Overview

Dataset statistics

Number of variables6
Number of observations3552
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory180.5 KiB
Average record size in memory52.0 B

Variable types

Numeric4
Categorical2

Dataset

Description대전광역시 동구 관내 인구 정보로서, 읍면동명, 읍면동코드, 성별, 연령(1세단위) 및 인구수 정보를 포함하고 있습니다.
Author대전광역시 동구
URLhttps://www.data.go.kr/data/15111118/fileData.do

Alerts

순번 is highly overall correlated with 법정읍면동명High correlation
법정동코드 is highly overall correlated with 법정읍면동명High correlation
법정읍면동명 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
순번 has unique valuesUnique
인구수 has 400 (11.3%) zerosZeros

Reproduction

Analysis started2023-12-12 10:12:23.915153
Analysis finished2023-12-12 10:12:26.301741
Duration2.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3552
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1775.5
Minimum0
Maximum3551
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size31.3 KiB
2023-12-12T19:12:26.385128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile177.55
Q1887.75
median1775.5
Q32663.25
95-th percentile3373.45
Maximum3551
Range3551
Interquartile range (IQR)1775.5

Descriptive statistics

Standard deviation1025.5184
Coefficient of variation (CV)0.57759415
Kurtosis-1.2
Mean1775.5
Median Absolute Deviation (MAD)888
Skewness0
Sum6306576
Variance1051688
MonotonicityStrictly increasing
2023-12-12T19:12:26.537348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
2373 1
 
< 0.1%
2362 1
 
< 0.1%
2363 1
 
< 0.1%
2364 1
 
< 0.1%
2365 1
 
< 0.1%
2366 1
 
< 0.1%
2367 1
 
< 0.1%
2368 1
 
< 0.1%
2369 1
 
< 0.1%
Other values (3542) 3542
99.7%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
3551 1
< 0.1%
3550 1
< 0.1%
3549 1
< 0.1%
3548 1
< 0.1%
3547 1
< 0.1%
3546 1
< 0.1%
3545 1
< 0.1%
3544 1
< 0.1%
3543 1
< 0.1%
3542 1
< 0.1%

법정읍면동명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size27.9 KiB
중앙동
 
222
신인동
 
222
효동
 
222
판암1동
 
222
판암2동
 
222
Other values (11)
2442 

Length

Max length4
Median length3
Mean length3.125
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
중앙동 222
 
6.2%
신인동 222
 
6.2%
효동 222
 
6.2%
판암1동 222
 
6.2%
판암2동 222
 
6.2%
용운동 222
 
6.2%
대동 222
 
6.2%
자양동 222
 
6.2%
가양1동 222
 
6.2%
가양2동 222
 
6.2%
Other values (6) 1332
37.5%

Length

2023-12-12T19:12:26.689808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중앙동 222
 
6.2%
신인동 222
 
6.2%
효동 222
 
6.2%
판암1동 222
 
6.2%
판암2동 222
 
6.2%
용운동 222
 
6.2%
대동 222
 
6.2%
자양동 222
 
6.2%
가양1동 222
 
6.2%
가양2동 222
 
6.2%
Other values (6) 1332
37.5%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0110121 × 109
Minimum3.0110103 × 109
Maximum3.0110149 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.3 KiB
2023-12-12T19:12:26.892824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0110103 × 109
5-th percentile3.0110103 × 109
Q13.011011 × 109
median3.0110114 × 109
Q33.0110125 × 109
95-th percentile3.0110149 × 109
Maximum3.0110149 × 109
Range4600
Interquartile range (IQR)1525

Descriptive statistics

Standard deviation1596.5952
Coefficient of variation (CV)5.30252 × 10-7
Kurtosis-0.78281124
Mean3.0110121 × 109
Median Absolute Deviation (MAD)500
Skewness0.95540622
Sum1.0695115 × 1013
Variance2549116.1
MonotonicityNot monotonic
2023-12-12T19:12:27.032473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3011010700 444
12.5%
3011011400 444
12.5%
3011014900 222
 
6.2%
3011014800 222
 
6.2%
3011010300 222
 
6.2%
3011010900 222
 
6.2%
3011011000 222
 
6.2%
3011011100 222
 
6.2%
3011011500 222
 
6.2%
3011011600 222
 
6.2%
Other values (4) 888
25.0%
ValueCountFrequency (%)
3011010300 222
6.2%
3011010700 444
12.5%
3011010900 222
6.2%
3011011000 222
6.2%
3011011100 222
6.2%
3011011400 444
12.5%
3011011500 222
6.2%
3011011600 222
6.2%
3011011700 222
6.2%
3011011800 222
6.2%
ValueCountFrequency (%)
3011014900 222
6.2%
3011014800 222
6.2%
3011014700 222
6.2%
3011014600 222
6.2%
3011011800 222
6.2%
3011011700 222
6.2%
3011011600 222
6.2%
3011011500 222
6.2%
3011011400 444
12.5%
3011011100 222
6.2%

성별
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.9 KiB
남성
1776 
여성
1776 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남성
2nd row여성
3rd row남성
4th row여성
5th row남성

Common Values

ValueCountFrequency (%)
남성 1776
50.0%
여성 1776
50.0%

Length

2023-12-12T19:12:27.172055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:12:27.287725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남성 1776
50.0%
여성 1776
50.0%

연령
Real number (ℝ)

Distinct111
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55
Minimum0
Maximum110
Zeros32
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size31.3 KiB
2023-12-12T19:12:27.711420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q127
median55
Q383
95-th percentile105
Maximum110
Range110
Interquartile range (IQR)56

Descriptive statistics

Standard deviation32.046151
Coefficient of variation (CV)0.58265729
Kurtosis-1.2001949
Mean55
Median Absolute Deviation (MAD)28
Skewness0
Sum195360
Variance1026.9558
MonotonicityNot monotonic
2023-12-12T19:12:27.868282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32
 
0.9%
1 32
 
0.9%
82 32
 
0.9%
81 32
 
0.9%
80 32
 
0.9%
79 32
 
0.9%
78 32
 
0.9%
77 32
 
0.9%
76 32
 
0.9%
75 32
 
0.9%
Other values (101) 3232
91.0%
ValueCountFrequency (%)
0 32
0.9%
1 32
0.9%
2 32
0.9%
3 32
0.9%
4 32
0.9%
5 32
0.9%
6 32
0.9%
7 32
0.9%
8 32
0.9%
9 32
0.9%
ValueCountFrequency (%)
110 32
0.9%
109 32
0.9%
108 32
0.9%
107 32
0.9%
106 32
0.9%
105 32
0.9%
104 32
0.9%
103 32
0.9%
102 32
0.9%
101 32
0.9%

인구수
Real number (ℝ)

ZEROS 

Distinct235
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.0808
Minimum0
Maximum281
Zeros400
Zeros (%)11.3%
Negative0
Negative (%)0.0%
Memory size31.3 KiB
2023-12-12T19:12:28.039781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113
median54
Q395
95-th percentile168
Maximum281
Range281
Interquartile range (IQR)82

Descriptive statistics

Standard deviation54.50928
Coefficient of variation (CV)0.87803765
Kurtosis0.2385766
Mean62.0808
Median Absolute Deviation (MAD)41
Skewness0.84867262
Sum220511
Variance2971.2616
MonotonicityNot monotonic
2023-12-12T19:12:28.177039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 400
 
11.3%
1 91
 
2.6%
2 55
 
1.5%
3 42
 
1.2%
4 41
 
1.2%
6 37
 
1.0%
15 35
 
1.0%
5 34
 
1.0%
7 34
 
1.0%
8 33
 
0.9%
Other values (225) 2750
77.4%
ValueCountFrequency (%)
0 400
11.3%
1 91
 
2.6%
2 55
 
1.5%
3 42
 
1.2%
4 41
 
1.2%
5 34
 
1.0%
6 37
 
1.0%
7 34
 
1.0%
8 33
 
0.9%
9 31
 
0.9%
ValueCountFrequency (%)
281 1
 
< 0.1%
268 1
 
< 0.1%
260 2
0.1%
259 1
 
< 0.1%
258 1
 
< 0.1%
256 1
 
< 0.1%
255 1
 
< 0.1%
247 3
0.1%
245 1
 
< 0.1%
243 2
0.1%

Interactions

2023-12-12T19:12:25.742702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:12:24.397783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:12:24.924767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:12:25.371240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:12:25.831064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:12:24.532234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:12:25.043829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:12:25.465403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:12:25.921090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:12:24.649943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:12:25.165249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:12:25.557004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:12:26.019441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:12:24.797875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:12:25.281581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:12:25.654938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:12:28.269160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번법정읍면동명법정동코드성별연령인구수
순번1.0000.9750.9950.0000.2750.423
법정읍면동명0.9751.0001.0000.0000.0000.502
법정동코드0.9951.0001.0000.0000.0000.351
성별0.0000.0000.0001.0000.0000.000
연령0.2750.0000.0000.0001.0000.723
인구수0.4230.5020.3510.0000.7231.000
2023-12-12T19:12:28.404950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정읍면동명성별
법정읍면동명1.0000.000
성별0.0001.000
2023-12-12T19:12:28.501624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번법정동코드연령인구수법정읍면동명성별
순번1.0000.3350.062-0.0350.8810.000
법정동코드0.3351.0000.000-0.1550.9980.000
연령0.0620.0001.000-0.4460.0000.000
인구수-0.035-0.155-0.4461.0000.2220.000
법정읍면동명0.8810.9980.0000.2221.0000.000
성별0.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T19:12:26.138636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:12:26.258923image/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

순번법정읍면동명법정동코드성별연령인구수
00중앙동3011014900남성03
11중앙동3011014900여성03
22중앙동3011014900남성19
33중앙동3011014900여성16
44중앙동3011014900남성22
55중앙동3011014900여성24
66중앙동3011014900남성33
77중앙동3011014900여성36
88중앙동3011014900남성44
99중앙동3011014900여성44
순번법정읍면동명법정동코드성별연령인구수
35423542산내동3011014700남성1060
35433543산내동3011014700여성1060
35443544산내동3011014700남성1070
35453545산내동3011014700여성1070
35463546산내동3011014700남성1080
35473547산내동3011014700여성1080
35483548산내동3011014700남성1090
35493549산내동3011014700여성1090
35503550산내동3011014700남성1100
35513551산내동3011014700여성1100