Overview

Dataset statistics

Number of variables14
Number of observations94
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.6 KiB
Average record size in memory126.4 B

Variable types

Text1
Numeric11
Categorical2

Dataset

Description대검찰청에서 발간하는 범죄분석은 3종의 범죄통계원표를 기반으로 작성하는 자료이며 이 중 본 데이터는 외국인범죄자의 처분결과에 관한 통계임.
Author대검찰청
URLhttps://www.data.go.kr/data/15085228/fileData.do

Alerts

구공판_구속 is highly overall correlated with 구공판_불구속 and 7 other fieldsHigh correlation
구공판_불구속 is highly overall correlated with 구공판_구속 and 5 other fieldsHigh correlation
구약식 is highly overall correlated with 구공판_구속 and 6 other fieldsHigh correlation
소년보호송치 is highly overall correlated with 구공판_구속 and 5 other fieldsHigh correlation
가정보호송치 is highly overall correlated with 아동보호송치High correlation
기소유예 is highly overall correlated with 구공판_구속 and 8 other fieldsHigh correlation
혐의없음 is highly overall correlated with 구공판_구속 and 7 other fieldsHigh correlation
죄가안됨 is highly overall correlated with 아동보호송치High correlation
공소권없음 is highly overall correlated with 구공판_구속 and 8 other fieldsHigh correlation
기소중지 is highly overall correlated with 구공판_구속 and 7 other fieldsHigh correlation
참고인중지 is highly overall correlated with 구공판_구속 and 4 other fieldsHigh correlation
성매매보호송치 is highly overall correlated with 기소유예High correlation
아동보호송치 is highly overall correlated with 가정보호송치 and 2 other fieldsHigh correlation
성매매보호송치 is highly imbalanced (91.5%)Imbalance
아동보호송치 is highly imbalanced (87.3%)Imbalance
범죄분류 has unique valuesUnique
구공판_구속 has 46 (48.9%) zerosZeros
구공판_불구속 has 35 (37.2%) zerosZeros
구약식 has 18 (19.1%) zerosZeros
소년보호송치 has 80 (85.1%) zerosZeros
가정보호송치 has 84 (89.4%) zerosZeros
기소유예 has 18 (19.1%) zerosZeros
혐의없음 has 15 (16.0%) zerosZeros
죄가안됨 has 82 (87.2%) zerosZeros
공소권없음 has 41 (43.6%) zerosZeros
기소중지 has 29 (30.9%) zerosZeros
참고인중지 has 62 (66.0%) zerosZeros

Reproduction

Analysis started2023-12-12 18:21:14.836601
Analysis finished2023-12-12 18:21:27.434147
Duration12.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄분류
Text

UNIQUE 

Distinct94
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size884.0 B
2023-12-13T03:21:27.571829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length17.5
Mean length6.7978723
Min length2

Characters and Unicode

Total characters639
Distinct characters177
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

Unique94 ?
Unique (%)100.0%

Sample

1st row절도
2nd row장물
3rd row사기
4th row횡령
5th row배임
ValueCountFrequency (%)
절도 1
 
1.1%
도로교통법 1
 
1.1%
선박직원법 1
 
1.1%
상표법 1
 
1.1%
산지관리법 1
 
1.1%
산업안전보건법 1
 
1.1%
부정경쟁방지및영업비밀보호에관한법률 1
 
1.1%
병역법 1
 
1.1%
물환경보전법 1
 
1.1%
마약류관리에관한법률 1
 
1.1%
Other values (84) 84
89.4%
2023-12-13T03:21:27.957320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
53
 
8.3%
23
 
3.6%
15
 
2.3%
14
 
2.2%
13
 
2.0%
12
 
1.9%
12
 
1.9%
11
 
1.7%
11
 
1.7%
10
 
1.6%
Other values (167) 465
72.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 622
97.3%
Other Punctuation 7
 
1.1%
Open Punctuation 5
 
0.8%
Close Punctuation 5
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
53
 
8.5%
23
 
3.7%
15
 
2.4%
14
 
2.3%
13
 
2.1%
12
 
1.9%
12
 
1.9%
11
 
1.8%
11
 
1.8%
10
 
1.6%
Other values (163) 448
72.0%
Other Punctuation
ValueCountFrequency (%)
, 5
71.4%
· 2
 
28.6%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 622
97.3%
Common 17
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
53
 
8.5%
23
 
3.7%
15
 
2.4%
14
 
2.3%
13
 
2.1%
12
 
1.9%
12
 
1.9%
11
 
1.8%
11
 
1.8%
10
 
1.6%
Other values (163) 448
72.0%
Common
ValueCountFrequency (%)
( 5
29.4%
) 5
29.4%
, 5
29.4%
· 2
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 622
97.3%
ASCII 15
 
2.3%
None 2
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
53
 
8.5%
23
 
3.7%
15
 
2.4%
14
 
2.3%
13
 
2.1%
12
 
1.9%
12
 
1.9%
11
 
1.8%
11
 
1.8%
10
 
1.6%
Other values (163) 448
72.0%
ASCII
ValueCountFrequency (%)
( 5
33.3%
) 5
33.3%
, 5
33.3%
None
ValueCountFrequency (%)
· 2
100.0%

구공판_구속
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.691489
Minimum0
Maximum489
Zeros46
Zeros (%)48.9%
Negative0
Negative (%)0.0%
Memory size978.0 B
2023-12-13T03:21:28.085289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36.75
95-th percentile102.95
Maximum489
Range489
Interquartile range (IQR)6.75

Descriptive statistics

Standard deviation65.47166
Coefficient of variation (CV)3.164183
Kurtosis30.797101
Mean20.691489
Median Absolute Deviation (MAD)1
Skewness5.1241533
Sum1945
Variance4286.5382
MonotonicityNot monotonic
2023-12-13T03:21:28.207384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 46
48.9%
1 12
 
12.8%
5 5
 
5.3%
9 3
 
3.2%
2 3
 
3.2%
11 2
 
2.1%
8 2
 
2.1%
3 2
 
2.1%
90 1
 
1.1%
7 1
 
1.1%
Other values (17) 17
 
18.1%
ValueCountFrequency (%)
0 46
48.9%
1 12
 
12.8%
2 3
 
3.2%
3 2
 
2.1%
4 1
 
1.1%
5 5
 
5.3%
6 1
 
1.1%
7 1
 
1.1%
8 2
 
2.1%
9 3
 
3.2%
ValueCountFrequency (%)
489 1
1.1%
278 1
1.1%
197 1
1.1%
181 1
1.1%
127 1
1.1%
90 1
1.1%
84 1
1.1%
80 1
1.1%
62 1
1.1%
52 1
1.1%

구공판_불구속
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.978723
Minimum0
Maximum281
Zeros35
Zeros (%)37.2%
Negative0
Negative (%)0.0%
Memory size978.0 B
2023-12-13T03:21:28.340974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39.75
95-th percentile108.45
Maximum281
Range281
Interquartile range (IQR)9.75

Descriptive statistics

Standard deviation49.169223
Coefficient of variation (CV)2.590755
Kurtosis16.012441
Mean18.978723
Median Absolute Deviation (MAD)1
Skewness3.8328766
Sum1784
Variance2417.6124
MonotonicityNot monotonic
2023-12-13T03:21:28.449100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 35
37.2%
1 15
16.0%
3 6
 
6.4%
6 4
 
4.3%
5 3
 
3.2%
2 3
 
3.2%
19 2
 
2.1%
14 2
 
2.1%
13 2
 
2.1%
54 1
 
1.1%
Other values (21) 21
22.3%
ValueCountFrequency (%)
0 35
37.2%
1 15
16.0%
2 3
 
3.2%
3 6
 
6.4%
4 1
 
1.1%
5 3
 
3.2%
6 4
 
4.3%
7 1
 
1.1%
8 1
 
1.1%
9 1
 
1.1%
ValueCountFrequency (%)
281 1
1.1%
272 1
1.1%
171 1
1.1%
131 1
1.1%
113 1
1.1%
106 1
1.1%
102 1
1.1%
88 1
1.1%
87 1
1.1%
54 1
1.1%

구약식
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)48.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.21277
Minimum0
Maximum4443
Zeros18
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size978.0 B
2023-12-13T03:21:28.585199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7.5
Q355.75
95-th percentile464
Maximum4443
Range4443
Interquartile range (IQR)54.75

Descriptive statistics

Standard deviation475.47438
Coefficient of variation (CV)4.3141498
Kurtosis76.112246
Mean110.21277
Median Absolute Deviation (MAD)7.5
Skewness8.3948189
Sum10360
Variance226075.89
MonotonicityNot monotonic
2023-12-13T03:21:28.719074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 18
19.1%
3 9
 
9.6%
1 7
 
7.4%
8 4
 
4.3%
4 4
 
4.3%
6 3
 
3.2%
7 3
 
3.2%
13 3
 
3.2%
2 2
 
2.1%
114 2
 
2.1%
Other values (36) 39
41.5%
ValueCountFrequency (%)
0 18
19.1%
1 7
 
7.4%
2 2
 
2.1%
3 9
9.6%
4 4
 
4.3%
5 1
 
1.1%
6 3
 
3.2%
7 3
 
3.2%
8 4
 
4.3%
9 1
 
1.1%
ValueCountFrequency (%)
4443 1
1.1%
866 1
1.1%
693 1
1.1%
586 1
1.1%
581 1
1.1%
401 1
1.1%
301 1
1.1%
206 1
1.1%
167 1
1.1%
164 1
1.1%

소년보호송치
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62765957
Minimum0
Maximum20
Zeros80
Zeros (%)85.1%
Negative0
Negative (%)0.0%
Memory size978.0 B
2023-12-13T03:21:28.833041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3.35
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.5058385
Coefficient of variation (CV)3.9923529
Kurtosis40.887718
Mean0.62765957
Median Absolute Deviation (MAD)0
Skewness5.9643811
Sum59
Variance6.2792267
MonotonicityNot monotonic
2023-12-13T03:21:28.959394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 80
85.1%
1 7
 
7.4%
20 1
 
1.1%
7 1
 
1.1%
3 1
 
1.1%
10 1
 
1.1%
4 1
 
1.1%
6 1
 
1.1%
2 1
 
1.1%
ValueCountFrequency (%)
0 80
85.1%
1 7
 
7.4%
2 1
 
1.1%
3 1
 
1.1%
4 1
 
1.1%
6 1
 
1.1%
7 1
 
1.1%
10 1
 
1.1%
20 1
 
1.1%
ValueCountFrequency (%)
20 1
 
1.1%
10 1
 
1.1%
7 1
 
1.1%
6 1
 
1.1%
4 1
 
1.1%
3 1
 
1.1%
2 1
 
1.1%
1 7
 
7.4%
0 80
85.1%

가정보호송치
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2021277
Minimum0
Maximum186
Zeros84
Zeros (%)89.4%
Negative0
Negative (%)0.0%
Memory size978.0 B
2023-12-13T03:21:29.082307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum186
Range186
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20.198254
Coefficient of variation (CV)6.3077603
Kurtosis74.257267
Mean3.2021277
Median Absolute Deviation (MAD)0
Skewness8.3342965
Sum301
Variance407.96946
MonotonicityNot monotonic
2023-12-13T03:21:29.236963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 84
89.4%
1 4
 
4.3%
2 2
 
2.1%
18 1
 
1.1%
186 1
 
1.1%
47 1
 
1.1%
42 1
 
1.1%
ValueCountFrequency (%)
0 84
89.4%
1 4
 
4.3%
2 2
 
2.1%
18 1
 
1.1%
42 1
 
1.1%
47 1
 
1.1%
186 1
 
1.1%
ValueCountFrequency (%)
186 1
 
1.1%
47 1
 
1.1%
42 1
 
1.1%
18 1
 
1.1%
2 2
 
2.1%
1 4
 
4.3%
0 84
89.4%

성매매보호송치
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size884.0 B
0
93 
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 93
98.9%
3 1
 
1.1%

Length

2023-12-13T03:21:29.388101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:21:29.481302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 93
98.9%
3 1
 
1.1%

아동보호송치
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size884.0 B
0
91 
3
 
1
1
 
1
23
 
1

Length

Max length2
Median length1
Mean length1.0106383
Min length1

Unique

Unique3 ?
Unique (%)3.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 91
96.8%
3 1
 
1.1%
1 1
 
1.1%
23 1
 
1.1%

Length

2023-12-13T03:21:29.603084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:21:29.729147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 91
96.8%
3 1
 
1.1%
1 1
 
1.1%
23 1
 
1.1%

기소유예
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)46.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.62766
Minimum0
Maximum1713
Zeros18
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size978.0 B
2023-12-13T03:21:29.854989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q361.5
95-th percentile634.05
Maximum1713
Range1713
Interquartile range (IQR)60.5

Descriptive statistics

Standard deviation257.63966
Coefficient of variation (CV)2.5351332
Kurtosis18.478174
Mean101.62766
Median Absolute Deviation (MAD)5
Skewness3.9609833
Sum9553
Variance66378.193
MonotonicityNot monotonic
2023-12-13T03:21:30.044458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 18
19.1%
1 12
 
12.8%
3 7
 
7.4%
2 6
 
6.4%
5 4
 
4.3%
4 3
 
3.2%
10 2
 
2.1%
15 2
 
2.1%
6 2
 
2.1%
7 2
 
2.1%
Other values (34) 36
38.3%
ValueCountFrequency (%)
0 18
19.1%
1 12
12.8%
2 6
 
6.4%
3 7
 
7.4%
4 3
 
3.2%
5 4
 
4.3%
6 2
 
2.1%
7 2
 
2.1%
10 2
 
2.1%
11 2
 
2.1%
ValueCountFrequency (%)
1713 1
1.1%
947 1
1.1%
922 1
1.1%
828 1
1.1%
805 1
1.1%
542 1
1.1%
366 1
1.1%
360 1
1.1%
354 1
1.1%
308 1
1.1%

혐의없음
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.553191
Minimum0
Maximum1099
Zeros15
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size978.0 B
2023-12-13T03:21:30.196379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q331.25
95-th percentile312.05
Maximum1099
Range1099
Interquartile range (IQR)29.25

Descriptive statistics

Standard deviation150.84312
Coefficient of variation (CV)2.6209341
Kurtosis26.105773
Mean57.553191
Median Absolute Deviation (MAD)6
Skewness4.6397111
Sum5410
Variance22753.648
MonotonicityNot monotonic
2023-12-13T03:21:30.338241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 15
16.0%
2 12
 
12.8%
1 8
 
8.5%
5 5
 
5.3%
12 4
 
4.3%
27 4
 
4.3%
4 4
 
4.3%
8 3
 
3.2%
9 3
 
3.2%
6 3
 
3.2%
Other values (27) 33
35.1%
ValueCountFrequency (%)
0 15
16.0%
1 8
8.5%
2 12
12.8%
3 2
 
2.1%
4 4
 
4.3%
5 5
 
5.3%
6 3
 
3.2%
8 3
 
3.2%
9 3
 
3.2%
11 1
 
1.1%
ValueCountFrequency (%)
1099 1
1.1%
547 1
1.1%
542 1
1.1%
402 1
1.1%
366 1
1.1%
283 1
1.1%
260 1
1.1%
250 1
1.1%
158 1
1.1%
141 1
1.1%

죄가안됨
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57446809
Minimum0
Maximum23
Zeros82
Zeros (%)87.2%
Negative0
Negative (%)0.0%
Memory size978.0 B
2023-12-13T03:21:30.460620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum23
Range23
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.7997287
Coefficient of variation (CV)4.8736018
Kurtosis50.193622
Mean0.57446809
Median Absolute Deviation (MAD)0
Skewness6.8684496
Sum54
Variance7.8384809
MonotonicityNot monotonic
2023-12-13T03:21:30.584461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 82
87.2%
1 5
 
5.3%
2 3
 
3.2%
3 2
 
2.1%
23 1
 
1.1%
14 1
 
1.1%
ValueCountFrequency (%)
0 82
87.2%
1 5
 
5.3%
2 3
 
3.2%
3 2
 
2.1%
14 1
 
1.1%
23 1
 
1.1%
ValueCountFrequency (%)
23 1
 
1.1%
14 1
 
1.1%
3 2
 
2.1%
2 3
 
3.2%
1 5
 
5.3%
0 82
87.2%

공소권없음
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)30.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.829787
Minimum0
Maximum2769
Zeros41
Zeros (%)43.6%
Negative0
Negative (%)0.0%
Memory size978.0 B
2023-12-13T03:21:30.729210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q311.75
95-th percentile100.2
Maximum2769
Range2769
Interquartile range (IQR)11.75

Descriptive statistics

Standard deviation304.71766
Coefficient of variation (CV)5.4579764
Kurtosis70.271889
Mean55.829787
Median Absolute Deviation (MAD)1
Skewness8.1333252
Sum5248
Variance92852.852
MonotonicityNot monotonic
2023-12-13T03:21:30.880606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 41
43.6%
1 15
 
16.0%
2 5
 
5.3%
15 3
 
3.2%
4 3
 
3.2%
6 3
 
3.2%
11 2
 
2.1%
84 1
 
1.1%
12 1
 
1.1%
41 1
 
1.1%
Other values (19) 19
20.2%
ValueCountFrequency (%)
0 41
43.6%
1 15
 
16.0%
2 5
 
5.3%
3 1
 
1.1%
4 3
 
3.2%
6 3
 
3.2%
11 2
 
2.1%
12 1
 
1.1%
15 3
 
3.2%
21 1
 
1.1%
ValueCountFrequency (%)
2769 1
1.1%
1046 1
1.1%
296 1
1.1%
146 1
1.1%
108 1
1.1%
96 1
1.1%
84 1
1.1%
80 1
1.1%
73 1
1.1%
55 1
1.1%

기소중지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.861702
Minimum0
Maximum810
Zeros29
Zeros (%)30.9%
Negative0
Negative (%)0.0%
Memory size978.0 B
2023-12-13T03:21:31.012165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q314
95-th percentile123.85
Maximum810
Range810
Interquartile range (IQR)14

Descriptive statistics

Standard deviation114.22479
Coefficient of variation (CV)3.2765122
Kurtosis32.126928
Mean34.861702
Median Absolute Deviation (MAD)2
Skewness5.4662381
Sum3277
Variance13047.303
MonotonicityNot monotonic
2023-12-13T03:21:31.172016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 29
30.9%
1 12
12.8%
2 8
 
8.5%
4 5
 
5.3%
8 4
 
4.3%
18 3
 
3.2%
9 3
 
3.2%
61 2
 
2.1%
7 2
 
2.1%
29 2
 
2.1%
Other values (22) 24
25.5%
ValueCountFrequency (%)
0 29
30.9%
1 12
12.8%
2 8
 
8.5%
3 1
 
1.1%
4 5
 
5.3%
5 2
 
2.1%
6 1
 
1.1%
7 2
 
2.1%
8 4
 
4.3%
9 3
 
3.2%
ValueCountFrequency (%)
810 1
1.1%
659 1
1.1%
347 1
1.1%
169 1
1.1%
131 1
1.1%
120 1
1.1%
111 1
1.1%
78 1
1.1%
76 1
1.1%
75 1
1.1%

참고인중지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7765957
Minimum0
Maximum56
Zeros62
Zeros (%)66.0%
Negative0
Negative (%)0.0%
Memory size978.0 B
2023-12-13T03:21:31.314624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6.7
Maximum56
Range56
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.1542087
Coefficient of variation (CV)3.4640456
Kurtosis66.215213
Mean1.7765957
Median Absolute Deviation (MAD)0
Skewness7.6316085
Sum167
Variance37.874285
MonotonicityNot monotonic
2023-12-13T03:21:31.769081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 62
66.0%
1 11
 
11.7%
3 5
 
5.3%
6 4
 
4.3%
2 4
 
4.3%
4 2
 
2.1%
56 1
 
1.1%
11 1
 
1.1%
12 1
 
1.1%
8 1
 
1.1%
Other values (2) 2
 
2.1%
ValueCountFrequency (%)
0 62
66.0%
1 11
 
11.7%
2 4
 
4.3%
3 5
 
5.3%
4 2
 
2.1%
5 1
 
1.1%
6 4
 
4.3%
8 1
 
1.1%
9 1
 
1.1%
11 1
 
1.1%
ValueCountFrequency (%)
56 1
 
1.1%
12 1
 
1.1%
11 1
 
1.1%
9 1
 
1.1%
8 1
 
1.1%
6 4
4.3%
5 1
 
1.1%
4 2
 
2.1%
3 5
5.3%
2 4
4.3%

Interactions

2023-12-13T03:21:26.059580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:15.335795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:16.312717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:17.469372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:18.387952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:19.650736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:20.537726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:21.473688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:22.387844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:23.355142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:24.598303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:26.143580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:15.415192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:16.435609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:17.548007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:18.486757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:19.735301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:20.622390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:21.551027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:22.474380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:23.464670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:24.715278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:26.231285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:15.505960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:16.542846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:17.629918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:18.575423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:19.811713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:20.721699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:21.638929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:22.553654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:23.557543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:24.806914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:26.308424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:15.595220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:16.635504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:17.709908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:18.652315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:19.888731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:20.799906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:21.725216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:22.626251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:23.658595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:24.921202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:26.411298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:15.690246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:16.743785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:17.798587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:18.748070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:19.970450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:20.900002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:21.822215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:22.715812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:23.843335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:25.022715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:26.518165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:15.780685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:16.856232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:17.882844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:18.829110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:20.053309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:20.986328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:21.905251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:22.809299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:23.973736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:25.453886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:26.621349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:15.877863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:16.958899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:17.965480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:18.916428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:20.131626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:21.081791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:21.989167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:22.896533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:24.063692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:25.545681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:26.715475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:15.966941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:17.064651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:18.050599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:18.998020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:20.204093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:21.167011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:22.058117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:22.988868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:24.163731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:25.650282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:26.795460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:16.045014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:17.177464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:18.126141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:19.079234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:20.278155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:21.241115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:22.128472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:23.066011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:24.275527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:25.737620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:26.913625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:16.133263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:17.271670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:18.201158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:19.157780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:20.361502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:21.314013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:22.207515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:23.151298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:24.386287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:25.844010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:27.023278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:16.228298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:17.385409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:18.290440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:19.579047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:20.449403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:21.399411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:22.301035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:23.256507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:24.499063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:21:25.951957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:21:31.870904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
범죄분류구공판_구속구공판_불구속구약식소년보호송치가정보호송치성매매보호송치아동보호송치기소유예혐의없음죄가안됨공소권없음기소중지참고인중지
범죄분류1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
구공판_구속1.0001.0000.7890.5520.8410.5550.0000.0000.9140.8190.6740.0000.8490.885
구공판_불구속1.0000.7891.0000.8490.8880.8260.0000.4920.6850.8730.6220.4920.8740.740
구약식1.0000.5520.8491.0000.9030.7450.0000.3150.6920.8910.4730.4720.8460.335
소년보호송치1.0000.8410.8880.9031.0000.9310.0000.4990.7820.9240.8290.4990.9240.864
가정보호송치1.0000.5550.8260.7450.9311.0000.0000.7980.6640.8720.7980.6670.3610.536
성매매보호송치1.0000.0000.0000.0000.0000.0001.0000.0000.6290.0000.0000.0000.0000.000
아동보호송치1.0000.0000.4920.3150.4990.7980.0001.0000.4560.5000.8840.8840.0000.290
기소유예1.0000.9140.6850.6920.7820.6640.6290.4561.0000.7300.7130.5430.7350.688
혐의없음1.0000.8190.8730.8910.9240.8720.0000.5000.7301.0000.7090.7090.9660.901
죄가안됨1.0000.6740.6220.4730.8290.7980.0000.8840.7130.7091.0000.8840.3660.906
공소권없음1.0000.0000.4920.4720.4990.6670.0000.8840.5430.7090.8841.0000.7120.559
기소중지1.0000.8490.8740.8460.9240.3610.0000.0000.7350.9660.3660.7121.0000.848
참고인중지1.0000.8850.7400.3350.8640.5360.0000.2900.6880.9010.9060.5590.8481.000
2023-12-13T03:21:32.032265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아동보호송치성매매보호송치
아동보호송치1.0000.000
성매매보호송치0.0001.000
2023-12-13T03:21:32.134967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구공판_구속구공판_불구속구약식소년보호송치가정보호송치기소유예혐의없음죄가안됨공소권없음기소중지참고인중지성매매보호송치아동보호송치
구공판_구속1.0000.8170.5730.5400.2620.6380.6910.4200.6120.6910.5700.0000.000
구공판_불구속0.8171.0000.5670.4940.3010.6570.7110.4800.6480.7500.4770.0000.334
구약식0.5730.5671.0000.5580.1950.8410.7940.4420.7100.7050.4580.0000.302
소년보호송치0.5400.4940.5581.0000.3560.5280.5750.4160.5620.5530.3660.0000.339
가정보호송치0.2620.3010.1950.3561.0000.3020.2670.2290.2580.1640.1190.0000.855
기소유예0.6380.6570.8410.5280.3021.0000.7930.4320.6440.7540.5090.6590.324
혐의없음0.6910.7110.7940.5750.2670.7931.0000.4440.7520.8250.5610.0000.340
죄가안됨0.4200.4800.4420.4160.2290.4320.4441.0000.3730.3420.3130.0000.558
공소권없음0.6120.6480.7100.5620.2580.6440.7520.3731.0000.8040.5190.0000.558
기소중지0.6910.7500.7050.5530.1640.7540.8250.3420.8041.0000.6210.0000.000
참고인중지0.5700.4770.4580.3660.1190.5090.5610.3130.5190.6211.0000.0000.116
성매매보호송치0.0000.0000.0000.0000.0000.6590.0000.0000.0000.0000.0001.0000.000
아동보호송치0.0000.3340.3020.3390.8550.3240.3400.5580.5580.0000.1160.0001.000

Missing values

2023-12-13T03:21:27.182705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:21:27.369727image/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

범죄분류구공판_구속구공판_불구속구약식소년보호송치가정보호송치성매매보호송치아동보호송치기소유예혐의없음죄가안됨공소권없음기소중지참고인중지
0절도1971134012000017133662401696
1장물518000013120060
2사기4892721647000227109917381056
3횡령31511400003081410156811
4배임11300000120120
5손괴81415311800366108329180
6살인84600000260470
7강도381100000140194
8방화11310100600000
9성폭력801311671200232250026441
범죄분류구공판_구속구공판_불구속구약식소년보호송치가정보호송치성매매보호송치아동보호송치기소유예혐의없음죄가안됨공소권없음기소중지참고인중지
84주민등록법00100000580220
85청소년보호법00750000106320130
86축산물위생관리법00800001200000
87출입국관리법90191560000542440108781
88특가법(도주차량)112221000010230291
89폐기물관리법0030000000000
90풍속영업의규제에관한법률0000000100000
91학원의설립운영및과외교습에관한법률0030000400000
92화물자동차운수사업법00150000140000
93기타특별법1818869310003602602231112