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 05:00:00부터 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 60 (0.6%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-11 19:58:23.840584
Analysis finished2023-12-11 19:58:26.149143
Duration2.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct1056
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T04:58:26.459394image/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

Unique262 ?
Unique (%)2.6%

Sample

1st row4610078f
2nd row461010b2
3rd row461006ba
4th row461017ff
5th row4610112e
ValueCountFrequency (%)
46100441 710
 
7.1%
4610078f 475
 
4.8%
461002e0 392
 
3.9%
461012cf 270
 
2.7%
461006ba 266
 
2.7%
46100704 206
 
2.1%
461002b9 201
 
2.0%
461000f6 189
 
1.9%
46100283 174
 
1.7%
461006da 160
 
1.6%
Other values (1046) 6957
69.6%
2023-12-12T04:58:27.023937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18912
23.6%
1 16168
20.2%
4 12848
16.1%
6 11818
14.8%
2 3267
 
4.1%
7 2250
 
2.8%
8 1994
 
2.5%
f 1960
 
2.5%
3 1710
 
2.1%
d 1402
 
1.8%
Other values (6) 7671
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71665
89.6%
Lowercase Letter 8335
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18912
26.4%
1 16168
22.6%
4 12848
17.9%
6 11818
16.5%
2 3267
 
4.6%
7 2250
 
3.1%
8 1994
 
2.8%
3 1710
 
2.4%
5 1373
 
1.9%
9 1325
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
f 1960
23.5%
d 1402
16.8%
b 1382
16.6%
a 1240
14.9%
e 1217
14.6%
c 1134
13.6%

Most occurring scripts

ValueCountFrequency (%)
Common 71665
89.6%
Latin 8335
 
10.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18912
26.4%
1 16168
22.6%
4 12848
17.9%
6 11818
16.5%
2 3267
 
4.6%
7 2250
 
3.1%
8 1994
 
2.8%
3 1710
 
2.4%
5 1373
 
1.9%
9 1325
 
1.8%
Latin
ValueCountFrequency (%)
f 1960
23.5%
d 1402
16.8%
b 1382
16.6%
a 1240
14.9%
e 1217
14.6%
c 1134
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18912
23.6%
1 16168
20.2%
4 12848
16.1%
6 11818
14.8%
2 3267
 
4.1%
7 2250
 
2.8%
8 1994
 
2.5%
f 1960
 
2.5%
3 1710
 
2.1%
d 1402
 
1.8%
Other values (6) 7671
9.6%

collection_dt
Real number (ℝ)

Distinct8952
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0210227 × 1016
Minimum2.0210227 × 1016
Maximum2.0210227 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:58:27.206502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0210227 × 1016
5-th percentile2.0210227 × 1016
Q12.0210227 × 1016
median2.0210227 × 1016
Q32.0210227 × 1016
95-th percentile2.0210227 × 1016
Maximum2.0210227 × 1016
Range55700092
Interquartile range (IQR)14770540

Descriptive statistics

Standard deviation11330312
Coefficient of variation (CV)5.6062271 × 10-10
Kurtosis-0.46045029
Mean2.0210227 × 1016
Median Absolute Deviation (MAD)9183244
Skewness-0.23935387
Sum-8.1191395 × 1017
Variance1.2837598 × 1014
MonotonicityNot monotonic
2023-12-12T04:58:27.353736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20210227100900426 5
 
0.1%
20210227104300047 5
 
0.1%
20210227103330974 5
 
0.1%
20210227094900585 5
 
0.1%
20210227092700587 4
 
< 0.1%
20210227095830533 4
 
< 0.1%
20210227103830669 4
 
< 0.1%
20210227084530940 4
 
< 0.1%
20210227090230993 4
 
< 0.1%
20210227090830867 4
 
< 0.1%
Other values (8942) 9956
99.6%
ValueCountFrequency (%)
20210227050230098 1
< 0.1%
20210227050300118 1
< 0.1%
20210227050300477 1
< 0.1%
20210227050400626 1
< 0.1%
20210227050430176 1
< 0.1%
20210227050730559 1
< 0.1%
20210227050830895 1
< 0.1%
20210227050900367 1
< 0.1%
20210227051000890 1
< 0.1%
20210227051030051 1
< 0.1%
ValueCountFrequency (%)
20210227105930190 1
< 0.1%
20210227105930159 1
< 0.1%
20210227105930143 1
< 0.1%
20210227105930127 1
< 0.1%
20210227105930096 1
< 0.1%
20210227105930081 2
< 0.1%
20210227105930049 1
< 0.1%
20210227105930034 2
< 0.1%
20210227105930018 2
< 0.1%
20210227105929940 1
< 0.1%

longitude
Real number (ℝ)

Distinct8409
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.93396
Minimum126.1633
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:58:27.498724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.1633
5-th percentile126.34073
Q1126.46375
median126.50171
Q3126.64102
95-th percentile126.93008
Maximum180
Range53.836705
Interquartile range (IQR)0.177266

Descriptive statistics

Standard deviation11.06073
Coefficient of variation (CV)0.085786014
Kurtosis17.370524
Mean128.93396
Median Absolute Deviation (MAD)0.0602675
Skewness4.400338
Sum1289339.6
Variance122.33976
MonotonicityNot monotonic
2023-12-12T04:58:27.646391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180.0000001 448
 
4.5%
126.527271 51
 
0.5%
126.491748 16
 
0.2%
126.491749 15
 
0.1%
126.49175 13
 
0.1%
126.491896 11
 
0.1%
126.491892 11
 
0.1%
126.480247 10
 
0.1%
126.477872 9
 
0.1%
126.527283 9
 
0.1%
Other values (8399) 9407
94.1%
ValueCountFrequency (%)
126.163295 1
< 0.1%
126.163389 1
< 0.1%
126.165033 1
< 0.1%
126.165323 1
< 0.1%
126.16648 1
< 0.1%
126.166663 1
< 0.1%
126.167066 1
< 0.1%
126.168666 1
< 0.1%
126.169139 1
< 0.1%
126.172061 1
< 0.1%
ValueCountFrequency (%)
180.0000001 448
4.5%
126.935793 1
 
< 0.1%
126.935744 1
 
< 0.1%
126.93574 1
 
< 0.1%
126.935707 1
 
< 0.1%
126.935626 1
 
< 0.1%
126.935604 1
 
< 0.1%
126.935503 1
 
< 0.1%
126.935501 1
 
< 0.1%
126.935457 1
 
< 0.1%

latitude
Real number (ℝ)

Distinct8209
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.982233
Minimum33.200941
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:58:27.797400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.200941
5-th percentile33.253835
Q133.438012
median33.487971
Q333.50389
95-th percentile33.554023
Maximum90
Range56.799059
Interquartile range (IQR)0.065878

Descriptive statistics

Standard deviation11.699323
Coefficient of variation (CV)0.32514165
Kurtosis17.375655
Mean35.982233
Median Absolute Deviation (MAD)0.025192
Skewness4.4012547
Sum359822.33
Variance136.87415
MonotonicityNot monotonic
2023-12-12T04:58:27.945570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.0000001 448
 
4.5%
33.517272 51
 
0.5%
33.495866 18
 
0.2%
33.495863 10
 
0.1%
33.489866 9
 
0.1%
33.495891 9
 
0.1%
33.495877 9
 
0.1%
33.509462 9
 
0.1%
33.477961 9
 
0.1%
33.495895 8
 
0.1%
Other values (8199) 9420
94.2%
ValueCountFrequency (%)
33.200941 1
< 0.1%
33.203975 1
< 0.1%
33.203978 1
< 0.1%
33.204153 1
< 0.1%
33.213332 1
< 0.1%
33.215962 1
< 0.1%
33.219676 1
< 0.1%
33.219936 1
< 0.1%
33.219944 1
< 0.1%
33.221182 1
< 0.1%
ValueCountFrequency (%)
90.0000001 448
4.5%
33.5616 1
 
< 0.1%
33.560358 1
 
< 0.1%
33.559859 1
 
< 0.1%
33.55819 1
 
< 0.1%
33.557997 1
 
< 0.1%
33.557586 1
 
< 0.1%
33.557024 1
 
< 0.1%
33.556482 1
 
< 0.1%
33.556204 1
 
< 0.1%

Interactions

2023-12-12T04:58:25.513231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:24.584982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:25.050695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:25.652411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:24.769664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:25.217067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:25.776718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:24.908270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:25.377210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T04:58:28.049753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.0000.0790.079
longitude0.0791.0001.000
latitude0.0791.0001.000
2023-12-12T04:58:28.142729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.0000.0540.025
longitude0.0541.0000.335
latitude0.0250.3351.000

Missing values

2023-12-12T04:58:25.964867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T04:58:26.089400image/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
899294610078f20210227103557743126.45558833.493215
4203461010b220210227065300933126.53827633.486328
13428461006ba20210227073731729126.49994133.518023
48370461017ff20210227085330454126.50006133.498544
365414610112e20210227083430496126.50574233.4897
192854610038d20210227074930087126.31125633.233823
378414610078f20210227083644982126.73890233.436962
40581461004f820210227084130221126.43041533.472707
65364610044120210227070932139126.50951433.250709
494744610026120210227085500638126.479533.498086
oidcollection_dtlongitudelatitude
553844610032420210227090300684126.45146633.462836
126334610044120210227073543309126.36198333.357358
459504610034a20210227084956286126.85162933.346541
45497461018d220210227084900842180.090.0
907954610028320210227103610401126.46850633.478492
365504610106a20210227083430543126.46254933.472738
874534610028320210227103125208126.47435233.482467
81429461002d520210227100853519126.69902433.501224
91324610181d20210227072230113126.72199733.445835
7546046100fbc20210227095300225126.5139733.5124

Duplicate rows

Most frequently occurring

oidcollection_dtlongitudelatitude# duplicates
0461000b920210227095200202126.89246933.4974012
1461000c620210227073900256180.090.02
2461000d920210227092700587126.40828633.422822
34610010020210227095800342126.90881733.4650942
44610010020210227101331883126.83739533.5308792
54610012c20210227090200317126.42601833.4925162
6461001c420210227103830325126.37678333.3955382
7461001fa20210227095300022126.47514733.4830052
84610028320210227103334834126.47205733.4806322
9461002de20210227104115487126.48580233.4896752