Overview

Dataset statistics

Number of variables5
Number of observations82
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory43.6 B

Variable types

Categorical1
Text2
Numeric2

Dataset

Description2020년 1월~9월 군사우체국 우편물 접수 및 배달물량 정보입니다.해당 데이터가 보유한 컬럼은 다음과 같습니다.컬럼명 : 지방우정청, 총괄국명, 우체국명, 우편물 접수물량, 우편물 배달물량
Author과학기술정보통신부 우정사업본부
URLhttps://www.data.go.kr/data/15072617/fileData.do

Alerts

우체국명 has unique valuesUnique
우편물 접수물량(건) has unique valuesUnique
우편물 배달물량(건) has 9 (11.0%) zerosZeros

Reproduction

Analysis started2023-12-12 22:45:19.880243
Analysis finished2023-12-12 22:45:20.675924
Duration0.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지방우정청
Categorical

Distinct8
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size788.0 B
경인지방우정청
27 
강원지방우정청
19 
충청지방우정청
12 
부산지방우정청
9 
경북지방우정청
8 
Other values (3)
7 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row전남지방우정청
2nd row부산지방우정청
3rd row경인지방우정청
4th row전남지방우정청
5th row전북지방우정청

Common Values

ValueCountFrequency (%)
경인지방우정청 27
32.9%
강원지방우정청 19
23.2%
충청지방우정청 12
14.6%
부산지방우정청 9
 
11.0%
경북지방우정청 8
 
9.8%
전남지방우정청 4
 
4.9%
서울지방우정청 2
 
2.4%
전북지방우정청 1
 
1.2%

Length

2023-12-13T07:45:20.744560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:45:20.866302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경인지방우정청 27
32.9%
강원지방우정청 19
23.2%
충청지방우정청 12
14.6%
부산지방우정청 9
 
11.0%
경북지방우정청 8
 
9.8%
전남지방우정청 4
 
4.9%
서울지방우정청 2
 
2.4%
전북지방우정청 1
 
1.2%

총괄국명
Text

Distinct55
Distinct (%)67.1%
Missing0
Missing (%)0.0%
Memory size788.0 B
2023-12-13T07:45:21.154554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.3902439
Min length5

Characters and Unicode

Total characters442
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)43.9%

Sample

1st row광주광산우체국
2nd row부산강서우체국
3rd row용인우체국
4th row북광주우체국
5th row임실우체국
ValueCountFrequency (%)
진해우체국 4
 
4.9%
인제우체국 3
 
3.7%
이천우체국 3
 
3.7%
화천우체국 3
 
3.7%
포천우체국 3
 
3.7%
대구수성우체국 3
 
3.7%
원주우체국 3
 
3.7%
고양덕양우체국 2
 
2.4%
의정부우체국 2
 
2.4%
양양우체국 2
 
2.4%
Other values (45) 54
65.9%
2023-12-13T07:45:21.551871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
우 82
18.6%
체 82
18.6%
국 82
18.6%
천 17
 
3.8%
양 12
 
2.7%
주 10
 
2.3%
성 9
 
2.0%
대 8
 
1.8%
산 8
 
1.8%
평 6
 
1.4%
Other values (61) 126
28.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 442
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
우 82
18.6%
체 82
18.6%
국 82
18.6%
천 17
 
3.8%
양 12
 
2.7%
주 10
 
2.3%
성 9
 
2.0%
대 8
 
1.8%
산 8
 
1.8%
평 6
 
1.4%
Other values (61) 126
28.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 442
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
우 82
18.6%
체 82
18.6%
국 82
18.6%
천 17
 
3.8%
양 12
 
2.7%
주 10
 
2.3%
성 9
 
2.0%
대 8
 
1.8%
산 8
 
1.8%
평 6
 
1.4%
Other values (61) 126
28.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 442
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
우 82
18.6%
체 82
18.6%
국 82
18.6%
천 17
 
3.8%
양 12
 
2.7%
주 10
 
2.3%
성 9
 
2.0%
대 8
 
1.8%
산 8
 
1.8%
평 6
 
1.4%
Other values (61) 126
28.5%

우체국명
Text

UNIQUE 

Distinct82
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size788.0 B
2023-12-13T07:45:21.796848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10.5
Mean length10.060976
Min length6

Characters and Unicode

Total characters825
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)100.0%

Sample

1st row305군사출장소
2nd row307군사우편출장소
3rd row505군사우편출장소
4th row85군사출장소
5th row89군사우편출장소
ValueCountFrequency (%)
305군사출장소 1
 
1.2%
제76군사우편출장소 1
 
1.2%
제73군사우체국 1
 
1.2%
제71군사우체국 1
 
1.2%
제605군사우편출장소 1
 
1.2%
제604군사우편출장소 1
 
1.2%
제603군사우체국 1
 
1.2%
제602군사우체국 1
 
1.2%
제601군사우편출장소 1
 
1.2%
제567군사우편출장소 1
 
1.2%
Other values (72) 72
87.8%
2023-12-13T07:45:22.110147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 82
9.9%
사 82
9.9%
우 79
9.6%
제 76
9.2%
출 64
 
7.8%
장 64
 
7.8%
소 64
 
7.8%
편 61
 
7.4%
1 44
 
5.3%
3 36
 
4.4%
Other values (10) 173
21.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 608
73.7%
Decimal Number 217
 
26.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 82
13.5%
사 82
13.5%
우 79
13.0%
제 76
12.5%
출 64
10.5%
장 64
10.5%
소 64
10.5%
편 61
10.0%
체 18
 
3.0%
국 18
 
3.0%
Decimal Number
ValueCountFrequency (%)
1 44
20.3%
3 36
16.6%
0 34
15.7%
8 18
8.3%
9 17
 
7.8%
7 17
 
7.8%
2 15
 
6.9%
6 14
 
6.5%
5 13
 
6.0%
4 9
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 608
73.7%
Common 217
 
26.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 82
13.5%
사 82
13.5%
우 79
13.0%
제 76
12.5%
출 64
10.5%
장 64
10.5%
소 64
10.5%
편 61
10.0%
체 18
 
3.0%
국 18
 
3.0%
Common
ValueCountFrequency (%)
1 44
20.3%
3 36
16.6%
0 34
15.7%
8 18
8.3%
9 17
 
7.8%
7 17
 
7.8%
2 15
 
6.9%
6 14
 
6.5%
5 13
 
6.0%
4 9
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 608
73.7%
ASCII 217
 
26.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 82
13.5%
사 82
13.5%
우 79
13.0%
제 76
12.5%
출 64
10.5%
장 64
10.5%
소 64
10.5%
편 61
10.0%
체 18
 
3.0%
국 18
 
3.0%
ASCII
ValueCountFrequency (%)
1 44
20.3%
3 36
16.6%
0 34
15.7%
8 18
8.3%
9 17
 
7.8%
7 17
 
7.8%
2 15
 
6.9%
6 14
 
6.5%
5 13
 
6.0%
4 9
 
4.1%

우편물 접수물량(건)
Real number (ℝ)

UNIQUE 

Distinct82
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15066.5
Minimum1696
Maximum132132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-13T07:45:22.224437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1696
5-th percentile3031.25
Q15909.25
median8903.5
Q316855.25
95-th percentile46452.5
Maximum132132
Range130436
Interquartile range (IQR)10946

Descriptive statistics

Standard deviation18897.189
Coefficient of variation (CV)1.2542521
Kurtosis19.593966
Mean15066.5
Median Absolute Deviation (MAD)4215
Skewness3.9218373
Sum1235453
Variance3.5710374 × 108
MonotonicityNot monotonic
2023-12-13T07:45:22.340011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6278 1
 
1.2%
51483 1
 
1.2%
8053 1
 
1.2%
27678 1
 
1.2%
20977 1
 
1.2%
11005 1
 
1.2%
9719 1
 
1.2%
3574 1
 
1.2%
16196 1
 
1.2%
31865 1
 
1.2%
Other values (72) 72
87.8%
ValueCountFrequency (%)
1696 1
1.2%
1932 1
1.2%
2393 1
1.2%
2469 1
1.2%
3011 1
1.2%
3416 1
1.2%
3418 1
1.2%
3428 1
1.2%
3522 1
1.2%
3544 1
1.2%
ValueCountFrequency (%)
132132 1
1.2%
83013 1
1.2%
62366 1
1.2%
51483 1
1.2%
47072 1
1.2%
34682 1
1.2%
33456 1
1.2%
31865 1
1.2%
27925 1
1.2%
27678 1
1.2%

우편물 배달물량(건)
Real number (ℝ)

ZEROS 

Distinct74
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21052.415
Minimum0
Maximum145395
Zeros9
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-13T07:45:22.450924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17801
median12518
Q326726.25
95-th percentile57141
Maximum145395
Range145395
Interquartile range (IQR)18925.25

Descriptive statistics

Standard deviation23106.732
Coefficient of variation (CV)1.0975811
Kurtosis9.6708838
Mean21052.415
Median Absolute Deviation (MAD)7017.5
Skewness2.5530872
Sum1726298
Variance5.3392105 × 108
MonotonicityNot monotonic
2023-12-13T07:45:22.567986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
11.0%
7203 1
 
1.2%
12298 1
 
1.2%
32675 1
 
1.2%
145395 1
 
1.2%
33857 1
 
1.2%
19811 1
 
1.2%
40791 1
 
1.2%
10260 1
 
1.2%
8013 1
 
1.2%
Other values (64) 64
78.0%
ValueCountFrequency (%)
0 9
11.0%
291 1
 
1.2%
2874 1
 
1.2%
4439 1
 
1.2%
4859 1
 
1.2%
5438 1
 
1.2%
5563 1
 
1.2%
5753 1
 
1.2%
6950 1
 
1.2%
7203 1
 
1.2%
ValueCountFrequency (%)
145395 1
1.2%
82404 1
1.2%
68835 1
1.2%
59463 1
1.2%
57179 1
1.2%
56419 1
1.2%
56250 1
1.2%
55736 1
1.2%
54870 1
1.2%
52713 1
1.2%

Interactions

2023-12-13T07:45:20.334149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:45:20.125903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:45:20.428377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:45:20.228709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:45:22.644300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지방우정청총괄국명우체국명우편물 접수물량(건)우편물 배달물량(건)
지방우정청1.0001.0001.0000.0750.000
총괄국명1.0001.0001.0000.6420.472
우체국명1.0001.0001.0001.0001.000
우편물 접수물량(건)0.0750.6421.0001.0000.908
우편물 배달물량(건)0.0000.4721.0000.9081.000
2023-12-13T07:45:22.719419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편물 접수물량(건)우편물 배달물량(건)지방우정청
우편물 접수물량(건)1.0000.4740.038
우편물 배달물량(건)0.4741.0000.000
지방우정청0.0380.0001.000

Missing values

2023-12-13T07:45:20.540322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:45:20.628242image/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

지방우정청총괄국명우체국명우편물 접수물량(건)우편물 배달물량(건)
0전남지방우정청광주광산우체국305군사출장소62787203
1부산지방우정청부산강서우체국307군사우편출장소950914036
2경인지방우정청용인우체국505군사우편출장소707512263
3전남지방우정청북광주우체국85군사출장소1695513644
4전북지방우정청임실우체국89군사우편출장소231540
5전남지방우정청영암우체국8군사출장소59620
6강원지방우정청인제우체국제100군사우체국3468259463
7강원지방우정청홍천우체국제101군사우체국1705418100
8경인지방우정청가평우체국제102군사우편출장소703325809
9강원지방우정청화천우체국제104군사우체국1226055736
지방우정청총괄국명우체국명우편물 접수물량(건)우편물 배달물량(건)
72경인지방우정청이천우체국제900군사우편출장소1127919208
73충청지방우정청세종우체국제90군사우편출장소35447578
74경인지방우정청양평우체국제91군사우편출장소35225438
75경인지방우정청동두천우체국제92군사우편출장소2730552152
76강원지방우정청화천우체국제93군사우체국505031727
77경인지방우정청의정부우체국제94군사우편출장소536014622
78경인지방우정청동두천우체국제95군사우편출장소983152713
79경북지방우정청대구수성우체국제96군사우편출장소24692874
80강원지방우정청양구우체국제97군사우체국1331750716
81강원지방우정청화천우체국제99군사우체국454556419