Overview

Dataset statistics

Number of variables4
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory880.0 B
Average record size in memory40.0 B

Variable types

Numeric2
Text2

Dataset

Description결혼이민자,귀화자 관련 정보(경상북도 결혼이민자, 귀화자의 시군별, 결혼이민자.귀화자 수, 비율 현황입니다. )
Author경상북도
URLhttps://www.data.go.kr/data/15004867/fileData.do

Alerts

연번 is highly overall correlated with 결혼이민자 및 귀화자High correlation
결혼이민자 및 귀화자 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
시군별 has unique valuesUnique
결혼이민자 및 귀화자 has unique valuesUnique

Reproduction

Analysis started2024-04-06 08:06:42.469303
Analysis finished2024-04-06 08:06:43.632944
Duration1.16 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-04-06T17:06:43.743561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.05
Q16.25
median11.5
Q316.75
95-th percentile20.95
Maximum22
Range21
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation6.4935866
Coefficient of variation (CV)0.5646597
Kurtosis-1.2
Mean11.5
Median Absolute Deviation (MAD)5.5
Skewness0
Sum253
Variance42.166667
MonotonicityStrictly increasing
2024-04-06T17:06:43.989137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 1
 
4.5%
13 1
 
4.5%
22 1
 
4.5%
21 1
 
4.5%
20 1
 
4.5%
19 1
 
4.5%
18 1
 
4.5%
17 1
 
4.5%
16 1
 
4.5%
15 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1 1
4.5%
2 1
4.5%
3 1
4.5%
4 1
4.5%
5 1
4.5%
6 1
4.5%
7 1
4.5%
8 1
4.5%
9 1
4.5%
10 1
4.5%
ValueCountFrequency (%)
22 1
4.5%
21 1
4.5%
20 1
4.5%
19 1
4.5%
18 1
4.5%
17 1
4.5%
16 1
4.5%
15 1
4.5%
14 1
4.5%
13 1
4.5%

시군별
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-04-06T17:06:44.314531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters44
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
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 row포항
2nd row경주
3rd row김천
4th row안동
5th row구미
ValueCountFrequency (%)
포항 1
 
4.5%
경주 1
 
4.5%
울진 1
 
4.5%
봉화 1
 
4.5%
예천 1
 
4.5%
칠곡 1
 
4.5%
성주 1
 
4.5%
고령 1
 
4.5%
청도 1
 
4.5%
영덕 1
 
4.5%
Other values (12) 12
54.5%
2024-04-06T17:06:44.866889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 4
 
9.1%
영 4
 
9.1%
경 3
 
6.8%
천 3
 
6.8%
성 2
 
4.5%
청 2
 
4.5%
울 2
 
4.5%
칠 1
 
2.3%
도 1
 
2.3%
고 1
 
2.3%
Other values (21) 21
47.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 44
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 4
 
9.1%
영 4
 
9.1%
경 3
 
6.8%
천 3
 
6.8%
성 2
 
4.5%
청 2
 
4.5%
울 2
 
4.5%
칠 1
 
2.3%
도 1
 
2.3%
고 1
 
2.3%
Other values (21) 21
47.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 44
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 4
 
9.1%
영 4
 
9.1%
경 3
 
6.8%
천 3
 
6.8%
성 2
 
4.5%
청 2
 
4.5%
울 2
 
4.5%
칠 1
 
2.3%
도 1
 
2.3%
고 1
 
2.3%
Other values (21) 21
47.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 44
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 4
 
9.1%
영 4
 
9.1%
경 3
 
6.8%
천 3
 
6.8%
성 2
 
4.5%
청 2
 
4.5%
울 2
 
4.5%
칠 1
 
2.3%
도 1
 
2.3%
고 1
 
2.3%
Other values (21) 21
47.7%

결혼이민자 및 귀화자
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean755.18182
Minimum25
Maximum2301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-04-06T17:06:45.091156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile165.5
Q1260.25
median471.5
Q3894.5
95-th percentile2193.7
Maximum2301
Range2276
Interquartile range (IQR)634.25

Descriptive statistics

Standard deviation700.47206
Coefficient of variation (CV)0.92755419
Kurtosis0.42499666
Mean755.18182
Median Absolute Deviation (MAD)261
Skewness1.2749358
Sum16614
Variance490661.11
MonotonicityNot monotonic
2024-04-06T17:06:45.367085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2201 1
 
4.5%
164 1
 
4.5%
25 1
 
4.5%
273 1
 
4.5%
253 1
 
4.5%
429 1
 
4.5%
1097 1
 
4.5%
317 1
 
4.5%
261 1
 
4.5%
260 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
25 1
4.5%
164 1
4.5%
194 1
4.5%
253 1
4.5%
255 1
4.5%
260 1
4.5%
261 1
4.5%
273 1
4.5%
317 1
4.5%
336 1
4.5%
ValueCountFrequency (%)
2301 1
4.5%
2201 1
4.5%
2055 1
4.5%
1754 1
4.5%
1097 1
4.5%
897 1
4.5%
887 1
4.5%
820 1
4.5%
716 1
4.5%
605 1
4.5%

비율
Text

Distinct19
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-04-06T17:06:45.655898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.1818182
Min length5

Characters and Unicode

Total characters114
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)77.3%

Sample

1st row13.20%
2nd row12.40%
3rd row5.30%
4th row5.40%
5th row13.80%
ValueCountFrequency (%)
1.60 3
 
13.6%
1.50 2
 
9.1%
10.60 1
 
4.5%
13.20 1
 
4.5%
2.00 1
 
4.5%
2.60 1
 
4.5%
6.60 1
 
4.5%
1.90 1
 
4.5%
1.00 1
 
4.5%
1.20 1
 
4.5%
Other values (9) 9
40.9%
2024-04-06T17:06:46.383173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 26
22.8%
. 22
19.3%
% 22
19.3%
1 13
11.4%
6 8
 
7.0%
2 6
 
5.3%
3 6
 
5.3%
5 4
 
3.5%
4 4
 
3.5%
9 2
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70
61.4%
Other Punctuation 44
38.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26
37.1%
1 13
18.6%
6 8
 
11.4%
2 6
 
8.6%
3 6
 
8.6%
5 4
 
5.7%
4 4
 
5.7%
9 2
 
2.9%
8 1
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 22
50.0%
% 22
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 114
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 26
22.8%
. 22
19.3%
% 22
19.3%
1 13
11.4%
6 8
 
7.0%
2 6
 
5.3%
3 6
 
5.3%
5 4
 
3.5%
4 4
 
3.5%
9 2
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26
22.8%
. 22
19.3%
% 22
19.3%
1 13
11.4%
6 8
 
7.0%
2 6
 
5.3%
3 6
 
5.3%
5 4
 
3.5%
4 4
 
3.5%
9 2
 
1.8%

Interactions

2024-04-06T17:06:43.087942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:42.772074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:43.235626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:06:42.945609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:06:46.573791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군별결혼이민자 및 귀화자비율
연번1.0001.0000.5710.795
시군별1.0001.0001.0001.000
결혼이민자 및 귀화자0.5711.0001.0001.000
비율0.7951.0001.0001.000
2024-04-06T17:06:46.725052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번결혼이민자 및 귀화자
연번1.000-0.711
결혼이민자 및 귀화자-0.7111.000

Missing values

2024-04-06T17:06:43.419967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:06:43.573070image/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

연번시군별결혼이민자 및 귀화자비율
01포항220113.20%
12경주205512.40%
23김천8875.30%
34안동8975.40%
45구미230113.80%
56영주6053.60%
67영천8204.90%
78상주7164.30%
89문경5143.10%
910경산175410.60%
연번시군별결혼이민자 및 귀화자비율
1213영양1641.00%
1314영덕2551.50%
1415청도2601.60%
1516고령2611.60%
1617성주3171.90%
1718칠곡10976.60%
1819예천4292.60%
1920봉화2531.50%
2021울진2731.60%
2122울릉250.20%