Overview

Dataset statistics

Number of variables7
Number of observations43
Missing cells1
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory63.0 B

Variable types

Categorical1
Text1
Numeric4
DateTime1

Dataset

Description제주도 내 노인인구와 관련된 정보로 읍면동별 65세 이상, 80세 이상, 85세 이상, 100세 이상 노인 인구 현황을 제공합니다.
Author제주특별자치도
URLhttps://www.data.go.kr/data/15056374/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
65세 이상 인구 is highly overall correlated with 80세 이상 인구 and 2 other fieldsHigh correlation
80세 이상 인구 is highly overall correlated with 65세 이상 인구 and 2 other fieldsHigh correlation
85세 이상 인구 is highly overall correlated with 65세 이상 인구 and 2 other fieldsHigh correlation
100세 이상 인구 is highly overall correlated with 65세 이상 인구 and 2 other fieldsHigh correlation
100세 이상 인구 has 1 (2.3%) missing valuesMissing
읍면동 has unique valuesUnique
65세 이상 인구 has unique valuesUnique
80세 이상 인구 has unique valuesUnique
100세 이상 인구 has 5 (11.6%) zerosZeros

Reproduction

Analysis started2024-03-14 19:33:15.956294
Analysis finished2024-03-14 19:33:20.272447
Duration4.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정시
Categorical

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size472.0 B
제주시
26 
서귀포시
17 

Length

Max length4
Median length3
Mean length3.3953488
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row제주시
2nd row제주시
3rd row제주시
4th row제주시
5th row제주시

Common Values

ValueCountFrequency (%)
제주시 26
60.5%
서귀포시 17
39.5%

Length

2024-03-15T04:33:20.489003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T04:33:20.814526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주시 26
60.5%
서귀포시 17
39.5%

읍면동
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size472.0 B
2024-03-15T04:33:21.601503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1627907
Min length2

Characters and Unicode

Total characters136
Distinct characters63
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

Unique43 ?
Unique (%)100.0%

Sample

1st row한림읍
2nd row애월읍
3rd row구좌읍
4th row조천읍
5th row한경면
ValueCountFrequency (%)
한림읍 1
 
2.3%
노형동 1
 
2.3%
이호동 1
 
2.3%
도두동 1
 
2.3%
대정읍 1
 
2.3%
남원읍 1
 
2.3%
성산읍 1
 
2.3%
안덕면 1
 
2.3%
표선면 1
 
2.3%
송산동 1
 
2.3%
Other values (33) 33
76.7%
2024-03-15T04:33:23.091440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 32
23.5%
도 9
 
6.6%
읍 7
 
5.1%
면 5
 
3.7%
2 4
 
2.9%
1 4
 
2.9%
천 4
 
2.9%
대 3
 
2.2%
삼 3
 
2.2%
이 3
 
2.2%
Other values (53) 62
45.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 128
94.1%
Decimal Number 8
 
5.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 32
25.0%
도 9
 
7.0%
읍 7
 
5.5%
면 5
 
3.9%
천 4
 
3.1%
대 3
 
2.3%
삼 3
 
2.3%
이 3
 
2.3%
중 2
 
1.6%
정 2
 
1.6%
Other values (51) 58
45.3%
Decimal Number
ValueCountFrequency (%)
2 4
50.0%
1 4
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 128
94.1%
Common 8
 
5.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 32
25.0%
도 9
 
7.0%
읍 7
 
5.5%
면 5
 
3.9%
천 4
 
3.1%
대 3
 
2.3%
삼 3
 
2.3%
이 3
 
2.3%
중 2
 
1.6%
정 2
 
1.6%
Other values (51) 58
45.3%
Common
ValueCountFrequency (%)
2 4
50.0%
1 4
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 128
94.1%
ASCII 8
 
5.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 32
25.0%
도 9
 
7.0%
읍 7
 
5.5%
면 5
 
3.9%
천 4
 
3.1%
대 3
 
2.3%
삼 3
 
2.3%
이 3
 
2.3%
중 2
 
1.6%
정 2
 
1.6%
Other values (51) 58
45.3%
ASCII
ValueCountFrequency (%)
2 4
50.0%
1 4
50.0%

65세 이상 인구
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2817.5814
Minimum445
Maximum7245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size515.0 B
2024-03-15T04:33:23.318396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum445
5-th percentile581
Q11249
median2404
Q34161.5
95-th percentile6013.5
Maximum7245
Range6800
Interquartile range (IQR)2912.5

Descriptive statistics

Standard deviation1874.3103
Coefficient of variation (CV)0.66521959
Kurtosis-0.52610237
Mean2817.5814
Median Absolute Deviation (MAD)1390
Skewness0.67094947
Sum121156
Variance3513039.2
MonotonicityNot monotonic
2024-03-15T04:33:23.597415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
5021 1
 
2.3%
7245 1
 
2.3%
798 1
 
2.3%
544 1
 
2.3%
4774 1
 
2.3%
5018 1
 
2.3%
4217 1
 
2.3%
2806 1
 
2.3%
3098 1
 
2.3%
1092 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
445 1
2.3%
544 1
2.3%
576 1
2.3%
626 1
2.3%
707 1
2.3%
798 1
2.3%
885 1
2.3%
955 1
2.3%
979 1
2.3%
1014 1
2.3%
ValueCountFrequency (%)
7245 1
2.3%
6774 1
2.3%
6020 1
2.3%
5955 1
2.3%
5502 1
2.3%
5324 1
2.3%
5021 1
2.3%
5018 1
2.3%
4774 1
2.3%
4274 1
2.3%

80세 이상 인구
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean746.62791
Minimum128
Maximum2130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size515.0 B
2024-03-15T04:33:23.968032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128
5-th percentile155.9
Q1361
median618
Q31028.5
95-th percentile1519.6
Maximum2130
Range2002
Interquartile range (IQR)667.5

Descriptive statistics

Standard deviation498.48346
Coefficient of variation (CV)0.66764644
Kurtosis-0.12401163
Mean746.62791
Median Absolute Deviation (MAD)317
Skewness0.77370379
Sum32105
Variance248485.76
MonotonicityNot monotonic
2024-03-15T04:33:24.213925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1480 1
 
2.3%
2130 1
 
2.3%
217 1
 
2.3%
155 1
 
2.3%
1465 1
 
2.3%
1623 1
 
2.3%
1237 1
 
2.3%
847 1
 
2.3%
837 1
 
2.3%
273 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
128 1
2.3%
141 1
2.3%
155 1
2.3%
164 1
2.3%
185 1
2.3%
217 1
2.3%
227 1
2.3%
235 1
2.3%
273 1
2.3%
312 1
2.3%
ValueCountFrequency (%)
2130 1
2.3%
1623 1
2.3%
1524 1
2.3%
1480 1
2.3%
1465 1
2.3%
1437 1
2.3%
1435 1
2.3%
1394 1
2.3%
1269 1
2.3%
1237 1
2.3%

85세 이상 인구
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean357.51163
Minimum59
Maximum1060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size515.0 B
2024-03-15T04:33:24.463080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum59
5-th percentile81.2
Q1169
median296
Q3501.5
95-th percentile763.9
Maximum1060
Range1001
Interquartile range (IQR)332.5

Descriptive statistics

Standard deviation246.25484
Coefficient of variation (CV)0.68880232
Kurtosis0.095020171
Mean357.51163
Median Absolute Deviation (MAD)173
Skewness0.8827449
Sum15373
Variance60641.446
MonotonicityNot monotonic
2024-03-15T04:33:24.696710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
104 2
 
4.7%
746 1
 
2.3%
59 1
 
2.3%
81 1
 
2.3%
726 1
 
2.3%
754 1
 
2.3%
610 1
 
2.3%
406 1
 
2.3%
394 1
 
2.3%
123 1
 
2.3%
Other values (32) 32
74.4%
ValueCountFrequency (%)
59 1
2.3%
71 1
2.3%
81 1
2.3%
83 1
2.3%
89 1
2.3%
101 1
2.3%
104 2
4.7%
123 1
2.3%
157 1
2.3%
168 1
2.3%
ValueCountFrequency (%)
1060 1
2.3%
777 1
2.3%
765 1
2.3%
754 1
2.3%
746 1
2.3%
726 1
2.3%
685 1
2.3%
610 1
2.3%
600 1
2.3%
580 1
2.3%

100세 이상 인구
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct18
Distinct (%)42.9%
Missing1
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean6.952381
Minimum0
Maximum21
Zeros5
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size515.0 B
2024-03-15T04:33:25.080378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q311.75
95-th percentile15.95
Maximum21
Range21
Interquartile range (IQR)8.75

Descriptive statistics

Standard deviation5.5478072
Coefficient of variation (CV)0.79797227
Kurtosis-0.29922206
Mean6.952381
Median Absolute Deviation (MAD)3.5
Skewness0.72235849
Sum292
Variance30.778165
MonotonicityNot monotonic
2024-03-15T04:33:25.441852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 5
11.6%
5 5
11.6%
12 4
9.3%
6 4
9.3%
2 4
9.3%
4 4
9.3%
3 3
 
7.0%
15 2
 
4.7%
10 2
 
4.7%
16 1
 
2.3%
Other values (8) 8
18.6%
ValueCountFrequency (%)
0 5
11.6%
1 1
 
2.3%
2 4
9.3%
3 3
7.0%
4 4
9.3%
5 5
11.6%
6 4
9.3%
8 1
 
2.3%
9 1
 
2.3%
10 2
 
4.7%
ValueCountFrequency (%)
21 1
 
2.3%
19 1
 
2.3%
16 1
 
2.3%
15 2
4.7%
14 1
 
2.3%
13 1
 
2.3%
12 4
9.3%
11 1
 
2.3%
10 2
4.7%
9 1
 
2.3%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size472.0 B
Minimum2023-12-31 00:00:00
Maximum2023-12-31 00:00:00
2024-03-15T04:33:25.761518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:26.032573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-15T04:33:18.914255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:16.235797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:17.159155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:18.026218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:19.162495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:16.485165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:17.379106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:18.267674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:19.361898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:16.825502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:17.539334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:18.518161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:19.495259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:17.015341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:17.692839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:33:18.738642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T04:33:26.186089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정시읍면동65세 이상 인구80세 이상 인구85세 이상 인구100세 이상 인구
행정시1.0001.0000.0000.0000.0000.408
읍면동1.0001.0001.0001.0001.0001.000
65세 이상 인구0.0001.0001.0000.8820.8640.821
80세 이상 인구0.0001.0000.8821.0000.9780.882
85세 이상 인구0.0001.0000.8640.9781.0000.895
100세 이상 인구0.4081.0000.8210.8820.8951.000
2024-03-15T04:33:26.367787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
65세 이상 인구80세 이상 인구85세 이상 인구100세 이상 인구행정시
65세 이상 인구1.0000.9730.9560.8590.000
80세 이상 인구0.9731.0000.9900.8890.000
85세 이상 인구0.9560.9901.0000.8990.000
100세 이상 인구0.8590.8890.8991.0000.365
행정시0.0000.0000.0000.3651.000

Missing values

2024-03-15T04:33:19.716665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T04:33:20.111474image/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

행정시읍면동65세 이상 인구80세 이상 인구85세 이상 인구100세 이상 인구데이터기준일자
0제주시한림읍50211480746152023-12-31
1제주시애월읍724521301060192023-12-31
2제주시구좌읍42741437777102023-12-31
3제주시조천읍53241524765212023-12-31
4제주시한경면2769935508122023-12-31
5제주시추자면6261858902023-12-31
6제주시우도면4451418302023-12-31
7제주시일도1동7071647112023-12-31
8제주시일도2동5955139458062023-12-31
9제주시이도1동155242021052023-12-31
행정시읍면동65세 이상 인구80세 이상 인구85세 이상 인구100세 이상 인구데이터기준일자
33서귀포시중앙동95523510402023-12-31
34서귀포시천지동88522710122023-12-31
35서귀포시효돈동140643221322023-12-31
36서귀포시영천동142846924852023-12-31
37서귀포시동홍동378585835342023-12-31
38서귀포시서홍동173240715732023-12-31
39서귀포시대륜동278164829752023-12-31
40서귀포시대천동228052925642023-12-31
41서귀포시중문동219459829632023-12-31
42서귀포시예래동101431517022023-12-31