Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows50
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 50 (0.5%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-11 19:44:09.824804
Analysis finished2023-12-11 19:44:12.687911
Duration2.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct718
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T04:44:13.046342image/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

Unique79 ?
Unique (%)0.8%

Sample

1st row4610115f
2nd row461002e3
3rd row4610032b
4th row46100635
5th row461018ec
ValueCountFrequency (%)
4610078f 1043
 
10.4%
461002e3 1028
 
10.3%
461002e2 570
 
5.7%
46100108 353
 
3.5%
461006b5 222
 
2.2%
46100159 200
 
2.0%
4610014e 182
 
1.8%
46100156 120
 
1.2%
46101027 105
 
1.1%
46101185 98
 
1.0%
Other values (708) 6079
60.8%
2023-12-12T04:44:13.723162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18722
23.4%
1 16304
20.4%
4 11492
14.4%
6 11303
14.1%
2 3342
 
4.2%
8 3104
 
3.9%
7 3004
 
3.8%
e 2469
 
3.1%
3 2222
 
2.8%
f 2011
 
2.5%
Other values (6) 6027
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72337
90.4%
Lowercase Letter 7663
 
9.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18722
25.9%
1 16304
22.5%
4 11492
15.9%
6 11303
15.6%
2 3342
 
4.6%
8 3104
 
4.3%
7 3004
 
4.2%
3 2222
 
3.1%
5 1785
 
2.5%
9 1059
 
1.5%
Lowercase Letter
ValueCountFrequency (%)
e 2469
32.2%
f 2011
26.2%
b 1048
13.7%
c 776
 
10.1%
a 724
 
9.4%
d 635
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Common 72337
90.4%
Latin 7663
 
9.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18722
25.9%
1 16304
22.5%
4 11492
15.9%
6 11303
15.6%
2 3342
 
4.6%
8 3104
 
4.3%
7 3004
 
4.2%
3 2222
 
3.1%
5 1785
 
2.5%
9 1059
 
1.5%
Latin
ValueCountFrequency (%)
e 2469
32.2%
f 2011
26.2%
b 1048
13.7%
c 776
 
10.1%
a 724
 
9.4%
d 635
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18722
23.4%
1 16304
20.4%
4 11492
14.4%
6 11303
14.1%
2 3342
 
4.2%
8 3104
 
3.9%
7 3004
 
3.8%
e 2469
 
3.1%
3 2222
 
2.8%
f 2011
 
2.5%
Other values (6) 6027
 
7.5%

collection_dt
Real number (ℝ)

Distinct9250
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.020101 × 1016
Minimum2.020101 × 1016
Maximum2.0201011 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:44:14.039339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.020101 × 1016
5-th percentile2.020101 × 1016
Q12.020101 × 1016
median2.020101 × 1016
Q32.020101 × 1016
95-th percentile2.0201011 × 1016
Maximum2.0201011 × 1016
Range8.4880041 × 108
Interquartile range (IQR)13595876

Descriptive statistics

Standard deviation1.8234184 × 108
Coefficient of variation (CV)9.0263725 × 10-9
Kurtosis13.573609
Mean2.020101 × 1016
Median Absolute Deviation (MAD)7082468
Skewness3.9384345
Sum-9.040823 × 1017
Variance3.3248548 × 1016
MonotonicityNot monotonic
2023-12-12T04:44:14.347897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20201010194900134 4
 
< 0.1%
20201010194700945 4
 
< 0.1%
20201010202900918 4
 
< 0.1%
20201010200830093 4
 
< 0.1%
20201010194530619 4
 
< 0.1%
20201010200830078 4
 
< 0.1%
20201010205200240 4
 
< 0.1%
20201010194900149 4
 
< 0.1%
20201010201300114 4
 
< 0.1%
20201010195530873 3
 
< 0.1%
Other values (9240) 9961
99.6%
ValueCountFrequency (%)
20201010194330367 2
< 0.1%
20201010194330429 1
 
< 0.1%
20201010194330508 2
< 0.1%
20201010194330523 1
 
< 0.1%
20201010194330555 1
 
< 0.1%
20201010194330633 3
< 0.1%
20201010194330648 1
 
< 0.1%
20201010194330711 1
 
< 0.1%
20201010194330758 1
 
< 0.1%
20201010194330805 1
 
< 0.1%
ValueCountFrequency (%)
20201011043130780 1
< 0.1%
20201011043100353 1
< 0.1%
20201011043000330 1
< 0.1%
20201011042930076 1
< 0.1%
20201011042600231 1
< 0.1%
20201011042530758 1
< 0.1%
20201011042507816 1
< 0.1%
20201011042500362 1
< 0.1%
20201011042330724 1
< 0.1%
20201011042100061 1
< 0.1%

longitude
Real number (ℝ)

Distinct8253
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.04534
Minimum126.16871
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:44:14.555615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16871
5-th percentile126.31384
Q1126.45364
median126.50919
Q3126.58548
95-th percentile180
Maximum180
Range53.831286
Interquartile range (IQR)0.131835

Descriptive statistics

Standard deviation13.269894
Coefficient of variation (CV)0.10204052
Kurtosis10.247938
Mean130.04534
Median Absolute Deviation (MAD)0.0629745
Skewness3.4991608
Sum1300453.4
Variance176.09008
MonotonicityNot monotonic
2023-12-12T04:44:14.793980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180.0000001 659
 
6.6%
126.566534 80
 
0.8%
126.553057 33
 
0.3%
126.555898 18
 
0.2%
126.910652 17
 
0.2%
126.910648 16
 
0.2%
126.910651 15
 
0.1%
126.910653 14
 
0.1%
126.407324 13
 
0.1%
126.910649 13
 
0.1%
Other values (8243) 9122
91.2%
ValueCountFrequency (%)
126.168714 1
< 0.1%
126.172852 1
< 0.1%
126.183489 1
< 0.1%
126.185792 1
< 0.1%
126.18686 1
< 0.1%
126.187329 1
< 0.1%
126.187928 1
< 0.1%
126.192072 1
< 0.1%
126.192295 1
< 0.1%
126.196017 1
< 0.1%
ValueCountFrequency (%)
180.0000001 659
6.6%
126.933958 1
 
< 0.1%
126.932839 1
 
< 0.1%
126.932639 1
 
< 0.1%
126.932564 1
 
< 0.1%
126.932325 1
 
< 0.1%
126.932243 1
 
< 0.1%
126.932136 1
 
< 0.1%
126.932035 1
 
< 0.1%
126.931897 1
 
< 0.1%

latitude
Real number (ℝ)

Distinct8002
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.140805
Minimum33.206069
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:44:14.978050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.206069
5-th percentile33.245524
Q133.306502
median33.473031
Q333.499157
95-th percentile90
Maximum90
Range56.793931
Interquartile range (IQR)0.1926545

Descriptive statistics

Standard deviation14.041044
Coefficient of variation (CV)0.37804898
Kurtosis10.249447
Mean37.140805
Median Absolute Deviation (MAD)0.044703
Skewness3.4995092
Sum371408.05
Variance197.1509
MonotonicityNot monotonic
2023-12-12T04:44:15.177652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.0000001 659
 
6.6%
33.245458 80
 
0.8%
33.474837 25
 
0.2%
33.474836 20
 
0.2%
33.24394 19
 
0.2%
33.243941 19
 
0.2%
33.243938 15
 
0.1%
33.484154 14
 
0.1%
33.474838 14
 
0.1%
33.484147 13
 
0.1%
Other values (7992) 9122
91.2%
ValueCountFrequency (%)
33.206069 1
< 0.1%
33.20607 1
< 0.1%
33.206544 1
< 0.1%
33.206565 1
< 0.1%
33.206581 1
< 0.1%
33.206594 1
< 0.1%
33.206628 1
< 0.1%
33.207242 1
< 0.1%
33.207754 1
< 0.1%
33.207917 1
< 0.1%
ValueCountFrequency (%)
90.0000001 659
6.6%
33.562766 1
 
< 0.1%
33.558341 1
 
< 0.1%
33.557589 1
 
< 0.1%
33.55732 1
 
< 0.1%
33.557297 1
 
< 0.1%
33.557016 1
 
< 0.1%
33.556884 1
 
< 0.1%
33.556879 1
 
< 0.1%
33.556875 1
 
< 0.1%

Interactions

2023-12-12T04:44:11.822704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:10.475914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:11.365811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:12.036569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:10.639242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:11.521281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:12.238660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:10.803977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:11.682694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T04:44:15.329079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.0000.0230.023
longitude0.0231.0001.000
latitude0.0231.0001.000
2023-12-12T04:44:15.463433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.0000.090-0.001
longitude0.0901.0000.357
latitude-0.0010.3571.000

Missing values

2023-12-12T04:44:12.464880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T04:44:12.615107image/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
899054610115f20201010235700427126.42348933.457211
68625461002e320201010214143697126.50280733.499284
699134610032b20201010214500105126.48802633.48859
230544610063520201010201030093126.4564833.49539
27569461018ec20201010201530151126.56678133.248011
92572461002e320201011012525790126.63634733.535777
14329461000d620201010200100140126.36073833.468532
713594610116f20201010214830667126.89394733.44427
370514610050420201010203000582126.60784933.475869
933884610102720201011014430266126.56653433.245458
oidcollection_dtlongitudelatitude
531894610075c20201010210330090126.3078433.227994
920184610105420201011011230492126.55305733.243943
47444610078f20201010194909963126.49590733.493822
4307461010f920201010194830988126.34894233.466292
58474610190d20201010195030770126.35472933.259436
503614610033b20201010205650236126.50472233.249895
834204610182c20201010224900502126.40009633.415308
54414610011d20201010195000735126.56311833.251576
760924610078f20201010220835629126.78645233.399918
900644610102720201011000230025126.56653433.245458

Duplicate rows

Most frequently occurring

oidcollection_dtlongitudelatitude# duplicates
0461000f720201010234700437180.090.02
14610010820201010205218258126.52152433.4922682
24610014e20201010202027361180.090.02
34610014e20201010202106038180.090.02
44610015920201010195020613126.51967633.4916482
54610017d20201010214630260180.090.02
64610028e20201010200000241126.89834733.4099112
74610028e20201010205330942126.73641833.4357082
8461002e220201010200746728126.45572133.4654042
9461002e220201010201322570126.42053233.4338992