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-15441/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 399 (4.0%) zerosZeros

Reproduction

Analysis started2024-04-27 12:13:32.380358
Analysis finished2024-04-27 12:13:46.500796
Duration14.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준일ID
Real number (ℝ)

Distinct168
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20237223
Minimum20231105
Maximum20240422
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-27T12:13:46.780967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20231105
5-th percentile20231113
Q120231218
median20240129
Q320240312
95-th percentile20240414
Maximum20240422
Range9317
Interquartile range (IQR)9094

Descriptive statistics

Standard deviation4285.1072
Coefficient of variation (CV)0.00021174384
Kurtosis-1.5035571
Mean20237223
Median Absolute Deviation (MAD)197
Skewness-0.70329183
Sum2.0237223 × 1011
Variance18362144
MonotonicityNot monotonic
2024-04-27T12:13:47.221882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20240114 78
 
0.8%
20240330 74
 
0.7%
20240403 74
 
0.7%
20240124 74
 
0.7%
20231113 72
 
0.7%
20240402 72
 
0.7%
20240129 72
 
0.7%
20240418 71
 
0.7%
20231227 71
 
0.7%
20240324 71
 
0.7%
Other values (158) 9271
92.7%
ValueCountFrequency (%)
20231105 21
 
0.2%
20231106 60
0.6%
20231107 60
0.6%
20231108 55
0.5%
20231109 53
0.5%
20231110 64
0.6%
20231111 65
0.7%
20231112 54
0.5%
20231113 72
0.7%
20231114 57
0.6%
ValueCountFrequency (%)
20240422 60
0.6%
20240421 52
0.5%
20240420 60
0.6%
20240419 61
0.6%
20240418 71
0.7%
20240417 67
0.7%
20240416 62
0.6%
20240415 58
0.6%
20240414 66
0.7%
20240413 62
0.6%

시간대구분
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.4827
Minimum0
Maximum23
Zeros399
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-27T12:13:47.589553image/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 deviation6.9059445
Coefficient of variation (CV)0.60142166
Kurtosis-1.2019555
Mean11.4827
Median Absolute Deviation (MAD)6
Skewness0.0092111134
Sum114827
Variance47.69207
MonotonicityNot monotonic
2024-04-27T12:13:48.133333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
5 454
 
4.5%
4 444
 
4.4%
18 436
 
4.4%
12 433
 
4.3%
11 428
 
4.3%
10 424
 
4.2%
7 423
 
4.2%
13 423
 
4.2%
17 423
 
4.2%
23 420
 
4.2%
Other values (14) 5692
56.9%
ValueCountFrequency (%)
0 399
4.0%
1 415
4.2%
2 408
4.1%
3 418
4.2%
4 444
4.4%
5 454
4.5%
6 401
4.0%
7 423
4.2%
8 384
3.8%
9 413
4.1%
ValueCountFrequency (%)
23 420
4.2%
22 420
4.2%
21 405
4.0%
20 400
4.0%
19 404
4.0%
18 436
4.4%
17 423
4.2%
16 406
4.1%
15 415
4.2%
14 404
4.0%

자치구코드
Real number (ℝ)

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11419.004
Minimum11110
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-27T12:13:48.574950image/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 deviation187.87332
Coefficient of variation (CV)0.016452689
Kurtosis-1.2220098
Mean11419.004
Median Absolute Deviation (MAD)150
Skewness0.072117367
Sum1.1419004 × 108
Variance35296.386
MonotonicityNot monotonic
2024-04-27T12:13:48.950911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11650 445
 
4.5%
11710 432
 
4.3%
11740 420
 
4.2%
11260 419
 
4.2%
11215 416
 
4.2%
11305 413
 
4.1%
11410 411
 
4.1%
11680 410
 
4.1%
11140 405
 
4.0%
11560 402
 
4.0%
Other values (15) 5827
58.3%
ValueCountFrequency (%)
11110 387
3.9%
11140 405
4.0%
11170 397
4.0%
11200 400
4.0%
11215 416
4.2%
11230 383
3.8%
11260 419
4.2%
11290 370
3.7%
11305 413
4.1%
11320 399
4.0%
ValueCountFrequency (%)
11740 420
4.2%
11710 432
4.3%
11680 410
4.1%
11650 445
4.5%
11620 388
3.9%
11590 377
3.8%
11560 402
4.0%
11545 394
3.9%
11530 401
4.0%
11500 392
3.9%

총생활인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct9998
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6382.5351
Minimum5.4134
Maximum60907.219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-27T12:13:49.360633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.4134
5-th percentile473.36721
Q11079.0049
median2566.6182
Q38291.261
95-th percentile21631.661
Maximum60907.219
Range60901.806
Interquartile range (IQR)7212.2561

Descriptive statistics

Standard deviation8641.6161
Coefficient of variation (CV)1.3539473
Kurtosis7.5029458
Mean6382.5351
Median Absolute Deviation (MAD)1929.352
Skewness2.5750394
Sum63825351
Variance74677529
MonotonicityNot monotonic
2024-04-27T12:13:49.818786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1025.3789 2
 
< 0.1%
453.1381 2
 
< 0.1%
8521.9879 1
 
< 0.1%
5747.4172 1
 
< 0.1%
396.0813 1
 
< 0.1%
905.8511 1
 
< 0.1%
717.741 1
 
< 0.1%
905.1006 1
 
< 0.1%
408.8683 1
 
< 0.1%
1267.0907 1
 
< 0.1%
Other values (9988) 9988
99.9%
ValueCountFrequency (%)
5.4134 1
< 0.1%
23.7785 1
< 0.1%
232.2135 1
< 0.1%
237.3234 1
< 0.1%
246.7048 1
< 0.1%
251.7377 1
< 0.1%
253.2236 1
< 0.1%
253.9798 1
< 0.1%
254.4399 1
< 0.1%
264.5747 1
< 0.1%
ValueCountFrequency (%)
60907.2193 1
< 0.1%
55310.8135 1
< 0.1%
53005.4824 1
< 0.1%
51496.5041 1
< 0.1%
50350.8079 1
< 0.1%
50336.6109 1
< 0.1%
49994.3495 1
< 0.1%
49511.9133 1
< 0.1%
49470.5314 1
< 0.1%
49251.728 1
< 0.1%

중국인체류인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct9988
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2276.6416
Minimum0
Maximum24751.122
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-27T12:13:50.264267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile140.58524
Q1390.96382
median1175.5949
Q33106.7569
95-th percentile7508.5416
Maximum24751.122
Range24751.122
Interquartile range (IQR)2715.7931

Descriptive statistics

Standard deviation2904.8289
Coefficient of variation (CV)1.2759272
Kurtosis9.0000671
Mean2276.6416
Median Absolute Deviation (MAD)922.71485
Skewness2.678423
Sum22766416
Variance8438030.9
MonotonicityNot monotonic
2024-04-27T12:13:50.742749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 8
 
0.1%
1025.3789 2
 
< 0.1%
410.1516 2
 
< 0.1%
340.7355 2
 
< 0.1%
1118.7571 2
 
< 0.1%
899.8706 2
 
< 0.1%
3431.1486 1
 
< 0.1%
5354.6306 1
 
< 0.1%
4398.0711 1
 
< 0.1%
3450.5676 1
 
< 0.1%
Other values (9978) 9978
99.8%
ValueCountFrequency (%)
0.0 8
0.1%
1.8818 1
 
< 0.1%
2.0363 1
 
< 0.1%
15.9824 1
 
< 0.1%
16.5313 1
 
< 0.1%
18.1991 1
 
< 0.1%
31.3454 1
 
< 0.1%
36.6374 1
 
< 0.1%
40.1585 1
 
< 0.1%
43.3679 1
 
< 0.1%
ValueCountFrequency (%)
24751.1221 1
< 0.1%
21864.9878 1
< 0.1%
21099.1884 1
< 0.1%
20425.3954 1
< 0.1%
19868.9851 1
< 0.1%
19825.7709 1
< 0.1%
19707.9953 1
< 0.1%
19595.5526 1
< 0.1%
19323.0595 1
< 0.1%
19179.98 1
< 0.1%

중국외외국인체류인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct9961
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4105.8935
Minimum0
Maximum38188.036
Zeros36
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-27T12:13:51.112933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile292.72963
Q1639.81158
median1196.0422
Q35188.4257
95-th percentile14664.477
Maximum38188.036
Range38188.036
Interquartile range (IQR)4548.6141

Descriptive statistics

Standard deviation5912.3337
Coefficient of variation (CV)1.4399628
Kurtosis7.5191675
Mean4105.8935
Median Absolute Deviation (MAD)857.61765
Skewness2.5765143
Sum41058935
Variance34955690
MonotonicityNot monotonic
2024-04-27T12:13:51.573700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 36
 
0.4%
14.7667 3
 
< 0.1%
1036.9407 2
 
< 0.1%
858.1361 2
 
< 0.1%
557.2317 1
 
< 0.1%
3176.4103 1
 
< 0.1%
6753.622 1
 
< 0.1%
3933.9876 1
 
< 0.1%
242.8726 1
 
< 0.1%
374.4787 1
 
< 0.1%
Other values (9951) 9951
99.5%
ValueCountFrequency (%)
0.0 36
0.4%
5.4135 1
 
< 0.1%
13.2 1
 
< 0.1%
14.7667 3
 
< 0.1%
23.7785 1
 
< 0.1%
142.4066 1
 
< 0.1%
155.4047 1
 
< 0.1%
163.2802 1
 
< 0.1%
164.9894 1
 
< 0.1%
167.5512 1
 
< 0.1%
ValueCountFrequency (%)
38188.0356 1
< 0.1%
37795.098 1
< 0.1%
36156.0962 1
< 0.1%
36141.8982 1
< 0.1%
36011.9205 1
< 0.1%
35844.6693 1
< 0.1%
35602.8176 1
< 0.1%
35296.8224 1
< 0.1%
35256.5809 1
< 0.1%
34768.5941 1
< 0.1%

Interactions

2024-04-27T12:13:44.579232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:37.026393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:38.622747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:40.082978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:41.433567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:42.957566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:44.861929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:37.287383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:38.893313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:40.357093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:41.610823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:43.219497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:45.132914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:37.546566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:39.143593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:40.615805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:41.844922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:43.477111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:45.407768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:37.900297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:39.363095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:40.861468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:42.127428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:43.749840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:45.597338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:38.161921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:39.535570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:41.057585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:42.394938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:44.031160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:45.898817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:38.362306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:39.799590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:41.235199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:42.660355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T12:13:44.288697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-27T12:13:51.859332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일ID시간대구분자치구코드총생활인구수중국인체류인구수중국외외국인체류인구수
기준일ID1.0000.0000.0220.1090.0640.061
시간대구분0.0001.0000.0160.1730.0710.182
자치구코드0.0220.0161.0000.7700.7100.772
총생활인구수0.1090.1730.7701.0000.9280.958
중국인체류인구수0.0640.0710.7100.9281.0000.866
중국외외국인체류인구수0.0610.1820.7720.9580.8661.000
2024-04-27T12:13:52.145855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일ID시간대구분자치구코드총생활인구수중국인체류인구수중국외외국인체류인구수
기준일ID1.0000.0160.001-0.001-0.0160.011
시간대구분0.0161.0000.004-0.012-0.004-0.021
자치구코드0.0010.0041.000-0.024-0.035-0.049
총생활인구수-0.001-0.012-0.0241.0000.9620.967
중국인체류인구수-0.016-0.004-0.0350.9621.0000.875
중국외외국인체류인구수0.011-0.021-0.0490.9670.8751.000

Missing values

2024-04-27T12:13:46.204251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-27T12:13:46.410367image/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
1696420240325611470748.624249.0273499.596
30633202403021113051060.9666592.4438468.5225
856312023112910115302823.45362047.928775.5241
516202024012518112302647.42471274.73171372.6934
57723202401152211305848.3883283.1766565.2114
281692024030722115902345.45431595.2759750.1786
306002024030201111011898.84944694.56377204.2873
838552023120211115008674.90124070.7984604.1047
846842023120120115901491.2464558.1678933.08
91175202311201611350632.9448131.6381501.3075
기준일ID시간대구분자치구코드총생활인구수중국인체류인구수중국외외국인체류인구수
9697420231110811320449.4406168.7768280.6645
3509720240224111168018153.90916269.114311884.796
11224202404041611740787.519177.1313610.3876
818402023120531111012505.72243706.14728799.5751
805602023120823116201852.1595786.68421065.4756
1393420240330511320412.4315126.3829286.0493
23520202403144116202486.12511213.58441272.5416
8446520231201121111017439.20454814.150812625.0544
17302024042021112302200.7587912.93541287.8241
504892024012713112001487.42941001.6554485.7739