Overview

Dataset statistics

Number of variables14
Number of observations57
Missing cells211
Missing cells (%)26.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.4 KiB
Average record size in memory114.3 B

Variable types

Text6
Categorical8

Dataset

Description수출입식물 품목별 농약 등록 현황을 전국민이 활용하도록 정보 제공 제공 목록 : 유형, 대상, 품목, 대상해충, 검토항목, 농약잔류, 등록여부
URLhttps://www.data.go.kr/data/15047511/fileData.do

Alerts

Unnamed: 10 is highly overall correlated with Unnamed: 6 and 2 other fieldsHigh correlation
Unnamed: 4 is highly overall correlated with Unnamed: 6 and 2 other fieldsHigh correlation
Unnamed: 7 is highly overall correlated with Unnamed: 4 and 6 other fieldsHigh correlation
Unnamed: 5 is highly overall correlated with Unnamed: 6 and 2 other fieldsHigh correlation
Unnamed: 6 is highly overall correlated with Unnamed: 4 and 6 other fieldsHigh correlation
Unnamed: 12 is highly overall correlated with Unnamed: 6 and 2 other fieldsHigh correlation
Unnamed: 11 is highly overall correlated with Unnamed: 6 and 2 other fieldsHigh correlation
Unnamed: 9 is highly overall correlated with Unnamed: 4 and 6 other fieldsHigh correlation
Unnamed: 9 is highly imbalanced (52.3%)Imbalance
수출입 식물 품목별 농약 등록 현황 has 36 (63.2%) missing valuesMissing
Unnamed: 1 has 24 (42.1%) missing valuesMissing
Unnamed: 2 has 7 (12.3%) missing valuesMissing
Unnamed: 3 has 35 (61.4%) missing valuesMissing
Unnamed: 8 has 55 (96.5%) missing valuesMissing
Unnamed: 13 has 54 (94.7%) missing valuesMissing

Reproduction

Analysis started2023-12-12 13:13:59.669939
Analysis finished2023-12-12 13:14:01.186514
Duration1.52 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

수출입 식물 품목별 농약 등록 현황
Text

MISSING 

Distinct21
Distinct (%)100.0%
Missing36
Missing (%)63.2%
Memory size588.0 B
2023-12-12T22:14:01.352866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.2380952
Min length2

Characters and Unicode

Total characters68
Distinct characters42
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

Unique21 ?
Unique (%)100.0%

Sample

1st row유형
2nd row종자류
3rd row묘목류
4th row절화류
5th row구근류
ValueCountFrequency (%)
유형 1
 
4.3%
버섯류 1
 
4.3%
목재류 1
 
4.3%
향신료 1
 
4.3%
및 1
 
4.3%
기호 1
 
4.3%
한약재 1
 
4.3%
유료류 1
 
4.3%
섬유류 1
 
4.3%
건초류 1
 
4.3%
Other values (13) 13
56.5%
2023-12-12T22:14:01.761011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
류 17
25.0%
유 3
 
4.4%
건 3
 
4.4%
재 2
 
2.9%
목 2
 
2.9%
채 2
 
2.9%
과 2
 
2.9%
료 2
 
2.9%
2
 
2.9%
및 1
 
1.5%
Other values (32) 32
47.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 62
91.2%
Space Separator 2
 
2.9%
Control 1
 
1.5%
Open Punctuation 1
 
1.5%
Close Punctuation 1
 
1.5%
Other Punctuation 1
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
류 17
27.4%
유 3
 
4.8%
건 3
 
4.8%
재 2
 
3.2%
목 2
 
3.2%
채 2
 
3.2%
과 2
 
3.2%
료 2
 
3.2%
및 1
 
1.6%
약 1
 
1.6%
Other values (27) 27
43.5%
Space Separator
ValueCountFrequency (%)
2
100.0%
Control
ValueCountFrequency (%)
1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Other Punctuation
ValueCountFrequency (%)
: 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 62
91.2%
Common 6
 
8.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
류 17
27.4%
유 3
 
4.8%
건 3
 
4.8%
재 2
 
3.2%
목 2
 
3.2%
채 2
 
3.2%
과 2
 
3.2%
료 2
 
3.2%
및 1
 
1.6%
약 1
 
1.6%
Other values (27) 27
43.5%
Common
ValueCountFrequency (%)
2
33.3%
1
16.7%
( 1
16.7%
) 1
16.7%
: 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 62
91.2%
ASCII 6
 
8.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
류 17
27.4%
유 3
 
4.8%
건 3
 
4.8%
재 2
 
3.2%
목 2
 
3.2%
채 2
 
3.2%
과 2
 
3.2%
료 2
 
3.2%
및 1
 
1.6%
약 1
 
1.6%
Other values (27) 27
43.5%
ASCII
ValueCountFrequency (%)
2
33.3%
1
16.7%
( 1
16.7%
) 1
16.7%
: 1
16.7%

Unnamed: 1
Text

MISSING 

Distinct19
Distinct (%)57.6%
Missing24
Missing (%)42.1%
Memory size588.0 B
2023-12-12T22:14:01.990585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length185
Median length7
Mean length7.6060606
Min length1

Characters and Unicode

Total characters251
Distinct characters97
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)51.5%

Sample

1st row대상
2nd row곡류
3rd row특작
4th row목초
5th row수목
ValueCountFrequency (%)
14
27.5%
에틸포메이트 2
 
3.9%
메틸브로마이드 2
 
3.9%
수목 2
 
3.9%
퓨메이트(세이프퓸 1
 
2.0%
포스핀 1
 
2.0%
비바킬(팜한농 1
 
2.0%
16.6 1
 
2.0%
99 1
 
2.0%
견과류 1
 
2.0%
Other values (25) 25
49.0%
2023-12-12T22:14:02.405300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18
 
7.2%
, 15
 
6.0%
- 14
 
5.6%
이 11
 
4.4%
포 8
 
3.2%
( 8
 
3.2%
) 8
 
3.2%
스 8
 
3.2%
트 7
 
2.8%
메 6
 
2.4%
Other values (87) 148
59.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 178
70.9%
Space Separator 18
 
7.2%
Other Punctuation 16
 
6.4%
Dash Punctuation 14
 
5.6%
Decimal Number 9
 
3.6%
Open Punctuation 8
 
3.2%
Close Punctuation 8
 
3.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 11
 
6.2%
포 8
 
4.5%
스 8
 
4.5%
트 7
 
3.9%
메 6
 
3.4%
드 5
 
2.8%
류 5
 
2.8%
틸 4
 
2.2%
농 4
 
2.2%
에 4
 
2.2%
Other values (75) 116
65.2%
Decimal Number
ValueCountFrequency (%)
6 2
22.2%
9 2
22.2%
2 2
22.2%
1 1
11.1%
0 1
11.1%
3 1
11.1%
Other Punctuation
ValueCountFrequency (%)
, 15
93.8%
. 1
 
6.2%
Space Separator
ValueCountFrequency (%)
18
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 178
70.9%
Common 73
29.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 11
 
6.2%
포 8
 
4.5%
스 8
 
4.5%
트 7
 
3.9%
메 6
 
3.4%
드 5
 
2.8%
류 5
 
2.8%
틸 4
 
2.2%
농 4
 
2.2%
에 4
 
2.2%
Other values (75) 116
65.2%
Common
ValueCountFrequency (%)
18
24.7%
, 15
20.5%
- 14
19.2%
( 8
11.0%
) 8
11.0%
6 2
 
2.7%
9 2
 
2.7%
2 2
 
2.7%
1 1
 
1.4%
. 1
 
1.4%
Other values (2) 2
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 178
70.9%
ASCII 73
29.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18
24.7%
, 15
20.5%
- 14
19.2%
( 8
11.0%
) 8
11.0%
6 2
 
2.7%
9 2
 
2.7%
2 2
 
2.7%
1 1
 
1.4%
. 1
 
1.4%
Other values (2) 2
 
2.7%
Hangul
ValueCountFrequency (%)
이 11
 
6.2%
포 8
 
4.5%
스 8
 
4.5%
트 7
 
3.9%
메 6
 
3.4%
드 5
 
2.8%
류 5
 
2.8%
틸 4
 
2.2%
농 4
 
2.2%
에 4
 
2.2%
Other values (75) 116
65.2%

Unnamed: 2
Text

MISSING 

Distinct46
Distinct (%)92.0%
Missing7
Missing (%)12.3%
Memory size588.0 B
2023-12-12T22:14:02.674492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length3.14
Min length1

Characters and Unicode

Total characters157
Distinct characters95
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)84.0%

Sample

1st row품목
2nd row호맥
3rd row벼
4th row유채
5th row참깨
ValueCountFrequency (%)
땅콩 2
 
3.9%
소나무 2
 
3.9%
참깨 2
 
3.9%
옥수수 2
 
3.9%
호두 2
 
3.9%
알팔파건초 1
 
2.0%
새송이버섯 1
 
2.0%
파인애플(바나나 1
 
2.0%
건포도 1
 
2.0%
건대추 1
 
2.0%
Other values (36) 36
70.6%
2023-12-12T22:14:03.116115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
나 6
 
3.8%
두 5
 
3.2%
건 5
 
3.2%
추 5
 
3.2%
라 4
 
2.5%
수 4
 
2.5%
호 3
 
1.9%
감 3
 
1.9%
리 3
 
1.9%
오 3
 
1.9%
Other values (85) 116
73.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 149
94.9%
Close Punctuation 3
 
1.9%
Open Punctuation 3
 
1.9%
Other Punctuation 1
 
0.6%
Space Separator 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
나 6
 
4.0%
두 5
 
3.4%
건 5
 
3.4%
추 5
 
3.4%
라 4
 
2.7%
수 4
 
2.7%
호 3
 
2.0%
감 3
 
2.0%
리 3
 
2.0%
오 3
 
2.0%
Other values (81) 108
72.5%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 149
94.9%
Common 8
 
5.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
나 6
 
4.0%
두 5
 
3.4%
건 5
 
3.4%
추 5
 
3.4%
라 4
 
2.7%
수 4
 
2.7%
호 3
 
2.0%
감 3
 
2.0%
리 3
 
2.0%
오 3
 
2.0%
Other values (81) 108
72.5%
Common
ValueCountFrequency (%)
) 3
37.5%
( 3
37.5%
, 1
 
12.5%
1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 149
94.9%
ASCII 8
 
5.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
나 6
 
4.0%
두 5
 
3.4%
건 5
 
3.4%
추 5
 
3.4%
라 4
 
2.7%
수 4
 
2.7%
호 3
 
2.0%
감 3
 
2.0%
리 3
 
2.0%
오 3
 
2.0%
Other values (81) 108
72.5%
ASCII
ValueCountFrequency (%)
) 3
37.5%
( 3
37.5%
, 1
 
12.5%
1
 
12.5%

Unnamed: 3
Text

MISSING 

Distinct11
Distinct (%)50.0%
Missing35
Missing (%)61.4%
Memory size588.0 B
2023-12-12T22:14:03.331388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length9
Mean length9.1818182
Min length4

Characters and Unicode

Total characters202
Distinct characters53
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

Unique7 ?
Unique (%)31.8%

Sample

1st row대상해충
2nd row거짓쌀도둑거저리 어리쌀바구미
3rd row귤가루깍지벌레
4th row점박이응애
5th row뿌리응애
ValueCountFrequency (%)
거짓쌀도둑거저리 8
25.0%
어리쌀바구미 8
25.0%
귤가루깍지벌레 3
 
9.4%
점박이응애 2
 
6.2%
화랑곡나방 2
 
6.2%
대상해충 1
 
3.1%
뿌리응애 1
 
3.1%
감자뿔나방 1
 
3.1%
밤바구미 1
 
3.1%
긴수염버섯파리 1
 
3.1%
Other values (4) 4
12.5%
2023-12-12T22:14:03.634211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 18
 
8.9%
거 16
 
7.9%
쌀 16
 
7.9%
미 10
 
5.0%
10
 
5.0%
바 9
 
4.5%
구 9
 
4.5%
짓 8
 
4.0%
어 8
 
4.0%
저 8
 
4.0%
Other values (43) 90
44.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 192
95.0%
Control 10
 
5.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 18
 
9.4%
거 16
 
8.3%
쌀 16
 
8.3%
미 10
 
5.2%
바 9
 
4.7%
구 9
 
4.7%
짓 8
 
4.2%
어 8
 
4.2%
저 8
 
4.2%
둑 8
 
4.2%
Other values (42) 82
42.7%
Control
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 192
95.0%
Common 10
 
5.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 18
 
9.4%
거 16
 
8.3%
쌀 16
 
8.3%
미 10
 
5.2%
바 9
 
4.7%
구 9
 
4.7%
짓 8
 
4.2%
어 8
 
4.2%
저 8
 
4.2%
둑 8
 
4.2%
Other values (42) 82
42.7%
Common
ValueCountFrequency (%)
10
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 192
95.0%
ASCII 10
 
5.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 18
 
9.4%
거 16
 
8.3%
쌀 16
 
8.3%
미 10
 
5.2%
바 9
 
4.7%
구 9
 
4.7%
짓 8
 
4.2%
어 8
 
4.2%
저 8
 
4.2%
둑 8
 
4.2%
Other values (42) 82
42.7%
ASCII
ValueCountFrequency (%)
10
100.0%

Unnamed: 4
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size588.0 B
<NA>
29 
√
26 
검토항목
 
1
약효
 
1

Length

Max length4
Median length4
Mean length2.5964912
Min length1

Unique

Unique2 ?
Unique (%)3.5%

Sample

1st row<NA>
2nd row검토항목
3rd row약효
4th row√
5th row√

Common Values

ValueCountFrequency (%)
<NA> 29
50.9%
√ 26
45.6%
검토항목 1
 
1.8%
약효 1
 
1.8%

Length

2023-12-12T22:14:03.773717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:14:03.868278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 29
50.9%
√ 26
45.6%
검토항목 1
 
1.8%
약효 1
 
1.8%

Unnamed: 5
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size588.0 B
√
44 
<NA>
12 
약해
 
1

Length

Max length4
Median length1
Mean length1.6491228
Min length1

Unique

Unique1 ?
Unique (%)1.8%

Sample

1st row<NA>
2nd row<NA>
3rd row약해
4th row√
5th row√

Common Values

ValueCountFrequency (%)
√ 44
77.2%
<NA> 12
 
21.1%
약해 1
 
1.8%

Length

2023-12-12T22:14:03.979844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:14:04.076197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
√ 44
77.2%
na 12
 
21.1%
약해 1
 
1.8%

Unnamed: 6
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Memory size588.0 B
<NA>
29 
√
23 
농약잔류
 
1
방울토마토 풋고추
 
1
표고버섯
 
1
Other values (2)
 
2

Length

Max length9
Median length4
Mean length2.8070175
Min length1

Unique

Unique5 ?
Unique (%)8.8%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 29
50.9%
√ 23
40.4%
농약잔류 1
 
1.8%
방울토마토 풋고추 1
 
1.8%
표고버섯 1
 
1.8%
유채 1
 
1.8%
담배 1
 
1.8%

Length

2023-12-12T22:14:04.187399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:14:04.302298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 29
50.0%
√ 23
39.7%
농약잔류 1
 
1.7%
방울토마토 1
 
1.7%
풋고추 1
 
1.7%
표고버섯 1
 
1.7%
유채 1
 
1.7%
담배 1
 
1.7%

Unnamed: 7
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size588.0 B
<NA>
31 
√(영일) √(아트)
22 
등록여부
 
1
MB
 
1
√(아트)
 
1

Length

Max length11
Median length4
Mean length6.7017544
Min length2

Unique

Unique4 ?
Unique (%)7.0%

Sample

1st row<NA>
2nd row등록여부
3rd rowMB
4th row√(아트)
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 31
54.4%
√(영일) √(아트) 22
38.6%
등록여부 1
 
1.8%
MB 1
 
1.8%
√(아트) 1
 
1.8%
√(영일) 1
 
1.8%

Length

2023-12-12T22:14:04.420749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:14:04.508984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 31
39.2%
√(영일 23
29.1%
√(아트 23
29.1%
등록여부 1
 
1.3%
mb 1
 
1.3%

Unnamed: 8
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing55
Missing (%)96.5%
Memory size588.0 B
2023-12-12T22:14:04.621417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4.5
Mean length4.5
Min length3

Characters and Unicode

Total characters9
Distinct characters9
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowMgP
2nd row√(마그나)
ValueCountFrequency (%)
mgp 1
50.0%
√(마그나 1
50.0%
2023-12-12T22:14:04.876181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 1
11.1%
g 1
11.1%
P 1
11.1%
√ 1
11.1%
( 1
11.1%
마 1
11.1%
그 1
11.1%
나 1
11.1%
) 1
11.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3
33.3%
Uppercase Letter 2
22.2%
Lowercase Letter 1
 
11.1%
Math Symbol 1
 
11.1%
Open Punctuation 1
 
11.1%
Close Punctuation 1
 
11.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
마 1
33.3%
그 1
33.3%
나 1
33.3%
Uppercase Letter
ValueCountFrequency (%)
M 1
50.0%
P 1
50.0%
Lowercase Letter
ValueCountFrequency (%)
g 1
100.0%
Math Symbol
ValueCountFrequency (%)
√ 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3
33.3%
Common 3
33.3%
Hangul 3
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 1
33.3%
g 1
33.3%
P 1
33.3%
Common
ValueCountFrequency (%)
√ 1
33.3%
( 1
33.3%
) 1
33.3%
Hangul
ValueCountFrequency (%)
마 1
33.3%
그 1
33.3%
나 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5
55.6%
Hangul 3
33.3%
Math Operators 1
 
11.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 1
20.0%
g 1
20.0%
P 1
20.0%
( 1
20.0%
) 1
20.0%
Math Operators
ValueCountFrequency (%)
√ 1
100.0%
Hangul
ValueCountFrequency (%)
마 1
33.3%
그 1
33.3%
나 1
33.3%

Unnamed: 9
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size588.0 B
<NA>
40 
√(에피흄)
11 
AIP
 
1
√(에피흄) 마늘
 
1
√(에피흄) 쌀
 
1
Other values (3)
 
3

Length

Max length15
Median length4
Mean length4.9298246
Min length3

Unique

Unique6 ?
Unique (%)10.5%

Sample

1st row<NA>
2nd row<NA>
3rd rowAIP
4th row√(에피흄)
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 40
70.2%
√(에피흄) 11
 
19.3%
AIP 1
 
1.8%
√(에피흄) 마늘 1
 
1.8%
√(에피흄) 쌀 1
 
1.8%
√(에피흄) 바나나 1
 
1.8%
√(에피흄) +저장 1
 
1.8%
√(에피흄) +담배+저장해충 1
 
1.8%

Length

2023-12-12T22:14:05.014596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:14:05.120714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 40
64.5%
√(에피흄 16
 
25.8%
aip 1
 
1.6%
마늘 1
 
1.6%
쌀 1
 
1.6%
바나나 1
 
1.6%
저장 1
 
1.6%
담배+저장해충 1
 
1.6%

Unnamed: 10
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size588.0 B
<NA>
43 
√(비바킬)
13 
PH3+CO2
 
1

Length

Max length7
Median length4
Mean length4.5087719
Min length4

Unique

Unique1 ?
Unique (%)1.8%

Sample

1st row<NA>
2nd row<NA>
3rd rowPH3+CO2
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 43
75.4%
√(비바킬) 13
 
22.8%
PH3+CO2 1
 
1.8%

Length

2023-12-12T22:14:05.251804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:14:05.371079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 43
75.4%
√(비바킬 13
 
22.8%
ph3+co2 1
 
1.8%

Unnamed: 11
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size588.0 B
<NA>
45 
√(베이퍼)
11 
EF(16.6)
 
1

Length

Max length8
Median length4
Mean length4.4561404
Min length4

Unique

Unique1 ?
Unique (%)1.8%

Sample

1st row<NA>
2nd row<NA>
3rd rowEF(16.6)
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 45
78.9%
√(베이퍼) 11
 
19.3%
EF(16.6) 1
 
1.8%

Length

2023-12-12T22:14:05.493536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:14:05.604546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 45
78.9%
√(베이퍼 11
 
19.3%
ef(16.6 1
 
1.8%

Unnamed: 12
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size588.0 B
<NA>
46 
√(퓨메)
10 
EF(99)
 
1

Length

Max length6
Median length4
Mean length4.2105263
Min length4

Unique

Unique1 ?
Unique (%)1.8%

Sample

1st row<NA>
2nd row<NA>
3rd rowEF(99)
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 46
80.7%
√(퓨메) 10
 
17.5%
EF(99) 1
 
1.8%

Length

2023-12-12T22:14:05.839514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:14:05.985844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 46
80.7%
√(퓨메 10
 
17.5%
ef(99 1
 
1.8%

Unnamed: 13
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing54
Missing (%)94.7%
Memory size588.0 B
2023-12-12T22:14:06.187038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length6
Mean length6.6666667
Min length3

Characters and Unicode

Total characters20
Distinct characters17
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row2023.7.31현재
2nd rowEDN
3rd row√(스테리)
ValueCountFrequency (%)
2023.7.31현재 1
33.3%
edn 1
33.3%
√(스테리 1
33.3%
2023-12-12T22:14:06.486361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2
 
10.0%
3 2
 
10.0%
. 2
 
10.0%
N 1
 
5.0%
리 1
 
5.0%
테 1
 
5.0%
스 1
 
5.0%
( 1
 
5.0%
√ 1
 
5.0%
E 1
 
5.0%
Other values (7) 7
35.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7
35.0%
Other Letter 5
25.0%
Uppercase Letter 3
15.0%
Other Punctuation 2
 
10.0%
Open Punctuation 1
 
5.0%
Math Symbol 1
 
5.0%
Close Punctuation 1
 
5.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2
28.6%
3 2
28.6%
0 1
14.3%
1 1
14.3%
7 1
14.3%
Other Letter
ValueCountFrequency (%)
리 1
20.0%
테 1
20.0%
스 1
20.0%
재 1
20.0%
현 1
20.0%
Uppercase Letter
ValueCountFrequency (%)
N 1
33.3%
E 1
33.3%
D 1
33.3%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Math Symbol
ValueCountFrequency (%)
√ 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12
60.0%
Hangul 5
25.0%
Latin 3
 
15.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 2
16.7%
3 2
16.7%
. 2
16.7%
( 1
8.3%
√ 1
8.3%
0 1
8.3%
1 1
8.3%
7 1
8.3%
) 1
8.3%
Hangul
ValueCountFrequency (%)
리 1
20.0%
테 1
20.0%
스 1
20.0%
재 1
20.0%
현 1
20.0%
Latin
ValueCountFrequency (%)
N 1
33.3%
E 1
33.3%
D 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14
70.0%
Hangul 5
 
25.0%
Math Operators 1
 
5.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2
14.3%
3 2
14.3%
. 2
14.3%
N 1
7.1%
( 1
7.1%
E 1
7.1%
D 1
7.1%
0 1
7.1%
1 1
7.1%
7 1
7.1%
Hangul
ValueCountFrequency (%)
리 1
20.0%
테 1
20.0%
스 1
20.0%
재 1
20.0%
현 1
20.0%
Math Operators
ValueCountFrequency (%)
√ 1
100.0%

Correlations

2023-12-12T22:14:06.606974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수출입 식물 품목별 농약 등록 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13
수출입 식물 품목별 농약 등록 현황1.0001.0001.0001.0001.000NaN1.0001.000NaN1.000NaNNaNNaNNaN
Unnamed: 11.0001.0001.0000.7381.000NaN0.7930.749NaN0.431NaNNaNNaNNaN
Unnamed: 21.0001.0001.0001.0001.000NaN1.0001.000NaN1.000NaNNaNNaNNaN
Unnamed: 31.0000.7381.0001.0001.000NaN1.0000.000NaN0.632NaNNaNNaNNaN
Unnamed: 41.0001.0001.0001.0001.0000.6401.0001.0000.0001.0000.5230.4550.4550.000
Unnamed: 5NaNNaNNaNNaN0.6401.000NaN1.0000.0001.0000.5450.5450.494NaN
Unnamed: 61.0000.7931.0001.0001.000NaN1.0000.921NaNNaNNaNNaNNaNNaN
Unnamed: 71.0000.7491.0000.0001.0001.0000.9211.0000.0000.8051.0001.0001.000NaN
Unnamed: 8NaNNaNNaNNaN0.0000.000NaN0.0001.0000.000NaNNaNNaNNaN
Unnamed: 91.0000.4311.0000.6321.0001.000NaN0.8050.0001.0001.0001.0001.000NaN
Unnamed: 10NaNNaNNaNNaN0.5230.545NaN1.000NaN1.0001.0000.5230.5230.000
Unnamed: 11NaNNaNNaNNaN0.4550.545NaN1.000NaN1.0000.5231.0000.494NaN
Unnamed: 12NaNNaNNaNNaN0.4550.494NaN1.000NaN1.0000.5230.4941.000NaN
Unnamed: 13NaNNaNNaNNaN0.000NaNNaNNaNNaNNaN0.000NaNNaN1.000
2023-12-12T22:14:06.796402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 10Unnamed: 4Unnamed: 7Unnamed: 5Unnamed: 6Unnamed: 12Unnamed: 11Unnamed: 9
Unnamed: 101.0000.3370.9430.3571.0000.3370.3371.000
Unnamed: 40.3371.0000.9130.4400.9260.2750.2750.707
Unnamed: 70.9430.9131.0000.9510.6530.9350.9430.608
Unnamed: 50.3570.4400.9511.0001.0000.3120.3570.784
Unnamed: 61.0000.9260.6531.0001.0001.0001.0001.000
Unnamed: 120.3370.2750.9350.3121.0001.0000.3121.000
Unnamed: 110.3370.2750.9430.3571.0000.3121.0001.000
Unnamed: 91.0000.7070.6080.7841.0001.0001.0001.000
2023-12-12T22:14:07.227802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12
Unnamed: 41.0000.4400.9260.9130.7070.3370.2750.275
Unnamed: 50.4401.0001.0000.9510.7840.3570.3570.312
Unnamed: 60.9261.0001.0000.6531.0001.0001.0001.000
Unnamed: 70.9130.9510.6531.0000.6080.9430.9430.935
Unnamed: 90.7070.7841.0000.6081.0001.0001.0001.000
Unnamed: 100.3370.3571.0000.9431.0001.0000.3370.337
Unnamed: 110.2750.3571.0000.9431.0000.3371.0000.312
Unnamed: 120.2750.3121.0000.9351.0000.3370.3121.000

Missing values

2023-12-12T22:14:00.531674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:14:00.760944image/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.
2023-12-12T22:14:00.980559image/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

수출입 식물 품목별 농약 등록 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2023.7.31현재
1유형대상품목대상해충검토항목<NA>농약잔류등록여부<NA><NA><NA><NA><NA><NA>
2<NA><NA><NA><NA>약효약해<NA>MBMgPAIPPH3+CO2EF(16.6)EF(99)EDN
3종자류곡류호맥거짓쌀도둑거저리 어리쌀바구미√√<NA>√(아트)√(마그나)√(에피흄)<NA><NA><NA><NA>
4<NA><NA>벼<NA>√√<NA><NA><NA><NA><NA><NA><NA><NA>
5<NA>특작유채<NA><NA>√<NA>√(영일) √(아트)<NA>√(에피흄)<NA><NA><NA><NA>
6<NA><NA>참깨<NA><NA>√<NA><NA><NA><NA><NA><NA><NA><NA>
7<NA><NA>땅콩<NA><NA>√<NA><NA><NA><NA><NA><NA><NA><NA>
8<NA>목초오차드그라스<NA>√√<NA>√(영일) √(아트)<NA>√(에피흄)<NA><NA><NA><NA>
9<NA><NA>옥수수<NA>√√<NA><NA><NA><NA><NA><NA><NA><NA>
수출입 식물 품목별 농약 등록 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13
47<NA><NA>인삼<NA>√√√<NA><NA><NA><NA><NA><NA><NA>
48기호 및 향신료-커피원두거짓쌀도둑거저리 어리쌀바구미<NA>√√√(영일) √(아트)<NA>√(에피흄) +담배+저장해충<NA><NA><NA><NA>
49<NA><NA>엽연초<NA><NA>√담배<NA><NA><NA><NA><NA><NA><NA>
50<NA><NA>후추<NA><NA>√√<NA><NA><NA><NA><NA><NA><NA>
51목재류-소나무흰개미 소나무좀√<NA><NA><NA><NA><NA>√(비바킬)<NA><NA>√(스테리)
52<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
53<NA>식물검역용<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54<NA>비검역용<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
55<NA>2023 예정<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
56상표(업체):영일엠비(농협케미컬, 메틸브로마이드), 아트라텍엠비(아트라텍, 메틸브로마이드), 마그나포스(유나이티드포스포로스코리아, 마그네슘포스파이드), 에피흄(농협케미컬, 알루미늄포스파이드), 베이퍼메이트(린데코리아, 에틸포메이트 16.6), 비바킬(팜한농, 포스핀), 퓨메이트(세이프퓸, 에틸포메이트 99), 스테리가스(팜한농, 에탄디니트릴)<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>