Overview

Dataset statistics

Number of variables5
Number of observations44
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory47.0 B

Variable types

Text1
Numeric4

Dataset

Description고양시 주민등록인구현황에 관한 데이터로, 2023년 12월 31일말 기준 고양시 세대수, 동별인구(남,여), 전월말 인구 자료를 제공합니다.
Author경기도 고양시
URLhttps://www.data.go.kr/data/3070908/fileData.do

Alerts

세대수 is highly overall correlated with 남 자 and 2 other fieldsHigh correlation
남 자 is highly overall correlated with 세대수 and 2 other fieldsHigh correlation
여 자 is highly overall correlated with 세대수 and 2 other fieldsHigh correlation
전월말인구 is highly overall correlated with 세대수 and 2 other fieldsHigh correlation
구분 has unique valuesUnique
세대수 has unique valuesUnique
남 자 has unique valuesUnique
여 자 has unique valuesUnique
전월말인구 has unique valuesUnique

Reproduction

Analysis started2024-04-21 02:47:04.116580
Analysis finished2024-04-21 02:47:07.161536
Duration3.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size484.0 B
2024-04-21T11:47:07.262909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0681818
Min length4

Characters and Unicode

Total characters223
Distinct characters51
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

Unique44 ?
Unique (%)100.0%

Sample

1st row주 교 동
2nd row원 신 동
3rd row흥 도 동
4th row성사 1동
5th row성사 2동
ValueCountFrequency (%)
동 22
20.2%
1동 9
 
8.3%
2동 9
 
8.3%
행신 4
 
3.7%
일산 3
 
2.8%
주 2
 
1.8%
화정 2
 
1.8%
3동 2
 
1.8%
화 2
 
1.8%
백석 2
 
1.8%
Other values (39) 52
47.7%
2024-04-21T11:47:07.555587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
65
29.1%
동 44
19.7%
1 11
 
4.9%
2 11
 
4.9%
산 8
 
3.6%
행 5
 
2.2%
신 5
 
2.2%
주 4
 
1.8%
화 4
 
1.8%
일 3
 
1.3%
Other values (41) 63
28.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 133
59.6%
Space Separator 65
29.1%
Decimal Number 25
 
11.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 44
33.1%
산 8
 
6.0%
행 5
 
3.8%
신 5
 
3.8%
주 4
 
3.0%
화 4
 
3.0%
일 3
 
2.3%
사 3
 
2.3%
송 3
 
2.3%
정 3
 
2.3%
Other values (36) 51
38.3%
Decimal Number
ValueCountFrequency (%)
1 11
44.0%
2 11
44.0%
3 2
 
8.0%
4 1
 
4.0%
Space Separator
ValueCountFrequency (%)
65
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 133
59.6%
Common 90
40.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 44
33.1%
산 8
 
6.0%
행 5
 
3.8%
신 5
 
3.8%
주 4
 
3.0%
화 4
 
3.0%
일 3
 
2.3%
사 3
 
2.3%
송 3
 
2.3%
정 3
 
2.3%
Other values (36) 51
38.3%
Common
ValueCountFrequency (%)
65
72.2%
1 11
 
12.2%
2 11
 
12.2%
3 2
 
2.2%
4 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 133
59.6%
ASCII 90
40.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
65
72.2%
1 11
 
12.2%
2 11
 
12.2%
3 2
 
2.2%
4 1
 
1.1%
Hangul
ValueCountFrequency (%)
동 44
33.1%
산 8
 
6.0%
행 5
 
3.8%
신 5
 
3.8%
주 4
 
3.0%
화 4
 
3.0%
일 3
 
2.3%
사 3
 
2.3%
송 3
 
2.3%
정 3
 
2.3%
Other values (36) 51
38.3%

세대수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10554.091
Minimum4589
Maximum16439
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-04-21T11:47:07.675390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4589
5-th percentile5491.6
Q18745
median10078
Q312552.5
95-th percentile15839.85
Maximum16439
Range11850
Interquartile range (IQR)3807.5

Descriptive statistics

Standard deviation3043.0858
Coefficient of variation (CV)0.28833234
Kurtosis-0.43208333
Mean10554.091
Median Absolute Deviation (MAD)2039
Skewness0.055418189
Sum464380
Variance9260371.1
MonotonicityNot monotonic
2024-04-21T11:47:07.785602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
5446 1
 
2.3%
9619 1
 
2.3%
14343 1
 
2.3%
12822 1
 
2.3%
11575 1
 
2.3%
8751 1
 
2.3%
5750 1
 
2.3%
4589 1
 
2.3%
15754 1
 
2.3%
10779 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
4589 1
2.3%
4688 1
2.3%
5446 1
2.3%
5750 1
2.3%
6490 1
2.3%
7195 1
2.3%
7286 1
2.3%
8006 1
2.3%
8027 1
2.3%
8481 1
2.3%
ValueCountFrequency (%)
16439 1
2.3%
16188 1
2.3%
15855 1
2.3%
15754 1
2.3%
14871 1
2.3%
14343 1
2.3%
14198 1
2.3%
13125 1
2.3%
12947 1
2.3%
12822 1
2.3%

남 자
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11917.455
Minimum5166
Maximum19824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-04-21T11:47:07.904068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5166
5-th percentile5920.3
Q19885
median11436
Q314371.25
95-th percentile17695.6
Maximum19824
Range14658
Interquartile range (IQR)4486.25

Descriptive statistics

Standard deviation3398.558
Coefficient of variation (CV)0.28517482
Kurtosis-0.19808871
Mean11917.455
Median Absolute Deviation (MAD)2120
Skewness0.19283045
Sum524368
Variance11550196
MonotonicityNot monotonic
2024-04-21T11:47:08.017433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
5677 1
 
2.3%
11422 1
 
2.3%
17807 1
 
2.3%
13790 1
 
2.3%
9582 1
 
2.3%
11029 1
 
2.3%
7299 1
 
2.3%
5438 1
 
2.3%
12979 1
 
2.3%
11548 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
5166 1
2.3%
5438 1
2.3%
5677 1
2.3%
7299 1
2.3%
8097 1
2.3%
8653 1
2.3%
9194 1
2.3%
9308 1
2.3%
9324 1
2.3%
9463 1
2.3%
ValueCountFrequency (%)
19824 1
2.3%
17807 1
2.3%
17740 1
2.3%
17444 1
2.3%
16901 1
2.3%
16372 1
2.3%
15130 1
2.3%
15109 1
2.3%
14828 1
2.3%
14802 1
2.3%

여 자
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12512.25
Minimum5284
Maximum20996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-04-21T11:47:08.137825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5284
5-th percentile5888.25
Q110319.75
median12048
Q315274
95-th percentile18423
Maximum20996
Range15712
Interquartile range (IQR)4954.25

Descriptive statistics

Standard deviation3708.2106
Coefficient of variation (CV)0.29636641
Kurtosis-0.3003477
Mean12512.25
Median Absolute Deviation (MAD)2443
Skewness0.10125531
Sum550539
Variance13750826
MonotonicityNot monotonic
2024-04-21T11:47:08.259522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
5284 1
 
2.3%
12376 1
 
2.3%
19355 1
 
2.3%
15258 1
 
2.3%
10515 1
 
2.3%
11997 1
 
2.3%
8258 1
 
2.3%
5425 1
 
2.3%
13552 1
 
2.3%
10466 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
5284 1
2.3%
5425 1
2.3%
5574 1
2.3%
7669 1
2.3%
7917 1
2.3%
8258 1
2.3%
8878 1
2.3%
9475 1
2.3%
9682 1
2.3%
9870 1
2.3%
ValueCountFrequency (%)
20996 1
2.3%
19355 1
2.3%
18555 1
2.3%
17675 1
2.3%
17564 1
2.3%
17312 1
2.3%
16319 1
2.3%
15668 1
2.3%
15551 1
2.3%
15347 1
2.3%

전월말인구
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24433.841
Minimum10728
Maximum40804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2024-04-21T11:47:08.390972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10728
5-th percentile11657.35
Q120014.75
median23158.5
Q329168.75
95-th percentile35879.75
Maximum40804
Range30076
Interquartile range (IQR)9154

Descriptive statistics

Standard deviation7077.083
Coefficient of variation (CV)0.28964268
Kurtosis-0.24387168
Mean24433.841
Median Absolute Deviation (MAD)4567
Skewness0.14722617
Sum1075089
Variance50085104
MonotonicityNot monotonic
2024-04-21T11:47:08.510492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
10972 1
 
2.3%
23794 1
 
2.3%
37168 1
 
2.3%
29043 1
 
2.3%
20058 1
 
2.3%
23040 1
 
2.3%
15541 1
 
2.3%
10859 1
 
2.3%
26507 1
 
2.3%
22063 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
10728 1
2.3%
10859 1
2.3%
10972 1
2.3%
15541 1
2.3%
15810 1
2.3%
16809 1
2.3%
18162 1
2.3%
18767 1
2.3%
19128 1
2.3%
19183 1
2.3%
ValueCountFrequency (%)
40804 1
2.3%
37168 1
2.3%
36014 1
2.3%
35119 1
2.3%
34576 1
2.3%
33954 1
2.3%
31469 1
2.3%
30822 1
2.3%
30415 1
2.3%
30126 1
2.3%

Interactions

2024-04-21T11:47:06.711162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:47:05.666515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:47:06.020915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:47:06.346022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:47:06.785358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:47:05.796418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:47:06.095721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:47:06.437588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:47:06.866406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:47:05.872476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:47:06.187768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:47:06.540393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:47:06.950251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:47:05.950561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:47:06.270583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:47:06.632770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T11:47:08.592175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분세대수남 자여 자전월말인구
구분1.0001.0001.0001.0001.000
세대수1.0001.0000.9120.8350.898
남 자1.0000.9121.0000.9730.993
여 자1.0000.8350.9731.0000.992
전월말인구1.0000.8980.9930.9921.000
2024-04-21T11:47:08.677474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수남 자여 자전월말인구
세대수1.0000.9190.8970.911
남 자0.9191.0000.9760.994
여 자0.8970.9761.0000.991
전월말인구0.9110.9940.9911.000

Missing values

2024-04-21T11:47:07.042585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T11:47:07.127672image/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

구분세대수남 자여 자전월말인구
0주 교 동54465677528410972
1원 신 동9402108971199322889
2흥 도 동12701145431503829546
3성사 1동10057105461065821199
4성사 2동46885166557410728
5효 자 동10016116811225323831
6삼송 1 동10099105001141621845
7삼송 2 동14198132321534728529
8창 릉 동10308114501186323277
9고 양 동12503143141411628469
구분세대수남 자여 자전월말인구
34일산 2동84819463968219128
35일산 3동12105163721756433954
36탄현 1동12381148021532230126
37탄현 2동72869194999219183
38주엽 1동10969125931424726794
39주엽 2동11450127171456827308
40대 화 동16188169011767534576
41송 포 동8027101411042920560
42덕 이 동11688151091566830822
43가 좌 동71959308947518767