Overview

Dataset statistics

Number of variables10
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory94.1 B

Variable types

Text1
Numeric9

Dataset

Description부산광역시 북구 등록외국인 현황에 대한 데이터로 총 외국인 수, 국적별, 거주지역별, 성별 등의 항목을 제공합니다.
Author부산광역시 북구
URLhttps://www.data.go.kr/data/15026047/fileData.do

Alerts

소계 is highly overall correlated with 중국 and 4 other fieldsHigh correlation
중국 is highly overall correlated with 소계 and 2 other fieldsHigh correlation
한국계 중국인 is highly overall correlated with 소계 and 1 other fieldsHigh correlation
베트남 is highly overall correlated with 소계 and 3 other fieldsHigh correlation
일본 is highly overall correlated with 미국 and 1 other fieldsHigh correlation
미국 is highly overall correlated with 일본 and 1 other fieldsHigh correlation
필리핀 is highly overall correlated with 소계 and 3 other fieldsHigh correlation
기타 is highly overall correlated with 소계 and 1 other fieldsHigh correlation
구분 has unique valuesUnique
중국 has 1 (3.8%) zerosZeros
한국계 중국인 has 2 (7.7%) zerosZeros
일본 has 8 (30.8%) zerosZeros
미국 has 12 (46.2%) zerosZeros
필리핀 has 11 (42.3%) zerosZeros
타이완 has 8 (30.8%) zerosZeros
기타 has 2 (7.7%) zerosZeros

Reproduction

Analysis started2023-12-16 16:02:01.818279
Analysis finished2023-12-16 16:02:35.956969
Duration34.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-16T16:02:36.316796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9230769
Min length6

Characters and Unicode

Total characters180
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st row구포1동(남)
2nd row구포1동(여)
3rd row구포2동(남)
4th row구포2동(여)
5th row구포3동(남)
ValueCountFrequency (%)
구포1동(남 1
 
3.8%
구포1동(여 1
 
3.8%
만덕3동(남 1
 
3.8%
만덕2동(여 1
 
3.8%
만덕2동(남 1
 
3.8%
만덕1동(여 1
 
3.8%
만덕1동(남 1
 
3.8%
덕천3동(여 1
 
3.8%
덕천3동(남 1
 
3.8%
덕천2동(여 1
 
3.8%
Other values (16) 16
61.5%
2023-12-16T16:02:37.887690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 26
14.4%
( 26
14.4%
) 26
14.4%
남 13
 
7.2%
여 13
 
7.2%
덕 12
 
6.7%
2 8
 
4.4%
1 8
 
4.4%
3 8
 
4.4%
화 6
 
3.3%
Other values (7) 34
18.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 104
57.8%
Open Punctuation 26
 
14.4%
Close Punctuation 26
 
14.4%
Decimal Number 24
 
13.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 26
25.0%
남 13
12.5%
여 13
12.5%
덕 12
11.5%
화 6
 
5.8%
천 6
 
5.8%
명 6
 
5.8%
구 6
 
5.8%
포 6
 
5.8%
만 6
 
5.8%
Other values (2) 4
 
3.8%
Decimal Number
ValueCountFrequency (%)
2 8
33.3%
1 8
33.3%
3 8
33.3%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 104
57.8%
Common 76
42.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 26
25.0%
남 13
12.5%
여 13
12.5%
덕 12
11.5%
화 6
 
5.8%
천 6
 
5.8%
명 6
 
5.8%
구 6
 
5.8%
포 6
 
5.8%
만 6
 
5.8%
Other values (2) 4
 
3.8%
Common
ValueCountFrequency (%)
( 26
34.2%
) 26
34.2%
2 8
 
10.5%
1 8
 
10.5%
3 8
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 104
57.8%
ASCII 76
42.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 26
25.0%
남 13
12.5%
여 13
12.5%
덕 12
11.5%
화 6
 
5.8%
천 6
 
5.8%
명 6
 
5.8%
구 6
 
5.8%
포 6
 
5.8%
만 6
 
5.8%
Other values (2) 4
 
3.8%
ASCII
ValueCountFrequency (%)
( 26
34.2%
) 26
34.2%
2 8
 
10.5%
1 8
 
10.5%
3 8
 
10.5%

소계
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.192308
Minimum10
Maximum550
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-16T16:02:38.614066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile11.25
Q122.5
median39.5
Q356.5
95-th percentile308.25
Maximum550
Range540
Interquartile range (IQR)34

Descriptive statistics

Standard deviation119.99134
Coefficient of variation (CV)1.6393982
Kurtosis11.430309
Mean73.192308
Median Absolute Deviation (MAD)17.5
Skewness3.3814652
Sum1903
Variance14397.922
MonotonicityNot monotonic
2023-12-16T16:02:39.363966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
22 2
 
7.7%
39 1
 
3.8%
14 1
 
3.8%
30 1
 
3.8%
50 1
 
3.8%
10 1
 
3.8%
42 1
 
3.8%
11 1
 
3.8%
28 1
 
3.8%
16 1
 
3.8%
Other values (15) 15
57.7%
ValueCountFrequency (%)
10 1
3.8%
11 1
3.8%
12 1
3.8%
14 1
3.8%
16 1
3.8%
22 2
7.7%
24 1
3.8%
28 1
3.8%
30 1
3.8%
37 1
3.8%
ValueCountFrequency (%)
550 1
3.8%
378 1
3.8%
99 1
3.8%
98 1
3.8%
72 1
3.8%
68 1
3.8%
58 1
3.8%
52 1
3.8%
50 1
3.8%
47 1
3.8%

중국
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8076923
Minimum0
Maximum33
Zeros1
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-16T16:02:39.819672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q312
95-th percentile26
Maximum33
Range33
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.226879
Coefficient of variation (CV)0.93405613
Kurtosis2.4045145
Mean8.8076923
Median Absolute Deviation (MAD)4
Skewness1.5875716
Sum229
Variance67.681538
MonotonicityNot monotonic
2023-12-16T16:02:40.484401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
6 4
15.4%
3 3
11.5%
12 3
11.5%
7 3
11.5%
1 2
 
7.7%
2 2
 
7.7%
20 1
 
3.8%
33 1
 
3.8%
28 1
 
3.8%
17 1
 
3.8%
Other values (5) 5
19.2%
ValueCountFrequency (%)
0 1
 
3.8%
1 2
7.7%
2 2
7.7%
3 3
11.5%
4 1
 
3.8%
5 1
 
3.8%
6 4
15.4%
7 3
11.5%
11 1
 
3.8%
12 3
11.5%
ValueCountFrequency (%)
33 1
 
3.8%
28 1
 
3.8%
20 1
 
3.8%
17 1
 
3.8%
15 1
 
3.8%
12 3
11.5%
11 1
 
3.8%
7 3
11.5%
6 4
15.4%
5 1
 
3.8%

한국계 중국인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4615385
Minimum0
Maximum12
Zeros2
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-16T16:02:41.126075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q11
median4
Q36.75
95-th percentile9.75
Maximum12
Range12
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation3.5126146
Coefficient of variation (CV)0.78731018
Kurtosis-0.83972135
Mean4.4615385
Median Absolute Deviation (MAD)3
Skewness0.53625747
Sum116
Variance12.338462
MonotonicityNot monotonic
2023-12-16T16:02:41.406782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 6
23.1%
9 4
15.4%
4 4
15.4%
6 2
 
7.7%
5 2
 
7.7%
0 2
 
7.7%
2 2
 
7.7%
12 1
 
3.8%
10 1
 
3.8%
7 1
 
3.8%
ValueCountFrequency (%)
0 2
 
7.7%
1 6
23.1%
2 2
 
7.7%
3 1
 
3.8%
4 4
15.4%
5 2
 
7.7%
6 2
 
7.7%
7 1
 
3.8%
9 4
15.4%
10 1
 
3.8%
ValueCountFrequency (%)
12 1
 
3.8%
10 1
 
3.8%
9 4
15.4%
7 1
 
3.8%
6 2
 
7.7%
5 2
 
7.7%
4 4
15.4%
3 1
 
3.8%
2 2
 
7.7%
1 6
23.1%

베트남
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.230769
Minimum1
Maximum507
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-16T16:02:41.731335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q17.25
median14
Q319.5
95-th percentile242
Maximum507
Range506
Interquartile range (IQR)12.25

Descriptive statistics

Standard deviation111.2483
Coefficient of variation (CV)2.515179
Kurtosis13.526703
Mean44.230769
Median Absolute Deviation (MAD)6.5
Skewness3.6903743
Sum1150
Variance12376.185
MonotonicityNot monotonic
2023-12-16T16:02:42.099221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
14 3
 
11.5%
8 2
 
7.7%
5 2
 
7.7%
15 2
 
7.7%
2 1
 
3.8%
18 1
 
3.8%
1 1
 
3.8%
20 1
 
3.8%
4 1
 
3.8%
27 1
 
3.8%
Other values (11) 11
42.3%
ValueCountFrequency (%)
1 1
 
3.8%
2 1
 
3.8%
4 1
 
3.8%
5 2
7.7%
6 1
 
3.8%
7 1
 
3.8%
8 2
7.7%
11 1
 
3.8%
13 1
 
3.8%
14 3
11.5%
ValueCountFrequency (%)
507 1
3.8%
311 1
3.8%
35 1
3.8%
33 1
3.8%
27 1
3.8%
24 1
3.8%
20 1
3.8%
18 1
3.8%
17 1
3.8%
16 1
3.8%

일본
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)30.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3846154
Minimum0
Maximum16
Zeros8
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-16T16:02:42.418700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q32
95-th percentile8.5
Maximum16
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.5335752
Coefficient of variation (CV)1.4818219
Kurtosis8.6841362
Mean2.3846154
Median Absolute Deviation (MAD)1.5
Skewness2.7484007
Sum62
Variance12.486154
MonotonicityNot monotonic
2023-12-16T16:02:42.784750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 8
30.8%
2 7
26.9%
1 5
19.2%
4 2
 
7.7%
3 1
 
3.8%
9 1
 
3.8%
16 1
 
3.8%
7 1
 
3.8%
ValueCountFrequency (%)
0 8
30.8%
1 5
19.2%
2 7
26.9%
3 1
 
3.8%
4 2
 
7.7%
7 1
 
3.8%
9 1
 
3.8%
16 1
 
3.8%
ValueCountFrequency (%)
16 1
 
3.8%
9 1
 
3.8%
7 1
 
3.8%
4 2
 
7.7%
3 1
 
3.8%
2 7
26.9%
1 5
19.2%
0 8
30.8%

미국
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6153846
Minimum0
Maximum10
Zeros12
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-16T16:02:43.176055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6.25
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.4343693
Coefficient of variation (CV)1.5069905
Kurtosis5.203622
Mean1.6153846
Median Absolute Deviation (MAD)1
Skewness2.1819968
Sum42
Variance5.9261538
MonotonicityNot monotonic
2023-12-16T16:02:43.721723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 12
46.2%
1 5
19.2%
2 3
 
11.5%
3 2
 
7.7%
4 2
 
7.7%
10 1
 
3.8%
7 1
 
3.8%
ValueCountFrequency (%)
0 12
46.2%
1 5
19.2%
2 3
 
11.5%
3 2
 
7.7%
4 2
 
7.7%
7 1
 
3.8%
10 1
 
3.8%
ValueCountFrequency (%)
10 1
 
3.8%
7 1
 
3.8%
4 2
 
7.7%
3 2
 
7.7%
2 3
 
11.5%
1 5
19.2%
0 12
46.2%

필리핀
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2692308
Minimum0
Maximum9
Zeros11
Zeros (%)42.3%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-16T16:02:44.162831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35
95-th percentile6
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.7793192
Coefficient of variation (CV)1.2247847
Kurtosis-0.46891372
Mean2.2692308
Median Absolute Deviation (MAD)1
Skewness0.94564931
Sum59
Variance7.7246154
MonotonicityNot monotonic
2023-12-16T16:02:44.644761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 11
42.3%
6 5
19.2%
1 4
 
15.4%
2 3
 
11.5%
5 2
 
7.7%
9 1
 
3.8%
ValueCountFrequency (%)
0 11
42.3%
1 4
 
15.4%
2 3
 
11.5%
5 2
 
7.7%
6 5
19.2%
9 1
 
3.8%
ValueCountFrequency (%)
9 1
 
3.8%
6 5
19.2%
5 2
 
7.7%
2 3
 
11.5%
1 4
 
15.4%
0 11
42.3%

타이완
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5
Minimum0
Maximum6
Zeros8
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-16T16:02:45.048599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3.75
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5033296
Coefficient of variation (CV)1.0022198
Kurtosis1.6888728
Mean1.5
Median Absolute Deviation (MAD)1
Skewness1.1861576
Sum39
Variance2.26
MonotonicityNot monotonic
2023-12-16T16:02:45.456784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 8
30.8%
1 7
26.9%
2 5
19.2%
3 4
15.4%
6 1
 
3.8%
4 1
 
3.8%
ValueCountFrequency (%)
0 8
30.8%
1 7
26.9%
2 5
19.2%
3 4
15.4%
4 1
 
3.8%
6 1
 
3.8%
ValueCountFrequency (%)
6 1
 
3.8%
4 1
 
3.8%
3 4
15.4%
2 5
19.2%
1 7
26.9%
0 8
30.8%

기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9230769
Minimum0
Maximum19
Zeros2
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-16T16:02:45.816124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q14
median7
Q310
95-th percentile18
Maximum19
Range19
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.5923024
Coefficient of variation (CV)0.70582457
Kurtosis-0.36707721
Mean7.9230769
Median Absolute Deviation (MAD)3
Skewness0.65075466
Sum206
Variance31.273846
MonotonicityNot monotonic
2023-12-16T16:02:46.218280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7 5
19.2%
8 2
 
7.7%
10 2
 
7.7%
4 2
 
7.7%
3 2
 
7.7%
0 2
 
7.7%
18 2
 
7.7%
1 1
 
3.8%
17 1
 
3.8%
5 1
 
3.8%
Other values (6) 6
23.1%
ValueCountFrequency (%)
0 2
 
7.7%
1 1
 
3.8%
2 1
 
3.8%
3 2
 
7.7%
4 2
 
7.7%
5 1
 
3.8%
6 1
 
3.8%
7 5
19.2%
8 2
 
7.7%
9 1
 
3.8%
ValueCountFrequency (%)
19 1
 
3.8%
18 2
 
7.7%
17 1
 
3.8%
15 1
 
3.8%
11 1
 
3.8%
10 2
 
7.7%
9 1
 
3.8%
8 2
 
7.7%
7 5
19.2%
6 1
 
3.8%

Interactions

2023-12-16T16:02:32.025802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:02.766201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:07.926096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:11.569052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:14.474382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:18.192662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:21.780658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:24.380216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:27.499053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:32.360318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:03.320787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:08.220043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:11.870620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:14.853603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:18.601361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:22.015525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:24.704868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:28.276391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:32.714220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:03.994529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:08.681254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:12.246531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:15.293287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:19.080460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:22.245782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:24.950409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:28.644339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:32.998565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:04.693679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:09.048424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:12.581994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:15.617580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:19.570741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:22.521204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:25.244112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:29.003900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:33.256998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:05.467517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:09.301541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:12.827958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:15.952713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:20.045400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:22.839014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:25.607793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:29.468902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:33.567663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:06.810638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:09.903591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:13.102838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:16.457401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:20.379760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:23.144621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:25.976912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:30.045748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:33.853064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:07.057431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:10.355481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:13.429706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:16.833242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:20.873468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:23.407419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:26.327689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:30.405619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:34.152245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:07.320447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:10.750177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:13.802413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:17.291319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:21.199239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:23.717493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:26.723027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:30.906434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:34.460121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:07.609622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:11.193009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:14.117224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:17.785584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:21.481987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:23.999881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:27.072803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:02:31.352150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-16T16:02:46.552157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분소계중국한국계 중국인베트남일본미국필리핀타이완기타
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소계1.0001.0000.9610.8051.0000.3310.0000.3830.1620.850
중국1.0000.9611.0000.8871.0000.9040.2000.8460.7120.755
한국계 중국인1.0000.8050.8871.0000.9370.2390.3890.3710.6700.764
베트남1.0001.0001.0000.9371.0000.0000.0000.0000.0000.937
일본1.0000.3310.9040.2390.0001.0000.6660.8590.8100.000
미국1.0000.0000.2000.3890.0000.6661.0000.4600.0000.403
필리핀1.0000.3830.8460.3710.0000.8590.4601.0000.7130.000
타이완1.0000.1620.7120.6700.0000.8100.0000.7131.0000.000
기타1.0000.8500.7550.7640.9370.0000.4030.0000.0001.000
2023-12-16T16:02:46.938639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소계중국한국계 중국인베트남일본미국필리핀타이완기타
소계1.0000.9220.5320.8850.4440.4480.6440.2850.644
중국0.9221.0000.4890.8420.4480.3720.5860.1680.453
한국계 중국인0.5320.4891.0000.5540.0470.0640.1650.0170.391
베트남0.8850.8420.5541.0000.2570.2430.5800.1820.410
일본0.4440.4480.0470.2571.0000.5120.6190.1380.415
미국0.4480.3720.0640.2430.5121.0000.3180.2050.584
필리핀0.6440.5860.1650.5800.6190.3181.0000.3970.484
타이완0.2850.1680.0170.1820.1380.2050.3971.0000.234
기타0.6440.4530.3910.4100.4150.5840.4840.2341.000

Missing values

2023-12-16T16:02:34.870728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T16:02:35.535948image/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

구분소계중국한국계 중국인베트남일본미국필리핀타이완기타
0구포1동(남)39391411038
1구포1동(여)7212924006318
2구포2동(남)5212917030110
3구포2동(여)98201233445218
4구포3동(남)55033650700004
5구포3동(여)3782810311326315
6금곡동(남)38651324008
7금곡동(여)9917935919019
8화명1동(남)2240713007
9화명1동(여)6815215161667
구분소계중국한국계 중국인베트남일본미국필리핀타이완기타
16덕천2동(남)2466520005
17덕천2동(여)46742710214
18덕천3동(남)1633800020
19덕천3동(여)28711520120
20만덕1동(남)1121400013
21만덕1동(여)42742001217
22만덕2동(남)1004100113
23만덕2동(여)505418225410
24만덕3동(남)22102100117
25만덕3동(여)30311421207