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/1206

Alerts

longitude is highly skewed (γ1 = 99.6666112)Skewed
latitude is highly skewed (γ1 = 33.77955466)Skewed

Reproduction

Analysis started2023-12-11 20:06:34.788020
Analysis finished2023-12-11 20:06:37.164981
Duration2.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct1864
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T05:06:37.428159image/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

Unique124 ?
Unique (%)1.2%

Sample

1st row46100446
2nd row46101089
3rd row461018a8
4th row46100707
5th row461001d1
ValueCountFrequency (%)
46101032 15
 
0.1%
461010be 15
 
0.1%
461004cb 15
 
0.1%
4610067f 14
 
0.1%
461011c0 14
 
0.1%
4610181b 14
 
0.1%
4610043f 13
 
0.1%
46100378 13
 
0.1%
46100603 13
 
0.1%
46101781 13
 
0.1%
Other values (1854) 9861
98.6%
2023-12-12T05:06:37.887026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 17946
22.4%
0 17903
22.4%
4 11949
14.9%
6 11735
14.7%
7 2467
 
3.1%
8 2428
 
3.0%
3 2101
 
2.6%
2 1883
 
2.4%
5 1850
 
2.3%
f 1679
 
2.1%
Other values (6) 8059
10.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71833
89.8%
Lowercase Letter 8167
 
10.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 17946
25.0%
0 17903
24.9%
4 11949
16.6%
6 11735
16.3%
7 2467
 
3.4%
8 2428
 
3.4%
3 2101
 
2.9%
2 1883
 
2.6%
5 1850
 
2.6%
9 1571
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
f 1679
20.6%
b 1420
17.4%
a 1325
16.2%
d 1286
15.7%
e 1253
15.3%
c 1204
14.7%

Most occurring scripts

ValueCountFrequency (%)
Common 71833
89.8%
Latin 8167
 
10.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 17946
25.0%
0 17903
24.9%
4 11949
16.6%
6 11735
16.3%
7 2467
 
3.4%
8 2428
 
3.4%
3 2101
 
2.9%
2 1883
 
2.6%
5 1850
 
2.6%
9 1571
 
2.2%
Latin
ValueCountFrequency (%)
f 1679
20.6%
b 1420
17.4%
a 1325
16.2%
d 1286
15.7%
e 1253
15.3%
c 1204
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 17946
22.4%
0 17903
22.4%
4 11949
14.9%
6 11735
14.7%
7 2467
 
3.1%
8 2428
 
3.0%
3 2101
 
2.6%
2 1883
 
2.4%
5 1850
 
2.3%
f 1679
 
2.1%
Other values (6) 8059
10.1%

collection_dt
Real number (ℝ)

Distinct6888
Distinct (%)68.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0201017 × 1013
Minimum2.0201003 × 1013
Maximum2.0201101 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:06:38.121166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0201003 × 1013
5-th percentile2.0201003 × 1013
Q12.020101 × 1013
median2.0201017 × 1013
Q32.0201024 × 1013
95-th percentile2.0201031 × 1013
Maximum2.0201101 × 1013
Range97958597
Interquartile range (IQR)14048230

Descriptive statistics

Standard deviation9721586.2
Coefficient of variation (CV)4.8124241 × 10-7
Kurtosis-0.19814011
Mean2.0201017 × 1013
Median Absolute Deviation (MAD)7022850
Skewness0.11341941
Sum2.0201017 × 1017
Variance9.4509239 × 1013
MonotonicityNot monotonic
2023-12-12T05:06:38.316204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20201024154430 6
 
0.1%
20201010151600 6
 
0.1%
20201010150700 6
 
0.1%
20201010153030 6
 
0.1%
20201024153800 6
 
0.1%
20201010185430 5
 
0.1%
20201024115630 5
 
0.1%
20201017154830 5
 
0.1%
20201024114230 5
 
0.1%
20201024165600 5
 
0.1%
Other values (6878) 9945
99.5%
ValueCountFrequency (%)
20201003052333 1
< 0.1%
20201003054216 1
< 0.1%
20201003054830 2
< 0.1%
20201003055000 1
< 0.1%
20201003055514 1
< 0.1%
20201003055930 1
< 0.1%
20201003060100 1
< 0.1%
20201003061230 1
< 0.1%
20201003061330 1
< 0.1%
20201003061800 1
< 0.1%
ValueCountFrequency (%)
20201101010930 1
< 0.1%
20201101001500 1
< 0.1%
20201031225330 1
< 0.1%
20201031225100 1
< 0.1%
20201031224509 1
< 0.1%
20201031224500 1
< 0.1%
20201031224200 1
< 0.1%
20201031224100 1
< 0.1%
20201031222300 1
< 0.1%
20201031221730 1
< 0.1%

longitude
Real number (ℝ)

SKEWED 

Distinct9823
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.57755
Minimum122.61736
Maximum550.10995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:06:38.449182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum122.61736
5-th percentile126.25097
Q1126.3931
median126.50218
Q3126.66635
95-th percentile126.92008
Maximum550.10995
Range427.49259
Interquartile range (IQR)0.27325625

Descriptive statistics

Standard deviation4.2404639
Coefficient of variation (CV)0.033500917
Kurtosis9955.5725
Mean126.57755
Median Absolute Deviation (MAD)0.1314285
Skewness99.666611
Sum1265775.5
Variance17.981534
MonotonicityNot monotonic
2023-12-12T05:06:38.598378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.560012 3
 
< 0.1%
126.508653 3
 
< 0.1%
126.5622 3
 
< 0.1%
126.502937 3
 
< 0.1%
126.311074 3
 
< 0.1%
126.562073 2
 
< 0.1%
126.486157 2
 
< 0.1%
126.23127 2
 
< 0.1%
126.933302 2
 
< 0.1%
126.483373 2
 
< 0.1%
Other values (9813) 9975
99.8%
ValueCountFrequency (%)
122.617357 1
< 0.1%
126.16348 1
< 0.1%
126.163759 1
< 0.1%
126.163894 1
< 0.1%
126.164096 1
< 0.1%
126.164132 1
< 0.1%
126.16442 1
< 0.1%
126.164422 1
< 0.1%
126.164427 1
< 0.1%
126.164494 1
< 0.1%
ValueCountFrequency (%)
550.109948 1
< 0.1%
126.969913 1
< 0.1%
126.969601 1
< 0.1%
126.969512 1
< 0.1%
126.968762 1
< 0.1%
126.968665 1
< 0.1%
126.968663 1
< 0.1%
126.968637 1
< 0.1%
126.968609 1
< 0.1%
126.968574 1
< 0.1%

latitude
Real number (ℝ)

SKEWED 

Distinct9682
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.40732
Minimum33.200242
Maximum43.768599
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:06:38.728006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.200242
5-th percentile33.239492
Q133.292987
median33.447483
Q333.496446
95-th percentile33.537502
Maximum43.768599
Range10.568357
Interquartile range (IQR)0.203459

Descriptive statistics

Standard deviation0.14852448
Coefficient of variation (CV)0.0044458666
Kurtosis2367.5424
Mean33.40732
Median Absolute Deviation (MAD)0.06430755
Skewness33.779555
Sum334073.2
Variance0.022059523
MonotonicityNot monotonic
2023-12-12T05:06:38.898670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.496506 4
 
< 0.1%
33.250326 3
 
< 0.1%
33.512157 3
 
< 0.1%
33.512165 3
 
< 0.1%
33.503138 3
 
< 0.1%
33.489708 3
 
< 0.1%
33.512133 3
 
< 0.1%
33.502885 3
 
< 0.1%
33.496417 3
 
< 0.1%
33.517719 3
 
< 0.1%
Other values (9672) 9969
99.7%
ValueCountFrequency (%)
33.200242 1
< 0.1%
33.204151 1
< 0.1%
33.204215 1
< 0.1%
33.204404 1
< 0.1%
33.20441 1
< 0.1%
33.204417 1
< 0.1%
33.204453 1
< 0.1%
33.204464 1
< 0.1%
33.204483 1
< 0.1%
33.204662 1
< 0.1%
ValueCountFrequency (%)
43.768599 1
< 0.1%
33.710665 1
< 0.1%
33.563883 1
< 0.1%
33.563858 1
< 0.1%
33.563856 1
< 0.1%
33.563791 1
< 0.1%
33.56378 1
< 0.1%
33.56376 1
< 0.1%
33.563391 1
< 0.1%
33.563367 1
< 0.1%

time
Date

Distinct6888
Distinct (%)68.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-10-03 05:23:33
Maximum2020-11-01 01:09:30
2023-12-12T05:06:39.065197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:39.251420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Diff
Real number (ℝ)

Distinct5286
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91812.622
Minimum1200
Maximum1789744
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:06:39.393858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile1457.95
Q12484.75
median4008.5
Q38790
95-th percentile575929.55
Maximum1789744
Range1788544
Interquartile range (IQR)6305.25

Descriptive statistics

Standard deviation210290.4
Coefficient of variation (CV)2.2904302
Kurtosis4.1677431
Mean91812.622
Median Absolute Deviation (MAD)1944.5
Skewness2.2070874
Sum9.1812622 × 108
Variance4.4222053 × 1010
MonotonicityNot monotonic
2023-12-12T05:06:39.643872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1290 46
 
0.5%
2130 42
 
0.4%
1950 39
 
0.4%
2430 37
 
0.4%
1380 34
 
0.3%
2490 34
 
0.3%
3180 33
 
0.3%
2550 33
 
0.3%
2970 33
 
0.3%
2910 33
 
0.3%
Other values (5276) 9636
96.4%
ValueCountFrequency (%)
1200 24
0.2%
1201 2
 
< 0.1%
1202 2
 
< 0.1%
1205 1
 
< 0.1%
1206 2
 
< 0.1%
1207 1
 
< 0.1%
1209 2
 
< 0.1%
1210 1
 
< 0.1%
1215 1
 
< 0.1%
1218 2
 
< 0.1%
ValueCountFrequency (%)
1789744 1
< 0.1%
1786153 1
< 0.1%
1775469 1
< 0.1%
1221870 1
< 0.1%
1212784 1
< 0.1%
1202991 1
< 0.1%
1200090 1
< 0.1%
1199150 1
< 0.1%
1196177 1
< 0.1%
1192150 1
< 0.1%

Interactions

2023-12-12T05:06:36.670724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:35.468859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:35.910759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:36.341619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:36.763463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:35.593718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:36.000715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:36.426424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:36.838482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:35.688640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:36.093384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:36.500087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:36.918535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:35.785950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:36.203723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:36.582856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:06:39.776718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.0000.0140.0140.084
longitude0.0141.0000.7070.000
latitude0.0140.7071.0000.000
Diff0.0840.0000.0001.000
2023-12-12T05:06:39.903775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.0000.009-0.012-0.049
longitude0.0091.0000.290-0.004
latitude-0.0120.2901.000-0.011
Diff-0.049-0.004-0.0111.000

Missing values

2023-12-12T05:06:37.032134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:06:37.121194image/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
105174610044620201024125930126.31172833.2358292020-10-24 12:59:301830
257264610108920201010135530126.50038633.5032152020-10-10 13:55:301650
40982461018a820201031073600126.48310633.4965532020-10-31 07:36:003041
185994610070720201017081600126.45578533.4923582020-10-17 08:16:003088
3458461001d120201003104009126.38468833.4766722020-10-03 10:40:094342
83184610038220201010114100126.75317933.4521252020-10-10 11:41:001990
78204610036a20201010120000126.76290833.5580152020-10-10 12:00:001653
2247046100ff820201003195700126.91222733.4459862020-10-03 19:57:002067
2103746100fb320201031110630126.93326533.4723832020-10-31 11:06:3023303
102584610042c20201003155130126.83108633.4632572020-10-03 15:51:303240
oidcollection_dtlongitudelatitudetimeDiff
251594610106e20201024115630126.93579733.4656622020-10-24 11:56:303180
189394610073a20201031185004126.41085733.2503212020-10-31 18:50:044231
343024610178720201010121700126.2897433.3041262020-10-10 12:17:001710
17793461006ae20201003191313126.45567933.4940752020-10-03 19:13:13573472
116454610049a20201003124430126.84288533.3241712020-10-03 12:44:308397
2074146100fa320201024075600126.73781433.5206772020-10-24 07:56:0022460
39334610020520201031192900126.35599133.4750452020-10-31 19:29:006902
2192446100fe020201017131700126.62032633.3847562020-10-17 13:17:001860
402354610188820201024201100126.66556133.5423052020-10-24 20:11:00566892
197104610078420201024185400126.42428933.2424642020-10-24 18:54:00568947