Overview

Dataset statistics

Number of variables9
Number of observations139
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.0 KiB
Average record size in memory80.9 B

Variable types

Text1
Numeric7
Categorical1

Dataset

Description대검찰청에서 발간하는 범죄분석은 3종의 범죄통계원표를 기반으로 작성하는 자료이며 이 중 본 데이터는 범죄자 처분결과(법인체)에 관한 통계임.
Author대검찰청
URLhttps://www.data.go.kr/data/15084897/fileData.do

Alerts

구공판_불구속 is highly overall correlated with 구약식 and 4 other fieldsHigh correlation
구약식 is highly overall correlated with 구공판_불구속 and 4 other fieldsHigh correlation
기소유예 is highly overall correlated with 구공판_불구속 and 3 other fieldsHigh correlation
혐의없음 is highly overall correlated with 구공판_불구속 and 4 other fieldsHigh correlation
공소권없음 is highly overall correlated with 구공판_불구속 and 3 other fieldsHigh correlation
참고인중지 is highly overall correlated with 공소권없음 and 1 other fieldsHigh correlation
죄가안됨 is highly overall correlated with 구공판_불구속 and 4 other fieldsHigh correlation
죄가안됨 is highly imbalanced (90.8%)Imbalance
범죄분류 has unique valuesUnique
구공판_불구속 has 55 (39.6%) zerosZeros
구약식 has 46 (33.1%) zerosZeros
기소유예 has 36 (25.9%) zerosZeros
혐의없음 has 20 (14.4%) zerosZeros
공소권없음 has 73 (52.5%) zerosZeros
기소중지 has 98 (70.5%) zerosZeros
참고인중지 has 105 (75.5%) zerosZeros

Reproduction

Analysis started2023-12-12 15:04:45.025957
Analysis finished2023-12-12 15:04:50.720096
Duration5.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄분류
Text

UNIQUE 

Distinct139
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-13T00:04:50.907453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length16
Mean length7.7482014
Min length2

Characters and Unicode

Total characters1077
Distinct characters219
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique139 ?
Unique (%)100.0%

Sample

1st row절도
2nd row사기
3rd row컴퓨터등사용사기
4th row부당이득
5th row편의시설부정이용
ValueCountFrequency (%)
절도 1
 
0.7%
여신전문금융업법 1
 
0.7%
약사법 1
 
0.7%
액화석유가스의안전관리및사업법 1
 
0.7%
아동복지법 1
 
0.7%
신용정보의이용및보호에관한법률 1
 
0.7%
식품위생법 1
 
0.7%
수산자원관리법 1
 
0.7%
수산업법 1
 
0.7%
도로교통법(사고후미조치 1
 
0.7%
Other values (129) 129
92.8%
2023-12-13T00:04:51.321370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
법 100
 
9.3%
관 56
 
5.2%
한 31
 
2.9%
에 30
 
2.8%
률 28
 
2.6%
리 27
 
2.5%
보 20
 
1.9%
의 19
 
1.8%
및 19
 
1.8%
업 18
 
1.7%
Other values (209) 729
67.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1056
98.1%
Open Punctuation 8
 
0.7%
Close Punctuation 8
 
0.7%
Other Punctuation 5
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
법 100
 
9.5%
관 56
 
5.3%
한 31
 
2.9%
에 30
 
2.8%
률 28
 
2.7%
리 27
 
2.6%
보 20
 
1.9%
의 19
 
1.8%
및 19
 
1.8%
업 18
 
1.7%
Other values (206) 708
67.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1056
98.1%
Common 21
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
법 100
 
9.5%
관 56
 
5.3%
한 31
 
2.9%
에 30
 
2.8%
률 28
 
2.7%
리 27
 
2.6%
보 20
 
1.9%
의 19
 
1.8%
및 19
 
1.8%
업 18
 
1.7%
Other values (206) 708
67.0%
Common
ValueCountFrequency (%)
( 8
38.1%
) 8
38.1%
, 5
23.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1056
98.1%
ASCII 21
 
1.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
법 100
 
9.5%
관 56
 
5.3%
한 31
 
2.9%
에 30
 
2.8%
률 28
 
2.7%
리 27
 
2.6%
보 20
 
1.9%
의 19
 
1.8%
및 19
 
1.8%
업 18
 
1.7%
Other values (206) 708
67.0%
ASCII
ValueCountFrequency (%)
( 8
38.1%
) 8
38.1%
, 5
23.8%

구공판_불구속
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.258993
Minimum0
Maximum280
Zeros55
Zeros (%)39.6%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T00:04:51.459612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37.5
95-th percentile28.5
Maximum280
Range280
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation35.122836
Coefficient of variation (CV)3.4236144
Kurtosis45.74089
Mean10.258993
Median Absolute Deviation (MAD)1
Skewness6.54214
Sum1426
Variance1233.6136
MonotonicityNot monotonic
2023-12-13T00:04:51.599655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 55
39.6%
1 17
 
12.2%
2 12
 
8.6%
3 6
 
4.3%
5 5
 
3.6%
6 5
 
3.6%
10 4
 
2.9%
8 4
 
2.9%
11 3
 
2.2%
22 3
 
2.2%
Other values (19) 25
18.0%
ValueCountFrequency (%)
0 55
39.6%
1 17
 
12.2%
2 12
 
8.6%
3 6
 
4.3%
4 2
 
1.4%
5 5
 
3.6%
6 5
 
3.6%
7 2
 
1.4%
8 4
 
2.9%
9 1
 
0.7%
ValueCountFrequency (%)
280 1
0.7%
265 1
0.7%
142 1
0.7%
50 2
1.4%
42 1
0.7%
33 1
0.7%
28 1
0.7%
27 1
0.7%
26 1
0.7%
25 1
0.7%

구약식
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)40.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.517986
Minimum0
Maximum5306
Zeros46
Zeros (%)33.1%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T00:04:51.759820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q328.5
95-th percentile239.1
Maximum5306
Range5306
Interquartile range (IQR)28.5

Descriptive statistics

Standard deviation468.00132
Coefficient of variation (CV)5.6035993
Kurtosis114.62928
Mean83.517986
Median Absolute Deviation (MAD)4
Skewness10.36343
Sum11609
Variance219025.24
MonotonicityNot monotonic
2023-12-13T00:04:51.916953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46
33.1%
1 10
 
7.2%
4 8
 
5.8%
2 7
 
5.0%
3 6
 
4.3%
23 3
 
2.2%
71 3
 
2.2%
9 3
 
2.2%
5 2
 
1.4%
6 2
 
1.4%
Other values (46) 49
35.3%
ValueCountFrequency (%)
0 46
33.1%
1 10
 
7.2%
2 7
 
5.0%
3 6
 
4.3%
4 8
 
5.8%
5 2
 
1.4%
6 2
 
1.4%
7 2
 
1.4%
8 1
 
0.7%
9 3
 
2.2%
ValueCountFrequency (%)
5306 1
0.7%
1179 1
0.7%
987 1
0.7%
327 1
0.7%
314 1
0.7%
291 1
0.7%
267 1
0.7%
236 1
0.7%
196 1
0.7%
177 1
0.7%

기소유예
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.863309
Minimum0
Maximum641
Zeros36
Zeros (%)25.9%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T00:04:52.089555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q314
95-th percentile106.8
Maximum641
Range641
Interquartile range (IQR)14

Descriptive statistics

Standard deviation76.15128
Coefficient of variation (CV)2.9443749
Kurtosis37.98971
Mean25.863309
Median Absolute Deviation (MAD)3
Skewness5.6742202
Sum3595
Variance5799.0174
MonotonicityNot monotonic
2023-12-13T00:04:52.246401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 36
25.9%
1 19
13.7%
2 12
 
8.6%
3 10
 
7.2%
4 6
 
4.3%
6 5
 
3.6%
10 4
 
2.9%
5 4
 
2.9%
14 3
 
2.2%
16 2
 
1.4%
Other values (30) 38
27.3%
ValueCountFrequency (%)
0 36
25.9%
1 19
13.7%
2 12
 
8.6%
3 10
 
7.2%
4 6
 
4.3%
5 4
 
2.9%
6 5
 
3.6%
7 1
 
0.7%
8 2
 
1.4%
9 1
 
0.7%
ValueCountFrequency (%)
641 1
0.7%
443 1
0.7%
320 1
0.7%
189 1
0.7%
162 1
0.7%
132 2
1.4%
104 2
1.4%
97 1
0.7%
83 1
0.7%
81 1
0.7%

혐의없음
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.093525
Minimum0
Maximum654
Zeros20
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T00:04:52.701476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q324.5
95-th percentile217.7
Maximum654
Range654
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation79.343127
Coefficient of variation (CV)2.4722472
Kurtosis29.881103
Mean32.093525
Median Absolute Deviation (MAD)7
Skewness4.877196
Sum4461
Variance6295.3318
MonotonicityNot monotonic
2023-12-13T00:04:52.865157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20
 
14.4%
2 13
 
9.4%
1 13
 
9.4%
4 9
 
6.5%
3 8
 
5.8%
7 6
 
4.3%
10 5
 
3.6%
16 4
 
2.9%
19 3
 
2.2%
9 3
 
2.2%
Other values (40) 55
39.6%
ValueCountFrequency (%)
0 20
14.4%
1 13
9.4%
2 13
9.4%
3 8
 
5.8%
4 9
6.5%
5 2
 
1.4%
6 3
 
2.2%
7 6
 
4.3%
8 1
 
0.7%
9 3
 
2.2%
ValueCountFrequency (%)
654 1
0.7%
340 1
0.7%
279 1
0.7%
275 1
0.7%
251 1
0.7%
234 1
0.7%
224 1
0.7%
217 1
0.7%
142 1
0.7%
103 1
0.7%

죄가안됨
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
0
136 
2
 
1
1
 
1
11
 
1

Length

Max length2
Median length1
Mean length1.0071942
Min length1

Unique

Unique3 ?
Unique (%)2.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 136
97.8%
2 1
 
0.7%
1 1
 
0.7%
11 1
 
0.7%

Length

2023-12-13T00:04:53.039790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:04:53.149061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 136
97.8%
2 1
 
0.7%
1 1
 
0.7%
11 1
 
0.7%

공소권없음
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7194245
Minimum0
Maximum750
Zeros73
Zeros (%)52.5%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T00:04:53.246450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile19.1
Maximum750
Range750
Interquartile range (IQR)3

Descriptive statistics

Standard deviation64.056477
Coefficient of variation (CV)7.3464112
Kurtosis132.68219
Mean8.7194245
Median Absolute Deviation (MAD)0
Skewness11.407685
Sum1212
Variance4103.2323
MonotonicityNot monotonic
2023-12-13T00:04:53.383917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 73
52.5%
1 20
 
14.4%
2 10
 
7.2%
4 8
 
5.8%
3 6
 
4.3%
5 3
 
2.2%
10 3
 
2.2%
8 2
 
1.4%
20 2
 
1.4%
7 1
 
0.7%
Other values (11) 11
 
7.9%
ValueCountFrequency (%)
0 73
52.5%
1 20
 
14.4%
2 10
 
7.2%
3 6
 
4.3%
4 8
 
5.8%
5 3
 
2.2%
6 1
 
0.7%
7 1
 
0.7%
8 2
 
1.4%
9 1
 
0.7%
ValueCountFrequency (%)
750 1
0.7%
79 1
0.7%
67 1
0.7%
23 1
0.7%
21 1
0.7%
20 2
1.4%
19 1
0.7%
15 1
0.7%
14 1
0.7%
11 1
0.7%

기소중지
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7553957
Minimum0
Maximum57
Zeros98
Zeros (%)70.5%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T00:04:53.539278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile10.3
Maximum57
Range57
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.1618764
Coefficient of variation (CV)3.5102493
Kurtosis48.904219
Mean1.7553957
Median Absolute Deviation (MAD)0
Skewness6.2920204
Sum244
Variance37.968721
MonotonicityNot monotonic
2023-12-13T00:04:53.730474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 98
70.5%
1 18
 
12.9%
2 6
 
4.3%
3 5
 
3.6%
6 2
 
1.4%
57 1
 
0.7%
20 1
 
0.7%
5 1
 
0.7%
4 1
 
0.7%
18 1
 
0.7%
Other values (5) 5
 
3.6%
ValueCountFrequency (%)
0 98
70.5%
1 18
 
12.9%
2 6
 
4.3%
3 5
 
3.6%
4 1
 
0.7%
5 1
 
0.7%
6 2
 
1.4%
10 1
 
0.7%
13 1
 
0.7%
16 1
 
0.7%
ValueCountFrequency (%)
57 1
0.7%
23 1
0.7%
21 1
0.7%
20 1
0.7%
18 1
0.7%
16 1
0.7%
13 1
0.7%
10 1
0.7%
6 2
1.4%
5 1
0.7%

참고인중지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.618705
Minimum0
Maximum115
Zeros105
Zeros (%)75.5%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T00:04:53.862200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3.1
Maximum115
Range115
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.167721
Coefficient of variation (CV)6.281392
Kurtosis114.56991
Mean1.618705
Median Absolute Deviation (MAD)0
Skewness10.398051
Sum225
Variance103.38255
MonotonicityNot monotonic
2023-12-13T00:04:53.991251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 105
75.5%
1 17
 
12.2%
2 6
 
4.3%
3 4
 
2.9%
10 2
 
1.4%
8 1
 
0.7%
4 1
 
0.7%
115 1
 
0.7%
5 1
 
0.7%
32 1
 
0.7%
ValueCountFrequency (%)
0 105
75.5%
1 17
 
12.2%
2 6
 
4.3%
3 4
 
2.9%
4 1
 
0.7%
5 1
 
0.7%
8 1
 
0.7%
10 2
 
1.4%
32 1
 
0.7%
115 1
 
0.7%
ValueCountFrequency (%)
115 1
 
0.7%
32 1
 
0.7%
10 2
 
1.4%
8 1
 
0.7%
5 1
 
0.7%
4 1
 
0.7%
3 4
 
2.9%
2 6
 
4.3%
1 17
 
12.2%
0 105
75.5%

Interactions

2023-12-13T00:04:49.839257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:45.402958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:46.373145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:47.076329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:47.723637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:48.435860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:49.201932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:49.921391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:45.509607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:46.467364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:47.160686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:47.835858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:48.545711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:49.292013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:50.008567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:45.605868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:46.575666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:47.245149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:47.935952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:48.693960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:49.382561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:50.112821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:45.699436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:46.671198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:47.330305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:48.029373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:48.789324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:49.485437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:50.203325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:45.792626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:46.771404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:47.427953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:48.123874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:48.898584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:49.588535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:50.292624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:45.900470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:46.883820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:47.533223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:48.234002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:49.011632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:49.670081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:50.379137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:46.002653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:46.983294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:47.634672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:48.331541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:49.093193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:49.747595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:04:54.113187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구공판_불구속구약식기소유예혐의없음죄가안됨공소권없음기소중지참고인중지
구공판_불구속1.0000.9490.9210.8310.9170.4840.7760.800
구약식0.9491.0000.9540.7940.8880.0000.8340.670
기소유예0.9210.9541.0000.8110.7500.9360.9311.000
혐의없음0.8310.7940.8111.0000.7340.6770.8280.818
죄가안됨0.9170.8880.7500.7341.0000.0000.5270.670
공소권없음0.4840.0000.9360.6770.0001.0000.9020.941
기소중지0.7760.8340.9310.8280.5270.9021.0000.902
참고인중지0.8000.6701.0000.8180.6700.9410.9021.000
2023-12-13T00:04:54.256136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구공판_불구속구약식기소유예혐의없음공소권없음기소중지참고인중지죄가안됨
구공판_불구속1.0000.6900.5640.6710.5860.2940.4700.634
구약식0.6901.0000.7760.7180.5240.1810.3180.564
기소유예0.5640.7761.0000.6660.4350.2710.4000.583
혐의없음0.6710.7180.6661.0000.6340.4040.4770.602
공소권없음0.5860.5240.4350.6341.0000.3760.5400.000
기소중지0.2940.1810.2710.4040.3761.0000.3930.364
참고인중지0.4700.3180.4000.4770.5400.3931.0000.697
죄가안됨0.6340.5640.5830.6020.0000.3640.6971.000

Missing values

2023-12-13T00:04:50.499540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:04:50.641838image/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_불구속구약식기소유예혐의없음죄가안됨공소권없음기소중지참고인중지
0절도11350011
1사기2619673400235710
2컴퓨터등사용사기00130000
3부당이득00100000
4편의시설부정이용000160501
5전기통신금융사기피해금환급에관한특별법00300000
6횡령7443005202
7배임1001600151
8배임수재10010100
9배임증재00100000
범죄분류구공판_불구속구약식기소유예혐의없음죄가안됨공소권없음기소중지참고인중지
129출입국관리법3326725310515
130통신비밀보호법00010000
131특가법(도주차량)11500011
132특허법4324401410
133폐기물관리법5031478871221
134하천법23140000
135화물자동차운수사업법24110260200
136화재예방,소방시설설치유지및안전관리에관한법률02600000
137화학물질관리법251745780000
138기타특별법142117964165411671332