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

Description부산광역시북구_외국인등록현황_20230912
Author부산광역시 북구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15026047

Alerts

소계 is highly overall correlated with 중국 and 6 other fieldsHigh correlation
중국 is highly overall correlated with 소계 and 3 other fieldsHigh correlation
한국계 중국인 is highly overall correlated with 소계 and 1 other fieldsHigh correlation
베트남 is highly overall correlated with 소계 and 2 other fieldsHigh correlation
일본 is highly overall correlated with 소계 and 2 other fieldsHigh correlation
미국 is highly overall correlated with 소계 and 1 other fieldsHigh correlation
필리핀 is highly overall correlated with 소계 and 2 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 1 (3.8%) zerosZeros
일본 has 9 (34.6%) zerosZeros
미국 has 11 (42.3%) zerosZeros
필리핀 has 12 (46.2%) zerosZeros
타이완 has 8 (30.8%) zerosZeros
기타 has 3 (11.5%) zerosZeros

Reproduction

Analysis started2023-12-10 16:31:23.398983
Analysis finished2023-12-10 16:31:38.464035
Duration15.07 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
2023-12-11T01:31:38.659314image/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%
2023-12-11T01:31:39.112923image/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 

Distinct24
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.961538
Minimum10
Maximum534
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:31:39.251681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile11
Q122.25
median38
Q355
95-th percentile303.75
Maximum534
Range524
Interquartile range (IQR)32.75

Descriptive statistics

Standard deviation117.1493
Coefficient of variation (CV)1.6508844
Kurtosis11.234291
Mean70.961538
Median Absolute Deviation (MAD)17.5
Skewness3.3549599
Sum1845
Variance13723.958
MonotonicityNot monotonic
2023-12-11T01:31:39.410343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
30 2
 
7.7%
11 2
 
7.7%
35 1
 
3.8%
57 1
 
3.8%
13 1
 
3.8%
49 1
 
3.8%
12 1
 
3.8%
39 1
 
3.8%
15 1
 
3.8%
43 1
 
3.8%
Other values (14) 14
53.8%
ValueCountFrequency (%)
10 1
3.8%
11 2
7.7%
12 1
3.8%
13 1
3.8%
15 1
3.8%
22 1
3.8%
23 1
3.8%
30 2
7.7%
35 1
3.8%
36 1
3.8%
ValueCountFrequency (%)
534 1
3.8%
371 1
3.8%
102 1
3.8%
92 1
3.8%
75 1
3.8%
65 1
3.8%
57 1
3.8%
49 1
3.8%
46 1
3.8%
45 1
3.8%

중국
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4615385
Minimum0
Maximum35
Zeros1
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:31:39.559518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12.25
median6.5
Q310.75
95-th percentile27.75
Maximum35
Range35
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation8.6312491
Coefficient of variation (CV)1.0200567
Kurtosis3.8124838
Mean8.4615385
Median Absolute Deviation (MAD)4.5
Skewness1.93551
Sum220
Variance74.498462
MonotonicityNot monotonic
2023-12-11T01:31:39.729408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
7 5
19.2%
2 4
15.4%
5 3
11.5%
13 2
 
7.7%
1 2
 
7.7%
3 1
 
3.8%
18 1
 
3.8%
35 1
 
3.8%
31 1
 
3.8%
16 1
 
3.8%
Other values (5) 5
19.2%
ValueCountFrequency (%)
0 1
 
3.8%
1 2
 
7.7%
2 4
15.4%
3 1
 
3.8%
4 1
 
3.8%
5 3
11.5%
6 1
 
3.8%
7 5
19.2%
10 1
 
3.8%
11 1
 
3.8%
ValueCountFrequency (%)
35 1
 
3.8%
31 1
 
3.8%
18 1
 
3.8%
16 1
 
3.8%
13 2
 
7.7%
11 1
 
3.8%
10 1
 
3.8%
7 5
19.2%
6 1
 
3.8%
5 3
11.5%

한국계 중국인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6538462
Minimum0
Maximum13
Zeros1
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:31:39.903685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median4
Q37.5
95-th percentile10
Maximum13
Range13
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation3.5433578
Coefficient of variation (CV)0.76138267
Kurtosis-0.48899102
Mean4.6538462
Median Absolute Deviation (MAD)3
Skewness0.60199168
Sum121
Variance12.555385
MonotonicityNot monotonic
2023-12-11T01:31:40.064523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 7
26.9%
8 3
11.5%
6 3
11.5%
4 3
11.5%
10 2
 
7.7%
5 2
 
7.7%
3 2
 
7.7%
9 1
 
3.8%
13 1
 
3.8%
2 1
 
3.8%
ValueCountFrequency (%)
0 1
 
3.8%
1 7
26.9%
2 1
 
3.8%
3 2
 
7.7%
4 3
11.5%
5 2
 
7.7%
6 3
11.5%
8 3
11.5%
9 1
 
3.8%
10 2
 
7.7%
ValueCountFrequency (%)
13 1
 
3.8%
10 2
 
7.7%
9 1
 
3.8%
8 3
11.5%
6 3
11.5%
5 2
 
7.7%
4 3
11.5%
3 2
 
7.7%
2 1
 
3.8%
1 7
26.9%

베트남
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.923077
Minimum1
Maximum490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:31:40.218114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5
Q16.5
median14.5
Q319
95-th percentile236.5
Maximum490
Range489
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation107.77195
Coefficient of variation (CV)2.5108161
Kurtosis13.40359
Mean42.923077
Median Absolute Deviation (MAD)7.5
Skewness3.6756221
Sum1116
Variance11614.794
MonotonicityNot monotonic
2023-12-11T01:31:40.366046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
15 3
 
11.5%
10 2
 
7.7%
19 2
 
7.7%
1 2
 
7.7%
6 2
 
7.7%
3 1
 
3.8%
17 1
 
3.8%
4 1
 
3.8%
16 1
 
3.8%
8 1
 
3.8%
Other values (10) 10
38.5%
ValueCountFrequency (%)
1 2
7.7%
3 1
3.8%
4 1
3.8%
5 1
3.8%
6 2
7.7%
8 1
3.8%
9 1
3.8%
10 2
7.7%
12 1
3.8%
14 1
3.8%
ValueCountFrequency (%)
490 1
 
3.8%
303 1
 
3.8%
37 1
 
3.8%
33 1
 
3.8%
25 1
 
3.8%
23 1
 
3.8%
19 2
7.7%
17 1
 
3.8%
16 1
 
3.8%
15 3
11.5%

일본
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3846154
Minimum0
Maximum16
Zeros9
Zeros (%)34.6%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:31:40.517064image/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 deviation3.5785687
Coefficient of variation (CV)1.5006901
Kurtosis8.1092168
Mean2.3846154
Median Absolute Deviation (MAD)1.5
Skewness2.64865
Sum62
Variance12.806154
MonotonicityNot monotonic
2023-12-11T01:31:40.694819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 9
34.6%
2 7
26.9%
1 4
15.4%
5 1
 
3.8%
3 1
 
3.8%
9 1
 
3.8%
16 1
 
3.8%
4 1
 
3.8%
7 1
 
3.8%
ValueCountFrequency (%)
0 9
34.6%
1 4
15.4%
2 7
26.9%
3 1
 
3.8%
4 1
 
3.8%
5 1
 
3.8%
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%
5 1
 
3.8%
4 1
 
3.8%
3 1
 
3.8%
2 7
26.9%
1 4
15.4%
0 9
34.6%

미국
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6538462
Minimum0
Maximum10
Zeros11
Zeros (%)42.3%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:31:40.882433image/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 deviation2.4156541
Coefficient of variation (CV)1.460628
Kurtosis5.2973667
Mean1.6538462
Median Absolute Deviation (MAD)1
Skewness2.1935971
Sum43
Variance5.8353846
MonotonicityNot monotonic
2023-12-11T01:31:41.049066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 11
42.3%
1 6
23.1%
2 3
 
11.5%
3 2
 
7.7%
4 2
 
7.7%
10 1
 
3.8%
7 1
 
3.8%
ValueCountFrequency (%)
0 11
42.3%
1 6
23.1%
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 6
23.1%
0 11
42.3%

필리핀
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1538462
Minimum0
Maximum9
Zeros12
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:31:41.193929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34.75
95-th percentile6
Maximum9
Range9
Interquartile range (IQR)4.75

Descriptive statistics

Standard deviation2.7812559
Coefficient of variation (CV)1.2912974
Kurtosis-0.30772315
Mean2.1538462
Median Absolute Deviation (MAD)1
Skewness1.0245167
Sum56
Variance7.7353846
MonotonicityNot monotonic
2023-12-11T01:31:41.334499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 12
46.2%
6 5
19.2%
1 4
 
15.4%
2 2
 
7.7%
4 1
 
3.8%
5 1
 
3.8%
9 1
 
3.8%
ValueCountFrequency (%)
0 12
46.2%
1 4
 
15.4%
2 2
 
7.7%
4 1
 
3.8%
5 1
 
3.8%
6 5
19.2%
9 1
 
3.8%
ValueCountFrequency (%)
9 1
 
3.8%
6 5
19.2%
5 1
 
3.8%
4 1
 
3.8%
2 2
 
7.7%
1 4
 
15.4%
0 12
46.2%

타이완
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4615385
Minimum0
Maximum5
Zeros8
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:31:41.543849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3.75
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3922864
Coefficient of variation (CV)0.95261704
Kurtosis0.095164153
Mean1.4615385
Median Absolute Deviation (MAD)1
Skewness0.8083084
Sum38
Variance1.9384615
MonotonicityNot monotonic
2023-12-11T01:31:41.750475image/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%
5 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%
5 1
 
3.8%
ValueCountFrequency (%)
5 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 

Distinct13
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2692308
Minimum0
Maximum18
Zeros3
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:31:41.921498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.25
median7
Q39
95-th percentile17.5
Maximum18
Range18
Interquartile range (IQR)4.75

Descriptive statistics

Standard deviation5.1190444
Coefficient of variation (CV)0.70420716
Kurtosis0.029371334
Mean7.2692308
Median Absolute Deviation (MAD)2
Skewness0.65491734
Sum189
Variance26.204615
MonotonicityNot monotonic
2023-12-11T01:31:42.110392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
7 4
15.4%
6 3
11.5%
9 3
11.5%
0 3
11.5%
8 2
7.7%
18 2
7.7%
3 2
7.7%
5 2
7.7%
14 1
 
3.8%
16 1
 
3.8%
Other values (3) 3
11.5%
ValueCountFrequency (%)
0 3
11.5%
1 1
 
3.8%
3 2
7.7%
4 1
 
3.8%
5 2
7.7%
6 3
11.5%
7 4
15.4%
8 2
7.7%
9 3
11.5%
13 1
 
3.8%
ValueCountFrequency (%)
18 2
7.7%
16 1
 
3.8%
14 1
 
3.8%
13 1
 
3.8%
9 3
11.5%
8 2
7.7%
7 4
15.4%
6 3
11.5%
5 2
7.7%
4 1
 
3.8%

Interactions

2023-12-11T01:31:35.326550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:23.867052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:25.075589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:27.072517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:28.212087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:29.338229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:30.764054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:32.621840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:34.123192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:35.445516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:24.027598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:25.204968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:27.192307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:28.331857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:29.523191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:30.902188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:32.797854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:34.246978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:35.594330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:24.173452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:25.326514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:27.335526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:28.462159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:29.682958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:31.040791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:32.983932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:34.391431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:36.311001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:24.308054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:25.483970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:27.457949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:28.598505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:29.843938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:31.184031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:33.113952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:34.537006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:36.651766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:24.431283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:25.669190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:27.576259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:28.727826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:29.982853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:31.303979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:33.294659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:34.660862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:37.026214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:24.576571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:26.381937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:27.707335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:28.866311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:30.134409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:31.441212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:33.499507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:34.829396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:37.320374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:24.684342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:26.629895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:27.823493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:28.982256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:30.279571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:31.575628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:33.640417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:34.952447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:37.654569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:24.784606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:26.773048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:27.944998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:29.093664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:30.466528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:31.870020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:33.767455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:35.081126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:37.907192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:24.906715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:26.924520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:28.069476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:29.208091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:30.608068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:32.266043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:33.904897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:31:35.196317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:31:42.287672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분소계중국한국계 중국인베트남일본미국필리핀타이완기타
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소계1.0001.0000.9990.8071.0000.3610.0000.4820.1620.761
중국1.0000.9991.0000.8281.0000.7880.3280.7790.0000.757
한국계 중국인1.0000.8070.8281.0000.5450.5500.5770.6110.5100.320
베트남1.0001.0001.0000.5451.0000.0000.0000.0000.0000.792
일본1.0000.3610.7880.5500.0001.0000.8340.9130.6130.574
미국1.0000.0000.3280.5770.0000.8341.0000.6860.1910.553
필리핀1.0000.4820.7790.6110.0000.9130.6861.0000.0000.719
타이완1.0000.1620.0000.5100.0000.6130.1910.0001.0000.514
기타1.0000.7610.7570.3200.7920.5740.5530.7190.5141.000
2023-12-11T01:31:42.488579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소계중국한국계 중국인베트남일본미국필리핀타이완기타
소계1.0000.9240.5200.8750.5100.5120.6480.2880.585
중국0.9241.0000.4790.8300.5050.4130.5850.1920.421
한국계 중국인0.5200.4791.0000.5690.0080.1080.1490.0270.313
베트남0.8750.8300.5691.0000.2980.3090.4980.1570.395
일본0.5100.5050.0080.2981.0000.4350.7330.1800.472
미국0.5120.4130.1080.3090.4351.0000.3420.2660.708
필리핀0.6480.5850.1490.4980.7330.3421.0000.4320.483
타이완0.2880.1920.0270.1570.1800.2660.4321.0000.284
기타0.5850.4210.3130.3950.4720.7080.4830.2841.000

Missing values

2023-12-11T01:31:38.150107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:31:38.370348image/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)35391011038
1구포1동(여)75131025024318
2구포2동(남)45781903017
3구포2동(여)102181337545218
4구포3동(남)53435649000003
5구포3동(여)3713110303316314
6금곡동(남)37551424007
7금곡동(여)9216833919016
8화명1동(남)2241613007
9화명1동(여)6513315161656
구분소계중국한국계 중국인베트남일본미국필리핀타이완기타
16덕천2동(남)2355620005
17덕천2동(여)43662310214
18덕천3동(남)1523800020
19덕천3동(여)30711620220
20만덕1동(남)1121400013
21만덕1동(여)39741901017
22만덕2동(남)1204100115
23만덕2동(여)49541722649
24만덕3동(남)1310110019
25만덕3동(여)30211521108