Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory585.9 KiB
Average record size in memory60.0 B

Variable types

Text1
Numeric4
DateTime1

Dataset

Description- 월별 토요일의 렌터카 체류 거점 위치정보 입니다. - 체류 거점은 수집시간 전로그와 후로그의 시각차이가 20분 이상인 경우, 전로그의 위치를 의미합니다. - 기간: 2020년 1월부터 2021년 12월 까지
Author제주특별자치도 미래성장과
URLhttps://www.jejudatahub.net/data/view/data/1205

Reproduction

Analysis started2023-12-11 19:59:48.327489
Analysis finished2023-12-11 19:59:52.797914
Duration4.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

oid
Text

Distinct1508
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T04:59:53.249462image/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

Unique126 ?
Unique (%)1.3%

Sample

1st row46100c0f
2nd row46100720
3rd row46100be5
4th row4610045c
5th row461006b6
ValueCountFrequency (%)
4610041d 26
 
0.3%
46100386 20
 
0.2%
4610045c 19
 
0.2%
461001f8 19
 
0.2%
46100121 18
 
0.2%
46100565 18
 
0.2%
4610074c 18
 
0.2%
461006ee 18
 
0.2%
461006c2 18
 
0.2%
461007a7 18
 
0.2%
Other values (1498) 9808
98.1%
2023-12-12T04:59:54.671196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 21837
27.3%
1 12495
15.6%
4 12348
15.4%
6 12256
15.3%
5 2517
 
3.1%
c 2362
 
3.0%
7 2226
 
2.8%
2 2160
 
2.7%
3 2061
 
2.6%
d 1742
 
2.2%
Other values (6) 7996
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70334
87.9%
Lowercase Letter 9666
 
12.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21837
31.0%
1 12495
17.8%
4 12348
17.6%
6 12256
17.4%
5 2517
 
3.6%
7 2226
 
3.2%
2 2160
 
3.1%
3 2061
 
2.9%
9 1258
 
1.8%
8 1176
 
1.7%
Lowercase Letter
ValueCountFrequency (%)
c 2362
24.4%
d 1742
18.0%
b 1694
17.5%
f 1317
13.6%
e 1290
13.3%
a 1261
13.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70334
87.9%
Latin 9666
 
12.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21837
31.0%
1 12495
17.8%
4 12348
17.6%
6 12256
17.4%
5 2517
 
3.6%
7 2226
 
3.2%
2 2160
 
3.1%
3 2061
 
2.9%
9 1258
 
1.8%
8 1176
 
1.7%
Latin
ValueCountFrequency (%)
c 2362
24.4%
d 1742
18.0%
b 1694
17.5%
f 1317
13.6%
e 1290
13.3%
a 1261
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21837
27.3%
1 12495
15.6%
4 12348
15.4%
6 12256
15.3%
5 2517
 
3.1%
c 2362
 
3.0%
7 2226
 
2.8%
2 2160
 
2.7%
3 2061
 
2.6%
d 1742
 
2.2%
Other values (6) 7996
 
10.0%

collection_dt
Real number (ℝ)

Distinct9715
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0200216 × 1013
Minimum2.0200201 × 1013
Maximum2.0200301 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:59:55.232919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0200201 × 1013
5-th percentile2.0200201 × 1013
Q12.0200201 × 1013
median2.0200215 × 1013
Q32.0200222 × 1013
95-th percentile2.0200229 × 1013
Maximum2.0200301 × 1013
Range99981324
Interquartile range (IQR)20951105

Descriptive statistics

Standard deviation9984381.6
Coefficient of variation (CV)4.9427104 × 10-7
Kurtosis1.4236042
Mean2.0200216 × 1013
Median Absolute Deviation (MAD)6982102.5
Skewness-0.032370157
Sum2.0200216 × 1017
Variance9.9687876 × 1013
MonotonicityNot monotonic
2023-12-12T04:59:55.572299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200215162233 3
 
< 0.1%
20200215133953 3
 
< 0.1%
20200222172926 3
 
< 0.1%
20200201104802 3
 
< 0.1%
20200215172202 3
 
< 0.1%
20200215130731 3
 
< 0.1%
20200201152439 3
 
< 0.1%
20200215142849 3
 
< 0.1%
20200215171548 3
 
< 0.1%
20200215104050 3
 
< 0.1%
Other values (9705) 9970
99.7%
ValueCountFrequency (%)
20200201051326 1
< 0.1%
20200201052054 1
< 0.1%
20200201052434 1
< 0.1%
20200201052616 1
< 0.1%
20200201055044 1
< 0.1%
20200201055114 1
< 0.1%
20200201055215 1
< 0.1%
20200201060530 1
< 0.1%
20200201062727 1
< 0.1%
20200201063147 1
< 0.1%
ValueCountFrequency (%)
20200301032650 1
< 0.1%
20200301022609 1
< 0.1%
20200301021257 1
< 0.1%
20200301005828 1
< 0.1%
20200301002151 1
< 0.1%
20200229234240 1
< 0.1%
20200229230139 1
< 0.1%
20200229224416 1
< 0.1%
20200229224143 1
< 0.1%
20200229223347 1
< 0.1%

longitude
Real number (ℝ)

Distinct9970
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.51512
Minimum126.16404
Maximum126.96883
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:59:55.905606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16404
5-th percentile126.25343
Q1126.39503
median126.49502
Q3126.56437
95-th percentile126.91348
Maximum126.96883
Range0.8047941
Interquartile range (IQR)0.16933758

Descriptive statistics

Standard deviation0.18070666
Coefficient of variation (CV)0.0014283405
Kurtosis0.080418364
Mean126.51512
Median Absolute Deviation (MAD)0.08324215
Skewness0.71626851
Sum1265151.2
Variance0.032654899
MonotonicityNot monotonic
2023-12-12T04:59:56.246085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.242215 2
 
< 0.1%
126.5125118 2
 
< 0.1%
126.4263725 2
 
< 0.1%
126.3682913 2
 
< 0.1%
126.9332875 2
 
< 0.1%
126.528244 2
 
< 0.1%
126.4630518 2
 
< 0.1%
126.9302356 2
 
< 0.1%
126.9354133 2
 
< 0.1%
126.9288238 2
 
< 0.1%
Other values (9960) 9980
99.8%
ValueCountFrequency (%)
126.1640375 1
< 0.1%
126.1640776 1
< 0.1%
126.1641378 1
< 0.1%
126.1642621 1
< 0.1%
126.1643786 1
< 0.1%
126.1645881 1
< 0.1%
126.1646433 1
< 0.1%
126.1647113 1
< 0.1%
126.1648403 1
< 0.1%
126.1656423 1
< 0.1%
ValueCountFrequency (%)
126.9688316 1
< 0.1%
126.9674571 1
< 0.1%
126.9671116 1
< 0.1%
126.9670345 1
< 0.1%
126.9608211 1
< 0.1%
126.960611 1
< 0.1%
126.9600073 1
< 0.1%
126.9599773 1
< 0.1%
126.9591198 1
< 0.1%
126.9578493 1
< 0.1%

latitude
Real number (ℝ)

Distinct9924
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.412942
Minimum33.199936
Maximum33.563837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:59:56.557397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.199936
5-th percentile33.240107
Q133.303359
median33.462339
Q333.497234
95-th percentile33.526886
Maximum33.563837
Range0.3639008
Interquartile range (IQR)0.1938744

Descriptive statistics

Standard deviation0.10545254
Coefficient of variation (CV)0.0031560389
Kurtosis-1.2103149
Mean33.412942
Median Absolute Deviation (MAD)0.049385
Skewness-0.61926113
Sum334129.42
Variance0.011120239
MonotonicityNot monotonic
2023-12-12T04:59:56.865788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.4691931 2
 
< 0.1%
33.4960781 2
 
< 0.1%
33.2550051 2
 
< 0.1%
33.4852783 2
 
< 0.1%
33.5120018 2
 
< 0.1%
33.4924043 2
 
< 0.1%
33.4937179 2
 
< 0.1%
33.4937501 2
 
< 0.1%
33.5022321 2
 
< 0.1%
33.4924335 2
 
< 0.1%
Other values (9914) 9980
99.8%
ValueCountFrequency (%)
33.199936 1
< 0.1%
33.2024243 1
< 0.1%
33.2029416 1
< 0.1%
33.2040603 1
< 0.1%
33.2041346 1
< 0.1%
33.2041573 1
< 0.1%
33.2047075 1
< 0.1%
33.204797 1
< 0.1%
33.2049611 1
< 0.1%
33.2056008 1
< 0.1%
ValueCountFrequency (%)
33.5638368 1
< 0.1%
33.560669 1
< 0.1%
33.5602328 1
< 0.1%
33.560155 1
< 0.1%
33.5601373 1
< 0.1%
33.5596131 1
< 0.1%
33.5594441 1
< 0.1%
33.5594401 1
< 0.1%
33.5593653 1
< 0.1%
33.5582151 1
< 0.1%

time
Date

Distinct9715
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-02-01 05:13:26
Maximum2020-03-01 03:26:50
2023-12-12T04:59:57.188024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:57.456349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Diff
Real number (ℝ)

Distinct6790
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136959.12
Minimum1200
Maximum2428766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:59:57.731550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile1445.9
Q12491.75
median4140
Q310582
95-th percentile1166578.5
Maximum2428766
Range2427566
Interquartile range (IQR)8090.25

Descriptive statistics

Standard deviation334667.93
Coefficient of variation (CV)2.4435607
Kurtosis8.1645746
Mean136959.12
Median Absolute Deviation (MAD)2182.5
Skewness2.8060326
Sum1.3695912 × 109
Variance1.1200262 × 1011
MonotonicityNot monotonic
2023-12-12T04:59:58.019581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1260 9
 
0.1%
1380 8
 
0.1%
1606 8
 
0.1%
1768 8
 
0.1%
2884 8
 
0.1%
1230 8
 
0.1%
1740 8
 
0.1%
2726 7
 
0.1%
1290 7
 
0.1%
2400 7
 
0.1%
Other values (6780) 9922
99.2%
ValueCountFrequency (%)
1200 3
< 0.1%
1201 2
< 0.1%
1202 1
 
< 0.1%
1204 1
 
< 0.1%
1205 2
< 0.1%
1206 1
 
< 0.1%
1207 1
 
< 0.1%
1208 1
 
< 0.1%
1209 4
< 0.1%
1211 1
 
< 0.1%
ValueCountFrequency (%)
2428766 1
< 0.1%
2423936 1
< 0.1%
2416509 1
< 0.1%
2394083 1
< 0.1%
2392126 1
< 0.1%
2391906 1
< 0.1%
2389394 1
< 0.1%
2388981 1
< 0.1%
2386030 1
< 0.1%
2378328 1
< 0.1%

Interactions

2023-12-12T04:59:51.732480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:49.536960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:50.152209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:50.831607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:51.901845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:49.685262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:50.317941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:51.018052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:52.067564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:49.859112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:50.498597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:51.258383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:52.224985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:49.999649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:50.694111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:59:51.540820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T04:59:58.207380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.0000.0540.0690.379
longitude0.0541.0000.8410.055
latitude0.0690.8411.0000.058
Diff0.3790.0550.0581.000
2023-12-12T04:59:58.465944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
collection_dtlongitudelatitudeDiff
collection_dt1.000-0.002-0.004-0.043
longitude-0.0021.0000.300-0.019
latitude-0.0040.3001.0000.038
Diff-0.043-0.0190.0381.000

Missing values

2023-12-12T04:59:52.469021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T04:59:52.686027image/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_dtlongitudelatitudetimeDiff
1598146100c0f20200222143756126.31056733.2364122020-02-22 14:37:562021
135034610072020200222173453126.8549933.5270322020-02-22 17:34:532803
1558846100be520200222182847126.37959233.475462020-02-22 18:28:479162
72404610045c20200215144053126.41472333.2449442020-02-15 14:40:532511
12288461006b620200229183818126.56125133.4489512020-02-29 18:38:182483
55634610035f20200215170044126.56804133.2434572020-02-15 17:00:444385
142984610076220200215161003126.50941333.2520962020-02-15 16:10:03586364
8274461004b120200201173041126.50654933.4896392020-02-01 17:30:414873
139704610074620200215165752126.48565933.4715412020-02-15 16:57:523665
100294610057a20200222135416126.38609533.4761772020-02-22 13:54:164004
oidcollection_dtlongitudelatitudetimeDiff
112344610061720200222210628126.79118133.3059022020-02-22 21:06:28565297
95754610055220200222123043126.92897233.4603842020-02-22 12:30:431628
140244610074b20200222111749126.93392733.4720152020-02-22 11:17:4918350
118014610067820200201163424126.50245633.5129232020-02-01 16:34:242374
36674610021a20200215140704126.31324733.4629622020-02-15 14:07:049665
12732461006d920200215094217126.4556433.4933392020-02-15 09:42:172532
13113461006fd20200201141305126.50756333.2308732020-02-01 14:13:054984
1852346100d9b20200229141514126.31088933.233692020-02-29 14:15:143827
20604610018220200201105933126.67431333.54432020-02-01 10:59:333827
56064610036620200222093819126.53784533.4901882020-02-22 09:38:191354