Overview

Dataset statistics

Number of variables5
Number of observations25
Missing cells4
Missing cells (%)3.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory48.3 B

Variable types

Text1
Numeric3
Categorical1

Dataset

Description인천광역시 서구 동별 독거노인 현황에 대한 데이터로 구분, 노인수(명), 독거노인(명), 100세 이상(명) 등의 정보가 포함되어 있습니다.
Author인천광역시 서구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15090750&srcSe=7661IVAWM27C61E190

Alerts

노인수(명) is highly overall correlated with 독거노인(명) and 2 other fieldsHigh correlation
독거노인(명) is highly overall correlated with 노인수(명) and 2 other fieldsHigh correlation
100세이상(명) is highly overall correlated with 노인수(명) and 2 other fieldsHigh correlation
데이터기준일자 is highly overall correlated with 노인수(명) and 2 other fieldsHigh correlation
데이터기준일자 is highly imbalanced (75.8%)Imbalance
구분 has 1 (4.0%) missing valuesMissing
노인수(명) has 1 (4.0%) missing valuesMissing
독거노인(명) has 1 (4.0%) missing valuesMissing
100세이상(명) has 1 (4.0%) missing valuesMissing
100세이상(명) has 1 (4.0%) zerosZeros

Reproduction

Analysis started2024-01-28 17:26:15.831981
Analysis finished2024-01-28 17:26:17.106828
Duration1.27 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

MISSING 

Distinct24
Distinct (%)100.0%
Missing1
Missing (%)4.0%
Memory size332.0 B
2024-01-29T02:26:17.220951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.8333333
Min length2

Characters and Unicode

Total characters92
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

Unique24 ?
Unique (%)100.0%

Sample

1st row서구
2nd row검암경서동
3rd row연희동
4th row청라1동
5th row청라2동
ValueCountFrequency (%)
서구 1
 
4.2%
검암경서동 1
 
4.2%
마전동 1
 
4.2%
오류왕길동 1
 
4.2%
당하동 1
 
4.2%
원당동 1
 
4.2%
불로대곡동 1
 
4.2%
검단동 1
 
4.2%
가좌4동 1
 
4.2%
가좌3동 1
 
4.2%
Other values (14) 14
58.3%
2024-01-29T02:26:17.495846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 23
25.0%
가 7
 
7.6%
3 4
 
4.3%
좌 4
 
4.3%
라 4
 
4.3%
1 4
 
4.3%
2 4
 
4.3%
남 3
 
3.3%
석 3
 
3.3%
청 3
 
3.3%
Other values (27) 33
35.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79
85.9%
Decimal Number 13
 
14.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 23
29.1%
가 7
 
8.9%
좌 4
 
5.1%
라 4
 
5.1%
남 3
 
3.8%
석 3
 
3.8%
청 3
 
3.8%
정 3
 
3.8%
당 2
 
2.5%
원 2
 
2.5%
Other values (23) 25
31.6%
Decimal Number
ValueCountFrequency (%)
3 4
30.8%
1 4
30.8%
2 4
30.8%
4 1
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79
85.9%
Common 13
 
14.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 23
29.1%
가 7
 
8.9%
좌 4
 
5.1%
라 4
 
5.1%
남 3
 
3.8%
석 3
 
3.8%
청 3
 
3.8%
정 3
 
3.8%
당 2
 
2.5%
원 2
 
2.5%
Other values (23) 25
31.6%
Common
ValueCountFrequency (%)
3 4
30.8%
1 4
30.8%
2 4
30.8%
4 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79
85.9%
ASCII 13
 
14.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 23
29.1%
가 7
 
8.9%
좌 4
 
5.1%
라 4
 
5.1%
남 3
 
3.8%
석 3
 
3.8%
청 3
 
3.8%
정 3
 
3.8%
당 2
 
2.5%
원 2
 
2.5%
Other values (23) 25
31.6%
ASCII
ValueCountFrequency (%)
3 4
30.8%
1 4
30.8%
2 4
30.8%
4 1
 
7.7%

노인수(명)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing1
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean5622.25
Minimum901
Maximum67467
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-01-29T02:26:17.594695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum901
5-th percentile1442.45
Q12096.75
median2884.5
Q33924.75
95-th percentile5884.15
Maximum67467
Range66566
Interquartile range (IQR)1828

Descriptive statistics

Standard deviation13225.056
Coefficient of variation (CV)2.3522711
Kurtosis23.571904
Mean5622.25
Median Absolute Deviation (MAD)1000.5
Skewness4.8364776
Sum134934
Variance1.7490211 × 108
MonotonicityNot monotonic
2024-01-29T02:26:17.689392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
67467 1
 
4.0%
2158 1
 
4.0%
1649 1
 
4.0%
2472 1
 
4.0%
3291 1
 
4.0%
2522 1
 
4.0%
2477 1
 
4.0%
3226 1
 
4.0%
4109 1
 
4.0%
1837 1
 
4.0%
Other values (14) 14
56.0%
ValueCountFrequency (%)
901 1
4.0%
1406 1
4.0%
1649 1
4.0%
1812 1
4.0%
1837 1
4.0%
1913 1
4.0%
2158 1
4.0%
2430 1
4.0%
2472 1
4.0%
2477 1
4.0%
ValueCountFrequency (%)
67467 1
4.0%
6130 1
4.0%
4491 1
4.0%
4322 1
4.0%
4109 1
4.0%
3957 1
4.0%
3914 1
4.0%
3361 1
4.0%
3320 1
4.0%
3291 1
4.0%

독거노인(명)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing1
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean1412.1667
Minimum196
Maximum16946
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-01-29T02:26:17.778625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum196
5-th percentile262.95
Q1439.75
median703.5
Q31011
95-th percentile1870.25
Maximum16946
Range16750
Interquartile range (IQR)571.25

Descriptive statistics

Standard deviation3332.8715
Coefficient of variation (CV)2.360112
Kurtosis23.216297
Mean1412.1667
Median Absolute Deviation (MAD)283.5
Skewness4.7857335
Sum33892
Variance11108032
MonotonicityNot monotonic
2024-01-29T02:26:17.869919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
16946 1
 
4.0%
694 1
 
4.0%
285 1
 
4.0%
436 1
 
4.0%
805 1
 
4.0%
441 1
 
4.0%
620 1
 
4.0%
727 1
 
4.0%
1161 1
 
4.0%
576 1
 
4.0%
Other values (14) 14
56.0%
ValueCountFrequency (%)
196 1
4.0%
261 1
4.0%
274 1
4.0%
285 1
4.0%
404 1
4.0%
436 1
4.0%
441 1
4.0%
518 1
4.0%
576 1
4.0%
620 1
4.0%
ValueCountFrequency (%)
16946 1
4.0%
1985 1
4.0%
1220 1
4.0%
1177 1
4.0%
1161 1
4.0%
1017 1
4.0%
1009 1
4.0%
966 1
4.0%
805 1
4.0%
767 1
4.0%

100세이상(명)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)37.5%
Missing1
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean6
Minimum0
Maximum72
Zeros1
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-01-29T02:26:17.971490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34.25
95-th percentile7.85
Maximum72
Range72
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation14.17898
Coefficient of variation (CV)2.3631634
Kurtosis23.079762
Mean6
Median Absolute Deviation (MAD)1
Skewness4.7655597
Sum144
Variance201.04348
MonotonicityNot monotonic
2024-01-29T02:26:18.058721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 7
28.0%
3 6
24.0%
5 3
12.0%
4 2
 
8.0%
1 2
 
8.0%
72 1
 
4.0%
8 1
 
4.0%
7 1
 
4.0%
0 1
 
4.0%
(Missing) 1
 
4.0%
ValueCountFrequency (%)
0 1
 
4.0%
1 2
 
8.0%
2 7
28.0%
3 6
24.0%
4 2
 
8.0%
5 3
12.0%
7 1
 
4.0%
8 1
 
4.0%
72 1
 
4.0%
ValueCountFrequency (%)
72 1
 
4.0%
8 1
 
4.0%
7 1
 
4.0%
5 3
12.0%
4 2
 
8.0%
3 6
24.0%
2 7
28.0%
1 2
 
8.0%
0 1
 
4.0%

데이터기준일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2022-09-01
24 
<NA>
 
1

Length

Max length10
Median length10
Mean length9.76
Min length4

Unique

Unique1 ?
Unique (%)4.0%

Sample

1st row2022-09-01
2nd row2022-09-01
3rd row2022-09-01
4th row2022-09-01
5th row2022-09-01

Common Values

ValueCountFrequency (%)
2022-09-01 24
96.0%
<NA> 1
 
4.0%

Length

2024-01-29T02:26:18.164586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T02:26:18.252736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-09-01 24
96.0%
na 1
 
4.0%

Interactions

2024-01-29T02:26:16.408287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:26:15.958422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:26:16.192322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:26:16.476226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:26:16.030237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:26:16.267775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:26:16.799175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:26:16.105212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:26:16.338309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-29T02:26:18.302001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분노인수(명)독거노인(명)100세이상(명)
구분1.0001.0001.0001.000
노인수(명)1.0001.0001.0001.000
독거노인(명)1.0001.0001.0001.000
100세이상(명)1.0001.0001.0001.000
2024-01-29T02:26:18.382652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노인수(명)독거노인(명)100세이상(명)데이터기준일자
노인수(명)1.0000.8900.7091.000
독거노인(명)0.8901.0000.7231.000
100세이상(명)0.7090.7231.0001.000
데이터기준일자1.0001.0001.0001.000

Missing values

2024-01-29T02:26:16.894648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T02:26:16.975244image/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.
2024-01-29T02:26:17.055541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

구분노인수(명)독거노인(명)100세이상(명)데이터기준일자
0서구6746716946722022-09-01
1검암경서동4491100952022-09-01
2연희동6130198582022-09-01
3청라1동191327422022-09-01
4청라2동332051842022-09-01
5청라3동181219622022-09-01
6가정1동3914117772022-09-01
7가정2동90126112022-09-01
8가정3동140640432022-09-01
9신현원창동432296652022-09-01
구분노인수(명)독거노인(명)100세이상(명)데이터기준일자
15가좌3동3361101722022-09-01
16가좌4동183757622022-09-01
17검단동4109116132022-09-01
18불로대곡동322672732022-09-01
19원당동247762032022-09-01
20당하동252244112022-09-01
21오류왕길동329180552022-09-01
22마전동247243622022-09-01
23아라동164928502022-09-01
24<NA><NA><NA><NA><NA>