Overview

Dataset statistics

Number of variables3
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory807.0 B
Average record size in memory29.9 B

Variable types

Text1
Numeric1
Categorical1

Dataset

Description송파구의 행정동별 노인 인구 현황을 나타내는 데이터로 행정동, 노인인구, 데이터기준일자 등을 포함하고 있습니다.
Author서울특별시 송파구
URLhttps://www.data.go.kr/data/15112505/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
행정동 has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:49:07.328771
Analysis finished2023-12-12 11:49:07.659002
Duration0.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-12T20:49:07.812068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.7777778
Min length3

Characters and Unicode

Total characters102
Distinct characters35
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

Unique27 ?
Unique (%)100.0%

Sample

1st row풍납1동
2nd row풍납2동
3rd row거여1동
4th row거여2동
5th row마천1동
ValueCountFrequency (%)
풍납1동 1
 
3.7%
가락본동 1
 
3.7%
잠실6동 1
 
3.7%
잠실4동 1
 
3.7%
잠실3동 1
 
3.7%
잠실2동 1
 
3.7%
잠실본동 1
 
3.7%
위례동 1
 
3.7%
장지동 1
 
3.7%
문정2동 1
 
3.7%
Other values (17) 17
63.0%
2023-12-12T20:49:08.178779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 27
26.5%
2 8
 
7.8%
1 7
 
6.9%
잠 6
 
5.9%
실 6
 
5.9%
가 3
 
2.9%
락 3
 
2.9%
문 2
 
2.0%
납 2
 
2.0%
본 2
 
2.0%
Other values (25) 36
35.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 83
81.4%
Decimal Number 19
 
18.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 27
32.5%
잠 6
 
7.2%
실 6
 
7.2%
가 3
 
3.6%
락 3
 
3.6%
문 2
 
2.4%
납 2
 
2.4%
본 2
 
2.4%
파 2
 
2.4%
송 2
 
2.4%
Other values (19) 28
33.7%
Decimal Number
ValueCountFrequency (%)
2 8
42.1%
1 7
36.8%
4 1
 
5.3%
3 1
 
5.3%
6 1
 
5.3%
7 1
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 83
81.4%
Common 19
 
18.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 27
32.5%
잠 6
 
7.2%
실 6
 
7.2%
가 3
 
3.6%
락 3
 
3.6%
문 2
 
2.4%
납 2
 
2.4%
본 2
 
2.4%
파 2
 
2.4%
송 2
 
2.4%
Other values (19) 28
33.7%
Common
ValueCountFrequency (%)
2 8
42.1%
1 7
36.8%
4 1
 
5.3%
3 1
 
5.3%
6 1
 
5.3%
7 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 83
81.4%
ASCII 19
 
18.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 27
32.5%
잠 6
 
7.2%
실 6
 
7.2%
가 3
 
3.6%
락 3
 
3.6%
문 2
 
2.4%
납 2
 
2.4%
본 2
 
2.4%
파 2
 
2.4%
송 2
 
2.4%
Other values (19) 28
33.7%
ASCII
ValueCountFrequency (%)
2 8
42.1%
1 7
36.8%
4 1
 
5.3%
3 1
 
5.3%
6 1
 
5.3%
7 1
 
5.3%

노인인구
Real number (ℝ)

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3850.9259
Minimum2247
Maximum6839
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T20:49:08.376796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2247
5-th percentile2390.1
Q13067
median3920
Q34249.5
95-th percentile5679.7
Maximum6839
Range4592
Interquartile range (IQR)1182.5

Descriptive statistics

Standard deviation1029.645
Coefficient of variation (CV)0.26737596
Kurtosis1.9986452
Mean3850.9259
Median Absolute Deviation (MAD)617
Skewness0.99821938
Sum103975
Variance1060168.8
MonotonicityNot monotonic
2023-12-12T20:49:08.536776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
4194 2
 
7.4%
2799 1
 
3.7%
4035 1
 
3.7%
2247 1
 
3.7%
3031 1
 
3.7%
3001 1
 
3.7%
6100 1
 
3.7%
3920 1
 
3.7%
3927 1
 
3.7%
3620 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
2247 1
3.7%
2337 1
3.7%
2514 1
3.7%
2799 1
3.7%
3001 1
3.7%
3031 1
3.7%
3047 1
3.7%
3087 1
3.7%
3214 1
3.7%
3620 1
3.7%
ValueCountFrequency (%)
6839 1
3.7%
6100 1
3.7%
4699 1
3.7%
4630 1
3.7%
4537 1
3.7%
4336 1
3.7%
4305 1
3.7%
4194 2
7.4%
4177 1
3.7%
4037 1
3.7%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-01-31
27 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-01-31
2nd row2023-01-31
3rd row2023-01-31
4th row2023-01-31
5th row2023-01-31

Common Values

ValueCountFrequency (%)
2023-01-31 27
100.0%

Length

2023-12-12T20:49:08.690133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:49:08.820200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-01-31 27
100.0%

Interactions

2023-12-12T20:49:07.426848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:49:08.900858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동노인인구
행정동1.0001.000
노인인구1.0001.000

Missing values

2023-12-12T20:49:07.555861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:49:07.629190image/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

행정동노인인구데이터기준일자
0풍납1동27992023-01-31
1풍납2동40372023-01-31
2거여1동25142023-01-31
3거여2동43052023-01-31
4마천1동43362023-01-31
5마천2동41942023-01-31
6방이1동23372023-01-31
7방이2동36492023-01-31
8오륜동30872023-01-31
9오금동68392023-01-31
행정동노인인구데이터기준일자
17문정1동32142023-01-31
18문정2동41942023-01-31
19장지동46302023-01-31
20위례동36202023-01-31
21잠실본동39272023-01-31
22잠실2동39202023-01-31
23잠실3동61002023-01-31
24잠실4동30012023-01-31
25잠실6동30312023-01-31
26잠실7동22472023-01-31