Overview

Dataset statistics

Number of variables16
Number of observations120
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.8 KiB
Average record size in memory143.1 B

Variable types

Numeric14
Categorical2

Dataset

Description경기북부경찰청 관할 지역에서 발생한 5대범죄(살인, 강도, 성범죄, 절도, 폭력)에 대하여 발생건수, 검거건수, 검거인원을 경찰서별 구분하여 연도별로 데이터를 제공합니다.
Author경찰청 경기도북부경찰청
URLhttps://www.data.go.kr/data/15086083/fileData.do

Alerts

연도 is highly overall correlated with 남양주북부서High correlation
의정부서 is highly overall correlated with 고양서 and 11 other fieldsHigh correlation
고양서 is highly overall correlated with 의정부서 and 11 other fieldsHigh correlation
일산동부서 is highly overall correlated with 의정부서 and 11 other fieldsHigh correlation
일산서부서 is highly overall correlated with 의정부서 and 11 other fieldsHigh correlation
남양주남부서 is highly overall correlated with 의정부서 and 11 other fieldsHigh correlation
남양주북부서 is highly overall correlated with 연도High correlation
파주서 is highly overall correlated with 의정부서 and 11 other fieldsHigh correlation
양주서 is highly overall correlated with 의정부서 and 11 other fieldsHigh correlation
동두천서 is highly overall correlated with 의정부서 and 11 other fieldsHigh correlation
구리서 is highly overall correlated with 의정부서 and 11 other fieldsHigh correlation
포천서 is highly overall correlated with 의정부서 and 11 other fieldsHigh correlation
가평서 is highly overall correlated with 의정부서 and 11 other fieldsHigh correlation
연천서 is highly overall correlated with 의정부서 and 11 other fieldsHigh correlation
범죄유형 is highly overall correlated with 의정부서 and 11 other fieldsHigh correlation
고양서 has 3 (2.5%) zerosZeros
일산동부서 has 3 (2.5%) zerosZeros
일산서부서 has 9 (7.5%) zerosZeros
남양주북부서 has 77 (64.2%) zerosZeros
양주서 has 3 (2.5%) zerosZeros
동두천서 has 17 (14.2%) zerosZeros
구리서 has 8 (6.7%) zerosZeros
가평서 has 19 (15.8%) zerosZeros
연천서 has 23 (19.2%) zerosZeros

Reproduction

Analysis started2024-03-14 17:28:17.954200
Analysis finished2024-03-14 17:29:09.093020
Duration51.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5
Minimum2016
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T02:29:09.177675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017.75
median2019.5
Q32021.25
95-th percentile2023
Maximum2023
Range7
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.300895
Coefficient of variation (CV)0.0011393389
Kurtosis-1.2395577
Mean2019.5
Median Absolute Deviation (MAD)2
Skewness0
Sum242340
Variance5.2941176
MonotonicityDecreasing
2024-03-15T02:29:09.400702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2023 15
12.5%
2022 15
12.5%
2021 15
12.5%
2020 15
12.5%
2019 15
12.5%
2018 15
12.5%
2017 15
12.5%
2016 15
12.5%
ValueCountFrequency (%)
2016 15
12.5%
2017 15
12.5%
2018 15
12.5%
2019 15
12.5%
2020 15
12.5%
2021 15
12.5%
2022 15
12.5%
2023 15
12.5%
ValueCountFrequency (%)
2023 15
12.5%
2022 15
12.5%
2021 15
12.5%
2020 15
12.5%
2019 15
12.5%
2018 15
12.5%
2017 15
12.5%
2016 15
12.5%

범죄유형
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
살인
24 
강도
24 
성범죄
24 
절도
24 
폭력
24 

Length

Max length3
Median length2
Mean length2.2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row살인
2nd row살인
3rd row살인
4th row강도
5th row강도

Common Values

ValueCountFrequency (%)
살인 24
20.0%
강도 24
20.0%
성범죄 24
20.0%
절도 24
20.0%
폭력 24
20.0%

Length

2024-03-15T02:29:09.681716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:29:10.000996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
살인 24
20.0%
강도 24
20.0%
성범죄 24
20.0%
절도 24
20.0%
폭력 24
20.0%

구분
Categorical

Distinct3
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
발생건수
40 
검거건수
40 
검거인원
40 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row발생건수
2nd row검거건수
3rd row검거인원
4th row발생건수
5th row검거건수

Common Values

ValueCountFrequency (%)
발생건수 40
33.3%
검거건수 40
33.3%
검거인원 40
33.3%

Length

2024-03-15T02:29:10.236535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:29:10.411923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
발생건수 40
33.3%
검거건수 40
33.3%
검거인원 40
33.3%

의정부서
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean896.04167
Minimum2
Maximum4618
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T02:29:10.723281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q110
median199.5
Q31312
95-th percentile3332.15
Maximum4618
Range4616
Interquartile range (IQR)1302

Descriptive statistics

Standard deviation1225.6966
Coefficient of variation (CV)1.3679014
Kurtosis0.66572843
Mean896.04167
Median Absolute Deviation (MAD)195.5
Skewness1.3411528
Sum107525
Variance1502332.2
MonotonicityNot monotonic
2024-03-15T02:29:11.233766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 7
 
5.8%
6 6
 
5.0%
2 4
 
3.3%
4 4
 
3.3%
7 4
 
3.3%
10 4
 
3.3%
11 3
 
2.5%
16 3
 
2.5%
221 3
 
2.5%
5 3
 
2.5%
Other values (76) 79
65.8%
ValueCountFrequency (%)
2 4
3.3%
3 7
5.8%
4 4
3.3%
5 3
2.5%
6 6
5.0%
7 4
3.3%
9 1
 
0.8%
10 4
3.3%
11 3
2.5%
12 2
 
1.7%
ValueCountFrequency (%)
4618 1
0.8%
4205 1
0.8%
4128 1
0.8%
4014 1
0.8%
3983 1
0.8%
3468 1
0.8%
3325 1
0.8%
3312 1
0.8%
3295 1
0.8%
3094 1
0.8%

고양서
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean616.51667
Minimum0
Maximum2778
Zeros3
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T02:29:11.621609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median114
Q31038.75
95-th percentile2280.45
Maximum2778
Range2778
Interquartile range (IQR)1033.75

Descriptive statistics

Standard deviation822.85732
Coefficient of variation (CV)1.3346879
Kurtosis-0.0018724854
Mean616.51667
Median Absolute Deviation (MAD)111
Skewness1.1564089
Sum73982
Variance677094.17
MonotonicityNot monotonic
2024-03-15T02:29:12.040738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 8
 
6.7%
5 8
 
6.7%
1 6
 
5.0%
3 5
 
4.2%
8 5
 
4.2%
6 5
 
4.2%
7 5
 
4.2%
0 3
 
2.5%
114 2
 
1.7%
118 2
 
1.7%
Other values (68) 71
59.2%
ValueCountFrequency (%)
0 3
 
2.5%
1 6
5.0%
2 2
 
1.7%
3 5
4.2%
4 8
6.7%
5 8
6.7%
6 5
4.2%
7 5
4.2%
8 5
4.2%
11 1
 
0.8%
ValueCountFrequency (%)
2778 1
0.8%
2698 1
0.8%
2652 1
0.8%
2536 1
0.8%
2501 1
0.8%
2346 1
0.8%
2277 1
0.8%
2186 1
0.8%
2153 1
0.8%
2144 1
0.8%

일산동부서
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct81
Distinct (%)67.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean512.55
Minimum0
Maximum3359
Zeros3
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T02:29:12.510547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median138
Q3819
95-th percentile1832.8
Maximum3359
Range3359
Interquartile range (IQR)814

Descriptive statistics

Standard deviation705.2188
Coefficient of variation (CV)1.3759025
Kurtosis2.2349018
Mean512.55
Median Absolute Deviation (MAD)136
Skewness1.6027354
Sum61506
Variance497333.56
MonotonicityNot monotonic
2024-03-15T02:29:12.957681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 8
 
6.7%
5 7
 
5.8%
4 6
 
5.0%
1 6
 
5.0%
2 5
 
4.2%
3 5
 
4.2%
138 3
 
2.5%
0 3
 
2.5%
7 2
 
1.7%
8 2
 
1.7%
Other values (71) 73
60.8%
ValueCountFrequency (%)
0 3
 
2.5%
1 6
5.0%
2 5
4.2%
3 5
4.2%
4 6
5.0%
5 7
5.8%
6 8
6.7%
7 2
 
1.7%
8 2
 
1.7%
9 2
 
1.7%
ValueCountFrequency (%)
3359 1
0.8%
2564 1
0.8%
2475 1
0.8%
2307 1
0.8%
2284 1
0.8%
2152 1
0.8%
1816 1
0.8%
1772 1
0.8%
1739 1
0.8%
1653 1
0.8%

일산서부서
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.675
Minimum0
Maximum1395
Zeros9
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T02:29:13.371782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median50.5
Q3394.75
95-th percentile1020.85
Maximum1395
Range1395
Interquartile range (IQR)392.75

Descriptive statistics

Standard deviation367.48494
Coefficient of variation (CV)1.4718532
Kurtosis0.65561335
Mean249.675
Median Absolute Deviation (MAD)49.5
Skewness1.3874601
Sum29961
Variance135045.18
MonotonicityNot monotonic
2024-03-15T02:29:13.690124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 16
 
13.3%
4 9
 
7.5%
0 9
 
7.5%
2 8
 
6.7%
3 7
 
5.8%
57 2
 
1.7%
8 2
 
1.7%
49 2
 
1.7%
51 2
 
1.7%
59 2
 
1.7%
Other values (60) 61
50.8%
ValueCountFrequency (%)
0 9
7.5%
1 16
13.3%
2 8
6.7%
3 7
5.8%
4 9
7.5%
5 1
 
0.8%
8 2
 
1.7%
9 1
 
0.8%
22 1
 
0.8%
27 1
 
0.8%
ValueCountFrequency (%)
1395 1
0.8%
1219 1
0.8%
1209 1
0.8%
1103 1
0.8%
1078 1
0.8%
1075 1
0.8%
1018 1
0.8%
956 1
0.8%
947 1
0.8%
937 1
0.8%

남양주남부서
Real number (ℝ)

HIGH CORRELATION 

Distinct84
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean692.775
Minimum2
Maximum4392
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T02:29:13.960535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q18
median176.5
Q31073
95-th percentile3012.15
Maximum4392
Range4390
Interquartile range (IQR)1065

Descriptive statistics

Standard deviation1023.6968
Coefficient of variation (CV)1.4776757
Kurtosis2.4547714
Mean692.775
Median Absolute Deviation (MAD)172
Skewness1.7644343
Sum83133
Variance1047955.2
MonotonicityNot monotonic
2024-03-15T02:29:14.325488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 7
 
5.8%
3 5
 
4.2%
5 5
 
4.2%
8 5
 
4.2%
4 4
 
3.3%
6 4
 
3.3%
9 3
 
2.5%
2 3
 
2.5%
10 3
 
2.5%
14 3
 
2.5%
Other values (74) 78
65.0%
ValueCountFrequency (%)
2 3
2.5%
3 5
4.2%
4 4
3.3%
5 5
4.2%
6 4
3.3%
7 7
5.8%
8 5
4.2%
9 3
2.5%
10 3
2.5%
11 2
 
1.7%
ValueCountFrequency (%)
4392 1
0.8%
3989 1
0.8%
3804 1
0.8%
3401 1
0.8%
3282 1
0.8%
3015 1
0.8%
3012 1
0.8%
2997 1
0.8%
2983 1
0.8%
2691 1
0.8%

남양주북부서
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.64167
Minimum0
Maximum1269
Zeros77
Zeros (%)64.2%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T02:29:14.716334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile950
Maximum1269
Range1269
Interquartile range (IQR)4

Descriptive statistics

Standard deviation298.80327
Coefficient of variation (CV)2.4767833
Kurtosis5.4953254
Mean120.64167
Median Absolute Deviation (MAD)0
Skewness2.5708359
Sum14477
Variance89283.392
MonotonicityNot monotonic
2024-03-15T02:29:14.997153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 77
64.2%
4 6
 
5.0%
1 5
 
4.2%
3 3
 
2.5%
2 2
 
1.7%
950 2
 
1.7%
1128 1
 
0.8%
845 1
 
0.8%
345 1
 
0.8%
339 1
 
0.8%
Other values (21) 21
 
17.5%
ValueCountFrequency (%)
0 77
64.2%
1 5
 
4.2%
2 2
 
1.7%
3 3
 
2.5%
4 6
 
5.0%
53 1
 
0.8%
56 1
 
0.8%
57 1
 
0.8%
60 1
 
0.8%
64 1
 
0.8%
ValueCountFrequency (%)
1269 1
0.8%
1184 1
0.8%
1128 1
0.8%
1107 1
0.8%
1071 1
0.8%
950 2
1.7%
907 1
0.8%
845 1
0.8%
700 1
0.8%
664 1
0.8%

파주서
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean607.55
Minimum2
Maximum2577
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T02:29:15.413459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q16
median136
Q3995.75
95-th percentile2302.3
Maximum2577
Range2575
Interquartile range (IQR)989.75

Descriptive statistics

Standard deviation806.69691
Coefficient of variation (CV)1.3277869
Kurtosis-0.11884342
Mean607.55
Median Absolute Deviation (MAD)133
Skewness1.1439344
Sum72906
Variance650759.9
MonotonicityNot monotonic
2024-03-15T02:29:15.881886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 8
 
6.7%
2 7
 
5.8%
3 7
 
5.8%
6 6
 
5.0%
5 5
 
4.2%
12 4
 
3.3%
8 4
 
3.3%
9 4
 
3.3%
93 2
 
1.7%
167 2
 
1.7%
Other values (70) 71
59.2%
ValueCountFrequency (%)
2 7
5.8%
3 7
5.8%
4 8
6.7%
5 5
4.2%
6 6
5.0%
7 2
 
1.7%
8 4
3.3%
9 4
3.3%
11 1
 
0.8%
12 4
3.3%
ValueCountFrequency (%)
2577 1
0.8%
2540 1
0.8%
2513 1
0.8%
2499 1
0.8%
2350 1
0.8%
2327 1
0.8%
2301 1
0.8%
2236 1
0.8%
2217 1
0.8%
2098 1
0.8%

양주서
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73
Distinct (%)60.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean332.15833
Minimum0
Maximum1795
Zeros3
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T02:29:16.130036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median64
Q3477.5
95-th percentile1322.25
Maximum1795
Range1795
Interquartile range (IQR)474.5

Descriptive statistics

Standard deviation475.81112
Coefficient of variation (CV)1.4324829
Kurtosis1.2251848
Mean332.15833
Median Absolute Deviation (MAD)62
Skewness1.4950169
Sum39859
Variance226396.22
MonotonicityNot monotonic
2024-03-15T02:29:16.414366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 19
 
15.8%
1 7
 
5.8%
4 6
 
5.0%
3 5
 
4.2%
5 5
 
4.2%
0 3
 
2.5%
57 3
 
2.5%
69 2
 
1.7%
6 2
 
1.7%
66 2
 
1.7%
Other values (63) 66
55.0%
ValueCountFrequency (%)
0 3
 
2.5%
1 7
 
5.8%
2 19
15.8%
3 5
 
4.2%
4 6
 
5.0%
5 5
 
4.2%
6 2
 
1.7%
7 1
 
0.8%
48 1
 
0.8%
52 1
 
0.8%
ValueCountFrequency (%)
1795 1
0.8%
1751 1
0.8%
1727 1
0.8%
1619 1
0.8%
1553 1
0.8%
1327 1
0.8%
1322 1
0.8%
1254 1
0.8%
1230 1
0.8%
1194 1
0.8%

동두천서
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184.73333
Minimum0
Maximum926
Zeros17
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T02:29:16.779779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median51
Q3290
95-th percentile723.3
Maximum926
Range926
Interquartile range (IQR)289

Descriptive statistics

Standard deviation251.13366
Coefficient of variation (CV)1.3594388
Kurtosis0.4661331
Mean184.73333
Median Absolute Deviation (MAD)51
Skewness1.3077446
Sum22168
Variance63068.113
MonotonicityNot monotonic
2024-03-15T02:29:17.421470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 17
 
14.2%
0 17
 
14.2%
2 6
 
5.0%
40 4
 
3.3%
51 3
 
2.5%
3 3
 
2.5%
203 2
 
1.7%
52 2
 
1.7%
59 2
 
1.7%
46 2
 
1.7%
Other values (60) 62
51.7%
ValueCountFrequency (%)
0 17
14.2%
1 17
14.2%
2 6
 
5.0%
3 3
 
2.5%
4 2
 
1.7%
5 1
 
0.8%
7 1
 
0.8%
14 1
 
0.8%
37 1
 
0.8%
40 4
 
3.3%
ValueCountFrequency (%)
926 1
0.8%
872 1
0.8%
835 1
0.8%
760 1
0.8%
750 1
0.8%
729 1
0.8%
723 1
0.8%
692 1
0.8%
673 1
0.8%
641 1
0.8%

구리서
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean367.15833
Minimum0
Maximum2032
Zeros8
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T02:29:17.741440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median87
Q3603
95-th percentile1543.25
Maximum2032
Range2032
Interquartile range (IQR)601

Descriptive statistics

Standard deviation518.221
Coefficient of variation (CV)1.4114374
Kurtosis1.7534285
Mean367.15833
Median Absolute Deviation (MAD)86
Skewness1.5774528
Sum44059
Variance268553.01
MonotonicityNot monotonic
2024-03-15T02:29:18.228599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 13
 
10.8%
2 13
 
10.8%
4 8
 
6.7%
0 8
 
6.7%
46 2
 
1.7%
6 2
 
1.7%
101 1
 
0.8%
1945 1
 
0.8%
1319 1
 
0.8%
1548 1
 
0.8%
Other values (70) 70
58.3%
ValueCountFrequency (%)
0 8
6.7%
1 13
10.8%
2 13
10.8%
4 8
6.7%
5 1
 
0.8%
6 2
 
1.7%
7 1
 
0.8%
13 1
 
0.8%
15 1
 
0.8%
46 2
 
1.7%
ValueCountFrequency (%)
2032 1
0.8%
1957 1
0.8%
1945 1
0.8%
1922 1
0.8%
1577 1
0.8%
1548 1
0.8%
1543 1
0.8%
1436 1
0.8%
1367 1
0.8%
1363 1
0.8%

포천서
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean277.78333
Minimum0
Maximum1480
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T02:29:18.545436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median59
Q3422.5
95-th percentile1091.05
Maximum1480
Range1480
Interquartile range (IQR)418.5

Descriptive statistics

Standard deviation384.2211
Coefficient of variation (CV)1.3831683
Kurtosis1.0117701
Mean277.78333
Median Absolute Deviation (MAD)58
Skewness1.4116183
Sum33334
Variance147625.85
MonotonicityNot monotonic
2024-03-15T02:29:18.920543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 12
 
10.0%
5 8
 
6.7%
2 7
 
5.8%
3 6
 
5.0%
4 5
 
4.2%
71 3
 
2.5%
12 2
 
1.7%
59 2
 
1.7%
6 2
 
1.7%
47 2
 
1.7%
Other values (65) 71
59.2%
ValueCountFrequency (%)
0 1
 
0.8%
1 12
10.0%
2 7
5.8%
3 6
5.0%
4 5
4.2%
5 8
6.7%
6 2
 
1.7%
7 2
 
1.7%
9 1
 
0.8%
10 2
 
1.7%
ValueCountFrequency (%)
1480 1
0.8%
1445 1
0.8%
1324 1
0.8%
1315 1
0.8%
1123 1
0.8%
1111 1
0.8%
1090 1
0.8%
1048 1
0.8%
996 1
0.8%
985 1
0.8%

가평서
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)52.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.75
Minimum0
Maximum687
Zeros19
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T02:29:19.451643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median44
Q3158
95-th percentile472.6
Maximum687
Range687
Interquartile range (IQR)157

Descriptive statistics

Standard deviation170.36314
Coefficient of variation (CV)1.4108749
Kurtosis1.4926778
Mean120.75
Median Absolute Deviation (MAD)44
Skewness1.5546572
Sum14490
Variance29023.601
MonotonicityNot monotonic
2024-03-15T02:29:19.913328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
15.8%
1 16
 
13.3%
2 14
 
11.7%
51 3
 
2.5%
45 2
 
1.7%
50 2
 
1.7%
158 2
 
1.7%
42 2
 
1.7%
131 2
 
1.7%
599 2
 
1.7%
Other values (53) 56
46.7%
ValueCountFrequency (%)
0 19
15.8%
1 16
13.3%
2 14
11.7%
3 2
 
1.7%
4 1
 
0.8%
11 1
 
0.8%
14 1
 
0.8%
35 1
 
0.8%
36 1
 
0.8%
38 1
 
0.8%
ValueCountFrequency (%)
687 1
0.8%
599 2
1.7%
596 1
0.8%
593 1
0.8%
484 1
0.8%
472 1
0.8%
450 1
0.8%
447 1
0.8%
445 1
0.8%
444 1
0.8%

연천서
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.666667
Minimum0
Maximum312
Zeros23
Zeros (%)19.2%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T02:29:20.372327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median9.5
Q3108.5
95-th percentile234.6
Maximum312
Range312
Interquartile range (IQR)107.5

Descriptive statistics

Standard deviation83.449107
Coefficient of variation (CV)1.3985884
Kurtosis0.7108319
Mean59.666667
Median Absolute Deviation (MAD)9.5
Skewness1.3564569
Sum7160
Variance6963.7535
MonotonicityNot monotonic
2024-03-15T02:29:20.913152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
19.2%
1 16
 
13.3%
2 7
 
5.8%
3 5
 
4.2%
13 4
 
3.3%
9 4
 
3.3%
66 4
 
3.3%
16 2
 
1.7%
195 2
 
1.7%
10 2
 
1.7%
Other values (45) 51
42.5%
ValueCountFrequency (%)
0 23
19.2%
1 16
13.3%
2 7
 
5.8%
3 5
 
4.2%
6 2
 
1.7%
7 1
 
0.8%
8 2
 
1.7%
9 4
 
3.3%
10 2
 
1.7%
11 1
 
0.8%
ValueCountFrequency (%)
312 1
0.8%
296 1
0.8%
277 1
0.8%
272 1
0.8%
267 1
0.8%
246 1
0.8%
234 1
0.8%
213 2
1.7%
212 1
0.8%
202 1
0.8%

Interactions

2024-03-15T02:29:04.823314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:18.925509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:22.712686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:26.362416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:29.870017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:33.353474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:36.939236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:40.317875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:43.837500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:47.422386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:51.082001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:54.173724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:57.788354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:01.281951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:05.087489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:19.188634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:22.984280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:26.624250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:30.132100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:33.616683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:37.193015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:40.593488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:44.085711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:47.705590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:51.274520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:54.426475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:58.080444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:01.531273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:05.325786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:19.429679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:23.222836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:26.864649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:30.417574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:33.855236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:37.419405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:40.736255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:44.271277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:47.960409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:51.415885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:54.569653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:58.333994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:01.772444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:05.568901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:19.684175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:23.507939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:27.106119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:30.850364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:34.105068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:37.658176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:40.979232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:44.409161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:48.242396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:51.551995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:54.743523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:58.603238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:02.043111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:05.798797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:19.952774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:23.778030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:27.360734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:31.018817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:34.367622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:37.913434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:41.270079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:44.661612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:48.621867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:51.700003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:55.016519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:58.880007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:02.326850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:06.057602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:20.236076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:24.040749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:27.625455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:31.177276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:34.627950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:38.167048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:41.435325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:44.920052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:48.929915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:52.053534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:55.201969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:59.189587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:02.588039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:06.298615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:20.522837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:24.279563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:27.867737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:31.319375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:34.870400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:38.395987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:41.574070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:45.155023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:49.155592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:52.283029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:55.361117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:59.440096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:02.839560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:06.552021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:20.780827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:24.530311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:28.114825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:31.474471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:35.129354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:38.636485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:41.897449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:45.376767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:49.413660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:52.529337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:55.619517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:59.699764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:03.096722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:06.796826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:20.975629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:24.798890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:28.355998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:31.788646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:35.379426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:38.881647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:42.140145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:45.715759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:49.664001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:52.765319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:55.868195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:59.955853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:03.342191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:07.055146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:21.240532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:25.066095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:28.610005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:32.054633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:35.646741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:39.129051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:42.402018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:45.977767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:49.918662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:53.027857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:56.346937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:00.219616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:03.604973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:07.288818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:21.558437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:25.314563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:28.845934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:32.301532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:35.886752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:39.358689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:42.639512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:46.219398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:50.160589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:53.214668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:56.649713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:00.464765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:03.847829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:07.555830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:21.801064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:25.588859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:29.104026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:32.566819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:36.157458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:39.611589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:42.919415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:46.566368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:50.424253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:53.417029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:56.964538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:00.783146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:04.128862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:07.734508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:22.112631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:25.861082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:29.359664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:32.835404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:36.416894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:39.831446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:43.179773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:46.849947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:50.750879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:53.669440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:57.242002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:00.942777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:04.307200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:07.890665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:22.416055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:26.116029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:29.617864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:33.101513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:36.687766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:40.070358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:43.626242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:47.167686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:50.924149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:53.924354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:28:57.511758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:01.124788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:29:04.570415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T02:29:21.258680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도범죄유형구분의정부서고양서일산동부서일산서부서남양주남부서남양주북부서파주서양주서동두천서구리서포천서가평서연천서
연도1.0000.0000.0000.0000.0000.0000.0000.0000.2010.0000.0000.0000.0000.0000.0000.000
범죄유형0.0001.0000.0000.9440.8290.8270.9130.8990.4980.8320.8370.9390.7960.9440.9380.932
구분0.0000.0001.0000.4680.5890.2310.4320.1920.2110.6630.5070.4030.4810.3190.3610.350
의정부서0.0000.9440.4681.0000.8890.9160.9680.9710.7380.8560.9300.9650.9440.9870.9750.965
고양서0.0000.8290.5890.8891.0000.9320.8510.8280.8450.9500.9610.9220.9450.8520.8810.885
일산동부서0.0000.8270.2310.9160.9321.0000.8620.9020.8140.9340.9590.8950.9460.9170.8970.902
일산서부서0.0000.9130.4320.9680.8510.8621.0000.9480.7090.8380.8780.9430.8590.9480.9360.937
남양주남부서0.0000.8990.1920.9710.8280.9020.9481.0000.5460.8430.8560.9440.9020.9710.9380.952
남양주북부서0.2010.4980.2110.7380.8450.8140.7090.5461.0000.8930.8310.7300.8330.7600.7210.693
파주서0.0000.8320.6630.8560.9500.9340.8380.8430.8931.0000.9550.8540.9370.8470.8430.859
양주서0.0000.8370.5070.9300.9610.9590.8780.8560.8310.9551.0000.9250.9590.9000.9120.912
동두천서0.0000.9390.4030.9650.9220.8950.9430.9440.7300.8540.9251.0000.8680.9580.9610.967
구리서0.0000.7960.4810.9440.9450.9460.8590.9020.8330.9370.9590.8681.0000.9300.8950.882
포천서0.0000.9440.3190.9870.8520.9170.9480.9710.7600.8470.9000.9580.9301.0000.9630.952
가평서0.0000.9380.3610.9750.8810.8970.9360.9380.7210.8430.9120.9610.8950.9631.0000.956
연천서0.0000.9320.3500.9650.8850.9020.9370.9520.6930.8590.9120.9670.8820.9520.9561.000
2024-03-15T02:29:21.644982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
범죄유형구분
범죄유형1.0000.000
구분0.0001.000
2024-03-15T02:29:21.962270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도의정부서고양서일산동부서일산서부서남양주남부서남양주북부서파주서양주서동두천서구리서포천서가평서연천서범죄유형구분
연도1.000-0.153-0.106-0.0560.244-0.1980.7950.0040.019-0.039-0.210-0.152-0.092-0.1250.0000.000
의정부서-0.1531.0000.9510.9150.8220.9470.0980.9150.9360.9560.9490.9380.8980.9070.6630.308
고양서-0.1060.9511.0000.9190.8410.9590.1620.9490.9350.9430.9380.9460.9210.9070.6570.308
일산동부서-0.0560.9150.9191.0000.8370.9320.1950.9230.9190.9310.9310.9430.9110.9130.6550.098
일산서부서0.2440.8220.8410.8371.0000.8120.3370.8660.8640.8530.8080.8330.8490.7860.5980.278
남양주남부서-0.1980.9470.9590.9320.8121.0000.0390.9170.9390.9320.9470.9430.9140.9060.5740.110
남양주북부서0.7950.0980.1620.1950.3370.0391.0000.2500.2030.1680.0520.1230.1260.1290.3100.089
파주서0.0040.9150.9490.9230.8660.9170.2501.0000.9280.9350.9270.9380.9100.9030.6610.366
양주서0.0190.9360.9350.9190.8640.9390.2030.9281.0000.9490.8950.9240.9020.8980.6690.251
동두천서-0.0390.9560.9430.9310.8530.9320.1680.9350.9491.0000.9410.9440.9340.8900.6520.255
구리서-0.2100.9490.9380.9310.8080.9470.0520.9270.8950.9411.0000.9510.9150.9290.6080.234
포천서-0.1520.9380.9460.9430.8330.9430.1230.9380.9240.9440.9511.0000.9250.9210.6630.194
가평서-0.0920.8980.9210.9110.8490.9140.1260.9100.9020.9340.9150.9251.0000.9040.6500.224
연천서-0.1250.9070.9070.9130.7860.9060.1290.9030.8980.8900.9290.9210.9041.0000.6360.216
범죄유형0.0000.6630.6570.6550.5980.5740.3100.6610.6690.6520.6080.6630.6500.6361.0000.000
구분0.0000.3080.3080.0980.2780.1100.0890.3660.2510.2550.2340.1940.2240.2160.0001.000

Missing values

2024-03-15T02:29:08.328776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T02:29:08.884612image/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

연도범죄유형구분의정부서고양서일산동부서일산서부서남양주남부서남양주북부서파주서양주서동두천서구리서포천서가평서연천서
02023살인발생건수33643412210420
12023살인검거건수33643412210420
22023살인검거인원34853412210530
32023강도발생건수3192413200301
42023강도검거건수21102512200101
52023강도검거인원411881512400101
62023성범죄발생건수2311181355511577199806166574513
72023성범죄검거건수187931284993671676345594200
82023성범죄검거인원1949513751100661736446614300
92023절도발생건수147414318145449077001241525380539356157113
연도범죄유형구분의정부서고양서일산동부서일산서부서남양주남부서남양주북부서파주서양주서동두천서구리서포천서가평서연천서
1102016강도검거인원268401003201400
1112016성범죄발생건수197118219022901246346119784820
1122016성범죄검거건수172111199121001015751111603816
1132016성범죄검거인원184113217125001096453129704521
1142016절도발생건수17521123145822147601293446344936548201143
1152016절도검거건수10917119278957091525320359733814478
1162016절도검거인원1096710898911240685329163658370148120
1172016폭력발생건수33122059256441340102085132272915431111472234
1182016폭력검거건수2915174023072730150184011166411304981401198
1192016폭력검거인원41282501335936439202540175192620321480687272