Overview

Dataset statistics

Number of variables8
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory75.7 B

Variable types

Text1
Numeric6
Categorical1

Dataset

Description인천광역시 서구 내 동별, 국가별 외국인 등록 현황(타이완(대만), 미국, 일본, 필리핀, 중국 국적, 데이터 기준일자 등)입니다.
Author인천광역시 서구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15068522&srcSe=7661IVAWM27C61E190

Alerts

데이터기준일자 has constant value ""Constant
타이완(대만) is highly overall correlated with 미국 and 2 other fieldsHigh correlation
미국 is highly overall correlated with 타이완(대만) and 2 other fieldsHigh correlation
일본 is highly overall correlated with 타이완(대만) and 2 other fieldsHigh correlation
필리핀 is highly overall correlated with 기타High correlation
중국 is highly overall correlated with 타이완(대만) and 3 other fieldsHigh correlation
기타 is highly overall correlated with 필리핀 and 1 other fieldsHigh correlation
구분 has unique valuesUnique
기타 has unique valuesUnique
타이완(대만) has 3 (13.0%) zerosZeros
미국 has 3 (13.0%) zerosZeros

Reproduction

Analysis started2024-01-28 07:16:51.524994
Analysis finished2024-01-28 07:16:54.245937
Duration2.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2024-01-28T16:16:54.353709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.9130435
Min length3

Characters and Unicode

Total characters90
Distinct characters36
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

Unique23 ?
Unique (%)100.0%

Sample

1st row검암경서동
2nd row연희동
3rd row청라1동
4th row청라2동
5th row청라3동
ValueCountFrequency (%)
검암경서동 1
 
4.3%
가좌1동 1
 
4.3%
마전동 1
 
4.3%
오류왕길동 1
 
4.3%
당하동 1
 
4.3%
원당동 1
 
4.3%
불로대곡동 1
 
4.3%
검단동 1
 
4.3%
가좌4동 1
 
4.3%
가좌3동 1
 
4.3%
Other values (13) 13
56.5%
2024-01-28T16:16:54.638442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 23
25.6%
가 7
 
7.8%
좌 4
 
4.4%
라 4
 
4.4%
1 4
 
4.4%
2 4
 
4.4%
3 4
 
4.4%
석 3
 
3.3%
정 3
 
3.3%
남 3
 
3.3%
Other values (26) 31
34.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77
85.6%
Decimal Number 13
 
14.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 23
29.9%
가 7
 
9.1%
좌 4
 
5.2%
라 4
 
5.2%
석 3
 
3.9%
정 3
 
3.9%
남 3
 
3.9%
청 3
 
3.9%
당 2
 
2.6%
원 2
 
2.6%
Other values (22) 23
29.9%
Decimal Number
ValueCountFrequency (%)
1 4
30.8%
2 4
30.8%
3 4
30.8%
4 1
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77
85.6%
Common 13
 
14.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 23
29.9%
가 7
 
9.1%
좌 4
 
5.2%
라 4
 
5.2%
석 3
 
3.9%
정 3
 
3.9%
남 3
 
3.9%
청 3
 
3.9%
당 2
 
2.6%
원 2
 
2.6%
Other values (22) 23
29.9%
Common
ValueCountFrequency (%)
1 4
30.8%
2 4
30.8%
3 4
30.8%
4 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77
85.6%
ASCII 13
 
14.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 23
29.9%
가 7
 
9.1%
좌 4
 
5.2%
라 4
 
5.2%
석 3
 
3.9%
정 3
 
3.9%
남 3
 
3.9%
청 3
 
3.9%
당 2
 
2.6%
원 2
 
2.6%
Other values (22) 23
29.9%
ASCII
ValueCountFrequency (%)
1 4
30.8%
2 4
30.8%
3 4
30.8%
4 1
 
7.7%

타이완(대만)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0869565
Minimum0
Maximum39
Zeros3
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-28T16:16:54.729575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.5
median4
Q313.5
95-th percentile30.5
Maximum39
Range39
Interquartile range (IQR)11

Descriptive statistics

Standard deviation10.833085
Coefficient of variation (CV)1.1921577
Kurtosis1.6515054
Mean9.0869565
Median Absolute Deviation (MAD)3
Skewness1.5561107
Sum209
Variance117.35573
MonotonicityNot monotonic
2024-01-28T16:16:54.822135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
3 3
13.0%
4 3
13.0%
0 3
13.0%
1 2
 
8.7%
5 2
 
8.7%
26 1
 
4.3%
21 1
 
4.3%
39 1
 
4.3%
31 1
 
4.3%
17 1
 
4.3%
Other values (5) 5
21.7%
ValueCountFrequency (%)
0 3
13.0%
1 2
8.7%
2 1
 
4.3%
3 3
13.0%
4 3
13.0%
5 2
8.7%
6 1
 
4.3%
7 1
 
4.3%
11 1
 
4.3%
16 1
 
4.3%
ValueCountFrequency (%)
39 1
4.3%
31 1
4.3%
26 1
4.3%
21 1
4.3%
17 1
4.3%
16 1
4.3%
11 1
4.3%
7 1
4.3%
6 1
4.3%
5 2
8.7%

미국
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6956522
Minimum0
Maximum50
Zeros3
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-28T16:16:54.919273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile23.2
Maximum50
Range50
Interquartile range (IQR)5

Descriptive statistics

Standard deviation11.133221
Coefficient of variation (CV)1.6627538
Kurtosis10.653197
Mean6.6956522
Median Absolute Deviation (MAD)2
Skewness3.06871
Sum154
Variance123.94862
MonotonicityNot monotonic
2024-01-28T16:16:55.010735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 6
26.1%
0 3
13.0%
2 2
 
8.7%
6 2
 
8.7%
3 2
 
8.7%
4 2
 
8.7%
11 1
 
4.3%
16 1
 
4.3%
24 1
 
4.3%
50 1
 
4.3%
Other values (2) 2
 
8.7%
ValueCountFrequency (%)
0 3
13.0%
1 6
26.1%
2 2
 
8.7%
3 2
 
8.7%
4 2
 
8.7%
5 1
 
4.3%
6 2
 
8.7%
11 1
 
4.3%
12 1
 
4.3%
16 1
 
4.3%
ValueCountFrequency (%)
50 1
4.3%
24 1
4.3%
16 1
4.3%
12 1
4.3%
11 1
4.3%
6 2
8.7%
5 1
4.3%
4 2
8.7%
3 2
8.7%
2 2
8.7%

일본
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.304348
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-28T16:16:55.089714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median9
Q314
95-th percentile29.7
Maximum32
Range31
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.9464812
Coefficient of variation (CV)0.79141949
Kurtosis0.43326805
Mean11.304348
Median Absolute Deviation (MAD)3
Skewness1.159687
Sum260
Variance80.039526
MonotonicityNot monotonic
2024-01-28T16:16:55.171623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
6 4
17.4%
11 2
 
8.7%
10 2
 
8.7%
7 2
 
8.7%
2 2
 
8.7%
4 2
 
8.7%
27 1
 
4.3%
16 1
 
4.3%
21 1
 
4.3%
30 1
 
4.3%
Other values (5) 5
21.7%
ValueCountFrequency (%)
1 1
 
4.3%
2 2
8.7%
4 2
8.7%
6 4
17.4%
7 2
8.7%
9 1
 
4.3%
10 2
8.7%
11 2
8.7%
12 1
 
4.3%
16 1
 
4.3%
ValueCountFrequency (%)
32 1
4.3%
30 1
4.3%
27 1
4.3%
21 1
4.3%
20 1
4.3%
16 1
4.3%
12 1
4.3%
11 2
8.7%
10 2
8.7%
9 1
4.3%

필리핀
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.826087
Minimum4
Maximum215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-28T16:16:55.258639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q16.5
median12
Q338
95-th percentile163
Maximum215
Range211
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation55.885965
Coefficient of variation (CV)1.4774451
Kurtosis4.6685902
Mean37.826087
Median Absolute Deviation (MAD)8
Skewness2.2648995
Sum870
Variance3123.2411
MonotonicityNot monotonic
2024-01-28T16:16:55.336937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
4 4
17.4%
12 2
 
8.7%
6 2
 
8.7%
8 2
 
8.7%
71 1
 
4.3%
168 1
 
4.3%
24 1
 
4.3%
33 1
 
4.3%
51 1
 
4.3%
18 1
 
4.3%
Other values (7) 7
30.4%
ValueCountFrequency (%)
4 4
17.4%
6 2
8.7%
7 1
 
4.3%
8 2
8.7%
10 1
 
4.3%
12 2
8.7%
15 1
 
4.3%
18 1
 
4.3%
24 1
 
4.3%
29 1
 
4.3%
ValueCountFrequency (%)
215 1
4.3%
168 1
4.3%
118 1
4.3%
71 1
4.3%
51 1
4.3%
43 1
4.3%
33 1
4.3%
29 1
4.3%
24 1
4.3%
18 1
4.3%

중국
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.782609
Minimum7
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-28T16:16:55.627283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile12
Q132.5
median55
Q383.5
95-th percentile102.6
Maximum123
Range116
Interquartile range (IQR)51

Descriptive statistics

Standard deviation31.615526
Coefficient of variation (CV)0.5378381
Kurtosis-0.85270134
Mean58.782609
Median Absolute Deviation (MAD)27
Skewness0.17184608
Sum1352
Variance999.5415
MonotonicityNot monotonic
2024-01-28T16:16:55.711832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
82 2
 
8.7%
123 1
 
4.3%
64 1
 
4.3%
33 1
 
4.3%
63 1
 
4.3%
50 1
 
4.3%
30 1
 
4.3%
32 1
 
4.3%
103 1
 
4.3%
26 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
7 1
4.3%
11 1
4.3%
21 1
4.3%
26 1
4.3%
30 1
4.3%
32 1
4.3%
33 1
4.3%
41 1
4.3%
44 1
4.3%
48 1
4.3%
ValueCountFrequency (%)
123 1
4.3%
103 1
4.3%
99 1
4.3%
89 1
4.3%
88 1
4.3%
85 1
4.3%
82 2
8.7%
76 1
4.3%
64 1
4.3%
63 1
4.3%

기타
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean433.3913
Minimum54
Maximum1855
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-28T16:16:55.799181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum54
5-th percentile65.1
Q1177
median294
Q3543.5
95-th percentile1123.5
Maximum1855
Range1801
Interquartile range (IQR)366.5

Descriptive statistics

Standard deviation433.0598
Coefficient of variation (CV)0.9992351
Kurtosis4.3561238
Mean433.3913
Median Absolute Deviation (MAD)154
Skewness2.0026909
Sum9968
Variance187540.79
MonotonicityNot monotonic
2024-01-28T16:16:55.882036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
739 1
 
4.3%
371 1
 
4.3%
241 1
 
4.3%
102 1
 
4.3%
1855 1
 
4.3%
226 1
 
4.3%
294 1
 
4.3%
311 1
 
4.3%
1074 1
 
4.3%
140 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
54 1
4.3%
61 1
4.3%
102 1
4.3%
106 1
4.3%
140 1
4.3%
166 1
4.3%
188 1
4.3%
216 1
4.3%
222 1
4.3%
226 1
4.3%
ValueCountFrequency (%)
1855 1
4.3%
1129 1
4.3%
1074 1
4.3%
785 1
4.3%
739 1
4.3%
600 1
4.3%
487 1
4.3%
371 1
4.3%
311 1
4.3%
303 1
4.3%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-02-28
23 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-02-28
2nd row2023-02-28
3rd row2023-02-28
4th row2023-02-28
5th row2023-02-28

Common Values

ValueCountFrequency (%)
2023-02-28 23
100.0%

Length

2024-01-28T16:16:55.977700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T16:16:56.042582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-02-28 23
100.0%

Interactions

2024-01-28T16:16:53.692019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:51.714189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:52.145340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:52.528279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:52.933855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:53.313590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:53.762072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:51.806092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:52.212475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:52.608032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:52.998310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:53.385030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:53.824935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:51.881193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:52.268823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:52.668355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:53.053175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:53.444081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:53.890131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:51.948575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:52.329664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:52.739967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:53.124028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:53.505125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:53.954594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:52.009421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:52.398953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:52.808530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:53.175553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:53.562620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:54.021025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:52.078186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:52.456112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:52.867880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:53.244247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T16:16:53.622865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T16:16:56.087657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분타이완(대만)미국일본필리핀중국기타
구분1.0001.0001.0001.0001.0001.0001.000
타이완(대만)1.0001.0000.8330.9460.0000.5860.000
미국1.0000.8331.0000.6090.0000.6540.000
일본1.0000.9460.6091.0000.3720.1300.000
필리핀1.0000.0000.0000.3721.0000.6030.961
중국1.0000.5860.6540.1300.6031.0000.550
기타1.0000.0000.0000.0000.9610.5501.000
2024-01-28T16:16:56.179654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
타이완(대만)미국일본필리핀중국기타
타이완(대만)1.0000.6420.690-0.2050.6830.076
미국0.6421.0000.679-0.1550.5060.132
일본0.6900.6791.000-0.2110.5900.062
필리핀-0.205-0.155-0.2111.0000.3170.735
중국0.6830.5060.5900.3171.0000.573
기타0.0760.1320.0620.7350.5731.000

Missing values

2024-01-28T16:16:54.121566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T16:16:54.210110image/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

구분타이완(대만)미국일본필리핀중국기타데이터기준일자
0검암경서동261127711237392023-02-28
1연희동2121610853712023-02-28
2청라1동3916216821882023-02-28
3청라2동3124308892162023-02-28
4청라3동17501115762222023-02-28
5가정1동66108882982023-02-28
6가정2동11147542023-02-28
7가정3동3076211062023-02-28
8신현원창동21743556002023-02-28
9석남1동72629994872023-02-28
구분타이완(대만)미국일본필리핀중국기타데이터기준일자
13가좌2동0061211612023-02-28
14가좌3동10618483032023-02-28
15가좌4동01451261402023-02-28
16검단동43103310310742023-02-28
17불로대곡동35624323112023-02-28
18원당동41294302942023-02-28
19당하동54204502262023-02-28
20오류왕길동06111686318552023-02-28
21마전동114124331022023-02-28
22아라동1633212822412023-02-28