Overview

Dataset statistics

Number of variables12
Number of observations101
Missing cells146
Missing cells (%)12.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.1 KiB
Average record size in memory102.3 B

Variable types

Numeric5
Categorical2
Text4
DateTime1

Dataset

Description전라북도 시군별 쌀가공식품업체 현황(시군명, 상호, 월사용능력, 가공용 쌀 매입대상자 지정일, 주생산품, 연락처등)
Author전라북도
URLhttps://www.data.go.kr/data/15055751/fileData.do

Alerts

순번 is highly overall correlated with 시군명High correlation
월사용능력(톤) is highly overall correlated with 2019회계년도 사용량 총계(톤) and 2 other fieldsHigh correlation
2019회계년도 사용량 총계(톤) is highly overall correlated with 월사용능력(톤) and 2 other fieldsHigh correlation
2019회계년도 국내산 사용량(톤) is highly overall correlated with 월사용능력(톤) and 2 other fieldsHigh correlation
2019회계년도 외국산 사용량(톤) is highly overall correlated with 월사용능력(톤) and 2 other fieldsHigh correlation
시군명 is highly overall correlated with 순번High correlation
월사용능력(톤) has 2 (2.0%) missing valuesMissing
연락처 has 5 (5.0%) missing valuesMissing
팩스번호 has 5 (5.0%) missing valuesMissing
2019회계년도 사용량 총계(톤) has 24 (23.8%) missing valuesMissing
2019회계년도 국내산 사용량(톤) has 40 (39.6%) missing valuesMissing
2019회계년도 외국산 사용량(톤) has 70 (69.3%) missing valuesMissing
순번 has unique valuesUnique
상호 has unique valuesUnique
주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:02:03.152126
Analysis finished2023-12-12 20:02:07.442568
Duration4.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51
Minimum1
Maximum101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T05:02:07.543216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q126
median51
Q376
95-th percentile96
Maximum101
Range100
Interquartile range (IQR)50

Descriptive statistics

Standard deviation29.300171
Coefficient of variation (CV)0.57451315
Kurtosis-1.2
Mean51
Median Absolute Deviation (MAD)25
Skewness0
Sum5151
Variance858.5
MonotonicityStrictly increasing
2023-12-13T05:02:07.731024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (91) 91
90.1%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
101 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%

시군명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Memory size940.0 B
김제시청
15 
전주시청
15 
익산시청
12 
군산시청
11 
완주군청
11 
Other values (9)
37 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row고창군청
2nd row고창군청
3rd row고창군청
4th row군산시청
5th row군산시청

Common Values

ValueCountFrequency (%)
김제시청 15
14.9%
전주시청 15
14.9%
익산시청 12
11.9%
군산시청 11
10.9%
완주군청 11
10.9%
정읍시청 7
6.9%
남원시청 6
 
5.9%
순창군청 6
 
5.9%
부안군청 5
 
5.0%
진안군청 5
 
5.0%
Other values (4) 8
7.9%

Length

2023-12-13T05:02:07.875784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
김제시청 15
14.9%
전주시청 15
14.9%
익산시청 12
11.9%
군산시청 11
10.9%
완주군청 11
10.9%
정읍시청 7
6.9%
남원시청 6
 
5.9%
순창군청 6
 
5.9%
부안군청 5
 
5.0%
진안군청 5
 
5.0%
Other values (4) 8
7.9%

상호
Text

UNIQUE 

Distinct101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
2023-12-13T05:02:08.139908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length14
Mean length9.2079208
Min length2

Characters and Unicode

Total characters930
Distinct characters209
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)100.0%

Sample

1st row농업회사법인고은(유)
2nd row사임당푸드영농조합법인
3rd row선운산쌀과방영농조합법인
4th row롯데칠성음료(주)군산공장
5th row대광제면
ValueCountFrequency (%)
농업회사법인 8
 
6.4%
유한회사 5
 
4.0%
주식회사 3
 
2.4%
영농조합법인 2
 
1.6%
농업회사법인고은(유 1
 
0.8%
원광탁주 1
 
0.8%
미광식품 1
 
0.8%
한떡협전북전주시지부 1
 
0.8%
주)천본 1
 
0.8%
라이스영농조합법인 1
 
0.8%
Other values (101) 101
80.8%
2023-12-13T05:02:08.543685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
농 45
 
4.8%
사 41
 
4.4%
인 41
 
4.4%
주 40
 
4.3%
법 40
 
4.3%
회 36
 
3.9%
조 28
 
3.0%
) 25
 
2.7%
업 25
 
2.7%
( 25
 
2.7%
Other values (199) 584
62.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 856
92.0%
Close Punctuation 25
 
2.7%
Open Punctuation 25
 
2.7%
Space Separator 24
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
농 45
 
5.3%
사 41
 
4.8%
인 41
 
4.8%
주 40
 
4.7%
법 40
 
4.7%
회 36
 
4.2%
조 28
 
3.3%
업 25
 
2.9%
합 23
 
2.7%
식 22
 
2.6%
Other values (196) 515
60.2%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%
Space Separator
ValueCountFrequency (%)
24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 856
92.0%
Common 74
 
8.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
농 45
 
5.3%
사 41
 
4.8%
인 41
 
4.8%
주 40
 
4.7%
법 40
 
4.7%
회 36
 
4.2%
조 28
 
3.3%
업 25
 
2.9%
합 23
 
2.7%
식 22
 
2.6%
Other values (196) 515
60.2%
Common
ValueCountFrequency (%)
) 25
33.8%
( 25
33.8%
24
32.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 856
92.0%
ASCII 74
 
8.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
농 45
 
5.3%
사 41
 
4.8%
인 41
 
4.8%
주 40
 
4.7%
법 40
 
4.7%
회 36
 
4.2%
조 28
 
3.3%
업 25
 
2.9%
합 23
 
2.7%
식 22
 
2.6%
Other values (196) 515
60.2%
ASCII
ValueCountFrequency (%)
) 25
33.8%
( 25
33.8%
24
32.4%

월사용능력(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct37
Distinct (%)37.4%
Missing2
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean86.111111
Minimum2
Maximum1500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T05:02:08.673182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q110
median20
Q340
95-th percentile410
Maximum1500
Range1498
Interquartile range (IQR)30

Descriptive statistics

Standard deviation228.7183
Coefficient of variation (CV)2.6560834
Kurtosis22.661172
Mean86.111111
Median Absolute Deviation (MAD)10
Skewness4.5919698
Sum8525
Variance52312.059
MonotonicityNot monotonic
2023-12-13T05:02:08.801503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
10 24
23.8%
20 8
 
7.9%
15 6
 
5.9%
40 5
 
5.0%
24 4
 
4.0%
30 4
 
4.0%
36 3
 
3.0%
50 3
 
3.0%
60 3
 
3.0%
9 2
 
2.0%
Other values (27) 37
36.6%
ValueCountFrequency (%)
2 2
 
2.0%
3 2
 
2.0%
4 2
 
2.0%
6 2
 
2.0%
8 2
 
2.0%
9 2
 
2.0%
10 24
23.8%
11 2
 
2.0%
12 1
 
1.0%
15 6
 
5.9%
ValueCountFrequency (%)
1500 1
1.0%
1232 1
1.0%
1000 1
1.0%
500 2
2.0%
400 1
1.0%
360 1
1.0%
300 1
1.0%
250 1
1.0%
160 1
1.0%
144 1
1.0%

가공용쌀매입대상자 지정일
Date

Distinct97
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
Minimum1991-01-18 00:00:00
Maximum2020-09-21 00:00:00
2023-12-13T05:02:08.927847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:09.040277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

주생산품
Categorical

Distinct9
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size940.0 B
누룽지
30 
떡류
29 
주류
21 
조미식품
7 
제과·제빵
4 
Other values (4)
10 

Length

Max length5
Median length2
Mean length2.7029703
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row주류
2nd row떡류
3rd row제과·제빵
4th row주류
5th row떡류

Common Values

ValueCountFrequency (%)
누룽지 30
29.7%
떡류 29
28.7%
주류 21
20.8%
조미식품 7
 
6.9%
제과·제빵 4
 
4.0%
가공밥류 4
 
4.0%
쌀가루 3
 
3.0%
곡물가공 2
 
2.0%
음료 1
 
1.0%

Length

2023-12-13T05:02:09.145834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:02:09.245944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
누룽지 30
29.7%
떡류 29
28.7%
주류 21
20.8%
조미식품 7
 
6.9%
제과·제빵 4
 
4.0%
가공밥류 4
 
4.0%
쌀가루 3
 
3.0%
곡물가공 2
 
2.0%
음료 1
 
1.0%

연락처
Text

MISSING 

Distinct96
Distinct (%)100.0%
Missing5
Missing (%)5.0%
Memory size940.0 B
2023-12-13T05:02:09.450846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.010417
Min length11

Characters and Unicode

Total characters1153
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

Unique96 ?
Unique (%)100.0%

Sample

1st row063-562-2008
2nd row063-561-5986
3rd row063-564-6696
4th row02-3459-1378
5th row063-446-4082
ValueCountFrequency (%)
063-245-1164 1
 
1.0%
063-564-6696 1
 
1.0%
063-254-9424 1
 
1.0%
063-351-9200 1
 
1.0%
063-644-7799 1
 
1.0%
063-843-7778 1
 
1.0%
063-832-7065 1
 
1.0%
063-831-7576 1
 
1.0%
063-842-5552 1
 
1.0%
063-855-3337 1
 
1.0%
Other values (86) 86
89.6%
2023-12-13T05:02:09.802978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 192
16.7%
3 164
14.2%
0 157
13.6%
6 149
12.9%
2 89
7.7%
5 87
7.5%
4 85
7.4%
8 70
 
6.1%
1 57
 
4.9%
7 54
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 961
83.3%
Dash Punctuation 192
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 164
17.1%
0 157
16.3%
6 149
15.5%
2 89
9.3%
5 87
9.1%
4 85
8.8%
8 70
7.3%
1 57
 
5.9%
7 54
 
5.6%
9 49
 
5.1%
Dash Punctuation
ValueCountFrequency (%)
- 192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1153
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 192
16.7%
3 164
14.2%
0 157
13.6%
6 149
12.9%
2 89
7.7%
5 87
7.5%
4 85
7.4%
8 70
 
6.1%
1 57
 
4.9%
7 54
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1153
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 192
16.7%
3 164
14.2%
0 157
13.6%
6 149
12.9%
2 89
7.7%
5 87
7.5%
4 85
7.4%
8 70
 
6.1%
1 57
 
4.9%
7 54
 
4.7%

팩스번호
Text

MISSING 

Distinct94
Distinct (%)97.9%
Missing5
Missing (%)5.0%
Memory size940.0 B
2023-12-13T05:02:10.008509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.072917
Min length11

Characters and Unicode

Total characters1159
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

Unique92 ?
Unique (%)95.8%

Sample

1st row063-564-2008
2nd row063-562-5989
3rd row063-564-6696
4th row02-3452-1407
5th row063-443-4879
ValueCountFrequency (%)
063-543-4782 2
 
2.1%
063-274-1806 2
 
2.1%
063-861-8609 1
 
1.0%
050-4484-5959 1
 
1.0%
063-564-2008 1
 
1.0%
063-855-3337 1
 
1.0%
063-241-2220 1
 
1.0%
063-212-7440 1
 
1.0%
063-351-9922 1
 
1.0%
063-644-7988 1
 
1.0%
Other values (84) 84
87.5%
2023-12-13T05:02:10.330336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 193
16.7%
0 165
14.2%
3 165
14.2%
6 144
12.4%
4 95
8.2%
5 88
7.6%
2 85
7.3%
8 76
 
6.6%
1 63
 
5.4%
9 43
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 966
83.3%
Dash Punctuation 193
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 165
17.1%
3 165
17.1%
6 144
14.9%
4 95
9.8%
5 88
9.1%
2 85
8.8%
8 76
7.9%
1 63
 
6.5%
9 43
 
4.5%
7 42
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 193
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1159
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 193
16.7%
0 165
14.2%
3 165
14.2%
6 144
12.4%
4 95
8.2%
5 88
7.6%
2 85
7.3%
8 76
 
6.6%
1 63
 
5.4%
9 43
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1159
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 193
16.7%
0 165
14.2%
3 165
14.2%
6 144
12.4%
4 95
8.2%
5 88
7.6%
2 85
7.3%
8 76
 
6.6%
1 63
 
5.4%
9 43
 
3.7%

주소
Text

UNIQUE 

Distinct101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
2023-12-13T05:02:10.541035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length35
Mean length23.871287
Min length15

Characters and Unicode

Total characters2411
Distinct characters220
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)100.0%

Sample

1st row전북 고창군 아산면 계산리 384 32
2nd row전북 고창군 고창읍 태봉로 551
3rd row전북 고창군 무장면 무장남북로 20 1 선운산쌀과방
4th row전북 군산시 소룡동 176 1
5th row전북 군산시 경암동 683 4
ValueCountFrequency (%)
전북 100
 
16.2%
1 18
 
2.9%
김제시 15
 
2.4%
전주시 15
 
2.4%
익산시 12
 
1.9%
완주군 11
 
1.8%
군산시 11
 
1.8%
덕진구 8
 
1.3%
2 7
 
1.1%
완산구 7
 
1.1%
Other values (320) 412
66.9%
2023-12-13T05:02:10.950937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
563
23.4%
전 120
 
5.0%
북 107
 
4.4%
1 101
 
4.2%
시 68
 
2.8%
2 68
 
2.8%
산 59
 
2.4%
3 51
 
2.1%
면 48
 
2.0%
리 46
 
1.9%
Other values (210) 1180
48.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1385
57.4%
Space Separator 563
23.4%
Decimal Number 428
 
17.8%
Open Punctuation 16
 
0.7%
Close Punctuation 16
 
0.7%
Uppercase Letter 2
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 120
 
8.7%
북 107
 
7.7%
시 68
 
4.9%
산 59
 
4.3%
면 48
 
3.5%
리 46
 
3.3%
군 46
 
3.3%
동 44
 
3.2%
주 34
 
2.5%
길 28
 
2.0%
Other values (195) 785
56.7%
Decimal Number
ValueCountFrequency (%)
1 101
23.6%
2 68
15.9%
3 51
11.9%
7 39
 
9.1%
5 34
 
7.9%
4 33
 
7.7%
8 28
 
6.5%
6 27
 
6.3%
0 26
 
6.1%
9 21
 
4.9%
Space Separator
ValueCountFrequency (%)
563
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Uppercase Letter
ValueCountFrequency (%)
F 2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1385
57.4%
Common 1024
42.5%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 120
 
8.7%
북 107
 
7.7%
시 68
 
4.9%
산 59
 
4.3%
면 48
 
3.5%
리 46
 
3.3%
군 46
 
3.3%
동 44
 
3.2%
주 34
 
2.5%
길 28
 
2.0%
Other values (195) 785
56.7%
Common
ValueCountFrequency (%)
563
55.0%
1 101
 
9.9%
2 68
 
6.6%
3 51
 
5.0%
7 39
 
3.8%
5 34
 
3.3%
4 33
 
3.2%
8 28
 
2.7%
6 27
 
2.6%
0 26
 
2.5%
Other values (4) 54
 
5.3%
Latin
ValueCountFrequency (%)
F 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1385
57.4%
ASCII 1026
42.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
563
54.9%
1 101
 
9.8%
2 68
 
6.6%
3 51
 
5.0%
7 39
 
3.8%
5 34
 
3.3%
4 33
 
3.2%
8 28
 
2.7%
6 27
 
2.6%
0 26
 
2.5%
Other values (5) 56
 
5.5%
Hangul
ValueCountFrequency (%)
전 120
 
8.7%
북 107
 
7.7%
시 68
 
4.9%
산 59
 
4.3%
면 48
 
3.5%
리 46
 
3.3%
군 46
 
3.3%
동 44
 
3.2%
주 34
 
2.5%
길 28
 
2.0%
Other values (195) 785
56.7%

2019회계년도 사용량 총계(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct62
Distinct (%)80.5%
Missing24
Missing (%)23.8%
Infinite0
Infinite (%)0.0%
Mean310.72416
Minimum0.48
Maximum6400.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T05:02:11.087178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.48
5-th percentile1.92
Q19
median32
Q384
95-th percentile2115
Maximum6400.04
Range6399.56
Interquartile range (IQR)75

Descriptive statistics

Standard deviation938.73578
Coefficient of variation (CV)3.0211226
Kurtosis24.909745
Mean310.72416
Median Absolute Deviation (MAD)25
Skewness4.6371927
Sum23925.76
Variance881224.86
MonotonicityNot monotonic
2023-12-13T05:02:11.220656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.0 4
 
4.0%
2.0 3
 
3.0%
4.0 3
 
3.0%
53.0 2
 
2.0%
20.0 2
 
2.0%
50.0 2
 
2.0%
16.0 2
 
2.0%
5.0 2
 
2.0%
56.0 2
 
2.0%
28.0 2
 
2.0%
Other values (52) 53
52.5%
(Missing) 24
23.8%
ValueCountFrequency (%)
0.48 1
 
1.0%
0.8 1
 
1.0%
1.0 1
 
1.0%
1.6 1
 
1.0%
2.0 3
3.0%
3.0 4
4.0%
4.0 3
3.0%
5.0 2
2.0%
5.4 1
 
1.0%
7.0 2
2.0%
ValueCountFrequency (%)
6400.04 1
1.0%
3420.0 1
1.0%
2562.0 1
1.0%
2495.0 1
1.0%
2020.0 1
1.0%
1484.0 1
1.0%
1341.0 1
1.0%
459.0 1
1.0%
416.0 1
1.0%
341.2 1
1.0%

2019회계년도 국내산 사용량(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct48
Distinct (%)78.7%
Missing40
Missing (%)39.6%
Infinite0
Infinite (%)0.0%
Mean259.99869
Minimum0.4
Maximum3908
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T05:02:11.370484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile1
Q15
median25
Q356
95-th percentile2333
Maximum3908
Range3907.6
Interquartile range (IQR)51

Descriptive statistics

Standard deviation768.50209
Coefficient of variation (CV)2.9557922
Kurtosis13.763851
Mean259.99869
Median Absolute Deviation (MAD)22
Skewness3.7787518
Sum15859.92
Variance590595.46
MonotonicityNot monotonic
2023-12-13T05:02:11.505416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
3.0 4
 
4.0%
2.0 3
 
3.0%
4.0 3
 
3.0%
5.0 3
 
3.0%
16.0 2
 
2.0%
1.0 2
 
2.0%
53.0 2
 
2.0%
56.0 2
 
2.0%
44.8 1
 
1.0%
0.48 1
 
1.0%
Other values (38) 38
37.6%
(Missing) 40
39.6%
ValueCountFrequency (%)
0.4 1
 
1.0%
0.48 1
 
1.0%
1.0 2
2.0%
2.0 3
3.0%
3.0 4
4.0%
4.0 3
3.0%
4.8 1
 
1.0%
5.0 3
3.0%
5.4 1
 
1.0%
7.0 1
 
1.0%
ValueCountFrequency (%)
3908.0 1
1.0%
3420.0 1
1.0%
2495.0 1
1.0%
2333.0 1
1.0%
468.0 1
1.0%
459.0 1
1.0%
341.0 1
1.0%
334.0 1
1.0%
264.0 1
1.0%
238.0 1
1.0%

2019회계년도 외국산 사용량(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)96.8%
Missing70
Missing (%)69.3%
Infinite0
Infinite (%)0.0%
Mean260.18839
Minimum0.4
Maximum2492.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T05:02:11.636114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile3.2
Q118.5
median50
Q3146.8
95-th percentile1424.5
Maximum2492.04
Range2491.64
Interquartile range (IQR)128.3

Descriptive statistics

Standard deviation564.73249
Coefficient of variation (CV)2.1704754
Kurtosis8.3895986
Mean260.18839
Median Absolute Deviation (MAD)40
Skewness2.9013538
Sum8065.84
Variance318922.78
MonotonicityNot monotonic
2023-12-13T05:02:11.778646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
50.0 2
 
2.0%
1077.0 1
 
1.0%
45.0 1
 
1.0%
150.0 1
 
1.0%
17.0 1
 
1.0%
10.0 1
 
1.0%
83.0 1
 
1.0%
143.6 1
 
1.0%
20.0 1
 
1.0%
9.8 1
 
1.0%
Other values (20) 20
 
19.8%
(Missing) 70
69.3%
ValueCountFrequency (%)
0.4 1
1.0%
1.6 1
1.0%
4.8 1
1.0%
7.0 1
1.0%
9.8 1
1.0%
10.0 1
1.0%
14.0 1
1.0%
17.0 1
1.0%
20.0 1
1.0%
21.0 1
1.0%
ValueCountFrequency (%)
2492.04 1
1.0%
1552.0 1
1.0%
1297.0 1
1.0%
1077.0 1
1.0%
229.0 1
1.0%
206.2 1
1.0%
155.0 1
1.0%
150.0 1
1.0%
143.6 1
1.0%
96.0 1
1.0%

Interactions

2023-12-13T05:02:05.962227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:04.066849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:04.480007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:04.943959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:05.391301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:06.059849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:04.146685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:04.572206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:05.022625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:05.486682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:06.168328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:04.226653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:04.672645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:05.115104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:05.622719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:06.278619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:04.303944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:04.762177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:05.197834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:05.739729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:06.395332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:04.391089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:04.857520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:05.301843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:05.866207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:02:11.869656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시군명월사용능력(톤)가공용쌀매입대상자 지정일주생산품연락처팩스번호2019회계년도 사용량 총계(톤)2019회계년도 국내산 사용량(톤)2019회계년도 외국산 사용량(톤)
순번1.0000.9380.2000.9890.3551.0000.9730.0000.0000.172
시군명0.9381.0000.0000.9940.6121.0000.9930.0000.0000.000
월사용능력(톤)0.2000.0001.0000.0000.6211.0001.0000.9220.9491.000
가공용쌀매입대상자 지정일0.9890.9940.0001.0000.8891.0000.9910.0001.0001.000
주생산품0.3550.6120.6210.8891.0001.0001.0000.6040.6750.504
연락처1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
팩스번호0.9730.9931.0000.9911.0001.0001.0001.0001.0001.000
2019회계년도 사용량 총계(톤)0.0000.0000.9220.0000.6041.0001.0001.0000.9910.988
2019회계년도 국내산 사용량(톤)0.0000.0000.9491.0000.6751.0001.0000.9911.0000.788
2019회계년도 외국산 사용량(톤)0.1720.0001.0001.0000.5041.0001.0000.9880.7881.000
2023-12-13T05:02:11.995978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주생산품시군명
주생산품1.0000.302
시군명0.3021.000
2023-12-13T05:02:12.084734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번월사용능력(톤)2019회계년도 사용량 총계(톤)2019회계년도 국내산 사용량(톤)2019회계년도 외국산 사용량(톤)시군명주생산품
순번1.000-0.082-0.049-0.164-0.0230.7370.171
월사용능력(톤)-0.0821.0000.7210.6870.7280.0000.384
2019회계년도 사용량 총계(톤)-0.0490.7211.0000.9180.9720.0000.384
2019회계년도 국내산 사용량(톤)-0.1640.6870.9181.0000.8290.0000.448
2019회계년도 외국산 사용량(톤)-0.0230.7280.9720.8291.0000.0000.420
시군명0.7370.0000.0000.0000.0001.0000.302
주생산품0.1710.3840.3840.4480.4200.3021.000

Missing values

2023-12-13T05:02:06.553659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:02:06.813766image/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.
2023-12-13T05:02:07.030380image/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

순번시군명상호월사용능력(톤)가공용쌀매입대상자 지정일주생산품연락처팩스번호주소2019회계년도 사용량 총계(톤)2019회계년도 국내산 사용량(톤)2019회계년도 외국산 사용량(톤)
01고창군청농업회사법인고은(유)362013-03-28주류063-562-2008063-564-2008전북 고창군 아산면 계산리 384 3232.0<NA>32.0
12고창군청사임당푸드영농조합법인102015-01-15떡류063-561-5986063-562-5989전북 고창군 고창읍 태봉로 55137.037.0<NA>
23고창군청선운산쌀과방영농조합법인102017-11-22제과·제빵063-564-6696063-564-6696전북 고창군 무장면 무장남북로 20 1 선운산쌀과방2.02.0<NA>
34군산시청롯데칠성음료(주)군산공장15001991-01-18주류02-3459-137802-3452-1407전북 군산시 소룡동 176 16400.043908.02492.04
45군산시청대광제면102000-06-27떡류063-446-4082063-443-4879전북 군산시 경암동 683 49.64.84.8
56군산시청(주)대두식품5002005-05-12쌀가루063-450-3597063-450-3517전북 군산시 서수면 마룡리 93 192562.02333.0229.0
67군산시청군산양조공사222010-02-24주류063-462-3657063-461-2463전북 군산시 옥산면 대위로 13795.036.059.0
78군산시청농업회사법인주식회사백화202010-07-14주류063-468-2703063-467-9898전북 군산시 나포면 주곡리 677번지7.0<NA>7.0
89군산시청실로암식품152011-11-25누룽지063-446-4984<NA>전북 군산시 경암동 679 5 실로암식품4.04.0<NA>
910군산시청옹고집영농조합법인252012-09-18조미식품063-453-8877063-453-4133전북 군산시 나포면 서왕길 34 (공장동)<NA><NA><NA>
순번시군명상호월사용능력(톤)가공용쌀매입대상자 지정일주생산품연락처팩스번호주소2019회계년도 사용량 총계(톤)2019회계년도 국내산 사용량(톤)2019회계년도 외국산 사용량(톤)
9192정읍시청마이코 인터내셔널(방아다리)102017-12-20누룽지063-571-0606063-571-2004전북 정읍시 감곡면 원삼1길 27 원삼1길 279.09.0<NA>
9293정읍시청작은농부이야기302018-01-22누룽지031-8059-4854031-629-5561전북 정읍시 2산단5길 37 (하북동) 1동81.081.0<NA>
9394정읍시청농부의선물302019-01-10누룽지070-8616-83280303-3442-8328전북 정읍시 소성면 등계리 1342 농부의선물48.048.0<NA>
9495정읍시청농업회사법인주식회사 고부5002019-11-15쌀가루063-537-7533063-537-7544전북 정읍시 고부면 덕안리 957 고부150.0<NA>150.0
9596정읍시청농업회사법인 유한회사 일등라이스푸드402020-07-15떡류063-571-8688063-571-8677전북 정읍시 감곡면 삼평리 609 12 일등라이스푸드<NA><NA><NA>
9697진안군청매일제과산업(주)482005-04-30누룽지063-433-9988063-433-6688전북 진안군 진안읍 연장리 1066 7<NA><NA><NA>
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