Overview

Dataset statistics

Number of variables5
Number of observations26
Missing cells20
Missing cells (%)15.4%
Duplicate rows1
Duplicate rows (%)3.8%
Total size in memory1.2 KiB
Average record size in memory48.1 B

Variable types

Text1
Numeric3
DateTime1

Dataset

Description대구광역시 달서구_월별노인_인구현황_20190331
Author대구광역시 달서구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=3074876&dataSetDetailId=307487619431f7a179f7&provdMethod=FILE

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 1 (3.8%) duplicate rowsDuplicates
계 is highly overall correlated with 남 and 1 other fieldsHigh correlation
남 is highly overall correlated with 계 and 1 other fieldsHigh correlation
여 is highly overall correlated with 계 and 1 other fieldsHigh correlation
동 명 has 4 (15.4%) missing valuesMissing
계 has 4 (15.4%) missing valuesMissing
남 has 4 (15.4%) missing valuesMissing
여 has 4 (15.4%) missing valuesMissing
데이터기준일자 has 4 (15.4%) missing valuesMissing

Reproduction

Analysis started2023-12-10 20:03:20.950319
Analysis finished2023-12-10 20:03:22.850898
Duration1.9 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

동 명
Text

MISSING 

Distinct22
Distinct (%)100.0%
Missing4
Missing (%)15.4%
Memory size340.0 B
2023-12-11T05:03:23.024515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length3.8636364
Min length3

Characters and Unicode

Total characters85
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row성당동
2nd row두류1.2동
3rd row두류3동
4th row본리동
5th row감삼동
ValueCountFrequency (%)
두류1.2동 1
 
4.3%
월성2동 1
 
4.3%
동 1
 
4.3%
본 1
 
4.3%
송현2동 1
 
4.3%
송현1동 1
 
4.3%
도원동 1
 
4.3%
상인3동 1
 
4.3%
상인2동 1
 
4.3%
상인1동 1
 
4.3%
Other values (13) 13
56.5%
2023-12-11T05:03:23.460798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 22
25.9%
1 6
 
7.1%
2 6
 
7.1%
5
 
5.9%
인 3
 
3.5%
상 3
 
3.5%
성 3
 
3.5%
두 2
 
2.4%
류 2
 
2.4%
현 2
 
2.4%
Other values (22) 31
36.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 65
76.5%
Decimal Number 14
 
16.5%
Space Separator 5
 
5.9%
Other Punctuation 1
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 22
33.8%
인 3
 
4.6%
상 3
 
4.6%
성 3
 
4.6%
두 2
 
3.1%
류 2
 
3.1%
현 2
 
3.1%
송 2
 
3.1%
월 2
 
3.1%
당 2
 
3.1%
Other values (17) 22
33.8%
Decimal Number
ValueCountFrequency (%)
1 6
42.9%
2 6
42.9%
3 2
 
14.3%
Space Separator
ValueCountFrequency (%)
5
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 65
76.5%
Common 20
 
23.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 22
33.8%
인 3
 
4.6%
상 3
 
4.6%
성 3
 
4.6%
두 2
 
3.1%
류 2
 
3.1%
현 2
 
3.1%
송 2
 
3.1%
월 2
 
3.1%
당 2
 
3.1%
Other values (17) 22
33.8%
Common
ValueCountFrequency (%)
1 6
30.0%
2 6
30.0%
5
25.0%
3 2
 
10.0%
. 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 65
76.5%
ASCII 20
 
23.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 22
33.8%
인 3
 
4.6%
상 3
 
4.6%
성 3
 
4.6%
두 2
 
3.1%
류 2
 
3.1%
현 2
 
3.1%
송 2
 
3.1%
월 2
 
3.1%
당 2
 
3.1%
Other values (17) 22
33.8%
ASCII
ValueCountFrequency (%)
1 6
30.0%
2 6
30.0%
5
25.0%
3 2
 
10.0%
. 1
 
5.0%

계
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing4
Missing (%)15.4%
Infinite0
Infinite (%)0.0%
Mean3286.9091
Minimum1876
Maximum7380
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T05:03:23.639693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1876
5-th percentile1990.65
Q12322
median3018.5
Q33864.5
95-th percentile4787.15
Maximum7380
Range5504
Interquartile range (IQR)1542.5

Descriptive statistics

Standard deviation1278.4919
Coefficient of variation (CV)0.38896479
Kurtosis3.8040047
Mean3286.9091
Median Absolute Deviation (MAD)799.5
Skewness1.6221743
Sum72312
Variance1634541.5
MonotonicityNot monotonic
2023-12-11T05:03:23.762368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2929 1
 
3.8%
2331 1
 
3.8%
3894 1
 
3.8%
3602 1
 
3.8%
4733 1
 
3.8%
2793 1
 
3.8%
2794 1
 
3.8%
4790 1
 
3.8%
7380 1
 
3.8%
4305 1
 
3.8%
Other values (12) 12
46.2%
(Missing) 4
 
15.4%
ValueCountFrequency (%)
1876 1
3.8%
1989 1
3.8%
2022 1
3.8%
2070 1
3.8%
2177 1
3.8%
2319 1
3.8%
2331 1
3.8%
2793 1
3.8%
2794 1
3.8%
2814 1
3.8%
ValueCountFrequency (%)
7380 1
3.8%
4790 1
3.8%
4733 1
3.8%
4305 1
3.8%
4159 1
3.8%
3894 1
3.8%
3776 1
3.8%
3602 1
3.8%
3243 1
3.8%
3208 1
3.8%

남
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing4
Missing (%)15.4%
Infinite0
Infinite (%)0.0%
Mean1400.1818
Minimum855
Maximum3166
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T05:03:23.917934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum855
5-th percentile869.1
Q1971.75
median1282.5
Q31584
95-th percentile2099.3
Maximum3166
Range2311
Interquartile range (IQR)612.25

Descriptive statistics

Standard deviation542.29895
Coefficient of variation (CV)0.3873061
Kurtosis4.2948293
Mean1400.1818
Median Absolute Deviation (MAD)322
Skewness1.7669209
Sum30804
Variance294088.16
MonotonicityNot monotonic
2023-12-11T05:03:24.064919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1215 1
 
3.8%
936 1
 
3.8%
1572 1
 
3.8%
1588 1
 
3.8%
2067 1
 
3.8%
1126 1
 
3.8%
1217 1
 
3.8%
2101 1
 
3.8%
3166 1
 
3.8%
1669 1
 
3.8%
Other values (12) 12
46.2%
(Missing) 4
 
15.4%
ValueCountFrequency (%)
855 1
3.8%
868 1
3.8%
890 1
3.8%
891 1
3.8%
936 1
3.8%
944 1
3.8%
1055 1
3.8%
1126 1
3.8%
1206 1
3.8%
1215 1
3.8%
ValueCountFrequency (%)
3166 1
3.8%
2101 1
3.8%
2067 1
3.8%
1781 1
3.8%
1669 1
3.8%
1588 1
3.8%
1572 1
3.8%
1489 1
3.8%
1463 1
3.8%
1357 1
3.8%

여
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing4
Missing (%)15.4%
Infinite0
Infinite (%)0.0%
Mean1886.7273
Minimum1008
Maximum4214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T05:03:24.214628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1008
5-th percentile1101.45
Q11296.75
median1732.5
Q32313.25
95-th percentile2687.85
Maximum4214
Range3206
Interquartile range (IQR)1016.5

Descriptive statistics

Standard deviation742.79627
Coefficient of variation (CV)0.39369563
Kurtosis3.2805499
Mean1886.7273
Median Absolute Deviation (MAD)526
Skewness1.4924146
Sum41508
Variance551746.3
MonotonicityNot monotonic
2023-12-11T05:03:24.369701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1714 1
 
3.8%
1395 1
 
3.8%
2322 1
 
3.8%
2014 1
 
3.8%
2666 1
 
3.8%
1667 1
 
3.8%
1577 1
 
3.8%
2689 1
 
3.8%
4214 1
 
3.8%
2636 1
 
3.8%
Other values (12) 12
46.2%
(Missing) 4
 
15.4%
ValueCountFrequency (%)
1008 1
3.8%
1098 1
3.8%
1167 1
3.8%
1180 1
3.8%
1233 1
3.8%
1264 1
3.8%
1395 1
3.8%
1577 1
3.8%
1608 1
3.8%
1667 1
3.8%
ValueCountFrequency (%)
4214 1
3.8%
2689 1
3.8%
2666 1
3.8%
2636 1
3.8%
2378 1
3.8%
2322 1
3.8%
2287 1
3.8%
2014 1
3.8%
1860 1
3.8%
1780 1
3.8%

데이터기준일자
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)4.5%
Missing4
Missing (%)15.4%
Memory size340.0 B
Minimum2019-03-31 00:00:00
Maximum2019-03-31 00:00:00
2023-12-11T05:03:24.499202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:03:24.639617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-11T05:03:22.116894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:03:21.102830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:03:21.755391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:03:22.242425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:03:21.544260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:03:21.874161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:03:22.369134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:03:21.624745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T05:03:22.003603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T05:03:24.738178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동 명계남여
동 명1.0001.0001.0001.000
계1.0001.0000.9930.967
남1.0000.9931.0000.948
여1.0000.9670.9481.000
2023-12-11T05:03:24.855865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계남여
계1.0000.9820.995
남0.9821.0000.975
여0.9950.9751.000

Missing values

2023-12-11T05:03:22.514011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T05:03:22.639063image/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.
2023-12-11T05:03:22.764428image/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

동 명계남여데이터기준일자
0성당동4159178123782019-03-31
1두류1.2동3208134818602019-03-31
2두류3동202285511672019-03-31
3본리동2319105512642019-03-31
4감삼동3243146317802019-03-31
5죽전동207089011802019-03-31
6장기동198989110982019-03-31
7용산1동3108135717512019-03-31
8용산2동2814120616082019-03-31
9이곡1동217794412332019-03-31
동 명계남여데이터기준일자
16상인2동2794121715772019-03-31
17상인3동2793112616672019-03-31
18도원동4733206726662019-03-31
19송현1동3602158820142019-03-31
20송현2동3894157223222019-03-31
21본 동233193613952019-03-31
22<NA><NA><NA><NA><NA>
23<NA><NA><NA><NA><NA>
24<NA><NA><NA><NA><NA>
25<NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

동 명계남여데이터기준일자# duplicates
0<NA><NA><NA><NA><NA>4