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 20:00:02.439506
Analysis finished2023-12-11 20:00:07.921197
Duration5.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct1400
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T05:00:08.240136image/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

Unique102 ?
Unique (%)1.0%

Sample

1st row46100d48
2nd row46100c24
3rd row461000bc
4th row46100cbd
5th row461001a2
ValueCountFrequency (%)
4610019f 19
 
0.2%
4610041d 19
 
0.2%
46100da2 19
 
0.2%
461001b8 19
 
0.2%
461001ce 18
 
0.2%
461001bc 18
 
0.2%
461006ea 18
 
0.2%
46100de3 17
 
0.2%
46100193 17
 
0.2%
461006ed 17
 
0.2%
Other values (1390) 9819
98.2%
2023-12-12T05:00:08.857877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 21770
27.2%
1 12311
15.4%
4 12261
15.3%
6 12249
15.3%
c 2593
 
3.2%
d 2446
 
3.1%
7 2185
 
2.7%
5 2111
 
2.6%
3 2025
 
2.5%
2 2011
 
2.5%
Other values (6) 8038
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69243
86.6%
Lowercase Letter 10757
 
13.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21770
31.4%
1 12311
17.8%
4 12261
17.7%
6 12249
17.7%
7 2185
 
3.2%
5 2111
 
3.0%
3 2025
 
2.9%
2 2011
 
2.9%
9 1211
 
1.7%
8 1109
 
1.6%
Lowercase Letter
ValueCountFrequency (%)
c 2593
24.1%
d 2446
22.7%
b 1706
15.9%
e 1398
13.0%
f 1316
12.2%
a 1298
12.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69243
86.6%
Latin 10757
 
13.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21770
31.4%
1 12311
17.8%
4 12261
17.7%
6 12249
17.7%
7 2185
 
3.2%
5 2111
 
3.0%
3 2025
 
2.9%
2 2011
 
2.9%
9 1211
 
1.7%
8 1109
 
1.6%
Latin
ValueCountFrequency (%)
c 2593
24.1%
d 2446
22.7%
b 1706
15.9%
e 1398
13.0%
f 1316
12.2%
a 1298
12.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21770
27.2%
1 12311
15.4%
4 12261
15.3%
6 12249
15.3%
c 2593
 
3.2%
d 2446
 
3.1%
7 2185
 
2.7%
5 2111
 
2.6%
3 2025
 
2.5%
2 2011
 
2.5%
Other values (6) 8038
 
10.0%

collection_dt
Real number (ℝ)

Distinct9717
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0200317 × 1013
Minimum2.0200307 × 1013
Maximum2.0200329 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:00:09.082304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0200307 × 1013
5-th percentile2.0200307 × 1013
Q12.0200307 × 1013
median2.0200314 × 1013
Q32.0200321 × 1013
95-th percentile2.0200328 × 1013
Maximum2.0200329 × 1013
Range21975037
Interquartile range (IQR)13962777

Descriptive statistics

Standard deviation7383939.1
Coefficient of variation (CV)3.6553581 × 10-7
Kurtosis-1.2004473
Mean2.0200317 × 1013
Median Absolute Deviation (MAD)6985214.5
Skewness0.13773177
Sum2.0200317 × 1017
Variance5.4522556 × 1013
MonotonicityNot monotonic
2023-12-12T05:00:09.257084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200307102129 3
 
< 0.1%
20200307125818 3
 
< 0.1%
20200307094345 3
 
< 0.1%
20200314102740 3
 
< 0.1%
20200314181641 3
 
< 0.1%
20200307123001 3
 
< 0.1%
20200321113023 3
 
< 0.1%
20200321120743 3
 
< 0.1%
20200314122608 3
 
< 0.1%
20200328074002 2
 
< 0.1%
Other values (9707) 9971
99.7%
ValueCountFrequency (%)
20200307050603 1
< 0.1%
20200307054010 1
< 0.1%
20200307054100 1
< 0.1%
20200307054536 1
< 0.1%
20200307054641 1
< 0.1%
20200307063327 1
< 0.1%
20200307063826 1
< 0.1%
20200307064814 1
< 0.1%
20200307064959 1
< 0.1%
20200307065106 1
< 0.1%
ValueCountFrequency (%)
20200329025640 1
< 0.1%
20200329024806 1
< 0.1%
20200329015710 1
< 0.1%
20200329015341 1
< 0.1%
20200329013134 1
< 0.1%
20200329012443 1
< 0.1%
20200329005845 1
< 0.1%
20200329004532 1
< 0.1%
20200329001457 1
< 0.1%
20200329001033 1
< 0.1%

longitude
Real number (ℝ)

Distinct9961
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.51986
Minimum126.16376
Maximum126.96947
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:00:09.421921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16376
5-th percentile126.25448
Q1126.41202
median126.4967
Q3126.56686
95-th percentile126.91288
Maximum126.96947
Range0.8057066
Interquartile range (IQR)0.15484102

Descriptive statistics

Standard deviation0.18023543
Coefficient of variation (CV)0.0014245624
Kurtosis0.068480371
Mean126.51986
Median Absolute Deviation (MAD)0.0819968
Skewness0.68286111
Sum1265198.6
Variance0.032484812
MonotonicityNot monotonic
2023-12-12T05:00:09.577721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.4966389 2
 
< 0.1%
126.8991328 2
 
< 0.1%
126.562498 2
 
< 0.1%
126.5056804 2
 
< 0.1%
126.4964865 2
 
< 0.1%
126.4934536 2
 
< 0.1%
126.5006693 2
 
< 0.1%
126.4909041 2
 
< 0.1%
126.4973486 2
 
< 0.1%
126.3923548 2
 
< 0.1%
Other values (9951) 9980
99.8%
ValueCountFrequency (%)
126.163761 1
< 0.1%
126.1637689 1
< 0.1%
126.1637706 1
< 0.1%
126.1637741 1
< 0.1%
126.1638438 1
< 0.1%
126.1645143 1
< 0.1%
126.1647685 1
< 0.1%
126.1653891 1
< 0.1%
126.165394 1
< 0.1%
126.1655766 1
< 0.1%
ValueCountFrequency (%)
126.9694676 1
< 0.1%
126.9685925 1
< 0.1%
126.9684948 1
< 0.1%
126.9677491 1
< 0.1%
126.9671493 1
< 0.1%
126.9671418 1
< 0.1%
126.9670891 1
< 0.1%
126.9657166 1
< 0.1%
126.9654288 1
< 0.1%
126.9649981 1
< 0.1%

latitude
Real number (ℝ)

Distinct9927
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.417943
Minimum33.200029
Maximum33.564462
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:00:09.773672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.200029
5-th percentile33.241107
Q133.305801
median33.468933
Q333.498121
95-th percentile33.528966
Maximum33.564462
Range0.3644332
Interquartile range (IQR)0.19231973

Descriptive statistics

Standard deviation0.10495993
Coefficient of variation (CV)0.0031408254
Kurtosis-1.1065976
Mean33.417943
Median Absolute Deviation (MAD)0.04286365
Skewness-0.70044252
Sum334179.43
Variance0.011016586
MonotonicityNot monotonic
2023-12-12T05:00:10.020122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.5028491 3
 
< 0.1%
33.2484366 3
 
< 0.1%
33.4936756 2
 
< 0.1%
33.4890178 2
 
< 0.1%
33.2542568 2
 
< 0.1%
33.4876614 2
 
< 0.1%
33.4643211 2
 
< 0.1%
33.4798711 2
 
< 0.1%
33.4961141 2
 
< 0.1%
33.4729031 2
 
< 0.1%
Other values (9917) 9978
99.8%
ValueCountFrequency (%)
33.2000288 1
< 0.1%
33.2040723 1
< 0.1%
33.2040749 1
< 0.1%
33.2040828 1
< 0.1%
33.2041043 1
< 0.1%
33.2042098 1
< 0.1%
33.2043314 1
< 0.1%
33.2043641 1
< 0.1%
33.2045911 1
< 0.1%
33.2047986 1
< 0.1%
ValueCountFrequency (%)
33.564462 1
< 0.1%
33.5636905 1
< 0.1%
33.5608905 1
< 0.1%
33.5606026 1
< 0.1%
33.5605941 1
< 0.1%
33.5604635 1
< 0.1%
33.5602121 1
< 0.1%
33.5600901 1
< 0.1%
33.5594395 1
< 0.1%
33.5594256 1
< 0.1%

time
Date

Distinct9717
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-03-07 05:06:03
Maximum2020-03-29 02:56:40
2023-12-12T05:00:10.207608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:10.364132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Diff
Real number (ℝ)

Distinct6832
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110179.22
Minimum1200
Maximum1821486
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:00:10.552380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile1472
Q12557
median4296
Q311923.75
95-th percentile586388.4
Maximum1821486
Range1820286
Interquartile range (IQR)9366.75

Descriptive statistics

Standard deviation247515.03
Coefficient of variation (CV)2.2464765
Kurtosis7.1414947
Mean110179.22
Median Absolute Deviation (MAD)2321
Skewness2.5086306
Sum1.1017922 × 109
Variance6.1263688 × 1010
MonotonicityNot monotonic
2023-12-12T05:00:10.711061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1230 13
 
0.1%
1650 11
 
0.1%
1350 9
 
0.1%
2611 8
 
0.1%
2443 7
 
0.1%
1410 7
 
0.1%
1590 7
 
0.1%
2933 7
 
0.1%
2678 7
 
0.1%
1830 7
 
0.1%
Other values (6822) 9917
99.2%
ValueCountFrequency (%)
1200 2
< 0.1%
1201 1
 
< 0.1%
1203 1
 
< 0.1%
1204 4
< 0.1%
1205 2
< 0.1%
1206 2
< 0.1%
1209 1
 
< 0.1%
1210 3
< 0.1%
1211 1
 
< 0.1%
1213 1
 
< 0.1%
ValueCountFrequency (%)
1821486 1
< 0.1%
1821033 1
< 0.1%
1815940 1
< 0.1%
1809906 1
< 0.1%
1806673 1
< 0.1%
1803000 1
< 0.1%
1798333 1
< 0.1%
1791095 1
< 0.1%
1784673 1
< 0.1%
1784417 1
< 0.1%

Interactions

2023-12-12T05:00:06.675904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:03.543648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:04.835456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:05.787120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:06.903873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:03.758721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:05.051494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:06.026041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:07.129530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:03.937048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:05.308647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:06.234955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:07.351518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:04.147308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:05.562093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:06.442934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:00:10.825480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.0000.0680.0820.166
longitude0.0681.0000.8430.106
latitude0.0820.8431.0000.086
Diff0.1660.1060.0861.000
2023-12-12T05:00:10.983228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.000-0.012-0.045-0.051
longitude-0.0121.0000.329-0.028
latitude-0.0450.3291.0000.042
Diff-0.051-0.0280.0421.000

Missing values

2023-12-12T05:00:07.625289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:00:07.841851image/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
1678846100d4820200328115521126.35295633.2860792020-03-28 11:55:213695
1413246100c2420200314114335126.49563533.5060172020-03-14 11:43:351217555
186461000bc20200321150018126.63351433.534992020-03-21 15:00:187282
1526946100cbd20200328202230126.41853233.4889172020-03-28 20:22:302225
2129461001a220200321181726126.56462633.2414082020-03-21 18:17:263030
33064610021b20200307194119126.53219233.4983242020-03-07 19:41:193655
48704610037420200307131714126.50295333.510622020-03-07 13:17:14600148
1415046100c2520200321182513126.4295333.4884532020-03-21 18:25:136959
2954461001fc20200321104230126.37320933.3887442020-03-21 10:42:3038879
7260461004a920200328120855126.51897533.5166872020-03-28 12:08:553778
oidcollection_dtlongitudelatitudetimeDiff
13004461007c620200321085200126.51955433.5120082020-03-21 08:52:002535
16274610016420200314121201126.56506233.2452952020-03-14 12:12:015190
1396346100c0c20200314153228126.5030633.5154692020-03-14 15:32:281183702
1611446100d0620200314083132126.35657933.2610072020-03-14 08:31:328771
1590246100cf020200314195503126.45933133.3118062020-03-14 19:55:03565681
81174610052820200328130928126.50675333.2338962020-03-28 13:09:282873
103074610068220200328142129126.56714133.2515872020-03-28 14:21:292376
4235461002db20200328161725126.7213733.39432020-03-28 16:17:251419
49684610037e20200307100046126.48634533.5028562020-03-07 10:00:466718
1596746100cf720200322012717126.82451733.3120052020-03-22 01:27:17566961