Overview

Dataset statistics

Number of variables6
Number of observations73
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory54.8 B

Variable types

Text1
Numeric4
Categorical1

Dataset

Description대검찰청에서 발간하는 범죄분석은 3종의 범죄통계원표를 기반으로 작성하는 자료이며, 이 중 본 데이터는 정신장애범죄자의 범죄별 처분횟수와 관련된 통계임. (단위: 명)
Author대검찰청
URLhttps://www.data.go.kr/data/15085431/fileData.do

Alerts

없음 is highly overall correlated with 1회 and 3 other fieldsHigh correlation
1회 is highly overall correlated with 없음 and 3 other fieldsHigh correlation
2회 is highly overall correlated with 없음 and 3 other fieldsHigh correlation
3회 이상 is highly overall correlated with 없음 and 3 other fieldsHigh correlation
미상 is highly overall correlated with 없음 and 3 other fieldsHigh correlation
범죄분류 has unique valuesUnique
없음 has 4 (5.5%) zerosZeros
1회 has 41 (56.2%) zerosZeros
2회 has 46 (63.0%) zerosZeros
3회 이상 has 47 (64.4%) zerosZeros

Reproduction

Analysis started2023-12-12 23:33:17.022542
Analysis finished2023-12-12 23:33:19.076358
Duration2.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄분류
Text

UNIQUE 

Distinct73
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size716.0 B
2023-12-13T08:33:19.247588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length5.7123288
Min length2

Characters and Unicode

Total characters417
Distinct characters151
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

Unique73 ?
Unique (%)100.0%

Sample

1st row절도
2nd row장물
3rd row사기
4th row횡령
5th row배임
ValueCountFrequency (%)
절도 1
 
1.4%
공인중개사법 1
 
1.4%
식품위생법 1
 
1.4%
수산업법 1
 
1.4%
성매매알선등행위의처벌에관한법률 1
 
1.4%
산지관리법 1
 
1.4%
부정수표단속법 1
 
1.4%
병역법 1
 
1.4%
마약류관리에관한법률 1
 
1.4%
도로법 1
 
1.4%
Other values (63) 63
86.3%
2023-12-13T08:33:19.610423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
법 39
 
9.4%
관 12
 
2.9%
리 8
 
1.9%
사 8
 
1.9%
보 7
 
1.7%
도 7
 
1.7%
업 7
 
1.7%
기 6
 
1.4%
에 6
 
1.4%
한 6
 
1.4%
Other values (141) 311
74.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 408
97.8%
Open Punctuation 3
 
0.7%
Close Punctuation 3
 
0.7%
Other Punctuation 3
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
법 39
 
9.6%
관 12
 
2.9%
리 8
 
2.0%
사 8
 
2.0%
보 7
 
1.7%
도 7
 
1.7%
업 7
 
1.7%
기 6
 
1.5%
에 6
 
1.5%
한 6
 
1.5%
Other values (137) 302
74.0%
Other Punctuation
ValueCountFrequency (%)
, 2
66.7%
· 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 408
97.8%
Common 9
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
법 39
 
9.6%
관 12
 
2.9%
리 8
 
2.0%
사 8
 
2.0%
보 7
 
1.7%
도 7
 
1.7%
업 7
 
1.7%
기 6
 
1.5%
에 6
 
1.5%
한 6
 
1.5%
Other values (137) 302
74.0%
Common
ValueCountFrequency (%)
( 3
33.3%
) 3
33.3%
, 2
22.2%
· 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 408
97.8%
ASCII 8
 
1.9%
None 1
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
법 39
 
9.6%
관 12
 
2.9%
리 8
 
2.0%
사 8
 
2.0%
보 7
 
1.7%
도 7
 
1.7%
업 7
 
1.7%
기 6
 
1.5%
에 6
 
1.5%
한 6
 
1.5%
Other values (137) 302
74.0%
ASCII
ValueCountFrequency (%)
( 3
37.5%
) 3
37.5%
, 2
25.0%
None
ValueCountFrequency (%)
· 1
100.0%

없음
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)58.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.86301
Minimum0
Maximum2085
Zeros4
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size789.0 B
2023-12-13T08:33:19.748053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6
Q11
median8
Q364
95-th percentile594.2
Maximum2085
Range2085
Interquartile range (IQR)63

Descriptive statistics

Standard deviation297.76698
Coefficient of variation (CV)2.6859001
Kurtosis28.313697
Mean110.86301
Median Absolute Deviation (MAD)7
Skewness4.8907678
Sum8093
Variance88665.175
MonotonicityNot monotonic
2023-12-13T08:33:19.855065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1 15
20.5%
6 4
 
5.5%
0 4
 
5.5%
5 3
 
4.1%
3 3
 
4.1%
8 3
 
4.1%
2 3
 
4.1%
21 2
 
2.7%
4 2
 
2.7%
2085 1
 
1.4%
Other values (33) 33
45.2%
ValueCountFrequency (%)
0 4
 
5.5%
1 15
20.5%
2 3
 
4.1%
3 3
 
4.1%
4 2
 
2.7%
5 3
 
4.1%
6 4
 
5.5%
7 1
 
1.4%
8 3
 
4.1%
9 1
 
1.4%
ValueCountFrequency (%)
2085 1
1.4%
1098 1
1.4%
694 1
1.4%
611 1
1.4%
583 1
1.4%
272 1
1.4%
271 1
1.4%
247 1
1.4%
233 1
1.4%
228 1
1.4%

1회
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.630137
Minimum0
Maximum136
Zeros41
Zeros (%)56.2%
Negative0
Negative (%)0.0%
Memory size789.0 B
2023-12-13T08:33:19.958661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile34
Maximum136
Range136
Interquartile range (IQR)5

Descriptive statistics

Standard deviation18.339019
Coefficient of variation (CV)2.7660091
Kurtosis34.967292
Mean6.630137
Median Absolute Deviation (MAD)0
Skewness5.3535392
Sum484
Variance336.31963
MonotonicityNot monotonic
2023-12-13T08:33:20.069607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 41
56.2%
1 7
 
9.6%
5 3
 
4.1%
3 3
 
4.1%
6 2
 
2.7%
2 2
 
2.7%
15 1
 
1.4%
24 1
 
1.4%
38 1
 
1.4%
12 1
 
1.4%
Other values (11) 11
 
15.1%
ValueCountFrequency (%)
0 41
56.2%
1 7
 
9.6%
2 2
 
2.7%
3 3
 
4.1%
4 1
 
1.4%
5 3
 
4.1%
6 2
 
2.7%
7 1
 
1.4%
9 1
 
1.4%
11 1
 
1.4%
ValueCountFrequency (%)
136 1
1.4%
47 1
1.4%
38 1
1.4%
37 1
1.4%
32 1
1.4%
28 1
1.4%
24 1
1.4%
21 1
1.4%
16 1
1.4%
15 1
1.4%

2회
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6986301
Minimum0
Maximum70
Zeros46
Zeros (%)63.0%
Negative0
Negative (%)0.0%
Memory size789.0 B
2023-12-13T08:33:20.167109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile14.4
Maximum70
Range70
Interquartile range (IQR)1

Descriptive statistics

Standard deviation9.0334024
Coefficient of variation (CV)3.3474029
Kurtosis43.896382
Mean2.6986301
Median Absolute Deviation (MAD)0
Skewness6.1623209
Sum197
Variance81.602359
MonotonicityNot monotonic
2023-12-13T08:33:20.263733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 46
63.0%
1 13
 
17.8%
8 3
 
4.1%
4 2
 
2.7%
3 2
 
2.7%
70 1
 
1.4%
14 1
 
1.4%
21 1
 
1.4%
2 1
 
1.4%
5 1
 
1.4%
Other values (2) 2
 
2.7%
ValueCountFrequency (%)
0 46
63.0%
1 13
 
17.8%
2 1
 
1.4%
3 2
 
2.7%
4 2
 
2.7%
5 1
 
1.4%
8 3
 
4.1%
14 1
 
1.4%
15 1
 
1.4%
19 1
 
1.4%
ValueCountFrequency (%)
70 1
 
1.4%
21 1
 
1.4%
19 1
 
1.4%
15 1
 
1.4%
14 1
 
1.4%
8 3
4.1%
5 1
 
1.4%
4 2
2.7%
3 2
2.7%
2 1
 
1.4%

3회 이상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8767123
Minimum0
Maximum117
Zeros47
Zeros (%)64.4%
Negative0
Negative (%)0.0%
Memory size789.0 B
2023-12-13T08:33:20.404284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile17
Maximum117
Range117
Interquartile range (IQR)1

Descriptive statistics

Standard deviation14.4414
Coefficient of variation (CV)3.7251668
Kurtosis53.85721
Mean3.8767123
Median Absolute Deviation (MAD)0
Skewness6.9608901
Sum283
Variance208.55403
MonotonicityNot monotonic
2023-12-13T08:33:20.536612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 47
64.4%
1 10
 
13.7%
5 3
 
4.1%
3 3
 
4.1%
17 2
 
2.7%
117 1
 
1.4%
15 1
 
1.4%
13 1
 
1.4%
11 1
 
1.4%
4 1
 
1.4%
Other values (3) 3
 
4.1%
ValueCountFrequency (%)
0 47
64.4%
1 10
 
13.7%
3 3
 
4.1%
4 1
 
1.4%
5 3
 
4.1%
7 1
 
1.4%
11 1
 
1.4%
13 1
 
1.4%
15 1
 
1.4%
17 2
 
2.7%
ValueCountFrequency (%)
117 1
 
1.4%
25 1
 
1.4%
23 1
 
1.4%
17 2
2.7%
15 1
 
1.4%
13 1
 
1.4%
11 1
 
1.4%
7 1
 
1.4%
5 3
4.1%
4 1
 
1.4%

미상
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size716.0 B
0
54 
1
13 
2
 
4
5
 
1
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)2.7%

Sample

1st row5
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 54
74.0%
1 13
 
17.8%
2 4
 
5.5%
5 1
 
1.4%
6 1
 
1.4%

Length

2023-12-13T08:33:20.675243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:33:20.793058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 54
74.0%
1 13
 
17.8%
2 4
 
5.5%
5 1
 
1.4%
6 1
 
1.4%

Interactions

2023-12-13T08:33:18.529975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:33:17.254138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:33:17.874218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:33:18.195890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:33:18.619026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:33:17.587083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:33:17.952945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:33:18.280813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:33:18.731709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:33:17.678564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:33:18.032953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:33:18.368475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:33:18.814323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:33:17.788543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:33:18.116232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:33:18.448308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:33:20.863602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
범죄분류없음1회2회3회 이상미상
범죄분류1.0001.0001.0001.0001.0001.000
없음1.0001.0000.9190.8840.8560.826
1회1.0000.9191.0000.9840.8040.969
2회1.0000.8840.9841.0000.8080.971
3회 이상1.0000.8560.8040.8081.0000.726
미상1.0000.8260.9690.9710.7261.000
2023-12-13T08:33:20.978236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
없음1회2회3회 이상미상
없음1.0000.8590.7900.7340.719
1회0.8591.0000.7820.7950.749
2회0.7900.7821.0000.8310.680
3회 이상0.7340.7950.8311.0000.661
미상0.7190.7490.6800.6611.000

Missing values

2023-12-13T08:33:18.938972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:33:19.040817image/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

범죄분류없음1회2회3회 이상미상
0절도2085136701175
1장물60100
2사기272214171
3횡령1566151
4배임30001
5손괴694288151
6살인655110
7강도375100
8방화1347130
9성폭력6113714132
범죄분류없음1회2회3회 이상미상
63전자금융거래법211000
64정보통신망이용촉진및정보보호등에관한법률573000
65조세범처벌법10000
66주민등록법52000
67청소년보호법110000
68출입국관리법20000
69특가법(도주차량)240100
70폐기물관리법10000
71화물자동차운수사업법10000
72기타특별법1922415232