Overview

Dataset statistics

Number of variables8
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory75.9 B

Variable types

Text1
Numeric7

Dataset

Description대검찰청에서 발간하는 범죄분석은 3종의 범죄통계원표를 기반으로 작성하는 자료이며, 이 중 본 데이터는 절도 범죄의 발생장소와 수법에 관한 통계임. (단위: 건)
Author대검찰청
URLhttps://www.data.go.kr/data/15085797/fileData.do

Alerts

침입절도 is highly overall correlated with 들치기 and 2 other fieldsHigh correlation
소매치기 is highly overall correlated with 날치기 and 3 other fieldsHigh correlation
날치기 is highly overall correlated with 소매치기 and 3 other fieldsHigh correlation
들치기 is highly overall correlated with 침입절도 and 4 other fieldsHigh correlation
속임수절도 is highly overall correlated with 침입절도 and 4 other fieldsHigh correlation
기타 is highly overall correlated with 침입절도 and 4 other fieldsHigh correlation
미상 is highly overall correlated with 소매치기 and 1 other fieldsHigh correlation
장소 has unique valuesUnique
침입절도 has unique valuesUnique
기타 has unique valuesUnique
침입절도 has 1 (3.7%) zerosZeros
소매치기 has 9 (33.3%) zerosZeros
날치기 has 15 (55.6%) zerosZeros
들치기 has 2 (7.4%) zerosZeros
속임수절도 has 3 (11.1%) zerosZeros
미상 has 11 (40.7%) zerosZeros

Reproduction

Analysis started2023-12-12 10:02:04.778868
Analysis finished2023-12-12 10:02:11.385168
Duration6.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

장소
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-12T19:02:11.538771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length3.8148148
Min length2

Characters and Unicode

Total characters103
Distinct characters64
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row아파트·연립·다세대
2nd row단독주택
3rd row고속도로
4th row노상
5th row상점
ValueCountFrequency (%)
아파트·연립·다세대 1
 
3.7%
기타교통수단내 1
 
3.7%
공지 1
 
3.7%
구금장소 1
 
3.7%
부대 1
 
3.7%
해상 1
 
3.7%
산야 1
 
3.7%
종교기관 1
 
3.7%
의료기관 1
 
3.7%
금융기관 1
 
3.7%
Other values (17) 17
63.0%
2023-12-12T19:02:11.959541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
· 6
 
5.8%
장 5
 
4.9%
기 5
 
4.9%
소 3
 
2.9%
지 3
 
2.9%
공 3
 
2.9%
교 3
 
2.9%
대 3
 
2.9%
관 3
 
2.9%
상 3
 
2.9%
Other values (54) 66
64.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 97
94.2%
Other Punctuation 6
 
5.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 5
 
5.2%
기 5
 
5.2%
소 3
 
3.1%
지 3
 
3.1%
공 3
 
3.1%
교 3
 
3.1%
대 3
 
3.1%
관 3
 
3.1%
상 3
 
3.1%
흥 2
 
2.1%
Other values (53) 64
66.0%
Other Punctuation
ValueCountFrequency (%)
· 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 97
94.2%
Common 6
 
5.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 5
 
5.2%
기 5
 
5.2%
소 3
 
3.1%
지 3
 
3.1%
공 3
 
3.1%
교 3
 
3.1%
대 3
 
3.1%
관 3
 
3.1%
상 3
 
3.1%
흥 2
 
2.1%
Other values (53) 64
66.0%
Common
ValueCountFrequency (%)
· 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 97
94.2%
None 6
 
5.8%

Most frequent character per block

None
ValueCountFrequency (%)
· 6
100.0%
Hangul
ValueCountFrequency (%)
장 5
 
5.2%
기 5
 
5.2%
소 3
 
3.1%
지 3
 
3.1%
공 3
 
3.1%
교 3
 
3.1%
대 3
 
3.1%
관 3
 
3.1%
상 3
 
3.1%
흥 2
 
2.1%
Other values (53) 64
66.0%

침입절도
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1034.1481
Minimum0
Maximum9560
Zeros1
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T19:02:12.096210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.3
Q124
median283
Q3715
95-th percentile4711.1
Maximum9560
Range9560
Interquartile range (IQR)691

Descriptive statistics

Standard deviation2116.4998
Coefficient of variation (CV)2.0466118
Kurtosis10.537384
Mean1034.1481
Median Absolute Deviation (MAD)269
Skewness3.1174114
Sum27922
Variance4479571.2
MonotonicityNot monotonic
2023-12-12T19:02:12.225940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
2451 1
 
3.7%
5642 1
 
3.7%
9560 1
 
3.7%
2 1
 
3.7%
0 1
 
3.7%
5 1
 
3.7%
28 1
 
3.7%
86 1
 
3.7%
814 1
 
3.7%
367 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0 1
3.7%
1 1
3.7%
2 1
3.7%
4 1
3.7%
5 1
3.7%
14 1
3.7%
21 1
3.7%
27 1
3.7%
28 1
3.7%
45 1
3.7%
ValueCountFrequency (%)
9560 1
3.7%
5642 1
3.7%
2539 1
3.7%
2467 1
3.7%
2451 1
3.7%
1306 1
3.7%
814 1
3.7%
616 1
3.7%
434 1
3.7%
426 1
3.7%

소매치기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.407407
Minimum0
Maximum273
Zeros9
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T19:02:12.357829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q330.5
95-th percentile197.4
Maximum273
Range273
Interquartile range (IQR)30.5

Descriptive statistics

Standard deviation69.163274
Coefficient of variation (CV)2.2745535
Kurtosis8.3396891
Mean30.407407
Median Absolute Deviation (MAD)1
Skewness2.9679082
Sum821
Variance4783.5584
MonotonicityNot monotonic
2023-12-12T19:02:12.462757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 9
33.3%
1 6
22.2%
3 2
 
7.4%
273 1
 
3.7%
32 1
 
3.7%
47 1
 
3.7%
36 1
 
3.7%
2 1
 
3.7%
44 1
 
3.7%
91 1
 
3.7%
Other values (3) 3
 
11.1%
ValueCountFrequency (%)
0 9
33.3%
1 6
22.2%
2 1
 
3.7%
3 2
 
7.4%
12 1
 
3.7%
29 1
 
3.7%
32 1
 
3.7%
36 1
 
3.7%
44 1
 
3.7%
47 1
 
3.7%
ValueCountFrequency (%)
273 1
3.7%
243 1
3.7%
91 1
3.7%
47 1
3.7%
44 1
3.7%
36 1
3.7%
32 1
3.7%
29 1
3.7%
12 1
3.7%
3 2
7.4%

날치기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6
Minimum0
Maximum118
Zeros15
Zeros (%)55.6%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T19:02:12.566208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile19.4
Maximum118
Range118
Interquartile range (IQR)1

Descriptive statistics

Standard deviation22.931336
Coefficient of variation (CV)3.8218893
Kurtosis24.229491
Mean6
Median Absolute Deviation (MAD)0
Skewness4.8530571
Sum162
Variance525.84615
MonotonicityNot monotonic
2023-12-12T19:02:12.666287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 15
55.6%
1 6
 
22.2%
3 2
 
7.4%
2 1
 
3.7%
118 1
 
3.7%
4 1
 
3.7%
26 1
 
3.7%
ValueCountFrequency (%)
0 15
55.6%
1 6
 
22.2%
2 1
 
3.7%
3 2
 
7.4%
4 1
 
3.7%
26 1
 
3.7%
118 1
 
3.7%
ValueCountFrequency (%)
118 1
 
3.7%
26 1
 
3.7%
4 1
 
3.7%
3 2
 
7.4%
2 1
 
3.7%
1 6
 
22.2%
0 15
55.6%

들치기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean731.22222
Minimum0
Maximum7509
Zeros2
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T19:02:12.793813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6
Q139.5
median127
Q3341.5
95-th percentile4278.3
Maximum7509
Range7509
Interquartile range (IQR)302

Descriptive statistics

Standard deviation1711.9618
Coefficient of variation (CV)2.3412333
Kurtosis10.416389
Mean731.22222
Median Absolute Deviation (MAD)102
Skewness3.2190471
Sum19743
Variance2930813.3
MonotonicityNot monotonic
2023-12-12T19:02:12.947952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 2
 
7.4%
360 1
 
3.7%
178 1
 
3.7%
7509 1
 
3.7%
13 1
 
3.7%
2 1
 
3.7%
532 1
 
3.7%
25 1
 
3.7%
125 1
 
3.7%
477 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
0 2
7.4%
2 1
3.7%
12 1
3.7%
13 1
3.7%
25 1
3.7%
39 1
3.7%
40 1
3.7%
46 1
3.7%
63 1
3.7%
78 1
3.7%
ValueCountFrequency (%)
7509 1
3.7%
4947 1
3.7%
2718 1
3.7%
1173 1
3.7%
532 1
3.7%
477 1
3.7%
360 1
3.7%
323 1
3.7%
227 1
3.7%
215 1
3.7%

속임수절도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.37037
Minimum0
Maximum646
Zeros3
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T19:02:13.061561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q334
95-th percentile401.5
Maximum646
Range646
Interquartile range (IQR)32

Descriptive statistics

Standard deviation152.77665
Coefficient of variation (CV)2.3018803
Kurtosis9.5008485
Mean66.37037
Median Absolute Deviation (MAD)7
Skewness3.1145114
Sum1792
Variance23340.704
MonotonicityNot monotonic
2023-12-12T19:02:13.170818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 5
18.5%
1 3
 
11.1%
0 3
 
11.1%
14 2
 
7.4%
487 1
 
3.7%
10 1
 
3.7%
30 1
 
3.7%
5 1
 
3.7%
3 1
 
3.7%
7 1
 
3.7%
Other values (8) 8
29.6%
ValueCountFrequency (%)
0 3
11.1%
1 3
11.1%
2 5
18.5%
3 1
 
3.7%
5 1
 
3.7%
7 1
 
3.7%
10 1
 
3.7%
14 2
 
7.4%
20 1
 
3.7%
28 1
 
3.7%
ValueCountFrequency (%)
646 1
3.7%
487 1
3.7%
202 1
3.7%
119 1
3.7%
83 1
3.7%
73 1
3.7%
38 1
3.7%
30 1
3.7%
28 1
3.7%
20 1
3.7%

기타
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4938.7778
Minimum5
Maximum56604
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T19:02:13.280308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile14.2
Q1296
median903
Q32907
95-th percentile26200.3
Maximum56604
Range56599
Interquartile range (IQR)2611

Descriptive statistics

Standard deviation12130.411
Coefficient of variation (CV)2.4561566
Kurtosis13.685647
Mean4938.7778
Median Absolute Deviation (MAD)752
Skewness3.6177781
Sum133347
Variance1.4714688 × 108
MonotonicityNot monotonic
2023-12-12T19:02:13.384975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
4138 1
 
3.7%
3099 1
 
3.7%
56604 1
 
3.7%
17 1
 
3.7%
5 1
 
3.7%
13 1
 
3.7%
49 1
 
3.7%
1655 1
 
3.7%
211 1
 
3.7%
797 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
5 1
3.7%
13 1
3.7%
17 1
3.7%
49 1
3.7%
126 1
3.7%
211 1
3.7%
280 1
3.7%
312 1
3.7%
408 1
3.7%
535 1
3.7%
ValueCountFrequency (%)
56604 1
3.7%
31327 1
3.7%
14238 1
3.7%
6031 1
3.7%
4138 1
3.7%
3741 1
3.7%
3099 1
3.7%
2715 1
3.7%
1655 1
3.7%
1618 1
3.7%

미상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.037037
Minimum0
Maximum468
Zeros11
Zeros (%)40.7%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T19:02:13.481329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33.5
95-th percentile31.5
Maximum468
Range468
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation89.690271
Coefficient of variation (CV)4.263446
Kurtosis26.511168
Mean21.037037
Median Absolute Deviation (MAD)1
Skewness5.1307106
Sum568
Variance8044.3447
MonotonicityNot monotonic
2023-12-12T19:02:13.591643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 11
40.7%
1 7
25.9%
2 2
 
7.4%
10 2
 
7.4%
5 1
 
3.7%
39 1
 
3.7%
11 1
 
3.7%
14 1
 
3.7%
468 1
 
3.7%
ValueCountFrequency (%)
0 11
40.7%
1 7
25.9%
2 2
 
7.4%
5 1
 
3.7%
10 2
 
7.4%
11 1
 
3.7%
14 1
 
3.7%
39 1
 
3.7%
468 1
 
3.7%
ValueCountFrequency (%)
468 1
 
3.7%
39 1
 
3.7%
14 1
 
3.7%
11 1
 
3.7%
10 2
 
7.4%
5 1
 
3.7%
2 2
 
7.4%
1 7
25.9%
0 11
40.7%

Interactions

2023-12-12T19:02:10.517762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:05.181286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:06.323870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:07.543976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:08.311216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:09.135771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:09.845229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:10.599142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:05.299860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:06.449295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:07.642739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:08.429143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:09.235103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:09.939854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:10.709362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:05.437574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:06.589038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:07.755764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:08.560549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:09.339905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:10.042684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:10.819012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:05.600551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:06.713424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:07.853681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:08.693685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:09.447599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:10.142601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:10.910097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:05.771307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:06.824068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:07.981613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:08.818024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:09.576601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:10.256052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:11.003115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:05.989189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:06.937301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:08.092522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:08.937039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:09.666848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:10.349289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:11.090869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:06.160708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:07.057333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:08.209710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:09.038586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:09.756508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:02:10.432065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:02:13.685616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
장소침입절도소매치기날치기들치기속임수절도기타미상
장소1.0001.0001.0001.0001.0001.0001.0001.000
침입절도1.0001.0000.8030.6730.8930.9420.8931.000
소매치기1.0000.8031.0001.0000.9600.8960.9601.000
날치기1.0000.6731.0001.0001.0000.7641.0001.000
들치기1.0000.8930.9601.0001.0000.9951.0001.000
속임수절도1.0000.9420.8960.7640.9951.0000.9951.000
기타1.0000.8930.9601.0001.0000.9951.0001.000
미상1.0001.0001.0001.0001.0001.0001.0001.000
2023-12-12T19:02:13.798360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
침입절도소매치기날치기들치기속임수절도기타미상
침입절도1.0000.2040.2060.5020.6150.6270.170
소매치기0.2041.0000.5650.5820.4940.5030.588
날치기0.2060.5651.0000.7560.5900.6600.415
들치기0.5020.5820.7561.0000.7890.9530.405
속임수절도0.6150.4940.5900.7891.0000.8570.526
기타0.6270.5030.6600.9530.8571.0000.431
미상0.1700.5880.4150.4050.5260.4311.000

Missing values

2023-12-12T19:02:11.201633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:02:11.337186image/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

장소침입절도소매치기날치기들치기속임수절도기타미상
0아파트·연립·다세대2451133608341382
1단독주택5642122277330991
2고속도로4101211260
3노상1427311849471193132710
4상점246732027186461423810
5시장·노점3804713232010311
6숙박업소·목욕탕616311952827151
7유흥접객업소2539363117320260311
8사무실1306201273816185
9공장4260046145351
장소침입절도소매치기날치기들치기속임수절도기타미상
17학교283106316570
18금융기관27014773037410
19의료기관36710125107970
20종교기관814102502110
21산야8601532216550
22해상2800014491
23부대50021130
24구금장소0000052
25공지200130170
26기타956024326750948756604468