Overview

Dataset statistics

Number of variables9
Number of observations35
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory82.8 B

Variable types

Categorical1
Text1
Numeric7

Dataset

Description전국 경찰관서에 고소, 고발, 인지 등으로 형사입건된 사건의 발생, 검거, 피의자에 대한 죄종별 분석 현황
Author경찰청
URLhttps://www.data.go.kr/data/3074458/fileData.do

Alerts

10일이내 is highly overall correlated with 20일이내 and 5 other fieldsHigh correlation
20일이내 is highly overall correlated with 10일이내 and 6 other fieldsHigh correlation
1개월이내 is highly overall correlated with 10일이내 and 5 other fieldsHigh correlation
2개월이내 is highly overall correlated with 10일이내 and 5 other fieldsHigh correlation
3개월이내 is highly overall correlated with 10일이내 and 5 other fieldsHigh correlation
6개월이내 is highly overall correlated with 10일이내 and 5 other fieldsHigh correlation
6개월초과 is highly overall correlated with 10일이내 and 5 other fieldsHigh correlation
범죄대분류 is highly overall correlated with 20일이내High correlation
범죄중분류 has unique valuesUnique
10일이내 has unique valuesUnique
1개월이내 has unique valuesUnique
2개월이내 has unique valuesUnique
3개월이내 has unique valuesUnique
6개월이내 has unique valuesUnique

Reproduction

Analysis started2023-12-12 14:38:59.391887
Analysis finished2023-12-12 14:39:04.725060
Duration5.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄대분류
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
지능범죄
9 
폭력범죄
8 
강력범죄
5 
풍속범죄
2 
절도범죄
 
1
Other values (10)
10 

Length

Max length6
Median length4
Mean length4.0571429
Min length4

Unique

Unique11 ?
Unique (%)31.4%

Sample

1st row강력범죄
2nd row강력범죄
3rd row강력범죄
4th row강력범죄
5th row강력범죄

Common Values

ValueCountFrequency (%)
지능범죄 9
25.7%
폭력범죄 8
22.9%
강력범죄 5
14.3%
풍속범죄 2
 
5.7%
절도범죄 1
 
2.9%
특별경제범죄 1
 
2.9%
마약범죄 1
 
2.9%
보건범죄 1
 
2.9%
환경범죄 1
 
2.9%
교통범죄 1
 
2.9%
Other values (5) 5
14.3%

Length

2023-12-12T23:39:04.824980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지능범죄 9
25.7%
폭력범죄 8
22.9%
강력범죄 5
14.3%
풍속범죄 2
 
5.7%
절도범죄 1
 
2.9%
특별경제범죄 1
 
2.9%
마약범죄 1
 
2.9%
보건범죄 1
 
2.9%
환경범죄 1
 
2.9%
교통범죄 1
 
2.9%
Other values (5) 5
14.3%

범죄중분류
Text

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-12-12T23:39:05.035973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.5428571
Min length2

Characters and Unicode

Total characters124
Distinct characters72
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

Unique35 ?
Unique (%)100.0%

Sample

1st row살인기수
2nd row살인미수등
3rd row강도
4th row강간강제추행
5th row방화
ValueCountFrequency (%)
살인기수 1
 
2.9%
문서인장 1
 
2.9%
사기 1
 
2.9%
횡령 1
 
2.9%
배임 1
 
2.9%
성풍속범죄 1
 
2.9%
도박범죄 1
 
2.9%
특별경제범죄 1
 
2.9%
유가증권인지 1
 
2.9%
마약범죄 1
 
2.9%
Other values (25) 25
71.4%
2023-12-12T23:39:05.447732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
죄 12
 
9.7%
범 12
 
9.7%
인 5
 
4.0%
기 4
 
3.2%
수 3
 
2.4%
강 3
 
2.4%
도 3
 
2.4%
유 3
 
2.4%
행 3
 
2.4%
살 2
 
1.6%
Other values (62) 74
59.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 124
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
죄 12
 
9.7%
범 12
 
9.7%
인 5
 
4.0%
기 4
 
3.2%
수 3
 
2.4%
강 3
 
2.4%
도 3
 
2.4%
유 3
 
2.4%
행 3
 
2.4%
살 2
 
1.6%
Other values (62) 74
59.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 124
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
죄 12
 
9.7%
범 12
 
9.7%
인 5
 
4.0%
기 4
 
3.2%
수 3
 
2.4%
강 3
 
2.4%
도 3
 
2.4%
유 3
 
2.4%
행 3
 
2.4%
살 2
 
1.6%
Other values (62) 74
59.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 124
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
죄 12
 
9.7%
범 12
 
9.7%
인 5
 
4.0%
기 4
 
3.2%
수 3
 
2.4%
강 3
 
2.4%
도 3
 
2.4%
유 3
 
2.4%
행 3
 
2.4%
살 2
 
1.6%
Other values (62) 74
59.7%

10일이내
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16491.057
Minimum9
Maximum257309
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T23:39:05.594705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile18.9
Q1198
median1129
Q312604
95-th percentile58228.7
Maximum257309
Range257300
Interquartile range (IQR)12406

Descriptive statistics

Standard deviation45346.063
Coefficient of variation (CV)2.7497365
Kurtosis24.826316
Mean16491.057
Median Absolute Deviation (MAD)1115
Skewness4.7584938
Sum577187
Variance2.0562654 × 109
MonotonicityNot monotonic
2023-12-12T23:39:05.746665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
244 1
 
2.9%
371 1
 
2.9%
32536 1
 
2.9%
3050 1
 
2.9%
181 1
 
2.9%
4140 1
 
2.9%
13445 1
 
2.9%
18250 1
 
2.9%
1216 1
 
2.9%
4899 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
9 1
2.9%
14 1
2.9%
21 1
2.9%
29 1
2.9%
55 1
2.9%
75 1
2.9%
181 1
2.9%
185 1
2.9%
188 1
2.9%
208 1
2.9%
ValueCountFrequency (%)
257309 1
2.9%
84394 1
2.9%
47015 1
2.9%
32536 1
2.9%
28053 1
2.9%
25241 1
2.9%
24697 1
2.9%
18250 1
2.9%
13445 1
2.9%
11763 1
2.9%

20일이내
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8743.0286
Minimum9
Maximum115105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T23:39:05.859804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile28.4
Q1183.5
median721
Q35768
95-th percentile31377.2
Maximum115105
Range115096
Interquartile range (IQR)5584.5

Descriptive statistics

Standard deviation20626.719
Coefficient of variation (CV)2.359219
Kurtosis21.677397
Mean8743.0286
Median Absolute Deviation (MAD)694
Skewness4.3443289
Sum306006
Variance4.2546154 × 108
MonotonicityNot monotonic
2023-12-12T23:39:05.986240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
34 2
 
5.7%
27 1
 
2.9%
64 1
 
2.9%
3449 1
 
2.9%
406 1
 
2.9%
2918 1
 
2.9%
5649 1
 
2.9%
13296 1
 
2.9%
664 1
 
2.9%
3525 1
 
2.9%
Other values (24) 24
68.6%
ValueCountFrequency (%)
9 1
2.9%
27 1
2.9%
29 1
2.9%
34 2
5.7%
41 1
2.9%
47 1
2.9%
64 1
2.9%
161 1
2.9%
206 1
2.9%
215 1
2.9%
ValueCountFrequency (%)
115105 1
2.9%
32241 1
2.9%
31007 1
2.9%
26732 1
2.9%
17515 1
2.9%
17228 1
2.9%
15879 1
2.9%
13296 1
2.9%
5887 1
2.9%
5649 1
2.9%

1개월이내
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5593.4571
Minimum1
Maximum54791
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T23:39:06.125149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19.7
Q1132
median607
Q33764.5
95-th percentile25609.2
Maximum54791
Range54790
Interquartile range (IQR)3632.5

Descriptive statistics

Standard deviation11001.977
Coefficient of variation (CV)1.9669369
Kurtosis11.726589
Mean5593.4571
Median Absolute Deviation (MAD)588
Skewness3.1777456
Sum195771
Variance1.210435 × 108
MonotonicityNot monotonic
2023-12-12T23:39:06.254990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
20 1
 
2.9%
32 1
 
2.9%
27901 1
 
2.9%
3679 1
 
2.9%
547 1
 
2.9%
2421 1
 
2.9%
3572 1
 
2.9%
11224 1
 
2.9%
576 1
 
2.9%
2538 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
1 1
2.9%
19 1
2.9%
20 1
2.9%
23 1
2.9%
32 1
2.9%
47 1
2.9%
48 1
2.9%
60 1
2.9%
100 1
2.9%
164 1
2.9%
ValueCountFrequency (%)
54791 1
2.9%
27901 1
2.9%
24627 1
2.9%
15074 1
2.9%
12297 1
2.9%
12287 1
2.9%
11224 1
2.9%
9719 1
2.9%
3850 1
2.9%
3679 1
2.9%

2개월이내
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10811.714
Minimum17
Maximum91771
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T23:39:06.408795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile47.4
Q1315
median1325
Q38396
95-th percentile56544.2
Maximum91771
Range91754
Interquartile range (IQR)8081

Descriptive statistics

Standard deviation20457.438
Coefficient of variation (CV)1.8921549
Kurtosis7.7442399
Mean10811.714
Median Absolute Deviation (MAD)1279
Skewness2.7412683
Sum378410
Variance4.1850679 × 108
MonotonicityNot monotonic
2023-12-12T23:39:06.552419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
48 1
 
2.9%
46 1
 
2.9%
91771 1
 
2.9%
10395 1
 
2.9%
2047 1
 
2.9%
4203 1
 
2.9%
5839 1
 
2.9%
29198 1
 
2.9%
904 1
 
2.9%
4755 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
17 1
2.9%
46 1
2.9%
48 1
2.9%
67 1
2.9%
136 1
2.9%
169 1
2.9%
188 1
2.9%
238 1
2.9%
253 1
2.9%
377 1
2.9%
ValueCountFrequency (%)
91771 1
2.9%
64856 1
2.9%
52982 1
2.9%
29198 1
2.9%
23840 1
2.9%
21294 1
2.9%
21113 1
2.9%
15867 1
2.9%
10395 1
2.9%
6397 1
2.9%

3개월이내
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6695
Minimum5
Maximum80921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T23:39:06.670198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile31.9
Q1231.5
median859
Q36144
95-th percentile26303.1
Maximum80921
Range80916
Interquartile range (IQR)5912.5

Descriptive statistics

Standard deviation14996.649
Coefficient of variation (CV)2.2399774
Kurtosis18.283031
Mean6695
Median Absolute Deviation (MAD)854
Skewness3.9936895
Sum234325
Variance2.2489948 × 108
MonotonicityNot monotonic
2023-12-12T23:39:06.813838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
36 1
 
2.9%
34 1
 
2.9%
80921 1
 
2.9%
8442 1
 
2.9%
2199 1
 
2.9%
2067 1
 
2.9%
3452 1
 
2.9%
22758 1
 
2.9%
521 1
 
2.9%
2538 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
5 1
2.9%
27 1
2.9%
34 1
2.9%
36 1
2.9%
100 1
2.9%
106 1
2.9%
131 1
2.9%
186 1
2.9%
197 1
2.9%
266 1
2.9%
ValueCountFrequency (%)
80921 1
2.9%
34575 1
2.9%
22758 1
2.9%
19069 1
2.9%
13355 1
2.9%
11782 1
2.9%
8859 1
2.9%
8442 1
2.9%
7330 1
2.9%
4958 1
2.9%

6개월이내
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4532.8857
Minimum16
Maximum47175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T23:39:06.993598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile40.4
Q1210
median789
Q34254.5
95-th percentile17671.6
Maximum47175
Range47159
Interquartile range (IQR)4044.5

Descriptive statistics

Standard deviation9208.318
Coefficient of variation (CV)2.0314472
Kurtosis13.709571
Mean4532.8857
Median Absolute Deviation (MAD)745
Skewness3.4317079
Sum158651
Variance84793121
MonotonicityNot monotonic
2023-12-12T23:39:07.141679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
32 1
 
2.9%
45 1
 
2.9%
47175 1
 
2.9%
5305 1
 
2.9%
2049 1
 
2.9%
979 1
 
2.9%
2845 1
 
2.9%
14929 1
 
2.9%
789 1
 
2.9%
1655 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
16 1
2.9%
32 1
2.9%
44 1
2.9%
45 1
2.9%
83 1
2.9%
118 1
2.9%
147 1
2.9%
152 1
2.9%
189 1
2.9%
231 1
2.9%
ValueCountFrequency (%)
47175 1
2.9%
24071 1
2.9%
14929 1
2.9%
13323 1
2.9%
11653 1
2.9%
11595 1
2.9%
5923 1
2.9%
5366 1
2.9%
5305 1
2.9%
3204 1
2.9%

6개월초과
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2556.2571
Minimum30
Maximum28408
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T23:39:07.274757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile35.2
Q169.5
median432
Q31658
95-th percentile9530.9
Maximum28408
Range28378
Interquartile range (IQR)1588.5

Descriptive statistics

Standard deviation5347.5981
Coefficient of variation (CV)2.0919641
Kurtosis16.196246
Mean2556.2571
Median Absolute Deviation (MAD)385
Skewness3.6753247
Sum89469
Variance28596805
MonotonicityNot monotonic
2023-12-12T23:39:07.395813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
47 2
 
5.7%
55 2
 
5.7%
629 1
 
2.9%
1307 1
 
2.9%
864 1
 
2.9%
694 1
 
2.9%
1206 1
 
2.9%
5039 1
 
2.9%
432 1
 
2.9%
256 1
 
2.9%
Other values (23) 23
65.7%
ValueCountFrequency (%)
30 1
2.9%
31 1
2.9%
37 1
2.9%
47 2
5.7%
55 2
5.7%
62 1
2.9%
69 1
2.9%
70 1
2.9%
77 1
2.9%
124 1
2.9%
ValueCountFrequency (%)
28408 1
2.9%
9911 1
2.9%
9368 1
2.9%
7718 1
2.9%
7005 1
2.9%
6953 1
2.9%
5039 1
2.9%
3788 1
2.9%
2009 1
2.9%
1307 1
2.9%

Interactions

2023-12-12T23:39:03.735837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:38:59.696090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:00.292231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:00.806263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:01.452197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:02.354933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:03.065066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:03.841426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:38:59.768342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:00.368917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:00.901225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:01.525566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:02.462831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:03.170757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:03.951497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:38:59.855690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:00.444132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:00.985645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:01.611542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:02.546426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:03.263577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:04.054658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:38:59.941550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:00.516706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:01.065971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:01.995754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:02.640651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:03.341800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:04.149179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:00.020699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:00.585509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:01.143217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:02.082844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:02.736817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:03.419157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:04.257626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:00.147225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:00.670343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:01.242971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:02.187723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:02.840411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:03.553298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:04.349372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:00.222341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:00.737003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:01.378341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:02.260799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:02.943003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:39:03.640718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:39:07.526648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
범죄대분류범죄중분류10일이내20일이내1개월이내2개월이내3개월이내6개월이내6개월초과
범죄대분류1.0001.0000.7710.8270.7780.8350.8790.8100.904
범죄중분류1.0001.0001.0001.0001.0001.0001.0001.0001.000
10일이내0.7711.0001.0000.9680.8940.8630.7080.7730.861
20일이내0.8271.0000.9681.0000.9820.9160.7370.8530.928
1개월이내0.7781.0000.8940.9821.0000.9640.9150.9780.915
2개월이내0.8351.0000.8630.9160.9641.0000.9730.9680.919
3개월이내0.8791.0000.7080.7370.9150.9731.0000.9280.953
6개월이내0.8101.0000.7730.8530.9780.9680.9281.0000.847
6개월초과0.9041.0000.8610.9280.9150.9190.9530.8471.000
2023-12-12T23:39:07.662972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
10일이내20일이내1개월이내2개월이내3개월이내6개월이내6개월초과범죄대분류
10일이내1.0000.9640.9470.9020.8800.8510.7880.442
20일이내0.9641.0000.9930.9680.9490.9120.8010.506
1개월이내0.9470.9931.0000.9830.9680.9300.8110.405
2개월이내0.9020.9680.9831.0000.9910.9530.8170.478
3개월이내0.8800.9490.9680.9911.0000.9710.8430.462
6개월이내0.8510.9120.9300.9530.9711.0000.9120.442
6개월초과0.7880.8010.8110.8170.8430.9121.0000.496
범죄대분류0.4420.5060.4050.4780.4620.4420.4961.000

Missing values

2023-12-12T23:39:04.480019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:39:04.653885image/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

범죄대분류범죄중분류10일이내20일이내1개월이내2개월이내3개월이내6개월이내6개월초과
0강력범죄살인기수244272048363247
1강력범죄살인미수등371473246344555
2강력범죄강도1129485321528285358467
3강력범죄강간강제추행5421262318223255176216932009
4강력범죄방화50221516423810611877
5절도범죄절도2805317228122872111311782115957718
6폭력범죄상해2524115879971915867733053666953
7폭력범죄폭행84394310071507421294885959233788
8폭력범죄체포감금188161100253197152145
9폭력범죄협박10277606071325711443124
범죄대분류범죄중분류10일이내20일이내1개월이내2개월이내3개월이내6개월이내6개월초과
25특별경제범죄특별경제범죄1825013296112242919822758149295039
26마약범죄마약범죄1216664576904521789432
27보건범죄보건범죄489935252538475525381655629
28환경범죄환경범죄5696755821154652371256
29교통범죄교통범죄2573091151055479164856190691332328408
30노동범죄노동범죄427320259608517370140
31안보범죄안보범죄9911751670
32선거범죄선거범죄18520620158334738562
33병역범죄병역범죄7420588738506149186326430
34기타범죄기타범죄4701532241246275298234575240719368