1260 18
 
0.2%
2160 17
 
0.2%
2190 17
 
0.2%
Other values (6115) 9814
98.1%
ValueCountFrequency (%)
1200 12
0.1%
1201 1
 
< 0.1%
1202 1
 
< 0.1%
1205 2
 
< 0.1%
1207 2
 
< 0.1%
1208 1
 
< 0.1%
1209 2
 
< 0.1%
1210 2
 
< 0.1%
1211 2
 
< 0.1%
1212 3
 
< 0.1%
ValueCountFrequency (%)
1806588 1
< 0.1%
1802976 1
< 0.1%
1791211 1
< 0.1%
1781730 1
< 0.1%
1777560 1
< 0.1%
1775286 1
< 0.1%
1770378 1
< 0.1%
1244397 1
< 0.1%
1223857 1
< 0.1%
1215118 1
< 0.1%

Interactions

2023-12-12T05:03:23.830599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:22.466851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:22.916077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:23.397558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:23.944045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:22.550648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:23.041345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:23.519728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:24.083447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:22.661605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:23.165512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:23.626529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:24.194688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:22.790437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:23.267929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:23.714691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:03:27.027060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.0000.0170.0400.177
longitude0.0171.0000.8310.000
latitude0.0400.8311.0000.000
Diff0.1770.0000.0001.000
2023-12-12T05:03:27.149505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.0000.006-0.028-0.064
longitude0.0061.0000.3320.019
latitude-0.0280.3321.0000.020
Diff-0.0640.0190.0201.000

Missing values

2023-12-12T05:03:24.361980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:03:24.494153image/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
153534610107220210911165000126.79124233.3060652021-09-11 16:50:00591588
12034610015a20210918195700126.48616933.5026212021-09-18 19:57:00569547
624461000f620210918153126126.75224433.4129912021-09-18 15:31:262209
7450461004d720210925130030126.24710133.3925362021-09-25 13:00:306391
205324610123420210918211000126.2539933.3348012021-09-18 21:10:00555901
204434610122b20210911143330126.96189533.5130112021-09-11 14:33:303168
5178461003c620210925175230126.31751333.3028492021-09-25 17:52:301608
89014610058a20210925155200126.36984433.4787352021-09-25 15:52:004568
147604610103020210904203700126.48843133.5170842021-09-04 20:37:00563190
122214610077720210911155930126.91412933.4499932021-09-11 15:59:303750
oidcollection_dtlongitudelatitudetimeDiff
21034610020520210911204930126.32516233.2465912021-09-11 20:49:30570070
17012461010fa20210911202200126.61539133.4520442021-09-11 20:22:00582480
11316461006dd20210904181600126.52823733.5121732021-09-04 18:16:001410
27851461018e620210911122900126.17948133.3040962021-09-11 12:29:002374
11476461006f520210904132400126.77193333.5291782021-09-04 13:24:003392
226644610131620210911195930126.52810533.5120442021-09-11 19:59:303090
105224610065f20210918114700126.56401433.5046192021-09-18 11:47:001776
102514610062f20210918095030126.52167433.4771682021-09-18 09:50:302867
67774610049420210911223700126.93164433.4659512021-09-11 22:37:00558810
124034610078420210911102630126.84304333.3243082021-09-11 10:26:3022245