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 20:00:30.678316
Analysis finished2023-12-11 20:00:33.063164
Duration2.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct1597
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T05:00:33.284650image/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

Unique73 ?
Unique (%)0.7%

Sample

1st row461005d7
2nd row4610070f
3rd row46100d37
4th row46100deb
5th row461005e3
ValueCountFrequency (%)
46100d91 18
 
0.2%
461000b5 18
 
0.2%
46100504 17
 
0.2%
4610036a 17
 
0.2%
46100cc8 17
 
0.2%
461002aa 16
 
0.2%
4610034e 16
 
0.2%
461004f3 16
 
0.2%
46100da3 15
 
0.1%
461001db 15
 
0.1%
Other values (1587) 9835
98.4%
2023-12-12T05:00:33.651500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 21584
27.0%
1 12494
15.6%
4 12330
15.4%
6 12081
15.1%
c 2545
 
3.2%
d 2479
 
3.1%
3 2233
 
2.8%
2 2174
 
2.7%
5 2153
 
2.7%
7 1922
 
2.4%
Other values (6) 8005
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69431
86.8%
Lowercase Letter 10569
 
13.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21584
31.1%
1 12494
18.0%
4 12330
17.8%
6 12081
17.4%
3 2233
 
3.2%
2 2174
 
3.1%
5 2153
 
3.1%
7 1922
 
2.8%
8 1259
 
1.8%
9 1201
 
1.7%
Lowercase Letter
ValueCountFrequency (%)
c 2545
24.1%
d 2479
23.5%
b 1684
15.9%
f 1308
12.4%
e 1277
12.1%
a 1276
12.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69431
86.8%
Latin 10569
 
13.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21584
31.1%
1 12494
18.0%
4 12330
17.8%
6 12081
17.4%
3 2233
 
3.2%
2 2174
 
3.1%
5 2153
 
3.1%
7 1922
 
2.8%
8 1259
 
1.8%
9 1201
 
1.7%
Latin
ValueCountFrequency (%)
c 2545
24.1%
d 2479
23.5%
b 1684
15.9%
f 1308
12.4%
e 1277
12.1%
a 1276
12.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21584
27.0%
1 12494
15.6%
4 12330
15.4%
6 12081
15.1%
c 2545
 
3.2%
d 2479
 
3.1%
3 2233
 
2.8%
2 2174
 
2.7%
5 2153
 
2.7%
7 1922
 
2.4%
Other values (6) 8005
 
10.0%

collection_dt
Real number (ℝ)

Distinct7399
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0200616 × 1013
Minimum2.0200606 × 1013
Maximum2.0200628 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:00:33.787141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0200606 × 1013
5-th percentile2.0200606 × 1013
Q12.0200606 × 1013
median2.020062 × 1013
Q32.020062 × 1013
95-th percentile2.0200627 × 1013
Maximum2.0200628 × 1013
Range21981390
Interquartile range (IQR)13993222

Descriptive statistics

Standard deviation7767626.1
Coefficient of variation (CV)3.8452421 × 10-7
Kurtosis-1.3484248
Mean2.0200616 × 1013
Median Absolute Deviation (MAD)6990479
Skewness-0.011854414
Sum2.0200616 × 1017
Variance6.0336015 × 1013
MonotonicityNot monotonic
2023-12-12T05:00:33.912447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200606175700 7
 
0.1%
20200627140800 7
 
0.1%
20200613143700 7
 
0.1%
20200627115600 6
 
0.1%
20200620185830 6
 
0.1%
20200620152000 6
 
0.1%
20200606201400 6
 
0.1%
20200620113930 6
 
0.1%
20200627110230 6
 
0.1%
20200627173000 5
 
0.1%
Other values (7389) 9938
99.4%
ValueCountFrequency (%)
20200606050640 1
< 0.1%
20200606050730 1
< 0.1%
20200606050930 1
< 0.1%
20200606051208 1
< 0.1%
20200606051946 1
< 0.1%
20200606053000 1
< 0.1%
20200606053225 1
< 0.1%
20200606053648 1
< 0.1%
20200606053730 1
< 0.1%
20200606055600 1
< 0.1%
ValueCountFrequency (%)
20200628032030 1
< 0.1%
20200628002400 1
< 0.1%
20200628001930 1
< 0.1%
20200628001836 1
< 0.1%
20200627234230 1
< 0.1%
20200627232730 1
< 0.1%
20200627225730 1
< 0.1%
20200627224530 1
< 0.1%
20200627224130 1
< 0.1%
20200627222800 1
< 0.1%

longitude
Real number (ℝ)

Distinct9851
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.52836
Minimum126.1639
Maximum126.96942
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:00:34.069922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.1639
5-th percentile126.24717
Q1126.39735
median126.50065
Q3126.6357
95-th percentile126.91244
Maximum126.96942
Range0.805523
Interquartile range (IQR)0.23834637

Descriptive statistics

Standard deviation0.19218406
Coefficient of variation (CV)0.001518901
Kurtosis-0.41872558
Mean126.52836
Median Absolute Deviation (MAD)0.11735735
Skewness0.51587342
Sum1265283.6
Variance0.036934713
MonotonicityNot monotonic
2023-12-12T05:00:34.227214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.310887 3
 
< 0.1%
126.415048 3
 
< 0.1%
126.502809 3
 
< 0.1%
126.502936 3
 
< 0.1%
126.91754 3
 
< 0.1%
126.503325 2
 
< 0.1%
126.48614 2
 
< 0.1%
126.4928568 2
 
< 0.1%
126.414433 2
 
< 0.1%
126.305606 2
 
< 0.1%
Other values (9841) 9975
99.8%
ValueCountFrequency (%)
126.163896 1
< 0.1%
126.164055 1
< 0.1%
126.1643475 1
< 0.1%
126.164415 1
< 0.1%
126.164426 1
< 0.1%
126.164489 1
< 0.1%
126.164494 1
< 0.1%
126.164546 1
< 0.1%
126.164712 1
< 0.1%
126.164721 1
< 0.1%
ValueCountFrequency (%)
126.969419 1
< 0.1%
126.969321 1
< 0.1%
126.96929 1
< 0.1%
126.969251 1
< 0.1%
126.969204 1
< 0.1%
126.968989 1
< 0.1%
126.968452 1
< 0.1%
126.967488 1
< 0.1%
126.967324 1
< 0.1%
126.966915 1
< 0.1%

latitude
Real number (ℝ)

Distinct9778
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.410561
Minimum33.200805
Maximum33.563844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:00:34.392886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.200805
5-th percentile33.24126
Q133.299316
median33.454451
Q333.497796
95-th percentile33.537579
Maximum33.563844
Range0.363039
Interquartile range (IQR)0.1984799

Descriptive statistics

Standard deviation0.10624181
Coefficient of variation (CV)0.003179887
Kurtosis-1.2807413
Mean33.410561
Median Absolute Deviation (MAD)0.0583126
Skewness-0.52290633
Sum334105.61
Variance0.011287322
MonotonicityNot monotonic
2023-12-12T05:00:34.545395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.448662 3
 
< 0.1%
33.481676 3
 
< 0.1%
33.505264 3
 
< 0.1%
33.498317 2
 
< 0.1%
33.463204 2
 
< 0.1%
33.444649 2
 
< 0.1%
33.464503 2
 
< 0.1%
33.249979 2
 
< 0.1%
33.2500195 2
 
< 0.1%
33.255083 2
 
< 0.1%
Other values (9768) 9977
99.8%
ValueCountFrequency (%)
33.200805 1
< 0.1%
33.204812 1
< 0.1%
33.204826 1
< 0.1%
33.204864 1
< 0.1%
33.205529 1
< 0.1%
33.205657 1
< 0.1%
33.205816 1
< 0.1%
33.205953 1
< 0.1%
33.205997 1
< 0.1%
33.206004 1
< 0.1%
ValueCountFrequency (%)
33.563844 1
< 0.1%
33.563826 1
< 0.1%
33.563522 1
< 0.1%
33.56107 1
< 0.1%
33.561005 1
< 0.1%
33.560154 1
< 0.1%
33.559844 1
< 0.1%
33.559758 1
< 0.1%
33.559481 2
< 0.1%
33.559468 1
< 0.1%

time
Date

Distinct7399
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-06-06 05:06:40
Maximum2020-06-28 03:20:30
2023-12-12T05:00:34.730791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:34.880586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Diff
Real number (ℝ)

Distinct6365
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95196.928
Minimum1200
Maximum1811171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:00:35.033148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile1436.95
Q12490
median4290
Q310419.75
95-th percentile578507.75
Maximum1811171
Range1809971
Interquartile range (IQR)7929.75

Descriptive statistics

Standard deviation220077.99
Coefficient of variation (CV)2.3118182
Kurtosis5.3061922
Mean95196.928
Median Absolute Deviation (MAD)2311.5
Skewness2.3624934
Sum9.5196928 × 108
Variance4.8434324 × 1010
MonotonicityNot monotonic
2023-12-12T05:00:35.185372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1350 30
 
0.3%
1230 27
 
0.3%
1290 24
 
0.2%
2370 23
 
0.2%
1470 22
 
0.2%
1530 22
 
0.2%
1770 20
 
0.2%
2310 20
 
0.2%
2010 19
 
0.2%
3390 19
 
0.2%
Other values (6355) 9774
97.7%
ValueCountFrequency (%)
1200 12
0.1%
1202 1
 
< 0.1%
1203 2
 
< 0.1%
1204 4
 
< 0.1%
1206 4
 
< 0.1%
1207 2
 
< 0.1%
1209 2
 
< 0.1%
1210 2
 
< 0.1%
1211 1
 
< 0.1%
1212 1
 
< 0.1%
ValueCountFrequency (%)
1811171 1
< 0.1%
1792080 1
< 0.1%
1783101 1
< 0.1%
1231798 1
< 0.1%
1220880 1
< 0.1%
1219532 1
< 0.1%
1205995 1
< 0.1%
1205472 1
< 0.1%
1203794 1
< 0.1%
1202081 1
< 0.1%

Interactions

2023-12-12T05:00:32.549319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:31.281193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:31.614391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:32.008354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:32.628143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:31.357050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:31.711101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:32.097955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:32.733524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:31.439882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:31.813213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:32.384196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:32.825552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:31.520853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:31.904513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:00:32.460769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:00:35.277786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.0000.0320.0450.173
longitude0.0321.0000.8300.039
latitude0.0450.8301.0000.073
Diff0.1730.0390.0731.000
2023-12-12T05:00:35.366421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.000-0.0220.014-0.055
longitude-0.0221.0000.310-0.008
latitude0.0140.3101.000-0.013
Diff-0.055-0.008-0.0131.000

Missing values

2023-12-12T05:00:32.931921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:00:33.018559image/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
13270461005d720200620195630126.67442633.5370672020-06-20 19:56:301600
162694610070f20200627132130126.32823833.3477442020-06-27 13:21:301569
2295046100d3720200613190430126.48629833.5027152020-06-13 19:04:305651
2501046100deb20200620192230126.52857733.5120272020-06-20 19:22:304680
13418461005e320200620165830126.66934233.5409942020-06-20 16:58:302042
2351246100d9b20200606123300126.90400133.4051952020-06-06 12:33:001290
149194610068c20200627131030126.1796433.3458642020-06-27 13:10:303623
2383746100dab20200613212600126.50930433.2486612020-06-13 21:26:001230
259874610112420200627122300126.52831433.5121792020-06-27 12:23:002103
170324610075920200620081200126.47223433.4800562020-06-20 08:12:003282
oidcollection_dtlongitudelatitudetimeDiff
22754610019320200620192430126.24128833.39192020-06-20 19:24:303883
2208146100ced20200620191600126.4868833.487022020-06-20 19:16:002602
170624610075c20200620111000126.9331533.4724482020-06-20 11:10:0016680
45484610027e20200627143930126.47525333.4787242020-06-27 14:39:301533
1968246100c2520200606202700126.59510933.2374872020-06-06 20:27:003000
2439746100dca20200613172100126.56199533.2454922020-06-13 17:21:003420
58304610032020200620124454126.88931433.5272972020-06-20 12:44:542562
110414610050420200620122630126.31087133.2336472020-06-20 12:26:303523
18074461007ff20200627145200126.55760833.2642020-06-27 14:52:0015941
582461000e220200606224030126.48950233.4929062020-06-06 22:40:30561102