Overview

Dataset statistics

Number of variables10
Number of observations84
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 KiB
Average record size in memory90.6 B

Variable types

Text1
Numeric8
Categorical1

Dataset

Description2018-10-31일 기준 운전면허소지자 서울지역 대장별 현황입니다.항목 : 구분, 1종대형, 1종보통, 1종소형, 대형견인, 소형견인, 구난, 2종보통, 2종소형, 원자
Author경찰청
URLhttps://www.data.go.kr/data/15048424/fileData.do

Alerts

1종대형 is highly overall correlated with 1종보통 and 5 other fieldsHigh correlation
1종보통 is highly overall correlated with 1종대형 and 5 other fieldsHigh correlation
대형견인 is highly overall correlated with 1종대형 and 3 other fieldsHigh correlation
소형견인 is highly overall correlated with 1종대형 and 5 other fieldsHigh correlation
구난 is highly overall correlated with 1종대형 and 4 other fieldsHigh correlation
2종보통 is highly overall correlated with 1종대형 and 5 other fieldsHigh correlation
2종소형 is highly overall correlated with 1종대형 and 4 other fieldsHigh correlation
1종소형 is highly imbalanced (83.8%)Imbalance
구분 has unique valuesUnique
1종대형 has 3 (3.6%) zerosZeros
1종보통 has 2 (2.4%) zerosZeros
대형견인 has 35 (41.7%) zerosZeros
소형견인 has 51 (60.7%) zerosZeros
구난 has 47 (56.0%) zerosZeros
2종보통 has 2 (2.4%) zerosZeros
2종소형 has 14 (16.7%) zerosZeros

Reproduction

Analysis started2023-12-12 07:17:18.667884
Analysis finished2023-12-12 07:17:25.854555
Duration7.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size804.0 B
2023-12-12T16:17:26.066237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.0357143
Min length2

Characters and Unicode

Total characters171
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)100.0%

Sample

1st row16
2nd row17
3rd row18
4th row19
5th row20
ValueCountFrequency (%)
16 1
 
1.2%
70 1
 
1.2%
60 1
 
1.2%
77 1
 
1.2%
76 1
 
1.2%
75 1
 
1.2%
74 1
 
1.2%
73 1
 
1.2%
72 1
 
1.2%
71 1
 
1.2%
Other values (75) 75
88.2%
2023-12-12T16:17:26.487642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 19
11.1%
8 19
11.1%
9 19
11.1%
7 19
11.1%
2 18
10.5%
3 18
10.5%
4 18
10.5%
5 18
10.5%
1 12
7.0%
0 8
4.7%
Other values (3) 3
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 168
98.2%
Other Letter 2
 
1.2%
Space Separator 1
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 19
11.3%
8 19
11.3%
9 19
11.3%
7 19
11.3%
2 18
10.7%
3 18
10.7%
4 18
10.7%
5 18
10.7%
1 12
7.1%
0 8
4.8%
Other Letter
ValueCountFrequency (%)
이 1
50.0%
상 1
50.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 169
98.8%
Hangul 2
 
1.2%

Most frequent character per script

Common
ValueCountFrequency (%)
6 19
11.2%
8 19
11.2%
9 19
11.2%
7 19
11.2%
2 18
10.7%
3 18
10.7%
4 18
10.7%
5 18
10.7%
1 12
7.1%
0 8
4.7%
Hangul
ValueCountFrequency (%)
이 1
50.0%
상 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 169
98.8%
Hangul 2
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 19
11.2%
8 19
11.2%
9 19
11.2%
7 19
11.2%
2 18
10.7%
3 18
10.7%
4 18
10.7%
5 18
10.7%
1 12
7.1%
0 8
4.7%
Hangul
ValueCountFrequency (%)
이 1
50.0%
상 1
50.0%

1종대형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3074.2381
Minimum0
Maximum7826
Zeros3
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-12T16:17:26.669565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q1308.25
median3256.5
Q34904.5
95-th percentile6947.4
Maximum7826
Range7826
Interquartile range (IQR)4596.25

Descriptive statistics

Standard deviation2549.8985
Coefficient of variation (CV)0.82944079
Kurtosis-1.30551
Mean3074.2381
Median Absolute Deviation (MAD)2595.5
Skewness0.2302454
Sum258236
Variance6501982.3
MonotonicityNot monotonic
2023-12-12T16:17:26.806624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
3.6%
7 2
 
2.4%
4 2
 
2.4%
3271 1
 
1.2%
1818 1
 
1.2%
1775 1
 
1.2%
1874 1
 
1.2%
1892 1
 
1.2%
2075 1
 
1.2%
2872 1
 
1.2%
Other values (70) 70
83.3%
ValueCountFrequency (%)
0 3
3.6%
2 1
 
1.2%
4 2
2.4%
5 1
 
1.2%
6 1
 
1.2%
7 2
2.4%
11 1
 
1.2%
17 1
 
1.2%
19 1
 
1.2%
38 1
 
1.2%
ValueCountFrequency (%)
7826 1
1.2%
7748 1
1.2%
7088 1
1.2%
7052 1
1.2%
6957 1
1.2%
6893 1
1.2%
6857 1
1.2%
6851 1
1.2%
6784 1
1.2%
6684 1
1.2%

1종보통
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct83
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37180.702
Minimum0
Maximum85433
Zeros2
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-12T16:17:26.963744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.75
Q12661.5
median36242.5
Q367796.25
95-th percentile81576.8
Maximum85433
Range85433
Interquartile range (IQR)65134.75

Descriptive statistics

Standard deviation31342.057
Coefficient of variation (CV)0.8429657
Kurtosis-1.6561082
Mean37180.702
Median Absolute Deviation (MAD)32833.5
Skewness0.088926864
Sum3123179
Variance9.8232452 × 108
MonotonicityNot monotonic
2023-12-12T16:17:27.148514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
2.4%
22265 1
 
1.2%
5588 1
 
1.2%
7468 1
 
1.2%
10633 1
 
1.2%
10486 1
 
1.2%
11863 1
 
1.2%
13133 1
 
1.2%
15130 1
 
1.2%
20819 1
 
1.2%
Other values (73) 73
86.9%
ValueCountFrequency (%)
0 2
2.4%
17 1
1.2%
18 1
1.2%
33 1
1.2%
38 1
1.2%
39 1
1.2%
40 1
1.2%
45 1
1.2%
49 1
1.2%
95 1
1.2%
ValueCountFrequency (%)
85433 1
1.2%
83720 1
1.2%
83194 1
1.2%
83067 1
1.2%
81743 1
1.2%
80635 1
1.2%
80519 1
1.2%
79079 1
1.2%
78668 1
1.2%
78566 1
1.2%

1종소형
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size804.0 B
0
82 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 82
97.6%
1 2
 
2.4%

Length

2023-12-12T16:17:27.296017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:17:27.404493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 82
97.6%
1 2
 
2.4%

대형견인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0714286
Minimum0
Maximum9
Zeros35
Zeros (%)41.7%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-12T16:17:27.517126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile7
Maximum9
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.472917
Coefficient of variation (CV)1.193822
Kurtosis0.25138628
Mean2.0714286
Median Absolute Deviation (MAD)1
Skewness1.0913308
Sum174
Variance6.1153184
MonotonicityNot monotonic
2023-12-12T16:17:27.655603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 35
41.7%
1 12
 
14.3%
4 8
 
9.5%
2 8
 
9.5%
3 6
 
7.1%
5 5
 
6.0%
6 4
 
4.8%
7 3
 
3.6%
9 2
 
2.4%
8 1
 
1.2%
ValueCountFrequency (%)
0 35
41.7%
1 12
 
14.3%
2 8
 
9.5%
3 6
 
7.1%
4 8
 
9.5%
5 5
 
6.0%
6 4
 
4.8%
7 3
 
3.6%
8 1
 
1.2%
9 2
 
2.4%
ValueCountFrequency (%)
9 2
 
2.4%
8 1
 
1.2%
7 3
 
3.6%
6 4
 
4.8%
5 5
 
6.0%
4 8
 
9.5%
3 6
 
7.1%
2 8
 
9.5%
1 12
 
14.3%
0 35
41.7%

소형견인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0595238
Minimum0
Maximum7
Zeros51
Zeros (%)60.7%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-12T16:17:27.827563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile5
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6671973
Coefficient of variation (CV)1.5735345
Kurtosis2.1280971
Mean1.0595238
Median Absolute Deviation (MAD)0
Skewness1.6613517
Sum89
Variance2.7795468
MonotonicityNot monotonic
2023-12-12T16:17:27.950108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 51
60.7%
1 9
 
10.7%
2 9
 
10.7%
3 7
 
8.3%
5 4
 
4.8%
4 2
 
2.4%
6 1
 
1.2%
7 1
 
1.2%
ValueCountFrequency (%)
0 51
60.7%
1 9
 
10.7%
2 9
 
10.7%
3 7
 
8.3%
4 2
 
2.4%
5 4
 
4.8%
6 1
 
1.2%
7 1
 
1.2%
ValueCountFrequency (%)
7 1
 
1.2%
6 1
 
1.2%
5 4
 
4.8%
4 2
 
2.4%
3 7
 
8.3%
2 9
 
10.7%
1 9
 
10.7%
0 51
60.7%

구난
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.94047619
Minimum0
Maximum6
Zeros47
Zeros (%)56.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-12T16:17:28.085702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3385047
Coefficient of variation (CV)1.4232202
Kurtosis2.1205505
Mean0.94047619
Median Absolute Deviation (MAD)0
Skewness1.5315079
Sum79
Variance1.791595
MonotonicityNot monotonic
2023-12-12T16:17:28.230821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 47
56.0%
1 14
 
16.7%
2 11
 
13.1%
3 8
 
9.5%
4 2
 
2.4%
5 1
 
1.2%
6 1
 
1.2%
ValueCountFrequency (%)
0 47
56.0%
1 14
 
16.7%
2 11
 
13.1%
3 8
 
9.5%
4 2
 
2.4%
5 1
 
1.2%
6 1
 
1.2%
ValueCountFrequency (%)
6 1
 
1.2%
5 1
 
1.2%
4 2
 
2.4%
3 8
 
9.5%
2 11
 
13.1%
1 14
 
16.7%
0 47
56.0%

2종보통
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct83
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32002.393
Minimum0
Maximum64997
Zeros2
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-12T16:17:28.354280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile88.8
Q15474.75
median34625
Q356751
95-th percentile63899
Maximum64997
Range64997
Interquartile range (IQR)51276.25

Descriptive statistics

Standard deviation24581.081
Coefficient of variation (CV)0.76810136
Kurtosis-1.6666574
Mean32002.393
Median Absolute Deviation (MAD)25002
Skewness-0.059341781
Sum2688201
Variance6.0422956 × 108
MonotonicityNot monotonic
2023-12-12T16:17:28.540270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
2.4%
20358 1
 
1.2%
9113 1
 
1.2%
10682 1
 
1.2%
13448 1
 
1.2%
12511 1
 
1.2%
13037 1
 
1.2%
14372 1
 
1.2%
15749 1
 
1.2%
20345 1
 
1.2%
Other values (73) 73
86.9%
ValueCountFrequency (%)
0 2
2.4%
39 1
1.2%
54 1
1.2%
84 1
1.2%
116 1
1.2%
148 1
1.2%
155 1
1.2%
190 1
1.2%
227 1
1.2%
366 1
1.2%
ValueCountFrequency (%)
64997 1
1.2%
64722 1
1.2%
64307 1
1.2%
64280 1
1.2%
64016 1
1.2%
63236 1
1.2%
63120 1
1.2%
62413 1
1.2%
61948 1
1.2%
61374 1
1.2%

2종소형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.547619
Minimum0
Maximum106
Zeros14
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-12T16:17:28.729641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median42.5
Q359
95-th percentile86.85
Maximum106
Range106
Interquartile range (IQR)54

Descriptive statistics

Standard deviation30.224623
Coefficient of variation (CV)0.80496776
Kurtosis-0.97770053
Mean37.547619
Median Absolute Deviation (MAD)24.5
Skewness0.23910506
Sum3154
Variance913.52783
MonotonicityNot monotonic
2023-12-12T16:17:28.928468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 14
 
16.7%
58 5
 
6.0%
50 3
 
3.6%
15 3
 
3.6%
54 3
 
3.6%
59 2
 
2.4%
65 2
 
2.4%
56 2
 
2.4%
76 2
 
2.4%
5 2
 
2.4%
Other values (39) 46
54.8%
ValueCountFrequency (%)
0 14
16.7%
1 2
 
2.4%
2 2
 
2.4%
3 1
 
1.2%
4 1
 
1.2%
5 2
 
2.4%
6 2
 
2.4%
9 1
 
1.2%
14 1
 
1.2%
15 3
 
3.6%
ValueCountFrequency (%)
106 1
1.2%
103 1
1.2%
102 1
1.2%
94 1
1.2%
87 1
1.2%
86 1
1.2%
76 2
2.4%
72 1
1.2%
70 1
1.2%
69 1
1.2%

원자
Real number (ℝ)

Distinct78
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean455.38095
Minimum2
Maximum1341
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-12T16:17:29.093069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8.3
Q1286.5
median451
Q3623
95-th percentile982.2
Maximum1341
Range1339
Interquartile range (IQR)336.5

Descriptive statistics

Standard deviation292.56621
Coefficient of variation (CV)0.64246474
Kurtosis0.4286794
Mean455.38095
Median Absolute Deviation (MAD)171
Skewness0.46711608
Sum38252
Variance85594.986
MonotonicityNot monotonic
2023-12-12T16:17:29.293691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450 2
 
2.4%
2 2
 
2.4%
623 2
 
2.4%
339 2
 
2.4%
548 2
 
2.4%
110 2
 
2.4%
596 1
 
1.2%
551 1
 
1.2%
452 1
 
1.2%
474 1
 
1.2%
Other values (68) 68
81.0%
ValueCountFrequency (%)
2 2
2.4%
3 1
1.2%
7 1
1.2%
8 1
1.2%
10 1
1.2%
11 1
1.2%
12 1
1.2%
25 1
1.2%
37 1
1.2%
38 1
1.2%
ValueCountFrequency (%)
1341 1
1.2%
1233 1
1.2%
1088 1
1.2%
1048 1
1.2%
999 1
1.2%
887 1
1.2%
879 1
1.2%
785 1
1.2%
776 1
1.2%
775 1
1.2%

Interactions

2023-12-12T16:17:24.628666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:18.984167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:19.887210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:20.570599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:21.173935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:21.956630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:22.735415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:23.478696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:24.761132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:19.166141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:19.975123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:20.664419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:21.279027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:22.048817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:22.846108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:23.597483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:24.861660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:19.322713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:20.051119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:20.733588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:21.364482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:22.130362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:22.941996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:23.703627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:24.963634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:19.446569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:20.163157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:20.795297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:21.449095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:22.215548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:23.039404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:23.789898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:25.101232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:19.548029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:20.268492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:20.871294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:21.551107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:22.317936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:23.121139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:24.194988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:25.221905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:19.642307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:20.357363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:20.940569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:21.649492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:22.433720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:23.206566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:24.298495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:25.334330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:19.722575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:20.428518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:21.014296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:21.768355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:22.546923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:23.296794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:24.408626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:25.453123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:19.805279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:20.495470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:21.087581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:21.862688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:22.643383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:23.393537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:17:24.510580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:17:29.436594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분1종대형1종보통1종소형대형견인소형견인구난2종보통2종소형원자
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
1종대형1.0001.0000.8990.0000.7000.5030.4730.9080.8480.809
1종보통1.0000.8991.0000.0000.5550.4000.4620.9390.9010.820
1종소형1.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
대형견인1.0000.7000.5550.0001.0000.6890.4710.5800.6440.000
소형견인1.0000.5030.4000.0000.6891.0000.5350.4730.4000.212
구난1.0000.4730.4620.0000.4710.5351.0000.4100.6890.351
2종보통1.0000.9080.9390.0000.5800.4730.4101.0000.9000.818
2종소형1.0000.8480.9010.0000.6440.4000.6890.9001.0000.779
원자1.0000.8090.8200.0000.0000.2120.3510.8180.7791.000
2023-12-12T16:17:29.596426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1종대형1종보통대형견인소형견인구난2종보통2종소형원자1종소형
1종대형1.0000.8850.7980.6090.5130.7430.6430.4320.000
1종보통0.8851.0000.7120.7810.6980.9130.8040.2790.000
대형견인0.7980.7121.0000.5380.4520.5370.4060.1960.000
소형견인0.6090.7810.5381.0000.5910.6690.573-0.0350.000
구난0.5130.6980.4520.5911.0000.7270.7110.1190.000
2종보통0.7430.9130.5370.6690.7271.0000.9100.3060.000
2종소형0.6430.8040.4060.5730.7110.9101.0000.4700.000
원자0.4320.2790.196-0.0350.1190.3060.4701.0000.000
1종소형0.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T16:17:25.619494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:17:25.798198image/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

구분1종대형1종보통1종소형대형견인소형견인구난2종보통2종소형원자
01600000000411
117000000001233
2180233900002971441341
31971959200001296150999
4203221498000022368538775
5216791941500013236261879
622131824159001340354861048
723187630018000047498661088
824233336921010154686106887
92530044480700006054294703
구분1종대형1종보통1종소형대형견인소형견인구난2종보통2종소형원자
7490171350000544237
759119950000366025
76924490000227011
77935400000190012
789411450000148010
7995738100015508
809643300008407
819721800005403
829861700003902
8399 이상739000011602