Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows10
Duplicate rows (%)0.1%
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 추축 - 기간: 2020년 1월부터 2021년 12월 까지
Author제주특별자치도 미래성장과
URLhttps://www.jejudatahub.net/data/view/data/1204

Alerts

Dataset has 10 (0.1%) duplicate rowsDuplicates

Reproduction

Analysis started2023-12-11 20:05:34.578275
Analysis finished2023-12-11 20:05:35.921490
Duration1.34 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct733
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T05:05:36.158937image/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

Unique63 ?
Unique (%)0.6%

Sample

1st row461006e5
2nd row461018a7
3rd row46101369
4th row461006e5
5th row46101078
ValueCountFrequency (%)
461006e5 711
 
7.1%
46100100 676
 
6.8%
461007cc 473
 
4.7%
461012cf 442
 
4.4%
46101369 392
 
3.9%
461000ec 297
 
3.0%
461006da 196
 
2.0%
4610045a 161
 
1.6%
46100367 145
 
1.5%
461002db 88
 
0.9%
Other values (723) 6419
64.2%
2023-12-12T05:05:36.575940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18943
23.7%
1 16776
21.0%
6 12595
15.7%
4 11532
14.4%
c 2506
 
3.1%
2 2318
 
2.9%
7 2082
 
2.6%
5 1926
 
2.4%
3 1920
 
2.4%
9 1731
 
2.2%
Other values (6) 7671
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71409
89.3%
Lowercase Letter 8591
 
10.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18943
26.5%
1 16776
23.5%
6 12595
17.6%
4 11532
16.1%
2 2318
 
3.2%
7 2082
 
2.9%
5 1926
 
2.7%
3 1920
 
2.7%
9 1731
 
2.4%
8 1586
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
c 2506
29.2%
e 1646
19.2%
f 1359
15.8%
a 1125
13.1%
b 985
 
11.5%
d 970
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
Common 71409
89.3%
Latin 8591
 
10.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18943
26.5%
1 16776
23.5%
6 12595
17.6%
4 11532
16.1%
2 2318
 
3.2%
7 2082
 
2.9%
5 1926
 
2.7%
3 1920
 
2.7%
9 1731
 
2.4%
8 1586
 
2.2%
Latin
ValueCountFrequency (%)
c 2506
29.2%
e 1646
19.2%
f 1359
15.8%
a 1125
13.1%
b 985
 
11.5%
d 970
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18943
23.7%
1 16776
21.0%
6 12595
15.7%
4 11532
14.4%
c 2506
 
3.1%
2 2318
 
2.9%
7 2082
 
2.6%
5 1926
 
2.4%
3 1920
 
2.4%
9 1731
 
2.2%
Other values (6) 7671
9.6%

collection_dt
Real number (ℝ)

Distinct9036
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0211113 × 1016
Minimum2.0211113 × 1016
Maximum2.0211113 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:05:36.723322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0211113 × 1016
5-th percentile2.0211113 × 1016
Q12.0211113 × 1016
median2.0211113 × 1016
Q32.0211113 × 1016
95-th percentile2.0211113 × 1016
Maximum2.0211113 × 1016
Range43581129
Interquartile range (IQR)11145992

Descriptive statistics

Standard deviation11165324
Coefficient of variation (CV)5.5243489 × 10-10
Kurtosis0.66040379
Mean2.0211113 × 1016
Median Absolute Deviation (MAD)6819990
Skewness-1.236347
Sum-8.0305399 × 1017
Variance1.2466446 × 1014
MonotonicityNot monotonic
2023-12-12T05:05:36.859855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20211113084900634 6
 
0.1%
20211113092600986 5
 
0.1%
20211113083830828 4
 
< 0.1%
20211113090600219 4
 
< 0.1%
20211113092500687 4
 
< 0.1%
20211113092300291 4
 
< 0.1%
20211113091430302 4
 
< 0.1%
20211113093500326 4
 
< 0.1%
20211113081900726 4
 
< 0.1%
20211113083030502 4
 
< 0.1%
Other values (9026) 9957
99.6%
ValueCountFrequency (%)
20211113050019746 1
< 0.1%
20211113050024826 1
< 0.1%
20211113050030268 1
< 0.1%
20211113050030611 1
< 0.1%
20211113050042162 1
< 0.1%
20211113050052339 1
< 0.1%
20211113050055387 1
< 0.1%
20211113050100874 1
< 0.1%
20211113050102530 1
< 0.1%
20211113050106626 1
< 0.1%
ValueCountFrequency (%)
20211113093600875 1
< 0.1%
20211113093600750 1
< 0.1%
20211113093600719 1
< 0.1%
20211113093600704 2
< 0.1%
20211113093600672 1
< 0.1%
20211113093600594 1
< 0.1%
20211113093600579 1
< 0.1%
20211113093600516 1
< 0.1%
20211113093600500 2
< 0.1%
20211113093600485 1
< 0.1%

longitude
Real number (ℝ)

Distinct8369
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.73427
Minimum126.16439
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:05:37.007478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16439
5-th percentile126.26665
Q1126.4195
median126.49739
Q3126.55256
95-th percentile126.89057
Maximum180
Range53.83561
Interquartile range (IQR)0.1330615

Descriptive statistics

Standard deviation8.0388099
Coefficient of variation (CV)0.062933853
Kurtosis38.306057
Mean127.73427
Median Absolute Deviation (MAD)0.064097
Skewness6.3469478
Sum1277342.7
Variance64.622465
MonotonicityNot monotonic
2023-12-12T05:05:37.140683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180.0000001 231
 
2.3%
126.528807 70
 
0.7%
126.52881 63
 
0.6%
126.528808 61
 
0.6%
126.528809 60
 
0.6%
126.528811 42
 
0.4%
126.528806 38
 
0.4%
126.528812 32
 
0.3%
126.528805 31
 
0.3%
126.528802 30
 
0.3%
Other values (8359) 9342
93.4%
ValueCountFrequency (%)
126.16439 1
< 0.1%
126.167171 1
< 0.1%
126.167172 1
< 0.1%
126.167173 2
< 0.1%
126.167174 1
< 0.1%
126.167183 1
< 0.1%
126.167184 1
< 0.1%
126.167185 2
< 0.1%
126.167186 1
< 0.1%
126.167188 1
< 0.1%
ValueCountFrequency (%)
180.0000001 231
2.3%
127.1161825 1
 
< 0.1%
127.1161361 1
 
< 0.1%
127.1161263 1
 
< 0.1%
127.1161221 1
 
< 0.1%
127.1161113 1
 
< 0.1%
127.1160816 1
 
< 0.1%
127.1160605 1
 
< 0.1%
127.1160588 1
 
< 0.1%
127.1160583 1
 
< 0.1%

latitude
Real number (ℝ)

Distinct8184
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.715308
Minimum33.198721
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:05:37.271982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.198721
5-th percentile33.242455
Q133.29827
median33.447261
Q333.496286
95-th percentile33.531661
Maximum90
Range56.801279
Interquartile range (IQR)0.198016

Descriptive statistics

Standard deviation8.5027921
Coefficient of variation (CV)0.24492918
Kurtosis38.312921
Mean34.715308
Median Absolute Deviation (MAD)0.06149
Skewness6.3478027
Sum347153.08
Variance72.297474
MonotonicityNot monotonic
2023-12-12T05:05:37.403022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.0000001 231
 
2.3%
33.242458 52
 
0.5%
33.242461 50
 
0.5%
33.242455 47
 
0.5%
33.242462 39
 
0.4%
33.242457 37
 
0.4%
33.242454 36
 
0.4%
33.242469 35
 
0.4%
33.242466 34
 
0.3%
33.242464 34
 
0.3%
Other values (8174) 9405
94.0%
ValueCountFrequency (%)
33.198721 1
< 0.1%
33.199896 1
< 0.1%
33.200009 1
< 0.1%
33.20041 1
< 0.1%
33.200463 1
< 0.1%
33.20064 1
< 0.1%
33.200723 1
< 0.1%
33.200778 1
< 0.1%
33.201732 1
< 0.1%
33.201873 1
< 0.1%
ValueCountFrequency (%)
90.0000001 231
2.3%
35.7942221 1
 
< 0.1%
35.7942141 1
 
< 0.1%
35.794202 1
 
< 0.1%
35.7942001 1
 
< 0.1%
35.7941896 1
 
< 0.1%
35.794188 1
 
< 0.1%
35.7941831 1
 
< 0.1%
35.7941788 1
 
< 0.1%
35.7941778 1
 
< 0.1%

Interactions

2023-12-12T05:05:35.483023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:34.913177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:35.195387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:35.572874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:35.005806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:35.276413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:35.659668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:35.108482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:35.374151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:05:37.486389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.0000.0370.037
longitude0.0371.0001.000
latitude0.0371.0001.000
2023-12-12T05:05:37.566922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitude
collection_dt1.000-0.1420.068
longitude-0.1421.0000.306
latitude0.0680.3061.000

Missing values

2023-12-12T05:05:35.775879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:05:35.874015image/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
4729461006e520211113055559705126.52881633.242457
17961461018a720211113073400873126.61766833.39069
355704610136920211113083244297126.20008633.347431
3765461006e520211113054551528126.52882333.242476
187214610107820211113073900485180.090.0
2948461006e520211113053605037126.52881933.242468
278974610054220211113081700013126.4723333.497889
545594610010020211113085406885126.37077233.379663
220604610134f20211113075700113180.090.0
544664610106e20211113085400821126.91393233.445377
oidcollection_dtlongitudelatitude
773174610196c20211113092100878126.34800133.440168
151564610043720211113071230951126.56014133.285644
66875461007cc20211113090849191126.67973433.531137
440894610136920211113084239624126.26899833.33802
47569461000ec20211113084620442126.36691233.364829
342724610010020211113083050823126.47600433.48377
393944610109f20211113083730153126.52551833.247201
54014610179f20211113060230169126.48747133.485319
53594610198a20211113060200770126.49144433.482875
783624610177c20211113092200742126.6741833.469324

Duplicate rows

Most frequently occurring

oidcollection_dtlongitudelatitude# duplicates
0461000ea20211113092800868126.7365233.5552922
1461000ec20211113084607219126.36786133.3670612
2461000ee20211113083030065126.49628833.4972562
34610010020211113083148139126.47599833.4837352
4461002ff20211113093202787126.51364333.2480452
5461007e420211113090600219126.49642633.5050032
64610104b20211113085300537126.44510733.4587062
74610124520211113060730285126.55620733.507242
8461012cf20211113084939411126.52933333.5009112
9461018c420211113084130085126.3535233.3282352