Overview

Dataset statistics

Number of variables6
Number of observations280
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.3 KiB
Average record size in memory52.5 B

Variable types

Text1
Numeric4
Categorical1

Dataset

Description경상남도 거창군 행정마을별 데이터로 마을명, 세대수, 인구(전체), 인구(남), 인구(여) 등의 항목을 제공합니다.
Author경상남도 거창군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3035560

Alerts

데이터기준일 has constant value ""Constant
세대수 is highly overall correlated with 인구(전체) and 2 other fieldsHigh correlation
인구(전체) is highly overall correlated with 세대수 and 2 other fieldsHigh correlation
인구(남) is highly overall correlated with 세대수 and 2 other fieldsHigh correlation
인구(여) is highly overall correlated with 세대수 and 2 other fieldsHigh correlation

Reproduction

Analysis started2024-04-17 18:10:50.083876
Analysis finished2024-04-17 18:10:51.697761
Duration1.61 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

마을명
Text

Distinct262
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-04-18T03:10:52.012339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.2178571
Min length2

Characters and Unicode

Total characters621
Distinct characters170
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique247 ?
Unique (%)88.2%

Sample

1st row거창군
2nd row거창읍
3rd row원상동
4th row상동
5th row하동
ValueCountFrequency (%)
신기 4
 
1.3%
창촌 3
 
1.0%
당동 2
 
0.7%
촌 2
 
0.7%
암 2
 
0.7%
용 2
 
0.7%
산포 2
 
0.7%
학동 2
 
0.7%
신촌 2
 
0.7%
월포 2
 
0.7%
Other values (267) 276
92.3%
2024-04-18T03:10:52.486007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 41
 
6.6%
산 28
 
4.5%
19
 
3.1%
촌 15
 
2.4%
원 15
 
2.4%
대 13
 
2.1%
기 13
 
2.1%
곡 12
 
1.9%
상 12
 
1.9%
면 12
 
1.9%
Other values (160) 441
71.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 596
96.0%
Space Separator 19
 
3.1%
Decimal Number 6
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 41
 
6.9%
산 28
 
4.7%
촌 15
 
2.5%
원 15
 
2.5%
대 13
 
2.2%
기 13
 
2.2%
곡 12
 
2.0%
상 12
 
2.0%
면 12
 
2.0%
신 10
 
1.7%
Other values (157) 425
71.3%
Decimal Number
ValueCountFrequency (%)
2 3
50.0%
1 3
50.0%
Space Separator
ValueCountFrequency (%)
19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 596
96.0%
Common 25
 
4.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 41
 
6.9%
산 28
 
4.7%
촌 15
 
2.5%
원 15
 
2.5%
대 13
 
2.2%
기 13
 
2.2%
곡 12
 
2.0%
상 12
 
2.0%
면 12
 
2.0%
신 10
 
1.7%
Other values (157) 425
71.3%
Common
ValueCountFrequency (%)
19
76.0%
2 3
 
12.0%
1 3
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 596
96.0%
ASCII 25
 
4.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 41
 
6.9%
산 28
 
4.7%
촌 15
 
2.5%
원 15
 
2.5%
대 13
 
2.2%
기 13
 
2.2%
곡 12
 
2.0%
상 12
 
2.0%
면 12
 
2.0%
신 10
 
1.7%
Other values (157) 425
71.3%
ASCII
ValueCountFrequency (%)
19
76.0%
2 3
 
12.0%
1 3
 
12.0%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct123
Distinct (%)43.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean328.97143
Minimum11
Maximum30704
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-18T03:10:52.597101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile21.95
Q137
median50
Q380
95-th percentile932.65
Maximum30704
Range30693
Interquartile range (IQR)43

Descriptive statistics

Standard deviation2163.5255
Coefficient of variation (CV)6.5766366
Kurtosis158.1152
Mean328.97143
Median Absolute Deviation (MAD)17
Skewness12.200288
Sum92112
Variance4680842.8
MonotonicityNot monotonic
2024-04-18T03:10:52.702705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 10
 
3.6%
42 10
 
3.6%
51 7
 
2.5%
47 7
 
2.5%
50 7
 
2.5%
52 6
 
2.1%
45 5
 
1.8%
41 5
 
1.8%
44 5
 
1.8%
25 5
 
1.8%
Other values (113) 213
76.1%
ValueCountFrequency (%)
11 1
 
0.4%
13 1
 
0.4%
15 1
 
0.4%
16 1
 
0.4%
17 5
1.8%
18 1
 
0.4%
19 3
1.1%
21 1
 
0.4%
22 2
 
0.7%
23 1
 
0.4%
ValueCountFrequency (%)
30704 1
0.4%
18653 1
0.4%
3350 1
0.4%
2162 1
0.4%
2129 1
0.4%
1846 1
0.4%
1654 1
0.4%
1638 1
0.4%
1342 1
0.4%
1329 1
0.4%

인구(전체)
Real number (ℝ)

HIGH CORRELATION 

Distinct154
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean650.95714
Minimum19
Maximum60756
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-18T03:10:52.817252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile35.85
Q161.75
median82.5
Q3138.25
95-th percentile1768
Maximum60756
Range60737
Interquartile range (IQR)76.5

Descriptive statistics

Standard deviation4395.7832
Coefficient of variation (CV)6.7527997
Kurtosis149.30861
Mean650.95714
Median Absolute Deviation (MAD)28.5
Skewness11.904808
Sum182268
Variance19322910
MonotonicityNot monotonic
2024-04-18T03:10:52.921398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 8
 
2.9%
64 5
 
1.8%
65 5
 
1.8%
67 5
 
1.8%
68 5
 
1.8%
82 5
 
1.8%
84 5
 
1.8%
66 5
 
1.8%
79 5
 
1.8%
52 5
 
1.8%
Other values (144) 227
81.1%
ValueCountFrequency (%)
19 1
0.4%
21 2
0.7%
23 1
0.4%
25 1
0.4%
26 1
0.4%
27 2
0.7%
28 2
0.7%
30 1
0.4%
31 1
0.4%
33 2
0.7%
ValueCountFrequency (%)
60756 1
0.4%
40326 1
0.4%
7701 1
0.4%
4600 1
0.4%
4036 1
0.4%
3932 1
0.4%
3651 1
0.4%
3209 1
0.4%
2778 1
0.4%
2322 1
0.4%

인구(남)
Real number (ℝ)

HIGH CORRELATION 

Distinct115
Distinct (%)41.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean319.93929
Minimum10
Maximum29861
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-18T03:10:53.024725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile17
Q130
median40
Q368
95-th percentile859.15
Maximum29861
Range29851
Interquartile range (IQR)38

Descriptive statistics

Standard deviation2164.3959
Coefficient of variation (CV)6.7650206
Kurtosis148.80166
Mean319.93929
Median Absolute Deviation (MAD)14
Skewness11.888951
Sum89583
Variance4684609.4
MonotonicityNot monotonic
2024-04-18T03:10:53.127744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 12
 
4.3%
38 10
 
3.6%
31 8
 
2.9%
33 8
 
2.9%
36 8
 
2.9%
37 8
 
2.9%
26 7
 
2.5%
19 7
 
2.5%
28 7
 
2.5%
29 7
 
2.5%
Other values (105) 198
70.7%
ValueCountFrequency (%)
10 2
 
0.7%
11 1
 
0.4%
12 3
1.1%
13 1
 
0.4%
14 3
1.1%
15 2
 
0.7%
16 1
 
0.4%
17 2
 
0.7%
18 4
1.4%
19 7
2.5%
ValueCountFrequency (%)
29861 1
0.4%
19938 1
0.4%
3815 1
0.4%
2316 1
0.4%
1917 1
0.4%
1906 1
0.4%
1750 1
0.4%
1547 1
0.4%
1321 1
0.4%
1305 1
0.4%

인구(여)
Real number (ℝ)

HIGH CORRELATION 

Distinct119
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean331.01786
Minimum6
Maximum30895
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-18T03:10:53.236091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile17.95
Q131
median43
Q372
95-th percentile908.85
Maximum30895
Range30889
Interquartile range (IQR)41

Descriptive statistics

Standard deviation2231.4854
Coefficient of variation (CV)6.7412841
Kurtosis149.78339
Mean331.01786
Median Absolute Deviation (MAD)15
Skewness11.919162
Sum92685
Variance4979527.2
MonotonicityNot monotonic
2024-04-18T03:10:53.366510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 11
 
3.9%
28 10
 
3.6%
39 9
 
3.2%
35 9
 
3.2%
31 9
 
3.2%
38 8
 
2.9%
47 7
 
2.5%
45 6
 
2.1%
24 6
 
2.1%
46 6
 
2.1%
Other values (109) 199
71.1%
ValueCountFrequency (%)
6 1
 
0.4%
7 1
 
0.4%
9 2
0.7%
12 1
 
0.4%
13 3
1.1%
14 2
0.7%
15 2
0.7%
16 1
 
0.4%
17 1
 
0.4%
18 2
0.7%
ValueCountFrequency (%)
30895 1
0.4%
20388 1
0.4%
3886 1
0.4%
2284 1
0.4%
2119 1
0.4%
2026 1
0.4%
1901 1
0.4%
1662 1
0.4%
1457 1
0.4%
1176 1
0.4%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2022-04-30
280 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-04-30
2nd row2022-04-30
3rd row2022-04-30
4th row2022-04-30
5th row2022-04-30

Common Values

ValueCountFrequency (%)
2022-04-30 280
100.0%

Length

2024-04-18T03:10:53.463392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:10:53.528573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-04-30 280
100.0%

Interactions

2024-04-18T03:10:51.278674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:50.254583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:50.532071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:51.013946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:51.341294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:50.328930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:50.599663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:51.077107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:51.419616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:50.400258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:50.885653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:51.147801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:51.483775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:50.465747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:50.949089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:51.212151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T03:10:53.571797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수인구(전체)인구(남)인구(여)
세대수1.0001.0001.0001.000
인구(전체)1.0001.0001.0001.000
인구(남)1.0001.0001.0001.000
인구(여)1.0001.0001.0001.000
2024-04-18T03:10:53.646196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수인구(전체)인구(남)인구(여)
세대수1.0000.9830.9690.970
인구(전체)0.9831.0000.9850.987
인구(남)0.9690.9851.0000.946
인구(여)0.9700.9870.9461.000

Missing values

2024-04-18T03:10:51.567079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T03:10:51.655288image/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

마을명세대수인구(전체)인구(남)인구(여)데이터기준일
0거창군307046075629861308952022-04-30
1거창읍186534032619938203882022-04-30
2원상동78017658579082022-04-30
3상동33507701381538862022-04-30
4하동65711925796132022-04-30
5죽전13422778132114572022-04-30
6동동18464036191721192022-04-30
7강양73515978017962022-04-30
8개봉16543932190620262022-04-30
9동산244926232022-04-30
마을명세대수인구(전체)인구(남)인구(여)데이터기준일
270송 정427336372022-04-30
271용 암243921182022-04-30
272개 금6310049512022-04-30
273심 방517537382022-04-30
274중 촌528446382022-04-30
275회 남254622242022-04-30
276추 동458241412022-04-30
277해 평284214282022-04-30
278용 산114194941002022-04-30
279율 리233317162022-04-30