Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows60
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 추축 - 기간: 2020년 1월부터 2021년 12월 까지
Author제주특별자치도 미래성장과
URLhttps://www.jejudatahub.net/data/view/data/1202

Alerts

Dataset has 60 (0.6%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-11 19:44:28.684982
Analysis finished2023-12-11 19:44:31.128006
Duration2.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct965
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T04:44:31.421130image/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

Unique114 ?
Unique (%)1.1%

Sample

1st row46101783
2nd row46101025
3rd row461000cf
4th row4610102e
5th row46101774
ValueCountFrequency (%)
461006da 472
 
4.7%
461006e5 327
 
3.3%
461006ed 278
 
2.8%
46100108 260
 
2.6%
461000ec 241
 
2.4%
46100704 207
 
2.1%
461000f6 193
 
1.9%
46100100 158
 
1.6%
461002dd 143
 
1.4%
46100103 106
 
1.1%
Other values (955) 7615
76.1%
2023-12-12T04:44:31.959484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 19494
24.4%
1 15860
19.8%
6 12765
16.0%
4 11541
14.4%
2 2295
 
2.9%
5 2183
 
2.7%
7 2114
 
2.6%
d 2095
 
2.6%
3 1920
 
2.4%
8 1881
 
2.4%
Other values (6) 7852
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71086
88.9%
Lowercase Letter 8914
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19494
27.4%
1 15860
22.3%
6 12765
18.0%
4 11541
16.2%
2 2295
 
3.2%
5 2183
 
3.1%
7 2114
 
3.0%
3 1920
 
2.7%
8 1881
 
2.6%
9 1033
 
1.5%
Lowercase Letter
ValueCountFrequency (%)
d 2095
23.5%
e 1789
20.1%
a 1392
15.6%
c 1352
15.2%
f 1154
12.9%
b 1132
12.7%

Most occurring scripts

ValueCountFrequency (%)
Common 71086
88.9%
Latin 8914
 
11.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19494
27.4%
1 15860
22.3%
6 12765
18.0%
4 11541
16.2%
2 2295
 
3.2%
5 2183
 
3.1%
7 2114
 
3.0%
3 1920
 
2.7%
8 1881
 
2.6%
9 1033
 
1.5%
Latin
ValueCountFrequency (%)
d 2095
23.5%
e 1789
20.1%
a 1392
15.6%
c 1352
15.2%
f 1154
12.9%
b 1132
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19494
24.4%
1 15860
19.8%
6 12765
16.0%
4 11541
14.4%
2 2295
 
2.9%
5 2183
 
2.7%
7 2114
 
2.6%
d 2095
 
2.6%
3 1920
 
2.4%
8 1881
 
2.4%
Other values (6) 7852
9.8%

collection_dt
Real number (ℝ)

Distinct8102
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0201213 × 1016
Minimum2.0201212 × 1016
Maximum2.020122 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:44:32.168792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0201212 × 1016
5-th percentile2.0201212 × 1016
Q12.0201212 × 1016
median2.0201212 × 1016
Q32.0201212 × 1016
95-th percentile2.020122 × 1016
Maximum2.020122 × 1016
Range7.8638998 × 109
Interquartile range (IQR)8069940

Descriptive statistics

Standard deviation1.8790645 × 109
Coefficient of variation (CV)9.3017409 × 10-8
Kurtosis11.35843
Mean2.0201213 × 1016
Median Absolute Deviation (MAD)2987784
Skewness3.6545899
Sum-9.0205813 × 1017
Variance3.5308832 × 1018
MonotonicityNot monotonic
2023-12-12T04:44:32.382010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20201212192800631 6
 
0.1%
20201212191030206 5
 
0.1%
20201212183930654 5
 
0.1%
20201212184500712 5
 
0.1%
20201212183300166 5
 
0.1%
20201212183600267 5
 
0.1%
20201212183030224 5
 
0.1%
20201212183100134 5
 
0.1%
20201212182300822 5
 
0.1%
20201212182830755 5
 
0.1%
Other values (8092) 9949
99.5%
ValueCountFrequency (%)
20201212182030928 2
< 0.1%
20201212182030975 2
< 0.1%
20201212182030990 3
< 0.1%
20201212182031537 1
 
< 0.1%
20201212182033084 1
 
< 0.1%
20201212182035132 1
 
< 0.1%
20201212182037882 1
 
< 0.1%
20201212182039210 1
 
< 0.1%
20201212182039648 1
 
< 0.1%
20201212182040242 1
 
< 0.1%
ValueCountFrequency (%)
20201220045930697 1
< 0.1%
20201220045900677 1
< 0.1%
20201220045900615 1
< 0.1%
20201220045730945 1
< 0.1%
20201220045708661 1
< 0.1%
20201220045600289 1
< 0.1%
20201220045538661 1
< 0.1%
20201220045508670 1
< 0.1%
20201220045500575 1
< 0.1%
20201220045500075 1
< 0.1%

longitude
Real number (ℝ)

Distinct8824
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.40831
Minimum126.16483
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:44:32.594524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16483
5-th percentile126.27891
Q1126.44582
median126.50713
Q3126.6375
95-th percentile126.91273
Maximum180
Range53.835172
Interquartile range (IQR)0.1916805

Descriptive statistics

Standard deviation9.8417214
Coefficient of variation (CV)0.07664396
Kurtosis23.524852
Mean128.40831
Median Absolute Deviation (MAD)0.0839015
Skewness5.0509807
Sum1284083.1
Variance96.85948
MonotonicityNot monotonic
2023-12-12T04:44:32.793921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180.0000001 351
 
3.5%
126.53565 60
 
0.6%
126.829592 11
 
0.1%
126.916253 10
 
0.1%
126.491514 9
 
0.1%
126.829597 7
 
0.1%
126.496676 7
 
0.1%
126.491516 7
 
0.1%
126.804778 7
 
0.1%
126.496677 7
 
0.1%
Other values (8814) 9524
95.2%
ValueCountFrequency (%)
126.164828 1
< 0.1%
126.166605 1
< 0.1%
126.169202 1
< 0.1%
126.171371 1
< 0.1%
126.180032 1
< 0.1%
126.180459 1
< 0.1%
126.180468 1
< 0.1%
126.181773 1
< 0.1%
126.18181 1
< 0.1%
126.181874 1
< 0.1%
ValueCountFrequency (%)
180.0000001 351
3.5%
127.5093496 1
 
< 0.1%
127.5090096 1
 
< 0.1%
127.5088956 1
 
< 0.1%
127.508867 1
 
< 0.1%
127.5085491 1
 
< 0.1%
127.5085201 1
 
< 0.1%
126.969664 1
 
< 0.1%
126.967944 1
 
< 0.1%
126.967195 1
 
< 0.1%

latitude
Real number (ℝ)

Distinct8686
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.402284
Minimum33.21678
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:44:32.992478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.21678
5-th percentile33.249894
Q133.312712
median33.463598
Q333.499938
95-th percentile33.543005
Maximum90
Range56.78322
Interquartile range (IQR)0.18722575

Descriptive statistics

Standard deviation10.41455
Coefficient of variation (CV)0.29417733
Kurtosis23.531002
Mean35.402284
Median Absolute Deviation (MAD)0.0537815
Skewness5.051944
Sum354022.84
Variance108.46284
MonotonicityNot monotonic
2023-12-12T04:44:33.174804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.0000001 351
 
3.5%
33.494216 60
 
0.6%
33.505448 20
 
0.2%
33.447859 10
 
0.1%
33.496581 9
 
0.1%
33.326925 9
 
0.1%
33.288552 8
 
0.1%
33.496084 7
 
0.1%
33.527637 7
 
0.1%
33.398774 7
 
0.1%
Other values (8676) 9512
95.1%
ValueCountFrequency (%)
33.2167796 1
< 0.1%
33.217709 1
< 0.1%
33.217859 1
< 0.1%
33.217862 1
< 0.1%
33.217919 1
< 0.1%
33.217942 1
< 0.1%
33.218192 1
< 0.1%
33.218195 1
< 0.1%
33.218257 1
< 0.1%
33.218278 1
< 0.1%
ValueCountFrequency (%)
90.0000001 351
3.5%
36.6171636 1
 
< 0.1%
36.6159296 1
 
< 0.1%
36.6134583 1
 
< 0.1%
36.6133881 1
 
< 0.1%
36.6133509 1
 
< 0.1%
36.6133396 1
 
< 0.1%
33.563968 1
 
< 0.1%
33.559626 1
 
< 0.1%
33.559611 1
 
< 0.1%

Interactions

2023-12-12T04:44:30.486528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:29.175056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:30.055549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:30.665185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:29.743842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:30.201910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:30.793882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:29.908030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:30.337285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T04:44:33.303718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.0000.0590.059
longitude0.0591.0001.000
latitude0.0591.0001.000
2023-12-12T04:44:33.432536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.000-0.0280.018
longitude-0.0281.0000.262
latitude0.0180.2621.000

Missing values

2023-12-12T04:44:30.968625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T04:44:31.073709image/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
577274610178320201212185800068126.67046633.540006
867724610102520201212192700185126.56599533.245947
73827461000cf20201212191300164126.49159733.495949
589454610102e20201212185900468126.42386933.436361
459884610177420201212184800111126.51286533.504295
11636461000cf20201212182400533126.5123933.500151
649944610013020201212190435672126.93313833.461824
258164610181520201212183330732126.53273433.508173
14685461011bd20201212182600423126.50163533.485982
3337461000ec20201220010756010126.26804433.40344
oidcollection_dtlongitudelatitude
29055461018a520201212183600267126.45377533.501149
657304610010320201212190524725126.67395433.275064
22589461012b420201212183130028126.52058233.492256
531744610179820201212185400113126.48765633.490859
93179461011a820201212193300841126.30034933.444308
55088461003dc20201212185530547126.91485633.452037
43837461002d520201212184611674126.82959733.326926
486564610029f20201212185000423126.53046233.247763
126804610183b20201212182430974180.090.0
25601461010ee20201212183330310126.51325833.500068

Duplicate rows

Most frequently occurring

oidcollection_dtlongitudelatitude# duplicates
41461012b020201212192130903126.48813333.4909813
04610010320201212191529512126.62522533.2635652
14610010820201212185017410126.49510533.5040662
24610010820201212190004882126.42947233.4928452
34610013420201212184100259126.59897533.520672
44610013d20201212192330921126.51908133.4923182
54610015220201212192910843126.89501833.3997542
64610023520201212193430744126.35506733.3326442
7461002dd20201212185307902126.4954933.5038762
84610031b20201212184530669126.37271933.3862482