Overview

Dataset statistics

Number of variables16
Number of observations28
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory147.6 B

Variable types

Text1
Numeric14
Categorical1

Dataset

Description전북특별자치도 농기계 보유 현황시군별 동력 경운기 농용트랙터 소형 농용트랙터 중형 농용트랙터 대형 스피드스 프레이어 동력이앙기 보행형 동력이앙기 승용형 관리기 보행형 관리기 승용형 콤바인 3조 이하 콤바인 4조 콤바인 5조 이상 곡물건조기 농산물건조기 연도
Author전북특별자치도
URLhttps://www.data.go.kr/data/15120227/fileData.do

Alerts

동력 경운기 is highly overall correlated with 농용트랙터 소형 and 10 other fieldsHigh correlation
농용트랙터 소형 is highly overall correlated with 동력 경운기 and 9 other fieldsHigh correlation
농용트랙터 중형 is highly overall correlated with 동력 경운기 and 10 other fieldsHigh correlation
농용트랙터 대형 is highly overall correlated with 동력 경운기 and 9 other fieldsHigh correlation
동력이앙기 보행형 is highly overall correlated with 동력 경운기 and 9 other fieldsHigh correlation
동력이앙기 승용형 is highly overall correlated with 동력 경운기 and 9 other fieldsHigh correlation
관리기 보행형 is highly overall correlated with 동력 경운기 and 2 other fieldsHigh correlation
관리기 승용형 is highly overall correlated with 동력 경운기 and 7 other fieldsHigh correlation
콤바인 3조 이하 is highly overall correlated with 동력 경운기 and 7 other fieldsHigh correlation
콤바인 4조 is highly overall correlated with 동력 경운기 and 9 other fieldsHigh correlation
콤바인 5조 이상 is highly overall correlated with 동력 경운기 and 8 other fieldsHigh correlation
곡물건조기 is highly overall correlated with 동력 경운기 and 9 other fieldsHigh correlation
동력 경운기 has unique valuesUnique
농용트랙터 중형 has unique valuesUnique
농용트랙터 대형 has unique valuesUnique
동력이앙기 보행형 has unique valuesUnique
동력이앙기 승용형 has unique valuesUnique
관리기 보행형 has unique valuesUnique
곡물건조기 has unique valuesUnique
농산물건조기 has unique valuesUnique

Reproduction

Analysis started2024-03-14 11:38:33.179390
Analysis finished2024-03-14 11:39:17.952334
Duration44.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct14
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size352.0 B
2024-03-14T20:39:18.459115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters84
Distinct characters24
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

Unique0 ?
Unique (%)0.0%

Sample

1st row전주시
2nd row군산시
3rd row익산시
4th row정읍시
5th row남원시
ValueCountFrequency (%)
전주시 2
 
7.1%
군산시 2
 
7.1%
익산시 2
 
7.1%
정읍시 2
 
7.1%
남원시 2
 
7.1%
김제시 2
 
7.1%
완주군 2
 
7.1%
진안군 2
 
7.1%
무주군 2
 
7.1%
장수군 2
 
7.1%
Other values (4) 8
28.6%
2024-03-14T20:39:19.323920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18
21.4%
12
14.3%
6
 
7.1%
4
 
4.8%
4
 
4.8%
4
 
4.8%
2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
Other values (14) 28
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 84
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
18
21.4%
12
14.3%
6
 
7.1%
4
 
4.8%
4
 
4.8%
4
 
4.8%
2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
Other values (14) 28
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 84
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
18
21.4%
12
14.3%
6
 
7.1%
4
 
4.8%
4
 
4.8%
4
 
4.8%
2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
Other values (14) 28
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 84
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
18
21.4%
12
14.3%
6
 
7.1%
4
 
4.8%
4
 
4.8%
4
 
4.8%
2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
Other values (14) 28
33.3%

동력 경운기
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3839.4643
Minimum1208
Maximum6588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-03-14T20:39:19.707405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1208
5-th percentile1733.85
Q12699.75
median3725.5
Q35185.75
95-th percentile6071.15
Maximum6588
Range5380
Interquartile range (IQR)2486

Descriptive statistics

Standard deviation1442.4814
Coefficient of variation (CV)0.37569862
Kurtosis-0.86460342
Mean3839.4643
Median Absolute Deviation (MAD)1190.5
Skewness0.12619786
Sum107505
Variance2080752.7
MonotonicityNot monotonic
2024-03-14T20:39:20.111194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1208 1
 
3.6%
2794 1
 
3.6%
4408 1
 
3.6%
6588 1
 
3.6%
3481 1
 
3.6%
3707 1
 
3.6%
2738 1
 
3.6%
2144 1
 
3.6%
3253 1
 
3.6%
4028 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
1208 1
3.6%
1513 1
3.6%
2144 1
3.6%
2341 1
3.6%
2395 1
3.6%
2485 1
3.6%
2585 1
3.6%
2738 1
3.6%
2794 1
3.6%
2993 1
3.6%
ValueCountFrequency (%)
6588 1
3.6%
6103 1
3.6%
6012 1
3.6%
5440 1
3.6%
5358 1
3.6%
5300 1
3.6%
5257 1
3.6%
5162 1
3.6%
4710 1
3.6%
4408 1
3.6%

농용트랙터 소형
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean424.92857
Minimum136
Maximum1053
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-03-14T20:39:20.493338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum136
5-th percentile171.05
Q1222
median355.5
Q3552.25
95-th percentile882.1
Maximum1053
Range917
Interquartile range (IQR)330.25

Descriptive statistics

Standard deviation243.88976
Coefficient of variation (CV)0.57395473
Kurtosis0.33953328
Mean424.92857
Median Absolute Deviation (MAD)154.5
Skewness0.95046577
Sum11898
Variance59482.217
MonotonicityNot monotonic
2024-03-14T20:39:20.865295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
222 2
 
7.1%
174 1
 
3.6%
552 1
 
3.6%
644 1
 
3.6%
593 1
 
3.6%
247 1
 
3.6%
136 1
 
3.6%
310 1
 
3.6%
170 1
 
3.6%
316 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
136 1
3.6%
170 1
3.6%
173 1
3.6%
174 1
3.6%
193 1
3.6%
209 1
3.6%
222 2
7.1%
240 1
3.6%
247 1
3.6%
258 1
3.6%
ValueCountFrequency (%)
1053 1
3.6%
929 1
3.6%
795 1
3.6%
737 1
3.6%
644 1
3.6%
593 1
3.6%
553 1
3.6%
552 1
3.6%
523 1
3.6%
501 1
3.6%

농용트랙터 중형
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1134.9643
Minimum354
Maximum2123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-03-14T20:39:21.219954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum354
5-th percentile406.65
Q1731.75
median1041.5
Q31720
95-th percentile1939.1
Maximum2123
Range1769
Interquartile range (IQR)988.25

Descriptive statistics

Standard deviation526.93189
Coefficient of variation (CV)0.46427178
Kurtosis-1.1425377
Mean1134.9643
Median Absolute Deviation (MAD)354
Skewness0.33640845
Sum31779
Variance277657.22
MonotonicityNot monotonic
2024-03-14T20:39:21.603221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
358 1
 
3.6%
1017 1
 
3.6%
1348 1
 
3.6%
1318 1
 
3.6%
775 1
 
3.6%
653 1
 
3.6%
713 1
 
3.6%
497 1
 
3.6%
836 1
 
3.6%
1066 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
354 1
3.6%
358 1
3.6%
497 1
3.6%
579 1
3.6%
653 1
3.6%
662 1
3.6%
713 1
3.6%
738 1
3.6%
757 1
3.6%
765 1
3.6%
ValueCountFrequency (%)
2123 1
3.6%
1944 1
3.6%
1930 1
3.6%
1761 1
3.6%
1755 1
3.6%
1750 1
3.6%
1747 1
3.6%
1711 1
3.6%
1348 1
3.6%
1318 1
3.6%

농용트랙터 대형
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean679.82143
Minimum105
Maximum1593
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-03-14T20:39:21.960122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum105
5-th percentile152.4
Q1304.5
median510.5
Q31080.25
95-th percentile1401.15
Maximum1593
Range1488
Interquartile range (IQR)775.75

Descriptive statistics

Standard deviation459.50167
Coefficient of variation (CV)0.67591524
Kurtosis-1.1993511
Mean679.82143
Median Absolute Deviation (MAD)355.5
Skewness0.46246904
Sum19035
Variance211141.78
MonotonicityNot monotonic
2024-03-14T20:39:22.345119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
179 1
 
3.6%
866 1
 
3.6%
867 1
 
3.6%
914 1
 
3.6%
349 1
 
3.6%
318 1
 
3.6%
253 1
 
3.6%
105 1
 
3.6%
303 1
 
3.6%
281 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
105 1
3.6%
151 1
3.6%
155 1
3.6%
179 1
3.6%
253 1
3.6%
281 1
3.6%
303 1
3.6%
305 1
3.6%
318 1
3.6%
321 1
3.6%
ValueCountFrequency (%)
1593 1
3.6%
1419 1
3.6%
1368 1
3.6%
1346 1
3.6%
1184 1
3.6%
1126 1
3.6%
1123 1
3.6%
1066 1
3.6%
945 1
3.6%
914 1
3.6%

스피드스 프레이어
Real number (ℝ)

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean206.57143
Minimum41
Maximum639
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-03-14T20:39:22.701526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile56
Q1100.75
median162.5
Q3260.75
95-th percentile481.65
Maximum639
Range598
Interquartile range (IQR)160

Descriptive statistics

Standard deviation151.64931
Coefficient of variation (CV)0.73412529
Kurtosis1.2120138
Mean206.57143
Median Absolute Deviation (MAD)79.5
Skewness1.2711542
Sum5784
Variance22997.513
MonotonicityNot monotonic
2024-03-14T20:39:22.902567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
56 2
 
7.1%
101 2
 
7.1%
263 1
 
3.6%
234 1
 
3.6%
151 1
 
3.6%
130 1
 
3.6%
102 1
 
3.6%
406 1
 
3.6%
342 1
 
3.6%
184 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
41 1
3.6%
56 2
7.1%
63 1
3.6%
66 1
3.6%
79 1
3.6%
100 1
3.6%
101 2
7.1%
102 1
3.6%
130 1
3.6%
134 1
3.6%
ValueCountFrequency (%)
639 1
3.6%
510 1
3.6%
429 1
3.6%
406 1
3.6%
386 1
3.6%
342 1
3.6%
263 1
3.6%
260 1
3.6%
238 1
3.6%
234 1
3.6%

동력이앙기 보행형
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean735.25
Minimum132
Maximum1921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-03-14T20:39:23.215907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum132
5-th percentile265.5
Q1428
median613
Q31065.5
95-th percentile1689.8
Maximum1921
Range1789
Interquartile range (IQR)637.5

Descriptive statistics

Standard deviation456.89683
Coefficient of variation (CV)0.62141697
Kurtosis0.88471869
Mean735.25
Median Absolute Deviation (MAD)238
Skewness1.1820325
Sum20587
Variance208754.71
MonotonicityNot monotonic
2024-03-14T20:39:23.619258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
132 1
 
3.6%
1070 1
 
3.6%
1071 1
 
3.6%
1201 1
 
3.6%
598 1
 
3.6%
389 1
 
3.6%
272 1
 
3.6%
262 1
 
3.6%
344 1
 
3.6%
361 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
132 1
3.6%
262 1
3.6%
272 1
3.6%
277 1
3.6%
344 1
3.6%
361 1
3.6%
389 1
3.6%
441 1
3.6%
470 1
3.6%
488 1
3.6%
ValueCountFrequency (%)
1921 1
3.6%
1757 1
3.6%
1565 1
3.6%
1201 1
3.6%
1141 1
3.6%
1071 1
3.6%
1070 1
3.6%
1064 1
3.6%
719 1
3.6%
713 1
3.6%

동력이앙기 승용형
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean781.75
Minimum131
Maximum1673
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-03-14T20:39:23.990577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum131
5-th percentile203.5
Q1434
median795
Q3976.5
95-th percentile1622.7
Maximum1673
Range1542
Interquartile range (IQR)542.5

Descriptive statistics

Standard deviation437.0895
Coefficient of variation (CV)0.55911673
Kurtosis-0.32401397
Mean781.75
Median Absolute Deviation (MAD)265
Skewness0.46660229
Sum21889
Variance191047.23
MonotonicityNot monotonic
2024-03-14T20:39:24.387285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
200 1
 
3.6%
227 1
 
3.6%
622 1
 
3.6%
1051 1
 
3.6%
771 1
 
3.6%
898 1
 
3.6%
819 1
 
3.6%
389 1
 
3.6%
643 1
 
3.6%
730 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
131 1
3.6%
200 1
3.6%
210 1
3.6%
227 1
3.6%
293 1
3.6%
307 1
3.6%
389 1
3.6%
449 1
3.6%
479 1
3.6%
622 1
3.6%
ValueCountFrequency (%)
1673 1
3.6%
1643 1
3.6%
1585 1
3.6%
1386 1
3.6%
1069 1
3.6%
1051 1
3.6%
1029 1
3.6%
959 1
3.6%
958 1
3.6%
954 1
3.6%

관리기 보행형
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2165.6786
Minimum973
Maximum3758
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-03-14T20:39:24.948730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum973
5-th percentile1139.15
Q11467.75
median1891.5
Q32791.75
95-th percentile3664.6
Maximum3758
Range2785
Interquartile range (IQR)1324

Descriptive statistics

Standard deviation859.23222
Coefficient of variation (CV)0.39674965
Kurtosis-0.84739088
Mean2165.6786
Median Absolute Deviation (MAD)546
Skewness0.59836465
Sum60639
Variance738280
MonotonicityNot monotonic
2024-03-14T20:39:25.324361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
973 1
 
3.6%
1448 1
 
3.6%
1473 1
 
3.6%
2047 1
 
3.6%
1249 1
 
3.6%
1702 1
 
3.6%
1910 1
 
3.6%
1873 1
 
3.6%
2388 1
 
3.6%
3478 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
973 1
3.6%
1080 1
3.6%
1249 1
3.6%
1260 1
3.6%
1296 1
3.6%
1448 1
3.6%
1452 1
3.6%
1473 1
3.6%
1593 1
3.6%
1612 1
3.6%
ValueCountFrequency (%)
3758 1
3.6%
3715 1
3.6%
3571 1
3.6%
3543 1
3.6%
3478 1
3.6%
2824 1
3.6%
2803 1
3.6%
2788 1
3.6%
2681 1
3.6%
2388 1
3.6%

관리기 승용형
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean336.67857
Minimum10
Maximum1998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-03-14T20:39:25.700982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile38.25
Q1150.75
median245.5
Q3406.75
95-th percentile866.65
Maximum1998
Range1988
Interquartile range (IQR)256

Descriptive statistics

Standard deviation388.37277
Coefficient of variation (CV)1.1535417
Kurtosis12.76993
Mean336.67857
Median Absolute Deviation (MAD)123.5
Skewness3.2671957
Sum9427
Variance150833.41
MonotonicityNot monotonic
2024-03-14T20:39:26.107116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
48 2
 
7.1%
147 1
 
3.6%
462 1
 
3.6%
292 1
 
3.6%
515 1
 
3.6%
155 1
 
3.6%
10 1
 
3.6%
239 1
 
3.6%
217 1
 
3.6%
1998 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
10 1
3.6%
33 1
3.6%
48 2
7.1%
75 1
3.6%
126 1
3.6%
147 1
3.6%
152 1
3.6%
155 1
3.6%
164 1
3.6%
178 1
3.6%
ValueCountFrequency (%)
1998 1
3.6%
1056 1
3.6%
515 1
3.6%
470 1
3.6%
468 1
3.6%
462 1
3.6%
454 1
3.6%
391 1
3.6%
373 1
3.6%
323 1
3.6%

콤바인 3조 이하
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.321429
Minimum22
Maximum156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-03-14T20:39:26.497216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile27.35
Q139.25
median62.5
Q3105.75
95-th percentile135.7
Maximum156
Range134
Interquartile range (IQR)66.5

Descriptive statistics

Standard deviation39.134198
Coefficient of variation (CV)0.54111484
Kurtosis-0.9683159
Mean72.321429
Median Absolute Deviation (MAD)29.5
Skewness0.50180757
Sum2025
Variance1531.4854
MonotonicityNot monotonic
2024-03-14T20:39:26.915819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
44 2
 
7.1%
28 1
 
3.6%
37 1
 
3.6%
104 1
 
3.6%
124 1
 
3.6%
121 1
 
3.6%
60 1
 
3.6%
35 1
 
3.6%
95 1
 
3.6%
40 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
22 1
3.6%
27 1
3.6%
28 1
3.6%
32 1
3.6%
34 1
3.6%
35 1
3.6%
37 1
3.6%
40 1
3.6%
41 1
3.6%
42 1
3.6%
ValueCountFrequency (%)
156 1
3.6%
142 1
3.6%
124 1
3.6%
121 1
3.6%
113 1
3.6%
112 1
3.6%
111 1
3.6%
104 1
3.6%
95 1
3.6%
94 1
3.6%

콤바인 4조
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean360.5
Minimum67
Maximum862
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-03-14T20:39:27.296455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum67
5-th percentile84.25
Q1175.25
median330
Q3538.75
95-th percentile725.9
Maximum862
Range795
Interquartile range (IQR)363.5

Descriptive statistics

Standard deviation225.30054
Coefficient of variation (CV)0.62496682
Kurtosis-0.72229665
Mean360.5
Median Absolute Deviation (MAD)195.5
Skewness0.52579591
Sum10094
Variance50760.333
MonotonicityNot monotonic
2024-03-14T20:39:27.717222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
436 2
 
7.1%
122 1
 
3.6%
467 1
 
3.6%
553 1
 
3.6%
285 1
 
3.6%
229 1
 
3.6%
120 1
 
3.6%
67 1
 
3.6%
140 1
 
3.6%
199 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
67 1
3.6%
72 1
3.6%
107 1
3.6%
120 1
3.6%
122 1
3.6%
129 1
3.6%
140 1
3.6%
187 1
3.6%
199 1
3.6%
213 1
3.6%
ValueCountFrequency (%)
862 1
3.6%
756 1
3.6%
670 1
3.6%
662 1
3.6%
630 1
3.6%
553 1
3.6%
541 1
3.6%
538 1
3.6%
467 1
3.6%
436 2
7.1%

콤바인 5조 이상
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean232.21429
Minimum21
Maximum707
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-03-14T20:39:28.097972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23.75
Q176
median220.5
Q3374
95-th percentile492.2
Maximum707
Range686
Interquartile range (IQR)298

Descriptive statistics

Standard deviation179.54827
Coefficient of variation (CV)0.7732008
Kurtosis0.030711119
Mean232.21429
Median Absolute Deviation (MAD)153.5
Skewness0.75047808
Sum6502
Variance32237.582
MonotonicityNot monotonic
2024-03-14T20:39:28.367234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
236 2
 
7.1%
152 2
 
7.1%
374 2
 
7.1%
82 1
 
3.6%
106 1
 
3.6%
308 1
 
3.6%
55 1
 
3.6%
58 1
 
3.6%
21 1
 
3.6%
22 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
21 1
3.6%
22 1
3.6%
27 1
3.6%
38 1
3.6%
55 1
3.6%
57 1
3.6%
58 1
3.6%
82 1
3.6%
100 1
3.6%
106 1
3.6%
ValueCountFrequency (%)
707 1
3.6%
502 1
3.6%
474 1
3.6%
436 1
3.6%
433 1
3.6%
417 1
3.6%
374 2
7.1%
309 1
3.6%
308 1
3.6%
259 1
3.6%

곡물건조기
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean869.32143
Minimum47
Maximum2378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-03-14T20:39:28.588434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile82.7
Q1258.5
median662
Q31409.25
95-th percentile2167.55
Maximum2378
Range2331
Interquartile range (IQR)1150.75

Descriptive statistics

Standard deviation725.88557
Coefficient of variation (CV)0.8350025
Kurtosis-0.84114873
Mean869.32143
Median Absolute Deviation (MAD)493.5
Skewness0.65334009
Sum24341
Variance526909.86
MonotonicityNot monotonic
2024-03-14T20:39:28.911061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
278 1
 
3.6%
1588 1
 
3.6%
1385 1
 
3.6%
694 1
 
3.6%
630 1
 
3.6%
328 1
 
3.6%
179 1
 
3.6%
47 1
 
3.6%
169 1
 
3.6%
200 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
47 1
3.6%
61 1
3.6%
123 1
3.6%
168 1
3.6%
169 1
3.6%
179 1
3.6%
200 1
3.6%
278 1
3.6%
283 1
3.6%
292 1
3.6%
ValueCountFrequency (%)
2378 1
3.6%
2247 1
3.6%
2020 1
3.6%
1908 1
3.6%
1588 1
3.6%
1574 1
3.6%
1482 1
3.6%
1385 1
3.6%
1384 1
3.6%
1303 1
3.6%

농산물건조기
Real number (ℝ)

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1770.3571
Minimum407
Maximum2912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-03-14T20:39:29.277004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum407
5-th percentile548.35
Q11230
median1831
Q32441.25
95-th percentile2736.05
Maximum2912
Range2505
Interquartile range (IQR)1211.25

Descriptive statistics

Standard deviation721.35019
Coefficient of variation (CV)0.40746026
Kurtosis-0.91981012
Mean1770.3571
Median Absolute Deviation (MAD)615.5
Skewness-0.19605765
Sum49570
Variance520346.09
MonotonicityNot monotonic
2024-03-14T20:39:29.667742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
471 1
 
3.6%
1390 1
 
3.6%
1194 1
 
3.6%
1105 1
 
3.6%
2156 1
 
3.6%
2654 1
 
3.6%
692 1
 
3.6%
1242 1
 
3.6%
2749 1
 
3.6%
1835 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
407 1
3.6%
471 1
3.6%
692 1
3.6%
885 1
3.6%
1105 1
3.6%
1145 1
3.6%
1194 1
3.6%
1242 1
3.6%
1330 1
3.6%
1390 1
3.6%
ValueCountFrequency (%)
2912 1
3.6%
2749 1
3.6%
2712 1
3.6%
2654 1
3.6%
2621 1
3.6%
2554 1
3.6%
2490 1
3.6%
2425 1
3.6%
2228 1
3.6%
2156 1
3.6%

연도
Categorical

Distinct2
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size352.0 B
2016
14 
2013
14 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2016 14
50.0%
2013 14
50.0%

Length

2024-03-14T20:39:30.055524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T20:39:30.359499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 14
50.0%
2013 14
50.0%

Interactions

2024-03-14T20:39:13.640200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:34.035022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:37.508689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:40.416327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:43.543901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:47.127175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:49.857108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:52.545683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:55.603401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:59.050532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:02.288287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:04.951355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:07.490355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:10.067698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:13.883981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:34.271223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:37.748186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:40.568405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:43.800615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:47.369394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:50.036531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:52.719221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:55.839649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:59.300124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:02.533003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:05.103386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:07.635977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:10.300229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:14.116374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:34.504444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:37.973056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:40.985705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:44.039069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:47.552212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:50.241449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:52.857517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:56.074996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:59.543329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:02.986779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:05.305988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:07.880547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:10.530360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:14.361702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:34.749618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:38.216478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:41.142640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:44.300375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:47.805862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:50.396026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:53.020192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:56.319405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:59.747288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:03.164388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:05.554221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:08.033324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:10.770936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:14.612317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:35.006375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:38.466111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:41.300343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:44.557364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:47.959235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:50.557234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:53.179082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:56.571624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:59.910923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:03.324687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:05.757142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:08.194840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:11.021009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:14.854734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:35.246255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:38.702516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:41.444447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:44.807113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:48.100415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:50.708479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:53.329031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:56.809889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:00.085695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:03.471264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:05.913786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:08.338861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:11.256991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:15.106887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:35.499535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:38.950456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:41.602323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:45.073087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:48.254519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:50.865334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:53.537297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:57.059566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:00.245963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:03.633196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:06.083312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:08.535104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:11.506317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:15.360745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:35.752100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:39.199553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:41.768775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:45.339673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:48.409753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:51.060430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:53.810012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:57.319339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:00.427890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:03.791647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:06.284541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:08.696356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:11.942069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:15.597135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:35.992658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:39.434030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:42.015722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:45.585655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:48.607696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:51.311484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:54.057271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:57.555787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:00.690882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:03.939360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:06.432870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:08.922185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:12.176063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:15.855452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:36.253565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:39.693564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:42.278492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:45.850684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:48.871098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:51.505791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:54.326581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:57.819939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:00.971290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:04.149664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:06.699686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:09.207177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:12.431582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:16.111126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:36.509557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:39.842864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:42.535672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:46.108643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:49.126714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:51.856114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:54.590087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:58.070824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:01.244386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:04.345565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:06.865021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:09.451255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:12.681593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:16.372569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:36.772538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:39.998448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:42.802267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:46.374500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:49.385195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:52.023312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:54.859101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:58.331695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:01.526534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:04.509755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:07.031407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:09.619075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:12.937236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:16.626825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:37.028762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:40.147558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:43.063135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:46.638686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:49.581163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:52.182902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:55.119321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:58.583528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:01.795181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:04.666431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:07.195843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:09.776776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:13.182853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:16.861537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:37.265354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:40.279289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:43.300719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:46.880683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:49.717481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:52.323618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:55.356351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:38:58.814535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:02.038498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:04.805827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:07.337855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:09.914494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:39:13.407614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T20:39:30.573000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군별동력 경운기농용트랙터 소형농용트랙터 중형농용트랙터 대형스피드스 프레이어동력이앙기 보행형동력이앙기 승용형관리기 보행형관리기 승용형콤바인 3조 이하콤바인 4조콤바인 5조 이상곡물건조기농산물건조기연도
시군별1.0000.8160.6470.9270.7460.8350.0000.5800.7610.6730.5500.7430.7150.9010.8940.000
동력 경운기0.8161.0000.8320.9060.7320.7210.6470.3750.4560.9330.6910.8340.5230.5470.8290.000
농용트랙터 소형0.6470.8321.0000.7690.9160.1870.7070.6570.0000.9460.8900.8820.8150.9440.6800.000
농용트랙터 중형0.9270.9060.7691.0000.6730.4120.6350.1720.3710.7450.6630.7000.7830.7050.7670.000
농용트랙터 대형0.7460.7320.9160.6731.0000.6200.7400.7130.0000.8770.8660.8450.7720.9140.7010.000
스피드스 프레이어0.8350.7210.1870.4120.6201.0000.3690.4110.0000.6780.6070.0000.4650.0000.6520.000
동력이앙기 보행형0.0000.6470.7070.6350.7400.3691.0000.3230.3440.4710.6330.7410.5960.5670.5030.586
동력이앙기 승용형0.5800.3750.6570.1720.7130.4110.3231.0000.0000.0000.6200.7330.6980.7630.4330.000
관리기 보행형0.7610.4560.0000.3710.0000.0000.3440.0001.0000.2080.0000.5300.0000.0000.5370.468
관리기 승용형0.6730.9330.9460.7450.8770.6780.4710.0000.2081.0000.8710.9200.7090.7410.7660.000
콤바인 3조 이하0.5500.6910.8900.6630.8660.6070.6330.6200.0000.8711.0000.8860.5680.9160.3660.000
콤바인 4조0.7430.8340.8820.7000.8450.0000.7410.7330.5300.9200.8861.0000.7470.9280.8430.269
콤바인 5조 이상0.7150.5230.8150.7830.7720.4650.5960.6980.0000.7090.5680.7471.0000.7440.6810.000
곡물건조기0.9010.5470.9440.7050.9140.0000.5670.7630.0000.7410.9160.9280.7441.0000.3860.000
농산물건조기0.8940.8290.6800.7670.7010.6520.5030.4330.5370.7660.3660.8430.6810.3861.0000.000
연도0.0000.0000.0000.0000.0000.0000.5860.0000.4680.0000.0000.2690.0000.0000.0001.000
2024-03-14T20:39:30.965128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동력 경운기농용트랙터 소형농용트랙터 중형농용트랙터 대형스피드스 프레이어동력이앙기 보행형동력이앙기 승용형관리기 보행형관리기 승용형콤바인 3조 이하콤바인 4조콤바인 5조 이상곡물건조기농산물건조기연도
동력 경운기1.0000.7490.8740.746-0.0900.7760.8470.6390.5280.6890.7380.6110.6350.3560.000
농용트랙터 소형0.7491.0000.9250.843-0.1490.7980.6920.4570.6470.6530.8340.8040.8480.1270.000
농용트랙터 중형0.8740.9251.0000.885-0.1360.8320.7990.6310.6370.7340.8380.7870.8110.3340.000
농용트랙터 대형0.7460.8430.8851.000-0.1400.7950.7570.3680.6790.6540.8800.9250.9000.3260.000
스피드스 프레이어-0.090-0.149-0.136-0.1401.000-0.0850.0400.120-0.115-0.137-0.143-0.043-0.192-0.2010.000
동력이앙기 보행형0.7760.7980.8320.795-0.0851.0000.5580.4080.5810.5810.8190.6930.7550.1710.478
동력이앙기 승용형0.8470.6920.7990.7570.0400.5581.0000.5970.4410.7210.7020.6600.6680.3930.000
관리기 보행형0.6390.4570.6310.3680.1200.4080.5971.0000.1750.4570.2520.2010.1940.3630.288
관리기 승용형0.5280.6470.6370.679-0.1150.5810.4410.1751.0000.4490.6470.5910.5700.1470.000
콤바인 3조 이하0.6890.6530.7340.654-0.1370.5810.7210.4570.4491.0000.6110.4870.5320.2420.000
콤바인 4조0.7380.8340.8380.880-0.1430.8190.7020.2520.6470.6111.0000.8810.9340.1950.133
콤바인 5조 이상0.6110.8040.7870.925-0.0430.6930.6600.2010.5910.4870.8811.0000.9300.1880.000
곡물건조기0.6350.8480.8110.900-0.1920.7550.6680.1940.5700.5320.9340.9301.0000.2330.000
농산물건조기0.3560.1270.3340.326-0.2010.1710.3930.3630.1470.2420.1950.1880.2331.0000.000
연도0.0000.0000.0000.0000.0000.4780.0000.2880.0000.0000.1330.0000.0000.0001.000

Missing values

2024-03-14T20:39:17.242427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T20:39:17.812942image/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

시군별동력 경운기농용트랙터 소형농용트랙터 중형농용트랙터 대형스피드스 프레이어동력이앙기 보행형동력이앙기 승용형관리기 보행형관리기 승용형콤바인 3조 이하콤바인 4조콤바인 5조 이상곡물건조기농산물건조기연도
0전주시120817435817926313220097314728122822784712016
1군산시2485467959106679646822129646242436374157416472016
2익산시42727371750134614062213862788323113538502190818272016
3정읍시5440523175514192387101673280347089670474148229122016
4남원시535848317117422601141106937581268337523699024902016
5김제시4710105319301593639713158528241056142630707224727122016
6완주군4080395119540313466547937151783218711329218522016
7진안군2585222757321665102932217283651073816826212016
8무주군239519357915538647013114524543472276113302016
9장수군234124073830551057430717577544129571238852016
시군별동력 경운기농용트랙터 소형농용트랙터 중형농용트랙터 대형스피드스 프레이어동력이앙기 보행형동력이앙기 승용형관리기 보행형관리기 승용형콤바인 3조 이하콤바인 4조콤바인 5조 이상곡물건조기농산물건조기연도
18남원시610355317476181991064164335433739443615289919172013
19김제시601292921231368184192110291811199875862436237822282013
20완주군4028316106628110136173034782174019910020018352013
21진안군3253222836303563446432388239951402216927492013
22무주군21441704971053422623891873103567214712422013
23장수군273831071325340627281919104860120581796922013
24임실군3707136653318102389898170248442295532826542013
25순창군3481247775349130598771124915512128515263021562013
26고창군6588593131891415112011051204751512455323669411052013
27부안군440864413488675610716221473292104467308138511942013