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/1205

Reproduction

Analysis started2023-12-11 19:59:34.111860
Analysis finished2023-12-11 19:59:39.588213
Duration5.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct1330
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T04:59:40.028241image/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

Unique60 ?
Unique (%)0.6%

Sample

1st row46100378
2nd row461004aa
3rd row461003f5
4th row461003c9
5th row461000fb
ValueCountFrequency (%)
46100164 20
 
0.2%
461005d2 18
 
0.2%
461001ab 18
 
0.2%
46100476 18
 
0.2%
461006cb 18
 
0.2%
46100556 17
 
0.2%
461006fd 17
 
0.2%
461000e7 17
 
0.2%
461001ba 17
 
0.2%
461000f6 16
 
0.2%
Other values (1320) 9824
98.2%
2023-12-12T04:59:40.820812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22087
27.6%
4 12682
15.9%
1 12496
15.6%
6 12453
15.6%
5 2824
 
3.5%
3 2484
 
3.1%
7 2329
 
2.9%
2 2269
 
2.8%
b 1473
 
1.8%
8 1364
 
1.7%
Other values (6) 7539
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72217
90.3%
Lowercase Letter 7783
 
9.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22087
30.6%
4 12682
17.6%
1 12496
17.3%
6 12453
17.2%
5 2824
 
3.9%
3 2484
 
3.4%
7 2329
 
3.2%
2 2269
 
3.1%
8 1364
 
1.9%
9 1229
 
1.7%
Lowercase Letter
ValueCountFrequency (%)
b 1473
18.9%
c 1340
17.2%
e 1301
16.7%
a 1268
16.3%
f 1211
15.6%
d 1190
15.3%

Most occurring scripts

ValueCountFrequency (%)
Common 72217
90.3%
Latin 7783
 
9.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22087
30.6%
4 12682
17.6%
1 12496
17.3%
6 12453
17.2%
5 2824
 
3.9%
3 2484
 
3.4%
7 2329
 
3.2%
2 2269
 
3.1%
8 1364
 
1.9%
9 1229
 
1.7%
Latin
ValueCountFrequency (%)
b 1473
18.9%
c 1340
17.2%
e 1301
16.7%
a 1268
16.3%
f 1211
15.6%
d 1190
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22087
27.6%
4 12682
15.9%
1 12496
15.6%
6 12453
15.6%
5 2824
 
3.5%
3 2484
 
3.1%
7 2329
 
2.9%
2 2269
 
2.8%
b 1473
 
1.8%
8 1364
 
1.7%
Other values (6) 7539
 
9.4%

collection_dt
Real number (ℝ)

Distinct9718
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0200114 × 1013
Minimum2.0200104 × 1013
Maximum2.0200126 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:59:41.110373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0200104 × 1013
5-th percentile2.0200104 × 1013
Q12.0200104 × 1013
median2.0200111 × 1013
Q32.0200118 × 1013
95-th percentile2.0200125 × 1013
Maximum2.0200126 × 1013
Range21968570
Interquartile range (IQR)13969994

Descriptive statistics

Standard deviation7400321
Coefficient of variation (CV)3.6635047 × 10-7
Kurtosis-1.2085368
Mean2.0200114 × 1013
Median Absolute Deviation (MAD)6986818
Skewness0.14463253
Sum2.0200114 × 1017
Variance5.4764752 × 1013
MonotonicityNot monotonic
2023-12-12T04:59:41.245989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200118132809 3
 
< 0.1%
20200111150009 3
 
< 0.1%
20200111195418 3
 
< 0.1%
20200125125705 3
 
< 0.1%
20200104185408 3
 
< 0.1%
20200111172042 3
 
< 0.1%
20200111134407 3
 
< 0.1%
20200111161044 3
 
< 0.1%
20200104130532 3
 
< 0.1%
20200118121330 2
 
< 0.1%
Other values (9708) 9971
99.7%
ValueCountFrequency (%)
20200104052741 1
< 0.1%
20200104053018 1
< 0.1%
20200104060219 1
< 0.1%
20200104060249 1
< 0.1%
20200104062855 1
< 0.1%
20200104063146 1
< 0.1%
20200104063302 1
< 0.1%
20200104064114 1
< 0.1%
20200104064256 1
< 0.1%
20200104064813 1
< 0.1%
ValueCountFrequency (%)
20200126021311 1
< 0.1%
20200126014356 1
< 0.1%
20200126001129 1
< 0.1%
20200126001018 1
< 0.1%
20200125235025 1
< 0.1%
20200125232219 1
< 0.1%
20200125231732 1
< 0.1%
20200125230050 1
< 0.1%
20200125225917 1
< 0.1%
20200125225832 1
< 0.1%

longitude
Real number (ℝ)

Distinct9965
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.53396
Minimum126.16365
Maximum126.9697
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:59:41.454931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16365
5-th percentile126.26389
Q1126.41228
median126.50021
Q3126.63451
95-th percentile126.91946
Maximum126.9697
Range0.8060517
Interquartile range (IQR)0.22222625

Descriptive statistics

Standard deviation0.18850979
Coefficient of variation (CV)0.0014897961
Kurtosis-0.27319751
Mean126.53396
Median Absolute Deviation (MAD)0.10461755
Skewness0.61412319
Sum1265339.6
Variance0.035535942
MonotonicityNot monotonic
2023-12-12T04:59:41.737244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.4859555 2
 
< 0.1%
126.3120041 2
 
< 0.1%
126.5620493 2
 
< 0.1%
126.8358358 2
 
< 0.1%
126.6787661 2
 
< 0.1%
126.5030155 2
 
< 0.1%
126.9316341 2
 
< 0.1%
126.4920588 2
 
< 0.1%
126.511673 2
 
< 0.1%
126.4553115 2
 
< 0.1%
Other values (9955) 9980
99.8%
ValueCountFrequency (%)
126.1636476 1
< 0.1%
126.1636796 1
< 0.1%
126.163888 1
< 0.1%
126.1639406 1
< 0.1%
126.1645223 1
< 0.1%
126.164737 1
< 0.1%
126.1647468 1
< 0.1%
126.1653048 1
< 0.1%
126.165365 1
< 0.1%
126.1653678 1
< 0.1%
ValueCountFrequency (%)
126.9696993 1
< 0.1%
126.9669493 1
< 0.1%
126.964942 1
< 0.1%
126.9597448 1
< 0.1%
126.959499 1
< 0.1%
126.9594613 1
< 0.1%
126.9594431 1
< 0.1%
126.959397 1
< 0.1%
126.959327 1
< 0.1%
126.957889 1
< 0.1%

latitude
Real number (ℝ)

Distinct9931
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.403648
Minimum33.203209
Maximum33.563892
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:59:41.991992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.203209
5-th percentile33.240876
Q133.281126
median33.449277
Q333.49609
95-th percentile33.529037
Maximum33.563892
Range0.3606826
Interquartile range (IQR)0.21496362

Descriptive statistics

Standard deviation0.10781369
Coefficient of variation (CV)0.0032276023
Kurtosis-1.4416807
Mean33.403648
Median Absolute Deviation (MAD)0.0616787
Skewness-0.43068132
Sum334036.48
Variance0.011623791
MonotonicityNot monotonic
2023-12-12T04:59:42.247196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.4967789 2
 
< 0.1%
33.2393168 2
 
< 0.1%
33.5048474 2
 
< 0.1%
33.4960466 2
 
< 0.1%
33.2453604 2
 
< 0.1%
33.4977266 2
 
< 0.1%
33.4962946 2
 
< 0.1%
33.3328599 2
 
< 0.1%
33.495991 2
 
< 0.1%
33.4980718 2
 
< 0.1%
Other values (9921) 9980
99.8%
ValueCountFrequency (%)
33.2032095 1
< 0.1%
33.204062 1
< 0.1%
33.2041511 1
< 0.1%
33.2043546 1
< 0.1%
33.2043626 1
< 0.1%
33.2044015 1
< 0.1%
33.2052508 1
< 0.1%
33.2059511 1
< 0.1%
33.2059796 1
< 0.1%
33.2060085 1
< 0.1%
ValueCountFrequency (%)
33.5638921 1
< 0.1%
33.563663 1
< 0.1%
33.5636316 1
< 0.1%
33.5602153 1
< 0.1%
33.5594608 1
< 0.1%
33.5594526 1
< 0.1%
33.5594053 1
< 0.1%
33.5592403 1
< 0.1%
33.5591693 1
< 0.1%
33.5590576 1
< 0.1%

time
Date

Distinct9718
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-01-04 05:27:41
Maximum2020-01-26 02:13:11
2023-12-12T04:59:42.447831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:42.708565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Diff
Real number (ℝ)

Distinct6752
Distinct (%)67.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97016.205
Minimum1200
Maximum1823710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:59:42.985368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile1492
Q12527.75
median4115
Q39584.5
95-th percentile578655.7
Maximum1823710
Range1822510
Interquartile range (IQR)7056.75

Descriptive statistics

Standard deviation222278.14
Coefficient of variation (CV)2.2911444
Kurtosis5.9854662
Mean97016.205
Median Absolute Deviation (MAD)2077
Skewness2.3837004
Sum9.7016204 × 108
Variance4.940757 × 1010
MonotonicityNot monotonic
2023-12-12T04:59:43.254575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1240 8
 
0.1%
3160 7
 
0.1%
2351 7
 
0.1%
2281 7
 
0.1%
3299 7
 
0.1%
1602 7
 
0.1%
3505 7
 
0.1%
1611 7
 
0.1%
1485 7
 
0.1%
2000 7
 
0.1%
Other values (6742) 9929
99.3%
ValueCountFrequency (%)
1200 4
< 0.1%
1201 1
 
< 0.1%
1202 3
< 0.1%
1203 1
 
< 0.1%
1204 1
 
< 0.1%
1205 2
< 0.1%
1207 3
< 0.1%
1208 2
< 0.1%
1209 3
< 0.1%
1210 1
 
< 0.1%
ValueCountFrequency (%)
1823710 1
< 0.1%
1818866 1
< 0.1%
1806039 1
< 0.1%
1805898 1
< 0.1%
1800411 1
< 0.1%
1799268 1
< 0.1%
1785655 1
< 0.1%
1771004 1
< 0.1%
1244283 1
< 0.1%
1235502 1
< 0.1%

Interactions

2023-12-12T04:59:38.264235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:35.975099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:36.662246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:37.472606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:38.469569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:36.201334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:36.841135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:37.657879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:38.651954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:36.384153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:37.081806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:37.877489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:38.895237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:36.545777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:37.287560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:38.059957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T04:59:43.389900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.0000.0570.0440.149
longitude0.0571.0000.8190.088
latitude0.0440.8191.0000.081
Diff0.1490.0880.0811.000
2023-12-12T04:59:43.587317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.0000.017-0.020-0.007
longitude0.0171.0000.263-0.050
latitude-0.0200.2631.0000.027
Diff-0.007-0.0500.0271.000

Missing values

2023-12-12T04:59:39.242642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T04:59:39.484949image/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
83254610037820200118192607126.7954133.533452020-01-18 19:26:07563553
11635461004aa20200118130134126.48784533.4503652020-01-18 13:01:341508
9377461003f520200118183646126.28412333.3746562020-01-18 18:36:46586690
8989461003c920200111173517126.23945333.39322020-01-11 17:35:171404
1370461000fb20200118154111126.62280233.5386122020-01-18 15:41:1112432
14872461005a820200125112226126.36903433.2913342020-01-25 11:22:266673
137114610055320200125120130126.67864633.2758132020-01-25 12:01:302352
26234610016920200104144424126.49265633.4960962020-01-04 14:44:244053
192534610073620200118094357126.93270533.4722020-01-18 09:43:5713215
9077461003d620200111195220126.47978133.4935792020-01-11 19:52:206379
oidcollection_dtlongitudelatitudetimeDiff
12161461004d420200118131402126.56006233.2510952020-01-18 13:14:022622
20873461007ff20200118134748126.53500633.4916552020-01-18 13:47:4814345
1347461000f920200118104306126.54913133.5127512020-01-18 10:43:061922
20970461008a120200118170759126.30685633.4454412020-01-18 17:07:594741
15260461005cb20200118093208126.61022533.2721042020-01-18 09:32:083845
140924610056c20200104093856126.60061533.3255552020-01-04 09:38:561519
15344461005d220200104105720126.6858533.393172020-01-04 10:57:203286
100414610044420200111180100126.30475833.2400592020-01-11 18:01:005534
6109461002a520200125095030126.88015533.5217252020-01-25 09:50:3011532
9382461003f620200111130122126.42409233.2504112020-01-11 13:01:222564