Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows36
Duplicate rows (%)0.4%
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 05:00:00부터 익일 04:59:59까지의 데이터를 추출 - 기간: 2020년 1월부터 2021년 12월 까지
Author제주특별자치도 미래성장과
URLhttps://www.jejudatahub.net/data/view/data/1203

Alerts

Dataset has 36 (0.4%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-11 19:58:31.551724
Analysis finished2023-12-11 19:58:33.622678
Duration2.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct1131
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T04:58:33.919958image/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

Unique128 ?
Unique (%)1.3%

Sample

1st row46100571
2nd row46101122
3rd row4610112f
4th row461017b9
5th row46101840
ValueCountFrequency (%)
46100366 243
 
2.4%
461006ba 199
 
2.0%
46100367 192
 
1.9%
46100283 180
 
1.8%
46100704 179
 
1.8%
4610078d 178
 
1.8%
46100158 167
 
1.7%
461007cc 165
 
1.7%
46101339 165
 
1.7%
461000f6 162
 
1.6%
Other values (1121) 8170
81.7%
2023-12-12T04:58:34.455521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18605
23.3%
1 15948
19.9%
6 12283
15.4%
4 11843
14.8%
2 2773
 
3.5%
3 2622
 
3.3%
7 2385
 
3.0%
8 2355
 
2.9%
d 1626
 
2.0%
5 1607
 
2.0%
Other values (6) 7953
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71755
89.7%
Lowercase Letter 8245
 
10.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18605
25.9%
1 15948
22.2%
6 12283
17.1%
4 11843
16.5%
2 2773
 
3.9%
3 2622
 
3.7%
7 2385
 
3.3%
8 2355
 
3.3%
5 1607
 
2.2%
9 1334
 
1.9%
Lowercase Letter
ValueCountFrequency (%)
d 1626
19.7%
b 1520
18.4%
f 1375
16.7%
e 1341
16.3%
a 1240
15.0%
c 1143
13.9%

Most occurring scripts

ValueCountFrequency (%)
Common 71755
89.7%
Latin 8245
 
10.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18605
25.9%
1 15948
22.2%
6 12283
17.1%
4 11843
16.5%
2 2773
 
3.9%
3 2622
 
3.7%
7 2385
 
3.3%
8 2355
 
3.3%
5 1607
 
2.2%
9 1334
 
1.9%
Latin
ValueCountFrequency (%)
d 1626
19.7%
b 1520
18.4%
f 1375
16.7%
e 1341
16.3%
a 1240
15.0%
c 1143
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18605
23.3%
1 15948
19.9%
6 12283
15.4%
4 11843
14.8%
2 2773
 
3.5%
3 2622
 
3.3%
7 2385
 
3.0%
8 2355
 
2.9%
d 1626
 
2.0%
5 1607
 
2.0%
Other values (6) 7953
9.9%

collection_dt
Real number (ℝ)

Distinct7316
Distinct (%)73.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0210327 × 1016
Minimum2.0210327 × 1016
Maximum2.0210327 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:58:34.647453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0210327 × 1016
5-th percentile2.0210327 × 1016
Q12.0210327 × 1016
median2.0210327 × 1016
Q32.0210327 × 1016
95-th percentile2.0210327 × 1016
Maximum2.0210327 × 1016
Range2609706
Interquartile range (IQR)1400400

Descriptive statistics

Standard deviation769691.74
Coefficient of variation (CV)3.8084081 × 10-11
Kurtosis-1.2067972
Mean2.0210327 × 1016
Median Absolute Deviation (MAD)699992
Skewness-0.10597619
Sum-8.1091359 × 1017
Variance5.9242537 × 1011
MonotonicityNot monotonic
2023-12-12T04:58:34.869771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20210327121200336 9
 
0.1%
20210327120730097 7
 
0.1%
20210327121530973 6
 
0.1%
20210327121300563 6
 
0.1%
20210327121630309 6
 
0.1%
20210327122730473 6
 
0.1%
20210327122930630 6
 
0.1%
20210327121730880 6
 
0.1%
20210327121530488 6
 
0.1%
20210327122900454 6
 
0.1%
Other values (7306) 9936
99.4%
ValueCountFrequency (%)
20210327120430917 1
< 0.1%
20210327120430948 1
< 0.1%
20210327120430964 1
< 0.1%
20210327120430980 1
< 0.1%
20210327120430995 1
< 0.1%
20210327120431448 1
< 0.1%
20210327120431698 1
< 0.1%
20210327120431964 1
< 0.1%
20210327120432230 1
< 0.1%
20210327120432620 1
< 0.1%
ValueCountFrequency (%)
20210327123040623 1
< 0.1%
20210327123040404 1
< 0.1%
20210327123040201 1
< 0.1%
20210327123040107 2
< 0.1%
20210327123040092 1
< 0.1%
20210327123040013 1
< 0.1%
20210327123039967 1
< 0.1%
20210327123039920 1
< 0.1%
20210327123039904 1
< 0.1%
20210327123039857 1
< 0.1%

longitude
Real number (ℝ)

Distinct8588
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.93173
Minimum126.16324
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:58:35.048251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16324
5-th percentile126.27286
Q1126.38314
median126.50238
Q3126.6794
95-th percentile126.93432
Maximum180
Range53.836762
Interquartile range (IQR)0.2962635

Descriptive statistics

Standard deviation11.074879
Coefficient of variation (CV)0.085897231
Kurtosis17.31545
Mean128.93173
Median Absolute Deviation (MAD)0.1330455
Skewness4.3937302
Sum1289317.3
Variance122.65295
MonotonicityNot monotonic
2023-12-12T04:58:35.260214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180.0000001 449
 
4.5%
126.272795 20
 
0.2%
126.455549 15
 
0.1%
126.272798 15
 
0.1%
126.455543 15
 
0.1%
126.892943 14
 
0.1%
126.272799 14
 
0.1%
126.455547 14
 
0.1%
126.455542 12
 
0.1%
126.455545 12
 
0.1%
Other values (8578) 9420
94.2%
ValueCountFrequency (%)
126.163238 1
< 0.1%
126.165512 1
< 0.1%
126.165517 2
< 0.1%
126.166051 1
< 0.1%
126.167242 1
< 0.1%
126.167446 1
< 0.1%
126.167953 1
< 0.1%
126.170046 1
< 0.1%
126.170991 1
< 0.1%
126.171498 1
< 0.1%
ValueCountFrequency (%)
180.0000001 449
4.5%
129.220745 1
 
< 0.1%
129.2206 1
 
< 0.1%
129.220515 1
 
< 0.1%
129.220396 2
 
< 0.1%
129.174097 1
 
< 0.1%
129.172541 1
 
< 0.1%
126.936358 1
 
< 0.1%
126.936347 1
 
< 0.1%
126.936268 2
 
< 0.1%

latitude
Real number (ℝ)

Distinct8390
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.941957
Minimum33.198559
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:58:35.446441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.198559
5-th percentile33.247311
Q133.306769
median33.441138
Q333.494727
95-th percentile33.556116
Maximum90
Range56.801441
Interquartile range (IQR)0.18795875

Descriptive statistics

Standard deviation11.721947
Coefficient of variation (CV)0.32613548
Kurtosis17.324456
Mean35.941957
Median Absolute Deviation (MAD)0.073921
Skewness4.3953357
Sum359419.57
Variance137.40405
MonotonicityNot monotonic
2023-12-12T04:58:36.111154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.0000001 449
 
4.5%
33.493199 19
 
0.2%
33.493198 16
 
0.2%
33.313885 13
 
0.1%
33.313886 12
 
0.1%
33.313893 12
 
0.1%
33.496681 11
 
0.1%
33.493197 11
 
0.1%
33.313883 10
 
0.1%
33.493189 10
 
0.1%
Other values (8380) 9437
94.4%
ValueCountFrequency (%)
33.198559 1
< 0.1%
33.199985 1
< 0.1%
33.200562 1
< 0.1%
33.201351 1
< 0.1%
33.201522 1
< 0.1%
33.201701 1
< 0.1%
33.202661 1
< 0.1%
33.204098 1
< 0.1%
33.204675 1
< 0.1%
33.204676 1
< 0.1%
ValueCountFrequency (%)
90.0000001 449
4.5%
35.244015 1
 
< 0.1%
35.243165 1
 
< 0.1%
35.24264 1
 
< 0.1%
35.241764 2
 
< 0.1%
35.238449 1
 
< 0.1%
35.237619 1
 
< 0.1%
33.563788 1
 
< 0.1%
33.560163 1
 
< 0.1%
33.560031 1
 
< 0.1%

Interactions

2023-12-12T04:58:32.930458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:32.042914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:32.477562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:33.108771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:32.165892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:32.621624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:33.259570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:32.316551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:32.770555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T04:58:36.259346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.0000.0940.094
longitude0.0941.0001.000
latitude0.0941.0001.000
2023-12-12T04:58:36.373659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.000-0.006-0.021
longitude-0.0061.0000.401
latitude-0.0210.4011.000

Missing values

2023-12-12T04:58:33.423892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T04:58:33.569591image/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
727274610057120210327122530380126.5830133.256574
497014610112220210327121930067126.31749333.302659
72224610112f20210327120600492126.30176933.444574
29440461017b920210327121309580126.93634733.462901
948244610184020210327123038654126.51932433.460594
931674610042420210327123030887126.55120833.509724
772914610189420210327122548616126.35614533.281632
394804610036720210327121554632126.61993433.261372
3035461008ac20210327120526097126.50535133.503976
21264610133320210327120521690126.49216133.496017
oidcollection_dtlongitudelatitude
841334610070420210327122745069126.31701533.303271
90470461006da20210327122944444126.59442933.25949
567194610197720210327122058828126.55245333.509257
178534610107920210327121000195126.67405633.330263
733734610177120210327122532224126.57863933.46833
649004610194d20210327122300938126.30570933.350973
162094610077320210327120926566126.49192333.493574
332944610195120210327121430715126.91384133.453421
8149461002de20210327120628308126.51049933.51282
688884610036720210327122425356126.57561533.254827

Duplicate rows

Most frequently occurring

oidcollection_dtlongitudelatitude# duplicates
0461000f620210327122115033126.34037433.3106432
14610013020210327120942036126.47734633.4822712
2461001e620210327122500188126.44437333.4580252
3461001e620210327122700141126.44930733.461512
4461001ec20210327121500672126.5142133.4607912
54610020520210327120900828126.2405633.3936892
64610028320210327121224089126.83980233.5288232
7461002df20210327122220511126.49894833.5050352
8461002df20210327122334802126.49642433.5054672
94610036620210327122028371126.35229733.324932