Overview

Dataset statistics

Number of variables17
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory158.0 B

Variable types

Text1
Categorical4
Numeric12

Dataset

Description전라남도 행정구역(읍면동)현황입니다. 읍/면/동/통/리/반 수, 출장소 수, 면적, 세대 수, 남성/여성 인구 수 등 전라남도 내 시군별 현황을 참고할 수 있습니다.
Author전라남도
URLhttps://www.data.go.kr/data/3036133/fileData.do

Alerts

출장소(도) has constant value ""Constant
출장소(읍면) is highly overall correlated with 출장소(시군)High correlation
출장소(시군) is highly overall correlated with and 4 other fieldsHigh correlation
is highly overall correlated with 법정리 and 2 other fieldsHigh correlation
is highly overall correlated with 법정동 and 6 other fieldsHigh correlation
법정동 is highly overall correlated with and 6 other fieldsHigh correlation
출장소(통) is highly overall correlated with and 6 other fieldsHigh correlation
법정리 is highly overall correlated with and 3 other fieldsHigh correlation
행정리 is highly overall correlated with and 2 other fieldsHigh correlation
반(도시) is highly overall correlated with and 6 other fieldsHigh correlation
면적 is highly overall correlated with and 3 other fieldsHigh correlation
세대수 is highly overall correlated with and 6 other fieldsHigh correlation
인구(남) is highly overall correlated with and 5 other fieldsHigh correlation
인구(여) is highly overall correlated with and 5 other fieldsHigh correlation
is highly overall correlated with 법정리 and 2 other fieldsHigh correlation
출장소(시군) is highly imbalanced (73.3%)Imbalance
구분 has unique valuesUnique
면적 has unique valuesUnique
세대수 has unique valuesUnique
인구(남) has unique valuesUnique
인구(여) has unique valuesUnique
has 1 (4.5%) zerosZeros
has 17 (77.3%) zerosZeros
법정동 has 17 (77.3%) zerosZeros
출장소(통) has 17 (77.3%) zerosZeros
법정리 has 1 (4.5%) zerosZeros
행정리 has 1 (4.5%) zerosZeros
반(도시) has 17 (77.3%) zerosZeros
반(농촌) has 1 (4.5%) zerosZeros

Reproduction

Analysis started2023-12-12 11:59:18.813644
Analysis finished2023-12-12 11:59:36.432965
Duration17.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T20:59:36.574439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters66
Distinct characters35
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

Unique22 ?
Unique (%)100.0%

Sample

1st row목포시
2nd row여수시
3rd row순천시
4th row나주시
5th row광양시
ValueCountFrequency (%)
목포시 1
 
4.5%
여수시 1
 
4.5%
진도군 1
 
4.5%
완도군 1
 
4.5%
장성군 1
 
4.5%
영광군 1
 
4.5%
함평군 1
 
4.5%
무안군 1
 
4.5%
영암군 1
 
4.5%
해남군 1
 
4.5%
Other values (12) 12
54.5%
2023-12-12T20:59:36.962235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17
25.8%
5
 
7.6%
3
 
4.5%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
Other values (25) 27
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
17
25.8%
5
 
7.6%
3
 
4.5%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
Other values (25) 27
40.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
17
25.8%
5
 
7.6%
3
 
4.5%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
Other values (25) 27
40.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
17
25.8%
5
 
7.6%
3
 
4.5%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
Other values (25) 27
40.9%


Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size308.0 B
1
13 
2
3
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)4.5%

Sample

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

Common Values

ValueCountFrequency (%)
1 13
59.1%
2 4
 
18.2%
3 4
 
18.2%
0 1
 
4.5%

Length

2023-12-12T20:59:37.124990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:59:37.289464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 13
59.1%
2 4
 
18.2%
3 4
 
18.2%
0 1
 
4.5%


Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9090909
Minimum0
Maximum14
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T20:59:37.407862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q17
median9.5
Q310.75
95-th percentile12.95
Maximum14
Range14
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation3.1153856
Coefficient of variation (CV)0.34968614
Kurtosis1.8133538
Mean8.9090909
Median Absolute Deviation (MAD)2.5
Skewness-0.89458457
Sum196
Variance9.7056277
MonotonicityNot monotonic
2023-12-12T20:59:37.655443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10 5
22.7%
6 4
18.2%
12 3
13.6%
7 2
 
9.1%
9 2
 
9.1%
8 2
 
9.1%
0 1
 
4.5%
11 1
 
4.5%
14 1
 
4.5%
13 1
 
4.5%
ValueCountFrequency (%)
0 1
 
4.5%
6 4
18.2%
7 2
 
9.1%
8 2
 
9.1%
9 2
 
9.1%
10 5
22.7%
11 1
 
4.5%
12 3
13.6%
13 1
 
4.5%
14 1
 
4.5%
ValueCountFrequency (%)
14 1
 
4.5%
13 1
 
4.5%
12 3
13.6%
11 1
 
4.5%
10 5
22.7%
9 2
 
9.1%
8 2
 
9.1%
7 2
 
9.1%
6 4
18.2%
0 1
 
4.5%


Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0909091
Minimum0
Maximum23
Zeros17
Zeros (%)77.3%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T20:59:37.856571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile19.65
Maximum23
Range23
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.7676337
Coefficient of variation (CV)2.1895285
Kurtosis4.0843042
Mean3.0909091
Median Absolute Deviation (MAD)0
Skewness2.2414371
Sum68
Variance45.800866
MonotonicityDecreasing
2023-12-12T20:59:37.984297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 17
77.3%
23 1
 
4.5%
20 1
 
4.5%
13 1
 
4.5%
7 1
 
4.5%
5 1
 
4.5%
ValueCountFrequency (%)
0 17
77.3%
5 1
 
4.5%
7 1
 
4.5%
13 1
 
4.5%
20 1
 
4.5%
23 1
 
4.5%
ValueCountFrequency (%)
23 1
 
4.5%
20 1
 
4.5%
13 1
 
4.5%
7 1
 
4.5%
5 1
 
4.5%
0 17
77.3%

법정동
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7272727
Minimum0
Maximum64
Zeros17
Zeros (%)77.3%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T20:59:38.136371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile50.15
Maximum64
Range64
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.729101
Coefficient of variation (CV)2.1460428
Kurtosis3.4136955
Mean8.7272727
Median Absolute Deviation (MAD)0
Skewness2.101268
Sum192
Variance350.77922
MonotonicityNot monotonic
2023-12-12T20:59:38.409580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 17
77.3%
64 1
 
4.5%
51 1
 
4.5%
33 1
 
4.5%
34 1
 
4.5%
10 1
 
4.5%
ValueCountFrequency (%)
0 17
77.3%
10 1
 
4.5%
33 1
 
4.5%
34 1
 
4.5%
51 1
 
4.5%
64 1
 
4.5%
ValueCountFrequency (%)
64 1
 
4.5%
51 1
 
4.5%
34 1
 
4.5%
33 1
 
4.5%
10 1
 
4.5%
0 17
77.3%

출장소(도)
Categorical

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
0
22 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 22
100.0%

Length

2023-12-12T20:59:38.778648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:59:39.100822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22
100.0%

출장소(시군)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size308.0 B
0
21 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)4.5%

Sample

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

Common Values

ValueCountFrequency (%)
0 21
95.5%
1 1
 
4.5%

Length

2023-12-12T20:59:39.298358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:59:39.433482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21
95.5%
1 1
 
4.5%

출장소(읍면)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Memory size308.0 B
0
11 
1
2
3
 
1
13
 
1

Length

Max length2
Median length1
Mean length1.0454545
Min length1

Unique

Unique2 ?
Unique (%)9.1%

Sample

1st row0
2nd row3
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11
50.0%
1 5
22.7%
2 4
 
18.2%
3 1
 
4.5%
13 1
 
4.5%

Length

2023-12-12T20:59:39.578212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:59:39.723022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 11
50.0%
1 5
22.7%
2 4
 
18.2%
3 1
 
4.5%
13 1
 
4.5%

출장소(통)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.681818
Minimum0
Maximum623
Zeros17
Zeros (%)77.3%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T20:59:39.842548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile487.25
Maximum623
Range623
Interquartile range (IQR)0

Descriptive statistics

Standard deviation180.71666
Coefficient of variation (CV)2.1856881
Kurtosis3.8871005
Mean82.681818
Median Absolute Deviation (MAD)0
Skewness2.20772
Sum1819
Variance32658.513
MonotonicityDecreasing
2023-12-12T20:59:39.980971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 17
77.3%
623 1
 
4.5%
492 1
 
4.5%
397 1
 
4.5%
179 1
 
4.5%
128 1
 
4.5%
ValueCountFrequency (%)
0 17
77.3%
128 1
 
4.5%
179 1
 
4.5%
397 1
 
4.5%
492 1
 
4.5%
623 1
 
4.5%
ValueCountFrequency (%)
623 1
 
4.5%
492 1
 
4.5%
397 1
 
4.5%
179 1
 
4.5%
128 1
 
4.5%
0 17
77.3%

법정리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.04545
Minimum0
Maximum188
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T20:59:40.135828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile59.2
Q199.5
median124
Q3139
95-th percentile177.25
Maximum188
Range188
Interquartile range (IQR)39.5

Descriptive statistics

Standard deviation42.575957
Coefficient of variation (CV)0.36689035
Kurtosis1.4393824
Mean116.04545
Median Absolute Deviation (MAD)20
Skewness-0.82417278
Sum2553
Variance1812.7121
MonotonicityNot monotonic
2023-12-12T20:59:40.300894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
104 2
 
9.1%
0 1
 
4.5%
63 1
 
4.5%
143 1
 
4.5%
98 1
 
4.5%
89 1
 
4.5%
123 1
 
4.5%
127 1
 
4.5%
121 1
 
4.5%
178 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
0 1
4.5%
59 1
4.5%
63 1
4.5%
69 1
4.5%
89 1
4.5%
98 1
4.5%
104 2
9.1%
112 1
4.5%
121 1
4.5%
123 1
4.5%
ValueCountFrequency (%)
188 1
4.5%
178 1
4.5%
163 1
4.5%
154 1
4.5%
143 1
4.5%
140 1
4.5%
136 1
4.5%
131 1
4.5%
127 1
4.5%
126 1
4.5%

행정리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean312.81818
Minimum0
Maximum515
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T20:59:40.454038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile157.5
Q1252.75
median292.5
Q3390
95-th percentile512.85
Maximum515
Range515
Interquartile range (IQR)137.25

Descriptive statistics

Standard deviation121.45218
Coefficient of variation (CV)0.38825167
Kurtosis0.92070614
Mean312.81818
Median Absolute Deviation (MAD)54.5
Skewness-0.34578118
Sum6882
Variance14750.632
MonotonicityNot monotonic
2023-12-12T20:59:40.624384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
292 2
 
9.1%
273 2
 
9.1%
515 2
 
9.1%
0 1
 
4.5%
281 1
 
4.5%
343 1
 
4.5%
242 1
 
4.5%
246 1
 
4.5%
427 1
 
4.5%
403 1
 
4.5%
Other values (9) 9
40.9%
ValueCountFrequency (%)
0 1
4.5%
155 1
4.5%
205 1
4.5%
215 1
4.5%
242 1
4.5%
246 1
4.5%
273 2
9.1%
281 1
4.5%
292 2
9.1%
293 1
4.5%
ValueCountFrequency (%)
515 2
9.1%
472 1
4.5%
455 1
4.5%
427 1
4.5%
403 1
4.5%
351 1
4.5%
343 1
4.5%
318 1
4.5%
316 1
4.5%
293 1
4.5%

반(도시)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean433.90909
Minimum0
Maximum3576
Zeros17
Zeros (%)77.3%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T20:59:40.763090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2643.65
Maximum3576
Range3576
Interquartile range (IQR)0

Descriptive statistics

Standard deviation975.78086
Coefficient of variation (CV)2.248814
Kurtosis5.3716178
Mean433.90909
Median Absolute Deviation (MAD)0
Skewness2.4348323
Sum9546
Variance952148.28
MonotonicityNot monotonic
2023-12-12T20:59:40.919240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 17
77.3%
2695 1
 
4.5%
3576 1
 
4.5%
1668 1
 
4.5%
884 1
 
4.5%
723 1
 
4.5%
ValueCountFrequency (%)
0 17
77.3%
723 1
 
4.5%
884 1
 
4.5%
1668 1
 
4.5%
2695 1
 
4.5%
3576 1
 
4.5%
ValueCountFrequency (%)
3576 1
 
4.5%
2695 1
 
4.5%
1668 1
 
4.5%
884 1
 
4.5%
723 1
 
4.5%
0 17
77.3%

반(농촌)
Real number (ℝ)

ZEROS 

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean710.09091
Minimum0
Maximum1392
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T20:59:41.080981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile372.6
Q1539
median714
Q3830.25
95-th percentile1327.3
Maximum1392
Range1392
Interquartile range (IQR)291.25

Descriptive statistics

Standard deviation299.0601
Coefficient of variation (CV)0.42115748
Kurtosis1.7837818
Mean710.09091
Median Absolute Deviation (MAD)150
Skewness0.3299293
Sum15622
Variance89436.944
MonotonicityNot monotonic
2023-12-12T20:59:41.256938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
714 2
 
9.1%
0 1
 
4.5%
875 1
 
4.5%
844 1
 
4.5%
1345 1
 
4.5%
575 1
 
4.5%
897 1
 
4.5%
517 1
 
4.5%
789 1
 
4.5%
368 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
0 1
4.5%
368 1
4.5%
460 1
4.5%
497 1
4.5%
517 1
4.5%
527 1
4.5%
575 1
4.5%
612 1
4.5%
623 1
4.5%
650 1
4.5%
ValueCountFrequency (%)
1392 1
4.5%
1345 1
4.5%
991 1
4.5%
897 1
4.5%
875 1
4.5%
844 1
4.5%
789 1
4.5%
783 1
4.5%
728 1
4.5%
721 1
4.5%

면적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean561.76955
Minimum51.66
Maximum1043.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T20:59:41.412830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51.66
5-th percentile392.2665
Q1451.58
median515.305
Q3647.3075
95-th percentile905.7685
Maximum1043.76
Range992.1
Interquartile range (IQR)195.7275

Descriptive statistics

Standard deviation205.55287
Coefficient of variation (CV)0.36590248
Kurtosis1.6832842
Mean561.76955
Median Absolute Deviation (MAD)95.16
Skewness0.19854926
Sum12358.93
Variance42251.982
MonotonicityNot monotonic
2023-12-12T20:59:41.567776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
51.66 1
 
4.5%
500.9 1
 
4.5%
655.62 1
 
4.5%
440.11 1
 
4.5%
396.76 1
 
4.5%
518.35 1
 
4.5%
474.71 1
 
4.5%
392.03 1
 
4.5%
450.41 1
 
4.5%
612.48 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
51.66 1
4.5%
392.03 1
4.5%
396.76 1
4.5%
440.11 1
4.5%
442.94 1
4.5%
450.41 1
4.5%
455.09 1
4.5%
464.13 1
4.5%
474.71 1
4.5%
500.9 1
4.5%
ValueCountFrequency (%)
1043.76 1
4.5%
910.95 1
4.5%
807.32 1
4.5%
787.03 1
4.5%
664.09 1
4.5%
655.62 1
4.5%
622.37 1
4.5%
612.48 1
4.5%
608.45 1
4.5%
547.51 1
4.5%

세대수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41259.545
Minimum13429
Maximum128720
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T20:59:41.728541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13429
5-th percentile15565.3
Q120295
median26665
Q341200.5
95-th percentile123788.55
Maximum128720
Range115291
Interquartile range (IQR)20905.5

Descriptive statistics

Standard deviation34725.411
Coefficient of variation (CV)0.84163339
Kurtosis2.0245961
Mean41259.545
Median Absolute Deviation (MAD)8532
Skewness1.7580202
Sum907710
Variance1.2058542 × 109
MonotonicityNot monotonic
2023-12-12T20:59:41.895831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
104039 1
 
4.5%
18550 1
 
4.5%
21816 1
 
4.5%
16578 1
 
4.5%
25781 1
 
4.5%
23358 1
 
4.5%
27549 1
 
4.5%
18010 1
 
4.5%
43119 1
 
4.5%
28353 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
13429 1
4.5%
15512 1
4.5%
16578 1
4.5%
18010 1
4.5%
18550 1
4.5%
19788 1
4.5%
21816 1
4.5%
22282 1
4.5%
23358 1
4.5%
24787 1
4.5%
ValueCountFrequency (%)
128720 1
4.5%
124828 1
4.5%
104039 1
4.5%
68734 1
4.5%
59865 1
4.5%
43119 1
4.5%
35445 1
4.5%
35074 1
4.5%
32093 1
4.5%
28353 1
4.5%

인구(남)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41603.273
Minimum12052
Maximum139821
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T20:59:42.058943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12052
5-th percentile13450.9
Q117757.25
median25049
Q342398.25
95-th percentile137454.6
Maximum139821
Range127769
Interquartile range (IQR)24641

Descriptive statistics

Standard deviation39247.506
Coefficient of variation (CV)0.94337544
Kurtosis2.0929056
Mean41603.273
Median Absolute Deviation (MAD)8322
Skewness1.7805603
Sum915272
Variance1.5403667 × 109
MonotonicityNot monotonic
2023-12-12T20:59:42.203315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
107826 1
 
4.5%
16128 1
 
4.5%
20346 1
 
4.5%
14684 1
 
4.5%
24017 1
 
4.5%
22162 1
 
4.5%
26081 1
 
4.5%
15539 1
 
4.5%
45607 1
 
4.5%
27359 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
12052 1
4.5%
13386 1
4.5%
14684 1
4.5%
15539 1
4.5%
16128 1
4.5%
17402 1
4.5%
18823 1
4.5%
20346 1
4.5%
22162 1
4.5%
23168 1
4.5%
ValueCountFrequency (%)
139821 1
4.5%
139014 1
4.5%
107826 1
4.5%
79523 1
4.5%
58749 1
4.5%
45607 1
4.5%
32772 1
4.5%
30643 1
4.5%
30170 1
4.5%
27359 1
4.5%

인구(여)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41019.318
Minimum12603
Maximum139723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T20:59:42.337856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12603
5-th percentile13731.45
Q117696
median24308
Q342015.5
95-th percentile133652.45
Maximum139723
Range127120
Interquartile range (IQR)24319.5

Descriptive statistics

Standard deviation38587.078
Coefficient of variation (CV)0.94070502
Kurtosis2.2116225
Mean41019.318
Median Absolute Deviation (MAD)8076.5
Skewness1.8108747
Sum902425
Variance1.4889626 × 109
MonotonicityNot monotonic
2023-12-12T20:59:42.483115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
109113 1
 
4.5%
17049 1
 
4.5%
17512 1
 
4.5%
14823 1
 
4.5%
23580 1
 
4.5%
20984 1
 
4.5%
26116 1
 
4.5%
15245 1
 
4.5%
45001 1
 
4.5%
25036 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
12603 1
4.5%
13674 1
4.5%
14823 1
4.5%
15245 1
4.5%
17049 1
4.5%
17512 1
4.5%
18248 1
4.5%
19648 1
4.5%
20984 1
4.5%
22624 1
4.5%
ValueCountFrequency (%)
139723 1
4.5%
134944 1
4.5%
109113 1
4.5%
72645 1
4.5%
57707 1
4.5%
45001 1
4.5%
33059 1
4.5%
31710 1
4.5%
31381 1
4.5%
26116 1
4.5%

Interactions

2023-12-12T20:59:34.752080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:19.502092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:20.956428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:22.049985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:23.216658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:24.586335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:26.015873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:27.708338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:29.023501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:30.390643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:31.562867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:32.949386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:34.864783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:19.604476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:21.037496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:22.143640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:23.324025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:24.743702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:26.137824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:27.811735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:29.131268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:30.497321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:31.670300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:33.072417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:34.976415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:19.698215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:21.117317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:22.221718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:23.410440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:24.837314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:26.263201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:27.918786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:29.237435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:30.599026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:31.788840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:33.194123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:35.110153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:19.792518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:21.196045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:22.308225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:23.516661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:24.972857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:26.385304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:28.030958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:29.341410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:30.692466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:31.896754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:33.322565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:35.225920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:19.879827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:21.275105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:22.410830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:23.618098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:25.066135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:26.496143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:28.135134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:29.446366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:30.789283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:32.016238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:33.449158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:35.326193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:19.983455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:21.365332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:22.522693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:23.777973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:25.149994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:26.611274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:28.235384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:29.577853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:30.882361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:32.150814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:33.573443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:35.422986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:20.090687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:21.478307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:22.615742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:23.898302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:25.235273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:26.709075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:28.344964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:29.685173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:30.965849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:32.283350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:33.706414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:35.535483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:20.197813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:21.573291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:22.711686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:24.012568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:25.391401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:26.829056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:28.451884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:29.812460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:31.064386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:32.409782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:33.869651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:35.641573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:20.584162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:21.669261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:22.818917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:24.113430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:25.537532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:26.929008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:28.551527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:29.917895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:31.156720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:32.515319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:34.350593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:35.720642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:20.676402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:21.757121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:22.899844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:24.221243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:25.658477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:27.390319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:28.654974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:30.006250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:31.243523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:32.608604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:34.440379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:35.799108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:20.779322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:21.856574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:22.989593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:24.335668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:25.769963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:27.490674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:28.763950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:30.159107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:31.356006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:32.710246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:34.534453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:35.901001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:20.867280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:21.963948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:23.101422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:24.454713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:25.905940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:27.599365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:28.889045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:30.293352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:31.454784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:32.824254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:59:34.643256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:59:42.939277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분법정동출장소(시군)출장소(읍면)출장소(통)법정리행정리반(도시)반(농촌)면적세대수인구(남)인구(여)
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
1.0001.0000.7010.6170.5670.0000.2180.5670.8990.8870.5670.8290.9060.7310.6430.625
1.0000.7011.0000.5490.5780.0000.0000.5590.6540.8060.5590.7000.6900.6560.7840.814
1.0000.6170.5491.0001.0001.0000.5121.0000.7560.7351.0000.3760.7160.9560.9260.926
법정동1.0000.5670.5781.0001.0001.0000.7490.9930.8300.7230.9930.5960.6120.8170.9070.907
출장소(시군)1.0000.0000.0001.0001.0001.0001.0001.0000.0000.0001.0000.0000.0000.7500.4800.480
출장소(읍면)1.0000.2180.0000.5120.7491.0001.0000.8110.0000.4660.8110.6270.1460.3770.0000.000
출장소(통)1.0000.5670.5591.0000.9931.0000.8111.0000.7480.6991.0000.4760.7520.8870.8780.878
법정리1.0000.8990.6540.7560.8300.0000.0000.7481.0000.8370.7480.8770.8210.7710.7700.771
행정리1.0000.8870.8060.7350.7230.0000.4660.6990.8371.0000.6990.7970.8260.7900.8350.823
반(도시)1.0000.5670.5591.0000.9931.0000.8111.0000.7480.6991.0000.4760.7520.8870.8780.878
반(농촌)1.0000.8290.7000.3760.5960.0000.6270.4760.8770.7970.4761.0000.8940.5790.3940.483
면적1.0000.9060.6900.7160.6120.0000.1460.7520.8210.8260.7520.8941.0000.6050.6030.642
세대수1.0000.7310.6560.9560.8170.7500.3770.8870.7710.7900.8870.5790.6051.0000.9760.964
인구(남)1.0000.6430.7840.9260.9070.4800.0000.8780.7700.8350.8780.3940.6030.9761.0001.000
인구(여)1.0000.6250.8140.9260.9070.4800.0000.8780.7710.8230.8780.4830.6420.9641.0001.000
2023-12-12T20:59:43.160784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
출장소(읍면)출장소(시군)
출장소(읍면)1.0000.9220.143
출장소(시군)0.9221.0000.000
0.1430.0001.000
2023-12-12T20:59:43.312738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동출장소(통)법정리행정리반(도시)반(농촌)면적세대수인구(남)인구(여)출장소(시군)출장소(읍면)
1.000-0.314-0.306-0.3140.8190.750-0.3100.2430.706-0.039-0.038-0.0450.4990.0000.000
-0.3141.0000.9981.000-0.239-0.2070.998-0.070-0.0680.7280.7280.7270.4090.8940.351
법정동-0.3060.9981.0000.998-0.241-0.2090.996-0.068-0.0790.7240.7240.7200.4720.9220.359
출장소(통)-0.3141.0000.9981.000-0.239-0.2070.998-0.070-0.0680.7280.7280.7270.4720.9220.426
법정리0.819-0.239-0.241-0.2391.0000.800-0.2360.2760.7850.0410.0610.0690.5160.0000.000
행정리0.750-0.207-0.209-0.2070.8001.000-0.2020.3750.7320.2790.2750.2830.4980.0000.249
반(도시)-0.3100.9980.9960.998-0.236-0.2021.000-0.044-0.0530.7310.7310.7280.4720.9220.426
반(농촌)0.243-0.070-0.068-0.0700.2760.375-0.0441.0000.2720.3030.2830.2770.4240.0000.389
면적0.706-0.068-0.079-0.0680.7850.732-0.0530.2721.0000.2140.2140.2200.5280.0000.000
세대수-0.0390.7280.7240.7280.0410.2790.7310.3030.2141.0000.9950.9950.5330.5000.259
인구(남)-0.0380.7280.7240.7280.0610.2750.7310.2830.2140.9951.0000.9930.4570.4470.000
인구(여)-0.0450.7270.7200.7270.0690.2830.7280.2770.2200.9950.9931.0000.4350.4470.000
0.4990.4090.4720.4720.5160.4980.4720.4240.5280.5330.4570.4351.0000.0000.143
출장소(시군)0.0000.8940.9220.9220.0000.0000.9220.0000.0000.5000.4470.4470.0001.0000.922
출장소(읍면)0.0000.3510.3590.4260.0000.2490.4260.3890.0000.2590.0000.0000.1430.9221.000

Missing values

2023-12-12T20:59:36.072895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:59:36.343541image/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

구분법정동출장소(도)출장소(시군)출장소(읍면)출장소(통)법정리행정리반(도시)반(농촌)면적세대수인구(남)인구(여)
0목포시002364000623002695051.66104039107826109113
1여수시162051013492632153576875512.26128720139821134944
2순천시11013330023971634721668728910.95124828139014139723
3나주시112734000179154455884783608.45598655874957707
4광양시1651000012859205723527464.13687347952372645
5담양군1110000001403180623455.09247872316822624
6곡성군1100000001252730497547.51155121338613674
7구례군17000000691550460442.94134291205212603
8고흥군21400002013151501392807.32354453017031710
9보성군2100000101263160721664.09222821882319648
구분법정동출장소(도)출장소(시군)출장소(읍면)출장소(통)법정리행정리반(도시)반(농촌)면적세대수인구(남)인구(여)
12강진군1100000001122930612500.9185501612817049
13해남군11300000017851503681043.76350743277233059
14영암군290000101214030714612.48283532735925036
15무안군360000101044270789450.41431194560745001
16함평군180000001042730517392.03180101553915245
17영광군380000101272920897474.71275492608126116
18장성군1100000001232920575518.35233582216220984
19완도군390000208924601345396.76257812401723580
20진도군16000020982420714440.11165781468414823
21신안군21200001301433430844655.62218162034617512