Overview

Dataset statistics

Number of variables19
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory175.7 B

Variable types

Text1
Numeric18

Dataset

Description경상북도 시군의 농기계(트랙트, 스피드스프레이어, 보행형동력경운기, 보행형동력이양기, 콤바인, 보행관리기) 등의 현황입니다.
Author경상북도
URLhttps://www.data.go.kr/data/15044809/fileData.do

Alerts

동력경운기 is highly overall correlated with 농용트랙터 (소형) and 14 other fieldsHigh correlation
농용트랙터 (소형) is highly overall correlated with 동력경운기 and 12 other fieldsHigh correlation
농용트랙터 (중형) is highly overall correlated with 동력경운기 and 12 other fieldsHigh correlation
농용트랙터 (대형) is highly overall correlated with 동력경운기 and 13 other fieldsHigh correlation
스피드스 프레이어 is highly overall correlated with 동력경운기 and 3 other fieldsHigh correlation
동력이앙기(보행형) is highly overall correlated with 동력경운기 and 12 other fieldsHigh correlation
동력이앙기(승용형) is highly overall correlated with 동력경운기 and 12 other fieldsHigh correlation
관리기(보행형) is highly overall correlated with 동력경운기 and 10 other fieldsHigh correlation
콤바인(3조이하) is highly overall correlated with 동력경운기 and 8 other fieldsHigh correlation
콤바인(4조) is highly overall correlated with 동력경운기 and 11 other fieldsHigh correlation
콤바인(5조이상) is highly overall correlated with 동력경운기 and 11 other fieldsHigh correlation
곡물건조기 is highly overall correlated with 동력경운기 and 11 other fieldsHigh correlation
농산물건조기 is highly overall correlated with 동력경운기 and 4 other fieldsHigh correlation
파종기 is highly overall correlated with 동력경운기 and 11 other fieldsHigh correlation
정식기 is highly overall correlated with 동력경운기 and 10 other fieldsHigh correlation
수확기 is highly overall correlated with 동력경운기 and 3 other fieldsHigh correlation
농업용멀티콥터 is highly overall correlated with 농용트랙터 (소형) and 8 other fieldsHigh correlation
기종별구 별 has unique valuesUnique
동력경운기 has unique valuesUnique
농용트랙터 (소형) has unique valuesUnique
농용트랙터 (중형) has unique valuesUnique
스피드스 프레이어 has unique valuesUnique
동력이앙기(보행형) has unique valuesUnique
동력이앙기(승용형) has unique valuesUnique
관리기(보행형) has unique valuesUnique
관리기(승용형) has unique valuesUnique
콤바인(4조) has unique valuesUnique
콤바인(5조이상) has unique valuesUnique
곡물건조기 has unique valuesUnique
농산물건조기 has unique valuesUnique
농용트랙터 (소형) has 1 (4.3%) zerosZeros
농용트랙터 (대형) has 1 (4.3%) zerosZeros
스피드스 프레이어 has 1 (4.3%) zerosZeros
동력이앙기(보행형) has 1 (4.3%) zerosZeros
동력이앙기(승용형) has 1 (4.3%) zerosZeros
관리기(승용형) has 1 (4.3%) zerosZeros
콤바인(3조이하) has 1 (4.3%) zerosZeros
콤바인(4조) has 1 (4.3%) zerosZeros
콤바인(5조이상) has 1 (4.3%) zerosZeros
곡물건조기 has 1 (4.3%) zerosZeros
파종기 has 1 (4.3%) zerosZeros
정식기 has 3 (13.0%) zerosZeros
수확기 has 1 (4.3%) zerosZeros
농업용멀티콥터 has 7 (30.4%) zerosZeros

Reproduction

Analysis started2024-01-06 12:59:55.072298
Analysis finished2024-01-06 13:01:55.196836
Duration2 minutes and 0.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기종별구 별
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2024-01-06T13:01:55.425140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69
Distinct characters34
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

Unique23 ?
Unique (%)100.0%

Sample

1st row포항시
2nd row경주시
3rd row김천시
4th row안동시
5th row구미시
ValueCountFrequency (%)
포항시 1
 
4.3%
청송군 1
 
4.3%
울진군 1
 
4.3%
봉화군 1
 
4.3%
예천군 1
 
4.3%
칠곡군 1
 
4.3%
성주군 1
 
4.3%
고령군 1
 
4.3%
청도군 1
 
4.3%
영덕군 1
 
4.3%
Other values (13) 13
56.5%
2024-01-06T13:01:56.443047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14
20.3%
10
14.5%
4
 
5.8%
4
 
5.8%
3
 
4.3%
3
 
4.3%
2
 
2.9%
2
 
2.9%
2
 
2.9%
1
 
1.4%
Other values (24) 24
34.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 69
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
14
20.3%
10
14.5%
4
 
5.8%
4
 
5.8%
3
 
4.3%
3
 
4.3%
2
 
2.9%
2
 
2.9%
2
 
2.9%
1
 
1.4%
Other values (24) 24
34.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 69
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
14
20.3%
10
14.5%
4
 
5.8%
4
 
5.8%
3
 
4.3%
3
 
4.3%
2
 
2.9%
2
 
2.9%
2
 
2.9%
1
 
1.4%
Other values (24) 24
34.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 69
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
14
20.3%
10
14.5%
4
 
5.8%
4
 
5.8%
3
 
4.3%
3
 
4.3%
2
 
2.9%
2
 
2.9%
2
 
2.9%
1
 
1.4%
Other values (24) 24
34.8%

동력경운기
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5259.6522
Minimum34
Maximum10598
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-06T13:01:56.938197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile2276.6
Q13245.5
median5044
Q36942
95-th percentile10036.2
Maximum10598
Range10564
Interquartile range (IQR)3696.5

Descriptive statistics

Standard deviation2773.8293
Coefficient of variation (CV)0.52737886
Kurtosis-0.49261515
Mean5259.6522
Median Absolute Deviation (MAD)1946
Skewness0.40449842
Sum120972
Variance7694129.2
MonotonicityNot monotonic
2024-01-06T13:01:57.383559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
4628 1
 
4.3%
8616 1
 
4.3%
34 1
 
4.3%
2426 1
 
4.3%
3417 1
 
4.3%
5368 1
 
4.3%
2572 1
 
4.3%
5044 1
 
4.3%
2929 1
 
4.3%
5687 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
34 1
4.3%
2260 1
4.3%
2426 1
4.3%
2572 1
4.3%
2929 1
4.3%
3098 1
4.3%
3393 1
4.3%
3417 1
4.3%
3840 1
4.3%
4628 1
4.3%
ValueCountFrequency (%)
10598 1
4.3%
10147 1
4.3%
9039 1
4.3%
8617 1
4.3%
8616 1
4.3%
8197 1
4.3%
5687 1
4.3%
5539 1
4.3%
5430 1
4.3%
5368 1
4.3%

농용트랙터 (소형)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean606.21739
Minimum0
Maximum1450
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-06T13:01:57.970810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile165.9
Q1309.5
median555
Q3818
95-th percentile1309.7
Maximum1450
Range1450
Interquartile range (IQR)508.5

Descriptive statistics

Standard deviation398.59879
Coefficient of variation (CV)0.65751791
Kurtosis-0.28440531
Mean606.21739
Median Absolute Deviation (MAD)249
Skewness0.79325112
Sum13943
Variance158881
MonotonicityNot monotonic
2024-01-06T13:01:58.470201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
689 1
 
4.3%
1289 1
 
4.3%
0 1
 
4.3%
306 1
 
4.3%
301 1
 
4.3%
566 1
 
4.3%
273 1
 
4.3%
897 1
 
4.3%
383 1
 
4.3%
570 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
0 1
4.3%
154 1
4.3%
273 1
4.3%
290 1
4.3%
301 1
4.3%
306 1
4.3%
313 1
4.3%
345 1
4.3%
369 1
4.3%
383 1
4.3%
ValueCountFrequency (%)
1450 1
4.3%
1312 1
4.3%
1289 1
4.3%
1191 1
4.3%
907 1
4.3%
897 1
4.3%
739 1
4.3%
689 1
4.3%
647 1
4.3%
570 1
4.3%

농용트랙터 (중형)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1262.6087
Minimum6
Maximum3036
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-06T13:01:59.025103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile480.3
Q1583
median1129
Q31797
95-th percentile2431.4
Maximum3036
Range3030
Interquartile range (IQR)1214

Descriptive statistics

Standard deviation771.54531
Coefficient of variation (CV)0.61107238
Kurtosis-0.31093916
Mean1262.6087
Median Absolute Deviation (MAD)598
Skewness0.57228695
Sum29040
Variance595282.16
MonotonicityNot monotonic
2024-01-06T13:01:59.728775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1279 1
 
4.3%
2068 1
 
4.3%
6 1
 
4.3%
483 1
 
4.3%
1067 1
 
4.3%
1985 1
 
4.3%
635 1
 
4.3%
1880 1
 
4.3%
1411 1
 
4.3%
850 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
6 1
4.3%
480 1
4.3%
483 1
4.3%
498 1
4.3%
522 1
4.3%
531 1
4.3%
635 1
4.3%
836 1
4.3%
850 1
4.3%
954 1
4.3%
ValueCountFrequency (%)
3036 1
4.3%
2437 1
4.3%
2381 1
4.3%
2068 1
4.3%
1985 1
4.3%
1880 1
4.3%
1714 1
4.3%
1677 1
4.3%
1411 1
4.3%
1279 1
4.3%

농용트랙터 (대형)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean440.34783
Minimum0
Maximum1353
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-06T13:02:00.560179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile117.4
Q1213.5
median394
Q3541
95-th percentile1020.1
Maximum1353
Range1353
Interquartile range (IQR)327.5

Descriptive statistics

Standard deviation325.56463
Coefficient of variation (CV)0.73933516
Kurtosis1.6357284
Mean440.34783
Median Absolute Deviation (MAD)171
Skewness1.2815716
Sum10128
Variance105992.33
MonotonicityNot monotonic
2024-01-06T13:02:01.613705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
267 2
 
8.7%
546 1
 
4.3%
121 1
 
4.3%
0 1
 
4.3%
435 1
 
4.3%
904 1
 
4.3%
144 1
 
4.3%
199 1
 
4.3%
328 1
 
4.3%
277 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
0 1
4.3%
117 1
4.3%
121 1
4.3%
144 1
4.3%
199 1
4.3%
204 1
4.3%
223 1
4.3%
267 2
8.7%
277 1
4.3%
328 1
4.3%
ValueCountFrequency (%)
1353 1
4.3%
1033 1
4.3%
904 1
4.3%
787 1
4.3%
642 1
4.3%
546 1
4.3%
536 1
4.3%
455 1
4.3%
451 1
4.3%
445 1
4.3%

스피드스 프레이어
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1135.2174
Minimum0
Maximum2762
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-06T13:02:02.325310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17.6
Q1345.5
median955
Q31854
95-th percentile2734.4
Maximum2762
Range2762
Interquartile range (IQR)1508.5

Descriptive statistics

Standard deviation944.40452
Coefficient of variation (CV)0.83191513
Kurtosis-1.1066422
Mean1135.2174
Median Absolute Deviation (MAD)712
Skewness0.53373548
Sum26110
Variance891899.91
MonotonicityNot monotonic
2024-01-06T13:02:02.714806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1120 1
 
4.3%
498 1
 
4.3%
0 1
 
4.3%
12 1
 
4.3%
955 1
 
4.3%
606 1
 
4.3%
146 1
 
4.3%
240 1
 
4.3%
68 1
 
4.3%
1289 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
0 1
4.3%
12 1
4.3%
68 1
4.3%
146 1
4.3%
240 1
4.3%
243 1
4.3%
448 1
4.3%
498 1
4.3%
606 1
4.3%
642 1
4.3%
ValueCountFrequency (%)
2762 1
4.3%
2752 1
4.3%
2576 1
4.3%
2334 1
4.3%
2306 1
4.3%
2300 1
4.3%
1408 1
4.3%
1380 1
4.3%
1354 1
4.3%
1289 1
4.3%

동력이앙기(보행형)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean879.26087
Minimum0
Maximum3126
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-06T13:02:03.144117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile185.8
Q1493.5
median791
Q31134
95-th percentile1712.7
Maximum3126
Range3126
Interquartile range (IQR)640.5

Descriptive statistics

Standard deviation654.06333
Coefficient of variation (CV)0.74387858
Kurtosis5.5348156
Mean879.26087
Median Absolute Deviation (MAD)361
Skewness1.895493
Sum20223
Variance427798.84
MonotonicityNot monotonic
2024-01-06T13:02:03.890580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
823 1
 
4.3%
3126 1
 
4.3%
0 1
 
4.3%
382 1
 
4.3%
557 1
 
4.3%
919 1
 
4.3%
675 1
 
4.3%
781 1
 
4.3%
791 1
 
4.3%
1088 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
0 1
4.3%
181 1
4.3%
229 1
4.3%
308 1
4.3%
382 1
4.3%
430 1
4.3%
557 1
4.3%
575 1
4.3%
648 1
4.3%
675 1
4.3%
ValueCountFrequency (%)
3126 1
4.3%
1725 1
4.3%
1602 1
4.3%
1236 1
4.3%
1182 1
4.3%
1180 1
4.3%
1088 1
4.3%
919 1
4.3%
898 1
4.3%
887 1
4.3%

동력이앙기(승용형)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean732.08696
Minimum0
Maximum1914
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-06T13:02:04.410366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile71.6
Q1345
median601
Q3973.5
95-th percentile1902.3
Maximum1914
Range1914
Interquartile range (IQR)628.5

Descriptive statistics

Standard deviation569.0742
Coefficient of variation (CV)0.77733143
Kurtosis0.10495521
Mean732.08696
Median Absolute Deviation (MAD)285
Skewness0.98996073
Sum16838
Variance323845.45
MonotonicityNot monotonic
2024-01-06T13:02:04.911976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1031 1
 
4.3%
1815 1
 
4.3%
0 1
 
4.3%
431 1
 
4.3%
532 1
 
4.3%
1360 1
 
4.3%
322 1
 
4.3%
437 1
 
4.3%
621 1
 
4.3%
363 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
0 1
4.3%
66 1
4.3%
122 1
4.3%
261 1
4.3%
322 1
4.3%
327 1
4.3%
363 1
4.3%
431 1
4.3%
437 1
4.3%
452 1
4.3%
ValueCountFrequency (%)
1914 1
4.3%
1912 1
4.3%
1815 1
4.3%
1360 1
4.3%
1213 1
4.3%
1031 1
4.3%
916 1
4.3%
886 1
4.3%
647 1
4.3%
621 1
4.3%

관리기(보행형)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4862.7391
Minimum182
Maximum10283
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-06T13:02:05.337980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum182
5-th percentile1369.3
Q12945
median4470
Q36325
95-th percentile9245.5
Maximum10283
Range10101
Interquartile range (IQR)3380

Descriptive statistics

Standard deviation2698.0343
Coefficient of variation (CV)0.55483839
Kurtosis-0.5290876
Mean4862.7391
Median Absolute Deviation (MAD)1614
Skewness0.42963952
Sum111843
Variance7279389.3
MonotonicityNot monotonic
2024-01-06T13:02:05.729241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
3691 1
 
4.3%
6026 1
 
4.3%
182 1
 
4.3%
1301 1
 
4.3%
4738 1
 
4.3%
4470 1
 
4.3%
2242 1
 
4.3%
5963 1
 
4.3%
2348 1
 
4.3%
4654 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
182 1
4.3%
1301 1
4.3%
1984 1
4.3%
2242 1
4.3%
2348 1
4.3%
2856 1
4.3%
3034 1
4.3%
3667 1
4.3%
3691 1
4.3%
4168 1
4.3%
ValueCountFrequency (%)
10283 1
4.3%
9291 1
4.3%
8836 1
4.3%
8487 1
4.3%
7911 1
4.3%
6624 1
4.3%
6026 1
4.3%
5963 1
4.3%
4798 1
4.3%
4738 1
4.3%

관리기(승용형)
Real number (ℝ)

UNIQUE  ZEROS 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean274.6087
Minimum0
Maximum741
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-06T13:02:06.204833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35.4
Q193.5
median227
Q3369
95-th percentile665.5
Maximum741
Range741
Interquartile range (IQR)275.5

Descriptive statistics

Standard deviation218.52454
Coefficient of variation (CV)0.79576702
Kurtosis-0.44568643
Mean274.6087
Median Absolute Deviation (MAD)136
Skewness0.70432137
Sum6316
Variance47752.976
MonotonicityNot monotonic
2024-01-06T13:02:06.783794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
221 1
 
4.3%
48 1
 
4.3%
0 1
 
4.3%
321 1
 
4.3%
174 1
 
4.3%
400 1
 
4.3%
325 1
 
4.3%
119 1
 
4.3%
96 1
 
4.3%
343 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
0 1
4.3%
35 1
4.3%
39 1
4.3%
48 1
4.3%
75 1
4.3%
91 1
4.3%
96 1
4.3%
99 1
4.3%
119 1
4.3%
174 1
4.3%
ValueCountFrequency (%)
741 1
4.3%
672 1
4.3%
607 1
4.3%
590 1
4.3%
400 1
4.3%
382 1
4.3%
356 1
4.3%
355 1
4.3%
343 1
4.3%
325 1
4.3%

콤바인(3조이하)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.26087
Minimum0
Maximum425
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-06T13:02:07.222677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22.1
Q140.5
median80
Q3187
95-th percentile272.5
Maximum425
Range425
Interquartile range (IQR)146.5

Descriptive statistics

Standard deviation101.71539
Coefficient of variation (CV)0.86742821
Kurtosis2.3882891
Mean117.26087
Median Absolute Deviation (MAD)47
Skewness1.4654354
Sum2697
Variance10346.02
MonotonicityNot monotonic
2024-01-06T13:02:07.745798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
80 2
 
8.7%
202 1
 
4.3%
32 1
 
4.3%
0 1
 
4.3%
36 1
 
4.3%
72 1
 
4.3%
55 1
 
4.3%
84 1
 
4.3%
277 1
 
4.3%
149 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
0 1
4.3%
21 1
4.3%
32 1
4.3%
33 1
4.3%
36 1
4.3%
38 1
4.3%
43 1
4.3%
55 1
4.3%
72 1
4.3%
74 1
4.3%
ValueCountFrequency (%)
425 1
4.3%
277 1
4.3%
232 1
4.3%
204 1
4.3%
202 1
4.3%
195 1
4.3%
179 1
4.3%
149 1
4.3%
96 1
4.3%
90 1
4.3%

콤바인(4조)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean275.3913
Minimum0
Maximum886
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-06T13:02:08.222025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile39.4
Q1105.5
median214
Q3349
95-th percentile656.6
Maximum886
Range886
Interquartile range (IQR)243.5

Descriptive statistics

Standard deviation224.84009
Coefficient of variation (CV)0.81643861
Kurtosis1.1982912
Mean275.3913
Median Absolute Deviation (MAD)122
Skewness1.2177612
Sum6334
Variance50553.067
MonotonicityNot monotonic
2024-01-06T13:02:08.570064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
644 1
 
4.3%
886 1
 
4.3%
0 1
 
4.3%
185 1
 
4.3%
92 1
 
4.3%
348 1
 
4.3%
146 1
 
4.3%
107 1
 
4.3%
332 1
 
4.3%
214 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
0 1
4.3%
37 1
4.3%
61 1
4.3%
62 1
4.3%
92 1
4.3%
104 1
4.3%
107 1
4.3%
146 1
4.3%
185 1
4.3%
201 1
4.3%
ValueCountFrequency (%)
886 1
4.3%
658 1
4.3%
644 1
4.3%
510 1
4.3%
441 1
4.3%
350 1
4.3%
348 1
4.3%
332 1
4.3%
300 1
4.3%
235 1
4.3%

콤바인(5조이상)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.43478
Minimum0
Maximum566
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-06T13:02:08.939317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.8
Q159.5
median89
Q3200.5
95-th percentile383.2
Maximum566
Range566
Interquartile range (IQR)141

Descriptive statistics

Standard deviation139.6648
Coefficient of variation (CV)0.92840764
Kurtosis2.3349573
Mean150.43478
Median Absolute Deviation (MAD)57
Skewness1.5422458
Sum3460
Variance19506.257
MonotonicityNot monotonic
2024-01-06T13:02:09.477010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
190 1
 
4.3%
358 1
 
4.3%
0 1
 
4.3%
80 1
 
4.3%
59 1
 
4.3%
300 1
 
4.3%
67 1
 
4.3%
211 1
 
4.3%
89 1
 
4.3%
76 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
0 1
4.3%
17 1
4.3%
35 1
4.3%
36 1
4.3%
49 1
4.3%
59 1
4.3%
60 1
4.3%
67 1
4.3%
76 1
4.3%
78 1
4.3%
ValueCountFrequency (%)
566 1
4.3%
386 1
4.3%
358 1
4.3%
300 1
4.3%
229 1
4.3%
211 1
4.3%
190 1
4.3%
171 1
4.3%
164 1
4.3%
146 1
4.3%

곡물건조기
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean423.47826
Minimum0
Maximum1176
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-06T13:02:10.120062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile70.1
Q1208
median330
Q3584
95-th percentile1008.5
Maximum1176
Range1176
Interquartile range (IQR)376

Descriptive statistics

Standard deviation308.95614
Coefficient of variation (CV)0.72956789
Kurtosis0.42318115
Mean423.47826
Median Absolute Deviation (MAD)189
Skewness0.94150935
Sum9740
Variance95453.897
MonotonicityNot monotonic
2024-01-06T13:02:10.623999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
507 1
 
4.3%
878 1
 
4.3%
0 1
 
4.3%
349 1
 
4.3%
287 1
 
4.3%
777 1
 
4.3%
145 1
 
4.3%
271 1
 
4.3%
326 1
 
4.3%
138 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
0 1
4.3%
68 1
4.3%
89 1
4.3%
138 1
4.3%
141 1
4.3%
145 1
4.3%
271 1
4.3%
287 1
4.3%
301 1
4.3%
315 1
4.3%
ValueCountFrequency (%)
1176 1
4.3%
1023 1
4.3%
878 1
4.3%
777 1
4.3%
606 1
4.3%
596 1
4.3%
572 1
4.3%
507 1
4.3%
452 1
4.3%
393 1
4.3%

농산물건조기
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2721.4348
Minimum67
Maximum7724
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-06T13:02:11.027450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum67
5-th percentile625.6
Q11409
median2587
Q33610.5
95-th percentile5899.5
Maximum7724
Range7657
Interquartile range (IQR)2201.5

Descriptive statistics

Standard deviation1827.6915
Coefficient of variation (CV)0.67159116
Kurtosis1.2855864
Mean2721.4348
Median Absolute Deviation (MAD)1338
Skewness1.0419564
Sum62593
Variance3340456.3
MonotonicityNot monotonic
2024-01-06T13:02:11.394222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1724 1
 
4.3%
2417 1
 
4.3%
67 1
 
4.3%
925 1
 
4.3%
4009 1
 
4.3%
4375 1
 
4.3%
597 1
 
4.3%
1025 1
 
4.3%
883 1
 
4.3%
2701 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
67 1
4.3%
597 1
4.3%
883 1
4.3%
925 1
4.3%
1025 1
4.3%
1249 1
4.3%
1569 1
4.3%
1724 1
4.3%
1781 1
4.3%
2417 1
4.3%
ValueCountFrequency (%)
7724 1
4.3%
6032 1
4.3%
4707 1
4.3%
4375 1
4.3%
4236 1
4.3%
4009 1
4.3%
3212 1
4.3%
2921 1
4.3%
2722 1
4.3%
2701 1
4.3%

파종기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.521739
Minimum0
Maximum690
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-06T13:02:11.749347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.5
Q118.5
median55
Q381.5
95-th percentile201.8
Maximum690
Range690
Interquartile range (IQR)63

Descriptive statistics

Standard deviation142.78046
Coefficient of variation (CV)1.594925
Kurtosis15.317632
Mean89.521739
Median Absolute Deviation (MAD)32
Skewness3.6694043
Sum2059
Variance20386.261
MonotonicityNot monotonic
2024-01-06T13:02:12.108694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
164 2
 
8.7%
23 2
 
8.7%
55 1
 
4.3%
2 1
 
4.3%
0 1
 
4.3%
7 1
 
4.3%
12 1
 
4.3%
72 1
 
4.3%
74 1
 
4.3%
9 1
 
4.3%
Other values (11) 11
47.8%
ValueCountFrequency (%)
0 1
4.3%
2 1
4.3%
7 1
4.3%
9 1
4.3%
12 1
4.3%
14 1
4.3%
23 2
8.7%
28 1
4.3%
34 1
4.3%
54 1
4.3%
ValueCountFrequency (%)
690 1
4.3%
206 1
4.3%
164 2
8.7%
138 1
4.3%
85 1
4.3%
78 1
4.3%
74 1
4.3%
72 1
4.3%
65 1
4.3%
62 1
4.3%

정식기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)69.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.869565
Minimum0
Maximum57
Zeros3
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-06T13:02:12.488503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.5
median5
Q317
95-th percentile40.7
Maximum57
Range57
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation14.498603
Coefficient of variation (CV)1.221494
Kurtosis3.5743453
Mean11.869565
Median Absolute Deviation (MAD)5
Skewness1.8752837
Sum273
Variance210.20949
MonotonicityNot monotonic
2024-01-06T13:02:12.773216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 3
13.0%
5 2
 
8.7%
3 2
 
8.7%
19 2
 
8.7%
4 2
 
8.7%
1 2
 
8.7%
10 1
 
4.3%
29 1
 
4.3%
12 1
 
4.3%
15 1
 
4.3%
Other values (6) 6
26.1%
ValueCountFrequency (%)
0 3
13.0%
1 2
8.7%
2 1
 
4.3%
3 2
8.7%
4 2
8.7%
5 2
8.7%
8 1
 
4.3%
10 1
 
4.3%
11 1
 
4.3%
12 1
 
4.3%
ValueCountFrequency (%)
57 1
4.3%
42 1
4.3%
29 1
4.3%
23 1
4.3%
19 2
8.7%
15 1
4.3%
12 1
4.3%
11 1
4.3%
10 1
4.3%
8 1
4.3%

수확기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.30435
Minimum0
Maximum751
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-06T13:02:13.074631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median31
Q3135
95-th percentile289.8
Maximum751
Range751
Interquartile range (IQR)127

Descriptive statistics

Standard deviation168.56653
Coefficient of variation (CV)1.6476967
Kurtosis9.9140928
Mean102.30435
Median Absolute Deviation (MAD)30
Skewness2.8879431
Sum2353
Variance28414.676
MonotonicityNot monotonic
2024-01-06T13:02:13.404422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
11 2
 
8.7%
13 2
 
8.7%
1 2
 
8.7%
7 1
 
4.3%
0 1
 
4.3%
2 1
 
4.3%
56 1
 
4.3%
9 1
 
4.3%
144 1
 
4.3%
290 1
 
4.3%
Other values (10) 10
43.5%
ValueCountFrequency (%)
0 1
4.3%
1 2
8.7%
2 1
4.3%
6 1
4.3%
7 1
4.3%
9 1
4.3%
11 2
8.7%
13 2
8.7%
31 1
4.3%
54 1
4.3%
ValueCountFrequency (%)
751 1
4.3%
290 1
4.3%
288 1
4.3%
221 1
4.3%
178 1
4.3%
144 1
4.3%
126 1
4.3%
77 1
4.3%
63 1
4.3%
56 1
4.3%

농업용멀티콥터
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6521739
Minimum0
Maximum20
Zeros7
Zeros (%)30.4%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-06T13:02:13.781271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q35.5
95-th percentile16.9
Maximum20
Range20
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation5.9818566
Coefficient of variation (CV)1.2858196
Kurtosis1.3902577
Mean4.6521739
Median Absolute Deviation (MAD)3
Skewness1.5597971
Sum107
Variance35.782609
MonotonicityNot monotonic
2024-01-06T13:02:14.101585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 7
30.4%
3 4
17.4%
4 2
 
8.7%
6 2
 
8.7%
1 2
 
8.7%
5 1
 
4.3%
16 1
 
4.3%
13 1
 
4.3%
2 1
 
4.3%
17 1
 
4.3%
ValueCountFrequency (%)
0 7
30.4%
1 2
 
8.7%
2 1
 
4.3%
3 4
17.4%
4 2
 
8.7%
5 1
 
4.3%
6 2
 
8.7%
13 1
 
4.3%
16 1
 
4.3%
17 1
 
4.3%
ValueCountFrequency (%)
20 1
 
4.3%
17 1
 
4.3%
16 1
 
4.3%
13 1
 
4.3%
6 2
8.7%
5 1
 
4.3%
4 2
8.7%
3 4
17.4%
2 1
 
4.3%
1 2
8.7%

Interactions

2024-01-06T13:01:47.095351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T12:59:56.542132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:04.073528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:10.452296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:14.915170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:22.150259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:29.268609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:35.492144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:42.814996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:48.309810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:54.514770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-01-06T13:01:23.766175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:30.775509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:37.543200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:44.290497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:51.518665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:00.990651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:08.313972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:13.495610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:19.234759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:26.493487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:33.529940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:40.410084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:46.396223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:52.470471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:58.371068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:05.948776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:12.143126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:17.465159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:24.032856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:31.027529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:37.888109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:44.642517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:51.879984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:01.416105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:08.713616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:13.745961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:19.604109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:26.887849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:33.800267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:40.783520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:46.644408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:52.847720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:58.746305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:06.256265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:12.545185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:17.718297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:24.319152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:31.340260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:38.154062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:45.098254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:52.214821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:01.796584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:09.064880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:14.012780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:19.967178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:27.537768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:34.080607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:41.139426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:46.917716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:53.230807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:59.159806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:06.557022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:12.952659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:18.063078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:24.648704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:31.715275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:38.488737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:45.530603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:52.506336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:02.133508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:09.396974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:14.285651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:20.308542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:28.025600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:34.371627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:41.494167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:47.179994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:53.491888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:59.542090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:06.894604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:13.246629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:18.477472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:24.961672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:32.114218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:38.977188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:45.946869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:52.801966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:02.521861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:09.749174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:14.496132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:20.723160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:28.496946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:34.719559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:42.145849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:47.471912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:53.765652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:59.916734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:07.178495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:13.508434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:18.993455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:25.279967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:32.365003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:39.419384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:46.346085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:53.240657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:03.535266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:10.109020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:14.656493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:21.642809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:28.956882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:35.150153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:42.448843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:47.964339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:00:54.170609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:00.349588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:07.452508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:13.765279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:19.500474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:25.699471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:32.671575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:39.840796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T13:01:46.732056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-06T13:02:14.390116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기종별구 별동력경운기농용트랙터 (소형)농용트랙터 (중형)농용트랙터 (대형)스피드스 프레이어동력이앙기(보행형)동력이앙기(승용형)관리기(보행형)관리기(승용형)콤바인(3조이하)콤바인(4조)콤바인(5조이상)곡물건조기농산물건조기파종기정식기수확기농업용멀티콥터
기종별구 별1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
동력경운기1.0001.0000.8010.7220.4950.6800.6560.4310.7590.0000.0000.0000.7090.6210.4850.7340.8790.7140.121
농용트랙터 (소형)1.0000.8011.0000.7880.8430.0000.7210.8730.6260.0000.3420.6340.8680.8190.7440.4010.4980.3790.390
농용트랙터 (중형)1.0000.7220.7881.0000.8310.0000.6880.6020.7300.0000.7250.7570.7460.8990.7920.8650.7690.7900.811
농용트랙터 (대형)1.0000.4950.8430.8311.0000.0000.3480.9200.0000.0000.5520.8360.9180.8360.8720.8510.7440.5040.827
스피드스 프레이어1.0000.6800.0000.0000.0001.0000.0000.0000.4840.6080.0000.0000.0000.0000.3540.0000.0000.3940.000
동력이앙기(보행형)1.0000.6560.7210.6880.3480.0001.0000.6510.4320.1690.8010.7440.6410.8160.0000.4320.4980.0000.544
동력이앙기(승용형)1.0000.4310.8730.6020.9200.0000.6511.0000.0000.8120.0000.7810.8570.7870.6290.2550.5920.1610.764
관리기(보행형)1.0000.7590.6260.7300.0000.4840.4320.0001.0000.6350.4290.0000.0000.4130.0000.7820.7450.6460.000
관리기(승용형)1.0000.0000.0000.0000.0000.6080.1690.8120.6351.0000.0000.0000.3750.3560.7860.0000.7420.5220.373
콤바인(3조이하)1.0000.0000.3420.7250.5520.0000.8010.0000.4290.0001.0000.9340.8160.7690.0000.6040.5780.3310.505
콤바인(4조)1.0000.0000.6340.7570.8360.0000.7440.7810.0000.0000.9341.0000.9420.8540.5500.8280.7680.0000.749
콤바인(5조이상)1.0000.7090.8680.7460.9180.0000.6410.8570.0000.3750.8160.9421.0000.8980.6450.8370.8920.0000.792
곡물건조기1.0000.6210.8190.8990.8360.0000.8160.7870.4130.3560.7690.8540.8981.0000.8010.8580.7990.5500.867
농산물건조기1.0000.4850.7440.7920.8720.3540.0000.6290.0000.7860.0000.5500.6450.8011.0000.8030.6150.6310.706
파종기1.0000.7340.4010.8650.8510.0000.4320.2550.7820.0000.6040.8280.8370.8580.8031.0000.9170.6160.667
정식기1.0000.8790.4980.7690.7440.0000.4980.5920.7450.7420.5780.7680.8920.7990.6150.9171.0000.6190.753
수확기1.0000.7140.3790.7900.5040.3940.0000.1610.6460.5220.3310.0000.0000.5500.6310.6160.6191.0000.000
농업용멀티콥터1.0000.1210.3900.8110.8270.0000.5440.7640.0000.3730.5050.7490.7920.8670.7060.6670.7530.0001.000
2024-01-06T13:02:14.835662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동력경운기농용트랙터 (소형)농용트랙터 (중형)농용트랙터 (대형)스피드스 프레이어동력이앙기(보행형)동력이앙기(승용형)관리기(보행형)관리기(승용형)콤바인(3조이하)콤바인(4조)콤바인(5조이상)곡물건조기농산물건조기파종기정식기수확기농업용멀티콥터
동력경운기1.0000.8190.7500.6800.6610.8350.6930.9050.3850.5320.6250.5990.6110.6330.7090.5050.6380.408
농용트랙터 (소형)0.8191.0000.8320.7090.4250.8180.7690.7080.4010.7110.7620.8430.7040.3420.6570.5170.3240.612
농용트랙터 (중형)0.7500.8321.0000.8270.2810.8800.9000.7490.1920.6880.8260.8620.7960.4280.8310.6710.3880.789
농용트랙터 (대형)0.6800.7090.8271.0000.3550.7880.9520.5790.3070.6240.8660.8230.9050.5610.6910.6020.3230.760
스피드스 프레이어0.6610.4250.2810.3551.0000.3420.3000.6440.2830.0440.2220.1350.2710.7060.3100.3400.5060.194
동력이앙기(보행형)0.8350.8180.8800.7880.3421.0000.8350.7740.2410.7560.8720.7500.7390.3790.7650.5930.4740.576
동력이앙기(승용형)0.6930.7690.9000.9520.3000.8351.0000.5980.2520.7550.9310.8790.9520.4310.7450.6530.2760.778
관리기(보행형)0.9050.7080.7490.5790.6440.7740.5981.0000.2620.3760.4940.4700.4890.6130.7100.5300.6380.420
관리기(승용형)0.3850.4010.1920.3070.2830.2410.2520.2621.0000.1740.1830.3240.2530.1230.2410.3420.1600.063
콤바인(3조이하)0.5320.7110.6880.6240.0440.7560.7550.3760.1741.0000.8600.7160.6980.0140.4830.4620.2440.428
콤바인(4조)0.6250.7620.8260.8660.2220.8720.9310.4940.1830.8601.0000.8370.8890.2600.6790.5710.2750.689
콤바인(5조이상)0.5990.8430.8620.8230.1350.7500.8790.4700.3240.7160.8371.0000.8600.2420.7250.5600.0870.776
곡물건조기0.6110.7040.7960.9050.2710.7390.9520.4890.2530.6980.8890.8601.0000.4150.7390.6120.1440.759
농산물건조기0.6330.3420.4280.5610.7060.3790.4310.6130.1230.0140.2600.2420.4151.0000.4670.3560.5460.355
파종기0.7090.6570.8310.6910.3100.7650.7450.7100.2410.4830.6790.7250.7390.4671.0000.7120.3490.628
정식기0.5050.5170.6710.6020.3400.5930.6530.5300.3420.4620.5710.5600.6120.3560.7121.0000.2860.466
수확기0.6380.3240.3880.3230.5060.4740.2760.6380.1600.2440.2750.0870.1440.5460.3490.2861.0000.148
농업용멀티콥터0.4080.6120.7890.7600.1940.5760.7780.4200.0630.4280.6890.7760.7590.3550.6280.4660.1481.000

Missing values

2024-01-06T13:01:53.833621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-06T13:01:54.796202image/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

기종별구 별동력경운기농용트랙터 (소형)농용트랙터 (중형)농용트랙터 (대형)스피드스 프레이어동력이앙기(보행형)동력이앙기(승용형)관리기(보행형)관리기(승용형)콤바인(3조이하)콤바인(4조)콤바인(5조이상)곡물건조기농산물건조기파종기정식기수확기농업용멀티콥터
0포항시4628689127954611208231031369122120264419050717245510114
1경주시861612892068536498312618156026484258863588782417852115
2김천시819713121714451135416026478836672195350146452258713842773
3안동시9039907238164223001182916102832277430017159677242061922116
4구미시543073916777872431180121342893552044412296061569624136
5영주시49013131181394140889860166249143219603152476548633
6영천시8617397954445233488760979115908020178330292178571780
7상주시10598145030361353257617251914929135623251056611764707164233113
8문경시5192647112945513806488863667607902351645723212341161
9경산시5539555531117230643026147987413861368917811402880
기종별구 별동력경운기농용트랙터 (소형)농용트랙터 (중형)농용트랙터 (대형)스피드스 프레이어동력이앙기(보행형)동력이앙기(승용형)관리기(보행형)관리기(승용형)콤바인(3조이하)콤바인(4조)콤바인(5조이상)곡물건조기농산물건조기파종기정식기수확기농업용멀티콥터
13영양군3098154498204448229663034352137176842362801260
14영덕군226036952226764218132719843823310493301124923116
15청도군5687570850277128910883634654343149214761382701912900
16고령군29293831411328687916212348962773328932688323191444
17성주군504489718801992407814375963119841072112711025741293
18칠곡군25722736351441466753222242325551466714559772510
19예천군5368566198590460691913604470400723483007774375164295620
20봉화군3417301106743595555753247381743692592874009123133
21울진군242630648326712382431130132180185803499257320
22울릉군3406000018200000670000