Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows53
Duplicate rows (%)0.5%
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 53 (0.5%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-11 19:43:51.743268
Analysis finished2023-12-11 19:43:54.332852
Duration2.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct656
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T04:43:54.787318image/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

Unique85 ?
Unique (%)0.9%

Sample

1st row4610078d
2nd row4610014e
3rd row461011d6
4th row461006b5
5th row461011ed
ValueCountFrequency (%)
46100156 607
 
6.1%
461006b5 576
 
5.8%
4610078d 552
 
5.5%
461002e2 522
 
5.2%
46100347 485
 
4.9%
461000ec 433
 
4.3%
461002d7 366
 
3.7%
46100734 327
 
3.3%
461006ba 323
 
3.2%
4610014e 207
 
2.1%
Other values (646) 5602
56.0%
2023-12-12T04:43:55.732757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 19765
24.7%
1 15066
18.8%
6 12493
15.6%
4 12040
15.0%
7 3225
 
4.0%
2 2662
 
3.3%
5 2217
 
2.8%
e 2090
 
2.6%
3 1717
 
2.1%
d 1678
 
2.1%
Other values (6) 7047
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71554
89.4%
Lowercase Letter 8446
 
10.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19765
27.6%
1 15066
21.1%
6 12493
17.5%
4 12040
16.8%
7 3225
 
4.5%
2 2662
 
3.7%
5 2217
 
3.1%
3 1717
 
2.4%
8 1395
 
1.9%
9 974
 
1.4%
Lowercase Letter
ValueCountFrequency (%)
e 2090
24.7%
d 1678
19.9%
b 1529
18.1%
c 1179
14.0%
a 1069
12.7%
f 901
10.7%

Most occurring scripts

ValueCountFrequency (%)
Common 71554
89.4%
Latin 8446
 
10.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19765
27.6%
1 15066
21.1%
6 12493
17.5%
4 12040
16.8%
7 3225
 
4.5%
2 2662
 
3.7%
5 2217
 
3.1%
3 1717
 
2.4%
8 1395
 
1.9%
9 974
 
1.4%
Latin
ValueCountFrequency (%)
e 2090
24.7%
d 1678
19.9%
b 1529
18.1%
c 1179
14.0%
a 1069
12.7%
f 901
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19765
24.7%
1 15066
18.8%
6 12493
15.6%
4 12040
15.0%
7 3225
 
4.0%
2 2662
 
3.3%
5 2217
 
2.8%
e 2090
 
2.6%
3 1717
 
2.1%
d 1678
 
2.1%
Other values (6) 7047
 
8.8%

collection_dt
Real number (ℝ)

Distinct9351
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0200801 × 1016
Minimum2.0200801 × 1016
Maximum2.0200801 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:43:56.018868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0200801 × 1016
5-th percentile2.0200801 × 1016
Q12.0200801 × 1016
median2.0200801 × 1016
Q32.0200801 × 1016
95-th percentile2.0200801 × 1016
Maximum2.0200801 × 1016
Range45169507
Interquartile range (IQR)12169544

Descriptive statistics

Standard deviation9477834.6
Coefficient of variation (CV)4.6918113 × 10-10
Kurtosis0.15662112
Mean2.0200801 × 1016
Median Absolute Deviation (MAD)7712484
Skewness-0.84867951
Sum-9.0617397 × 1017
Variance8.9829349 × 1013
MonotonicityNot monotonic
2023-12-12T04:43:56.279758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200801091400286 4
 
< 0.1%
20200801093300535 4
 
< 0.1%
20200801083200494 4
 
< 0.1%
20200801092500773 4
 
< 0.1%
20200801094130074 4
 
< 0.1%
20200801093300160 4
 
< 0.1%
20200801094700143 3
 
< 0.1%
20200801091430093 3
 
< 0.1%
20200801083130561 3
 
< 0.1%
20200801084330713 3
 
< 0.1%
Other values (9341) 9964
99.6%
ValueCountFrequency (%)
20200801050030835 1
< 0.1%
20200801050100410 1
< 0.1%
20200801050100988 1
< 0.1%
20200801050130780 1
< 0.1%
20200801050230475 1
< 0.1%
20200801050430908 1
< 0.1%
20200801050600082 1
< 0.1%
20200801050630016 1
< 0.1%
20200801050730662 2
< 0.1%
20200801050730678 1
< 0.1%
ValueCountFrequency (%)
20200801095200342 2
< 0.1%
20200801095200264 1
< 0.1%
20200801095200233 2
< 0.1%
20200801095200217 1
< 0.1%
20200801095200202 1
< 0.1%
20200801095200155 1
< 0.1%
20200801095200139 1
< 0.1%
20200801095200077 1
< 0.1%
20200801095200014 1
< 0.1%
20200801095159608 1
< 0.1%

longitude
Real number (ℝ)

Distinct8416
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.71705
Minimum126.1655
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:43:56.546518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.1655
5-th percentile126.31049
Q1126.44475
median126.50307
Q3126.64053
95-th percentile180
Maximum180
Range53.834502
Interquartile range (IQR)0.19578075

Descriptive statistics

Standard deviation12.671421
Coefficient of variation (CV)0.09768508
Kurtosis11.816522
Mean129.71705
Median Absolute Deviation (MAD)0.095082
Skewness3.7164226
Sum1297170.5
Variance160.5649
MonotonicityNot monotonic
2023-12-12T04:43:56.785785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180.0000001 597
 
6.0%
126.486382 52
 
0.5%
126.483139 17
 
0.2%
126.483138 17
 
0.2%
126.366224 16
 
0.2%
126.49697 15
 
0.1%
126.483137 13
 
0.1%
126.366227 10
 
0.1%
126.366223 10
 
0.1%
126.655935 9
 
0.1%
Other values (8406) 9244
92.4%
ValueCountFrequency (%)
126.165498 1
< 0.1%
126.165693 1
< 0.1%
126.177994 1
< 0.1%
126.177996 1
< 0.1%
126.178072 1
< 0.1%
126.178076 1
< 0.1%
126.179296 1
< 0.1%
126.179512 1
< 0.1%
126.179513 1
< 0.1%
126.179534 1
< 0.1%
ValueCountFrequency (%)
180.0000001 597
6.0%
126.969663 1
 
< 0.1%
126.96835 1
 
< 0.1%
126.966929 1
 
< 0.1%
126.965435 1
 
< 0.1%
126.964997 1
 
< 0.1%
126.964951 1
 
< 0.1%
126.959683 1
 
< 0.1%
126.953048 1
 
< 0.1%
126.948088 1
 
< 0.1%

latitude
Real number (ℝ)

Distinct8150
Distinct (%)81.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.815981
Minimum33.199116
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:43:56.996288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.199116
5-th percentile33.250378
Q133.417057
median33.486052
Q333.504673
95-th percentile90
Maximum90
Range56.800884
Interquartile range (IQR)0.087616

Descriptive statistics

Standard deviation13.401893
Coefficient of variation (CV)0.3640238
Kurtosis11.81916
Mean36.815981
Median Absolute Deviation (MAD)0.029935
Skewness3.7169917
Sum368159.81
Variance179.61074
MonotonicityNot monotonic
2023-12-12T04:43:57.209031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.0000001 597
 
6.0%
33.490372 52
 
0.5%
33.503736 27
 
0.3%
33.496229 23
 
0.2%
33.23796 17
 
0.2%
33.497176 15
 
0.1%
33.496235 14
 
0.1%
33.49623 12
 
0.1%
33.468117 11
 
0.1%
33.50506 10
 
0.1%
Other values (8140) 9222
92.2%
ValueCountFrequency (%)
33.199116 1
< 0.1%
33.199117 1
< 0.1%
33.199121 1
< 0.1%
33.203962 1
< 0.1%
33.20729 1
< 0.1%
33.207644 1
< 0.1%
33.207847 1
< 0.1%
33.208323 1
< 0.1%
33.208383 1
< 0.1%
33.208583 1
< 0.1%
ValueCountFrequency (%)
90.0000001 597
6.0%
33.563801 1
 
< 0.1%
33.5637255 1
 
< 0.1%
33.562511 1
 
< 0.1%
33.5615279 1
 
< 0.1%
33.561386 1
 
< 0.1%
33.560029 1
 
< 0.1%
33.559983 1
 
< 0.1%
33.559547 1
 
< 0.1%
33.558059 1
 
< 0.1%

Interactions

2023-12-12T04:43:53.580340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:43:52.223887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:43:52.675652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:43:53.744891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:43:52.374925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:43:52.815820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:43:53.919306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:43:52.543382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:43:53.419119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T04:43:57.392430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.0000.1860.186
longitude0.1861.0001.000
latitude0.1861.0001.000
2023-12-12T04:43:57.568046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.000-0.1380.076
longitude-0.1381.0000.409
latitude0.0760.4091.000

Missing values

2023-12-12T04:43:54.130129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T04:43:54.265252image/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
745734610078d20200801092548687126.48489833.493699
818914610014e20200801093500451180.090.0
63706461011d620200801091000700126.48643733.503026
9299461006b520200801070951902126.64084433.473264
77965461011ed20200801093000690126.92882333.468415
37153461010a120200801082730891126.60687833.52346
37752461004a420200801082830399126.45436733.495569
26600461003bc20200801081000212126.52427333.250614
450444610110720200801084030257126.36483933.477078
651724610015620200801091201696126.36840633.252845
oidcollection_dtlongitudelatitude
631984610078d20200801090916847126.52032533.511046
32407461000ec20200801082029702126.47979433.48619
4435461006b520200801064705790126.87428533.438992
699334610116920200801091930045126.4198533.46185
291104610078d20200801081516018126.56183433.510428
14533461002e220200801073051836126.55766833.506798
822854610104820200801093530556126.51210333.481237
9315146100fa220200801094930602126.36506133.333016
483994610178120200801084530927126.49435533.493637
68905461001ba20200801091753279126.49865233.504764

Duplicate rows

Most frequently occurring

oidcollection_dtlongitudelatitude# duplicates
0461000ec20200801093406398126.26723433.4025472
14610010720200801073500547126.49173933.4937542
24610015620200801081306758126.42040333.4340742
3461002d720200801084955757126.42447533.4922582
4461002d720200801090039648126.49050233.4937442
5461002d720200801090131014126.49378333.4936592
6461002d720200801090315686126.4969733.4971762
7461002d720200801090712038126.49633833.5051932
8461002d720200801090810060126.49432433.5067142
9461002d720200801090911098126.49301633.5064652