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 20:00:21.646562
Analysis finished2023-12-11 20:00:24.443331
Duration2.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct1529
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T05:00:24.656369image/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

Unique59 ?
Unique (%)0.6%

Sample

1st row4610022c
2nd row461000bf
3rd row46100dc9
4th row46100549
5th row4610023e
ValueCountFrequency (%)
46100c11 18
 
0.2%
46100772 18
 
0.2%
46100107 17
 
0.2%
461001b3 17
 
0.2%
46100db8 17
 
0.2%
4610012a 17
 
0.2%
461003f6 16
 
0.2%
461004c9 16
 
0.2%
46100173 16
 
0.2%
46100320 16
 
0.2%
Other values (1519) 9832
98.3%
2023-12-12T05:00:25.063482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 21570
27.0%
4 12262
15.3%
1 12242
15.3%
6 12080
15.1%
c 2782
 
3.5%
d 2492
 
3.1%
5 2352
 
2.9%
3 2139
 
2.7%
2 2087
 
2.6%
7 2033
 
2.5%
Other values (6) 7961
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69200
86.5%
Lowercase Letter 10800
 
13.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21570
31.2%
4 12262
17.7%
1 12242
17.7%
6 12080
17.5%
5 2352
 
3.4%
3 2139
 
3.1%
2 2087
 
3.0%
7 2033
 
2.9%
9 1242
 
1.8%
8 1193
 
1.7%
Lowercase Letter
ValueCountFrequency (%)
c 2782
25.8%
d 2492
23.1%
b 1709
15.8%
a 1310
12.1%
e 1255
11.6%
f 1252
11.6%

Most occurring scripts

ValueCountFrequency (%)
Common 69200
86.5%
Latin 10800
 
13.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21570
31.2%
4 12262
17.7%
1 12242
17.7%
6 12080
17.5%
5 2352
 
3.4%
3 2139
 
3.1%
2 2087
 
3.0%
7 2033
 
2.9%
9 1242
 
1.8%
8 1193
 
1.7%
Latin
ValueCountFrequency (%)
c 2782
25.8%
d 2492
23.1%
b 1709
15.8%
a 1310
12.1%
e 1255
11.6%
f 1252
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21570
27.0%
4 12262
15.3%
1 12242
15.3%
6 12080
15.1%
c 2782
 
3.5%
d 2492
 
3.1%
5 2352
 
2.9%
3 2139
 
2.7%
2 2087
 
2.6%
7 2033
 
2.5%
Other values (6) 7961
 
10.0%

collection_dt
Real number (ℝ)

Distinct8365
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0200515 × 1013
Minimum2.0200502 × 1013
Maximum2.0200531 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:00:25.196085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0200502 × 1013
5-th percentile2.0200502 × 1013
Q12.0200502 × 1013
median2.0200516 × 1013
Q32.0200523 × 1013
95-th percentile2.020053 × 1013
Maximum2.0200531 × 1013
Range28975987
Interquartile range (IQR)20949196

Descriptive statistics

Standard deviation10100271
Coefficient of variation (CV)5.0000065 × 10-7
Kurtosis-1.3639849
Mean2.0200515 × 1013
Median Absolute Deviation (MAD)7038535
Skewness0.025314412
Sum2.0200515 × 1017
Variance1.0201547 × 1014
MonotonicityNot monotonic
2023-12-12T05:00:25.356441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200530123200 6
 
0.1%
20200509113130 6
 
0.1%
20200523141030 6
 
0.1%
20200523180900 5
 
0.1%
20200516134100 5
 
0.1%
20200516103130 5
 
0.1%
20200516163630 5
 
0.1%
20200523150600 5
 
0.1%
20200516180900 5
 
0.1%
20200509162230 5
 
0.1%
Other values (8355) 9947
99.5%
ValueCountFrequency (%)
20200502054735 1
< 0.1%
20200502055625 1
< 0.1%
20200502055837 1
< 0.1%
20200502055844 1
< 0.1%
20200502060240 1
< 0.1%
20200502060639 1
< 0.1%
20200502060930 1
< 0.1%
20200502061819 1
< 0.1%
20200502061948 1
< 0.1%
20200502062348 1
< 0.1%
ValueCountFrequency (%)
20200531030722 1
< 0.1%
20200531012830 1
< 0.1%
20200531012600 1
< 0.1%
20200531004150 1
< 0.1%
20200531003112 1
< 0.1%
20200531001729 1
< 0.1%
20200531000400 1
< 0.1%
20200530223526 1
< 0.1%
20200530223300 1
< 0.1%
20200530221530 1
< 0.1%

longitude
Real number (ℝ)

Distinct9935
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.53465
Minimum126.16298
Maximum126.9924
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:00:25.518607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16298
5-th percentile126.25652
Q1126.41233
median126.50324
Q3126.64229
95-th percentile126.91133
Maximum126.9924
Range0.8294221
Interquartile range (IQR)0.22995935

Descriptive statistics

Standard deviation0.18705882
Coefficient of variation (CV)0.001478321
Kurtosis-0.37365653
Mean126.53465
Median Absolute Deviation (MAD)0.1095306
Skewness0.50490643
Sum1265346.5
Variance0.034991003
MonotonicityNot monotonic
2023-12-12T05:00:25.653239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.503007 3
 
< 0.1%
126.526221 3
 
< 0.1%
126.413496 2
 
< 0.1%
126.36553 2
 
< 0.1%
126.4628645 2
 
< 0.1%
126.496784 2
 
< 0.1%
126.493118 2
 
< 0.1%
126.50052 2
 
< 0.1%
126.5029521 2
 
< 0.1%
126.502691 2
 
< 0.1%
Other values (9925) 9978
99.8%
ValueCountFrequency (%)
126.162982 1
< 0.1%
126.163405 1
< 0.1%
126.163762 1
< 0.1%
126.1643096 1
< 0.1%
126.1643468 1
< 0.1%
126.164441 1
< 0.1%
126.164754 1
< 0.1%
126.164895 1
< 0.1%
126.165243 1
< 0.1%
126.16534 1
< 0.1%
ValueCountFrequency (%)
126.9924041 1
< 0.1%
126.9922338 1
< 0.1%
126.969322 1
< 0.1%
126.969243 1
< 0.1%
126.968817 1
< 0.1%
126.968722 1
< 0.1%
126.9686553 1
< 0.1%
126.967372 1
< 0.1%
126.967096 1
< 0.1%
126.967003 1
< 0.1%

latitude
Real number (ℝ)

Distinct9879
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.410226
Minimum33.197053
Maximum37.251222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:00:25.788145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.197053
5-th percentile33.242389
Q133.291363
median33.452666
Q333.496356
95-th percentile33.536039
Maximum37.251222
Range4.0541692
Interquartile range (IQR)0.20499248

Descriptive statistics

Standard deviation0.119477
Coefficient of variation (CV)0.0035760609
Kurtosis211.84612
Mean33.410226
Median Absolute Deviation (MAD)0.05924045
Skewness6.2723625
Sum334102.26
Variance0.014274754
MonotonicityNot monotonic
2023-12-12T05:00:25.918216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.510602 3
 
< 0.1%
33.496138 3
 
< 0.1%
33.503077 3
 
< 0.1%
33.512202 2
 
< 0.1%
33.238526 2
 
< 0.1%
33.493344 2
 
< 0.1%
33.504374 2
 
< 0.1%
33.423403 2
 
< 0.1%
33.479509 2
 
< 0.1%
33.5195705 2
 
< 0.1%
Other values (9869) 9977
99.8%
ValueCountFrequency (%)
33.197053 1
< 0.1%
33.200857 1
< 0.1%
33.2041929 1
< 0.1%
33.2044801 1
< 0.1%
33.204586 1
< 0.1%
33.2050952 1
< 0.1%
33.205339 1
< 0.1%
33.2054188 1
< 0.1%
33.2055513 1
< 0.1%
33.2055549 1
< 0.1%
ValueCountFrequency (%)
37.2512222 1
< 0.1%
37.2511649 1
< 0.1%
33.5638783 1
< 0.1%
33.5634635 1
< 0.1%
33.5613393 1
< 0.1%
33.561012 1
< 0.1%
33.5604205 1
< 0.1%
33.5603822 1
< 0.1%
33.560064 1
< 0.1%
33.5595172 1
< 0.1%

time
Date

Distinct8365
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-05-02 05:47:35
Maximum2020-05-31 03:07:22
2023-12-12T05:00:26.046567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:26.189507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Diff
Real number (ℝ)

Distinct6687
Distinct (%)66.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110311.27
Minimum1200
Maximum2409940
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:00:26.310535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile1441.9
Q12540
median4290
Q310407.25
95-th percentile585001.5
Maximum2409940
Range2408740
Interquartile range (IQR)7867.25

Descriptive statistics

Standard deviation263302.26
Coefficient of variation (CV)2.3869027
Kurtosis12.052119
Mean110311.27
Median Absolute Deviation (MAD)2276
Skewness3.0331351
Sum1.1031127 × 109
Variance6.9328079 × 1010
MonotonicityNot monotonic
2023-12-12T05:00:26.435990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1230 27
 
0.3%
1290 18
 
0.2%
2130 16
 
0.2%
1470 16
 
0.2%
1350 15
 
0.1%
2190 14
 
0.1%
1590 14
 
0.1%
2310 14
 
0.1%
1770 13
 
0.1%
3210 13
 
0.1%
Other values (6677) 9840
98.4%
ValueCountFrequency (%)
1200 6
0.1%
1201 1
 
< 0.1%
1203 1
 
< 0.1%
1204 3
< 0.1%
1205 1
 
< 0.1%
1206 1
 
< 0.1%
1207 1
 
< 0.1%
1208 1
 
< 0.1%
1209 2
 
< 0.1%
1211 1
 
< 0.1%
ValueCountFrequency (%)
2409940 1
< 0.1%
2402926 1
< 0.1%
2396013 1
< 0.1%
2390208 1
< 0.1%
2388887 1
< 0.1%
2382825 1
< 0.1%
2380791 1
< 0.1%
1815759 1
< 0.1%
1815664 1
< 0.1%
1808742 1
< 0.1%

Interactions

2023-12-12T05:00:23.897393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:22.287685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:22.748901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:23.429506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:23.999263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:22.415986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:22.836461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:23.536310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:24.102821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:22.517781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:23.193279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:23.645924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:24.203779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:22.642240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:23.319834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:23.776293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:00:26.509033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.0000.0850.0130.305
longitude0.0851.0000.0630.039
latitude0.0130.0631.0000.000
Diff0.3050.0390.0001.000
2023-12-12T05:00:26.589136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.000-0.0210.033-0.042
longitude-0.0211.0000.2810.001
latitude0.0330.2811.0000.010
Diff-0.0420.0010.0101.000

Missing values

2023-12-12T05:00:24.311640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:00:24.402813image/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
50464610022c20200516102011126.378133.4751382020-05-16 10:20:113750
283461000bf20200516114147126.57690533.2541932020-05-16 11:41:477254
3017646100dc920200516195230126.48959833.2341812020-05-16 19:52:30559997
137384610054920200502134401126.28832333.3043912020-05-02 13:44:014890
53504610023e20200523114414126.51408333.5152842020-05-23 11:44:142231
9014461003c820200502172532126.48002733.4862642020-05-02 17:25:325747
8835461003af20200502134841126.83174633.4631842020-05-02 13:48:413236
2979746100dba20200509130030126.42624833.2385932020-05-09 13:00:302111
15623461005d520200530084700126.51825333.2522372020-05-30 08:47:0033668
196994610073d20200516184532126.66682333.5421492020-05-16 18:45:321166273
oidcollection_dtlongitudelatitudetimeDiff
2356546100c2020200502211709126.36708133.3651882020-05-02 21:17:09565910
139294610055220200523212930126.36643233.4493992020-05-23 21:29:30566790
17944610013d20200516191900126.49327333.4960152020-05-16 19:19:005127
97804610041c20200523082131126.49648633.5034112020-05-23 08:21:313569
6354461002ac20200523140200126.25608833.3354832020-05-23 14:02:006685
135814610053f20200509141641126.56054433.2531552020-05-09 14:16:4116525
313461000c020200530123700126.91651233.4486692020-05-30 12:37:005046
2975346100db820200516195000126.50051233.5032962020-05-16 19:50:00564232
586461000dd20200523172712126.35476533.2813512020-05-23 17:27:127257
105604610045720200530064230126.66537833.5426032020-05-30 06:42:3032995