Overview

Dataset statistics

Number of variables7
Number of observations38
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory64.5 B

Variable types

Categorical1
Text1
Numeric5

Dataset

Description전국 경찰관서에 고소, 고발, 인지 등으로 형사입건된 사건의 발생, 검거, 피의자에 대한 죄종별 분석 현황
Author경찰청
URLhttps://www.data.go.kr/data/3074481/fileData.do

Alerts

자백여부(자백) is highly overall correlated with 자백여부(일부자백) and 4 other fieldsHigh correlation
자백여부(일부자백) is highly overall correlated with 자백여부(자백) and 3 other fieldsHigh correlation
자백여부(부인) is highly overall correlated with 자백여부(자백) and 3 other fieldsHigh correlation
자백여부(묵비) is highly overall correlated with 자백여부(자백) and 4 other fieldsHigh correlation
미상 is highly overall correlated with 자백여부(자백) and 3 other fieldsHigh correlation
범죄대분류 is highly overall correlated with 자백여부(자백) and 1 other fieldsHigh correlation
범죄중분류 has unique valuesUnique
자백여부(자백) has unique valuesUnique
자백여부(일부자백) has unique valuesUnique
미상 has unique valuesUnique
자백여부(묵비) has 6 (15.8%) zerosZeros

Reproduction

Analysis started2023-12-12 03:30:06.938515
Analysis finished2023-12-12 03:30:10.391000
Duration3.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄대분류
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)39.5%
Missing0
Missing (%)0.0%
Memory size436.0 B
지능범죄
9 
강력범죄
8 
폭력범죄
8 
풍속범죄
2 
절도범죄
 
1
Other values (10)
10 

Length

Max length7
Median length4
Mean length4.0263158
Min length2

Unique

Unique11 ?
Unique (%)28.9%

Sample

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

Common Values

ValueCountFrequency (%)
지능범죄 9
23.7%
강력범죄 8
21.1%
폭력범죄 8
21.1%
풍속범죄 2
 
5.3%
절도범죄 1
 
2.6%
특별경제범죄 1
 
2.6%
마약범죄 1
 
2.6%
보건범죄 1
 
2.6%
환경범죄 1
 
2.6%
교통범죄 1
 
2.6%
Other values (5) 5
13.2%

Length

2023-12-12T12:30:10.462470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지능범죄 9
23.7%
강력범죄 8
21.1%
폭력범죄 8
21.1%
풍속범죄 2
 
5.3%
절도범죄 1
 
2.6%
특별경제범죄 1
 
2.6%
마약범죄 1
 
2.6%
보건범죄 1
 
2.6%
환경범죄 1
 
2.6%
교통범죄 1
 
2.6%
Other values (5) 5
13.2%

범죄중분류
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-12T12:30:10.683226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length6
Mean length3.7105263
Min length2

Characters and Unicode

Total characters141
Distinct characters73
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)100.0%

Sample

1st row살인기수
2nd row살인미수등
3rd row강도
4th row강간
5th row유사강간
ValueCountFrequency (%)
살인기수 1
 
2.6%
도박범죄 1
 
2.6%
병역범죄 1
 
2.6%
문서·인장 1
 
2.6%
유가증권인지 1
 
2.6%
사기 1
 
2.6%
횡령 1
 
2.6%
배임 1
 
2.6%
성풍속범죄 1
 
2.6%
마약범죄 1
 
2.6%
Other values (28) 28
73.7%
2023-12-12T12:30:11.125597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
범 12
 
8.5%
죄 12
 
8.5%
강 6
 
4.3%
기 5
 
3.5%
인 5
 
3.5%
유 4
 
2.8%
· 4
 
2.8%
행 4
 
2.8%
제 3
 
2.1%
간 3
 
2.1%
Other values (63) 83
58.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 137
97.2%
Other Punctuation 4
 
2.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
범 12
 
8.8%
죄 12
 
8.8%
강 6
 
4.4%
기 5
 
3.6%
인 5
 
3.6%
유 4
 
2.9%
행 4
 
2.9%
제 3
 
2.2%
간 3
 
2.2%
등 3
 
2.2%
Other values (62) 80
58.4%
Other Punctuation
ValueCountFrequency (%)
· 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 137
97.2%
Common 4
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
범 12
 
8.8%
죄 12
 
8.8%
강 6
 
4.4%
기 5
 
3.6%
인 5
 
3.6%
유 4
 
2.9%
행 4
 
2.9%
제 3
 
2.2%
간 3
 
2.2%
등 3
 
2.2%
Other values (62) 80
58.4%
Common
ValueCountFrequency (%)
· 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 137
97.2%
None 4
 
2.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
범 12
 
8.8%
죄 12
 
8.8%
강 6
 
4.4%
기 5
 
3.6%
인 5
 
3.6%
유 4
 
2.9%
행 4
 
2.9%
제 3
 
2.2%
간 3
 
2.2%
등 3
 
2.2%
Other values (62) 80
58.4%
None
ValueCountFrequency (%)
· 4
100.0%

자백여부(자백)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24655.579
Minimum18
Maximum376010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T12:30:11.271165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile26.65
Q1288.25
median1777
Q318681.5
95-th percentile81805.95
Maximum376010
Range375992
Interquartile range (IQR)18393.25

Descriptive statistics

Standard deviation64483.966
Coefficient of variation (CV)2.6153905
Kurtosis25.032967
Mean24655.579
Median Absolute Deviation (MAD)1733
Skewness4.7339463
Sum936912
Variance4.1581819 × 109
MonotonicityNot monotonic
2023-12-12T12:30:11.441754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
229 1
 
2.6%
3486 1
 
2.6%
98 1
 
2.6%
63889 1
 
2.6%
10741 1
 
2.6%
1964 1
 
2.6%
9301 1
 
2.6%
23157 1
 
2.6%
40985 1
 
2.6%
15321 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
18 1
2.6%
19 1
2.6%
28 1
2.6%
60 1
2.6%
77 1
2.6%
98 1
2.6%
156 1
2.6%
229 1
2.6%
266 1
2.6%
287 1
2.6%
ValueCountFrequency (%)
376010 1
2.6%
132052 1
2.6%
72939 1
2.6%
63889 1
2.6%
50174 1
2.6%
40985 1
2.6%
38233 1
2.6%
36481 1
2.6%
23157 1
2.6%
19301 1
2.6%

자백여부(일부자백)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4464.4474
Minimum19
Maximum28225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T12:30:11.648933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile22.85
Q1191.25
median744
Q33105.25
95-th percentile21730.3
Maximum28225
Range28206
Interquartile range (IQR)2914

Descriptive statistics

Standard deviation7654.8923
Coefficient of variation (CV)1.7146338
Kurtosis2.4560348
Mean4464.4474
Median Absolute Deviation (MAD)680.5
Skewness1.8896784
Sum169649
Variance58597376
MonotonicityNot monotonic
2023-12-12T12:30:11.791203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
88 1
 
2.6%
707 1
 
2.6%
63 1
 
2.6%
21460 1
 
2.6%
3106 1
 
2.6%
848 1
 
2.6%
1015 1
 
2.6%
1863 1
 
2.6%
11557 1
 
2.6%
2808 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
19 1
2.6%
22 1
2.6%
23 1
2.6%
30 1
2.6%
63 1
2.6%
64 1
2.6%
88 1
2.6%
89 1
2.6%
174 1
2.6%
183 1
2.6%
ValueCountFrequency (%)
28225 1
2.6%
23262 1
2.6%
21460 1
2.6%
19192 1
2.6%
17236 1
2.6%
13415 1
2.6%
11557 1
2.6%
9442 1
2.6%
4461 1
2.6%
3106 1
2.6%

자백여부(부인)
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2835.2632
Minimum4
Maximum22070
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T12:30:11.954827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile15.8
Q190.75
median498
Q32819.75
95-th percentile13112.7
Maximum22070
Range22066
Interquartile range (IQR)2729

Descriptive statistics

Standard deviation5113.8329
Coefficient of variation (CV)1.8036537
Kurtosis6.2562182
Mean2835.2632
Median Absolute Deviation (MAD)470
Skewness2.5113242
Sum107740
Variance26151287
MonotonicityNot monotonic
2023-12-12T12:30:12.116868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
63 2
 
5.3%
555 1
 
2.6%
1783 1
 
2.6%
22070 1
 
2.6%
3031 1
 
2.6%
1096 1
 
2.6%
955 1
 
2.6%
1293 1
 
2.6%
6415 1
 
2.6%
17 1
 
2.6%
Other values (27) 27
71.1%
ValueCountFrequency (%)
4 1
2.6%
9 1
2.6%
17 1
2.6%
39 1
2.6%
45 1
2.6%
62 1
2.6%
63 2
5.3%
88 1
2.6%
90 1
2.6%
93 1
2.6%
ValueCountFrequency (%)
22070 1
2.6%
18075 1
2.6%
12237 1
2.6%
10829 1
2.6%
7880 1
2.6%
6415 1
2.6%
5597 1
2.6%
4455 1
2.6%
3398 1
2.6%
3031 1
2.6%

자백여부(묵비)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)63.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.315789
Minimum0
Maximum667
Zeros6
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T12:30:12.625104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median9
Q323.75
95-th percentile247.25
Maximum667
Range667
Interquartile range (IQR)21.75

Descriptive statistics

Standard deviation128.09458
Coefficient of variation (CV)2.2745767
Kurtosis14.61121
Mean56.315789
Median Absolute Deviation (MAD)9
Skewness3.6111595
Sum2140
Variance16408.222
MonotonicityNot monotonic
2023-12-12T12:30:12.799460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 6
15.8%
2 4
 
10.5%
8 3
 
7.9%
9 3
 
7.9%
3 3
 
7.9%
21 1
 
2.6%
667 1
 
2.6%
13 1
 
2.6%
379 1
 
2.6%
1 1
 
2.6%
Other values (14) 14
36.8%
ValueCountFrequency (%)
0 6
15.8%
1 1
 
2.6%
2 4
10.5%
3 3
7.9%
5 1
 
2.6%
8 3
7.9%
9 3
7.9%
10 1
 
2.6%
13 1
 
2.6%
16 1
 
2.6%
ValueCountFrequency (%)
667 1
2.6%
379 1
2.6%
224 1
2.6%
197 1
2.6%
142 1
2.6%
104 1
2.6%
96 1
2.6%
68 1
2.6%
45 1
2.6%
24 1
2.6%

미상
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13052.474
Minimum28
Maximum138867
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T12:30:12.994785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile67.6
Q1253.5
median851
Q35382.75
95-th percentile89681.1
Maximum138867
Range138839
Interquartile range (IQR)5129.25

Descriptive statistics

Standard deviation31756.343
Coefficient of variation (CV)2.4329751
Kurtosis8.8308127
Mean13052.474
Median Absolute Deviation (MAD)780
Skewness3.0505297
Sum495994
Variance1.0084653 × 109
MonotonicityNot monotonic
2023-12-12T12:30:13.156150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
105 1
 
2.6%
498 1
 
2.6%
326 1
 
2.6%
114530 1
 
2.6%
15076 1
 
2.6%
5437 1
 
2.6%
2651 1
 
2.6%
1427 1
 
2.6%
28549 1
 
2.6%
2546 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
28 1
2.6%
54 1
2.6%
70 1
2.6%
72 1
2.6%
104 1
2.6%
105 1
2.6%
151 1
2.6%
174 1
2.6%
191 1
2.6%
246 1
2.6%
ValueCountFrequency (%)
138867 1
2.6%
114530 1
2.6%
85296 1
2.6%
51835 1
2.6%
28549 1
2.6%
15076 1
2.6%
11755 1
2.6%
9200 1
2.6%
7571 1
2.6%
5437 1
2.6%

Interactions

2023-12-12T12:30:09.580847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:30:07.237324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:30:07.859826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:30:08.463448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:30:09.008115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:30:09.743140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:30:07.370932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:30:07.981580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:30:08.584882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:30:09.129852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:30:09.875968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:30:07.495005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:30:08.111499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:30:08.690828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:30:09.246452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:30:09.962027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:30:07.605608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:30:08.215068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:30:08.779341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:30:09.342734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:30:10.085355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:30:07.721138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:30:08.330652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:30:08.898515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:30:09.464044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:30:13.254472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
범죄대분류범죄중분류자백여부(자백)자백여부(일부자백)자백여부(부인)자백여부(묵비)미상
범죄대분류1.0001.0000.9370.7490.0000.8630.803
범죄중분류1.0001.0001.0001.0001.0001.0001.000
자백여부(자백)0.9371.0001.0000.9860.9820.9980.929
자백여부(일부자백)0.7491.0000.9861.0000.9940.9620.924
자백여부(부인)0.0001.0000.9820.9941.0000.9310.913
자백여부(묵비)0.8631.0000.9980.9620.9311.0000.938
미상0.8031.0000.9290.9240.9130.9381.000
2023-12-12T12:30:13.371225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자백여부(자백)자백여부(일부자백)자백여부(부인)자백여부(묵비)미상범죄대분류
자백여부(자백)1.0000.9490.8840.8400.8540.689
자백여부(일부자백)0.9491.0000.9670.8630.8850.374
자백여부(부인)0.8840.9671.0000.8520.9040.000
자백여부(묵비)0.8400.8630.8521.0000.8080.524
미상0.8540.8850.9040.8081.0000.446
범죄대분류0.6890.3740.0000.5240.4461.000

Missing values

2023-12-12T12:30:10.222645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:30:10.346881image/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

범죄대분류범죄중분류자백여부(자백)자백여부(일부자백)자백여부(부인)자백여부(묵비)미상
0강력범죄살인기수22988178105
1강력범죄살인미수등26617462370
2강력범죄강도12704112212174
3강력범죄강간14601256148491308
4강력범죄유사강간1568990054
5강력범죄강제추행518131033398241694
6강력범죄기타강간·강제추행등4223222953151
7강력범죄방화103621610010104
8절도범죄절도범죄7293994425597967571
9폭력범죄상해36481172367880685220
범죄대분류범죄중분류자백여부(자백)자백여부(일부자백)자백여부(부인)자백여부(묵비)미상
28특별경제범죄특별경제범죄4098511557641514228549
29마약범죄마약범죄348670755518498
30보건범죄보건범죄153212808140282546
31환경범죄환경범죄2397249881401
32교통범죄교통범죄376010134154455379138867
33노동범죄노동범죄1590220450276
34안보범죄안보범죄19304928
35선거범죄선거범죄10935483852772
36병역범죄병역범죄1682371315313821
37기타기타132052282251807566751835