Overview

Dataset statistics

Number of variables6
Number of observations104
Missing cells144
Missing cells (%)23.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.4 KiB
Average record size in memory53.2 B

Variable types

Categorical2
Numeric4

Dataset

Description파일데이터 요청건어업별_업종별_어선세력(전북특별자치도 연근해어업 동력어선의 척수, 톤수, 마력수 )(1980년~2019년)
Author전북특별자치도
URLhttps://www.data.go.kr/data/15074823/fileData.do

Alerts

연도별 is highly overall correlated with 척수(척) and 1 other fieldsHigh correlation
척수(척) is highly overall correlated with 연도별 and 2 other fieldsHigh correlation
톤수(톤) is highly overall correlated with 연도별 and 1 other fieldsHigh correlation
마력수(마력) is highly overall correlated with 척수(척) and 2 other fieldsHigh correlation
업종별 is highly overall correlated with 척수(척) and 1 other fieldsHigh correlation
시도별 is highly overall correlated with 톤수(톤) and 1 other fieldsHigh correlation
척수(척) has 40 (38.5%) missing valuesMissing
톤수(톤) has 52 (50.0%) missing valuesMissing
마력수(마력) has 52 (50.0%) missing valuesMissing

Reproduction

Analysis started2024-03-14 09:54:34.013799
Analysis finished2024-03-14 09:54:39.352063
Duration5.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종별
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size960.0 B
근해어업
52 
연안어업
52 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
근해어업 52
50.0%
연안어업 52
50.0%

Length

2024-03-14T18:54:39.558792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:54:39.862232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
근해어업 52
50.0%
연안어업 52
50.0%

연도별
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.2692
Minimum1992
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-14T18:54:40.178828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1992
5-th percentile1993
Q12000
median2006.5
Q32013
95-th percentile2018
Maximum2019
Range27
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.9099676
Coefficient of variation (CV)0.0039426252
Kurtosis-1.0550642
Mean2006.2692
Median Absolute Deviation (MAD)6.5
Skewness-0.13719665
Sum208652
Variance62.567588
MonotonicityNot monotonic
2024-03-14T18:54:40.571047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1992 4
 
3.8%
2008 4
 
3.8%
2019 4
 
3.8%
2018 4
 
3.8%
2017 4
 
3.8%
2016 4
 
3.8%
2015 4
 
3.8%
2014 4
 
3.8%
2013 4
 
3.8%
2012 4
 
3.8%
Other values (16) 64
61.5%
ValueCountFrequency (%)
1992 4
3.8%
1993 4
3.8%
1994 4
3.8%
1997 4
3.8%
1998 4
3.8%
1999 4
3.8%
2000 4
3.8%
2001 4
3.8%
2002 4
3.8%
2003 4
3.8%
ValueCountFrequency (%)
2019 4
3.8%
2018 4
3.8%
2017 4
3.8%
2016 4
3.8%
2015 4
3.8%
2014 4
3.8%
2013 4
3.8%
2012 4
3.8%
2011 4
3.8%
2010 4
3.8%

시도별
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size960.0 B
전국
52 
전라북도
51 
<NA>
 
1

Length

Max length4
Median length3
Mean length3
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row전국
2nd row전라북도
3rd row전국
4th row전라북도
5th row전국

Common Values

ValueCountFrequency (%)
전국 52
50.0%
전라북도 51
49.0%
<NA> 1
 
1.0%

Length

2024-03-14T18:54:41.002315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:54:41.341817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전국 52
50.0%
전라북도 51
49.0%
na 1
 
1.0%

척수(척)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct64
Distinct (%)100.0%
Missing40
Missing (%)38.5%
Infinite0
Infinite (%)0.0%
Mean21745.344
Minimum20
Maximum59822
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-14T18:54:41.681736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile74.55
Q12718.5
median5611
Q344074.25
95-th percentile59449.1
Maximum59822
Range59802
Interquartile range (IQR)41355.75

Descriptive statistics

Standard deviation23379.647
Coefficient of variation (CV)1.0751565
Kurtosis-1.5605848
Mean21745.344
Median Absolute Deviation (MAD)4799
Skewness0.52736562
Sum1391702
Variance5.4660788 × 108
MonotonicityNot monotonic
2024-03-14T18:54:42.123235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40525 1
 
1.0%
45252 1
 
1.0%
52315 1
 
1.0%
55776 1
 
1.0%
59064 1
 
1.0%
59486 1
 
1.0%
59769 1
 
1.0%
59820 1
 
1.0%
59822 1
 
1.0%
59240 1
 
1.0%
Other values (54) 54
51.9%
(Missing) 40
38.5%
ValueCountFrequency (%)
20 1
1.0%
61 1
1.0%
64 1
1.0%
72 1
1.0%
89 1
1.0%
95 1
1.0%
1529 1
1.0%
1530 1
1.0%
1532 1
1.0%
1734 1
1.0%
ValueCountFrequency (%)
59822 1
1.0%
59820 1
1.0%
59769 1
1.0%
59486 1
1.0%
59240 1
1.0%
59064 1
1.0%
58390 1
1.0%
57843 1
1.0%
55776 1
1.0%
52659 1
1.0%

톤수(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct52
Distinct (%)100.0%
Missing52
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean6564387.9
Minimum107844.55
Maximum3.3332258 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-14T18:54:42.542759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum107844.55
5-th percentile110190.2
Q1121350.87
median131651.12
Q3162405.85
95-th percentile321369.99
Maximum3.3332258 × 108
Range3.3321473 × 108
Interquartile range (IQR)41054.985

Descriptive statistics

Standard deviation46201740
Coefficient of variation (CV)7.0382404
Kurtosis51.99982
Mean6564387.9
Median Absolute Deviation (MAD)15918.475
Skewness7.2110842
Sum3.4134817 × 108
Variance2.1346008 × 1015
MonotonicityNot monotonic
2024-03-14T18:54:42.988621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
108615.73 1
 
1.0%
120282.4 1
 
1.0%
131766.3 1
 
1.0%
139138.16 1
 
1.0%
147637.71 1
 
1.0%
151853.2 1
 
1.0%
155808.06 1
 
1.0%
157358.47 1
 
1.0%
158578.35 1
 
1.0%
157215.11 1
 
1.0%
Other values (42) 42
40.4%
(Missing) 52
50.0%
ValueCountFrequency (%)
107844.55 1
1.0%
108615.73 1
1.0%
109056.55 1
1.0%
111117.74 1
1.0%
115800.76 1
1.0%
117106.88 1
1.0%
117968.97 1
1.0%
118149.91 1
1.0%
118314.01 1
1.0%
119461.04 1
1.0%
ValueCountFrequency (%)
333322578.0 1
1.0%
331273.38 1
1.0%
328876.37 1
1.0%
315228.4 1
1.0%
302322.8 1
1.0%
291599.82 1
1.0%
247274.62 1
1.0%
231909.49 1
1.0%
204200.11 1
1.0%
185773.09 1
1.0%

마력수(마력)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct52
Distinct (%)100.0%
Missing52
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean5077289.2
Minimum1699986
Maximum11721227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-14T18:54:43.429868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1699986
5-th percentile1763867.8
Q11999840.2
median3292533
Q38398971.8
95-th percentile10417564
Maximum11721227
Range10021241
Interquartile range (IQR)6399131.5

Descriptive statistics

Standard deviation3314729.2
Coefficient of variation (CV)0.65285414
Kurtosis-1.4213838
Mean5077289.2
Median Absolute Deviation (MAD)1528717
Skewness0.44401991
Sum2.6401904 × 108
Variance1.098743 × 1013
MonotonicityNot monotonic
2024-03-14T18:54:43.880082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3049791 1
 
1.0%
5256199 1
 
1.0%
5481894 1
 
1.0%
6154172 1
 
1.0%
7803944 1
 
1.0%
8937270 1
 
1.0%
11168198 1
 
1.0%
11149253 1
 
1.0%
11721227 1
 
1.0%
8325925 1
 
1.0%
Other values (42) 42
40.4%
(Missing) 52
50.0%
ValueCountFrequency (%)
1699986 1
1.0%
1739900 1
1.0%
1763298 1
1.0%
1764334 1
1.0%
1777286 1
1.0%
1783003 1
1.0%
1795167 1
1.0%
1828445 1
1.0%
1880114 1
1.0%
1907612 1
1.0%
ValueCountFrequency (%)
11721227 1
1.0%
11168198 1
1.0%
11149253 1
1.0%
9818909 1
1.0%
9554037 1
1.0%
8937270 1
1.0%
8919423 1
1.0%
8698444 1
1.0%
8596506 1
1.0%
8561963 1
1.0%

Interactions

2024-03-14T18:54:37.250913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:54:34.261472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:54:35.252218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:54:36.253744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:54:37.493884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:54:34.504576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:54:35.497237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:54:36.498559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:54:37.743549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:54:34.752330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:54:35.746672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:54:36.749897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:54:37.995306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:54:35.006152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:54:36.000236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:54:36.999972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T18:54:44.148407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종별연도별시도별척수(척)톤수(톤)마력수(마력)
업종별1.0000.0000.0000.9580.0000.994
연도별0.0001.0000.0000.611NaN0.670
시도별0.0000.0001.0000.511NaNNaN
척수(척)0.9580.6110.5111.0000.4280.740
톤수(톤)0.000NaNNaN0.4281.0000.000
마력수(마력)0.9940.670NaN0.7400.0001.000
2024-03-14T18:54:44.427037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종별시도별
업종별1.0000.000
시도별0.0001.000
2024-03-14T18:54:44.671385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도별척수(척)톤수(톤)마력수(마력)업종별시도별
연도별1.000-0.574-0.510-0.0440.0000.000
척수(척)-0.5741.000-0.0810.8740.7900.355
톤수(톤)-0.510-0.0811.000-0.1890.0001.000
마력수(마력)-0.0440.874-0.1891.0000.8541.000
업종별0.0000.7900.0000.8541.0000.000
시도별0.0000.3551.0001.0000.0001.000

Missing values

2024-03-14T18:54:38.330171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T18:54:38.682447image/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.
2024-03-14T18:54:39.193189image/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

업종별연도별시도별척수(척)톤수(톤)마력수(마력)
0근해어업1992전국6762333322578.02065475
1근해어업1992전라북도<NA><NA><NA>
2근해어업1993전국6674331273.382177521
3근해어업1993전라북도<NA><NA><NA>
4근해어업1994전국6536328876.372385936
5근해어업1994전라북도<NA><NA><NA>
6근해어업1997전국6344315228.44771713
7근해어업1997전라북도<NA><NA><NA>
8근해어업1998전국6164302322.84695732
9근해어업1998전라북도<NA><NA><NA>
업종별연도별시도별척수(척)톤수(톤)마력수(마력)
94연안어업2015전국42339119736.48412741
95연안어업2015전라북도1966<NA><NA>
96연안어업2016전국40791119461.048561963
97연안어업2016전라북도1971<NA><NA>
98연안어업2017전국39206117968.978596506
99연안어업2017전라북도1530<NA><NA>
100연안어업2018전국38015117106.888698444
101연안어업2018전라북도1532<NA><NA>
102연안어업2019전국37453118314.018919423
103연안어업2019전라북도1529<NA><NA>