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

Reproduction

Analysis started2023-12-11 20:06:42.298136
Analysis finished2023-12-11 20:06:45.515988
Duration3.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct2180
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T05:06:45.825704image/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

Unique209 ?
Unique (%)2.1%

Sample

1st row461005c2
2nd row46100320
3rd row46101242
4th row4610116e
5th row461010b8
ValueCountFrequency (%)
4610186b 14
 
0.1%
461000ec 13
 
0.1%
46101950 13
 
0.1%
46101003 13
 
0.1%
46101201 13
 
0.1%
46101199 13
 
0.1%
46100494 13
 
0.1%
4610182c 13
 
0.1%
4610067f 13
 
0.1%
46100700 12
 
0.1%
Other values (2170) 9870
98.7%
2023-12-12T05:06:46.331184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 18487
23.1%
0 17040
21.3%
4 11928
14.9%
6 11769
14.7%
2 2414
 
3.0%
7 2382
 
3.0%
8 2283
 
2.9%
3 2118
 
2.6%
5 1948
 
2.4%
9 1810
 
2.3%
Other values (6) 7821
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72179
90.2%
Lowercase Letter 7821
 
9.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 18487
25.6%
0 17040
23.6%
4 11928
16.5%
6 11769
16.3%
2 2414
 
3.3%
7 2382
 
3.3%
8 2283
 
3.2%
3 2118
 
2.9%
5 1948
 
2.7%
9 1810
 
2.5%
Lowercase Letter
ValueCountFrequency (%)
f 1583
20.2%
b 1287
16.5%
a 1270
16.2%
c 1263
16.1%
d 1222
15.6%
e 1196
15.3%

Most occurring scripts

ValueCountFrequency (%)
Common 72179
90.2%
Latin 7821
 
9.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1 18487
25.6%
0 17040
23.6%
4 11928
16.5%
6 11769
16.3%
2 2414
 
3.3%
7 2382
 
3.3%
8 2283
 
3.2%
3 2118
 
2.9%
5 1948
 
2.7%
9 1810
 
2.5%
Latin
ValueCountFrequency (%)
f 1583
20.2%
b 1287
16.5%
a 1270
16.2%
c 1263
16.1%
d 1222
15.6%
e 1196
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 18487
23.1%
0 17040
21.3%
4 11928
14.9%
6 11769
14.7%
2 2414
 
3.0%
7 2382
 
3.0%
8 2283
 
2.9%
3 2118
 
2.6%
5 1948
 
2.4%
9 1810
 
2.3%
Other values (6) 7821
9.8%

collection_dt
Real number (ℝ)

Distinct5999
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0201117 × 1013
Minimum2.0201107 × 1013
Maximum2.0201129 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:06:46.520518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0201107 × 1013
5-th percentile2.0201107 × 1013
Q12.0201107 × 1013
median2.0201114 × 1013
Q32.0201121 × 1013
95-th percentile2.0201128 × 1013
Maximum2.0201129 × 1013
Range21989400
Interquartile range (IQR)13956865

Descriptive statistics

Standard deviation7396060.6
Coefficient of variation (CV)3.6612137 × 10-7
Kurtosis-1.2136758
Mean2.0201117 × 1013
Median Absolute Deviation (MAD)6981750
Skewness0.089084845
Sum2.0201117 × 1017
Variance5.4701712 × 1013
MonotonicityNot monotonic
2023-12-12T05:06:46.690455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20201121105930 8
 
0.1%
20201121113630 7
 
0.1%
20201121170230 7
 
0.1%
20201114113730 7
 
0.1%
20201107125400 7
 
0.1%
20201121150130 6
 
0.1%
20201114101600 6
 
0.1%
20201114121300 6
 
0.1%
20201107182930 6
 
0.1%
20201128154100 6
 
0.1%
Other values (5989) 9934
99.3%
ValueCountFrequency (%)
20201107051500 1
< 0.1%
20201107051900 1
< 0.1%
20201107052230 1
< 0.1%
20201107053230 1
< 0.1%
20201107053800 1
< 0.1%
20201107055130 1
< 0.1%
20201107060700 1
< 0.1%
20201107061030 1
< 0.1%
20201107061330 1
< 0.1%
20201107061500 1
< 0.1%
ValueCountFrequency (%)
20201129040900 1
< 0.1%
20201129030930 1
< 0.1%
20201129021500 1
< 0.1%
20201129003530 1
< 0.1%
20201129000400 1
< 0.1%
20201128235730 1
< 0.1%
20201128235330 1
< 0.1%
20201128235200 1
< 0.1%
20201128230830 1
< 0.1%
20201128225600 1
< 0.1%

longitude
Real number (ℝ)

Distinct9765
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.52951
Minimum126.16321
Maximum126.96967
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:06:46.849204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16321
5-th percentile126.25109
Q1126.39106
median126.5001
Q3126.64553
95-th percentile126.91905
Maximum126.96967
Range0.806459
Interquartile range (IQR)0.2544745

Descriptive statistics

Standard deviation0.19333417
Coefficient of variation (CV)0.0015279769
Kurtosis-0.4200476
Mean126.52951
Median Absolute Deviation (MAD)0.12712525
Skewness0.54342623
Sum1265295.1
Variance0.0373781
MonotonicityNot monotonic
2023-12-12T05:06:47.016867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.497675 4
 
< 0.1%
126.503134 3
 
< 0.1%
126.465616 3
 
< 0.1%
126.526119 3
 
< 0.1%
126.500675 3
 
< 0.1%
126.310363 3
 
< 0.1%
126.500503 3
 
< 0.1%
126.497656 2
 
< 0.1%
126.289778 2
 
< 0.1%
126.46563 2
 
< 0.1%
Other values (9755) 9972
99.7%
ValueCountFrequency (%)
126.163208 1
< 0.1%
126.163982 1
< 0.1%
126.164079 1
< 0.1%
126.164098 1
< 0.1%
126.16431 1
< 0.1%
126.164356 1
< 0.1%
126.164374 1
< 0.1%
126.164462 1
< 0.1%
126.164652 1
< 0.1%
126.164657 1
< 0.1%
ValueCountFrequency (%)
126.969667 1
< 0.1%
126.969506 1
< 0.1%
126.969427 1
< 0.1%
126.969152 1
< 0.1%
126.96889 1
< 0.1%
126.968885 1
< 0.1%
126.968826 1
< 0.1%
126.968633 1
< 0.1%
126.968431 1
< 0.1%
126.967977 1
< 0.1%

latitude
Real number (ℝ)

Distinct9616
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.404237
Minimum33.200061
Maximum33.561078
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:06:47.193209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.200061
5-th percentile33.238534
Q133.289161
median33.447603
Q333.496524
95-th percentile33.5324
Maximum33.561078
Range0.361017
Interquartile range (IQR)0.20736225

Descriptive statistics

Standard deviation0.10747293
Coefficient of variation (CV)0.0032173443
Kurtosis-1.3807078
Mean33.404237
Median Absolute Deviation (MAD)0.0646835
Skewness-0.43487012
Sum334042.37
Variance0.011550432
MonotonicityNot monotonic
2023-12-12T05:06:47.419161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.304201 3
 
< 0.1%
33.249722 3
 
< 0.1%
33.510629 3
 
< 0.1%
33.496462 3
 
< 0.1%
33.496137 3
 
< 0.1%
33.502866 3
 
< 0.1%
33.289012 3
 
< 0.1%
33.502901 3
 
< 0.1%
33.496525 3
 
< 0.1%
33.244583 3
 
< 0.1%
Other values (9606) 9970
99.7%
ValueCountFrequency (%)
33.200061 1
< 0.1%
33.200112 1
< 0.1%
33.200131 1
< 0.1%
33.203028 1
< 0.1%
33.20428 1
< 0.1%
33.204343 1
< 0.1%
33.204544 1
< 0.1%
33.204554 1
< 0.1%
33.204825 1
< 0.1%
33.204949 1
< 0.1%
ValueCountFrequency (%)
33.561078 1
< 0.1%
33.560381 1
< 0.1%
33.560157 1
< 0.1%
33.559723 1
< 0.1%
33.559475 1
< 0.1%
33.559402 1
< 0.1%
33.559346 1
< 0.1%
33.558659 1
< 0.1%
33.558604 1
< 0.1%
33.558554 1
< 0.1%

time
Date

Distinct5999
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-11-07 05:15:00
Maximum2020-11-29 04:09:00
2023-12-12T05:06:47.603245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:47.763464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Diff
Real number (ℝ)

Distinct6075
Distinct (%)60.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83542.375
Minimum1200
Maximum1792068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:06:47.992984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile1451.95
Q12500.5
median3910.5
Q37872.75
95-th percentile573044.25
Maximum1792068
Range1790868
Interquartile range (IQR)5372.25

Descriptive statistics

Standard deviation199864.19
Coefficient of variation (CV)2.392369
Kurtosis4.3075896
Mean83542.375
Median Absolute Deviation (MAD)1831.5
Skewness2.2929827
Sum8.3542375 × 108
Variance3.9945694 × 1010
MonotonicityNot monotonic
2023-12-12T05:06:48.230866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 22
 
0.2%
1500 19
 
0.2%
2850 19
 
0.2%
2730 19
 
0.2%
2070 18
 
0.2%
2670 18
 
0.2%
2130 18
 
0.2%
2550 18
 
0.2%
2250 18
 
0.2%
1290 17
 
0.2%
Other values (6065) 9814
98.1%
ValueCountFrequency (%)
1200 4
< 0.1%
1201 3
< 0.1%
1202 1
 
< 0.1%
1203 1
 
< 0.1%
1204 2
 
< 0.1%
1205 2
 
< 0.1%
1206 5
0.1%
1208 5
0.1%
1209 1
 
< 0.1%
1210 4
< 0.1%
ValueCountFrequency (%)
1792068 1
< 0.1%
1785750 1
< 0.1%
1213204 1
< 0.1%
1191870 1
< 0.1%
1191029 1
< 0.1%
1190409 1
< 0.1%
1188030 1
< 0.1%
1187125 1
< 0.1%
1184504 1
< 0.1%
1177147 1
< 0.1%

Interactions

2023-12-12T05:06:44.865042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:43.095777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:43.682759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:44.274836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:44.966943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:43.229384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:43.832749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:44.412048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:45.090752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:43.385622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:43.979580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:44.569292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:45.191655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:43.542302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:44.121326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:06:44.737739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:06:48.364374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.0000.0290.0320.136
longitude0.0291.0000.8260.038
latitude0.0320.8261.0000.075
Diff0.1360.0380.0751.000
2023-12-12T05:06:48.501715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.000-0.010-0.025-0.034
longitude-0.0101.0000.3070.002
latitude-0.0250.3071.000-0.017
Diff-0.0340.002-0.0171.000

Missing values

2023-12-12T05:06:45.322304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:06:45.451135image/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
11776461005c220201128144628126.3891533.4781892020-11-28 14:46:286323
51534610032020201121200147126.33539533.4664122020-11-21 20:01:47579106
275934610124220201128100630126.48274133.4964482020-11-28 10:06:308782
237894610116e20201114115730126.83679233.3803022020-11-14 11:57:301770
20720461010b820201114091830126.42645733.238182020-11-14 09:18:302445
277494610124e20201128105200126.56830133.2642662020-11-28 10:52:002680
201614610109120201114103530126.4064133.4233752020-11-14 10:35:301729
20628461010b320201121164831126.50912233.2516482020-11-21 16:48:312609
297484610177d20201128165430126.77960433.5131452020-11-28 16:54:302590
21537461010e920201107101500126.68896833.4351232020-11-07 10:15:004057
oidcollection_dtlongitudelatitudetimeDiff
325224610181620201114084600126.2885633.2045442020-11-14 08:46:005519
36098461018fb20201121103430126.75643433.4536682020-11-21 10:34:304655
20966461010c420201114123500126.23960933.3933482020-11-14 12:35:002940
34966461018ab20201114092930126.16502233.3091632020-11-14 09:29:309159
34943461018aa20201114145630126.46691333.5058222020-11-14 14:56:303044
39954610028e20201107183500126.48047733.4849242020-11-07 18:35:002670
327164610182020201114132000126.48733133.4913982020-11-14 13:20:001663
233774610115920201107085300126.4831433.4967442020-11-07 08:53:005009
181934610101920201121104000126.30323533.448672020-11-21 10:40:004185
20874461010c020201107181400126.27610233.4329042020-11-07 18:14:002523