Overview

Dataset statistics

Number of variables11
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory99.4 B

Variable types

Categorical2
Text2
Numeric7

Dataset

Description샘플 데이터
Author경기콘텐츠진흥원
URLhttps://bigdata-region.kr/#/dataset/465870ec-38b7-4d00-8bb2-c896314c503f

Alerts

기준년월 has constant value ""Constant
시도명 has constant value ""Constant
전체 인구 is highly overall correlated with 유소년 인구 and 1 other fieldsHigh correlation
유소년 인구 is highly overall correlated with 전체 인구 and 4 other fieldsHigh correlation
고령 인구 is highly overall correlated with 전체 인구 and 1 other fieldsHigh correlation
고령 인구 구성비 is highly overall correlated with 유소년 인구 and 2 other fieldsHigh correlation
고령화 지수 is highly overall correlated with 유소년 인구 and 2 other fieldsHigh correlation
고령화 보조 지수 is highly overall correlated with 유소년 인구 and 2 other fieldsHigh correlation
행정동 코드 has unique valuesUnique
전체 인구 has unique valuesUnique
유소년 인구 has unique valuesUnique
고령 인구 has unique valuesUnique
고령화 지수 has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:55:43.075334
Analysis finished2023-12-10 13:55:54.293025
Duration11.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2019-01
30 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-01
2nd row2019-01
3rd row2019-01
4th row2019-01
5th row2019-01

Common Values

ValueCountFrequency (%)
2019-01 30
100.0%

Length

2023-12-10T22:55:54.418939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:55:54.593216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-01 30
100.0%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
30 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 30
100.0%

Length

2023-12-10T22:55:54.756298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:55:54.898217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Text

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:55:55.268326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length4.9666667
Min length3

Characters and Unicode

Total characters149
Distinct characters42
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

Unique17 ?
Unique (%)56.7%

Sample

1st row광명시
2nd row고양시 일산동구
3rd row구리시
4th row성남시 분당구
5th row군포시
ValueCountFrequency (%)
광명시 3
 
6.8%
고양시 3
 
6.8%
성남시 3
 
6.8%
파주시 2
 
4.5%
안산시 2
 
4.5%
상록구 2
 
4.5%
안성시 2
 
4.5%
일산동구 2
 
4.5%
평택시 2
 
4.5%
안양시 2
 
4.5%
Other values (19) 21
47.7%
2023-12-10T22:55:55.793664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
19.5%
구 15
 
10.1%
14
 
9.4%
안 9
 
6.0%
양 7
 
4.7%
산 6
 
4.0%
성 6
 
4.0%
광 3
 
2.0%
평 3
 
2.0%
인 3
 
2.0%
Other values (32) 54
36.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 135
90.6%
Space Separator 14
 
9.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
21.5%
구 15
 
11.1%
안 9
 
6.7%
양 7
 
5.2%
산 6
 
4.4%
성 6
 
4.4%
광 3
 
2.2%
평 3
 
2.2%
인 3
 
2.2%
원 3
 
2.2%
Other values (31) 51
37.8%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 135
90.6%
Common 14
 
9.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
21.5%
구 15
 
11.1%
안 9
 
6.7%
양 7
 
5.2%
산 6
 
4.4%
성 6
 
4.4%
광 3
 
2.2%
평 3
 
2.2%
인 3
 
2.2%
원 3
 
2.2%
Other values (31) 51
37.8%
Common
ValueCountFrequency (%)
14
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 135
90.6%
ASCII 14
 
9.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
21.5%
구 15
 
11.1%
안 9
 
6.7%
양 7
 
5.2%
산 6
 
4.4%
성 6
 
4.4%
광 3
 
2.2%
평 3
 
2.2%
인 3
 
2.2%
원 3
 
2.2%
Other values (31) 51
37.8%
ASCII
ValueCountFrequency (%)
14
100.0%

행정동명
Text

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:55:56.090068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.4
Min length2

Characters and Unicode

Total characters102
Distinct characters53
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

Unique28 ?
Unique (%)93.3%

Sample

1st row광명6동
2nd row마두1동
3rd row동구동
4th row정자3동
5th row군포2동
ValueCountFrequency (%)
정자3동 2
 
6.7%
광명6동 1
 
3.3%
옥천면 1
 
3.3%
철산2동 1
 
3.3%
갈현동 1
 
3.3%
장항2동 1
 
3.3%
일산2동 1
 
3.3%
마도면 1
 
3.3%
청북읍 1
 
3.3%
송탄동 1
 
3.3%
Other values (19) 19
63.3%
2023-12-10T22:55:56.709735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 24
23.5%
2 7
 
6.9%
산 4
 
3.9%
면 4
 
3.9%
읍 3
 
2.9%
1 3
 
2.9%
북 2
 
2.0%
하 2
 
2.0%
마 2
 
2.0%
안 2
 
2.0%
Other values (43) 49
48.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89
87.3%
Decimal Number 13
 
12.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 24
27.0%
산 4
 
4.5%
면 4
 
4.5%
읍 3
 
3.4%
북 2
 
2.2%
하 2
 
2.2%
마 2
 
2.2%
안 2
 
2.2%
자 2
 
2.2%
일 2
 
2.2%
Other values (39) 42
47.2%
Decimal Number
ValueCountFrequency (%)
2 7
53.8%
1 3
23.1%
3 2
 
15.4%
6 1
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89
87.3%
Common 13
 
12.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 24
27.0%
산 4
 
4.5%
면 4
 
4.5%
읍 3
 
3.4%
북 2
 
2.2%
하 2
 
2.2%
마 2
 
2.2%
안 2
 
2.2%
자 2
 
2.2%
일 2
 
2.2%
Other values (39) 42
47.2%
Common
ValueCountFrequency (%)
2 7
53.8%
1 3
23.1%
3 2
 
15.4%
6 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89
87.3%
ASCII 13
 
12.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 24
27.0%
산 4
 
4.5%
면 4
 
4.5%
읍 3
 
3.4%
북 2
 
2.2%
하 2
 
2.2%
마 2
 
2.2%
안 2
 
2.2%
자 2
 
2.2%
일 2
 
2.2%
Other values (39) 42
47.2%
ASCII
ValueCountFrequency (%)
2 7
53.8%
1 3
23.1%
3 2
 
15.4%
6 1
 
7.7%

행정동 코드
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1328838 × 109
Minimum4.1111573 × 109
Maximum4.183034 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:55:56.924479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1111573 × 109
5-th percentile4.1121768 × 109
Q14.1210578 × 109
median4.1285575 × 109
Q34.1463072 × 109
95-th percentile4.161244 × 109
Maximum4.183034 × 109
Range71876700
Interquartile range (IQR)25249450

Descriptive statistics

Standard deviation17956763
Coefficient of variation (CV)0.0043448507
Kurtosis0.48120401
Mean4.1328838 × 109
Median Absolute Deviation (MAD)11950000
Skewness0.939184
Sum1.2398651 × 1011
Variance3.2244534 × 1014
MonotonicityNot monotonic
2023-12-10T22:55:57.164820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4121057000 1
 
3.3%
4183034000 1
 
3.3%
4121063100 1
 
3.3%
4121060000 1
 
3.3%
4129052000 1
 
3.3%
4128559000 1
 
3.3%
4128752000 1
 
3.3%
4159033000 1
 
3.3%
4122025900 1
 
3.3%
4122053500 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
4111157300 1
3.3%
4111370000 1
3.3%
4113163000 1
3.3%
4113356000 1
3.3%
4113557000 1
3.3%
4117152000 1
3.3%
4117352000 1
3.3%
4121057000 1
3.3%
4121060000 1
3.3%
4121063100 1
3.3%
ValueCountFrequency (%)
4183034000 1
3.3%
4163053000 1
3.3%
4159033000 1
3.3%
4155039000 1
3.3%
4155034000 1
3.3%
4148025600 1
3.3%
4148025000 1
3.3%
4146358600 1
3.3%
4146153000 1
3.3%
4141052000 1
3.3%

전체 인구
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31325.443
Minimum5041.79
Maximum83428.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:55:57.360141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5041.79
5-th percentile8502.776
Q116629.8
median25690.995
Q341689.283
95-th percentile64956.242
Maximum83428.47
Range78386.68
Interquartile range (IQR)25059.483

Descriptive statistics

Standard deviation19917.108
Coefficient of variation (CV)0.6358125
Kurtosis0.12306763
Mean31325.443
Median Absolute Deviation (MAD)13594.995
Skewness0.86196238
Sum939763.29
Variance3.966912 × 108
MonotonicityNot monotonic
2023-12-10T22:55:57.579236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
9831.51 1
 
3.3%
12479.66 1
 
3.3%
28207.08 1
 
3.3%
10157.04 1
 
3.3%
7415.63 1
 
3.3%
83428.47 1
 
3.3%
25328.15 1
 
3.3%
25538.7 1
 
3.3%
52295.79 1
 
3.3%
52060.02 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
5041.79 1
3.3%
7415.63 1
3.3%
9831.51 1
3.3%
10157.04 1
3.3%
10875.61 1
3.3%
12479.66 1
3.3%
16466.06 1
3.3%
16627.89 1
3.3%
16635.53 1
3.3%
17733.99 1
3.3%
ValueCountFrequency (%)
83428.47 1
3.3%
66318.46 1
3.3%
63291.31 1
3.3%
59966.16 1
3.3%
52295.79 1
3.3%
52060.02 1
3.3%
49329.46 1
3.3%
41961.08 1
3.3%
40873.89 1
3.3%
39669.65 1
3.3%

유소년 인구
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2116.8533
Minimum312.7
Maximum6112.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:55:57.798430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum312.7
5-th percentile590.7855
Q1721.5875
median1626.095
Q33026.9275
95-th percentile5676.203
Maximum6112.62
Range5799.92
Interquartile range (IQR)2305.34

Descriptive statistics

Standard deviation1715.046
Coefficient of variation (CV)0.81018652
Kurtosis0.231448
Mean2116.8533
Median Absolute Deviation (MAD)956.26
Skewness1.1415433
Sum63505.6
Variance2941382.9
MonotonicityNot monotonic
2023-12-10T22:55:57.994161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
613.33 1
 
3.3%
670.42 1
 
3.3%
1927.65 1
 
3.3%
710.28 1
 
3.3%
669.25 1
 
3.3%
4879.08 1
 
3.3%
1389.41 1
 
3.3%
649.16 1
 
3.3%
3217.48 1
 
3.3%
2957.35 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
312.7 1
3.3%
572.34 1
3.3%
613.33 1
3.3%
622.25 1
3.3%
649.16 1
3.3%
669.25 1
3.3%
670.42 1
3.3%
710.28 1
3.3%
755.51 1
3.3%
794.36 1
3.3%
ValueCountFrequency (%)
6112.62 1
3.3%
5684.78 1
3.3%
5665.72 1
3.3%
4879.08 1
3.3%
4566.59 1
3.3%
3217.48 1
3.3%
3064.06 1
3.3%
3050.12 1
3.3%
2957.35 1
3.3%
2388.42 1
3.3%

고령 인구
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2888.3093
Minimum415.52
Maximum7538.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:55:58.182794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum415.52
5-th percentile842.3785
Q11899.535
median2546.845
Q33538.4975
95-th percentile6299.947
Maximum7538.87
Range7123.35
Interquartile range (IQR)1638.9625

Descriptive statistics

Standard deviation1681.9108
Coefficient of variation (CV)0.5823167
Kurtosis1.570049
Mean2888.3093
Median Absolute Deviation (MAD)851.175
Skewness1.1614674
Sum86649.28
Variance2828823.8
MonotonicityNot monotonic
2023-12-10T22:55:58.361808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1142.71 1
 
3.3%
2202.21 1
 
3.3%
2825.56 1
 
3.3%
1015.04 1
 
3.3%
701.11 1
 
3.3%
7538.87 1
 
3.3%
2691.69 1
 
3.3%
2616.55 1
 
3.3%
3319.06 1
 
3.3%
3471.83 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
415.52 1
3.3%
701.11 1
3.3%
1015.04 1
3.3%
1142.71 1
3.3%
1250.85 1
3.3%
1334.39 1
3.3%
1808.81 1
3.3%
1880.34 1
3.3%
1957.12 1
3.3%
2092.93 1
3.3%
ValueCountFrequency (%)
7538.87 1
3.3%
7056.01 1
3.3%
5375.87 1
3.3%
4812.1 1
3.3%
4151.54 1
3.3%
4030.37 1
3.3%
3623.27 1
3.3%
3560.72 1
3.3%
3471.83 1
3.3%
3324.21 1
3.3%

고령 인구 구성비
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8573333
Minimum6.35
Maximum17.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:55:58.543141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.35
5-th percentile6.637
Q18.1425
median9.925
Q310.675
95-th percentile13.7525
Maximum17.65
Range11.3
Interquartile range (IQR)2.5325

Descriptive statistics

Standard deviation2.425208
Coefficient of variation (CV)0.24603084
Kurtosis2.7527035
Mean9.8573333
Median Absolute Deviation (MAD)1.3
Skewness1.1970989
Sum295.72
Variance5.881634
MonotonicityNot monotonic
2023-12-10T22:55:58.721598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
10.02 2
 
6.7%
11.62 1
 
3.3%
17.65 1
 
3.3%
9.99 1
 
3.3%
9.45 1
 
3.3%
9.04 1
 
3.3%
10.63 1
 
3.3%
10.25 1
 
3.3%
6.35 1
 
3.3%
6.67 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
6.35 1
3.3%
6.61 1
3.3%
6.67 1
3.3%
6.74 1
3.3%
7.57 1
3.3%
7.6 1
3.3%
8.02 1
3.3%
8.11 1
3.3%
8.24 1
3.3%
8.98 1
3.3%
ValueCountFrequency (%)
17.65 1
3.3%
14.9 1
3.3%
12.35 1
3.3%
12.27 1
3.3%
11.62 1
3.3%
11.3 1
3.3%
11.15 1
3.3%
10.69 1
3.3%
10.63 1
3.3%
10.25 1
3.3%

고령화 지수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.718667
Minimum62.28
Maximum173.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:55:58.927585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum62.28
5-th percentile65.1155
Q179.8725
median97.395
Q3104.7375
95-th percentile134.949
Maximum173.16
Range110.88
Interquartile range (IQR)24.865

Descriptive statistics

Standard deviation23.797364
Coefficient of variation (CV)0.24604727
Kurtosis2.747262
Mean96.718667
Median Absolute Deviation (MAD)12.765
Skewness1.1958916
Sum2901.56
Variance566.31453
MonotonicityNot monotonic
2023-12-10T22:55:59.136088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
114.05 1
 
3.3%
173.16 1
 
3.3%
98.3 1
 
3.3%
98.06 1
 
3.3%
92.78 1
 
3.3%
88.67 1
 
3.3%
104.28 1
 
3.3%
100.54 1
 
3.3%
62.28 1
 
3.3%
65.44 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
62.28 1
3.3%
64.85 1
3.3%
65.44 1
3.3%
66.13 1
3.3%
74.31 1
3.3%
74.54 1
3.3%
78.74 1
3.3%
79.54 1
3.3%
80.87 1
3.3%
88.08 1
3.3%
ValueCountFrequency (%)
173.16 1
3.3%
146.19 1
3.3%
121.21 1
3.3%
120.4 1
3.3%
114.05 1
3.3%
110.92 1
3.3%
109.4 1
3.3%
104.89 1
3.3%
104.28 1
3.3%
100.54 1
3.3%

고령화 보조 지수
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.87333
Minimum58.48
Maximum432.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:55:59.329749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum58.48
5-th percentile86.289
Q1116.9575
median149.14
Q3191.9375
95-th percentile369.5045
Maximum432.81
Range374.33
Interquartile range (IQR)74.98

Descriptive statistics

Standard deviation90.590252
Coefficient of variation (CV)0.51803354
Kurtosis2.0336493
Mean174.87333
Median Absolute Deviation (MAD)37.825
Skewness1.5347984
Sum5246.2
Variance8206.5938
MonotonicityNot monotonic
2023-12-10T22:55:59.576970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
154.51 2
 
6.7%
186.31 1
 
3.3%
131.54 1
 
3.3%
146.58 1
 
3.3%
142.91 1
 
3.3%
104.76 1
 
3.3%
193.73 1
 
3.3%
403.07 1
 
3.3%
103.16 1
 
3.3%
117.4 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
58.48 1
3.3%
84.93 1
3.3%
87.95 1
3.3%
97.05 1
3.3%
103.16 1
3.3%
104.76 1
3.3%
110.91 1
3.3%
116.81 1
3.3%
117.4 1
3.3%
131.54 1
3.3%
ValueCountFrequency (%)
432.81 1
3.3%
403.07 1
3.3%
328.48 1
3.3%
299.92 1
3.3%
283.78 1
3.3%
214.45 1
3.3%
199.96 1
3.3%
193.73 1
3.3%
186.56 1
3.3%
186.31 1
3.3%

Interactions

2023-12-10T22:55:51.998796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:43.495436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:44.913947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:46.480268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:47.957016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:49.404007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:50.696080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:52.216107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:43.627858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:45.099367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:46.792435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:48.132515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:49.563056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:50.862590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:52.735199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:43.772367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:45.259943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:47.009408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:48.296454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:49.735608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:51.009584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:52.903265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:43.881393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:45.498097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:47.199877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:48.429507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:49.934602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:51.182070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:53.079798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:44.033425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:45.785785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:47.390259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:48.639059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:50.094929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:51.411167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:53.236571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:44.312813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:45.969831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:47.636651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:48.895741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:50.296342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:51.629672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:53.400609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:44.507954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:46.222310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:47.782341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:49.117353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:50.437960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:51.839455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:55:59.751522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명행정동명행정동 코드전체 인구유소년 인구고령 인구고령 인구 구성비고령화 지수고령화 보조 지수
시군구명1.0000.9391.0000.0000.7410.6830.7750.7750.844
행정동명0.9391.0001.0000.8930.9180.9490.9530.9530.929
행정동 코드1.0001.0001.0000.0000.2620.1680.7290.7290.744
전체 인구0.0000.8930.0001.0000.8360.7830.3780.3780.000
유소년 인구0.7410.9180.2620.8361.0000.8890.0000.0000.643
고령 인구0.6830.9490.1680.7830.8891.0000.0000.0000.000
고령 인구 구성비0.7750.9530.7290.3780.0000.0001.0001.0000.908
고령화 지수0.7750.9530.7290.3780.0000.0001.0001.0000.908
고령화 보조 지수0.8440.9290.7440.0000.6430.0000.9080.9081.000
2023-12-10T22:56:00.089619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동 코드전체 인구유소년 인구고령 인구고령 인구 구성비고령화 지수고령화 보조 지수
행정동 코드1.0000.072-0.0790.2670.3870.3840.420
전체 인구0.0721.0000.9280.919-0.452-0.451-0.391
유소년 인구-0.0790.9281.0000.811-0.545-0.543-0.603
고령 인구0.2670.9190.8111.000-0.158-0.157-0.142
고령 인구 구성비0.387-0.452-0.545-0.1581.0001.0000.850
고령화 지수0.384-0.451-0.543-0.1571.0001.0000.847
고령화 보조 지수0.420-0.391-0.603-0.1420.8500.8471.000

Missing values

2023-12-10T22:55:53.755215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:55:54.145470image/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

기준년월시도명시군구명행정동명행정동 코드전체 인구유소년 인구고령 인구고령 인구 구성비고령화 지수고령화 보조 지수
02019-01경기도광명시광명6동41210570009831.51613.331142.7111.62114.05186.31
12019-01경기도고양시 일산동구마두1동412855600041961.083064.064030.379.6194.25131.54
22019-01경기도구리시동구동413105200036755.742388.423623.279.8696.73151.7
32019-01경기도성남시 분당구정자3동411355700017733.991128.291808.8110.2100.09160.31
42019-01경기도군포시군포2동414105200059966.165665.724812.18.0278.7484.93
52019-01경기도성남시 수정구신촌동41131630005041.79312.7415.528.2480.87132.88
62019-01경기도성남시 중원구은행2동411335600010875.61622.251334.3912.27120.4214.45
72019-01경기도수원시 권선구입북동411137000025843.292016.571957.127.5774.3197.05
82019-01경기도수원시 장안구정자3동411115730049329.465684.783324.216.7466.1358.48
92019-01경기도안산시 상록구반월동412716000039669.651908.623560.728.9888.08186.56
기준년월시도명시군구명행정동명행정동 코드전체 인구유소년 인구고령 인구고령 인구 구성비고령화 지수고령화 보조 지수
202019-01경기도파주시문산읍414802500063291.314566.597056.0111.15109.4154.51
212019-01경기도파주시법원읍414802560018344.59755.512265.9112.35121.21299.92
222019-01경기도평택시송탄동412205350052060.022957.353471.836.6765.44117.4
232019-01경기도평택시청북읍412202590052295.793217.483319.066.3562.28103.16
242019-01경기도화성시마도면415903300025538.7649.162616.5510.25100.54403.07
252019-01경기도고양시 일산서구일산2동412875200025328.151389.412691.6910.63104.28193.73
262019-01경기도고양시 일산동구장항2동412855900083428.474879.087538.879.0488.67154.51
272019-01경기도과천시갈현동41290520007415.63669.25701.119.4592.78104.76
282019-01경기도광명시철산2동412106000010157.04710.281015.049.9998.06142.91
292019-01경기도광명시하안1동412106310028207.081927.652825.5610.0298.3146.58