Overview

Dataset statistics

Number of variables10
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory95.3 B

Variable types

Text1
Numeric9

Dataset

Description부산광역시부산진구_인구현황_20201130
Author부산광역시 부산진구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15074078

Alerts

인구수(합계) is highly overall correlated with 인구수(남) and 5 other fieldsHigh correlation
인구수(남) is highly overall correlated with 인구수(합계) and 5 other fieldsHigh correlation
인구수(여) is highly overall correlated with 인구수(합계) and 5 other fieldsHigh correlation
구성비(%) is highly overall correlated with 인구수(합계) and 5 other fieldsHigh correlation
남(%) is highly overall correlated with 인구수(합계) and 5 other fieldsHigh correlation
여(%) is highly overall correlated with 인구수(합계) and 5 other fieldsHigh correlation
세대수 is highly overall correlated with 인구수(합계) and 5 other fieldsHigh correlation
행정기관 has unique valuesUnique
인구수(합계) has unique valuesUnique
인구수(남) has unique valuesUnique
인구수(여) has unique valuesUnique
여(%) has unique valuesUnique
성비 has unique valuesUnique
세대수 has unique valuesUnique

Reproduction

Analysis started2023-12-10 17:13:49.295803
Analysis finished2023-12-10 17:14:10.625321
Duration21.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정기관
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-11T02:14:10.978462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.7619048
Min length3

Characters and Unicode

Total characters100
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row부산진구
2nd row부전제1동
3rd row부전제2동
4th row연지동
5th row초읍동
ValueCountFrequency (%)
부산진구 1
 
4.8%
당감제1동 1
 
4.8%
범천제1동 1
 
4.8%
개금제3동 1
 
4.8%
개금제2동 1
 
4.8%
개금제1동 1
 
4.8%
가야제2동 1
 
4.8%
가야제1동 1
 
4.8%
당감제4동 1
 
4.8%
당감제2동 1
 
4.8%
Other values (11) 11
52.4%
2023-12-11T02:14:11.854580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 20
20.0%
제 18
18.0%
1 8
 
8.0%
2 7
 
7.0%
부 5
 
5.0%
전 4
 
4.0%
금 3
 
3.0%
개 3
 
3.0%
감 3
 
3.0%
당 3
 
3.0%
Other values (17) 26
26.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 82
82.0%
Decimal Number 18
 
18.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 20
24.4%
제 18
22.0%
부 5
 
6.1%
전 4
 
4.9%
금 3
 
3.7%
개 3
 
3.7%
감 3
 
3.7%
당 3
 
3.7%
암 2
 
2.4%
범 2
 
2.4%
Other values (13) 19
23.2%
Decimal Number
ValueCountFrequency (%)
1 8
44.4%
2 7
38.9%
3 2
 
11.1%
4 1
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 82
82.0%
Common 18
 
18.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 20
24.4%
제 18
22.0%
부 5
 
6.1%
전 4
 
4.9%
금 3
 
3.7%
개 3
 
3.7%
감 3
 
3.7%
당 3
 
3.7%
암 2
 
2.4%
범 2
 
2.4%
Other values (13) 19
23.2%
Common
ValueCountFrequency (%)
1 8
44.4%
2 7
38.9%
3 2
 
11.1%
4 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 82
82.0%
ASCII 18
 
18.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 20
24.4%
제 18
22.0%
부 5
 
6.1%
전 4
 
4.9%
금 3
 
3.7%
개 3
 
3.7%
감 3
 
3.7%
당 3
 
3.7%
암 2
 
2.4%
범 2
 
2.4%
Other values (13) 19
23.2%
ASCII
ValueCountFrequency (%)
1 8
44.4%
2 7
38.9%
3 2
 
11.1%
4 1
 
5.6%

인구수(합계)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34226.19
Minimum9947
Maximum359375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-11T02:14:12.124379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9947
5-th percentile10333
Q112976
median19989
Q321101
95-th percentile29547
Maximum359375
Range349428
Interquartile range (IQR)8125

Descriptive statistics

Standard deviation74679.11
Coefficient of variation (CV)2.1819288
Kurtosis20.768823
Mean34226.19
Median Absolute Deviation (MAD)3020
Skewness4.5464519
Sum718750
Variance5.5769695 × 109
MonotonicityNot monotonic
2023-12-11T02:14:12.405447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
359375 1
 
4.8%
15552 1
 
4.8%
20709 1
 
4.8%
12046 1
 
4.8%
29547 1
 
4.8%
11357 1
 
4.8%
17202 1
 
4.8%
17199 1
 
4.8%
19989 1
 
4.8%
11757 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
9947 1
4.8%
10333 1
4.8%
11357 1
4.8%
11757 1
4.8%
12046 1
4.8%
12976 1
4.8%
15552 1
4.8%
17199 1
4.8%
17202 1
4.8%
18011 1
4.8%
ValueCountFrequency (%)
359375 1
4.8%
29547 1
4.8%
23900 1
4.8%
23009 1
4.8%
22431 1
4.8%
21101 1
4.8%
20927 1
4.8%
20842 1
4.8%
20709 1
4.8%
20540 1
4.8%

인구수(남)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16543.714
Minimum4974
Maximum173709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-11T02:14:12.717996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4974
5-th percentile4995
Q15961
median9732
Q310077
95-th percentile14298
Maximum173709
Range168735
Interquartile range (IQR)4116

Descriptive statistics

Standard deviation36096.188
Coefficient of variation (CV)2.1818672
Kurtosis20.771539
Mean16543.714
Median Absolute Deviation (MAD)1505
Skewness4.5468802
Sum347418
Variance1.3029348 × 109
MonotonicityNot monotonic
2023-12-11T02:14:13.006270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
173709 1
 
4.8%
7823 1
 
4.8%
10077 1
 
4.8%
5726 1
 
4.8%
14298 1
 
4.8%
5556 1
 
4.8%
8227 1
 
4.8%
8378 1
 
4.8%
9942 1
 
4.8%
5761 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
4974 1
4.8%
4995 1
4.8%
5556 1
4.8%
5726 1
4.8%
5761 1
4.8%
5961 1
4.8%
7823 1
4.8%
8227 1
4.8%
8378 1
4.8%
8869 1
4.8%
ValueCountFrequency (%)
173709 1
4.8%
14298 1
4.8%
11398 1
4.8%
11232 1
4.8%
10906 1
4.8%
10077 1
4.8%
10071 1
4.8%
9942 1
4.8%
9939 1
4.8%
9844 1
4.8%

인구수(여)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17682.476
Minimum4952
Maximum185666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-11T02:14:13.285800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4952
5-th percentile5359
Q17015
median10047
Q311162
95-th percentile15249
Maximum185666
Range180714
Interquartile range (IQR)4147

Descriptive statistics

Standard deviation38583.685
Coefficient of variation (CV)2.1820295
Kurtosis20.764381
Mean17682.476
Median Absolute Deviation (MAD)1730
Skewness4.5457524
Sum371332
Variance1.4887007 × 109
MonotonicityNot monotonic
2023-12-11T02:14:13.572867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
185666 1
 
4.8%
7729 1
 
4.8%
10632 1
 
4.8%
6320 1
 
4.8%
15249 1
 
4.8%
5801 1
 
4.8%
8975 1
 
4.8%
8821 1
 
4.8%
10047 1
 
4.8%
5996 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
4952 1
4.8%
5359 1
4.8%
5801 1
4.8%
5996 1
4.8%
6320 1
4.8%
7015 1
4.8%
7729 1
4.8%
8821 1
4.8%
8975 1
4.8%
9142 1
4.8%
ValueCountFrequency (%)
185666 1
4.8%
15249 1
4.8%
12502 1
4.8%
11777 1
4.8%
11525 1
4.8%
11162 1
4.8%
10998 1
4.8%
10856 1
4.8%
10808 1
4.8%
10632 1
4.8%

구성비(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5238095
Minimum2.77
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-11T02:14:13.844056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.77
5-th percentile2.88
Q13.61
median5.56
Q35.87
95-th percentile8.22
Maximum100
Range97.23
Interquartile range (IQR)2.26

Descriptive statistics

Standard deviation20.780211
Coefficient of variation (CV)2.1819221
Kurtosis20.769118
Mean9.5238095
Median Absolute Deviation (MAD)0.84
Skewness4.5464978
Sum200
Variance431.81715
MonotonicityNot monotonic
2023-12-11T02:14:14.131469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4.79 2
 
9.5%
100.0 1
 
4.8%
6.65 1
 
4.8%
5.76 1
 
4.8%
3.35 1
 
4.8%
8.22 1
 
4.8%
3.16 1
 
4.8%
5.56 1
 
4.8%
3.27 1
 
4.8%
2.88 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
2.77 1
4.8%
2.88 1
4.8%
3.16 1
4.8%
3.27 1
4.8%
3.35 1
4.8%
3.61 1
4.8%
4.33 1
4.8%
4.79 2
9.5%
5.01 1
4.8%
5.56 1
4.8%
ValueCountFrequency (%)
100.0 1
4.8%
8.22 1
4.8%
6.65 1
4.8%
6.4 1
4.8%
6.24 1
4.8%
5.87 1
4.8%
5.82 1
4.8%
5.8 1
4.8%
5.76 1
4.8%
5.72 1
4.8%

남(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6038095
Minimum1.38
Maximum48.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-11T02:14:14.394056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.38
5-th percentile1.39
Q11.66
median2.71
Q32.8
95-th percentile3.98
Maximum48.34
Range46.96
Interquartile range (IQR)1.14

Descriptive statistics

Standard deviation10.044931
Coefficient of variation (CV)2.1818738
Kurtosis20.771249
Mean4.6038095
Median Absolute Deviation (MAD)0.42
Skewness4.5468342
Sum96.68
Variance100.90064
MonotonicityNot monotonic
2023-12-11T02:14:14.676799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2.8 2
 
9.5%
2.77 2
 
9.5%
48.34 1
 
4.8%
3.17 1
 
4.8%
1.59 1
 
4.8%
3.98 1
 
4.8%
1.55 1
 
4.8%
2.29 1
 
4.8%
2.33 1
 
4.8%
1.6 1
 
4.8%
Other values (9) 9
42.9%
ValueCountFrequency (%)
1.38 1
4.8%
1.39 1
4.8%
1.55 1
4.8%
1.59 1
4.8%
1.6 1
4.8%
1.66 1
4.8%
2.18 1
4.8%
2.29 1
4.8%
2.33 1
4.8%
2.47 1
4.8%
ValueCountFrequency (%)
48.34 1
4.8%
3.98 1
4.8%
3.17 1
4.8%
3.13 1
4.8%
3.03 1
4.8%
2.8 2
9.5%
2.77 2
9.5%
2.74 1
4.8%
2.71 1
4.8%
2.47 1
4.8%

여(%)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9204762
Minimum1.38
Maximum51.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-11T02:14:15.123746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.38
5-th percentile1.49
Q11.95
median2.8
Q33.11
95-th percentile4.24
Maximum51.66
Range50.28
Interquartile range (IQR)1.16

Descriptive statistics

Standard deviation10.73551
Coefficient of variation (CV)2.181803
Kurtosis20.76408
Mean4.9204762
Median Absolute Deviation (MAD)0.48
Skewness4.545704
Sum103.33
Variance115.25116
MonotonicityNot monotonic
2023-12-11T02:14:15.410403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
51.66 1
 
4.8%
2.15 1
 
4.8%
2.96 1
 
4.8%
1.76 1
 
4.8%
4.24 1
 
4.8%
1.61 1
 
4.8%
2.5 1
 
4.8%
2.45 1
 
4.8%
2.8 1
 
4.8%
1.67 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
1.38 1
4.8%
1.49 1
4.8%
1.61 1
4.8%
1.67 1
4.8%
1.76 1
4.8%
1.95 1
4.8%
2.15 1
4.8%
2.45 1
4.8%
2.5 1
4.8%
2.54 1
4.8%
ValueCountFrequency (%)
51.66 1
4.8%
4.24 1
4.8%
3.48 1
4.8%
3.28 1
4.8%
3.21 1
4.8%
3.11 1
4.8%
3.06 1
4.8%
3.02 1
4.8%
3.01 1
4.8%
2.96 1
4.8%

성비
Real number (ℝ)

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.79
Minimum84.98
Maximum101.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-11T02:14:15.719035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum84.98
5-th percentile89.04
Q191.17
median93.76
Q395.78
95-th percentile100.87
Maximum101.22
Range16.24
Interquartile range (IQR)4.61

Descriptive statistics

Standard deviation3.9576331
Coefficient of variation (CV)0.042196749
Kurtosis0.25463676
Mean93.79
Median Absolute Deviation (MAD)2.32
Skewness-0.018850041
Sum1969.59
Variance15.66286
MonotonicityNot monotonic
2023-12-11T02:14:16.010866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
93.56 1
 
4.8%
101.22 1
 
4.8%
94.78 1
 
4.8%
90.6 1
 
4.8%
93.76 1
 
4.8%
95.78 1
 
4.8%
91.67 1
 
4.8%
94.98 1
 
4.8%
98.95 1
 
4.8%
96.08 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
84.98 1
4.8%
89.04 1
4.8%
89.51 1
4.8%
90.04 1
4.8%
90.6 1
4.8%
91.17 1
4.8%
91.67 1
4.8%
92.77 1
4.8%
92.82 1
4.8%
93.56 1
4.8%
ValueCountFrequency (%)
101.22 1
4.8%
100.87 1
4.8%
98.95 1
4.8%
97.01 1
4.8%
96.08 1
4.8%
95.78 1
4.8%
95.37 1
4.8%
94.98 1
4.8%
94.78 1
4.8%
94.63 1
4.8%

세대수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16434.381
Minimum4584
Maximum172561
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-11T02:14:16.296805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4584
5-th percentile5058
Q17197
median9230
Q310324
95-th percentile12418
Maximum172561
Range167977
Interquartile range (IQR)3127

Descriptive statistics

Standard deviation35844.384
Coefficient of variation (CV)2.1810608
Kurtosis20.807283
Mean16434.381
Median Absolute Deviation (MAD)1909
Skewness4.5523394
Sum345122
Variance1.2848198 × 109
MonotonicityNot monotonic
2023-12-11T02:14:16.581831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
172561 1
 
4.8%
9807 1
 
4.8%
10319 1
 
4.8%
7197 1
 
4.8%
12418 1
 
4.8%
5058 1
 
4.8%
8859 1
 
4.8%
7321 1
 
4.8%
10324 1
 
4.8%
5206 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
4584 1
4.8%
5058 1
4.8%
5206 1
4.8%
5518 1
4.8%
7036 1
4.8%
7197 1
4.8%
7321 1
4.8%
7841 1
4.8%
8670 1
4.8%
8859 1
4.8%
ValueCountFrequency (%)
172561 1
4.8%
12418 1
4.8%
11684 1
4.8%
11245 1
4.8%
10453 1
4.8%
10324 1
4.8%
10319 1
4.8%
10082 1
4.8%
9807 1
4.8%
9709 1
4.8%

세대당인구
Real number (ℝ)

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0909524
Minimum1.59
Maximum2.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-11T02:14:16.899002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.59
5-th percentile1.65
Q11.88
median2.22
Q32.29
95-th percentile2.4
Maximum2.56
Range0.97
Interquartile range (IQR)0.41

Descriptive statistics

Standard deviation0.28225706
Coefficient of variation (CV)0.13498971
Kurtosis-1.0611581
Mean2.0909524
Median Absolute Deviation (MAD)0.18
Skewness-0.32724784
Sum43.91
Variance0.079669048
MonotonicityNot monotonic
2023-12-11T02:14:17.172869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1.94 2
 
9.5%
2.25 2
 
9.5%
2.08 1
 
4.8%
1.59 1
 
4.8%
2.01 1
 
4.8%
1.67 1
 
4.8%
2.38 1
 
4.8%
2.35 1
 
4.8%
2.26 1
 
4.8%
2.29 1
 
4.8%
Other values (9) 9
42.9%
ValueCountFrequency (%)
1.59 1
4.8%
1.65 1
4.8%
1.67 1
4.8%
1.79 1
4.8%
1.8 1
4.8%
1.88 1
4.8%
1.94 2
9.5%
2.01 1
4.8%
2.08 1
4.8%
2.22 1
4.8%
ValueCountFrequency (%)
2.56 1
4.8%
2.4 1
4.8%
2.38 1
4.8%
2.37 1
4.8%
2.35 1
4.8%
2.29 1
4.8%
2.26 1
4.8%
2.25 2
9.5%
2.23 1
4.8%
2.22 1
4.8%

Interactions

2023-12-11T02:14:07.565630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:49.901437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:51.890240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:53.719579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:55.706325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:58.029872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:00.285657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:02.428563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:05.486013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:07.812621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:50.192406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:52.106031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:53.915473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:55.954528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:58.351270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:00.531212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:02.710463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:05.783726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:08.028727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:50.415279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:52.312285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:54.141927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:56.220039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:58.567845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:00.762340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:03.576071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:06.029214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:08.355214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:50.616550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:52.521890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:54.354343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:56.461523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:58.790207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:01.007589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:03.886772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:06.280951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:08.566947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:50.809369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:52.719371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:54.556509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:56.752566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:58.984715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:01.256167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:04.160594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:06.504155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:08.746709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:51.020103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:52.914936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:54.745206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:57.010273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:59.159306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:01.512323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:04.462897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:06.715089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:08.956110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:51.238057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:53.099215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:54.960486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:57.256274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:59.357937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:01.773434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:04.703826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:06.934816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:09.224991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:51.463996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:53.319428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:55.238924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:57.549065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:59.600992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:01.988862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:04.944823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:07.143333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:09.510758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:51.685913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:53.534885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:55.480402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:57.809940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:13:59.989713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:02.217439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:05.205708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:14:07.359809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:14:17.369797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정기관인구수(합계)인구수(남)인구수(여)구성비(%)남(%)여(%)성비세대수세대당인구
행정기관1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
인구수(합계)1.0001.0000.6180.6230.6230.6230.6230.0000.6181.000
인구수(남)1.0000.6181.0000.6180.6230.6230.6230.0000.6231.000
인구수(여)1.0000.6230.6181.0000.6230.6230.6230.0000.6181.000
구성비(%)1.0000.6230.6230.6231.0000.6280.6280.0000.6231.000
남(%)1.0000.6230.6230.6230.6281.0000.6280.0000.6231.000
여(%)1.0000.6230.6230.6230.6280.6281.0000.0000.6231.000
성비1.0000.0000.0000.0000.0000.0000.0001.0000.0000.522
세대수1.0000.6180.6230.6180.6230.6230.6230.0001.0001.000
세대당인구1.0001.0001.0001.0001.0001.0001.0000.5221.0001.000
2023-12-11T02:14:18.256465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인구수(합계)인구수(남)인구수(여)구성비(%)남(%)여(%)성비세대수세대당인구
인구수(합계)1.0000.9750.9971.0000.9810.997-0.2810.8680.290
인구수(남)0.9751.0000.9650.9760.9990.965-0.1380.8770.281
인구수(여)0.9970.9651.0000.9970.9701.000-0.2990.8490.315
구성비(%)1.0000.9760.9971.0000.9810.997-0.2760.8660.297
남(%)0.9810.9990.9700.9811.0000.970-0.1520.8810.277
여(%)0.9970.9651.0000.9970.9701.000-0.2990.8490.315
성비-0.281-0.138-0.299-0.276-0.152-0.2991.000-0.2100.099
세대수0.8680.8770.8490.8660.8810.849-0.2101.000-0.138
세대당인구0.2900.2810.3150.2970.2770.3150.099-0.1381.000

Missing values

2023-12-11T02:14:10.042938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:14:10.474162image/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

행정기관인구수(합계)인구수(남)인구수(여)구성비(%)남(%)여(%)성비세대수세대당인구
0부산진구359375173709185666100.048.3451.6693.561725612.08
1부전제1동15552782377294.332.182.15101.2298071.59
2부전제2동9947499549522.771.391.38100.8755181.8
3연지동208429844109985.82.743.0689.5186702.4
4초읍동2243110906115256.243.033.2194.63100822.22
5양정제1동205409732108085.722.713.0190.0492302.23
6양정제2동12976596170153.611.661.9584.9878411.65
7전포제1동2092710071108565.822.83.0292.77116841.79
8전포제2동211019939111625.872.773.1189.04112451.88
9부암제1동2300911232117776.43.133.2895.3797092.37
행정기관인구수(합계)인구수(남)인구수(여)구성비(%)남(%)여(%)성비세대수세대당인구
11당감제1동2390011398125026.653.173.4891.17104532.29
12당감제2동10333497453592.881.381.4992.8245842.25
13당감제4동11757576159963.271.61.6796.0852062.26
14가야제1동199899942100475.562.772.898.95103241.94
15가야제2동17199837888214.792.332.4594.9873212.35
16개금제1동17202822789754.792.292.591.6788591.94
17개금제2동11357555658013.161.551.6195.7850582.25
18개금제3동2954714298152498.223.984.2493.76124182.38
19범천제1동12046572663203.351.591.7690.671971.67
20범천제2동2070910077106325.762.82.9694.78103192.01