Overview

Dataset statistics

Number of variables8
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory74.7 B

Variable types

Text1
Numeric7

Dataset

Description대검찰청에서 발간하는 범죄분석은 3종의 범죄통계원표를 기반으로 작성하는 자료이며 이 중 본 데이터는 요일별 범죄발생건수에 관한 통계임. (단위: 건)
Author대검찰청
URLhttps://www.data.go.kr/data/15085723/fileData.do

Alerts

일 is highly overall correlated with 월 and 5 other fieldsHigh correlation
월 is highly overall correlated with 일 and 5 other fieldsHigh correlation
화 is highly overall correlated with 일 and 5 other fieldsHigh correlation
수 is highly overall correlated with 일 and 5 other fieldsHigh correlation
목 is highly overall correlated with 일 and 5 other fieldsHigh correlation
금 is highly overall correlated with 일 and 5 other fieldsHigh correlation
토 is highly overall correlated with 일 and 5 other fieldsHigh correlation
범죄분류 has unique valuesUnique
월 has unique valuesUnique
화 has unique valuesUnique
목 has unique valuesUnique
금 has unique valuesUnique
토 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:06:37.143246
Analysis finished2023-12-12 23:06:42.098966
Duration4.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄분류
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-13T08:06:42.243669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length16
Mean length7.9722222
Min length2

Characters and Unicode

Total characters287
Distinct characters99
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)100.0%

Sample

1st row절도
2nd row장물
3rd row손괴
4th row살인
5th row강도
ValueCountFrequency (%)
절도 1
 
2.8%
장물 1
 
2.8%
도로교통법(사고후미조치 1
 
2.8%
유기 1
 
2.8%
가정폭력범죄의처벌등에관한특례법 1
 
2.8%
경범죄처벌법 1
 
2.8%
교통사고처리특례법 1
 
2.8%
도로교통법 1
 
2.8%
도로교통법(무면허운전 1
 
2.8%
도로교통법(음주운전 1
 
2.8%
Other values (26) 26
72.2%
2023-12-13T08:06:42.601669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
법 15
 
5.2%
( 12
 
4.2%
) 12
 
4.2%
관 10
 
3.5%
도 9
 
3.1%
등 8
 
2.8%
에 7
 
2.4%
한 7
 
2.4%
통 6
 
2.1%
교 6
 
2.1%
Other values (89) 195
67.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 258
89.9%
Open Punctuation 12
 
4.2%
Close Punctuation 12
 
4.2%
Other Punctuation 5
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
법 15
 
5.8%
관 10
 
3.9%
도 9
 
3.5%
등 8
 
3.1%
에 7
 
2.7%
한 7
 
2.7%
통 6
 
2.3%
교 6
 
2.3%
률 6
 
2.3%
성 6
 
2.3%
Other values (86) 178
69.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Other Punctuation
ValueCountFrequency (%)
· 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 258
89.9%
Common 29
 
10.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
법 15
 
5.8%
관 10
 
3.9%
도 9
 
3.5%
등 8
 
3.1%
에 7
 
2.7%
한 7
 
2.7%
통 6
 
2.3%
교 6
 
2.3%
률 6
 
2.3%
성 6
 
2.3%
Other values (86) 178
69.0%
Common
ValueCountFrequency (%)
( 12
41.4%
) 12
41.4%
· 5
17.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 258
89.9%
ASCII 24
 
8.4%
None 5
 
1.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
법 15
 
5.8%
관 10
 
3.9%
도 9
 
3.5%
등 8
 
3.1%
에 7
 
2.7%
한 7
 
2.7%
통 6
 
2.3%
교 6
 
2.3%
률 6
 
2.3%
성 6
 
2.3%
Other values (86) 178
69.0%
ASCII
ValueCountFrequency (%)
( 12
50.0%
) 12
50.0%
None
ValueCountFrequency (%)
· 5
100.0%

일
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4162.8611
Minimum19
Maximum27651
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T08:06:42.781282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile21.25
Q1132.75
median525.5
Q32866.25
95-th percentile26231
Maximum27651
Range27632
Interquartile range (IQR)2733.5

Descriptive statistics

Standard deviation7989.4514
Coefficient of variation (CV)1.9192212
Kurtosis4.0686932
Mean4162.8611
Median Absolute Deviation (MAD)484
Skewness2.2847322
Sum149863
Variance63831334
MonotonicityNot monotonic
2023-12-13T08:06:42.893531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
19 2
 
5.6%
26008 1
 
2.8%
27 1
 
2.8%
1005 1
 
2.8%
20809 1
 
2.8%
2225 1
 
2.8%
6274 1
 
2.8%
6502 1
 
2.8%
26900 1
 
2.8%
436 1
 
2.8%
Other values (25) 25
69.4%
ValueCountFrequency (%)
19 2
5.6%
22 1
2.8%
27 1
2.8%
37 1
2.8%
62 1
2.8%
84 1
2.8%
106 1
2.8%
123 1
2.8%
136 1
2.8%
153 1
2.8%
ValueCountFrequency (%)
27651 1
2.8%
26900 1
2.8%
26008 1
2.8%
20809 1
2.8%
8551 1
2.8%
8014 1
2.8%
6502 1
2.8%
6274 1
2.8%
4790 1
2.8%
2225 1
2.8%

월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3926.1111
Minimum15
Maximum27124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T08:06:43.017493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile26
Q1167.25
median569
Q32789.75
95-th percentile21774.75
Maximum27124
Range27109
Interquartile range (IQR)2622.5

Descriptive statistics

Standard deviation7351.1649
Coefficient of variation (CV)1.8723782
Kurtosis4.250648
Mean3926.1111
Median Absolute Deviation (MAD)504.5
Skewness2.297976
Sum141340
Variance54039625
MonotonicityNot monotonic
2023-12-13T08:06:43.148268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
25101 1
 
2.8%
1632 1
 
2.8%
23 1
 
2.8%
856 1
 
2.8%
27124 1
 
2.8%
2296 1
 
2.8%
6942 1
 
2.8%
7073 1
 
2.8%
20440 1
 
2.8%
339 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
15 1
2.8%
23 1
2.8%
27 1
2.8%
61 1
2.8%
68 1
2.8%
86 1
2.8%
126 1
2.8%
129 1
2.8%
135 1
2.8%
178 1
2.8%
ValueCountFrequency (%)
27124 1
2.8%
25101 1
2.8%
20666 1
2.8%
20440 1
2.8%
7478 1
2.8%
7073 1
2.8%
6942 1
2.8%
6494 1
2.8%
4271 1
2.8%
2296 1
2.8%

화
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4070
Minimum18
Maximum26454
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T08:06:43.290589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile34.25
Q1160.5
median608.5
Q32928.25
95-th percentile24265
Maximum26454
Range26436
Interquartile range (IQR)2767.75

Descriptive statistics

Standard deviation7620.875
Coefficient of variation (CV)1.8724509
Kurtosis3.8547799
Mean4070
Median Absolute Deviation (MAD)545
Skewness2.2517004
Sum146520
Variance58077735
MonotonicityNot monotonic
2023-12-13T08:06:43.473497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
24886 1
 
2.8%
1733 1
 
2.8%
29 1
 
2.8%
887 1
 
2.8%
26454 1
 
2.8%
2430 1
 
2.8%
6932 1
 
2.8%
7037 1
 
2.8%
24058 1
 
2.8%
419 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
18 1
2.8%
29 1
2.8%
36 1
2.8%
42 1
2.8%
85 1
2.8%
109 1
2.8%
117 1
2.8%
127 1
2.8%
144 1
2.8%
166 1
2.8%
ValueCountFrequency (%)
26454 1
2.8%
24886 1
2.8%
24058 1
2.8%
22097 1
2.8%
7549 1
2.8%
7037 1
2.8%
6932 1
2.8%
6772 1
2.8%
4423 1
2.8%
2430 1
2.8%

수
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4119.4722
Minimum20
Maximum27284
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T08:06:43.612253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile35.25
Q1173.25
median613.5
Q33071.75
95-th percentile24751.25
Maximum27284
Range27264
Interquartile range (IQR)2898.5

Descriptive statistics

Standard deviation7753.7129
Coefficient of variation (CV)1.8822103
Kurtosis3.9567328
Mean4119.4722
Median Absolute Deviation (MAD)560
Skewness2.2705274
Sum148301
Variance60120063
MonotonicityNot monotonic
2023-12-13T08:06:43.747711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
40 2
 
5.6%
25226 1
 
2.8%
1702 1
 
2.8%
934 1
 
2.8%
27284 1
 
2.8%
2590 1
 
2.8%
6711 1
 
2.8%
7178 1
 
2.8%
24593 1
 
2.8%
390 1
 
2.8%
Other values (25) 25
69.4%
ValueCountFrequency (%)
20 1
2.8%
21 1
2.8%
40 2
5.6%
67 1
2.8%
118 1
2.8%
122 1
2.8%
129 1
2.8%
144 1
2.8%
183 1
2.8%
187 1
2.8%
ValueCountFrequency (%)
27284 1
2.8%
25226 1
2.8%
24593 1
2.8%
22048 1
2.8%
7540 1
2.8%
7178 1
2.8%
6806 1
2.8%
6711 1
2.8%
4517 1
2.8%
2590 1
2.8%

목
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4198.2222
Minimum28
Maximum27268
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T08:06:43.908915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile32.75
Q1155.25
median635
Q33097.5
95-th percentile26010.75
Maximum27268
Range27240
Interquartile range (IQR)2942.25

Descriptive statistics

Standard deviation7933.4843
Coefficient of variation (CV)1.8897247
Kurtosis4.0246157
Mean4198.2222
Median Absolute Deviation (MAD)540
Skewness2.2845265
Sum151136
Variance62940173
MonotonicityNot monotonic
2023-12-13T08:06:44.099246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
25831 1
 
2.8%
1723 1
 
2.8%
34 1
 
2.8%
910 1
 
2.8%
27268 1
 
2.8%
2578 1
 
2.8%
6950 1
 
2.8%
7199 1
 
2.8%
26550 1
 
2.8%
375 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
28 1
2.8%
29 1
2.8%
34 1
2.8%
56 1
2.8%
80 1
2.8%
110 1
2.8%
120 1
2.8%
126 1
2.8%
144 1
2.8%
159 1
2.8%
ValueCountFrequency (%)
27268 1
2.8%
26550 1
2.8%
25831 1
2.8%
21757 1
2.8%
7719 1
2.8%
7199 1
2.8%
6950 1
2.8%
6486 1
2.8%
4656 1
2.8%
2578 1
2.8%

금
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4395.4167
Minimum25
Maximum29556
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T08:06:44.277170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile39.5
Q1144.5
median622.5
Q33192.75
95-th percentile27040.75
Maximum29556
Range29531
Interquartile range (IQR)3048.25

Descriptive statistics

Standard deviation8414.8354
Coefficient of variation (CV)1.9144568
Kurtosis4.0374383
Mean4395.4167
Median Absolute Deviation (MAD)559
Skewness2.2878815
Sum158235
Variance70809456
MonotonicityNot monotonic
2023-12-13T08:06:44.475672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
27601 1
 
2.8%
1724 1
 
2.8%
41 1
 
2.8%
873 1
 
2.8%
29556 1
 
2.8%
2610 1
 
2.8%
6904 1
 
2.8%
7659 1
 
2.8%
26854 1
 
2.8%
384 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
25 1
2.8%
35 1
2.8%
41 1
2.8%
48 1
2.8%
79 1
2.8%
105 1
2.8%
106 1
2.8%
124 1
2.8%
137 1
2.8%
147 1
2.8%
ValueCountFrequency (%)
29556 1
2.8%
27601 1
2.8%
26854 1
2.8%
23340 1
2.8%
8373 1
2.8%
7659 1
2.8%
7007 1
2.8%
6904 1
2.8%
4941 1
2.8%
2610 1
2.8%

토
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4603.1389
Minimum14
Maximum29702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T08:06:44.681013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile30
Q1131
median530.5
Q33208.5
95-th percentile28278.75
Maximum29702
Range29688
Interquartile range (IQR)3077.5

Descriptive statistics

Standard deviation8917.4114
Coefficient of variation (CV)1.9372458
Kurtosis3.8393746
Mean4603.1389
Median Absolute Deviation (MAD)474
Skewness2.2653478
Sum165713
Variance79520226
MonotonicityNot monotonic
2023-12-13T08:06:44.880852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
29702 1
 
2.8%
1723 1
 
2.8%
27 1
 
2.8%
1026 1
 
2.8%
27014 1
 
2.8%
2536 1
 
2.8%
6374 1
 
2.8%
7599 1
 
2.8%
29298 1
 
2.8%
462 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
14 1
2.8%
27 1
2.8%
31 1
2.8%
38 1
2.8%
75 1
2.8%
111 1
2.8%
122 1
2.8%
125 1
2.8%
128 1
2.8%
132 1
2.8%
ValueCountFrequency (%)
29702 1
2.8%
29298 1
2.8%
27939 1
2.8%
27014 1
2.8%
9348 1
2.8%
7797 1
2.8%
7599 1
2.8%
6374 1
2.8%
5226 1
2.8%
2536 1
2.8%

Interactions

2023-12-13T08:06:41.351238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:37.409571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:37.971984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:38.502263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:39.413546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:40.004936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:40.645211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:41.433265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:37.479503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:38.044653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:38.585074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:39.502946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:40.092116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:40.735725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:41.538245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:37.549617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:38.116941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:38.665234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:39.590908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:40.174343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:40.898006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:41.616311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:37.630527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:38.200409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:38.739325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:39.670101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:40.255451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:40.990717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:41.696325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:37.716983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:38.272337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:38.814317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:39.749568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:40.339343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:41.072741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:41.772888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:37.798933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:38.353878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:39.202174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:39.837792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:40.433430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:41.153019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:41.851219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:37.887909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:38.431533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:39.294379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:39.917619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:40.537980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:06:41.257229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:06:45.067181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
범죄분류일월화수목금토
범죄분류1.0001.0001.0001.0001.0001.0001.0001.000
일1.0001.0000.9300.9110.9110.9110.9111.000
월1.0000.9301.0000.9950.9950.9950.9950.989
화1.0000.9110.9951.0001.0001.0001.0000.989
수1.0000.9110.9951.0001.0001.0001.0000.989
목1.0000.9110.9951.0001.0001.0001.0000.989
금1.0000.9110.9951.0001.0001.0001.0000.989
토1.0001.0000.9890.9890.9890.9890.9891.000
2023-12-13T08:06:45.509291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일월화수목금토
일1.0000.9840.9850.9860.9860.9880.996
월0.9841.0000.9980.9960.9960.9970.988
화0.9850.9981.0000.9980.9990.9990.988
수0.9860.9960.9981.0000.9970.9980.992
목0.9860.9960.9990.9971.0000.9980.987
금0.9880.9970.9990.9980.9981.0000.991
토0.9960.9880.9880.9920.9870.9911.000

Missing values

2023-12-13T08:06:41.954414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:06:42.059239image/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

범죄분류일월화수목금토
0절도26008251012488625226258312760129702
1장물237286298308273283249
2손괴8551747875497540771983739348
3살인106135127118126124122
4강도153126144144144147132
5방화218195190187207177184
6성폭력4790427144234517465649415226
7폭행27651206662209722048217572334027939
8상해8014649467726806648670077797
9협박2218220322092330233623832328
범죄분류일월화수목금토
26도로교통법(사고후미조치)6502707370377178719976597599
27도로교통법(음주운전)26900204402405824593265502685429298
28도로교통법(음주측정거부)436339419390375384462
29마약류관리에관한법률(대마)136178166183159137128
30마약류관리에관한법률(마약)62237222239224196125
31마약류관리에관한법률(향정)94110169751043993947883
32성매매알선등행위의처벌에관한법률85317311899160621641349813
33아동·청소년의성보호에관한법률(성매수등)8486117122120105111
34아동·청소년의성보호에관한법률(음란물등)37688567807975
35특가법(도주차량)633626651699721686795