Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells694
Missing cells (%)0.5%
Duplicate rows5
Duplicate rows (%)0.1%
Total size in memory1.1 MiB
Average record size in memory120.0 B

Variable types

Categorical3
Text3
Numeric7

Dataset

Description면세유의 지역별, 재배품목별 공급현황 자료
Author농림축산식품부
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220217000000002049

Alerts

Dataset has 5 (0.1%) duplicate rowsDuplicates
영농합계면적(㎡) is highly overall correlated with 경유High correlation
경유 is highly overall correlated with 영농합계면적(㎡)High correlation
유종명 is highly imbalanced (58.5%)Imbalance
시지부 has 694 (6.9%) missing valuesMissing
영농합계면적(㎡) is highly skewed (γ1 = 71.44870442)Skewed
사육두수 is highly skewed (γ1 = 99.80775523)Skewed
실내등유 is highly skewed (γ1 = 24.49037947)Skewed
보일러등유 is highly skewed (γ1 = 44.81499001)Skewed
중유 is highly skewed (γ1 = 39.92645134)Skewed
가스 is highly skewed (γ1 = 68.49484185)Skewed
사육두수 has 7721 (77.2%) zerosZeros
경유 has 1591 (15.9%) zerosZeros
실내등유 has 6935 (69.3%) zerosZeros
보일러등유 has 9968 (99.7%) zerosZeros
중유 has 9421 (94.2%) zerosZeros
가스 has 9981 (99.8%) zerosZeros

Reproduction

Analysis started2023-12-11 03:32:18.131419
Analysis finished2023-12-11 03:32:26.062538
Duration7.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역본부
Categorical

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경기지역본부
1783 
충남지역본부
1283 
전남지역본부
1223 
경남지역본부
1171 
전북지역본부
1168 
Other values (11)
3372 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전북지역본부
2nd row전북지역본부
3rd row경기지역본부
4th row경남지역본부
5th row경남지역본부

Common Values

ValueCountFrequency (%)
경기지역본부 1783
17.8%
충남지역본부 1283
12.8%
전남지역본부 1223
12.2%
경남지역본부 1171
11.7%
전북지역본부 1168
11.7%
경북지역본부 1098
11.0%
충북지역본부 567
 
5.7%
강원지역본부 489
 
4.9%
제주지역본부 428
 
4.3%
부산지역본부 170
 
1.7%
Other values (6) 620
 
6.2%

Length

2023-12-11T12:32:26.122985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기지역본부 1783
17.8%
충남지역본부 1283
12.8%
전남지역본부 1223
12.2%
경남지역본부 1171
11.7%
전북지역본부 1168
11.7%
경북지역본부 1098
11.0%
충북지역본부 567
 
5.7%
강원지역본부 489
 
4.9%
제주지역본부 428
 
4.3%
부산지역본부 170
 
1.7%
Other values (6) 620
 
6.2%

시지부
Text

MISSING 

Distinct155
Distinct (%)1.7%
Missing694
Missing (%)6.9%
Memory size156.2 KiB
2023-12-11T12:32:26.315485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length8.130346
Min length8

Characters and Unicode

Total characters75661
Distinct characters117
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st row임실군농정지원단
2nd row순창군농정지원단
3rd row광명시농정지원단
4th row산청군농정지원단
5th row밀양시농정지원단
ValueCountFrequency (%)
서귀포시농정지원단 275
 
3.0%
논산시농정지원단 183
 
2.0%
익산시농정지원단 172
 
1.8%
나주시농정지원단 161
 
1.7%
이천시농정지원단 160
 
1.7%
진주시농정지원단 159
 
1.7%
김해시농정지원단 155
 
1.7%
제주시농정지원단 153
 
1.6%
남원시농정지원단 145
 
1.6%
화성시농정지원단 141
 
1.5%
Other values (145) 7602
81.7%
2023-12-11T12:32:26.758337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9817
13.0%
9457
12.5%
9315
12.3%
9306
12.3%
9306
12.3%
5440
 
7.2%
4242
 
5.6%
1804
 
2.4%
1100
 
1.5%
985
 
1.3%
Other values (107) 14889
19.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 75661
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
9817
13.0%
9457
12.5%
9315
12.3%
9306
12.3%
9306
12.3%
5440
 
7.2%
4242
 
5.6%
1804
 
2.4%
1100
 
1.5%
985
 
1.3%
Other values (107) 14889
19.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 75661
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
9817
13.0%
9457
12.5%
9315
12.3%
9306
12.3%
9306
12.3%
5440
 
7.2%
4242
 
5.6%
1804
 
2.4%
1100
 
1.5%
985
 
1.3%
Other values (107) 14889
19.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 75661
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
9817
13.0%
9457
12.5%
9315
12.3%
9306
12.3%
9306
12.3%
5440
 
7.2%
4242
 
5.6%
1804
 
2.4%
1100
 
1.5%
985
 
1.3%
Other values (107) 14889
19.7%
Distinct1693
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T12:32:27.035711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length6.7016
Min length4

Characters and Unicode

Total characters67016
Distinct characters310
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

Unique243 ?
Unique (%)2.4%

Sample

1st row임실농협 청웅지점
2nd row동계농협
3rd row광명농협 학온지점
4th row산청군농협 오전지점
5th row부북농협
ValueCountFrequency (%)
경제사업소 117
 
0.8%
남원농협 82
 
0.6%
산청군농협 77
 
0.5%
서부지점 75
 
0.5%
서귀포농협 68
 
0.5%
과천농협 63
 
0.4%
대저농협 61
 
0.4%
춘향골농협 61
 
0.4%
제주시농협 61
 
0.4%
대동농협 60
 
0.4%
Other values (1666) 14049
95.1%
2023-12-11T12:32:27.444537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10079
 
15.0%
10000
 
14.9%
4774
 
7.1%
4579
 
6.8%
4289
 
6.4%
1453
 
2.2%
1233
 
1.8%
969
 
1.4%
852
 
1.3%
813
 
1.2%
Other values (300) 27975
41.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 61968
92.5%
Space Separator 4774
 
7.1%
Math Symbol 270
 
0.4%
Decimal Number 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10079
 
16.3%
10000
 
16.1%
4579
 
7.4%
4289
 
6.9%
1453
 
2.3%
1233
 
2.0%
969
 
1.6%
852
 
1.4%
813
 
1.3%
753
 
1.2%
Other values (295) 26948
43.5%
Math Symbol
ValueCountFrequency (%)
< 135
50.0%
> 135
50.0%
Decimal Number
ValueCountFrequency (%)
2 3
75.0%
4 1
 
25.0%
Space Separator
ValueCountFrequency (%)
4774
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 61968
92.5%
Common 5048
 
7.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10079
 
16.3%
10000
 
16.1%
4579
 
7.4%
4289
 
6.9%
1453
 
2.3%
1233
 
2.0%
969
 
1.6%
852
 
1.4%
813
 
1.3%
753
 
1.2%
Other values (295) 26948
43.5%
Common
ValueCountFrequency (%)
4774
94.6%
< 135
 
2.7%
> 135
 
2.7%
2 3
 
0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 61968
92.5%
ASCII 5048
 
7.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
10079
 
16.3%
10000
 
16.1%
4579
 
7.4%
4289
 
6.9%
1453
 
2.3%
1233
 
2.0%
969
 
1.6%
852
 
1.4%
813
 
1.3%
753
 
1.2%
Other values (295) 26948
43.5%
ASCII
ValueCountFrequency (%)
4774
94.6%
< 135
 
2.7%
> 135
 
2.7%
2 3
 
0.1%
4 1
 
< 0.1%

등록년도
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2013
3414 
2012
3372 
2011
3214 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2011
2nd row2012
3rd row2012
4th row2011
5th row2011

Common Values

ValueCountFrequency (%)
2013 3414
34.1%
2012 3372
33.7%
2011 3214
32.1%

Length

2023-12-11T12:32:27.586309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:32:27.696452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2013 3414
34.1%
2012 3372
33.7%
2011 3214
32.1%
Distinct222
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T12:32:28.036555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length3.378
Min length1

Characters and Unicode

Total characters33780
Distinct characters228
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)0.3%

Sample

1st row토마토
2nd row닭(육계)
3rd row튜울립
4th row상추
5th row백합(나리)
ValueCountFrequency (%)
닭(육계 779
 
7.1%
고추 725
 
6.6%
돼지 703
 
6.4%
토마토 614
 
5.6%
느타리버섯 440
 
4.0%
채소 421
 
3.8%
오이 395
 
3.6%
딸기 373
 
3.4%
오리 282
 
2.6%
상추 281
 
2.6%
Other values (178) 5947
54.3%
2023-12-11T12:32:28.517151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1605
 
4.8%
) 1464
 
4.3%
( 1464
 
4.3%
1335
 
4.0%
1095
 
3.2%
1049
 
3.1%
1049
 
3.1%
973
 
2.9%
960
 
2.8%
952
 
2.8%
Other values (218) 21834
64.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 27236
80.6%
Decimal Number 1704
 
5.0%
Close Punctuation 1464
 
4.3%
Open Punctuation 1464
 
4.3%
Space Separator 960
 
2.8%
Other Symbol 952
 
2.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1605
 
5.9%
1335
 
4.9%
1095
 
4.0%
1049
 
3.9%
1049
 
3.9%
973
 
3.6%
855
 
3.1%
809
 
3.0%
805
 
3.0%
786
 
2.9%
Other values (204) 16875
62.0%
Decimal Number
ValueCountFrequency (%)
1 520
30.5%
2 301
17.7%
0 229
13.4%
3 192
 
11.3%
5 187
 
11.0%
8 118
 
6.9%
7 49
 
2.9%
6 45
 
2.6%
4 39
 
2.3%
9 24
 
1.4%
Close Punctuation
ValueCountFrequency (%)
) 1464
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1464
100.0%
Space Separator
ValueCountFrequency (%)
960
100.0%
Other Symbol
ValueCountFrequency (%)
952
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 27236
80.6%
Common 6544
 
19.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1605
 
5.9%
1335
 
4.9%
1095
 
4.0%
1049
 
3.9%
1049
 
3.9%
973
 
3.6%
855
 
3.1%
809
 
3.0%
805
 
3.0%
786
 
2.9%
Other values (204) 16875
62.0%
Common
ValueCountFrequency (%)
) 1464
22.4%
( 1464
22.4%
960
14.7%
952
14.5%
1 520
 
7.9%
2 301
 
4.6%
0 229
 
3.5%
3 192
 
2.9%
5 187
 
2.9%
8 118
 
1.8%
Other values (4) 157
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 27236
80.6%
ASCII 5592
 
16.6%
Letterlike Symbols 952
 
2.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1605
 
5.9%
1335
 
4.9%
1095
 
4.0%
1049
 
3.9%
1049
 
3.9%
973
 
3.6%
855
 
3.1%
809
 
3.0%
805
 
3.0%
786
 
2.9%
Other values (204) 16875
62.0%
ASCII
ValueCountFrequency (%)
) 1464
26.2%
( 1464
26.2%
960
17.2%
1 520
 
9.3%
2 301
 
5.4%
0 229
 
4.1%
3 192
 
3.4%
5 187
 
3.3%
8 118
 
2.1%
7 49
 
0.9%
Other values (3) 108
 
1.9%
Letterlike Symbols
ValueCountFrequency (%)
952
100.0%

유종명
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경유
7453 
실내등유
2245 
중유
 
285
가스(난방)
 
15
부생연료유1호
 
2

Length

Max length7
Median length2
Mean length2.456
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경유
2nd row경유
3rd row경유
4th row실내등유
5th row경유

Common Values

ValueCountFrequency (%)
경유 7453
74.5%
실내등유 2245
 
22.4%
중유 285
 
2.9%
가스(난방) 15
 
0.1%
부생연료유1호 2
 
< 0.1%

Length

2023-12-11T12:32:28.673492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:32:28.813350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경유 7453
74.5%
실내등유 2245
 
22.4%
중유 285
 
2.9%
가스(난방 15
 
0.1%
부생연료유1호 2
 
< 0.1%

영농합계면적(㎡)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1944
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13419.26
Minimum1
Maximum11146720
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:32:29.295680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile330
Q11320
median3300
Q37920
95-th percentile36795
Maximum11146720
Range11146719
Interquartile range (IQR)6600

Descriptive statistics

Standard deviation125935.33
Coefficient of variation (CV)9.3846706
Kurtosis6150.6847
Mean13419.26
Median Absolute Deviation (MAD)2343
Skewness71.448704
Sum1.341926 × 108
Variance1.5859708 × 1010
MonotonicityNot monotonic
2023-12-11T12:32:29.457512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1980.0 469
 
4.7%
3300.0 425
 
4.2%
660.0 409
 
4.1%
990.0 370
 
3.7%
1650.0 349
 
3.5%
1320.0 339
 
3.4%
330.0 303
 
3.0%
2640.0 299
 
3.0%
3960.0 269
 
2.7%
6600.0 220
 
2.2%
Other values (1934) 6548
65.5%
ValueCountFrequency (%)
1.0 1
 
< 0.1%
3.3 3
< 0.1%
6.6 1
 
< 0.1%
13.2 1
 
< 0.1%
16.5 3
< 0.1%
17.2 1
 
< 0.1%
23.1 1
 
< 0.1%
29.7 1
 
< 0.1%
33.0 3
< 0.1%
36.3 1
 
< 0.1%
ValueCountFrequency (%)
11146720.2 1
< 0.1%
3047639.1 1
< 0.1%
1530507.0 1
< 0.1%
1393887.3 1
< 0.1%
1251979.4 1
< 0.1%
1124529.8 1
< 0.1%
1026531.0 1
< 0.1%
1007685.4 1
< 0.1%
940044.9 1
< 0.1%
777176.4 1
< 0.1%

사육두수
Real number (ℝ)

SKEWED  ZEROS 

Distinct963
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161368.03
Minimum0
Maximum1.000708 × 109
Zeros7721
Zeros (%)77.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:32:29.618610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile300000
Maximum1.000708 × 109
Range1.000708 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10012912
Coefficient of variation (CV)62.05016
Kurtosis9974.27
Mean161368.03
Median Absolute Deviation (MAD)0
Skewness99.807755
Sum1.6136803 × 109
Variance1.0025841 × 1014
MonotonicityNot monotonic
2023-12-11T12:32:29.810189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7721
77.2%
100 49
 
0.5%
60000 36
 
0.4%
100000 33
 
0.3%
150000 32
 
0.3%
300000 29
 
0.3%
200 28
 
0.3%
1000 26
 
0.3%
50 26
 
0.3%
150 24
 
0.2%
Other values (953) 1996
 
20.0%
ValueCountFrequency (%)
0 7721
77.2%
1 2
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 2
 
< 0.1%
6 3
 
< 0.1%
7 5
 
0.1%
8 3
 
< 0.1%
10 11
 
0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
1000707999 1
< 0.1%
14720280 1
< 0.1%
12111200 1
< 0.1%
8831000 1
< 0.1%
5718000 1
< 0.1%
5390500 1
< 0.1%
4548000 1
< 0.1%
4443100 1
< 0.1%
4272600 1
< 0.1%
4184500 1
< 0.1%

경유
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct853
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13883.882
Minimum0
Maximum2430200
Zeros1591
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:32:29.962580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11000
median3000
Q38200
95-th percentile46000
Maximum2430200
Range2430200
Interquartile range (IQR)7200

Descriptive statistics

Standard deviation66953.2
Coefficient of variation (CV)4.8223688
Kurtosis471.60124
Mean13883.882
Median Absolute Deviation (MAD)2800
Skewness18.422381
Sum1.3883882 × 108
Variance4.482731 × 109
MonotonicityNot monotonic
2023-12-11T12:32:30.132447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1591
 
15.9%
2000 844
 
8.4%
1000 624
 
6.2%
3000 482
 
4.8%
4000 397
 
4.0%
5000 322
 
3.2%
6000 290
 
2.9%
600 266
 
2.7%
400 231
 
2.3%
10000 181
 
1.8%
Other values (843) 4772
47.7%
ValueCountFrequency (%)
0 1591
15.9%
1 3
 
< 0.1%
4 1
 
< 0.1%
20 1
 
< 0.1%
40 2
 
< 0.1%
45 2
 
< 0.1%
50 2
 
< 0.1%
60 2
 
< 0.1%
65 1
 
< 0.1%
80 4
 
< 0.1%
ValueCountFrequency (%)
2430200 1
< 0.1%
2128700 1
< 0.1%
1753200 1
< 0.1%
1697300 1
< 0.1%
1660000 1
< 0.1%
1539700 1
< 0.1%
1273900 1
< 0.1%
1250000 1
< 0.1%
1075000 1
< 0.1%
999700 1
< 0.1%

실내등유
Real number (ℝ)

SKEWED  ZEROS 

Distinct277
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1385.8421
Minimum0
Maximum345000
Zeros6935
Zeros (%)69.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:32:30.284093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3800
95-th percentile6205
Maximum345000
Range345000
Interquartile range (IQR)800

Descriptive statistics

Standard deviation6014.9138
Coefficient of variation (CV)4.3402591
Kurtosis1143.7751
Mean1385.8421
Median Absolute Deviation (MAD)0
Skewness24.490379
Sum13858421
Variance36179188
MonotonicityNot monotonic
2023-12-11T12:32:30.454599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6935
69.3%
1000 363
 
3.6%
2000 352
 
3.5%
600 244
 
2.4%
3000 198
 
2.0%
400 186
 
1.9%
4000 116
 
1.2%
5000 103
 
1.0%
1200 97
 
1.0%
800 70
 
0.7%
Other values (267) 1336
 
13.4%
ValueCountFrequency (%)
0 6935
69.3%
20 1
 
< 0.1%
40 1
 
< 0.1%
45 1
 
< 0.1%
50 2
 
< 0.1%
60 1
 
< 0.1%
80 1
 
< 0.1%
100 6
 
0.1%
110 1
 
< 0.1%
120 2
 
< 0.1%
ValueCountFrequency (%)
345000 1
< 0.1%
141000 1
< 0.1%
103000 1
< 0.1%
100000 1
< 0.1%
96600 1
< 0.1%
96000 1
< 0.1%
87000 1
< 0.1%
76500 1
< 0.1%
72400 1
< 0.1%
68500 1
< 0.1%

보일러등유
Real number (ℝ)

SKEWED  ZEROS 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.61
Minimum0
Maximum28000
Zeros9968
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:32:30.607793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum28000
Range28000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation453.01222
Coefficient of variation (CV)31.006996
Kurtosis2284.1091
Mean14.61
Median Absolute Deviation (MAD)0
Skewness44.81499
Sum146100
Variance205220.07
MonotonicityNot monotonic
2023-12-11T12:32:30.734125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 9968
99.7%
2000 3
 
< 0.1%
800 3
 
< 0.1%
400 3
 
< 0.1%
600 3
 
< 0.1%
1000 2
 
< 0.1%
100 2
 
< 0.1%
5000 2
 
< 0.1%
6000 2
 
< 0.1%
4500 2
 
< 0.1%
Other values (9) 10
 
0.1%
ValueCountFrequency (%)
0 9968
99.7%
100 2
 
< 0.1%
200 1
 
< 0.1%
400 3
 
< 0.1%
500 1
 
< 0.1%
600 3
 
< 0.1%
800 3
 
< 0.1%
1000 2
 
< 0.1%
2000 3
 
< 0.1%
2800 1
 
< 0.1%
ValueCountFrequency (%)
28000 1
< 0.1%
20000 1
< 0.1%
19000 1
< 0.1%
15000 1
< 0.1%
8000 1
< 0.1%
6000 2
< 0.1%
5000 2
< 0.1%
4500 2
< 0.1%
4000 2
< 0.1%
2800 1
< 0.1%

중유
Real number (ℝ)

SKEWED  ZEROS 

Distinct144
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2595.89
Minimum0
Maximum2070200
Zeros9421
Zeros (%)94.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:32:30.882126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6000
Maximum2070200
Range2070200
Interquartile range (IQR)0

Descriptive statistics

Standard deviation34939.33
Coefficient of variation (CV)13.45948
Kurtosis1979.1693
Mean2595.89
Median Absolute Deviation (MAD)0
Skewness39.926451
Sum25958900
Variance1.2207568 × 109
MonotonicityNot monotonic
2023-12-11T12:32:31.094001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9421
94.2%
20000 52
 
0.5%
10000 52
 
0.5%
6000 32
 
0.3%
8000 31
 
0.3%
5000 30
 
0.3%
12000 23
 
0.2%
7000 18
 
0.2%
4000 17
 
0.2%
16000 16
 
0.2%
Other values (134) 308
 
3.1%
ValueCountFrequency (%)
0 9421
94.2%
500 1
 
< 0.1%
1000 7
 
0.1%
1400 2
 
< 0.1%
2000 9
 
0.1%
2500 1
 
< 0.1%
3000 6
 
0.1%
4000 17
 
0.2%
4700 2
 
< 0.1%
5000 30
 
0.3%
ValueCountFrequency (%)
2070200 1
< 0.1%
1595000 1
< 0.1%
1431200 1
< 0.1%
771000 1
< 0.1%
595200 1
< 0.1%
436000 1
< 0.1%
421600 1
< 0.1%
381000 1
< 0.1%
367000 1
< 0.1%
350000 1
< 0.1%

가스
Real number (ℝ)

SKEWED  ZEROS 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.11
Minimum0
Maximum228000
Zeros9981
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T12:32:31.238047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum228000
Range228000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2695.2062
Coefficient of variation (CV)48.034329
Kurtosis5353.7639
Mean56.11
Median Absolute Deviation (MAD)0
Skewness68.494842
Sum561100
Variance7264136.6
MonotonicityNot monotonic
2023-12-11T12:32:31.371870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 9981
99.8%
3000 4
 
< 0.1%
2900 2
 
< 0.1%
86000 2
 
< 0.1%
1800 1
 
< 0.1%
50 1
 
< 0.1%
1900 1
 
< 0.1%
60000 1
 
< 0.1%
1000 1
 
< 0.1%
250 1
 
< 0.1%
Other values (5) 5
 
0.1%
ValueCountFrequency (%)
0 9981
99.8%
50 1
 
< 0.1%
250 1
 
< 0.1%
1000 1
 
< 0.1%
1800 1
 
< 0.1%
1900 1
 
< 0.1%
2900 2
 
< 0.1%
3000 4
 
< 0.1%
5600 1
 
< 0.1%
13200 1
 
< 0.1%
ValueCountFrequency (%)
228000 1
 
< 0.1%
86000 2
< 0.1%
60000 1
 
< 0.1%
41000 1
 
< 0.1%
18500 1
 
< 0.1%
13200 1
 
< 0.1%
5600 1
 
< 0.1%
3000 4
< 0.1%
2900 2
< 0.1%
1900 1
 
< 0.1%

Interactions

2023-12-11T12:32:25.176853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:19.921171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:21.056973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:21.789207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:22.825896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:23.647187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:24.407246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:25.263208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:20.032684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:21.177293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:21.879664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:22.929473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:23.747998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:24.520974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:25.352862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:20.167795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:21.295585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:21.980446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:23.065459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:23.854478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:24.631480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:25.442685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:20.279597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:21.402771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:22.083035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:23.194480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:23.991135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:24.744573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:25.524813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:20.725916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:21.523849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:22.416072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:23.317133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:24.108041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:24.851175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:25.621948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:20.824694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:21.616779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:22.579749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:23.421510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:24.199749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:24.950004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:25.700740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:20.939522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:21.707176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:22.708316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:23.534878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:24.307860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:25.085447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:32:31.486357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역본부등록년도유종명영농합계면적(㎡)사육두수경유실내등유보일러등유중유가스
지역본부1.0000.0000.2480.0140.0000.1070.0210.0520.1250.000
등록년도0.0001.0000.0520.0000.0000.0300.0160.0000.0000.013
유종명0.2480.0521.0000.0000.0000.0000.0790.0000.1100.565
영농합계면적(㎡)0.0140.0000.0001.0000.0000.6840.0570.0000.2630.000
사육두수0.0000.0000.0000.0001.0000.0000.0000.0000.0000.000
경유0.1070.0300.0000.6840.0001.0000.7630.2220.5900.000
실내등유0.0210.0160.0790.0570.0000.7631.0000.1140.2000.000
보일러등유0.0520.0000.0000.0000.0000.2220.1141.0000.1710.000
중유0.1250.0000.1100.2630.0000.5900.2000.1711.0000.000
가스0.0000.0130.5650.0000.0000.0000.0000.0000.0001.000
2023-12-11T12:32:31.640433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유종명지역본부등록년도
유종명1.0000.1290.039
지역본부0.1291.0000.000
등록년도0.0390.0001.000
2023-12-11T12:32:31.750380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영농합계면적(㎡)사육두수경유실내등유보일러등유중유가스지역본부등록년도유종명
영농합계면적(㎡)1.0000.0320.5080.1280.0250.1780.0370.0070.0000.000
사육두수0.0321.000-0.1070.348-0.006-0.1310.0700.0000.0000.000
경유0.508-0.1071.000-0.1570.0070.187-0.0110.0420.0180.000
실내등유0.1280.348-0.1571.0000.019-0.0120.0020.0110.0120.030
보일러등유0.025-0.0060.0070.0191.0000.072-0.0020.0240.0000.000
중유0.178-0.1310.187-0.0120.0721.000-0.0110.0570.0000.070
가스0.0370.070-0.0110.002-0.002-0.0111.0000.0000.0100.241
지역본부0.0070.0000.0420.0110.0240.0570.0001.0000.0000.129
등록년도0.0000.0000.0180.0120.0000.0000.0100.0001.0000.039
유종명0.0000.0000.0000.0300.0000.0700.2410.1290.0391.000

Missing values

2023-12-11T12:32:25.817788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:32:25.983197image/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.

Sample

지역본부시지부지역농협등록년도작목명유종명영농합계면적(㎡)사육두수경유실내등유보일러등유중유가스
17299전북지역본부임실군농정지원단임실농협 청웅지점2011토마토경유7920.00600000320000
42182전북지역본부순창군농정지원단동계농협2012닭(육계)경유5874.0840000196003500000
25521경기지역본부광명시농정지원단광명농협 학온지점2012튜울립경유990.00120000000
6614경남지역본부산청군농정지원단산청군농협 오전지점2011상추실내등유1320.000800000
6285경남지역본부밀양시농정지원단부북농협2011백합(나리)경유3960.0082000000
71558제주지역본부제주시농정지원단함덕농협2013깻잎경유2409.0022000000
47007충남지역본부서천군농정지원단장항농협20127℃ 채소실내등유1996.5040003000000
9651경북지역본부영천시농정지원단금호농협 대창지점2011포도경유4851.0030000000
16120전북지역본부김제시농정지원단용지농협2011닭(육계)경유6930.06000066000000
53771경기지역본부연천군농정지원단전곡농협2013소(한우)경유3405.614630000000
지역본부시지부지역농협등록년도작목명유종명영농합계면적(㎡)사육두수경유실내등유보일러등유중유가스
56361경남지역본부김해시농정지원단김해농협 서김해지점2013벤자민경유2310.0050000000
68222전북지역본부남원시농정지원단운봉농협2013파프리카실내등유28875.00860015600000
9058경북지역본부상주시농정지원단은척농협2011닭(육계)실내등유6642.94500003000000
63422서울지역본부<NA>남서울농협201315℃ 채소경유330.0040000000
69048전북지역본부익산시농정지원단익산농협2013쑥갓경유3960.0030000000
71946충남지역본부금산군농정지원단만인산농협 내부지점2013깻잎경유1155.0020000000
63905인천지역본부<NA>서인천농협 서곶지점2013장미경유55869.00749000000
35609경북지역본부포항시농정지원단포항농협2012토마토경유11880.0084500000
11345대전지역본부<NA>진잠농협2011방울토마토경유990.0078000000
35987광주지역본부<NA>송정농협2012오이경유1320.00137000000

Duplicate rows

Most frequently occurring

지역본부시지부지역농협등록년도작목명유종명영농합계면적(㎡)사육두수경유실내등유보일러등유중유가스# duplicates
3전남지역본부해남군농정지원단화원농협2013닭(육계)실내등유330.030000010000003
0경북지역본부의성군농정지원단서의성농협 단밀지점2011닭(육계)경유3960.0738000030000002
1인천지역본부강화군농정지원단강화농협2012돼지실내등유749.1100010000002
2전남지역본부고흥군농정지원단녹동농협2013방울토마토경유1650.00400000002
4전북지역본부순창군농정지원단순창농협 팔덕지점2013닭(육계)경유5960.11200001300000002