Overview

Dataset statistics

Number of variables16
Number of observations208
Missing cells125
Missing cells (%)3.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.9 KiB
Average record size in memory132.6 B

Variable types

Text8
Categorical4
Numeric4

Dataset

Description산청군 약초 정보(약초명, 지역, 효능, 사용부위, 생약명, 학명, 과명, 생육상명, 결실시기, 용도 등 )에 대한 공공데이터 자료입니다.
Author경상남도 산청군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15041774

Alerts

데이터기준일자 has constant value ""Constant
개화시기 시작(월) is highly overall correlated with 개화시기 종료(월) and 2 other fieldsHigh correlation
개화시기 종료(월) is highly overall correlated with 개화시기 시작(월) and 1 other fieldsHigh correlation
결실시기 시작(월) is highly overall correlated with 개화시기 시작(월) and 1 other fieldsHigh correlation
결실시기 종료(월) is highly overall correlated with 개화시기 시작(월) and 2 other fieldsHigh correlation
효능별 구분 has 49 (23.6%) missing valuesMissing
이명 has 28 (13.5%) missing valuesMissing
개화시기 시작(월) has 7 (3.4%) missing valuesMissing
개화시기 종료(월) has 7 (3.4%) missing valuesMissing
결실시기 시작(월) has 16 (7.7%) missing valuesMissing
결실시기 종료(월) has 16 (7.7%) missing valuesMissing
약초명 has unique valuesUnique
식물명 has unique valuesUnique
학명 has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:43:13.465038
Analysis finished2023-12-11 00:43:17.455304
Duration3.99 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

약초명
Text

UNIQUE 

Distinct208
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023-12-11T09:43:17.771445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length3.3798077
Min length1

Characters and Unicode

Total characters703
Distinct characters243
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique208 ?
Unique (%)100.0%

Sample

1st row가새뽕나무
2nd row가시오갈피
3rd row감국(甘菊)
4th row회화나무
5th row황벽나무
ValueCountFrequency (%)
가새뽕나무 1
 
0.5%
가시오갈피 1
 
0.5%
두충 1
 
0.5%
인동초 1
 
0.5%
산딸나무 1
 
0.5%
인동덩굴 1
 
0.5%
도꼬마리 1
 
0.5%
산마늘 1
 
0.5%
도라지 1
 
0.5%
산벗나무 1
 
0.5%
Other values (198) 198
95.2%
2023-12-11T09:43:18.289503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
나 42
 
6.0%
무 39
 
5.5%
리 21
 
3.0%
산 15
 
2.1%
초 14
 
2.0%
지 13
 
1.8%
풀 13
 
1.8%
오 12
 
1.7%
기 11
 
1.6%
이 11
 
1.6%
Other values (233) 512
72.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 685
97.4%
Close Punctuation 9
 
1.3%
Open Punctuation 9
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
나 42
 
6.1%
무 39
 
5.7%
리 21
 
3.1%
산 15
 
2.2%
초 14
 
2.0%
지 13
 
1.9%
풀 13
 
1.9%
오 12
 
1.8%
기 11
 
1.6%
이 11
 
1.6%
Other values (231) 494
72.1%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 682
97.0%
Common 18
 
2.6%
Han 3
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
나 42
 
6.2%
무 39
 
5.7%
리 21
 
3.1%
산 15
 
2.2%
초 14
 
2.1%
지 13
 
1.9%
풀 13
 
1.9%
오 12
 
1.8%
기 11
 
1.6%
이 11
 
1.6%
Other values (228) 491
72.0%
Han
ValueCountFrequency (%)
葛 1
33.3%
甘 1
33.3%
菊 1
33.3%
Common
ValueCountFrequency (%)
) 9
50.0%
( 9
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 682
97.0%
ASCII 18
 
2.6%
CJK 3
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
나 42
 
6.2%
무 39
 
5.7%
리 21
 
3.1%
산 15
 
2.2%
초 14
 
2.1%
지 13
 
1.9%
풀 13
 
1.9%
오 12
 
1.8%
기 11
 
1.6%
이 11
 
1.6%
Other values (228) 491
72.0%
ASCII
ValueCountFrequency (%)
) 9
50.0%
( 9
50.0%
CJK
ValueCountFrequency (%)
葛 1
33.3%
甘 1
33.3%
菊 1
33.3%

권역별 구분
Categorical

Distinct30
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
113 
지리산권
21 
집현산권
 
11
웅석산권
 
8
왕산권
 
7
Other values (25)
48 

Length

Max length27
Median length4
Mean length5.8125
Min length3

Unique

Unique14 ?
Unique (%)6.7%

Sample

1st row<NA>
2nd row지리산권
3rd row왕산권
4th row<NA>
5th row지리산권

Common Values

ValueCountFrequency (%)
<NA> 113
54.3%
지리산권 21
 
10.1%
집현산권 11
 
5.3%
웅석산권 8
 
3.8%
왕산권 7
 
3.4%
황매산권 6
 
2.9%
왕산권, 웅석산권 5
 
2.4%
지리산권, 왕산권, 집현산권 4
 
1.9%
지리산권, 왕산권 3
 
1.4%
지리산권, 집현산권 3
 
1.4%
Other values (20) 27
 
13.0%

Length

2023-12-11T09:43:18.441966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 113
42.2%
지리산권 42
 
15.7%
집현산권 33
 
12.3%
왕산권 31
 
11.6%
웅석산권 28
 
10.4%
황매산권 17
 
6.3%
지리산권,황매산권,집현산권,웅석산권 1
 
0.4%
지리산권,황매산권,왕산권,집현산권,웅석산권 1
 
0.4%
왕산권,집현산권,웅석산권 1
 
0.4%
지리산권,집현산권 1
 
0.4%

효능별 구분
Text

MISSING 

Distinct115
Distinct (%)72.3%
Missing49
Missing (%)23.6%
Memory size1.8 KiB
2023-12-11T09:43:18.619809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length91
Median length42
Mean length17.660377
Min length2

Characters and Unicode

Total characters2808
Distinct characters60
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

Unique97 ?
Unique (%)61.0%

Sample

1st row기침가래
2nd row자양강장
3rd row고혈압, 기침가래
4th row위장질환,당뇨병
5th row해열,진통,치통,비뇨,생식기,성병 ,부종,이뇨,간장질환
ValueCountFrequency (%)
기침가래 24
 
8.2%
부인병 23
 
7.9%
부종,이뇨 20
 
6.9%
비뇨,생식기,성병 18
 
6.2%
독사,독충 15
 
5.2%
해열,진통,치통 14
 
4.8%
지혈제 11
 
3.8%
11
 
3.8%
심장병 11
 
3.8%
정신,신경,불면증 8
 
2.7%
Other values (80) 136
46.7%
2023-12-11T09:43:18.971519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 629
22.4%
167
 
5.9%
통 134
 
4.8%
신 105
 
3.7%
뇨 89
 
3.2%
장 83
 
3.0%
병 81
 
2.9%
기 76
 
2.7%
부 68
 
2.4%
경 58
 
2.1%
Other values (50) 1318
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2012
71.7%
Other Punctuation 629
 
22.4%
Space Separator 167
 
5.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
통 134
 
6.7%
신 105
 
5.2%
뇨 89
 
4.4%
장 83
 
4.1%
병 81
 
4.0%
기 76
 
3.8%
부 68
 
3.4%
경 58
 
2.9%
환 53
 
2.6%
질 53
 
2.6%
Other values (48) 1212
60.2%
Other Punctuation
ValueCountFrequency (%)
, 629
100.0%
Space Separator
ValueCountFrequency (%)
167
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2012
71.7%
Common 796
 
28.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
통 134
 
6.7%
신 105
 
5.2%
뇨 89
 
4.4%
장 83
 
4.1%
병 81
 
4.0%
기 76
 
3.8%
부 68
 
3.4%
경 58
 
2.9%
환 53
 
2.6%
질 53
 
2.6%
Other values (48) 1212
60.2%
Common
ValueCountFrequency (%)
, 629
79.0%
167
 
21.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2012
71.7%
ASCII 796
 
28.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 629
79.0%
167
 
21.0%
Hangul
ValueCountFrequency (%)
통 134
 
6.7%
신 105
 
5.2%
뇨 89
 
4.4%
장 83
 
4.1%
병 81
 
4.0%
기 76
 
3.8%
부 68
 
3.4%
경 58
 
2.9%
환 53
 
2.6%
질 53
 
2.6%
Other values (48) 1212
60.2%

사용부위별 구분
Categorical

Distinct36
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
<NA>
57 
전초
29 
줄기와 잎
23 
줄기와 잎
18 
과실
15 
Other values (31)
66 

Length

Max length30
Median length20
Mean length5.375
Min length2

Unique

Unique19 ?
Unique (%)9.1%

Sample

1st row 줄기와 잎, 과실
2nd row줄기와 잎
3rd row꽃류
4th row줄기와 잎, 꽃류, 과실
5th row줄기와 잎

Common Values

ValueCountFrequency (%)
<NA> 57
27.4%
전초 29
13.9%
줄기와 잎 23
11.1%
줄기와 잎 18
 
8.7%
과실 15
 
7.2%
줄기와 잎, 과실 12
 
5.8%
줄기와 잎, 과실 5
 
2.4%
꽃류 4
 
1.9%
전초 4
 
1.9%
꽃류 4
 
1.9%
Other values (26) 37
17.8%

Length

2023-12-11T09:43:19.091522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
줄기와 80
23.0%
잎 77
22.1%
na 57
16.4%
전초 46
13.2%
과실 45
12.9%
꽃류 24
 
6.9%
종자/씨앗 13
 
3.7%
잎,과실 1
 
0.3%
종자/씨앗,전초 1
 
0.3%
수지류 1
 
0.3%
Other values (3) 3
 
0.9%

생약명
Text

Distinct201
Distinct (%)97.1%
Missing1
Missing (%)0.5%
Memory size1.8 KiB
2023-12-11T09:43:19.351681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length6.7004831
Min length2

Characters and Unicode

Total characters1387
Distinct characters451
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique195 ?
Unique (%)94.2%

Sample

1st row상기(桑起),상근(桑根), 상실(桑實)
2nd row자오가근(刺五加根)
3rd row야국(野菊)
4th row괴화(傀花)
5th row황백(黃栢)
ValueCountFrequency (%)
산약(山藥 2
 
0.9%
미후리 2
 
0.9%
용담(龍膽 2
 
0.9%
당귀(當歸 2
 
0.9%
야국(野菊 2
 
0.9%
황금(黃芩 2
 
0.9%
초오(草烏 2
 
0.9%
찰진흙버섯 1
 
0.5%
총목피(曾木皮 1
 
0.5%
독활(獨活 1
 
0.5%
Other values (201) 201
92.2%
2023-12-11T09:43:19.835833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 174
 
12.5%
) 174
 
12.5%
초 30
 
2.2%
자 24
 
1.7%
草 18
 
1.3%
산 13
 
0.9%
수 13
 
0.9%
, 13
 
0.9%
子 12
 
0.9%
11
 
0.8%
Other values (441) 905
65.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 975
70.3%
Open Punctuation 174
 
12.5%
Close Punctuation 174
 
12.5%
Other Punctuation 28
 
2.0%
Decimal Number 25
 
1.8%
Space Separator 11
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
초 30
 
3.1%
자 24
 
2.5%
草 18
 
1.8%
산 13
 
1.3%
수 13
 
1.3%
子 12
 
1.2%
근 10
 
1.0%
오 10
 
1.0%
백 10
 
1.0%
삼 9
 
0.9%
Other values (424) 826
84.7%
Decimal Number
ValueCountFrequency (%)
8 5
20.0%
6 4
16.0%
2 3
12.0%
9 3
12.0%
4 2
 
8.0%
3 2
 
8.0%
1 2
 
8.0%
7 2
 
8.0%
5 1
 
4.0%
0 1
 
4.0%
Other Punctuation
ValueCountFrequency (%)
, 13
46.4%
& 5
 
17.9%
# 5
 
17.9%
; 5
 
17.9%
Open Punctuation
ValueCountFrequency (%)
( 174
100.0%
Close Punctuation
ValueCountFrequency (%)
) 174
100.0%
Space Separator
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 551
39.7%
Han 424
30.6%
Common 412
29.7%

Most frequent character per script

Han
ValueCountFrequency (%)
草 18
 
4.2%
子 12
 
2.8%
黃 9
 
2.1%
皮 9
 
2.1%
山 8
 
1.9%
白 7
 
1.7%
根 7
 
1.7%
蔘 6
 
1.4%
木 5
 
1.2%
菜 5
 
1.2%
Other values (238) 338
79.7%
Hangul
ValueCountFrequency (%)
초 30
 
5.4%
자 24
 
4.4%
산 13
 
2.4%
수 13
 
2.4%
근 10
 
1.8%
오 10
 
1.8%
백 10
 
1.8%
삼 9
 
1.6%
천 9
 
1.6%
황 9
 
1.6%
Other values (176) 414
75.1%
Common
ValueCountFrequency (%)
( 174
42.2%
) 174
42.2%
, 13
 
3.2%
11
 
2.7%
& 5
 
1.2%
# 5
 
1.2%
; 5
 
1.2%
8 5
 
1.2%
6 4
 
1.0%
2 3
 
0.7%
Other values (7) 13
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 551
39.7%
ASCII 412
29.7%
CJK 412
29.7%
CJK Compat Ideographs 12
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 174
42.2%
) 174
42.2%
, 13
 
3.2%
11
 
2.7%
& 5
 
1.2%
# 5
 
1.2%
; 5
 
1.2%
8 5
 
1.2%
6 4
 
1.0%
2 3
 
0.7%
Other values (7) 13
 
3.2%
Hangul
ValueCountFrequency (%)
초 30
 
5.4%
자 24
 
4.4%
산 13
 
2.4%
수 13
 
2.4%
근 10
 
1.8%
오 10
 
1.8%
백 10
 
1.8%
삼 9
 
1.6%
천 9
 
1.6%
황 9
 
1.6%
Other values (176) 414
75.1%
CJK
ValueCountFrequency (%)
草 18
 
4.4%
子 12
 
2.9%
黃 9
 
2.2%
皮 9
 
2.2%
山 8
 
1.9%
白 7
 
1.7%
根 7
 
1.7%
蔘 6
 
1.5%
木 5
 
1.2%
菜 5
 
1.2%
Other values (228) 326
79.1%
CJK Compat Ideographs
ValueCountFrequency (%)
龍 3
25.0%
律 1
 
8.3%
旅 1
 
8.3%
鈴 1
 
8.3%
雷 1
 
8.3%
參 1
 
8.3%
金 1
 
8.3%
狼 1
 
8.3%
蓮 1
 
8.3%
連 1
 
8.3%

식물명
Text

UNIQUE 

Distinct208
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023-12-11T09:43:20.171550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length3.3798077
Min length1

Characters and Unicode

Total characters703
Distinct characters243
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique208 ?
Unique (%)100.0%

Sample

1st row가새뽕나무
2nd row가시오갈피
3rd row감국(甘菊)
4th row회화나무
5th row황벽나무
ValueCountFrequency (%)
가새뽕나무 1
 
0.5%
가시오갈피 1
 
0.5%
두충 1
 
0.5%
인동초 1
 
0.5%
산딸나무 1
 
0.5%
인동덩굴 1
 
0.5%
도꼬마리 1
 
0.5%
산마늘 1
 
0.5%
도라지 1
 
0.5%
산벗나무 1
 
0.5%
Other values (198) 198
95.2%
2023-12-11T09:43:20.617988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
나 42
 
6.0%
무 39
 
5.5%
리 21
 
3.0%
산 15
 
2.1%
초 14
 
2.0%
지 13
 
1.8%
풀 13
 
1.8%
오 12
 
1.7%
기 11
 
1.6%
이 11
 
1.6%
Other values (233) 512
72.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 685
97.4%
Close Punctuation 9
 
1.3%
Open Punctuation 9
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
나 42
 
6.1%
무 39
 
5.7%
리 21
 
3.1%
산 15
 
2.2%
초 14
 
2.0%
지 13
 
1.9%
풀 13
 
1.9%
오 12
 
1.8%
기 11
 
1.6%
이 11
 
1.6%
Other values (231) 494
72.1%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 682
97.0%
Common 18
 
2.6%
Han 3
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
나 42
 
6.2%
무 39
 
5.7%
리 21
 
3.1%
산 15
 
2.2%
초 14
 
2.1%
지 13
 
1.9%
풀 13
 
1.9%
오 12
 
1.8%
기 11
 
1.6%
이 11
 
1.6%
Other values (228) 491
72.0%
Han
ValueCountFrequency (%)
葛 1
33.3%
甘 1
33.3%
菊 1
33.3%
Common
ValueCountFrequency (%)
) 9
50.0%
( 9
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 682
97.0%
ASCII 18
 
2.6%
CJK 3
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
나 42
 
6.2%
무 39
 
5.7%
리 21
 
3.1%
산 15
 
2.2%
초 14
 
2.1%
지 13
 
1.9%
풀 13
 
1.9%
오 12
 
1.8%
기 11
 
1.6%
이 11
 
1.6%
Other values (228) 491
72.0%
ASCII
ValueCountFrequency (%)
) 9
50.0%
( 9
50.0%
CJK
ValueCountFrequency (%)
葛 1
33.3%
甘 1
33.3%
菊 1
33.3%

학명
Text

UNIQUE 

Distinct208
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023-12-11T09:43:20.957143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length70
Median length45
Mean length29.432692
Min length11

Characters and Unicode

Total characters6122
Distinct characters58
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique208 ?
Unique (%)100.0%

Sample

1st rowMorus bombycis L. for. dissecta NAKAI
2nd rowAcanthopanax Senticosus HARMS.
3rd rowChrysanthemum indicum L.
4th rowSophora japonica
5th rowPhellodendron amurense
ValueCountFrequency (%)
l 33
 
4.3%
var 26
 
3.4%
et 19
 
2.5%
nakai 18
 
2.3%
thunb 16
 
2.1%
japonica 13
 
1.7%
miq 11
 
1.4%
sieb 9
 
1.2%
zucc 8
 
1.0%
kitamura 7
 
0.9%
Other values (490) 614
79.3%
2023-12-11T09:43:21.421457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
567
 
9.3%
a 529
 
8.6%
i 446
 
7.3%
e 286
 
4.7%
s 285
 
4.7%
r 276
 
4.5%
n 270
 
4.4%
. 264
 
4.3%
u 256
 
4.2%
o 254
 
4.1%
Other values (48) 2689
43.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4012
65.5%
Uppercase Letter 1177
 
19.2%
Space Separator 567
 
9.3%
Other Punctuation 265
 
4.3%
Open Punctuation 50
 
0.8%
Close Punctuation 50
 
0.8%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 529
13.2%
i 446
11.1%
e 286
 
7.1%
s 285
 
7.1%
r 276
 
6.9%
n 270
 
6.7%
u 256
 
6.4%
o 254
 
6.3%
l 210
 
5.2%
t 198
 
4.9%
Other values (16) 1002
25.0%
Uppercase Letter
ValueCountFrequency (%)
A 147
 
12.5%
L 80
 
6.8%
M 76
 
6.5%
E 71
 
6.0%
I 70
 
5.9%
C 69
 
5.9%
N 68
 
5.8%
R 62
 
5.3%
S 57
 
4.8%
H 55
 
4.7%
Other values (16) 422
35.9%
Other Punctuation
ValueCountFrequency (%)
. 264
99.6%
, 1
 
0.4%
Space Separator
ValueCountFrequency (%)
567
100.0%
Open Punctuation
ValueCountFrequency (%)
( 50
100.0%
Close Punctuation
ValueCountFrequency (%)
) 50
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5189
84.8%
Common 933
 
15.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 529
 
10.2%
i 446
 
8.6%
e 286
 
5.5%
s 285
 
5.5%
r 276
 
5.3%
n 270
 
5.2%
u 256
 
4.9%
o 254
 
4.9%
l 210
 
4.0%
t 198
 
3.8%
Other values (42) 2179
42.0%
Common
ValueCountFrequency (%)
567
60.8%
. 264
28.3%
( 50
 
5.4%
) 50
 
5.4%
, 1
 
0.1%
- 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6122
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
567
 
9.3%
a 529
 
8.6%
i 446
 
7.3%
e 286
 
4.7%
s 285
 
4.7%
r 276
 
4.5%
n 270
 
4.4%
. 264
 
4.3%
u 256
 
4.2%
o 254
 
4.1%
Other values (48) 2689
43.9%

이명
Text

MISSING 

Distinct179
Distinct (%)99.4%
Missing28
Missing (%)13.5%
Memory size1.8 KiB
2023-12-11T09:43:21.715110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length25
Mean length10.327778
Min length2

Characters and Unicode

Total characters1859
Distinct characters461
Distinct categories7 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique178 ?
Unique (%)98.9%

Sample

1st row가새뽕
2nd row자오가(刺五加)
3rd row고의(苦薏), 야산국
4th row괴화나무
5th row벽목(蘗木), 황경나무, 황경피나무, 단항(檀杭)
ValueCountFrequency (%)
끼무릇 2
 
0.5%
적작약 2
 
0.5%
건귀(乾歸 2
 
0.5%
박달나무 1
 
0.3%
쥐손이풀 1
 
0.3%
개발초 1
 
0.3%
염교 1
 
0.3%
염 1
 
0.3%
산총 1
 
0.3%
저이(猪耳 1
 
0.3%
Other values (356) 356
96.5%
2023-12-11T09:43:22.132472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
189
 
10.2%
, 181
 
9.7%
) 73
 
3.9%
( 73
 
3.9%
나 49
 
2.6%
무 48
 
2.6%
초 41
 
2.2%
산 24
 
1.3%
자 21
 
1.1%
수 17
 
0.9%
Other values (451) 1143
61.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1308
70.4%
Other Punctuation 197
 
10.6%
Space Separator 189
 
10.2%
Close Punctuation 73
 
3.9%
Open Punctuation 73
 
3.9%
Lowercase Letter 18
 
1.0%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
나 49
 
3.7%
무 48
 
3.7%
초 41
 
3.1%
산 24
 
1.8%
자 21
 
1.6%
수 17
 
1.3%
지 17
 
1.3%
화 16
 
1.2%
기 15
 
1.1%
삼 14
 
1.1%
Other values (432) 1046
80.0%
Lowercase Letter
ValueCountFrequency (%)
r 3
16.7%
e 2
11.1%
h 2
11.1%
w 2
11.1%
i 2
11.1%
t 1
 
5.6%
u 1
 
5.6%
m 1
 
5.6%
f 1
 
5.6%
l 1
 
5.6%
Other values (2) 2
11.1%
Other Punctuation
ValueCountFrequency (%)
, 181
91.9%
. 12
 
6.1%
· 4
 
2.0%
Space Separator
ValueCountFrequency (%)
189
100.0%
Close Punctuation
ValueCountFrequency (%)
) 73
100.0%
Open Punctuation
ValueCountFrequency (%)
( 73
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1130
60.8%
Common 532
28.6%
Han 178
 
9.6%
Latin 19
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
나 49
 
4.3%
무 48
 
4.2%
초 41
 
3.6%
산 24
 
2.1%
자 21
 
1.9%
수 17
 
1.5%
지 17
 
1.5%
화 16
 
1.4%
기 15
 
1.3%
삼 14
 
1.2%
Other values (310) 868
76.8%
Han
ValueCountFrequency (%)
草 8
 
4.5%
花 6
 
3.4%
蔘 5
 
2.8%
歸 4
 
2.2%
香 4
 
2.2%
黃 3
 
1.7%
子 3
 
1.7%
蓮 3
 
1.7%
萎 3
 
1.7%
石 3
 
1.7%
Other values (112) 136
76.4%
Latin
ValueCountFrequency (%)
r 3
15.8%
e 2
10.5%
h 2
10.5%
w 2
10.5%
i 2
10.5%
P 1
 
5.3%
t 1
 
5.3%
u 1
 
5.3%
m 1
 
5.3%
f 1
 
5.3%
Other values (3) 3
15.8%
Common
ValueCountFrequency (%)
189
35.5%
, 181
34.0%
) 73
 
13.7%
( 73
 
13.7%
. 12
 
2.3%
· 4
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1130
60.8%
ASCII 547
29.4%
CJK 175
 
9.4%
None 4
 
0.2%
CJK Compat Ideographs 3
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
189
34.6%
, 181
33.1%
) 73
 
13.3%
( 73
 
13.3%
. 12
 
2.2%
r 3
 
0.5%
e 2
 
0.4%
h 2
 
0.4%
w 2
 
0.4%
i 2
 
0.4%
Other values (8) 8
 
1.5%
Hangul
ValueCountFrequency (%)
나 49
 
4.3%
무 48
 
4.2%
초 41
 
3.6%
산 24
 
2.1%
자 21
 
1.9%
수 17
 
1.5%
지 17
 
1.5%
화 16
 
1.4%
기 15
 
1.3%
삼 14
 
1.2%
Other values (310) 868
76.8%
CJK
ValueCountFrequency (%)
草 8
 
4.6%
花 6
 
3.4%
蔘 5
 
2.9%
歸 4
 
2.3%
香 4
 
2.3%
黃 3
 
1.7%
子 3
 
1.7%
蓮 3
 
1.7%
萎 3
 
1.7%
石 3
 
1.7%
Other values (109) 133
76.0%
None
ValueCountFrequency (%)
· 4
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
利 1
33.3%
女 1
33.3%
落 1
33.3%

과명
Text

Distinct92
Distinct (%)44.4%
Missing1
Missing (%)0.5%
Memory size1.8 KiB
2023-12-11T09:43:22.375159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length3.9130435
Min length2

Characters and Unicode

Total characters810
Distinct characters155
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)27.5%

Sample

1st row뽕나무과
2nd row오갈피과
3rd row국화과
4th row콩과
5th row운향과
ValueCountFrequency (%)
국화과 19
 
9.1%
백합과 15
 
7.2%
꿀풀과 11
 
5.3%
장미과 10
 
4.8%
미나리아재비과 7
 
3.3%
미나리과 7
 
3.3%
콩과 6
 
2.9%
마디풀과 5
 
2.4%
초롱꽃과 5
 
2.4%
가지과 4
 
1.9%
Other values (83) 120
57.4%
2023-12-11T09:43:22.699761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 208
25.7%
나 54
 
6.7%
무 35
 
4.3%
미 26
 
3.2%
리 24
 
3.0%
화 20
 
2.5%
국 19
 
2.3%
풀 19
 
2.3%
백 16
 
2.0%
합 15
 
1.9%
Other values (145) 374
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 784
96.8%
Lowercase Letter 18
 
2.2%
Open Punctuation 2
 
0.2%
Close Punctuation 2
 
0.2%
Space Separator 2
 
0.2%
Uppercase Letter 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 208
26.5%
나 54
 
6.9%
무 35
 
4.5%
미 26
 
3.3%
리 24
 
3.1%
화 20
 
2.6%
국 19
 
2.4%
풀 19
 
2.4%
백 16
 
2.0%
합 15
 
1.9%
Other values (129) 348
44.4%
Lowercase Letter
ValueCountFrequency (%)
e 5
27.8%
a 3
16.7%
l 2
 
11.1%
c 1
 
5.6%
t 1
 
5.6%
u 1
 
5.6%
m 1
 
5.6%
r 1
 
5.6%
f 1
 
5.6%
i 1
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
R 1
50.0%
U 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 784
96.8%
Latin 20
 
2.5%
Common 6
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 208
26.5%
나 54
 
6.9%
무 35
 
4.5%
미 26
 
3.3%
리 24
 
3.1%
화 20
 
2.6%
국 19
 
2.4%
풀 19
 
2.4%
백 16
 
2.0%
합 15
 
1.9%
Other values (129) 348
44.4%
Latin
ValueCountFrequency (%)
e 5
25.0%
a 3
15.0%
l 2
 
10.0%
c 1
 
5.0%
t 1
 
5.0%
u 1
 
5.0%
R 1
 
5.0%
U 1
 
5.0%
m 1
 
5.0%
r 1
 
5.0%
Other values (3) 3
15.0%
Common
ValueCountFrequency (%)
( 2
33.3%
) 2
33.3%
2
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 784
96.8%
ASCII 26
 
3.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 208
26.5%
나 54
 
6.9%
무 35
 
4.5%
미 26
 
3.3%
리 24
 
3.1%
화 20
 
2.6%
국 19
 
2.4%
풀 19
 
2.4%
백 16
 
2.0%
합 15
 
1.9%
Other values (129) 348
44.4%
ASCII
ValueCountFrequency (%)
e 5
19.2%
a 3
11.5%
( 2
 
7.7%
) 2
 
7.7%
l 2
 
7.7%
2
 
7.7%
c 1
 
3.8%
t 1
 
3.8%
u 1
 
3.8%
R 1
 
3.8%
Other values (6) 6
23.1%

생육상명
Categorical

Distinct50
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
다년생초본
83 
목본식물
35 
여러해살이 풀
15 
다년생 초본
9 
낙엽교목
 
5
Other values (45)
61 

Length

Max length12
Median length11
Mean length5.4182692
Min length3

Unique

Unique36 ?
Unique (%)17.3%

Sample

1st row목본식물
2nd row목본식물
3rd row다년생초본
4th row낙엽교목
5th row낙엽교목

Common Values

ValueCountFrequency (%)
다년생초본 83
39.9%
목본식물 35
16.8%
여러해살이 풀 15
 
7.2%
다년생 초본 9
 
4.3%
낙엽교목 5
 
2.4%
한해살이 풀 4
 
1.9%
여러해살이풀 3
 
1.4%
여러해살이 초본 3
 
1.4%
두해살이 풀 3
 
1.4%
<NA> 3
 
1.4%
Other values (40) 45
21.6%

Length

2023-12-11T09:43:22.804700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
다년생초본 84
32.2%
목본식물 36
13.8%
풀 25
 
9.6%
여러해살이 23
 
8.8%
초본 15
 
5.7%
다년생 12
 
4.6%
낙엽교목 5
 
1.9%
한해살이 5
 
1.9%
na 3
 
1.1%
덩굴 3
 
1.1%
Other values (39) 50
19.2%

개화시기 시작(월)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)4.5%
Missing7
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean6.0746269
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-11T09:43:22.883198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median6
Q37
95-th percentile8
Maximum10
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5904097
Coefficient of variation (CV)0.26181191
Kurtosis-0.26104718
Mean6.0746269
Median Absolute Deviation (MAD)1
Skewness-0.11592514
Sum1221
Variance2.529403
MonotonicityNot monotonic
2023-12-11T09:43:22.971929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 45
21.6%
6 44
21.2%
7 42
20.2%
8 30
14.4%
4 19
9.1%
9 8
 
3.8%
3 8
 
3.8%
2 3
 
1.4%
10 2
 
1.0%
(Missing) 7
 
3.4%
ValueCountFrequency (%)
2 3
 
1.4%
3 8
 
3.8%
4 19
9.1%
5 45
21.6%
6 44
21.2%
7 42
20.2%
8 30
14.4%
9 8
 
3.8%
10 2
 
1.0%
ValueCountFrequency (%)
10 2
 
1.0%
9 8
 
3.8%
8 30
14.4%
7 42
20.2%
6 44
21.2%
5 45
21.6%
4 19
9.1%
3 8
 
3.8%
2 3
 
1.4%

개화시기 종료(월)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)4.5%
Missing7
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean7.1840796
Minimum3
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-11T09:43:23.056020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q16
median7
Q39
95-th percentile10
Maximum11
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7438306
Coefficient of variation (CV)0.24273543
Kurtosis-0.67818222
Mean7.1840796
Median Absolute Deviation (MAD)1
Skewness-0.29724926
Sum1444
Variance3.0409453
MonotonicityNot monotonic
2023-12-11T09:43:23.145593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
9 42
20.2%
8 41
19.7%
6 33
15.9%
7 33
15.9%
5 26
12.5%
10 12
 
5.8%
4 9
 
4.3%
3 4
 
1.9%
11 1
 
0.5%
(Missing) 7
 
3.4%
ValueCountFrequency (%)
3 4
 
1.9%
4 9
 
4.3%
5 26
12.5%
6 33
15.9%
7 33
15.9%
8 41
19.7%
9 42
20.2%
10 12
 
5.8%
11 1
 
0.5%
ValueCountFrequency (%)
11 1
 
0.5%
10 12
 
5.8%
9 42
20.2%
8 41
19.7%
7 33
15.9%
6 33
15.9%
5 26
12.5%
4 9
 
4.3%
3 4
 
1.9%

결실시기 시작(월)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)3.6%
Missing16
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean8.96875
Minimum5
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-11T09:43:23.462909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q19
median9
Q310
95-th percentile10
Maximum11
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2735992
Coefficient of variation (CV)0.14200409
Kurtosis0.83135052
Mean8.96875
Median Absolute Deviation (MAD)1
Skewness-1.0473342
Sum1722
Variance1.622055
MonotonicityNot monotonic
2023-12-11T09:43:23.547591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
9 75
36.1%
10 64
30.8%
7 19
 
9.1%
8 14
 
6.7%
11 9
 
4.3%
6 8
 
3.8%
5 3
 
1.4%
(Missing) 16
 
7.7%
ValueCountFrequency (%)
5 3
 
1.4%
6 8
 
3.8%
7 19
 
9.1%
8 14
 
6.7%
9 75
36.1%
10 64
30.8%
11 9
 
4.3%
ValueCountFrequency (%)
11 9
 
4.3%
10 64
30.8%
9 75
36.1%
8 14
 
6.7%
7 19
 
9.1%
6 8
 
3.8%
5 3
 
1.4%

결실시기 종료(월)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)3.6%
Missing16
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean9.5
Minimum6
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-11T09:43:23.635352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile7
Q19
median10
Q310
95-th percentile11
Maximum12
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2193896
Coefficient of variation (CV)0.1283568
Kurtosis1.2384676
Mean9.5
Median Absolute Deviation (MAD)0
Skewness-1.1030078
Sum1824
Variance1.486911
MonotonicityNot monotonic
2023-12-11T09:43:23.721448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
10 99
47.6%
9 36
 
17.3%
11 22
 
10.6%
8 15
 
7.2%
7 10
 
4.8%
6 7
 
3.4%
12 3
 
1.4%
(Missing) 16
 
7.7%
ValueCountFrequency (%)
6 7
 
3.4%
7 10
 
4.8%
8 15
 
7.2%
9 36
 
17.3%
10 99
47.6%
11 22
 
10.6%
12 3
 
1.4%
ValueCountFrequency (%)
12 3
 
1.4%
11 22
 
10.6%
10 99
47.6%
9 36
 
17.3%
8 15
 
7.2%
7 10
 
4.8%
6 7
 
3.4%

용도
Text

Distinct84
Distinct (%)40.4%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023-12-11T09:43:23.898652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length21
Mean length7.8076923
Min length2

Characters and Unicode

Total characters1624
Distinct characters80
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)30.8%

Sample

1st row식용. 약용
2nd row식용, 약용
3rd row약용, 식용(어린순), 관상용
4th row관상, 약재, 목재
5th row목재, 조경, 염료, 약재
ValueCountFrequency (%)
약용 169
36.6%
식용 109
23.6%
관상용 69
14.9%
약재 13
 
2.8%
밀원용 9
 
1.9%
관상 9
 
1.9%
목재 6
 
1.3%
염료 5
 
1.1%
공업용 5
 
1.1%
3
 
0.6%
Other values (58) 65
 
14.1%
2023-12-11T09:43:24.222327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
용 396
24.4%
254
15.6%
약 195
12.0%
, 190
11.7%
식 120
 
7.4%
관 84
 
5.2%
상 82
 
5.0%
. 70
 
4.3%
재 25
 
1.5%
료 15
 
0.9%
Other values (70) 193
11.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1084
66.7%
Other Punctuation 260
 
16.0%
Space Separator 254
 
15.6%
Close Punctuation 13
 
0.8%
Open Punctuation 13
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
용 396
36.5%
약 195
18.0%
식 120
 
11.1%
관 84
 
7.7%
상 82
 
7.6%
재 25
 
2.3%
료 15
 
1.4%
원 13
 
1.2%
수 10
 
0.9%
목 10
 
0.9%
Other values (65) 134
 
12.4%
Other Punctuation
ValueCountFrequency (%)
, 190
73.1%
. 70
 
26.9%
Space Separator
ValueCountFrequency (%)
254
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1082
66.6%
Common 540
33.3%
Han 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
용 396
36.6%
약 195
18.0%
식 120
 
11.1%
관 84
 
7.8%
상 82
 
7.6%
재 25
 
2.3%
료 15
 
1.4%
원 13
 
1.2%
수 10
 
0.9%
목 10
 
0.9%
Other values (63) 132
 
12.2%
Common
ValueCountFrequency (%)
254
47.0%
, 190
35.2%
. 70
 
13.0%
) 13
 
2.4%
( 13
 
2.4%
Han
ValueCountFrequency (%)
食 1
50.0%
用 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1082
66.6%
ASCII 540
33.3%
CJK 2
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
용 396
36.6%
약 195
18.0%
식 120
 
11.1%
관 84
 
7.8%
상 82
 
7.6%
재 25
 
2.3%
료 15
 
1.4%
원 13
 
1.2%
수 10
 
0.9%
목 10
 
0.9%
Other values (63) 132
 
12.2%
ASCII
ValueCountFrequency (%)
254
47.0%
, 190
35.2%
. 70
 
13.0%
) 13
 
2.4%
( 13
 
2.4%
CJK
ValueCountFrequency (%)
食 1
50.0%
用 1
50.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2020-11-20
208 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-11-20
2nd row2020-11-20
3rd row2020-11-20
4th row2020-11-20
5th row2020-11-20

Common Values

ValueCountFrequency (%)
2020-11-20 208
100.0%

Length

2023-12-11T09:43:24.356491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:43:24.444099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-11-20 208
100.0%

Interactions

2023-12-11T09:43:16.318936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:43:14.988770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:43:15.421942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:43:15.893402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:43:16.423271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:43:15.098917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:43:15.515687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:43:15.997976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:43:16.544782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:43:15.219892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:43:15.636781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:43:16.109161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:43:16.657168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:43:15.325462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:43:15.755269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:43:16.212902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:43:24.519729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
권역별 구분사용부위별 구분과명생육상명개화시기 시작(월)개화시기 종료(월)결실시기 시작(월)결실시기 종료(월)용도
권역별 구분1.0000.7920.2450.0000.0000.1940.0000.0000.000
사용부위별 구분0.7921.0000.8410.3600.1880.0000.0000.0000.000
과명0.2450.8411.0000.7320.7980.7480.5680.1830.610
생육상명0.0000.3600.7321.0000.0000.0000.0000.0000.840
개화시기 시작(월)0.0000.1880.7980.0001.0000.9380.5340.5460.640
개화시기 종료(월)0.1940.0000.7480.0000.9381.0000.5120.5330.381
결실시기 시작(월)0.0000.0000.5680.0000.5340.5121.0000.9070.439
결실시기 종료(월)0.0000.0000.1830.0000.5460.5330.9071.0000.000
용도0.0000.0000.6100.8400.6400.3810.4390.0001.000
2023-12-11T09:43:24.636324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용부위별 구분생육상명권역별 구분
사용부위별 구분1.0000.0550.312
생육상명0.0551.0000.000
권역별 구분0.3120.0001.000
2023-12-11T09:43:24.734993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
개화시기 시작(월)개화시기 종료(월)결실시기 시작(월)결실시기 종료(월)권역별 구분사용부위별 구분생육상명
개화시기 시작(월)1.0000.8940.5340.5180.0000.0480.000
개화시기 종료(월)0.8941.0000.4960.5170.0350.0000.000
결실시기 시작(월)0.5340.4961.0000.7820.0000.0000.000
결실시기 종료(월)0.5180.5170.7821.0000.0000.0000.000
권역별 구분0.0000.0350.0000.0001.0000.3120.000
사용부위별 구분0.0480.0000.0000.0000.3121.0000.055
생육상명0.0000.0000.0000.0000.0000.0551.000

Missing values

2023-12-11T09:43:16.810123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:43:17.098614image/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.
2023-12-11T09:43:17.297077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

약초명권역별 구분효능별 구분사용부위별 구분생약명식물명학명이명과명생육상명개화시기 시작(월)개화시기 종료(월)결실시기 시작(월)결실시기 종료(월)용도데이터기준일자
0가새뽕나무<NA>기침가래줄기와 잎, 과실상기(桑起),상근(桑根), 상실(桑實)가새뽕나무Morus bombycis L. for. dissecta NAKAI가새뽕뽕나무과목본식물6678식용. 약용2020-11-20
1가시오갈피지리산권자양강장줄기와 잎자오가근(刺五加根)가시오갈피Acanthopanax Senticosus HARMS.자오가(刺五加)오갈피과목본식물671010식용, 약용2020-11-20
2감국(甘菊)왕산권고혈압, 기침가래꽃류야국(野菊)감국(甘菊)Chrysanthemum indicum L.고의(苦薏), 야산국국화과다년생초본9101111약용, 식용(어린순), 관상용2020-11-20
3회화나무<NA><NA>줄기와 잎, 꽃류, 과실괴화(傀花)회화나무Sophora japonica괴화나무콩과낙엽교목78910관상, 약재, 목재2020-11-20
4황벽나무지리산권위장질환,당뇨병줄기와 잎황백(黃栢)황벽나무Phellodendron amurense벽목(蘗木), 황경나무, 황경피나무, 단항(檀杭)운향과낙엽교목66710목재, 조경, 염료, 약재2020-11-20
5황기<NA><NA><NA>황기(黃기)황기Astragalus membranaceus(Fisch) Bge.단너삼콩과다년생 초본8999약용2020-11-20
6황금<NA>해열,진통,치통,비뇨,생식기,성병 ,부종,이뇨,간장질환<NA>황금(黃芩)황금Scutellaria baicalensis Georgi속썩은풀꿀풀과여러해살이 풀7899약용. 식용. 공업용. 밀원용2020-11-20
7환삼덩굴<NA>비뇨,생식기,성병 ,부종,이뇨과실, 전초율초(律草)환삼덩굴Humulus japonicus Siebold et Zuccarini좀환삼덩굴, 범상덩굴삼과한해살이 풀79910식용, 약재, 섬유2020-11-20
8화살나무지리산권항암제,당뇨병, 부인병 ,정신,신경,불면증줄기와 잎, 과실귀전우(鬼箭羽)화살나무Euonymus alatus Sieb홑잎나무, 참빗나무노박덩굴과낙엽교목551010관상, 약용2020-11-20
9호장근<NA>신경통,관절염,비뇨,생식기,성병 ,부종,이뇨<NA>호장근(虎杖根)호장근Reynoutria japonica Houtt.까치수영여뀌과여러해살이 풀681010약용, 식용2020-11-20
약초명권역별 구분효능별 구분사용부위별 구분생약명식물명학명이명과명생육상명개화시기 시작(월)개화시기 종료(월)결실시기 시작(월)결실시기 종료(월)용도데이터기준일자
198쑥황매산권해열,진통,치통,위장질환,고혈압,비뇨,생식기,성병 ,지혈제 ,기생충,구제, 부인병 ,정신,신경,불면증,부종,이뇨,간장질환줄기와 잎, 종자/씨앗애(艾)쑥Artemisia princeps var. orientalis (PAMPAN.) HARA.<NA>국화과다년생초본791011약용, 식용2020-11-20
199애기똥풀<NA>해열,진통,치통,위장질환,항암제,간장질환전초백굴채(白屈菜)애기똥풀Chelidonium majus L.씨아똥, 까치다리, 젖풀, 아기똥풀양귀비과다년생초본5879약용2020-11-20
200약모밀<NA>기침가래전초어성초(漁腥草)약모밀Houttuynia cordata THUNB.집약초, 즙채, 십자풀삼백초과다년생초본6789약용(땅속줄기, 잎), 식용2020-11-20
201억새<NA>기침가래줄기와 잎망경(芒莖)억새Miscanthus sinensis var. purpurascens RENDLE.<NA>벼과다년생초본<NA><NA><NA><NA>관상용, 식용, 약용2020-11-20
202엉겅퀴집현산권지혈제 ,부종,이뇨전초대계엉겅퀴Cirsium japonicum var. ussuriense (REGEL) KITAMURA.향가새, 가시나물국화과다년생초본10899식용(과실주, 양주, 탄산음료), 약용2020-11-20
203여뀌<NA>비뇨,생식기,성병 ,부종,이뇨줄기와 잎, 과실수료여뀌Polygonum hydropiper L.버들어뀌마디풀과일년새초본691010식용, 약용, 밀원용2020-11-20
204여로왕산권, 웅석산권해열,진통,치통,고혈압,중풍,기생충,구제<NA>여로(旅路)여로veratrum kaackii var. japonicum T. SHMIZU<NA>백합과다년생초본781010관상용, 약용2020-11-20
205오갈피나무집현산권<NA>줄기와 잎오가피(汚加皮)오갈피나무Acanthopanax sessiliflorum (RUPR. et MAX.) SEEm.참오갈피나무오갈피과목본식물891010식용, 약용2020-11-20
206산짚신나물<NA><NA><NA>선학초(仙鶴草)산짚신나물Agrimonia coreana<NA>장미과다년생 초본78<NA><NA>식용, 약용2020-11-20
207자리공<NA><NA>꽃류상륙(商陸)자리공Phytolacca esculenta VAN. HOUTT.장녹자리공과다년생초본691010약용2020-11-20