Overview

Dataset statistics

Number of variables13
Number of observations180
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.5 KiB
Average record size in memory116.7 B

Variable types

Text1
Numeric11
Categorical1

Dataset

Description대검찰청에서 발간하는 범죄분석은 3종의 범죄통계원표를 기반으로 작성하는 자료이며 이 중 본 데이터는 서울북부지방검찰청이 관할하는 범죄 발생 검거상황과 관련된 통계임.
Author대검찰청
URLhttps://www.data.go.kr/data/15084757/fileData.do

Alerts

직수_인지_발생건수(건) is highly overall correlated with 직수_인지_검거건수(건) and 8 other fieldsHigh correlation
직수_인지_검거건수(건) is highly overall correlated with 직수_인지_발생건수(건) and 8 other fieldsHigh correlation
직수_인지_남자검거인원(명) is highly overall correlated with 직수_인지_발생건수(건) and 8 other fieldsHigh correlation
직수_인지_여자검거인원(명) is highly overall correlated with 직수_인지_발생건수(건) and 8 other fieldsHigh correlation
직수_인지_미상검거인원(명) is highly overall correlated with 직수_인지_발생건수(건) and 4 other fieldsHigh correlation
지휘관할_발생건수(건) is highly overall correlated with 직수_인지_발생건수(건) and 7 other fieldsHigh correlation
지휘관할_검거건수(건) is highly overall correlated with 직수_인지_발생건수(건) and 7 other fieldsHigh correlation
지휘관할_남자검거인원(명) is highly overall correlated with 직수_인지_발생건수(건) and 7 other fieldsHigh correlation
지휘관할_여자검거인원(명) is highly overall correlated with 직수_인지_발생건수(건) and 7 other fieldsHigh correlation
지휘관할_미상검거인원(명) is highly overall correlated with 직수_인지_발생건수(건) and 8 other fieldsHigh correlation
지휘관할_법인(개) is highly overall correlated with 직수_인지_법인(개)High correlation
직수_인지_법인(개) is highly overall correlated with 지휘관할_법인(개)High correlation
직수_인지_법인(개) is highly imbalanced (77.7%)Imbalance
범죄분류 has unique valuesUnique
직수_인지_발생건수(건) has 94 (52.2%) zerosZeros
직수_인지_검거건수(건) has 101 (56.1%) zerosZeros
직수_인지_남자검거인원(명) has 101 (56.1%) zerosZeros
직수_인지_여자검거인원(명) has 140 (77.8%) zerosZeros
직수_인지_미상검거인원(명) has 154 (85.6%) zerosZeros
지휘관할_검거건수(건) has 3 (1.7%) zerosZeros
지휘관할_남자검거인원(명) has 5 (2.8%) zerosZeros
지휘관할_여자검거인원(명) has 34 (18.9%) zerosZeros
지휘관할_미상검거인원(명) has 121 (67.2%) zerosZeros
지휘관할_법인(개) has 136 (75.6%) zerosZeros

Reproduction

Analysis started2023-12-12 23:04:17.186399
Analysis finished2023-12-12 23:04:29.105830
Duration11.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄분류
Text

UNIQUE 

Distinct180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-13T08:04:29.279692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length8.0222222
Min length2

Characters and Unicode

Total characters1444
Distinct characters234
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique180 ?
Unique (%)100.0%

Sample

1st row절도
2nd row불법사용
3rd row침입절도
4th row장물
5th row사기
ValueCountFrequency (%)
관한법률 23
 
8.2%
16
 
5.7%
관리에 4
 
1.4%
마약류관리에 3
 
1.1%
안전관리에 2
 
0.7%
처벌등에 2
 
0.7%
보호에 2
 
0.7%
성보호에 2
 
0.7%
아동·청소년의 2
 
0.7%
규제 2
 
0.7%
Other values (221) 222
79.3%
2023-12-13T08:04:29.687000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
103
 
7.1%
100
 
6.9%
54
 
3.7%
33
 
2.3%
32
 
2.2%
28
 
1.9%
25
 
1.7%
24
 
1.7%
24
 
1.7%
24
 
1.7%
Other values (224) 997
69.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1296
89.8%
Space Separator 100
 
6.9%
Other Punctuation 18
 
1.2%
Open Punctuation 15
 
1.0%
Close Punctuation 15
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
103
 
7.9%
54
 
4.2%
33
 
2.5%
32
 
2.5%
28
 
2.2%
25
 
1.9%
24
 
1.9%
24
 
1.9%
24
 
1.9%
23
 
1.8%
Other values (218) 926
71.5%
Other Punctuation
ValueCountFrequency (%)
, 10
55.6%
· 7
38.9%
/ 1
 
5.6%
Space Separator
ValueCountFrequency (%)
100
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1296
89.8%
Common 148
 
10.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
103
 
7.9%
54
 
4.2%
33
 
2.5%
32
 
2.5%
28
 
2.2%
25
 
1.9%
24
 
1.9%
24
 
1.9%
24
 
1.9%
23
 
1.8%
Other values (218) 926
71.5%
Common
ValueCountFrequency (%)
100
67.6%
( 15
 
10.1%
) 15
 
10.1%
, 10
 
6.8%
· 7
 
4.7%
/ 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1296
89.8%
ASCII 141
 
9.8%
None 7
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
103
 
7.9%
54
 
4.2%
33
 
2.5%
32
 
2.5%
28
 
2.2%
25
 
1.9%
24
 
1.9%
24
 
1.9%
24
 
1.9%
23
 
1.8%
Other values (218) 926
71.5%
ASCII
ValueCountFrequency (%)
100
70.9%
( 15
 
10.6%
) 15
 
10.6%
, 10
 
7.1%
/ 1
 
0.7%
None
ValueCountFrequency (%)
· 7
100.0%

직수_인지_발생건수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9888889
Minimum0
Maximum341
Zeros94
Zeros (%)52.2%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T08:04:29.839918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.25
95-th percentile24.25
Maximum341
Range341
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation29.437177
Coefficient of variation (CV)4.2119966
Kurtosis96.119532
Mean6.9888889
Median Absolute Deviation (MAD)0
Skewness9.0747008
Sum1258
Variance866.54736
MonotonicityNot monotonic
2023-12-13T08:04:29.983195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 94
52.2%
1 25
 
13.9%
3 8
 
4.4%
2 8
 
4.4%
5 6
 
3.3%
4 5
 
2.8%
6 4
 
2.2%
14 4
 
2.2%
8 3
 
1.7%
10 3
 
1.7%
Other values (15) 20
 
11.1%
ValueCountFrequency (%)
0 94
52.2%
1 25
 
13.9%
2 8
 
4.4%
3 8
 
4.4%
4 5
 
2.8%
5 6
 
3.3%
6 4
 
2.2%
7 3
 
1.7%
8 3
 
1.7%
10 3
 
1.7%
ValueCountFrequency (%)
341 1
0.6%
146 1
0.6%
88 1
0.6%
67 1
0.6%
62 1
0.6%
47 1
0.6%
41 1
0.6%
36 1
0.6%
29 1
0.6%
24 1
0.6%

직수_인지_검거건수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5388889
Minimum0
Maximum252
Zeros101
Zeros (%)56.1%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T08:04:30.149366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile16.05
Maximum252
Range252
Interquartile range (IQR)2

Descriptive statistics

Standard deviation22.706375
Coefficient of variation (CV)4.0994459
Kurtosis82.969559
Mean5.5388889
Median Absolute Deviation (MAD)0
Skewness8.4172844
Sum997
Variance515.57948
MonotonicityNot monotonic
2023-12-13T08:04:30.296475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 101
56.1%
1 23
 
12.8%
2 12
 
6.7%
4 10
 
5.6%
11 4
 
2.2%
6 3
 
1.7%
3 3
 
1.7%
8 3
 
1.7%
5 2
 
1.1%
10 2
 
1.1%
Other values (14) 17
 
9.4%
ValueCountFrequency (%)
0 101
56.1%
1 23
 
12.8%
2 12
 
6.7%
3 3
 
1.7%
4 10
 
5.6%
5 2
 
1.1%
6 3
 
1.7%
7 2
 
1.1%
8 3
 
1.7%
10 2
 
1.1%
ValueCountFrequency (%)
252 1
0.6%
130 1
0.6%
64 1
0.6%
59 1
0.6%
54 1
0.6%
41 1
0.6%
35 1
0.6%
33 1
0.6%
17 1
0.6%
16 1
0.6%

직수_인지_남자검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9166667
Minimum0
Maximum325
Zeros101
Zeros (%)56.1%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T08:04:30.443937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile20.2
Maximum325
Range325
Interquartile range (IQR)3

Descriptive statistics

Standard deviation26.444392
Coefficient of variation (CV)4.4694747
Kurtosis120.40419
Mean5.9166667
Median Absolute Deviation (MAD)0
Skewness10.278446
Sum1065
Variance699.30587
MonotonicityNot monotonic
2023-12-13T08:04:30.570948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 101
56.1%
1 19
 
10.6%
2 13
 
7.2%
3 8
 
4.4%
6 5
 
2.8%
7 3
 
1.7%
5 3
 
1.7%
11 3
 
1.7%
12 3
 
1.7%
4 2
 
1.1%
Other values (18) 20
 
11.1%
ValueCountFrequency (%)
0 101
56.1%
1 19
 
10.6%
2 13
 
7.2%
3 8
 
4.4%
4 2
 
1.1%
5 3
 
1.7%
6 5
 
2.8%
7 3
 
1.7%
8 1
 
0.6%
9 2
 
1.1%
ValueCountFrequency (%)
325 1
0.6%
88 1
0.6%
81 1
0.6%
46 1
0.6%
44 1
0.6%
40 1
0.6%
35 1
0.6%
25 1
0.6%
24 1
0.6%
20 1
0.6%

직수_인지_여자검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5055556
Minimum0
Maximum105
Zeros140
Zeros (%)77.8%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T08:04:30.704009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.1
Maximum105
Range105
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.4546092
Coefficient of variation (CV)5.6156076
Kurtosis127.50555
Mean1.5055556
Median Absolute Deviation (MAD)0
Skewness10.681964
Sum271
Variance71.480416
MonotonicityNot monotonic
2023-12-13T08:04:30.837508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 140
77.8%
1 19
 
10.6%
2 6
 
3.3%
4 4
 
2.2%
6 2
 
1.1%
3 2
 
1.1%
105 1
 
0.6%
24 1
 
0.6%
16 1
 
0.6%
18 1
 
0.6%
Other values (3) 3
 
1.7%
ValueCountFrequency (%)
0 140
77.8%
1 19
 
10.6%
2 6
 
3.3%
3 2
 
1.1%
4 4
 
2.2%
6 2
 
1.1%
7 1
 
0.6%
10 1
 
0.6%
16 1
 
0.6%
18 1
 
0.6%
ValueCountFrequency (%)
105 1
 
0.6%
26 1
 
0.6%
24 1
 
0.6%
18 1
 
0.6%
16 1
 
0.6%
10 1
 
0.6%
7 1
 
0.6%
6 2
1.1%
4 4
2.2%
3 2
1.1%

직수_인지_미상검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97222222
Minimum0
Maximum52
Zeros154
Zeros (%)85.6%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T08:04:30.964519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum52
Range52
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.1831598
Coefficient of variation (CV)5.3312501
Kurtosis65.774296
Mean0.97222222
Median Absolute Deviation (MAD)0
Skewness7.7930287
Sum175
Variance26.865146
MonotonicityNot monotonic
2023-12-13T08:04:31.071650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 154
85.6%
1 10
 
5.6%
3 5
 
2.8%
2 4
 
2.2%
5 2
 
1.1%
29 1
 
0.6%
6 1
 
0.6%
11 1
 
0.6%
34 1
 
0.6%
52 1
 
0.6%
ValueCountFrequency (%)
0 154
85.6%
1 10
 
5.6%
2 4
 
2.2%
3 5
 
2.8%
5 2
 
1.1%
6 1
 
0.6%
11 1
 
0.6%
29 1
 
0.6%
34 1
 
0.6%
52 1
 
0.6%
ValueCountFrequency (%)
52 1
 
0.6%
34 1
 
0.6%
29 1
 
0.6%
11 1
 
0.6%
6 1
 
0.6%
5 2
 
1.1%
3 5
 
2.8%
2 4
 
2.2%
1 10
 
5.6%
0 154
85.6%

직수_인지_법인(개)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
0
168 
1
 
7
2
 
3
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row2

Common Values

ValueCountFrequency (%)
0 168
93.3%
1 7
 
3.9%
2 3
 
1.7%
3 2
 
1.1%

Length

2023-12-13T08:04:31.195608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:04:31.316864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 168
93.3%
1 7
 
3.9%
2 3
 
1.7%
3 2
 
1.1%

지휘관할_발생건수(건)
Real number (ℝ)

HIGH CORRELATION 

Distinct102
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean351.97778
Minimum0
Maximum8325
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T08:04:31.480491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median23
Q3149.25
95-th percentile1225
Maximum8325
Range8325
Interquartile range (IQR)143.25

Descriptive statistics

Standard deviation1213.1086
Coefficient of variation (CV)3.4465488
Kurtosis31.242384
Mean351.97778
Median Absolute Deviation (MAD)22
Skewness5.4858228
Sum63356
Variance1471632.4
MonotonicityNot monotonic
2023-12-13T08:04:31.650808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 17
 
9.4%
2 10
 
5.6%
4 9
 
5.0%
12 6
 
3.3%
8 6
 
3.3%
5 5
 
2.8%
6 5
 
2.8%
9 4
 
2.2%
15 4
 
2.2%
29 3
 
1.7%
Other values (92) 111
61.7%
ValueCountFrequency (%)
0 1
 
0.6%
1 17
9.4%
2 10
5.6%
3 2
 
1.1%
4 9
5.0%
5 5
 
2.8%
6 5
 
2.8%
7 1
 
0.6%
8 6
 
3.3%
9 4
 
2.2%
ValueCountFrequency (%)
8325 1
0.6%
8254 1
0.6%
7389 1
0.6%
7290 1
0.6%
3533 1
0.6%
2503 1
0.6%
2346 1
0.6%
1588 1
0.6%
1320 1
0.6%
1220 1
0.6%

지휘관할_검거건수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct100
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean300.34444
Minimum0
Maximum7940
Zeros3
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T08:04:31.820661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median20
Q3127
95-th percentile1204.65
Maximum7940
Range7940
Interquartile range (IQR)122

Descriptive statistics

Standard deviation1036.379
Coefficient of variation (CV)3.4506348
Kurtosis35.023602
Mean300.34444
Median Absolute Deviation (MAD)19
Skewness5.723126
Sum54062
Variance1074081.4
MonotonicityNot monotonic
2023-12-13T08:04:31.992713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 19
 
10.6%
2 10
 
5.6%
7 8
 
4.4%
4 7
 
3.9%
3 5
 
2.8%
9 4
 
2.2%
18 4
 
2.2%
12 4
 
2.2%
10 4
 
2.2%
21 3
 
1.7%
Other values (90) 112
62.2%
ValueCountFrequency (%)
0 3
 
1.7%
1 19
10.6%
2 10
5.6%
3 5
 
2.8%
4 7
 
3.9%
5 3
 
1.7%
6 2
 
1.1%
7 8
4.4%
8 3
 
1.7%
9 4
 
2.2%
ValueCountFrequency (%)
7940 1
0.6%
7155 1
0.6%
6633 1
0.6%
4149 1
0.6%
3814 1
0.6%
1507 1
0.6%
1438 1
0.6%
1332 1
0.6%
1274 1
0.6%
1201 1
0.6%

지휘관할_남자검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct104
Distinct (%)57.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean297.38889
Minimum0
Maximum9536
Zeros5
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T08:04:32.168538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median27
Q3144.5
95-th percentile1225.15
Maximum9536
Range9536
Interquartile range (IQR)139.5

Descriptive statistics

Standard deviation1032.9618
Coefficient of variation (CV)3.4734377
Kurtosis46.614857
Mean297.38889
Median Absolute Deviation (MAD)26
Skewness6.3914824
Sum53530
Variance1067010
MonotonicityNot monotonic
2023-12-13T08:04:32.309878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 18
 
10.0%
2 8
 
4.4%
4 7
 
3.9%
3 6
 
3.3%
0 5
 
2.8%
7 4
 
2.2%
12 4
 
2.2%
6 4
 
2.2%
17 4
 
2.2%
22 3
 
1.7%
Other values (94) 117
65.0%
ValueCountFrequency (%)
0 5
 
2.8%
1 18
10.0%
2 8
4.4%
3 6
 
3.3%
4 7
 
3.9%
5 3
 
1.7%
6 4
 
2.2%
7 4
 
2.2%
8 2
 
1.1%
9 2
 
1.1%
ValueCountFrequency (%)
9536 1
0.6%
6244 1
0.6%
6032 1
0.6%
3493 1
0.6%
3033 1
0.6%
1687 1
0.6%
1408 1
0.6%
1325 1
0.6%
1285 1
0.6%
1222 1
0.6%

지휘관할_여자검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct67
Distinct (%)37.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.85
Minimum0
Maximum1957
Zeros34
Zeros (%)18.9%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T08:04:32.439370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q331.25
95-th percentile301.25
Maximum1957
Range1957
Interquartile range (IQR)30.25

Descriptive statistics

Standard deviation242.83492
Coefficient of variation (CV)3.3797484
Kurtosis36.623039
Mean71.85
Median Absolute Deviation (MAD)4
Skewness5.7816737
Sum12933
Variance58968.799
MonotonicityNot monotonic
2023-12-13T08:04:32.580463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34
18.9%
1 21
 
11.7%
2 19
 
10.6%
3 13
 
7.2%
10 5
 
2.8%
5 4
 
2.2%
7 4
 
2.2%
4 4
 
2.2%
12 3
 
1.7%
6 3
 
1.7%
Other values (57) 70
38.9%
ValueCountFrequency (%)
0 34
18.9%
1 21
11.7%
2 19
10.6%
3 13
 
7.2%
4 4
 
2.2%
5 4
 
2.2%
6 3
 
1.7%
7 4
 
2.2%
8 3
 
1.7%
9 2
 
1.1%
ValueCountFrequency (%)
1957 1
0.6%
1727 1
0.6%
1264 1
0.6%
1184 1
0.6%
696 1
0.6%
441 1
0.6%
362 1
0.6%
345 1
0.6%
306 1
0.6%
301 1
0.6%

지휘관할_미상검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0722222
Minimum0
Maximum183
Zeros121
Zeros (%)67.2%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T08:04:32.709605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile21.1
Maximum183
Range183
Interquartile range (IQR)2

Descriptive statistics

Standard deviation24.872643
Coefficient of variation (CV)4.0961352
Kurtosis31.800618
Mean6.0722222
Median Absolute Deviation (MAD)0
Skewness5.5520788
Sum1093
Variance618.64839
MonotonicityNot monotonic
2023-12-13T08:04:32.820881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 121
67.2%
2 12
 
6.7%
1 11
 
6.1%
3 10
 
5.6%
4 5
 
2.8%
5 2
 
1.1%
14 2
 
1.1%
15 2
 
1.1%
7 2
 
1.1%
171 1
 
0.6%
Other values (12) 12
 
6.7%
ValueCountFrequency (%)
0 121
67.2%
1 11
 
6.1%
2 12
 
6.7%
3 10
 
5.6%
4 5
 
2.8%
5 2
 
1.1%
7 2
 
1.1%
8 1
 
0.6%
9 1
 
0.6%
14 2
 
1.1%
ValueCountFrequency (%)
183 1
0.6%
171 1
0.6%
139 1
0.6%
113 1
0.6%
109 1
0.6%
82 1
0.6%
28 1
0.6%
24 1
0.6%
23 1
0.6%
21 1
0.6%

지휘관할_법인(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9222222
Minimum0
Maximum131
Zeros136
Zeros (%)75.6%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T08:04:32.922687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum131
Range131
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.002517
Coefficient of variation (CV)5.7238525
Kurtosis109.92115
Mean1.9222222
Median Absolute Deviation (MAD)0
Skewness9.9308329
Sum346
Variance121.05537
MonotonicityNot monotonic
2023-12-13T08:04:33.021096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 136
75.6%
2 15
 
8.3%
1 14
 
7.8%
3 4
 
2.2%
5 3
 
1.7%
19 1
 
0.6%
131 1
 
0.6%
4 1
 
0.6%
9 1
 
0.6%
14 1
 
0.6%
Other values (3) 3
 
1.7%
ValueCountFrequency (%)
0 136
75.6%
1 14
 
7.8%
2 15
 
8.3%
3 4
 
2.2%
4 1
 
0.6%
5 3
 
1.7%
7 1
 
0.6%
9 1
 
0.6%
14 1
 
0.6%
19 1
 
0.6%
ValueCountFrequency (%)
131 1
 
0.6%
54 1
 
0.6%
37 1
 
0.6%
19 1
 
0.6%
14 1
 
0.6%
9 1
 
0.6%
7 1
 
0.6%
5 3
1.7%
4 1
 
0.6%
3 4
2.2%

Interactions

2023-12-13T08:04:27.382340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:17.609525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:18.424751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:19.208998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:20.207394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:21.199942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:22.472319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:23.502565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:24.574852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:25.503111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:26.415935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:27.476431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:17.681170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:18.495277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:19.282967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:20.303996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:21.294600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:22.572803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:23.611672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:24.663395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:25.589873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:26.509023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:27.568456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:17.751730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:18.558972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:19.440991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:20.403549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:21.373488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:22.664833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:23.728355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:24.734883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:25.666410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:26.597031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:27.893528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:17.820360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:18.633785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:19.514636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:20.482947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:21.460384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:22.750419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:23.865661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:24.820286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:25.743405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:26.676688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:27.973213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:17.889512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:18.702052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:19.592881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:20.573036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:21.537436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:22.834754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:23.982244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:24.905502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:25.828777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:26.758358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:28.050067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:17.956662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:18.770310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:19.685175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:20.685947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:21.612465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:22.946915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:24.079288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:24.987380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:25.909009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:26.837559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:28.119200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:18.029759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:18.833324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:19.768020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:20.775494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:21.695133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:23.052981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:24.165548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:25.059437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:25.983156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:26.911927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:28.208905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:18.113042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:18.905230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:19.849946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:20.873281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:21.813421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:23.150474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:24.248041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:25.156645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:26.069754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:27.010112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:28.303241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:18.202322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:18.973785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:19.921643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:20.952605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:21.900240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:23.224306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:24.323186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:25.237407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:26.147849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:27.106800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:28.419300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:18.282136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:19.046065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:20.037635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:21.040812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:22.272149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:23.330742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:24.411480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:25.337250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:26.232055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:27.217897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:28.568457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:18.356312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:19.133443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:20.121591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:21.117509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:22.372548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:23.420213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:24.497454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:25.419594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:26.331887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:27.303883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:04:33.105455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
직수_인지_발생건수(건)직수_인지_검거건수(건)직수_인지_남자검거인원(명)직수_인지_여자검거인원(명)직수_인지_미상검거인원(명)직수_인지_법인(개)지휘관할_발생건수(건)지휘관할_검거건수(건)지휘관할_남자검거인원(명)지휘관할_여자검거인원(명)지휘관할_미상검거인원(명)지휘관할_법인(개)
직수_인지_발생건수(건)1.0000.9940.9430.8660.8060.4770.7400.6460.7450.8320.6990.771
직수_인지_검거건수(건)0.9941.0000.8840.8850.8060.4630.5930.6320.7200.7630.6700.810
직수_인지_남자검거인원(명)0.9430.8841.0000.9680.8370.7550.6960.7420.4830.8370.7340.553
직수_인지_여자검거인원(명)0.8660.8850.9681.0000.8370.6980.6900.7380.4740.7970.7210.452
직수_인지_미상검거인원(명)0.8060.8060.8370.8371.0000.4310.4150.8110.4390.6040.8830.438
직수_인지_법인(개)0.4770.4630.7550.6980.4311.0000.4090.4820.2950.6350.6150.729
지휘관할_발생건수(건)0.7400.5930.6960.6900.4150.4091.0000.9170.8780.9740.8550.367
지휘관할_검거건수(건)0.6460.6320.7420.7380.8110.4820.9171.0000.9230.8600.7700.482
지휘관할_남자검거인원(명)0.7450.7200.4830.4740.4390.2950.8780.9231.0000.8590.7790.597
지휘관할_여자검거인원(명)0.8320.7630.8370.7970.6040.6350.9740.8600.8591.0000.9480.737
지휘관할_미상검거인원(명)0.6990.6700.7340.7210.8830.6150.8550.7700.7790.9481.0000.630
지휘관할_법인(개)0.7710.8100.5530.4520.4380.7290.3670.4820.5970.7370.6301.000
2023-12-13T08:04:33.231310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
직수_인지_발생건수(건)직수_인지_검거건수(건)직수_인지_남자검거인원(명)직수_인지_여자검거인원(명)직수_인지_미상검거인원(명)지휘관할_발생건수(건)지휘관할_검거건수(건)지휘관할_남자검거인원(명)지휘관할_여자검거인원(명)지휘관할_미상검거인원(명)지휘관할_법인(개)직수_인지_법인(개)
직수_인지_발생건수(건)1.0000.9560.8770.7250.5340.5900.5750.5940.5610.5570.2560.406
직수_인지_검거건수(건)0.9561.0000.8780.7140.5280.5890.5870.6070.5620.5180.2890.392
직수_인지_남자검거인원(명)0.8770.8781.0000.6860.5110.6160.6100.6430.5700.5430.3110.392
직수_인지_여자검거인원(명)0.7250.7140.6861.0000.5220.5070.5040.5080.5350.5140.2190.341
직수_인지_미상검거인원(명)0.5340.5280.5110.5221.0000.3810.3720.3680.3930.6690.2170.290
지휘관할_발생건수(건)0.5900.5890.6160.5070.3811.0000.9800.9500.8160.5600.3500.289
지휘관할_검거건수(건)0.5750.5870.6100.5040.3720.9801.0000.9710.8270.5330.3690.328
지휘관할_남자검거인원(명)0.5940.6070.6430.5080.3680.9500.9711.0000.8120.5390.3600.244
지휘관할_여자검거인원(명)0.5610.5620.5700.5350.3930.8160.8270.8121.0000.5680.3670.491
지휘관할_미상검거인원(명)0.5570.5180.5430.5140.6690.5600.5330.5390.5681.0000.3480.471
지휘관할_법인(개)0.2560.2890.3110.2190.2170.3500.3690.3600.3670.3481.0000.668
직수_인지_법인(개)0.4060.3920.3920.3410.2900.2890.3280.2440.4910.4710.6681.000

Missing values

2023-12-13T08:04:28.754115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:04:29.017089image/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

범죄분류직수_인지_발생건수(건)직수_인지_검거건수(건)직수_인지_남자검거인원(명)직수_인지_여자검거인원(명)직수_인지_미상검거인원(명)직수_인지_법인(개)지휘관할_발생건수(건)지휘관할_검거건수(건)지휘관할_남자검거인원(명)지휘관할_여자검거인원(명)지휘관할_미상검거인원(명)지휘관할_법인(개)
0절도5461207389414930331184240
1불법사용00000013917000
2침입절도221000214190107500
3장물00000060641133200
4사기341252325105292825466336244172713919
5컴퓨터등사용사기20020017969641510
6부당이득100000531220
7편의시설부정이용000000181930300
8전기통신금융사기피해금환급에관한특별법1110008931291000
9보험사기방지특별법000000551021663100
범죄분류직수_인지_발생건수(건)직수_인지_검거건수(건)직수_인지_남자검거인원(명)직수_인지_여자검거인원(명)직수_인지_미상검거인원(명)직수_인지_법인(개)지휘관할_발생건수(건)지휘관할_검거건수(건)지휘관할_남자검거인원(명)지휘관할_여자검거인원(명)지휘관할_미상검거인원(명)지휘관할_법인(개)
170특허법000000679202
171폐기물관리법000000211100
172풍속영업의 규제에 관한법률000000875200
173하천법000000111000
174학교보건법000000443100
175학원의설립운영 및 과외교습에 관한법률000000242461900
176화물자동차 운수사업법000000109137155307
177화재예방·소방시설설치유지 및 안전관리에 관한법률000000111100
178화학물질관리법000000121213600
179기타특별법47413510321588150712856962854