Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory605.5 KiB
Average record size in memory62.0 B

Variable types

Numeric6

Dataset

Description기준일ID,시간대구분,행정동코드,총생활인구수,중국인체류인구수,중국외외국인체류인구수
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-14992/S/1/datasetView.do

Alerts

총생활인구수 is highly overall correlated with 중국인체류인구수 and 1 other fieldsHigh correlation
중국인체류인구수 is highly overall correlated with 총생활인구수 and 1 other fieldsHigh correlation
중국외외국인체류인구수 is highly overall correlated with 총생활인구수 and 1 other fieldsHigh correlation
시간대구분 has 381 (3.8%) zerosZeros

Reproduction

Analysis started2024-04-27 12:15:08.288232
Analysis finished2024-04-27 12:15:19.878286
Duration11.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준일ID
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20240418
Minimum20240413
Maximum20240422
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-27T12:15:20.049376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20240413
5-th percentile20240413
Q120240415
median20240418
Q320240420
95-th percentile20240422
Maximum20240422
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8292059
Coefficient of variation (CV)1.3978002 × 10-7
Kurtosis-1.2031415
Mean20240418
Median Absolute Deviation (MAD)2
Skewness-0.0084896557
Sum2.0240418 × 1011
Variance8.0044059
MonotonicityNot monotonic
2024-04-27T12:15:20.403908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
20240415 1047
10.5%
20240417 1044
10.4%
20240419 1030
10.3%
20240420 1017
10.2%
20240418 1006
10.1%
20240421 1004
10.0%
20240422 999
10.0%
20240416 999
10.0%
20240414 997
10.0%
20240413 857
8.6%
ValueCountFrequency (%)
20240413 857
8.6%
20240414 997
10.0%
20240415 1047
10.5%
20240416 999
10.0%
20240417 1044
10.4%
20240418 1006
10.1%
20240419 1030
10.3%
20240420 1017
10.2%
20240421 1004
10.0%
20240422 999
10.0%
ValueCountFrequency (%)
20240422 999
10.0%
20240421 1004
10.0%
20240420 1017
10.2%
20240419 1030
10.3%
20240418 1006
10.1%
20240417 1044
10.4%
20240416 999
10.0%
20240415 1047
10.5%
20240414 997
10.0%
20240413 857
8.6%

시간대구분
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.3049
Minimum0
Maximum23
Zeros381
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-27T12:15:20.749682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q317
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.8188552
Coefficient of variation (CV)0.60317695
Kurtosis-1.1837232
Mean11.3049
Median Absolute Deviation (MAD)6
Skewness0.023434517
Sum113049
Variance46.496786
MonotonicityNot monotonic
2024-04-27T12:15:21.110737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2 468
 
4.7%
15 452
 
4.5%
8 449
 
4.5%
7 443
 
4.4%
14 441
 
4.4%
11 439
 
4.4%
3 438
 
4.4%
17 430
 
4.3%
10 430
 
4.3%
1 429
 
4.3%
Other values (14) 5581
55.8%
ValueCountFrequency (%)
0 381
3.8%
1 429
4.3%
2 468
4.7%
3 438
4.4%
4 407
4.1%
5 427
4.3%
6 394
3.9%
7 443
4.4%
8 449
4.5%
9 402
4.0%
ValueCountFrequency (%)
23 349
3.5%
22 394
3.9%
21 367
3.7%
20 401
4.0%
19 406
4.1%
18 416
4.2%
17 430
4.3%
16 414
4.1%
15 452
4.5%
14 441
4.4%

행정동코드
Real number (ℝ)

Distinct424
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11432405
Minimum11110515
Maximum11740700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-27T12:15:21.478247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110515
5-th percentile11140570
Q111260660
median11440630
Q311590670
95-th percentile11710670
Maximum11740700
Range630185
Interquartile range (IQR)330010

Descriptive statistics

Standard deviation191468.03
Coefficient of variation (CV)0.016747835
Kurtosis-1.2601643
Mean11432405
Median Absolute Deviation (MAD)179915
Skewness-0.017301693
Sum1.1432405 × 1011
Variance3.6660006 × 1010
MonotonicityNot monotonic
2024-04-27T12:15:21.934464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11290610 37
 
0.4%
11260655 37
 
0.4%
11500535 36
 
0.4%
11620765 35
 
0.4%
11305534 33
 
0.3%
11290650 33
 
0.3%
11215840 33
 
0.3%
11530530 33
 
0.3%
11140625 33
 
0.3%
11230660 33
 
0.3%
Other values (414) 9657
96.6%
ValueCountFrequency (%)
11110515 21
0.2%
11110530 33
0.3%
11110540 25
0.2%
11110550 26
0.3%
11110560 28
0.3%
11110570 27
0.3%
11110580 30
0.3%
11110600 27
0.3%
11110615 27
0.3%
11110630 17
0.2%
ValueCountFrequency (%)
11740700 20
0.2%
11740690 31
0.3%
11740685 19
0.2%
11740660 20
0.2%
11740650 16
0.2%
11740640 24
0.2%
11740620 17
0.2%
11740610 28
0.3%
11740600 30
0.3%
11740590 30
0.3%

총생활인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct9991
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean911.00837
Minimum28.1772
Maximum11471.292
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-27T12:15:22.353753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28.1772
5-th percentile117.93625
Q1264.32587
median509.59545
Q3968.45605
95-th percentile3132.5554
Maximum11471.292
Range11443.115
Interquartile range (IQR)704.13018

Descriptive statistics

Standard deviation1281.4844
Coefficient of variation (CV)1.4066659
Kurtosis19.100359
Mean911.00837
Median Absolute Deviation (MAD)293.90255
Skewness3.8683914
Sum9110083.7
Variance1642202.3
MonotonicityNot monotonic
2024-04-27T12:15:22.751983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
298.4176 2
 
< 0.1%
637.2482 2
 
< 0.1%
145.1286 2
 
< 0.1%
298.6341 2
 
< 0.1%
309.8201 2
 
< 0.1%
202.641 2
 
< 0.1%
293.5205 2
 
< 0.1%
224.4185 2
 
< 0.1%
332.3811 2
 
< 0.1%
309.0393 1
 
< 0.1%
Other values (9981) 9981
99.8%
ValueCountFrequency (%)
28.1772 1
< 0.1%
31.1679 1
< 0.1%
32.1183 1
< 0.1%
33.5427 1
< 0.1%
33.7055 1
< 0.1%
34.7013 1
< 0.1%
36.5185 1
< 0.1%
37.9389 1
< 0.1%
38.4441 1
< 0.1%
38.6488 1
< 0.1%
ValueCountFrequency (%)
11471.2923 1
< 0.1%
11382.7138 1
< 0.1%
11294.332 1
< 0.1%
11250.1179 1
< 0.1%
11229.6476 1
< 0.1%
11147.5955 1
< 0.1%
11142.7937 1
< 0.1%
11086.9333 1
< 0.1%
11034.4718 1
< 0.1%
10966.8885 1
< 0.1%

중국인체류인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct9994
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean619.96449
Minimum4.7572
Maximum11050.831
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-27T12:15:23.294508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.7572
5-th percentile54.67156
Q1140.41575
median288.8159
Q3589.55367
95-th percentile2387.1072
Maximum11050.831
Range11046.074
Interquartile range (IQR)449.13792

Descriptive statistics

Standard deviation1071.7011
Coefficient of variation (CV)1.7286492
Kurtosis25.426256
Mean619.96449
Median Absolute Deviation (MAD)177.4946
Skewness4.488935
Sum6199644.9
Variance1148543.3
MonotonicityNot monotonic
2024-04-27T12:15:23.726484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
324.5816 2
 
< 0.1%
141.8273 2
 
< 0.1%
28.821 2
 
< 0.1%
105.8848 2
 
< 0.1%
103.8121 2
 
< 0.1%
242.8092 2
 
< 0.1%
136.5073 1
 
< 0.1%
110.8548 1
 
< 0.1%
66.8655 1
 
< 0.1%
156.112 1
 
< 0.1%
Other values (9984) 9984
99.8%
ValueCountFrequency (%)
4.7572 1
< 0.1%
7.0791 1
< 0.1%
8.6039 1
< 0.1%
11.178 1
< 0.1%
12.0613 1
< 0.1%
12.6796 1
< 0.1%
13.7555 1
< 0.1%
13.8051 1
< 0.1%
13.8733 1
< 0.1%
14.1712 1
< 0.1%
ValueCountFrequency (%)
11050.8311 1
< 0.1%
10957.0354 1
< 0.1%
10789.6368 1
< 0.1%
10689.7637 1
< 0.1%
10554.5455 1
< 0.1%
10495.8486 1
< 0.1%
10475.8319 1
< 0.1%
10340.3878 1
< 0.1%
10338.7585 1
< 0.1%
10222.7286 1
< 0.1%

중국외외국인체류인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct9990
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.04387
Minimum6.1954
Maximum4430.3171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-27T12:15:24.135661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.1954
5-th percentile43.006095
Q194.05655
median174.24515
Q3333.92185
95-th percentile921.05033
Maximum4430.3171
Range4424.1217
Interquartile range (IQR)239.8653

Descriptive statistics

Standard deviation380.42468
Coefficient of variation (CV)1.3071043
Kurtosis30.799967
Mean291.04387
Median Absolute Deviation (MAD)95.39185
Skewness4.4862578
Sum2910438.7
Variance144722.93
MonotonicityNot monotonic
2024-04-27T12:15:24.545823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
279.2066 2
 
< 0.1%
55.9383 2
 
< 0.1%
313.314 2
 
< 0.1%
84.4861 2
 
< 0.1%
54.0481 2
 
< 0.1%
211.7413 2
 
< 0.1%
96.7437 2
 
< 0.1%
84.5715 2
 
< 0.1%
70.1201 2
 
< 0.1%
36.7782 2
 
< 0.1%
Other values (9980) 9980
99.8%
ValueCountFrequency (%)
6.1954 1
< 0.1%
7.6489 1
< 0.1%
7.6894 1
< 0.1%
7.7577 1
< 0.1%
7.9613 1
< 0.1%
8.0195 1
< 0.1%
8.871 1
< 0.1%
9.1284 1
< 0.1%
9.3134 1
< 0.1%
9.9181 1
< 0.1%
ValueCountFrequency (%)
4430.3171 1
< 0.1%
4383.3845 1
< 0.1%
4375.019 1
< 0.1%
4374.5863 1
< 0.1%
4370.9492 1
< 0.1%
4362.1665 1
< 0.1%
4288.9644 1
< 0.1%
4218.6847 1
< 0.1%
4188.1712 1
< 0.1%
4153.4214 1
< 0.1%

Interactions

2024-04-27T12:15:17.564912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:10.187409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:11.761731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:13.045110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:14.479026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:15.978955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:17.834340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:10.441955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:12.012956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:13.221619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:14.747624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:16.251825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:18.088749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:10.696213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:12.257707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:13.396694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:15.007737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:16.502469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:18.362103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:10.972045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:12.488569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:13.673228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:15.287844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:16.787543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:18.631554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:11.242699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:12.718396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:13.948677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:15.477722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:17.053840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:18.888209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:11.503947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:12.886335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:14.214590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:15.708544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:15:17.313219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-27T12:15:24.809236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일ID시간대구분행정동코드총생활인구수중국인체류인구수중국외외국인체류인구수
기준일ID1.0000.0000.0000.0170.0080.040
시간대구분0.0001.0000.0000.0750.0800.046
행정동코드0.0000.0001.0000.3770.3910.272
총생활인구수0.0170.0750.3771.0000.9620.731
중국인체류인구수0.0080.0800.3910.9621.0000.543
중국외외국인체류인구수0.0400.0460.2720.7310.5431.000
2024-04-27T12:15:25.092319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일ID시간대구분행정동코드총생활인구수중국인체류인구수중국외외국인체류인구수
기준일ID1.0000.0240.0130.0060.005-0.000
시간대구분0.0241.0000.0110.0020.020-0.018
행정동코드0.0130.0111.000-0.0070.040-0.120
총생활인구수0.0060.002-0.0071.0000.9460.785
중국인체류인구수0.0050.0200.0400.9461.0000.612
중국외외국인체류인구수-0.000-0.018-0.1200.7850.6121.000

Missing values

2024-04-27T12:15:19.374852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-27T12:15:19.725965image/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

기준일ID시간대구분행정동코드총생활인구수중국인체류인구수중국외외국인체류인구수
602742024041722112157302599.50921552.98091046.5294
29373202404202111290620257.9768170.729287.2478
18804202404212011320521197.1519127.357169.7952
18209202404211811710670396.4159162.5569233.8594
306620240422711260570308.5104182.0366126.4752
6246720240416311305620314.2835143.0526171.2311
60080202404172111560660898.3952863.414534.9805
99137202404131711650560876.2479413.2728462.9757
88180202404141511740570309.5775216.543393.0339
96319202404131111215780959.2036645.7942313.4097
기준일ID시간대구분행정동코드총생활인구수중국인체류인구수중국외외국인체류인구수
90257202404142011680655494.8492201.8333293.0159
63379202404165114107001508.4584626.4993881.9594
80381202404152111500550277.5814197.723879.8577
197692024042122115305604022.87813706.6569316.2191
21102024042241174059058.857527.835131.0227
69426202404161911590651202.656583.5622119.0946
20062202404212311305575135.173163.801571.3712
66041202404161111620575538.0587309.5001228.5596
8210420240414111530770828.9662718.301110.6658
5447120240417811410655112.756848.115864.6409