Overview

Dataset statistics

Number of variables14
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory129.1 B

Variable types

Text1
Numeric13

Dataset

Description<2011~2023년까지 서울특별시경찰청 경찰서의 경찰관 인원 현황>중부, 종로, 남대문, 서대문, 혜화, 용산, 성북, 동대문, 마포, 영등포, 성동, 동작, 광진, 서부, 강북, 금천, 중랑, 강남, 관악강서, 강동, 종암, 구로, 서초, 양천, 송파, 노원, 방배, 은평, 도봉, 수서경찰서의 2011~2023년까지의 경찰관 인원 현황을 제공합니다.
Author경찰청 서울특별시경찰청
URLhttps://www.data.go.kr/data/15114290/fileData.do

Alerts

2011년 is highly overall correlated with 2012년 and 11 other fieldsHigh correlation
2012년 is highly overall correlated with 2011년 and 11 other fieldsHigh correlation
2013년 is highly overall correlated with 2011년 and 11 other fieldsHigh correlation
2014년 is highly overall correlated with 2011년 and 11 other fieldsHigh correlation
2015년 is highly overall correlated with 2011년 and 11 other fieldsHigh correlation
2016년 is highly overall correlated with 2011년 and 11 other fieldsHigh correlation
2017년 is highly overall correlated with 2011년 and 11 other fieldsHigh correlation
2018년 is highly overall correlated with 2011년 and 11 other fieldsHigh correlation
2019년 is highly overall correlated with 2011년 and 11 other fieldsHigh correlation
2020년 is highly overall correlated with 2011년 and 11 other fieldsHigh correlation
2021년 is highly overall correlated with 2011년 and 11 other fieldsHigh correlation
2022년 is highly overall correlated with 2011년 and 11 other fieldsHigh correlation
2023년 is highly overall correlated with 2011년 and 11 other fieldsHigh correlation
경찰서 has unique valuesUnique
2018년 has unique valuesUnique
2021년 has unique valuesUnique
2023년 has unique valuesUnique

Reproduction

Analysis started2024-04-21 13:29:31.594271
Analysis finished2024-04-21 13:30:02.787309
Duration31.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

경찰서
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size376.0 B
2024-04-21T22:30:03.374171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.1290323
Min length2

Characters and Unicode

Total characters66
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)100.0%

Sample

1st row중부
2nd row종로
3rd row남대문
4th row서대문
5th row혜화
ValueCountFrequency (%)
중부 1
 
3.2%
중랑 1
 
3.2%
도봉 1
 
3.2%
은평 1
 
3.2%
방배 1
 
3.2%
노원 1
 
3.2%
송파 1
 
3.2%
양천 1
 
3.2%
서초 1
 
3.2%
구로 1
 
3.2%
Other values (21) 21
67.7%
2024-04-21T22:30:04.548688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5
 
7.6%
4
 
6.1%
4
 
6.1%
3
 
4.5%
3
 
4.5%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
Other values (33) 37
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
5
 
7.6%
4
 
6.1%
4
 
6.1%
3
 
4.5%
3
 
4.5%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
Other values (33) 37
56.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
5
 
7.6%
4
 
6.1%
4
 
6.1%
3
 
4.5%
3
 
4.5%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
Other values (33) 37
56.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
5
 
7.6%
4
 
6.1%
4
 
6.1%
3
 
4.5%
3
 
4.5%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
Other values (33) 37
56.1%

2011년
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean591.70968
Minimum339
Maximum883
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-04-21T22:30:04.929767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum339
5-th percentile376.5
Q1485
median594
Q3678.5
95-th percentile796.5
Maximum883
Range544
Interquartile range (IQR)193.5

Descriptive statistics

Standard deviation133.85993
Coefficient of variation (CV)0.22622568
Kurtosis-0.3783319
Mean591.70968
Median Absolute Deviation (MAD)108
Skewness0.055235062
Sum18343
Variance17918.48
MonotonicityNot monotonic
2024-04-21T22:30:05.323732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
606 2
 
6.5%
724 2
 
6.5%
475 1
 
3.2%
681 1
 
3.2%
537 1
 
3.2%
486 1
 
3.2%
436 1
 
3.2%
339 1
 
3.2%
883 1
 
3.2%
676 1
 
3.2%
Other values (19) 19
61.3%
ValueCountFrequency (%)
339 1
3.2%
367 1
3.2%
386 1
3.2%
436 1
3.2%
446 1
3.2%
448 1
3.2%
475 1
3.2%
484 1
3.2%
486 1
3.2%
501 1
3.2%
ValueCountFrequency (%)
883 1
3.2%
837 1
3.2%
756 1
3.2%
724 2
6.5%
721 1
3.2%
711 1
3.2%
681 1
3.2%
676 1
3.2%
675 1
3.2%
657 1
3.2%

2012년
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean601.32258
Minimum342
Maximum895
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-04-21T22:30:05.679252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum342
5-th percentile385.5
Q1498.5
median609
Q3689
95-th percentile805
Maximum895
Range553
Interquartile range (IQR)190.5

Descriptive statistics

Standard deviation134.43471
Coefficient of variation (CV)0.22356505
Kurtosis-0.34928431
Mean601.32258
Median Absolute Deviation (MAD)108
Skewness0.034409939
Sum18641
Variance18072.692
MonotonicityNot monotonic
2024-04-21T22:30:06.271825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
458 2
 
6.5%
484 1
 
3.2%
695 1
 
3.2%
548 1
 
3.2%
496 1
 
3.2%
437 1
 
3.2%
342 1
 
3.2%
734 1
 
3.2%
895 1
 
3.2%
683 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
342 1
3.2%
382 1
3.2%
389 1
3.2%
437 1
3.2%
458 2
6.5%
484 1
3.2%
496 1
3.2%
501 1
3.2%
511 1
3.2%
548 1
3.2%
ValueCountFrequency (%)
895 1
3.2%
844 1
3.2%
766 1
3.2%
735 1
3.2%
734 1
3.2%
731 1
3.2%
721 1
3.2%
695 1
3.2%
683 1
3.2%
678 1
3.2%

2013년
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean605.67742
Minimum341
Maximum906
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-04-21T22:30:06.622078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum341
5-th percentile382.5
Q1499.5
median609
Q3690
95-th percentile820
Maximum906
Range565
Interquartile range (IQR)190.5

Descriptive statistics

Standard deviation137.96893
Coefficient of variation (CV)0.22779276
Kurtosis-0.3021124
Mean605.67742
Median Absolute Deviation (MAD)109
Skewness0.037276493
Sum18776
Variance19035.426
MonotonicityNot monotonic
2024-04-21T22:30:06.994018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
748 2
 
6.5%
604 2
 
6.5%
499 1
 
3.2%
687 1
 
3.2%
553 1
 
3.2%
500 1
 
3.2%
437 1
 
3.2%
341 1
 
3.2%
724 1
 
3.2%
906 1
 
3.2%
Other values (19) 19
61.3%
ValueCountFrequency (%)
341 1
3.2%
376 1
3.2%
389 1
3.2%
437 1
3.2%
449 1
3.2%
459 1
3.2%
497 1
3.2%
499 1
3.2%
500 1
3.2%
513 1
3.2%
ValueCountFrequency (%)
906 1
3.2%
861 1
3.2%
779 1
3.2%
748 2
6.5%
725 1
3.2%
724 1
3.2%
693 1
3.2%
687 1
3.2%
677 1
3.2%
664 1
3.2%

2014년
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean630.29032
Minimum354
Maximum964
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-04-21T22:30:07.348440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum354
5-th percentile398
Q1523.5
median637
Q3732
95-th percentile835.5
Maximum964
Range610
Interquartile range (IQR)208.5

Descriptive statistics

Standard deviation143.04456
Coefficient of variation (CV)0.22695027
Kurtosis-0.21097468
Mean630.29032
Median Absolute Deviation (MAD)112
Skewness0.047959329
Sum19539
Variance20461.746
MonotonicityNot monotonic
2024-04-21T22:30:07.730746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
715 2
 
6.5%
468 2
 
6.5%
501 1
 
3.2%
579 1
 
3.2%
526 1
 
3.2%
354 1
 
3.2%
751 1
 
3.2%
964 1
 
3.2%
637 1
 
3.2%
712 1
 
3.2%
Other values (19) 19
61.3%
ValueCountFrequency (%)
354 1
3.2%
394 1
3.2%
402 1
3.2%
468 2
6.5%
484 1
3.2%
501 1
3.2%
521 1
3.2%
526 1
3.2%
529 1
3.2%
579 1
3.2%
ValueCountFrequency (%)
964 1
3.2%
867 1
3.2%
804 1
3.2%
761 1
3.2%
760 1
3.2%
757 1
3.2%
751 1
3.2%
749 1
3.2%
715 2
6.5%
712 1
3.2%

2015년
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean636.70968
Minimum352
Maximum983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-04-21T22:30:08.100897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum352
5-th percentile387
Q1525
median644
Q3743
95-th percentile866
Maximum983
Range631
Interquartile range (IQR)218

Descriptive statistics

Standard deviation153.44015
Coefficient of variation (CV)0.24098919
Kurtosis-0.32483138
Mean636.70968
Median Absolute Deviation (MAD)114
Skewness0.068574288
Sum19738
Variance23543.88
MonotonicityNot monotonic
2024-04-21T22:30:08.486197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
387 2
 
6.5%
497 1
 
3.2%
721 1
 
3.2%
594 1
 
3.2%
529 1
 
3.2%
468 1
 
3.2%
352 1
 
3.2%
756 1
 
3.2%
983 1
 
3.2%
723 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
352 1
3.2%
387 2
6.5%
460 1
3.2%
462 1
3.2%
468 1
3.2%
497 1
3.2%
521 1
3.2%
529 1
3.2%
535 1
3.2%
594 1
3.2%
ValueCountFrequency (%)
983 1
3.2%
904 1
3.2%
828 1
3.2%
799 1
3.2%
787 1
3.2%
771 1
3.2%
758 1
3.2%
756 1
3.2%
730 1
3.2%
723 1
3.2%

2016년
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean671.3871
Minimum372
Maximum1020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-04-21T22:30:08.857272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum372
5-th percentile409.5
Q1539.5
median672
Q3782
95-th percentile922.5
Maximum1020
Range648
Interquartile range (IQR)242.5

Descriptive statistics

Standard deviation162.42674
Coefficient of variation (CV)0.24192711
Kurtosis-0.44752919
Mean671.3871
Median Absolute Deviation (MAD)124
Skewness0.050304436
Sum20813
Variance26382.445
MonotonicityNot monotonic
2024-04-21T22:30:09.246968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
483 2
 
6.5%
518 1
 
3.2%
769 1
 
3.2%
635 1
 
3.2%
540 1
 
3.2%
372 1
 
3.2%
798 1
 
3.2%
1020 1
 
3.2%
759 1
 
3.2%
669 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
372 1
3.2%
407 1
3.2%
412 1
3.2%
483 2
6.5%
501 1
3.2%
518 1
3.2%
539 1
3.2%
540 1
3.2%
563 1
3.2%
635 1
3.2%
ValueCountFrequency (%)
1020 1
3.2%
964 1
3.2%
881 1
3.2%
853 1
3.2%
849 1
3.2%
798 1
3.2%
796 1
3.2%
795 1
3.2%
769 1
3.2%
763 1
3.2%

2017년
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean679.3871
Minimum374
Maximum1026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-04-21T22:30:09.619421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum374
5-th percentile415
Q1560
median682
Q3791
95-th percentile936.5
Maximum1026
Range652
Interquartile range (IQR)231

Descriptive statistics

Standard deviation162.80698
Coefficient of variation (CV)0.23963802
Kurtosis-0.4154952
Mean679.3871
Median Absolute Deviation (MAD)125
Skewness0.055619819
Sum21061
Variance26506.112
MonotonicityNot monotonic
2024-04-21T22:30:10.016884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
697 2
 
6.5%
526 1
 
3.2%
748 1
 
3.2%
635 1
 
3.2%
563 1
 
3.2%
507 1
 
3.2%
374 1
 
3.2%
811 1
 
3.2%
1026 1
 
3.2%
771 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
374 1
3.2%
410 1
3.2%
420 1
3.2%
485 1
3.2%
503 1
3.2%
507 1
3.2%
526 1
3.2%
557 1
3.2%
563 1
3.2%
580 1
3.2%
ValueCountFrequency (%)
1026 1
3.2%
971 1
3.2%
902 1
3.2%
861 1
3.2%
858 1
3.2%
817 1
3.2%
816 1
3.2%
811 1
3.2%
771 1
3.2%
749 1
3.2%

2018년
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean691.51613
Minimum377
Maximum1042
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-04-21T22:30:10.399121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum377
5-th percentile428.5
Q1564
median688
Q3805
95-th percentile962
Maximum1042
Range665
Interquartile range (IQR)241

Descriptive statistics

Standard deviation167.94401
Coefficient of variation (CV)0.24286348
Kurtosis-0.53680593
Mean691.51613
Median Absolute Deviation (MAD)135
Skewness0.084470817
Sum21437
Variance28205.191
MonotonicityNot monotonic
2024-04-21T22:30:10.791306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
528 1
 
3.2%
657 1
 
3.2%
646 1
 
3.2%
578 1
 
3.2%
517 1
 
3.2%
377 1
 
3.2%
833 1
 
3.2%
1042 1
 
3.2%
787 1
 
3.2%
725 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
377 1
3.2%
428 1
3.2%
429 1
3.2%
493 1
3.2%
512 1
3.2%
517 1
3.2%
528 1
3.2%
550 1
3.2%
578 1
3.2%
583 1
3.2%
ValueCountFrequency (%)
1042 1
3.2%
980 1
3.2%
944 1
3.2%
885 1
3.2%
884 1
3.2%
836 1
3.2%
833 1
3.2%
823 1
3.2%
787 1
3.2%
768 1
3.2%

2019년
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean691.80645
Minimum379
Maximum1063
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-04-21T22:30:11.162943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum379
5-th percentile417
Q1552.5
median674
Q3791
95-th percentile965.5
Maximum1063
Range684
Interquartile range (IQR)238.5

Descriptive statistics

Standard deviation173.41711
Coefficient of variation (CV)0.25067287
Kurtosis-0.50177355
Mean691.80645
Median Absolute Deviation (MAD)130
Skewness0.12931973
Sum21446
Variance30073.495
MonotonicityNot monotonic
2024-04-21T22:30:11.547466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
674 2
 
6.5%
417 2
 
6.5%
533 1
 
3.2%
866 1
 
3.2%
666 1
 
3.2%
561 1
 
3.2%
508 1
 
3.2%
379 1
 
3.2%
809 1
 
3.2%
1063 1
 
3.2%
Other values (19) 19
61.3%
ValueCountFrequency (%)
379 1
3.2%
417 2
6.5%
493 1
3.2%
507 1
3.2%
508 1
3.2%
533 1
3.2%
544 1
3.2%
561 1
3.2%
575 1
3.2%
642 1
3.2%
ValueCountFrequency (%)
1063 1
3.2%
990 1
3.2%
941 1
3.2%
918 1
3.2%
883 1
3.2%
866 1
3.2%
826 1
3.2%
809 1
3.2%
773 1
3.2%
766 1
3.2%

2020년
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean685.48387
Minimum357
Maximum1050
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-04-21T22:30:11.917819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum357
5-th percentile411.5
Q1547.5
median681
Q3780
95-th percentile974.5
Maximum1050
Range693
Interquartile range (IQR)232.5

Descriptive statistics

Standard deviation178.29131
Coefficient of variation (CV)0.26009556
Kurtosis-0.54456682
Mean685.48387
Median Absolute Deviation (MAD)123
Skewness0.12691441
Sum21250
Variance31787.791
MonotonicityNot monotonic
2024-04-21T22:30:12.324455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
668 2
 
6.5%
753 2
 
6.5%
517 1
 
3.2%
883 1
 
3.2%
646 1
 
3.2%
567 1
 
3.2%
492 1
 
3.2%
357 1
 
3.2%
804 1
 
3.2%
1050 1
 
3.2%
Other values (19) 19
61.3%
ValueCountFrequency (%)
357 1
3.2%
408 1
3.2%
415 1
3.2%
470 1
3.2%
492 1
3.2%
500 1
3.2%
517 1
3.2%
529 1
3.2%
566 1
3.2%
567 1
3.2%
ValueCountFrequency (%)
1050 1
3.2%
1010 1
3.2%
939 1
3.2%
913 1
3.2%
891 1
3.2%
883 1
3.2%
824 1
3.2%
804 1
3.2%
756 1
3.2%
753 2
6.5%

2021년
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean694.22581
Minimum362
Maximum1047
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-04-21T22:30:12.706660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum362
5-th percentile429.5
Q1547
median692
Q3793
95-th percentile977.5
Maximum1047
Range685
Interquartile range (IQR)246

Descriptive statistics

Standard deviation179.57129
Coefficient of variation (CV)0.25866409
Kurtosis-0.68173184
Mean694.22581
Median Absolute Deviation (MAD)125
Skewness0.05081059
Sum21521
Variance32245.847
MonotonicityNot monotonic
2024-04-21T22:30:13.105915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
519 1
 
3.2%
662 1
 
3.2%
661 1
 
3.2%
567 1
 
3.2%
470 1
 
3.2%
362 1
 
3.2%
810 1
 
3.2%
1047 1
 
3.2%
754 1
 
3.2%
759 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
362 1
3.2%
403 1
3.2%
456 1
3.2%
469 1
3.2%
470 1
3.2%
502 1
3.2%
519 1
3.2%
527 1
3.2%
567 1
3.2%
585 1
3.2%
ValueCountFrequency (%)
1047 1
3.2%
1009 1
3.2%
946 1
3.2%
913 1
3.2%
908 1
3.2%
902 1
3.2%
837 1
3.2%
810 1
3.2%
776 1
3.2%
768 1
3.2%

2022년
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean705.16129
Minimum367
Maximum1084
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-04-21T22:30:13.485526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum367
5-th percentile438.5
Q1550.5
median691
Q3815
95-th percentile985.5
Maximum1084
Range717
Interquartile range (IQR)264.5

Descriptive statistics

Standard deviation186.15676
Coefficient of variation (CV)0.26399175
Kurtosis-0.74357514
Mean705.16129
Median Absolute Deviation (MAD)133
Skewness0.072310933
Sum21860
Variance34654.34
MonotonicityNot monotonic
2024-04-21T22:30:13.881876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
924 2
 
6.5%
517 1
 
3.2%
781 1
 
3.2%
684 1
 
3.2%
574 1
 
3.2%
474 1
 
3.2%
367 1
 
3.2%
824 1
 
3.2%
1084 1
 
3.2%
764 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
367 1
3.2%
413 1
3.2%
464 1
3.2%
471 1
3.2%
474 1
3.2%
502 1
3.2%
517 1
3.2%
527 1
3.2%
574 1
3.2%
600 1
3.2%
ValueCountFrequency (%)
1084 1
3.2%
1008 1
3.2%
963 1
3.2%
945 1
3.2%
924 2
6.5%
837 1
3.2%
824 1
3.2%
806 1
3.2%
787 1
3.2%
781 1
3.2%

2023년
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean706.54839
Minimum364
Maximum1095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-04-21T22:30:14.257548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum364
5-th percentile440
Q1545
median693
Q3827
95-th percentile992.5
Maximum1095
Range731
Interquartile range (IQR)282

Descriptive statistics

Standard deviation190.27346
Coefficient of variation (CV)0.26929997
Kurtosis-0.78735604
Mean706.54839
Median Absolute Deviation (MAD)135
Skewness0.094711691
Sum21903
Variance36203.989
MonotonicityNot monotonic
2024-04-21T22:30:14.656685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
514 1
 
3.2%
599 1
 
3.2%
693 1
 
3.2%
573 1
 
3.2%
475 1
 
3.2%
364 1
 
3.2%
826 1
 
3.2%
1095 1
 
3.2%
758 1
 
3.2%
786 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
364 1
3.2%
414 1
3.2%
466 1
3.2%
475 1
3.2%
477 1
3.2%
491 1
3.2%
514 1
3.2%
517 1
3.2%
573 1
3.2%
595 1
3.2%
ValueCountFrequency (%)
1095 1
3.2%
1014 1
3.2%
971 1
3.2%
949 1
3.2%
944 1
3.2%
918 1
3.2%
842 1
3.2%
828 1
3.2%
826 1
3.2%
786 1
3.2%

Interactions

2024-04-21T22:29:59.273236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:32.352046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:34.196932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:36.033736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:38.085115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:39.923393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:41.821634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:43.713449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:45.808991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:48.183137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:51.353419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:53.741688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:56.416143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:59.416815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:32.489157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:34.331852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:36.171192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:38.219892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:40.066900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:41.961671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:43.855397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:45.945936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:48.422441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:51.590707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:53.881821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:56.661761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:59.558363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:32.624744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:34.467854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:36.306370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:38.356746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:40.204981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:42.099629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:43.994573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:46.086565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:48.663656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:51.831474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:54.020930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:56.905977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:59.702859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:32.761489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:34.602253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:36.441968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:38.491837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:40.348374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:42.241732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:44.135464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:46.226020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:48.901742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:52.067907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:54.161404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:57.154683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:59.845686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:32.895944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:34.739970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:36.577941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:38.628503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:40.488350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:42.379979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:44.273197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:46.369799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:49.139584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:52.306639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:54.299867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:57.395978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:59.997468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:33.040804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:34.881113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:36.722314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:38.769964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:40.636724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:42.527957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:44.420977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:46.516997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:49.382230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:52.549924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:54.445902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:57.645666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:30:00.243619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:33.182065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:35.023703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:36.864880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:38.914395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:40.784567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:42.672679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:44.566266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:46.662618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:49.627124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:52.696570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:54.590121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:57.892614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:30:00.493909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:33.326707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:35.164486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:37.009368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:39.054449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:40.929940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:42.821555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:44.714040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:46.809039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:49.870726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:52.840942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:54.952670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:58.142105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:30:00.745910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:33.467514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:35.309582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:37.150445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:39.198686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:41.077148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:42.965674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:44.858860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:46.954771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:50.117006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:52.996420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:55.168689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:58.390981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:30:00.893677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:33.612581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:35.450485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:37.294119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:39.340300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:41.220953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:43.112316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:45.214207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:47.181656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:50.360053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:53.145070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:55.415770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:58.638249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:30:01.104921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:33.753547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:35.594537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:37.434503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:39.484150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:41.370158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:43.256924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:45.359078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:47.427445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:50.606040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:53.291786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:55.661395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:58.812479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:30:01.356619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:33.897960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:35.735556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:37.784507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:39.624117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:41.516095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:43.406440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:45.503865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:47.675950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:50.848591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:53.434824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:55.909080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:58.962125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:30:01.615386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:34.046540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:35.885773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:37.935661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:39.774782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:41.670403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:43.557789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:45.656864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:47.928254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:51.101359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:53.586771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:56.163957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:29:59.119482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T22:30:14.938085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경찰서2011년2012년2013년2014년2015년2016년2017년2018년2019년2020년2021년2022년2023년
경찰서1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2011년1.0001.0000.9930.9730.9640.9300.9030.8630.8780.8720.9280.8910.8040.804
2012년1.0000.9931.0000.9920.9060.8820.8960.9120.8370.8170.8900.8970.7930.793
2013년1.0000.9730.9921.0000.8870.9090.9220.8860.8360.8240.8590.8860.8030.803
2014년1.0000.9640.9060.8871.0000.9940.9170.9630.9680.9770.9690.9410.9310.931
2015년1.0000.9300.8820.9090.9941.0000.9520.9640.9550.9820.9740.9530.9630.963
2016년1.0000.9030.8960.9220.9170.9521.0000.9500.9180.9180.9100.9140.8530.853
2017년1.0000.8630.9120.8860.9630.9640.9501.0000.9910.9540.9450.9590.9100.910
2018년1.0000.8780.8370.8360.9680.9550.9180.9911.0000.9700.9830.9540.9070.907
2019년1.0000.8720.8170.8240.9770.9820.9180.9540.9701.0000.9860.9500.9830.983
2020년1.0000.9280.8900.8590.9690.9740.9100.9450.9830.9861.0000.9870.9750.975
2021년1.0000.8910.8970.8860.9410.9530.9140.9590.9540.9500.9871.0000.9820.982
2022년1.0000.8040.7930.8030.9310.9630.8530.9100.9070.9830.9750.9821.0001.000
2023년1.0000.8040.7930.8030.9310.9630.8530.9100.9070.9830.9750.9821.0001.000
2024-04-21T22:30:15.293153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2011년2012년2013년2014년2015년2016년2017년2018년2019년2020년2021년2022년2023년
2011년1.0000.9950.9940.9860.9810.9830.9840.9810.9790.9790.9730.9570.936
2012년0.9951.0000.9950.9900.9890.9880.9910.9880.9860.9860.9820.9700.953
2013년0.9940.9951.0000.9870.9850.9870.9890.9830.9830.9850.9800.9670.948
2014년0.9860.9900.9871.0000.9920.9900.9930.9910.9890.9900.9880.9750.961
2015년0.9810.9890.9850.9921.0000.9940.9970.9950.9910.9900.9870.9760.962
2016년0.9830.9880.9870.9900.9941.0000.9920.9900.9860.9880.9850.9750.957
2017년0.9840.9910.9890.9930.9970.9921.0000.9970.9950.9940.9900.9800.966
2018년0.9810.9880.9830.9910.9950.9900.9971.0000.9970.9950.9920.9770.963
2019년0.9790.9860.9830.9890.9910.9860.9950.9971.0000.9970.9940.9830.970
2020년0.9790.9860.9850.9900.9900.9880.9940.9950.9971.0000.9970.9840.971
2021년0.9730.9820.9800.9880.9870.9850.9900.9920.9940.9971.0000.9860.975
2022년0.9570.9700.9670.9750.9760.9750.9800.9770.9830.9840.9861.0000.995
2023년0.9360.9530.9480.9610.9620.9570.9660.9630.9700.9710.9750.9951.000

Missing values

2024-04-21T22:30:01.994783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T22:30:02.565262image/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

경찰서2011년2012년2013년2014년2015년2016년2017년2018년2019년2020년2021년2022년2023년
0중부475484499501497518526528533517519517514
1종로591599604615604652655657662661662616599
2남대문448458449484460501503512507500502502491
3서대문606616630641646678682688674681692679674
4혜화446458459468462483485493493470469464466
5용산601623631655664714711701712703719806842
6성북484501497521521539557550544529527527517
7동대문724731748760758795817823826824837837828
8마포711721725749787853858884883891902924918
9영등포8378448618679049649719809901010100910081014
경찰서2011년2012년2013년2014년2015년2016년2017년2018년2019년2020년2021년2022년2023년
21종암386389389402387412420429417408403413414
22구로639649658712730763749768752737761772779
23서초594610609637644669697725736733759775786
24양천676683677715723759771787773753754764758
25송파88389590696498310201026104210631050104710841095
26노원724734724751756798811833809804810824826
27방배339342341354352372374377379357362367364
28은평436437437468468483507517508492470474475
29도봉486496500526529540563578561567567574573
30수서537548553579594635635646666646661684693