Overview

Dataset statistics

Number of variables14
Number of observations162
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.9 KiB
Average record size in memory125.8 B

Variable types

Text1
Numeric13

Dataset

Description대검찰청에서 발간하는 범죄분석은 3종의 범죄통계원표를 기반으로 작성하는 자료이며 이 중 본 데이터는 범죄의 수사 단서에 관한 통계임. (단위: 건)
Author대검찰청
URLhttps://www.data.go.kr/data/15085610/fileData.do

Alerts

현행범 is highly overall correlated with 피해자신고 and 7 other fieldsHigh correlation
피해자신고 is highly overall correlated with 현행범 and 6 other fieldsHigh correlation
고소 is highly overall correlated with 피해자신고 and 1 other fieldsHigh correlation
고발 is highly overall correlated with 진정투서High correlation
자수 is highly overall correlated with 현행범 and 5 other fieldsHigh correlation
진정투서 is highly overall correlated with 현행범 and 5 other fieldsHigh correlation
타인신고 is highly overall correlated with 현행범 and 6 other fieldsHigh correlation
불심검문 is highly overall correlated with 현행범 and 4 other fieldsHigh correlation
피해품발견 is highly overall correlated with 피해자신고 and 2 other fieldsHigh correlation
탐문정보 is highly overall correlated with 현행범 and 3 other fieldsHigh correlation
여죄 is highly overall correlated with 현행범 and 4 other fieldsHigh correlation
기타 is highly overall correlated with 현행범 and 7 other fieldsHigh correlation
범죄분류 has unique valuesUnique
현행범 has 24 (14.8%) zerosZeros
피해자신고 has 19 (11.7%) zerosZeros
고소 has 13 (8.0%) zerosZeros
고발 has 14 (8.6%) zerosZeros
자수 has 87 (53.7%) zerosZeros
진정투서 has 16 (9.9%) zerosZeros
타인신고 has 11 (6.8%) zerosZeros
불심검문 has 67 (41.4%) zerosZeros
피해품발견 has 121 (74.7%) zerosZeros
변사체 has 134 (82.7%) zerosZeros
탐문정보 has 17 (10.5%) zerosZeros
여죄 has 29 (17.9%) zerosZeros
기타 has 2 (1.2%) zerosZeros

Reproduction

Analysis started2023-12-12 23:27:45.303151
Analysis finished2023-12-12 23:28:00.527448
Duration15.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄분류
Text

UNIQUE 

Distinct162
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-13T08:28:00.686362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length19
Mean length8.037037
Min length2

Characters and Unicode

Total characters1302
Distinct characters225
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

Unique162 ?
Unique (%)100.0%

Sample

1st row절도
2nd row장물
3rd row사기
4th row횡령
5th row배임
ValueCountFrequency (%)
절도 1
 
0.6%
약사법 1
 
0.6%
사행행위등규제및처벌특례법 1
 
0.6%
성매매알선등행위의처벌에관한법률 1
 
0.6%
산림자원의조성및관리에관한법률 1
 
0.6%
산업안전보건법 1
 
0.6%
산지관리법 1
 
0.6%
상표법 1
 
0.6%
석유및석유대체연료사업법 1
 
0.6%
선박안전법 1
 
0.6%
Other values (152) 152
93.8%
2023-12-13T08:28:01.004806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
114
 
8.8%
67
 
5.1%
41
 
3.1%
40
 
3.1%
34
 
2.6%
31
 
2.4%
27
 
2.1%
26
 
2.0%
23
 
1.8%
19
 
1.5%
Other values (215) 880
67.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1268
97.4%
Open Punctuation 12
 
0.9%
Close Punctuation 12
 
0.9%
Other Punctuation 10
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
114
 
9.0%
67
 
5.3%
41
 
3.2%
40
 
3.2%
34
 
2.7%
31
 
2.4%
27
 
2.1%
26
 
2.1%
23
 
1.8%
19
 
1.5%
Other values (211) 846
66.7%
Other Punctuation
ValueCountFrequency (%)
, 8
80.0%
· 2
 
20.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1268
97.4%
Common 34
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
114
 
9.0%
67
 
5.3%
41
 
3.2%
40
 
3.2%
34
 
2.7%
31
 
2.4%
27
 
2.1%
26
 
2.1%
23
 
1.8%
19
 
1.5%
Other values (211) 846
66.7%
Common
ValueCountFrequency (%)
( 12
35.3%
) 12
35.3%
, 8
23.5%
· 2
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1268
97.4%
ASCII 32
 
2.5%
None 2
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
114
 
9.0%
67
 
5.3%
41
 
3.2%
40
 
3.2%
34
 
2.7%
31
 
2.4%
27
 
2.1%
26
 
2.1%
23
 
1.8%
19
 
1.5%
Other values (211) 846
66.7%
ASCII
ValueCountFrequency (%)
( 12
37.5%
) 12
37.5%
, 8
25.0%
None
ValueCountFrequency (%)
· 2
100.0%

현행범
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct93
Distinct (%)57.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean878.45062
Minimum0
Maximum46624
Zeros24
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T08:28:01.118474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median24
Q3175
95-th percentile4889.2
Maximum46624
Range46624
Interquartile range (IQR)173

Descriptive statistics

Standard deviation4075.3693
Coefficient of variation (CV)4.6392697
Kurtosis100.13991
Mean878.45062
Median Absolute Deviation (MAD)24
Skewness9.2637666
Sum142309
Variance16608635
MonotonicityNot monotonic
2023-12-13T08:28:01.224449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24
 
14.8%
1 13
 
8.0%
2 9
 
5.6%
4 6
 
3.7%
3 4
 
2.5%
10 3
 
1.9%
8 3
 
1.9%
19 3
 
1.9%
16 3
 
1.9%
62 2
 
1.2%
Other values (83) 92
56.8%
ValueCountFrequency (%)
0 24
14.8%
1 13
8.0%
2 9
 
5.6%
3 4
 
2.5%
4 6
 
3.7%
5 2
 
1.2%
6 2
 
1.2%
7 1
 
0.6%
8 3
 
1.9%
9 2
 
1.2%
ValueCountFrequency (%)
46624 1
0.6%
11849 1
0.6%
10316 1
0.6%
9254 1
0.6%
8573 1
0.6%
8449 1
0.6%
6449 1
0.6%
6030 1
0.6%
4960 1
0.6%
3544 1
0.6%

피해자신고
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct89
Distinct (%)54.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4081.463
Minimum0
Maximum146812
Zeros19
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T08:28:01.334976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median19.5
Q3262.75
95-th percentile20525.85
Maximum146812
Range146812
Interquartile range (IQR)260.75

Descriptive statistics

Standard deviation18227.736
Coefficient of variation (CV)4.4659809
Kurtosis42.791474
Mean4081.463
Median Absolute Deviation (MAD)19.5
Skewness6.3255954
Sum661197
Variance3.3225035 × 108
MonotonicityNot monotonic
2023-12-13T08:28:01.443496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
11.7%
1 18
 
11.1%
3 8
 
4.9%
10 5
 
3.1%
2 5
 
3.1%
5 5
 
3.1%
6 4
 
2.5%
23 4
 
2.5%
9 3
 
1.9%
7 3
 
1.9%
Other values (79) 88
54.3%
ValueCountFrequency (%)
0 19
11.7%
1 18
11.1%
2 5
 
3.1%
3 8
4.9%
4 2
 
1.2%
5 5
 
3.1%
6 4
 
2.5%
7 3
 
1.9%
8 1
 
0.6%
9 3
 
1.9%
ValueCountFrequency (%)
146812 1
0.6%
134416 1
0.6%
97219 1
0.6%
46625 1
0.6%
41980 1
0.6%
29120 1
0.6%
24768 1
0.6%
24382 1
0.6%
20914 1
0.6%
13151 1
0.6%

고소
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct98
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1559.6049
Minimum0
Maximum96434
Zeros13
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T08:28:01.561949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median26
Q3224.75
95-th percentile6990.1
Maximum96434
Range96434
Interquartile range (IQR)220.75

Descriptive statistics

Standard deviation8048.1641
Coefficient of variation (CV)5.1603864
Kurtosis121.76671
Mean1559.6049
Median Absolute Deviation (MAD)25
Skewness10.470726
Sum252656
Variance64772945
MonotonicityNot monotonic
2023-12-13T08:28:01.694227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
8.0%
1 10
 
6.2%
3 7
 
4.3%
2 7
 
4.3%
4 6
 
3.7%
15 6
 
3.7%
6 4
 
2.5%
11 4
 
2.5%
7 3
 
1.9%
12 3
 
1.9%
Other values (88) 99
61.1%
ValueCountFrequency (%)
0 13
8.0%
1 10
6.2%
2 7
4.3%
3 7
4.3%
4 6
3.7%
5 1
 
0.6%
6 4
 
2.5%
7 3
 
1.9%
8 2
 
1.2%
9 3
 
1.9%
ValueCountFrequency (%)
96434 1
0.6%
20334 1
0.6%
14876 1
0.6%
13602 1
0.6%
11839 1
0.6%
11426 1
0.6%
10462 1
0.6%
9660 1
0.6%
6996 1
0.6%
6878 1
0.6%

고발
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct110
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean395.17901
Minimum0
Maximum8044
Zeros14
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T08:28:01.814247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median60.5
Q3261
95-th percentile1726.8
Maximum8044
Range8044
Interquartile range (IQR)251

Descriptive statistics

Standard deviation1102.3419
Coefficient of variation (CV)2.7894749
Kurtosis28.002936
Mean395.17901
Median Absolute Deviation (MAD)58
Skewness5.0096931
Sum64019
Variance1215157.8
MonotonicityNot monotonic
2023-12-13T08:28:01.925714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
8.6%
2 5
 
3.1%
1 5
 
3.1%
4 4
 
2.5%
10 3
 
1.9%
13 3
 
1.9%
26 3
 
1.9%
20 3
 
1.9%
9 3
 
1.9%
3 3
 
1.9%
Other values (100) 116
71.6%
ValueCountFrequency (%)
0 14
8.6%
1 5
 
3.1%
2 5
 
3.1%
3 3
 
1.9%
4 4
 
2.5%
5 1
 
0.6%
6 1
 
0.6%
7 1
 
0.6%
8 2
 
1.2%
9 3
 
1.9%
ValueCountFrequency (%)
8044 1
0.6%
7241 1
0.6%
6313 1
0.6%
3808 1
0.6%
3133 1
0.6%
2978 1
0.6%
2421 1
0.6%
1855 1
0.6%
1741 1
0.6%
1457 1
0.6%

자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.722222
Minimum0
Maximum8267
Zeros87
Zeros (%)53.7%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T08:28:02.032642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.75
95-th percentile95.45
Maximum8267
Range8267
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation649.83062
Coefficient of variation (CV)10.197865
Kurtosis160.66635
Mean63.722222
Median Absolute Deviation (MAD)0
Skewness12.651233
Sum10323
Variance422279.83
MonotonicityNot monotonic
2023-12-13T08:28:02.125272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 87
53.7%
1 20
 
12.3%
3 8
 
4.9%
2 6
 
3.7%
5 5
 
3.1%
4 3
 
1.9%
9 2
 
1.2%
14 2
 
1.2%
21 2
 
1.2%
6 2
 
1.2%
Other values (25) 25
 
15.4%
ValueCountFrequency (%)
0 87
53.7%
1 20
 
12.3%
2 6
 
3.7%
3 8
 
4.9%
4 3
 
1.9%
5 5
 
3.1%
6 2
 
1.2%
9 2
 
1.2%
10 1
 
0.6%
11 1
 
0.6%
ValueCountFrequency (%)
8267 1
0.6%
382 1
0.6%
215 1
0.6%
149 1
0.6%
124 1
0.6%
107 1
0.6%
100 1
0.6%
98 1
0.6%
96 1
0.6%
85 1
0.6%

진정투서
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct87
Distinct (%)53.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean855.92593
Minimum0
Maximum93930
Zeros16
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T08:28:02.453648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median19
Q3113.75
95-th percentile1527.1
Maximum93930
Range93930
Interquartile range (IQR)108.75

Descriptive statistics

Standard deviation7426.046
Coefficient of variation (CV)8.6760381
Kurtosis156.05077
Mean855.92593
Median Absolute Deviation (MAD)18
Skewness12.394553
Sum138660
Variance55146159
MonotonicityNot monotonic
2023-12-13T08:28:02.554363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
9.9%
1 7
 
4.3%
6 7
 
4.3%
2 6
 
3.7%
3 5
 
3.1%
12 5
 
3.1%
5 5
 
3.1%
4 5
 
3.1%
13 4
 
2.5%
7 3
 
1.9%
Other values (77) 99
61.1%
ValueCountFrequency (%)
0 16
9.9%
1 7
4.3%
2 6
 
3.7%
3 5
 
3.1%
4 5
 
3.1%
5 5
 
3.1%
6 7
4.3%
7 3
 
1.9%
8 2
 
1.2%
9 2
 
1.2%
ValueCountFrequency (%)
93930 1
0.6%
10242 1
0.6%
4161 1
0.6%
3568 1
0.6%
3190 1
0.6%
2859 1
0.6%
2663 1
0.6%
1734 1
0.6%
1537 1
0.6%
1339 1
0.6%

타인신고
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct99
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean697.01852
Minimum0
Maximum25291
Zeros11
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T08:28:02.663444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median46
Q3205
95-th percentile2564.35
Maximum25291
Range25291
Interquartile range (IQR)200

Descriptive statistics

Standard deviation2862.9022
Coefficient of variation (CV)4.1073545
Kurtosis58.021915
Mean697.01852
Median Absolute Deviation (MAD)45
Skewness7.3192294
Sum112917
Variance8196208.9
MonotonicityNot monotonic
2023-12-13T08:28:02.773415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 13
 
8.0%
5 12
 
7.4%
0 11
 
6.8%
2 9
 
5.6%
4 5
 
3.1%
12 3
 
1.9%
26 3
 
1.9%
19 3
 
1.9%
205 2
 
1.2%
20 2
 
1.2%
Other values (89) 99
61.1%
ValueCountFrequency (%)
0 11
6.8%
1 13
8.0%
2 9
5.6%
3 2
 
1.2%
4 5
 
3.1%
5 12
7.4%
6 1
 
0.6%
7 1
 
0.6%
8 2
 
1.2%
9 1
 
0.6%
ValueCountFrequency (%)
25291 1
0.6%
23411 1
0.6%
7087 1
0.6%
7075 1
0.6%
6209 1
0.6%
3357 1
0.6%
2864 1
0.6%
2601 1
0.6%
2570 1
0.6%
2457 1
0.6%

불심검문
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1145.9877
Minimum0
Maximum124184
Zeros67
Zeros (%)41.4%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T08:28:02.888349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q322.75
95-th percentile498.5
Maximum124184
Range124184
Interquartile range (IQR)22.75

Descriptive statistics

Standard deviation10170.499
Coefficient of variation (CV)8.8748769
Kurtosis135.86372
Mean1145.9877
Median Absolute Deviation (MAD)1
Skewness11.381582
Sum185650
Variance1.0343906 × 108
MonotonicityNot monotonic
2023-12-13T08:28:03.016479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 67
41.4%
1 16
 
9.9%
2 6
 
3.7%
3 6
 
3.7%
7 3
 
1.9%
6 3
 
1.9%
11 3
 
1.9%
16 3
 
1.9%
25 3
 
1.9%
23 2
 
1.2%
Other values (46) 50
30.9%
ValueCountFrequency (%)
0 67
41.4%
1 16
 
9.9%
2 6
 
3.7%
3 6
 
3.7%
4 1
 
0.6%
5 1
 
0.6%
6 3
 
1.9%
7 3
 
1.9%
8 2
 
1.2%
9 1
 
0.6%
ValueCountFrequency (%)
124184 1
0.6%
35343 1
0.6%
12688 1
0.6%
5240 1
0.6%
2029 1
0.6%
1169 1
0.6%
862 1
0.6%
545 1
0.6%
509 1
0.6%
299 1
0.6%

피해품발견
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.12963
Minimum0
Maximum1178
Zeros121
Zeros (%)74.7%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T08:28:03.179145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.75
95-th percentile7.95
Maximum1178
Range1178
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation98.345451
Coefficient of variation (CV)8.1078693
Kurtosis125.37191
Mean12.12963
Median Absolute Deviation (MAD)0
Skewness10.824681
Sum1965
Variance9671.8278
MonotonicityNot monotonic
2023-12-13T08:28:03.285199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 121
74.7%
1 14
 
8.6%
2 9
 
5.6%
7 2
 
1.2%
10 2
 
1.2%
6 2
 
1.2%
3 2
 
1.2%
4 2
 
1.2%
17 1
 
0.6%
5 1
 
0.6%
Other values (6) 6
 
3.7%
ValueCountFrequency (%)
0 121
74.7%
1 14
 
8.6%
2 9
 
5.6%
3 2
 
1.2%
4 2
 
1.2%
5 1
 
0.6%
6 2
 
1.2%
7 2
 
1.2%
8 1
 
0.6%
9 1
 
0.6%
ValueCountFrequency (%)
1178 1
0.6%
331 1
0.6%
285 1
0.6%
40 1
0.6%
17 1
0.6%
10 2
1.2%
9 1
0.6%
8 1
0.6%
7 2
1.2%
6 2
1.2%

변사체
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.962963
Minimum0
Maximum311
Zeros134
Zeros (%)82.7%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T08:28:03.383873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.95
Maximum311
Range311
Interquartile range (IQR)0

Descriptive statistics

Standard deviation25.032622
Coefficient of variation (CV)8.4485099
Kurtosis145.08091
Mean2.962963
Median Absolute Deviation (MAD)0
Skewness11.833949
Sum480
Variance626.63216
MonotonicityNot monotonic
2023-12-13T08:28:03.504879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 134
82.7%
1 11
 
6.8%
2 5
 
3.1%
6 2
 
1.2%
3 1
 
0.6%
69 1
 
0.6%
16 1
 
0.6%
15 1
 
0.6%
9 1
 
0.6%
311 1
 
0.6%
Other values (4) 4
 
2.5%
ValueCountFrequency (%)
0 134
82.7%
1 11
 
6.8%
2 5
 
3.1%
3 1
 
0.6%
4 1
 
0.6%
5 1
 
0.6%
6 2
 
1.2%
7 1
 
0.6%
8 1
 
0.6%
9 1
 
0.6%
ValueCountFrequency (%)
311 1
0.6%
69 1
0.6%
16 1
0.6%
15 1
0.6%
9 1
0.6%
8 1
0.6%
7 1
0.6%
6 2
1.2%
5 1
0.6%
4 1
0.6%

탐문정보
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct102
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean257.2037
Minimum0
Maximum10546
Zeros17
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T08:28:03.663019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median39
Q3144.5
95-th percentile1274.35
Maximum10546
Range10546
Interquartile range (IQR)137.5

Descriptive statistics

Standard deviation930.65645
Coefficient of variation (CV)3.6183633
Kurtosis94.16144
Mean257.2037
Median Absolute Deviation (MAD)37
Skewness8.8855723
Sum41667
Variance866121.43
MonotonicityNot monotonic
2023-12-13T08:28:03.809522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
10.5%
1 7
 
4.3%
6 6
 
3.7%
7 6
 
3.7%
24 3
 
1.9%
20 3
 
1.9%
2 3
 
1.9%
14 3
 
1.9%
4 3
 
1.9%
66 2
 
1.2%
Other values (92) 109
67.3%
ValueCountFrequency (%)
0 17
10.5%
1 7
4.3%
2 3
 
1.9%
3 2
 
1.2%
4 3
 
1.9%
6 6
 
3.7%
7 6
 
3.7%
8 2
 
1.2%
9 2
 
1.2%
10 1
 
0.6%
ValueCountFrequency (%)
10546 1
0.6%
2755 1
0.6%
2543 1
0.6%
2153 1
0.6%
2144 1
0.6%
1979 1
0.6%
1387 1
0.6%
1365 1
0.6%
1288 1
0.6%
1015 1
0.6%

여죄
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean252.58642
Minimum0
Maximum9373
Zeros29
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T08:28:03.942089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q353.75
95-th percentile817.25
Maximum9373
Range9373
Interquartile range (IQR)52.75

Descriptive statistics

Standard deviation1041.0926
Coefficient of variation (CV)4.1217285
Kurtosis49.876102
Mean252.58642
Median Absolute Deviation (MAD)8
Skewness6.7294843
Sum40919
Variance1083873.9
MonotonicityNot monotonic
2023-12-13T08:28:04.110401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29
17.9%
1 22
 
13.6%
2 7
 
4.3%
3 7
 
4.3%
8 6
 
3.7%
4 6
 
3.7%
10 4
 
2.5%
18 4
 
2.5%
13 4
 
2.5%
12 4
 
2.5%
Other values (59) 69
42.6%
ValueCountFrequency (%)
0 29
17.9%
1 22
13.6%
2 7
 
4.3%
3 7
 
4.3%
4 6
 
3.7%
5 2
 
1.2%
6 3
 
1.9%
7 1
 
0.6%
8 6
 
3.7%
9 3
 
1.9%
ValueCountFrequency (%)
9373 1
0.6%
7139 1
0.6%
4578 1
0.6%
3538 1
0.6%
1866 1
0.6%
1516 1
0.6%
1107 1
0.6%
901 1
0.6%
818 1
0.6%
803 1
0.6%

기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct139
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1062.4321
Minimum0
Maximum23685
Zeros2
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T08:28:04.252194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.05
Q154
median197.5
Q3622.75
95-th percentile4677.6
Maximum23685
Range23685
Interquartile range (IQR)568.75

Descriptive statistics

Standard deviation2875.2105
Coefficient of variation (CV)2.7062534
Kurtosis31.021371
Mean1062.4321
Median Absolute Deviation (MAD)175.5
Skewness5.0965706
Sum172114
Variance8266835.4
MonotonicityNot monotonic
2023-12-13T08:28:04.434964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125 3
 
1.9%
6 3
 
1.9%
84 3
 
1.9%
0 2
 
1.2%
140 2
 
1.2%
44 2
 
1.2%
2 2
 
1.2%
388 2
 
1.2%
20 2
 
1.2%
54 2
 
1.2%
Other values (129) 139
85.8%
ValueCountFrequency (%)
0 2
1.2%
1 1
 
0.6%
2 2
1.2%
4 1
 
0.6%
6 3
1.9%
7 1
 
0.6%
9 1
 
0.6%
11 1
 
0.6%
12 1
 
0.6%
13 1
 
0.6%
ValueCountFrequency (%)
23685 1
0.6%
17272 1
0.6%
11941 1
0.6%
9080 1
0.6%
9030 1
0.6%
8715 1
0.6%
8180 1
0.6%
7372 1
0.6%
4718 1
0.6%
3910 1
0.6%

Interactions

2023-12-13T08:27:59.103250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:45.756709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:46.770688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:47.934395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:49.016179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:50.222429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:51.444145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:52.414556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:53.552101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:54.741031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:55.862343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:57.185725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:58.083903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:59.176325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:45.822517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:46.860368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:48.006078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:49.102342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:50.293014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:51.502872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:52.494281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:53.628955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:54.828120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:55.936891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:57.260632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:58.147536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:59.267699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:45.905640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:46.956185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:48.083116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:49.209362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:50.383212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:51.568980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:52.578445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:53.718503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:54.929339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:56.028102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:57.345959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:58.218147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:59.352996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:45.974834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:47.052701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:48.161914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:49.296603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:50.480274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:51.630960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:52.660849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:53.795289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:55.009079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:56.108715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:57.422254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:58.281949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:59.440487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:46.060502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:47.148471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:48.260712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:49.400103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:50.572823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:51.701547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:52.782923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:53.900472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:55.104187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:56.203683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:57.495519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:58.374182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:59.514977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:46.139947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:47.235099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:48.342582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:49.512531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:50.652063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:51.761326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:52.858828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:54.029040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:55.179101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:56.273920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:57.557817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:58.455987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:59.597160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:46.220528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:47.318786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:48.426246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:49.602529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:50.720323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:51.817040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:52.945700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:54.128759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:55.273705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:56.348897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:57.619587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:58.525716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:59.677942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:46.295166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:47.398400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:48.524696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:49.709993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:50.790520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:51.883037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:53.025867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:54.207980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:55.349070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:56.436990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:57.682629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:58.602637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:59.773270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:46.371900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:47.494905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:48.617345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:49.804962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:50.862777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:51.967165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:53.121673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:54.291858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:55.432609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:56.516693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:57.756900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:58.703175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:59.888833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:46.445487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:47.583078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:48.691449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:49.887521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:50.934994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:52.052469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:53.214263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:54.385105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:55.548324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:56.599864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:57.824688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:58.778695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:59.979985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:46.532463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:47.678056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:48.773753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:49.997626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:51.004033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:52.148792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:53.302660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:54.499654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:55.649096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:56.678754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:57.891592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:58.856195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:28:00.070218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:46.615524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:47.768097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:48.846980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:50.075548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:51.319090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:52.275295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:53.379286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:54.585320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:55.722373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:56.765309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:57.953096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:58.941424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:28:00.143450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:46.698663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:47.862268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:48.927550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:50.150609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:51.385167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:52.342696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:53.461785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:54.658674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:55.791350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:57.117114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:58.018490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:27:59.031725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:28:04.563526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
현행범피해자신고고소고발자수진정투서타인신고불심검문피해품발견변사체탐문정보여죄기타
현행범1.0000.8170.6630.0000.0000.2640.6750.0000.2640.0000.6420.4590.000
피해자신고0.8171.0000.5710.0000.8810.6060.6470.3470.8430.0000.5890.7370.735
고소0.6630.5711.0000.0980.0000.7160.2320.0000.1480.0000.6070.7250.322
고발0.0000.0000.0981.0000.0000.2250.3900.0000.0000.0000.3330.6390.622
자수0.0000.8810.0000.0001.0000.0000.8890.0000.0000.0000.0000.0001.000
진정투서0.2640.6060.7160.2250.0001.0000.0000.0000.0000.0000.3370.9370.734
타인신고0.6750.6470.2320.3900.8890.0001.0000.8340.0000.0000.2120.4250.765
불심검문0.0000.3470.0000.0000.0000.0000.8341.0000.0000.0000.5730.0000.797
피해품발견0.2640.8430.1480.0000.0000.0000.0000.0001.0000.0000.7040.9370.346
변사체0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
탐문정보0.6420.5890.6070.3330.0000.3370.2120.5730.7040.0001.0000.8490.623
여죄0.4590.7370.7250.6390.0000.9370.4250.0000.9370.0000.8491.0000.865
기타0.0000.7350.3220.6221.0000.7340.7650.7970.3460.0000.6230.8651.000
2023-12-13T08:28:04.718346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
현행범피해자신고고소고발자수진정투서타인신고불심검문피해품발견변사체탐문정보여죄기타
현행범1.0000.7750.4330.1680.6240.5390.7530.5840.4860.3460.5730.5430.695
피해자신고0.7751.0000.5490.0030.6800.5690.7520.4820.5820.4280.4410.4220.593
고소0.4330.5491.0000.2420.3500.6300.1800.0360.2800.2860.3930.3240.428
고발0.1680.0030.2421.0000.0680.5100.1820.2090.094-0.0120.3950.2340.347
자수0.6240.6800.3500.0681.0000.4220.6610.5120.5920.4580.4840.4560.605
진정투서0.5390.5690.6300.5100.4221.0000.4760.3590.4070.2480.6600.4550.630
타인신고0.7530.7520.1800.1820.6610.4761.0000.6920.5210.4260.4970.5080.621
불심검문0.5840.4820.0360.2090.5120.3590.6921.0000.4710.1620.4560.5230.636
피해품발견0.4860.5820.2800.0940.5920.4070.5210.4711.0000.2830.3460.3520.468
변사체0.3460.4280.286-0.0120.4580.2480.4260.1620.2831.0000.1460.1640.238
탐문정보0.5730.4410.3930.3950.4840.6600.4970.4560.3460.1461.0000.7010.748
여죄0.5430.4220.3240.2340.4560.4550.5080.5230.3520.1640.7011.0000.699
기타0.6950.5930.4280.3470.6050.6300.6210.6360.4680.2380.7480.6991.000

Missing values

2023-12-13T08:28:00.268849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:28:00.462397image/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

범죄분류현행범피해자신고고소고발자수진정투서타인신고불심검문피해품발견변사체탐문정보여죄기타
0절도603014681228706314917342457545117801054693732598
1장물271706001648162850323546452
2사기85732438296434144262939306095171254345789030
3횡령3212912014876763562663384106331013873052298
4배임2325315037137410100219391322
5손괴844941980356787388538651103167135403
6살인296127482192205116918565
7강도15849453225730021243146
8방화46344512051357000171345
9성폭력64491315153431092175921292631221441516937
범죄분류현행범피해자신고고소고발자수진정투서타인신고불심검문피해품발견변사체탐문정보여죄기타
152폐기물관리법5743568900667821009425308
153풍속영업의규제에관한법률10000235250011140
154하천법0011750619000606
155학교보건법101230010000155
156학원의설립운영및과외교습에관한법률1024940140000000
157화물자동차운수사업법1626857302769056600271212
158화재로인한재해보상과보험가입에관한법률0600005000004
159화재예방,소방시설설치유지및안전관리에관한법률10220000000020
160화학물질관리법182132264793200733274
161기타특별법1526109331699631312415376209862261979713911941