Overview

Dataset statistics

Number of variables9
Number of observations38
Missing cells30
Missing cells (%)8.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory82.5 B

Variable types

Text2
Numeric7

Dataset

Description국제식물검역인증원에서조사한아시아매미나방에발생정보에대한것으로월별지역,항구별,예찰트랩별,아시아매미나방채집수를제공한다
Author국제식물검역인증원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220714000000002159

Alerts

예찰트랩 설치수 is highly overall correlated with 7월 and 1 other fieldsHigh correlation
6월 is highly overall correlated with 7월 and 2 other fieldsHigh correlation
7월 is highly overall correlated with 예찰트랩 설치수 and 4 other fieldsHigh correlation
8월 is highly overall correlated with 6월 and 4 other fieldsHigh correlation
9월 is highly overall correlated with 8월High correlation
합계 is highly overall correlated with 예찰트랩 설치수 and 4 other fieldsHigh correlation
트랩당 채집수 is highly overall correlated with 7월 and 2 other fieldsHigh correlation
사무소 has 29 (76.3%) missing valuesMissing
항구명 has 1 (2.6%) missing valuesMissing
트랩당 채집수 has unique valuesUnique
6월 has 3 (7.9%) zerosZeros
8월 has 8 (21.1%) zerosZeros
9월 has 31 (81.6%) zerosZeros

Reproduction

Analysis started2023-12-11 03:26:57.874918
Analysis finished2023-12-11 03:27:04.049703
Duration6.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사무소
Text

MISSING 

Distinct9
Distinct (%)100.0%
Missing29
Missing (%)76.3%
Memory size436.0 B
2023-12-11T12:27:04.172822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.6666667
Min length2

Characters and Unicode

Total characters42
Distinct characters19
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

Unique9 ?
Unique (%)100.0%

Sample

1st row부산사무소
2nd row광양사무소
3rd row군산사무소
4th row평택사무소
5th row인천사무소
ValueCountFrequency (%)
부산사무소 1
11.1%
광양사무소 1
11.1%
군산사무소 1
11.1%
평택사무소 1
11.1%
인천사무소 1
11.1%
동해사무소 1
11.1%
포항사무소 1
11.1%
울산사무소 1
11.1%
합계 1
11.1%
2023-12-11T12:27:04.522834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 8
19.0%
무 8
19.0%
소 8
19.0%
산 3
 
7.1%
부 1
 
2.4%
동 1
 
2.4%
합 1
 
2.4%
울 1
 
2.4%
항 1
 
2.4%
포 1
 
2.4%
Other values (9) 9
21.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 8
19.0%
무 8
19.0%
소 8
19.0%
산 3
 
7.1%
부 1
 
2.4%
동 1
 
2.4%
합 1
 
2.4%
울 1
 
2.4%
항 1
 
2.4%
포 1
 
2.4%
Other values (9) 9
21.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 8
19.0%
무 8
19.0%
소 8
19.0%
산 3
 
7.1%
부 1
 
2.4%
동 1
 
2.4%
합 1
 
2.4%
울 1
 
2.4%
항 1
 
2.4%
포 1
 
2.4%
Other values (9) 9
21.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 8
19.0%
무 8
19.0%
소 8
19.0%
산 3
 
7.1%
부 1
 
2.4%
동 1
 
2.4%
합 1
 
2.4%
울 1
 
2.4%
항 1
 
2.4%
포 1
 
2.4%
Other values (9) 9
21.4%

항구명
Text

MISSING 

Distinct30
Distinct (%)81.1%
Missing1
Missing (%)2.6%
Memory size436.0 B
2023-12-11T12:27:04.683162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.8108108
Min length1

Characters and Unicode

Total characters104
Distinct characters43
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

Unique29 ?
Unique (%)78.4%

Sample

1st row부산항
2nd row부산신항
3rd row마산항
4th row진해항
5th row통영항
ValueCountFrequency (%)
계 8
 
21.6%
보령화력 1
 
2.7%
온산항 1
 
2.7%
울산항 1
 
2.7%
영일만항 1
 
2.7%
포항신항 1
 
2.7%
호산항 1
 
2.7%
옥계항 1
 
2.7%
동해항 1
 
2.7%
영흥화력 1
 
2.7%
Other values (20) 20
54.1%
2023-12-11T12:27:04.940685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
항 26
25.0%
계 9
 
8.7%
산 8
 
7.7%
포 5
 
4.8%
화 4
 
3.8%
력 4
 
3.8%
진 3
 
2.9%
영 3
 
2.9%
옥 2
 
1.9%
당 2
 
1.9%
Other values (33) 38
36.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 104
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
항 26
25.0%
계 9
 
8.7%
산 8
 
7.7%
포 5
 
4.8%
화 4
 
3.8%
력 4
 
3.8%
진 3
 
2.9%
영 3
 
2.9%
옥 2
 
1.9%
당 2
 
1.9%
Other values (33) 38
36.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 104
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
항 26
25.0%
계 9
 
8.7%
산 8
 
7.7%
포 5
 
4.8%
화 4
 
3.8%
력 4
 
3.8%
진 3
 
2.9%
영 3
 
2.9%
옥 2
 
1.9%
당 2
 
1.9%
Other values (33) 38
36.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 104
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
항 26
25.0%
계 9
 
8.7%
산 8
 
7.7%
포 5
 
4.8%
화 4
 
3.8%
력 4
 
3.8%
진 3
 
2.9%
영 3
 
2.9%
옥 2
 
1.9%
당 2
 
1.9%
Other values (33) 38
36.5%

예찰트랩 설치수
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)63.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.815789
Minimum5
Maximum555
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-11T12:27:05.045394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q110
median20
Q340.75
95-th percentile103.4
Maximum555
Range550
Interquartile range (IQR)30.75

Descriptive statistics

Standard deviation89.925151
Coefficient of variation (CV)2.0523458
Kurtosis30.014508
Mean43.815789
Median Absolute Deviation (MAD)13
Skewness5.2663579
Sum1665
Variance8086.5327
MonotonicityNot monotonic
2023-12-11T12:27:05.152809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
20 6
15.8%
7 5
 
13.2%
6 3
 
7.9%
10 2
 
5.3%
35 2
 
5.3%
23 2
 
5.3%
61 1
 
2.6%
11 1
 
2.6%
555 1
 
2.6%
60 1
 
2.6%
Other values (14) 14
36.8%
ValueCountFrequency (%)
5 1
 
2.6%
6 3
7.9%
7 5
13.2%
10 2
 
5.3%
11 1
 
2.6%
15 1
 
2.6%
18 1
 
2.6%
20 6
15.8%
23 2
 
5.3%
28 1
 
2.6%
ValueCountFrequency (%)
555 1
2.6%
151 1
2.6%
95 1
2.6%
70 1
2.6%
61 1
2.6%
60 1
2.6%
52 1
2.6%
47 1
2.6%
45 1
2.6%
41 1
2.6%

6월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.447368
Minimum0
Maximum677
Zeros3
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-11T12:27:05.268455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median14
Q332.5
95-th percentile217.55
Maximum677
Range677
Interquartile range (IQR)28.5

Descriptive statistics

Standard deviation122.15048
Coefficient of variation (CV)2.2854349
Kurtosis19.127984
Mean53.447368
Median Absolute Deviation (MAD)12
Skewness4.1186293
Sum2031
Variance14920.74
MonotonicityNot monotonic
2023-12-11T12:27:05.378453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 3
 
7.9%
4 3
 
7.9%
12 3
 
7.9%
0 3
 
7.9%
5 2
 
5.3%
2 2
 
5.3%
55 2
 
5.3%
8 2
 
5.3%
19 2
 
5.3%
200 1
 
2.6%
Other values (15) 15
39.5%
ValueCountFrequency (%)
0 3
7.9%
1 3
7.9%
2 2
5.3%
4 3
7.9%
5 2
5.3%
7 1
 
2.6%
8 2
5.3%
12 3
7.9%
16 1
 
2.6%
17 1
 
2.6%
ValueCountFrequency (%)
677 1
2.6%
317 1
2.6%
200 1
2.6%
171 1
2.6%
107 1
2.6%
74 1
2.6%
57 1
2.6%
55 2
5.3%
33 1
2.6%
31 1
2.6%

7월
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean553.26316
Minimum20
Maximum7008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-11T12:27:05.491363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile27.55
Q1103
median187.5
Q3448.25
95-th percentile1558.8
Maximum7008
Range6988
Interquartile range (IQR)345.25

Descriptive statistics

Standard deviation1172.3615
Coefficient of variation (CV)2.1189943
Kurtosis26.095249
Mean553.26316
Median Absolute Deviation (MAD)137
Skewness4.8219726
Sum21024
Variance1374431.4
MonotonicityNot monotonic
2023-12-11T12:27:06.027097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
103 2
 
5.3%
88 2
 
5.3%
814 1
 
2.6%
323 1
 
2.6%
1303 1
 
2.6%
1190 1
 
2.6%
265 1
 
2.6%
1455 1
 
2.6%
354 1
 
2.6%
116 1
 
2.6%
Other values (26) 26
68.4%
ValueCountFrequency (%)
20 1
2.6%
25 1
2.6%
28 1
2.6%
32 1
2.6%
42 1
2.6%
49 1
2.6%
56 1
2.6%
88 2
5.3%
103 2
5.3%
116 1
2.6%
ValueCountFrequency (%)
7008 1
2.6%
2147 1
2.6%
1455 1
2.6%
1303 1
2.6%
1190 1
2.6%
814 1
2.6%
793 1
2.6%
749 1
2.6%
671 1
2.6%
466 1
2.6%

8월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)57.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.973684
Minimum0
Maximum709
Zeros8
Zeros (%)21.1%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-11T12:27:06.154537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3.5
Q333.25
95-th percentile280.25
Maximum709
Range709
Interquartile range (IQR)32.25

Descriptive statistics

Standard deviation132.89835
Coefficient of variation (CV)2.3743006
Kurtosis16.061947
Mean55.973684
Median Absolute Deviation (MAD)3.5
Skewness3.7395172
Sum2127
Variance17661.972
MonotonicityNot monotonic
2023-12-11T12:27:06.329245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 8
21.1%
1 5
13.2%
3 4
 
10.5%
2 2
 
5.3%
4 2
 
5.3%
28 1
 
2.6%
310 1
 
2.6%
709 1
 
2.6%
10 1
 
2.6%
6 1
 
2.6%
Other values (12) 12
31.6%
ValueCountFrequency (%)
0 8
21.1%
1 5
13.2%
2 2
 
5.3%
3 4
10.5%
4 2
 
5.3%
6 1
 
2.6%
9 1
 
2.6%
10 1
 
2.6%
11 1
 
2.6%
23 1
 
2.6%
ValueCountFrequency (%)
709 1
2.6%
310 1
2.6%
275 1
2.6%
236 1
2.6%
120 1
2.6%
100 1
2.6%
93 1
2.6%
58 1
2.6%
49 1
2.6%
35 1
2.6%

9월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1315789
Minimum0
Maximum27
Zeros31
Zeros (%)81.6%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-11T12:27:06.503881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile16.65
Maximum27
Range27
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.4101737
Coefficient of variation (CV)3.007242
Kurtosis10.586027
Mean2.1315789
Median Absolute Deviation (MAD)0
Skewness3.3453579
Sum81
Variance41.090327
MonotonicityNot monotonic
2023-12-11T12:27:06.639240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 31
81.6%
1 2
 
5.3%
6 1
 
2.6%
5 1
 
2.6%
15 1
 
2.6%
26 1
 
2.6%
27 1
 
2.6%
ValueCountFrequency (%)
0 31
81.6%
1 2
 
5.3%
5 1
 
2.6%
6 1
 
2.6%
15 1
 
2.6%
26 1
 
2.6%
27 1
 
2.6%
ValueCountFrequency (%)
27 1
 
2.6%
26 1
 
2.6%
15 1
 
2.6%
6 1
 
2.6%
5 1
 
2.6%
1 2
 
5.3%
0 31
81.6%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean664.81579
Minimum21
Maximum8421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-11T12:27:06.803465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile30.1
Q1113
median233.5
Q3620.25
95-th percentile1895.05
Maximum8421
Range8400
Interquartile range (IQR)507.25

Descriptive statistics

Standard deviation1403.2333
Coefficient of variation (CV)2.11071
Kurtosis26.542467
Mean664.81579
Median Absolute Deviation (MAD)176
Skewness4.8684681
Sum25263
Variance1969063.7
MonotonicityNot monotonic
2023-12-11T12:27:06.965990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
161 2
 
5.3%
51 2
 
5.3%
113 2
 
5.3%
897 1
 
2.6%
253 1
 
2.6%
1477 1
 
2.6%
1234 1
 
2.6%
552 1
 
2.6%
1786 1
 
2.6%
470 1
 
2.6%
Other values (25) 25
65.8%
ValueCountFrequency (%)
21 1
2.6%
25 1
2.6%
31 1
2.6%
51 2
5.3%
53 1
2.6%
62 1
2.6%
92 1
2.6%
105 1
2.6%
113 2
5.3%
140 1
2.6%
ValueCountFrequency (%)
8421 1
2.6%
2513 1
2.6%
1786 1
2.6%
1477 1
2.6%
1234 1
2.6%
1088 1
2.6%
897 1
2.6%
881 1
2.6%
838 1
2.6%
643 1
2.6%

트랩당 채집수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.90497
Minimum0.7
Maximum77.666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-11T12:27:07.158724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile2.4
Q15.6875
median13.702778
Q323.412234
95-th percentile50.475974
Maximum77.666667
Range76.966667
Interquartile range (IQR)17.724734

Descriptive statistics

Standard deviation16.618001
Coefficient of variation (CV)0.92812226
Kurtosis3.4884346
Mean17.90497
Median Absolute Deviation (MAD)9.0742063
Skewness1.706542
Sum680.38886
Variance276.15797
MonotonicityNot monotonic
2023-12-11T12:27:07.361754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
14.704918032786885 1
 
2.6%
52.142857142857146 1
 
2.6%
7.285714285714286 1
 
2.6%
21.1 1
 
2.6%
30.097560975609756 1
 
2.6%
50.18181818181818 1
 
2.6%
34.34615384615385 1
 
2.6%
23.5 1
 
2.6%
12.65 1
 
2.6%
23.148936170212767 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
0.7 1
2.6%
1.55 1
2.6%
2.55 1
2.6%
3.2285714285714286 1
2.6%
3.577777777777778 1
2.6%
4.133333333333334 1
2.6%
4.231578947368421 1
2.6%
4.257142857142857 1
2.6%
5.0 1
2.6%
5.25 1
2.6%
ValueCountFrequency (%)
77.66666666666667 1
2.6%
52.142857142857146 1
2.6%
50.18181818181818 1
2.6%
38.57142857142857 1
2.6%
36.43478260869565 1
2.6%
34.34615384615385 1
2.6%
31.0 1
2.6%
30.097560975609756 1
2.6%
29.142857142857142 1
2.6%
23.5 1
2.6%

Interactions

2023-12-11T12:27:02.902322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:26:58.213827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:26:59.027539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:00.062087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:00.833436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:01.490597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:02.182879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:02.999933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:26:58.342088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:26:59.124831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:00.186125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:00.931006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:01.587685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:02.280535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:03.111787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:26:58.448953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:26:59.518179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:00.281747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:01.038087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:01.691094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:02.385840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:03.228491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:26:58.555307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:26:59.604323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:00.403421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:01.132294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:01.777074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:02.496031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:03.336402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:26:58.641346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:26:59.704818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:00.532906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:01.224247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:01.872208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:02.619425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:03.432040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:26:58.777887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:26:59.826928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:00.637897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:01.312784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:01.975842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:02.704625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:03.536028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:26:58.922209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:26:59.936801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:00.721208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:01.392948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:02.072238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:02.800684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:27:07.492980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사무소항구명예찰트랩 설치수6월7월8월9월합계트랩당 채집수
사무소1.0001.0001.0001.0001.0001.0001.0001.0001.000
항구명1.0001.0000.0000.0000.0000.0000.0000.0000.876
예찰트랩\n설치수1.0000.0001.0000.8550.8470.5990.3180.9710.000
6월1.0000.0000.8551.0000.9480.8240.3840.7520.380
7월1.0000.0000.8470.9481.0000.9500.5270.9120.226
8월1.0000.0000.5990.8240.9501.0000.8720.7440.380
9월1.0000.0000.3180.3840.5270.8721.0000.3840.314
합계1.0000.0000.9710.7520.9120.7440.3841.0000.000
트랩당 채집수1.0000.8760.0000.3800.2260.3800.3140.0001.000
2023-12-11T12:27:07.621626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
예찰트랩 설치수6월7월8월9월합계트랩당 채집수
예찰트랩\n설치수1.0000.4730.5870.3770.2230.587-0.156
6월0.4731.0000.7100.5100.1380.7410.424
7월0.5870.7101.0000.7160.4140.9830.671
8월0.3770.5100.7161.0000.5880.7800.633
9월0.2230.1380.4140.5881.0000.4730.372
합계0.5870.7410.9830.7800.4731.0000.676
트랩당 채집수-0.1560.4240.6710.6330.3720.6761.000

Missing values

2023-12-11T12:27:03.721822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:27:03.879606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-11T12:27:03.991453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

사무소항구명예찰트랩 설치수6월7월8월9월합계트랩당 채집수
0부산사무소부산항615581428089714.704918
1<NA>부산신항40552151102817.025
2<NA>마산항18571711022912.722222
3<NA>진해항7121893020429.142857
4<NA>통영항7192483027038.571429
5<NA>고현항61073581046677.666667
6<NA>옥포항681032011318.833333
7<NA>장승포항644900538.833333
8<NA>계1513172147490251316.642384
9광양사무소광양항3012000210.7
사무소항구명예찰트랩 설치수6월7월8월9월합계트랩당 채집수
28<NA>옥계항2012116120525312.65
29<NA>호산항74323231536552.142857
30<NA>계473379323626108823.148936
31포항사무소포항신항20193200512.55
32<NA>영일만항1555610624.133333
33<NA>계352488101133.228571
34울산사무소울산항2829205402388.5
35<NA>온산항321714666064320.09375
36<NA>계6020067110088114.683333
37합계<NA>555677700870927842115.172973