Overview

Dataset statistics

Number of variables12
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory111.3 B

Variable types

Text1
Numeric11

Dataset

Description<2011~2021년까지 서울특별시경찰청 경찰서의 경찰관 인원 현황>
Author경찰청 서울특별시경찰청
URLhttps://www.data.go.kr/data/15036460/fileData.do

Alerts

2011년 is highly overall correlated with 2012년 and 9 other fieldsHigh correlation
2012년 is highly overall correlated with 2011년 and 9 other fieldsHigh correlation
2013년 is highly overall correlated with 2011년 and 9 other fieldsHigh correlation
2014년 is highly overall correlated with 2011년 and 9 other fieldsHigh correlation
2015년 is highly overall correlated with 2011년 and 9 other fieldsHigh correlation
2016년 is highly overall correlated with 2011년 and 9 other fieldsHigh correlation
2017년 is highly overall correlated with 2011년 and 9 other fieldsHigh correlation
2018년 is highly overall correlated with 2011년 and 9 other fieldsHigh correlation
2019년 is highly overall correlated with 2011년 and 9 other fieldsHigh correlation
2020년 is highly overall correlated with 2011년 and 9 other fieldsHigh correlation
2021년 is highly overall correlated with 2011년 and 9 other fieldsHigh correlation
경찰서 has unique valuesUnique
2018년 has unique valuesUnique
2021년 has unique valuesUnique

Reproduction

Analysis started2023-12-12 02:36:13.244628
Analysis finished2023-12-12 02:36:25.785649
Duration12.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

경찰서
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-12T11:36:26.078070image/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%
2023-12-12T11:36:26.749300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
서 5
 
7.6%
동 4
 
6.1%
강 4
 
6.1%
대 3
 
4.5%
문 3
 
4.5%
성 2
 
3.0%
천 2
 
3.0%
부 2
 
3.0%
포 2
 
3.0%
북 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 (%)
서 5
 
7.6%
동 4
 
6.1%
강 4
 
6.1%
대 3
 
4.5%
문 3
 
4.5%
성 2
 
3.0%
천 2
 
3.0%
부 2
 
3.0%
포 2
 
3.0%
북 2
 
3.0%
Other values (33) 37
56.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 66
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 5
 
7.6%
동 4
 
6.1%
강 4
 
6.1%
대 3
 
4.5%
문 3
 
4.5%
성 2
 
3.0%
천 2
 
3.0%
부 2
 
3.0%
포 2
 
3.0%
북 2
 
3.0%
Other values (33) 37
56.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 66
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
서 5
 
7.6%
동 4
 
6.1%
강 4
 
6.1%
대 3
 
4.5%
문 3
 
4.5%
성 2
 
3.0%
천 2
 
3.0%
부 2
 
3.0%
포 2
 
3.0%
북 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 size411.0 B
2023-12-12T11:36:27.020401image/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
2023-12-12T11:36:27.284555image/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 size411.0 B
2023-12-12T11:36:27.586580image/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
2023-12-12T11:36:27.871174image/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 size411.0 B
2023-12-12T11:36:28.114955image/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
2023-12-12T11:36:28.347553image/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 size411.0 B
2023-12-12T11:36:28.563333image/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
2023-12-12T11:36:28.755061image/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 size411.0 B
2023-12-12T11:36:28.983637image/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
2023-12-12T11:36:29.209165image/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 size411.0 B
2023-12-12T11:36:29.384330image/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
2023-12-12T11:36:29.512596image/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 size411.0 B
2023-12-12T11:36:29.646052image/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
2023-12-12T11:36:29.806772image/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 size411.0 B
2023-12-12T11:36:29.963400image/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
2023-12-12T11:36:30.096573image/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 size411.0 B
2023-12-12T11:36:30.610524image/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
2023-12-12T11:36:30.741446image/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 size411.0 B
2023-12-12T11:36:30.885243image/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
2023-12-12T11:36:31.130097image/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 size411.0 B
2023-12-12T11:36:31.304719image/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
2023-12-12T11:36:31.472977image/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%

Interactions

2023-12-12T11:36:23.796809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:13.553850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:14.458248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:15.496754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:16.794454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:17.667423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:18.689477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:19.575844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:20.677310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:21.696126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:22.868074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:23.884750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:13.639157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:14.561200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:15.606689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:16.878745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:17.742135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:18.780894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:19.654612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:20.756813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:21.784766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:22.944684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:23.968925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:13.715970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:14.661596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:15.691722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:16.948236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:17.824785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:18.858614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:19.737771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:20.844994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:22.112347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:23.016964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:24.061146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:13.792902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:14.749690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:15.770433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:17.016227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:17.922059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:18.933292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:19.826478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:20.946981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:22.207979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:23.112567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:24.145674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:13.872283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:14.857947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:16.158985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:17.085953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:18.009341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:19.007231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:19.921280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:21.076137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:22.294942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:23.191267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:24.238683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:13.965282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:14.961924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:16.245567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:17.177140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:18.096822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:19.085890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:20.046236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:21.178669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:22.370678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:23.286180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:24.380442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:14.046828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:15.065939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:16.346161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:17.251392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:18.210268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:19.182753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:20.142872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:21.272105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:22.475403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:23.365994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:24.512743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:14.128113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:15.142434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:16.436091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:17.324981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:18.292563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:19.263353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:20.289541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:21.352455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:22.556573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:23.445899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:24.655277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:14.210596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:15.226692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:16.534176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:17.401002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:18.382642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:19.339786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:20.400568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:21.437296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:22.636309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:23.521031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:24.810479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:14.300263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:15.303404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:16.632056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:17.479882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:18.489536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:19.421999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:20.485639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:21.525875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:22.709999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:23.621745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:25.071519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:14.379105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:15.397330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:16.711680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:17.569554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:18.593279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:19.499354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:20.576512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:21.611322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:22.789511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:23.714030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:36:31.588574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경찰서2011년2012년2013년2014년2015년2016년2017년2018년2019년2020년2021년
경찰서1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2011년1.0001.0000.9930.9730.9640.9300.9030.8630.8780.8720.9280.891
2012년1.0000.9931.0000.9920.9060.8820.8960.9120.8370.8170.8900.897
2013년1.0000.9730.9921.0000.8870.9090.9220.8860.8360.8240.8590.886
2014년1.0000.9640.9060.8871.0000.9940.9170.9630.9680.9770.9690.941
2015년1.0000.9300.8820.9090.9941.0000.9520.9640.9550.9820.9740.953
2016년1.0000.9030.8960.9220.9170.9521.0000.9500.9180.9180.9100.914
2017년1.0000.8630.9120.8860.9630.9640.9501.0000.9910.9540.9450.959
2018년1.0000.8780.8370.8360.9680.9550.9180.9911.0000.9700.9830.954
2019년1.0000.8720.8170.8240.9770.9820.9180.9540.9701.0000.9860.950
2020년1.0000.9280.8900.8590.9690.9740.9100.9450.9830.9861.0000.987
2021년1.0000.8910.8970.8860.9410.9530.9140.9590.9540.9500.9871.000
2023-12-12T11:36:31.783743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2011년2012년2013년2014년2015년2016년2017년2018년2019년2020년2021년
2011년1.0000.9950.9940.9860.9810.9830.9840.9810.9790.9790.973
2012년0.9951.0000.9950.9900.9890.9880.9910.9880.9860.9860.982
2013년0.9940.9951.0000.9870.9850.9870.9890.9830.9830.9850.980
2014년0.9860.9900.9871.0000.9920.9900.9930.9910.9890.9900.988
2015년0.9810.9890.9850.9921.0000.9940.9970.9950.9910.9900.987
2016년0.9830.9880.9870.9900.9941.0000.9920.9900.9860.9880.985
2017년0.9840.9910.9890.9930.9970.9921.0000.9970.9950.9940.990
2018년0.9810.9880.9830.9910.9950.9900.9971.0000.9970.9950.992
2019년0.9790.9860.9830.9890.9910.9860.9950.9971.0000.9970.994
2020년0.9790.9860.9850.9900.9900.9880.9940.9950.9971.0000.997
2021년0.9730.9820.9800.9880.9870.9850.9900.9920.9940.9971.000

Missing values

2023-12-12T11:36:25.355165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:36:25.646694image/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

경찰서2011년2012년2013년2014년2015년2016년2017년2018년2019년2020년2021년
0중부475484499501497518526528533517519
1종로591599604615604652655657662661662
2남대문448458449484460501503512507500502
3서대문606616630641646678682688674681692
4혜화446458459468462483485493493470469
5용산601623631655664714711701712703719
6성북484501497521521539557550544529527
7동대문724731748760758795817823826824837
8마포711721725749787853858884883891902
9영등포83784486186790496497198099010101009
경찰서2011년2012년2013년2014년2015년2016년2017년2018년2019년2020년2021년
21종암386389389402387412420429417408403
22구로639649658712730763749768752737761
23서초594610609637644669697725736733759
24양천676683677715723759771787773753754
25송파883895906964983102010261042106310501047
26노원724734724751756798811833809804810
27방배339342341354352372374377379357362
28은평436437437468468483507517508492470
29도봉486496500526529540563578561567567
30수서537548553579594635635646666646661