Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows55
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 05:00:00부터 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 55 (0.5%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-11 19:58:40.171380
Analysis finished2023-12-11 19:58:42.125038
Duration1.95 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct997
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T04:58:42.391889image/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

Unique132 ?
Unique (%)1.3%

Sample

1st row461018a2
2nd row461002d5
3rd row46101893
4th row4610012c
5th row46101184
ValueCountFrequency (%)
46100283 310
 
3.1%
461006e5 279
 
2.8%
461002e2 274
 
2.7%
461006b5 243
 
2.4%
46100156 234
 
2.3%
46100441 225
 
2.2%
461006ed 204
 
2.0%
4610078f 202
 
2.0%
46100351 199
 
2.0%
461000f6 186
 
1.9%
Other values (987) 7644
76.4%
2023-12-12T04:58:42.906852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18694
23.4%
1 15796
19.7%
6 13035
16.3%
4 11817
14.8%
2 3016
 
3.8%
3 2566
 
3.2%
5 2010
 
2.5%
7 1886
 
2.4%
8 1737
 
2.2%
e 1700
 
2.1%
Other values (6) 7743
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71831
89.8%
Lowercase Letter 8169
 
10.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18694
26.0%
1 15796
22.0%
6 13035
18.1%
4 11817
16.5%
2 3016
 
4.2%
3 2566
 
3.6%
5 2010
 
2.8%
7 1886
 
2.6%
8 1737
 
2.4%
9 1274
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
e 1700
20.8%
b 1583
19.4%
d 1487
18.2%
f 1323
16.2%
a 1292
15.8%
c 784
9.6%

Most occurring scripts

ValueCountFrequency (%)
Common 71831
89.8%
Latin 8169
 
10.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18694
26.0%
1 15796
22.0%
6 13035
18.1%
4 11817
16.5%
2 3016
 
4.2%
3 2566
 
3.6%
5 2010
 
2.8%
7 1886
 
2.6%
8 1737
 
2.4%
9 1274
 
1.8%
Latin
ValueCountFrequency (%)
e 1700
20.8%
b 1583
19.4%
d 1487
18.2%
f 1323
16.2%
a 1292
15.8%
c 784
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18694
23.4%
1 15796
19.7%
6 13035
16.3%
4 11817
14.8%
2 3016
 
3.8%
3 2566
 
3.2%
5 2010
 
2.5%
7 1886
 
2.4%
8 1737
 
2.2%
e 1700
 
2.1%
Other values (6) 7743
9.7%

collection_dt
Real number (ℝ)

Distinct7901
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0210417 × 1016
Minimum2.0210417 × 1016
Maximum2.0210417 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:58:43.102875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0210417 × 1016
5-th percentile2.0210417 × 1016
Q12.0210417 × 1016
median2.0210417 × 1016
Q32.0210417 × 1016
95-th percentile2.0210417 × 1016
Maximum2.0210417 × 1016
Range8180313
Interquartile range (IQR)6005464

Descriptive statistics

Standard deviation2973849.8
Coefficient of variation (CV)1.471444 × 10-10
Kurtosis-1.5687736
Mean2.0210417 × 1016
Median Absolute Deviation (MAD)1499860
Skewness0.47680366
Sum-8.1001294 × 1017
Variance8.8437826 × 1012
MonotonicityNot monotonic
2023-12-12T04:58:43.418187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20210417184030355 9
 
0.1%
20210417185330806 8
 
0.1%
20210417183700654 7
 
0.1%
20210417184530704 7
 
0.1%
20210417185000310 6
 
0.1%
20210417183930307 6
 
0.1%
20210417185700834 6
 
0.1%
20210417185200796 6
 
0.1%
20210417183930119 6
 
0.1%
20210417184100567 5
 
0.1%
Other values (7891) 9934
99.3%
ValueCountFrequency (%)
20210417183333518 1
< 0.1%
20210417183334252 1
< 0.1%
20210417183334690 1
< 0.1%
20210417183335221 1
< 0.1%
20210417183336065 1
< 0.1%
20210417183336706 1
< 0.1%
20210417183337300 1
< 0.1%
20210417183337988 1
< 0.1%
20210417183339082 1
< 0.1%
20210417183340004 1
< 0.1%
ValueCountFrequency (%)
20210417191513831 1
< 0.1%
20210417191513347 1
< 0.1%
20210417191513159 1
< 0.1%
20210417191512815 1
< 0.1%
20210417191511831 1
< 0.1%
20210417191510815 1
< 0.1%
20210417191509721 1
< 0.1%
20210417191509564 1
< 0.1%
20210417191508767 1
< 0.1%
20210417191507267 1
< 0.1%

longitude
Real number (ℝ)

Distinct8704
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.03367
Minimum126.16296
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:58:43.854313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16296
5-th percentile126.26539
Q1126.42436
median126.50343
Q3126.57839
95-th percentile126.89713
Maximum180
Range53.837039
Interquartile range (IQR)0.15402825

Descriptive statistics

Standard deviation8.8867882
Coefficient of variation (CV)0.069409777
Kurtosis30.230066
Mean128.03367
Median Absolute Deviation (MAD)0.077897
Skewness5.6753895
Sum1280336.7
Variance78.975005
MonotonicityNot monotonic
2023-12-12T04:58:44.249730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180.0000001 284
 
2.8%
126.49188 13
 
0.1%
126.265404 12
 
0.1%
126.265405 10
 
0.1%
126.491881 10
 
0.1%
126.454394 8
 
0.1%
126.265398 8
 
0.1%
126.265403 8
 
0.1%
126.1972 8
 
0.1%
126.491879 8
 
0.1%
Other values (8694) 9631
96.3%
ValueCountFrequency (%)
126.162961 1
< 0.1%
126.16394 1
< 0.1%
126.164237 1
< 0.1%
126.164249 1
< 0.1%
126.164251 1
< 0.1%
126.164252 1
< 0.1%
126.164325 1
< 0.1%
126.164421 1
< 0.1%
126.164506 1
< 0.1%
126.165577 1
< 0.1%
ValueCountFrequency (%)
180.0000001 284
2.8%
129.084032 1
 
< 0.1%
129.023234 1
 
< 0.1%
129.014025 1
 
< 0.1%
128.993827 1
 
< 0.1%
128.990704 1
 
< 0.1%
128.959913 1
 
< 0.1%
128.953018 1
 
< 0.1%
128.952851 1
 
< 0.1%
128.952846 1
 
< 0.1%

latitude
Real number (ℝ)

Distinct8403
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.024758
Minimum33.199458
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:58:44.507951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.199458
5-th percentile33.248294
Q133.316077
median33.46474
Q333.504256
95-th percentile33.552362
Maximum90
Range56.800542
Interquartile range (IQR)0.1881795

Descriptive statistics

Standard deviation9.4002391
Coefficient of variation (CV)0.26838841
Kurtosis30.245648
Mean35.024758
Median Absolute Deviation (MAD)0.0528265
Skewness5.6774908
Sum350247.58
Variance88.364495
MonotonicityNot monotonic
2023-12-12T04:58:44.710084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.0000001 284
 
2.8%
33.495756 21
 
0.2%
33.495761 12
 
0.1%
33.411914 10
 
0.1%
33.395896 9
 
0.1%
33.364783 9
 
0.1%
33.411907 9
 
0.1%
33.411918 9
 
0.1%
33.495755 8
 
0.1%
33.411915 8
 
0.1%
Other values (8393) 9621
96.2%
ValueCountFrequency (%)
33.199458 1
< 0.1%
33.200221 1
< 0.1%
33.201682 1
< 0.1%
33.204193 1
< 0.1%
33.208543 1
< 0.1%
33.210894 1
< 0.1%
33.216724 1
< 0.1%
33.216754 1
< 0.1%
33.216774 1
< 0.1%
33.217992 1
< 0.1%
ValueCountFrequency (%)
90.0000001 284
2.8%
35.239965 1
 
< 0.1%
35.238974 1
 
< 0.1%
35.234541 1
 
< 0.1%
35.233228 1
 
< 0.1%
35.218511 1
 
< 0.1%
35.185789 1
 
< 0.1%
35.176084 1
 
< 0.1%
35.168255 1
 
< 0.1%
35.168055 1
 
< 0.1%

Interactions

2023-12-12T04:58:41.485440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:40.613441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:41.070226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:41.618687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:40.782943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:41.223181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:41.758311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:40.939566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:58:41.363148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T04:58:44.853982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.0000.0000.000
longitude0.0001.0001.000
latitude0.0001.0001.000
2023-12-12T04:58:45.012907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.000-0.1280.012
longitude-0.1281.0000.299
latitude0.0120.2991.000

Missing values

2023-12-12T04:58:41.954782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T04:58:42.069163image/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
10899461018a220210417183800984126.58857533.514791
8717461002d520210417183709579126.55988833.2529
820954610189320210417190900664126.5019433.499082
494104610012c20210417185500284126.39450833.263829
408364610118420210417185130616126.72555833.442892
80250461002e120210417190810556126.26539533.411905
303284610179b20210417184700667126.56233933.249814
318034610028320210417184738740126.53731333.49729
85130461010bb20210417191027548126.4666333.501593
94096461017b520210417191430928126.43809733.239825
oidcollection_dtlongitudelatitude
57472461006e520210417185808118126.27841833.43606
68654461012fb20210417190300576126.34699933.25609
21893461006e520210417184314917126.40928233.473549
653584610055620210417190130676126.48606833.502767
16435461000ec20210417184034419126.73943233.553618
32783461018e120210417184800949126.4953733.503838
18778461006b520210417184146470126.23047533.236804
458944610182720210417185330837126.66914133.540875
177254610028320210417184116493126.56189433.510402
54514461017f620210417185700272126.54100733.505785

Duplicate rows

Most frequently occurring

oidcollection_dtlongitudelatitude# duplicates
0461000ec20210417185028693126.83734633.5308432
1461000ec20210417185053340126.83879433.5296082
24610010f20210417191200277126.50850333.2494252
34610019320210417183600779126.42900533.4927492
44610020d20210417190030142126.55677433.4979062
54610022920210417185000404126.46556833.4752632
64610023120210417190200387126.66981133.4320232
74610028320210417184118588126.56189333.5104022
84610028320210417184130028126.56189233.5104032
94610028320210417184711982126.53917933.4983622