Overview

Dataset statistics

Number of variables10
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory94.7 B

Variable types

Text1
Numeric7
Categorical2

Dataset

Description경상북도 해양수산분야에 대한 데이터로 각 시군별(도내 23개 시,군) 등록된 어선등록현황 및 통계자료를 제공합니다.
Author경상북도
URLhttps://www.data.go.kr/data/15044799/fileData.do

Alerts

100톤이상 is highly overall correlated with 동력 and 7 other fieldsHigh correlation
30-50톤 is highly overall correlated with 동력 and 7 other fieldsHigh correlation
동력 is highly overall correlated with 무동력 and 7 other fieldsHigh correlation
무동력 is highly overall correlated with 동력 and 7 other fieldsHigh correlation
1톤미만 is highly overall correlated with 동력 and 7 other fieldsHigh correlation
1-10톤 is highly overall correlated with 동력 and 7 other fieldsHigh correlation
10-20톤 is highly overall correlated with 동력 and 7 other fieldsHigh correlation
20-30톤 is highly overall correlated with 동력 and 7 other fieldsHigh correlation
50-100톤 is highly overall correlated with 동력 and 7 other fieldsHigh correlation
30-50톤 is highly imbalanced (56.3%)Imbalance
100톤이상 is highly imbalanced (67.6%)Imbalance
지역 has unique valuesUnique
동력 has 8 (34.8%) zerosZeros
무동력 has 16 (69.6%) zerosZeros
1톤미만 has 7 (30.4%) zerosZeros
1-10톤 has 15 (65.2%) zerosZeros
10-20톤 has 18 (78.3%) zerosZeros
20-30톤 has 18 (78.3%) zerosZeros
50-100톤 has 18 (78.3%) zerosZeros

Reproduction

Analysis started2023-12-12 18:26:07.941649
Analysis finished2023-12-12 18:26:14.422681
Duration6.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-13T03:26:14.562918image/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 row영천시
4th row김천시
5th row안동시
ValueCountFrequency (%)
포항시 1
 
4.3%
청송군 1
 
4.3%
울진군 1
 
4.3%
고령군 1
 
4.3%
영양군 1
 
4.3%
봉화군 1
 
4.3%
예천군 1
 
4.3%
칠곡군 1
 
4.3%
성주군 1
 
4.3%
청도군 1
 
4.3%
Other values (13) 13
56.5%
2023-12-13T03:26:14.927154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 14
20.3%
시 10
14.5%
주 4
 
5.8%
영 4
 
5.8%
경 3
 
4.3%
천 3
 
4.3%
청 2
 
2.9%
울 2
 
2.9%
성 2
 
2.9%
포 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 (%)
군 14
20.3%
시 10
14.5%
주 4
 
5.8%
영 4
 
5.8%
경 3
 
4.3%
천 3
 
4.3%
청 2
 
2.9%
울 2
 
2.9%
성 2
 
2.9%
포 1
 
1.4%
Other values (24) 24
34.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 69
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 14
20.3%
시 10
14.5%
주 4
 
5.8%
영 4
 
5.8%
경 3
 
4.3%
천 3
 
4.3%
청 2
 
2.9%
울 2
 
2.9%
성 2
 
2.9%
포 1
 
1.4%
Other values (24) 24
34.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 69
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 14
20.3%
시 10
14.5%
주 4
 
5.8%
영 4
 
5.8%
경 3
 
4.3%
천 3
 
4.3%
청 2
 
2.9%
울 2
 
2.9%
성 2
 
2.9%
포 1
 
1.4%
Other values (24) 24
34.8%

동력
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.86957
Minimum0
Maximum1282
Zeros8
Zeros (%)34.8%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T03:26:15.073859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9
Q347.5
95-th percentile580.5
Maximum1282
Range1282
Interquartile range (IQR)47.5

Descriptive statistics

Standard deviation306.8992
Coefficient of variation (CV)2.2260112
Kurtosis8.6486437
Mean137.86957
Median Absolute Deviation (MAD)9
Skewness2.8336115
Sum3171
Variance94187.119
MonotonicityNot monotonic
2023-12-13T03:26:15.191799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 8
34.8%
9 3
 
13.0%
1282 1
 
4.3%
415 1
 
4.3%
81 1
 
4.3%
12 1
 
4.3%
13 1
 
4.3%
8 1
 
4.3%
582 1
 
4.3%
5 1
 
4.3%
Other values (4) 4
17.4%
ValueCountFrequency (%)
0 8
34.8%
4 1
 
4.3%
5 1
 
4.3%
8 1
 
4.3%
9 3
 
13.0%
12 1
 
4.3%
13 1
 
4.3%
14 1
 
4.3%
81 1
 
4.3%
161 1
 
4.3%
ValueCountFrequency (%)
1282 1
 
4.3%
582 1
 
4.3%
567 1
 
4.3%
415 1
 
4.3%
161 1
 
4.3%
81 1
 
4.3%
14 1
 
4.3%
13 1
 
4.3%
12 1
 
4.3%
9 3
13.0%

무동력
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9565217
Minimum0
Maximum37
Zeros16
Zeros (%)69.6%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T03:26:15.332531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile32.9
Maximum37
Range37
Interquartile range (IQR)1

Descriptive statistics

Standard deviation11.01849
Coefficient of variation (CV)2.2230286
Kurtosis4.1804572
Mean4.9565217
Median Absolute Deviation (MAD)0
Skewness2.2907179
Sum114
Variance121.40711
MonotonicityNot monotonic
2023-12-13T03:26:15.487139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 16
69.6%
1 2
 
8.7%
37 1
 
4.3%
6 1
 
4.3%
34 1
 
4.3%
12 1
 
4.3%
23 1
 
4.3%
ValueCountFrequency (%)
0 16
69.6%
1 2
 
8.7%
6 1
 
4.3%
12 1
 
4.3%
23 1
 
4.3%
34 1
 
4.3%
37 1
 
4.3%
ValueCountFrequency (%)
37 1
 
4.3%
34 1
 
4.3%
23 1
 
4.3%
12 1
 
4.3%
6 1
 
4.3%
1 2
 
8.7%
0 16
69.6%

1톤미만
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.869565
Minimum0
Maximum132
Zeros7
Zeros (%)30.4%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T03:26:15.660018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9
Q313
95-th percentile109.3
Maximum132
Range132
Interquartile range (IQR)13

Descriptive statistics

Standard deviation38.563176
Coefficient of variation (CV)1.6155793
Kurtosis2.4399546
Mean23.869565
Median Absolute Deviation (MAD)9
Skewness1.8612532
Sum549
Variance1487.1186
MonotonicityNot monotonic
2023-12-13T03:26:15.816087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 7
30.4%
9 3
13.0%
12 2
 
8.7%
13 2
 
8.7%
113 1
 
4.3%
57 1
 
4.3%
76 1
 
4.3%
8 1
 
4.3%
132 1
 
4.3%
5 1
 
4.3%
Other values (3) 3
13.0%
ValueCountFrequency (%)
0 7
30.4%
2 1
 
4.3%
4 1
 
4.3%
5 1
 
4.3%
8 1
 
4.3%
9 3
13.0%
12 2
 
8.7%
13 2
 
8.7%
57 1
 
4.3%
75 1
 
4.3%
ValueCountFrequency (%)
132 1
 
4.3%
113 1
 
4.3%
76 1
 
4.3%
75 1
 
4.3%
57 1
 
4.3%
13 2
8.7%
12 2
8.7%
9 3
13.0%
8 1
 
4.3%
5 1
 
4.3%

1-10톤
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.913043
Minimum0
Maximum1038
Zeros15
Zeros (%)65.2%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T03:26:15.957186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile411.8
Maximum1038
Range1038
Interquartile range (IQR)3

Descriptive statistics

Standard deviation242.012
Coefficient of variation (CV)2.4222263
Kurtosis10.407076
Mean99.913043
Median Absolute Deviation (MAD)0
Skewness3.0714606
Sum2298
Variance58569.81
MonotonicityNot monotonic
2023-12-13T03:26:16.107083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 15
65.2%
1 2
 
8.7%
1038 1
 
4.3%
286 1
 
4.3%
5 1
 
4.3%
412 1
 
4.3%
410 1
 
4.3%
145 1
 
4.3%
ValueCountFrequency (%)
0 15
65.2%
1 2
 
8.7%
5 1
 
4.3%
145 1
 
4.3%
286 1
 
4.3%
410 1
 
4.3%
412 1
 
4.3%
1038 1
 
4.3%
ValueCountFrequency (%)
1038 1
 
4.3%
412 1
 
4.3%
410 1
 
4.3%
286 1
 
4.3%
145 1
 
4.3%
5 1
 
4.3%
1 2
 
8.7%
0 15
65.2%

10-20톤
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9130435
Minimum0
Maximum14
Zeros18
Zeros (%)78.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T03:26:16.230230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile12.5
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.2524115
Coefficient of variation (CV)2.2228515
Kurtosis3.6198241
Mean1.9130435
Median Absolute Deviation (MAD)0
Skewness2.1751684
Sum44
Variance18.083004
MonotonicityNot monotonic
2023-12-13T03:26:16.362117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 18
78.3%
14 1
 
4.3%
2 1
 
4.3%
8 1
 
4.3%
13 1
 
4.3%
7 1
 
4.3%
ValueCountFrequency (%)
0 18
78.3%
2 1
 
4.3%
7 1
 
4.3%
8 1
 
4.3%
13 1
 
4.3%
14 1
 
4.3%
ValueCountFrequency (%)
14 1
 
4.3%
13 1
 
4.3%
8 1
 
4.3%
7 1
 
4.3%
2 1
 
4.3%
0 18
78.3%

20-30톤
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3913043
Minimum0
Maximum62
Zeros18
Zeros (%)78.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T03:26:16.539836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile58.8
Maximum62
Range62
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.177968
Coefficient of variation (CV)2.2854574
Kurtosis4.0265347
Mean8.3913043
Median Absolute Deviation (MAD)0
Skewness2.2692054
Sum193
Variance367.79447
MonotonicityNot monotonic
2023-12-13T03:26:16.673466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 18
78.3%
62 1
 
4.3%
24 1
 
4.3%
39 1
 
4.3%
61 1
 
4.3%
7 1
 
4.3%
ValueCountFrequency (%)
0 18
78.3%
7 1
 
4.3%
24 1
 
4.3%
39 1
 
4.3%
61 1
 
4.3%
62 1
 
4.3%
ValueCountFrequency (%)
62 1
 
4.3%
61 1
 
4.3%
39 1
 
4.3%
24 1
 
4.3%
7 1
 
4.3%
0 18
78.3%

30-50톤
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
0
19 
45
 
1
15
 
1
10
 
1
13
 
1

Length

Max length2
Median length1
Mean length1.173913
Min length1

Unique

Unique4 ?
Unique (%)17.4%

Sample

1st row45
2nd row15
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
82.6%
45 1
 
4.3%
15 1
 
4.3%
10 1
 
4.3%
13 1
 
4.3%

Length

2023-12-13T03:26:16.875950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:26:17.021613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 19
82.6%
45 1
 
4.3%
15 1
 
4.3%
10 1
 
4.3%
13 1
 
4.3%

50-100톤
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7391304
Minimum0
Maximum39
Zeros18
Zeros (%)78.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T03:26:17.148636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile34.2
Maximum39
Range39
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.382672
Coefficient of variation (CV)2.4018482
Kurtosis5.0271628
Mean4.7391304
Median Absolute Deviation (MAD)0
Skewness2.4375058
Sum109
Variance129.56522
MonotonicityNot monotonic
2023-12-13T03:26:17.304195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 18
78.3%
39 1
 
4.3%
36 1
 
4.3%
15 1
 
4.3%
18 1
 
4.3%
1 1
 
4.3%
ValueCountFrequency (%)
0 18
78.3%
1 1
 
4.3%
15 1
 
4.3%
18 1
 
4.3%
36 1
 
4.3%
39 1
 
4.3%
ValueCountFrequency (%)
39 1
 
4.3%
36 1
 
4.3%
18 1
 
4.3%
15 1
 
4.3%
1 1
 
4.3%
0 18
78.3%

100톤이상
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
0
21 
8
 
1
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)8.7%

Sample

1st row8
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21
91.3%
8 1
 
4.3%
1 1
 
4.3%

Length

2023-12-13T03:26:17.498772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:26:17.628469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21
91.3%
8 1
 
4.3%
1 1
 
4.3%

Interactions

2023-12-13T03:26:13.376790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:08.307020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:09.179272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:10.043231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:10.771375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:11.473268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:12.647553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:13.482080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:08.413663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:09.291537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:10.143240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:10.867253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:11.570831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:12.753401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:13.588676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:08.516184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:09.419574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:10.250801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:10.968000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:12.033784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:12.863359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:13.694937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:08.625375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:09.527984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:10.357063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:11.068476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:12.164249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:12.971588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:13.824745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:08.721048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:09.636011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:10.454890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:11.150816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:12.309421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:13.058292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:13.957079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:08.899609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:09.793363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:10.556669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:11.270472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:12.432578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:13.161323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:14.057198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:09.018127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:09.914010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:10.659195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:11.367395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:12.538252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:13.268562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:26:17.726893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역동력무동력1톤미만1-10톤10-20톤20-30톤30-50톤50-100톤100톤이상
지역1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
동력1.0001.0000.9510.9771.0000.8940.9930.9870.8101.000
무동력1.0000.9511.0000.9650.9510.8050.9510.9870.8550.804
1톤미만1.0000.9770.9651.0000.9770.8460.9770.9970.8901.000
1-10톤1.0001.0000.9510.9771.0000.8940.9930.9870.8101.000
10-20톤1.0000.8940.8050.8460.8941.0001.0000.8900.9550.779
20-30톤1.0000.9930.9510.9770.9931.0001.0000.9870.8550.804
30-50톤1.0000.9870.9870.9970.9870.8900.9871.0001.0001.000
50-100톤1.0000.8100.8550.8900.8100.9550.8551.0001.0000.631
100톤이상1.0001.0000.8041.0001.0000.7790.8041.0000.6311.000
2023-12-13T03:26:17.888829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
100톤이상30-50톤
100톤이상1.0000.949
30-50톤0.9491.000
2023-12-13T03:26:18.006490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동력무동력1톤미만1-10톤10-20톤20-30톤50-100톤30-50톤100톤이상
동력1.0000.5690.8670.8190.7350.7370.7330.8330.949
무동력0.5691.0000.5700.7830.8410.8430.8350.8330.782
1톤미만0.8670.5701.0000.6800.5370.5520.5450.9160.949
1-10톤0.8190.7830.6801.0000.8460.8480.8430.8330.949
10-20톤0.7350.8410.5370.8461.0000.9980.9890.8330.782
20-30톤0.7370.8430.5520.8480.9981.0000.9940.8330.782
50-100톤0.7330.8350.5450.8430.9890.9941.0000.9730.632
30-50톤0.8330.8330.9160.8330.8330.8330.9731.0000.949
100톤이상0.9490.7820.9490.9490.7820.7820.6320.9491.000

Missing values

2023-12-13T03:26:14.201569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:26:14.369733image/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

지역동력무동력1톤미만1-10톤10-20톤20-30톤30-50톤50-100톤100톤이상
0포항시1282371131038146245398
1경주시41565728622415361
2영천시000000000
3김천시000000000
4안동시81076500000
5구미시909000000
6영주시000000000
7상주시12012000000
8경산시000000000
9문경시13013000000
지역동력무동력1톤미만1-10톤10-20톤20-30톤30-50톤50-100톤100톤이상
13영덕군5823413241283910150
14청도군01212000000
15성주군505000000
16칠곡군14013100000
17예천군404000000
18봉화군000000000
19영양군000000000
20고령군909000000
21울진군5672375410136113180
22울릉군1611214577010