Overview

Dataset statistics

Number of variables11
Number of observations6862
Missing cells9385
Missing cells (%)12.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory636.7 KiB
Average record size in memory95.0 B

Variable types

Numeric4
Categorical3
DateTime2
Text2

Dataset

Description농가경영종합관리시스템의 농가 영농활동 일지에 대한 테이블을 공개합니다. (품목재배일련번호 년도 등록일시 수정일시 면적 주요품종 재배유형 재배기간(시작월) 재배기간(종료월) 조성주수 비고)
Author충청북도
URLhttps://www.data.go.kr/data/15050307/fileData.do

Alerts

재배유형 is highly overall correlated with 품목재배일련번호 and 5 other fieldsHigh correlation
년도 is highly overall correlated with 재배유형High correlation
재배기간(시작월) is highly overall correlated with 재배기간(종료월) and 1 other fieldsHigh correlation
품목재배일련번호 is highly overall correlated with 재배유형High correlation
면적 is highly overall correlated with 재배유형High correlation
재배기간(종료월) is highly overall correlated with 재배유형 and 1 other fieldsHigh correlation
조성주수 is highly overall correlated with 재배유형High correlation
년도 is highly imbalanced (99.6%)Imbalance
재배유형 is highly imbalanced (99.6%)Imbalance
재배기간(시작월) is highly imbalanced (98.5%)Imbalance
주요품종 has 2893 (42.2%) missing valuesMissing
비고 has 6486 (94.5%) missing valuesMissing
면적 is highly skewed (γ1 = 82.75143612)Skewed
조성주수 is highly skewed (γ1 = 56.88726895)Skewed
품목재배일련번호 has unique valuesUnique
면적 has 74 (1.1%) zerosZeros
조성주수 has 3495 (50.9%) zerosZeros

Reproduction

Analysis started2023-12-12 07:58:44.194843
Analysis finished2023-12-12 07:58:47.826883
Duration3.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목재배일련번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct6862
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19347.948
Minimum15613
Maximum23010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.4 KiB
2023-12-12T16:58:47.974131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15613
5-th percentile15999.05
Q117491.25
median19367.5
Q321204.75
95-th percentile22636.8
Maximum23010
Range7397
Interquartile range (IQR)3713.5

Descriptive statistics

Standard deviation2133.4962
Coefficient of variation (CV)0.11026989
Kurtosis-1.2068031
Mean19347.948
Median Absolute Deviation (MAD)1856
Skewness-0.025856031
Sum1.3276562 × 108
Variance4551805.8
MonotonicityStrictly increasing
2023-12-12T16:58:48.149191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15613 1
 
< 0.1%
20630 1
 
< 0.1%
20627 1
 
< 0.1%
20625 1
 
< 0.1%
20624 1
 
< 0.1%
20623 1
 
< 0.1%
20622 1
 
< 0.1%
20620 1
 
< 0.1%
20619 1
 
< 0.1%
20618 1
 
< 0.1%
Other values (6852) 6852
99.9%
ValueCountFrequency (%)
15613 1
< 0.1%
15614 1
< 0.1%
15615 1
< 0.1%
15616 1
< 0.1%
15617 1
< 0.1%
15622 1
< 0.1%
15623 1
< 0.1%
15624 1
< 0.1%
15625 1
< 0.1%
15626 1
< 0.1%
ValueCountFrequency (%)
23010 1
< 0.1%
23009 1
< 0.1%
23008 1
< 0.1%
23007 1
< 0.1%
23006 1
< 0.1%
23005 1
< 0.1%
23004 1
< 0.1%
23003 1
< 0.1%
23002 1
< 0.1%
23001 1
< 0.1%

년도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.7 KiB
1905
6859 
1900
 
2
1907
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1905
2nd row1905
3rd row1905
4th row1905
5th row1905

Common Values

ValueCountFrequency (%)
1905 6859
> 99.9%
1900 2
 
< 0.1%
1907 1
 
< 0.1%

Length

2023-12-12T16:58:48.323038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:58:48.444303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1905 6859
> 99.9%
1900 2
 
< 0.1%
1907 1
 
< 0.1%

등록일시
Date

Distinct936
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Memory size53.7 KiB
Minimum2017-03-09 00:00:00
Maximum2019-11-11 00:00:00
2023-12-12T16:58:48.593820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:58:48.772220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

수정일시
Date

Distinct938
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Memory size53.7 KiB
Minimum2017-03-09 00:00:00
Maximum2019-11-11 00:00:00
2023-12-12T16:58:48.938906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:58:49.078616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

면적
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct389
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20173.816
Minimum0
Maximum1.2312312 × 108
Zeros74
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size60.4 KiB
2023-12-12T16:58:49.227629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q150
median356
Q31000
95-th percentile5000
Maximum1.2312312 × 108
Range1.2312312 × 108
Interquartile range (IQR)950

Descriptive statistics

Standard deviation1486820.3
Coefficient of variation (CV)73.700497
Kurtosis6852.3932
Mean20173.816
Median Absolute Deviation (MAD)344
Skewness82.751436
Sum1.3843273 × 108
Variance2.2106345 × 1012
MonotonicityNot monotonic
2023-12-12T16:58:49.405186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 472
 
6.9%
1000 424
 
6.2%
300 389
 
5.7%
200 360
 
5.2%
10 314
 
4.6%
1 299
 
4.4%
500 294
 
4.3%
50 294
 
4.3%
600 266
 
3.9%
400 219
 
3.2%
Other values (379) 3531
51.5%
ValueCountFrequency (%)
0 74
 
1.1%
1 299
4.4%
2 123
1.8%
3 92
 
1.3%
4 13
 
0.2%
5 125
1.8%
6 10
 
0.1%
7 9
 
0.1%
8 5
 
0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
123123123 1
 
< 0.1%
3000000 1
 
< 0.1%
1000000 1
 
< 0.1%
600002 1
 
< 0.1%
160000 1
 
< 0.1%
123123 3
< 0.1%
100000 5
0.1%
95000 1
 
< 0.1%
90000 1
 
< 0.1%
80000 2
 
< 0.1%

주요품종
Text

MISSING 

Distinct2063
Distinct (%)52.0%
Missing2893
Missing (%)42.2%
Memory size53.7 KiB
2023-12-12T16:58:49.824589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length48
Mean length4.1025447
Min length1

Characters and Unicode

Total characters16283
Distinct characters666
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1599 ?
Unique (%)40.3%

Sample

1st row궁천
2nd row양파
3rd row청양 마니따
4th row몽부사 대옥계 대황모 양홍장
5th row듀크
ValueCountFrequency (%)
설향 83
 
1.8%
수미 77
 
1.7%
고추 70
 
1.5%
홍로 45
 
1.0%
벼 43
 
0.9%
부사 41
 
0.9%
표고버섯 38
 
0.8%
한우 32
 
0.7%
아로니아 31
 
0.7%
감자 28
 
0.6%
Other values (2130) 4106
89.4%
2023-12-12T16:58:50.432810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
736
 
4.5%
추 366
 
2.2%
고 344
 
2.1%
, 339
 
2.1%
수 320
 
2.0%
마 288
 
1.8%
대 264
 
1.6%
미 250
 
1.5%
리 228
 
1.4%
스 222
 
1.4%
Other values (656) 12926
79.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14122
86.7%
Space Separator 736
 
4.5%
Other Punctuation 572
 
3.5%
Decimal Number 510
 
3.1%
Lowercase Letter 102
 
0.6%
Uppercase Letter 76
 
0.5%
Open Punctuation 74
 
0.5%
Close Punctuation 74
 
0.5%
Dash Punctuation 14
 
0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
추 366
 
2.6%
고 344
 
2.4%
수 320
 
2.3%
마 288
 
2.0%
대 264
 
1.9%
미 250
 
1.8%
리 228
 
1.6%
스 222
 
1.6%
아 192
 
1.4%
자 187
 
1.3%
Other values (592) 11461
81.2%
Lowercase Letter
ValueCountFrequency (%)
r 15
14.7%
g 10
 
9.8%
k 10
 
9.8%
p 7
 
6.9%
e 7
 
6.9%
t 6
 
5.9%
o 6
 
5.9%
a 5
 
4.9%
s 5
 
4.9%
n 5
 
4.9%
Other values (12) 26
25.5%
Uppercase Letter
ValueCountFrequency (%)
R 12
15.8%
P 10
13.2%
Y 9
11.8%
T 8
10.5%
M 7
9.2%
L 5
6.6%
A 5
6.6%
K 4
 
5.3%
H 4
 
5.3%
B 2
 
2.6%
Other values (8) 10
13.2%
Decimal Number
ValueCountFrequency (%)
0 120
23.5%
1 117
22.9%
7 57
11.2%
2 57
11.2%
3 44
 
8.6%
5 33
 
6.5%
8 30
 
5.9%
6 25
 
4.9%
4 22
 
4.3%
9 5
 
1.0%
Other Punctuation
ValueCountFrequency (%)
, 339
59.3%
. 217
37.9%
/ 10
 
1.7%
· 2
 
0.3%
* 2
 
0.3%
' 1
 
0.2%
; 1
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 73
98.6%
[ 1
 
1.4%
Close Punctuation
ValueCountFrequency (%)
) 73
98.6%
] 1
 
1.4%
Space Separator
ValueCountFrequency (%)
736
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14122
86.7%
Common 1983
 
12.2%
Latin 178
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
추 366
 
2.6%
고 344
 
2.4%
수 320
 
2.3%
마 288
 
2.0%
대 264
 
1.9%
미 250
 
1.8%
리 228
 
1.6%
스 222
 
1.6%
아 192
 
1.4%
자 187
 
1.3%
Other values (592) 11461
81.2%
Latin
ValueCountFrequency (%)
r 15
 
8.4%
R 12
 
6.7%
g 10
 
5.6%
P 10
 
5.6%
k 10
 
5.6%
Y 9
 
5.1%
T 8
 
4.5%
p 7
 
3.9%
M 7
 
3.9%
e 7
 
3.9%
Other values (30) 83
46.6%
Common
ValueCountFrequency (%)
736
37.1%
, 339
17.1%
. 217
 
10.9%
0 120
 
6.1%
1 117
 
5.9%
( 73
 
3.7%
) 73
 
3.7%
7 57
 
2.9%
2 57
 
2.9%
3 44
 
2.2%
Other values (14) 150
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14074
86.4%
ASCII 2159
 
13.3%
Compat Jamo 48
 
0.3%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
736
34.1%
, 339
15.7%
. 217
 
10.1%
0 120
 
5.6%
1 117
 
5.4%
( 73
 
3.4%
) 73
 
3.4%
7 57
 
2.6%
2 57
 
2.6%
3 44
 
2.0%
Other values (53) 326
15.1%
Hangul
ValueCountFrequency (%)
추 366
 
2.6%
고 344
 
2.4%
수 320
 
2.3%
마 288
 
2.0%
대 264
 
1.9%
미 250
 
1.8%
리 228
 
1.6%
스 222
 
1.6%
아 192
 
1.4%
자 187
 
1.3%
Other values (585) 11413
81.1%
Compat Jamo
ValueCountFrequency (%)
ㆍ 28
58.3%
ㅣ 8
 
16.7%
ㅇ 6
 
12.5%
ㅡ 2
 
4.2%
ㄹ 2
 
4.2%
ㅈ 1
 
2.1%
ㅂ 1
 
2.1%
None
ValueCountFrequency (%)
· 2
100.0%

재배유형
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.7 KiB
1
6860 
<NA>
 
2

Length

Max length4
Median length1
Mean length1.0008744
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 6860
> 99.9%
<NA> 2
 
< 0.1%

Length

2023-12-12T16:58:50.612257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:58:50.749381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 6860
> 99.9%
na 2
 
< 0.1%

재배기간(시작월)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.7 KiB
1
6848 
2
 
8
<NA>
 
6

Length

Max length4
Median length1
Mean length1.0026231
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 6848
99.8%
2 8
 
0.1%
<NA> 6
 
0.1%

Length

2023-12-12T16:58:50.860467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:58:50.975327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 6848
99.8%
2 8
 
0.1%
na 6
 
0.1%

재배기간(종료월)
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)0.5%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean9.5300467
Minimum0
Maximum80
Zeros31
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size60.4 KiB
2023-12-12T16:58:51.084051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q18
median10
Q312
95-th percentile12
Maximum80
Range80
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.4108381
Coefficient of variation (CV)0.3579036
Kurtosis58.326119
Mean9.5300467
Median Absolute Deviation (MAD)2
Skewness3.147185
Sum65338
Variance11.633816
MonotonicityNot monotonic
2023-12-12T16:58:51.531687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
12 2204
32.1%
10 1139
16.6%
11 942
13.7%
6 584
 
8.5%
9 525
 
7.7%
8 334
 
4.9%
7 326
 
4.8%
5 289
 
4.2%
3 143
 
2.1%
4 131
 
1.9%
Other values (22) 239
 
3.5%
ValueCountFrequency (%)
0 31
 
0.5%
1 69
 
1.0%
2 91
 
1.3%
3 143
 
2.1%
4 131
 
1.9%
5 289
4.2%
6 584
8.5%
7 326
4.8%
8 334
4.9%
9 525
7.7%
ValueCountFrequency (%)
80 1
 
< 0.1%
71 1
 
< 0.1%
60 1
 
< 0.1%
48 2
 
< 0.1%
42 1
 
< 0.1%
36 5
0.1%
35 2
 
< 0.1%
33 1
 
< 0.1%
30 1
 
< 0.1%
27 1
 
< 0.1%

조성주수
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct386
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18683.511
Minimum0
Maximum50000000
Zeros3495
Zeros (%)50.9%
Negative0
Negative (%)0.0%
Memory size60.4 KiB
2023-12-12T16:58:51.715297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3300
95-th percentile8000
Maximum50000000
Range50000000
Interquartile range (IQR)300

Descriptive statistics

Standard deviation716078.42
Coefficient of variation (CV)38.326759
Kurtosis3654.1745
Mean18683.511
Median Absolute Deviation (MAD)0
Skewness56.887269
Sum1.2820625 × 108
Variance5.1276831 × 1011
MonotonicityNot monotonic
2023-12-12T16:58:51.923364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3495
50.9%
100 239
 
3.5%
200 160
 
2.3%
1000 159
 
2.3%
300 138
 
2.0%
50 115
 
1.7%
1 106
 
1.5%
2000 96
 
1.4%
500 87
 
1.3%
10 81
 
1.2%
Other values (376) 2186
31.9%
ValueCountFrequency (%)
0 3495
50.9%
1 106
 
1.5%
2 40
 
0.6%
3 29
 
0.4%
4 21
 
0.3%
5 50
 
0.7%
6 27
 
0.4%
7 15
 
0.2%
8 13
 
0.2%
9 7
 
0.1%
ValueCountFrequency (%)
50000000 1
< 0.1%
20190720 1
< 0.1%
20190701 1
< 0.1%
10000000 2
< 0.1%
1671000 1
< 0.1%
1000000 1
< 0.1%
400000 1
< 0.1%
320000 1
< 0.1%
300000 2
< 0.1%
222222 1
< 0.1%

비고
Text

MISSING 

Distinct353
Distinct (%)93.9%
Missing6486
Missing (%)94.5%
Memory size53.7 KiB
2023-12-12T16:58:52.214869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length70
Median length37
Mean length8.6515957
Min length1

Characters and Unicode

Total characters3253
Distinct characters419
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique338 ?
Unique (%)89.9%

Sample

1st row납읍리
2nd row옥천면 신복리 238
3rd row텃밭
4th row회관텃밭
5th row임대
ValueCountFrequency (%)
임대 10
 
1.5%
6
 
0.9%
밭 4
 
0.6%
무농약 4
 
0.6%
농장 4
 
0.6%
하우스 4
 
0.6%
파종 4
 
0.6%
비닐하우스 4
 
0.6%
최영회 3
 
0.4%
친환경 3
 
0.4%
Other values (571) 642
93.3%
2023-12-12T16:58:52.640371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
332
 
10.2%
0 180
 
5.5%
1 129
 
4.0%
2 95
 
2.9%
, 58
 
1.8%
. 57
 
1.8%
지 56
 
1.7%
리 50
 
1.5%
5 47
 
1.4%
4 42
 
1.3%
Other values (409) 2207
67.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2081
64.0%
Decimal Number 653
 
20.1%
Space Separator 332
 
10.2%
Other Punctuation 130
 
4.0%
Dash Punctuation 22
 
0.7%
Open Punctuation 11
 
0.3%
Close Punctuation 11
 
0.3%
Lowercase Letter 7
 
0.2%
Math Symbol 3
 
0.1%
Connector Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 56
 
2.7%
리 50
 
2.4%
종 36
 
1.7%
이 34
 
1.6%
기 33
 
1.6%
대 32
 
1.5%
농 29
 
1.4%
동 29
 
1.4%
파 28
 
1.3%
배 28
 
1.3%
Other values (381) 1726
82.9%
Decimal Number
ValueCountFrequency (%)
0 180
27.6%
1 129
19.8%
2 95
14.5%
5 47
 
7.2%
4 42
 
6.4%
3 39
 
6.0%
7 38
 
5.8%
8 31
 
4.7%
9 30
 
4.6%
6 22
 
3.4%
Lowercase Letter
ValueCountFrequency (%)
k 2
28.6%
g 2
28.6%
t 1
14.3%
a 1
14.3%
b 1
14.3%
Other Punctuation
ValueCountFrequency (%)
, 58
44.6%
. 57
43.8%
/ 10
 
7.7%
: 5
 
3.8%
Math Symbol
ValueCountFrequency (%)
+ 1
33.3%
= 1
33.3%
~ 1
33.3%
Space Separator
ValueCountFrequency (%)
332
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2081
64.0%
Common 1164
35.8%
Latin 8
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 56
 
2.7%
리 50
 
2.4%
종 36
 
1.7%
이 34
 
1.6%
기 33
 
1.6%
대 32
 
1.5%
농 29
 
1.4%
동 29
 
1.4%
파 28
 
1.3%
배 28
 
1.3%
Other values (381) 1726
82.9%
Common
ValueCountFrequency (%)
332
28.5%
0 180
15.5%
1 129
 
11.1%
2 95
 
8.2%
, 58
 
5.0%
. 57
 
4.9%
5 47
 
4.0%
4 42
 
3.6%
3 39
 
3.4%
7 38
 
3.3%
Other values (12) 147
12.6%
Latin
ValueCountFrequency (%)
k 2
25.0%
g 2
25.0%
t 1
12.5%
a 1
12.5%
B 1
12.5%
b 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2076
63.8%
ASCII 1172
36.0%
Compat Jamo 5
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
332
28.3%
0 180
15.4%
1 129
 
11.0%
2 95
 
8.1%
, 58
 
4.9%
. 57
 
4.9%
5 47
 
4.0%
4 42
 
3.6%
3 39
 
3.3%
7 38
 
3.2%
Other values (18) 155
13.2%
Hangul
ValueCountFrequency (%)
지 56
 
2.7%
리 50
 
2.4%
종 36
 
1.7%
이 34
 
1.6%
기 33
 
1.6%
대 32
 
1.5%
농 29
 
1.4%
동 29
 
1.4%
파 28
 
1.3%
배 28
 
1.3%
Other values (376) 1721
82.9%
Compat Jamo
ValueCountFrequency (%)
ㅇ 1
20.0%
ㆍ 1
20.0%
ㅣ 1
20.0%
ㅡ 1
20.0%
ㅠ 1
20.0%

Interactions

2023-12-12T16:58:46.861095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:58:45.361066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:58:45.924609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:58:46.412941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:58:47.019001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:58:45.549540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:58:46.061674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:58:46.526652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:58:47.137158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:58:45.681769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:58:46.197196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:58:46.642717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:58:47.275049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:58:45.811103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:58:46.306856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:58:46.749394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:58:52.734843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품목재배일련번호년도면적재배기간(시작월)재배기간(종료월)조성주수
품목재배일련번호1.0000.0460.0000.0290.0760.031
년도0.0461.0000.0000.0000.0000.000
면적0.0000.0001.0000.0000.0000.000
재배기간(시작월)0.0290.0000.0001.0000.6530.000
재배기간(종료월)0.0760.0000.0000.6531.0000.000
조성주수0.0310.0000.0000.0000.0001.000
2023-12-12T16:58:52.839576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재배유형년도재배기간(시작월)
재배유형1.0001.0001.000
년도1.0001.0000.000
재배기간(시작월)1.0000.0001.000
2023-12-12T16:58:52.918130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품목재배일련번호면적재배기간(종료월)조성주수년도재배유형재배기간(시작월)
품목재배일련번호1.0000.0460.0030.0020.0271.0000.022
면적0.0461.0000.0910.0950.0001.0000.000
재배기간(종료월)0.0030.0911.0000.0090.0001.0000.660
조성주수0.0020.0950.0091.0000.0001.0000.000
년도0.0270.0000.0000.0001.0001.0000.000
재배유형1.0001.0001.0001.0001.0001.0001.000
재배기간(시작월)0.0220.0000.6600.0000.0001.0001.000

Missing values

2023-12-12T16:58:47.442653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:58:47.615372image/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.
2023-12-12T16:58:47.751551image/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

품목재배일련번호년도등록일시수정일시면적주요품종재배유형재배기간(시작월)재배기간(종료월)조성주수비고
01561319052017-03-092017-03-091000<NA>1190<NA>
11561419052017-03-102017-04-03500궁천111280납읍리
21561519052017-03-102017-03-101600양파1180<NA>
31561619052017-03-102017-03-10700<NA>1190<NA>
41561719052017-03-102017-03-10500<NA>1170<NA>
51562219052017-03-102017-05-19150청양 마니따1110600<NA>
61562319052017-03-102017-03-10150<NA>1110150<NA>
71562419052017-03-102017-03-103000몽부사 대옥계 대황모 양홍장1111155<NA>
81562519072017-03-102017-03-10500듀크1111100<NA>
91562619052017-03-102017-03-10300하루키11100<NA>
품목재배일련번호년도등록일시수정일시면적주요품종재배유형재배기간(시작월)재배기간(종료월)조성주수비고
68522300119052019-11-102019-11-1020<NA>1140<NA>
68532300219052019-11-102019-11-102400<NA>1160<NA>
68542300319052019-11-112019-11-111000<NA>11110<NA>
68552300419052019-11-112019-11-111000<NA>11110<NA>
68562300519052019-11-112019-11-11480한우11390<NA>
68572300619052019-11-112019-11-112500새누리11100<NA>
68582300719052019-11-112019-11-111000아로니아11101000<NA>
68592300819052019-11-112019-11-111000다음11116000<NA>
68602300919052019-11-112019-11-11600난지형1120<NA>
68612301019052019-11-112019-11-11300c동2동 꽃적상추2/청상 추2/치마상추2줄(1줄*6주)1129240<NA>