Overview

Dataset statistics

Number of variables4
Number of observations39
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 KiB
Average record size in memory37.4 B

Variable types

Categorical1
Text1
Numeric2

Dataset

Description경기도 고양시 2015년 전출입 현황 데이터로 구별, 동별, 전입인구, 전출인구 등의 항목을 제공하는 데이터입니다.
Author경기도 고양시
URLhttps://www.data.go.kr/data/15107540/fileData.do

Alerts

전입(명) is highly overall correlated with 전출(명)High correlation
전출(명) is highly overall correlated with 전입(명)High correlation
전입(명) has unique valuesUnique
전출(명) has unique valuesUnique

Reproduction

Analysis started2023-12-12 10:52:02.280650
Analysis finished2023-12-12 10:52:03.495602
Duration1.21 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct3
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size444.0 B
덕양구
19 
일산동구
11 
일산서구
9 

Length

Max length4
Median length4
Mean length3.5128205
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
덕양구 19
48.7%
일산동구 11
28.2%
일산서구 9
23.1%

Length

2023-12-12T19:52:03.644491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:52:03.860863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
덕양구 19
48.7%
일산동구 11
28.2%
일산서구 9
23.1%

동별
Text

Distinct38
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-12T19:52:04.193852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.4871795
Min length3

Characters and Unicode

Total characters136
Distinct characters44
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

Unique37 ?
Unique (%)94.9%

Sample

1st row주교동
2nd row원신동
3rd row흥도동
4th row성사1동
5th row성사2동
ValueCountFrequency (%)
중산동 2
 
5.1%
장항2동 1
 
2.6%
송포동 1
 
2.6%
정발산동 1
 
2.6%
백석1동 1
 
2.6%
백석2동 1
 
2.6%
마두1동 1
 
2.6%
마두2동 1
 
2.6%
장항1동 1
 
2.6%
주교동 1
 
2.6%
Other values (28) 28
71.8%
2023-12-12T19:52:04.860196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 39
28.7%
산 8
 
5.9%
2 8
 
5.9%
1 8
 
5.9%
신 5
 
3.7%
행 4
 
2.9%
주 4
 
2.9%
화 4
 
2.9%
일 3
 
2.2%
정 3
 
2.2%
Other values (34) 50
36.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 118
86.8%
Decimal Number 18
 
13.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 39
33.1%
산 8
 
6.8%
신 5
 
4.2%
행 4
 
3.4%
주 4
 
3.4%
화 4
 
3.4%
일 3
 
2.5%
정 3
 
2.5%
사 3
 
2.5%
백 2
 
1.7%
Other values (31) 43
36.4%
Decimal Number
ValueCountFrequency (%)
2 8
44.4%
1 8
44.4%
3 2
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 118
86.8%
Common 18
 
13.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 39
33.1%
산 8
 
6.8%
신 5
 
4.2%
행 4
 
3.4%
주 4
 
3.4%
화 4
 
3.4%
일 3
 
2.5%
정 3
 
2.5%
사 3
 
2.5%
백 2
 
1.7%
Other values (31) 43
36.4%
Common
ValueCountFrequency (%)
2 8
44.4%
1 8
44.4%
3 2
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 118
86.8%
ASCII 18
 
13.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 39
33.1%
산 8
 
6.8%
신 5
 
4.2%
행 4
 
3.4%
주 4
 
3.4%
화 4
 
3.4%
일 3
 
2.5%
정 3
 
2.5%
사 3
 
2.5%
백 2
 
1.7%
Other values (31) 43
36.4%
ASCII
ValueCountFrequency (%)
2 8
44.4%
1 8
44.4%
3 2
 
11.1%

전입(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4540.4615
Minimum468
Maximum11477
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T19:52:05.687985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum468
5-th percentile816.4
Q13340
median4480
Q35534.5
95-th percentile9075.9
Maximum11477
Range11009
Interquartile range (IQR)2194.5

Descriptive statistics

Standard deviation2360.0598
Coefficient of variation (CV)0.51978411
Kurtosis1.3721858
Mean4540.4615
Median Absolute Deviation (MAD)1181
Skewness0.71559543
Sum177078
Variance5569882.1
MonotonicityNot monotonic
2023-12-12T19:52:05.941423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
2240 1
 
2.6%
4280 1
 
2.6%
5288 1
 
2.6%
4480 1
 
2.6%
4754 1
 
2.6%
4031 1
 
2.6%
2568 1
 
2.6%
468 1
 
2.6%
6419 1
 
2.6%
4206 1
 
2.6%
Other values (29) 29
74.4%
ValueCountFrequency (%)
468 1
2.6%
550 1
2.6%
846 1
2.6%
1001 1
2.6%
1788 1
2.6%
2205 1
2.6%
2240 1
2.6%
2568 1
2.6%
2991 1
2.6%
3131 1
2.6%
ValueCountFrequency (%)
11477 1
2.6%
9876 1
2.6%
8987 1
2.6%
7443 1
2.6%
6556 1
2.6%
6466 1
2.6%
6419 1
2.6%
6108 1
2.6%
5670 1
2.6%
5661 1
2.6%

전출(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4106.4872
Minimum447
Maximum7750
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-12T19:52:06.180032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum447
5-th percentile967
Q12687.5
median4448
Q35258.5
95-th percentile6761.8
Maximum7750
Range7303
Interquartile range (IQR)2571

Descriptive statistics

Standard deviation1874.0505
Coefficient of variation (CV)0.45636341
Kurtosis-0.70825028
Mean4106.4872
Median Absolute Deviation (MAD)1289
Skewness-0.24088795
Sum160153
Variance3512065.3
MonotonicityNot monotonic
2023-12-12T19:52:06.434799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
2744 1
 
2.6%
2152 1
 
2.6%
5699 1
 
2.6%
5001 1
 
2.6%
4829 1
 
2.6%
4448 1
 
2.6%
2631 1
 
2.6%
447 1
 
2.6%
6348 1
 
2.6%
2805 1
 
2.6%
Other values (29) 29
74.4%
ValueCountFrequency (%)
447 1
2.6%
652 1
2.6%
1002 1
2.6%
1032 1
2.6%
1700 1
2.6%
1949 1
2.6%
2017 1
2.6%
2152 1
2.6%
2349 1
2.6%
2631 1
2.6%
ValueCountFrequency (%)
7750 1
2.6%
6850 1
2.6%
6752 1
2.6%
6573 1
2.6%
6389 1
2.6%
6348 1
2.6%
5981 1
2.6%
5737 1
2.6%
5699 1
2.6%
5324 1
2.6%

Interactions

2023-12-12T19:52:02.905684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:52:02.525049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:52:03.055347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:52:02.699790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:52:06.627476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분동별전입(명)전출(명)
구분1.0001.0000.0000.000
동별1.0001.0000.9540.927
전입(명)0.0000.9541.0000.974
전출(명)0.0000.9270.9741.000
2023-12-12T19:52:06.798491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전입(명)전출(명)구분
전입(명)1.0000.7290.000
전출(명)0.7291.0000.000
구분0.0000.0001.000

Missing values

2023-12-12T19:52:03.272796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:52:03.434249image/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

구분동별전입(명)전출(명)
0덕양구주교동22402744
1덕양구원신동42802152
2덕양구흥도동98763196
3덕양구성사1동42524695
4덕양구성사2동17881949
5덕양구효자동550652
6덕양구신도동114771700
7덕양구창릉동29912017
8덕양구고양동45354726
9덕양구관산동56614244
구분동별전입(명)전출(명)
29일산동구고봉동42062805
30일산서구일산1동48874838
31일산서구일산2동35523916
32일산서구일산3동47085324
33일산서구탄현동74437750
34일산서구주엽1동41354558
35일산서구주엽2동44694845
36일산서구대화동65566389
37일산서구송포동22052349
38일산서구송산동89875981