Overview

Dataset statistics

Number of variables13
Number of observations191
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.8 KiB
Average record size in memory116.7 B

Variable types

Text1
Numeric12

Dataset

Description대검찰청에서 발간하는 범죄분석은 3종의 범죄통계원표를 기반으로 작성하는 자료이며 이 중 본 데이터는 수원지방검찰청이 관할하는 범죄 발생의 검거상황과 관련된 통계임
Author대검찰청
URLhttps://www.data.go.kr/data/15084765/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 9 other fieldsHigh correlation
직수_인지_여자검거인원(명) is highly overall correlated with 직수_인지_발생건수(건) and 6 other fieldsHigh correlation
직수_인지_미상검거인원(명) is highly overall correlated with 직수_인지_발생건수(건) and 4 other fieldsHigh correlation
직수_인지_법인(개) is highly overall correlated with 직수_인지_남자검거인원(명) and 1 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 6 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) and 1 other fieldsHigh correlation
범죄분류 has unique valuesUnique
직수_인지_발생건수(건) has 48 (25.1%) zerosZeros
직수_인지_검거건수(건) has 50 (26.2%) zerosZeros
직수_인지_남자검거인원(명) has 64 (33.5%) zerosZeros
직수_인지_여자검거인원(명) has 114 (59.7%) zerosZeros
직수_인지_미상검거인원(명) has 131 (68.6%) zerosZeros
직수_인지_법인(개) has 148 (77.5%) zerosZeros
지휘관할_여자검거인원(명) has 15 (7.9%) zerosZeros
지휘관할_미상검거인원(명) has 78 (40.8%) zerosZeros
지휘관할_법인(개) has 88 (46.1%) zerosZeros

Reproduction

Analysis started2023-12-12 18:11:22.122385
Analysis finished2023-12-12 18:11:39.598664
Duration17.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄분류
Text

UNIQUE 

Distinct191
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-13T03:11:39.779154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22
Mean length8.2408377
Min length2

Characters and Unicode

Total characters1574
Distinct characters242
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

Unique191 ?
Unique (%)100.0%

Sample

1st row절도
2nd row불법사용
3rd row침입절도
4th row장물
5th row사기
ValueCountFrequency (%)
관한법률 27
 
8.7%
21
 
6.8%
관리에 4
 
1.3%
마약류관리에 3
 
1.0%
이용에 2
 
0.6%
안전관리에 2
 
0.6%
규제에 2
 
0.6%
처벌등에 2
 
0.6%
성보호에 2
 
0.6%
아동·청소년의 2
 
0.6%
Other values (241) 242
78.3%
2023-12-13T03:11:40.220651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
118
 
7.5%
114
 
7.2%
64
 
4.1%
38
 
2.4%
36
 
2.3%
32
 
2.0%
29
 
1.8%
25
 
1.6%
25
 
1.6%
23
 
1.5%
Other values (232) 1070
68.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1407
89.4%
Space Separator 118
 
7.5%
Other Punctuation 19
 
1.2%
Close Punctuation 15
 
1.0%
Open Punctuation 15
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
114
 
8.1%
64
 
4.5%
38
 
2.7%
36
 
2.6%
32
 
2.3%
29
 
2.1%
25
 
1.8%
25
 
1.8%
23
 
1.6%
23
 
1.6%
Other values (226) 998
70.9%
Other Punctuation
ValueCountFrequency (%)
, 10
52.6%
· 7
36.8%
/ 2
 
10.5%
Space Separator
ValueCountFrequency (%)
118
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1407
89.4%
Common 167
 
10.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
114
 
8.1%
64
 
4.5%
38
 
2.7%
36
 
2.6%
32
 
2.3%
29
 
2.1%
25
 
1.8%
25
 
1.8%
23
 
1.6%
23
 
1.6%
Other values (226) 998
70.9%
Common
ValueCountFrequency (%)
118
70.7%
) 15
 
9.0%
( 15
 
9.0%
, 10
 
6.0%
· 7
 
4.2%
/ 2
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1407
89.4%
ASCII 160
 
10.2%
None 7
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
118
73.8%
) 15
 
9.4%
( 15
 
9.4%
, 10
 
6.2%
/ 2
 
1.2%
Hangul
ValueCountFrequency (%)
114
 
8.1%
64
 
4.5%
38
 
2.7%
36
 
2.6%
32
 
2.3%
29
 
2.1%
25
 
1.8%
25
 
1.8%
23
 
1.6%
23
 
1.6%
Other values (226) 998
70.9%
None
ValueCountFrequency (%)
· 7
100.0%

직수_인지_발생건수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.675393
Minimum0
Maximum898
Zeros48
Zeros (%)25.1%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T03:11:40.416083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median4
Q320
95-th percentile125.5
Maximum898
Range898
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation85.464178
Coefficient of variation (CV)2.9804013
Kurtosis59.906555
Mean28.675393
Median Absolute Deviation (MAD)4
Skewness6.8481024
Sum5477
Variance7304.1257
MonotonicityNot monotonic
2023-12-13T03:11:40.563419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48
25.1%
1 24
 
12.6%
2 17
 
8.9%
4 9
 
4.7%
20 6
 
3.1%
7 5
 
2.6%
6 5
 
2.6%
3 5
 
2.6%
12 5
 
2.6%
5 4
 
2.1%
Other values (47) 63
33.0%
ValueCountFrequency (%)
0 48
25.1%
1 24
12.6%
2 17
 
8.9%
3 5
 
2.6%
4 9
 
4.7%
5 4
 
2.1%
6 5
 
2.6%
7 5
 
2.6%
8 1
 
0.5%
9 3
 
1.6%
ValueCountFrequency (%)
898 1
0.5%
424 1
0.5%
356 1
0.5%
289 1
0.5%
261 1
0.5%
243 1
0.5%
182 1
0.5%
177 1
0.5%
161 1
0.5%
132 1
0.5%

직수_인지_검거건수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)25.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.712042
Minimum0
Maximum603
Zeros50
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T03:11:40.702901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q317.5
95-th percentile78
Maximum603
Range603
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation61.293048
Coefficient of variation (CV)2.8229979
Kurtosis47.132627
Mean21.712042
Median Absolute Deviation (MAD)3
Skewness6.0984525
Sum4147
Variance3756.8377
MonotonicityNot monotonic
2023-12-13T03:11:40.876024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 50
26.2%
1 32
16.8%
2 12
 
6.3%
3 10
 
5.2%
5 7
 
3.7%
10 5
 
2.6%
4 5
 
2.6%
9 4
 
2.1%
23 4
 
2.1%
26 4
 
2.1%
Other values (39) 58
30.4%
ValueCountFrequency (%)
0 50
26.2%
1 32
16.8%
2 12
 
6.3%
3 10
 
5.2%
4 5
 
2.6%
5 7
 
3.7%
6 3
 
1.6%
7 3
 
1.6%
8 2
 
1.0%
9 4
 
2.1%
ValueCountFrequency (%)
603 1
0.5%
298 1
0.5%
283 1
0.5%
254 1
0.5%
223 1
0.5%
173 1
0.5%
160 1
0.5%
138 1
0.5%
94 1
0.5%
85 1
0.5%

직수_인지_남자검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.272251
Minimum0
Maximum900
Zeros64
Zeros (%)33.5%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T03:11:41.027786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q316
95-th percentile132
Maximum900
Range900
Interquartile range (IQR)16

Descriptive statistics

Standard deviation80.600112
Coefficient of variation (CV)3.1892731
Kurtosis75.868451
Mean25.272251
Median Absolute Deviation (MAD)3
Skewness7.7406491
Sum4827
Variance6496.3781
MonotonicityNot monotonic
2023-12-13T03:11:41.199501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 64
33.5%
1 17
 
8.9%
2 11
 
5.8%
3 10
 
5.2%
4 9
 
4.7%
5 8
 
4.2%
6 4
 
2.1%
12 3
 
1.6%
15 3
 
1.6%
16 3
 
1.6%
Other values (45) 59
30.9%
ValueCountFrequency (%)
0 64
33.5%
1 17
 
8.9%
2 11
 
5.8%
3 10
 
5.2%
4 9
 
4.7%
5 8
 
4.2%
6 4
 
2.1%
7 3
 
1.6%
8 1
 
0.5%
9 3
 
1.6%
ValueCountFrequency (%)
900 1
0.5%
402 1
0.5%
265 1
0.5%
222 1
0.5%
183 1
0.5%
173 1
0.5%
172 1
0.5%
165 1
0.5%
159 1
0.5%
157 1
0.5%

직수_인지_여자검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6963351
Minimum0
Maximum224
Zeros114
Zeros (%)59.7%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T03:11:41.370052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile24.5
Maximum224
Range224
Interquartile range (IQR)2

Descriptive statistics

Standard deviation21.97132
Coefficient of variation (CV)3.8570975
Kurtosis60.605598
Mean5.6963351
Median Absolute Deviation (MAD)0
Skewness7.1890596
Sum1088
Variance482.73888
MonotonicityNot monotonic
2023-12-13T03:11:41.523309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 114
59.7%
1 22
 
11.5%
2 10
 
5.2%
4 7
 
3.7%
8 6
 
3.1%
3 4
 
2.1%
5 4
 
2.1%
6 3
 
1.6%
9 3
 
1.6%
21 1
 
0.5%
Other values (17) 17
 
8.9%
ValueCountFrequency (%)
0 114
59.7%
1 22
 
11.5%
2 10
 
5.2%
3 4
 
2.1%
4 7
 
3.7%
5 4
 
2.1%
6 3
 
1.6%
8 6
 
3.1%
9 3
 
1.6%
10 1
 
0.5%
ValueCountFrequency (%)
224 1
0.5%
141 1
0.5%
107 1
0.5%
58 1
0.5%
52 1
0.5%
49 1
0.5%
41 1
0.5%
37 1
0.5%
33 1
0.5%
28 1
0.5%

직수_인지_미상검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3560209
Minimum0
Maximum189
Zeros131
Zeros (%)68.6%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T03:11:41.648472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile26
Maximum189
Range189
Interquartile range (IQR)1

Descriptive statistics

Standard deviation24.670224
Coefficient of variation (CV)3.8813944
Kurtosis36.643523
Mean6.3560209
Median Absolute Deviation (MAD)0
Skewness5.8513768
Sum1214
Variance608.61995
MonotonicityNot monotonic
2023-12-13T03:11:41.789087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 131
68.6%
1 15
 
7.9%
2 8
 
4.2%
13 5
 
2.6%
3 4
 
2.1%
11 2
 
1.0%
86 2
 
1.0%
4 2
 
1.0%
26 2
 
1.0%
12 2
 
1.0%
Other values (16) 18
 
9.4%
ValueCountFrequency (%)
0 131
68.6%
1 15
 
7.9%
2 8
 
4.2%
3 4
 
2.1%
4 2
 
1.0%
5 2
 
1.0%
6 2
 
1.0%
7 1
 
0.5%
8 1
 
0.5%
10 1
 
0.5%
ValueCountFrequency (%)
189 1
0.5%
177 1
0.5%
165 1
0.5%
86 2
1.0%
80 1
0.5%
37 1
0.5%
36 1
0.5%
30 1
0.5%
26 2
1.0%
20 1
0.5%

직수_인지_법인(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7172775
Minimum0
Maximum70
Zeros148
Zeros (%)77.5%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T03:11:41.922435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8.5
Maximum70
Range70
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.8043071
Coefficient of variation (CV)3.9622642
Kurtosis58.149734
Mean1.7172775
Median Absolute Deviation (MAD)0
Skewness6.869507
Sum328
Variance46.298595
MonotonicityNot monotonic
2023-12-13T03:11:42.064527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 148
77.5%
1 15
 
7.9%
2 9
 
4.7%
8 3
 
1.6%
3 3
 
1.6%
24 2
 
1.0%
4 2
 
1.0%
13 2
 
1.0%
31 1
 
0.5%
9 1
 
0.5%
Other values (5) 5
 
2.6%
ValueCountFrequency (%)
0 148
77.5%
1 15
 
7.9%
2 9
 
4.7%
3 3
 
1.6%
4 2
 
1.0%
7 1
 
0.5%
8 3
 
1.6%
9 1
 
0.5%
13 2
 
1.0%
14 1
 
0.5%
ValueCountFrequency (%)
70 1
 
0.5%
34 1
 
0.5%
31 1
 
0.5%
24 2
1.0%
15 1
 
0.5%
14 1
 
0.5%
13 2
1.0%
9 1
 
0.5%
8 3
1.6%
7 1
 
0.5%

지휘관할_발생건수(건)
Real number (ℝ)

HIGH CORRELATION 

Distinct155
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1562.0157
Minimum1
Maximum33651
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T03:11:42.218899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q134.5
median142
Q3645
95-th percentile7897.5
Maximum33651
Range33650
Interquartile range (IQR)610.5

Descriptive statistics

Standard deviation5045.1963
Coefficient of variation (CV)3.2299267
Kurtosis25.095885
Mean1562.0157
Median Absolute Deviation (MAD)129
Skewness4.9204309
Sum298345
Variance25454006
MonotonicityNot monotonic
2023-12-13T03:11:42.416143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 5
 
2.6%
3 5
 
2.6%
37 4
 
2.1%
32 4
 
2.1%
5 3
 
1.6%
4 3
 
1.6%
18 2
 
1.0%
849 2
 
1.0%
16 2
 
1.0%
224 2
 
1.0%
Other values (145) 159
83.2%
ValueCountFrequency (%)
1 1
 
0.5%
2 2
 
1.0%
3 5
2.6%
4 3
1.6%
5 3
1.6%
6 2
 
1.0%
7 2
 
1.0%
8 1
 
0.5%
9 2
 
1.0%
11 1
 
0.5%
ValueCountFrequency (%)
33651 1
0.5%
31217 1
0.5%
29717 1
0.5%
27659 1
0.5%
26561 1
0.5%
12340 1
0.5%
11633 1
0.5%
10764 1
0.5%
9530 1
0.5%
8281 1
0.5%

지휘관할_검거건수(건)
Real number (ℝ)

HIGH CORRELATION 

Distinct148
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1346.5916
Minimum0
Maximum31314
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T03:11:42.596948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q133.5
median123
Q3584.5
95-th percentile5949.5
Maximum31314
Range31314
Interquartile range (IQR)551

Descriptive statistics

Standard deviation4449.0101
Coefficient of variation (CV)3.3039045
Kurtosis28.965294
Mean1346.5916
Median Absolute Deviation (MAD)112
Skewness5.2462162
Sum257199
Variance19793691
MonotonicityNot monotonic
2023-12-13T03:11:42.782797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 5
 
2.6%
4 5
 
2.6%
34 3
 
1.6%
20 3
 
1.6%
19 3
 
1.6%
17 3
 
1.6%
9 3
 
1.6%
55 3
 
1.6%
61 3
 
1.6%
35 3
 
1.6%
Other values (138) 157
82.2%
ValueCountFrequency (%)
0 1
 
0.5%
1 1
 
0.5%
2 2
 
1.0%
3 5
2.6%
4 5
2.6%
5 2
 
1.0%
6 2
 
1.0%
8 1
 
0.5%
9 3
1.6%
10 1
 
0.5%
ValueCountFrequency (%)
31314 1
0.5%
28880 1
0.5%
27658 1
0.5%
26174 1
0.5%
15728 1
0.5%
11945 1
0.5%
11155 1
0.5%
7327 1
0.5%
6816 1
0.5%
6017 1
0.5%

지휘관할_남자검거인원(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct158
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1303.2461
Minimum0
Maximum32142
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T03:11:43.042409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.5
Q139
median148
Q3646.5
95-th percentile5413
Maximum32142
Range32142
Interquartile range (IQR)607.5

Descriptive statistics

Standard deviation4211.1529
Coefficient of variation (CV)3.23128
Kurtosis31.67831
Mean1303.2461
Median Absolute Deviation (MAD)138
Skewness5.4177839
Sum248920
Variance17733809
MonotonicityNot monotonic
2023-12-13T03:11:43.223609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5
 
2.6%
4 5
 
2.6%
2 3
 
1.6%
8 3
 
1.6%
78 2
 
1.0%
7 2
 
1.0%
14 2
 
1.0%
40 2
 
1.0%
33 2
 
1.0%
44 2
 
1.0%
Other values (148) 163
85.3%
ValueCountFrequency (%)
0 1
 
0.5%
1 5
2.6%
2 3
1.6%
3 1
 
0.5%
4 5
2.6%
5 1
 
0.5%
6 2
 
1.0%
7 2
 
1.0%
8 3
1.6%
10 1
 
0.5%
ValueCountFrequency (%)
32142 1
0.5%
28492 1
0.5%
24403 1
0.5%
23456 1
0.5%
12078 1
0.5%
11077 1
0.5%
10628 1
0.5%
8075 1
0.5%
5736 1
0.5%
5626 1
0.5%

지휘관할_여자검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct104
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean292
Minimum0
Maximum6784
Zeros15
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T03:11:43.740875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median23
Q3121
95-th percentile1157
Maximum6784
Range6784
Interquartile range (IQR)116

Descriptive statistics

Standard deviation947.94429
Coefficient of variation (CV)3.2463846
Kurtosis32.905183
Mean292
Median Absolute Deviation (MAD)22
Skewness5.5134223
Sum55772
Variance898598.38
MonotonicityNot monotonic
2023-12-13T03:11:43.932498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
7.9%
1 8
 
4.2%
3 8
 
4.2%
2 7
 
3.7%
4 6
 
3.1%
6 5
 
2.6%
14 5
 
2.6%
5 5
 
2.6%
9 4
 
2.1%
16 4
 
2.1%
Other values (94) 124
64.9%
ValueCountFrequency (%)
0 15
7.9%
1 8
4.2%
2 7
3.7%
3 8
4.2%
4 6
 
3.1%
5 5
 
2.6%
6 5
 
2.6%
7 2
 
1.0%
8 3
 
1.6%
9 4
 
2.1%
ValueCountFrequency (%)
6784 1
0.5%
6744 1
0.5%
6535 1
0.5%
4056 1
0.5%
3250 1
0.5%
2617 1
0.5%
1568 1
0.5%
1532 1
0.5%
1424 1
0.5%
1187 1
0.5%

지휘관할_미상검거인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.418848
Minimum0
Maximum585
Zeros78
Zeros (%)40.8%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T03:11:44.127500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q310.5
95-th percentile102.5
Maximum585
Range585
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation70.944845
Coefficient of variation (CV)3.3122624
Kurtosis38.384022
Mean21.418848
Median Absolute Deviation (MAD)1
Skewness5.8026048
Sum4091
Variance5033.171
MonotonicityNot monotonic
2023-12-13T03:11:44.321887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 78
40.8%
1 19
 
9.9%
2 12
 
6.3%
3 10
 
5.2%
4 5
 
2.6%
5 4
 
2.1%
7 4
 
2.1%
9 4
 
2.1%
6 3
 
1.6%
17 3
 
1.6%
Other values (38) 49
25.7%
ValueCountFrequency (%)
0 78
40.8%
1 19
 
9.9%
2 12
 
6.3%
3 10
 
5.2%
4 5
 
2.6%
5 4
 
2.1%
6 3
 
1.6%
7 4
 
2.1%
8 2
 
1.0%
9 4
 
2.1%
ValueCountFrequency (%)
585 1
0.5%
546 1
0.5%
312 1
0.5%
295 1
0.5%
219 1
0.5%
217 1
0.5%
176 1
0.5%
130 1
0.5%
104 2
1.0%
101 1
0.5%

지휘관할_법인(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.17801
Minimum0
Maximum902
Zeros88
Zeros (%)46.1%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T03:11:44.541118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile92
Maximum902
Range902
Interquartile range (IQR)6

Descriptive statistics

Standard deviation83.209816
Coefficient of variation (CV)3.9290667
Kurtosis70.884337
Mean21.17801
Median Absolute Deviation (MAD)1
Skewness7.6390533
Sum4045
Variance6923.8734
MonotonicityNot monotonic
2023-12-13T03:11:44.734731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 88
46.1%
2 17
 
8.9%
1 16
 
8.4%
3 11
 
5.8%
6 6
 
3.1%
5 6
 
3.1%
4 3
 
1.6%
9 3
 
1.6%
14 3
 
1.6%
8 3
 
1.6%
Other values (31) 35
 
18.3%
ValueCountFrequency (%)
0 88
46.1%
1 16
 
8.4%
2 17
 
8.9%
3 11
 
5.8%
4 3
 
1.6%
5 6
 
3.1%
6 6
 
3.1%
7 1
 
0.5%
8 3
 
1.6%
9 3
 
1.6%
ValueCountFrequency (%)
902 1
0.5%
429 1
0.5%
379 1
0.5%
275 1
0.5%
218 1
0.5%
158 1
0.5%
133 1
0.5%
126 1
0.5%
116 1
0.5%
96 1
0.5%

Interactions

2023-12-13T03:11:38.037835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:22.610160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:24.040305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:25.489105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:26.926232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:28.152107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:29.469493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:31.047637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:32.274311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:33.509919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:34.888655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:36.297617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:38.123945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:22.686705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:24.173526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:25.595299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:27.041795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:28.253600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:29.565182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:31.134190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:32.363684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:33.594945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:35.012984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:36.408812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:38.224981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:22.781660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:24.293215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:25.702260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:27.138734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:28.342288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:29.688901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:31.247310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:32.471020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:33.679775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:35.141356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:36.535259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:38.324384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:22.858332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:24.399731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:25.814608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:27.225833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:28.439020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:29.803033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:31.353759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:32.570781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:33.758206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:35.259327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:36.666610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:38.412249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:22.956728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:24.507728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:25.908941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:27.308897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:28.539573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:29.903542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:31.459240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:32.651845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:33.873217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:35.365405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:36.814697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:38.521069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:23.048923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:24.619749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:26.034155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:27.394559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:28.647831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:30.011121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:31.557697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:32.748330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:34.018275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:35.480883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:36.957726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:38.612935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:23.430099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:24.763455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:26.152352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:27.484514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:28.760207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:30.116589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:31.688557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:32.843411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:34.155678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:35.615096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:37.394945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:38.741175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:23.516668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:24.885915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:26.274382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:27.594315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:28.873886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:30.240188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:31.785295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:32.946765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:34.281473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:35.744843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:37.519180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:38.850848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:23.603897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:25.022340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:26.385685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:27.698337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:28.992889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:30.671200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:31.881501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:33.050480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:34.397884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:35.852207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:37.616954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:38.950255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:23.690332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:25.142959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:26.513900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:27.802608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:29.122623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:30.773534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:31.967879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:33.144292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:34.505980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:35.986066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:37.720136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:39.068544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:23.799633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:25.271964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:26.682847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:27.914651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:29.248695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:30.865243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:32.074425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:33.241983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:34.612312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:36.097552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:37.842599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:39.159489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:23.919369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:25.391090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:26.821386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:28.053099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:29.365216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:30.956798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:32.183330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:33.389929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:34.760178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:36.203596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:11:37.947333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:11:44.866487image/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)1.0000.9780.8030.9630.8720.8630.5250.5440.4730.5960.9410.599
직수_인지_검거건수(건)0.9781.0000.8180.9500.8490.7970.5160.5390.4820.5330.8600.634
직수_인지_남자검거인원(명)0.8030.8181.0000.8370.9020.5770.6680.6980.6110.5870.5990.440
직수_인지_여자검거인원(명)0.9630.9500.8371.0000.7970.8610.6340.6720.6020.7180.8930.727
직수_인지_미상검거인원(명)0.8720.8490.9020.7971.0000.5930.5190.5320.5010.5560.7810.362
직수_인지_법인(개)0.8630.7970.5770.8610.5931.0000.3580.4240.2940.5140.8460.946
지휘관할_발생건수(건)0.5250.5160.6680.6340.5190.3581.0000.9810.9720.9650.5780.181
지휘관할_검거건수(건)0.5440.5390.6980.6720.5320.4240.9811.0000.9740.9700.5710.317
지휘관할_남자검거인원(명)0.4730.4820.6110.6020.5010.2940.9720.9741.0000.9680.5900.000
지휘관할_여자검거인원(명)0.5960.5330.5870.7180.5560.5140.9650.9700.9681.0000.6020.456
지휘관할_미상검거인원(명)0.9410.8600.5990.8930.7810.8460.5780.5710.5900.6021.0000.788
지휘관할_법인(개)0.5990.6340.4400.7270.3620.9460.1810.3170.0000.4560.7881.000
2023-12-13T03:11:45.067367image/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)1.0000.9820.9250.7670.6460.4950.6360.6210.6540.6310.6500.381
직수_인지_검거건수(건)0.9821.0000.9290.7630.6390.4900.6410.6310.6580.6410.6480.408
직수_인지_남자검거인원(명)0.9250.9291.0000.7450.6430.5090.5980.5900.6310.6090.6140.374
직수_인지_여자검거인원(명)0.7670.7630.7451.0000.5640.3650.5050.4980.5120.5880.4980.284
직수_인지_미상검거인원(명)0.6460.6390.6430.5641.0000.3970.4340.4180.4480.4570.7170.314
직수_인지_법인(개)0.4950.4900.5090.3650.3971.0000.2760.2660.2880.2950.4380.575
지휘관할_발생건수(건)0.6360.6410.5980.5050.4340.2761.0000.9960.9710.8580.6760.397
지휘관할_검거건수(건)0.6210.6310.5900.4980.4180.2660.9961.0000.9730.8630.6570.400
지휘관할_남자검거인원(명)0.6540.6580.6310.5120.4480.2880.9710.9731.0000.8420.6720.403
지휘관할_여자검거인원(명)0.6310.6410.6090.5880.4570.2950.8580.8630.8421.0000.6510.431
지휘관할_미상검거인원(명)0.6500.6480.6140.4980.7170.4380.6760.6570.6720.6511.0000.501
지휘관할_법인(개)0.3810.4080.3740.2840.3140.5750.3970.4000.4030.4310.5011.000

Missing values

2023-12-13T03:11:39.281923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:11:39.500700image/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)
0절도43332621122656115728120784056653
1불법사용000000645490400
2침입절도7340008638164392810
3장물6320002161313005310
4사기898603900224165313365127658244036535546116
5컴퓨터등사용사기2012166006553153017440
6부당이득41321014912310
7편의시설부정이용0000009167761622
8전기통신금융사기피해금환급에관한특별법3100005163211758320
9보험사기방지특별법11000015914528710600
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)
181폐기물관리법292324111428625729531683
182풍속영업의 규제에 관한법률000000181818500
183하천법010000262633403
184학교보건법111000222100
185학원의설립운영 및 과외교습에 관한법률0010001641634812900
186화물자동차 운수사업법33300048447764033423
187화재로 인한 재해보상과 보험가입에 관한법률000000500000
188화재예방·소방시설설치유지 및 안전관리에 관한법률000000333403
189화학물질관리법3340011211231536913
190기타특별법35629826510786157514732756262617176429