Overview

Dataset statistics

Number of variables9
Number of observations42
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory83.0 B

Variable types

Numeric8
Categorical1

Dataset

Description대전지역 요일별 강력범, 절도범, 폭력범, 지능범, 풍속범, 기타형법범, 특별법범 범죄발생 현황으로 어느 요일에 범죄발생이 많고 적은지 파악할 수 있는 자료 활용
Author경찰청 대전광역시경찰청
URLhttps://www.data.go.kr/data/15094057/fileData.do

Alerts

is highly overall correlated with and 6 other fieldsHigh correlation
is highly overall correlated with and 6 other fieldsHigh correlation
is highly overall correlated with and 6 other fieldsHigh correlation
is highly overall correlated with and 6 other fieldsHigh correlation
is highly overall correlated with and 6 other fieldsHigh correlation
is highly overall correlated with and 6 other fieldsHigh correlation
is highly overall correlated with and 6 other fieldsHigh correlation
항목 is highly overall correlated with and 6 other fieldsHigh correlation
has unique valuesUnique
has unique valuesUnique
has unique valuesUnique
has unique valuesUnique

Reproduction

Analysis started2024-03-14 12:00:39.401083
Analysis finished2024-03-14 12:00:50.927551
Duration11.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct6
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-14T21:00:51.017374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019.5
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7285268
Coefficient of variation (CV)0.00085591819
Kurtosis-1.275956
Mean2019.5
Median Absolute Deviation (MAD)1.5
Skewness0
Sum84819
Variance2.9878049
MonotonicityIncreasing
2024-03-14T21:00:51.211571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 7
16.7%
2018 7
16.7%
2019 7
16.7%
2020 7
16.7%
2021 7
16.7%
2022 7
16.7%
ValueCountFrequency (%)
2017 7
16.7%
2018 7
16.7%
2019 7
16.7%
2020 7
16.7%
2021 7
16.7%
2022 7
16.7%
ValueCountFrequency (%)
2022 7
16.7%
2021 7
16.7%
2020 7
16.7%
2019 7
16.7%
2018 7
16.7%
2017 7
16.7%

항목
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size464.0 B
강력범
절도범
폭력범
지능범
풍속범
Other values (2)
12 

Length

Max length5
Median length3
Mean length3.4285714
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강력범
2nd row절도범
3rd row폭력범
4th row지능범
5th row풍속범

Common Values

ValueCountFrequency (%)
강력범 6
14.3%
절도범 6
14.3%
폭력범 6
14.3%
지능범 6
14.3%
풍속범 6
14.3%
기타형법범 6
14.3%
특별법범 6
14.3%

Length

2024-03-14T21:00:51.447160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:00:51.702510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강력범 6
14.3%
절도범 6
14.3%
폭력범 6
14.3%
지능범 6
14.3%
풍속범 6
14.3%
기타형법범 6
14.3%
특별법범 6
14.3%


Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean717.97619
Minimum17
Maximum2173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-14T21:00:52.005875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile25.2
Q1127.5
median748
Q31188.25
95-th percentile1739.2
Maximum2173
Range2156
Interquartile range (IQR)1060.75

Descriptive statistics

Standard deviation592.10116
Coefficient of variation (CV)0.82468077
Kurtosis-0.63847675
Mean717.97619
Median Absolute Deviation (MAD)487
Skewness0.53092067
Sum30155
Variance350583.78
MonotonicityNot monotonic
2024-03-14T21:00:52.240190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
103 1
 
2.4%
29 1
 
2.4%
905 1
 
2.4%
25 1
 
2.4%
295 1
 
2.4%
1667 1
 
2.4%
89 1
 
2.4%
654 1
 
2.4%
1036 1
 
2.4%
832 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
17 1
2.4%
19 1
2.4%
25 1
2.4%
29 1
2.4%
31 1
2.4%
37 1
2.4%
89 1
2.4%
103 1
2.4%
110 1
2.4%
116 1
2.4%
ValueCountFrequency (%)
2173 1
2.4%
1819 1
2.4%
1743 1
2.4%
1667 1
2.4%
1431 1
2.4%
1429 1
2.4%
1369 1
2.4%
1313 1
2.4%
1246 1
2.4%
1221 1
2.4%


Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean961.42857
Minimum25
Maximum2963
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-14T21:00:52.474627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile29.35
Q1112.5
median804.5
Q31572.25
95-th percentile2520.5
Maximum2963
Range2938
Interquartile range (IQR)1459.75

Descriptive statistics

Standard deviation859.27576
Coefficient of variation (CV)0.89374894
Kurtosis-0.62118305
Mean961.42857
Median Absolute Deviation (MAD)695
Skewness0.71026109
Sum40380
Variance738354.84
MonotonicityNot monotonic
2024-03-14T21:00:52.722585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
114 1
 
2.4%
28 1
 
2.4%
2172 1
 
2.4%
38 1
 
2.4%
449 1
 
2.4%
2266 1
 
2.4%
107 1
 
2.4%
748 1
 
2.4%
981 1
 
2.4%
1961 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
25 1
2.4%
28 1
2.4%
29 1
2.4%
36 1
2.4%
37 1
2.4%
38 1
2.4%
92 1
2.4%
94 1
2.4%
103 1
2.4%
107 1
2.4%
ValueCountFrequency (%)
2963 1
2.4%
2529 1
2.4%
2522 1
2.4%
2492 1
2.4%
2266 1
2.4%
2172 1
2.4%
2037 1
2.4%
1961 1
2.4%
1832 1
2.4%
1797 1
2.4%


Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean951.54762
Minimum21
Maximum3055
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-14T21:00:53.046999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile25.15
Q1119.75
median741
Q31582.25
95-th percentile2450.5
Maximum3055
Range3034
Interquartile range (IQR)1462.5

Descriptive statistics

Standard deviation861.25131
Coefficient of variation (CV)0.90510584
Kurtosis-0.48386273
Mean951.54762
Median Absolute Deviation (MAD)628.5
Skewness0.75110678
Sum39965
Variance741753.81
MonotonicityNot monotonic
2024-03-14T21:00:53.464732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
119 1
 
2.4%
24 1
 
2.4%
2171 1
 
2.4%
25 1
 
2.4%
455 1
 
2.4%
2206 1
 
2.4%
106 1
 
2.4%
737 1
 
2.4%
980 1
 
2.4%
1799 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
21 1
2.4%
24 1
2.4%
25 1
2.4%
28 1
2.4%
38 1
2.4%
39 1
2.4%
93 1
2.4%
99 1
2.4%
103 1
2.4%
106 1
2.4%
ValueCountFrequency (%)
3055 1
2.4%
2699 1
2.4%
2458 1
2.4%
2308 1
2.4%
2206 1
2.4%
2171 1
2.4%
2063 1
2.4%
1835 1
2.4%
1802 1
2.4%
1799 1
2.4%


Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean943.85714
Minimum20
Maximum3005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-14T21:00:53.861571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile24
Q1131
median804
Q31597.5
95-th percentile2397.65
Maximum3005
Range2985
Interquartile range (IQR)1466.5

Descriptive statistics

Standard deviation834.41583
Coefficient of variation (CV)0.88404886
Kurtosis-0.52700231
Mean943.85714
Median Absolute Deviation (MAD)687.5
Skewness0.70423586
Sum39642
Variance696249.78
MonotonicityNot monotonic
2024-03-14T21:00:54.265712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
456 2
 
4.8%
804 2
 
4.8%
24 2
 
4.8%
105 1
 
2.4%
1960 1
 
2.4%
2134 1
 
2.4%
90 1
 
2.4%
711 1
 
2.4%
939 1
 
2.4%
1798 1
 
2.4%
Other values (29) 29
69.0%
ValueCountFrequency (%)
20 1
2.4%
23 1
2.4%
24 2
4.8%
30 1
2.4%
43 1
2.4%
90 1
2.4%
91 1
2.4%
103 1
2.4%
105 1
2.4%
128 1
2.4%
ValueCountFrequency (%)
3005 1
2.4%
2514 1
2.4%
2401 1
2.4%
2334 1
2.4%
2134 1
2.4%
2037 1
2.4%
1960 1
2.4%
1839 1
2.4%
1798 1
2.4%
1792 1
2.4%


Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean954.40476
Minimum25
Maximum2966
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-14T21:00:54.494784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile30.1
Q1117.25
median797.5
Q31548
95-th percentile2419.95
Maximum2966
Range2941
Interquartile range (IQR)1430.75

Descriptive statistics

Standard deviation846.94591
Coefficient of variation (CV)0.88740746
Kurtosis-0.58519022
Mean954.40476
Median Absolute Deviation (MAD)684.5
Skewness0.70531958
Sum40085
Variance717317.37
MonotonicityNot monotonic
2024-03-14T21:00:54.733951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
124 1
 
2.4%
32 1
 
2.4%
1933 1
 
2.4%
30 1
 
2.4%
445 1
 
2.4%
2362 1
 
2.4%
100 1
 
2.4%
718 1
 
2.4%
966 1
 
2.4%
1834 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
25 1
2.4%
28 1
2.4%
30 1
2.4%
32 1
2.4%
38 1
2.4%
41 1
2.4%
98 1
2.4%
99 1
2.4%
100 1
2.4%
111 1
2.4%
ValueCountFrequency (%)
2966 1
2.4%
2589 1
2.4%
2423 1
2.4%
2362 1
2.4%
2338 1
2.4%
2046 1
2.4%
1933 1
2.4%
1924 1
2.4%
1834 1
2.4%
1818 1
2.4%


Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean993.40476
Minimum17
Maximum3165
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-14T21:00:54.992603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile31
Q1125.25
median868
Q31580
95-th percentile2618.8
Maximum3165
Range3148
Interquartile range (IQR)1454.75

Descriptive statistics

Standard deviation882.54734
Coefficient of variation (CV)0.88840659
Kurtosis-0.46140088
Mean993.40476
Median Absolute Deviation (MAD)745
Skewness0.71829539
Sum41723
Variance778889.81
MonotonicityNot monotonic
2024-03-14T21:00:55.415576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
31 2
 
4.8%
121 1
 
2.4%
38 1
 
2.4%
2024 1
 
2.4%
412 1
 
2.4%
2406 1
 
2.4%
93 1
 
2.4%
745 1
 
2.4%
1024 1
 
2.4%
1684 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
17 1
2.4%
24 1
2.4%
31 2
4.8%
36 1
2.4%
38 1
2.4%
93 1
2.4%
112 1
2.4%
119 1
2.4%
121 1
2.4%
125 1
2.4%
ValueCountFrequency (%)
3165 1
2.4%
2710 1
2.4%
2630 1
2.4%
2406 1
2.4%
2284 1
2.4%
2225 1
2.4%
2024 1
2.4%
1979 1
2.4%
1815 1
2.4%
1684 1
2.4%


Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean811.09524
Minimum14
Maximum2490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size506.0 B
2024-03-14T21:00:55.667359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile20.5
Q1135.75
median855.5
Q31236.5
95-th percentile2137.55
Maximum2490
Range2476
Interquartile range (IQR)1100.75

Descriptive statistics

Standard deviation685.87252
Coefficient of variation (CV)0.8456128
Kurtosis-0.38685634
Mean811.09524
Median Absolute Deviation (MAD)568.5
Skewness0.64243499
Sum34066
Variance470421.11
MonotonicityNot monotonic
2024-03-14T21:00:55.909967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
868 2
 
4.8%
115 1
 
2.4%
271 1
 
2.4%
14 1
 
2.4%
363 1
 
2.4%
1977 1
 
2.4%
131 1
 
2.4%
731 1
 
2.4%
1204 1
 
2.4%
986 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
14 1
2.4%
18 1
2.4%
20 1
2.4%
30 1
2.4%
34 1
2.4%
42 1
2.4%
114 1
2.4%
115 1
2.4%
125 1
2.4%
128 1
2.4%
ValueCountFrequency (%)
2490 1
2.4%
2209 1
2.4%
2146 1
2.4%
1977 1
2.4%
1729 1
2.4%
1508 1
2.4%
1486 1
2.4%
1435 1
2.4%
1367 1
2.4%
1330 1
2.4%

Interactions

2024-03-14T21:00:49.116073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:39.847097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:41.483226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:42.883285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:44.106436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:45.338168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:46.591354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:47.838803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:49.254275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:40.092304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:41.621402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:43.026407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:44.250605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:45.489146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:46.734269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:47.989822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:49.471287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:40.327342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:41.744340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:43.159643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:44.381914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:45.659815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:46.940079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:48.228822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:49.612050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:40.572629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:41.884772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:43.379589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:44.614001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:45.864575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:47.100103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:48.399233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:49.752499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:40.818266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:42.029517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:43.534460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:44.759534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:46.013359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:47.247261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:48.544641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:49.896825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:41.052530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:42.251512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:43.676810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:44.901569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:46.157063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:47.390846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:48.688725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:50.036974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:41.196105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:42.597777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:43.823137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:45.048376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:46.303520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:47.537468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:48.835260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:50.179929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:41.344625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:42.750386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:43.968474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:45.190506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:46.450027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:47.698442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:00:48.978191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T21:00:56.077903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도항목
연도1.0000.0000.0000.0000.0000.0000.0000.0000.000
항목0.0001.0000.8610.9110.9250.9140.9170.9260.899
0.0000.8611.0000.9660.9690.9630.9780.9750.995
0.0000.9110.9661.0000.9960.9940.9960.9890.973
0.0000.9250.9690.9961.0000.9950.9940.9950.981
0.0000.9140.9630.9940.9951.0000.9930.9890.964
0.0000.9170.9780.9960.9940.9931.0000.9970.978
0.0000.9260.9750.9890.9950.9890.9971.0000.978
0.0000.8990.9950.9730.9810.9640.9780.9781.000
2024-03-14T21:00:56.280721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도항목
연도1.000-0.047-0.042-0.045-0.026-0.020-0.029-0.0200.000
-0.0471.0000.9300.9360.9390.9420.9430.9890.642
-0.0420.9301.0000.9940.9890.9900.9890.9310.739
-0.0450.9360.9941.0000.9890.9920.9900.9400.771
-0.0260.9390.9890.9891.0000.9940.9950.9350.746
-0.0200.9420.9900.9920.9941.0000.9930.9420.752
-0.0290.9430.9890.9900.9950.9931.0000.9400.774
-0.0200.9890.9310.9400.9350.9420.9401.0000.714
항목0.0000.6420.7390.7710.7460.7520.7740.7141.000

Missing values

2024-03-14T21:00:50.595961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T21:00:50.844048image/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

연도항목
02017강력범103114119105124121115
12017절도범909900835824864954928
22017폭력범1369110711931207120612831435
32017지능범72613991319141013741421843
42017풍속범17373920282430
52017기타형법범285396364456436429299
62017특별법범2173296330553005296631652490
72018강력범153112103128111112128
82018절도범794773739804760802868
92018폭력범1431125012641217124713071486
연도항목
322021풍속범29282443323834
332021기타형법범272387388400392371271
342021특별법범1429203720631960204622251729
352022강력범1199299140115126125
362022절도범784836811865878910949
372022폭력범1215103410741026106111071241
382022지능범1108252923082334233822841223
392022풍속범37363830413618
402022기타형법범277447380424421407298
412022특별법범1313179718021792192419791508