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-14993/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 419 (4.2%) zerosZeros
중국인체류인구수 has 1056 (10.6%) zerosZeros

Reproduction

Analysis started2024-04-27 03:39:22.134483
Analysis finished2024-04-27 03:39:37.824841
Duration15.69 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%
Mean20240411
Minimum20240406
Maximum20240415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-27T03:39:38.266749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20240406
5-th percentile20240406
Q120240408
median20240411
Q320240413
95-th percentile20240415
Maximum20240415
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8327168
Coefficient of variation (CV)1.3995352 × 10-7
Kurtosis-1.2110366
Mean20240411
Median Absolute Deviation (MAD)2
Skewness-0.005947694
Sum2.0240411 × 1011
Variance8.0242844
MonotonicityNot monotonic
2024-04-27T03:39:38.662312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
20240415 1042
10.4%
20240408 1040
10.4%
20240410 1029
10.3%
20240409 1027
10.3%
20240407 1022
10.2%
20240412 1017
10.2%
20240414 1016
10.2%
20240413 1002
10.0%
20240411 996
10.0%
20240406 809
8.1%
ValueCountFrequency (%)
20240406 809
8.1%
20240407 1022
10.2%
20240408 1040
10.4%
20240409 1027
10.3%
20240410 1029
10.3%
20240411 996
10.0%
20240412 1017
10.2%
20240413 1002
10.0%
20240414 1016
10.2%
20240415 1042
10.4%
ValueCountFrequency (%)
20240415 1042
10.4%
20240414 1016
10.2%
20240413 1002
10.0%
20240412 1017
10.2%
20240411 996
10.0%
20240410 1029
10.3%
20240409 1027
10.3%
20240408 1040
10.4%
20240407 1022
10.2%
20240406 809
8.1%

시간대구분
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.2636
Minimum0
Maximum23
Zeros419
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-27T03:39:39.040745image/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 deviation6.8720476
Coefficient of variation (CV)0.61011111
Kurtosis-1.1953185
Mean11.2636
Median Absolute Deviation (MAD)6
Skewness0.031767373
Sum112636
Variance47.225038
MonotonicityNot monotonic
2024-04-27T03:39:39.329737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
15 463
 
4.6%
7 453
 
4.5%
2 448
 
4.5%
4 438
 
4.4%
3 435
 
4.3%
1 434
 
4.3%
12 431
 
4.3%
19 427
 
4.3%
14 426
 
4.3%
5 422
 
4.2%
Other values (14) 5623
56.2%
ValueCountFrequency (%)
0 419
4.2%
1 434
4.3%
2 448
4.5%
3 435
4.3%
4 438
4.4%
5 422
4.2%
6 420
4.2%
7 453
4.5%
8 414
4.1%
9 406
4.1%
ValueCountFrequency (%)
23 385
3.9%
22 365
3.6%
21 381
3.8%
20 384
3.8%
19 427
4.3%
18 404
4.0%
17 417
4.2%
16 410
4.1%
15 463
4.6%
14 426
4.3%

행정동코드
Real number (ℝ)

Distinct424
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11433044
Minimum11110515
Maximum11740700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-27T03:39:39.673073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110515
5-th percentile11140580
Q111260655
median11440600
Q311590680
95-th percentile11710680
Maximum11740700
Range630185
Interquartile range (IQR)330025

Descriptive statistics

Standard deviation191573.16
Coefficient of variation (CV)0.016756094
Kurtosis-1.2638537
Mean11433044
Median Absolute Deviation (MAD)179940
Skewness-0.0067294743
Sum1.1433044 × 1011
Variance3.6700276 × 1010
MonotonicityNot monotonic
2024-04-27T03:39:40.082685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11260690 37
 
0.4%
11590651 36
 
0.4%
11350695 36
 
0.4%
11530520 36
 
0.4%
11305600 35
 
0.4%
11740685 34
 
0.3%
11410700 34
 
0.3%
11380625 34
 
0.3%
11110680 34
 
0.3%
11290760 33
 
0.3%
Other values (414) 9651
96.5%
ValueCountFrequency (%)
11110515 26
0.3%
11110530 14
0.1%
11110540 29
0.3%
11110550 20
0.2%
11110560 22
0.2%
11110570 19
0.2%
11110580 25
0.2%
11110600 22
0.2%
11110615 17
0.2%
11110630 23
0.2%
ValueCountFrequency (%)
11740700 23
0.2%
11740690 24
0.2%
11740685 34
0.3%
11740660 24
0.2%
11740650 19
0.2%
11740640 24
0.2%
11740620 24
0.2%
11740610 22
0.2%
11740600 23
0.2%
11740590 22
0.2%

총생활인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct9975
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean411.72262
Minimum0.001
Maximum22794.677
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-27T03:39:40.402362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile11.57009
Q133.96665
median75.15705
Q3215.81375
95-th percentile1805.7595
Maximum22794.677
Range22794.676
Interquartile range (IQR)181.8471

Descriptive statistics

Standard deviation1318.7959
Coefficient of variation (CV)3.2031173
Kurtosis91.698279
Mean411.72262
Median Absolute Deviation (MAD)52.81445
Skewness8.2365875
Sum4117226.2
Variance1739222.5
MonotonicityNot monotonic
2024-04-27T03:39:40.841213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56.6781 2
 
< 0.1%
11.5026 2
 
< 0.1%
34.068 2
 
< 0.1%
79.6373 2
 
< 0.1%
37.991 2
 
< 0.1%
14.1159 2
 
< 0.1%
56.6637 2
 
< 0.1%
13.38 2
 
< 0.1%
105.815 2
 
< 0.1%
36.6743 2
 
< 0.1%
Other values (9965) 9980
99.8%
ValueCountFrequency (%)
0.001 1
< 0.1%
0.0267 1
< 0.1%
0.0973 1
< 0.1%
0.0981 1
< 0.1%
0.0989 1
< 0.1%
0.1408 1
< 0.1%
0.2776 1
< 0.1%
0.491 1
< 0.1%
0.5228 1
< 0.1%
0.5783 1
< 0.1%
ValueCountFrequency (%)
22794.6771 1
< 0.1%
22760.5918 1
< 0.1%
21694.7704 1
< 0.1%
21345.1819 1
< 0.1%
20314.0628 1
< 0.1%
20021.3915 1
< 0.1%
18924.9136 1
< 0.1%
18772.9866 1
< 0.1%
18636.1488 1
< 0.1%
18472.8182 1
< 0.1%

중국인체류인구수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8719
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.28509
Minimum0
Maximum5267.808
Zeros1056
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-27T03:39:41.227726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18.160575
median24.82545
Q381.02785
95-th percentile627.78951
Maximum5267.808
Range5267.808
Interquartile range (IQR)72.867275

Descriptive statistics

Standard deviation335.87074
Coefficient of variation (CV)2.6387282
Kurtosis45.812707
Mean127.28509
Median Absolute Deviation (MAD)23.5906
Skewness5.7956142
Sum1272850.9
Variance112809.16
MonotonicityNot monotonic
2024-04-27T03:39:41.535783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1056
 
10.6%
8.9471 5
 
0.1%
14.3346 4
 
< 0.1%
0.0002 4
 
< 0.1%
8.947 4
 
< 0.1%
10.0678 4
 
< 0.1%
0.001 4
 
< 0.1%
8.8822 4
 
< 0.1%
8.4244 4
 
< 0.1%
10.4512 4
 
< 0.1%
Other values (8709) 8907
89.1%
ValueCountFrequency (%)
0.0 1056
10.6%
0.0001 3
 
< 0.1%
0.0002 4
 
< 0.1%
0.0003 1
 
< 0.1%
0.0004 1
 
< 0.1%
0.0005 2
 
< 0.1%
0.0006 2
 
< 0.1%
0.0007 1
 
< 0.1%
0.0008 1
 
< 0.1%
0.001 4
 
< 0.1%
ValueCountFrequency (%)
5267.808 1
< 0.1%
4796.0224 1
< 0.1%
4577.2702 1
< 0.1%
4383.691 1
< 0.1%
4322.9618 1
< 0.1%
4047.9348 1
< 0.1%
3663.2629 1
< 0.1%
3660.5318 1
< 0.1%
3603.6023 1
< 0.1%
3554.3201 1
< 0.1%

중국외외국인체류인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct9954
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean284.43754
Minimum0
Maximum19464.051
Zeros10
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-27T03:39:41.886222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.919475
Q120.65635
median45.07205
Q3130.3883
95-th percentile1185.5078
Maximum19464.051
Range19464.051
Interquartile range (IQR)109.73195

Descriptive statistics

Standard deviation1038.0564
Coefficient of variation (CV)3.6495058
Kurtosis122.04243
Mean284.43754
Median Absolute Deviation (MAD)31.32315
Skewness9.6138303
Sum2844375.4
Variance1077561.2
MonotonicityNot monotonic
2024-04-27T03:39:42.217641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
0.1%
11.6658 2
 
< 0.1%
15.6238 2
 
< 0.1%
32.1075 2
 
< 0.1%
38.9559 2
 
< 0.1%
6.5746 2
 
< 0.1%
17.8053 2
 
< 0.1%
27.9373 2
 
< 0.1%
37.9483 2
 
< 0.1%
38.769 2
 
< 0.1%
Other values (9944) 9972
99.7%
ValueCountFrequency (%)
0.0 10
0.1%
0.0002 1
 
< 0.1%
0.001 1
 
< 0.1%
0.0075 1
 
< 0.1%
0.0104 1
 
< 0.1%
0.0199 1
 
< 0.1%
0.0267 1
 
< 0.1%
0.0305 1
 
< 0.1%
0.0349 1
 
< 0.1%
0.0432 1
 
< 0.1%
ValueCountFrequency (%)
19464.0506 1
< 0.1%
18746.7418 1
< 0.1%
18155.1922 1
< 0.1%
18080.0406 1
< 0.1%
17813.9593 1
< 0.1%
17745.585 1
< 0.1%
16399.2766 1
< 0.1%
16000.2235 1
< 0.1%
15987.4094 1
< 0.1%
15950.8058 1
< 0.1%

Interactions

2024-04-27T03:39:35.206904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:27.111835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:28.834333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:30.451888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:32.033781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:33.708714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:35.565387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:27.405939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:29.088042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:30.697582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:32.273388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:33.972646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:35.847778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:27.701444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:29.357975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:31.063105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:32.528928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:34.155542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:36.211230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:28.029971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:29.615893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:31.263485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:32.827479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:34.376163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:36.534881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:28.193962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:29.866956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:31.446486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:33.076445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:34.645533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:36.809158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:28.552415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:30.117331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:31.726993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:33.334641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-27T03:39:34.908258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-27T03:39:42.442553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일ID시간대구분행정동코드총생활인구수중국인체류인구수중국외외국인체류인구수
기준일ID1.0000.0210.0000.0460.0320.039
시간대구분0.0211.0000.0000.0500.0200.048
행정동코드0.0000.0001.0000.2950.3000.277
총생활인구수0.0460.0500.2951.0000.8710.982
중국인체류인구수0.0320.0200.3000.8711.0000.823
중국외외국인체류인구수0.0390.0480.2770.9820.8231.000
2024-04-27T03:39:42.713477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일ID시간대구분행정동코드총생활인구수중국인체류인구수중국외외국인체류인구수
기준일ID1.0000.042-0.007-0.015-0.003-0.018
시간대구분0.0421.000-0.008-0.022-0.011-0.029
행정동코드-0.007-0.0081.000-0.023-0.055-0.021
총생활인구수-0.015-0.022-0.0231.0000.8820.919
중국인체류인구수-0.003-0.011-0.0550.8821.0000.675
중국외외국인체류인구수-0.018-0.029-0.0210.9190.6751.000

Missing values

2024-04-27T03:39:37.211261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-27T03:39:37.649401image/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
5693320240410141129076043.4539.98833.4651
6009202404151411215820113.62279.942533.6798
37769202404121711170530932.4442118.5385813.9062
5060320240411231132071036.72439.165127.5593
158582024041413113506707.26417.26410.0
39820202404122111710580165.40455.6805109.7234
28687202404131911545670134.7205106.903227.8175
86194202404071111305535165.334261.4007103.9336
343002024041281171053142.98130.042.981
7908202404151811545610240.0126145.644894.3681
기준일ID시간대구분행정동코드총생활인구수중국인체류인구수중국외외국인체류인구수
1789820240414181123073069.824830.560239.2647
3947920240412211120052062.979326.649636.33
941102024040651174060052.852714.508638.3448
728782024040831171054028.92618.687420.2386
2774320240413171138059010.15280.010.1528
4593820240411121132066020.89320.020.8932
424862024041141123066071.56925.817145.7522
59417202404102011215730164.164757.4103106.7543
821382024040711159066090.610332.782557.8277
9845320240406161126055011.60982.6788.932