Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows57
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 추출 - 기간: 2020년 1월부터 2021년 12월 까지
Author제주특별자치도 미래성장과
URLhttps://www.jejudatahub.net/data/view/data/1201

Alerts

Dataset has 57 (0.6%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-11 19:51:06.331583
Analysis finished2023-12-11 19:51:08.481284
Duration2.15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct704
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T04:51:08.741653image/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

Unique70 ?
Unique (%)0.7%

Sample

1st row46100169
2nd row4610076d
3rd row461005e9
4th row46100c76
5th row46100d04
ValueCountFrequency (%)
461006a4 105
 
1.1%
46100cba 51
 
0.5%
46100cb0 50
 
0.5%
46100d2b 49
 
0.5%
461001a0 48
 
0.5%
4610061c 46
 
0.5%
46100c18 45
 
0.4%
46100bf0 45
 
0.4%
461006ed 45
 
0.4%
46100198 44
 
0.4%
Other values (694) 9472
94.7%
2023-12-12T04:51:09.293854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22169
27.7%
1 12707
15.9%
4 12276
15.3%
6 12210
15.3%
d 2820
 
3.5%
5 2146
 
2.7%
7 2101
 
2.6%
c 2048
 
2.6%
2 1783
 
2.2%
b 1753
 
2.2%
Other values (6) 7987
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69312
86.6%
Lowercase Letter 10688
 
13.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22169
32.0%
1 12707
18.3%
4 12276
17.7%
6 12210
17.6%
5 2146
 
3.1%
7 2101
 
3.0%
2 1783
 
2.6%
3 1734
 
2.5%
9 1375
 
2.0%
8 811
 
1.2%
Lowercase Letter
ValueCountFrequency (%)
d 2820
26.4%
c 2048
19.2%
b 1753
16.4%
e 1483
13.9%
a 1295
12.1%
f 1289
12.1%

Most occurring scripts

ValueCountFrequency (%)
Common 69312
86.6%
Latin 10688
 
13.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22169
32.0%
1 12707
18.3%
4 12276
17.7%
6 12210
17.6%
5 2146
 
3.1%
7 2101
 
3.0%
2 1783
 
2.6%
3 1734
 
2.5%
9 1375
 
2.0%
8 811
 
1.2%
Latin
ValueCountFrequency (%)
d 2820
26.4%
c 2048
19.2%
b 1753
16.4%
e 1483
13.9%
a 1295
12.1%
f 1289
12.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22169
27.7%
1 12707
15.9%
4 12276
15.3%
6 12210
15.3%
d 2820
 
3.5%
5 2146
 
2.7%
7 2101
 
2.6%
c 2048
 
2.6%
2 1783
 
2.2%
b 1753
 
2.2%
Other values (6) 7987
 
10.0%

collection_dt
Real number (ℝ)

Distinct7823
Distinct (%)78.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.020023 × 1016
Minimum2.0200229 × 1016
Maximum2.0200301 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:51:09.503799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0200229 × 1016
5-th percentile2.0200229 × 1016
Q12.0200229 × 1016
median2.0200229 × 1016
Q32.0200229 × 1016
95-th percentile2.0200229 × 1016
Maximum2.0200301 × 1016
Range7.1994937 × 1010
Interquartile range (IQR)21707248

Descriptive statistics

Standard deviation9.0500367 × 109
Coefficient of variation (CV)4.4801651 × 10-7
Kurtosis57.156986
Mean2.020023 × 1016
Median Absolute Deviation (MAD)11023000
Skewness7.6906021
Sum-9.1188209 × 1017
Variance8.1903164 × 1019
MonotonicityNot monotonic
2023-12-12T04:51:09.731836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200229113318000 6
 
0.1%
20200229132116000 5
 
0.1%
20200229110015000 5
 
0.1%
20200229125314000 5
 
0.1%
20200229120349000 4
 
< 0.1%
20200229112949000 4
 
< 0.1%
20200229114928000 4
 
< 0.1%
20200229131302000 4
 
< 0.1%
20200229123649000 4
 
< 0.1%
20200229134726000 4
 
< 0.1%
Other values (7813) 9955
99.6%
ValueCountFrequency (%)
20200229050407000 1
< 0.1%
20200229051056000 1
< 0.1%
20200229051807000 1
< 0.1%
20200229052707000 1
< 0.1%
20200229053655000 1
< 0.1%
20200229053737000 1
< 0.1%
20200229053755000 1
< 0.1%
20200229054025000 1
< 0.1%
20200229054205000 1
< 0.1%
20200229054435000 1
< 0.1%
ValueCountFrequency (%)
20200301045344000 1
< 0.1%
20200301045314000 1
< 0.1%
20200301045244000 1
< 0.1%
20200301044344000 1
< 0.1%
20200301044244000 1
< 0.1%
20200301043844000 1
< 0.1%
20200301043814000 1
< 0.1%
20200301043714000 1
< 0.1%
20200301043614000 1
< 0.1%
20200301041604000 1
< 0.1%

longitude
Real number (ℝ)

Distinct9478
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.81434
Minimum126.16295
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:51:09.945148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16295
5-th percentile126.26166
Q1126.41586
median126.5033
Q3126.62944
95-th percentile126.93546
Maximum180
Range53.837053
Interquartile range (IQR)0.21358205

Descriptive statistics

Standard deviation10.838615
Coefficient of variation (CV)0.084141373
Kurtosis18.354106
Mean128.81434
Median Absolute Deviation (MAD)0.09854215
Skewness4.5104793
Sum1288143.4
Variance117.47559
MonotonicityNot monotonic
2023-12-12T04:51:10.151334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180.0000001 429
 
4.3%
126.5629038 2
 
< 0.1%
126.5510093 2
 
< 0.1%
126.4557275 2
 
< 0.1%
126.4889224 2
 
< 0.1%
126.4120253 2
 
< 0.1%
126.5057273 2
 
< 0.1%
126.2898491 2
 
< 0.1%
126.5393063 2
 
< 0.1%
126.4932426 2
 
< 0.1%
Other values (9468) 9553
95.5%
ValueCountFrequency (%)
126.1629468 1
< 0.1%
126.163242 1
< 0.1%
126.163333 1
< 0.1%
126.163616 1
< 0.1%
126.1637333 1
< 0.1%
126.1637625 1
< 0.1%
126.1638888 1
< 0.1%
126.1638903 1
< 0.1%
126.1639146 1
< 0.1%
126.1639155 1
< 0.1%
ValueCountFrequency (%)
180.0000001 429
4.3%
126.9701846 1
 
< 0.1%
126.9700426 1
 
< 0.1%
126.9685524 1
 
< 0.1%
126.9676215 1
 
< 0.1%
126.9676175 1
 
< 0.1%
126.9672596 1
 
< 0.1%
126.9672456 1
 
< 0.1%
126.966719 1
 
< 0.1%
126.965753 1
 
< 0.1%

latitude
Real number (ℝ)

Distinct9422
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.843519
Minimum33.19912
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:51:10.359435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.19912
5-th percentile33.245967
Q133.316833
median33.46778
Q333.500419
95-th percentile33.555041
Maximum90
Range56.80088
Interquartile range (IQR)0.18358675

Descriptive statistics

Standard deviation11.466712
Coefficient of variation (CV)0.31991032
Kurtosis18.361353
Mean35.843519
Median Absolute Deviation (MAD)0.04552745
Skewness4.5117401
Sum358435.19
Variance131.48548
MonotonicityNot monotonic
2023-12-12T04:51:10.565506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.0000001 429
 
4.3%
33.513238 3
 
< 0.1%
33.5002953 3
 
< 0.1%
33.5030748 2
 
< 0.1%
33.5053795 2
 
< 0.1%
33.4828085 2
 
< 0.1%
33.4923969 2
 
< 0.1%
33.2507998 2
 
< 0.1%
33.2374285 2
 
< 0.1%
33.4622318 2
 
< 0.1%
Other values (9412) 9551
95.5%
ValueCountFrequency (%)
33.1991196 1
< 0.1%
33.2000285 1
< 0.1%
33.2009229 1
< 0.1%
33.2031498 1
< 0.1%
33.2032346 1
< 0.1%
33.2032353 1
< 0.1%
33.2046601 1
< 0.1%
33.2048238 1
< 0.1%
33.2049203 1
< 0.1%
33.2062783 1
< 0.1%
ValueCountFrequency (%)
90.0000001 429
4.3%
33.5643361 1
 
< 0.1%
33.5615145 1
 
< 0.1%
33.561186 1
 
< 0.1%
33.5607698 1
 
< 0.1%
33.5607691 1
 
< 0.1%
33.5607688 1
 
< 0.1%
33.5603665 1
 
< 0.1%
33.5602949 1
 
< 0.1%
33.5601426 1
 
< 0.1%

Interactions

2023-12-12T04:51:07.810481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:06.816405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:07.320220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:07.960296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:06.983825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:07.493908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:08.084762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/