Overview

Dataset statistics

Number of variables5
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 KiB
Average record size in memory50.3 B

Variable types

Text1
Numeric4

Dataset

Description인천광역시 미추홀구의 연간 동별 전출입 현황에 대한 데이터로 전입인구,전입세대,전출인구,전출세대 등의 정보를 제공하고 있습니다.
URLhttps://www.data.go.kr/data/15102061/fileData.do

Alerts

전입인구 is highly overall correlated with 전입세대 and 2 other fieldsHigh correlation
전입세대 is highly overall correlated with 전입인구 and 2 other fieldsHigh correlation
전출인구 is highly overall correlated with 전입인구 and 2 other fieldsHigh correlation
전출세대 is highly overall correlated with 전입인구 and 2 other fieldsHigh correlation
행정동 has unique valuesUnique
전입인구 has unique valuesUnique
전입세대 has unique valuesUnique
전출세대 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:35:58.810443
Analysis finished2023-12-12 23:36:00.460733
Duration1.65 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T08:36:00.598240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.1904762
Min length3

Characters and Unicode

Total characters88
Distinct characters23
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

Unique21 ?
Unique (%)100.0%

Sample

1st row숭의2동
2nd row숭의1.3동
3rd row숭의4동
4th row용현1.4동
5th row용현2동
ValueCountFrequency (%)
숭의2동 1
 
4.8%
주안1동 1
 
4.8%
관교동 1
 
4.8%
주안8동 1
 
4.8%
주안7동 1
 
4.8%
주안6동 1
 
4.8%
주안5동 1
 
4.8%
주안4동 1
 
4.8%
주안3동 1
 
4.8%
주안2동 1
 
4.8%
Other values (11) 11
52.4%
2023-12-13T08:36:00.951308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 21
23.9%
안 8
 
9.1%
주 8
 
9.1%
2 5
 
5.7%
1 5
 
5.7%
3 4
 
4.5%
용 4
 
4.5%
현 4
 
4.5%
숭 3
 
3.4%
의 3
 
3.4%
Other values (13) 23
26.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 63
71.6%
Decimal Number 22
 
25.0%
Other Punctuation 3
 
3.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 21
33.3%
안 8
 
12.7%
주 8
 
12.7%
용 4
 
6.3%
현 4
 
6.3%
숭 3
 
4.8%
의 3
 
4.8%
학 3
 
4.8%
익 2
 
3.2%
도 2
 
3.2%
Other values (4) 5
 
7.9%
Decimal Number
ValueCountFrequency (%)
2 5
22.7%
1 5
22.7%
3 4
18.2%
4 3
13.6%
5 2
 
9.1%
6 1
 
4.5%
7 1
 
4.5%
8 1
 
4.5%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 63
71.6%
Common 25
 
28.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 21
33.3%
안 8
 
12.7%
주 8
 
12.7%
용 4
 
6.3%
현 4
 
6.3%
숭 3
 
4.8%
의 3
 
4.8%
학 3
 
4.8%
익 2
 
3.2%
도 2
 
3.2%
Other values (4) 5
 
7.9%
Common
ValueCountFrequency (%)
2 5
20.0%
1 5
20.0%
3 4
16.0%
4 3
12.0%
. 3
12.0%
5 2
 
8.0%
6 1
 
4.0%
7 1
 
4.0%
8 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 63
71.6%
ASCII 25
 
28.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 21
33.3%
안 8
 
12.7%
주 8
 
12.7%
용 4
 
6.3%
현 4
 
6.3%
숭 3
 
4.8%
의 3
 
4.8%
학 3
 
4.8%
익 2
 
3.2%
도 2
 
3.2%
Other values (4) 5
 
7.9%
ASCII
ValueCountFrequency (%)
2 5
20.0%
1 5
20.0%
3 4
16.0%
4 3
12.0%
. 3
12.0%
5 2
 
8.0%
6 1
 
4.0%
7 1
 
4.0%
8 1
 
4.0%

전입인구
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2743.1429
Minimum1090
Maximum6859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T08:36:01.091722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1090
5-th percentile1175
Q11859
median2374
Q33185
95-th percentile4870
Maximum6859
Range5769
Interquartile range (IQR)1326

Descriptive statistics

Standard deviation1428.9087
Coefficient of variation (CV)0.52090203
Kurtosis2.0989255
Mean2743.1429
Median Absolute Deviation (MAD)633
Skewness1.3984717
Sum57606
Variance2041780
MonotonicityNot monotonic
2023-12-13T08:36:01.222351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1965 1
 
4.8%
4564 1
 
4.8%
2374 1
 
4.8%
1517 1
 
4.8%
1975 1
 
4.8%
1346 1
 
4.8%
3007 1
 
4.8%
2499 1
 
4.8%
6859 1
 
4.8%
1175 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
1090 1
4.8%
1175 1
4.8%
1346 1
4.8%
1517 1
4.8%
1817 1
4.8%
1859 1
4.8%
1965 1
4.8%
1975 1
4.8%
2042 1
4.8%
2267 1
4.8%
ValueCountFrequency (%)
6859 1
4.8%
4870 1
4.8%
4564 1
4.8%
4174 1
4.8%
3844 1
4.8%
3185 1
4.8%
3007 1
4.8%
2798 1
4.8%
2499 1
4.8%
2379 1
4.8%

전입세대
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1376.5238
Minimum596
Maximum3109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T08:36:01.348703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum596
5-th percentile627
Q1858
median1123
Q31533
95-th percentile2947
Maximum3109
Range2513
Interquartile range (IQR)675

Descriptive statistics

Standard deviation785.12544
Coefficient of variation (CV)0.57036823
Kurtosis0.46571072
Mean1376.5238
Median Absolute Deviation (MAD)312
Skewness1.2268986
Sum28907
Variance616421.96
MonotonicityNot monotonic
2023-12-13T08:36:01.492495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
849 1
 
4.8%
1947 1
 
4.8%
1366 1
 
4.8%
654 1
 
4.8%
876 1
 
4.8%
653 1
 
4.8%
1533 1
 
4.8%
1254 1
 
4.8%
2947 1
 
4.8%
596 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
596 1
4.8%
627 1
4.8%
653 1
4.8%
654 1
4.8%
849 1
4.8%
858 1
4.8%
859 1
4.8%
876 1
4.8%
889 1
4.8%
1055 1
4.8%
ValueCountFrequency (%)
3109 1
4.8%
2947 1
4.8%
2896 1
4.8%
2077 1
4.8%
1947 1
4.8%
1533 1
4.8%
1435 1
4.8%
1366 1
4.8%
1304 1
4.8%
1254 1
4.8%

전출인구
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2690.9048
Minimum1186
Maximum5014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T08:36:01.596540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1186
5-th percentile1253
Q12057
median2428
Q33435
95-th percentile4499
Maximum5014
Range3828
Interquartile range (IQR)1378

Descriptive statistics

Standard deviation1030.1657
Coefficient of variation (CV)0.38283245
Kurtosis-0.13247387
Mean2690.9048
Median Absolute Deviation (MAD)554
Skewness0.63960426
Sum56509
Variance1061241.3
MonotonicityNot monotonic
2023-12-13T08:36:01.709281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2428 2
 
9.5%
2057 1
 
4.8%
2184 1
 
4.8%
2064 1
 
4.8%
1874 1
 
4.8%
2548 1
 
4.8%
1507 1
 
4.8%
3233 1
 
4.8%
2567 1
 
4.8%
2938 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
1186 1
4.8%
1253 1
4.8%
1507 1
4.8%
1874 1
4.8%
2047 1
4.8%
2057 1
4.8%
2064 1
4.8%
2073 1
4.8%
2184 1
4.8%
2428 2
9.5%
ValueCountFrequency (%)
5014 1
4.8%
4499 1
4.8%
3840 1
4.8%
3687 1
4.8%
3647 1
4.8%
3435 1
4.8%
3233 1
4.8%
2938 1
4.8%
2567 1
4.8%
2548 1
4.8%

전출세대
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1220.5714
Minimum540
Maximum2632
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T08:36:01.813509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum540
5-th percentile596
Q1828
median1038
Q31470
95-th percentile2437
Maximum2632
Range2092
Interquartile range (IQR)642

Descriptive statistics

Standard deviation584.75282
Coefficient of variation (CV)0.47908119
Kurtosis0.86327115
Mean1220.5714
Median Absolute Deviation (MAD)368
Skewness1.1851783
Sum25632
Variance341935.86
MonotonicityNot monotonic
2023-12-13T08:36:01.909531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
811 1
 
4.8%
1038 1
 
4.8%
1077 1
 
4.8%
660 1
 
4.8%
959 1
 
4.8%
621 1
 
4.8%
1406 1
 
4.8%
1172 1
 
4.8%
1355 1
 
4.8%
540 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
540 1
4.8%
596 1
4.8%
621 1
4.8%
660 1
4.8%
811 1
4.8%
828 1
4.8%
865 1
4.8%
937 1
4.8%
959 1
4.8%
1030 1
4.8%
ValueCountFrequency (%)
2632 1
4.8%
2437 1
4.8%
2138 1
4.8%
1564 1
4.8%
1496 1
4.8%
1470 1
4.8%
1406 1
4.8%
1355 1
4.8%
1172 1
4.8%
1077 1
4.8%

Interactions

2023-12-13T08:35:59.890021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:58.952912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:59.281404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:59.575245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:59.980260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:59.054400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:59.357177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:59.657889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:00.071979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:59.129702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:59.421760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:59.733026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:00.172850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:59.209760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:59.502782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:35:59.816484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:36:01.978329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동전입인구전입세대전출인구전출세대
행정동1.0001.0001.0001.0001.000
전입인구1.0001.0000.9040.8600.852
전입세대1.0000.9041.0000.6690.780
전출인구1.0000.8600.6691.0000.907
전출세대1.0000.8520.7800.9071.000
2023-12-13T08:36:02.060830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전입인구전입세대전출인구전출세대
전입인구1.0000.9780.7790.843
전입세대0.9781.0000.7730.871
전출인구0.7790.7731.0000.954
전출세대0.8430.8710.9541.000

Missing values

2023-12-13T08:36:00.296015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:36:00.421250image/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

행정동전입인구전입세대전출인구전출세대
0숭의2동19658492057811
1숭의1.3동4564194724281038
2숭의4동185985924281030
3용현1.4동3844289634352437
4용현2동18178582073865
5용현3동10906271186596
6용현5동4174207750142138
7학익1동226788938401470
8학익2동237911232047828
9도화1동2798130436871564
행정동전입인구전입세대전출인구전출세대
11주안1동4870310944992632
12주안2동204210552184937
13주안3동11755961253540
14주안4동6859294729381355
15주안5동2499125425671172
16주안6동3007153332331406
17주안7동13466531507621
18주안8동19758762548959
19관교동15176541874660
20문학동2374136620641077