Overview

Dataset statistics

Number of variables14
Number of observations38
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.7 KiB
Average record size in memory127.5 B

Variable types

Categorical1
Text1
Numeric12

Dataset

Description전국 경찰관서에 고소, 고발, 인지 등으로 형사입건된 사건의 발생, 검거, 피의자에 대한 죄종별 분석 현황
Author경찰청
URLhttps://www.data.go.kr/data/3074477/fileData.do

Alerts

단독범 is highly overall correlated with 소계 and 11 other fieldsHigh correlation
소계 is highly overall correlated with 단독범 and 11 other fieldsHigh correlation
학교동창 is highly overall correlated with 단독범 and 10 other fieldsHigh correlation
교도소.소년원동료 is highly overall correlated with 단독범 and 11 other fieldsHigh correlation
직장동료 is highly overall correlated with 단독범 and 11 other fieldsHigh correlation
친인척 is highly overall correlated with 단독범 and 10 other fieldsHigh correlation
군동료 is highly overall correlated with 단독범 and 10 other fieldsHigh correlation
동네친구 is highly overall correlated with 단독범 and 10 other fieldsHigh correlation
고향친구 is highly overall correlated with 단독범 and 10 other fieldsHigh correlation
애인 is highly overall correlated with 단독범 and 11 other fieldsHigh correlation
기타 is highly overall correlated with 단독범 and 11 other fieldsHigh correlation
미상 is highly overall correlated with 단독범 and 10 other fieldsHigh correlation
범죄대분류 is highly overall correlated with 단독범 and 5 other fieldsHigh correlation
단독범 has unique valuesUnique
소계 has unique valuesUnique
기타 has unique valuesUnique
미상 has unique valuesUnique
학교동창 has 6 (15.8%) zerosZeros
교도소.소년원동료 has 12 (31.6%) zerosZeros
직장동료 has 6 (15.8%) zerosZeros
친인척 has 5 (13.2%) zerosZeros
군동료 has 16 (42.1%) zerosZeros
동네친구 has 7 (18.4%) zerosZeros
고향친구 has 11 (28.9%) zerosZeros
애인 has 7 (18.4%) zerosZeros

Reproduction

Analysis started2023-12-12 18:30:26.459806
Analysis finished2023-12-12 18:30:41.091110
Duration14.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄대분류
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)39.5%
Missing0
Missing (%)0.0%
Memory size436.0 B
지능범죄
강력범죄
폭력범죄
풍속범죄
절도범죄
 
1
Other values (10)
10 

Length

Max length6
Median length4
Mean length4.0526316
Min length4

Unique

Unique11 ?
Unique (%)28.9%

Sample

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

Common Values

ValueCountFrequency (%)
지능범죄 9
23.7%
강력범죄 8
21.1%
폭력범죄 8
21.1%
풍속범죄 2
 
5.3%
절도범죄 1
 
2.6%
특별경제범죄 1
 
2.6%
마약범죄 1
 
2.6%
보건범죄 1
 
2.6%
환경범죄 1
 
2.6%
교통범죄 1
 
2.6%
Other values (5) 5
13.2%

Length

2023-12-13T03:30:41.188303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지능범죄 9
23.7%
강력범죄 8
21.1%
폭력범죄 8
21.1%
풍속범죄 2
 
5.3%
절도범죄 1
 
2.6%
특별경제범죄 1
 
2.6%
마약범죄 1
 
2.6%
보건범죄 1
 
2.6%
환경범죄 1
 
2.6%
교통범죄 1
 
2.6%
Other values (5) 5
13.2%
Distinct28
Distinct (%)73.7%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-13T03:30:41.405447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length2
Mean length3.1578947
Min length2

Characters and Unicode

Total characters120
Distinct characters59
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 (%)71.1%

Sample

1st row살인기수
2nd row살인미수등
3rd row강도
4th row강간
5th row유사강간
ValueCountFrequency (%)
소계 11
25.6%
강간 2
 
4.7%
폭력행위등 1
 
2.3%
공갈 1
 
2.3%
성풍속범죄 1
 
2.3%
배임 1
 
2.3%
횡령 1
 
2.3%
사기 1
 
2.3%
유가증권인지 1
 
2.3%
인장 1
 
2.3%
Other values (22) 22
51.2%
2023-12-13T03:30:41.773849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11
 
9.2%
11
 
9.2%
6
 
5.0%
5
 
4.2%
5
 
4.2%
4
 
3.3%
4
 
3.3%
4
 
3.3%
3
 
2.5%
3
 
2.5%
Other values (49) 64
53.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 115
95.8%
Space Separator 5
 
4.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
11
 
9.6%
11
 
9.6%
6
 
5.2%
5
 
4.3%
4
 
3.5%
4
 
3.5%
4
 
3.5%
3
 
2.6%
3
 
2.6%
3
 
2.6%
Other values (48) 61
53.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 115
95.8%
Common 5
 
4.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
11
 
9.6%
11
 
9.6%
6
 
5.2%
5
 
4.3%
4
 
3.5%
4
 
3.5%
4
 
3.5%
3
 
2.6%
3
 
2.6%
3
 
2.6%
Other values (48) 61
53.0%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 115
95.8%
ASCII 5
 
4.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
11
 
9.6%
11
 
9.6%
6
 
5.2%
5
 
4.3%
4
 
3.5%
4
 
3.5%
4
 
3.5%
3
 
2.6%
3
 
2.6%
3
 
2.6%
Other values (48) 61
53.0%
ASCII
ValueCountFrequency (%)
5
100.0%

단독범
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21142.737
Minimum20
Maximum217809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T03:30:41.952504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile41.6
Q1323.5
median2147.5
Q314190
95-th percentile99232.7
Maximum217809
Range217789
Interquartile range (IQR)13866.5

Descriptive statistics

Standard deviation44621.514
Coefficient of variation (CV)2.110489
Kurtosis10.489644
Mean21142.737
Median Absolute Deviation (MAD)2101
Skewness3.0879745
Sum803424
Variance1.9910795 × 109
MonotonicityNot monotonic
2023-12-13T03:30:42.142754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
214 1
 
2.6%
5838 1
 
2.6%
28 1
 
2.6%
85112 1
 
2.6%
18103 1
 
2.6%
1092 1
 
2.6%
10961 1
 
2.6%
7537 1
 
2.6%
33545 1
 
2.6%
11758 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
20 1
2.6%
28 1
2.6%
44 1
2.6%
49 1
2.6%
65 1
2.6%
109 1
2.6%
214 1
2.6%
238 1
2.6%
255 1
2.6%
320 1
2.6%
ValueCountFrequency (%)
217809 1
2.6%
134676 1
2.6%
92978 1
2.6%
85112 1
2.6%
68651 1
2.6%
36929 1
2.6%
33545 1
2.6%
29634 1
2.6%
18103 1
2.6%
14747 1
2.6%

소계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4757.5263
Minimum7
Maximum45554
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T03:30:42.330157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile25.2
Q1104.25
median634.5
Q33713.25
95-th percentile21668.9
Maximum45554
Range45547
Interquartile range (IQR)3609

Descriptive statistics

Standard deviation9798.1901
Coefficient of variation (CV)2.0595136
Kurtosis9.0716914
Mean4757.5263
Median Absolute Deviation (MAD)592.5
Skewness2.9436123
Sum180786
Variance96004530
MonotonicityNot monotonic
2023-12-13T03:30:42.485315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
36 1
 
2.6%
4759 1
 
2.6%
15 1
 
2.6%
45554 1
 
2.6%
2919 1
 
2.6%
1191 1
 
2.6%
696 1
 
2.6%
11870 1
 
2.6%
14097 1
 
2.6%
6271 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
7 1
2.6%
15 1
2.6%
27 1
2.6%
36 1
2.6%
37 1
2.6%
47 1
2.6%
51 1
2.6%
65 1
2.6%
71 1
2.6%
101 1
2.6%
ValueCountFrequency (%)
45554 1
2.6%
33676 1
2.6%
19550 1
2.6%
18738 1
2.6%
14097 1
2.6%
11870 1
2.6%
6271 1
2.6%
5035 1
2.6%
4759 1
2.6%
3978 1
2.6%

학교동창
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)73.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268.42105
Minimum0
Maximum4079
Zeros6
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T03:30:42.647995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median38
Q3141
95-th percentile1203.05
Maximum4079
Range4079
Interquartile range (IQR)139

Descriptive statistics

Standard deviation741.54272
Coefficient of variation (CV)2.7626101
Kurtosis20.086783
Mean268.42105
Median Absolute Deviation (MAD)38
Skewness4.2994727
Sum10200
Variance549885.6
MonotonicityNot monotonic
2023-12-13T03:30:43.203829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 6
 
15.8%
2 4
 
10.5%
1 3
 
7.9%
62 1
 
2.6%
1049 1
 
2.6%
791 1
 
2.6%
25 1
 
2.6%
11 1
 
2.6%
271 1
 
2.6%
20 1
 
2.6%
Other values (18) 18
47.4%
ValueCountFrequency (%)
0 6
15.8%
1 3
7.9%
2 4
10.5%
9 1
 
2.6%
11 1
 
2.6%
20 1
 
2.6%
22 1
 
2.6%
25 1
 
2.6%
27 1
 
2.6%
49 1
 
2.6%
ValueCountFrequency (%)
4079 1
2.6%
2076 1
2.6%
1049 1
2.6%
791 1
2.6%
403 1
2.6%
271 1
2.6%
164 1
2.6%
156 1
2.6%
154 1
2.6%
145 1
2.6%

교도소.소년원동료
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.736842
Minimum0
Maximum255
Zeros12
Zeros (%)31.6%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T03:30:43.349320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.5
Q39
95-th percentile147.55
Maximum255
Range255
Interquartile range (IQR)9

Descriptive statistics

Standard deviation54.681231
Coefficient of variation (CV)2.6369121
Kurtosis11.514776
Mean20.736842
Median Absolute Deviation (MAD)2.5
Skewness3.4395913
Sum788
Variance2990.037
MonotonicityNot monotonic
2023-12-13T03:30:43.476548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 12
31.6%
2 5
13.2%
4 4
 
10.5%
3 3
 
7.9%
9 2
 
5.3%
1 2
 
5.3%
196 1
 
2.6%
19 1
 
2.6%
8 1
 
2.6%
46 1
 
2.6%
Other values (6) 6
15.8%
ValueCountFrequency (%)
0 12
31.6%
1 2
 
5.3%
2 5
13.2%
3 3
 
7.9%
4 4
 
10.5%
8 1
 
2.6%
9 2
 
5.3%
11 1
 
2.6%
12 1
 
2.6%
19 1
 
2.6%
ValueCountFrequency (%)
255 1
2.6%
196 1
2.6%
139 1
2.6%
46 1
2.6%
27 1
2.6%
20 1
2.6%
19 1
2.6%
12 1
2.6%
11 1
2.6%
9 2
5.3%

직장동료
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean508.97368
Minimum0
Maximum4966
Zeros6
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T03:30:43.630639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q122.75
median46.5
Q3414.25
95-th percentile2301.3
Maximum4966
Range4966
Interquartile range (IQR)391.5

Descriptive statistics

Standard deviation1036.6085
Coefficient of variation (CV)2.0366642
Kurtosis9.5296781
Mean508.97368
Median Absolute Deviation (MAD)46.5
Skewness2.9578002
Sum19341
Variance1074557.2
MonotonicityNot monotonic
2023-12-13T03:30:43.761450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 6
 
15.8%
5 2
 
5.3%
39 2
 
5.3%
1584 1
 
2.6%
469 1
 
2.6%
34 1
 
2.6%
1159 1
 
2.6%
2070 1
 
2.6%
199 1
 
2.6%
262 1
 
2.6%
Other values (21) 21
55.3%
ValueCountFrequency (%)
0 6
15.8%
5 2
 
5.3%
7 1
 
2.6%
22 1
 
2.6%
25 1
 
2.6%
26 1
 
2.6%
28 1
 
2.6%
33 1
 
2.6%
34 1
 
2.6%
39 2
 
5.3%
ValueCountFrequency (%)
4966 1
2.6%
3221 1
2.6%
2139 1
2.6%
2070 1
2.6%
1584 1
2.6%
1159 1
2.6%
880 1
2.6%
557 1
2.6%
469 1
2.6%
450 1
2.6%

친인척
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean237.26316
Minimum0
Maximum1944
Zeros5
Zeros (%)13.2%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T03:30:43.904058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.25
median17.5
Q3154.75
95-th percentile1589.7
Maximum1944
Range1944
Interquartile range (IQR)149.5

Descriptive statistics

Standard deviation492.52344
Coefficient of variation (CV)2.075853
Kurtosis5.6972338
Mean237.26316
Median Absolute Deviation (MAD)17.5
Skewness2.5548986
Sum9016
Variance242579.33
MonotonicityNot monotonic
2023-12-13T03:30:44.065636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 5
 
13.2%
16 2
 
5.3%
5 2
 
5.3%
10 2
 
5.3%
9 2
 
5.3%
1944 1
 
2.6%
6 1
 
2.6%
88 1
 
2.6%
162 1
 
2.6%
160 1
 
2.6%
Other values (20) 20
52.6%
ValueCountFrequency (%)
0 5
13.2%
1 1
 
2.6%
2 1
 
2.6%
3 1
 
2.6%
5 2
 
5.3%
6 1
 
2.6%
8 1
 
2.6%
9 2
 
5.3%
10 2
 
5.3%
11 1
 
2.6%
ValueCountFrequency (%)
1944 1
2.6%
1690 1
2.6%
1572 1
2.6%
1014 1
2.6%
655 1
2.6%
541 1
2.6%
299 1
2.6%
248 1
2.6%
162 1
2.6%
160 1
2.6%

군동료
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2368421
Minimum0
Maximum39
Zeros16
Zeros (%)42.1%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T03:30:44.201967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q33.75
95-th percentile28.15
Maximum39
Range39
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation9.4652543
Coefficient of variation (CV)1.8074355
Kurtosis4.6923761
Mean5.2368421
Median Absolute Deviation (MAD)1.5
Skewness2.2978598
Sum199
Variance89.591038
MonotonicityNot monotonic
2023-12-13T03:30:44.353196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 16
42.1%
3 5
 
13.2%
2 4
 
10.5%
1 3
 
7.9%
4 2
 
5.3%
28 1
 
2.6%
15 1
 
2.6%
22 1
 
2.6%
29 1
 
2.6%
9 1
 
2.6%
Other values (3) 3
 
7.9%
ValueCountFrequency (%)
0 16
42.1%
1 3
 
7.9%
2 4
 
10.5%
3 5
 
13.2%
4 2
 
5.3%
6 1
 
2.6%
9 1
 
2.6%
15 1
 
2.6%
17 1
 
2.6%
22 1
 
2.6%
ValueCountFrequency (%)
39 1
 
2.6%
29 1
 
2.6%
28 1
 
2.6%
22 1
 
2.6%
17 1
 
2.6%
15 1
 
2.6%
9 1
 
2.6%
6 1
 
2.6%
4 2
 
5.3%
3 5
13.2%

동네친구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean914
Minimum0
Maximum8425
Zeros7
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T03:30:44.482417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.25
median126.5
Q3521.75
95-th percentile6624.9
Maximum8425
Range8425
Interquartile range (IQR)512.5

Descriptive statistics

Standard deviation2054.5043
Coefficient of variation (CV)2.2478165
Kurtosis6.9715046
Mean914
Median Absolute Deviation (MAD)126.5
Skewness2.7896748
Sum34732
Variance4220987.7
MonotonicityNot monotonic
2023-12-13T03:30:44.605491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 7
 
18.4%
3 1
 
2.6%
2480 1
 
2.6%
23 1
 
2.6%
145 1
 
2.6%
673 1
 
2.6%
21 1
 
2.6%
163 1
 
2.6%
798 1
 
2.6%
707 1
 
2.6%
Other values (22) 22
57.9%
ValueCountFrequency (%)
0 7
18.4%
3 1
 
2.6%
4 1
 
2.6%
9 1
 
2.6%
10 1
 
2.6%
12 1
 
2.6%
17 1
 
2.6%
21 1
 
2.6%
23 1
 
2.6%
35 1
 
2.6%
ValueCountFrequency (%)
8425 1
2.6%
7106 1
2.6%
6540 1
2.6%
3691 1
2.6%
2480 1
2.6%
1179 1
2.6%
798 1
2.6%
707 1
2.6%
673 1
2.6%
568 1
2.6%

고향친구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.07895
Minimum0
Maximum875
Zeros11
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T03:30:44.723242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13.5
Q369.5
95-th percentile713.65
Maximum875
Range875
Interquartile range (IQR)69.5

Descriptive statistics

Standard deviation227.3085
Coefficient of variation (CV)1.9925544
Kurtosis4.4036032
Mean114.07895
Median Absolute Deviation (MAD)13.5
Skewness2.320366
Sum4335
Variance51669.156
MonotonicityNot monotonic
2023-12-13T03:30:44.831930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 11
28.9%
3 2
 
5.3%
6 2
 
5.3%
45 2
 
5.3%
2 1
 
2.6%
509 1
 
2.6%
463 1
 
2.6%
33 1
 
2.6%
77 1
 
2.6%
18 1
 
2.6%
Other values (15) 15
39.5%
ValueCountFrequency (%)
0 11
28.9%
2 1
 
2.6%
3 2
 
5.3%
4 1
 
2.6%
5 1
 
2.6%
6 2
 
5.3%
13 1
 
2.6%
14 1
 
2.6%
17 1
 
2.6%
18 1
 
2.6%
ValueCountFrequency (%)
875 1
2.6%
723 1
2.6%
712 1
2.6%
509 1
2.6%
463 1
2.6%
256 1
2.6%
179 1
2.6%
128 1
2.6%
80 1
2.6%
77 1
2.6%

애인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)63.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.92105
Minimum0
Maximum889
Zeros7
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T03:30:44.951571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median10
Q366.5
95-th percentile518.95
Maximum889
Range889
Interquartile range (IQR)64.5

Descriptive statistics

Standard deviation204.76003
Coefficient of variation (CV)2.0289129
Kurtosis6.2247298
Mean100.92105
Median Absolute Deviation (MAD)10
Skewness2.559665
Sum3835
Variance41926.669
MonotonicityNot monotonic
2023-12-13T03:30:45.053674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 7
18.4%
9 3
 
7.9%
5 3
 
7.9%
7 2
 
5.3%
36 2
 
5.3%
2 2
 
5.3%
1 2
 
5.3%
482 1
 
2.6%
151 1
 
2.6%
41 1
 
2.6%
Other values (14) 14
36.8%
ValueCountFrequency (%)
0 7
18.4%
1 2
 
5.3%
2 2
 
5.3%
5 3
7.9%
7 2
 
5.3%
9 3
7.9%
11 1
 
2.6%
15 1
 
2.6%
16 1
 
2.6%
20 1
 
2.6%
ValueCountFrequency (%)
889 1
2.6%
632 1
2.6%
499 1
2.6%
482 1
2.6%
424 1
2.6%
174 1
2.6%
151 1
2.6%
115 1
2.6%
81 1
2.6%
68 1
2.6%

기타
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2587.8947
Minimum1
Maximum31124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T03:30:45.153996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.95
Q154
median315
Q31852.75
95-th percentile12139.5
Maximum31124
Range31123
Interquartile range (IQR)1798.75

Descriptive statistics

Standard deviation6238.0571
Coefficient of variation (CV)2.4104756
Kurtosis14.047306
Mean2587.8947
Median Absolute Deviation (MAD)300.5
Skewness3.6752239
Sum98340
Variance38913357
MonotonicityNot monotonic
2023-12-13T03:30:45.260516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
14 1
 
2.6%
2772 1
 
2.6%
15 1
 
2.6%
31124 1
 
2.6%
1375 1
 
2.6%
605 1
 
2.6%
370 1
 
2.6%
6082 1
 
2.6%
10314 1
 
2.6%
4251 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
1 1
2.6%
6 1
2.6%
13 1
2.6%
14 1
2.6%
15 1
2.6%
17 1
2.6%
20 1
2.6%
26 1
2.6%
30 1
2.6%
41 1
2.6%
ValueCountFrequency (%)
31124 1
2.6%
22484 1
2.6%
10314 1
2.6%
6082 1
2.6%
4448 1
2.6%
4251 1
2.6%
3787 1
2.6%
2772 1
2.6%
2430 1
2.6%
2012 1
2.6%

미상
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13426.605
Minimum59
Maximum134659
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T03:30:45.380387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum59
5-th percentile71.9
Q1168.75
median1080
Q36879.5
95-th percentile85521.2
Maximum134659
Range134600
Interquartile range (IQR)6710.75

Descriptive statistics

Standard deviation31908.095
Coefficient of variation (CV)2.3764827
Kurtosis7.6602681
Mean13426.605
Median Absolute Deviation (MAD)1004
Skewness2.9020366
Sum510211
Variance1.0181265 × 109
MonotonicityNot monotonic
2023-12-13T03:30:45.504759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
91 1
 
2.6%
1033 1
 
2.6%
74 1
 
2.6%
114343 1
 
2.6%
14908 1
 
2.6%
6041 1
 
2.6%
1889 1
 
2.6%
1718 1
 
2.6%
20262 1
 
2.6%
3781 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
59 1
2.6%
60 1
2.6%
74 1
2.6%
78 1
2.6%
83 1
2.6%
91 1
2.6%
116 1
2.6%
133 1
2.6%
147 1
2.6%
148 1
2.6%
ValueCountFrequency (%)
134659 1
2.6%
114343 1
2.6%
80435 1
2.6%
76187 1
2.6%
20262 1
2.6%
14908 1
2.6%
10224 1
2.6%
8548 1
2.6%
7401 1
2.6%
7159 1
2.6%

Interactions

2023-12-13T03:30:39.488056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:26.942628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:27.977294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:28.918883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:30.323125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:31.426221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:32.612000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:33.874350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:35.096247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:36.500381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:37.504011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:38.368335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:39.580987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:27.029192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:28.064851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:29.021956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:30.400804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:31.517047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:32.715782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:33.981297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:35.194256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:36.572796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:37.570476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:38.448106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:39.666462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:27.135403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:28.139451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:29.123925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:30.477114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:31.622108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:32.805157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:34.073430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:35.287141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:36.652423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:37.639009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:38.527364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:39.779291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:27.237252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:28.225724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:29.226182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:30.576195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:31.726373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:32.909395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:34.182074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:35.378724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:36.756590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:37.726211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:38.610963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:39.871020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:27.344738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:28.318610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:29.327031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:30.656072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:31.811778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:33.016588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:34.284114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:35.466659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:36.842819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:37.793591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:38.691045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:39.953818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:27.419718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:28.384643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:29.415635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:30.749136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:31.905379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:33.116354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:34.388901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:35.550757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:36.914453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:37.862687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:38.774868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:40.071248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:27.509148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:28.473216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:29.515977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:30.850162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:32.005743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:33.217320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:34.516285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:35.644288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:37.001027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:37.940722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:38.925743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:40.170961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:27.592850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:28.559972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:29.611403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:30.945765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:32.118711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:33.335747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:34.620292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:35.749814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:37.108548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:38.019788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:39.040608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:40.280823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:27.665710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:28.625361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:29.988196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:31.033220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:32.205588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:33.434606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:34.709257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:35.839012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:37.190205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:38.088355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:39.116723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:40.367744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:27.740705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:28.698559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:30.067165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:31.118263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:32.301557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:33.535683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:34.798307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:35.915533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:37.261434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:38.160872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:39.198566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:40.457718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:27.809403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:28.766415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:30.147537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:31.195782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:32.396769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:33.635876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:34.899365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:36.007825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:37.341666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:38.223989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:39.273511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:40.552405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:27.892533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:28.843529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:30.229805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:31.287366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:32.492443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:33.752677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:34.998770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:36.400905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:37.415916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:38.295211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:30:39.360971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:30:45.599881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
범죄대분류범죄중분류단독범소계학교동창교도소.소년원동료직장동료친인척군동료동네친구고향친구애인기타미상
범죄대분류1.0000.0000.8640.8740.8460.9330.8570.7980.8270.6730.7220.8930.9430.837
범죄중분류0.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
단독범0.8640.0001.0000.7830.7730.6660.6970.8780.9510.8600.9360.9250.7590.860
소계0.8740.0000.7831.0000.8960.8490.9860.9670.8970.9860.9010.8580.9690.792
학교동창0.8460.0000.7730.8961.0000.9890.8930.8960.9031.0000.8440.9010.9710.889
교도소.소년원동료0.9330.0000.6660.8490.9891.0000.8040.8090.7760.8960.8421.0000.9300.842
직장동료0.8570.0000.6970.9860.8930.8041.0000.9420.8580.9790.8290.7860.9180.763
친인척0.7980.0000.8780.9670.8960.8090.9421.0000.8740.9800.8800.8490.8800.804
군동료0.8270.0000.9510.8970.9030.7760.8580.8741.0000.9570.9000.8410.8380.863
동네친구0.6730.0000.8600.9861.0000.8960.9790.9800.9571.0000.9370.8880.8680.771
고향친구0.7220.0000.9360.9010.8440.8420.8290.8800.9000.9371.0000.9670.8560.765
애인0.8930.0000.9250.8580.9011.0000.7860.8490.8410.8880.9671.0000.7980.752
기타0.9430.0000.7590.9690.9710.9300.9180.8800.8380.8680.8560.7981.0000.941
미상0.8370.0000.8600.7920.8890.8420.7630.8040.8630.7710.7650.7520.9411.000
2023-12-13T03:30:45.743066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단독범소계학교동창교도소.소년원동료직장동료친인척군동료동네친구고향친구애인기타미상범죄대분류
단독범1.0000.8250.8130.6990.7340.8170.7750.7720.7620.8130.7960.8920.526
소계0.8251.0000.8400.7910.9240.8790.8540.8930.9440.8600.9820.8250.548
학교동창0.8130.8401.0000.7800.6970.7910.8260.9560.8980.9280.7820.7570.434
교도소.소년원동료0.6990.7910.7801.0000.6840.7720.7650.8020.7790.8600.7320.7410.557
직장동료0.7340.9240.6970.6841.0000.8320.7620.7400.8070.7530.9190.8330.520
친인척0.8170.8790.7910.7720.8321.0000.7480.7950.7990.8220.8420.8400.441
군동료0.7750.8540.8260.7650.7620.7481.0000.8450.8620.8190.8390.7940.466
동네친구0.7720.8930.9560.8020.7400.7950.8451.0000.9650.9070.8400.7180.316
고향친구0.7620.9440.8980.7790.8070.7990.8620.9651.0000.8770.9100.7410.360
애인0.8130.8600.9280.8600.7530.8220.8190.9070.8771.0000.8000.7830.571
기타0.7960.9820.7820.7320.9190.8420.8390.8400.9100.8001.0000.7920.575
미상0.8920.8250.7570.7410.8330.8400.7940.7180.7410.7830.7921.0000.423
범죄대분류0.5260.5480.4340.5570.5200.4410.4660.3160.3600.5710.5750.4231.000

Missing values

2023-12-13T03:30:40.716433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:30:40.963306image/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

범죄대분류범죄중분류단독범소계학교동창교도소.소년원동료직장동료친인척군동료동네친구고향친구애인기타미상
0강력범죄살인기수214362001003071491
1강력범죄살인미수등334372003012002083
2강력범죄강도38766869844903211720180147
3강력범죄강간3464443629281631801891182206
4강력범죄유사강간6564723510100917231
5강력범죄강제추행1251937867139113793111643485
6강력범죄기타 강간 강제추행등238519005017251360
7강력범죄방화947101220720353230148
8절도범죄소계686511955040791968801014288425509632378710224
9폭력범죄상해3692923091564213248256880689704314
범죄대분류범죄중분류단독범소계학교동창교도소.소년원동료직장동료친인척군동료동네친구고향친구애인기타미상
28특별경제범죄소계335451409714520207054167071791151031420262
29마약범죄소계583847599613919972379825642427721033
30보건범죄소계11758627120315841602163474142513781
31환경범죄소계2649101012811912160879670
32교통범죄소계217809397827192628817673771512430134659
33노동범죄소계32016010496000599133
34안보범죄소계44556112250414533732978
35선거범죄소계422533250401002362427590
36병역범죄소계276411400090000105286
37기타범죄소계1346763367679127496619443924804634822248476187