Overview

Dataset statistics

Number of variables5
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory48.3 B

Variable types

Text1
Numeric3
DateTime1

Dataset

Description인천광역시 서구 동별 독거노인 현황에 대한 데이터로 구분, 노인수(명), 독거노인(명), 100세 이상(명) 등의 정보가 포함되어 있습니다.
Author인천광역시 서구
URLhttps://www.data.go.kr/data/15090750/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
노인수(명) is highly overall correlated with 독거노인(명) and 1 other fieldsHigh correlation
독거노인(명) is highly overall correlated with 노인수(명) and 1 other fieldsHigh correlation
100세이상(명) is highly overall correlated with 노인수(명) and 1 other fieldsHigh correlation
구분 has unique valuesUnique
노인수(명) has unique valuesUnique
독거노인(명) has unique valuesUnique
100세이상(명) has 1 (4.2%) zerosZeros

Reproduction

Analysis started2024-03-14 10:33:20.949004
Analysis finished2024-03-14 10:33:24.059882
Duration3.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size320.0 B
2024-03-14T19:33:24.688149image/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%
2024-03-14T19:33:25.941518image/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  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6448.6667
Minimum1438
Maximum77384
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2024-03-14T19:33:26.324541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1438
5-th percentile1608.4
Q12506
median3487
Q34258.5
95-th percentile6428.55
Maximum77384
Range75946
Interquartile range (IQR)1752.5

Descriptive statistics

Standard deviation15159.549
Coefficient of variation (CV)2.3508037
Kurtosis23.639017
Mean6448.6667
Median Absolute Deviation (MAD)927.5
Skewness4.8462575
Sum154768
Variance2.2981193 × 108
MonotonicityNot monotonic
2024-03-14T19:33:26.738414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
77384 1
 
4.2%
2368 1
 
4.2%
3571 1
 
4.2%
2691 1
 
4.2%
3575 1
 
4.2%
3386 1
 
4.2%
2687 1
 
4.2%
3553 1
 
4.2%
4407 1
 
4.2%
2204 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1438 1
4.2%
1525 1
4.2%
2081 1
4.2%
2116 1
4.2%
2204 1
4.2%
2368 1
4.2%
2552 1
4.2%
2687 1
4.2%
2691 1
4.2%
2775 1
4.2%
ValueCountFrequency (%)
77384 1
4.2%
6603 1
4.2%
5440 1
4.2%
4735 1
4.2%
4663 1
4.2%
4407 1
4.2%
4209 1
4.2%
3782 1
4.2%
3602 1
4.2%
3575 1
4.2%

독거노인(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1631.1667
Minimum239
Maximum19574
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2024-03-14T19:33:27.119801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum239
5-th percentile303.1
Q1639.25
median776
Q31140.25
95-th percentile2098.15
Maximum19574
Range19335
Interquartile range (IQR)501

Descriptive statistics

Standard deviation3846.3937
Coefficient of variation (CV)2.358063
Kurtosis23.308531
Mean1631.1667
Median Absolute Deviation (MAD)305
Skewness4.7990953
Sum39148
Variance14794744
MonotonicityNot monotonic
2024-03-14T19:33:27.526598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
19574 1
 
4.2%
825 1
 
4.2%
667 1
 
4.2%
469 1
 
4.2%
883 1
 
4.2%
718 1
 
4.2%
703 1
 
4.2%
786 1
 
4.2%
1311 1
 
4.2%
662 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
239 1
4.2%
301 1
4.2%
315 1
4.2%
447 1
4.2%
469 1
4.2%
571 1
4.2%
662 1
4.2%
667 1
4.2%
703 1
4.2%
718 1
4.2%
ValueCountFrequency (%)
19574 1
4.2%
2224 1
4.2%
1385 1
4.2%
1312 1
4.2%
1311 1
4.2%
1219 1
4.2%
1114 1
4.2%
1079 1
4.2%
883 1
4.2%
830 1
4.2%

100세이상(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5
Minimum0
Maximum78
Zeros1
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size344.0 B
2024-03-14T19:33:27.905624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34.25
95-th percentile8.55
Maximum78
Range78
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation15.350967
Coefficient of variation (CV)2.3616872
Kurtosis23.146354
Mean6.5
Median Absolute Deviation (MAD)1
Skewness4.7746311
Sum156
Variance235.65217
MonotonicityNot monotonic
2024-03-14T19:33:28.274547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4 7
29.2%
2 4
16.7%
3 3
12.5%
1 3
12.5%
5 3
12.5%
78 1
 
4.2%
9 1
 
4.2%
6 1
 
4.2%
0 1
 
4.2%
ValueCountFrequency (%)
0 1
 
4.2%
1 3
12.5%
2 4
16.7%
3 3
12.5%
4 7
29.2%
5 3
12.5%
6 1
 
4.2%
9 1
 
4.2%
78 1
 
4.2%
ValueCountFrequency (%)
78 1
 
4.2%
9 1
 
4.2%
6 1
 
4.2%
5 3
12.5%
4 7
29.2%
3 3
12.5%
2 4
16.7%
1 3
12.5%
0 1
 
4.2%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size320.0 B
Minimum2023-12-31 00:00:00
Maximum2023-12-31 00:00:00
2024-03-14T19:33:28.611952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:33:28.914315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-14T19:33:22.732376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:33:21.105886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:33:21.919130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:33:23.006306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:33:21.375001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:33:22.189970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:33:23.275391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:33:21.647218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:33:22.461436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T19:33:29.124517image/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
2024-03-14T19:33:29.377477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노인수(명)독거노인(명)100세이상(명)
노인수(명)1.0000.7980.501
독거노인(명)0.7981.0000.516
100세이상(명)0.5010.5161.000

Missing values

2024-03-14T19:33:23.620211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T19:33:23.936143image/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

구분노인수(명)독거노인(명)100세이상(명)데이터기준일자
0서구7738419574782023-12-31
1검암경서동5440121932023-12-31
2연희동6603222492023-12-31
3청라1동208130122023-12-31
4청라2동378257142023-12-31
5청라3동211623932023-12-31
6가정1동4663138542023-12-31
7가정2동143831512023-12-31
8가정3동152544712023-12-31
9신현원창동4209107912023-12-31
구분노인수(명)독거노인(명)100세이상(명)데이터기준일자
14가좌2동342174852023-12-31
15가좌3동3602111422023-12-31
16가좌4동220466242023-12-31
17검단동4407131162023-12-31
18불로대곡동355378642023-12-31
19원당동268770352023-12-31
20당하동338671832023-12-31
21오류왕길동357588342023-12-31
22마전동269146922023-12-31
23아라동357166702023-12-31