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,자치구코드,총생활인구수,중국인체류인구수,중국외외국인체류인구수
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15440/S/1/datasetView.do

Alerts

총생활인구수 is highly overall correlated with 중국인체류인구수 and 1 other fieldsHigh correlation
중국인체류인구수 is highly overall correlated with 총생활인구수High correlation
중국외외국인체류인구수 is highly overall correlated with 총생활인구수High correlation
총생활인구수 has unique valuesUnique
중국인체류인구수 has unique valuesUnique
시간대구분 has 422 (4.2%) zerosZeros

Reproduction

Analysis started2024-04-27 10:45:17.577429
Analysis finished2024-04-27 10:45:29.333582
Duration11.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준일ID
Real number (ℝ)

Distinct167
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20237208
Minimum20231106
Maximum20240422
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-27T10:45:29.778113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20231106
5-th percentile20231114
Q120231217
median20240128
Q320240312
95-th percentile20240415
Maximum20240422
Range9316
Interquartile range (IQR)9095

Descriptive statistics

Standard deviation4288.7254
Coefficient of variation (CV)0.00021192278
Kurtosis-1.5135574
Mean20237208
Median Absolute Deviation (MAD)198
Skewness-0.69614796
Sum2.0237208 × 1011
Variance18393166
MonotonicityNot monotonic
2024-04-27T10:45:30.181265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20240101 82
 
0.8%
20240107 80
 
0.8%
20240229 75
 
0.8%
20240112 73
 
0.7%
20240326 71
 
0.7%
20231130 71
 
0.7%
20240415 71
 
0.7%
20231210 70
 
0.7%
20240216 70
 
0.7%
20231120 70
 
0.7%
Other values (157) 9267
92.7%
ValueCountFrequency (%)
20231106 38
0.4%
20231107 57
0.6%
20231108 55
0.5%
20231109 61
0.6%
20231110 64
0.6%
20231111 65
0.7%
20231112 52
0.5%
20231113 66
0.7%
20231114 48
0.5%
20231115 69
0.7%
ValueCountFrequency (%)
20240422 62
0.6%
20240421 66
0.7%
20240420 61
0.6%
20240419 68
0.7%
20240418 56
0.6%
20240417 66
0.7%
20240416 62
0.6%
20240415 71
0.7%
20240414 58
0.6%
20240413 66
0.7%

시간대구분
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.4196
Minimum0
Maximum23
Zeros422
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-27T10:45:30.617286image/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 deviation6.885454
Coefficient of variation (CV)0.60295054
Kurtosis-1.1955398
Mean11.4196
Median Absolute Deviation (MAD)6
Skewness0.011998737
Sum114196
Variance47.409477
MonotonicityNot monotonic
2024-04-27T10:45:31.014004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
7 452
 
4.5%
2 445
 
4.5%
3 431
 
4.3%
19 431
 
4.3%
15 428
 
4.3%
11 426
 
4.3%
14 426
 
4.3%
21 425
 
4.2%
8 424
 
4.2%
0 422
 
4.2%
Other values (14) 5690
56.9%
ValueCountFrequency (%)
0 422
4.2%
1 390
3.9%
2 445
4.5%
3 431
4.3%
4 411
4.1%
5 415
4.2%
6 398
4.0%
7 452
4.5%
8 424
4.2%
9 417
4.2%
ValueCountFrequency (%)
23 403
4.0%
22 371
3.7%
21 425
4.2%
20 411
4.1%
19 431
4.3%
18 413
4.1%
17 412
4.1%
16 393
3.9%
15 428
4.3%
14 426
4.3%

자치구코드
Real number (ℝ)

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11417.239
Minimum11110
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-27T10:45:31.311198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11140
Q111260
median11410
Q311560
95-th percentile11710
Maximum11740
Range630
Interquartile range (IQR)300

Descriptive statistics

Standard deviation186.15157
Coefficient of variation (CV)0.01630443
Kurtosis-1.1915565
Mean11417.239
Median Absolute Deviation (MAD)150
Skewness0.077140007
Sum1.1417239 × 108
Variance34652.407
MonotonicityNot monotonic
2024-04-27T10:45:31.530102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11710 428
 
4.3%
11305 423
 
4.2%
11470 422
 
4.2%
11500 420
 
4.2%
11380 416
 
4.2%
11410 414
 
4.1%
11545 411
 
4.1%
11230 411
 
4.1%
11290 410
 
4.1%
11350 407
 
4.1%
Other values (15) 5838
58.4%
ValueCountFrequency (%)
11110 402
4.0%
11140 391
3.9%
11170 407
4.1%
11200 382
3.8%
11215 384
3.8%
11230 411
4.1%
11260 374
3.7%
11290 410
4.1%
11305 423
4.2%
11320 394
3.9%
ValueCountFrequency (%)
11740 401
4.0%
11710 428
4.3%
11680 385
3.9%
11650 393
3.9%
11620 388
3.9%
11590 391
3.9%
11560 406
4.1%
11545 411
4.1%
11530 387
3.9%
11500 420
4.2%

총생활인구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15540.613
Minimum2384.1339
Maximum169588.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-27T10:45:31.793806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2384.1339
5-th percentile4715.5652
Q18447.955
median14851.908
Q319766.497
95-th percentile34319.701
Maximum169588.47
Range167204.33
Interquartile range (IQR)11318.542

Descriptive statistics

Standard deviation9737.8418
Coefficient of variation (CV)0.62660604
Kurtosis16.221787
Mean15540.613
Median Absolute Deviation (MAD)5772.7609
Skewness2.2619221
Sum1.5540613 × 108
Variance94825562
MonotonicityNot monotonic
2024-04-27T10:45:32.135782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16938.9378 1
 
< 0.1%
40636.7536 1
 
< 0.1%
13379.4683 1
 
< 0.1%
2937.8859 1
 
< 0.1%
4916.1246 1
 
< 0.1%
9106.0265 1
 
< 0.1%
33027.9536 1
 
< 0.1%
28351.0632 1
 
< 0.1%
20867.7112 1
 
< 0.1%
20446.9656 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
2384.1339 1
< 0.1%
2396.1778 1
< 0.1%
2415.2601 1
< 0.1%
2421.9514 1
< 0.1%
2425.3915 1
< 0.1%
2431.5667 1
< 0.1%
2440.7683 1
< 0.1%
2446.999 1
< 0.1%
2457.2918 1
< 0.1%
2461.3083 1
< 0.1%
ValueCountFrequency (%)
169588.4673 1
< 0.1%
134110.2022 1
< 0.1%
131241.9135 1
< 0.1%
112508.7761 1
< 0.1%
104111.0056 1
< 0.1%
100738.6642 1
< 0.1%
93538.0099 1
< 0.1%
90755.3545 1
< 0.1%
90561.927 1
< 0.1%
86621.667 1
< 0.1%

중국인체류인구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10500.131
Minimum1296.6012
Maximum155769.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-27T10:45:32.554733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1296.6012
5-th percentile2287.9911
Q14009.9452
median8791.3887
Q312956.114
95-th percentile31447.063
Maximum155769.59
Range154472.99
Interquartile range (IQR)8946.1685

Descriptive statistics

Standard deviation8699.4721
Coefficient of variation (CV)0.82851082
Kurtosis18.395125
Mean10500.131
Median Absolute Deviation (MAD)4502.74
Skewness2.7441726
Sum1.0500131 × 108
Variance75680815
MonotonicityNot monotonic
2024-04-27T10:45:33.009947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11532.8597 1
 
< 0.1%
38722.9591 1
 
< 0.1%
8162.5843 1
 
< 0.1%
1585.4689 1
 
< 0.1%
3165.6781 1
 
< 0.1%
6048.4439 1
 
< 0.1%
31133.2488 1
 
< 0.1%
26409.3829 1
 
< 0.1%
10897.9198 1
 
< 0.1%
13655.7573 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1296.6012 1
< 0.1%
1301.9239 1
< 0.1%
1311.5067 1
< 0.1%
1314.5685 1
< 0.1%
1319.2163 1
< 0.1%
1326.259 1
< 0.1%
1332.6693 1
< 0.1%
1337.9142 1
< 0.1%
1346.7467 1
< 0.1%
1349.9349 1
< 0.1%
ValueCountFrequency (%)
155769.5899 1
< 0.1%
125982.6711 1
< 0.1%
123381.8278 1
< 0.1%
104493.4306 1
< 0.1%
69802.4667 1
< 0.1%
67568.8229 1
< 0.1%
63615.3082 1
< 0.1%
60744.8741 1
< 0.1%
59943.4311 1
< 0.1%
59296.4996 1
< 0.1%

중국외외국인체류인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct9999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5040.4816
Minimum949.5453
Maximum44814.504
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-27T10:45:33.388246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum949.5453
5-th percentile1469.2735
Q12481.6381
median4663.0469
Q36625.683
95-th percentile10817.856
Maximum44814.504
Range43864.959
Interquartile range (IQR)4144.045

Descriptive statistics

Standard deviation3244.9234
Coefficient of variation (CV)0.6437725
Kurtosis12.791265
Mean5040.4816
Median Absolute Deviation (MAD)2127.156
Skewness2.0452684
Sum50404816
Variance10529528
MonotonicityNot monotonic
2024-04-27T10:45:33.650985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1305.9883 2
 
< 0.1%
5406.076 1
 
< 0.1%
5496.5408 1
 
< 0.1%
5216.8851 1
 
< 0.1%
1352.4153 1
 
< 0.1%
1750.4476 1
 
< 0.1%
3057.5824 1
 
< 0.1%
1894.7041 1
 
< 0.1%
1941.681 1
 
< 0.1%
9969.7907 1
 
< 0.1%
Other values (9989) 9989
99.9%
ValueCountFrequency (%)
949.5453 1
< 0.1%
1006.4143 1
< 0.1%
1013.4359 1
< 0.1%
1013.8218 1
< 0.1%
1015.7656 1
< 0.1%
1024.1465 1
< 0.1%
1026.1452 1
< 0.1%
1028.5987 1
< 0.1%
1035.9074 1
< 0.1%
1045.788 1
< 0.1%
ValueCountFrequency (%)
44814.5041 1
< 0.1%
40068.5924 1
< 0.1%
38755.9558 1
< 0.1%
38370.2966 1
< 0.1%
37638.9321 1
< 0.1%
36402.3017 1
< 0.1%
36154.4448 1
< 0.1%
36107.926 1
< 0.1%
32820.5968 1
< 0.1%
31557.0317 1
< 0.1%

Interactions

2024-04-27T10:45:27.342861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:19.587283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:21.029720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:22.522875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:24.273256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:25.887634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:27.512786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:19.793829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:21.220882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:22.817105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:24.552086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:26.078601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:27.683830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:19.965853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:21.377012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:23.084042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:24.836874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:26.356851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:27.912458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:20.161562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:21.684137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:23.384467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:25.186762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:26.577159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:28.185709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:20.476816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:21.971702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:23.689358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:25.381555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:26.825428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:28.480340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:20.767928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:22.269367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:24.000640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:25.617832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T10:45:27.120325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-27T10:45:33.822123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일ID시간대구분자치구코드총생활인구수중국인체류인구수중국외외국인체류인구수
기준일ID1.0000.0000.0000.1120.0760.106
시간대구분0.0001.0000.0000.0870.0890.092
자치구코드0.0000.0001.0000.4760.4750.547
총생활인구수0.1120.0870.4761.0000.9790.797
중국인체류인구수0.0760.0890.4750.9791.0000.560
중국외외국인체류인구수0.1060.0920.5470.7970.5601.000
2024-04-27T10:45:34.104004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일ID시간대구분자치구코드총생활인구수중국인체류인구수중국외외국인체류인구수
기준일ID1.0000.0090.003-0.021-0.018-0.023
시간대구분0.0091.000-0.0020.0010.003-0.016
자치구코드0.003-0.0021.0000.0120.074-0.135
총생활인구수-0.0210.0010.0121.0000.9380.583
중국인체류인구수-0.0180.0030.0740.9381.0000.355
중국외외국인체류인구수-0.023-0.016-0.1350.5830.3551.000

Missing values

2024-04-27T10:45:28.816978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-27T10:45:29.180683image/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

기준일ID시간대구분자치구코드총생활인구수중국인체류인구수중국외외국인체류인구수
2692520240309211111016938.937811532.85975406.076
4294220240209131154520540.283918974.93011565.3528
7947020231210101162020971.28113871.77047099.5053
62471202401072116508997.5293214.25895783.2666
4936202404145113806366.20123526.43422839.7689
26472024041891168024636.46213214.24311422.2174
4969620240129191165015909.27958206.697702.5909
3406720240226181154517713.315816076.07621637.2395
7130320231224201120014361.12319362.28984998.837
8564420231130171159013594.599868.43973726.153
기준일ID시간대구분자치구코드총생활인구수중국인체류인구수중국외외국인체류인구수
4532220240205121168027886.459615084.071412802.3878
990420240406121121522249.235416272.76295976.4734
981152023110912115009577.89446557.46913020.4231
94934202311145113203179.60321688.51531491.0891
7010420231226201121521588.209915800.06755788.1427
47440202402011115009365.38546211.91443153.4727
633202404211113057249.3044558.94052690.3614
656442024010291159013150.44319028.10714122.3353
50033202401289113056561.62174454.93942106.6818
8256820231205141156032786.264628671.16534115.0989