Overview

Dataset statistics

Number of variables10
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory92.3 B

Variable types

Text1
Numeric8
DateTime1

Dataset

Description경기도 안양시에 등록된 외국인 현황(관내 외국인수합계, 미국인명, 타이완인명, 중국인명, 한국계중국인명, 베트남인명, 기타명)정보 데이터 입니다.
Author경기도 안양시
URLhttps://www.data.go.kr/data/3079514/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
외국인수합계 is highly overall correlated with 타이완인명 and 4 other fieldsHigh correlation
타이완인명 is highly overall correlated with 외국인수합계 and 4 other fieldsHigh correlation
중국인명 is highly overall correlated with 외국인수합계 and 4 other fieldsHigh correlation
한국계중국인명 is highly overall correlated with 외국인수합계 and 4 other fieldsHigh correlation
베트남인명 is highly overall correlated with 외국인수합계 and 4 other fieldsHigh correlation
기타명 is highly overall correlated with 외국인수합계 and 4 other fieldsHigh correlation
행정동명 has unique valuesUnique
미국인명 has 3 (9.7%) zerosZeros
타이완인명 has 9 (29.0%) zerosZeros
베트남인명 has 1 (3.2%) zerosZeros

Reproduction

Analysis started2024-01-14 13:39:06.082705
Analysis finished2024-01-14 13:39:16.083345
Duration10 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동명
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2024-01-14T22:39:16.292983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.7096774
Min length3

Characters and Unicode

Total characters115
Distinct characters31
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

Unique31 ?
Unique (%)100.0%

Sample

1st row안양1동
2nd row안양2동
3rd row안양3동
4th row안양4동
5th row안양5동
ValueCountFrequency (%)
안양1동 1
 
3.2%
비산3동 1
 
3.2%
신촌동 1
 
3.2%
범계동 1
 
3.2%
호계3동 1
 
3.2%
호계2동 1
 
3.2%
호계1동 1
 
3.2%
귀인동 1
 
3.2%
평안동 1
 
3.2%
평촌동 1
 
3.2%
Other values (21) 21
67.7%
2024-01-14T22:39:16.959164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 31
27.0%
안 11
 
9.6%
양 11
 
9.6%
1 6
 
5.2%
2 6
 
5.2%
3 4
 
3.5%
계 4
 
3.5%
산 4
 
3.5%
석 3
 
2.6%
호 3
 
2.6%
Other values (21) 32
27.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 93
80.9%
Decimal Number 22
 
19.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 31
33.3%
안 11
 
11.8%
양 11
 
11.8%
계 4
 
4.3%
산 4
 
4.3%
석 3
 
3.2%
호 3
 
3.2%
비 3
 
3.2%
수 3
 
3.2%
달 3
 
3.2%
Other values (12) 17
18.3%
Decimal Number
ValueCountFrequency (%)
1 6
27.3%
2 6
27.3%
3 4
18.2%
4 1
 
4.5%
5 1
 
4.5%
9 1
 
4.5%
6 1
 
4.5%
7 1
 
4.5%
8 1
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 93
80.9%
Common 22
 
19.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 31
33.3%
안 11
 
11.8%
양 11
 
11.8%
계 4
 
4.3%
산 4
 
4.3%
석 3
 
3.2%
호 3
 
3.2%
비 3
 
3.2%
수 3
 
3.2%
달 3
 
3.2%
Other values (12) 17
18.3%
Common
ValueCountFrequency (%)
1 6
27.3%
2 6
27.3%
3 4
18.2%
4 1
 
4.5%
5 1
 
4.5%
9 1
 
4.5%
6 1
 
4.5%
7 1
 
4.5%
8 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 93
80.9%
ASCII 22
 
19.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 31
33.3%
안 11
 
11.8%
양 11
 
11.8%
계 4
 
4.3%
산 4
 
4.3%
석 3
 
3.2%
호 3
 
3.2%
비 3
 
3.2%
수 3
 
3.2%
달 3
 
3.2%
Other values (12) 17
18.3%
ASCII
ValueCountFrequency (%)
1 6
27.3%
2 6
27.3%
3 4
18.2%
4 1
 
4.5%
5 1
 
4.5%
9 1
 
4.5%
6 1
 
4.5%
7 1
 
4.5%
8 1
 
4.5%

외국인수합계
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean208.29032
Minimum27
Maximum757
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-01-14T22:39:17.180798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile29
Q172.5
median106
Q3280
95-th percentile627.5
Maximum757
Range730
Interquartile range (IQR)207.5

Descriptive statistics

Standard deviation202.42368
Coefficient of variation (CV)0.9718343
Kurtosis1.0096029
Mean208.29032
Median Absolute Deviation (MAD)71
Skewness1.377288
Sum6457
Variance40975.346
MonotonicityNot monotonic
2024-01-14T22:39:17.381340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
106 2
 
6.5%
238 2
 
6.5%
27 2
 
6.5%
333 1
 
3.2%
31 1
 
3.2%
58 1
 
3.2%
36 1
 
3.2%
83 1
 
3.2%
137 1
 
3.2%
96 1
 
3.2%
Other values (18) 18
58.1%
ValueCountFrequency (%)
27 2
6.5%
31 1
3.2%
35 1
3.2%
36 1
3.2%
53 1
3.2%
58 1
3.2%
70 1
3.2%
75 1
3.2%
79 1
3.2%
83 1
3.2%
ValueCountFrequency (%)
757 1
3.2%
672 1
3.2%
583 1
3.2%
504 1
3.2%
458 1
3.2%
423 1
3.2%
333 1
3.2%
322 1
3.2%
238 2
6.5%
220 1
3.2%

미국인명
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0645161
Minimum0
Maximum39
Zeros3
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-01-14T22:39:17.560441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36.5
95-th percentile10.5
Maximum39
Range39
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation7.0754764
Coefficient of variation (CV)1.3970686
Kurtosis18.33186
Mean5.0645161
Median Absolute Deviation (MAD)2
Skewness3.8820835
Sum157
Variance50.062366
MonotonicityNot monotonic
2024-01-14T22:39:17.765715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 7
22.6%
3 4
12.9%
6 4
12.9%
0 3
9.7%
2 3
9.7%
4 2
 
6.5%
9 2
 
6.5%
7 2
 
6.5%
8 1
 
3.2%
10 1
 
3.2%
Other values (2) 2
 
6.5%
ValueCountFrequency (%)
0 3
9.7%
1 7
22.6%
2 3
9.7%
3 4
12.9%
4 2
 
6.5%
6 4
12.9%
7 2
 
6.5%
8 1
 
3.2%
9 2
 
6.5%
10 1
 
3.2%
ValueCountFrequency (%)
39 1
 
3.2%
11 1
 
3.2%
10 1
 
3.2%
9 2
6.5%
8 1
 
3.2%
7 2
6.5%
6 4
12.9%
4 2
6.5%
3 4
12.9%
2 3
9.7%

일본인명
Real number (ℝ)

Distinct12
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4193548
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-01-14T22:39:17.988861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36.5
95-th percentile12
Maximum27
Range26
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation5.2521373
Coefficient of variation (CV)0.96914438
Kurtosis8.7327444
Mean5.4193548
Median Absolute Deviation (MAD)2
Skewness2.5208171
Sum168
Variance27.584946
MonotonicityNot monotonic
2024-01-14T22:39:18.165821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 7
22.6%
1 5
16.1%
4 4
12.9%
6 3
9.7%
5 3
9.7%
12 2
 
6.5%
9 2
 
6.5%
3 1
 
3.2%
11 1
 
3.2%
10 1
 
3.2%
Other values (2) 2
 
6.5%
ValueCountFrequency (%)
1 5
16.1%
2 7
22.6%
3 1
 
3.2%
4 4
12.9%
5 3
9.7%
6 3
9.7%
7 1
 
3.2%
9 2
 
6.5%
10 1
 
3.2%
11 1
 
3.2%
ValueCountFrequency (%)
27 1
 
3.2%
12 2
6.5%
11 1
 
3.2%
10 1
 
3.2%
9 2
6.5%
7 1
 
3.2%
6 3
9.7%
5 3
9.7%
4 4
12.9%
3 1
 
3.2%

타이완인명
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6451613
Minimum0
Maximum14
Zeros9
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-01-14T22:39:18.343193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile6.5
Maximum14
Range14
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.050119
Coefficient of variation (CV)1.1530938
Kurtosis5.1063695
Mean2.6451613
Median Absolute Deviation (MAD)2
Skewness1.882934
Sum82
Variance9.3032258
MonotonicityNot monotonic
2024-01-14T22:39:18.493164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 9
29.0%
1 6
19.4%
6 3
 
9.7%
3 3
 
9.7%
4 3
 
9.7%
2 3
 
9.7%
5 2
 
6.5%
14 1
 
3.2%
7 1
 
3.2%
ValueCountFrequency (%)
0 9
29.0%
1 6
19.4%
2 3
 
9.7%
3 3
 
9.7%
4 3
 
9.7%
5 2
 
6.5%
6 3
 
9.7%
7 1
 
3.2%
14 1
 
3.2%
ValueCountFrequency (%)
14 1
 
3.2%
7 1
 
3.2%
6 3
 
9.7%
5 2
 
6.5%
4 3
 
9.7%
3 3
 
9.7%
2 3
 
9.7%
1 6
19.4%
0 9
29.0%

중국인명
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.451613
Minimum2
Maximum172
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-01-14T22:39:18.657387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q112.5
median17
Q356
95-th percentile134.5
Maximum172
Range170
Interquartile range (IQR)43.5

Descriptive statistics

Standard deviation45.405461
Coefficient of variation (CV)1.1224636
Kurtosis2.0665647
Mean40.451613
Median Absolute Deviation (MAD)14
Skewness1.6417772
Sum1254
Variance2061.6559
MonotonicityNot monotonic
2024-01-14T22:39:18.850480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
13 4
 
12.9%
3 3
 
9.7%
103 2
 
6.5%
14 2
 
6.5%
11 2
 
6.5%
2 1
 
3.2%
15 1
 
3.2%
24 1
 
3.2%
21 1
 
3.2%
42 1
 
3.2%
Other values (13) 13
41.9%
ValueCountFrequency (%)
2 1
 
3.2%
3 3
9.7%
10 1
 
3.2%
11 2
6.5%
12 1
 
3.2%
13 4
12.9%
14 2
6.5%
15 1
 
3.2%
17 1
 
3.2%
21 1
 
3.2%
ValueCountFrequency (%)
172 1
3.2%
158 1
3.2%
111 1
3.2%
103 2
6.5%
73 1
3.2%
63 1
3.2%
61 1
3.2%
51 1
3.2%
47 1
3.2%
42 1
3.2%

한국계중국인명
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.516129
Minimum3
Maximum453
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-01-14T22:39:19.044479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4.5
Q112
median29
Q3118
95-th percentile356
Maximum453
Range450
Interquartile range (IQR)106

Descriptive statistics

Standard deviation122.72024
Coefficient of variation (CV)1.3557832
Kurtosis2.2357733
Mean90.516129
Median Absolute Deviation (MAD)21
Skewness1.7636997
Sum2806
Variance15060.258
MonotonicityNot monotonic
2024-01-14T22:39:19.222767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
8 4
 
12.9%
12 2
 
6.5%
17 2
 
6.5%
27 2
 
6.5%
113 1
 
3.2%
29 1
 
3.2%
33 1
 
3.2%
5 1
 
3.2%
21 1
 
3.2%
82 1
 
3.2%
Other values (15) 15
48.4%
ValueCountFrequency (%)
3 1
 
3.2%
4 1
 
3.2%
5 1
 
3.2%
8 4
12.9%
12 2
6.5%
17 2
6.5%
21 1
 
3.2%
26 1
 
3.2%
27 2
6.5%
29 1
 
3.2%
ValueCountFrequency (%)
453 1
3.2%
384 1
3.2%
328 1
3.2%
307 1
3.2%
211 1
3.2%
189 1
3.2%
134 1
3.2%
123 1
3.2%
113 1
3.2%
82 1
3.2%

베트남인명
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.516129
Minimum0
Maximum133
Zeros1
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-01-14T22:39:19.435775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.5
Q18
median15
Q331.5
95-th percentile99.5
Maximum133
Range133
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation32.299506
Coefficient of variation (CV)1.2181079
Kurtosis6.0206754
Mean26.516129
Median Absolute Deviation (MAD)12
Skewness2.406175
Sum822
Variance1043.2581
MonotonicityNot monotonic
2024-01-14T22:39:19.708228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
13 3
 
9.7%
25 3
 
9.7%
3 3
 
9.7%
15 2
 
6.5%
10 2
 
6.5%
12 1
 
3.2%
1 1
 
3.2%
7 1
 
3.2%
30 1
 
3.2%
0 1
 
3.2%
Other values (13) 13
41.9%
ValueCountFrequency (%)
0 1
 
3.2%
1 1
 
3.2%
2 1
 
3.2%
3 3
9.7%
5 1
 
3.2%
7 1
 
3.2%
9 1
 
3.2%
10 2
6.5%
12 1
 
3.2%
13 3
9.7%
ValueCountFrequency (%)
133 1
3.2%
130 1
3.2%
69 1
3.2%
45 1
3.2%
44 1
3.2%
43 1
3.2%
37 1
3.2%
33 1
3.2%
30 1
3.2%
27 1
3.2%

기타명
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.677419
Minimum7
Maximum102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-01-14T22:39:19.893260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile10
Q118.5
median29
Q346
95-th percentile86.5
Maximum102
Range95
Interquartile range (IQR)27.5

Descriptive statistics

Standard deviation25.901592
Coefficient of variation (CV)0.68745664
Kurtosis0.098069781
Mean37.677419
Median Absolute Deviation (MAD)13
Skewness1.0351916
Sum1168
Variance670.89247
MonotonicityNot monotonic
2024-01-14T22:39:20.075644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
26 3
 
9.7%
75 2
 
6.5%
16 2
 
6.5%
40 2
 
6.5%
63 1
 
3.2%
31 1
 
3.2%
7 1
 
3.2%
24 1
 
3.2%
35 1
 
3.2%
29 1
 
3.2%
Other values (16) 16
51.6%
ValueCountFrequency (%)
7 1
3.2%
9 1
3.2%
11 1
3.2%
12 1
3.2%
13 1
3.2%
15 1
3.2%
16 2
6.5%
21 1
3.2%
23 1
3.2%
24 1
3.2%
ValueCountFrequency (%)
102 1
3.2%
87 1
3.2%
86 1
3.2%
75 2
6.5%
71 1
3.2%
63 1
3.2%
47 1
3.2%
45 1
3.2%
40 2
6.5%
36 1
3.2%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
Minimum2023-12-10 00:00:00
Maximum2023-12-10 00:00:00
2024-01-14T22:39:20.249156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:20.413424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-14T22:39:14.123625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:06.442951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:07.821200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:08.854496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:09.810316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:10.719290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:11.944283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:12.904386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:14.295896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:06.923096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:07.964814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:08.991055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:09.915290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:10.880348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:12.099821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:13.061589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:14.427669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:07.074544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:08.074944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:09.112395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:10.058604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:11.068769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:12.235142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:13.202150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:14.548747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:07.197897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:08.179805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:09.209681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:10.193952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:11.220505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:12.341427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:13.335693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:14.698860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:07.339828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:08.308120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:09.328650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:10.303901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:11.388647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:12.470935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:13.510846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:15.198370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:07.472254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:08.461846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:09.493635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:10.425402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:11.513045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:12.591197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:13.647973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:15.346768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:07.577951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:08.581877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:09.613455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:10.519097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:11.623185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:12.691300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:13.773007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:15.498110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:07.707530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:08.722014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:09.723911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:10.614805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:11.797823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:12.788387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:39:13.930892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T22:39:20.544063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명외국인수합계미국인명일본인명타이완인명중국인명한국계중국인명베트남인명기타명
행정동명1.0001.0001.0001.0001.0001.0001.0001.0001.000
외국인수합계1.0001.0000.0000.0000.5940.9330.9300.9380.814
미국인명1.0000.0001.0000.7950.6940.0000.0000.0000.666
일본인명1.0000.0000.7951.0000.7570.0000.0000.0000.572
타이완인명1.0000.5940.6940.7571.0000.6380.5290.3360.754
중국인명1.0000.9330.0000.0000.6381.0000.9620.8800.723
한국계중국인명1.0000.9300.0000.0000.5290.9621.0000.8360.793
베트남인명1.0000.9380.0000.0000.3360.8800.8361.0000.647
기타명1.0000.8140.6660.5720.7540.7230.7930.6471.000
2024-01-14T22:39:20.746022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
외국인수합계미국인명일본인명타이완인명중국인명한국계중국인명베트남인명기타명
외국인수합계1.000-0.0410.1180.7110.9490.9440.9470.772
미국인명-0.0411.0000.3740.063-0.086-0.191-0.1060.167
일본인명0.1180.3741.0000.2100.111-0.0390.0650.350
타이완인명0.7110.0630.2101.0000.6480.5950.5580.688
중국인명0.949-0.0860.1110.6481.0000.9030.8940.669
한국계중국인명0.944-0.191-0.0390.5950.9031.0000.9270.602
베트남인명0.947-0.1060.0650.5580.8940.9271.0000.695
기타명0.7720.1670.3500.6880.6690.6020.6951.000

Missing values

2024-01-14T22:39:15.697744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T22:39:15.987031image/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

행정동명외국인수합계미국인명일본인명타이완인명중국인명한국계중국인명베트남인명기타명데이터기준일자
0안양1동33303610311333752023-12-10
1안양2동75746317245344752023-12-10
2안양3동5041147332869282023-12-10
3안양4동2203235112322162023-12-10
4안양5동45822615821143362023-12-10
5안양6동67231151113071331022023-12-10
6안양7동147911142625712023-12-10
7안양8동42344461134130862023-12-10
8안양9동95122134915132023-12-10
9석수1동1061100173225212023-12-10
행정동명외국인수합계미국인명일본인명타이완인명중국인명한국계중국인명베트남인명기타명데이터기준일자
21부림동23839277422115872023-12-10
22평촌동35250385122023-12-10
23평안동533501389152023-12-10
24귀인동27020115092023-12-10
25호계1동106175132713402023-12-10
26호계2동96691211713292023-12-10
27호계3동137690243330352023-12-10
28범계동831112215127242023-12-10
29신촌동36711118172023-12-10
30갈산동58761283312023-12-10