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/1208

Reproduction

Analysis started2023-12-11 20:03:36.522535
Analysis finished2023-12-11 20:03:39.827324
Duration3.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct1627
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T05:03:40.107270image/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

Unique35 ?
Unique (%)0.4%

Sample

1st row46101136
2nd row46100480
3rd row46101943
4th row461012dc
5th row461011c5
ValueCountFrequency (%)
461007e4 16
 
0.2%
461017b7 16
 
0.2%
4610117f 15
 
0.1%
461018a8 15
 
0.1%
461017a2 14
 
0.1%
461017f0 14
 
0.1%
46100472 14
 
0.1%
46101962 14
 
0.1%
4610122a 14
 
0.1%
46100494 14
 
0.1%
Other values (1617) 9854
98.5%
2023-12-12T05:03:40.592885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 18317
22.9%
0 16803
21.0%
4 11949
14.9%
6 11771
14.7%
2 2561
 
3.2%
7 2550
 
3.2%
8 2427
 
3.0%
3 2183
 
2.7%
5 1873
 
2.3%
9 1838
 
2.3%
Other values (6) 7728
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72272
90.3%
Lowercase Letter 7728
 
9.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 18317
25.3%
0 16803
23.2%
4 11949
16.5%
6 11771
16.3%
2 2561
 
3.5%
7 2550
 
3.5%
8 2427
 
3.4%
3 2183
 
3.0%
5 1873
 
2.6%
9 1838
 
2.5%
Lowercase Letter
ValueCountFrequency (%)
f 1481
19.2%
b 1472
19.0%
a 1291
16.7%
d 1173
15.2%
c 1156
15.0%
e 1155
14.9%

Most occurring scripts

ValueCountFrequency (%)
Common 72272
90.3%
Latin 7728
 
9.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 18317
25.3%
0 16803
23.2%
4 11949
16.5%
6 11771
16.3%
2 2561
 
3.5%
7 2550
 
3.5%
8 2427
 
3.4%
3 2183
 
3.0%
5 1873
 
2.6%
9 1838
 
2.5%
Latin
ValueCountFrequency (%)
f 1481
19.2%
b 1472
19.0%
a 1291
16.7%
d 1173
15.2%
c 1156
15.0%
e 1155
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 18317
22.9%
0 16803
21.0%
4 11949
14.9%
6 11771
14.7%
2 2561
 
3.2%
7 2550
 
3.2%
8 2427
 
3.0%
3 2183
 
2.7%
5 1873
 
2.3%
9 1838
 
2.3%
Other values (6) 7728
9.7%

collection_dt
Real number (ℝ)

Distinct5645
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0211116 × 1013
Minimum2.0211106 × 1013
Maximum2.0211128 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:03:40.756689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0211106 × 1013
5-th percentile2.0211106 × 1013
Q12.0211106 × 1013
median2.0211113 × 1013
Q32.021112 × 1013
95-th percentile2.0211127 × 1013
Maximum2.0211128 × 1013
Range21990100
Interquartile range (IQR)13972808

Descriptive statistics

Standard deviation7568425.6
Coefficient of variation (CV)3.7446847 × 10-7
Kurtosis-1.2667228
Mean2.0211116 × 1013
Median Absolute Deviation (MAD)6990085
Skewness0.10894499
Sum2.0211116 × 1017
Variance5.7281067 × 1013
MonotonicityNot monotonic
2023-12-12T05:03:40.922182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20211120144730 9
 
0.1%
20211106165030 8
 
0.1%
20211106142100 8
 
0.1%
20211113162500 7
 
0.1%
20211106135430 7
 
0.1%
20211120104700 7
 
0.1%
20211106161430 7
 
0.1%
20211127141500 7
 
0.1%
20211120185830 7
 
0.1%
20211113170330 7
 
0.1%
Other values (5635) 9926
99.3%
ValueCountFrequency (%)
20211106050530 1
< 0.1%
20211106051600 1
< 0.1%
20211106053730 1
< 0.1%
20211106060400 1
< 0.1%
20211106060530 1
< 0.1%
20211106061030 1
< 0.1%
20211106061200 1
< 0.1%
20211106063030 1
< 0.1%
20211106064030 1
< 0.1%
20211106064200 1
< 0.1%
ValueCountFrequency (%)
20211128040630 1
< 0.1%
20211127235200 1
< 0.1%
20211127231500 1
< 0.1%
20211127225100 1
< 0.1%
20211127222800 1
< 0.1%
20211127220030 1
< 0.1%
20211127215400 1
< 0.1%
20211127214730 1
< 0.1%
20211127213700 1
< 0.1%
20211127212800 1
< 0.1%

longitude
Real number (ℝ)

Distinct9798
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.52951
Minimum126.16406
Maximum129.22387
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:03:41.063092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16406
5-th percentile126.25047
Q1126.37984
median126.49778
Q3126.63941
95-th percentile126.92042
Maximum129.22387
Range3.059814
Interquartile range (IQR)0.25956825

Descriptive statistics

Standard deviation0.21906325
Coefficient of variation (CV)0.0017313213
Kurtosis29.373856
Mean126.52951
Median Absolute Deviation (MAD)0.128939
Skewness2.9165572
Sum1265295.1
Variance0.047988706
MonotonicityNot monotonic
2023-12-12T05:03:41.236522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.528336 3
 
< 0.1%
126.50054 3
 
< 0.1%
126.502547 3
 
< 0.1%
126.497729 3
 
< 0.1%
126.482435 3
 
< 0.1%
126.483379 2
 
< 0.1%
126.250154 2
 
< 0.1%
126.318876 2
 
< 0.1%
126.48367 2
 
< 0.1%
126.24962 2
 
< 0.1%
Other values (9788) 9975
99.8%
ValueCountFrequency (%)
126.164061 1
< 0.1%
126.1641 1
< 0.1%
126.164304 1
< 0.1%
126.164407 1
< 0.1%
126.16441 1
< 0.1%
126.164418 1
< 0.1%
126.164451 1
< 0.1%
126.164461 1
< 0.1%
126.164486 1
< 0.1%
126.164523 1
< 0.1%
ValueCountFrequency (%)
129.223875 1
< 0.1%
129.220976 1
< 0.1%
129.205233 1
< 0.1%
129.20067 1
< 0.1%
129.174037 1
< 0.1%
129.169511 1
< 0.1%
129.166404 1
< 0.1%
129.164344 1
< 0.1%
129.161076 1
< 0.1%
129.153926 1
< 0.1%

latitude
Real number (ℝ)

Distinct9597
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.408822
Minimum33.199973
Maximum35.794064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:03:41.419129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.199973
5-th percentile33.238612
Q133.291487
median33.448479
Q333.496452
95-th percentile33.536454
Maximum35.794064
Range2.5940905
Interquartile range (IQR)0.20496525

Descriptive statistics

Standard deviation0.12893795
Coefficient of variation (CV)0.0038593984
Kurtosis61.331745
Mean33.408822
Median Absolute Deviation (MAD)0.0635565
Skewness4.1153145
Sum334088.22
Variance0.016624996
MonotonicityNot monotonic
2023-12-12T05:03:41.574378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.236308 3
 
< 0.1%
33.494758 3
 
< 0.1%
33.435203 3
 
< 0.1%
33.489635 3
 
< 0.1%
33.496451 3
 
< 0.1%
33.250025 3
 
< 0.1%
33.466554 3
 
< 0.1%
33.495994 3
 
< 0.1%
33.50291 3
 
< 0.1%
33.496606 3
 
< 0.1%
Other values (9587) 9970
99.7%
ValueCountFrequency (%)
33.199973 1
< 0.1%
33.200052 1
< 0.1%
33.200161 1
< 0.1%
33.20408 1
< 0.1%
33.204092 1
< 0.1%
33.204215 1
< 0.1%
33.20435 1
< 0.1%
33.204422 1
< 0.1%
33.204702 1
< 0.1%
33.204806 1
< 0.1%
ValueCountFrequency (%)
35.7940635 1
< 0.1%
35.283603 1
< 0.1%
35.190267 1
< 0.1%
35.181782 1
< 0.1%
35.180897 1
< 0.1%
35.163139 1
< 0.1%
35.162329 1
< 0.1%
35.160931 1
< 0.1%
35.159341 1
< 0.1%
35.158588 1
< 0.1%

time
Date

Distinct5645
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-11-06 05:05:30
Maximum2021-11-28 04:06:30
2023-12-12T05:03:41.721555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:41.877412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Diff
Real number (ℝ)

Distinct6111
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88569.785
Minimum1200
Maximum1791267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:03:42.330439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile1477.95
Q12497
median3921.5
Q37736
95-th percentile574066.45
Maximum1791267
Range1790067
Interquartile range (IQR)5239

Descriptive statistics

Standard deviation205943.1
Coefficient of variation (CV)2.3252072
Kurtosis4.1571607
Mean88569.785
Median Absolute Deviation (MAD)1803.5
Skewness2.2222775
Sum8.8569785 × 108
Variance4.241256 × 1010
MonotonicityNot monotonic
2023-12-12T05:03:42.491927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3990 21
 
0.2%
2970 21
 
0.2%
2250 21
 
0.2%
1950 20
 
0.2%
2670 18
 
0.2%
1890 17
 
0.2%
1830 17
 
0.2%
4170 16
 
0.2%
1650 16
 
0.2%
2580 16
 
0.2%
Other values (6101) 9817
98.2%
ValueCountFrequency (%)
1200 6
0.1%
1201 1
 
< 0.1%
1203 2
 
< 0.1%
1204 2
 
< 0.1%
1206 4
< 0.1%
1207 3
< 0.1%
1208 1
 
< 0.1%
1210 1
 
< 0.1%
1211 2
 
< 0.1%
1213 2
 
< 0.1%
ValueCountFrequency (%)
1791267 1
< 0.1%
1781059 1
< 0.1%
1779524 1
< 0.1%
1216772 1
< 0.1%
1204120 1
< 0.1%
1196490 1
< 0.1%
1194750 1
< 0.1%
1187598 1
< 0.1%
1186491 1
< 0.1%
1182450 1
< 0.1%

Interactions

2023-12-12T05:03:39.036164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:37.169221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:37.681818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:38.313867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:39.195051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:37.290506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:37.839104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:38.462775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:39.358174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:37.423835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:38.017965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:38.703927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:39.498621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:37.549474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:38.155887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:38.869454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:03:42.626669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.0000.0230.0110.149
longitude0.0231.0000.9650.084
latitude0.0110.9651.0000.025
Diff0.1490.0840.0251.000
2023-12-12T05:03:42.741002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.0000.002-0.016-0.025
longitude0.0021.0000.3300.011
latitude-0.0160.3301.0000.017
Diff-0.0250.0110.0171.000

Missing values

2023-12-12T05:03:39.638332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:03:39.767321image/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
179544610113620211127134646126.27131533.4010352021-11-27 13:46:467664
64054610048020211127133400126.67841933.2758292021-11-27 13:34:002509
305654610194320211127122830126.42415233.2517622021-11-27 12:28:302372
22944461012dc20211106164400126.77850733.443432021-11-06 16:44:003476
20115461011c520211106114930126.66598833.445532021-11-06 11:49:305836
54444610041220211120124600126.75641933.4537352021-11-20 12:46:002113
13038461008a520211113205700126.79278133.3062462021-11-13 20:57:00574920
211784610122820211113155530126.50037933.5031132021-11-13 15:55:301273
122554610078520211127160830126.57456533.5152942021-11-27 16:08:3010830
46574610038520211120122900126.40884333.4524682021-11-20 12:29:001908
oidcollection_dtlongitudelatitudetimeDiff
242214610135b20211106161100126.67068533.4927692021-11-06 16:11:005577
9430461005d820211106130700126.30351533.448852021-11-06 13:07:002485
213624610123820211113130500126.75450933.4528942021-11-13 13:05:002749
11279461006fc20211106143930126.36330233.2346152021-11-06 14:39:304341
16893461010d120211120161530126.49089733.4907342021-11-20 16:15:302161
21614610022120211106163730126.49226233.4961312021-11-06 16:37:302436
189924610117620211113211300126.48900433.4708442021-11-13 21:13:001686
12518461007d920211127182230126.85392733.5234992021-11-27 18:22:303027
122914610078c20211106143400126.81196933.4912622021-11-06 14:34:003818
79794610053f20211113182900126.28494433.2477642021-11-13 18:29:006307