Overview

Dataset statistics

Number of variables10
Number of observations1166
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory101.5 KiB
Average record size in memory89.1 B

Variable types

Categorical1
Numeric8
Text1

Dataset

Description경기도_주민등록인구통계주민등록인구및세대현황행정동별집계기본
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=KFR5ARH1QPRYM8ZMA4O234704360&infSeq=1

Alerts

총인구수 is highly overall correlated with 세대수 and 3 other fieldsHigh correlation
세대수 is highly overall correlated with 총인구수 and 2 other fieldsHigh correlation
세대당인구수 is highly overall correlated with 총인구수 and 4 other fieldsHigh correlation
남성여성비율 is highly overall correlated with 세대당인구수 and 1 other fieldsHigh correlation
남성비율 is highly overall correlated with 세대당인구수 and 1 other fieldsHigh correlation
남성인구수 is highly overall correlated with 총인구수 and 3 other fieldsHigh correlation
여성인구수 is highly overall correlated with 총인구수 and 3 other fieldsHigh correlation

Reproduction

Analysis started2024-03-13 00:01:03.862786
Analysis finished2024-03-13 00:01:09.399386
Duration5.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연월
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.2 KiB
2016
595 
2017
571 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2016 595
51.0%
2017 571
49.0%

Length

2024-03-13T09:01:09.467745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T09:01:09.545706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 595
51.0%
2017 571
49.0%

행정동코드
Real number (ℝ)

Distinct608
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41350936
Minimum41111560
Maximum41830410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2024-03-13T09:01:09.628855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41111560
5-th percentile41115720
Q141190580
median41285530
Q341480538
95-th percentile41670520
Maximum41830410
Range718850
Interquartile range (IQR)289957.5

Descriptive statistics

Standard deviation198119.58
Coefficient of variation (CV)0.0047911752
Kurtosis-0.55982077
Mean41350936
Median Absolute Deviation (MAD)149870.5
Skewness0.67679651
Sum4.8215191 × 1010
Variance3.9251367 × 1010
MonotonicityNot monotonic
2024-03-13T09:01:09.740016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41630520 2
 
0.2%
41171630 2
 
0.2%
41171580 2
 
0.2%
41171581 2
 
0.2%
41171590 2
 
0.2%
41171600 2
 
0.2%
41171610 2
 
0.2%
41171621 2
 
0.2%
41173510 2
 
0.2%
41273535 2
 
0.2%
Other values (598) 1146
98.3%
ValueCountFrequency (%)
41111560 2
0.2%
41111566 2
0.2%
41111571 2
0.2%
41111572 2
0.2%
41111573 2
0.2%
41111580 2
0.2%
41111591 2
0.2%
41111597 2
0.2%
41111598 2
0.2%
41111600 2
0.2%
ValueCountFrequency (%)
41830410 2
0.2%
41830400 2
0.2%
41830395 2
0.2%
41830380 2
0.2%
41830370 2
0.2%
41830360 2
0.2%
41830350 2
0.2%
41830340 2
0.2%
41830330 2
0.2%
41830320 2
0.2%
Distinct548
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Memory size9.2 KiB
2024-03-13T09:01:09.987738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.3893654
Min length2

Characters and Unicode

Total characters3952
Distinct characters197
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

Unique13 ?
Unique (%)1.1%

Sample

1st row양주2동
2nd row회천1동
3rd row회천2동
4th row회천3동
5th row회천4동
ValueCountFrequency (%)
중앙동 15
 
1.3%
금곡동 6
 
0.5%
대야동 4
 
0.3%
반월동 4
 
0.3%
고등동 4
 
0.3%
위례동 4
 
0.3%
부림동 4
 
0.3%
풍산동 4
 
0.3%
신장2동 4
 
0.3%
이동면 4
 
0.3%
Other values (538) 1113
95.5%
2024-03-13T09:01:10.342256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
920
23.3%
209
 
5.3%
1 171
 
4.3%
2 163
 
4.1%
3 77
 
1.9%
70
 
1.8%
69
 
1.7%
63
 
1.6%
62
 
1.6%
58
 
1.5%
Other values (187) 2090
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3504
88.7%
Decimal Number 448
 
11.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
920
26.3%
209
 
6.0%
70
 
2.0%
69
 
2.0%
63
 
1.8%
62
 
1.8%
58
 
1.7%
57
 
1.6%
55
 
1.6%
51
 
1.5%
Other values (178) 1890
53.9%
Decimal Number
ValueCountFrequency (%)
1 171
38.2%
2 163
36.4%
3 77
17.2%
4 21
 
4.7%
5 4
 
0.9%
6 4
 
0.9%
7 4
 
0.9%
8 2
 
0.4%
9 2
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3504
88.7%
Common 448
 
11.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
920
26.3%
209
 
6.0%
70
 
2.0%
69
 
2.0%
63
 
1.8%
62
 
1.8%
58
 
1.7%
57
 
1.6%
55
 
1.6%
51
 
1.5%
Other values (178) 1890
53.9%
Common
ValueCountFrequency (%)
1 171
38.2%
2 163
36.4%
3 77
17.2%
4 21
 
4.7%
5 4
 
0.9%
6 4
 
0.9%
7 4
 
0.9%
8 2
 
0.4%
9 2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3504
88.7%
ASCII 448
 
11.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
920
26.3%
209
 
6.0%
70
 
2.0%
69
 
2.0%
63
 
1.8%
62
 
1.8%
58
 
1.7%
57
 
1.6%
55
 
1.6%
51
 
1.5%
Other values (178) 1890
53.9%
ASCII
ValueCountFrequency (%)
1 171
38.2%
2 163
36.4%
3 77
17.2%
4 21
 
4.7%
5 4
 
0.9%
6 4
 
0.9%
7 4
 
0.9%
8 2
 
0.4%
9 2
 
0.4%

총인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct1147
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28577.576
Minimum172
Maximum425016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2024-03-13T09:01:10.481086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum172
5-th percentile3826.25
Q112073
median21380.5
Q332797.25
95-th percentile78666.75
Maximum425016
Range424844
Interquartile range (IQR)20724.25

Descriptive statistics

Standard deviation33383.278
Coefficient of variation (CV)1.1681634
Kurtosis31.286144
Mean28577.576
Median Absolute Deviation (MAD)10104
Skewness4.5944542
Sum33321454
Variance1.1144433 × 109
MonotonicityNot monotonic
2024-03-13T09:01:10.584230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16172 2
 
0.2%
12303 2
 
0.2%
21373 2
 
0.2%
24841 2
 
0.2%
17589 2
 
0.2%
12265 2
 
0.2%
10287 2
 
0.2%
19615 2
 
0.2%
10575 2
 
0.2%
20774 2
 
0.2%
Other values (1137) 1146
98.3%
ValueCountFrequency (%)
172 2
0.2%
208 1
0.1%
223 1
0.1%
623 1
0.1%
728 1
0.1%
755 1
0.1%
1093 1
0.1%
1101 1
0.1%
1601 1
0.1%
1626 1
0.1%
ValueCountFrequency (%)
425016 1
0.1%
290611 1
0.1%
270061 1
0.1%
235604 1
0.1%
231926 1
0.1%
214017 1
0.1%
211552 1
0.1%
211009 1
0.1%
203168 1
0.1%
202199 1
0.1%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct1138
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11270.368
Minimum73
Maximum148108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2024-03-13T09:01:10.685199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum73
5-th percentile1897
Q15220.5
median8560.5
Q312789.5
95-th percentile26744
Maximum148108
Range148035
Interquartile range (IQR)7569

Descriptive statistics

Standard deviation12673.788
Coefficient of variation (CV)1.124523
Kurtosis29.037252
Mean11270.368
Median Absolute Deviation (MAD)3802
Skewness4.5588061
Sum13141249
Variance1.6062491 × 108
MonotonicityNot monotonic
2024-03-13T09:01:10.789689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10486 3
 
0.3%
9450 2
 
0.2%
9063 2
 
0.2%
11922 2
 
0.2%
4068 2
 
0.2%
7429 2
 
0.2%
4692 2
 
0.2%
10756 2
 
0.2%
8233 2
 
0.2%
6375 2
 
0.2%
Other values (1128) 1145
98.2%
ValueCountFrequency (%)
73 1
0.1%
75 1
0.1%
111 1
0.1%
121 1
0.1%
239 1
0.1%
331 1
0.1%
347 1
0.1%
568 1
0.1%
574 1
0.1%
776 1
0.1%
ValueCountFrequency (%)
148108 1
0.1%
120578 1
0.1%
105095 1
0.1%
102119 1
0.1%
94263 1
0.1%
92785 1
0.1%
81779 1
0.1%
80884 1
0.1%
77093 1
0.1%
74684 1
0.1%

세대당인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct162
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4463036
Minimum1.31
Maximum3.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2024-03-13T09:01:10.895669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.31
5-th percentile1.9
Q12.18
median2.47
Q32.7
95-th percentile3.02
Maximum3.39
Range2.08
Interquartile range (IQR)0.52

Descriptive statistics

Standard deviation0.34880688
Coefficient of variation (CV)0.14258528
Kurtosis-0.54272204
Mean2.4463036
Median Absolute Deviation (MAD)0.27
Skewness0.01597778
Sum2852.39
Variance0.12166624
MonotonicityNot monotonic
2024-03-13T09:01:10.997216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.64 22
 
1.9%
2.54 21
 
1.8%
2.53 19
 
1.6%
2.49 18
 
1.5%
2.04 17
 
1.5%
2.61 17
 
1.5%
2.2 16
 
1.4%
2.55 15
 
1.3%
2.5 15
 
1.3%
2.58 15
 
1.3%
Other values (152) 991
85.0%
ValueCountFrequency (%)
1.31 1
0.1%
1.32 1
0.1%
1.46 1
0.1%
1.57 1
0.1%
1.6 1
0.1%
1.65 1
0.1%
1.66 1
0.1%
1.7 2
0.2%
1.72 1
0.1%
1.73 2
0.2%
ValueCountFrequency (%)
3.39 2
0.2%
3.36 1
0.1%
3.34 1
0.1%
3.27 1
0.1%
3.25 1
0.1%
3.23 1
0.1%
3.2 1
0.1%
3.19 1
0.1%
3.18 1
0.1%
3.17 1
0.1%

남성여성비율
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0357719
Minimum0.86
Maximum1.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2024-03-13T09:01:11.099366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.86
5-th percentile0.94
Q10.97
median1.015
Q31.07
95-th percentile1.21
Maximum1.62
Range0.76
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.095690198
Coefficient of variation (CV)0.0923854
Kurtosis6.916889
Mean1.0357719
Median Absolute Deviation (MAD)0.045
Skewness2.1290998
Sum1207.71
Variance0.009156614
MonotonicityNot monotonic
2024-03-13T09:01:11.208820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.02 83
 
7.1%
1.01 82
 
7.0%
0.99 78
 
6.7%
0.96 72
 
6.2%
0.97 71
 
6.1%
0.98 67
 
5.7%
0.95 63
 
5.4%
1.04 59
 
5.1%
1.0 56
 
4.8%
1.03 50
 
4.3%
Other values (50) 485
41.6%
ValueCountFrequency (%)
0.86 1
 
0.1%
0.87 1
 
0.1%
0.89 5
 
0.4%
0.9 6
 
0.5%
0.91 6
 
0.5%
0.92 16
 
1.4%
0.93 22
 
1.9%
0.94 37
3.2%
0.95 63
5.4%
0.96 72
6.2%
ValueCountFrequency (%)
1.62 1
 
0.1%
1.61 1
 
0.1%
1.55 3
0.3%
1.5 1
 
0.1%
1.48 1
 
0.1%
1.46 2
0.2%
1.43 1
 
0.1%
1.42 1
 
0.1%
1.41 2
0.2%
1.38 2
0.2%

남성비율
Real number (ℝ)

HIGH CORRELATION 

Distinct542
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.78084
Minimum46.35
Maximum61.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2024-03-13T09:01:11.541850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46.35
5-th percentile48.33
Q149.35
median50.375
Q351.69
95-th percentile54.7875
Maximum61.9
Range15.55
Interquartile range (IQR)2.34

Descriptive statistics

Standard deviation2.1258567
Coefficient of variation (CV)0.041863361
Kurtosis4.0814647
Mean50.78084
Median Absolute Deviation (MAD)1.125
Skewness1.6219623
Sum59210.46
Variance4.5192666
MonotonicityNot monotonic
2024-03-13T09:01:11.647629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.21 9
 
0.8%
50.57 8
 
0.7%
50.56 8
 
0.7%
50.4 8
 
0.7%
50.46 7
 
0.6%
50.19 7
 
0.6%
49.24 7
 
0.6%
49.7 7
 
0.6%
50.6 6
 
0.5%
50.75 6
 
0.5%
Other values (532) 1093
93.7%
ValueCountFrequency (%)
46.35 1
0.1%
46.41 1
0.1%
47.0 1
0.1%
47.05 1
0.1%
47.14 1
0.1%
47.15 1
0.1%
47.22 1
0.1%
47.25 1
0.1%
47.38 1
0.1%
47.41 1
0.1%
ValueCountFrequency (%)
61.9 1
0.1%
61.74 1
0.1%
60.85 1
0.1%
60.84 1
0.1%
60.74 1
0.1%
60.07 1
0.1%
59.64 1
0.1%
59.39 1
0.1%
59.3 1
0.1%
58.92 1
0.1%

남성인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct1135
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14370.094
Minimum91
Maximum206749
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2024-03-13T09:01:11.762959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum91
5-th percentile2011.75
Q16126
median10674.5
Q316154.75
95-th percentile39419.75
Maximum206749
Range206658
Interquartile range (IQR)10028.75

Descriptive statistics

Standard deviation16723.816
Coefficient of variation (CV)1.163793
Kurtosis30.317751
Mean14370.094
Median Absolute Deviation (MAD)4905.5
Skewness4.5709775
Sum16755530
Variance2.7968601 × 108
MonotonicityNot monotonic
2024-03-13T09:01:11.864149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15819 3
 
0.3%
11919 2
 
0.2%
1316 2
 
0.2%
12633 2
 
0.2%
1253 2
 
0.2%
9694 2
 
0.2%
2473 2
 
0.2%
12407 2
 
0.2%
5359 2
 
0.2%
8680 2
 
0.2%
Other values (1125) 1145
98.2%
ValueCountFrequency (%)
91 1
0.1%
92 1
0.1%
122 1
0.1%
133 1
0.1%
316 1
0.1%
363 1
0.1%
387 1
0.1%
570 1
0.1%
583 1
0.1%
825 1
0.1%
ValueCountFrequency (%)
206749 1
0.1%
152917 1
0.1%
136251 1
0.1%
120486 1
0.1%
119817 1
0.1%
108226 1
0.1%
106953 1
0.1%
104361 1
0.1%
102935 1
0.1%
100203 1
0.1%

여성인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct1145
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14207.482
Minimum80
Maximum218267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2024-03-13T09:01:11.964152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile1751.25
Q15818.5
median10620
Q316489
95-th percentile37476.5
Maximum218267
Range218187
Interquartile range (IQR)10670.5

Descriptive statistics

Standard deviation16683.398
Coefficient of variation (CV)1.1742685
Kurtosis32.531868
Mean14207.482
Median Absolute Deviation (MAD)5224
Skewness4.6307509
Sum16565924
Variance2.7833577 × 108
MonotonicityNot monotonic
2024-03-13T09:01:12.066061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5279 2
 
0.2%
5639 2
 
0.2%
14177 2
 
0.2%
2425 2
 
0.2%
1973 2
 
0.2%
7641 2
 
0.2%
6315 2
 
0.2%
7394 2
 
0.2%
8565 2
 
0.2%
13220 2
 
0.2%
Other values (1135) 1146
98.3%
ValueCountFrequency (%)
80 1
0.1%
81 1
0.1%
86 1
0.1%
90 1
0.1%
307 1
0.1%
365 1
0.1%
368 1
0.1%
518 1
0.1%
523 1
0.1%
776 1
0.1%
ValueCountFrequency (%)
218267 1
0.1%
137694 1
0.1%
133810 1
0.1%
115787 1
0.1%
111440 1
0.1%
108074 1
0.1%
107191 1
0.1%
105791 1
0.1%
103298 1
0.1%
102858 1
0.1%

Interactions

2024-03-13T09:01:08.602214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:04.304681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:04.918430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:05.461068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:06.080691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:06.623390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:07.422042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:08.012640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:08.677693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:04.382525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:04.995677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:05.539953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:06.161138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:06.703642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:07.500805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:08.089911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:08.740736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:04.452137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:05.061751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:05.606486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:06.224497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:06.769514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:07.571300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:08.165968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:08.812427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:04.529414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:05.131347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:05.681638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:06.298229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:06.844366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:07.649582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:08.254802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:08.872928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:04.600263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:05.192094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:05.746814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:06.356457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:06.910447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:07.717666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:08.333009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:08.949566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:04.677636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:05.266482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:05.822728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:06.429215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:06.998934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:07.794989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:08.404955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:09.027750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:04.757690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:05.337783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:05.902977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:06.500578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:07.090764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:07.872854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:08.479023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:09.092667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:04.844460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:05.399665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:05.989414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:06.562923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:07.157775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:07.942358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:01:08.539460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T09:01:12.137821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준연월행정동코드총인구수세대수세대당인구수남성여성비율남성비율남성인구수여성인구수
기준연월1.0000.0000.1210.1400.0000.0000.0000.1210.108
행정동코드0.0001.0000.2320.2690.3670.3580.3560.3560.228
총인구수0.1210.2321.0000.9360.2160.0000.0000.9750.998
세대수0.1400.2690.9361.0000.2560.0000.0000.9560.940
세대당인구수0.0000.3670.2160.2561.0000.7730.7690.2120.230
남성여성비율0.0000.3580.0000.0000.7731.0000.9770.0000.053
남성비율0.0000.3560.0000.0000.7690.9771.0000.0000.069
남성인구수0.1210.3560.9750.9560.2120.0000.0001.0000.957
여성인구수0.1080.2280.9980.9400.2300.0530.0690.9571.000
2024-03-13T09:01:12.230749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드총인구수세대수세대당인구수남성여성비율남성비율남성인구수여성인구수기준연월
행정동코드1.000-0.218-0.217-0.1870.3100.311-0.208-0.2270.000
총인구수-0.2181.0000.9810.593-0.421-0.4190.9990.9980.091
세대수-0.2170.9811.0000.447-0.333-0.3310.9860.9740.107
세대당인구수-0.1870.5930.4471.000-0.654-0.6550.5720.6120.000
남성여성비율0.310-0.421-0.333-0.6541.0000.999-0.384-0.4580.000
남성비율0.311-0.419-0.331-0.6550.9991.000-0.382-0.4560.000
남성인구수-0.2080.9990.9860.572-0.384-0.3821.0000.9940.121
여성인구수-0.2270.9980.9740.612-0.458-0.4560.9941.0000.081
기준연월0.0000.0910.1070.0000.0000.0000.1210.0811.000

Missing values

2024-03-13T09:01:09.201114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T09:01:09.334108image/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

기준연월행정동코드행정동명총인구수세대수세대당인구수남성여성비율남성비율남성인구수여성인구수
0201741630520양주2동53389189462.820.9649.12621327176
1201741630530회천1동1012244272.291.0751.7552384884
2201741630540회천2동28122112162.511.0350.751427113851
3201741630550회천3동29359106742.750.9849.521453814821
4201741630560회천4동2017283972.41.0250.61102099963
5201741650250소흘읍46294185972.491.0450.952358722707
6201741650310군내면737133892.171.1954.4240113360
7201741650320내촌면501726081.921.3256.9528572160
8201741650330가산면808740641.991.3757.8346773410
9201741650340신북면1303361682.111.1753.8970246009
기준연월행정동코드행정동명총인구수세대수세대당인구수남성여성비율남성비율남성인구수여성인구수
1156201641830320강하면442522182.01.0150.1922212204
1157201641830330양서면1213554562.221.0250.4361206015
1158201641830340옥천면734234982.11.0551.3237683574
1159201641830350서종면868842132.061.0350.6644014287
1160201641830360단월면369218082.041.0551.1418881804
1161201641830370청운면372419361.921.0350.8618941830
1162201641830380양동면462522372.070.9949.7923032322
1163201641830395지평면664533511.981.0250.433493296
1164201641830400용문면1617270702.291.0450.9882457927
1165201641830410개군면491823792.071.0350.6924932425