Overview

Dataset statistics

Number of variables9
Number of observations413
Missing cells28
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.4 KiB
Average record size in memory75.3 B

Variable types

Categorical3
Text3
Numeric3

Dataset

Description신선농산물 수출검역단지현황 20220711
Author농림축산검역본부
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220726000000002293

Alerts

농가수 is highly overall correlated with 재배면적 and 1 other fieldsHigh correlation
재배면적 is highly overall correlated with 농가수 and 1 other fieldsHigh correlation
검사계획량 is highly overall correlated with 농가수 and 1 other fieldsHigh correlation
시군명 has 8 (1.9%) missing valuesMissing
농가수 has 8 (1.9%) missing valuesMissing
재배면적 has 9 (2.2%) missing valuesMissing
검사계획량 has 24 (5.8%) zerosZeros

Reproduction

Analysis started2023-12-11 03:46:19.988939
Analysis finished2023-12-11 03:46:21.640409
Duration1.65 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관명
Categorical

Distinct20
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
창원사무소
101 
구미사무소
77 
천안사무소
66 
광주사무소
29 
평택사무소
21 
Other values (15)
119 

Length

Max length7
Median length5
Mean length5.1331719
Min length5

Unique

Unique2 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
창원사무소 101
24.5%
구미사무소 77
18.6%
천안사무소 66
16.0%
광주사무소 29
 
7.0%
평택사무소 21
 
5.1%
대구사무소 15
 
3.6%
의왕사무소 14
 
3.4%
호남지역본부 14
 
3.4%
청주사무소 13
 
3.1%
전주사무소 10
 
2.4%
Other values (10) 53
12.8%

Length

2023-12-11T12:46:21.727493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
창원사무소 101
24.5%
구미사무소 77
18.6%
천안사무소 66
16.0%
광주사무소 29
 
7.0%
평택사무소 21
 
5.1%
대구사무소 15
 
3.6%
의왕사무소 14
 
3.4%
호남지역본부 14
 
3.4%
청주사무소 13
 
3.1%
전주사무소 10
 
2.4%
Other values (10) 53
12.8%

수출품목
Categorical

Distinct13
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
딸기
123 
배
123 
포도
86 
파프리카
21 
사과
18 
Other values (8)
42 

Length

Max length4
Median length2
Mean length1.8087167
Min length1

Unique

Unique2 ?
Unique (%)0.5%

Sample

1st row감
2nd row감
3rd row감
4th row감
5th row딸기

Common Values

ValueCountFrequency (%)
딸기 123
29.8%
배 123
29.8%
포도 86
20.8%
파프리카 21
 
5.1%
사과 18
 
4.4%
단감 16
 
3.9%
감귤 8
 
1.9%
복숭아 6
 
1.5%
감 5
 
1.2%
밤 3
 
0.7%
Other values (3) 4
 
1.0%

Length

2023-12-11T12:46:21.960162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
딸기 123
29.8%
배 123
29.8%
포도 86
20.8%
파프리카 21
 
5.1%
사과 18
 
4.4%
단감 16
 
3.9%
감귤 8
 
1.9%
복숭아 6
 
1.5%
감 5
 
1.2%
밤 3
 
0.7%
Other values (3) 4
 
1.0%

수출국가
Categorical

Distinct13
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
태국
94 
대만
89 
베트남
67 
중국
44 
캐나다
40 
Other values (8)
79 

Length

Max length5
Median length2
Mean length2.3559322
Min length2

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row베트남
2nd row베트남
3rd row태국
4th row태국
5th row베트남

Common Values

ValueCountFrequency (%)
태국 94
22.8%
대만 89
21.5%
베트남 67
16.2%
중국 44
10.7%
캐나다 40
9.7%
미국 34
 
8.2%
필리핀 19
 
4.6%
호주 12
 
2.9%
뉴질랜드 5
 
1.2%
브라질 4
 
1.0%
Other values (3) 5
 
1.2%

Length

2023-12-11T12:46:22.136537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
태국 94
22.8%
대만 89
21.5%
베트남 67
16.2%
중국 44
10.7%
캐나다 40
9.7%
미국 34
 
8.2%
필리핀 19
 
4.6%
호주 12
 
2.9%
뉴질랜드 5
 
1.2%
브라질 4
 
1.0%
Other values (3) 5
 
1.2%

시군명
Text

MISSING 

Distinct75
Distinct (%)18.5%
Missing8
Missing (%)1.9%
Memory size3.4 KiB
2023-12-11T12:46:22.448953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.7580247
Min length2

Characters and Unicode

Total characters1117
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)5.4%

Sample

1st row김해
2nd row김해
3rd row김해
4th row김해
5th row김해
ValueCountFrequency (%)
상주 50
 
12.3%
진주시 49
 
12.1%
천안시 29
 
7.2%
논산시 19
 
4.7%
산청군 15
 
3.7%
김천 15
 
3.7%
나주시 13
 
3.2%
화성시 12
 
3.0%
평택시 9
 
2.2%
안성시 9
 
2.2%
Other values (65) 185
45.7%
2023-12-11T12:46:22.954739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 219
19.6%
주 133
 
11.9%
군 86
 
7.7%
천 67
 
6.0%
진 57
 
5.1%
산 57
 
5.1%
상 50
 
4.5%
안 43
 
3.8%
성 31
 
2.8%
김 28
 
2.5%
Other values (64) 346
31.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1117
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 219
19.6%
주 133
 
11.9%
군 86
 
7.7%
천 67
 
6.0%
진 57
 
5.1%
산 57
 
5.1%
상 50
 
4.5%
안 43
 
3.8%
성 31
 
2.8%
김 28
 
2.5%
Other values (64) 346
31.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1117
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 219
19.6%
주 133
 
11.9%
군 86
 
7.7%
천 67
 
6.0%
진 57
 
5.1%
산 57
 
5.1%
상 50
 
4.5%
안 43
 
3.8%
성 31
 
2.8%
김 28
 
2.5%
Other values (64) 346
31.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1117
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 219
19.6%
주 133
 
11.9%
군 86
 
7.7%
천 67
 
6.0%
진 57
 
5.1%
산 57
 
5.1%
상 50
 
4.5%
안 43
 
3.8%
성 31
 
2.8%
김 28
 
2.5%
Other values (64) 346
31.0%

단지명/선과장명
Text

Distinct232
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2023-12-11T12:46:23.243430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length27
Mean length9.5157385
Min length2

Characters and Unicode

Total characters3930
Distinct characters224
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)30.0%

Sample

1st row진영농협산지유통센터
2nd row경남단감원예농협APC
3rd row진영농협산지유통센터
4th row경남단감원예농협APC
5th row한림농협딸기수출농단
ValueCountFrequency (%)
농업회사법인 19
 
3.6%
apc 10
 
1.9%
상주원예농협 7
 
1.3%
조은팜 6
 
1.1%
농산물산지유통센터 6
 
1.1%
한국배영농조합법인 6
 
1.1%
조이팜 5
 
1.0%
영농조합법인 5
 
1.0%
아산원예농업협동조합 5
 
1.0%
포도수출협의회 5
 
1.0%
Other values (253) 449
85.9%
2023-12-11T12:46:23.708869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
농 372
 
9.5%
협 190
 
4.8%
조 142
 
3.6%
합 131
 
3.3%
인 129
 
3.3%
법 126
 
3.2%
영 120
 
3.1%
111
 
2.8%
수 110
 
2.8%
산 99
 
2.5%
Other values (214) 2400
61.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3662
93.2%
Space Separator 111
 
2.8%
Uppercase Letter 60
 
1.5%
Other Punctuation 37
 
0.9%
Close Punctuation 22
 
0.6%
Open Punctuation 22
 
0.6%
Other Symbol 10
 
0.3%
Decimal Number 6
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
농 372
 
10.2%
협 190
 
5.2%
조 142
 
3.9%
합 131
 
3.6%
인 129
 
3.5%
법 126
 
3.4%
영 120
 
3.3%
수 110
 
3.0%
산 99
 
2.7%
지 94
 
2.6%
Other values (201) 2149
58.7%
Other Punctuation
ValueCountFrequency (%)
/ 26
70.3%
, 7
 
18.9%
? 2
 
5.4%
. 2
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
A 20
33.3%
P 20
33.3%
C 20
33.3%
Decimal Number
ValueCountFrequency (%)
1 3
50.0%
2 3
50.0%
Space Separator
ValueCountFrequency (%)
111
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3672
93.4%
Common 198
 
5.0%
Latin 60
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
농 372
 
10.1%
협 190
 
5.2%
조 142
 
3.9%
합 131
 
3.6%
인 129
 
3.5%
법 126
 
3.4%
영 120
 
3.3%
수 110
 
3.0%
산 99
 
2.7%
지 94
 
2.6%
Other values (202) 2159
58.8%
Common
ValueCountFrequency (%)
111
56.1%
/ 26
 
13.1%
) 22
 
11.1%
( 22
 
11.1%
, 7
 
3.5%
1 3
 
1.5%
2 3
 
1.5%
? 2
 
1.0%
. 2
 
1.0%
Latin
ValueCountFrequency (%)
A 20
33.3%
P 20
33.3%
C 20
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3662
93.2%
ASCII 258
 
6.6%
None 10
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
농 372
 
10.2%
협 190
 
5.2%
조 142
 
3.9%
합 131
 
3.6%
인 129
 
3.5%
법 126
 
3.4%
영 120
 
3.3%
수 110
 
3.0%
산 99
 
2.7%
지 94
 
2.6%
Other values (201) 2149
58.7%
ASCII
ValueCountFrequency (%)
111
43.0%
/ 26
 
10.1%
) 22
 
8.5%
( 22
 
8.5%
A 20
 
7.8%
P 20
 
7.8%
C 20
 
7.8%
, 7
 
2.7%
1 3
 
1.2%
2 3
 
1.2%
Other values (2) 4
 
1.6%
None
ValueCountFrequency (%)
㈜ 10
100.0%

농가수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct59
Distinct (%)14.6%
Missing8
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean16.353086
Minimum1
Maximum326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-11T12:46:23.901856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median9
Q318
95-th percentile52.6
Maximum326
Range325
Interquartile range (IQR)14

Descriptive statistics

Standard deviation26.929157
Coefficient of variation (CV)1.6467324
Kurtosis54.205438
Mean16.353086
Median Absolute Deviation (MAD)6
Skewness6.119587
Sum6623
Variance725.17948
MonotonicityNot monotonic
2023-12-11T12:46:24.074046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 37
 
9.0%
1 34
 
8.2%
5 25
 
6.1%
3 24
 
5.8%
8 21
 
5.1%
9 20
 
4.8%
6 19
 
4.6%
7 17
 
4.1%
12 16
 
3.9%
2 16
 
3.9%
Other values (49) 176
42.6%
ValueCountFrequency (%)
1 34
8.2%
2 16
3.9%
3 24
5.8%
4 37
9.0%
5 25
6.1%
6 19
4.6%
7 17
4.1%
8 21
5.1%
9 20
4.8%
10 14
 
3.4%
ValueCountFrequency (%)
326 1
 
0.2%
224 1
 
0.2%
136 1
 
0.2%
135 2
0.5%
103 1
 
0.2%
100 1
 
0.2%
95 1
 
0.2%
86 4
1.0%
80 1
 
0.2%
78 1
 
0.2%

재배면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct270
Distinct (%)66.8%
Missing9
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean15.742644
Minimum0.3
Maximum427
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-11T12:46:24.276450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.9045
Q13
median8.0937
Q316.625
95-th percentile52.49182
Maximum427
Range426.7
Interquartile range (IQR)13.625

Descriptive statistics

Standard deviation32.643572
Coefficient of variation (CV)2.0735762
Kurtosis90.139906
Mean15.742644
Median Absolute Deviation (MAD)5.5087
Skewness8.3175999
Sum6360.0282
Variance1065.6028
MonotonicityNot monotonic
2023-12-11T12:46:24.446860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.0 7
 
1.7%
8.9 5
 
1.2%
2.1 5
 
1.2%
4.8 5
 
1.2%
3.3935 4
 
1.0%
2.3 4
 
1.0%
15.3 4
 
1.0%
0.4 4
 
1.0%
25.5 4
 
1.0%
7.7792 4
 
1.0%
Other values (260) 358
86.7%
(Missing) 9
 
2.2%
ValueCountFrequency (%)
0.3 2
0.5%
0.4 4
1.0%
0.45 1
 
0.2%
0.5 1
 
0.2%
0.53 1
 
0.2%
0.59 1
 
0.2%
0.645 1
 
0.2%
0.66 1
 
0.2%
0.69 1
 
0.2%
0.71 1
 
0.2%
ValueCountFrequency (%)
427.0 1
0.2%
348.5 1
0.2%
177.4 1
0.2%
120.7 1
0.2%
110.4 1
0.2%
103.664 1
0.2%
102.3 1
0.2%
88.2 1
0.2%
81.63 1
0.2%
76.9 1
0.2%

검사계획량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct83
Distinct (%)20.2%
Missing2
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean95.470803
Minimum0
Maximum4500
Zeros24
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-11T12:46:24.592125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median20
Q368
95-th percentile400
Maximum4500
Range4500
Interquartile range (IQR)63

Descriptive statistics

Standard deviation311.88788
Coefficient of variation (CV)3.2668404
Kurtosis116.62893
Mean95.470803
Median Absolute Deviation (MAD)19
Skewness9.5836322
Sum39238.5
Variance97274.048
MonotonicityNot monotonic
2023-12-11T12:46:24.772232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 44
 
10.7%
5.0 39
 
9.4%
10.0 39
 
9.4%
20.0 34
 
8.2%
0.0 24
 
5.8%
50.0 22
 
5.3%
100.0 19
 
4.6%
2.0 12
 
2.9%
30.0 11
 
2.7%
150.0 11
 
2.7%
Other values (73) 156
37.8%
ValueCountFrequency (%)
0.0 24
5.8%
1.0 44
10.7%
2.0 12
 
2.9%
2.5 2
 
0.5%
3.0 8
 
1.9%
4.0 7
 
1.7%
5.0 39
9.4%
6.0 6
 
1.5%
7.0 2
 
0.5%
8.0 3
 
0.7%
ValueCountFrequency (%)
4500.0 1
0.2%
3000.0 1
0.2%
1650.0 1
0.2%
1104.0 1
0.2%
1007.0 1
0.2%
1000.0 2
0.5%
900.0 1
0.2%
750.0 1
0.2%
610.0 1
0.2%
600.0 1
0.2%

'21년산
Text

Distinct217
Distinct (%)52.7%
Missing1
Missing (%)0.2%
Memory size3.4 KiB
2023-12-11T12:46:25.190926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length2.9271845
Min length1

Characters and Unicode

Total characters1206
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

Unique201 ?
Unique (%)48.8%

Sample

1st row37.73
2nd row1.92
3rd row15.68
4th row0.74
5th row13.35
ValueCountFrequency (%)
0 175
42.5%
32.4 5
 
1.2%
4 3
 
0.7%
1.2 3
 
0.7%
48.6 3
 
0.7%
0.1 2
 
0.5%
4.61 2
 
0.5%
64.8 2
 
0.5%
52.92 2
 
0.5%
7.5 2
 
0.5%
Other values (207) 213
51.7%
2023-12-11T12:46:25.862737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 243
20.1%
. 215
17.8%
1 120
10.0%
2 119
9.9%
6 100
8.3%
4 86
 
7.1%
3 83
 
6.9%
5 67
 
5.6%
8 63
 
5.2%
7 54
 
4.5%
Other values (2) 56
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 988
81.9%
Other Punctuation 218
 
18.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 243
24.6%
1 120
12.1%
2 119
12.0%
6 100
10.1%
4 86
 
8.7%
3 83
 
8.4%
5 67
 
6.8%
8 63
 
6.4%
7 54
 
5.5%
9 53
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 215
98.6%
, 3
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1206
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 243
20.1%
. 215
17.8%
1 120
10.0%
2 119
9.9%
6 100
8.3%
4 86
 
7.1%
3 83
 
6.9%
5 67
 
5.6%
8 63
 
5.2%
7 54
 
4.5%
Other values (2) 56
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1206
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 243
20.1%
. 215
17.8%
1 120
10.0%
2 119
9.9%
6 100
8.3%
4 86
 
7.1%
3 83
 
6.9%
5 67
 
5.6%
8 63
 
5.2%
7 54
 
4.5%
Other values (2) 56
 
4.6%

Interactions

2023-12-11T12:46:20.931396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:20.415948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:20.645536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:21.119173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:20.498653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:20.726215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:21.201302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:20.573748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:46:20.808693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:46:26.002343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기관명수출품목수출국가시군명농가수재배면적검사계획량
기관명1.0000.8190.4691.0000.1880.1370.000
수출품목0.8191.0000.8000.9440.0000.0000.000
수출국가0.4690.8001.0000.0000.2380.2190.151
시군명1.0000.9440.0001.0000.0000.0000.000
농가수0.1880.0000.2380.0001.0000.8880.847
재배면적0.1370.0000.2190.0000.8881.0000.949
검사계획량0.0000.0000.1510.0000.8470.9491.000
2023-12-11T12:46:26.161050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수출국가기관명수출품목
수출국가1.0000.1750.337
기관명0.1751.0000.440
수출품목0.3370.4401.000
2023-12-11T12:46:26.284851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
농가수재배면적검사계획량기관명수출품목수출국가
농가수1.0000.8330.5580.0800.0000.111
재배면적0.8331.0000.6180.0610.0000.108
검사계획량0.5580.6181.0000.0000.0000.074
기관명0.0800.0610.0001.0000.4400.175
수출품목0.0000.0000.0000.4401.0000.337
수출국가0.1110.1080.0740.1750.3371.000

Missing values

2023-12-11T12:46:21.329025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:46:21.461960image/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.
2023-12-11T12:46:21.566594image/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

기관명수출품목수출국가시군명단지명/선과장명농가수재배면적검사계획량'21년산
0김해공항사무소감베트남김해진영농협산지유통센터59.14200.037.73
1김해공항사무소감베트남김해경남단감원예농협APC919.43100.01.92
2김해공항사무소감태국김해진영농협산지유통센터59.1460.015.68
3김해공항사무소감태국김해경남단감원예농협APC919.4350.00.74
4김해공항사무소딸기베트남김해한림농협딸기수출농단52.9220.013.35
5김해공항사무소딸기태국김해한림농협딸기수출농단52.925.07.67
6김해공항사무소딸기캐나다김해한림농협딸기수출농단52.925.00
7김해공항사무소딸기중국부산김해부경파프리카 수출선별장86.88100.00
8김해공항사무소토마토베트남부산김해부경파프리카 수출선별장10.5950.00
9대구사무소포도중국경산다모아수출영농조합124.720.00
기관명수출품목수출국가시군명단지명/선과장명농가수재배면적검사계획량'21년산
403무안공항사무소배대만영암군신북농협 농산물산지유통센터5381.63750.0607
404제주지역본부감귤미국제주시제주시농협3317.036.00
405제주지역본부감귤미국<NA>조천농협3527.0100.00
406제주지역본부감귤미국서귀포시서귀포농협<NA><NA>0.00
407제주지역본부감귤미국<NA>중문농협<NA><NA>0.00
408제주지역본부감귤미국<NA>감협1712.0100.00
409제주지역본부감귤미국<NA>성산농협2730.0100.00
410제주지역본부감귤태국서귀포시감협1712.030.00
411제주지역본부감귤태국<NA>중문농협<NA><NA>0.00
412제주지역본부참다래대만제주시한라골드<NA><NA>0.00