Overview

Dataset statistics

Number of variables10
Number of observations528
Missing cells66
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.4 KiB
Average record size in memory84.2 B

Variable types

Numeric4
Categorical6

Dataset

Description대전광역시 서구 독거노인 (순번, 시군구명, 행정동명, 행정동코드, 법정동코드, 인구구분명, 연령구분명, 남여구분명, 인구수, 데이터기준일현황) 데이터 입니다.
Author대전광역시 서구
URLhttps://www.data.go.kr/data/15113285/fileData.do

Alerts

시군구명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
순번 is highly overall correlated with 행정동코드 and 2 other fieldsHigh correlation
행정동코드 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
법정동코드 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
행정동명 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
인구구분명 is highly overall correlated with 연령구분명 and 1 other fieldsHigh correlation
연령구분명 is highly overall correlated with 인구구분명 and 1 other fieldsHigh correlation
남여구분명 is highly overall correlated with 인구구분명 and 1 other fieldsHigh correlation
법정동코드 has 66 (12.5%) missing valuesMissing
인구수 is highly skewed (γ1 = 20.59876276)Skewed
순번 has unique valuesUnique
인구수 has 94 (17.8%) zerosZeros

Reproduction

Analysis started2024-04-13 12:32:25.651696
Analysis finished2024-04-13 12:32:32.240144
Duration6.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct528
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean264.5
Minimum1
Maximum528
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2024-04-13T21:32:32.452898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile27.35
Q1132.75
median264.5
Q3396.25
95-th percentile501.65
Maximum528
Range527
Interquartile range (IQR)263.5

Descriptive statistics

Standard deviation152.56474
Coefficient of variation (CV)0.57680431
Kurtosis-1.2
Mean264.5
Median Absolute Deviation (MAD)132
Skewness0
Sum139656
Variance23276
MonotonicityStrictly increasing
2024-04-13T21:32:32.891291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
349 1
 
0.2%
363 1
 
0.2%
362 1
 
0.2%
361 1
 
0.2%
360 1
 
0.2%
359 1
 
0.2%
358 1
 
0.2%
357 1
 
0.2%
356 1
 
0.2%
Other values (518) 518
98.1%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
528 1
0.2%
527 1
0.2%
526 1
0.2%
525 1
0.2%
524 1
0.2%
523 1
0.2%
522 1
0.2%
521 1
0.2%
520 1
0.2%
519 1
0.2%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
서구
528 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서구 528
100.0%

Length

2024-04-13T21:32:33.167438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T21:32:33.471477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서구 528
100.0%

행정동명
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
전체
 
22
복수동
 
22
도마1동
 
22
도마2동
 
22
정림동
 
22
Other values (19)
418 

Length

Max length4
Median length4
Mean length3.4166667
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전체 22
 
4.2%
복수동 22
 
4.2%
도마1동 22
 
4.2%
도마2동 22
 
4.2%
정림동 22
 
4.2%
변동 22
 
4.2%
용문동 22
 
4.2%
탄방동 22
 
4.2%
괴정동 22
 
4.2%
가장동 22
 
4.2%
Other values (14) 308
58.3%

Length

2024-04-13T21:32:33.658394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전체 22
 
4.2%
복수동 22
 
4.2%
만년동 22
 
4.2%
둔산2동 22
 
4.2%
둔산1동 22
 
4.2%
기성동 22
 
4.2%
관저2동 22
 
4.2%
관저1동 22
 
4.2%
가수원동 22
 
4.2%
월평3동 22
 
4.2%
Other values (14) 308
58.3%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0170556 × 109
Minimum3.017 × 109
Maximum3.017066 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2024-04-13T21:32:33.870326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.017 × 109
5-th percentile3.017051 × 109
Q13.0170548 × 109
median3.0170582 × 109
Q33.0170596 × 109
95-th percentile3.017065 × 109
Maximum3.017066 × 109
Range66000
Interquartile range (IQR)4875

Descriptive statistics

Standard deviation12212.458
Coefficient of variation (CV)4.0478069 × 10-6
Kurtosis15.115337
Mean3.0170556 × 109
Median Absolute Deviation (MAD)2400
Skewness-3.8691366
Sum1.5930053 × 1012
Variance1.4914414 × 108
MonotonicityIncreasing
2024-04-13T21:32:34.078022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3017000000 22
 
4.2%
3017058600 22
 
4.2%
3017066000 22
 
4.2%
3017065000 22
 
4.2%
3017064000 22
 
4.2%
3017063000 22
 
4.2%
3017060000 22
 
4.2%
3017059700 22
 
4.2%
3017059600 22
 
4.2%
3017059000 22
 
4.2%
Other values (14) 308
58.3%
ValueCountFrequency (%)
3017000000 22
4.2%
3017051000 22
4.2%
3017052000 22
4.2%
3017053000 22
4.2%
3017053500 22
4.2%
3017054000 22
4.2%
3017055000 22
4.2%
3017055500 22
4.2%
3017056000 22
4.2%
3017057000 22
4.2%
ValueCountFrequency (%)
3017066000 22
4.2%
3017065000 22
4.2%
3017064000 22
4.2%
3017063000 22
4.2%
3017060000 22
4.2%
3017059700 22
4.2%
3017059600 22
4.2%
3017059000 22
4.2%
3017058800 22
4.2%
3017058700 22
4.2%

법정동코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)3.0%
Missing66
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean3.0170105 × 109
Minimum3.017 × 109
Maximum3.0170128 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2024-04-13T21:32:34.284853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.017 × 109
5-th percentile3.0170101 × 109
Q13.0170105 × 109
median3.0170111 × 109
Q33.0170113 × 109
95-th percentile3.0170116 × 109
Maximum3.0170128 × 109
Range12800
Interquartile range (IQR)800

Descriptive statistics

Standard deviation2421.8857
Coefficient of variation (CV)8.0274355 × 10-7
Kurtosis14.077765
Mean3.0170105 × 109
Median Absolute Deviation (MAD)300
Skewness-3.8393854
Sum1.3938588 × 1012
Variance5865530.4
MonotonicityNot monotonic
2024-04-13T21:32:34.490566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3017011300 66
12.5%
3017011200 66
12.5%
3017010300 44
8.3%
3017011100 44
8.3%
3017011600 44
8.3%
3017000000 22
 
4.2%
3017010100 22
 
4.2%
3017010200 22
 
4.2%
3017010500 22
 
4.2%
3017010600 22
 
4.2%
Other values (4) 88
16.7%
(Missing) 66
12.5%
ValueCountFrequency (%)
3017000000 22
4.2%
3017010100 22
4.2%
3017010200 22
4.2%
3017010300 44
8.3%
3017010500 22
4.2%
3017010600 22
4.2%
3017010800 22
4.2%
3017010900 22
4.2%
3017011000 22
4.2%
3017011100 44
8.3%
ValueCountFrequency (%)
3017012800 22
 
4.2%
3017011600 44
8.3%
3017011300 66
12.5%
3017011200 66
12.5%
3017011100 44
8.3%
3017011000 22
 
4.2%
3017010900 22
 
4.2%
3017010800 22
 
4.2%
3017010600 22
 
4.2%
3017010500 22
 
4.2%

인구구분명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
맞춤형급여 연령대별 독거노인수(생계,의료,주거)
240 
일반 연령대별 독거노인수
240 
전체 인구
 
24
만65세 이상 노인 인구
 
24

Length

Max length26
Median length19.5
Mean length18.545455
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전체 인구
2nd row만65세 이상 노인 인구
3rd row맞춤형급여 연령대별 독거노인수(생계,의료,주거)
4th row맞춤형급여 연령대별 독거노인수(생계,의료,주거)
5th row맞춤형급여 연령대별 독거노인수(생계,의료,주거)

Common Values

ValueCountFrequency (%)
맞춤형급여 연령대별 독거노인수(생계,의료,주거) 240
45.5%
일반 연령대별 독거노인수 240
45.5%
전체 인구 24
 
4.5%
만65세 이상 노인 인구 24
 
4.5%

Length

2024-04-13T21:32:34.703087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T21:32:34.902055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
연령대별 480
30.3%
맞춤형급여 240
15.2%
독거노인수(생계,의료,주거 240
15.2%
일반 240
15.2%
독거노인수 240
15.2%
인구 48
 
3.0%
전체 24
 
1.5%
만65세 24
 
1.5%
이상 24
 
1.5%
노인 24
 
1.5%

연령구분명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
65세이상
96 
70세이상
96 
80세이상
96 
90세이상
96 
100세이상
96 

Length

Max length6
Median length5
Mean length4.9090909
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전체
2nd row전체
3rd row65세이상
4th row65세이상
5th row70세이상

Common Values

ValueCountFrequency (%)
65세이상 96
18.2%
70세이상 96
18.2%
80세이상 96
18.2%
90세이상 96
18.2%
100세이상 96
18.2%
전체 48
9.1%

Length

2024-04-13T21:32:35.296026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T21:32:35.599264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
65세이상 96
18.2%
70세이상 96
18.2%
80세이상 96
18.2%
90세이상 96
18.2%
100세이상 96
18.2%
전체 48
9.1%

남여구분명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
남
240 
여
240 
전체
48 

Length

Max length2
Median length1
Mean length1.0909091
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남 240
45.5%
여 240
45.5%
전체 48
 
9.1%

Length

2024-04-13T21:32:35.980478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T21:32:36.307010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남 240
45.5%
여 240
45.5%
전체 48
 
9.1%

인구수
Real number (ℝ)

SKEWED  ZEROS 

Distinct184
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2039.5587
Minimum0
Maximum444903
Zeros94
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2024-04-13T21:32:36.652426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median21.5
Q376
95-th percentile3999.25
Maximum444903
Range444903
Interquartile range (IQR)74

Descriptive statistics

Standard deviation20068.843
Coefficient of variation (CV)9.8397965
Kurtosis452.41343
Mean2039.5587
Median Absolute Deviation (MAD)21.5
Skewness20.598763
Sum1076887
Variance4.0275845 × 108
MonotonicityNot monotonic
2024-04-13T21:32:37.066317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 94
 
17.8%
1 33
 
6.2%
2 18
 
3.4%
4 15
 
2.8%
3 10
 
1.9%
7 9
 
1.7%
10 8
 
1.5%
9 8
 
1.5%
22 8
 
1.5%
5 8
 
1.5%
Other values (174) 317
60.0%
ValueCountFrequency (%)
0 94
17.8%
1 33
 
6.2%
2 18
 
3.4%
3 10
 
1.9%
4 15
 
2.8%
5 8
 
1.5%
6 7
 
1.3%
7 9
 
1.7%
8 6
 
1.1%
9 8
 
1.5%
ValueCountFrequency (%)
444903 1
0.2%
63740 1
0.2%
49362 1
0.2%
46552 1
0.2%
35228 1
0.2%
26897 1
0.2%
24625 1
0.2%
23467 1
0.2%
22177 1
0.2%
21620 1
0.2%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2024-04-08
528 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-04-08
2nd row2024-04-08
3rd row2024-04-08
4th row2024-04-08
5th row2024-04-08

Common Values

ValueCountFrequency (%)
2024-04-08 528
100.0%

Length

2024-04-13T21:32:37.459782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T21:32:37.749875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-04-08 528
100.0%

Interactions

2024-04-13T21:32:30.540155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:32:27.470427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:32:28.485260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:32:29.517942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:32:30.732901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:32:27.727511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:32:28.740639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:32:29.768182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:32:30.971412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:32:27.985944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:32:29.006252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:32:30.031777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:32:31.225512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:32:28.244822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:32:29.268624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T21:32:30.292317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-13T21:32:37.929082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번행정동명행정동코드법정동코드인구구분명연령구분명남여구분명인구수
순번1.0000.9870.9380.8690.0000.0000.0000.000
행정동명0.9871.0001.0001.0000.0000.0000.0000.000
행정동코드0.9381.0001.0000.8570.0000.0000.0000.000
법정동코드0.8691.0000.8571.0000.0000.0000.0000.055
인구구분명0.0000.0000.0000.0001.0000.7360.6750.227
연령구분명0.0000.0000.0000.0000.7361.0000.9400.380
남여구분명0.0000.0000.0000.0000.6750.9401.0000.471
인구수0.0000.0000.0000.0550.2270.3800.4711.000
2024-04-13T21:32:38.216227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
남여구분명연령구분명인구구분명행정동명
남여구분명1.0000.7020.7040.000
연령구분명0.7021.0000.5710.000
인구구분명0.7040.5711.0000.000
행정동명0.0000.0000.0001.000
2024-04-13T21:32:38.485310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번행정동코드법정동코드인구수행정동명인구구분명연령구분명남여구분명
순번1.0000.9990.904-0.1580.9060.0000.0000.000
행정동코드0.9991.0000.905-0.1460.9810.0000.0000.000
법정동코드0.9040.9051.000-0.1350.9810.0000.0000.000
인구수-0.158-0.146-0.1351.0000.0000.2160.1700.186
행정동명0.9060.9810.9810.0001.0000.0000.0000.000
인구구분명0.0000.0000.0000.2160.0001.0000.5710.704
연령구분명0.0000.0000.0000.1700.0000.5711.0000.702
남여구분명0.0000.0000.0000.1860.0000.7040.7021.000

Missing values

2024-04-13T21:32:31.563895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-13T21:32:32.039817image/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

순번시군구명행정동명행정동코드법정동코드인구구분명연령구분명남여구분명인구수데이터기준일자
01서구전체30170000003017000000전체 인구전체전체4449032024-04-08
12서구전체30170000003017000000만65세 이상 노인 인구전체전체637402024-04-08
23서구전체30170000003017000000맞춤형급여 연령대별 독거노인수(생계,의료,주거)65세이상남5212024-04-08
34서구전체30170000003017000000맞춤형급여 연령대별 독거노인수(생계,의료,주거)65세이상여6912024-04-08
45서구전체30170000003017000000맞춤형급여 연령대별 독거노인수(생계,의료,주거)70세이상남6662024-04-08
56서구전체30170000003017000000맞춤형급여 연령대별 독거노인수(생계,의료,주거)70세이상여8982024-04-08
67서구전체30170000003017000000맞춤형급여 연령대별 독거노인수(생계,의료,주거)80세이상남2892024-04-08
78서구전체30170000003017000000맞춤형급여 연령대별 독거노인수(생계,의료,주거)80세이상여8812024-04-08
89서구전체30170000003017000000맞춤형급여 연령대별 독거노인수(생계,의료,주거)90세이상남202024-04-08
910서구전체30170000003017000000맞춤형급여 연령대별 독거노인수(생계,의료,주거)90세이상여1872024-04-08
순번시군구명행정동명행정동코드법정동코드인구구분명연령구분명남여구분명인구수데이터기준일자
518519서구둔산3동30170660003017011200일반 연령대별 독거노인수65세이상남452024-04-08
519520서구둔산3동30170660003017011200일반 연령대별 독거노인수65세이상여1222024-04-08
520521서구둔산3동30170660003017011200일반 연령대별 독거노인수70세이상남522024-04-08
521522서구둔산3동30170660003017011200일반 연령대별 독거노인수70세이상여1952024-04-08
522523서구둔산3동30170660003017011200일반 연령대별 독거노인수80세이상남252024-04-08
523524서구둔산3동30170660003017011200일반 연령대별 독거노인수80세이상여1262024-04-08
524525서구둔산3동30170660003017011200일반 연령대별 독거노인수90세이상남32024-04-08
525526서구둔산3동30170660003017011200일반 연령대별 독거노인수90세이상여92024-04-08
526527서구둔산3동30170660003017011200일반 연령대별 독거노인수100세이상남02024-04-08
527528서구둔산3동30170660003017011200일반 연령대별 독거노인수100세이상여12024-04-08