Overview

Dataset statistics

Number of variables11
Number of observations165
Missing cells595
Missing cells (%)32.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.3 KiB
Average record size in memory88.8 B

Variable types

Categorical3
Text8

Dataset

Description잠업에 종사하는 전국의 모든 잠업가구의 양잠형태별 생산현황, 누에 생산 및 판매현황 조회 서비스
Author농림축산식품부
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220215000000001944

Alerts

2004 1/2 has 79 (47.9%) missing valuesMissing
2005 has 81 (49.1%) missing valuesMissing
2005 1/2 has 61 (37.0%) missing valuesMissing
2006 has 82 (49.7%) missing valuesMissing
2006 1/2 has 66 (40.0%) missing valuesMissing
2007 has 82 (49.7%) missing valuesMissing
2007 1/2 has 79 (47.9%) missing valuesMissing
2008 has 65 (39.4%) missing valuesMissing

Reproduction

Analysis started2023-12-11 03:26:40.339745
Analysis finished2023-12-11 03:26:42.133457
Duration1.79 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정구역별(1)
Categorical

Distinct11
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
대구
15 
인천
15 
광주
15 
경기
15 
강원
15 
Other values (6)
90 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대구 15
9.1%
인천 15
9.1%
광주 15
9.1%
경기 15
9.1%
강원 15
9.1%
충북 15
9.1%
충남 15
9.1%
전북 15
9.1%
전남 15
9.1%
경북 15
9.1%

Length

2023-12-11T12:26:42.202016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대구 15
9.1%
인천 15
9.1%
광주 15
9.1%
경기 15
9.1%
강원 15
9.1%
충북 15
9.1%
충남 15
9.1%
전북 15
9.1%
전남 15
9.1%
경북 15
9.1%

구분별(1)
Categorical

Distinct5
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
동충하초
44 
누에고치
33 
건조누에
33 
숫나방
33 
오디
22 

Length

Max length4
Median length4
Mean length3.5333333
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row누에고치
2nd row누에고치
3rd row누에고치
4th row건조누에
5th row건조누에

Common Values

ValueCountFrequency (%)
동충하초 44
26.7%
누에고치 33
20.0%
건조누에 33
20.0%
숫나방 33
20.0%
오디 22
13.3%

Length

2023-12-11T12:26:42.343481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:26:42.472180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동충하초 44
26.7%
누에고치 33
20.0%
건조누에 33
20.0%
숫나방 33
20.0%
오디 22
13.3%

구분별(2)
Categorical

Distinct5
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
농가수(호)
55 
생산량(kg)
55 
누에사육량(상자)
33 
종균공급량(ℓ)
11 
사육량(상자)
11 

Length

Max length9
Median length8
Mean length7.1333333
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row농가수(호)
2nd row누에사육량(상자)
3rd row생산량(kg)
4th row농가수(호)
5th row누에사육량(상자)

Common Values

ValueCountFrequency (%)
농가수(호) 55
33.3%
생산량(kg) 55
33.3%
누에사육량(상자) 33
20.0%
종균공급량(ℓ) 11
 
6.7%
사육량(상자) 11
 
6.7%

Length

2023-12-11T12:26:42.607830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:26:42.726288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농가수(호 55
33.3%
생산량(kg 55
33.3%
누에사육량(상자 33
20.0%
종균공급량(ℓ 11
 
6.7%
사육량(상자 11
 
6.7%

2004 1/2
Text

MISSING 

Distinct75
Distinct (%)87.2%
Missing79
Missing (%)47.9%
Memory size1.4 KiB
2023-12-11T12:26:43.066509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.8023256
Min length1

Characters and Unicode

Total characters241
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)76.7%

Sample

1st row13
2nd row64
3rd row320
4th row31
5th row124
ValueCountFrequency (%)
1 3
 
3.5%
13 3
 
3.5%
53 2
 
2.3%
10 2
 
2.3%
23 2
 
2.3%
5 2
 
2.3%
3 2
 
2.3%
293 2
 
2.3%
2 2
 
2.3%
447 1
 
1.2%
Other values (65) 65
75.6%
2023-12-11T12:26:43.763676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 46
19.1%
3 31
12.9%
2 23
9.5%
4 22
9.1%
5 20
8.3%
7 20
8.3%
9 17
 
7.1%
, 17
 
7.1%
8 16
 
6.6%
0 15
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 224
92.9%
Other Punctuation 17
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 46
20.5%
3 31
13.8%
2 23
10.3%
4 22
9.8%
5 20
8.9%
7 20
8.9%
9 17
 
7.6%
8 16
 
7.1%
0 15
 
6.7%
6 14
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 241
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 46
19.1%
3 31
12.9%
2 23
9.5%
4 22
9.1%
5 20
8.3%
7 20
8.3%
9 17
 
7.1%
, 17
 
7.1%
8 16
 
6.6%
0 15
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 241
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 46
19.1%
3 31
12.9%
2 23
9.5%
4 22
9.1%
5 20
8.3%
7 20
8.3%
9 17
 
7.1%
, 17
 
7.1%
8 16
 
6.6%
0 15
 
6.2%

2005
Text

MISSING 

Distinct65
Distinct (%)77.4%
Missing81
Missing (%)49.1%
Memory size1.4 KiB
2023-12-11T12:26:44.020934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.5714286
Min length1

Characters and Unicode

Total characters216
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)67.9%

Sample

1st row1
2nd row10
3rd row60
4th row1
5th row5
ValueCountFrequency (%)
1 8
 
9.5%
5 5
 
6.0%
8 3
 
3.6%
10 3
 
3.6%
27 2
 
2.4%
52 2
 
2.4%
409 2
 
2.4%
40 2
 
2.4%
378 1
 
1.2%
891 1
 
1.2%
Other values (55) 55
65.5%
2023-12-11T12:26:44.371294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 41
19.0%
2 29
13.4%
0 22
10.2%
5 19
8.8%
8 19
8.8%
3 18
8.3%
4 16
 
7.4%
9 16
 
7.4%
, 14
 
6.5%
7 12
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202
93.5%
Other Punctuation 14
 
6.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 41
20.3%
2 29
14.4%
0 22
10.9%
5 19
9.4%
8 19
9.4%
3 18
8.9%
4 16
 
7.9%
9 16
 
7.9%
7 12
 
5.9%
6 10
 
5.0%
Other Punctuation
ValueCountFrequency (%)
, 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 41
19.0%
2 29
13.4%
0 22
10.2%
5 19
8.8%
8 19
8.8%
3 18
8.3%
4 16
 
7.4%
9 16
 
7.4%
, 14
 
6.5%
7 12
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 41
19.0%
2 29
13.4%
0 22
10.2%
5 19
8.8%
8 19
8.8%
3 18
8.3%
4 16
 
7.4%
9 16
 
7.4%
, 14
 
6.5%
7 12
 
5.6%

2005 1/2
Text

MISSING 

Distinct69
Distinct (%)66.3%
Missing61
Missing (%)37.0%
Memory size1.4 KiB
2023-12-11T12:26:44.569665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.4519231
Min length1

Characters and Unicode

Total characters255
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)51.0%

Sample

1st row1
2nd row10
3rd row40
4th row1
5th row3
ValueCountFrequency (%)
1 14
 
13.5%
3 5
 
4.8%
10 4
 
3.8%
16 3
 
2.9%
143 3
 
2.9%
9 2
 
1.9%
4 2
 
1.9%
7 2
 
1.9%
40 2
 
1.9%
18 2
 
1.9%
Other values (59) 65
62.5%
2023-12-11T12:26:44.923914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 50
19.6%
2 31
12.2%
0 28
11.0%
3 25
9.8%
8 22
8.6%
4 19
 
7.5%
5 18
 
7.1%
, 17
 
6.7%
7 16
 
6.3%
6 15
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 238
93.3%
Other Punctuation 17
 
6.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 50
21.0%
2 31
13.0%
0 28
11.8%
3 25
10.5%
8 22
9.2%
4 19
 
8.0%
5 18
 
7.6%
7 16
 
6.7%
6 15
 
6.3%
9 14
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 255
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 50
19.6%
2 31
12.2%
0 28
11.0%
3 25
9.8%
8 22
8.6%
4 19
 
7.5%
5 18
 
7.1%
, 17
 
6.7%
7 16
 
6.3%
6 15
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 255
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 50
19.6%
2 31
12.2%
0 28
11.0%
3 25
9.8%
8 22
8.6%
4 19
 
7.5%
5 18
 
7.1%
, 17
 
6.7%
7 16
 
6.3%
6 15
 
5.9%

2006
Text

MISSING 

Distinct63
Distinct (%)75.9%
Missing82
Missing (%)49.7%
Memory size1.4 KiB
2023-12-11T12:26:45.130550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.5542169
Min length1

Characters and Unicode

Total characters212
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)65.1%

Sample

1st row1
2nd row90
3rd row250
4th row1
5th row10
ValueCountFrequency (%)
2 6
 
7.2%
1 5
 
6.0%
22 4
 
4.8%
10 4
 
4.8%
30 2
 
2.4%
4 2
 
2.4%
11 2
 
2.4%
1,831 2
 
2.4%
6 2
 
2.4%
9 1
 
1.2%
Other values (53) 53
63.9%
2023-12-11T12:26:45.477029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 44
20.8%
2 30
14.2%
0 24
11.3%
4 23
10.8%
3 17
 
8.0%
6 15
 
7.1%
5 14
 
6.6%
, 13
 
6.1%
9 13
 
6.1%
8 11
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 199
93.9%
Other Punctuation 13
 
6.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 44
22.1%
2 30
15.1%
0 24
12.1%
4 23
11.6%
3 17
 
8.5%
6 15
 
7.5%
5 14
 
7.0%
9 13
 
6.5%
8 11
 
5.5%
7 8
 
4.0%
Other Punctuation
ValueCountFrequency (%)
, 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 212
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 44
20.8%
2 30
14.2%
0 24
11.3%
4 23
10.8%
3 17
 
8.0%
6 15
 
7.1%
5 14
 
6.6%
, 13
 
6.1%
9 13
 
6.1%
8 11
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 212
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 44
20.8%
2 30
14.2%
0 24
11.3%
4 23
10.8%
3 17
 
8.0%
6 15
 
7.1%
5 14
 
6.6%
, 13
 
6.1%
9 13
 
6.1%
8 11
 
5.2%

2006 1/2
Text

MISSING 

Distinct68
Distinct (%)68.7%
Missing66
Missing (%)40.0%
Memory size1.4 KiB
2023-12-11T12:26:45.688707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.4545455
Min length1

Characters and Unicode

Total characters243
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)53.5%

Sample

1st row1
2nd row55
3rd row330
4th row1
5th row5
ValueCountFrequency (%)
1 8
 
8.1%
4 5
 
5.1%
10 4
 
4.0%
5 4
 
4.0%
24 3
 
3.0%
30 3
 
3.0%
50 3
 
3.0%
41 2
 
2.0%
103 2
 
2.0%
51 2
 
2.0%
Other values (58) 63
63.6%
2023-12-11T12:26:46.042584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 47
19.3%
2 36
14.8%
5 28
11.5%
0 24
9.9%
3 22
9.1%
4 21
8.6%
7 15
 
6.2%
, 15
 
6.2%
8 14
 
5.8%
6 13
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 228
93.8%
Other Punctuation 15
 
6.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 47
20.6%
2 36
15.8%
5 28
12.3%
0 24
10.5%
3 22
9.6%
4 21
9.2%
7 15
 
6.6%
8 14
 
6.1%
6 13
 
5.7%
9 8
 
3.5%
Other Punctuation
ValueCountFrequency (%)
, 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 243
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 47
19.3%
2 36
14.8%
5 28
11.5%
0 24
9.9%
3 22
9.1%
4 21
8.6%
7 15
 
6.2%
, 15
 
6.2%
8 14
 
5.8%
6 13
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 243
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 47
19.3%
2 36
14.8%
5 28
11.5%
0 24
9.9%
3 22
9.1%
4 21
8.6%
7 15
 
6.2%
, 15
 
6.2%
8 14
 
5.8%
6 13
 
5.3%

2007
Text

MISSING 

Distinct58
Distinct (%)69.9%
Missing82
Missing (%)49.7%
Memory size1.4 KiB
2023-12-11T12:26:46.268184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.4698795
Min length1

Characters and Unicode

Total characters205
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)51.8%

Sample

1st row1
2nd row13
3rd row100
4th row1
5th row7
ValueCountFrequency (%)
1 7
 
8.4%
6 4
 
4.8%
40 3
 
3.6%
5 3
 
3.6%
3 3
 
3.6%
15 2
 
2.4%
14 2
 
2.4%
29 2
 
2.4%
47 2
 
2.4%
20 2
 
2.4%
Other values (48) 53
63.9%
2023-12-11T12:26:46.617837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 39
19.0%
2 27
13.2%
0 24
11.7%
4 19
9.3%
5 17
8.3%
3 16
7.8%
7 16
7.8%
9 15
 
7.3%
, 13
 
6.3%
8 10
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 192
93.7%
Other Punctuation 13
 
6.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 39
20.3%
2 27
14.1%
0 24
12.5%
4 19
9.9%
5 17
8.9%
3 16
8.3%
7 16
8.3%
9 15
 
7.8%
8 10
 
5.2%
6 9
 
4.7%
Other Punctuation
ValueCountFrequency (%)
, 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 205
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 39
19.0%
2 27
13.2%
0 24
11.7%
4 19
9.3%
5 17
8.3%
3 16
7.8%
7 16
7.8%
9 15
 
7.3%
, 13
 
6.3%
8 10
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 205
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 39
19.0%
2 27
13.2%
0 24
11.7%
4 19
9.3%
5 17
8.3%
3 16
7.8%
7 16
7.8%
9 15
 
7.3%
, 13
 
6.3%
8 10
 
4.9%

2007 1/2
Text

MISSING 

Distinct66
Distinct (%)76.7%
Missing79
Missing (%)47.9%
Memory size1.4 KiB
2023-12-11T12:26:46.816372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length2.627907
Min length1

Characters and Unicode

Total characters226
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)65.1%

Sample

1st row1
2nd row23
3rd row160
4th row1
5th row2
ValueCountFrequency (%)
2 8
 
9.3%
1 4
 
4.7%
13 3
 
3.5%
7 3
 
3.5%
52 2
 
2.3%
4 2
 
2.3%
45 2
 
2.3%
10 2
 
2.3%
139 2
 
2.3%
26 2
 
2.3%
Other values (56) 56
65.1%
2023-12-11T12:26:47.158789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 39
17.3%
2 36
15.9%
5 22
9.7%
3 21
9.3%
6 20
8.8%
4 19
8.4%
0 17
7.5%
, 15
 
6.6%
7 13
 
5.8%
8 12
 
5.3%
Other values (2) 12
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 210
92.9%
Other Punctuation 16
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 39
18.6%
2 36
17.1%
5 22
10.5%
3 21
10.0%
6 20
9.5%
4 19
9.0%
0 17
8.1%
7 13
 
6.2%
8 12
 
5.7%
9 11
 
5.2%
Other Punctuation
ValueCountFrequency (%)
, 15
93.8%
. 1
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 226
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 39
17.3%
2 36
15.9%
5 22
9.7%
3 21
9.3%
6 20
8.8%
4 19
8.4%
0 17
7.5%
, 15
 
6.6%
7 13
 
5.8%
8 12
 
5.3%
Other values (2) 12
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 226
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 39
17.3%
2 36
15.9%
5 22
9.7%
3 21
9.3%
6 20
8.8%
4 19
8.4%
0 17
7.5%
, 15
 
6.6%
7 13
 
5.8%
8 12
 
5.3%
Other values (2) 12
 
5.3%

2008
Text

MISSING 

Distinct76
Distinct (%)76.0%
Missing65
Missing (%)39.4%
Memory size1.4 KiB
2023-12-11T12:26:47.397829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length2.86
Min length1

Characters and Unicode

Total characters286
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)65.0%

Sample

1st row1
2nd row30
3rd row200
4th row1
5th row10
ValueCountFrequency (%)
1 7
 
7.0%
10 5
 
5.0%
2 4
 
4.0%
3 3
 
3.0%
23 3
 
3.0%
4 3
 
3.0%
100 2
 
2.0%
37 2
 
2.0%
36 2
 
2.0%
18 2
 
2.0%
Other values (66) 67
67.0%
2023-12-11T12:26:47.849207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 51
17.8%
0 44
15.4%
3 37
12.9%
2 27
9.4%
5 25
8.7%
, 23
8.0%
6 19
 
6.6%
4 17
 
5.9%
8 16
 
5.6%
9 14
 
4.9%
Other values (2) 13
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 262
91.6%
Other Punctuation 24
 
8.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 51
19.5%
0 44
16.8%
3 37
14.1%
2 27
10.3%
5 25
9.5%
6 19
 
7.3%
4 17
 
6.5%
8 16
 
6.1%
9 14
 
5.3%
7 12
 
4.6%
Other Punctuation
ValueCountFrequency (%)
, 23
95.8%
. 1
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Common 286
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 51
17.8%
0 44
15.4%
3 37
12.9%
2 27
9.4%
5 25
8.7%
, 23
8.0%
6 19
 
6.6%
4 17
 
5.9%
8 16
 
5.6%
9 14
 
4.9%
Other values (2) 13
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 286
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 51
17.8%
0 44
15.4%
3 37
12.9%
2 27
9.4%
5 25
8.7%
, 23
8.0%
6 19
 
6.6%
4 17
 
5.9%
8 16
 
5.6%
9 14
 
4.9%
Other values (2) 13
 
4.5%

Correlations

2023-12-11T12:26:47.977156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역별(1)구분별(1)구분별(2)2004 1/220052005 1/220062006 1/220072007 1/22008
행정구역별(1)1.0000.0000.0000.0000.6560.0000.7830.0000.0000.8210.741
구분별(1)0.0001.0000.7310.9640.8980.6040.5510.1940.0000.0000.624
구분별(2)0.0000.7311.0000.0000.0000.0000.0000.0000.7780.2310.464
2004 1/20.0000.9640.0001.0000.9900.9970.9900.9850.9940.9900.991
20050.6560.8980.0000.9901.0000.9960.9910.9920.9850.9920.991
2005 1/20.0000.6040.0000.9970.9961.0000.9950.9930.9930.9970.987
20060.7830.5510.0000.9900.9910.9951.0000.9860.9890.9910.987
2006 1/20.0000.1940.0000.9850.9920.9930.9861.0000.9790.9970.988
20070.0000.0000.7780.9940.9850.9930.9890.9791.0000.9880.989
2007 1/20.8210.0000.2310.9900.9920.9970.9910.9970.9881.0000.993
20080.7410.6240.4640.9910.9910.9870.9870.9880.9890.9931.000
2023-12-11T12:26:48.118024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분별(1)구분별(2)행정구역별(1)
구분별(1)1.0000.3550.000
구분별(2)0.3551.0000.000
행정구역별(1)0.0000.0001.000
2023-12-11T12:26:48.234359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역별(1)구분별(1)구분별(2)
행정구역별(1)1.0000.0000.000
구분별(1)0.0001.0000.355
구분별(2)0.0000.3551.000

Missing values

2023-12-11T12:26:41.664841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:26:41.848831image/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.
2023-12-11T12:26:42.032698image/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

행정구역별(1)구분별(1)구분별(2)2004 1/220052005 1/220062006 1/220072007 1/22008
0대구누에고치농가수(호)<NA><NA><NA><NA><NA><NA><NA><NA>
1대구누에고치누에사육량(상자)<NA><NA><NA><NA><NA><NA><NA><NA>
2대구누에고치생산량(kg)<NA><NA><NA><NA><NA><NA><NA><NA>
3대구건조누에농가수(호)<NA>1111111
4대구건조누에누에사육량(상자)<NA>10109055132330
5대구건조누에생산량(kg)<NA>6040250330100160200
6대구동충하초농가수(호)<NA>1111111
7대구동충하초종균공급량(ℓ)<NA>531057210
8대구동충하초누에사육량(상자)<NA>531057210
9대구동충하초생산량(kg)<NA>5030120507025100
행정구역별(1)구분별(1)구분별(2)2004 1/220052005 1/220062006 1/220072007 1/22008
155경남건조누에생산량(kg)11,1077,1048,9886,0577,9014,10912,463.806,394.36
156경남동충하초농가수(호)135969574
157경남동충하초종균공급량(ℓ)5327184919475437
158경남동충하초누에사육량(상자)9127366051406337
159경남동충하초생산량(kg)711242208115515495573183
160경남숫나방농가수(호)12124321
161경남숫나방사육량(상자)212310420132
162경남숫나방생산량(kg)2013266428129712
163경남오디농가수(호)<NA><NA><NA><NA><NA><NA><NA>36
164경남오디생산량(kg)<NA><NA><NA><NA><NA><NA><NA>4,470