Overview

Dataset statistics

Number of variables10
Number of observations126
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.1 KiB
Average record size in memory90.0 B

Variable types

Numeric9
Categorical1

Dataset

Description신혼부부통계는 저출산 및 고령화 등 경제․사회 변화에 대응하기 위하여 신혼부부에 대한 결혼, 출산․보육, 경제활동 및 주거실태 등을 파악하여 정책맞춤형 통계정보 제공합니다.- 작성기준일 : 11월 1일 기준
Author통계청
URLhttps://www.data.go.kr/data/15101096/fileData.do

Alerts

시점 is highly overall correlated with 맞벌이 비율High correlation
신혼부부 수 (A) is highly overall correlated with 맞벌이 부부 수 (B) and 3 other fieldsHigh correlation
맞벌이 부부 수 (B) is highly overall correlated with 신혼부부 수 (A) and 3 other fieldsHigh correlation
맞벌이 비율 is highly overall correlated with 시점 and 1 other fieldsHigh correlation
주택소유 신혼부부 수 (C) is highly overall correlated with 신혼부부 수 (A) and 3 other fieldsHigh correlation
주택 소유율 is highly overall correlated with 행정구역별High correlation
총 소유 주택 수 (D) is highly overall correlated with 신혼부부 수 (A) and 3 other fieldsHigh correlation
평균 출생아 수 is highly overall correlated with 맞벌이 비율High correlation
행정구역별 is highly overall correlated with 신혼부부 수 (A) and 4 other fieldsHigh correlation
신혼부부 수 (A) has unique valuesUnique
맞벌이 부부 수 (B) has unique valuesUnique
총 소유 주택 수 (D) has unique valuesUnique

Reproduction

Analysis started2024-03-14 19:41:04.545659
Analysis finished2024-03-14 19:41:26.562513
Duration22.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시점
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018
Minimum2015
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T04:41:26.644573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12016
median2018
Q32020
95-th percentile2021
Maximum2021
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0079841
Coefficient of variation (CV)0.0009950367
Kurtosis-1.251885
Mean2018
Median Absolute Deviation (MAD)2
Skewness0
Sum254268
Variance4.032
MonotonicityIncreasing
2024-03-15T04:41:26.991183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2015 18
14.3%
2016 18
14.3%
2017 18
14.3%
2018 18
14.3%
2019 18
14.3%
2020 18
14.3%
2021 18
14.3%
ValueCountFrequency (%)
2015 18
14.3%
2016 18
14.3%
2017 18
14.3%
2018 18
14.3%
2019 18
14.3%
2020 18
14.3%
2021 18
14.3%
ValueCountFrequency (%)
2021 18
14.3%
2020 18
14.3%
2019 18
14.3%
2018 18
14.3%
2017 18
14.3%
2016 18
14.3%
2015 18
14.3%

행정구역별
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
전국
 
7
서울특별시
 
7
부산광역시
 
7
대구광역시
 
7
인천광역시
 
7
Other values (13)
91 

Length

Max length7
Median length5
Mean length4.5
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전국
2nd row서울특별시
3rd row부산광역시
4th row대구광역시
5th row인천광역시

Common Values

ValueCountFrequency (%)
전국 7
 
5.6%
서울특별시 7
 
5.6%
부산광역시 7
 
5.6%
대구광역시 7
 
5.6%
인천광역시 7
 
5.6%
광주광역시 7
 
5.6%
대전광역시 7
 
5.6%
울산광역시 7
 
5.6%
세종특별자치시 7
 
5.6%
경기도 7
 
5.6%
Other values (8) 56
44.4%

Length

2024-03-15T04:41:27.419983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전국 7
 
5.6%
서울특별시 7
 
5.6%
경상남도 7
 
5.6%
경상북도 7
 
5.6%
전라남도 7
 
5.6%
전라북도 7
 
5.6%
충청남도 7
 
5.6%
충청북도 7
 
5.6%
강원도 7
 
5.6%
경기도 7
 
5.6%
Other values (8) 56
44.4%

신혼부부 수 (A)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct126
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145335.02
Minimum8107
Maximum1471647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T04:41:27.783409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8107
5-th percentile13675.25
Q135446.75
median45283
Q379822.75
95-th percentile923088.5
Maximum1471647
Range1463540
Interquartile range (IQR)44376

Descriptive statistics

Standard deviation297395.88
Coefficient of variation (CV)2.0462781
Kurtosis11.618242
Mean145335.02
Median Absolute Deviation (MAD)16013.5
Skewness3.5066028
Sum18312213
Variance8.8444309 × 1010
MonotonicityNot monotonic
2024-03-15T04:41:28.237667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1471647 1
 
0.8%
12966 1
 
0.8%
49774 1
 
0.8%
67632 1
 
0.8%
219101 1
 
0.8%
1183750 1
 
0.8%
17682 1
 
0.8%
79222 1
 
0.8%
57670 1
 
0.8%
38275 1
 
0.8%
Other values (116) 116
92.1%
ValueCountFrequency (%)
8107 1
0.8%
9898 1
0.8%
11031 1
0.8%
11475 1
0.8%
12225 1
0.8%
12432 1
0.8%
12966 1
0.8%
15803 1
0.8%
16868 1
0.8%
17682 1
0.8%
ValueCountFrequency (%)
1471647 1
0.8%
1436948 1
0.8%
1379766 1
0.8%
1322406 1
0.8%
1260117 1
0.8%
1183750 1
0.8%
1101455 1
0.8%
387989 1
0.8%
383763 1
0.8%
372622 1
0.8%

맞벌이 부부 수 (B)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct126
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65270.738
Minimum3876
Maximum603281
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T04:41:28.682726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3876
5-th percentile7192.75
Q115641.25
median20003
Q333545.5
95-th percentile469160.5
Maximum603281
Range599405
Interquartile range (IQR)17904.25

Descriptive statistics

Standard deviation133660.62
Coefficient of variation (CV)2.0477877
Kurtosis10.680154
Mean65270.738
Median Absolute Deviation (MAD)5995
Skewness3.388944
Sum8224113
Variance1.786516 × 1010
MonotonicityNot monotonic
2024-03-15T04:41:29.122477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
595850 1
 
0.8%
6863 1
 
0.8%
23083 1
 
0.8%
32299 1
 
0.8%
124450 1
 
0.8%
580089 1
 
0.8%
8114 1
 
0.8%
32779 1
 
0.8%
22593 1
 
0.8%
15651 1
 
0.8%
Other values (116) 116
92.1%
ValueCountFrequency (%)
3876 1
0.8%
4846 1
0.8%
5357 1
0.8%
6351 1
0.8%
6863 1
0.8%
6905 1
0.8%
6970 1
0.8%
7861 1
0.8%
7891 1
0.8%
7941 1
0.8%
ValueCountFrequency (%)
603281 1
0.8%
595850 1
0.8%
594409 1
0.8%
585957 1
0.8%
582985 1
0.8%
580089 1
0.8%
569472 1
0.8%
168226 1
0.8%
167162 1
0.8%
166778 1
0.8%

맞벌이 비율
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.052381
Minimum33.9
Maximum60.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T04:41:29.555049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.9
5-th percentile36.225
Q139.825
median43.35
Q347.55
95-th percentile53.05
Maximum60.2
Range26.3
Interquartile range (IQR)7.725

Descriptive statistics

Standard deviation5.4101344
Coefficient of variation (CV)0.1228114
Kurtosis0.067276319
Mean44.052381
Median Absolute Deviation (MAD)3.8
Skewness0.49344162
Sum5550.6
Variance29.269554
MonotonicityNot monotonic
2024-03-15T04:41:29.846454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.3 4
 
3.2%
42.5 4
 
3.2%
46.4 3
 
2.4%
45.5 3
 
2.4%
49.0 3
 
2.4%
49.3 3
 
2.4%
44.8 2
 
1.6%
47.4 2
 
1.6%
41.7 2
 
1.6%
43.6 2
 
1.6%
Other values (85) 98
77.8%
ValueCountFrequency (%)
33.9 1
0.8%
34.0 1
0.8%
34.9 1
0.8%
35.1 1
0.8%
35.4 1
0.8%
35.5 1
0.8%
36.2 1
0.8%
36.3 1
0.8%
36.4 1
0.8%
37.0 1
0.8%
ValueCountFrequency (%)
60.2 1
0.8%
59.2 1
0.8%
57.0 1
0.8%
56.8 1
0.8%
53.9 1
0.8%
53.2 1
0.8%
53.1 1
0.8%
52.9 1
0.8%
52.5 1
0.8%
51.8 1
0.8%

주택소유 신혼부부 수 (C)
Real number (ℝ)

HIGH CORRELATION 

Distinct125
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64120.198
Minimum3675
Maximum640709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T04:41:30.297045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3675
5-th percentile5963
Q116508.5
median21238
Q338430.75
95-th percentile399635.25
Maximum640709
Range637034
Interquartile range (IQR)21922.25

Descriptive statistics

Standard deviation130222.54
Coefficient of variation (CV)2.030913
Kurtosis12.105758
Mean64120.198
Median Absolute Deviation (MAD)7569.5
Skewness3.5880311
Sum8079145
Variance1.6957911 × 1010
MonotonicityNot monotonic
2024-03-15T04:41:30.738674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15464 2
 
1.6%
640709 1
 
0.8%
16505 1
 
0.8%
29180 1
 
0.8%
80134 1
 
0.8%
516481 1
 
0.8%
7616 1
 
0.8%
40323 1
 
0.8%
29133 1
 
0.8%
19237 1
 
0.8%
Other values (115) 115
91.3%
ValueCountFrequency (%)
3675 1
0.8%
4154 1
0.8%
4350 1
0.8%
4684 1
0.8%
5035 1
0.8%
5263 1
0.8%
5611 1
0.8%
7019 1
0.8%
7161 1
0.8%
7616 1
0.8%
ValueCountFrequency (%)
640709 1
0.8%
633642 1
0.8%
616621 1
0.8%
594721 1
0.8%
557592 1
0.8%
516481 1
0.8%
479791 1
0.8%
159168 1
0.8%
158367 1
0.8%
155521 1
0.8%

주택 소유율
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.300794
Minimum36.2
Maximum55.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T04:41:31.159469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.2
5-th percentile38.525
Q142.85
median46.45
Q349.55
95-th percentile54.075
Maximum55.9
Range19.7
Interquartile range (IQR)6.7

Descriptive statistics

Standard deviation4.610796
Coefficient of variation (CV)0.099583519
Kurtosis-0.54781393
Mean46.300794
Median Absolute Deviation (MAD)3.4
Skewness-0.032382651
Sum5833.9
Variance21.259439
MonotonicityNot monotonic
2024-03-15T04:41:31.627379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48.0 3
 
2.4%
50.6 3
 
2.4%
49.4 3
 
2.4%
46.4 2
 
1.6%
45.6 2
 
1.6%
51.6 2
 
1.6%
42.8 2
 
1.6%
49.6 2
 
1.6%
42.7 2
 
1.6%
39.3 2
 
1.6%
Other values (84) 103
81.7%
ValueCountFrequency (%)
36.2 1
0.8%
36.6 1
0.8%
36.7 1
0.8%
37.2 1
0.8%
37.3 1
0.8%
38.3 2
1.6%
39.2 1
0.8%
39.3 2
1.6%
39.5 1
0.8%
40.5 2
1.6%
ValueCountFrequency (%)
55.9 1
0.8%
55.7 1
0.8%
55.5 1
0.8%
55.3 1
0.8%
54.9 2
1.6%
54.2 1
0.8%
53.7 1
0.8%
53.6 1
0.8%
52.5 2
1.6%
52.1 1
0.8%

총 소유 주택 수 (D)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct126
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82806.77
Minimum4836
Maximum828486
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T04:41:31.974404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4836
5-th percentile7845.25
Q121015
median27050
Q349945.5
95-th percentile504384.75
Maximum828486
Range823650
Interquartile range (IQR)28930.5

Descriptive statistics

Standard deviation168480.86
Coefficient of variation (CV)2.0346266
Kurtosis12.173504
Mean82806.77
Median Absolute Deviation (MAD)9840
Skewness3.5911155
Sum10433653
Variance2.8385799 × 1010
MonotonicityNot monotonic
2024-03-15T04:41:32.230474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
828486 1
 
0.8%
6826 1
 
0.8%
28836 1
 
0.8%
39052 1
 
0.8%
106151 1
 
0.8%
663221 1
 
0.8%
10455 1
 
0.8%
50819 1
 
0.8%
37105 1
 
0.8%
24948 1
 
0.8%
Other values (116) 116
92.1%
ValueCountFrequency (%)
4836 1
0.8%
5244 1
0.8%
5814 1
0.8%
6042 1
0.8%
6731 1
0.8%
6826 1
0.8%
7309 1
0.8%
9454 1
0.8%
9899 1
0.8%
9929 1
0.8%
ValueCountFrequency (%)
828486 1
0.8%
827329 1
0.8%
801757 1
0.8%
771206 1
0.8%
721051 1
0.8%
663221 1
0.8%
603763 1
0.8%
206250 1
0.8%
204851 1
0.8%
201541 1
0.8%

소유부부기준 1쌍당 평균 소유주택 수
Real number (ℝ)

Distinct7
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2388889
Minimum0.7
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T04:41:32.605308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile0.9
Q11.2
median1.3
Q31.3
95-th percentile1.4
Maximum1.4
Range0.7
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.1302135
Coefficient of variation (CV)0.10510507
Kurtosis3.0569027
Mean1.2388889
Median Absolute Deviation (MAD)0
Skewness-1.8176718
Sum156.1
Variance0.016955556
MonotonicityNot monotonic
2024-03-15T04:41:32.944419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1.3 77
61.1%
1.2 23
 
18.3%
1.4 8
 
6.3%
1.0 8
 
6.3%
0.9 7
 
5.6%
1.1 2
 
1.6%
0.7 1
 
0.8%
ValueCountFrequency (%)
0.7 1
 
0.8%
0.9 7
 
5.6%
1.0 8
 
6.3%
1.1 2
 
1.6%
1.2 23
 
18.3%
1.3 77
61.1%
1.4 8
 
6.3%
ValueCountFrequency (%)
1.4 8
 
6.3%
1.3 77
61.1%
1.2 23
 
18.3%
1.1 2
 
1.6%
1.0 8
 
6.3%
0.9 7
 
5.6%
0.7 1
 
0.8%

평균 출생아 수
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.90666667
Minimum0.62
Maximum1.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T04:41:33.432363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.62
5-th percentile0.7625
Q10.84
median0.915
Q30.9675
95-th percentile1.055
Maximum1.15
Range0.53
Interquartile range (IQR)0.1275

Descriptive statistics

Standard deviation0.098598174
Coefficient of variation (CV)0.10874799
Kurtosis0.47588944
Mean0.90666667
Median Absolute Deviation (MAD)0.065
Skewness-0.27303929
Sum114.24
Variance0.0097216
MonotonicityNot monotonic
2024-03-15T04:41:33.908346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.9 8
 
6.3%
0.92 8
 
6.3%
0.93 7
 
5.6%
0.95 7
 
5.6%
0.87 6
 
4.8%
0.84 6
 
4.8%
1.02 5
 
4.0%
0.94 5
 
4.0%
0.88 4
 
3.2%
0.97 4
 
3.2%
Other values (31) 66
52.4%
ValueCountFrequency (%)
0.62 1
 
0.8%
0.63 1
 
0.8%
0.66 1
 
0.8%
0.67 1
 
0.8%
0.7 1
 
0.8%
0.76 2
1.6%
0.77 3
2.4%
0.78 2
1.6%
0.79 3
2.4%
0.8 1
 
0.8%
ValueCountFrequency (%)
1.15 1
 
0.8%
1.14 1
 
0.8%
1.1 1
 
0.8%
1.09 1
 
0.8%
1.08 1
 
0.8%
1.07 1
 
0.8%
1.06 1
 
0.8%
1.04 3
2.4%
1.03 4
3.2%
1.02 5
4.0%

Interactions

2024-03-15T04:41:23.249364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:04.993639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:07.330498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:10.282420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:12.269247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:14.336787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:16.612922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:18.630729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:20.871620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:23.512378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:05.249244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:07.596797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:10.456383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:12.705676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:14.596268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:16.775259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:18.778527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:21.033276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:23.854009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:05.523936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:07.896137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:10.716306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:12.901467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:14.875982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:17.040838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:19.023834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:21.210128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:24.139286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:05.780112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:08.449728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:10.971129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:13.138176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:15.037211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:17.300559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:19.269083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:21.574361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:24.408640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:06.040681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:08.745409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:11.268989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:13.275752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:15.294542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:17.626609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:19.511731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:21.838052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:24.731174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:06.301415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:09.046561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:11.491477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:13.432136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:15.548918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:17.877571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:19.756986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:22.112899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:25.031708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:06.567228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:09.555306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:11.661187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:13.692670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:15.813024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:18.132554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:20.189526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:22.403269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:25.453512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:06.808747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:09.886678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:11.889000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:13.893831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:16.057747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:18.300708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:20.419741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:22.707741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:25.612430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:07.081238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:10.118907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:12.063975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:14.085150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:16.328677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:18.469474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:20.679598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:41:22.982876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T04:41:34.194463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시점행정구역별신혼부부 수 (A)맞벌이 부부 수 (B)맞벌이 비율주택소유 신혼부부 수 (C)주택 소유율총 소유 주택 수 (D)소유부부기준 1쌍당 평균 소유주택 수평균 출생아 수
시점1.0000.0000.0000.0000.5380.0000.0000.0000.6000.485
행정구역별0.0001.0000.9600.9300.1320.9600.8900.9450.6270.679
신혼부부 수 (A)0.0000.9601.0000.9300.3140.9990.7030.9890.2630.635
맞벌이 부부 수 (B)0.0000.9300.9301.0000.5820.9300.6810.9270.1580.656
맞벌이 비율0.5380.1320.3140.5821.0000.2720.7000.2310.0380.660
주택소유 신혼부부 수 (C)0.0000.9600.9990.9300.2721.0000.7050.9950.2490.625
주택 소유율0.0000.8900.7030.6810.7000.7051.0000.6850.4520.679
총 소유 주택 수 (D)0.0000.9450.9890.9270.2310.9950.6851.0000.2140.625
소유부부기준 1쌍당 평균 소유주택 수0.6000.6270.2630.1580.0380.2490.4520.2141.0000.543
평균 출생아 수0.4850.6790.6350.6560.6600.6250.6790.6250.5431.000
2024-03-15T04:41:34.610925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시점신혼부부 수 (A)맞벌이 부부 수 (B)맞벌이 비율주택소유 신혼부부 수 (C)주택 소유율총 소유 주택 수 (D)소유부부기준 1쌍당 평균 소유주택 수평균 출생아 수행정구역별
시점1.000-0.194-0.0630.697-0.1810.039-0.1830.092-0.3940.000
신혼부부 수 (A)-0.1941.0000.964-0.2010.984-0.1770.9870.030-0.3000.695
맞벌이 부부 수 (B)-0.0630.9641.000-0.0330.943-0.1970.9420.048-0.3830.755
맞벌이 비율0.697-0.201-0.0331.000-0.223-0.424-0.2260.217-0.5560.035
주택소유 신혼부부 수 (C)-0.1810.9840.943-0.2231.000-0.0610.9970.004-0.2730.695
주택 소유율0.039-0.177-0.197-0.424-0.0611.000-0.079-0.2770.4640.592
총 소유 주택 수 (D)-0.1830.9870.942-0.2260.997-0.0791.0000.030-0.2620.657
소유부부기준 1쌍당 평균 소유주택 수0.0920.0300.0480.2170.004-0.2770.0301.0000.2700.323
평균 출생아 수-0.394-0.300-0.383-0.556-0.2730.464-0.2620.2701.0000.325
행정구역별0.0000.6950.7550.0350.6950.5920.6570.3230.3251.000

Missing values

2024-03-15T04:41:25.898059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T04:41:26.391493image/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

시점행정구역별신혼부부 수 (A)맞벌이 부부 수 (B)맞벌이 비율주택소유 신혼부부 수 (C)주택 소유율총 소유 주택 수 (D)소유부부기준 1쌍당 평균 소유주택 수평균 출생아 수
02015전국147164759585040.564070943.58284861.30.93
12015서울특별시29134113798647.410680436.71480261.40.77
22015부산광역시905783575939.54396648.5582271.30.93
32015대구광역시628112364637.62935346.7359661.20.96
42015인천광역시897473429138.24078345.4524641.30.95
52015광주광역시407071700041.82108151.8258361.21.03
62015대전광역시438031819241.51897643.3234531.20.96
72015울산광역시385371308033.92117654.9261911.20.99
82015세종특별자치시8107387647.8367545.348361.30.93
92015경기도38798915770740.615916841.02048511.30.91
시점행정구역별신혼부부 수 (A)맞벌이 부부 수 (B)맞벌이 비율주택소유 신혼부부 수 (C)주택 소유율총 소유 주택 수 (D)소유부부기준 1쌍당 평균 소유주택 수평균 출생아 수
1162021세종특별자치시11475690560.2415436.252441.30.82
1172021경기도32506716822651.813152140.51632141.20.76
1182021강원도317671608850.61507147.4193091.30.89
1192021충청북도343411744150.81651948.1206571.30.9
1202021충청남도463732199447.42234848.2287221.30.9
1212021전라북도311581536049.31546449.6197031.30.96
1222021전라남도333511550646.51787953.6229591.31.03
1232021경상북도487072181444.82555352.5321591.30.92
1242021경상남도654093068146.93405952.1424031.20.9
1252021제주특별자치도15803789149.9701944.494541.30.92