Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory142.0 B

Variable types

Numeric14
Categorical1

Dataset

Description기준일ID,시군구코드,시군구명,총생활인구수,내국인생활인구수,장기체류외국인인구수,단기체류외국인인구수,일최대인구수,일최소인구수,주간인구수(09~18),야간인구수(19~08),일최대이동인구수,서울외유입인구수,동일자치구행정동간이동인구수,자치구간이동인구수
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15379/S/1/datasetView.do

Alerts

시군구코드 is highly overall correlated with 내국인생활인구수 and 3 other fieldsHigh correlation
총생활인구수 is highly overall correlated with 내국인생활인구수 and 8 other fieldsHigh correlation
내국인생활인구수 is highly overall correlated with 시군구코드 and 8 other fieldsHigh correlation
장기체류외국인인구수 is highly overall correlated with 단기체류외국인인구수 and 3 other fieldsHigh correlation
단기체류외국인인구수 is highly overall correlated with 장기체류외국인인구수 and 4 other fieldsHigh correlation
일최대인구수 is highly overall correlated with 총생활인구수 and 8 other fieldsHigh correlation
일최소인구수 is highly overall correlated with 시군구코드 and 8 other fieldsHigh correlation
주간인구수(09~18) is highly overall correlated with 총생활인구수 and 10 other fieldsHigh correlation
야간인구수(19~08) is highly overall correlated with 시군구코드 and 8 other fieldsHigh correlation
일최대이동인구수 is highly overall correlated with 총생활인구수 and 8 other fieldsHigh correlation
서울외유입인구수 is highly overall correlated with 총생활인구수 and 6 other fieldsHigh correlation
동일자치구행정동간이동인구수 is highly overall correlated with 총생활인구수 and 5 other fieldsHigh correlation
자치구간이동인구수 is highly overall correlated with 장기체류외국인인구수 and 4 other fieldsHigh correlation
시군구명 is highly overall correlated with 시군구코드 and 7 other fieldsHigh correlation
총생활인구수 has unique valuesUnique
내국인생활인구수 has unique valuesUnique
장기체류외국인인구수 has unique valuesUnique
일최대인구수 has unique valuesUnique
일최소인구수 has unique valuesUnique
주간인구수(09~18) has unique valuesUnique
야간인구수(19~08) has unique valuesUnique
일최대이동인구수 has unique valuesUnique
서울외유입인구수 has unique valuesUnique
동일자치구행정동간이동인구수 has unique valuesUnique
자치구간이동인구수 has unique valuesUnique

Reproduction

Analysis started2024-05-04 02:15:47.871069
Analysis finished2024-05-04 02:17:19.435362
Duration1 minute and 31.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준일ID
Real number (ℝ)

Distinct1890
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20213379
Minimum20190211
Maximum20240429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T02:17:19.680078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190211
5-th percentile20190520
Q120200619
median20211012
Q320230117
95-th percentile20240128
Maximum20240429
Range50218
Interquartile range (IQR)29498

Descriptive statistics

Standard deviation15145.407
Coefficient of variation (CV)0.00074927637
Kurtosis-1.120717
Mean20213379
Median Absolute Deviation (MAD)10410
Skewness0.03836692
Sum2.0213379 × 1011
Variance2.2938336 × 108
MonotonicityNot monotonic
2024-05-04T02:17:20.535485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20211007 15
 
0.1%
20231231 13
 
0.1%
20220814 13
 
0.1%
20200503 12
 
0.1%
20200616 12
 
0.1%
20230106 12
 
0.1%
20211128 12
 
0.1%
20210308 12
 
0.1%
20240417 12
 
0.1%
20240427 11
 
0.1%
Other values (1880) 9876
98.8%
ValueCountFrequency (%)
20190211 1
 
< 0.1%
20190212 3
 
< 0.1%
20190213 5
0.1%
20190214 9
0.1%
20190215 7
0.1%
20190216 6
0.1%
20190217 3
 
< 0.1%
20190218 6
0.1%
20190219 4
< 0.1%
20190220 2
 
< 0.1%
ValueCountFrequency (%)
20240429 5
0.1%
20240428 2
 
< 0.1%
20240427 11
0.1%
20240426 5
0.1%
20240425 4
 
< 0.1%
20240424 8
0.1%
20240423 3
 
< 0.1%
20240422 5
0.1%
20240421 4
 
< 0.1%
20240420 5
0.1%

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11399.428
Minimum11000
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T02:17:20.924550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11000
5-th percentile11110
Q111230
median11380
Q311560
95-th percentile11710
Maximum11740
Range740
Interquartile range (IQR)330

Descriptive statistics

Standard deviation198.20305
Coefficient of variation (CV)0.017387104
Kurtosis-1.0079976
Mean11399.428
Median Absolute Deviation (MAD)165
Skewness-0.046521861
Sum1.1399428 × 108
Variance39284.449
MonotonicityNot monotonic
2024-05-04T02:17:21.332906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
11110 408
 
4.1%
11530 405
 
4.0%
11305 403
 
4.0%
11650 402
 
4.0%
11560 399
 
4.0%
11170 397
 
4.0%
11290 396
 
4.0%
11260 395
 
4.0%
11590 395
 
4.0%
11350 393
 
3.9%
Other values (16) 6007
60.1%
ValueCountFrequency (%)
11000 368
3.7%
11110 408
4.1%
11140 388
3.9%
11170 397
4.0%
11200 375
3.8%
11215 357
3.6%
11230 382
3.8%
11260 395
4.0%
11290 396
4.0%
11305 403
4.0%
ValueCountFrequency (%)
11740 363
3.6%
11710 352
3.5%
11680 368
3.7%
11650 402
4.0%
11620 388
3.9%
11590 395
4.0%
11560 399
4.0%
11545 387
3.9%
11530 405
4.0%
11500 392
3.9%

시군구명
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
종로구
 
408
구로구
 
405
강북구
 
403
서초구
 
402
영등포구
 
399
Other values (21)
7983 

Length

Max length4
Median length3
Mean length3.0763
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강서구
2nd row도봉구
3rd row금천구
4th row동작구
5th row동작구

Common Values

ValueCountFrequency (%)
종로구 408
 
4.1%
구로구 405
 
4.0%
강북구 403
 
4.0%
서초구 402
 
4.0%
영등포구 399
 
4.0%
용산구 397
 
4.0%
성북구 396
 
4.0%
중랑구 395
 
4.0%
동작구 395
 
4.0%
노원구 393
 
3.9%
Other values (16) 6007
60.1%

Length

2024-05-04T02:17:21.808222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
종로구 408
 
4.1%
구로구 405
 
4.0%
강북구 403
 
4.0%
서초구 402
 
4.0%
영등포구 399
 
4.0%
용산구 397
 
4.0%
성북구 396
 
4.0%
중랑구 395
 
4.0%
동작구 395
 
4.0%
노원구 393
 
3.9%
Other values (16) 6007
60.1%

총생활인구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean820788.11
Minimum167693.93
Maximum11707668
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T02:17:22.326356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum167693.93
5-th percentile256318.27
Q1349446.85
median415212.14
Q3512331.05
95-th percentile886449.32
Maximum11707668
Range11539974
Interquartile range (IQR)162884.2

Descriptive statistics

Standard deviation1980390.1
Coefficient of variation (CV)2.412791
Kurtosis22.123941
Mean820788.11
Median Absolute Deviation (MAD)85933.953
Skewness4.8942926
Sum8.2078811 × 109
Variance3.9219451 × 1012
MonotonicityNot monotonic
2024-05-04T02:17:22.971329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
535536.7034 1
 
< 0.1%
244378.2987 1
 
< 0.1%
387148.0987 1
 
< 0.1%
274813.8606 1
 
< 0.1%
416917.9187 1
 
< 0.1%
423787.804 1
 
< 0.1%
384701.4375 1
 
< 0.1%
522149.3702 1
 
< 0.1%
468251.5329 1
 
< 0.1%
440181.4407 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
167693.9316 1
< 0.1%
183705.6238 1
< 0.1%
191213.9225 1
< 0.1%
191595.1951 1
< 0.1%
193043.4979 1
< 0.1%
193833.0584 1
< 0.1%
197063.5992 1
< 0.1%
197762.8474 1
< 0.1%
201824.6072 1
< 0.1%
202222.6991 1
< 0.1%
ValueCountFrequency (%)
11707668.4276 1
< 0.1%
11689487.7364 1
< 0.1%
11679296.2354 1
< 0.1%
11650356.3953 1
< 0.1%
11649131.3283 1
< 0.1%
11645600.3024 1
< 0.1%
11644670.2874 1
< 0.1%
11629252.4393 1
< 0.1%
11622506.1938 1
< 0.1%
11622215.2466 1
< 0.1%

내국인생활인구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean781424.34
Minimum143090.19
Maximum11117078
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T02:17:23.528145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum143090.19
5-th percentile233422.21
Q1330579.63
median388674.06
Q3491973.33
95-th percentile853207.02
Maximum11117078
Range10973987
Interquartile range (IQR)161393.7

Descriptive statistics

Standard deviation1886124.9
Coefficient of variation (CV)2.4137013
Kurtosis22.106494
Mean781424.34
Median Absolute Deviation (MAD)88555.58
Skewness4.8915354
Sum7.8142434 × 109
Variance3.5574673 × 1012
MonotonicityNot monotonic
2024-05-04T02:17:23.989030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
517423.7025 1
 
< 0.1%
225743.6 1
 
< 0.1%
380756.6703 1
 
< 0.1%
268046.1404 1
 
< 0.1%
388479.912 1
 
< 0.1%
387183.7229 1
 
< 0.1%
357463.4028 1
 
< 0.1%
511923.4571 1
 
< 0.1%
442291.9114 1
 
< 0.1%
399135.6911 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
143090.1851 1
< 0.1%
148379.927 1
< 0.1%
150959.5952 1
< 0.1%
156119.5522 1
< 0.1%
161276.8861 1
< 0.1%
163056.6672 1
< 0.1%
166406.4699 1
< 0.1%
168073.0858 1
< 0.1%
169043.4946 1
< 0.1%
171722.903 1
< 0.1%
ValueCountFrequency (%)
11117077.5196 1
< 0.1%
11102190.6456 1
< 0.1%
11084950.9846 1
< 0.1%
11055967.5317 1
< 0.1%
11042732.191 1
< 0.1%
11038427.6823 1
< 0.1%
11033697.2565 1
< 0.1%
11033696.5154 1
< 0.1%
11033690.0983 1
< 0.1%
11030871.6586 1
< 0.1%

장기체류외국인인구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29610.125
Minimum2525.0218
Maximum439789.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T02:17:24.541610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2525.0218
5-th percentile4768.2341
Q18872.2228
median16094.934
Q321050.942
95-th percentile39575.932
Maximum439789.07
Range437264.04
Interquartile range (IQR)12178.719

Descriptive statistics

Standard deviation71551.323
Coefficient of variation (CV)2.4164478
Kurtosis21.762218
Mean29610.125
Median Absolute Deviation (MAD)6077.0809
Skewness4.829795
Sum2.9610125 × 108
Variance5.1195918 × 109
MonotonicityNot monotonic
2024-05-04T02:17:25.161938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9565.1791 1
 
< 0.1%
16021.1049 1
 
< 0.1%
5504.4304 1
 
< 0.1%
5964.2154 1
 
< 0.1%
25792.1583 1
 
< 0.1%
30852.8671 1
 
< 0.1%
20293.999 1
 
< 0.1%
8664.7727 1
 
< 0.1%
19144.9808 1
 
< 0.1%
35449.7389 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
2525.0218 1
< 0.1%
2605.2692 1
< 0.1%
2607.73 1
< 0.1%
2611.6171 1
< 0.1%
2632.4769 1
< 0.1%
2639.2226 1
< 0.1%
2642.5847 1
< 0.1%
2647.1117 1
< 0.1%
2654.2574 1
< 0.1%
2663.0448 1
< 0.1%
ValueCountFrequency (%)
439789.0663 1
< 0.1%
437556.7659 1
< 0.1%
433154.2442 1
< 0.1%
430947.0239 1
< 0.1%
430915.5713 1
< 0.1%
430800.0369 1
< 0.1%
430122.0304 1
< 0.1%
429784.1145 1
< 0.1%
429130.6432 1
< 0.1%
428594.0727 1
< 0.1%

단기체류외국인인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct9996
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9753.6477
Minimum0
Maximum209320.73
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T02:17:25.795874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile616.41249
Q11500.8492
median3212.1144
Q36883.4957
95-th percentile34903.983
Maximum209320.73
Range209320.73
Interquartile range (IQR)5382.6464

Descriptive statistics

Standard deviation25478.787
Coefficient of variation (CV)2.6122316
Kurtosis27.346473
Mean9753.6477
Median Absolute Deviation (MAD)2133.6523
Skewness5.1223576
Sum97536477
Variance6.4916856 × 108
MonotonicityNot monotonic
2024-05-04T02:17:26.518423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4
 
< 0.1%
3895.0078 2
 
< 0.1%
2613.5938 1
 
< 0.1%
803.5049 1
 
< 0.1%
2645.8483 1
 
< 0.1%
5751.2139 1
 
< 0.1%
6944.0357 1
 
< 0.1%
1561.1404 1
 
< 0.1%
6814.6406 1
 
< 0.1%
5596.0107 1
 
< 0.1%
Other values (9986) 9986
99.9%
ValueCountFrequency (%)
0.0 4
< 0.1%
32.5101 1
 
< 0.1%
148.2856 1
 
< 0.1%
149.8783 1
 
< 0.1%
208.0576 1
 
< 0.1%
220.0543 1
 
< 0.1%
221.9365 1
 
< 0.1%
230.245 1
 
< 0.1%
249.8442 1
 
< 0.1%
263.3547 1
 
< 0.1%
ValueCountFrequency (%)
209320.733 1
< 0.1%
205783.8351 1
< 0.1%
204446.8745 1
< 0.1%
200221.18 1
< 0.1%
198611.2319 1
< 0.1%
198266.449 1
< 0.1%
196745.9236 1
< 0.1%
196297.2181 1
< 0.1%
194313.1657 1
< 0.1%
191411.9408 1
< 0.1%

일최대인구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean873642.92
Minimum185079.65
Maximum12140449
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T02:17:27.322161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum185079.65
5-th percentile286892.65
Q1386910.71
median445528.55
Q3552119.51
95-th percentile1123435.9
Maximum12140449
Range11955369
Interquartile range (IQR)165208.8

Descriptive statistics

Standard deviation2027478.2
Coefficient of variation (CV)2.3207173
Kurtosis22.093422
Mean873642.92
Median Absolute Deviation (MAD)87215.082
Skewness4.8861563
Sum8.7364292 × 109
Variance4.1106679 × 1012
MonotonicityNot monotonic
2024-05-04T02:17:28.325113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
574416.8825 1
 
< 0.1%
271519.7913 1
 
< 0.1%
407072.5026 1
 
< 0.1%
305200.7955 1
 
< 0.1%
437574.9723 1
 
< 0.1%
436104.0728 1
 
< 0.1%
396804.6351 1
 
< 0.1%
550487.4422 1
 
< 0.1%
506610.9161 1
 
< 0.1%
463602.4611 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
185079.6534 1
< 0.1%
212046.6412 1
< 0.1%
213783.2911 1
< 0.1%
214339.9388 1
< 0.1%
216630.5077 1
< 0.1%
218559.2919 1
< 0.1%
218622.9084 1
< 0.1%
218741.5321 1
< 0.1%
218949.9764 1
< 0.1%
219160.2724 1
< 0.1%
ValueCountFrequency (%)
12140448.7358 1
< 0.1%
12065970.1919 1
< 0.1%
12064531.2737 1
< 0.1%
12062971.8802 1
< 0.1%
12061411.3597 1
< 0.1%
12060838.3351 1
< 0.1%
12053347.1922 1
< 0.1%
12047327.0674 1
< 0.1%
12038082.3641 1
< 0.1%
12034277.9727 1
< 0.1%

일최소인구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean769846.29
Minimum158114.29
Maximum11309182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T02:17:28.817358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum158114.29
5-th percentile219715.7
Q1308576.47
median385347.72
Q3465766.88
95-th percentile752966.21
Maximum11309182
Range11151068
Interquartile range (IQR)157190.41

Descriptive statistics

Standard deviation1934569.3
Coefficient of variation (CV)2.5129292
Kurtosis22.123707
Mean769846.29
Median Absolute Deviation (MAD)79520.749
Skewness4.896741
Sum7.6984629 × 109
Variance3.7425582 × 1012
MonotonicityNot monotonic
2024-05-04T02:17:29.363196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
498845.6943 1
 
< 0.1%
221613.7204 1
 
< 0.1%
366057.9926 1
 
< 0.1%
244238.5241 1
 
< 0.1%
390078.8049 1
 
< 0.1%
407962.156 1
 
< 0.1%
375717.6772 1
 
< 0.1%
488988.0119 1
 
< 0.1%
434597.8393 1
 
< 0.1%
419235.119 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
158114.2912 1
< 0.1%
158141.8904 1
< 0.1%
166558.9125 1
< 0.1%
167398.7759 1
< 0.1%
167595.6716 1
< 0.1%
170744.9443 1
< 0.1%
171461.2935 1
< 0.1%
171570.3549 1
< 0.1%
172396.7713 1
< 0.1%
172586.4315 1
< 0.1%
ValueCountFrequency (%)
11309182.4429 1
< 0.1%
11303330.7113 1
< 0.1%
11293043.1003 1
< 0.1%
11277586.795 1
< 0.1%
11269320.9161 1
< 0.1%
11261800.1321 1
< 0.1%
11251014.62 1
< 0.1%
11249146.1148 1
< 0.1%
11247162.3893 1
< 0.1%
11244746.9059 1
< 0.1%

주간인구수(09~18)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean836038.73
Minimum175872.58
Maximum12053602
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T02:17:29.967060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum175872.58
5-th percentile260249.71
Q1355199.56
median408093.52
Q3507373.63
95-th percentile1078318.3
Maximum12053602
Range11877729
Interquartile range (IQR)152174.07

Descriptive statistics

Standard deviation2019399.4
Coefficient of variation (CV)2.4154377
Kurtosis22.121949
Mean836038.73
Median Absolute Deviation (MAD)72854.399
Skewness4.8886627
Sum8.3603873 × 109
Variance4.0779741 × 1012
MonotonicityNot monotonic
2024-05-04T02:17:30.494604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
504349.9163 1
 
< 0.1%
266977.5189 1
 
< 0.1%
373615.2054 1
 
< 0.1%
250176.8524 1
 
< 0.1%
398318.6137 1
 
< 0.1%
415815.1166 1
 
< 0.1%
390817.8009 1
 
< 0.1%
502579.2163 1
 
< 0.1%
495711.1621 1
 
< 0.1%
427658.2531 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
175872.5766 1
< 0.1%
201349.4512 1
< 0.1%
201880.7597 1
< 0.1%
203075.5121 1
< 0.1%
205973.8585 1
< 0.1%
207914.7579 1
< 0.1%
207947.6975 1
< 0.1%
208064.0435 1
< 0.1%
208169.8095 1
< 0.1%
208424.3334 1
< 0.1%
ValueCountFrequency (%)
12053601.8738 1
< 0.1%
11996911.9851 1
< 0.1%
11988824.922 1
< 0.1%
11984625.9356 1
< 0.1%
11980281.4474 1
< 0.1%
11977083.8694 1
< 0.1%
11977002.1804 1
< 0.1%
11971119.4079 1
< 0.1%
11968626.5987 1
< 0.1%
11962217.679 1
< 0.1%

야간인구수(19~08)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean809894.81
Minimum161852.04
Maximum11469899
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T02:17:31.252586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum161852.04
5-th percentile230775.94
Q1345337.57
median424314.25
Q3520891.56
95-th percentile772249.72
Maximum11469899
Range11308047
Interquartile range (IQR)175553.99

Descriptive statistics

Standard deviation1953383
Coefficient of variation (CV)2.4118972
Kurtosis22.106642
Mean809894.81
Median Absolute Deviation (MAD)91860.543
Skewness4.8942943
Sum8.0989481 × 109
Variance3.8157053 × 1012
MonotonicityNot monotonic
2024-05-04T02:17:31.923300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
557812.9799 1
 
< 0.1%
228235.9985 1
 
< 0.1%
396814.451 1
 
< 0.1%
292411.7237 1
 
< 0.1%
430203.1365 1
 
< 0.1%
429482.5807 1
 
< 0.1%
380332.6064 1
 
< 0.1%
536128.0515 1
 
< 0.1%
448637.512 1
 
< 0.1%
449126.5747 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
161852.0423 1
< 0.1%
171102.8899 1
< 0.1%
176374.7828 1
< 0.1%
178933.3405 1
< 0.1%
179006.3222 1
< 0.1%
182741.3585 1
< 0.1%
184057.5144 1
< 0.1%
185010.6108 1
< 0.1%
185146.3208 1
< 0.1%
185671.6122 1
< 0.1%
ValueCountFrequency (%)
11469898.9873 1
< 0.1%
11461203.5925 1
< 0.1%
11460573.1088 1
< 0.1%
11419445.0334 1
< 0.1%
11414695.6437 1
< 0.1%
11413272.9222 1
< 0.1%
11410974.6573 1
< 0.1%
11406493.0472 1
< 0.1%
11400230.4972 1
< 0.1%
11398610.0299 1
< 0.1%

일최대이동인구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean432274.27
Minimum66330.993
Maximum7125566.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T02:17:32.521436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum66330.993
5-th percentile133548.89
Q1165749.1
median198102.2
Q3266396.44
95-th percentile698471.51
Maximum7125566.3
Range7059235.3
Interquartile range (IQR)100647.34

Descriptive statistics

Standard deviation1053387
Coefficient of variation (CV)2.4368487
Kurtosis23.775838
Mean432274.27
Median Absolute Deviation (MAD)39753.954
Skewness5.002532
Sum4.3227427 × 109
Variance1.1096241 × 1012
MonotonicityNot monotonic
2024-05-04T02:17:33.138752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
256679.938 1
 
< 0.1%
163529.728 1
 
< 0.1%
142218.3963 1
 
< 0.1%
172599.9418 1
 
< 0.1%
221495.7786 1
 
< 0.1%
212926.5079 1
 
< 0.1%
193977.4154 1
 
< 0.1%
224384.6441 1
 
< 0.1%
290936.0729 1
 
< 0.1%
169629.0026 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
66330.9932 1
< 0.1%
77718.6758 1
< 0.1%
80317.1533 1
< 0.1%
81366.6602 1
< 0.1%
82669.8882 1
< 0.1%
82951.6596 1
< 0.1%
84565.0456 1
< 0.1%
85161.0382 1
< 0.1%
85614.0368 1
< 0.1%
85635.2761 1
< 0.1%
ValueCountFrequency (%)
7125566.3037 1
< 0.1%
7108895.8906 1
< 0.1%
7083713.3701 1
< 0.1%
7075117.6183 1
< 0.1%
7066265.5851 1
< 0.1%
7065268.0893 1
< 0.1%
7045772.7466 1
< 0.1%
7040061.8999 1
< 0.1%
7029642.9731 1
< 0.1%
6987285.9465 1
< 0.1%

서울외유입인구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105156.07
Minimum9894.4939
Maximum1792553.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T02:17:33.743749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9894.4939
5-th percentile17259.816
Q130111.506
median44255.61
Q373257.101
95-th percentile220116.03
Maximum1792553.6
Range1782659.1
Interquartile range (IQR)43145.595

Descriptive statistics

Standard deviation259669.14
Coefficient of variation (CV)2.469369
Kurtosis23.748854
Mean105156.07
Median Absolute Deviation (MAD)18242.3
Skewness4.9572632
Sum1.0515607 × 109
Variance6.7428064 × 1010
MonotonicityNot monotonic
2024-05-04T02:17:34.348975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62258.3194 1
 
< 0.1%
63179.0036 1
 
< 0.1%
27716.3202 1
 
< 0.1%
14772.3378 1
 
< 0.1%
41504.853 1
 
< 0.1%
65494.7089 1
 
< 0.1%
35222.3036 1
 
< 0.1%
43581.6058 1
 
< 0.1%
72472.2224 1
 
< 0.1%
44640.0806 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
9894.4939 1
< 0.1%
10142.6366 1
< 0.1%
10648.2952 1
< 0.1%
10709.522 1
< 0.1%
10729.2587 1
< 0.1%
10736.191 1
< 0.1%
10750.0245 1
< 0.1%
10751.7203 1
< 0.1%
10847.469 1
< 0.1%
10850.5409 1
< 0.1%
ValueCountFrequency (%)
1792553.6042 1
< 0.1%
1778231.0198 1
< 0.1%
1772039.7509 1
< 0.1%
1764361.8024 1
< 0.1%
1758324.9626 1
< 0.1%
1751420.3051 1
< 0.1%
1733165.5222 1
< 0.1%
1731402.0203 1
< 0.1%
1718177.8244 1
< 0.1%
1717574.4695 1
< 0.1%

동일자치구행정동간이동인구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156115.21
Minimum19706.421
Maximum2467021.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T02:17:35.011530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19706.421
5-th percentile32380.038
Q164918.649
median80664.426
Q3109003.76
95-th percentile160187.91
Maximum2467021.2
Range2447314.8
Interquartile range (IQR)44085.106

Descriptive statistics

Standard deviation376363.31
Coefficient of variation (CV)2.4108048
Kurtosis22.715308
Mean156115.21
Median Absolute Deviation (MAD)19924.035
Skewness4.9311655
Sum1.5611521 × 109
Variance1.4164934 × 1011
MonotonicityNot monotonic
2024-05-04T02:17:35.533705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126357.4914 1
 
< 0.1%
42613.9767 1
 
< 0.1%
63779.9552 1
 
< 0.1%
110442.1499 1
 
< 0.1%
96593.6928 1
 
< 0.1%
76757.2207 1
 
< 0.1%
66157.7468 1
 
< 0.1%
145145.4306 1
 
< 0.1%
85313.0768 1
 
< 0.1%
79987.5173 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
19706.4205 1
< 0.1%
20050.1464 1
< 0.1%
20222.7967 1
< 0.1%
21492.8739 1
< 0.1%
21636.85 1
< 0.1%
22073.9078 1
< 0.1%
23008.0706 1
< 0.1%
23011.1317 1
< 0.1%
23158.8403 1
< 0.1%
23186.9515 1
< 0.1%
ValueCountFrequency (%)
2467021.239 1
< 0.1%
2418017.8349 1
< 0.1%
2409674.3939 1
< 0.1%
2394839.6063 1
< 0.1%
2377319.0909 1
< 0.1%
2365832.3712 1
< 0.1%
2365582.2344 1
< 0.1%
2343561.0729 1
< 0.1%
2343350.6376 1
< 0.1%
2337837.412 1
< 0.1%

자치구간이동인구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean171002.98
Minimum17125.275
Maximum3030576.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T02:17:36.336792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17125.275
5-th percentile34018.698
Q150102.727
median69073.588
Q3116584.9
95-th percentile347677.78
Maximum3030576.4
Range3013451.2
Interquartile range (IQR)66482.174

Descriptive statistics

Standard deviation425739.88
Coefficient of variation (CV)2.4896635
Kurtosis25.304673
Mean171002.98
Median Absolute Deviation (MAD)25915.653
Skewness5.0870715
Sum1.7100298 × 109
Variance1.8125445 × 1011
MonotonicityNot monotonic
2024-05-04T02:17:36.845385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68064.1272 1
 
< 0.1%
57736.7477 1
 
< 0.1%
50722.1209 1
 
< 0.1%
47385.4541 1
 
< 0.1%
83397.2328 1
 
< 0.1%
70674.5783 1
 
< 0.1%
92597.365 1
 
< 0.1%
35657.6077 1
 
< 0.1%
133150.7737 1
 
< 0.1%
45001.4047 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
17125.2749 1
< 0.1%
20348.6737 1
< 0.1%
21142.4675 1
< 0.1%
21345.1191 1
< 0.1%
21788.0464 1
< 0.1%
21945.5068 1
< 0.1%
22540.3071 1
< 0.1%
22716.323 1
< 0.1%
22743.4668 1
< 0.1%
22825.7869 1
< 0.1%
ValueCountFrequency (%)
3030576.4411 1
< 0.1%
3010733.8334 1
< 0.1%
3006649.7555 1
< 0.1%
2984046.6635 1
< 0.1%
2957712.5335 1
< 0.1%
2950235.7741 1
< 0.1%
2943686.4319 1
< 0.1%
2941633.1272 1
< 0.1%
2937849.8713 1
< 0.1%
2934527.652 1
< 0.1%

Interactions

2024-05-04T02:17:12.821109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:00.523527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:06.498604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:12.152745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:17.796499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:23.533209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:28.832822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:34.226335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:41.809429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:47.040518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:52.426338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:58.641557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:03.590545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:07.411490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:13.249985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:01.104300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:06.998043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:12.493542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:18.350914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:23.943249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:29.135030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:34.565003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:42.178001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:47.394569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:52.907459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:59.142695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:03.867950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:07.783811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:13.665623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:01.459255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:07.457300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:12.911349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:18.723320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:24.349968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:29.500260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:35.045296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:42.584510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:47.847681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:53.475757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:59.520776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:04.165234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:08.094169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:13.981021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:01.950855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:07.986070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:13.300474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:19.222694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:24.660045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:29.877843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:35.466084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:42.947153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:48.237658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:53.999151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:59.929473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:04.452850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:08.372957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:14.326439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:02.251367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:08.395637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:13.682992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:19.606159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:24.949312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:30.252668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:35.916070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:43.298061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:48.614329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:54.529625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:00.339028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:04.748296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:08.650608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:14.684845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:02.651586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:08.929578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:14.066557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:19.897437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:25.285201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:30.555823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:36.483561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:43.636006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:49.046286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:55.060356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:00.638548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:05.022910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:08.915352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:15.038512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:03.110031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:09.226871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:14.379262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:20.294068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:25.631840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:30.938519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:37.018767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:43.990848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:49.419029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:55.402557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:00.931549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:05.294643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:09.725854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:15.397942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:03.508721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:09.565654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:14.862447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:20.787851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:26.038891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:31.348788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:38.896411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:44.385303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:49.826087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:55.720672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:01.253762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:05.566978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:10.199671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:15.753151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:04.000399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:09.928988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:15.272804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:21.152708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:26.409850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:31.786341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:39.303009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:44.708397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:50.125205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:56.219226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:01.559832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:05.775589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:10.596083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:16.170278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:04.444068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:10.337625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:15.646577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:21.694405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:26.751173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:32.185667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:39.647152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:45.088770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:50.489117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:56.665413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:02.054373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:06.012285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:10.980094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:16.565830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:04.873788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:10.672379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:16.021059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:22.121917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:27.147124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:32.570408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:40.054953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:45.398141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:50.888501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:57.011707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:02.350713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:06.244563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:11.363815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:16.957815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:05.291992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:11.017720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:16.502636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:22.610958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:27.596566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:33.040620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:40.474916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:45.762927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:51.260084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:57.340051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:02.669261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:06.535632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:11.772671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:17.288548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:05.680683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:11.370347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:16.858916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:22.924338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:28.030453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:33.424670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:40.982298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:46.233106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:51.635665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:57.864228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:02.959905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:06.830025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:12.072295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:17.592364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:06.084022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:11.752083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:17.335297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:23.227825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:28.508400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:33.830649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:41.369563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:46.648749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:52.054238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:16:58.218275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:03.298324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:07.117233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:17:12.394996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T02:17:37.255574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일ID시군구코드시군구명총생활인구수내국인생활인구수장기체류외국인인구수단기체류외국인인구수일최대인구수일최소인구수주간인구수(09~18)야간인구수(19~08)일최대이동인구수서울외유입인구수동일자치구행정동간이동인구수자치구간이동인구수
기준일ID1.0000.0000.0000.0380.0350.1630.3000.0310.0370.0380.0360.1050.0860.2040.102
시군구코드0.0001.0001.000NaNNaN0.0740.571NaNNaNNaNNaN0.0570.428NaN0.418
시군구명0.0001.0001.0000.8110.8110.8170.7550.8110.8720.8110.8720.7210.7900.7680.795
총생활인구수0.038NaN0.8111.0000.9990.8990.7710.9940.7780.9590.7820.8020.8160.7000.943
내국인생활인구수0.035NaN0.8110.9991.0000.8970.7720.9930.7860.9560.7910.8040.8060.7060.942
장기체류외국인인구수0.1630.0740.8170.8990.8971.0000.8280.8970.6790.8970.6800.7370.7520.6780.919
단기체류외국인인구수0.3000.5710.7550.7710.7720.8281.0000.7700.8200.7710.8180.7050.7010.9000.720
일최대인구수0.031NaN0.8110.9940.9930.8970.7701.0000.7270.9860.7290.8510.8570.7140.971
일최소인구수0.037NaN0.8720.7780.7860.6790.8200.7271.0000.7721.0000.8570.9580.7590.851
주간인구수(09~18)0.038NaN0.8110.9590.9560.8970.7710.9860.7721.0000.7700.8660.8570.7030.977
야간인구수(19~08)0.036NaN0.8720.7820.7910.6800.8180.7291.0000.7701.0000.8540.9590.7580.849
일최대이동인구수0.1050.0570.7210.8020.8040.7370.7050.8510.8570.8660.8541.0000.8040.7530.891
서울외유입인구수0.0860.4280.7900.8160.8060.7520.7010.8570.9580.8570.9590.8041.0000.7430.890
동일자치구행정동간이동인구수0.204NaN0.7680.7000.7060.6780.9000.7140.7590.7030.7580.7530.7431.0000.737
자치구간이동인구수0.1020.4180.7950.9430.9420.9190.7200.9710.8510.9770.8490.8910.8900.7371.000
2024-05-04T02:17:37.812454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일ID시군구코드총생활인구수내국인생활인구수장기체류외국인인구수단기체류외국인인구수일최대인구수일최소인구수주간인구수(09~18)야간인구수(19~08)일최대이동인구수서울외유입인구수동일자치구행정동간이동인구수자치구간이동인구수시군구명
기준일ID1.0000.013-0.057-0.055-0.0740.024-0.052-0.059-0.055-0.057-0.1060.036-0.166-0.0520.000
시군구코드0.0131.0000.5000.514-0.086-0.0810.4310.5110.3930.5250.1830.1990.379-0.0950.999
총생활인구수-0.0570.5001.0000.9930.2290.3520.9580.9800.9400.9810.6860.5420.7370.3750.575
내국인생활인구수-0.0550.5140.9931.0000.1400.2700.9420.9770.9100.9900.6420.4860.7750.3120.575
장기체류외국인인구수-0.074-0.0860.2290.1401.0000.7140.2470.2150.3520.1270.4280.540-0.1400.6050.584
단기체류외국인인구수0.024-0.0810.3520.2700.7141.0000.4290.3150.5340.2250.6500.752-0.1120.7980.392
일최대인구수-0.0520.4310.9580.9420.2470.4291.0000.8930.9700.9120.7820.6390.6600.4770.575
일최소인구수-0.0590.5110.9800.9770.2150.3150.8931.0000.8960.9760.6230.4880.7570.3120.705
주간인구수(09~18)-0.0550.3930.9400.9100.3520.5340.9700.8961.0000.8660.8380.7170.5780.5730.575
야간인구수(19~08)-0.0570.5250.9810.9900.1270.2250.9120.9760.8661.0000.5810.4230.8150.2370.705
일최대이동인구수-0.1060.1830.6860.6420.4280.6500.7820.6230.8380.5811.0000.8600.3880.8190.408
서울외유입인구수0.0360.1990.5420.4860.5400.7520.6390.4880.7170.4230.8601.0000.1050.8020.443
동일자치구행정동간이동인구수-0.1660.3790.7370.775-0.140-0.1120.6600.7570.5780.8150.3880.1051.000-0.0570.498
자치구간이동인구수-0.052-0.0950.3750.3120.6050.7980.4770.3120.5730.2370.8190.802-0.0571.0000.470
시군구명0.0000.9990.5750.5750.5840.3920.5750.7050.5750.7050.4080.4430.4980.4701.000

Missing values

2024-05-04T02:17:18.198499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T02:17:18.992225image/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

기준일ID시군구코드시군구명총생활인구수내국인생활인구수장기체류외국인인구수단기체류외국인인구수일최대인구수일최소인구수주간인구수(09~18)야간인구수(19~08)일최대이동인구수서울외유입인구수동일자치구행정동간이동인구수자치구간이동인구수
467642019051511500강서구535536.7034517423.70259565.17918547.8218574416.8825498845.6943504349.9163557812.9799256679.93862258.3194126357.491468064.1272
207062022022311320도봉구272543.0516268892.44272699.4535951.1554295000.9059245459.2855251796.0591287362.332125588.992616661.465472641.015536286.5117
351182020081811545금천구249946.1836230433.582617198.63112313.97278663.807224014.1137274744.5987232233.03171217.911764381.884347296.868159539.1593
346262020090611590동작구416539.6683396689.086815365.73354484.848436433.4138397799.2229404300.4529425281.9649129137.123117652.21265653.2245831.6911
293742021032711590동작구404161.5056386844.424314138.35493178.7264421816.6369381420.761389378.4134414720.8571150295.113130827.883863261.036856206.1925
421682019112111650서초구650740.9138624840.187418224.54637676.1801783013.8509520497.9035757615.0732574402.2285480069.3434159623.543986707.8158233737.9837
31102024010111500강서구550833.7351533898.1059086.7417848.8892565607.568540591.4907547070.54553521.7316190262.429253925.030689070.569847266.8288
96702023042411710송파구749238.5244731848.325813010.42044379.7782756838.3929739102.8765745599.7707751837.6342380458.0911118077.9246142816.7533119563.4132
432252019092811410서대문구398118.0906362755.738224917.690410444.6621405152.5587384758.257396409.8205399338.2836185914.751243412.342761285.044881217.3637
333322020102511000서울시10578580.869310108560.3911384405.298385615.1810746916.931610486513.319810552938.572910596896.79534729645.91741004365.11422126243.12781599037.6754
기준일ID시군구코드시군구명총생활인구수내국인생활인구수장기체류외국인인구수단기체류외국인인구수일최대인구수일최소인구수주간인구수(09~18)야간인구수(19~08)일최대이동인구수서울외유입인구수동일자치구행정동간이동인구수자치구간이동인구수
46942023110111440마포구487639.2591449286.882820307.341518045.0348525402.2768453444.1399513487.6823469176.0996294045.850778305.503577346.206138394.1412
361472020070911260중랑구353222.7565346041.01186043.74311138.0016392132.5308311395.3878320774.3845376400.1651153794.048723732.771992276.628737784.6481
222412021122611350노원구525891.253518988.81735383.1781519.2576541624.6342512821.3494516957.495532272.5086184294.921932289.1264109051.11542954.6805
229142021113011290성북구431459.1172413728.764215199.22572531.1272458279.7146397554.7585405722.5488449842.3803180649.244122259.065487211.256171178.9226
477432019040711260중랑구382099.319375612.81815925.1913561.3096402814.2894351418.0678363560.6667395341.2135171229.20930227.1626101700.639339301.4071
42462023111811290성북구437526.4624419161.336616700.18981664.936463707.8211407181.3611417622.7929451743.3692165943.749829584.468576934.095859425.1855
314372021010611170용산구300167.5501279851.148818264.68212051.7193320652.017274088.2081318024.2626287412.7555174012.544441505.385948847.838383659.3202
299522021030411000서울시11082237.833810613664.6512378397.249190175.933511408787.28710767324.954411358306.966210885045.59636255958.86351498985.83142167576.74332589396.2888
992024042611620관악구464199.2638443404.321518738.93242056.01524290.8839399809.0348413558.2583500371.4106184634.093334088.380992432.841458112.871
146392022101411110종로구336523.1851306917.782817738.256411867.1459467780.4611237314.6814429998.6618269754.9874293718.375985354.631330872.2701177491.4745