Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows53
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 53 (0.5%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-11 19:58:59.976628
Analysis finished2023-12-11 19:59:01.748864
Duration1.77 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct624
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T04:59:02.010155image/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

Unique76 ?
Unique (%)0.8%

Sample

1st row461006e5
2nd row46100776
3rd row46100130
4th row46100261
5th row4610017e
ValueCountFrequency (%)
46101369 645
 
6.5%
461002e3 399
 
4.0%
461002de 367
 
3.7%
461006e5 266
 
2.7%
46101027 252
 
2.5%
46100366 237
 
2.4%
461002df 234
 
2.3%
461002e0 230
 
2.3%
461002e1 225
 
2.2%
461002dd 214
 
2.1%
Other values (614) 6931
69.3%
2023-12-12T04:59:02.566916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18423
23.0%
1 16414
20.5%
6 12654
15.8%
4 10922
13.7%
2 3901
 
4.9%
e 2999
 
3.7%
3 2743
 
3.4%
d 2201
 
2.8%
9 1827
 
2.3%
7 1764
 
2.2%
Other values (6) 6152
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71366
89.2%
Lowercase Letter 8634
 
10.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18423
25.8%
1 16414
23.0%
6 12654
17.7%
4 10922
15.3%
2 3901
 
5.5%
3 2743
 
3.8%
9 1827
 
2.6%
7 1764
 
2.5%
8 1445
 
2.0%
5 1273
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
e 2999
34.7%
d 2201
25.5%
f 1081
 
12.5%
c 1037
 
12.0%
b 746
 
8.6%
a 570
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Common 71366
89.2%
Latin 8634
 
10.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18423
25.8%
1 16414
23.0%
6 12654
17.7%
4 10922
15.3%
2 3901
 
5.5%
3 2743
 
3.8%
9 1827
 
2.6%
7 1764
 
2.5%
8 1445
 
2.0%
5 1273
 
1.8%
Latin
ValueCountFrequency (%)
e 2999
34.7%
d 2201
25.5%
f 1081
 
12.5%
c 1037
 
12.0%
b 746
 
8.6%
a 570
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18423
23.0%
1 16414
20.5%
6 12654
15.8%
4 10922
13.7%
2 3901
 
4.9%
e 2999
 
3.7%
3 2743
 
3.4%
d 2201
 
2.8%
9 1827
 
2.3%
7 1764
 
2.2%
Other values (6) 6152
 
7.7%

collection_dt
Real number (ℝ)

Distinct9563
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0210516 × 1016
Minimum2.0210502 × 1016
Maximum2.0210523 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:59:02.779246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0210502 × 1016
5-th percentile2.0210502 × 1016
Q12.0210515 × 1016
median2.0210515 × 1016
Q32.0210522 × 1016
95-th percentile2.0210523 × 1016
Maximum2.0210523 × 1016
Range2.1045924 × 1010
Interquartile range (IQR)6.8486176 × 109

Descriptive statistics

Standard deviation6.0187531 × 109
Coefficient of variation (CV)2.9780305 × 10-7
Kurtosis0.72130159
Mean2.0210516 × 1016
Median Absolute Deviation (MAD)9829034
Skewness-1.0226235
Sum-8.0902945 × 1017
Variance3.6225389 × 1019
MonotonicityNot monotonic
2023-12-12T04:59:02.958487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20210515214300308 5
 
0.1%
20210515212800811 5
 
0.1%
20210515211530018 5
 
0.1%
20210515212330054 5
 
0.1%
20210515211430526 5
 
0.1%
20210515213300133 4
 
< 0.1%
20210515220600768 3
 
< 0.1%
20210515220830805 3
 
< 0.1%
20210523001100749 3
 
< 0.1%
20210515212030701 3
 
< 0.1%
Other values (9553) 9959
99.6%
ValueCountFrequency (%)
20210502000006297 1
< 0.1%
20210502000012423 1
< 0.1%
20210502000017503 1
< 0.1%
20210502000018362 1
< 0.1%
20210502000019378 1
< 0.1%
20210502000019550 1
< 0.1%
20210502000022613 1
< 0.1%
20210502000023628 1
< 0.1%
20210502000026691 1
< 0.1%
20210502000029754 1
< 0.1%
ValueCountFrequency (%)
20210523045930759 1
< 0.1%
20210523045930712 1
< 0.1%
20210523045900532 1
< 0.1%
20210523045900423 1
< 0.1%
20210523045900016 1
< 0.1%
20210523045800216 1
< 0.1%
20210523045730690 1
< 0.1%
20210523045700554 2
< 0.1%
20210523045700461 1
< 0.1%
20210523045700257 1
< 0.1%

longitude
Real number (ℝ)

Distinct8250
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.76144
Minimum126.17834
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:59:03.183210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.17834
5-th percentile126.27224
Q1126.38779
median126.49793
Q3126.59456
95-th percentile126.919
Maximum180
Range53.821663
Interquartile range (IQR)0.206769

Descriptive statistics

Standard deviation10.756854
Coefficient of variation (CV)0.083540957
Kurtosis18.74119
Mean128.76144
Median Absolute Deviation (MAD)0.104564
Skewness4.5532427
Sum1287614.4
Variance115.70991
MonotonicityNot monotonic
2023-12-12T04:59:03.395004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180.0000001 422
 
4.2%
126.497926 93
 
0.9%
126.667035 60
 
0.6%
126.522569 15
 
0.1%
126.269205 11
 
0.1%
126.39479 11
 
0.1%
126.51908 11
 
0.1%
126.265235 10
 
0.1%
126.265231 9
 
0.1%
126.479741 9
 
0.1%
Other values (8240) 9349
93.5%
ValueCountFrequency (%)
126.178337 1
< 0.1%
126.179033 1
< 0.1%
126.179445 1
< 0.1%
126.179896 1
< 0.1%
126.182921 1
< 0.1%
126.1855 1
< 0.1%
126.185841 1
< 0.1%
126.186943 1
< 0.1%
126.18695 1
< 0.1%
126.18698 1
< 0.1%
ValueCountFrequency (%)
180.0000001 422
4.2%
126.952355 1
 
< 0.1%
126.951489 1
 
< 0.1%
126.951481 1
 
< 0.1%
126.935747 1
 
< 0.1%
126.935599 1
 
< 0.1%
126.935492 1
 
< 0.1%
126.935334 1
 
< 0.1%
126.934957 1
 
< 0.1%
126.934925 1
 
< 0.1%

latitude
Real number (ℝ)

Distinct8129
Distinct (%)81.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.812591
Minimum33.206693
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:59:03.592243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.206693
5-th percentile33.25039
Q133.357714
median33.468146
Q333.500048
95-th percentile33.55203
Maximum90
Range56.793307
Interquartile range (IQR)0.14233425

Descriptive statistics

Standard deviation11.375062
Coefficient of variation (CV)0.31762744
Kurtosis18.747761
Mean35.812591
Median Absolute Deviation (MAD)0.0424275
Skewness4.5543732
Sum358125.91
Variance129.39203
MonotonicityNot monotonic
2023-12-12T04:59:03.775999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.0000001 422
 
4.2%
33.504227 94
 
0.9%
33.542916 60
 
0.6%
33.518376 15
 
0.1%
33.411365 10
 
0.1%
33.280875 10
 
0.1%
33.485997 9
 
0.1%
33.4858 8
 
0.1%
33.411363 8
 
0.1%
33.452289 8
 
0.1%
Other values (8119) 9356
93.6%
ValueCountFrequency (%)
33.206693 1
< 0.1%
33.206932 1
< 0.1%
33.210653 1
< 0.1%
33.215124 1
< 0.1%
33.215401 1
< 0.1%
33.216185 1
< 0.1%
33.216565 1
< 0.1%
33.220519 1
< 0.1%
33.221017 1
< 0.1%
33.221377 1
< 0.1%
ValueCountFrequency (%)
90.0000001 422
4.2%
33.564322 1
 
< 0.1%
33.563341 1
 
< 0.1%
33.563115 1
 
< 0.1%
33.561285 1
 
< 0.1%
33.559334 1
 
< 0.1%
33.558462 1
 
< 0.1%
33.558448 1
 
< 0.1%
33.55795 1
 
< 0.1%
33.55792 1
 
< 0.1%

Interactions

2023-12-12T04:59:01.171936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:00.464933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:00.818382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:01.336224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:00.564796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:00.929197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:01.463091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:00.707582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:01.053593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T04:59:03.909483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.0000.0540.054
longitude0.0541.0001.000
latitude0.0541.0001.000
2023-12-12T04:59:04.040113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.0000.0060.014
longitude0.0061.0000.461
latitude0.0140.4611.000

Missing values

2023-12-12T04:59:01.600197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T04:59:01.698366image/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
15722461006e520210515213337404126.39598833.263335
737724610077620210502005300113126.38275333.403974
42954610013020210515211835904126.40807633.250266
812904610026120210522052830178126.29012233.206693
424214610017e20210515223805617126.48492233.485069
20783461002e320210515213927156126.35736133.280877
480724610132920210515230800691126.29904533.353189
13868461002e320210515213130653126.40886633.26718
51725461006ae20210516000100568126.49477633.250038
85074461002de20210522063926102126.28778833.441536
oidcollection_dtlongitudelatitude
867764610136820210522065030600126.32175233.46656
24041461011e220210515214330720126.75415833.555552
595084610010320210523001909913126.90871533.446605
30587461002db20210515215411209126.32232133.310149
30515461005f820210515215400849126.56403433.253734
943854610031520210522073730557126.62050733.384533
459424610017e20210515225500374126.52256933.518376
27284610186320210515211700407126.48712733.469462
89140461005a320210522070500950126.3553233.333877
20580461002dd20210515213908700126.61393233.260534

Duplicate rows

Most frequently occurring

oidcollection_dtlongitudelatitude# duplicates
49461018be20210516041500748126.54071933.4877733
0461000ec20210523010907265126.47308833.4813752
14610014e20210522073924198180.090.02
24610017e20210515224610615126.49669533.5042992
3461001bd20210515224100289126.50451233.4998162
44610029320210502002700690126.60075733.4866982
5461002dd20210515213343920126.57170133.2547792
6461002de20210522064302600126.30674833.4475062
7461002e120210515215416023126.40907633.4733942
8461002e320210515211555115126.49972233.2513932