Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
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 1 (< 0.1%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-11 19:51:23.951021
Analysis finished2023-12-11 19:51:25.862610
Duration1.91 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct871
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T04:51:26.086316image/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 row461001ec
2nd row461004a7
3rd row46100378
4th row461001e2
5th row461003e5
ValueCountFrequency (%)
461003ab 103
 
1.0%
46100cd7 57
 
0.6%
46100169 53
 
0.5%
4610068c 52
 
0.5%
4610074c 51
 
0.5%
46100711 49
 
0.5%
46100dcb 48
 
0.5%
4610021c 45
 
0.4%
4610056f 44
 
0.4%
46100537 43
 
0.4%
Other values (861) 9455
94.5%
2023-12-12T04:51:26.522400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 21922
27.4%
1 12609
15.8%
6 12301
15.4%
4 12229
15.3%
c 2699
 
3.4%
d 2426
 
3.0%
5 2164
 
2.7%
3 2012
 
2.5%
2 2004
 
2.5%
7 1998
 
2.5%
Other values (6) 7636
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69381
86.7%
Lowercase Letter 10619
 
13.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21922
31.6%
1 12609
18.2%
6 12301
17.7%
4 12229
17.6%
5 2164
 
3.1%
3 2012
 
2.9%
2 2004
 
2.9%
7 1998
 
2.9%
9 1167
 
1.7%
8 975
 
1.4%
Lowercase Letter
ValueCountFrequency (%)
c 2699
25.4%
d 2426
22.8%
b 1779
16.8%
a 1453
13.7%
e 1216
11.5%
f 1046
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
Common 69381
86.7%
Latin 10619
 
13.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21922
31.6%
1 12609
18.2%
6 12301
17.7%
4 12229
17.6%
5 2164
 
3.1%
3 2012
 
2.9%
2 2004
 
2.9%
7 1998
 
2.9%
9 1167
 
1.7%
8 975
 
1.4%
Latin
ValueCountFrequency (%)
c 2699
25.4%
d 2426
22.8%
b 1779
16.8%
a 1453
13.7%
e 1216
11.5%
f 1046
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21922
27.4%
1 12609
15.8%
6 12301
15.4%
4 12229
15.3%
c 2699
 
3.4%
d 2426
 
3.0%
5 2164
 
2.7%
3 2012
 
2.5%
2 2004
 
2.5%
7 1998
 
2.5%
Other values (6) 7636
 
9.5%

collection_dt
Real number (ℝ)

Distinct9958
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0200418 × 1016
Minimum2.0200418 × 1016
Maximum2.0200418 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:51:26.750154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0200418 × 1016
5-th percentile2.0200418 × 1016
Q12.0200418 × 1016
median2.0200418 × 1016
Q32.0200418 × 1016
95-th percentile2.0200418 × 1016
Maximum2.0200418 × 1016
Range83800008
Interquartile range (IQR)27381276

Descriptive statistics

Standard deviation17581454
Coefficient of variation (CV)8.7035101 × 10-10
Kurtosis0.031155632
Mean2.0200418 × 1016
Median Absolute Deviation (MAD)11614864
Skewness-0.68879054
Sum-9.1000372 × 1017
Variance3.0910754 × 1014
MonotonicityNot monotonic
2023-12-12T04:51:26.931453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200418114327333 2
 
< 0.1%
20200418123959672 2
 
< 0.1%
20200418090126043 2
 
< 0.1%
20200418130845719 2
 
< 0.1%
20200418122742548 2
 
< 0.1%
20200418084742350 2
 
< 0.1%
20200418130923133 2
 
< 0.1%
20200418103409022 2
 
< 0.1%
20200418114020637 2
 
< 0.1%
20200418090853140 2
 
< 0.1%
Other values (9948) 9980
99.8%
ValueCountFrequency (%)
20200418050141797 1
< 0.1%
20200418051119914 1
< 0.1%
20200418051205796 1
< 0.1%
20200418051211203 1
< 0.1%
20200418051306226 1
< 0.1%
20200418051509899 1
< 0.1%
20200418051544685 1
< 0.1%
20200418051552671 1
< 0.1%
20200418051913526 1
< 0.1%
20200418051923450 1
< 0.1%
ValueCountFrequency (%)
20200418133941805 1
< 0.1%
20200418133940023 1
< 0.1%
20200418133937632 1
< 0.1%
20200418133936976 1
< 0.1%
20200418133934101 1
< 0.1%
20200418133933413 1
< 0.1%
20200418133931210 1
< 0.1%
20200418133930100 1
< 0.1%
20200418133927444 1
< 0.1%
20200418133923349 1
< 0.1%

longitude
Real number (ℝ)

Distinct9576
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.39001
Minimum126.16259
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:51:27.110835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16259
5-th percentile126.26503
Q1126.41684
median126.50336
Q3126.63932
95-th percentile126.9298
Maximum180
Range53.837406
Interquartile range (IQR)0.22247957

Descriptive statistics

Standard deviation9.8162798
Coefficient of variation (CV)0.076456728
Kurtosis23.686026
Mean128.39001
Median Absolute Deviation (MAD)0.1090054
Skewness5.0667996
Sum1283900.1
Variance96.359349
MonotonicityNot monotonic
2023-12-12T04:51:27.300052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180.0000001 349
 
3.5%
126.4158904 13
 
0.1%
126.6322172 4
 
< 0.1%
126.5711581 3
 
< 0.1%
126.9613698 2
 
< 0.1%
126.4641306 2
 
< 0.1%
126.2668946 2
 
< 0.1%
126.4922808 2
 
< 0.1%
126.2407875 2
 
< 0.1%
126.239416 2
 
< 0.1%
Other values (9566) 9619
96.2%
ValueCountFrequency (%)
126.1625941 1
< 0.1%
126.1625987 1
< 0.1%
126.1631489 1
< 0.1%
126.1642571 1
< 0.1%
126.1651012 1
< 0.1%
126.1653976 1
< 0.1%
126.165399 2
< 0.1%
126.1654034 1
< 0.1%
126.1654073 1
< 0.1%
126.1654081 1
< 0.1%
ValueCountFrequency (%)
180.0000001 349
3.5%
126.9694266 1
 
< 0.1%
126.9694261 1
 
< 0.1%
126.9694246 1
 
< 0.1%
126.969424 1
 
< 0.1%
126.968721 1
 
< 0.1%
126.9684895 1
 
< 0.1%
126.9675416 1
 
< 0.1%
126.9673214 1
 
< 0.1%
126.9660235 1
 
< 0.1%

latitude
Real number (ℝ)

Distinct9552
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.397664
Minimum33.198862
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:51:27.474636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.198862
5-th percentile33.248332
Q133.351047
median33.468099
Q333.500111
95-th percentile33.551741
Maximum90
Range56.801138
Interquartile range (IQR)0.14906395

Descriptive statistics

Standard deviation10.384313
Coefficient of variation (CV)0.29336152
Kurtosis23.697434
Mean35.397664
Median Absolute Deviation (MAD)0.04283765
Skewness5.0685492
Sum353976.64
Variance107.83395
MonotonicityNot monotonic
2023-12-12T04:51:27.677598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.0000001 349
 
3.5%
33.4839558 13
 
0.1%
33.4767031 4
 
< 0.1%
33.4962035 4
 
< 0.1%
33.4969449 3
 
< 0.1%
33.5005403 3
 
< 0.1%
33.5132173 2
 
< 0.1%
33.495047 2
 
< 0.1%
33.5088508 2
 
< 0.1%
33.2917261 2
 
< 0.1%
Other values (9542) 9616
96.2%
ValueCountFrequency (%)
33.1988622 1
< 0.1%
33.19964 1
< 0.1%
33.2004003 1
< 0.1%
33.2008426 1
< 0.1%
33.2008578 1
< 0.1%
33.2034636 1
< 0.1%
33.2041103 1
< 0.1%
33.2045353 1
< 0.1%
33.2061605 1
< 0.1%
33.2063739 1
< 0.1%
ValueCountFrequency (%)
90.0000001 349
3.5%
33.565282 1
 
< 0.1%
33.5652791 1
 
< 0.1%
33.5652781 1
 
< 0.1%
33.5652753 1
 
< 0.1%
33.5647676 1
 
< 0.1%
33.5646178 1
 
< 0.1%
33.5643795 1
 
< 0.1%
33.5642475 1
 
< 0.1%
33.5640735 1
 
< 0.1%

Interactions

2023-12-12T04:51:25.303074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:24.438456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:24.889282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:25.410927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:24.603746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:25.025264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:25.529138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:51:24.755345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/