Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows61
Duplicate rows (%)0.6%
Total size in memory419.9 KiB
Average record size in memory43.0 B

Variable types

Text1
Numeric3

Dataset

Description- 월별 토요일의 렌터카 위치 정보 입니다. - 수집시각 기준 당일 05:00:00 부터 익일 04:59:59까지의 데이터를 추축 - 기간: 2020년 1월부터 2021년 12월 까지
Author제주특별자치도 미래성장과
URLhttps://www.jejudatahub.net/data/view/data/1202

Alerts

Dataset has 61 (0.6%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-11 19:44:18.663993
Analysis finished2023-12-11 19:44:21.192395
Duration2.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct799
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T04:44:21.561479image/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

Unique83 ?
Unique (%)0.8%

Sample

1st row46101848
2nd row461018ce
3rd row46100334
4th row46101972
5th row4610116e
ValueCountFrequency (%)
46100100 752
 
7.5%
46100159 549
 
5.5%
461002df 524
 
5.2%
461006e5 383
 
3.8%
461002d5 321
 
3.2%
461000ec 228
 
2.3%
46100130 205
 
2.1%
461006b5 155
 
1.6%
461000f6 147
 
1.5%
461002de 102
 
1.0%
Other values (789) 6634
66.3%
2023-12-12T04:44:22.277993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 20428
25.5%
1 16976
21.2%
6 11937
14.9%
4 11158
13.9%
5 2580
 
3.2%
2 2434
 
3.0%
d 1768
 
2.2%
f 1748
 
2.2%
9 1744
 
2.2%
7 1634
 
2.0%
Other values (6) 7593
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72041
90.1%
Lowercase Letter 7959
 
9.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20428
28.4%
1 16976
23.6%
6 11937
16.6%
4 11158
15.5%
5 2580
 
3.6%
2 2434
 
3.4%
9 1744
 
2.4%
7 1634
 
2.3%
8 1588
 
2.2%
3 1562
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
d 1768
22.2%
f 1748
22.0%
e 1466
18.4%
b 1120
14.1%
c 935
11.7%
a 922
11.6%

Most occurring scripts

ValueCountFrequency (%)
Common 72041
90.1%
Latin 7959
 
9.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20428
28.4%
1 16976
23.6%
6 11937
16.6%
4 11158
15.5%
5 2580
 
3.6%
2 2434
 
3.4%
9 1744
 
2.4%
7 1634
 
2.3%
8 1588
 
2.2%
3 1562
 
2.2%
Latin
ValueCountFrequency (%)
d 1768
22.2%
f 1748
22.0%
e 1466
18.4%
b 1120
14.1%
c 935
11.7%
a 922
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20428
25.5%
1 16976
21.2%
6 11937
14.9%
4 11158
13.9%
5 2580
 
3.2%
2 2434
 
3.0%
d 1768
 
2.2%
f 1748
 
2.2%
9 1744
 
2.2%
7 1634
 
2.0%
Other values (6) 7593
 
9.5%

collection_dt
Real number (ℝ)

Distinct9042
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0201107 × 1016
Minimum2.0201107 × 1016
Maximum2.0201107 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:44:22.561763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0201107 × 1016
5-th percentile2.0201107 × 1016
Q12.0201107 × 1016
median2.0201107 × 1016
Q32.0201107 × 1016
95-th percentile2.0201107 × 1016
Maximum2.0201107 × 1016
Range43500561
Interquartile range (IQR)15109864

Descriptive statistics

Standard deviation9538469
Coefficient of variation (CV)4.7217556 × 10-10
Kurtosis0.48351424
Mean2.0201107 × 1016
Median Absolute Deviation (MAD)7016896
Skewness-1.0201005
Sum-9.0311399 × 1017
Variance9.0982391 × 1013
MonotonicityNot monotonic
2023-12-12T04:44:22.824089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20201107092630148 6
 
0.1%
20201107090200365 5
 
0.1%
20201107093330530 5
 
0.1%
20201107084000929 4
 
< 0.1%
20201107091630399 4
 
< 0.1%
20201107080830014 4
 
< 0.1%
20201107091930985 4
 
< 0.1%
20201107081700455 4
 
< 0.1%
20201107083830934 4
 
< 0.1%
20201107093330765 4
 
< 0.1%
Other values (9032) 9956
99.6%
ValueCountFrequency (%)
20201107050000012 1
< 0.1%
20201107050000387 1
< 0.1%
20201107050030391 1
< 0.1%
20201107050200449 1
< 0.1%
20201107050200793 1
< 0.1%
20201107050200808 1
< 0.1%
20201107050242986 1
< 0.1%
20201107050311927 1
< 0.1%
20201107050400777 1
< 0.1%
20201107050430609 1
< 0.1%
ValueCountFrequency (%)
20201107093500573 1
 
< 0.1%
20201107093500542 3
< 0.1%
20201107093500526 2
< 0.1%
20201107093500495 1
 
< 0.1%
20201107093500417 2
< 0.1%
20201107093500401 1
 
< 0.1%
20201107093500385 1
 
< 0.1%
20201107093500339 2
< 0.1%
20201107093500292 1
 
< 0.1%
20201107093500276 1
 
< 0.1%

longitude
Real number (ℝ)

Distinct8854
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.82643
Minimum126.16404
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:44:23.069024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16404
5-th percentile126.31417
Q1126.46014
median126.50022
Q3126.60263
95-th percentile126.92085
Maximum180
Range53.835963
Interquartile range (IQR)0.14248175

Descriptive statistics

Standard deviation8.2181076
Coefficient of variation (CV)0.064291143
Kurtosis36.339728
Mean127.82643
Median Absolute Deviation (MAD)0.066213
Skewness6.1901889
Sum1278264.3
Variance67.537293
MonotonicityNot monotonic
2023-12-12T04:44:23.300750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180.0000001 242
 
2.4%
126.497268 12
 
0.1%
126.50862 8
 
0.1%
126.492471 8
 
0.1%
126.508618 8
 
0.1%
126.492925 7
 
0.1%
126.535767 7
 
0.1%
126.297757 7
 
0.1%
126.492932 7
 
0.1%
126.596804 7
 
0.1%
Other values (8844) 9687
96.9%
ValueCountFrequency (%)
126.164037 1
< 0.1%
126.164416 1
< 0.1%
126.167902 1
< 0.1%
126.169071 1
< 0.1%
126.171728 1
< 0.1%
126.178349 1
< 0.1%
126.181375 1
< 0.1%
126.181868 1
< 0.1%
126.182698 1
< 0.1%
126.183334 1
< 0.1%
ValueCountFrequency (%)
180.0000001 242
2.4%
126.970327 1
 
< 0.1%
126.969756 1
 
< 0.1%
126.969747 1
 
< 0.1%
126.969735 1
 
< 0.1%
126.969678 1
 
< 0.1%
126.969666 1
 
< 0.1%
126.96955 1
 
< 0.1%
126.969351 1
 
< 0.1%
126.96934 1
 
< 0.1%

latitude
Real number (ℝ)

Distinct8660
Distinct (%)86.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.798061
Minimum33.204111
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:44:23.511820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.204111
5-th percentile33.251231
Q133.364525
median33.479269
Q333.498911
95-th percentile33.52378
Maximum90
Range56.795889
Interquartile range (IQR)0.1343855

Descriptive statistics

Standard deviation8.6941507
Coefficient of variation (CV)0.24984584
Kurtosis36.357269
Mean34.798061
Median Absolute Deviation (MAD)0.031434
Skewness6.1923611
Sum347980.61
Variance75.588256
MonotonicityNot monotonic
2023-12-12T04:44:23.775136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.0000001 242
 
2.4%
33.497659 16
 
0.2%
33.507547 13
 
0.1%
33.514847 10
 
0.1%
33.502741 9
 
0.1%
33.500594 9
 
0.1%
33.49709 8
 
0.1%
33.497092 8
 
0.1%
33.500202 7
 
0.1%
33.485677 7
 
0.1%
Other values (8650) 9671
96.7%
ValueCountFrequency (%)
33.204111 1
< 0.1%
33.2069543 1
< 0.1%
33.207385 1
< 0.1%
33.213037 1
< 0.1%
33.216085 1
< 0.1%
33.220423 1
< 0.1%
33.221549 1
< 0.1%
33.222075 1
< 0.1%
33.222723 1
< 0.1%
33.223903 1
< 0.1%
ValueCountFrequency (%)
90.0000001 242
2.4%
33.559146 1
 
< 0.1%
33.559133 1
 
< 0.1%
33.55659 1
 
< 0.1%
33.556043 1
 
< 0.1%
33.555832 1
 
< 0.1%
33.55556 1
 
< 0.1%
33.555396 1
 
< 0.1%
33.555392 1
 
< 0.1%
33.555226 1
 
< 0.1%

Interactions

2023-12-12T04:44:20.372545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:19.262100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:19.819409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:20.565719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:19.452520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:20.009170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:20.755257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:19.625393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:20.197833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T04:44:23.938531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.0000.1060.106
longitude0.1061.0001.000
latitude0.1061.0001.000
2023-12-12T04:44:24.078590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.000-0.0280.091
longitude-0.0281.0000.305
latitude0.0910.3051.000

Missing values

2023-12-12T04:44:20.973091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T04:44:21.122261image/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_dtlongitudelatitude
265444610184820201107080500331126.4359833.494041
86976461018ce20201107092630992180.090.0
838254610033420201107092330187126.26323133.322043
488014610197220201107084200179126.35084533.294778
344324610116e20201107082230560126.45559433.501204
903824610026120201107093030101126.68480533.378344
593624610013020201107085517860126.51205133.503543
730854610118b20201107091200411126.91264333.446959
40076461006e520201107083045014126.52433933.499877
4727046100fe020201107084000133126.49798533.504233
oidcollection_dtlongitudelatitude
12448461010f120201107071130168126.63895733.449165
151794610102720201107072430612126.2391733.337055
681864610177e20201107090600208126.35214633.331633
35182461006e520201107082338428126.50861833.497088
259784610039320201107080330241126.91431433.446088
476424610117d20201107084030246126.82617933.332195
508584610102720201107084430839126.23918233.337043
668074610015820201107090426759126.93511933.462319
63911461000fe20201107090100232126.57157633.438797
247814610188020201107080030515126.5025933.514486

Duplicate rows

Most frequently occurring

oidcollection_dtlongitudelatitude# duplicates
04610010020201107071940731126.37056833.3786952
14610010020201107072055069126.3751633.3925822
24610010020201107072153342126.38069233.402042
34610010020201107072213735126.38371733.4046922
44610010020201107072558780126.4213433.434382
54610010020201107072633550126.42723133.438732
64610010020201107073123259126.44464233.4583112
74610010020201107073339620126.4553533.4636922
84610010020201107073404076126.45718533.4666162
94610010020201107074357778126.48832733.4910442