Overview

Dataset statistics

Number of variables16
Number of observations21
Missing cells91
Missing cells (%)27.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory149.3 B

Variable types

Text1
Numeric11
Unsupported4

Dataset

Description전라남도 귀농산어촌 가구현황(시군별, 년도별로 귀농, 귀촌, 귀어 가구 현황)에 관한 데이터를 조회하실 수 있습니다.
URLhttps://www.data.go.kr/data/15053183/fileData.do

Alerts

2018년 귀농 is highly overall correlated with 2019년 귀농 and 3 other fieldsHigh correlation
2018년 귀촌 is highly overall correlated with 2019년 귀촌 and 3 other fieldsHigh correlation
2019년 귀농 is highly overall correlated with 2018년 귀농 and 3 other fieldsHigh correlation
2019년 귀촌 is highly overall correlated with 2018년 귀촌 and 3 other fieldsHigh correlation
2020년 귀농 is highly overall correlated with 2018년 귀농 and 3 other fieldsHigh correlation
2020년 귀촌 is highly overall correlated with 2018년 귀촌 and 3 other fieldsHigh correlation
2021년 귀농 is highly overall correlated with 2018년 귀농 and 3 other fieldsHigh correlation
2021년 귀촌 is highly overall correlated with 2018년 귀촌 and 3 other fieldsHigh correlation
2022년 귀농 is highly overall correlated with 2018년 귀농 and 3 other fieldsHigh correlation
2022년 귀촌 is highly overall correlated with 2018년 귀촌 and 3 other fieldsHigh correlation
2018년 귀어 has 7 (33.3%) missing valuesMissing
2019년 귀어 has 21 (100.0%) missing valuesMissing
2020년 귀어 has 21 (100.0%) missing valuesMissing
2021년 귀어 has 21 (100.0%) missing valuesMissing
2022년 귀어 has 21 (100.0%) missing valuesMissing
시군 has unique valuesUnique
2018년 귀촌 has unique valuesUnique
2019년 귀촌 has unique valuesUnique
2020년 귀농 has unique valuesUnique
2020년 귀촌 has unique valuesUnique
2021년 귀촌 has unique valuesUnique
2019년 귀어 is an unsupported type, check if it needs cleaning or further analysisUnsupported
2020년 귀어 is an unsupported type, check if it needs cleaning or further analysisUnsupported
2021년 귀어 is an unsupported type, check if it needs cleaning or further analysisUnsupported
2022년 귀어 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-13 00:33:41.024990
Analysis finished2023-12-13 00:33:50.524765
Duration9.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T09:33:50.613719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters63
Distinct characters32
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row여 수
2nd row순 천
3rd row나 주
4th row광 양
5th row담 양
ValueCountFrequency (%)
성 3
 
7.1%
안 2
 
4.8%
양 2
 
4.8%
흥 2
 
4.8%
영 2
 
4.8%
진 2
 
4.8%
장 2
 
4.8%
광 2
 
4.8%
도 2
 
4.8%
순 2
 
4.8%
Other values (21) 21
50.0%
2023-12-13T09:33:50.833219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21
33.3%
성 3
 
4.8%
안 2
 
3.2%
양 2
 
3.2%
흥 2
 
3.2%
영 2
 
3.2%
진 2
 
3.2%
장 2
 
3.2%
광 2
 
3.2%
순 2
 
3.2%
Other values (22) 23
36.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42
66.7%
Space Separator 21
33.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 3
 
7.1%
안 2
 
4.8%
양 2
 
4.8%
흥 2
 
4.8%
영 2
 
4.8%
진 2
 
4.8%
장 2
 
4.8%
광 2
 
4.8%
순 2
 
4.8%
도 2
 
4.8%
Other values (21) 21
50.0%
Space Separator
ValueCountFrequency (%)
21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42
66.7%
Common 21
33.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 3
 
7.1%
안 2
 
4.8%
양 2
 
4.8%
흥 2
 
4.8%
영 2
 
4.8%
진 2
 
4.8%
장 2
 
4.8%
광 2
 
4.8%
순 2
 
4.8%
도 2
 
4.8%
Other values (21) 21
50.0%
Common
ValueCountFrequency (%)
21
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42
66.7%
ASCII 21
33.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21
100.0%
Hangul
ValueCountFrequency (%)
성 3
 
7.1%
안 2
 
4.8%
양 2
 
4.8%
흥 2
 
4.8%
영 2
 
4.8%
진 2
 
4.8%
장 2
 
4.8%
광 2
 
4.8%
순 2
 
4.8%
도 2
 
4.8%
Other values (21) 21
50.0%

2018년 귀농
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.47619
Minimum35
Maximum175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T09:33:50.926516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile45
Q172
median96
Q3119
95-th percentile167
Maximum175
Range140
Interquartile range (IQR)47

Descriptive statistics

Standard deviation37.137069
Coefficient of variation (CV)0.38493507
Kurtosis-0.1087077
Mean96.47619
Median Absolute Deviation (MAD)23
Skewness0.37438302
Sum2026
Variance1379.1619
MonotonicityNot monotonic
2023-12-13T09:33:51.013743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
96 2
 
9.5%
119 2
 
9.5%
55 1
 
4.8%
72 1
 
4.8%
51 1
 
4.8%
128 1
 
4.8%
65 1
 
4.8%
118 1
 
4.8%
108 1
 
4.8%
129 1
 
4.8%
Other values (9) 9
42.9%
ValueCountFrequency (%)
35 1
4.8%
45 1
4.8%
51 1
4.8%
55 1
4.8%
65 1
4.8%
72 1
4.8%
78 1
4.8%
80 1
4.8%
87 1
4.8%
91 1
4.8%
ValueCountFrequency (%)
175 1
4.8%
167 1
4.8%
129 1
4.8%
128 1
4.8%
119 2
9.5%
118 1
4.8%
112 1
4.8%
108 1
4.8%
96 2
9.5%
91 1
4.8%

2018년 귀어
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)85.7%
Missing7
Missing (%)33.3%
Infinite0
Infinite (%)0.0%
Mean23
Minimum1
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T09:33:51.109134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.65
Q17.25
median14.5
Q339.25
95-th percentile55.2
Maximum76
Range75
Interquartile range (IQR)32

Descriptive statistics

Standard deviation21.72202
Coefficient of variation (CV)0.94443565
Kurtosis1.1319356
Mean23
Median Absolute Deviation (MAD)12
Skewness1.168813
Sum322
Variance471.84615
MonotonicityNot monotonic
2023-12-13T09:33:51.187137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11 3
14.3%
40 1
 
4.8%
1 1
 
4.8%
44 1
 
4.8%
6 1
 
4.8%
2 1
 
4.8%
3 1
 
4.8%
37 1
 
4.8%
21 1
 
4.8%
41 1
 
4.8%
Other values (2) 2
 
9.5%
(Missing) 7
33.3%
ValueCountFrequency (%)
1 1
 
4.8%
2 1
 
4.8%
3 1
 
4.8%
6 1
 
4.8%
11 3
14.3%
18 1
 
4.8%
21 1
 
4.8%
37 1
 
4.8%
40 1
 
4.8%
41 1
 
4.8%
ValueCountFrequency (%)
76 1
 
4.8%
44 1
 
4.8%
41 1
 
4.8%
40 1
 
4.8%
37 1
 
4.8%
21 1
 
4.8%
18 1
 
4.8%
11 3
14.3%
6 1
 
4.8%
3 1
 
4.8%

2018년 귀촌
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1360.0952
Minimum568
Maximum3687
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T09:33:51.268164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum568
5-th percentile648
Q1835
median1272
Q31560
95-th percentile2252
Maximum3687
Range3119
Interquartile range (IQR)725

Descriptive statistics

Standard deviation718.24856
Coefficient of variation (CV)0.52808696
Kurtosis4.576023
Mean1360.0952
Median Absolute Deviation (MAD)379
Skewness1.8166049
Sum28562
Variance515880.99
MonotonicityNot monotonic
2023-12-13T09:33:51.350202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1728 1
 
4.8%
3687 1
 
4.8%
1194 1
 
4.8%
568 1
 
4.8%
989 1
 
4.8%
1529 1
 
4.8%
1143 1
 
4.8%
835 1
 
4.8%
2252 1
 
4.8%
1379 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
568 1
4.8%
648 1
4.8%
698 1
4.8%
739 1
4.8%
751 1
4.8%
835 1
4.8%
921 1
4.8%
989 1
4.8%
1143 1
4.8%
1194 1
4.8%
ValueCountFrequency (%)
3687 1
4.8%
2252 1
4.8%
2245 1
4.8%
1728 1
4.8%
1651 1
4.8%
1560 1
4.8%
1529 1
4.8%
1476 1
4.8%
1379 1
4.8%
1297 1
4.8%

2019년 귀농
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.904762
Minimum44
Maximum176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T09:33:51.438785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile46
Q180
median95
Q3118
95-th percentile165
Maximum176
Range132
Interquartile range (IQR)38

Descriptive statistics

Standard deviation36.430626
Coefficient of variation (CV)0.37986253
Kurtosis0.093040876
Mean95.904762
Median Absolute Deviation (MAD)23
Skewness0.41578824
Sum2014
Variance1327.1905
MonotonicityNot monotonic
2023-12-13T09:33:51.530379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
95 2
 
9.5%
46 1
 
4.8%
48 1
 
4.8%
49 1
 
4.8%
106 1
 
4.8%
91 1
 
4.8%
120 1
 
4.8%
93 1
 
4.8%
119 1
 
4.8%
132 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
44 1
4.8%
46 1
4.8%
48 1
4.8%
49 1
4.8%
52 1
4.8%
80 1
4.8%
84 1
4.8%
88 1
4.8%
91 1
4.8%
93 1
4.8%
ValueCountFrequency (%)
176 1
4.8%
165 1
4.8%
132 1
4.8%
120 1
4.8%
119 1
4.8%
118 1
4.8%
110 1
4.8%
106 1
4.8%
103 1
4.8%
95 2
9.5%

2019년 귀어
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing21
Missing (%)100.0%
Memory size321.0 B

2019년 귀촌
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1346.3333
Minimum548
Maximum3446
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T09:33:51.613996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum548
5-th percentile556
Q1741
median1304
Q31606
95-th percentile2324
Maximum3446
Range2898
Interquartile range (IQR)865

Descriptive statistics

Standard deviation703.21443
Coefficient of variation (CV)0.52231822
Kurtosis2.6598724
Mean1346.3333
Median Absolute Deviation (MAD)466
Skewness1.3298454
Sum28273
Variance494510.53
MonotonicityNot monotonic
2023-12-13T09:33:51.693804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1747 1
 
4.8%
3446 1
 
4.8%
1043 1
 
4.8%
548 1
 
4.8%
1005 1
 
4.8%
1606 1
 
4.8%
1328 1
 
4.8%
838 1
 
4.8%
2324 1
 
4.8%
1536 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
548 1
4.8%
556 1
4.8%
628 1
4.8%
648 1
4.8%
733 1
4.8%
741 1
4.8%
838 1
4.8%
1005 1
4.8%
1043 1
4.8%
1286 1
4.8%
ValueCountFrequency (%)
3446 1
4.8%
2324 1
4.8%
2050 1
4.8%
1788 1
4.8%
1747 1
4.8%
1606 1
4.8%
1570 1
4.8%
1548 1
4.8%
1536 1
4.8%
1328 1
4.8%

2020년 귀농
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.7619
Minimum49
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T09:33:51.777018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile53
Q189
median120
Q3135
95-th percentile171
Maximum180
Range131
Interquartile range (IQR)46

Descriptive statistics

Standard deviation39.201919
Coefficient of variation (CV)0.35076281
Kurtosis-0.87623058
Mean111.7619
Median Absolute Deviation (MAD)26
Skewness-0.10856172
Sum2347
Variance1536.7905
MonotonicityNot monotonic
2023-12-13T09:33:51.868430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
57 1
 
4.8%
127 1
 
4.8%
94 1
 
4.8%
72 1
 
4.8%
53 1
 
4.8%
124 1
 
4.8%
98 1
 
4.8%
120 1
 
4.8%
153 1
 
4.8%
130 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
49 1
4.8%
53 1
4.8%
56 1
4.8%
57 1
4.8%
72 1
4.8%
89 1
4.8%
94 1
4.8%
98 1
4.8%
103 1
4.8%
107 1
4.8%
ValueCountFrequency (%)
180 1
4.8%
171 1
4.8%
159 1
4.8%
153 1
4.8%
139 1
4.8%
135 1
4.8%
131 1
4.8%
130 1
4.8%
127 1
4.8%
124 1
4.8%

2020년 귀어
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing21
Missing (%)100.0%
Memory size321.0 B

2020년 귀촌
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1446.5238
Minimum594
Maximum3921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T09:33:51.954729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum594
5-th percentile622
Q1799
median1338
Q31680
95-th percentile3226
Maximum3921
Range3327
Interquartile range (IQR)881

Descriptive statistics

Standard deviation844.76782
Coefficient of variation (CV)0.58399856
Kurtosis3.1033369
Mean1446.5238
Median Absolute Deviation (MAD)531
Skewness1.662754
Sum30377
Variance713632.66
MonotonicityNot monotonic
2023-12-13T09:33:52.038499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1904 1
 
4.8%
3226 1
 
4.8%
1031 1
 
4.8%
594 1
 
4.8%
1009 1
 
4.8%
1673 1
 
4.8%
1187 1
 
4.8%
799 1
 
4.8%
3921 1
 
4.8%
1561 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
594 1
4.8%
622 1
4.8%
680 1
4.8%
706 1
4.8%
791 1
4.8%
799 1
4.8%
853 1
4.8%
1009 1
4.8%
1031 1
4.8%
1187 1
4.8%
ValueCountFrequency (%)
3921 1
4.8%
3226 1
4.8%
1944 1
4.8%
1904 1
4.8%
1869 1
4.8%
1680 1
4.8%
1673 1
4.8%
1631 1
4.8%
1561 1
4.8%
1358 1
4.8%

2021년 귀농
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.09524
Minimum62
Maximum223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T09:33:52.122287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum62
5-th percentile64
Q188
median132
Q3149
95-th percentile172
Maximum223
Range161
Interquartile range (IQR)61

Descriptive statistics

Standard deviation42.780726
Coefficient of variation (CV)0.35038816
Kurtosis-0.15448473
Mean122.09524
Median Absolute Deviation (MAD)32
Skewness0.28047824
Sum2564
Variance1830.1905
MonotonicityNot monotonic
2023-12-13T09:33:52.206615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
136 2
 
9.5%
149 2
 
9.5%
62 1
 
4.8%
97 1
 
4.8%
72 1
 
4.8%
66 1
 
4.8%
132 1
 
4.8%
88 1
 
4.8%
148 1
 
4.8%
172 1
 
4.8%
Other values (9) 9
42.9%
ValueCountFrequency (%)
62 1
4.8%
64 1
4.8%
66 1
4.8%
71 1
4.8%
72 1
4.8%
88 1
4.8%
90 1
4.8%
97 1
4.8%
123 1
4.8%
127 1
4.8%
ValueCountFrequency (%)
223 1
4.8%
172 1
4.8%
164 1
4.8%
157 1
4.8%
149 2
9.5%
148 1
4.8%
138 1
4.8%
136 2
9.5%
132 1
4.8%
127 1
4.8%

2021년 귀어
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing21
Missing (%)100.0%
Memory size321.0 B

2021년 귀촌
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1569.7143
Minimum646
Maximum4544
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T09:33:52.295603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum646
5-th percentile713
Q1831
median1351
Q31924
95-th percentile3444
Maximum4544
Range3898
Interquartile range (IQR)1093

Descriptive statistics

Standard deviation976.60218
Coefficient of variation (CV)0.62215283
Kurtosis3.5269621
Mean1569.7143
Median Absolute Deviation (MAD)523
Skewness1.7823607
Sum32964
Variance953751.81
MonotonicityNot monotonic
2023-12-13T09:33:52.401082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1925 1
 
4.8%
3444 1
 
4.8%
1148 1
 
4.8%
831 1
 
4.8%
960 1
 
4.8%
1580 1
 
4.8%
1220 1
 
4.8%
828 1
 
4.8%
4544 1
 
4.8%
1772 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
646 1
4.8%
713 1
4.8%
726 1
4.8%
762 1
4.8%
828 1
4.8%
831 1
4.8%
923 1
4.8%
960 1
4.8%
1148 1
4.8%
1220 1
4.8%
ValueCountFrequency (%)
4544 1
4.8%
3444 1
4.8%
2546 1
4.8%
2039 1
4.8%
1925 1
4.8%
1924 1
4.8%
1772 1
4.8%
1718 1
4.8%
1580 1
4.8%
1364 1
4.8%

2022년 귀농
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.619048
Minimum44
Maximum178
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T09:33:52.493325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile49
Q163
median93
Q3111
95-th percentile157
Maximum178
Range134
Interquartile range (IQR)48

Descriptive statistics

Standard deviation35.830819
Coefficient of variation (CV)0.38273001
Kurtosis0.14097461
Mean93.619048
Median Absolute Deviation (MAD)25
Skewness0.71746382
Sum1966
Variance1283.8476
MonotonicityNot monotonic
2023-12-13T09:33:52.583351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
111 2
 
9.5%
60 1
 
4.8%
157 1
 
4.8%
98 1
 
4.8%
54 1
 
4.8%
49 1
 
4.8%
119 1
 
4.8%
68 1
 
4.8%
93 1
 
4.8%
97 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
44 1
4.8%
49 1
4.8%
54 1
4.8%
60 1
4.8%
62 1
4.8%
63 1
4.8%
68 1
4.8%
71 1
4.8%
80 1
4.8%
91 1
4.8%
ValueCountFrequency (%)
178 1
4.8%
157 1
4.8%
141 1
4.8%
119 1
4.8%
115 1
4.8%
111 2
9.5%
104 1
4.8%
98 1
4.8%
97 1
4.8%
93 1
4.8%

2022년 귀어
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing21
Missing (%)100.0%
Memory size321.0 B

2022년 귀촌
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1422.0952
Minimum610
Maximum3781
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T09:33:52.670030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum610
5-th percentile675
Q1736
median1323
Q31621
95-th percentile2982
Maximum3781
Range3171
Interquartile range (IQR)885

Descriptive statistics

Standard deviation822.46312
Coefficient of variation (CV)0.57834602
Kurtosis2.4264552
Mean1422.0952
Median Absolute Deviation (MAD)474
Skewness1.5227016
Sum29864
Variance676445.59
MonotonicityNot monotonic
2023-12-13T09:33:52.761219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
675 2
 
9.5%
2475 1
 
4.8%
1143 1
 
4.8%
922 1
 
4.8%
1543 1
 
4.8%
1412 1
 
4.8%
693 1
 
4.8%
2982 1
 
4.8%
1548 1
 
4.8%
1196 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
610 1
4.8%
675 2
9.5%
693 1
4.8%
726 1
4.8%
736 1
4.8%
761 1
4.8%
922 1
4.8%
1143 1
4.8%
1196 1
4.8%
1323 1
4.8%
ValueCountFrequency (%)
3781 1
4.8%
2982 1
4.8%
2475 1
4.8%
1797 1
4.8%
1769 1
4.8%
1621 1
4.8%
1548 1
4.8%
1543 1
4.8%
1476 1
4.8%
1412 1
4.8%

Interactions

2023-12-13T09:33:49.534105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:41.323202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:42.087652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:42.877245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:43.628604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:44.395937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:45.355730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:46.148138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:46.979457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:47.819719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:48.598143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:49.600526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:41.390551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:42.166280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:42.942768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:43.698334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:44.463545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:45.435349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:46.229580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:47.063456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:47.892422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:48.664042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:49.674409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:41.460353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:42.237164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:43.010193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:43.762892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:44.529956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:45.518083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:46.312726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:47.150014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:47.958719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:48.731339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:49.745615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:41.531683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:42.303567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:43.086339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:43.826741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:44.829463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:45.582113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:46.375314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:47.231394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:48.023399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:49.007926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:49.825355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:41.608047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:42.368218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:43.155859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:43.895491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:44.896909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:45.655444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:46.441688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:47.301331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:48.091916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:49.071728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:49.888743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:41.675453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:42.437885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:43.216060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:43.958544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:44.957788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:45.730196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:46.505010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:47.366298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:48.157147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:49.136941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:49.953303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:41.739827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:42.510254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:43.281010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:44.032105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:45.019146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:45.794743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:46.585079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:47.431155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:48.224261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:49.199616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:50.016826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:41.806719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:42.583402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:43.345562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:44.109004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:45.084667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:45.877475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:46.667344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:47.504204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:48.296817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:49.266752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:50.085586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:41.878118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:42.651995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:43.415052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:44.187927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:45.152161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:45.951451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:46.749576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:47.581104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:48.368335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:49.336240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:50.152090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:41.947991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:42.723822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:43.486130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:44.264675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:45.220840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:46.018574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:46.830478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:47.656672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:48.448175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:49.404032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:50.218549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:42.017344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:42.801668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:43.560894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:44.332460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:45.290990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:46.084558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:46.899318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:47.738407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:48.515827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:33:49.471391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T09:33:53.073806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군2018년 귀농2018년 귀어2018년 귀촌2019년 귀농2019년 귀촌2020년 귀농2020년 귀촌2021년 귀농2021년 귀촌2022년 귀농2022년 귀촌
시군1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2018년 귀농1.0001.0000.7130.0000.7630.0000.6260.0000.6690.1830.6610.000
2018년 귀어1.0000.7131.0000.4700.0000.5730.7320.5510.4930.4810.3410.716
2018년 귀촌1.0000.0000.4701.0000.0000.8500.6140.8600.0000.7880.0000.809
2019년 귀농1.0000.7630.0000.0001.0000.3570.7030.0000.6390.0000.8450.000
2019년 귀촌1.0000.0000.5730.8500.3571.0000.8780.8700.0000.9000.6230.864
2020년 귀농1.0000.6260.7320.6140.7030.8781.0000.6840.6810.4840.7600.427
2020년 귀촌1.0000.0000.5510.8600.0000.8700.6841.0000.0000.9530.0000.939
2021년 귀농1.0000.6690.4930.0000.6390.0000.6810.0001.0000.0000.6730.000
2021년 귀촌1.0000.1830.4810.7880.0000.9000.4840.9530.0001.0000.2680.965
2022년 귀농1.0000.6610.3410.0000.8450.6230.7600.0000.6730.2681.0000.100
2022년 귀촌1.0000.0000.7160.8090.0000.8640.4270.9390.0000.9650.1001.000
2023-12-13T09:33:53.180155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2018년 귀농2018년 귀어2018년 귀촌2019년 귀농2019년 귀촌2020년 귀농2020년 귀촌2021년 귀농2021년 귀촌2022년 귀농2022년 귀촌
2018년 귀농1.0000.0460.2470.8790.2030.8240.1920.8840.1350.8080.099
2018년 귀어0.0461.0000.2010.0110.139-0.0150.1790.0350.1300.2100.188
2018년 귀촌0.2470.2011.0000.2110.9810.4480.9880.3390.9420.3630.968
2019년 귀농0.8790.0110.2111.0000.1660.8480.1730.8400.1140.8180.067
2019년 귀촌0.2030.1390.9810.1661.0000.3710.9690.2690.9310.2960.964
2020년 귀농0.824-0.0150.4480.8480.3711.0000.4350.9110.3780.7820.329
2020년 귀촌0.1920.1790.9880.1730.9690.4351.0000.3210.9610.3200.976
2021년 귀농0.8840.0350.3390.8400.2690.9110.3211.0000.2790.7560.212
2021년 귀촌0.1350.1300.9420.1140.9310.3780.9610.2791.0000.2380.959
2022년 귀농0.8080.2100.3630.8180.2960.7820.3200.7560.2381.0000.277
2022년 귀촌0.0990.1880.9680.0670.9640.3290.9760.2120.9590.2771.000

Missing values

2023-12-13T09:33:50.318988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:33:50.468114image/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

시군2018년 귀농2018년 귀어2018년 귀촌2019년 귀농2019년 귀어2019년 귀촌2020년 귀농2020년 귀어2020년 귀촌2021년 귀농2021년 귀어2021년 귀촌2022년 귀농2022년 귀어2022년 귀촌
0여 수5540172846<NA>174757<NA>190462<NA>192560<NA>2475
1순 천80<NA>368788<NA>3446127<NA>3226127<NA>3444104<NA>3781
2나 주175<NA>2245165<NA>2050159<NA>1680157<NA>1718141<NA>1621
3광 양351156044<NA>178849<NA>186971<NA>254644<NA>1797
4담 양112<NA>1476118<NA>1570135<NA>1631138<NA>192491<NA>1476
5곡 성91<NA>73984<NA>64889<NA>706123<NA>762111<NA>761
6구 례45<NA>64852<NA>55656<NA>62264<NA>64662<NA>610
7고 흥167441297176<NA>1304180<NA>1358223<NA>1351178<NA>1323
8보 성1196921103<NA>741131<NA>853164<NA>92380<NA>736
9화 순87<NA>1651110<NA>1548171<NA>1944149<NA>2039111<NA>1769
시군2018년 귀농2018년 귀어2018년 귀촌2019년 귀농2019년 귀어2019년 귀촌2020년 귀농2020년 귀어2020년 귀촌2021년 귀농2021년 귀어2021년 귀촌2022년 귀농2022년 귀어2022년 귀촌
11강 진96269895<NA>628107<NA>68097<NA>72663<NA>675
12해 남129111272132<NA>1286139<NA>1338149<NA>1364157<NA>1196
13영 암10831379119<NA>1536130<NA>1561136<NA>1772115<NA>1548
14무 안11937225293<NA>2324153<NA>3921172<NA>454497<NA>2982
15함 평11811835120<NA>838120<NA>799148<NA>82893<NA>693
16영 광6521114391<NA>132898<NA>118788<NA>122068<NA>1412
17장 성128<NA>1529106<NA>1606124<NA>1673132<NA>1580119<NA>1543
18완 도514198949<NA>100553<NA>100966<NA>96049<NA>922
19진 도721856848<NA>54872<NA>59472<NA>83154<NA>675
20신 안9676119495<NA>104394<NA>1031136<NA>114898<NA>1143