Overview

Dataset statistics

Number of variables5
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory48.7 B

Variable types

Text2
Numeric3

Dataset

Description본 자료는 묘목생산현황정보에 대한 데이터로 국유시설을 통한 최근 3년간 묘목생산량 추이 등 묘목 현황에 대한 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15114160/fileData.do

Alerts

2020년 묘목생산량(천본) is highly overall correlated with 2021년 묘목생산량(천본) and 1 other fieldsHigh correlation
2021년 묘목생산량(천본) is highly overall correlated with 2020년 묘목생산량(천본) and 1 other fieldsHigh correlation
2022년 묘목생산량(천본) is highly overall correlated with 2020년 묘목생산량(천본) and 1 other fieldsHigh correlation
2021년 묘목생산량(천본) has unique valuesUnique
2022년 묘목생산량(천본) has unique valuesUnique
2020년 묘목생산량(천본) has 2 (8.7%) zerosZeros
2021년 묘목생산량(천본) has 1 (4.3%) zerosZeros
2022년 묘목생산량(천본) has 1 (4.3%) zerosZeros

Reproduction

Analysis started2023-12-12 14:46:50.361255
Analysis finished2023-12-12 14:46:51.666532
Duration1.31 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관
Text

Distinct14
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-12T23:46:51.771185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters161
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)39.1%

Sample

1st row국유(북부청)
2nd row국유(북부청)
3rd row국유(북부청)
4th row국유(북부청)
5th row국유(동부청)
ValueCountFrequency (%)
국유(북부청 4
17.4%
국유(동부청 4
17.4%
국유(남부청 2
8.7%
국유(중부청 2
8.7%
국유(서부청 2
8.7%
민유(경기도 1
 
4.3%
민유(강원도 1
 
4.3%
민유(충북도 1
 
4.3%
민유(충남도 1
 
4.3%
민유(전북도 1
 
4.3%
Other values (4) 4
17.4%
2023-12-12T23:46:52.122401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 23
14.3%
) 23
14.3%
유 23
14.3%
국 14
8.7%
부 14
8.7%
청 14
8.7%
도 9
 
5.6%
민 9
 
5.6%
북 7
 
4.3%
남 5
 
3.1%
Other values (11) 20
12.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 115
71.4%
Open Punctuation 23
 
14.3%
Close Punctuation 23
 
14.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
유 23
20.0%
국 14
12.2%
부 14
12.2%
청 14
12.2%
도 9
 
7.8%
민 9
 
7.8%
북 7
 
6.1%
남 5
 
4.3%
동 4
 
3.5%
경 3
 
2.6%
Other values (9) 13
11.3%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 115
71.4%
Common 46
 
28.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
유 23
20.0%
국 14
12.2%
부 14
12.2%
청 14
12.2%
도 9
 
7.8%
민 9
 
7.8%
북 7
 
6.1%
남 5
 
4.3%
동 4
 
3.5%
경 3
 
2.6%
Other values (9) 13
11.3%
Common
ValueCountFrequency (%)
( 23
50.0%
) 23
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 115
71.4%
ASCII 46
 
28.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 23
50.0%
) 23
50.0%
Hangul
ValueCountFrequency (%)
유 23
20.0%
국 14
12.2%
부 14
12.2%
청 14
12.2%
도 9
 
7.8%
민 9
 
7.8%
북 7
 
6.1%
남 5
 
4.3%
동 4
 
3.5%
경 3
 
2.6%
Other values (9) 13
11.3%

양묘장
Text

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-12T23:46:52.334606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.2608696
Min length2

Characters and Unicode

Total characters75
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)91.3%

Sample

1st row용문(양평)
2nd row화천
3rd row홍천
4th row양구
5th row정선
ValueCountFrequency (%)
8개소 2
 
8.7%
용문(양평 1
 
4.3%
화천 1
 
4.3%
5개소 1
 
4.3%
10개소 1
 
4.3%
13개소 1
 
4.3%
9개소 1
 
4.3%
7개소 1
 
4.3%
12개소 1
 
4.3%
남원 1
 
4.3%
Other values (12) 12
52.2%
2023-12-12T23:46:52.712441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
소 9
 
12.0%
개 9
 
12.0%
( 4
 
5.3%
) 4
 
5.3%
성 3
 
4.0%
화 3
 
4.0%
양 3
 
4.0%
1 3
 
4.0%
천 2
 
2.7%
봉 2
 
2.7%
Other values (30) 33
44.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 55
73.3%
Decimal Number 12
 
16.0%
Open Punctuation 4
 
5.3%
Close Punctuation 4
 
5.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
소 9
16.4%
개 9
16.4%
성 3
 
5.5%
화 3
 
5.5%
양 3
 
5.5%
천 2
 
3.6%
봉 2
 
3.6%
평 2
 
3.6%
삼 1
 
1.8%
원 1
 
1.8%
Other values (20) 20
36.4%
Decimal Number
ValueCountFrequency (%)
1 3
25.0%
8 2
16.7%
2 2
16.7%
7 1
 
8.3%
9 1
 
8.3%
3 1
 
8.3%
0 1
 
8.3%
5 1
 
8.3%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 55
73.3%
Common 20
 
26.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
소 9
16.4%
개 9
16.4%
성 3
 
5.5%
화 3
 
5.5%
양 3
 
5.5%
천 2
 
3.6%
봉 2
 
3.6%
평 2
 
3.6%
삼 1
 
1.8%
원 1
 
1.8%
Other values (20) 20
36.4%
Common
ValueCountFrequency (%)
( 4
20.0%
) 4
20.0%
1 3
15.0%
8 2
10.0%
2 2
10.0%
7 1
 
5.0%
9 1
 
5.0%
3 1
 
5.0%
0 1
 
5.0%
5 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 55
73.3%
ASCII 20
 
26.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
소 9
16.4%
개 9
16.4%
성 3
 
5.5%
화 3
 
5.5%
양 3
 
5.5%
천 2
 
3.6%
봉 2
 
3.6%
평 2
 
3.6%
삼 1
 
1.8%
원 1
 
1.8%
Other values (20) 20
36.4%
ASCII
ValueCountFrequency (%)
( 4
20.0%
) 4
20.0%
1 3
15.0%
8 2
10.0%
2 2
10.0%
7 1
 
5.0%
9 1
 
5.0%
3 1
 
5.0%
0 1
 
5.0%
5 1
 
5.0%

2020년 묘목생산량(천본)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2319
Minimum0
Maximum8247
Zeros2
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T23:46:52.846874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q1238.5
median914
Q34209
95-th percentile7863.4
Maximum8247
Range8247
Interquartile range (IQR)3970.5

Descriptive statistics

Standard deviation2726.2072
Coefficient of variation (CV)1.175596
Kurtosis-0.10138085
Mean2319
Median Absolute Deviation (MAD)851
Skewness1.115228
Sum53337
Variance7432205.5
MonotonicityNot monotonic
2023-12-12T23:46:53.001452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 2
 
8.7%
1765 1
 
4.3%
909 1
 
4.3%
60 1
 
4.3%
3624 1
 
4.3%
4794 1
 
4.3%
4824 1
 
4.3%
6904 1
 
4.3%
8247 1
 
4.3%
5618 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
0 2
8.7%
60 1
4.3%
108 1
4.3%
151 1
4.3%
153 1
4.3%
324 1
4.3%
420 1
4.3%
489 1
4.3%
867 1
4.3%
909 1
4.3%
ValueCountFrequency (%)
8247 1
4.3%
7970 1
4.3%
6904 1
4.3%
5618 1
4.3%
4824 1
4.3%
4794 1
4.3%
3624 1
4.3%
2869 1
4.3%
1765 1
4.3%
1164 1
4.3%

2021년 묘목생산량(천본)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2443.7391
Minimum0
Maximum8983
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T23:46:53.134219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.2
Q1259
median1029
Q34536
95-th percentile8288
Maximum8983
Range8983
Interquartile range (IQR)4277

Descriptive statistics

Standard deviation2930.3922
Coefficient of variation (CV)1.1991428
Kurtosis-0.089826604
Mean2443.7391
Median Absolute Deviation (MAD)981
Skewness1.1147422
Sum56206
Variance8587198.5
MonotonicityNot monotonic
2023-12-12T23:46:53.299001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1915 1
 
4.3%
353 1
 
4.3%
48 1
 
4.3%
4251 1
 
4.3%
4821 1
 
4.3%
5026 1
 
4.3%
6676 1
 
4.3%
8983 1
 
4.3%
6713 1
 
4.3%
8463 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
0 1
4.3%
29 1
4.3%
31 1
4.3%
48 1
4.3%
65 1
4.3%
165 1
4.3%
353 1
4.3%
406 1
4.3%
442 1
4.3%
581 1
4.3%
ValueCountFrequency (%)
8983 1
4.3%
8463 1
4.3%
6713 1
4.3%
6676 1
4.3%
5026 1
4.3%
4821 1
4.3%
4251 1
4.3%
2893 1
4.3%
1915 1
4.3%
1385 1
4.3%

2022년 묘목생산량(천본)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2178.913
Minimum0
Maximum8400
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T23:46:53.461725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.2
Q1283.5
median934
Q33759.5
95-th percentile7493.4
Maximum8400
Range8400
Interquartile range (IQR)3476

Descriptive statistics

Standard deviation2661.6677
Coefficient of variation (CV)1.2215576
Kurtosis0.21128881
Mean2178.913
Median Absolute Deviation (MAD)906
Skewness1.233146
Sum50115
Variance7084474.9
MonotonicityNot monotonic
2023-12-12T23:46:53.594358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
934 1
 
4.3%
402 1
 
4.3%
12 1
 
4.3%
3285 1
 
4.3%
4755 1
 
4.3%
4234 1
 
4.3%
6120 1
 
4.3%
8400 1
 
4.3%
6049 1
 
4.3%
7646 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
0 1
4.3%
12 1
4.3%
14 1
4.3%
28 1
4.3%
67 1
4.3%
165 1
4.3%
402 1
4.3%
484 1
4.3%
615 1
4.3%
905 1
4.3%
ValueCountFrequency (%)
8400 1
4.3%
7646 1
4.3%
6120 1
4.3%
6049 1
4.3%
4755 1
4.3%
4234 1
4.3%
3285 1
4.3%
2074 1
4.3%
1022 1
4.3%
1012 1
4.3%

Interactions

2023-12-12T23:46:51.003292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:46:50.541480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:46:50.775579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:46:51.081168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:46:50.636235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:46:50.846630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:46:51.421381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:46:50.704324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:46:50.917757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:46:53.694767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기관양묘장2020년 묘목생산량(천본)2021년 묘목생산량(천본)2022년 묘목생산량(천본)
기관1.0000.7400.9230.9030.984
양묘장0.7401.0000.0000.0000.000
2020년 묘목생산량(천본)0.9230.0001.0000.9670.970
2021년 묘목생산량(천본)0.9030.0000.9671.0000.976
2022년 묘목생산량(천본)0.9840.0000.9700.9761.000
2023-12-12T23:46:53.809310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2020년 묘목생산량(천본)2021년 묘목생산량(천본)2022년 묘목생산량(천본)
2020년 묘목생산량(천본)1.0000.9870.959
2021년 묘목생산량(천본)0.9871.0000.968
2022년 묘목생산량(천본)0.9590.9681.000

Missing values

2023-12-12T23:46:51.512994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:46:51.614157image/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

기관양묘장2020년 묘목생산량(천본)2021년 묘목생산량(천본)2022년 묘목생산량(천본)
0국유(북부청)용문(양평)17651915934
1국유(북부청)화천420353402
2국유(북부청)홍천1516567
3국유(북부청)양구1083114
4국유(동부청)정선8674421022
5국유(동부청)평창116413851012
6국유(동부청)연곡324406615
7국유(동부청)대관령02928
8국유(남부청)춘양(봉화)11631029912
9국유(남부청)서벽(봉화)000
기관양묘장2020년 묘목생산량(천본)2021년 묘목생산량(천본)2022년 묘목생산량(천본)
13국유(서부청)남원909581484
14민유(경기도)8개소286928932074
15민유(강원도)12개소797084637646
16민유(충북도)7개소561867136049
17민유(충남도)8개소824789838400
18민유(전북도)9개소690466766120
19민유(전남도)13개소482450264234
20민유(경북도)10개소479448214755
21민유(경남도)5개소362442513285
22민유(제주도)2개소604812