Overview

Dataset statistics

Number of variables4
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory35.3 B

Variable types

DateTime1
Numeric1
Text1
Categorical1

Alerts

생성날짜 is highly imbalanced (91.9%)Imbalance

Reproduction

Analysis started2023-12-10 12:27:03.339928
Analysis finished2023-12-10 12:27:04.624416
Duration1.28 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

발령일
Date

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2007-02-14 00:00:00
Maximum2011-05-13 00:00:00
2023-12-10T21:27:04.797949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:27:05.099645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

발령회차
Real number (ℝ)

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.89
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:27:05.343128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.95
Q14
median8
Q313
95-th percentile18.05
Maximum23
Range22
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.6262283
Coefficient of variation (CV)0.63287157
Kurtosis-0.71092575
Mean8.89
Median Absolute Deviation (MAD)4
Skewness0.48911319
Sum889
Variance31.654444
MonotonicityNot monotonic
2023-12-10T21:27:05.601697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
6 11
 
11.0%
5 9
 
9.0%
2 9
 
9.0%
4 7
 
7.0%
12 6
 
6.0%
1 5
 
5.0%
3 5
 
5.0%
15 5
 
5.0%
14 5
 
5.0%
10 5
 
5.0%
Other values (13) 33
33.0%
ValueCountFrequency (%)
1 5
5.0%
2 9
9.0%
3 5
5.0%
4 7
7.0%
5 9
9.0%
6 11
11.0%
7 3
 
3.0%
8 4
 
4.0%
9 3
 
3.0%
10 5
5.0%
ValueCountFrequency (%)
23 1
 
1.0%
22 1
 
1.0%
21 1
 
1.0%
20 1
 
1.0%
19 1
 
1.0%
18 3
3.0%
17 3
3.0%
16 3
3.0%
15 5
5.0%
14 5
5.0%

황사발생지역
Text

Distinct79
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:27:06.291960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length100
Median length98
Mean length64.08
Min length2

Characters and Unicode

Total characters6408
Distinct characters66
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

Unique71 ?
Unique (%)71.0%

Sample

1st row서울, 백령도, 경기(문산,동두천,수원), 강원(춘천,속초,철원)
2nd row광주, 흑산도, 제주(제주, 고산, 서귀포)
3rd row서울, 인천,경기(문산,동두천,수원),강원(강릉,춘천,원주,대관령,속초,철원,영월),충북(충주, 청주,추풍령),대전,충남(서산),전북(군산,전주),대구,경북(안동,상주)
4th row백령도
5th row서울, 백령도, 인천, 경기(문산,동두천,수원), 강원(강릉,춘천,원주,대관령,속초,동해,철원, 영월), 충북(충주, 청주, 추풍령), 대전, 충남(서산), 전북(전주, 군산),
ValueCountFrequency (%)
백령도 62
 
5.5%
서울 55
 
4.9%
인천 52
 
4.6%
충남(서산 46
 
4.1%
대전 46
 
4.1%
추풍령 38
 
3.4%
경기(문산 34
 
3.0%
수원 33
 
2.9%
동두천 32
 
2.8%
충북(충주 32
 
2.8%
Other values (100) 694
61.7%
2023-12-10T21:27:08.129279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 1204
18.8%
1047
16.3%
( 336
 
5.2%
) 308
 
4.8%
주 221
 
3.4%
원 217
 
3.4%
천 205
 
3.2%
령 184
 
2.9%
산 168
 
2.6%
충 151
 
2.4%
Other values (56) 2367
36.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3509
54.8%
Other Punctuation 1204
 
18.8%
Space Separator 1047
 
16.3%
Open Punctuation 336
 
5.2%
Close Punctuation 308
 
4.8%
Decimal Number 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 221
 
6.3%
원 217
 
6.2%
천 205
 
5.8%
령 184
 
5.2%
산 168
 
4.8%
충 151
 
4.3%
전 144
 
4.1%
도 137
 
3.9%
북 127
 
3.6%
대 115
 
3.3%
Other values (51) 1840
52.4%
Other Punctuation
ValueCountFrequency (%)
, 1204
100.0%
Space Separator
ValueCountFrequency (%)
1047
100.0%
Open Punctuation
ValueCountFrequency (%)
( 336
100.0%
Close Punctuation
ValueCountFrequency (%)
) 308
100.0%
Decimal Number
ValueCountFrequency (%)
5 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3509
54.8%
Common 2899
45.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 221
 
6.3%
원 217
 
6.2%
천 205
 
5.8%
령 184
 
5.2%
산 168
 
4.8%
충 151
 
4.3%
전 144
 
4.1%
도 137
 
3.9%
북 127
 
3.6%
대 115
 
3.3%
Other values (51) 1840
52.4%
Common
ValueCountFrequency (%)
, 1204
41.5%
1047
36.1%
( 336
 
11.6%
) 308
 
10.6%
5 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3509
54.8%
ASCII 2899
45.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 1204
41.5%
1047
36.1%
( 336
 
11.6%
) 308
 
10.6%
5 4
 
0.1%
Hangul
ValueCountFrequency (%)
주 221
 
6.3%
원 217
 
6.2%
천 205
 
5.8%
령 184
 
5.2%
산 168
 
4.8%
충 151
 
4.3%
전 144
 
4.1%
도 137
 
3.9%
북 127
 
3.6%
대 115
 
3.3%
Other values (51) 1840
52.4%

생성날짜
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20191217
99 
201912
 
1

Length

Max length8
Median length8
Mean length7.98
Min length6

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row20191217
2nd row20191217
3rd row20191217
4th row20191217
5th row20191217

Common Values

ValueCountFrequency (%)
20191217 99
99.0%
201912 1
 
1.0%

Length

2023-12-10T21:27:08.617079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:27:08.938597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20191217 99
99.0%
201912 1
 
1.0%

Interactions

2023-12-10T21:27:04.261086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:27:09.121006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발령일발령회차황사발생지역생성날짜
발령일1.0001.0000.9980.000
발령회차1.0001.0000.6460.287
황사발생지역0.9980.6461.0000.000
생성날짜0.0000.2870.0001.000
2023-12-10T21:27:09.440227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발령회차생성날짜
발령회차1.0000.209
생성날짜0.2091.000

Missing values

2023-12-10T21:27:04.417467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:27:04.570892image/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

발령일발령회차황사발생지역생성날짜
02007-02-14 00:00:001서울, 백령도, 경기(문산,동두천,수원), 강원(춘천,속초,철원)20191217
12007-02-23 00:00:002광주, 흑산도, 제주(제주, 고산, 서귀포)20191217
22007-03-06 00:00:003서울, 인천,경기(문산,동두천,수원),강원(강릉,춘천,원주,대관령,속초,철원,영월),충북(충주, 청주,추풍령),대전,충남(서산),전북(군산,전주),대구,경북(안동,상주)20191217
32007-03-25 00:00:004백령도20191217
42007-03-27 00:00:005서울, 백령도, 인천, 경기(문산,동두천,수원), 강원(강릉,춘천,원주,대관령,속초,동해,철원, 영월), 충북(충주, 청주, 추풍령), 대전, 충남(서산), 전북(전주, 군산),20191217
52007-03-28 00:00:006서울, 인천, 경기(문산,동두천,수원), 강원(강릉,춘천,원주,대관령,속초,동해,철원, 영월),울릉도, 충북(충주, 청주, 추풍령), 대전, 충남(서산), 전북(전주, 군산), 광20191217
62007-03-31 00:00:007서울, 백령도, 인천, 경기(문산,동두천,수원), 강원(강릉,춘천,원주,대관령,속초,동해,철원, 영월), 충북(충주, 청주, 추풍령), 대전, 충남(서산), 전북(전주, 군산),20191217
72007-04-01 00:00:008서울, 백령도, 인천, 경기(문산,동두천,수원), 강원(강릉,춘천,원주,대관령,속초,동해,철원, 영월), 충북(충주, 청주, 추풍령), 대전, 충남(서산), 전북(전주, 군산),20191217
82007-04-02 00:00:009서울, 백령도, 인천, 경기(문산,동두천), 강원(강릉,춘천,원주,대관령,속초,동해,철원, 영월), 충북(충주, 청주, 추풍령), 대전, 충남(서산), 전북(전주, 군산), 광주,20191217
92007-04-05 00:00:0010백령도20191217
발령일발령회차황사발생지역생성날짜
902011-03-22 00:00:002강원(북강릉, 원주, 대관령), 충북(추풍령), 대구, 경북(울진, 안동, 포항), 울산, 경남(마산, 진주, 거창), 제주(제주, 고산)20191217
912011-04-01 00:00:003백령도20191217
922011-04-15 00:00:004강원(대관령), 대구, 경북(울진,안동)20191217
932011-04-16 00:00:004대구, 경북(울진, 안동, 포항), 울산20191217
942011-05-01 00:00:005서울, 백령도, 인천, 경기(수원), 강원(북강릉, 춘천, 원주, 대관령, 속초, 철원, 울릉도), 충북(청주), 대전, 충남(서산, 천안, 보령), 전북(군산, 전주), 광주,20191217
952011-05-02 00:00:005서울, 백령도, 인천, 경기(수원), 강원(북강릉, 춘천, 원주, 대관령, 속초, 철원, 울릉도), 충북(청주), 대전, 충남(서산, 천안, 보령), 전북(군산, 전주, 장수),20191217
962011-05-03 00:00:005서울, 백령도, 인천, 경기(수원), 강원(춘천, 원주, 대관령, 철원, 울릉도), 충북(청주), 대전, 충남(서산, 천안, 보령), 전북(군산, 전주, 장수), 광주, 전남(목포20191217
972011-05-04 00:00:005경기(수원), 강원(원주), 대전, 충남(서산, 천안, 보령), 전북(군산, 전주, 장수), 광주, 전남(목포, 여수, 완도, 진도, 흑산도), 대구, 부산, 울산, 경남(마산,20191217
982011-05-12 00:00:006서울, 백령도, 인천, 경기(수원), 강원(북강릉, 춘천, 원주, 대관령 ,속초, 철원), 충북(청주), 대전, 충남(서산, 천안, 보령), 전북(군산, 전주), 광주, 전남(목포20191217
992011-05-13 00:00:006서울, 백령도, 인천, 경기(수원), 강원(북강릉, 춘천, 원주, 대관령 ,속초, 철원), 울릉도, 충북(청주), 대전, 충남(서산, 천안, 보령), 전북(군산, 전주), 광주,20191217