Overview

Dataset statistics

Number of variables16
Number of observations162
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.8 KiB
Average record size in memory143.8 B

Variable types

Text1
Numeric15

Dataset

Description대검찰청에서 발간하는 범죄분석은 3종의 범죄통계원표를 기반으로 작성하는 자료이며 이 중 본 데이터는 범죄자와 피해자의 관계에 따른 형법/특별법범 통계임. (단위: 명)
Author대검찰청
URLhttps://www.data.go.kr/data/15086103/fileData.do

Alerts

국가 is highly overall correlated with 기타High correlation
공무원 is highly overall correlated with 직장동료 and 3 other fieldsHigh correlation
고용자 is highly overall correlated with 피고용자 and 3 other fieldsHigh correlation
피고용자 is highly overall correlated with 고용자 and 11 other fieldsHigh correlation
직장동료 is highly overall correlated with 공무원 and 12 other fieldsHigh correlation
친구 is highly overall correlated with 피고용자 and 10 other fieldsHigh correlation
애인 is highly overall correlated with 피고용자 and 9 other fieldsHigh correlation
동거친족 is highly overall correlated with 피고용자 and 8 other fieldsHigh correlation
기타친족 is highly overall correlated with 피고용자 and 9 other fieldsHigh correlation
거래상대방 is highly overall correlated with 피고용자 and 9 other fieldsHigh correlation
이웃 is highly overall correlated with 피고용자 and 9 other fieldsHigh correlation
지인 is highly overall correlated with 피고용자 and 10 other fieldsHigh correlation
타인 is highly overall correlated with 공무원 and 12 other fieldsHigh correlation
기타 is highly overall correlated with 국가 and 9 other fieldsHigh correlation
미상 is highly overall correlated with 공무원 and 11 other fieldsHigh correlation
범죄분류 has unique valuesUnique
국가 has 11 (6.8%) zerosZeros
공무원 has 59 (36.4%) zerosZeros
고용자 has 50 (30.9%) zerosZeros
피고용자 has 69 (42.6%) zerosZeros
직장동료 has 64 (39.5%) zerosZeros
친구 has 89 (54.9%) zerosZeros
애인 has 100 (61.7%) zerosZeros
동거친족 has 89 (54.9%) zerosZeros
기타친족 has 75 (46.3%) zerosZeros
거래상대방 has 34 (21.0%) zerosZeros
이웃 has 73 (45.1%) zerosZeros
지인 has 53 (32.7%) zerosZeros
타인 has 3 (1.9%) zerosZeros
기타 has 2 (1.2%) zerosZeros
미상 has 3 (1.9%) zerosZeros

Reproduction

Analysis started2023-12-12 20:48:03.593463
Analysis finished2023-12-12 20:48:25.886391
Duration22.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범죄분류
Text

UNIQUE 

Distinct162
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-13T05:48:26.073685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length19
Mean length8.037037
Min length2

Characters and Unicode

Total characters1302
Distinct characters225
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

Unique162 ?
Unique (%)100.0%

Sample

1st row절도
2nd row장물
3rd row사기
4th row횡령
5th row배임
ValueCountFrequency (%)
절도 1
 
0.6%
약사법 1
 
0.6%
사행행위등규제및처벌특례법 1
 
0.6%
성매매알선등행위의처벌에관한법률 1
 
0.6%
산림자원의조성및관리에관한법률 1
 
0.6%
산업안전보건법 1
 
0.6%
산지관리법 1
 
0.6%
상표법 1
 
0.6%
석유및석유대체연료사업법 1
 
0.6%
선박안전법 1
 
0.6%
Other values (152) 152
93.8%
2023-12-13T05:48:26.497938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
114
 
8.8%
67
 
5.1%
41
 
3.1%
40
 
3.1%
34
 
2.6%
31
 
2.4%
27
 
2.1%
26
 
2.0%
23
 
1.8%
19
 
1.5%
Other values (215) 880
67.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1268
97.4%
Open Punctuation 12
 
0.9%
Close Punctuation 12
 
0.9%
Other Punctuation 10
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
114
 
9.0%
67
 
5.3%
41
 
3.2%
40
 
3.2%
34
 
2.7%
31
 
2.4%
27
 
2.1%
26
 
2.1%
23
 
1.8%
19
 
1.5%
Other values (211) 846
66.7%
Other Punctuation
ValueCountFrequency (%)
, 6
60.0%
· 4
40.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1268
97.4%
Common 34
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
114
 
9.0%
67
 
5.3%
41
 
3.2%
40
 
3.2%
34
 
2.7%
31
 
2.4%
27
 
2.1%
26
 
2.1%
23
 
1.8%
19
 
1.5%
Other values (211) 846
66.7%
Common
ValueCountFrequency (%)
( 12
35.3%
) 12
35.3%
, 6
17.6%
· 4
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1268
97.4%
ASCII 30
 
2.3%
None 4
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
114
 
9.0%
67
 
5.3%
41
 
3.2%
40
 
3.2%
34
 
2.7%
31
 
2.4%
27
 
2.1%
26
 
2.1%
23
 
1.8%
19
 
1.5%
Other values (211) 846
66.7%
ASCII
ValueCountFrequency (%)
( 12
40.0%
) 12
40.0%
, 6
20.0%
None
ValueCountFrequency (%)
· 4
100.0%

국가
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct139
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2437.3148
Minimum0
Maximum138426
Zeros11
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T05:48:26.644086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q151.25
median271.5
Q31031.25
95-th percentile6822.2
Maximum138426
Range138426
Interquartile range (IQR)980

Descriptive statistics

Standard deviation11550.696
Coefficient of variation (CV)4.7391072
Kurtosis121.3216
Mean2437.3148
Median Absolute Deviation (MAD)260.5
Skewness10.479593
Sum394845
Variance1.3341858 × 108
MonotonicityNot monotonic
2023-12-13T05:48:26.796055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
6.8%
1 4
 
2.5%
45 3
 
1.9%
163 2
 
1.2%
10 2
 
1.2%
352 2
 
1.2%
918 2
 
1.2%
199 2
 
1.2%
30 2
 
1.2%
48 2
 
1.2%
Other values (129) 130
80.2%
ValueCountFrequency (%)
0 11
6.8%
1 4
 
2.5%
2 1
 
0.6%
3 1
 
0.6%
4 1
 
0.6%
5 1
 
0.6%
7 1
 
0.6%
10 2
 
1.2%
12 1
 
0.6%
13 1
 
0.6%
ValueCountFrequency (%)
138426 1
0.6%
33850 1
0.6%
24664 1
0.6%
22177 1
0.6%
15240 1
0.6%
13030 1
0.6%
12809 1
0.6%
7147 1
0.6%
6828 1
0.6%
6712 1
0.6%

공무원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.481481
Minimum0
Maximum6131
Zeros59
Zeros (%)36.4%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T05:48:26.934306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q38
95-th percentile72.85
Maximum6131
Range6131
Interquartile range (IQR)8

Descriptive statistics

Standard deviation499.15856
Coefficient of variation (CV)7.9889041
Kurtosis138.2682
Mean62.481481
Median Absolute Deviation (MAD)1.5
Skewness11.476582
Sum10122
Variance249159.27
MonotonicityNot monotonic
2023-12-13T05:48:27.058277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 59
36.4%
1 22
 
13.6%
2 11
 
6.8%
3 10
 
6.2%
4 6
 
3.7%
5 5
 
3.1%
6 5
 
3.1%
22 3
 
1.9%
8 3
 
1.9%
15 2
 
1.2%
Other values (29) 36
22.2%
ValueCountFrequency (%)
0 59
36.4%
1 22
 
13.6%
2 11
 
6.8%
3 10
 
6.2%
4 6
 
3.7%
5 5
 
3.1%
6 5
 
3.1%
7 2
 
1.2%
8 3
 
1.9%
9 2
 
1.2%
ValueCountFrequency (%)
6131 1
0.6%
1485 1
0.6%
920 1
0.6%
149 1
0.6%
141 1
0.6%
124 1
0.6%
94 2
1.2%
73 1
0.6%
70 1
0.6%
69 1
0.6%

고용자
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254.92593
Minimum0
Maximum24845
Zeros50
Zeros (%)30.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T05:48:27.208467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q314
95-th percentile344.3
Maximum24845
Range24845
Interquartile range (IQR)14

Descriptive statistics

Standard deviation2018.429
Coefficient of variation (CV)7.9177078
Kurtosis139.50041
Mean254.92593
Median Absolute Deviation (MAD)2
Skewness11.547418
Sum41298
Variance4074055.5
MonotonicityNot monotonic
2023-12-13T05:48:27.359321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 50
30.9%
1 30
18.5%
2 9
 
5.6%
5 9
 
5.6%
3 7
 
4.3%
4 4
 
2.5%
6 4
 
2.5%
14 3
 
1.9%
18 2
 
1.2%
13 2
 
1.2%
Other values (41) 42
25.9%
ValueCountFrequency (%)
0 50
30.9%
1 30
18.5%
2 9
 
5.6%
3 7
 
4.3%
4 4
 
2.5%
5 9
 
5.6%
6 4
 
2.5%
8 1
 
0.6%
9 1
 
0.6%
10 1
 
0.6%
ValueCountFrequency (%)
24845 1
0.6%
6348 1
0.6%
1612 1
0.6%
1508 1
0.6%
1324 1
0.6%
1113 1
0.6%
372 1
0.6%
370 1
0.6%
345 1
0.6%
331 1
0.6%

피고용자
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.481481
Minimum0
Maximum2665
Zeros69
Zeros (%)42.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T05:48:27.497231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile289.2
Maximum2665
Range2665
Interquartile range (IQR)6

Descriptive statistics

Standard deviation230.26612
Coefficient of variation (CV)4.9539325
Kurtosis105.43192
Mean46.481481
Median Absolute Deviation (MAD)1
Skewness9.5652664
Sum7530
Variance53022.487
MonotonicityNot monotonic
2023-12-13T05:48:27.630168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 69
42.6%
1 22
 
13.6%
2 13
 
8.0%
3 11
 
6.8%
6 5
 
3.1%
27 3
 
1.9%
14 3
 
1.9%
7 2
 
1.2%
5 2
 
1.2%
11 1
 
0.6%
Other values (31) 31
19.1%
ValueCountFrequency (%)
0 69
42.6%
1 22
 
13.6%
2 13
 
8.0%
3 11
 
6.8%
4 1
 
0.6%
5 2
 
1.2%
6 5
 
3.1%
7 2
 
1.2%
8 1
 
0.6%
9 1
 
0.6%
ValueCountFrequency (%)
2665 1
0.6%
620 1
0.6%
561 1
0.6%
478 1
0.6%
459 1
0.6%
447 1
0.6%
389 1
0.6%
359 1
0.6%
295 1
0.6%
179 1
0.6%

직장동료
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.993827
Minimum0
Maximum3966
Zeros64
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T05:48:27.795486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37.75
95-th percentile221.7
Maximum3966
Range3966
Interquartile range (IQR)7.75

Descriptive statistics

Standard deviation430.6789
Coefficient of variation (CV)5.0082537
Kurtosis62.837995
Mean85.993827
Median Absolute Deviation (MAD)1
Skewness7.6700956
Sum13931
Variance185484.32
MonotonicityNot monotonic
2023-12-13T05:48:27.957852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 64
39.5%
1 21
 
13.0%
2 15
 
9.3%
3 7
 
4.3%
4 7
 
4.3%
7 4
 
2.5%
6 2
 
1.2%
8 2
 
1.2%
31 2
 
1.2%
731 1
 
0.6%
Other values (37) 37
22.8%
ValueCountFrequency (%)
0 64
39.5%
1 21
 
13.0%
2 15
 
9.3%
3 7
 
4.3%
4 7
 
4.3%
5 1
 
0.6%
6 2
 
1.2%
7 4
 
2.5%
8 2
 
1.2%
9 1
 
0.6%
ValueCountFrequency (%)
3966 1
0.6%
3418 1
0.6%
1053 1
0.6%
951 1
0.6%
731 1
0.6%
616 1
0.6%
550 1
0.6%
393 1
0.6%
222 1
0.6%
216 1
0.6%

친구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.75309
Minimum0
Maximum4470
Zeros89
Zeros (%)54.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T05:48:28.110625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37.75
95-th percentile288.75
Maximum4470
Range4470
Interquartile range (IQR)7.75

Descriptive statistics

Standard deviation520.9907
Coefficient of variation (CV)5.1709651
Kurtosis57.091801
Mean100.75309
Median Absolute Deviation (MAD)0
Skewness7.3436195
Sum16322
Variance271431.31
MonotonicityNot monotonic
2023-12-13T05:48:28.245247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 89
54.9%
2 11
 
6.8%
1 7
 
4.3%
6 5
 
3.1%
3 3
 
1.9%
9 3
 
1.9%
15 3
 
1.9%
4 3
 
1.9%
29 2
 
1.2%
30 2
 
1.2%
Other values (29) 34
 
21.0%
ValueCountFrequency (%)
0 89
54.9%
1 7
 
4.3%
2 11
 
6.8%
3 3
 
1.9%
4 3
 
1.9%
5 2
 
1.2%
6 5
 
3.1%
7 1
 
0.6%
8 1
 
0.6%
9 3
 
1.9%
ValueCountFrequency (%)
4470 1
0.6%
4300 1
0.6%
1857 1
0.6%
1193 1
0.6%
1101 1
0.6%
432 1
0.6%
393 1
0.6%
308 1
0.6%
290 1
0.6%
265 1
0.6%

애인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.734568
Minimum0
Maximum4640
Zeros100
Zeros (%)61.7%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T05:48:28.385979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile380.65
Maximum4640
Range4640
Interquartile range (IQR)3

Descriptive statistics

Standard deviation460.63635
Coefficient of variation (CV)4.9672561
Kurtosis65.022803
Mean92.734568
Median Absolute Deviation (MAD)0
Skewness7.4935619
Sum15023
Variance212185.85
MonotonicityNot monotonic
2023-12-13T05:48:28.541341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 100
61.7%
1 14
 
8.6%
5 4
 
2.5%
3 4
 
2.5%
6 4
 
2.5%
2 4
 
2.5%
4 2
 
1.2%
14 2
 
1.2%
675 1
 
0.6%
114 1
 
0.6%
Other values (26) 26
 
16.0%
ValueCountFrequency (%)
0 100
61.7%
1 14
 
8.6%
2 4
 
2.5%
3 4
 
2.5%
4 2
 
1.2%
5 4
 
2.5%
6 4
 
2.5%
7 1
 
0.6%
10 1
 
0.6%
11 1
 
0.6%
ValueCountFrequency (%)
4640 1
0.6%
2597 1
0.6%
1634 1
0.6%
1103 1
0.6%
1071 1
0.6%
974 1
0.6%
764 1
0.6%
675 1
0.6%
389 1
0.6%
222 1
0.6%

동거친족
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202.3642
Minimum0
Maximum18389
Zeros89
Zeros (%)54.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T05:48:28.669163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.75
95-th percentile191.4
Maximum18389
Range18389
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation1521.3955
Coefficient of variation (CV)7.5181062
Kurtosis129.2409
Mean202.3642
Median Absolute Deviation (MAD)0
Skewness10.977225
Sum32783
Variance2314644.3
MonotonicityNot monotonic
2023-12-13T05:48:28.793143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 89
54.9%
1 16
 
9.9%
2 15
 
9.3%
4 4
 
2.5%
11 3
 
1.9%
5 3
 
1.9%
21 2
 
1.2%
10 2
 
1.2%
51 1
 
0.6%
12 1
 
0.6%
Other values (26) 26
 
16.0%
ValueCountFrequency (%)
0 89
54.9%
1 16
 
9.9%
2 15
 
9.3%
3 1
 
0.6%
4 4
 
2.5%
5 3
 
1.9%
6 1
 
0.6%
7 1
 
0.6%
8 1
 
0.6%
9 1
 
0.6%
ValueCountFrequency (%)
18389 1
0.6%
4871 1
0.6%
2582 1
0.6%
2329 1
0.6%
2185 1
0.6%
539 1
0.6%
239 1
0.6%
200 1
0.6%
192 1
0.6%
180 1
0.6%

기타친족
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.87037
Minimum0
Maximum2474
Zeros75
Zeros (%)46.3%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T05:48:28.952661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36.5
95-th percentile233.95
Maximum2474
Range2474
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation238.94382
Coefficient of variation (CV)4.6971119
Kurtosis74.068637
Mean50.87037
Median Absolute Deviation (MAD)1
Skewness8.1162884
Sum8241
Variance57094.151
MonotonicityNot monotonic
2023-12-13T05:48:29.347327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 75
46.3%
1 22
 
13.6%
2 14
 
8.6%
4 6
 
3.7%
3 3
 
1.9%
30 2
 
1.2%
7 2
 
1.2%
8 2
 
1.2%
29 2
 
1.2%
233 1
 
0.6%
Other values (33) 33
20.4%
ValueCountFrequency (%)
0 75
46.3%
1 22
 
13.6%
2 14
 
8.6%
3 3
 
1.9%
4 6
 
3.7%
5 1
 
0.6%
7 2
 
1.2%
8 2
 
1.2%
10 1
 
0.6%
11 1
 
0.6%
ValueCountFrequency (%)
2474 1
0.6%
1492 1
0.6%
682 1
0.6%
434 1
0.6%
403 1
0.6%
324 1
0.6%
256 1
0.6%
245 1
0.6%
234 1
0.6%
233 1
0.6%

거래상대방
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean262.51235
Minimum0
Maximum23300
Zeros34
Zeros (%)21.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T05:48:29.467726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8.5
Q365.75
95-th percentile731.45
Maximum23300
Range23300
Interquartile range (IQR)64.75

Descriptive statistics

Standard deviation1852.0304
Coefficient of variation (CV)7.055022
Kurtosis151.3116
Mean262.51235
Median Absolute Deviation (MAD)8.5
Skewness12.121905
Sum42527
Variance3430016.5
MonotonicityNot monotonic
2023-12-13T05:48:29.591439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34
21.0%
3 13
 
8.0%
1 13
 
8.0%
2 6
 
3.7%
5 6
 
3.7%
11 5
 
3.1%
6 4
 
2.5%
9 4
 
2.5%
36 3
 
1.9%
16 3
 
1.9%
Other values (59) 71
43.8%
ValueCountFrequency (%)
0 34
21.0%
1 13
 
8.0%
2 6
 
3.7%
3 13
 
8.0%
4 2
 
1.2%
5 6
 
3.7%
6 4
 
2.5%
7 2
 
1.2%
8 1
 
0.6%
9 4
 
2.5%
ValueCountFrequency (%)
23300 1
0.6%
2046 1
0.6%
1971 1
0.6%
1946 1
0.6%
1615 1
0.6%
1448 1
0.6%
826 1
0.6%
758 1
0.6%
734 1
0.6%
683 1
0.6%

이웃
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.06173
Minimum0
Maximum3972
Zeros73
Zeros (%)45.1%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T05:48:29.740224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q310.75
95-th percentile522.3
Maximum3972
Range3972
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation456.24026
Coefficient of variation (CV)4.3425924
Kurtosis45.663372
Mean105.06173
Median Absolute Deviation (MAD)1
Skewness6.426418
Sum17020
Variance208155.18
MonotonicityNot monotonic
2023-12-13T05:48:29.892295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 73
45.1%
1 13
 
8.0%
2 9
 
5.6%
3 8
 
4.9%
4 5
 
3.1%
8 3
 
1.9%
5 3
 
1.9%
6 3
 
1.9%
9 2
 
1.2%
70 2
 
1.2%
Other values (37) 41
25.3%
ValueCountFrequency (%)
0 73
45.1%
1 13
 
8.0%
2 9
 
5.6%
3 8
 
4.9%
4 5
 
3.1%
5 3
 
1.9%
6 3
 
1.9%
7 1
 
0.6%
8 3
 
1.9%
9 2
 
1.2%
ValueCountFrequency (%)
3972 1
0.6%
3147 1
0.6%
2036 1
0.6%
1367 1
0.6%
1153 1
0.6%
879 1
0.6%
634 1
0.6%
626 1
0.6%
529 1
0.6%
395 1
0.6%

지인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.11111
Minimum0
Maximum6785
Zeros53
Zeros (%)32.7%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T05:48:30.027277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q322
95-th percentile644.75
Maximum6785
Range6785
Interquartile range (IQR)22

Descriptive statistics

Standard deviation919.16664
Coefficient of variation (CV)4.6163503
Kurtosis42.078879
Mean199.11111
Median Absolute Deviation (MAD)3
Skewness6.4062256
Sum32256
Variance844867.3
MonotonicityNot monotonic
2023-12-13T05:48:30.150534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 53
32.7%
1 16
 
9.9%
2 10
 
6.2%
6 8
 
4.9%
3 6
 
3.7%
4 4
 
2.5%
5 4
 
2.5%
11 4
 
2.5%
22 3
 
1.9%
13 3
 
1.9%
Other values (45) 51
31.5%
ValueCountFrequency (%)
0 53
32.7%
1 16
 
9.9%
2 10
 
6.2%
3 6
 
3.7%
4 4
 
2.5%
5 4
 
2.5%
6 8
 
4.9%
7 2
 
1.2%
8 3
 
1.9%
10 1
 
0.6%
ValueCountFrequency (%)
6785 1
0.6%
6702 1
0.6%
6205 1
0.6%
2165 1
0.6%
2006 1
0.6%
1082 1
0.6%
824 1
0.6%
651 1
0.6%
647 1
0.6%
602 1
0.6%

타인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct124
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3147.5802
Minimum0
Maximum82119
Zeros3
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T05:48:30.268619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q124.5
median89
Q3785.75
95-th percentile15438.2
Maximum82119
Range82119
Interquartile range (IQR)761.25

Descriptive statistics

Standard deviation11462.595
Coefficient of variation (CV)3.6417167
Kurtosis29.566768
Mean3147.5802
Median Absolute Deviation (MAD)84
Skewness5.3121099
Sum509908
Variance1.3139109 × 108
MonotonicityNot monotonic
2023-12-13T05:48:30.382047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 5
 
3.1%
8 4
 
2.5%
0 3
 
1.9%
55 3
 
1.9%
18 3
 
1.9%
30 3
 
1.9%
9 3
 
1.9%
1 3
 
1.9%
13 3
 
1.9%
7 3
 
1.9%
Other values (114) 129
79.6%
ValueCountFrequency (%)
0 3
1.9%
1 3
1.9%
2 5
3.1%
3 1
 
0.6%
4 2
 
1.2%
5 2
 
1.2%
6 2
 
1.2%
7 3
1.9%
8 4
2.5%
9 3
1.9%
ValueCountFrequency (%)
82119 1
0.6%
71866 1
0.6%
68629 1
0.6%
53309 1
0.6%
36454 1
0.6%
18700 1
0.6%
18457 1
0.6%
18428 1
0.6%
15607 1
0.6%
12231 1
0.6%

기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct144
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1074.1481
Minimum0
Maximum21001
Zeros2
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T05:48:30.502303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.05
Q175
median269.5
Q3735
95-th percentile4495.7
Maximum21001
Range21001
Interquartile range (IQR)660

Descriptive statistics

Standard deviation2648.4125
Coefficient of variation (CV)2.4655933
Kurtosis30.407597
Mean1074.1481
Median Absolute Deviation (MAD)224.5
Skewness5.0866002
Sum174012
Variance7014088.5
MonotonicityNot monotonic
2023-12-13T05:48:30.619548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
247 3
 
1.9%
40 3
 
1.9%
12 2
 
1.2%
0 2
 
1.2%
275 2
 
1.2%
22 2
 
1.2%
396 2
 
1.2%
126 2
 
1.2%
269 2
 
1.2%
102 2
 
1.2%
Other values (134) 140
86.4%
ValueCountFrequency (%)
0 2
1.2%
2 1
0.6%
4 1
0.6%
5 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
11 1
0.6%
12 2
1.2%
17 2
1.2%
ValueCountFrequency (%)
21001 1
0.6%
17562 1
0.6%
11689 1
0.6%
9675 1
0.6%
9151 1
0.6%
5735 1
0.6%
4827 1
0.6%
4824 1
0.6%
4529 1
0.6%
3863 1
0.6%

미상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct146
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3370.2346
Minimum0
Maximum112235
Zeros3
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T05:48:30.747367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q160.75
median255
Q31096.25
95-th percentile8753.6
Maximum112235
Range112235
Interquartile range (IQR)1035.5

Descriptive statistics

Standard deviation14933.219
Coefficient of variation (CV)4.4309138
Kurtosis46.658636
Mean3370.2346
Median Absolute Deviation (MAD)238
Skewness6.8122895
Sum545978
Variance2.2300103 × 108
MonotonicityNot monotonic
2023-12-13T05:48:30.926224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 3
 
1.9%
11 3
 
1.9%
0 3
 
1.9%
39 2
 
1.2%
17 2
 
1.2%
12 2
 
1.2%
70 2
 
1.2%
90 2
 
1.2%
110 2
 
1.2%
162 2
 
1.2%
Other values (136) 139
85.8%
ValueCountFrequency (%)
0 3
1.9%
2 1
 
0.6%
3 1
 
0.6%
4 1
 
0.6%
5 1
 
0.6%
7 1
 
0.6%
8 3
1.9%
10 1
 
0.6%
11 3
1.9%
12 2
1.2%
ValueCountFrequency (%)
112235 1
0.6%
110097 1
0.6%
106510 1
0.6%
17773 1
0.6%
16802 1
0.6%
15172 1
0.6%
10356 1
0.6%
9468 1
0.6%
8790 1
0.6%
8062 1
0.6%

Interactions

2023-12-13T05:48:23.974058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:04.193638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:05.592791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:07.245377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:08.678390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:10.079504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:11.383752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:13.067644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:14.469714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:15.819217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:16.912297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:18.338155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:19.747000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:21.135046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:22.330552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:24.070902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:04.303785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:05.704471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:07.340276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:08.788092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:10.181164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:11.482944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:13.176255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:14.575353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:15.901547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:16.980230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:18.433299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:19.858466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:21.213384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:22.416217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:24.173815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:04.408118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:05.790209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:07.425951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:08.906889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:10.276837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:11.567071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:13.277102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:14.674749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:15.984563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:17.045287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:18.525147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:19.950835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:21.290522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:22.494989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:24.263635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:04.507767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:06.155927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:07.505568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:09.015808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:10.352673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:11.642702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:13.361543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:14.764079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:16.061270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:17.111470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:18.624208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:20.040355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:21.367733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:22.576855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:24.364427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:04.590693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:06.247067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:07.584970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:09.107302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:10.425928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:12.095378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:13.439936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:14.852699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:16.141001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:17.176757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:18.722298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:20.123863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:21.440581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:22.662623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:24.466931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:04.682034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:06.358885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:07.674292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:09.203345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:10.517813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:12.190311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:13.540527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:14.949461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:16.225872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:17.257073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:18.812633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:20.219567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:21.538781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:22.759014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:24.559873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:04.766383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:06.446335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:07.747023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:09.290331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:10.598673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:12.275103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:13.624708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:15.034185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:16.293369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:17.322466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:18.893250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:20.301074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:21.623270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:22.826884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:24.654645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:04.867422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:06.532509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:07.821318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:09.377378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:10.696516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:12.361839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:13.697713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:15.123619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:16.356593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:17.388328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:18.975647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:20.387499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:21.694313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:22.903127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:24.780579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:04.962504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:06.620931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:07.907745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:09.471839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:10.786209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:12.472849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:13.795941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:15.232971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:16.429386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:17.461100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:19.121850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:20.480837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:21.774598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:22.991628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:24.873305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:05.064283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:06.697774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:07.994846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:09.558550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:10.862832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:12.571912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:13.878189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:15.324115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:16.499551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:17.529934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:19.222464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:20.559931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:21.846896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:23.084400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:24.958086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:05.142972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:06.765681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:08.089420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:09.650454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:10.942053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:12.655457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:13.984414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:15.405726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:16.563321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:17.890420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:19.302514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:20.637572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:21.916131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:23.163354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:25.054712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:05.243129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:06.857136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:08.210846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:09.751104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:11.047846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:12.745778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:14.084289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:15.497119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:16.639821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:17.993972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:19.400745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:20.759083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:22.001800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:23.249405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:25.147819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:05.329336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:06.959925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:08.373676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:09.839844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:11.137393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:12.831165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:14.189371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:15.590362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:16.715445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:18.094775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:19.491156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:20.861457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:22.099299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:23.702921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:25.228069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:05.409110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:07.054745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:08.464537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:09.916014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:11.209433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:12.910352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:14.277102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:15.663773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:16.777840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:18.178405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:19.567061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:20.964180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:22.177044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:23.782465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:25.315748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:05.497349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:07.149419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:08.579353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:09.990389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:11.298153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:12.981295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:14.369458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:15.739770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:16.839951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:18.252464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:19.662480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:21.047821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:22.250452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:48:23.878047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:48:31.046856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국가공무원고용자피고용자직장동료친구애인동거친족기타친족거래상대방이웃지인타인기타미상
국가1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4420.8470.000
공무원0.0001.0000.0000.0000.3540.0000.0000.0000.0000.0000.4980.4480.0000.0000.395
고용자0.0000.0001.0000.8000.0000.0000.0000.0000.0000.0000.0000.0000.0000.6180.000
피고용자0.0000.0000.8001.0000.5960.8550.5970.6480.3740.5570.5620.5810.5800.6680.348
직장동료0.0000.3540.0000.5961.0000.8240.8950.8380.9610.5760.9000.9430.8620.5620.581
친구0.0000.0000.0000.8550.8241.0000.8790.8890.6971.0000.8910.8380.9190.7400.484
애인0.0000.0000.0000.5970.8950.8791.0000.9760.9450.7460.9590.8190.8640.4280.762
동거친족0.0000.0000.0000.6480.8380.8890.9761.0000.8940.0000.9220.5790.8140.2130.395
기타친족0.0000.0000.0000.3740.9610.6970.9450.8941.0000.3900.9430.8850.8170.3940.494
거래상대방0.0000.0000.0000.5570.5761.0000.7460.0000.3901.0000.6410.4601.0001.0000.355
이웃0.0000.4980.0000.5620.9000.8910.9590.9220.9430.6411.0000.9010.9730.7110.649
지인0.0000.4480.0000.5810.9430.8380.8190.5790.8850.4600.9011.0000.8450.4780.581
타인0.4420.0000.0000.5800.8620.9190.8640.8140.8171.0000.9730.8451.0000.9080.778
기타0.8470.0000.6180.6680.5620.7400.4280.2130.3941.0000.7110.4780.9081.0000.606
미상0.0000.3950.0000.3480.5810.4840.7620.3950.4940.3550.6490.5810.7780.6061.000
2023-12-13T05:48:31.212359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국가공무원고용자피고용자직장동료친구애인동거친족기타친족거래상대방이웃지인타인기타미상
국가1.0000.3980.1950.0720.0140.050-0.069-0.114-0.0370.1900.045-0.0150.2530.6030.325
공무원0.3981.0000.3980.4820.5200.4780.4050.4420.4540.4740.4530.4830.5960.6450.603
고용자0.1950.3981.0000.6890.6170.4610.4590.4360.4730.4930.4310.4730.5800.6680.404
피고용자0.0720.4820.6891.0000.6610.6460.5880.5980.6580.5580.5860.6570.6590.5690.533
직장동료0.0140.5200.6170.6611.0000.7090.6260.5970.6660.6880.6770.7270.7460.5400.628
친구0.0500.4780.4610.6460.7091.0000.8090.7280.7620.6610.7670.8190.8010.5270.689
애인-0.0690.4050.4590.5880.6260.8091.0000.8110.7840.5160.6960.7380.6680.4080.564
동거친족-0.1140.4420.4360.5980.5970.7280.8111.0000.8260.4680.7120.7430.6450.3710.528
기타친족-0.0370.4540.4730.6580.6660.7620.7840.8261.0000.6170.7490.8160.6980.4460.672
거래상대방0.1900.4740.4930.5580.6880.6610.5160.4680.6171.0000.6030.7430.7910.6020.806
이웃0.0450.4530.4310.5860.6770.7670.6960.7120.7490.6031.0000.7640.7330.4590.665
지인-0.0150.4830.4730.6570.7270.8190.7380.7430.8160.7430.7641.0000.8020.5130.741
타인0.2530.5960.5800.6590.7460.8010.6680.6450.6980.7910.7330.8021.0000.7800.804
기타0.6030.6450.6680.5690.5400.5270.4080.3710.4460.6020.4590.5130.7801.0000.649
미상0.3250.6030.4040.5330.6280.6890.5640.5280.6720.8060.6650.7410.8040.6491.000

Missing values

2023-12-13T05:48:25.510980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:48:25.799340image/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

범죄분류국가공무원고용자피고용자직장동료친구애인동거친족기타친족거래상대방이웃지인타인기타미상
0절도262361508447731119367511523316151367216582119383410356
1장물135160315020664222274259344
2사기512794111347895118571103644032330087967855330917562110097
3횡령568513243893931906621234204626165112138285715172
4배임293033182120911455796513079114396183
5손괴78491024821625416342329682683203610821870016275965
6살인0613404086180489509223040161
7강도01668352867572580100274162
8방화6661331027217039197028470150145
9성폭력01724835910531101107153924522052920061845710635929
범죄분류국가공무원고용자피고용자직장동료친구애인동거친족기타친족거래상대방이웃지인타인기타미상
152폐기물관리법114494020000373093396330
153풍속영업의규제에관한법률1991000000010095239
154하천법1981000000000021866
155학교보건법450000000011021815
156학원의설립운영및과외교습에관한법률4330110000030185855
157화물자동차운수사업법181601100000141085469385
158화재로인한재해보상과보험가입에관한법률000000000000000
159화재예방,소방시설설치유지및안전관리에관한법률2900000000000244
160화학물질관리법439031051200215529639
161기타특별법2217714137079791275414478734892471223191519468