Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows37
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 05:00:00 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 37 (0.4%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-11 19:44:00.953899
Analysis finished2023-12-11 19:44:03.429396
Duration2.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct590
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T04:44:03.744883image/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

Unique65 ?
Unique (%)0.7%

Sample

1st row46101943
2nd row461011df
3rd row46100130
4th row4610076d
5th row46100156
ValueCountFrequency (%)
461000f6 1016
 
10.2%
46100156 838
 
8.4%
461006ba 692
 
6.9%
46100159 467
 
4.7%
46100152 426
 
4.3%
461006ed 277
 
2.8%
46100108 252
 
2.5%
46100130 251
 
2.5%
461002e1 105
 
1.1%
461006e5 101
 
1.0%
Other values (580) 5575
55.8%
2023-12-12T04:44:04.348315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 20766
26.0%
1 16416
20.5%
6 13954
17.4%
4 11317
14.1%
5 2911
 
3.6%
f 1871
 
2.3%
2 1853
 
2.3%
7 1517
 
1.9%
a 1394
 
1.7%
3 1312
 
1.6%
Other values (6) 6689
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72504
90.6%
Lowercase Letter 7496
 
9.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20766
28.6%
1 16416
22.6%
6 13954
19.2%
4 11317
15.6%
5 2911
 
4.0%
2 1853
 
2.6%
7 1517
 
2.1%
3 1312
 
1.8%
8 1267
 
1.7%
9 1191
 
1.6%
Lowercase Letter
ValueCountFrequency (%)
f 1871
25.0%
a 1394
18.6%
b 1302
17.4%
e 1286
17.2%
d 879
11.7%
c 764
10.2%

Most occurring scripts

ValueCountFrequency (%)
Common 72504
90.6%
Latin 7496
 
9.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20766
28.6%
1 16416
22.6%
6 13954
19.2%
4 11317
15.6%
5 2911
 
4.0%
2 1853
 
2.6%
7 1517
 
2.1%
3 1312
 
1.8%
8 1267
 
1.7%
9 1191
 
1.6%
Latin
ValueCountFrequency (%)
f 1871
25.0%
a 1394
18.6%
b 1302
17.4%
e 1286
17.2%
d 879
11.7%
c 764
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20766
26.0%
1 16416
20.5%
6 13954
17.4%
4 11317
14.1%
5 2911
 
3.6%
f 1871
 
2.3%
2 1853
 
2.3%
7 1517
 
1.9%
a 1394
 
1.7%
3 1312
 
1.6%
Other values (6) 6689
 
8.4%

collection_dt
Real number (ℝ)

Distinct9448
Distinct (%)94.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0200905 × 1016
Minimum2.0200905 × 1016
Maximum2.0200905 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:44:04.576817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0200905 × 1016
5-th percentile2.0200905 × 1016
Q12.0200905 × 1016
median2.0200905 × 1016
Q32.0200905 × 1016
95-th percentile2.0200905 × 1016
Maximum2.0200905 × 1016
Range54200393
Interquartile range (IQR)19204604

Descriptive statistics

Standard deviation12379994
Coefficient of variation (CV)6.1284353 × 10-10
Kurtosis0.058402272
Mean2.0200905 × 1016
Median Absolute Deviation (MAD)9121330
Skewness-0.85935043
Sum-9.0513391 × 1017
Variance1.5326425 × 1014
MonotonicityNot monotonic
2023-12-12T04:44:04.838308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200905094230144 4
 
< 0.1%
20200905103530182 4
 
< 0.1%
20200905100500015 3
 
< 0.1%
20200905082830596 3
 
< 0.1%
20200905102100130 3
 
< 0.1%
20200905091930766 3
 
< 0.1%
20200905093630568 3
 
< 0.1%
20200905073630667 3
 
< 0.1%
20200905103230857 3
 
< 0.1%
20200905103030542 3
 
< 0.1%
Other values (9438) 9968
99.7%
ValueCountFrequency (%)
20200905050000576 1
< 0.1%
20200905050030768 1
< 0.1%
20200905050030878 1
< 0.1%
20200905050100789 1
< 0.1%
20200905050130074 1
< 0.1%
20200905050130824 1
< 0.1%
20200905050130918 1
< 0.1%
20200905050200703 1
< 0.1%
20200905050230489 1
< 0.1%
20200905050230629 1
< 0.1%
ValueCountFrequency (%)
20200905104200969 1
< 0.1%
20200905104200953 2
< 0.1%
20200905104200891 1
< 0.1%
20200905104200859 1
< 0.1%
20200905104200844 1
< 0.1%
20200905104200828 2
< 0.1%
20200905104200813 2
< 0.1%
20200905104200781 2
< 0.1%
20200905104200531 1
< 0.1%
20200905104200516 1
< 0.1%

longitude
Real number (ℝ)

Distinct7921
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.88121
Minimum126.16329
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:44:05.071412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16329
5-th percentile126.27425
Q1126.43156
median126.49192
Q3126.54745
95-th percentile126.92456
Maximum180
Range53.836714
Interquartile range (IQR)0.1158922

Descriptive statistics

Standard deviation11.046069
Coefficient of variation (CV)0.085707368
Kurtosis17.471795
Mean128.88121
Median Absolute Deviation (MAD)0.05851455
Skewness4.4119155
Sum1288812.1
Variance122.01565
MonotonicityNot monotonic
2023-12-12T04:44:05.279388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180.0000001 446
 
4.5%
126.491917 53
 
0.5%
126.491916 44
 
0.4%
126.491867 43
 
0.4%
126.491864 36
 
0.4%
126.493115 33
 
0.3%
126.455809 32
 
0.3%
126.251955 29
 
0.3%
126.491912 29
 
0.3%
126.491911 27
 
0.3%
Other values (7911) 9228
92.3%
ValueCountFrequency (%)
126.163286 1
< 0.1%
126.163307 1
< 0.1%
126.164774 1
< 0.1%
126.165764 1
< 0.1%
126.165765 1
< 0.1%
126.165766 1
< 0.1%
126.166833 1
< 0.1%
126.167512 1
< 0.1%
126.167516 1
< 0.1%
126.16763 1
< 0.1%
ValueCountFrequency (%)
180.0000001 446
4.5%
126.959653 1
 
< 0.1%
126.95783 1
 
< 0.1%
126.935745 1
 
< 0.1%
126.935737 1
 
< 0.1%
126.935736 1
 
< 0.1%
126.9356989 1
 
< 0.1%
126.9356965 1
 
< 0.1%
126.93569 1
 
< 0.1%
126.935478 1
 
< 0.1%

latitude
Real number (ℝ)

Distinct7741
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.947951
Minimum33.206457
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:44:05.492645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.206457
5-th percentile33.249702
Q133.363576
median33.480941
Q333.497399
95-th percentile33.556593
Maximum90
Range56.793543
Interquartile range (IQR)0.1338235

Descriptive statistics

Standard deviation11.679472
Coefficient of variation (CV)0.32489953
Kurtosis17.474822
Mean35.947951
Median Absolute Deviation (MAD)0.031684
Skewness4.4124541
Sum359479.51
Variance136.41007
MonotonicityNot monotonic
2023-12-12T04:44:05.735882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.0000001 446
 
4.5%
33.495762 81
 
0.8%
33.495754 76
 
0.8%
33.49576 56
 
0.6%
33.495756 52
 
0.5%
33.496337 49
 
0.5%
33.495761 47
 
0.5%
33.495755 46
 
0.5%
33.495753 45
 
0.4%
33.495764 36
 
0.4%
Other values (7731) 9066
90.7%
ValueCountFrequency (%)
33.206457 1
< 0.1%
33.214169 1
< 0.1%
33.2186151 1
< 0.1%
33.220113 1
< 0.1%
33.2217386 1
< 0.1%
33.222918 1
< 0.1%
33.223447 1
< 0.1%
33.224918 1
< 0.1%
33.225968 1
< 0.1%
33.228193 1
< 0.1%
ValueCountFrequency (%)
90.0000001 446
4.5%
33.563029 1
 
< 0.1%
33.562965 1
 
< 0.1%
33.561214 1
 
< 0.1%
33.560365 1
 
< 0.1%
33.559626 1
 
< 0.1%
33.559606 1
 
< 0.1%
33.559175 1
 
< 0.1%
33.558999 1
 
< 0.1%
33.558808 1
 
< 0.1%

Interactions

2023-12-12T04:44:02.689309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:01.546441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:02.113434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:02.851949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:01.727347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:02.326254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:03.012094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:01.926747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:44:02.499072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T04:44:05.919612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.0000.1330.133
longitude0.1331.0001.000
latitude0.1331.0001.000
2023-12-12T04:44:06.080605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.0000.1060.134
longitude0.1061.0000.452
latitude0.1340.4521.000

Missing values

2023-12-12T04:44:03.215328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T04:44:03.365239image/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
173664610194320200905075700446126.56026933.245847
12579461011df20200905074000410126.46624733.475923
136244610013020200905074333634126.57467133.254876
606764610076d20200905100200504126.4773733.484865
353584610015620200905085730825126.36211433.275632
1548461002e120200905055643657126.37036833.479022
722154610036320200905101830387126.50997233.465525
71299461006ba20200905101723327126.3634133.27392
805634610179f20200905102730609126.49523433.489024
351844610077f20200905085700946126.48300633.261196
oidcollection_dtlongitudelatitude
50388461004eb20200905093730639126.39159333.478712
7821346100ff320200905102500822126.50572933.500649
355824610015620200905085801377126.35729233.280402
23161461006ba20200905082025444126.4155733.257899
610814610015920200905100232118126.51988333.5048
533634610055620200905094434035126.55583433.274952
710014610105420200905101700214126.25195533.379288
31316461017a020200905084830629126.55434333.508277
67378461006ba20200905101202405126.39551233.263547
25788461006ba20200905082946076126.41875533.244295

Duplicate rows

Most frequently occurring

oidcollection_dtlongitudelatitude# duplicates
0461000f620200905071559494126.50005633.2502472
1461000f620200905081445074126.49692633.4994442
2461000f620200905102717309126.47266133.4810822
3461000fe20200905095300456126.51074533.4617442
44610013020200905074406920126.57037533.2547742
54610013020200905074424313126.56812533.2546552
64610015220200905100930259126.54599433.5123172
74610015220200905101046442126.55562333.5075922
84610015620200905090829840126.38246233.4037852
94610015620200905091210932126.42010933.4334682