Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows16
Duplicate rows (%)0.2%
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 추출 - 기간: 2020년 1월부터 2021년 12월 까지
Author제주특별자치도 미래성장과
URLhttps://www.jejudatahub.net/data/view/data/1201

Alerts

Dataset has 16 (0.2%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-11 19:51:14.326479
Analysis finished2023-12-11 19:51:16.567155
Duration2.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct829
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T04:51:16.855145image/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

Unique77 ?
Unique (%)0.8%

Sample

1st row46100224
2nd row4610044f
3rd row4610013a
4th row46100224
5th row4610035f
ValueCountFrequency (%)
46100465 76
 
0.8%
461005ff 61
 
0.6%
46100593 53
 
0.5%
46100bc7 53
 
0.5%
461001b9 50
 
0.5%
4610072e 50
 
0.5%
461000c3 49
 
0.5%
46100304 47
 
0.5%
46100384 46
 
0.5%
4610054c 46
 
0.5%
Other values (819) 9469
94.7%
2023-12-12T04:51:17.474033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 21569
27.0%
1 12389
15.5%
4 12206
15.3%
6 12142
15.2%
c 2781
 
3.5%
d 2545
 
3.2%
5 2483
 
3.1%
7 2270
 
2.8%
3 1939
 
2.4%
2 1930
 
2.4%
Other values (6) 7746
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69329
86.7%
Lowercase Letter 10671
 
13.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21569
31.1%
1 12389
17.9%
4 12206
17.6%
6 12142
17.5%
5 2483
 
3.6%
7 2270
 
3.3%
3 1939
 
2.8%
2 1930
 
2.8%
9 1282
 
1.8%
8 1119
 
1.6%
Lowercase Letter
ValueCountFrequency (%)
c 2781
26.1%
d 2545
23.8%
b 1585
14.9%
e 1438
13.5%
f 1184
11.1%
a 1138
10.7%

Most occurring scripts

ValueCountFrequency (%)
Common 69329
86.7%
Latin 10671
 
13.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21569
31.1%
1 12389
17.9%
4 12206
17.6%
6 12142
17.5%
5 2483
 
3.6%
7 2270
 
3.3%
3 1939
 
2.8%
2 1930
 
2.8%
9 1282
 
1.8%
8 1119
 
1.6%
Latin
ValueCountFrequency (%)
c 2781
26.1%
d 2545
23.8%
b 1585
14.9%
e 1438
13.5%
f 1184
11.1%
a 1138
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21569
27.0%
1 12389
15.5%
4 12206
15.3%
6 12142
15.2%
c 2781
 
3.5%
d 2545
 
3.2%
5 2483
 
3.1%
7 2270
 
2.8%
3 1939
 
2.4%
2 1930
 
2.4%
Other values (6) 7746
 
9.7%

collection_dt
Real number (ℝ)

Distinct9932
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0200308 × 1016
Minimum2.0200307 × 1016
Maximum2.0200329 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:51:17.765745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0200307 × 1016
5-th percentile2.0200307 × 1016
Q12.0200307 × 1016
median2.0200307 × 1016
Q32.0200307 × 1016
95-th percentile2.0200307 × 1016
Maximum2.0200329 × 1016
Range2.1995131 × 1010
Interquartile range (IQR)20037124

Descriptive statistics

Standard deviation3.6142779 × 109
Coefficient of variation (CV)1.7892192 × 10-7
Kurtosis30.758117
Mean2.0200308 × 1016
Median Absolute Deviation (MAD)9929746
Skewness5.7228835
Sum-9.1110758 × 1017
Variance1.3063004 × 1019
MonotonicityNot monotonic
2023-12-12T04:51:18.007047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200307113433583 3
 
< 0.1%
20200307123820887 3
 
< 0.1%
20200307094658619 2
 
< 0.1%
20200307105154233 2
 
< 0.1%
20200307110303032 2
 
< 0.1%
20200307111845318 2
 
< 0.1%
20200307112647022 2
 
< 0.1%
20200307120038584 2
 
< 0.1%
20200307091055214 2
 
< 0.1%
20200307114138032 2
 
< 0.1%
Other values (9922) 9978
99.8%
ValueCountFrequency (%)
20200307050825264 1
< 0.1%
20200307051700291 1
< 0.1%
20200307052500312 1
< 0.1%
20200307052530708 1
< 0.1%
20200307052700721 1
< 0.1%
20200307052900707 1
< 0.1%
20200307053000717 1
< 0.1%
20200307053330733 1
< 0.1%
20200307053536671 1
< 0.1%
20200307053806692 1
< 0.1%
ValueCountFrequency (%)
20200329045956430 1
< 0.1%
20200329045826418 1
< 0.1%
20200329045804726 1
< 0.1%
20200329045756413 1
< 0.1%
20200329045733174 1
< 0.1%
20200329045304590 1
< 0.1%
20200329045216349 1
< 0.1%
20200329045146343 1
< 0.1%
20200329045133059 1
< 0.1%
20200329045033066 1
< 0.1%

longitude
Real number (ℝ)

Distinct9439
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.97392
Minimum126.16553
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:51:18.287802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16553
5-th percentile126.26952
Q1126.45086
median126.51931
Q3126.68565
95-th percentile126.95379
Maximum180
Range53.834467
Interquartile range (IQR)0.23478667

Descriptive statistics

Standard deviation11.129782
Coefficient of variation (CV)0.086294831
Kurtosis17.072936
Mean128.97392
Median Absolute Deviation (MAD)0.1093046
Skewness4.3661933
Sum1289739.2
Variance123.87206
MonotonicityNot monotonic
2023-12-12T04:51:18.577091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180.0000001 454
 
4.5%
126.9537701 3
 
< 0.1%
126.9537801 3
 
< 0.1%
126.9537893 3
 
< 0.1%
126.4812068 2
 
< 0.1%
126.5293003 2
 
< 0.1%
126.9537773 2
 
< 0.1%
126.4987188 2
 
< 0.1%
126.5345073 2
 
< 0.1%
126.9537816 2
 
< 0.1%
Other values (9429) 9525
95.2%
ValueCountFrequency (%)
126.1655326 1
< 0.1%
126.165704 1
< 0.1%
126.1659984 1
< 0.1%
126.1666808 1
< 0.1%
126.1667618 1
< 0.1%
126.1674248 1
< 0.1%
126.1691391 1
< 0.1%
126.1710803 1
< 0.1%
126.1744221 1
< 0.1%
126.176327 1
< 0.1%
ValueCountFrequency (%)
180.0000001 454
4.5%
126.9704053 1
 
< 0.1%
126.9700266 1
 
< 0.1%
126.9694111 1
 
< 0.1%
126.9693456 1
 
< 0.1%
126.9683276 1
 
< 0.1%
126.9672633 1
 
< 0.1%
126.9666411 1
 
< 0.1%
126.9664328 1
 
< 0.1%
126.9660865 1
 
< 0.1%

latitude
Real number (ℝ)

Distinct9415
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.993381
Minimum33.200242
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:51:18.831265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.200242
5-th percentile33.24782
Q133.344719
median33.477023
Q333.503848
95-th percentile33.557017
Maximum90
Range56.799758
Interquartile range (IQR)0.15912867

Descriptive statistics

Standard deviation11.778801
Coefficient of variation (CV)0.32724909
Kurtosis17.080343
Mean35.993381
Median Absolute Deviation (MAD)0.0389555
Skewness4.367529
Sum359933.81
Variance138.74016
MonotonicityNot monotonic
2023-12-12T04:51:19.076215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.0000001 454
 
4.5%
33.4957571 3
 
< 0.1%
33.5050488 3
 
< 0.1%
33.5054466 3
 
< 0.1%
33.5050529 3
 
< 0.1%
33.4934813 3
 
< 0.1%
33.5050555 3
 
< 0.1%
33.505051 3
 
< 0.1%
33.5050463 3
 
< 0.1%
33.5001948 3
 
< 0.1%
Other values (9405) 9519
95.2%
ValueCountFrequency (%)
33.2002421 1
< 0.1%
33.2002433 1
< 0.1%
33.2002436 1
< 0.1%
33.2035988 1
< 0.1%
33.2042078 1
< 0.1%
33.204471 1
< 0.1%
33.2047316 1
< 0.1%
33.2060588 1
< 0.1%
33.2060755 1
< 0.1%
33.206238 1
< 0.1%
ValueCountFrequency (%)
90.0000001 454
4.5%
33.5647276 1
 
< 0.1%
33.5641848 1
 
< 0.1%
33.5638863 1
 
< 0.1%
33.5638705 1
 
< 0.1%
33.5638378 1
 
< 0.1%
33.5638358 1
 
< 0.1%
33.5637311 1
 
< 0.1%
33.5633851 1
 
< 0.1%
33.5628515 1
 
< 0.1%

Interactions

2023-12-12T04:51:15.990472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:14.752595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:15.596662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:16.108617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:15.312174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:15.720740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:16.220748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/