Overview

Dataset statistics

Number of variables10
Number of observations42
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory90.1 B

Variable types

Categorical2
Text1
Numeric7

Dataset

Description어가 및 어가인구수 집계 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=2P5OO05GB1QW7234A8221675466&infSeq=1

Alerts

집계일자 has constant value ""Constant
전업어가수 is highly overall correlated with 1종겸업어가수 and 5 other fieldsHigh correlation
1종겸업어가수 is highly overall correlated with 전업어가수 and 5 other fieldsHigh correlation
2종겸업어가수 is highly overall correlated with 전업어가수 and 5 other fieldsHigh correlation
전업인구수(호) is highly overall correlated with 전업어가수 and 5 other fieldsHigh correlation
1종겸업인구수(호) is highly overall correlated with 전업어가수 and 5 other fieldsHigh correlation
2종겸업인구수(호) is highly overall correlated with 전업어가수 and 5 other fieldsHigh correlation
어업종사자수 is highly overall correlated with 전업어가수 and 5 other fieldsHigh correlation
전업어가수 has 13 (31.0%) zerosZeros
1종겸업어가수 has 10 (23.8%) zerosZeros
2종겸업어가수 has 12 (28.6%) zerosZeros
전업인구수(호) has 13 (31.0%) zerosZeros
1종겸업인구수(호) has 10 (23.8%) zerosZeros
2종겸업인구수(호) has 12 (28.6%) zerosZeros

Reproduction

Analysis started2023-12-10 21:05:18.644641
Analysis finished2023-12-10 21:05:23.009018
Duration4.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

집계일자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size468.0 B
2020-12-31
42 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-31
2nd row2020-12-31
3rd row2020-12-31
4th row2020-12-31
5th row2020-12-31

Common Values

ValueCountFrequency (%)
2020-12-31 42
100.0%

Length

2023-12-11T06:05:23.073162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:05:23.178016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-31 42
100.0%

시군명
Text

Distinct27
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Memory size468.0 B
2023-12-11T06:05:23.384491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0714286
Min length3

Characters and Unicode

Total characters129
Distinct characters33
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

Unique12 ?
Unique (%)28.6%

Sample

1st row가평군
2nd row가평군
3rd row고양시
4th row고양시
5th row광주시
ValueCountFrequency (%)
가평군 2
 
4.8%
남양주시 2
 
4.8%
여주시 2
 
4.8%
고양시 2
 
4.8%
파주시 2
 
4.8%
오산시 2
 
4.8%
연천군 2
 
4.8%
양평군 2
 
4.8%
평택시 2
 
4.8%
화성시 2
 
4.8%
Other values (17) 22
52.4%
2023-12-11T06:05:23.775763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 38
29.5%
주 9
 
7.0%
양 8
 
6.2%
군 7
 
5.4%
평 6
 
4.7%
천 5
 
3.9%
성 5
 
3.9%
포 4
 
3.1%
남 4
 
3.1%
산 3
 
2.3%
Other values (23) 40
31.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 129
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 38
29.5%
주 9
 
7.0%
양 8
 
6.2%
군 7
 
5.4%
평 6
 
4.7%
천 5
 
3.9%
성 5
 
3.9%
포 4
 
3.1%
남 4
 
3.1%
산 3
 
2.3%
Other values (23) 40
31.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 129
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 38
29.5%
주 9
 
7.0%
양 8
 
6.2%
군 7
 
5.4%
평 6
 
4.7%
천 5
 
3.9%
성 5
 
3.9%
포 4
 
3.1%
남 4
 
3.1%
산 3
 
2.3%
Other values (23) 40
31.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 129
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 38
29.5%
주 9
 
7.0%
양 8
 
6.2%
군 7
 
5.4%
평 6
 
4.7%
천 5
 
3.9%
성 5
 
3.9%
포 4
 
3.1%
남 4
 
3.1%
산 3
 
2.3%
Other values (23) 40
31.0%

해면내수면구분명
Categorical

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size468.0 B
내수면
25 
해면
17 

Length

Max length3
Median length3
Mean length2.5952381
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row해면
2nd row내수면
3rd row내수면
4th row해면
5th row내수면

Common Values

ValueCountFrequency (%)
내수면 25
59.5%
해면 17
40.5%

Length

2023-12-11T06:05:23.954720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:05:24.089958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
내수면 25
59.5%
해면 17
40.5%

전업어가수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)35.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.952381
Minimum0
Maximum107
Zeros13
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-11T06:05:24.200737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36.75
95-th percentile20.7
Maximum107
Range107
Interquartile range (IQR)6.75

Descriptive statistics

Standard deviation17.126434
Coefficient of variation (CV)2.4633912
Kurtosis29.789928
Mean6.952381
Median Absolute Deviation (MAD)2
Skewness5.1642591
Sum292
Variance293.31475
MonotonicityNot monotonic
2023-12-11T06:05:24.328207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 13
31.0%
1 7
16.7%
2 4
 
9.5%
9 3
 
7.1%
4 3
 
7.1%
15 2
 
4.8%
3 2
 
4.8%
12 1
 
2.4%
6 1
 
2.4%
31 1
 
2.4%
Other values (5) 5
 
11.9%
ValueCountFrequency (%)
0 13
31.0%
1 7
16.7%
2 4
 
9.5%
3 2
 
4.8%
4 3
 
7.1%
5 1
 
2.4%
6 1
 
2.4%
7 1
 
2.4%
9 3
 
7.1%
12 1
 
2.4%
ValueCountFrequency (%)
107 1
 
2.4%
31 1
 
2.4%
21 1
 
2.4%
15 2
4.8%
13 1
 
2.4%
12 1
 
2.4%
9 3
7.1%
7 1
 
2.4%
6 1
 
2.4%
5 1
 
2.4%

1종겸업어가수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.452381
Minimum0
Maximum91
Zeros10
Zeros (%)23.8%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-11T06:05:24.458490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35.75
95-th percentile17
Maximum91
Range91
Interquartile range (IQR)4.75

Descriptive statistics

Standard deviation14.979991
Coefficient of variation (CV)2.3216221
Kurtosis25.828538
Mean6.452381
Median Absolute Deviation (MAD)2
Skewness4.7720821
Sum271
Variance224.40012
MonotonicityNot monotonic
2023-12-11T06:05:24.564431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 10
23.8%
1 10
23.8%
2 5
11.9%
4 3
 
7.1%
8 2
 
4.8%
3 2
 
4.8%
15 2
 
4.8%
17 2
 
4.8%
6 1
 
2.4%
7 1
 
2.4%
Other values (4) 4
 
9.5%
ValueCountFrequency (%)
0 10
23.8%
1 10
23.8%
2 5
11.9%
3 2
 
4.8%
4 3
 
7.1%
5 1
 
2.4%
6 1
 
2.4%
7 1
 
2.4%
8 2
 
4.8%
9 1
 
2.4%
ValueCountFrequency (%)
91 1
 
2.4%
35 1
 
2.4%
17 2
4.8%
15 2
4.8%
9 1
 
2.4%
8 2
4.8%
7 1
 
2.4%
6 1
 
2.4%
5 1
 
2.4%
4 3
7.1%

2종겸업어가수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8333333
Minimum0
Maximum153
Zeros12
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-11T06:05:24.678680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39.75
95-th percentile38.1
Maximum153
Range153
Interquartile range (IQR)9.75

Descriptive statistics

Standard deviation24.763187
Coefficient of variation (CV)2.5182902
Kurtosis28.487498
Mean9.8333333
Median Absolute Deviation (MAD)1
Skewness5.0305334
Sum413
Variance613.21545
MonotonicityNot monotonic
2023-12-11T06:05:24.862885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 12
28.6%
1 10
23.8%
11 2
 
4.8%
21 2
 
4.8%
2 2
 
4.8%
3 2
 
4.8%
39 1
 
2.4%
153 1
 
2.4%
4 1
 
2.4%
15 1
 
2.4%
Other values (8) 8
19.0%
ValueCountFrequency (%)
0 12
28.6%
1 10
23.8%
2 2
 
4.8%
3 2
 
4.8%
4 1
 
2.4%
5 1
 
2.4%
7 1
 
2.4%
8 1
 
2.4%
9 1
 
2.4%
10 1
 
2.4%
ValueCountFrequency (%)
153 1
2.4%
44 1
2.4%
39 1
2.4%
21 2
4.8%
19 1
2.4%
16 1
2.4%
15 1
2.4%
11 2
4.8%
10 1
2.4%
9 1
2.4%

전업인구수(호)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.952381
Minimum0
Maximum213
Zeros13
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-11T06:05:25.012580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q314.25
95-th percentile34.9
Maximum213
Range213
Interquartile range (IQR)14.25

Descriptive statistics

Standard deviation33.890752
Coefficient of variation (CV)2.42903
Kurtosis30.457192
Mean13.952381
Median Absolute Deviation (MAD)4
Skewness5.2205149
Sum586
Variance1148.583
MonotonicityNot monotonic
2023-12-11T06:05:25.397713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 13
31.0%
2 6
14.3%
5 3
 
7.1%
9 2
 
4.8%
15 2
 
4.8%
29 1
 
2.4%
213 1
 
2.4%
8 1
 
2.4%
17 1
 
2.4%
27 1
 
2.4%
Other values (11) 11
26.2%
ValueCountFrequency (%)
0 13
31.0%
1 1
 
2.4%
2 6
14.3%
3 1
 
2.4%
5 3
 
7.1%
6 1
 
2.4%
7 1
 
2.4%
8 1
 
2.4%
9 2
 
4.8%
10 1
 
2.4%
ValueCountFrequency (%)
213 1
2.4%
56 1
2.4%
35 1
2.4%
33 1
2.4%
30 1
2.4%
29 1
2.4%
27 1
2.4%
24 1
2.4%
17 1
2.4%
15 2
4.8%

1종겸업인구수(호)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.785714
Minimum0
Maximum199
Zeros10
Zeros (%)23.8%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-11T06:05:25.539024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q314
95-th percentile51.85
Maximum199
Range199
Interquartile range (IQR)13

Descriptive statistics

Standard deviation33.775107
Coefficient of variation (CV)2.1395995
Kurtosis21.69667
Mean15.785714
Median Absolute Deviation (MAD)5
Skewness4.2985373
Sum663
Variance1140.7578
MonotonicityNot monotonic
2023-12-11T06:05:25.669793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 10
23.8%
3 3
 
7.1%
1 3
 
7.1%
6 3
 
7.1%
2 3
 
7.1%
5 3
 
7.1%
9 2
 
4.8%
11 2
 
4.8%
23 1
 
2.4%
21 1
 
2.4%
Other values (11) 11
26.2%
ValueCountFrequency (%)
0 10
23.8%
1 3
 
7.1%
2 3
 
7.1%
3 3
 
7.1%
4 1
 
2.4%
5 3
 
7.1%
6 3
 
7.1%
9 2
 
4.8%
10 1
 
2.4%
11 2
 
4.8%
ValueCountFrequency (%)
199 1
2.4%
80 1
2.4%
52 1
2.4%
49 1
2.4%
47 1
2.4%
37 1
2.4%
23 1
2.4%
21 1
2.4%
18 1
2.4%
17 1
2.4%

2종겸업인구수(호)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.5
Minimum0
Maximum355
Zeros12
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-11T06:05:25.797096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.5
Q327.75
95-th percentile95.65
Maximum355
Range355
Interquartile range (IQR)27.75

Descriptive statistics

Standard deviation58.165974
Coefficient of variation (CV)2.2810186
Kurtosis26.013758
Mean25.5
Median Absolute Deviation (MAD)3.5
Skewness4.7294991
Sum1071
Variance3383.2805
MonotonicityNot monotonic
2023-12-11T06:05:25.954530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 12
28.6%
2 4
 
9.5%
3 4
 
9.5%
10 3
 
7.1%
98 1
 
2.4%
355 1
 
2.4%
8 1
 
2.4%
9 1
 
2.4%
46 1
 
2.4%
20 1
 
2.4%
Other values (13) 13
31.0%
ValueCountFrequency (%)
0 12
28.6%
1 1
 
2.4%
2 4
 
9.5%
3 4
 
9.5%
4 1
 
2.4%
7 1
 
2.4%
8 1
 
2.4%
9 1
 
2.4%
10 3
 
7.1%
20 1
 
2.4%
ValueCountFrequency (%)
355 1
2.4%
98 1
2.4%
97 1
2.4%
70 1
2.4%
62 1
2.4%
51 1
2.4%
46 1
2.4%
43 1
2.4%
38 1
2.4%
37 1
2.4%

어업종사자수
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)59.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.52381
Minimum1
Maximum539
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-11T06:05:26.112215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q338.75
95-th percentile110.35
Maximum539
Range538
Interquartile range (IQR)36.75

Descriptive statistics

Standard deviation85.942476
Coefficient of variation (CV)2.4192922
Kurtosis30.122252
Mean35.52381
Median Absolute Deviation (MAD)5
Skewness5.1872665
Sum1492
Variance7386.1092
MonotonicityNot monotonic
2023-12-11T06:05:26.258781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2 7
16.7%
1 6
 
14.3%
6 4
 
9.5%
23 2
 
4.8%
5 2
 
4.8%
3 2
 
4.8%
16 1
 
2.4%
539 1
 
2.4%
20 1
 
2.4%
19 1
 
2.4%
Other values (15) 15
35.7%
ValueCountFrequency (%)
1 6
14.3%
2 7
16.7%
3 2
 
4.8%
4 1
 
2.4%
5 2
 
4.8%
6 4
9.5%
9 1
 
2.4%
10 1
 
2.4%
12 1
 
2.4%
16 1
 
2.4%
ValueCountFrequency (%)
539 1
2.4%
140 1
2.4%
112 1
2.4%
79 1
2.4%
74 1
2.4%
67 1
2.4%
58 1
2.4%
57 1
2.4%
56 1
2.4%
45 1
2.4%

Interactions

2023-12-11T06:05:22.199205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:18.893428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:19.398996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:20.069677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:20.558293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:21.045228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:21.607855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:22.273410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:18.962017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:19.475899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:20.134195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:20.627859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:21.121231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:21.681985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:22.344419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:19.024928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:19.544607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:20.204141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:20.699577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:21.190172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:21.751853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:22.420289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:19.105629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:19.615500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:20.276851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:20.787755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:21.283478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:21.836861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:22.487788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:19.182086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:19.671876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:20.343477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:20.848095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:21.362102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:21.909892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:22.575894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:19.263157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:19.948702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:20.418037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:20.915533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:21.456718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:22.042691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:22.683687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:19.334517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:20.010631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:20.491959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:20.983064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:21.530977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:05:22.125221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:05:26.374431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명해면내수면구분명전업어가수1종겸업어가수2종겸업어가수전업인구수(호)1종겸업인구수(호)2종겸업인구수(호)어업종사자수
시군명1.0000.0000.0000.4530.0000.0000.0000.0000.000
해면내수면구분명0.0001.0000.0780.3910.2790.0900.2380.1990.000
전업어가수0.0000.0781.0000.9520.9470.9990.7220.9500.969
1종겸업어가수0.4530.3910.9521.0000.9690.9410.9440.9730.976
2종겸업어가수0.0000.2790.9470.9691.0000.9360.8300.9960.991
전업인구수(호)0.0000.0900.9990.9410.9361.0000.7390.9530.959
1종겸업인구수(호)0.0000.2380.7220.9440.8300.7391.0000.8640.835
2종겸업인구수(호)0.0000.1990.9500.9730.9960.9530.8641.0000.996
어업종사자수0.0000.0000.9690.9760.9910.9590.8350.9961.000
2023-12-11T06:05:26.501375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전업어가수1종겸업어가수2종겸업어가수전업인구수(호)1종겸업인구수(호)2종겸업인구수(호)어업종사자수해면내수면구분명
전업어가수1.0000.7710.8190.9960.7490.7960.9330.027
1종겸업어가수0.7711.0000.7610.7700.9870.7610.8530.252
2종겸업어가수0.8190.7611.0000.8150.7460.9870.8960.176
전업인구수(호)0.9960.7700.8151.0000.7460.7920.9320.040
1종겸업인구수(호)0.7490.9870.7460.7461.0000.7450.8440.276
2종겸업인구수(호)0.7960.7610.9870.7920.7451.0000.8900.122
어업종사자수0.9330.8530.8960.9320.8440.8901.0000.000
해면내수면구분명0.0270.2520.1760.0400.2760.1220.0001.000

Missing values

2023-12-11T06:05:22.804990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:05:22.951022image/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

집계일자시군명해면내수면구분명전업어가수1종겸업어가수2종겸업어가수전업인구수(호)1종겸업인구수(호)2종겸업인구수(호)어업종사자수
02020-12-31가평군해면0010022
12020-12-31가평군내수면981615174343
22020-12-31고양시내수면4689152923
32020-12-31고양시해면2003002
42020-12-31광주시내수면1351111012
52020-12-31광주시해면0100502
62020-12-31구리시내수면0100301
72020-12-31군포시내수면1102305
82020-12-31김포시해면1281030182458
92020-12-31김포시내수면971124213745
집계일자시군명해면내수면구분명전업어가수1종겸업어가수2종겸업어가수전업인구수(호)1종겸업인구수(호)2종겸업인구수(호)어업종사자수
322020-12-31의왕시내수면0010033
332020-12-31의정부시내수면0100101
342020-12-31이천시내수면3026036
352020-12-31파주시내수면13172127526267
362020-12-31파주시해면0200603
372020-12-31평택시해면427962023
382020-12-31평택시내수면9151517474656
392020-12-31포천시내수면723154919
402020-12-31화성시내수면444811820
412020-12-31화성시해면10791153213199355539