Overview

Dataset statistics

Number of variables5
Number of observations80
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory45.6 B

Variable types

DateTime1
Numeric4

Dataset

Description인천광역시 남동구 독거노인수에 대한 데이터로 남동구 전체인구수, 65세 이상 인구수, 65세 이상 인구비율, 독거노인수 등을 제공합니다.
Author인천광역시 남동구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15113333&srcSe=7661IVAWM27C61E190

Alerts

남동구 전체인구수(명) is highly overall correlated with 65세 이상 인구수(명) and 2 other fieldsHigh correlation
65세 이상 인구수(명) is highly overall correlated with 남동구 전체인구수(명) and 2 other fieldsHigh correlation
65세 이상 인구비율(퍼센트) is highly overall correlated with 남동구 전체인구수(명) and 2 other fieldsHigh correlation
독거노인수(명) is highly overall correlated with 남동구 전체인구수(명) and 2 other fieldsHigh correlation
기준일 has unique valuesUnique
65세 이상 인구수(명) has unique valuesUnique

Reproduction

Analysis started2024-03-18 05:11:29.303218
Analysis finished2024-03-18 05:11:32.337106
Duration3.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준일
Date

UNIQUE 

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
Minimum2017-01-31 00:00:00
Maximum2023-08-31 00:00:00
2024-03-18T14:11:32.413688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:11:32.528295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

남동구 전체인구수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct78
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean525442.04
Minimum497181
Maximum538976
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-03-18T14:11:32.659985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum497181
5-th percentile501433.65
Q1518177.5
median531075.5
Q3535600.25
95-th percentile538575.05
Maximum538976
Range41795
Interquartile range (IQR)17422.75

Descriptive statistics

Standard deviation12108.51
Coefficient of variation (CV)0.023044425
Kurtosis-0.50426773
Mean525442.04
Median Absolute Deviation (MAD)6570
Skewness-0.82866354
Sum42035363
Variance1.4661601 × 108
MonotonicityNot monotonic
2024-03-18T14:11:32.798081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
531526 2
 
2.5%
532257 2
 
2.5%
531364 1
 
1.2%
521719 1
 
1.2%
518272 1
 
1.2%
518372 1
 
1.2%
518662 1
 
1.2%
518988 1
 
1.2%
519667 1
 
1.2%
520683 1
 
1.2%
Other values (68) 68
85.0%
ValueCountFrequency (%)
497181 1
1.2%
498379 1
1.2%
499891 1
1.2%
500534 1
1.2%
501481 1
1.2%
502526 1
1.2%
503689 1
1.2%
505213 1
1.2%
506181 1
1.2%
506911 1
1.2%
ValueCountFrequency (%)
538976 1
1.2%
538936 1
1.2%
538847 1
1.2%
538709 1
1.2%
538568 1
1.2%
538360 1
1.2%
538352 1
1.2%
537923 1
1.2%
537795 1
1.2%
537496 1
1.2%

65세 이상 인구수(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68662.875
Minimum54389
Maximum83797
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-03-18T14:11:32.932741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum54389
5-th percentile56032.75
Q161363.5
median68862.5
Q375774.25
95-th percentile82129.85
Maximum83797
Range29408
Interquartile range (IQR)14410.75

Descriptive statistics

Standard deviation8504.4965
Coefficient of variation (CV)0.12385873
Kurtosis-1.2041641
Mean68662.875
Median Absolute Deviation (MAD)7378.5
Skewness0.058252504
Sum5493030
Variance72326461
MonotonicityStrictly increasing
2024-03-18T14:11:33.056611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54389 1
 
1.2%
69324 1
 
1.2%
75633 1
 
1.2%
75273 1
 
1.2%
74956 1
 
1.2%
74603 1
 
1.2%
74235 1
 
1.2%
73883 1
 
1.2%
73583 1
 
1.2%
73300 1
 
1.2%
Other values (70) 70
87.5%
ValueCountFrequency (%)
54389 1
1.2%
54738 1
1.2%
55186 1
1.2%
55534 1
1.2%
56059 1
1.2%
56347 1
1.2%
56826 1
1.2%
57193 1
1.2%
57520 1
1.2%
57873 1
1.2%
ValueCountFrequency (%)
83797 1
1.2%
83263 1
1.2%
82887 1
1.2%
82507 1
1.2%
82110 1
1.2%
81682 1
1.2%
81089 1
1.2%
80600 1
1.2%
80000 1
1.2%
79601 1
1.2%

65세 이상 인구비율(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.09125
Minimum10.2
Maximum16.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-03-18T14:11:33.183020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.2
5-th percentile10.495
Q111.375
median12.95
Q314.625
95-th percentile16.405
Maximum16.9
Range6.7
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation1.9420851
Coefficient of variation (CV)0.14834986
Kurtosis-1.1450839
Mean13.09125
Median Absolute Deviation (MAD)1.65
Skewness0.26304757
Sum1047.3
Variance3.7716946
MonotonicityNot monotonic
2024-03-18T14:11:33.299996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.1 3
 
3.8%
14.2 3
 
3.8%
11.2 3
 
3.8%
11.4 2
 
2.5%
14.5 2
 
2.5%
10.3 2
 
2.5%
15.2 2
 
2.5%
15.6 2
 
2.5%
12.3 2
 
2.5%
11.8 2
 
2.5%
Other values (49) 57
71.2%
ValueCountFrequency (%)
10.2 1
1.2%
10.3 2
2.5%
10.4 1
1.2%
10.5 2
2.5%
10.6 2
2.5%
10.7 2
2.5%
10.8 2
2.5%
10.9 1
1.2%
11.0 1
1.2%
11.1 2
2.5%
ValueCountFrequency (%)
16.9 1
1.2%
16.7 1
1.2%
16.6 1
1.2%
16.5 1
1.2%
16.4 1
1.2%
16.3 1
1.2%
16.1 1
1.2%
15.9 1
1.2%
15.8 1
1.2%
15.6 2
2.5%

독거노인수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct79
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17649.45
Minimum12610
Maximum23520
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-03-18T14:11:33.438874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12610
5-th percentile13444.45
Q114976.75
median17176.5
Q320136.5
95-th percentile22907.15
Maximum23520
Range10910
Interquartile range (IQR)5159.75

Descriptive statistics

Standard deviation3070.2721
Coefficient of variation (CV)0.17395852
Kurtosis-1.1048553
Mean17649.45
Median Absolute Deviation (MAD)2618.5
Skewness0.22966208
Sum1411956
Variance9426570.9
MonotonicityNot monotonic
2024-03-18T14:11:33.731688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16443 2
 
2.5%
12610 1
 
1.2%
19167 1
 
1.2%
20192 1
 
1.2%
20094 1
 
1.2%
19931 1
 
1.2%
19766 1
 
1.2%
19621 1
 
1.2%
19438 1
 
1.2%
19309 1
 
1.2%
Other values (69) 69
86.2%
ValueCountFrequency (%)
12610 1
1.2%
13132 1
1.2%
13228 1
1.2%
13301 1
1.2%
13452 1
1.2%
13458 1
1.2%
13554 1
1.2%
13626 1
1.2%
13689 1
1.2%
13769 1
1.2%
ValueCountFrequency (%)
23520 1
1.2%
23352 1
1.2%
23228 1
1.2%
23062 1
1.2%
22899 1
1.2%
22501 1
1.2%
22246 1
1.2%
22078 1
1.2%
21864 1
1.2%
21782 1
1.2%

Interactions

2024-03-18T14:11:31.859093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:11:30.695038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:11:31.190198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:11:31.528700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:11:31.934516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:11:30.831265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:11:31.278726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:11:31.602633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:11:32.023798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:11:30.910209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:11:31.368945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:11:31.680857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:11:32.103472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:11:31.123193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:11:31.458212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:11:31.771845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T14:11:33.824387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일남동구 전체인구수(명)65세 이상 인구수(명)65세 이상 인구비율(퍼센트)독거노인수(명)
기준일1.0001.0001.0001.0001.000
남동구 전체인구수(명)1.0001.0000.9630.9550.976
65세 이상 인구수(명)1.0000.9631.0000.9750.980
65세 이상 인구비율(퍼센트)1.0000.9550.9751.0000.985
독거노인수(명)1.0000.9760.9800.9851.000
2024-03-18T14:11:33.919118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
남동구 전체인구수(명)65세 이상 인구수(명)65세 이상 인구비율(퍼센트)독거노인수(명)
남동구 전체인구수(명)1.000-0.872-0.872-0.871
65세 이상 인구수(명)-0.8721.0001.0001.000
65세 이상 인구비율(퍼센트)-0.8721.0001.0000.999
독거노인수(명)-0.8711.0000.9991.000

Missing values

2024-03-18T14:11:32.193275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T14:11:32.277992image/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

기준일남동구 전체인구수(명)65세 이상 인구수(명)65세 이상 인구비율(퍼센트)독거노인수(명)
02017-01-315313645438910.212610
12017-02-285310225473810.313132
22017-03-315311295518610.313228
32017-04-305314905553410.413301
42017-05-315319235605910.513452
52017-06-305321845634710.513458
62017-07-315336555682610.613554
72017-08-315348605719310.613626
82017-09-305355915752010.713689
92017-10-315364285787310.713769
기준일남동구 전체인구수(명)65세 이상 인구수(명)65세 이상 인구비율(퍼센트)독거노인수(명)
702022-11-305069117960115.621782
712022-12-315061818000015.821864
722023-01-315052138060015.922078
732023-02-285036898108916.122246
742023-03-315025268168216.322501
752023-04-305014818211016.422899
762023-05-315005348250716.523062
772023-06-304998918288716.623228
782023-07-314983798326316.723352
792023-08-314971818379716.923520