Overview

Dataset statistics

Number of variables6
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory52.3 B

Variable types

Numeric3
DateTime1
Text2

Dataset

DescriptionSample
Author경기대학교 빅데이터센터
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=KGUDLIVERYMANLC00001

Alerts

순번 has unique valuesUnique
위치정보등록일시 has unique valuesUnique
경도X좌표 has unique valuesUnique
위도Y좌표 has unique valuesUnique

Reproduction

Analysis started2023-12-10 06:24:58.583568
Analysis finished2023-12-10 06:25:00.794391
Duration2.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15063250
Minimum533160
Maximum29402085
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T15:25:00.962405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum533160
5-th percentile2285338.4
Q17815247.8
median15579557
Q321906833
95-th percentile26700865
Maximum29402085
Range28868925
Interquartile range (IQR)14091585

Descriptive statistics

Standard deviation8102781
Coefficient of variation (CV)0.53791717
Kurtosis-1.1744209
Mean15063250
Median Absolute Deviation (MAD)7245096
Skewness-0.091072739
Sum1.506325 × 109
Variance6.565506 × 1013
MonotonicityNot monotonic
2023-12-10T15:25:01.216601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19007554 1
 
1.0%
25695109 1
 
1.0%
14847346 1
 
1.0%
22723470 1
 
1.0%
13749463 1
 
1.0%
25282363 1
 
1.0%
8472207 1
 
1.0%
6945745 1
 
1.0%
15382723 1
 
1.0%
533160 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
533160 1
1.0%
1075500 1
1.0%
1255747 1
1.0%
1927667 1
1.0%
1978381 1
1.0%
2301494 1
1.0%
2633191 1
1.0%
2844821 1
1.0%
3248653 1
1.0%
3342425 1
1.0%
ValueCountFrequency (%)
29402085 1
1.0%
28110154 1
1.0%
27992409 1
1.0%
27769864 1
1.0%
27139062 1
1.0%
26677802 1
1.0%
25987447 1
1.0%
25891375 1
1.0%
25824821 1
1.0%
25819634 1
1.0%

위치정보등록일시
Date

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2019-11-11 18:51:00
Maximum2020-07-26 16:48:00
2023-12-10T15:25:01.469468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:25:01.724039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

배달자ID
Text

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T15:25:02.237127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters800
Distinct characters11
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

Unique96 ?
Unique (%)96.0%

Sample

1st rowD0001366
2nd rowD0006333
3rd rowD0005108
4th rowD0008956
5th rowD0009053
ValueCountFrequency (%)
d0007565 2
 
2.0%
d0010088 2
 
2.0%
d0005173 1
 
1.0%
d0004854 1
 
1.0%
d0001366 1
 
1.0%
d0001647 1
 
1.0%
d0009393 1
 
1.0%
d0009009 1
 
1.0%
d0010047 1
 
1.0%
d0005449 1
 
1.0%
Other values (88) 88
88.0%
2023-12-10T15:25:03.082251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 342
42.8%
D 100
 
12.5%
7 53
 
6.6%
6 46
 
5.8%
1 44
 
5.5%
5 40
 
5.0%
8 40
 
5.0%
4 39
 
4.9%
3 32
 
4.0%
2 32
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700
87.5%
Uppercase Letter 100
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 342
48.9%
7 53
 
7.6%
6 46
 
6.6%
1 44
 
6.3%
5 40
 
5.7%
8 40
 
5.7%
4 39
 
5.6%
3 32
 
4.6%
2 32
 
4.6%
9 32
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
D 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 700
87.5%
Latin 100
 
12.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 342
48.9%
7 53
 
7.6%
6 46
 
6.6%
1 44
 
6.3%
5 40
 
5.7%
8 40
 
5.7%
4 39
 
5.6%
3 32
 
4.6%
2 32
 
4.6%
9 32
 
4.6%
Latin
ValueCountFrequency (%)
D 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 342
42.8%
D 100
 
12.5%
7 53
 
6.6%
6 46
 
5.8%
1 44
 
5.5%
5 40
 
5.0%
8 40
 
5.0%
4 39
 
4.9%
3 32
 
4.0%
2 32
 
4.0%

배달업체ID
Text

Distinct60
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T15:25:03.443837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters700
Distinct characters11
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

Unique39 ?
Unique (%)39.0%

Sample

1st rowC000109
2nd rowC000249
3rd rowC000447
4th rowC000305
5th rowC000077
ValueCountFrequency (%)
c000043 13
 
13.0%
c000309 5
 
5.0%
c000035 4
 
4.0%
c000224 4
 
4.0%
c000209 3
 
3.0%
c000057 2
 
2.0%
c000253 2
 
2.0%
c000278 2
 
2.0%
c000266 2
 
2.0%
c000249 2
 
2.0%
Other values (50) 61
61.0%
2023-12-10T15:25:04.033263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 356
50.9%
C 100
 
14.3%
3 51
 
7.3%
2 45
 
6.4%
4 37
 
5.3%
9 28
 
4.0%
1 23
 
3.3%
6 19
 
2.7%
5 16
 
2.3%
7 16
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 600
85.7%
Uppercase Letter 100
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 356
59.3%
3 51
 
8.5%
2 45
 
7.5%
4 37
 
6.2%
9 28
 
4.7%
1 23
 
3.8%
6 19
 
3.2%
5 16
 
2.7%
7 16
 
2.7%
8 9
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
C 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 600
85.7%
Latin 100
 
14.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 356
59.3%
3 51
 
8.5%
2 45
 
7.5%
4 37
 
6.2%
9 28
 
4.7%
1 23
 
3.8%
6 19
 
3.2%
5 16
 
2.7%
7 16
 
2.7%
8 9
 
1.5%
Latin
ValueCountFrequency (%)
C 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 356
50.9%
C 100
 
14.3%
3 51
 
7.3%
2 45
 
6.4%
4 37
 
5.3%
9 28
 
4.0%
1 23
 
3.3%
6 19
 
2.7%
5 16
 
2.3%
7 16
 
2.3%

경도X좌표
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.26264
Minimum126.45284
Maximum128.93899
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T15:25:04.309189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.45284
5-th percentile126.75602
Q1126.86663
median127.03851
Q3127.1979
95-th percentile128.61365
Maximum128.93899
Range2.4861548
Interquartile range (IQR)0.33127395

Descriptive statistics

Standard deviation0.65745303
Coefficient of variation (CV)0.0051661118
Kurtosis0.64145107
Mean127.26264
Median Absolute Deviation (MAD)0.1711807
Skewness1.4605928
Sum12726.264
Variance0.43224449
MonotonicityNot monotonic
2023-12-10T15:25:04.580594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.8591461 1
 
1.0%
127.2002332 1
 
1.0%
126.83992 1
 
1.0%
127.0995178 1
 
1.0%
127.0589294 1
 
1.0%
126.919226 1
 
1.0%
126.7826691 1
 
1.0%
126.866951 1
 
1.0%
126.9156799 1
 
1.0%
126.9152985 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
126.4528351 1
1.0%
126.4584198 1
1.0%
126.7067108 1
1.0%
126.7390137 1
1.0%
126.7546921 1
1.0%
126.7560883 1
1.0%
126.7616729 1
1.0%
126.7633209 1
1.0%
126.7694092 1
1.0%
126.7709404 1
1.0%
ValueCountFrequency (%)
128.9389899 1
1.0%
128.9168396 1
1.0%
128.8349152 1
1.0%
128.8190155 1
1.0%
128.6333313 1
1.0%
128.612609 1
1.0%
128.5872192 1
1.0%
128.5841064 1
1.0%
128.5809479 1
1.0%
128.5797272 1
1.0%

위도Y좌표
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.098661
Minimum35.015125
Maximum38.028969
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T15:25:04.875020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.015125
5-th percentile35.208023
Q136.930922
median37.479855
Q337.700291
95-th percentile37.79834
Maximum38.028969
Range3.0138435
Interquartile range (IQR)0.76936932

Descriptive statistics

Standard deviation0.86823338
Coefficient of variation (CV)0.023403361
Kurtosis0.26648445
Mean37.098661
Median Absolute Deviation (MAD)0.25879315
Skewness-1.3069839
Sum3709.8661
Variance0.75382919
MonotonicityNot monotonic
2023-12-10T15:25:05.268405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.38291168 1
 
1.0%
37.89758622 1
 
1.0%
37.49113846 1
 
1.0%
37.74855042 1
 
1.0%
37.74607468 1
 
1.0%
36.98148533 1
 
1.0%
35.01512527 1
 
1.0%
37.30797195 1
 
1.0%
37.0173378 1
 
1.0%
37.47803497 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
35.01512527 1
1.0%
35.09335709 1
1.0%
35.15421295 1
1.0%
35.17441177 1
1.0%
35.17793527 1
1.0%
35.20960617 1
1.0%
35.22242737 1
1.0%
35.23309326 1
1.0%
35.23368073 1
1.0%
35.24058914 1
1.0%
ValueCountFrequency (%)
38.02896881 1
1.0%
38.02377701 1
1.0%
37.89758622 1
1.0%
37.8960228 1
1.0%
37.88770676 1
1.0%
37.79363632 1
1.0%
37.76110458 1
1.0%
37.75641251 1
1.0%
37.75370789 1
1.0%
37.75205994 1
1.0%

Interactions

2023-12-10T15:24:59.975164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:59.000738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:59.460656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:25:00.134700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:59.185838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:59.634030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:25:00.269227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:59.311931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:24:59.785003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:25:05.440468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번위치정보등록일시배달자ID배달업체ID경도X좌표위도Y좌표
순번1.0001.0000.4140.5140.0940.000
위치정보등록일시1.0001.0001.0001.0001.0001.000
배달자ID0.4141.0001.0001.0001.0001.000
배달업체ID0.5141.0001.0001.0000.9820.999
경도X좌표0.0941.0001.0000.9821.0000.914
위도Y좌표0.0001.0001.0000.9990.9141.000
2023-12-10T15:25:05.618116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번경도X좌표위도Y좌표
순번1.0000.1090.079
경도X좌표0.1091.000-0.176
위도Y좌표0.079-0.1761.000

Missing values

2023-12-10T15:25:00.463866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:25:00.715762image/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

순번위치정보등록일시배달자ID배달업체ID경도X좌표위도Y좌표
0190075542020-03-23 17:13D0001366C000109126.85914637.382912
126331912019-11-27 17:49D0006333C000249126.87390137.459145
212557472019-11-16 17:41D0005108C000447128.50891135.857361
3193710072020-03-25 21:03D0008956C000305127.22514337.578323
4155532172020-03-01 20:14D0009053C000077126.82065637.467861
5184442432020-03-20 14:40D0001034C000043127.03472937.761105
678193822020-01-07 12:10D0002023C000162128.9168435.093357
7162973712020-03-06 20:07D0009133C000209126.86769937.482273
8254112332020-05-23 14:15D0003475C000264126.92922437.499689
9234782722020-04-20 12:24D0000817C000043127.07957537.756413
순번위치정보등록일시배달자ID배달업체ID경도X좌표위도Y좌표
90142299302020-02-22 20:23D0007674C000152126.4584236.779232
91178859822020-03-16 18:50D0000219C000035128.57611135.209606
92255248082020-05-24 19:40D0001754C000393128.54082135.853022
93266778022020-06-11 18:37D0005857C000428126.95177637.216852
9478028452020-01-06 22:04D0006043C000392128.5515935.854565
95258196342020-05-29 18:59D0012793C000254126.76167337.50341
96247499952020-05-12 19:02D0008843C000323128.20092736.598481
97281101542020-07-08 22:01D0013757C000543128.9389935.964769
98249727542020-05-16 12:37D0008168C000104126.89965337.5009
99187427822020-03-21 21:40D0004872C000253126.8089637.485817