Overview

Dataset statistics

Number of variables5
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory49.0 B

Variable types

Text1
Numeric3
DateTime1

Dataset

Description대구광역시 달서구_월별노인인구현황_20190630
Author대구광역시 달서구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=3074876&dataSetDetailId=30748761d4c9aa32ceaa&provdMethod=FILE

Alerts

데이터기준일자 has constant value ""Constant
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 unique valuesUnique
남 has unique valuesUnique
여 has unique valuesUnique

Reproduction

Analysis started2023-09-29 01:08:10.170459
Analysis finished2023-09-29 01:08:14.854633
Duration4.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

동 명
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-09-29T01:08:15.137142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.8181818
Min length3

Characters and Unicode

Total characters84
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 (%)
성당동 1
 
4.3%
월성1동 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%
진천동 1
 
4.3%
Other values (13) 13
56.5%
2023-09-29T01:08:16.641795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 22
26.2%
2 6
 
7.1%
1 6
 
7.1%
4
 
4.8%
인 3
 
3.6%
상 3
 
3.6%
성 3
 
3.6%
이 2
 
2.4%
본 2
 
2.4%
현 2
 
2.4%
Other values (22) 31
36.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 65
77.4%
Decimal Number 14
 
16.7%
Space Separator 4
 
4.8%
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%
이 2
 
3.1%
본 2
 
3.1%
현 2
 
3.1%
송 2
 
3.1%
두 2
 
3.1%
류 2
 
3.1%
Other values (17) 22
33.8%
Decimal Number
ValueCountFrequency (%)
2 6
42.9%
1 6
42.9%
3 2
 
14.3%
Space Separator
ValueCountFrequency (%)
4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 65
77.4%
Common 19
 
22.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 22
33.8%
인 3
 
4.6%
상 3
 
4.6%
성 3
 
4.6%
이 2
 
3.1%
본 2
 
3.1%
현 2
 
3.1%
송 2
 
3.1%
두 2
 
3.1%
류 2
 
3.1%
Other values (17) 22
33.8%
Common
ValueCountFrequency (%)
2 6
31.6%
1 6
31.6%
4
21.1%
3 2
 
10.5%
. 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 65
77.4%
ASCII 19
 
22.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 22
33.8%
인 3
 
4.6%
상 3
 
4.6%
성 3
 
4.6%
이 2
 
3.1%
본 2
 
3.1%
현 2
 
3.1%
송 2
 
3.1%
두 2
 
3.1%
류 2
 
3.1%
Other values (17) 22
33.8%
ASCII
ValueCountFrequency (%)
2 6
31.6%
1 6
31.6%
4
21.1%
3 2
 
10.5%
. 1
 
5.3%

계
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3340.0909
Minimum1927
Maximum7648
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-09-29T01:08:17.413014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1927
5-th percentile2026.7
Q12373
median3038.5
Q33856.5
95-th percentile4829.9
Maximum7648
Range5721
Interquartile range (IQR)1483.5

Descriptive statistics

Standard deviation1314.8551
Coefficient of variation (CV)0.39365847
Kurtosis4.3983513
Mean3340.0909
Median Absolute Deviation (MAD)802
Skewness1.7426959
Sum73482
Variance1728843.9
MonotonicityNot monotonic
2023-09-29T01:08:18.239738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2834 2
 
9.1%
4208 1
 
4.5%
3246 1
 
4.5%
2388 1
 
4.5%
3860 1
 
4.5%
3658 1
 
4.5%
4809 1
 
4.5%
4831 1
 
4.5%
7648 1
 
4.5%
4351 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
1927 1
4.5%
2026 1
4.5%
2040 1
4.5%
2084 1
4.5%
2242 1
4.5%
2368 1
4.5%
2388 1
4.5%
2834 2
9.1%
2893 1
4.5%
2961 1
4.5%
ValueCountFrequency (%)
7648 1
4.5%
4831 1
4.5%
4809 1
4.5%
4351 1
4.5%
4208 1
4.5%
3860 1
4.5%
3846 1
4.5%
3658 1
4.5%
3312 1
4.5%
3246 1
4.5%

남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1426.1364
Minimum870
Maximum3296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-09-29T01:08:18.973775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum870
5-th percentile882.85
Q11002
median1301.5
Q31601.25
95-th percentile2121.8
Maximum3296
Range2426
Interquartile range (IQR)599.25

Descriptive statistics

Standard deviation560.40914
Coefficient of variation (CV)0.39295621
Kurtosis4.9637689
Mean1426.1364
Median Absolute Deviation (MAD)319
Skewness1.8964259
Sum31375
Variance314058.41
MonotonicityNot monotonic
2023-09-29T01:08:19.754481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1809 1
 
4.5%
1231 1
 
4.5%
964 1
 
4.5%
1572 1
 
4.5%
1611 1
 
4.5%
2099 1
 
4.5%
1147 1
 
4.5%
1234 1
 
4.5%
2123 1
 
4.5%
3296 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
870 1
4.5%
882 1
4.5%
899 1
4.5%
907 1
4.5%
964 1
4.5%
973 1
4.5%
1089 1
4.5%
1147 1
4.5%
1231 1
4.5%
1234 1
4.5%
ValueCountFrequency (%)
3296 1
4.5%
2123 1
4.5%
2099 1
4.5%
1809 1
4.5%
1681 1
4.5%
1611 1
4.5%
1572 1
4.5%
1529 1
4.5%
1489 1
4.5%
1367 1
4.5%

여
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1913.9545
Minimum1045
Maximum4352
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-09-29T01:08:20.366915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1045
5-th percentile1121.55
Q11315.25
median1744.5
Q32309.75
95-th percentile2709.9
Maximum4352
Range3307
Interquartile range (IQR)994.5

Descriptive statistics

Standard deviation760.7936
Coefficient of variation (CV)0.39749826
Kurtosis3.8109708
Mean1913.9545
Median Absolute Deviation (MAD)509.5
Skewness1.6048493
Sum42107
Variance578806.9
MonotonicityNot monotonic
2023-09-29T01:08:21.415248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2399 1
 
4.5%
1730 1
 
4.5%
1424 1
 
4.5%
2288 1
 
4.5%
2047 1
 
4.5%
2710 1
 
4.5%
1687 1
 
4.5%
1600 1
 
4.5%
2708 1
 
4.5%
4352 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1045 1
4.5%
1119 1
4.5%
1170 1
4.5%
1185 1
4.5%
1269 1
4.5%
1279 1
4.5%
1424 1
4.5%
1600 1
4.5%
1647 1
4.5%
1687 1
4.5%
ValueCountFrequency (%)
4352 1
4.5%
2710 1
4.5%
2708 1
4.5%
2670 1
4.5%
2399 1
4.5%
2317 1
4.5%
2288 1
4.5%
2047 1
4.5%
1879 1
4.5%
1823 1
4.5%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
Minimum2019-06-30 00:00:00
Maximum2019-06-30 00:00:00
2023-09-29T01:08:22.027383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:08:22.867556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-09-29T01:08:12.759902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:08:10.548123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:08:11.522097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:08:13.052487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:08:10.850907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:08:12.034142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:08:13.362670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:08:11.161085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-29T01:08:12.383711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-29T01:08:23.520338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동 명계남여
동 명1.0001.0001.0001.000
계1.0001.0000.9580.988
남1.0000.9581.0000.922
여1.0000.9880.9221.000
2023-09-29T01:08:24.306627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계남여
계1.0000.9820.995
남0.9821.0000.974
여0.9950.9741.000

Missing values

2023-09-29T01:08:14.080859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-29T01:08:14.594098image/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

동 명계남여데이터기준일자
0성당동4208180923992019-06-30
1두류1.2동3246136718792019-06-30
2두류3동204087011702019-06-30
3본리동2368108912792019-06-30
4감삼동3312148918232019-06-30
5죽전동208489911852019-06-30
6장기동202690711192019-06-30
7용산1동3116135717592019-06-30
8용산2동2893124616472019-06-30
9이곡1동224297312692019-06-30
동 명계남여데이터기준일자
12월성1동2961123117302019-06-30
13월성2동4351168126702019-06-30
14진천동7648329643522019-06-30
15상인1동4831212327082019-06-30
16상인2동2834123416002019-06-30
17상인3동2834114716872019-06-30
18도원동4809209927102019-06-30
19송현1동3658161120472019-06-30
20송현2동3860157222882019-06-30
21본 동238896414242019-06-30