Overview

Dataset statistics

Number of variables12
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory105.4 B

Variable types

DateTime1
Categorical3
Text3
Numeric5

Dataset

Description샘플 데이터
Author경기콘텐츠진흥원
URLhttps://bigdata-region.kr/#/dataset/a0640334-56c7-40d8-a1be-dba9318b61c9

Alerts

기준년월 has constant value ""Constant
시도명 has constant value ""Constant
비교 시도명 has constant value ""Constant
행정동 코드 is highly overall correlated with 고령화 보조 지수 and 1 other fieldsHigh correlation
고령화 지수 is highly overall correlated with 고령화 보조 지수 and 1 other fieldsHigh correlation
고령화 보조 지수 is highly overall correlated with 행정동 코드 and 2 other fieldsHigh correlation
비교 행정동코드 is highly overall correlated with 행정동 코드 and 1 other fieldsHigh correlation
비교값 is highly overall correlated with 고령화 지수 and 1 other fieldsHigh correlation
비교 시군구명 is highly overall correlated with 비교 행정동코드High correlation
행정동 코드 has unique valuesUnique
고령화 지수 has unique valuesUnique
고령화 보조 지수 has unique valuesUnique
비교값 has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:05:59.264955
Analysis finished2023-12-10 14:06:05.559442
Duration6.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2019-01-01 00:00:00
Maximum2019-01-01 00:00:00
2023-12-10T23:06:05.642766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:06:05.829250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

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

2023-12-10T23:06:06.140479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:06:06.326048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Text

Distinct24
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:06:06.580226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length4.7666667
Min length3

Characters and Unicode

Total characters143
Distinct characters43
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

Unique18 ?
Unique (%)60.0%

Sample

1st row광명시
2nd row고양시 일산동구
3rd row구리시
4th row성남시 분당구
5th row군포시
ValueCountFrequency (%)
성남시 3
 
7.0%
고양시 2
 
4.7%
용인시 2
 
4.7%
안양시 2
 
4.7%
평택시 2
 
4.7%
수원시 2
 
4.7%
광명시 2
 
4.7%
군포시 2
 
4.7%
안성시 2
 
4.7%
상록구 2
 
4.7%
Other values (20) 22
51.2%
2023-12-10T23:06:07.211277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 28
19.6%
구 14
 
9.8%
13
 
9.1%
안 9
 
6.3%
양 7
 
4.9%
성 6
 
4.2%
산 4
 
2.8%
평 4
 
2.8%
군 4
 
2.8%
수 3
 
2.1%
Other values (33) 51
35.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 130
90.9%
Space Separator 13
 
9.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 28
21.5%
구 14
 
10.8%
안 9
 
6.9%
양 7
 
5.4%
성 6
 
4.6%
산 4
 
3.1%
평 4
 
3.1%
군 4
 
3.1%
수 3
 
2.3%
남 3
 
2.3%
Other values (32) 48
36.9%
Space Separator
ValueCountFrequency (%)
13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 130
90.9%
Common 13
 
9.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 28
21.5%
구 14
 
10.8%
안 9
 
6.9%
양 7
 
5.4%
성 6
 
4.6%
산 4
 
3.1%
평 4
 
3.1%
군 4
 
3.1%
수 3
 
2.3%
남 3
 
2.3%
Other values (32) 48
36.9%
Common
ValueCountFrequency (%)
13
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 130
90.9%
ASCII 13
 
9.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 28
21.5%
구 14
 
10.8%
안 9
 
6.9%
양 7
 
5.4%
성 6
 
4.6%
산 4
 
3.1%
평 4
 
3.1%
군 4
 
3.1%
수 3
 
2.3%
남 3
 
2.3%
Other values (32) 48
36.9%
ASCII
ValueCountFrequency (%)
13
100.0%

행정동명
Text

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:06:07.624071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.3333333
Min length2

Characters and Unicode

Total characters100
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%
광명4동 1
 
3.3%
문원동 1
 
3.3%
설악면 1
 
3.3%
화정2동 1
 
3.3%
마도면 1
 
3.3%
청북읍 1
 
3.3%
송탄동 1
 
3.3%
Other values (19) 19
63.3%
2023-12-10T23:06:08.216757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 23
23.0%
2 5
 
5.0%
면 5
 
5.0%
정 4
 
4.0%
읍 3
 
3.0%
북 2
 
2.0%
원 2
 
2.0%
문 2
 
2.0%
명 2
 
2.0%
광 2
 
2.0%
Other values (43) 50
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89
89.0%
Decimal Number 11
 
11.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 23
25.8%
면 5
 
5.6%
정 4
 
4.5%
읍 3
 
3.4%
북 2
 
2.2%
원 2
 
2.2%
문 2
 
2.2%
명 2
 
2.2%
광 2
 
2.2%
마 2
 
2.2%
Other values (38) 42
47.2%
Decimal Number
ValueCountFrequency (%)
2 5
45.5%
1 2
 
18.2%
3 2
 
18.2%
6 1
 
9.1%
4 1
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89
89.0%
Common 11
 
11.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 23
25.8%
면 5
 
5.6%
정 4
 
4.5%
읍 3
 
3.4%
북 2
 
2.2%
원 2
 
2.2%
문 2
 
2.2%
명 2
 
2.2%
광 2
 
2.2%
마 2
 
2.2%
Other values (38) 42
47.2%
Common
ValueCountFrequency (%)
2 5
45.5%
1 2
 
18.2%
3 2
 
18.2%
6 1
 
9.1%
4 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89
89.0%
ASCII 11
 
11.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 23
25.8%
면 5
 
5.6%
정 4
 
4.5%
읍 3
 
3.4%
북 2
 
2.2%
원 2
 
2.2%
문 2
 
2.2%
명 2
 
2.2%
광 2
 
2.2%
마 2
 
2.2%
Other values (38) 42
47.2%
ASCII
ValueCountFrequency (%)
2 5
45.5%
1 2
 
18.2%
3 2
 
18.2%
6 1
 
9.1%
4 1
 
9.1%

행정동 코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1353129 × 109
Minimum4.1111573 × 109
Maximum4.183034 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:06:08.436871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1111573 × 109
5-th percentile4.1121768 × 109
Q14.1210555 × 109
median4.128806 × 109
Q34.1476084 × 109
95-th percentile4.1734909 × 109
Maximum4.183034 × 109
Range71876700
Interquartile range (IQR)26552900

Descriptive statistics

Standard deviation19916208
Coefficient of variation (CV)0.0048161308
Kurtosis0.16317869
Mean4.1353129 × 109
Median Absolute Deviation (MAD)13749500
Skewness0.85998283
Sum1.2405939 × 1011
Variance3.9665534 × 1014
MonotonicityNot monotonic
2023-12-10T23:06:08.641641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4121057000 1
 
3.3%
4183034000 1
 
3.3%
4141056000 1
 
3.3%
4121055000 1
 
3.3%
4129056000 1
 
3.3%
4182031000 1
 
3.3%
4128162200 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%
4121055000 1
3.3%
4121057000 1
3.3%
4122025900 1
3.3%
ValueCountFrequency (%)
4183034000 1
3.3%
4182031000 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%

고령화 지수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.70967
Minimum62.28
Maximum173.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:06:08.873528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum62.28
5-th percentile65.1155
Q179.8725
median99.86
Q3113.2675
95-th percentile157.6795
Maximum173.16
Range110.88
Interquartile range (IQR)33.395

Descriptive statistics

Standard deviation28.10111
Coefficient of variation (CV)0.2762875
Kurtosis0.71516606
Mean101.70967
Median Absolute Deviation (MAD)19.655
Skewness0.86741097
Sum3051.29
Variance789.67237
MonotonicityNot monotonic
2023-12-10T23:06:09.085074image/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%
102.76 1
 
3.3%
127.61 1
 
3.3%
134.32 1
 
3.3%
167.08 1
 
3.3%
100.05 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%
167.08 1
3.3%
146.19 1
3.3%
134.32 1
3.3%
127.61 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%

고령화 보조 지수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190.395
Minimum58.48
Maximum452.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:06:09.275140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum58.48
5-th percentile86.289
Q1120.0825
median157.41
Q3210.8275
95-th percentile419.427
Maximum452.44
Range393.96
Interquartile range (IQR)90.745

Descriptive statistics

Standard deviation103.09343
Coefficient of variation (CV)0.54147129
Kurtosis1.0306603
Mean190.395
Median Absolute Deviation (MAD)41.67
Skewness1.2976335
Sum5711.85
Variance10628.254
MonotonicityNot monotonic
2023-12-10T23:06:09.491962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
186.31 1
 
3.3%
328.48 1
 
3.3%
246.48 1
 
3.3%
198.2 1
 
3.3%
182.89 1
 
3.3%
452.44 1
 
3.3%
128.13 1
 
3.3%
403.07 1
 
3.3%
103.16 1
 
3.3%
117.4 1
 
3.3%
Other values (20) 20
66.7%
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%
110.91 1
3.3%
116.81 1
3.3%
117.4 1
3.3%
128.13 1
3.3%
131.54 1
3.3%
ValueCountFrequency (%)
452.44 1
3.3%
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%
246.48 1
3.3%
214.45 1
3.3%
199.96 1
3.3%
198.2 1
3.3%

비교 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

2023-12-10T23:06:09.719992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:06:09.885673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

비교 시군구명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
연천군
8 
파주시
4 
양평군
4 
화성시
3 
안양시 동안구
2 
Other values (9)
9 

Length

Max length8
Median length3
Mean length3.5666667
Min length3

Unique

Unique9 ?
Unique (%)30.0%

Sample

1st row김포시
2nd row파주시
3rd row파주시
4th row광명시
5th row양평군

Common Values

ValueCountFrequency (%)
연천군 8
26.7%
파주시 4
13.3%
양평군 4
13.3%
화성시 3
 
10.0%
안양시 동안구 2
 
6.7%
김포시 1
 
3.3%
광명시 1
 
3.3%
수원시 팔달구 1
 
3.3%
고양시 일산서구 1
 
3.3%
양주시 1
 
3.3%
Other values (4) 4
13.3%

Length

2023-12-10T23:06:10.068337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
연천군 8
23.5%
양평군 4
11.8%
파주시 4
11.8%
화성시 3
 
8.8%
안양시 2
 
5.9%
동안구 2
 
5.9%
일산서구 1
 
2.9%
이천시 1
 
2.9%
부천시 1
 
2.9%
구리시 1
 
2.9%
Other values (7) 7
20.6%

비교 행정동명
Text

Distinct17
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:06:10.362077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.2666667
Min length2

Characters and Unicode

Total characters98
Distinct characters37
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)43.3%

Sample

1st row고촌읍
2nd row진서면
3rd row진서면
4th row소하2동
5th row청운면
ValueCountFrequency (%)
장남면 8
26.7%
청운면 4
13.3%
진서면 3
 
10.0%
동탄1동 2
 
6.7%
금촌2동 1
 
3.3%
고촌읍 1
 
3.3%
심곡2동 1
 
3.3%
율면 1
 
3.3%
부흥동 1
 
3.3%
반월동 1
 
3.3%
Other values (7) 7
23.3%
2023-12-10T23:06:10.873343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
면 16
16.3%
동 14
14.3%
장 8
 
8.2%
남 8
 
8.2%
2 5
 
5.1%
운 4
 
4.1%
청 4
 
4.1%
진 3
 
3.1%
서 3
 
3.1%
1 3
 
3.1%
Other values (27) 30
30.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 90
91.8%
Decimal Number 8
 
8.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 16
17.8%
동 14
15.6%
장 8
 
8.9%
남 8
 
8.9%
운 4
 
4.4%
청 4
 
4.4%
진 3
 
3.3%
서 3
 
3.3%
촌 2
 
2.2%
읍 2
 
2.2%
Other values (25) 26
28.9%
Decimal Number
ValueCountFrequency (%)
2 5
62.5%
1 3
37.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 90
91.8%
Common 8
 
8.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 16
17.8%
동 14
15.6%
장 8
 
8.9%
남 8
 
8.9%
운 4
 
4.4%
청 4
 
4.4%
진 3
 
3.3%
서 3
 
3.3%
촌 2
 
2.2%
읍 2
 
2.2%
Other values (25) 26
28.9%
Common
ValueCountFrequency (%)
2 5
62.5%
1 3
37.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 90
91.8%
ASCII 8
 
8.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
면 16
17.8%
동 14
15.6%
장 8
 
8.9%
남 8
 
8.9%
운 4
 
4.4%
청 4
 
4.4%
진 3
 
3.3%
서 3
 
3.3%
촌 2
 
2.2%
읍 2
 
2.2%
Other values (25) 26
28.9%
ASCII
ValueCountFrequency (%)
2 5
62.5%
1 3
37.5%

비교 행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1573047 × 109
Minimum4.111572 × 109
Maximum4.183037 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:06:11.106936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.111572 × 109
5-th percentile4.1173529 × 109
Q14.148041 × 109
median4.1590585 × 109
Q34.180038 × 109
95-th percentile4.183037 × 109
Maximum4.183037 × 109
Range71465000
Interquartile range (IQR)31997000

Descriptive statistics

Standard deviation24068611
Coefficient of variation (CV)0.0057894749
Kurtosis-1.0116096
Mean4.1573047 × 109
Median Absolute Deviation (MAD)20979500
Skewness-0.55454551
Sum1.2471914 × 1011
Variance5.7929806 × 1014
MonotonicityNot monotonic
2023-12-10T23:06:11.327029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
4180038000 8
26.7%
4183037000 4
13.3%
4148041000 3
 
10.0%
4159058500 2
 
6.7%
4157025300 1
 
3.3%
4159055000 1
 
3.3%
4161025900 1
 
3.3%
4150038000 1
 
3.3%
4117354000 1
 
3.3%
4148052000 1
 
3.3%
Other values (7) 7
23.3%
ValueCountFrequency (%)
4111572000 1
 
3.3%
4117352000 1
 
3.3%
4117354000 1
 
3.3%
4119051000 1
 
3.3%
4121065000 1
 
3.3%
4128758000 1
 
3.3%
4131051000 1
 
3.3%
4148041000 3
10.0%
4148052000 1
 
3.3%
4150038000 1
 
3.3%
ValueCountFrequency (%)
4183037000 4
13.3%
4180038000 8
26.7%
4163051000 1
 
3.3%
4161025900 1
 
3.3%
4159058500 2
 
6.7%
4159055000 1
 
3.3%
4157025300 1
 
3.3%
4150038000 1
 
3.3%
4148052000 1
 
3.3%
4148041000 3
 
10.0%

비교값
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.227333
Minimum25.54
Maximum239.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:06:11.568897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25.54
5-th percentile42.1925
Q166.5625
median88.385
Q3116.71
95-th percentile161.818
Maximum239.43
Range213.89
Interquartile range (IQR)50.1475

Descriptive statistics

Standard deviation42.973824
Coefficient of variation (CV)0.45127615
Kurtosis3.2665523
Mean95.227333
Median Absolute Deviation (MAD)23.3
Skewness1.3567767
Sum2856.82
Variance1846.7496
MonotonicityNot monotonic
2023-12-10T23:06:11.768422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
62.77 1
 
3.3%
25.54 1
 
3.3%
91.11 1
 
3.3%
66.69 1
 
3.3%
73.23 1
 
3.3%
31.82 1
 
3.3%
102.07 1
 
3.3%
59.07 1
 
3.3%
170.8 1
 
3.3%
121.93 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
25.54 1
3.3%
31.82 1
3.3%
54.87 1
3.3%
59.07 1
3.3%
62.77 1
3.3%
63.85 1
3.3%
66.38 1
3.3%
66.52 1
3.3%
66.69 1
3.3%
72.57 1
3.3%
ValueCountFrequency (%)
239.43 1
3.3%
170.8 1
3.3%
150.84 1
3.3%
142.52 1
3.3%
127.81 1
3.3%
121.93 1
3.3%
118.22 1
3.3%
118.16 1
3.3%
112.36 1
3.3%
111.01 1
3.3%

Interactions

2023-12-10T23:06:03.551519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:59.927132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:06:00.858277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:06:01.552917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:06:02.319636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:06:03.738677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:06:00.161219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:06:01.013903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:06:01.700229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:06:02.478761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:06:03.852989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:06:00.308929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:06:01.148010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:06:01.821727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:06:02.761475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:06:03.985903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:06:00.529125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:06:01.298641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:06:01.978019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:06:03.006897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:06:04.477796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:06:00.748531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:06:01.437663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:06:02.159462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:06:03.312333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:06:11.907500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명행정동명행정동 코드고령화 지수고령화 보조 지수비교 시군구명비교 행정동명비교 행정동코드비교값
시군구명1.0000.9481.0000.6520.0000.8400.8150.7410.827
행정동명0.9481.0001.0000.9630.9400.7840.6670.0000.856
행정동 코드1.0001.0001.0000.8060.6060.0000.0000.3210.588
고령화 지수0.6520.9630.8061.0000.5840.6340.5580.4260.703
고령화 보조 지수0.0000.9400.6060.5841.0000.0000.0000.0000.162
비교 시군구명0.8400.7840.0000.6340.0001.0001.0001.0000.000
비교 행정동명0.8150.6670.0000.5580.0001.0001.0001.0000.288
비교 행정동코드0.7410.0000.3210.4260.0001.0001.0001.0000.672
비교값0.8270.8560.5880.7030.1620.0000.2880.6721.000
2023-12-10T23:06:12.119553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동 코드고령화 지수고령화 보조 지수비교 행정동코드비교값비교 시군구명
행정동 코드1.0000.4430.5230.628-0.4330.000
고령화 지수0.4431.0000.8400.172-0.8970.254
고령화 보조 지수0.5230.8401.0000.183-0.8420.000
비교 행정동코드0.6280.1720.1831.000-0.0940.834
비교값-0.433-0.897-0.842-0.0941.0000.000
비교 시군구명0.0000.2540.0000.8340.0001.000

Missing values

2023-12-10T23:06:04.826520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:06:05.348268image/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 코드고령화 지수고령화 보조 지수비교 시도명비교 시군구명비교 행정동명비교 행정동코드비교값
02019-01경기도광명시광명6동4121057000114.05186.31경기도김포시고촌읍415702530062.77
12019-01경기도고양시 일산동구마두1동412855600094.25131.54경기도파주시진서면4148041000111.01
22019-01경기도구리시동구동413105200096.73151.7경기도파주시진서면414804100098.58
32019-01경기도성남시 분당구정자3동4113557000100.09160.31경기도광명시소하2동412106500074.28
42019-01경기도군포시군포2동414105200078.7484.93경기도양평군청운면4183037000118.16
52019-01경기도성남시 수정구신촌동411316300080.87132.88경기도수원시 팔달구우만2동411157200096.1
62019-01경기도성남시 중원구은행2동4113356000120.4214.45경기도고양시 일산서구송포동412875800063.85
72019-01경기도수원시 권선구입북동411137000074.3197.05경기도연천군장남면4180038000239.43
82019-01경기도수원시 장안구정자3동411115730066.1358.48경기도안양시 동안구비산2동4117352000142.52
92019-01경기도안산시 상록구반월동412716000088.08186.56경기도연천군장남면4180038000118.22
기준년월시도명시군구명행정동명행정동 코드고령화 지수고령화 보조 지수비교 시도명비교 시군구명비교 행정동명비교 행정동코드비교값
202019-01경기도파주시문산읍4148025000109.4154.51경기도연천군장남면418003800079.48
212019-01경기도파주시법원읍4148025600121.21299.92경기도파주시진서면414804100072.57
222019-01경기도평택시송탄동412205350065.44117.4경기도파주시금촌2동4148052000121.93
232019-01경기도평택시청북읍412202590062.28103.16경기도안양시 동안구부흥동4117354000170.8
242019-01경기도화성시마도면4159033000100.54403.07경기도화성시동탄1동415905850059.07
252019-01경기도고양시 덕양구화정2동4128162200100.05128.13경기도연천군장남면4180038000102.07
262019-01경기도가평군설악면4182031000167.08452.44경기도양평군청운면418303700031.82
272019-01경기도과천시문원동4129056000134.32182.89경기도이천시율면415003800073.23
282019-01경기도광명시광명4동4121055000127.61198.2경기도광주시곤지암읍416102590066.69
292019-01경기도군포시금정동4141056000102.76246.48경기도화성시동탄1동415905850091.11