Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory957.0 KiB
Average record size in memory98.0 B

Variable types

Categorical4
Text5
Numeric2

Dataset

Description2019년 10월 집중국간 차량운송데이터(발송집중국, 도착집중국, 운송거리 등)에 대한 정보입니다.
Author과학기술정보통신부 우정사업본부
URLhttps://www.data.go.kr/data/15064669/fileData.do

Alerts

발송관할청 is highly overall correlated with 발송집중국High correlation
발송집중국 is highly overall correlated with 발송관할청High correlation

Reproduction

Analysis started2023-12-12 22:55:56.386991
Analysis finished2023-12-12 22:55:58.132286
Duration1.75 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

발송일자
Categorical

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2019-10-15
 
494
2019-10-22
 
490
2019-10-28
 
478
2019-10-10
 
474
2019-10-29
 
464
Other values (26)
7600 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-10-26
2nd row2019-10-15
3rd row2019-10-14
4th row2019-10-07
5th row2019-10-25

Common Values

ValueCountFrequency (%)
2019-10-15 494
 
4.9%
2019-10-22 490
 
4.9%
2019-10-28 478
 
4.8%
2019-10-10 474
 
4.7%
2019-10-29 464
 
4.6%
2019-10-01 463
 
4.6%
2019-10-14 458
 
4.6%
2019-10-21 457
 
4.6%
2019-10-16 455
 
4.5%
2019-10-31 453
 
4.5%
Other values (21) 5314
53.1%

Length

2023-12-13T07:55:58.199992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-10-15 494
 
4.9%
2019-10-22 490
 
4.9%
2019-10-28 478
 
4.8%
2019-10-10 474
 
4.7%
2019-10-29 464
 
4.6%
2019-10-01 463
 
4.6%
2019-10-14 458
 
4.6%
2019-10-21 457
 
4.6%
2019-10-16 455
 
4.5%
2019-10-31 453
 
4.5%
Other values (21) 5314
53.1%

발송관할청
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경인청
3095 
충청청
2310 
서울청
1494 
부산청
916 
전남청
693 
Other values (4)
1492 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row충청청
2nd row서울청
3rd row서울청
4th row경인청
5th row전남청

Common Values

ValueCountFrequency (%)
경인청 3095
30.9%
충청청 2310
23.1%
서울청 1494
14.9%
부산청 916
 
9.2%
전남청 693
 
6.9%
경북청 682
 
6.8%
강원청 331
 
3.3%
전북청 291
 
2.9%
제주청 188
 
1.9%

Length

2023-12-13T07:55:58.367661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:55:58.485026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경인청 3095
30.9%
충청청 2310
23.1%
서울청 1494
14.9%
부산청 916
 
9.2%
전남청 693
 
6.9%
경북청 682
 
6.8%
강원청 331
 
3.3%
전북청 291
 
2.9%
제주청 188
 
1.9%

발송집중국
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대전교환센터
1679 
동서울우편집중국
713 
부평물류센터
673 
안양우편물류센터
 
575
동서울물류센터
 
497
Other values (24)
5863 

Length

Max length8
Median length7
Mean length6.8961
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대전교환센터
2nd row서서울물류센터
3rd row동서울우편집중국
4th row성남우편집중국
5th row영암우편집중국

Common Values

ValueCountFrequency (%)
대전교환센터 1679
16.8%
동서울우편집중국 713
 
7.1%
부평물류센터 673
 
6.7%
안양우편물류센터 575
 
5.8%
동서울물류센터 497
 
5.0%
부산우편집중국 390
 
3.9%
고양우편집중국 381
 
3.8%
성남우편집중국 380
 
3.8%
광주우편집중국 377
 
3.8%
대구우편집중국 361
 
3.6%
Other values (19) 3974
39.7%

Length

2023-12-13T07:55:58.626628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대전교환센터 1679
16.8%
동서울우편집중국 713
 
7.1%
부평물류센터 673
 
6.7%
안양우편물류센터 575
 
5.8%
동서울물류센터 497
 
5.0%
부산우편집중국 390
 
3.9%
고양우편집중국 381
 
3.8%
성남우편집중국 380
 
3.8%
광주우편집중국 377
 
3.8%
대구우편집중국 361
 
3.6%
Other values (19) 3974
39.7%

도착집중국
Categorical

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대전교환센터
1549 
동서울우편집중국
673 
부평물류센터
627 
안양우편물류센터
 
597
부산우편집중국
 
491
Other values (24)
6063 

Length

Max length8
Median length7
Mean length6.9248
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전주우편집중국
2nd row안양우편물류센터
3rd row부평물류센터
4th row대전교환센터
5th row대전교환센터

Common Values

ValueCountFrequency (%)
대전교환센터 1549
 
15.5%
동서울우편집중국 673
 
6.7%
부평물류센터 627
 
6.3%
안양우편물류센터 597
 
6.0%
부산우편집중국 491
 
4.9%
대구우편집중국 442
 
4.4%
의정부우편집중국 426
 
4.3%
성남우편집중국 423
 
4.2%
수원우편집중국 396
 
4.0%
고양우편집중국 387
 
3.9%
Other values (19) 3989
39.9%

Length

2023-12-13T07:55:58.767746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대전교환센터 1549
 
15.5%
동서울우편집중국 673
 
6.7%
부평물류센터 627
 
6.3%
안양우편물류센터 597
 
6.0%
부산우편집중국 491
 
4.9%
대구우편집중국 442
 
4.4%
의정부우편집중국 426
 
4.3%
성남우편집중국 423
 
4.2%
수원우편집중국 396
 
4.0%
고양우편집중국 387
 
3.9%
Other values (19) 3989
39.9%

운송선로편코드
Text

Distinct1370
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T07:55:59.049164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters110000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique221 ?
Unique (%)2.2%

Sample

1st row0511001-240
2nd row0462003-030
3rd row0112087-040
4th row0441002-039
5th row0541001-351
ValueCountFrequency (%)
0122027-020 53
 
0.5%
0322046-040 46
 
0.5%
0472048-010 32
 
0.3%
0452047-010 31
 
0.3%
0462043-010 29
 
0.3%
0432038-050 28
 
0.3%
0202062-020 28
 
0.3%
0462004-040 26
 
0.3%
0132018-030 25
 
0.2%
0462041-010 24
 
0.2%
Other values (1360) 9678
96.8%
2023-12-13T07:55:59.480405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 41419
37.7%
1 16008
 
14.6%
2 11523
 
10.5%
- 10000
 
9.1%
4 7464
 
6.8%
3 7080
 
6.4%
9 4666
 
4.2%
5 3598
 
3.3%
7 3322
 
3.0%
6 3247
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 100000
90.9%
Dash Punctuation 10000
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 41419
41.4%
1 16008
 
16.0%
2 11523
 
11.5%
4 7464
 
7.5%
3 7080
 
7.1%
9 4666
 
4.7%
5 3598
 
3.6%
7 3322
 
3.3%
6 3247
 
3.2%
8 1673
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 110000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 41419
37.7%
1 16008
 
14.6%
2 11523
 
10.5%
- 10000
 
9.1%
4 7464
 
6.8%
3 7080
 
6.4%
9 4666
 
4.2%
5 3598
 
3.3%
7 3322
 
3.0%
6 3247
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 110000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 41419
37.7%
1 16008
 
14.6%
2 11523
 
10.5%
- 10000
 
9.1%
4 7464
 
6.8%
3 7080
 
6.4%
9 4666
 
4.2%
5 3598
 
3.3%
7 3322
 
3.0%
6 3247
 
3.0%

운송선로명
Text

Distinct1254
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T07:55:59.798240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length10.7672
Min length7

Characters and Unicode

Total characters107672
Distinct characters81
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique217 ?
Unique (%)2.2%

Sample

1st row전주집센터왕복1-7
2nd row안양물서서울물왕복1-2
3rd row동집부평물왕복1-2
4th row성남집센터왕1-3
5th row영암집센터왕복1-4
ValueCountFrequency (%)
동서울물천안집청주집왕복1-1 53
 
0.5%
천안집수원집왕복2-1 46
 
0.5%
동서울물전주집광주집왕1-1 42
 
0.4%
동집센터왕복1-1 42
 
0.4%
수원집센터왕복1-1 35
 
0.3%
천안집센터왕복1-2 33
 
0.3%
순천집센터왕복1-1 33
 
0.3%
울산집센터왕복2-1 33
 
0.3%
부평물부천집동집왕복1-1 32
 
0.3%
부평물동집왕복1-1 31
 
0.3%
Other values (1248) 9630
96.2%
2023-12-13T07:56:00.275674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
집 14124
 
13.1%
1 12433
 
11.5%
- 10000
 
9.3%
왕 9833
 
9.1%
2 5920
 
5.5%
복 4653
 
4.3%
물 4194
 
3.9%
부 3682
 
3.4%
센 3245
 
3.0%
터 3245
 
3.0%
Other values (71) 36343
33.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77611
72.1%
Decimal Number 20017
 
18.6%
Dash Punctuation 10000
 
9.3%
Open Punctuation 17
 
< 0.1%
Close Punctuation 17
 
< 0.1%
Space Separator 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
집 14124
18.2%
왕 9833
 
12.7%
복 4653
 
6.0%
물 4194
 
5.4%
부 3682
 
4.7%
센 3245
 
4.2%
터 3245
 
4.2%
주 3191
 
4.1%
동 2620
 
3.4%
양 2450
 
3.2%
Other values (57) 26374
34.0%
Decimal Number
ValueCountFrequency (%)
1 12433
62.1%
2 5920
29.6%
3 844
 
4.2%
4 408
 
2.0%
5 199
 
1.0%
6 97
 
0.5%
7 79
 
0.4%
8 19
 
0.1%
9 15
 
0.1%
0 3
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77611
72.1%
Common 30061
 
27.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
집 14124
18.2%
왕 9833
 
12.7%
복 4653
 
6.0%
물 4194
 
5.4%
부 3682
 
4.7%
센 3245
 
4.2%
터 3245
 
4.2%
주 3191
 
4.1%
동 2620
 
3.4%
양 2450
 
3.2%
Other values (57) 26374
34.0%
Common
ValueCountFrequency (%)
1 12433
41.4%
- 10000
33.3%
2 5920
19.7%
3 844
 
2.8%
4 408
 
1.4%
5 199
 
0.7%
6 97
 
0.3%
7 79
 
0.3%
8 19
 
0.1%
( 17
 
0.1%
Other values (4) 45
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77611
72.1%
ASCII 30061
 
27.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
집 14124
18.2%
왕 9833
 
12.7%
복 4653
 
6.0%
물 4194
 
5.4%
부 3682
 
4.7%
센 3245
 
4.2%
터 3245
 
4.2%
주 3191
 
4.1%
동 2620
 
3.4%
양 2450
 
3.2%
Other values (57) 26374
34.0%
ASCII
ValueCountFrequency (%)
1 12433
41.4%
- 10000
33.3%
2 5920
19.7%
3 844
 
2.8%
4 408
 
1.4%
5 199
 
0.7%
6 97
 
0.3%
7 79
 
0.3%
8 19
 
0.1%
( 17
 
0.1%
Other values (4) 45
 
0.1%

운송거리(km)
Real number (ℝ)

Distinct337
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223.9044
Minimum0
Maximum591
Zeros30
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:56:00.445714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24
Q190
median221
Q3345
95-th percentile468
Maximum591
Range591
Interquartile range (IQR)255

Descriptive statistics

Standard deviation141.00068
Coefficient of variation (CV)0.62973608
Kurtosis-0.90275969
Mean223.9044
Median Absolute Deviation (MAD)126
Skewness0.21525559
Sum2239044
Variance19881.192
MonotonicityNot monotonic
2023-12-13T07:56:00.597361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
234 187
 
1.9%
371 184
 
1.8%
90 146
 
1.5%
56 146
 
1.5%
160 135
 
1.4%
422 132
 
1.3%
58 132
 
1.3%
9 131
 
1.3%
263 126
 
1.3%
350 125
 
1.2%
Other values (327) 8556
85.6%
ValueCountFrequency (%)
0 30
 
0.3%
1 64
0.6%
2 55
0.5%
3 3
 
< 0.1%
4 7
 
0.1%
5 28
 
0.3%
6 11
 
0.1%
9 131
1.3%
10 15
 
0.1%
12 10
 
0.1%
ValueCountFrequency (%)
591 53
0.5%
547 7
 
0.1%
527 102
1.0%
526 115
1.1%
479 82
0.8%
475 114
1.1%
468 72
0.7%
460 19
 
0.2%
442 5
 
0.1%
438 22
 
0.2%

차량톤급(톤)
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.8891
Minimum0
Maximum250
Zeros30
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:56:00.720475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q180
median110
Q3180
95-th percentile180
Maximum250
Range250
Interquartile range (IQR)100

Descriptive statistics

Standard deviation50.367008
Coefficient of variation (CV)0.40654915
Kurtosis-0.38899328
Mean123.8891
Median Absolute Deviation (MAD)30
Skewness0.41703368
Sum1238891
Variance2536.8355
MonotonicityNot monotonic
2023-12-13T07:56:00.834397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
110 3717
37.2%
180 3012
30.1%
80 2090
20.9%
50 533
 
5.3%
250 340
 
3.4%
25 130
 
1.3%
45 119
 
1.2%
0 30
 
0.3%
14 29
 
0.3%
ValueCountFrequency (%)
0 30
 
0.3%
14 29
 
0.3%
25 130
 
1.3%
45 119
 
1.2%
50 533
 
5.3%
80 2090
20.9%
110 3717
37.2%
180 3012
30.1%
250 340
 
3.4%
ValueCountFrequency (%)
250 340
 
3.4%
180 3012
30.1%
110 3717
37.2%
80 2090
20.9%
50 533
 
5.3%
45 119
 
1.2%
25 130
 
1.3%
14 29
 
0.3%
0 30
 
0.3%

등기소포물량(건)
Text

Distinct2458
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T07:56:01.193502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.8745
Min length1

Characters and Unicode

Total characters38745
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique643 ?
Unique (%)6.4%

Sample

1st row874
2nd row2,155
3rd row1,673
4th row1,771
5th row999
ValueCountFrequency (%)
0 478
 
4.8%
1 33
 
0.3%
917 17
 
0.2%
2 17
 
0.2%
689 16
 
0.2%
809 15
 
0.1%
908 14
 
0.1%
980 14
 
0.1%
599 14
 
0.1%
1,063 14
 
0.1%
Other values (2448) 9368
93.7%
2023-12-13T07:56:01.715626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 7028
18.1%
, 4973
12.8%
2 3430
8.9%
0 3287
8.5%
3 2987
7.7%
6 2910
7.5%
7 2898
7.5%
4 2885
7.4%
9 2802
 
7.2%
5 2779
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33772
87.2%
Other Punctuation 4973
 
12.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7028
20.8%
2 3430
10.2%
0 3287
9.7%
3 2987
8.8%
6 2910
8.6%
7 2898
8.6%
4 2885
8.5%
9 2802
 
8.3%
5 2779
 
8.2%
8 2766
 
8.2%
Other Punctuation
ValueCountFrequency (%)
, 4973
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 38745
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7028
18.1%
, 4973
12.8%
2 3430
8.9%
0 3287
8.5%
3 2987
7.7%
6 2910
7.5%
7 2898
7.5%
4 2885
7.4%
9 2802
 
7.2%
5 2779
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38745
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7028
18.1%
, 4973
12.8%
2 3430
8.9%
0 3287
8.5%
3 2987
7.7%
6 2910
7.5%
7 2898
7.5%
4 2885
7.4%
9 2802
 
7.2%
5 2779
 
7.2%

등기통상물량(건)
Text

Distinct603
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T07:56:02.083789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length1
Mean length1.1987
Min length1

Characters and Unicode

Total characters11987
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique540 ?
Unique (%)5.4%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 9192
91.9%
1 49
 
0.5%
2 33
 
0.3%
3 22
 
0.2%
4 14
 
0.1%
5 14
 
0.1%
7 7
 
0.1%
6 6
 
0.1%
11 4
 
< 0.1%
22 3
 
< 0.1%
Other values (593) 656
 
6.6%
2023-12-13T07:56:02.537960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9353
78.0%
1 424
 
3.5%
, 340
 
2.8%
2 315
 
2.6%
3 273
 
2.3%
6 258
 
2.2%
4 241
 
2.0%
5 220
 
1.8%
7 212
 
1.8%
8 182
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11647
97.2%
Other Punctuation 340
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9353
80.3%
1 424
 
3.6%
2 315
 
2.7%
3 273
 
2.3%
6 258
 
2.2%
4 241
 
2.1%
5 220
 
1.9%
7 212
 
1.8%
8 182
 
1.6%
9 169
 
1.5%
Other Punctuation
ValueCountFrequency (%)
, 340
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11987
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9353
78.0%
1 424
 
3.5%
, 340
 
2.8%
2 315
 
2.6%
3 273
 
2.3%
6 258
 
2.2%
4 241
 
2.0%
5 220
 
1.8%
7 212
 
1.8%
8 182
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11987
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9353
78.0%
1 424
 
3.5%
, 340
 
2.8%
2 315
 
2.6%
3 273
 
2.3%
6 258
 
2.2%
4 241
 
2.0%
5 220
 
1.8%
7 212
 
1.8%
8 182
 
1.5%

국내특급물량(건)
Text

Distinct1702
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T07:56:02.861279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length1
Mean length1.6963
Min length1

Characters and Unicode

Total characters16963
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1269 ?
Unique (%)12.7%

Sample

1st row100
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 7499
75.0%
1 51
 
0.5%
2 23
 
0.2%
6 13
 
0.1%
3 12
 
0.1%
4 11
 
0.1%
16 11
 
0.1%
22 11
 
0.1%
5 9
 
0.1%
12 8
 
0.1%
Other values (1692) 2352
 
23.5%
2023-12-13T07:56:03.597507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8137
48.0%
1 1384
 
8.2%
, 1167
 
6.9%
2 1105
 
6.5%
3 942
 
5.6%
5 787
 
4.6%
4 767
 
4.5%
6 729
 
4.3%
9 655
 
3.9%
8 653
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15796
93.1%
Other Punctuation 1167
 
6.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8137
51.5%
1 1384
 
8.8%
2 1105
 
7.0%
3 942
 
6.0%
5 787
 
5.0%
4 767
 
4.9%
6 729
 
4.6%
9 655
 
4.1%
8 653
 
4.1%
7 637
 
4.0%
Other Punctuation
ValueCountFrequency (%)
, 1167
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16963
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8137
48.0%
1 1384
 
8.2%
, 1167
 
6.9%
2 1105
 
6.5%
3 942
 
5.6%
5 787
 
4.6%
4 767
 
4.5%
6 729
 
4.3%
9 655
 
3.9%
8 653
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16963
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8137
48.0%
1 1384
 
8.2%
, 1167
 
6.9%
2 1105
 
6.5%
3 942
 
5.6%
5 787
 
4.6%
4 767
 
4.5%
6 729
 
4.3%
9 655
 
3.9%
8 653
 
3.8%

Interactions

2023-12-13T07:55:57.565139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:57.340929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:57.679873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:57.452675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:56:03.700600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발송일자발송관할청발송집중국도착집중국운송거리(km)차량톤급(톤)
발송일자1.0000.2910.2890.0700.0690.142
발송관할청0.2911.0001.0000.4770.5100.290
발송집중국0.2891.0001.0000.6250.7070.469
도착집중국0.0700.4770.6251.0000.6850.470
운송거리(km)0.0690.5100.7070.6851.0000.380
차량톤급(톤)0.1420.2900.4690.4700.3801.000
2023-12-13T07:56:03.820968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발송관할청도착집중국발송일자발송집중국
발송관할청1.0000.2000.1120.999
도착집중국0.2001.0000.0160.149
발송일자0.1120.0161.0000.070
발송집중국0.9990.1490.0701.000
2023-12-13T07:56:03.928585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운송거리(km)차량톤급(톤)발송일자발송관할청발송집중국도착집중국
운송거리(km)1.0000.3250.0240.2610.3380.320
차량톤급(톤)0.3251.0000.0660.1770.2350.244
발송일자0.0240.0661.0000.1120.0700.016
발송관할청0.2610.1770.1121.0000.9990.200
발송집중국0.3380.2350.0700.9991.0000.149
도착집중국0.3200.2440.0160.2000.1491.000

Missing values

2023-12-13T07:55:57.831803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:55:58.037649image/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

발송일자발송관할청발송집중국도착집중국운송선로편코드운송선로명운송거리(km)차량톤급(톤)등기소포물량(건)등기통상물량(건)국내특급물량(건)
225612019-10-26충청청대전교환센터전주우편집중국0511001-240전주집센터왕복1-7901108740100
110722019-10-15서울청서서울물류센터안양우편물류센터0462003-030안양물서서울물왕복1-242802,15500
96782019-10-14서울청동서울우편집중국부평물류센터0112087-040동집부평물왕복1-2811801,67300
44622019-10-07경인청성남우편집중국대전교환센터0441002-039성남집센터왕1-3138801,77100
220842019-10-25전남청영암우편집중국대전교환센터0541001-351영암집센터왕복1-442211099900
189532019-10-23경인청수원우편집중국성남우편집중국0312154-019청주집수원집성남집왕2-111118063300
86002019-10-11부산청창원우편집중국대전교환센터0611001-190창원집센터왕복1-14791101,00200
145382019-10-17경북청포항우편집중국서서울물류센터0732084-019포항집서서울물왕2-13368049000
117192019-10-15충청청대전교환센터동서울우편집중국0111001-160동집센터왕복1-13455074800
134032019-10-16강원청원주우편집중국대전교환센터0201001-170원주집센터왕복1-134911070400
발송일자발송관할청발송집중국도착집중국운송선로편코드운송선로명운송거리(km)차량톤급(톤)등기소포물량(건)등기통상물량(건)국내특급물량(건)
257312019-10-30경인청안양우편물류센터청주우편집중국0462008-019안양물천안집청주집왕1-113118057200
15122019-10-02경인청의정부우편집중국동서울우편집중국0112065-010동집의정부집왕복1-1561101,22100
274122019-10-31충청청대전교환센터순천집중과0001147-029센터순천집왕2-21751801,06600
157792019-10-18강원청강릉우편집중국원주우편집중국0213001-200강릉집원주집왕복1-2238803960896
102752019-10-14부산청부산우편집중국서서울물류센터0602031-030부산집서서울물왕2-1393801,76000
102882019-10-14부산청부산우편집중국고양우편집중국0602020-029부산집고양집왕3-24141101,21500
189032019-10-23경인청부천우편집중국동서울우편집중국0412036-080부천집동집왕복1-1688032000
152652019-10-18부산청울산우편집중국성남우편집중국0632007-030울산집성남집왕2-134318058700
226382019-10-27충청청대전교환센터창원우편집중국0001043-019센터창원집왕2-12401101,383220
164512019-10-21경인청의정부우편집중국안양우편물류센터0423034-020의정부집안양물안양집왕복1-11081101,12800