Overview

Dataset statistics

Number of variables12
Number of observations158
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.6 KiB
Average record size in memory107.8 B

Variable types

Text1
Numeric9
Categorical2

Dataset

Description대검찰청에서 발간하는 범죄분석은 3종의 범죄통계원표를 기반으로 작성하는 자료이며 이 중 본 데이터는 여성범죄자의 공범관계에 관한 통계임.
Author대검찰청
URLhttps://www.data.go.kr/data/15085085/fileData.do

Alerts

단독범 is highly overall correlated with 학교동창 and 8 other fieldsHigh correlation
학교동창 is highly overall correlated with 단독범 and 5 other fieldsHigh correlation
직장동료 is highly overall correlated with 단독범 and 8 other fieldsHigh correlation
친인척 is highly overall correlated with 단독범 and 8 other fieldsHigh correlation
동네친구 is highly overall correlated with 단독범 and 7 other fieldsHigh correlation
고향친구 is highly overall correlated with 단독범 and 8 other fieldsHigh correlation
애인 is highly overall correlated with 단독범 and 8 other fieldsHigh correlation
기타 is highly overall correlated with 단독범 and 6 other fieldsHigh correlation
미상 is highly overall correlated with 단독범 and 6 other fieldsHigh correlation
교도소_소년원동료 is highly overall correlated with 단독범 and 7 other fieldsHigh correlation
군동료 is highly overall correlated with 직장동료High correlation
교도소_소년원동료 is highly imbalanced (85.2%)Imbalance
군동료 is highly imbalanced (73.8%)Imbalance
범죄분류 has unique valuesUnique
단독범 has 2 (1.3%) zerosZeros
학교동창 has 106 (67.1%) zerosZeros
직장동료 has 37 (23.4%) zerosZeros
친인척 has 49 (31.0%) zerosZeros
동네친구 has 80 (50.6%) zerosZeros
고향친구 has 115 (72.8%) zerosZeros
애인 has 74 (46.8%) zerosZeros
기타 has 9 (5.7%) zerosZeros
미상 has 5 (3.2%) zerosZeros

Reproduction

Analysis started2023-12-12 07:22:24.724816
Analysis finished2023-12-12 07:22:33.950112
Duration9.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄분류
Text

UNIQUE 

Distinct158
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-12T16:22:34.174578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length17
Mean length7.8164557
Min length2

Characters and Unicode

Total characters1235
Distinct characters222
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

Unique158 ?
Unique (%)100.0%

Sample

1st row절도
2nd row장물
3rd row사기
4th row횡령
5th row배임
ValueCountFrequency (%)
절도 1
 
0.6%
성매매알선등행위의처벌에관한법률 1
 
0.6%
수산업법 1
 
0.6%
산업안전보건법 1
 
0.6%
산지관리법 1
 
0.6%
상표법 1
 
0.6%
석유및석유대체연료사업법 1
 
0.6%
선박안전법 1
 
0.6%
선박직원법 1
 
0.6%
사행행위등규제및처벌특례법 1
 
0.6%
Other values (148) 148
93.7%
2023-12-12T16:22:34.746111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
112
 
9.1%
62
 
5.0%
35
 
2.8%
34
 
2.8%
32
 
2.6%
29
 
2.3%
25
 
2.0%
24
 
1.9%
23
 
1.9%
18
 
1.5%
Other values (212) 841
68.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1201
97.2%
Close Punctuation 12
 
1.0%
Open Punctuation 12
 
1.0%
Other Punctuation 10
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
112
 
9.3%
62
 
5.2%
35
 
2.9%
34
 
2.8%
32
 
2.7%
29
 
2.4%
25
 
2.1%
24
 
2.0%
23
 
1.9%
18
 
1.5%
Other values (208) 807
67.2%
Other Punctuation
ValueCountFrequency (%)
, 8
80.0%
· 2
 
20.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1201
97.2%
Common 34
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
112
 
9.3%
62
 
5.2%
35
 
2.9%
34
 
2.8%
32
 
2.7%
29
 
2.4%
25
 
2.1%
24
 
2.0%
23
 
1.9%
18
 
1.5%
Other values (208) 807
67.2%
Common
ValueCountFrequency (%)
) 12
35.3%
( 12
35.3%
, 8
23.5%
· 2
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1201
97.2%
ASCII 32
 
2.6%
None 2
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
112
 
9.3%
62
 
5.2%
35
 
2.9%
34
 
2.8%
32
 
2.7%
29
 
2.4%
25
 
2.1%
24
 
2.0%
23
 
1.9%
18
 
1.5%
Other values (208) 807
67.2%
ASCII
ValueCountFrequency (%)
) 12
37.5%
( 12
37.5%
, 8
25.0%
None
ValueCountFrequency (%)
· 2
100.0%

단독범
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct129
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1562.8987
Minimum0
Maximum40780
Zeros2
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T16:22:34.919129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q128.25
median115.5
Q3673
95-th percentile5271.95
Maximum40780
Range40780
Interquartile range (IQR)644.75

Descriptive statistics

Standard deviation5116.936
Coefficient of variation (CV)3.2740036
Kurtosis32.274973
Mean1562.8987
Median Absolute Deviation (MAD)106
Skewness5.4199356
Sum246938
Variance26183035
MonotonicityNot monotonic
2023-12-12T16:22:35.101020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 4
 
2.5%
27 3
 
1.9%
4 3
 
1.9%
50 3
 
1.9%
111 2
 
1.3%
673 2
 
1.3%
53 2
 
1.3%
32 2
 
1.3%
138 2
 
1.3%
18 2
 
1.3%
Other values (119) 133
84.2%
ValueCountFrequency (%)
0 2
1.3%
1 1
 
0.6%
2 1
 
0.6%
3 2
1.3%
4 3
1.9%
5 1
 
0.6%
7 2
1.3%
8 4
2.5%
11 1
 
0.6%
12 2
1.3%
ValueCountFrequency (%)
40780 1
0.6%
30002 1
0.6%
26862 1
0.6%
20256 1
0.6%
19296 1
0.6%
10584 1
0.6%
8414 1
0.6%
5640 1
0.6%
5207 1
0.6%
5201 1
0.6%

학교동창
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.436709
Minimum0
Maximum755
Zeros106
Zeros (%)67.1%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T16:22:35.285970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile45.45
Maximum755
Range755
Interquartile range (IQR)2

Descriptive statistics

Standard deviation85.121901
Coefficient of variation (CV)5.1787679
Kurtosis55.932958
Mean16.436709
Median Absolute Deviation (MAD)0
Skewness7.3203974
Sum2597
Variance7245.738
MonotonicityNot monotonic
2023-12-12T16:22:35.429124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 106
67.1%
1 9
 
5.7%
2 8
 
5.1%
4 3
 
1.9%
7 3
 
1.9%
11 3
 
1.9%
10 2
 
1.3%
8 2
 
1.3%
21 2
 
1.3%
3 1
 
0.6%
Other values (19) 19
 
12.0%
ValueCountFrequency (%)
0 106
67.1%
1 9
 
5.7%
2 8
 
5.1%
3 1
 
0.6%
4 3
 
1.9%
5 1
 
0.6%
6 1
 
0.6%
7 3
 
1.9%
8 2
 
1.3%
9 1
 
0.6%
ValueCountFrequency (%)
755 1
0.6%
638 1
0.6%
397 1
0.6%
125 1
0.6%
109 1
0.6%
65 1
0.6%
51 1
0.6%
48 1
0.6%
45 1
0.6%
39 1
0.6%

교도소_소년원동료
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
0
151 
1
 
3
6
 
2
14
 
1
3
 
1

Length

Max length2
Median length1
Mean length1.0063291
Min length1

Unique

Unique2 ?
Unique (%)1.3%

Sample

1st row14
2nd row0
3rd row6
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 151
95.6%
1 3
 
1.9%
6 2
 
1.3%
14 1
 
0.6%
3 1
 
0.6%

Length

2023-12-12T16:22:35.575634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:22:35.672873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 151
95.6%
1 3
 
1.9%
6 2
 
1.3%
14 1
 
0.6%
3 1
 
0.6%

직장동료
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)29.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.955696
Minimum0
Maximum624
Zeros37
Zeros (%)23.4%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T16:22:35.779584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q314
95-th percentile169.25
Maximum624
Range624
Interquartile range (IQR)13

Descriptive statistics

Standard deviation81.623054
Coefficient of variation (CV)2.7247924
Kurtosis25.317072
Mean29.955696
Median Absolute Deviation (MAD)3
Skewness4.6402859
Sum4733
Variance6662.3229
MonotonicityNot monotonic
2023-12-12T16:22:35.938845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 37
23.4%
1 19
12.0%
2 17
 
10.8%
3 9
 
5.7%
4 7
 
4.4%
7 6
 
3.8%
8 5
 
3.2%
5 5
 
3.2%
6 5
 
3.2%
12 3
 
1.9%
Other values (37) 45
28.5%
ValueCountFrequency (%)
0 37
23.4%
1 19
12.0%
2 17
10.8%
3 9
 
5.7%
4 7
 
4.4%
5 5
 
3.2%
6 5
 
3.2%
7 6
 
3.8%
8 5
 
3.2%
9 2
 
1.3%
ValueCountFrequency (%)
624 1
0.6%
484 1
0.6%
354 1
0.6%
291 1
0.6%
277 1
0.6%
234 1
0.6%
204 1
0.6%
182 1
0.6%
167 1
0.6%
131 1
0.6%

친인척
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.658228
Minimum0
Maximum849
Zeros49
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T16:22:36.148521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q310.75
95-th percentile111.35
Maximum849
Range849
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation108.56357
Coefficient of variation (CV)3.6604873
Kurtosis36.123159
Mean29.658228
Median Absolute Deviation (MAD)2
Skewness5.8771581
Sum4686
Variance11786.048
MonotonicityNot monotonic
2023-12-12T16:22:36.268776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 49
31.0%
1 23
14.6%
2 12
 
7.6%
3 9
 
5.7%
9 6
 
3.8%
4 5
 
3.2%
5 4
 
2.5%
11 4
 
2.5%
6 4
 
2.5%
8 4
 
2.5%
Other values (35) 38
24.1%
ValueCountFrequency (%)
0 49
31.0%
1 23
14.6%
2 12
 
7.6%
3 9
 
5.7%
4 5
 
3.2%
5 4
 
2.5%
6 4
 
2.5%
7 1
 
0.6%
8 4
 
2.5%
9 6
 
3.8%
ValueCountFrequency (%)
849 1
0.6%
672 1
0.6%
652 1
0.6%
505 1
0.6%
132 1
0.6%
130 1
0.6%
119 2
1.3%
110 1
0.6%
87 1
0.6%
85 1
0.6%

군동료
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
0
151 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 151
95.6%
1 7
 
4.4%

Length

2023-12-12T16:22:36.386250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:22:36.487916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 151
95.6%
1 7
 
4.4%

동네친구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.841772
Minimum0
Maximum1229
Zeros80
Zeros (%)50.6%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T16:22:36.592704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37.75
95-th percentile83.15
Maximum1229
Range1229
Interquartile range (IQR)7.75

Descriptive statistics

Standard deviation166.45439
Coefficient of variation (CV)4.3986944
Kurtosis36.783354
Mean37.841772
Median Absolute Deviation (MAD)0
Skewness5.9895995
Sum5979
Variance27707.064
MonotonicityNot monotonic
2023-12-12T16:22:36.721974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 80
50.6%
2 11
 
7.0%
1 10
 
6.3%
3 6
 
3.8%
4 5
 
3.2%
8 4
 
2.5%
13 3
 
1.9%
7 3
 
1.9%
6 3
 
1.9%
11 2
 
1.3%
Other values (28) 31
 
19.6%
ValueCountFrequency (%)
0 80
50.6%
1 10
 
6.3%
2 11
 
7.0%
3 6
 
3.8%
4 5
 
3.2%
6 3
 
1.9%
7 3
 
1.9%
8 4
 
2.5%
9 1
 
0.6%
10 2
 
1.3%
ValueCountFrequency (%)
1229 1
0.6%
1198 1
0.6%
848 1
0.6%
806 1
0.6%
401 1
0.6%
184 1
0.6%
106 1
0.6%
84 1
0.6%
83 1
0.6%
79 1
0.6%

고향친구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8164557
Minimum0
Maximum97
Zeros115
Zeros (%)72.8%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T16:22:36.843348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile17.9
Maximum97
Range97
Interquartile range (IQR)1

Descriptive statistics

Standard deviation13.198069
Coefficient of variation (CV)3.4582005
Kurtosis26.030005
Mean3.8164557
Median Absolute Deviation (MAD)0
Skewness4.9349434
Sum603
Variance174.18903
MonotonicityNot monotonic
2023-12-12T16:22:36.975023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 115
72.8%
1 7
 
4.4%
2 6
 
3.8%
3 5
 
3.2%
4 4
 
2.5%
11 2
 
1.3%
8 2
 
1.3%
7 2
 
1.3%
5 2
 
1.3%
12 2
 
1.3%
Other values (11) 11
 
7.0%
ValueCountFrequency (%)
0 115
72.8%
1 7
 
4.4%
2 6
 
3.8%
3 5
 
3.2%
4 4
 
2.5%
5 2
 
1.3%
6 1
 
0.6%
7 2
 
1.3%
8 2
 
1.3%
9 1
 
0.6%
ValueCountFrequency (%)
97 1
0.6%
73 1
0.6%
67 1
0.6%
61 1
0.6%
56 1
0.6%
30 1
0.6%
28 1
0.6%
23 1
0.6%
17 1
0.6%
12 2
1.3%

애인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.550633
Minimum0
Maximum439
Zeros74
Zeros (%)46.8%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T16:22:37.119938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile35.6
Maximum439
Range439
Interquartile range (IQR)4

Descriptive statistics

Standard deviation55.422241
Coefficient of variation (CV)4.0900113
Kurtosis39.015099
Mean13.550633
Median Absolute Deviation (MAD)1
Skewness6.1195317
Sum2141
Variance3071.6248
MonotonicityNot monotonic
2023-12-12T16:22:37.581556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 74
46.8%
1 24
 
15.2%
3 10
 
6.3%
2 9
 
5.7%
8 4
 
2.5%
4 4
 
2.5%
6 3
 
1.9%
13 2
 
1.3%
9 2
 
1.3%
20 2
 
1.3%
Other values (19) 24
 
15.2%
ValueCountFrequency (%)
0 74
46.8%
1 24
 
15.2%
2 9
 
5.7%
3 10
 
6.3%
4 4
 
2.5%
6 3
 
1.9%
7 1
 
0.6%
8 4
 
2.5%
9 2
 
1.3%
10 2
 
1.3%
ValueCountFrequency (%)
439 1
0.6%
364 1
0.6%
335 1
0.6%
202 1
0.6%
131 1
0.6%
56 1
0.6%
52 1
0.6%
39 1
0.6%
35 1
0.6%
34 1
0.6%

기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173.91139
Minimum0
Maximum4697
Zeros9
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T16:22:37.725011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median19.5
Q371.75
95-th percentile858.6
Maximum4697
Range4697
Interquartile range (IQR)66.75

Descriptive statistics

Standard deviation587.08617
Coefficient of variation (CV)3.3757775
Kurtosis35.050906
Mean173.91139
Median Absolute Deviation (MAD)17.5
Skewness5.6542585
Sum27478
Variance344670.17
MonotonicityNot monotonic
2023-12-12T16:22:37.892272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 9
 
5.7%
0 9
 
5.7%
1 9
 
5.7%
10 7
 
4.4%
2 6
 
3.8%
9 6
 
3.8%
4 5
 
3.2%
3 5
 
3.2%
6 4
 
2.5%
34 3
 
1.9%
Other values (75) 95
60.1%
ValueCountFrequency (%)
0 9
5.7%
1 9
5.7%
2 6
3.8%
3 5
3.2%
4 5
3.2%
5 9
5.7%
6 4
2.5%
7 2
 
1.3%
8 3
 
1.9%
9 6
3.8%
ValueCountFrequency (%)
4697 1
0.6%
3734 1
0.6%
3224 1
0.6%
2065 1
0.6%
1505 1
0.6%
1256 1
0.6%
1004 1
0.6%
913 1
0.6%
849 1
0.6%
597 1
0.6%

미상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct111
Distinct (%)70.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean348.46203
Minimum0
Maximum14684
Zeros5
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-12T16:22:38.059156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q112.25
median50.5
Q3186
95-th percentile1370.15
Maximum14684
Range14684
Interquartile range (IQR)173.75

Descriptive statistics

Standard deviation1290.8254
Coefficient of variation (CV)3.7043502
Kurtosis98.845798
Mean348.46203
Median Absolute Deviation (MAD)45.5
Skewness9.2339083
Sum55057
Variance1666230.2
MonotonicityNot monotonic
2023-12-12T16:22:38.230000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 6
 
3.8%
1 6
 
3.8%
5 5
 
3.2%
15 5
 
3.2%
0 5
 
3.2%
9 3
 
1.9%
8 3
 
1.9%
22 3
 
1.9%
7 3
 
1.9%
37 2
 
1.3%
Other values (101) 117
74.1%
ValueCountFrequency (%)
0 5
3.2%
1 6
3.8%
2 6
3.8%
3 1
 
0.6%
4 2
 
1.3%
5 5
3.2%
6 2
 
1.3%
7 3
1.9%
8 3
1.9%
9 3
1.9%
ValueCountFrequency (%)
14684 1
0.6%
4934 1
0.6%
2724 1
0.6%
2434 1
0.6%
2229 1
0.6%
1888 1
0.6%
1634 1
0.6%
1507 1
0.6%
1346 1
0.6%
1291 1
0.6%

Interactions

2023-12-12T16:22:32.733210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:25.145115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:25.959497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:26.906869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:27.922414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:28.882372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:29.732315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:30.559492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:31.814011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:32.834079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:25.225670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:26.078490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:27.027384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:28.037579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:28.976252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:29.829045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:30.668371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:31.912505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:32.941968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:25.322863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:26.184882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:27.132938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:28.158005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:29.092480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:29.935721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:30.793589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:32.003610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:33.041232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:25.404454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:26.281594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:27.247333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:28.265635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:29.199207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:30.016181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:31.213624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:32.096977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:33.140395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:25.484354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:26.359073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:27.351512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:28.364054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:29.294466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:30.109302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:31.307105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:32.210042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:33.231613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:25.592096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:26.454005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:27.468537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:28.489716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:29.380963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:30.214721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:31.411909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:32.323742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:33.332304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:25.681765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:26.551687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:27.578547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:28.595220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:29.473743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:30.295515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:31.500254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:32.438165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:33.432742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:25.769035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:26.673114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:27.703831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:28.694141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:29.549532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:30.373170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:31.625741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:32.538414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:33.519475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:25.858406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:26.778431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:27.803568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:28.786951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:29.628984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:30.458219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:31.718738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:22:32.629790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:22:38.340116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단독범학교동창교도소_소년원동료직장동료친인척군동료동네친구고향친구애인기타미상
단독범1.0000.8620.7140.8260.8550.2040.8630.8650.9630.8040.895
학교동창0.8621.0000.9590.8060.9850.0000.9540.9340.9470.7740.767
교도소_소년원동료0.7140.9591.0000.7110.9230.0000.8730.7860.8460.6370.630
직장동료0.8260.8060.7111.0000.8180.8410.9280.9670.8630.9740.966
친인척0.8550.9850.9230.8181.0000.0000.9470.9150.9130.7700.787
군동료0.2040.0000.0000.8410.0001.0000.2790.5410.0000.6040.000
동네친구0.8630.9540.8730.9280.9470.2791.0000.9350.8630.9220.782
고향친구0.8650.9340.7860.9670.9150.5410.9351.0000.9240.9710.960
애인0.9630.9470.8460.8630.9130.0000.8630.9241.0000.8360.880
기타0.8040.7740.6370.9740.7700.6040.9220.9710.8361.0000.958
미상0.8950.7670.6300.9660.7870.0000.7820.9600.8800.9581.000
2023-12-12T16:22:38.468243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
군동료교도소_소년원동료
군동료1.0000.000
교도소_소년원동료0.0001.000
2023-12-12T16:22:38.590405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단독범학교동창직장동료친인척동네친구고향친구애인기타미상교도소_소년원동료군동료
단독범1.0000.5900.6120.7320.5830.5060.5910.7280.7660.5620.214
학교동창0.5901.0000.4270.5080.6980.6220.6400.4490.4090.7120.000
직장동료0.6120.4271.0000.6780.5380.5180.5860.7750.6250.5360.648
친인척0.7320.5080.6781.0000.6590.6400.6760.7870.7320.6140.000
동네친구0.5830.6980.5380.6591.0000.7050.7530.5990.4780.5210.337
고향친구0.5060.6220.5180.6400.7051.0000.6150.6280.5160.6320.400
애인0.5910.6400.5860.6760.7530.6151.0000.6150.5200.7470.000
기타0.7280.4490.7750.7870.5990.6280.6151.0000.8020.4550.448
미상0.7660.4090.6250.7320.4780.5160.5200.8021.0000.5560.000
교도소_소년원동료0.5620.7120.5360.6140.5210.6320.7470.4550.5561.0000.000
군동료0.2140.0000.6480.0000.3370.4000.0000.4480.0000.0001.000

Missing values

2023-12-12T16:22:33.669853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:22:33.879538image/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

범죄분류단독범학교동창교도소_소년원동료직장동료친인척군동료동네친구고향친구애인기타미상
0절도20256755142345050122973439913851
1장물37917077011082725
2사기268621256624672040161364469714684
3횡령520165013111908311563081888
4배임5510044190103119818
5손괴42571001340132234118290
6살인10520030306418
7강도31705110293172715
8방화2022001020326
9성폭력643130640352133776
범죄분류단독범학교동창교도소_소년원동료직장동료친인척군동료동네친구고향친구애인기타미상
148특허법2400000000022
149폐기물관리법85001200001845
150풍속영업의규제에관한법률4310710000169
151하천법420000000037
152학교보건법270000000012
153학원의설립운영및과외교습에관한법률3472091000167
154화물자동차운수사업법8200600001108
155화재예방,소방시설설치유지및안전관리에관한법률70000000006
156화학물질관리법432002004612426
157기타특별법10584704841301184303932241634