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 20:00:12.574239
Analysis finished2023-12-11 20:00:16.067323
Duration3.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct1344
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T05:00:16.309558image/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

Unique75 ?
Unique (%)0.8%

Sample

1st row46100c3e
2nd row46100155
3rd row46100d9b
4th row46100dcc
5th row4610078f
ValueCountFrequency (%)
4610041d 26
 
0.3%
46100cd2 21
 
0.2%
4610070a 20
 
0.2%
4610059e 20
 
0.2%
4610024b 19
 
0.2%
461006d9 19
 
0.2%
461001ad 19
 
0.2%
461001e6 19
 
0.2%
46100c76 18
 
0.2%
46100504 18
 
0.2%
Other values (1334) 9801
98.0%
2023-12-12T05:00:16.774584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 21679
27.1%
1 12390
15.5%
4 12249
15.3%
6 12211
15.3%
c 2739
 
3.4%
d 2545
 
3.2%
5 2279
 
2.8%
7 2069
 
2.6%
2 1976
 
2.5%
3 1943
 
2.4%
Other values (6) 7920
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69187
86.5%
Lowercase Letter 10813
 
13.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21679
31.3%
1 12390
17.9%
4 12249
17.7%
6 12211
17.6%
5 2279
 
3.3%
7 2069
 
3.0%
2 1976
 
2.9%
3 1943
 
2.8%
9 1259
 
1.8%
8 1132
 
1.6%
Lowercase Letter
ValueCountFrequency (%)
c 2739
25.3%
d 2545
23.5%
b 1755
16.2%
a 1362
12.6%
e 1243
11.5%
f 1169
10.8%

Most occurring scripts

ValueCountFrequency (%)
Common 69187
86.5%
Latin 10813
 
13.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21679
31.3%
1 12390
17.9%
4 12249
17.7%
6 12211
17.6%
5 2279
 
3.3%
7 2069
 
3.0%
2 1976
 
2.9%
3 1943
 
2.8%
9 1259
 
1.8%
8 1132
 
1.6%
Latin
ValueCountFrequency (%)
c 2739
25.3%
d 2545
23.5%
b 1755
16.2%
a 1362
12.6%
e 1243
11.5%
f 1169
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21679
27.1%
1 12390
15.5%
4 12249
15.3%
6 12211
15.3%
c 2739
 
3.4%
d 2545
 
3.2%
5 2279
 
2.8%
7 2069
 
2.6%
2 1976
 
2.5%
3 1943
 
2.4%
Other values (6) 7920
 
9.9%

collection_dt
Real number (ℝ)

Distinct9734
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0200415 × 1013
Minimum2.0200404 × 1013
Maximum2.0200426 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:00:16.938148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0200404 × 1013
5-th percentile2.0200404 × 1013
Q12.0200404 × 1013
median2.0200418 × 1013
Q32.0200418 × 1013
95-th percentile2.0200425 × 1013
Maximum2.0200426 × 1013
Range21988900
Interquartile range (IQR)14012515

Descriptive statistics

Standard deviation7879056.9
Coefficient of variation (CV)3.9004432 × 10-7
Kurtosis-1.3802499
Mean2.0200415 × 1013
Median Absolute Deviation (MAD)7018462
Skewness0.00028589707
Sum2.0200415 × 1017
Variance6.2079537 × 1013
MonotonicityNot monotonic
2023-12-12T05:00:17.102280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200404201232 3
 
< 0.1%
20200425094019 3
 
< 0.1%
20200404080816 3
 
< 0.1%
20200425122629 2
 
< 0.1%
20200425154116 2
 
< 0.1%
20200411123058 2
 
< 0.1%
20200404183944 2
 
< 0.1%
20200411115119 2
 
< 0.1%
20200425130423 2
 
< 0.1%
20200404120308 2
 
< 0.1%
Other values (9724) 9977
99.8%
ValueCountFrequency (%)
20200404051222 1
< 0.1%
20200404052246 1
< 0.1%
20200404053005 1
< 0.1%
20200404053435 1
< 0.1%
20200404053454 1
< 0.1%
20200404054942 1
< 0.1%
20200404055120 1
< 0.1%
20200404055209 1
< 0.1%
20200404055238 1
< 0.1%
20200404060219 1
< 0.1%
ValueCountFrequency (%)
20200426040122 1
< 0.1%
20200426022307 1
< 0.1%
20200426012546 1
< 0.1%
20200426003656 1
< 0.1%
20200426003103 1
< 0.1%
20200426002341 1
< 0.1%
20200426002232 1
< 0.1%
20200426002228 1
< 0.1%
20200426002047 1
< 0.1%
20200425235819 1
< 0.1%

longitude
Real number (ℝ)

Distinct9968
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.52843
Minimum126.16386
Maximum126.96949
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:00:17.327068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16386
5-th percentile126.258
Q1126.41279
median126.50212
Q3126.60923
95-th percentile126.91068
Maximum126.96949
Range0.8056333
Interquartile range (IQR)0.19643163

Descriptive statistics

Standard deviation0.18220883
Coefficient of variation (CV)0.0014400624
Kurtosis-0.17874818
Mean126.52843
Median Absolute Deviation (MAD)0.09268835
Skewness0.56365132
Sum1265284.3
Variance0.033200057
MonotonicityNot monotonic
2023-12-12T05:00:17.516759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.5028926 3
 
< 0.1%
126.9356891 2
 
< 0.1%
126.4928811 2
 
< 0.1%
126.5004925 2
 
< 0.1%
126.9293039 2
 
< 0.1%
126.4967333 2
 
< 0.1%
126.3056481 2
 
< 0.1%
126.5027673 2
 
< 0.1%
126.4966328 2
 
< 0.1%
126.562317 2
 
< 0.1%
Other values (9958) 9979
99.8%
ValueCountFrequency (%)
126.1638561 1
< 0.1%
126.1640308 1
< 0.1%
126.1643351 1
< 0.1%
126.1644616 1
< 0.1%
126.1645153 1
< 0.1%
126.1646761 1
< 0.1%
126.1647921 1
< 0.1%
126.1649985 1
< 0.1%
126.1652467 1
< 0.1%
126.1653348 1
< 0.1%
ValueCountFrequency (%)
126.9694894 1
< 0.1%
126.9688723 1
< 0.1%
126.9688671 1
< 0.1%
126.9684805 1
< 0.1%
126.9683973 1
< 0.1%
126.9683638 1
< 0.1%
126.9675166 1
< 0.1%
126.9672817 1
< 0.1%
126.9668493 1
< 0.1%
126.9594363 1
< 0.1%

latitude
Real number (ℝ)

Distinct9927
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.413913
Minimum33.203917
Maximum33.563296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:00:17.746013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.203917
5-th percentile33.242593
Q133.304967
median33.462416
Q333.496856
95-th percentile33.532407
Maximum33.563296
Range0.359379
Interquartile range (IQR)0.19188845

Descriptive statistics

Standard deviation0.1055007
Coefficient of variation (CV)0.0031573883
Kurtosis-1.1893324
Mean33.413913
Median Absolute Deviation (MAD)0.04996305
Skewness-0.6132058
Sum334139.13
Variance0.011130397
MonotonicityNot monotonic
2023-12-12T05:00:17.919585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.4977765 3
 
< 0.1%
33.5124483 2
 
< 0.1%
33.512351 2
 
< 0.1%
33.4639738 2
 
< 0.1%
33.2499303 2
 
< 0.1%
33.2500906 2
 
< 0.1%
33.5155128 2
 
< 0.1%
33.4631111 2
 
< 0.1%
33.5029916 2
 
< 0.1%
33.4617648 2
 
< 0.1%
Other values (9917) 9979
99.8%
ValueCountFrequency (%)
33.2039173 1
< 0.1%
33.2040718 1
< 0.1%
33.2042501 1
< 0.1%
33.2043542 1
< 0.1%
33.2044008 1
< 0.1%
33.2045017 1
< 0.1%
33.2045187 1
< 0.1%
33.2045353 1
< 0.1%
33.2046452 1
< 0.1%
33.20494 1
< 0.1%
ValueCountFrequency (%)
33.5632963 1
< 0.1%
33.5613851 1
< 0.1%
33.5607328 1
< 0.1%
33.5605555 1
< 0.1%
33.5604873 1
< 0.1%
33.5594306 1
< 0.1%
33.5594228 1
< 0.1%
33.5593932 1
< 0.1%
33.5593802 1
< 0.1%
33.5593798 1
< 0.1%

time
Date

Distinct9734
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-04-04 05:12:22
Maximum2020-04-26 04:01:22
2023-12-12T05:00:18.071663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:18.232480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Diff
Real number (ℝ)

Distinct6779
Distinct (%)67.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104400.89
Minimum1203
Maximum1821757
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:00:18.415759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1203
5-th percentile1455.95
Q12501.75
median4251.5
Q311654.5
95-th percentile584894.7
Maximum1821757
Range1820554
Interquartile range (IQR)9152.75

Descriptive statistics

Standard deviation244612.78
Coefficient of variation (CV)2.3430144
Kurtosis8.6493187
Mean104400.89
Median Absolute Deviation (MAD)2300.5
Skewness2.7148815
Sum1.0440089 × 109
Variance5.9835413 × 1010
MonotonicityNot monotonic
2023-12-12T05:00:18.601334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1230 19
 
0.2%
1290 18
 
0.2%
1710 13
 
0.1%
1350 10
 
0.1%
2568 9
 
0.1%
2346 8
 
0.1%
1410 8
 
0.1%
2490 8
 
0.1%
2012 8
 
0.1%
2370 8
 
0.1%
Other values (6769) 9891
98.9%
ValueCountFrequency (%)
1203 3
< 0.1%
1204 2
< 0.1%
1205 1
 
< 0.1%
1206 1
 
< 0.1%
1209 4
< 0.1%
1210 4
< 0.1%
1211 2
< 0.1%
1212 2
< 0.1%
1213 1
 
< 0.1%
1214 2
< 0.1%
ValueCountFrequency (%)
1821757 1
< 0.1%
1821449 1
< 0.1%
1807185 1
< 0.1%
1806602 1
< 0.1%
1792997 1
< 0.1%
1792715 1
< 0.1%
1791255 1
< 0.1%
1790959 1
< 0.1%
1789754 1
< 0.1%
1789146 1
< 0.1%

Interactions

2023-12-12T05:00:15.111969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:13.453837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:13.951692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:14.451604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:15.259881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:13.560355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:14.068265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:14.587013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:15.456471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:13.682416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:14.212369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:14.750766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:15.682215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:13.829763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:14.314578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:14.887851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:00:18.710043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.0000.0300.0320.192
longitude0.0301.0000.8220.091
latitude0.0320.8221.0000.097
Diff0.1920.0910.0971.000
2023-12-12T05:00:18.808892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.000-0.016-0.020-0.080
longitude-0.0161.0000.276-0.016
latitude-0.0200.2761.0000.037
Diff-0.080-0.0160.0371.000

Missing values

2023-12-12T05:00:15.879695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:00:16.012306image/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
1462346100c3e20200404172645126.57383833.254852020-04-04 17:26:453367
15854610015520200404101501126.53836433.4971942020-04-04 10:15:013084
1742646100d9b20200411132614126.37064933.2647472020-04-11 13:26:143368
1832246100dcc20200404085154126.53361333.51882020-04-04 08:51:542314
130614610078f20200411082217126.92956833.4595532020-04-11 08:22:17616479
1520746100c7a20200418153708126.45484533.4949992020-04-18 15:37:0816028
11668461006f020200418184511126.55353233.2517842020-04-18 18:45:11571535
16634610015920200411210800126.56943233.2533242020-04-11 21:08:002594
1658146100cfb20200404185700126.30248433.4487212020-04-04 18:57:002576
3047461001e420200404222556126.29172733.4357212020-04-04 22:25:56566096
oidcollection_dtlongitudelatitudetimeDiff
46974610030b20200404203025126.52667133.5108052020-04-04 20:30:251292
127704610076d20200404150146126.28890433.3052652020-04-04 15:01:463235
11729461006f720200418173920126.89998233.4133882020-04-18 17:39:203444
11308461006d020200418132814126.79542633.5560172020-04-18 13:28:148573
249461000c720200425151849126.71251833.5522792020-04-25 15:18:494813
1762146100da620200404100619126.56883133.2498462020-04-04 10:06:192451
1745246100d9c20200418100842126.69547633.5321762020-04-18 10:08:423437
16534610015920200404095606126.49231933.4961472020-04-04 09:56:064373
1586146100cbb20200418202620126.60391533.4847992020-04-18 20:26:202192
52074610037820200418150003126.79435233.4414322020-04-18 15:00:035032