Overview

Dataset statistics

Number of variables5
Number of observations178
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 KiB
Average record size in memory43.7 B

Variable types

Categorical1
Text1
Numeric3

Dataset

Description본 자료는 자원통합관리시스템 메타데이터 가공 자료로서 시스템 내 등록되어있는 원산지정보에 대한 데이터로 종자공급원, 수종, 조성연도 등에 대한 정보입니다.
URLhttps://www.data.go.kr/data/15116321/fileData.do

Reproduction

Analysis started2023-12-12 22:04:58.872528
Analysis finished2023-12-12 22:05:00.227690
Duration1.36 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

종자공급원
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
채종임분
125 
채종림
53 

Length

Max length4
Median length4
Mean length3.7022472
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row채종림
2nd row채종임분
3rd row채종임분
4th row채종임분
5th row채종임분

Common Values

ValueCountFrequency (%)
채종임분 125
70.2%
채종림 53
29.8%

Length

2023-12-13T07:05:00.309649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:05:00.425546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
채종임분 125
70.2%
채종림 53
29.8%

수종
Text

Distinct54
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-13T07:05:00.635540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length3.9382022
Min length2

Characters and Unicode

Total characters701
Distinct characters92
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

Unique25 ?
Unique (%)14.0%

Sample

1st row소나무
2nd row강원지방소나무
3rd row백합나무
4th row백합나무
5th row상수리나무
ValueCountFrequency (%)
낙엽송 19
 
10.7%
소나무 14
 
7.9%
백합나무 9
 
5.1%
상수리나무 9
 
5.1%
느티나무 8
 
4.5%
자작나무 8
 
4.5%
스트로브잣나무 8
 
4.5%
굴참나무 6
 
3.4%
전나무 6
 
3.4%
편백 6
 
3.4%
Other values (44) 85
47.8%
2023-12-13T07:05:01.006786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
나 143
20.4%
무 143
20.4%
송 20
 
2.9%
낙 19
 
2.7%
엽 19
 
2.7%
백 18
 
2.6%
소 15
 
2.1%
참 13
 
1.9%
로 13
 
1.9%
수 11
 
1.6%
Other values (82) 287
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 701
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
나 143
20.4%
무 143
20.4%
송 20
 
2.9%
낙 19
 
2.7%
엽 19
 
2.7%
백 18
 
2.6%
소 15
 
2.1%
참 13
 
1.9%
로 13
 
1.9%
수 11
 
1.6%
Other values (82) 287
40.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 701
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
나 143
20.4%
무 143
20.4%
송 20
 
2.9%
낙 19
 
2.7%
엽 19
 
2.7%
백 18
 
2.6%
소 15
 
2.1%
참 13
 
1.9%
로 13
 
1.9%
수 11
 
1.6%
Other values (82) 287
40.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 701
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
나 143
20.4%
무 143
20.4%
송 20
 
2.9%
낙 19
 
2.7%
엽 19
 
2.7%
백 18
 
2.6%
소 15
 
2.1%
참 13
 
1.9%
로 13
 
1.9%
수 11
 
1.6%
Other values (82) 287
40.9%

수령
Real number (ℝ)

Distinct54
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.662921
Minimum1
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T07:05:01.154457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q126.25
median35
Q341.75
95-th percentile59.15
Maximum200
Range199
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation18.869426
Coefficient of variation (CV)0.52910488
Kurtosis32.671951
Mean35.662921
Median Absolute Deviation (MAD)8
Skewness4.1444642
Sum6348
Variance356.05523
MonotonicityNot monotonic
2023-12-13T07:05:01.642354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 10
 
5.6%
40 10
 
5.6%
30 9
 
5.1%
31 7
 
3.9%
38 7
 
3.9%
36 7
 
3.9%
27 7
 
3.9%
28 7
 
3.9%
16 5
 
2.8%
33 5
 
2.8%
Other values (44) 104
58.4%
ValueCountFrequency (%)
1 1
 
0.6%
8 2
 
1.1%
9 1
 
0.6%
10 2
 
1.1%
11 1
 
0.6%
13 1
 
0.6%
15 3
1.7%
16 5
2.8%
17 2
 
1.1%
18 1
 
0.6%
ValueCountFrequency (%)
200 1
 
0.6%
106 1
 
0.6%
84 1
 
0.6%
80 1
 
0.6%
66 1
 
0.6%
65 1
 
0.6%
60 3
1.7%
59 1
 
0.6%
57 1
 
0.6%
56 2
1.1%

조성연도
Real number (ℝ)

Distinct20
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.2809
Minimum1986
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T07:05:01.800002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1986
5-th percentile2003
Q12007
median2009.5
Q32015
95-th percentile2016
Maximum2020
Range34
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.7035795
Coefficient of variation (CV)0.0028372053
Kurtosis3.8664629
Mean2010.2809
Median Absolute Deviation (MAD)4
Skewness-1.5700365
Sum357830
Variance32.53082
MonotonicityNot monotonic
2023-12-13T07:05:01.968465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2015 51
28.7%
2009 24
13.5%
2006 23
12.9%
2016 19
 
10.7%
2008 14
 
7.9%
2007 13
 
7.3%
2012 9
 
5.1%
2003 4
 
2.2%
2013 4
 
2.2%
2014 3
 
1.7%
Other values (10) 14
 
7.9%
ValueCountFrequency (%)
1986 2
 
1.1%
1992 3
 
1.7%
1994 1
 
0.6%
1996 1
 
0.6%
1997 1
 
0.6%
2003 4
 
2.2%
2004 1
 
0.6%
2005 2
 
1.1%
2006 23
12.9%
2007 13
7.3%
ValueCountFrequency (%)
2020 1
 
0.6%
2016 19
 
10.7%
2015 51
28.7%
2014 3
 
1.7%
2013 4
 
2.2%
2012 9
 
5.1%
2011 1
 
0.6%
2010 1
 
0.6%
2009 24
13.5%
2008 14
 
7.9%

본수
Real number (ℝ)

Distinct118
Distinct (%)66.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1638.4719
Minimum1
Maximum22000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T07:05:02.146956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile30.4
Q1250
median800
Q31750
95-th percentile6018
Maximum22000
Range21999
Interquartile range (IQR)1500

Descriptive statistics

Standard deviation2542.9984
Coefficient of variation (CV)1.5520549
Kurtosis24.688381
Mean1638.4719
Median Absolute Deviation (MAD)695
Skewness4.0445447
Sum291648
Variance6466840.6
MonotonicityNot monotonic
2023-12-13T07:05:02.301991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500 7
 
3.9%
1500 6
 
3.4%
1000 6
 
3.4%
3000 5
 
2.8%
900 5
 
2.8%
300 5
 
2.8%
600 5
 
2.8%
50 4
 
2.2%
1200 4
 
2.2%
800 3
 
1.7%
Other values (108) 128
71.9%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
10 1
0.6%
17 1
0.6%
18 1
0.6%
20 2
1.1%
21 1
0.6%
27 1
0.6%
31 1
0.6%
32 1
0.6%
ValueCountFrequency (%)
22000 1
0.6%
12500 1
0.6%
9225 1
0.6%
8262 1
0.6%
8000 1
0.6%
7900 1
0.6%
7250 1
0.6%
7000 1
0.6%
6120 1
0.6%
6000 1
0.6%

Interactions

2023-12-13T07:04:59.754903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:04:59.077445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:04:59.385384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:04:59.858140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:04:59.186037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:04:59.529904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:04:59.946568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:04:59.287479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:04:59.660896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:05:02.420320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종자공급원수종수령조성연도본수
종자공급원1.0000.5020.1410.4620.139
수종0.5021.0000.0000.7820.000
수령0.1410.0001.0000.5980.000
조성연도0.4620.7820.5981.0000.000
본수0.1390.0000.0000.0001.000
2023-12-13T07:05:02.554947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수령조성연도본수종자공급원
수령1.000-0.280-0.1420.148
조성연도-0.2801.0000.2650.452
본수-0.1420.2651.0000.146
종자공급원0.1480.4520.1461.000

Missing values

2023-12-13T07:05:00.076195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:05:00.192496image/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

종자공급원수종수령조성연도본수
0채종림소나무221994485
1채종임분강원지방소나무120201
2채종임분백합나무352008216
3채종임분백합나무45200820
4채종임분상수리나무44200950
5채종임분상수리나무362009180
6채종임분자작나무232009500
7채종임분물푸레나무2520091000
8채종임분백합나무48201195
9채종림고로쇠나무3520061600
종자공급원수종수령조성연도본수
168채종임분층층나무292007625
169채종임분편백3520071200
170채종임분편백362007500
171채종임분편백352012840
172채종임분편백2420158000
173채종임분독일가문비372015280
174채종임분황칠나무1320151000
175채종임분후박나무172015675
176채종임분황칠나무11201563
177채종임분편백3320151200