Overview

Dataset statistics

Number of variables10
Number of observations28
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory93.7 B

Variable types

Numeric9
Categorical1

Dataset

Description음주폐해예방 관련 지표- 국외통계지표 > 성인의 음주행동유형> 19세 이상 성인의 평생 음주율 지표데이터를 제공합니다.
Author한국건강증진개발원
URLhttps://www.data.go.kr/data/15050194/fileData.do

Alerts

동(거주지) is highly overall correlated with 읍면(거주지) and 7 other fieldsHigh correlation
읍면(거주지) is highly overall correlated with 동(거주지) and 7 other fieldsHigh correlation
19-29(연령별) is highly overall correlated with 동(거주지) and 1 other fieldsHigh correlation
30-39(연령별) is highly overall correlated with 동(거주지) and 6 other fieldsHigh correlation
40-49(연령별) is highly overall correlated with 동(거주지) and 6 other fieldsHigh correlation
50-59(연령별) is highly overall correlated with 동(거주지) and 6 other fieldsHigh correlation
60-69(연령별) is highly overall correlated with 동(거주지) and 6 other fieldsHigh correlation
70세 이상(연령별) is highly overall correlated with 동(거주지) and 6 other fieldsHigh correlation
구분 is highly overall correlated with 동(거주지) and 6 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 21:57:09.322757
Analysis finished2023-12-12 21:57:18.105073
Duration8.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct14
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.5
Minimum2007
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-13T06:57:18.162811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2007.35
Q12010
median2013.5
Q32017
95-th percentile2019.65
Maximum2020
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.1051007
Coefficient of variation (CV)0.0020387885
Kurtosis-1.2111337
Mean2013.5
Median Absolute Deviation (MAD)3.5
Skewness0
Sum56378
Variance16.851852
MonotonicityIncreasing
2023-12-13T06:57:18.278141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2007 2
 
7.1%
2008 2
 
7.1%
2009 2
 
7.1%
2010 2
 
7.1%
2011 2
 
7.1%
2012 2
 
7.1%
2013 2
 
7.1%
2014 2
 
7.1%
2015 2
 
7.1%
2016 2
 
7.1%
Other values (4) 8
28.6%
ValueCountFrequency (%)
2007 2
7.1%
2008 2
7.1%
2009 2
7.1%
2010 2
7.1%
2011 2
7.1%
2012 2
7.1%
2013 2
7.1%
2014 2
7.1%
2015 2
7.1%
2016 2
7.1%
ValueCountFrequency (%)
2020 2
7.1%
2019 2
7.1%
2018 2
7.1%
2017 2
7.1%
2016 2
7.1%
2015 2
7.1%
2014 2
7.1%
2013 2
7.1%
2012 2
7.1%
2011 2
7.1%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size356.0 B
남
14 
여
14 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남
2nd row여
3rd row남
4th row여
5th row남

Common Values

ValueCountFrequency (%)
남 14
50.0%
여 14
50.0%

Length

2023-12-13T06:57:18.397405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:57:18.493448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남 14
50.0%
여 14
50.0%

동(거주지)
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.461071
Minimum80.5
Maximum97.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-13T06:57:18.594098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum80.5
5-th percentile84.3
Q186.4
median93.05
Q396.3
95-th percentile97.115
Maximum97.5
Range17
Interquartile range (IQR)9.9

Descriptive statistics

Standard deviation5.2271289
Coefficient of variation (CV)0.057151407
Kurtosis-1.3426459
Mean91.461071
Median Absolute Deviation (MAD)3.35
Skewness-0.38767834
Sum2560.91
Variance27.322877
MonotonicityNot monotonic
2023-12-13T06:57:18.704023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
96.4 4
14.3%
86.4 3
 
10.7%
96.3 3
 
10.7%
84.3 2
 
7.1%
97.5 2
 
7.1%
95.8 1
 
3.6%
87.6 1
 
3.6%
90.9 1
 
3.6%
90.2 1
 
3.6%
89.7 1
 
3.6%
Other values (9) 9
32.1%
ValueCountFrequency (%)
80.5 1
 
3.6%
84.3 2
7.1%
85.5 1
 
3.6%
86.0 1
 
3.6%
86.4 3
10.7%
87.01 1
 
3.6%
87.6 1
 
3.6%
88.5 1
 
3.6%
89.7 1
 
3.6%
90.2 1
 
3.6%
ValueCountFrequency (%)
97.5 2
7.1%
96.4 4
14.3%
96.3 3
10.7%
95.9 1
 
3.6%
95.8 1
 
3.6%
95.5 1
 
3.6%
95.3 1
 
3.6%
95.2 1
 
3.6%
90.9 1
 
3.6%
90.2 1
 
3.6%

읍면(거주지)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.360714
Minimum76.8
Maximum95.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-13T06:57:18.833180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76.8
5-th percentile77.98
Q182.525
median90.85
Q394.05
95-th percentile95.165
Maximum95.6
Range18.8
Interquartile range (IQR)11.525

Descriptive statistics

Standard deviation6.3606044
Coefficient of variation (CV)0.071984529
Kurtosis-1.4641709
Mean88.360714
Median Absolute Deviation (MAD)4.3
Skewness-0.36827844
Sum2474.1
Variance40.457288
MonotonicityNot monotonic
2023-12-13T06:57:18.958476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
82.3 2
 
7.1%
94.0 1
 
3.6%
95.2 1
 
3.6%
76.8 1
 
3.6%
93.8 1
 
3.6%
79.8 1
 
3.6%
95.1 1
 
3.6%
88.8 1
 
3.6%
94.2 1
 
3.6%
86.2 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
76.8 1
3.6%
77.0 1
3.6%
79.8 1
3.6%
81.5 1
3.6%
81.9 1
3.6%
82.3 2
7.1%
82.6 1
3.6%
82.9 1
3.6%
83.4 1
3.6%
83.9 1
3.6%
ValueCountFrequency (%)
95.6 1
3.6%
95.2 1
3.6%
95.1 1
3.6%
95.0 1
3.6%
94.6 1
3.6%
94.3 1
3.6%
94.2 1
3.6%
94.0 1
3.6%
93.8 1
3.6%
93.5 1
3.6%

19-29(연령별)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.978571
Minimum92.2
Maximum98.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-13T06:57:19.085458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum92.2
5-th percentile93.84
Q194.8
median96.1
Q396.85
95-th percentile98.165
Maximum98.4
Range6.2
Interquartile range (IQR)2.05

Descriptive statistics

Standard deviation1.5215385
Coefficient of variation (CV)0.015852898
Kurtosis-0.068738523
Mean95.978571
Median Absolute Deviation (MAD)1.1
Skewness-0.35377773
Sum2687.4
Variance2.3150794
MonotonicityNot monotonic
2023-12-13T06:57:19.199198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
95.6 2
 
7.1%
94.8 2
 
7.1%
96.4 1
 
3.6%
94.5 1
 
3.6%
97.9 1
 
3.6%
96.6 1
 
3.6%
97.5 1
 
3.6%
96.3 1
 
3.6%
98.0 1
 
3.6%
94.2 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
92.2 1
3.6%
93.7 1
3.6%
94.1 1
3.6%
94.2 1
3.6%
94.5 1
3.6%
94.7 1
3.6%
94.8 2
7.1%
95.2 1
3.6%
95.5 1
3.6%
95.6 2
7.1%
ValueCountFrequency (%)
98.4 1
3.6%
98.2 1
3.6%
98.1 1
3.6%
98.0 1
3.6%
97.9 1
3.6%
97.5 1
3.6%
97.0 1
3.6%
96.8 1
3.6%
96.7 1
3.6%
96.6 1
3.6%

30-39(연령별)
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.628571
Minimum91.6
Maximum99.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-13T06:57:19.675329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum91.6
5-th percentile93.54
Q195.4
median96.9
Q398.175
95-th percentile99.065
Maximum99.5
Range7.9
Interquartile range (IQR)2.775

Descriptive statistics

Standard deviation1.9002645
Coefficient of variation (CV)0.019665659
Kurtosis0.31955333
Mean96.628571
Median Absolute Deviation (MAD)1.5
Skewness-0.68847813
Sum2705.6
Variance3.6110053
MonotonicityNot monotonic
2023-12-13T06:57:19.805399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
97.4 2
 
7.1%
94.9 2
 
7.1%
98.5 2
 
7.1%
97.2 2
 
7.1%
95.4 2
 
7.1%
97.9 1
 
3.6%
98.1 1
 
3.6%
95.0 1
 
3.6%
99.5 1
 
3.6%
95.9 1
 
3.6%
Other values (13) 13
46.4%
ValueCountFrequency (%)
91.6 1
3.6%
93.4 1
3.6%
93.8 1
3.6%
94.9 2
7.1%
95.0 1
3.6%
95.4 2
7.1%
95.5 1
3.6%
95.9 1
3.6%
96.2 1
3.6%
96.4 1
3.6%
ValueCountFrequency (%)
99.5 1
3.6%
99.1 1
3.6%
99.0 1
3.6%
98.6 1
3.6%
98.5 2
7.1%
98.4 1
3.6%
98.1 1
3.6%
97.9 1
3.6%
97.4 2
7.1%
97.3 1
3.6%

40-49(연령별)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.582143
Minimum78.5
Maximum98.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-13T06:57:19.935569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum78.5
5-th percentile85.835
Q191.2
median95.5
Q396.725
95-th percentile97.695
Maximum98.3
Range19.8
Interquartile range (IQR)5.525

Descriptive statistics

Standard deviation4.6240157
Coefficient of variation (CV)0.049411304
Kurtosis2.9167178
Mean93.582143
Median Absolute Deviation (MAD)1.85
Skewness-1.6292448
Sum2620.3
Variance21.381521
MonotonicityNot monotonic
2023-12-13T06:57:20.091166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
96.6 2
 
7.1%
95.9 2
 
7.1%
97.5 2
 
7.1%
97.2 2
 
7.1%
92.3 1
 
3.6%
94.5 1
 
3.6%
97.8 1
 
3.6%
94.7 1
 
3.6%
98.3 1
 
3.6%
95.1 1
 
3.6%
Other values (14) 14
50.0%
ValueCountFrequency (%)
78.5 1
3.6%
85.1 1
3.6%
87.2 1
3.6%
88.5 1
3.6%
89.1 1
3.6%
90.1 1
3.6%
90.3 1
3.6%
91.5 1
3.6%
92.3 1
3.6%
93.2 1
3.6%
ValueCountFrequency (%)
98.3 1
3.6%
97.8 1
3.6%
97.5 2
7.1%
97.2 2
7.1%
97.1 1
3.6%
96.6 2
7.1%
96.2 1
3.6%
96.1 1
3.6%
96.0 1
3.6%
95.9 2
7.1%

50-59(연령별)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.125
Minimum74.1
Maximum98.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-13T06:57:20.231334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum74.1
5-th percentile76.42
Q183.15
median91.25
Q395.7
95-th percentile96.765
Maximum98.2
Range24.1
Interquartile range (IQR)12.55

Descriptive statistics

Standard deviation7.6107441
Coefficient of variation (CV)0.085394043
Kurtosis-1.0706889
Mean89.125
Median Absolute Deviation (MAD)5.2
Skewness-0.58828841
Sum2495.5
Variance57.923426
MonotonicityNot monotonic
2023-12-13T06:57:20.376885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
77.2 2
 
7.1%
95.0 2
 
7.1%
87.4 2
 
7.1%
96.5 2
 
7.1%
92.6 1
 
3.6%
83.5 1
 
3.6%
89.6 1
 
3.6%
98.2 1
 
3.6%
95.6 1
 
3.6%
96.8 1
 
3.6%
Other values (14) 14
50.0%
ValueCountFrequency (%)
74.1 1
3.6%
76.0 1
3.6%
77.2 2
7.1%
80.4 1
3.6%
81.1 1
3.6%
83.0 1
3.6%
83.2 1
3.6%
83.5 1
3.6%
86.3 1
3.6%
87.4 2
7.1%
ValueCountFrequency (%)
98.2 1
3.6%
96.8 1
3.6%
96.7 1
3.6%
96.5 2
7.1%
96.4 1
3.6%
96.0 1
3.6%
95.6 1
3.6%
95.0 2
7.1%
94.9 1
3.6%
94.8 1
3.6%

60-69(연령별)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.189286
Minimum62.7
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-13T06:57:20.508353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum62.7
5-th percentile64.11
Q170.325
median85.65
Q393.65
95-th percentile96.045
Maximum97
Range34.3
Interquartile range (IQR)23.325

Descriptive statistics

Standard deviation12.383063
Coefficient of variation (CV)0.15066517
Kurtosis-1.6558158
Mean82.189286
Median Absolute Deviation (MAD)9.55
Skewness-0.27040177
Sum2301.3
Variance153.34025
MonotonicityNot monotonic
2023-12-13T06:57:20.665563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
91.1 2
 
7.1%
95.2 2
 
7.1%
69.5 2
 
7.1%
92.5 1
 
3.6%
74.5 1
 
3.6%
74.4 1
 
3.6%
94.7 1
 
3.6%
80.2 1
 
3.6%
78.4 1
 
3.6%
97.0 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
62.7 1
3.6%
63.9 1
3.6%
64.5 1
3.6%
64.7 1
3.6%
66.5 1
3.6%
69.5 2
7.1%
70.6 1
3.6%
74.4 1
3.6%
74.5 1
3.6%
74.9 1
3.6%
ValueCountFrequency (%)
97.0 1
3.6%
96.5 1
3.6%
95.2 2
7.1%
94.7 1
3.6%
94.2 1
3.6%
93.8 1
3.6%
93.6 1
3.6%
92.5 1
3.6%
92.2 1
3.6%
92.0 1
3.6%

70세 이상(연령별)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.453571
Minimum47.1
Maximum92.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-13T06:57:20.862609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47.1
5-th percentile50.345
Q154.025
median72.65
Q388.925
95-th percentile90.13
Maximum92.3
Range45.2
Interquartile range (IQR)34.9

Descriptive statistics

Standard deviation17.595254
Coefficient of variation (CV)0.24624736
Kurtosis-2.0316758
Mean71.453571
Median Absolute Deviation (MAD)16.5
Skewness-0.049881943
Sum2000.7
Variance309.59295
MonotonicityNot monotonic
2023-12-13T06:57:20.987881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
89.0 2
 
7.1%
85.2 1
 
3.6%
60.1 1
 
3.6%
54.2 1
 
3.6%
89.5 1
 
3.6%
57.1 1
 
3.6%
86.7 1
 
3.6%
59.9 1
 
3.6%
92.3 1
 
3.6%
56.8 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
47.1 1
3.6%
49.4 1
3.6%
52.1 1
3.6%
52.5 1
3.6%
53.1 1
3.6%
53.4 1
3.6%
53.5 1
3.6%
54.2 1
3.6%
55.8 1
3.6%
56.6 1
3.6%
ValueCountFrequency (%)
92.3 1
3.6%
90.2 1
3.6%
90.0 1
3.6%
89.5 1
3.6%
89.3 1
3.6%
89.0 2
7.1%
88.9 1
3.6%
87.7 1
3.6%
87.3 1
3.6%
87.1 1
3.6%

Interactions

2023-12-13T06:57:17.099724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:09.605676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:10.579212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:11.427540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:12.280869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:13.099773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:14.482983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:15.479951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:16.317256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:17.186515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:09.704303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:10.670808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:11.518397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:12.370407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:13.548127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:14.573819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:15.576377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:16.398478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:17.280592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:09.816243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:10.763860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:11.652233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:12.461954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:13.654373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:14.688807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:15.677022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:16.484094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:17.369987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:09.930589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:10.862622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:11.755861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:12.552526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:13.769173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:14.806599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:15.782648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:16.585278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:17.463200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:10.055280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:10.962875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:11.839132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:12.637015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:13.947976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:14.930913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:15.891155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:16.685249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:17.545535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:10.174940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:11.064353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:11.942186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:12.753129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:14.067389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:15.059480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:16.012402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:16.771866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:17.625161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:10.283437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:11.179564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:12.027087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:12.848344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:14.184750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:15.166411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:16.098349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:16.861056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:17.700617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:10.382145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:11.268831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:12.102736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:12.936297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:14.289431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:15.276281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:16.170277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:16.941231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:17.796057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:10.481744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:11.352994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:12.192670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:13.021223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:14.396480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:15.384389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:16.250048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:57:17.026052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:57:21.075698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도구분동(거주지)읍면(거주지)19-29(연령별)30-39(연령별)40-49(연령별)50-59(연령별)60-69(연령별)70세 이상(연령별)
연도1.0000.0000.0000.0000.1720.0000.3200.0000.0000.000
구분0.0001.0001.0001.0000.3420.8620.9481.0001.0001.000
동(거주지)0.0001.0001.0000.9390.5750.8890.9250.8140.9650.597
읍면(거주지)0.0001.0000.9391.0000.3670.7270.6470.6370.9390.693
19-29(연령별)0.1720.3420.5750.3671.0000.4390.7590.4430.5600.000
30-39(연령별)0.0000.8620.8890.7270.4391.0000.8590.5180.8390.488
40-49(연령별)0.3200.9480.9250.6470.7590.8591.0000.7400.8980.586
50-59(연령별)0.0001.0000.8140.6370.4430.5180.7401.0000.7570.763
60-69(연령별)0.0001.0000.9650.9390.5600.8390.8980.7571.0000.655
70세 이상(연령별)0.0001.0000.5970.6930.0000.4880.5860.7630.6551.000
2023-12-13T06:57:21.225044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도동(거주지)읍면(거주지)19-29(연령별)30-39(연령별)40-49(연령별)50-59(연령별)60-69(연령별)70세 이상(연령별)구분
연도1.0000.385-0.0360.1110.4430.3870.3690.3610.2690.000
동(거주지)0.3851.0000.7830.5040.8720.9130.9450.9410.9010.877
읍면(거주지)-0.0360.7831.0000.6240.7220.7430.7200.7590.7130.877
19-29(연령별)0.1110.5040.6241.0000.4240.4430.4550.4610.4180.275
30-39(연령별)0.4430.8720.7220.4241.0000.8430.7630.8230.7300.595
40-49(연령별)0.3870.9130.7430.4430.8431.0000.8950.8960.8470.701
50-59(연령별)0.3690.9450.7200.4550.7630.8951.0000.8960.9170.855
60-69(연령별)0.3610.9410.7590.4610.8230.8960.8961.0000.8570.877
70세 이상(연령별)0.2690.9010.7130.4180.7300.8470.9170.8571.0000.941
구분0.0000.8770.8770.2750.5950.7010.8550.8770.9411.000

Missing values

2023-12-13T06:57:17.913718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:57:18.051430image/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

연도구분동(거주지)읍면(거주지)19-29(연령별)30-39(연령별)40-49(연령별)50-59(연령별)60-69(연령별)70세 이상(연령별)
02007남95.594.096.497.996.692.692.585.2
12007여80.582.992.291.678.576.066.552.5
22008남95.293.595.997.496.094.292.086.9
32008여84.382.395.693.485.177.264.756.6
42009남95.394.696.896.697.194.991.187.1
52009여84.382.395.294.988.574.162.747.1
62010남95.995.698.298.596.295.091.187.3
72010여85.581.994.894.989.177.263.953.5
82011남96.393.297.097.295.996.495.287.7
92011여87.0186.698.195.490.380.469.549.4
연도구분동(거주지)읍면(거주지)19-29(연령별)30-39(연령별)40-49(연령별)50-59(연령별)60-69(연령별)70세 이상(연령별)
182016남96.494.396.798.196.196.793.690.2
192016여89.781.594.196.593.286.374.953.4
202017남96.492.994.299.097.596.094.289.0
212017여90.286.298.097.294.383.076.356.8
222018남97.594.296.399.197.596.897.092.3
232018여90.988.894.898.595.187.478.459.9
242019남96.395.197.598.698.395.695.286.7
252019여87.679.896.695.994.787.480.257.1
262020남97.593.897.999.597.898.294.789.5
272020여86.476.895.695.094.589.674.454.2