Overview

Dataset statistics

Number of variables16
Number of observations10000
Missing cells6
Missing cells (%)< 0.1%
Duplicate rows15
Duplicate rows (%)0.1%
Total size in memory1.4 MiB
Average record size in memory150.0 B

Variable types

Categorical3
Text1
Numeric12

Dataset

Description현재 농작물재해보험에 계약된 과수작물로 품목명, 품종명, 재배방법, 표준수확량, 평년수확량, 가입수확량, 가입가격, 평년과실수, 가입과실수 현황
Author농업정책보험금융원
URLhttps://www.data.go.kr/data/15090466/fileData.do

Alerts

기준년도 has constant value ""Constant
Dataset has 15 (0.1%) duplicate rowsDuplicates
주수 is highly overall correlated with 표준수확량 and 2 other fieldsHigh correlation
표준수확량 is highly overall correlated with 주수 and 4 other fieldsHigh correlation
평년수확량 is highly overall correlated with 주수 and 4 other fieldsHigh correlation
가입수확량 is highly overall correlated with 주수 and 4 other fieldsHigh correlation
가입가격 is highly overall correlated with 품목명High correlation
평년과실수 is highly overall correlated with 표준수확량 and 4 other fieldsHigh correlation
가입과실수 is highly overall correlated with 표준수확량 and 4 other fieldsHigh correlation
재식주간거리 is highly overall correlated with 재식열간거리 and 2 other fieldsHigh correlation
재식열간거리 is highly overall correlated with 재식주간거리 and 2 other fieldsHigh correlation
재식면적 is highly overall correlated with 재식주간거리 and 2 other fieldsHigh correlation
과중 is highly overall correlated with 평년과실수 and 6 other fieldsHigh correlation
품목명 is highly overall correlated with 가입가격 and 2 other fieldsHigh correlation
재배방법구분코드 is highly overall correlated with 과중 and 1 other fieldsHigh correlation
표준수확량 has 284 (2.8%) zerosZeros
평년수확량 has 286 (2.9%) zerosZeros
가입수확량 has 286 (2.9%) zerosZeros
가입가격 has 286 (2.9%) zerosZeros
평년과실수 has 3147 (31.5%) zerosZeros
가입과실수 has 3159 (31.6%) zerosZeros
재식주간거리 has 8586 (85.9%) zerosZeros
재식열간거리 has 8591 (85.9%) zerosZeros
재식면적 has 8591 (85.9%) zerosZeros
과중 has 3015 (30.1%) zerosZeros

Reproduction

Analysis started2024-04-21 11:20:20.326908
Analysis finished2024-04-21 11:20:55.904922
Duration35.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 10000
100.0%

Length

2024-04-21T20:20:56.009977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T20:20:56.165869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 10000
100.0%

품목명
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
(적과전종합Ⅱ)사과
5253 
복숭아
1198 
(적과전종합Ⅱ)배
914 
(적과전종합Ⅱ)떫은감
600 
(종합)감귤
563 
Other values (15)
1472 

Length

Max length11
Median length10
Mean length7.8626
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row복숭아
2nd row(적과전종합Ⅱ)사과
3rd row(적과전종합Ⅱ)사과
4th row(적과전종합Ⅱ)사과
5th row(적과전종합Ⅱ)사과

Common Values

ValueCountFrequency (%)
(적과전종합Ⅱ)사과 5253
52.5%
복숭아 1198
 
12.0%
(적과전종합Ⅱ)배 914
 
9.1%
(적과전종합Ⅱ)떫은감 600
 
6.0%
(종합)감귤 563
 
5.6%
410
 
4.1%
자두 259
 
2.6%
대추 233
 
2.3%
(적과전종합Ⅱ)단감 207
 
2.1%
포도 124
 
1.2%
Other values (10) 239
 
2.4%

Length

2024-04-21T20:20:56.333480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
적과전종합ⅱ)사과 5253
52.5%
복숭아 1198
 
12.0%
적과전종합ⅱ)배 914
 
9.1%
적과전종합ⅱ)떫은감 600
 
6.0%
종합)감귤 563
 
5.6%
410
 
4.1%
자두 259
 
2.6%
대추 233
 
2.3%
적과전종합ⅱ)단감 207
 
2.1%
포도 124
 
1.2%
Other values (10) 239
 
2.4%
Distinct180
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T20:20:57.368570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length4.9072
Min length2

Characters and Unicode

Total characters49072
Distinct characters204
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.2%

Sample

1st row천중도백도
2nd row부사(후지)
3rd row부사(후지)
4th row감홍
5th row아오리(쓰가루)
ValueCountFrequency (%)
부사(후지 2614
26.1%
홍로 1208
 
12.1%
신고 643
 
6.4%
미얀마 391
 
3.9%
갑주백목(봉옥/대봉시/하찌야/도호감 355
 
3.5%
아오리(쓰가루 302
 
3.0%
궁천 298
 
3.0%
복조 196
 
2.0%
중생기타백도계 170
 
1.7%
양광 156
 
1.6%
Other values (170) 3667
36.7%
2024-04-21T20:20:58.687778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 3831
 
7.8%
( 3831
 
7.8%
2805
 
5.7%
2730
 
5.6%
2714
 
5.5%
2623
 
5.3%
1414
 
2.9%
1414
 
2.9%
1335
 
2.7%
/ 1177
 
2.4%
Other values (194) 25198
51.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 39875
81.3%
Close Punctuation 3893
 
7.9%
Open Punctuation 3893
 
7.9%
Other Punctuation 1177
 
2.4%
Decimal Number 208
 
0.4%
Uppercase Letter 21
 
< 0.1%
Connector Punctuation 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2805
 
7.0%
2730
 
6.8%
2714
 
6.8%
2623
 
6.6%
1414
 
3.5%
1414
 
3.5%
1335
 
3.3%
1015
 
2.5%
945
 
2.4%
932
 
2.3%
Other values (180) 21948
55.0%
Decimal Number
ValueCountFrequency (%)
1 105
50.5%
7 48
23.1%
5 48
23.1%
2 6
 
2.9%
3 1
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
B 7
33.3%
A 7
33.3%
M 7
33.3%
Close Punctuation
ValueCountFrequency (%)
) 3831
98.4%
] 62
 
1.6%
Open Punctuation
ValueCountFrequency (%)
( 3831
98.4%
[ 62
 
1.6%
Other Punctuation
ValueCountFrequency (%)
/ 1177
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 39875
81.3%
Common 9176
 
18.7%
Latin 21
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2805
 
7.0%
2730
 
6.8%
2714
 
6.8%
2623
 
6.6%
1414
 
3.5%
1414
 
3.5%
1335
 
3.3%
1015
 
2.5%
945
 
2.4%
932
 
2.3%
Other values (180) 21948
55.0%
Common
ValueCountFrequency (%)
) 3831
41.8%
( 3831
41.8%
/ 1177
 
12.8%
1 105
 
1.1%
[ 62
 
0.7%
] 62
 
0.7%
7 48
 
0.5%
5 48
 
0.5%
2 6
 
0.1%
_ 5
 
0.1%
Latin
ValueCountFrequency (%)
B 7
33.3%
A 7
33.3%
M 7
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 39875
81.3%
ASCII 9197
 
18.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 3831
41.7%
( 3831
41.7%
/ 1177
 
12.8%
1 105
 
1.1%
[ 62
 
0.7%
] 62
 
0.7%
7 48
 
0.5%
5 48
 
0.5%
B 7
 
0.1%
A 7
 
0.1%
Other values (4) 19
 
0.2%
Hangul
ValueCountFrequency (%)
2805
 
7.0%
2730
 
6.8%
2714
 
6.8%
2623
 
6.6%
1414
 
3.5%
1414
 
3.5%
1335
 
3.3%
1015
 
2.5%
945
 
2.4%
932
 
2.3%
Other values (180) 21948
55.0%

수령
Real number (ℝ)

Distinct67
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.046
Minimum0
Maximum81
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T20:20:58.933356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q17
median12
Q321
95-th percentile35
Maximum81
Range81
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.243748
Coefficient of variation (CV)0.68082867
Kurtosis1.4779992
Mean15.046
Median Absolute Deviation (MAD)6
Skewness1.2302754
Sum150460
Variance104.93438
MonotonicityNot monotonic
2024-04-21T20:20:59.199581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 629
 
6.3%
8 628
 
6.3%
7 608
 
6.1%
10 557
 
5.6%
6 552
 
5.5%
5 524
 
5.2%
11 503
 
5.0%
4 464
 
4.6%
12 427
 
4.3%
13 352
 
3.5%
Other values (57) 4756
47.6%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 24
 
0.2%
2 85
 
0.9%
3 287
2.9%
4 464
4.6%
5 524
5.2%
6 552
5.5%
7 608
6.1%
8 628
6.3%
9 629
6.3%
ValueCountFrequency (%)
81 1
< 0.1%
80 1
< 0.1%
74 1
< 0.1%
70 2
< 0.1%
66 1
< 0.1%
62 2
< 0.1%
61 2
< 0.1%
60 2
< 0.1%
59 1
< 0.1%
57 2
< 0.1%

주수
Real number (ℝ)

HIGH CORRELATION 

Distinct715
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.2077
Minimum0
Maximum4295
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T20:20:59.465596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q130
median80
Q3200
95-th percentile500
Maximum4295
Range4295
Interquartile range (IQR)170

Descriptive statistics

Standard deviation217.39247
Coefficient of variation (CV)1.428262
Kurtosis51.272312
Mean152.2077
Median Absolute Deviation (MAD)62
Skewness5.1090263
Sum1522077
Variance47259.488
MonotonicityNot monotonic
2024-04-21T20:20:59.703584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 262
 
2.6%
50 236
 
2.4%
100 220
 
2.2%
10 219
 
2.2%
30 208
 
2.1%
200 156
 
1.6%
40 153
 
1.5%
150 142
 
1.4%
15 140
 
1.4%
60 133
 
1.3%
Other values (705) 8131
81.3%
ValueCountFrequency (%)
0 5
 
0.1%
1 53
0.5%
2 77
0.8%
3 89
0.9%
4 96
1.0%
5 132
1.3%
6 76
0.8%
7 76
0.8%
8 91
0.9%
9 64
0.6%
ValueCountFrequency (%)
4295 1
 
< 0.1%
3750 1
 
< 0.1%
3500 1
 
< 0.1%
3000 3
< 0.1%
2950 1
 
< 0.1%
2500 2
< 0.1%
2200 1
 
< 0.1%
2140 1
 
< 0.1%
2023 1
 
< 0.1%
2000 1
 
< 0.1%

재배방법구분코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
2987 
2
2916 
0
2551 
<NA>
1242 
3
304 

Length

Max length4
Median length1
Mean length1.3726
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row3
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 2987
29.9%
2 2916
29.2%
0 2551
25.5%
<NA> 1242
12.4%
3 304
 
3.0%

Length

2024-04-21T20:20:59.932042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T20:21:00.119809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2987
29.9%
2 2916
29.2%
0 2551
25.5%
na 1242
12.4%
3 304
 
3.0%

표준수확량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4333
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5436.9364
Minimum-1
Maximum125664
Zeros284
Zeros (%)2.8%
Negative2
Negative (%)< 0.1%
Memory size166.0 KiB
2024-04-21T20:21:00.340134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile104
Q1941
median2810.5
Q36832.5
95-th percentile19104.3
Maximum125664
Range125665
Interquartile range (IQR)5891.5

Descriptive statistics

Standard deviation8057.4378
Coefficient of variation (CV)1.4819812
Kurtosis39.148905
Mean5436.9364
Median Absolute Deviation (MAD)2282
Skewness4.6781699
Sum54369364
Variance64922304
MonotonicityNot monotonic
2024-04-21T20:21:00.595653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 284
 
2.8%
1200 22
 
0.2%
500 22
 
0.2%
420 22
 
0.2%
210 21
 
0.2%
2160 21
 
0.2%
1300 21
 
0.2%
1000 21
 
0.2%
1620 20
 
0.2%
450 20
 
0.2%
Other values (4323) 9526
95.3%
ValueCountFrequency (%)
-1 2
 
< 0.1%
0 284
2.8%
10 2
 
< 0.1%
13 1
 
< 0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
16 2
 
< 0.1%
20 3
 
< 0.1%
21 2
 
< 0.1%
22 1
 
< 0.1%
ValueCountFrequency (%)
125664 1
< 0.1%
124555 1
< 0.1%
121473 1
< 0.1%
117700 1
< 0.1%
116400 1
< 0.1%
111265 1
< 0.1%
96530 1
< 0.1%
90915 1
< 0.1%
80905 1
< 0.1%
72576 1
< 0.1%

평년수확량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6184
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5245.9766
Minimum0
Maximum156432
Zeros286
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T20:21:00.840333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile93
Q1887.75
median2646
Q36437.25
95-th percentile18327.85
Maximum156432
Range156432
Interquartile range (IQR)5549.5

Descriptive statistics

Standard deviation8116.5986
Coefficient of variation (CV)1.5472045
Kurtosis47.249782
Mean5245.9766
Median Absolute Deviation (MAD)2141
Skewness5.107663
Sum52459766
Variance65879174
MonotonicityNot monotonic
2024-04-21T20:21:01.094030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 286
 
2.9%
1530 9
 
0.1%
200 9
 
0.1%
209 9
 
0.1%
76 8
 
0.1%
213 8
 
0.1%
392 8
 
0.1%
328 8
 
0.1%
441 8
 
0.1%
505 8
 
0.1%
Other values (6174) 9639
96.4%
ValueCountFrequency (%)
0 286
2.9%
7 1
 
< 0.1%
9 1
 
< 0.1%
13 2
 
< 0.1%
14 2
 
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
17 1
 
< 0.1%
18 2
 
< 0.1%
19 2
 
< 0.1%
ValueCountFrequency (%)
156432 1
< 0.1%
146600 1
< 0.1%
111094 1
< 0.1%
99279 1
< 0.1%
99015 1
< 0.1%
97313 1
< 0.1%
90662 1
< 0.1%
89252 1
< 0.1%
88295 1
< 0.1%
85134 1
< 0.1%

가입수확량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6151
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5215.9152
Minimum0
Maximum156432
Zeros286
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T20:21:01.353937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile92
Q1869.75
median2607
Q36400.75
95-th percentile18293.2
Maximum156432
Range156432
Interquartile range (IQR)5531

Descriptive statistics

Standard deviation8102.5826
Coefficient of variation (CV)1.5534345
Kurtosis47.546338
Mean5215.9152
Median Absolute Deviation (MAD)2114.5
Skewness5.1265356
Sum52159152
Variance65651845
MonotonicityNot monotonic
2024-04-21T20:21:01.611073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 286
 
2.9%
1530 9
 
0.1%
76 9
 
0.1%
209 9
 
0.1%
4200 8
 
0.1%
505 8
 
0.1%
247 8
 
0.1%
213 8
 
0.1%
200 8
 
0.1%
441 8
 
0.1%
Other values (6141) 9639
96.4%
ValueCountFrequency (%)
0 286
2.9%
7 1
 
< 0.1%
13 1
 
< 0.1%
14 2
 
< 0.1%
15 1
 
< 0.1%
16 2
 
< 0.1%
17 1
 
< 0.1%
18 2
 
< 0.1%
19 2
 
< 0.1%
20 1
 
< 0.1%
ValueCountFrequency (%)
156432 1
< 0.1%
146600 1
< 0.1%
111094 1
< 0.1%
99279 1
< 0.1%
99015 1
< 0.1%
97313 1
< 0.1%
90662 1
< 0.1%
89252 1
< 0.1%
88295 1
< 0.1%
85134 1
< 0.1%

가입가격
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2098.0734
Minimum0
Maximum12159
Zeros286
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T20:21:01.875421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1045
Q11792
median1868
Q32210
95-th percentile4077
Maximum12159
Range12159
Interquartile range (IQR)418

Descriptive statistics

Standard deviation1351.861
Coefficient of variation (CV)0.64433446
Kurtosis28.78572
Mean2098.0734
Median Absolute Deviation (MAD)342
Skewness4.6277143
Sum20980734
Variance1827528.1
MonotonicityNot monotonic
2024-04-21T20:21:02.133729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1868 3026
30.3%
2210 1189
 
11.9%
1336 642
 
6.4%
2450 457
 
4.6%
1045 431
 
4.3%
1217 353
 
3.5%
2222 330
 
3.3%
2114 298
 
3.0%
0 286
 
2.9%
2979 284
 
2.8%
Other values (55) 2704
27.0%
ValueCountFrequency (%)
0 286
2.9%
640 4
 
< 0.1%
805 37
 
0.4%
1045 431
4.3%
1066 182
1.8%
1106 157
 
1.6%
1153 113
 
1.1%
1217 353
3.5%
1250 1
 
< 0.1%
1261 60
 
0.6%
ValueCountFrequency (%)
12159 16
 
0.2%
11870 38
0.4%
11135 46
0.5%
11090 6
 
0.1%
10942 21
0.2%
8586 5
 
0.1%
8263 30
0.3%
7928 6
 
0.1%
7656 1
 
< 0.1%
4954 14
 
0.1%

평년과실수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5795
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13095.001
Minimum0
Maximum521440
Zeros3147
Zeros (%)31.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T20:21:02.389166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3819
Q316570
95-th percentile53791.45
Maximum521440
Range521440
Interquartile range (IQR)16570

Descriptive statistics

Standard deviation24209.485
Coefficient of variation (CV)1.8487577
Kurtosis52.8209
Mean13095.001
Median Absolute Deviation (MAD)3819
Skewness5.2726924
Sum1.3095001 × 108
Variance5.8609917 × 108
MonotonicityNot monotonic
2024-04-21T20:21:02.644974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3147
31.5%
517.0 6
 
0.1%
1373.0 6
 
0.1%
770.0 6
 
0.1%
380.0 5
 
0.1%
200.0 5
 
0.1%
1527.0 5
 
0.1%
567.0 5
 
0.1%
823.0 5
 
0.1%
997.0 5
 
0.1%
Other values (5785) 6805
68.0%
ValueCountFrequency (%)
0.0 3147
31.5%
4.2 1
 
< 0.1%
4.5 1
 
< 0.1%
4.7 1
 
< 0.1%
5.0 3
 
< 0.1%
30.0 1
 
< 0.1%
42.0 1
 
< 0.1%
43.0 2
 
< 0.1%
44.0 1
 
< 0.1%
47.0 1
 
< 0.1%
ValueCountFrequency (%)
521440.0 1
< 0.1%
365675.0 1
< 0.1%
330930.0 1
< 0.1%
330173.0 1
< 0.1%
296905.0 1
< 0.1%
294827.0 1
< 0.1%
294317.0 1
< 0.1%
283780.0 1
< 0.1%
270477.0 1
< 0.1%
268157.0 1
< 0.1%

가입과실수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5776
Distinct (%)57.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13006.325
Minimum0
Maximum521440
Zeros3159
Zeros (%)31.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T20:21:02.894254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3700
Q316407.75
95-th percentile53236.65
Maximum521440
Range521440
Interquartile range (IQR)16407.75

Descriptive statistics

Standard deviation24176.035
Coefficient of variation (CV)1.8587907
Kurtosis53.229501
Mean13006.325
Median Absolute Deviation (MAD)3700
Skewness5.3026101
Sum1.3006325 × 108
Variance5.8448067 × 108
MonotonicityNot monotonic
2024-04-21T20:21:03.151041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3159
31.6%
1300 8
 
0.1%
770 6
 
0.1%
560 6
 
0.1%
823 5
 
0.1%
1527 5
 
0.1%
533 5
 
0.1%
1203 5
 
0.1%
697 5
 
0.1%
517 5
 
0.1%
Other values (5766) 6791
67.9%
ValueCountFrequency (%)
0 3159
31.6%
42 1
 
< 0.1%
43 1
 
< 0.1%
44 1
 
< 0.1%
47 1
 
< 0.1%
50 1
 
< 0.1%
52 1
 
< 0.1%
53 1
 
< 0.1%
64 2
 
< 0.1%
67 1
 
< 0.1%
ValueCountFrequency (%)
521440 1
< 0.1%
365675 1
< 0.1%
330930 1
< 0.1%
330173 1
< 0.1%
296905 1
< 0.1%
294827 1
< 0.1%
294317 1
< 0.1%
283780 1
< 0.1%
270477 1
< 0.1%
268157 1
< 0.1%

재식주간거리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47496
Minimum0
Maximum10
Zeros8586
Zeros (%)85.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T20:21:03.621067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3.5
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2409724
Coefficient of variation (CV)2.6127935
Kurtosis5.5919108
Mean0.47496
Median Absolute Deviation (MAD)0
Skewness2.5529979
Sum4749.6
Variance1.5400125
MonotonicityNot monotonic
2024-04-21T20:21:03.870907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 8586
85.9%
3.0 443
 
4.4%
4.0 212
 
2.1%
5.0 146
 
1.5%
2.0 123
 
1.2%
2.5 66
 
0.7%
3.5 44
 
0.4%
6.0 36
 
0.4%
4.5 34
 
0.3%
2.7 22
 
0.2%
Other values (43) 288
 
2.9%
ValueCountFrequency (%)
0.0 8586
85.9%
0.25 2
 
< 0.1%
0.3 5
 
0.1%
0.4 4
 
< 0.1%
1.0 1
 
< 0.1%
1.1 1
 
< 0.1%
1.2 7
 
0.1%
1.3 4
 
< 0.1%
1.4 2
 
< 0.1%
1.5 17
 
0.2%
ValueCountFrequency (%)
10.0 1
 
< 0.1%
7.0 4
 
< 0.1%
6.3 3
 
< 0.1%
6.0 36
 
0.4%
5.9 1
 
< 0.1%
5.6 1
 
< 0.1%
5.5 7
 
0.1%
5.4 4
 
< 0.1%
5.0 146
1.5%
4.9 5
 
0.1%

재식열간거리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.512105
Minimum0
Maximum8
Zeros8591
Zeros (%)85.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T20:21:04.212204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3313737
Coefficient of variation (CV)2.5998061
Kurtosis5.1633368
Mean0.512105
Median Absolute Deviation (MAD)0
Skewness2.5056033
Sum5121.05
Variance1.772556
MonotonicityNot monotonic
2024-04-21T20:21:04.658646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.0 8591
85.9%
4.0 265
 
2.6%
3.0 250
 
2.5%
5.0 164
 
1.6%
2.0 80
 
0.8%
6.0 74
 
0.7%
2.5 67
 
0.7%
3.5 61
 
0.6%
2.7 42
 
0.4%
4.5 42
 
0.4%
Other values (39) 364
 
3.6%
ValueCountFrequency (%)
0.0 8591
85.9%
1.0 1
 
< 0.1%
1.45 1
 
< 0.1%
1.5 7
 
0.1%
1.6 2
 
< 0.1%
1.7 1
 
< 0.1%
1.8 5
 
0.1%
1.9 3
 
< 0.1%
2.0 80
 
0.8%
2.1 7
 
0.1%
ValueCountFrequency (%)
8.0 1
 
< 0.1%
7.0 9
 
0.1%
6.3 3
 
< 0.1%
6.0 74
0.7%
5.9 1
 
< 0.1%
5.7 2
 
< 0.1%
5.6 1
 
< 0.1%
5.5 12
 
0.1%
5.4 1
 
< 0.1%
5.2 3
 
< 0.1%

재식면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct214
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.833122
Minimum0
Maximum49
Zeros8591
Zeros (%)85.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T20:21:05.083809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile12.96
Maximum49
Range49
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.3490472
Coefficient of variation (CV)2.9179985
Kurtosis14.249534
Mean1.833122
Median Absolute Deviation (MAD)0
Skewness3.5564742
Sum18331.22
Variance28.612306
MonotonicityNot monotonic
2024-04-21T20:21:05.513763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 8591
85.9%
9.0 147
 
1.5%
12.0 124
 
1.2%
25.0 78
 
0.8%
16.0 75
 
0.8%
20.0 71
 
0.7%
6.0 57
 
0.6%
30.0 34
 
0.3%
8.0 33
 
0.3%
7.5 29
 
0.3%
Other values (204) 761
 
7.6%
ValueCountFrequency (%)
0.0 8591
85.9%
0.36 1
 
< 0.1%
0.48 1
 
< 0.1%
0.6 1
 
< 0.1%
0.8 1
 
< 0.1%
0.88 2
 
< 0.1%
1.0 1
 
< 0.1%
1.8 1
 
< 0.1%
2.2 3
 
< 0.1%
2.25 2
 
< 0.1%
ValueCountFrequency (%)
49.0 2
 
< 0.1%
48.0 1
 
< 0.1%
42.0 3
 
< 0.1%
39.69 2
 
< 0.1%
39.6 1
 
< 0.1%
36.0 19
0.2%
35.0 2
 
< 0.1%
34.81 1
 
< 0.1%
33.0 1
 
< 0.1%
30.8 1
 
< 0.1%

과중
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1636
Distinct (%)16.4%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean228.38739
Minimum0
Maximum601.4325
Zeros3015
Zeros (%)30.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T20:21:05.912349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median300
Q3300
95-th percentile576.98962
Maximum601.4325
Range601.4325
Interquartile range (IQR)300

Descriptive statistics

Standard deviation171.57602
Coefficient of variation (CV)0.75125
Kurtosis-0.57020093
Mean228.38739
Median Absolute Deviation (MAD)12.5292
Skewness0.056580805
Sum2282503.5
Variance29438.332
MonotonicityNot monotonic
2024-04-21T20:21:06.340509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300.0 3748
37.5%
0.0 3015
30.1%
580.0 451
 
4.5%
270.0 326
 
3.3%
220.0 140
 
1.4%
154.0 53
 
0.5%
158.0 52
 
0.5%
180.0 35
 
0.4%
280.0 28
 
0.3%
370.0 25
 
0.2%
Other values (1626) 2121
21.2%
ValueCountFrequency (%)
0.0 3015
30.1%
40.0 2
 
< 0.1%
59.0 2
 
< 0.1%
75.0 3
 
< 0.1%
80.0 9
 
0.1%
154.0 53
 
0.5%
158.0 52
 
0.5%
159.3422 1
 
< 0.1%
160.0 21
 
0.2%
160.1952 1
 
< 0.1%
ValueCountFrequency (%)
601.4325 2
 
< 0.1%
598.4211 1
 
< 0.1%
586.3637 1
 
< 0.1%
583.9858 1
 
< 0.1%
581.65 3
 
< 0.1%
580.0 451
4.5%
579.9317 1
 
< 0.1%
579.4075 1
 
< 0.1%
579.2661 1
 
< 0.1%
579.2259 1
 
< 0.1%

Interactions

2024-04-21T20:20:53.066720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:26.093809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:28.395682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:30.336122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:32.388479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:34.678497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:38.207076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:41.445012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:44.652929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:46.766793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:48.961491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:51.070458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:53.248450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:26.272253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:28.566342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:30.516286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:32.579590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:34.964628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:38.485951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:41.722285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:44.931629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:46.943290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:49.149920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:51.243491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:53.402766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:26.431504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:28.714151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:30.674163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:32.740978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:35.229130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:38.741327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:41.972372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:45.097159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:47.092370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:49.311232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:51.396632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:53.569715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:26.603821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:28.875449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:30.840029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:32.918856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:35.502801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:39.009860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:42.241045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:45.266088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:47.258502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:49.488781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:51.560194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:53.753799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:26.791952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:29.051846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:31.021742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:33.105076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:35.788558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:39.294215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:42.519445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:45.443725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:47.433016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:49.676687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:51.740146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:53.935704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:26.977537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:29.226693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:31.202690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:33.293348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:36.073996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:39.579007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:42.800878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:45.624693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:47.613225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:49.866278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:51.917303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:54.101017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:27.153287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:29.386046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:31.376371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:33.467896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:36.346689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:39.843847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:43.065964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:45.787889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:47.778045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:50.038708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:52.083430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:54.264721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:27.320028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:29.538827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:31.539125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:33.639673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:36.832225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:40.108303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:43.325096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:45.948345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:48.144504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:50.208023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:52.249920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:54.426098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:27.490036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:29.699306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:31.706382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:33.812386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:37.099582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:40.370114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:43.581655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:46.104054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:48.302168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:50.376811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:52.412281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:54.589605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:27.658197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:29.850757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:31.869344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:33.981245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:37.368613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:40.634003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:43.845917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:46.265714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:48.455429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:50.543756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:52.566721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:54.768428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:28.053652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:30.023938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:32.054241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:34.169402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:37.658711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:40.913752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:44.126604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:46.442066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:48.631690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:50.728892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:52.744501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:54.931522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:28.221567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:30.174936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:32.217621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:34.400411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:37.930205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:41.178372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:44.388600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:46.603819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:48.790772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:50.896739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:20:52.899602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T20:21:06.626035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품목명수령주수재배방법구분코드표준수확량평년수확량가입수확량가입가격평년과실수가입과실수재식주간거리재식열간거리재식면적과중
품목명1.0000.6060.2760.7990.2520.1980.1960.8970.1610.1600.7940.8530.8290.899
수령0.6061.0000.0360.3330.2680.1670.1670.2820.1490.1480.2110.2650.2640.377
주수0.2760.0361.0000.0830.5810.7680.7690.0820.8470.8470.1420.1340.0580.053
재배방법구분코드0.7990.3330.0831.0000.1970.1230.1210.4640.0920.0910.5070.3710.3810.698
표준수확량0.2520.2680.5810.1971.0000.7430.7440.0910.6640.6650.0000.0000.0280.181
평년수확량0.1980.1670.7680.1230.7431.0001.0000.1000.9050.9020.0000.0000.0000.244
가입수확량0.1960.1670.7690.1210.7441.0001.0000.1010.9030.9050.0000.0000.0000.243
가입가격0.8970.2820.0820.4640.0910.1000.1011.0000.1120.1130.6240.4780.4340.669
평년과실수0.1610.1490.8470.0920.6640.9050.9030.1121.0001.0000.0560.0340.0270.371
가입과실수0.1600.1480.8470.0910.6650.9020.9050.1131.0001.0000.0540.0320.0250.371
재식주간거리0.7940.2110.1420.5070.0000.0000.0000.6240.0560.0541.0000.7770.8700.580
재식열간거리0.8530.2650.1340.3710.0000.0000.0000.4780.0340.0320.7771.0000.9480.442
재식면적0.8290.2640.0580.3810.0280.0000.0000.4340.0270.0250.8700.9481.0000.424
과중0.8990.3770.0530.6980.1810.2440.2430.6690.3710.3710.5800.4420.4241.000
2024-04-21T20:21:06.957884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
재배방법구분코드품목명
재배방법구분코드1.0000.581
품목명0.5811.000
2024-04-21T20:21:07.217082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수령주수표준수확량평년수확량가입수확량가입가격평년과실수가입과실수재식주간거리재식열간거리재식면적과중품목명재배방법구분코드
수령1.0000.1510.3710.3570.361-0.3620.1560.1590.1700.1710.172-0.0260.2350.205
주수0.1511.0000.8170.8050.812-0.1330.4680.4720.0990.1010.0990.0960.1130.053
표준수확량0.3710.8171.0000.9680.975-0.1910.6120.617-0.030-0.029-0.0300.1720.0820.119
평년수확량0.3570.8050.9681.0000.993-0.1780.6360.632-0.042-0.041-0.0410.1890.0790.079
가입수확량0.3610.8120.9750.9931.000-0.1780.6280.634-0.040-0.039-0.0390.1850.0790.077
가입가격-0.362-0.133-0.191-0.178-0.1781.000-0.325-0.3270.0230.0220.021-0.3260.6400.314
평년과실수0.1560.4680.6120.6360.628-0.3251.0000.996-0.484-0.483-0.4830.6470.0640.059
가입과실수0.1590.4720.6170.6320.634-0.3270.9961.000-0.485-0.484-0.4840.6480.0640.058
재식주간거리0.1700.099-0.030-0.042-0.0400.023-0.484-0.4851.0000.9960.998-0.5080.4750.246
재식열간거리0.1710.101-0.029-0.041-0.0390.022-0.483-0.4840.9961.0000.999-0.5070.4550.246
재식면적0.1720.099-0.030-0.041-0.0390.021-0.483-0.4840.9980.9991.000-0.5070.4210.237
과중-0.0260.0960.1720.1890.185-0.3260.6470.648-0.508-0.507-0.5071.0000.6390.531
품목명0.2350.1130.0820.0790.0790.6400.0640.0640.4750.4550.4210.6391.0000.581
재배방법구분코드0.2050.0530.1190.0790.0770.3140.0590.0580.2460.2460.2370.5310.5811.000

Missing values

2024-04-21T20:20:55.169680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T20:20:55.750553image/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

기준년도품목명품종명수령주수재배방법구분코드표준수확량평년수확량가입수확량가입가격평년과실수가입과실수재식주간거리재식열간거리재식면적과중
633162022복숭아천중도백도1215012751044104429790.000.00.00.00.0
216132022(적과전종합Ⅱ)사과부사(후지)294623452762612626126186887438.0874380.00.00.0298.7955
509922022(적과전종합Ⅱ)사과부사(후지)12100254002331233118687770.077700.00.00.0300.0
179632022(적과전종합Ⅱ)사과감홍52881403233363336280110610.0106100.00.00.0314.431
823242022(적과전종합Ⅱ)사과아오리(쓰가루)9822962802802114938.09380.00.00.0298.3607
251382022(종합)감귤궁천2760<NA>00000.003.04.012.00.0
610282022유마(아리마)40350<NA>24872487248715470.000.00.00.00.0
319742022복숭아중생기타황도계8100065005850585024500.000.00.00.00.0
622472022(종합)감귤궁천21240<NA>12802122601226010450.003.63.612.960.0
348702022복숭아유명백도2521012811515151529790.000.00.00.00.0
기준년도품목명품종명수령주수재배방법구분코드표준수확량평년수확량가입수확량가입가격평년과실수가입과실수재식주간거리재식열간거리재식면적과중
144992022(적과전종합Ⅱ)사과홍로2363232132584258422108815.088150.00.00.0293.125
324762022매실남고1655020242221222120280.004.03.012.00.0
31292022(적과전종합Ⅱ)사과홍로61701323038933893221012977.0129770.00.00.0300.0
553562022(적과전종합Ⅱ)떫은감고종시(산청)2142<NA>453628832860106616233.0161040.00.00.0177.6
494062022(적과전종합Ⅱ)사과홍로735177058658622101665.016650.00.00.0351.9893
113712022(적과전종합Ⅱ)배신고1817217852452245213364228.042280.00.00.0580.0
752242022(적과전종합Ⅱ)사과부사(후지)1716291277077018682567.025670.00.00.0300.0
521692022(적과전종합Ⅱ)사과부사(후지)8150137502932293218689773.097730.00.00.0300.0
618682022삼조생(모리와세)17100011801231123122220.000.00.00.00.0
970902022(종합)감귤궁천45400<NA>107358856885610450.003.03.09.00.0

Duplicate rows

Most frequently occurring

기준년도품목명품종명수령주수재배방법구분코드표준수확량평년수확량가입수확량가입가격평년과실수가입과실수재식주간거리재식열간거리재식면적과중# duplicates
12022(적과전종합Ⅱ)떫은감갑주백목(봉옥/대봉시/하찌야/도호감)1550<NA>24502450245012179074.090740.00.00.0270.03
02022(적과전종합Ⅱ)떫은감갑주백목(봉옥/대봉시/하찌야/도호감)1045<NA>17551755175512176500.065000.00.00.0270.02
22022(적과전종합Ⅱ)떫은감갑주백목(봉옥/대봉시/하찌야/도호감)2030<NA>23702370237012178778.087780.00.00.0270.02
32022(적과전종합Ⅱ)떫은감갑주백목(봉옥/대봉시/하찌야/도호감)2038<NA>300230023002121711119.0111190.00.00.0270.02
42022(적과전종합Ⅱ)사과부사(후지)35200000.000.00.00.0300.02
52022(적과전종합Ⅱ)사과부사(후지)320200000.000.00.00.0300.02
62022(적과전종합Ⅱ)사과부사(후지)330200000.000.00.00.0300.02
72022(적과전종합Ⅱ)사과부사(후지)4729188881868293.02930.00.00.0300.02
82022(적과전종합Ⅱ)사과부사(후지)4400140001765400018685883.0133330.00.00.0300.02
92022(적과전종합Ⅱ)사과부사(후지)82972118801454314543186848477.0484770.00.00.0300.02