Overview

Dataset statistics

Number of variables11
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory100.7 B

Variable types

Text1
Categorical1
Numeric9

Dataset

Description2020년 12월말 기준, 경찰청 관리 운전면허소지자(면허종별, 성별, 지역별)현황 자료로 단위는 건수 입니다.
Author경찰청
URLhttps://www.data.go.kr/data/15052311/fileData.do

Alerts

1종대형 is highly overall correlated with 1종보통 and 7 other fieldsHigh correlation
1종보통 is highly overall correlated with 1종대형 and 7 other fieldsHigh correlation
1종소형 is highly overall correlated with 1종대형 and 6 other fieldsHigh correlation
대형견인 is highly overall correlated with 1종대형 and 7 other fieldsHigh correlation
소형견인 is highly overall correlated with 1종대형 and 7 other fieldsHigh correlation
구난 is highly overall correlated with 1종대형 and 7 other fieldsHigh correlation
2종보통 is highly overall correlated with 성별High correlation
2종소형 is highly overall correlated with 1종대형 and 7 other fieldsHigh correlation
원자 is highly overall correlated with 1종대형 and 7 other fieldsHigh correlation
성별 is highly overall correlated with 1종대형 and 7 other fieldsHigh correlation
1종보통 has unique valuesUnique
대형견인 has unique valuesUnique
소형견인 has unique valuesUnique
2종보통 has unique valuesUnique
원자 has unique valuesUnique
1종소형 has 20 (55.6%) zerosZeros

Reproduction

Analysis started2023-12-12 02:41:49.624865
Analysis finished2023-12-12 02:41:58.197402
Duration8.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역별
Text

Distinct18
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-12T11:41:58.329725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.2222222
Min length2

Characters and Unicode

Total characters80
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울
2nd row서울
3rd row부산
4th row부산
5th row대구
ValueCountFrequency (%)
서울 2
 
5.6%
부산 2
 
5.6%
제주 2
 
5.6%
경남 2
 
5.6%
경북 2
 
5.6%
전남 2
 
5.6%
전북 2
 
5.6%
충남 2
 
5.6%
충북 2
 
5.6%
강원 2
 
5.6%
Other values (8) 16
44.4%
2023-12-12T11:41:58.679946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 8
 
10.0%
남 8
 
10.0%
북 8
 
10.0%
전 6
 
7.5%
부 6
 
7.5%
산 4
 
5.0%
대 4
 
5.0%
충 4
 
5.0%
주 4
 
5.0%
울 4
 
5.0%
Other values (11) 24
30.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 80
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 8
 
10.0%
남 8
 
10.0%
북 8
 
10.0%
전 6
 
7.5%
부 6
 
7.5%
산 4
 
5.0%
대 4
 
5.0%
충 4
 
5.0%
주 4
 
5.0%
울 4
 
5.0%
Other values (11) 24
30.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 80
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 8
 
10.0%
남 8
 
10.0%
북 8
 
10.0%
전 6
 
7.5%
부 6
 
7.5%
산 4
 
5.0%
대 4
 
5.0%
충 4
 
5.0%
주 4
 
5.0%
울 4
 
5.0%
Other values (11) 24
30.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 80
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 8
 
10.0%
남 8
 
10.0%
북 8
 
10.0%
전 6
 
7.5%
부 6
 
7.5%
산 4
 
5.0%
대 4
 
5.0%
충 4
 
5.0%
주 4
 
5.0%
울 4
 
5.0%
Other values (11) 24
30.0%

성별
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size420.0 B
남
18 
여
18 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남
2nd row여
3rd row남
4th row여
5th row남

Common Values

ValueCountFrequency (%)
남 18
50.0%
여 18
50.0%

Length

2023-12-12T11:41:58.815044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:41:58.915485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남 18
50.0%
여 18
50.0%

1종대형
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68186.944
Minimum551
Maximum409649
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T11:41:59.083076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum551
5-th percentile1537
Q12993.75
median13553.5
Q3123363
95-th percentile194762.75
Maximum409649
Range409098
Interquartile range (IQR)120369.25

Descriptive statistics

Standard deviation90000.632
Coefficient of variation (CV)1.31991
Kurtosis4.7362234
Mean68186.944
Median Absolute Deviation (MAD)12707.5
Skewness1.8935895
Sum2454730
Variance8.1001138 × 109
MonotonicityNot monotonic
2023-12-12T11:41:59.225071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2543 2
 
5.6%
253607 1
 
2.8%
162029 1
 
2.8%
130361 1
 
2.8%
3493 1
 
2.8%
121748 1
 
2.8%
3013 1
 
2.8%
128208 1
 
2.8%
3680 1
 
2.8%
3345 1
 
2.8%
Other values (25) 25
69.4%
ValueCountFrequency (%)
551 1
2.8%
1141 1
2.8%
1669 1
2.8%
1707 1
2.8%
2543 2
5.6%
2594 1
2.8%
2643 1
2.8%
2936 1
2.8%
3013 1
2.8%
3345 1
2.8%
ValueCountFrequency (%)
409649 1
2.8%
253607 1
2.8%
175148 1
2.8%
166322 1
2.8%
162029 1
2.8%
148307 1
2.8%
130361 1
2.8%
129926 1
2.8%
128208 1
2.8%
121748 1
2.8%

1종보통
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean565184.17
Minimum41908
Maximum2963785
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T11:41:59.342650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41908
5-th percentile100182.25
Q1197380.5
median366225.5
Q3756840
95-th percentile1395855.2
Maximum2963785
Range2921877
Interquartile range (IQR)559459.5

Descriptive statistics

Standard deviation612531.16
Coefficient of variation (CV)1.0837727
Kurtosis8.6240223
Mean565184.17
Median Absolute Deviation (MAD)185266.5
Skewness2.7763963
Sum20346630
Variance3.7519442 × 1011
MonotonicityNot monotonic
2023-12-12T11:41:59.481580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
2557639 1
 
2.8%
182436 1
 
2.8%
179482 1
 
2.8%
635460 1
 
2.8%
219303 1
 
2.8%
528743 1
 
2.8%
200377 1
 
2.8%
530016 1
 
2.8%
188391 1
 
2.8%
841617 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
41908 1
2.8%
96169 1
2.8%
101520 1
2.8%
136137 1
2.8%
164380 1
2.8%
176210 1
2.8%
179482 1
2.8%
182436 1
2.8%
188391 1
2.8%
200377 1
2.8%
ValueCountFrequency (%)
2963785 1
2.8%
2557639 1
2.8%
1008594 1
2.8%
1005184 1
2.8%
946232 1
2.8%
931782 1
2.8%
872475 1
2.8%
841617 1
2.8%
769668 1
2.8%
752564 1
2.8%

1종소형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0555556
Minimum0
Maximum97
Zeros20
Zeros (%)55.6%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T11:41:59.612162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35.5
95-th percentile32
Maximum97
Range97
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation18.074361
Coefficient of variation (CV)2.2437138
Kurtosis16.932407
Mean8.0555556
Median Absolute Deviation (MAD)0
Skewness3.7601684
Sum290
Variance326.68254
MonotonicityNot monotonic
2023-12-12T11:41:59.716049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 20
55.6%
32 2
 
5.6%
2 2
 
5.6%
4 2
 
5.6%
97 1
 
2.8%
12 1
 
2.8%
1 1
 
2.8%
28 1
 
2.8%
24 1
 
2.8%
7 1
 
2.8%
Other values (4) 4
 
11.1%
ValueCountFrequency (%)
0 20
55.6%
1 1
 
2.8%
2 2
 
5.6%
3 1
 
2.8%
4 2
 
5.6%
5 1
 
2.8%
7 1
 
2.8%
12 1
 
2.8%
18 1
 
2.8%
19 1
 
2.8%
ValueCountFrequency (%)
97 1
2.8%
32 2
5.6%
28 1
2.8%
24 1
2.8%
19 1
2.8%
18 1
2.8%
12 1
2.8%
7 1
2.8%
5 1
2.8%
4 2
5.6%

대형견인
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8973
Minimum9
Maximum47197
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T11:41:59.838906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile38.25
Q193.25
median1058
Q315882.5
95-th percentile29864.25
Maximum47197
Range47188
Interquartile range (IQR)15789.25

Descriptive statistics

Standard deviation12047.343
Coefficient of variation (CV)1.3426216
Kurtosis1.7171137
Mean8973
Median Absolute Deviation (MAD)1046
Skewness1.4296466
Sum323028
Variance1.4513848 × 108
MonotonicityNot monotonic
2023-12-12T11:41:59.962871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
24362 1
 
2.8%
67 1
 
2.8%
79 1
 
2.8%
16866 1
 
2.8%
112 1
 
2.8%
15852 1
 
2.8%
95 1
 
2.8%
21325 1
 
2.8%
130 1
 
2.8%
25056 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
9 1
2.8%
15 1
2.8%
46 1
2.8%
48 1
2.8%
49 1
2.8%
56 1
2.8%
67 1
2.8%
79 1
2.8%
88 1
2.8%
95 1
2.8%
ValueCountFrequency (%)
47197 1
2.8%
36015 1
2.8%
27814 1
2.8%
25056 1
2.8%
24362 1
2.8%
22566 1
2.8%
21325 1
2.8%
16866 1
2.8%
15974 1
2.8%
15852 1
2.8%

소형견인
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1201.75
Minimum12
Maximum8621
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T11:42:00.100171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile28.5
Q180.25
median495.5
Q31938
95-th percentile3261.75
Maximum8621
Range8609
Interquartile range (IQR)1857.75

Descriptive statistics

Standard deviation1694.9949
Coefficient of variation (CV)1.4104388
Kurtosis9.7217847
Mean1201.75
Median Absolute Deviation (MAD)456.5
Skewness2.6636908
Sum43263
Variance2873007.6
MonotonicityNot monotonic
2023-12-12T11:42:00.250537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
4143 1
 
2.8%
86 1
 
2.8%
82 1
 
2.8%
2835 1
 
2.8%
117 1
 
2.8%
1061 1
 
2.8%
48 1
 
2.8%
1386 1
 
2.8%
51 1
 
2.8%
2968 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
12 1
2.8%
24 1
2.8%
30 1
2.8%
48 1
2.8%
51 1
2.8%
52 1
2.8%
61 1
2.8%
68 1
2.8%
75 1
2.8%
82 1
2.8%
ValueCountFrequency (%)
8621 1
2.8%
4143 1
2.8%
2968 1
2.8%
2860 1
2.8%
2835 1
2.8%
2530 1
2.8%
2164 1
2.8%
2145 1
2.8%
2109 1
2.8%
1881 1
2.8%

구난
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3759.1667
Minimum8
Maximum26392
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T11:42:00.392145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile12.25
Q132
median478
Q36196.25
95-th percentile11113.25
Maximum26392
Range26384
Interquartile range (IQR)6164.25

Descriptive statistics

Standard deviation5522.2004
Coefficient of variation (CV)1.4689959
Kurtosis7.1617469
Mean3759.1667
Median Absolute Deviation (MAD)469
Skewness2.3113204
Sum135330
Variance30494698
MonotonicityNot monotonic
2023-12-12T11:42:00.518089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
18 2
 
5.6%
34 2
 
5.6%
14993 1
 
2.8%
35 1
 
2.8%
7560 1
 
2.8%
4888 1
 
2.8%
26 1
 
2.8%
5247 1
 
2.8%
21 1
 
2.8%
9736 1
 
2.8%
Other values (24) 24
66.7%
ValueCountFrequency (%)
8 1
2.8%
10 1
2.8%
13 1
2.8%
14 1
2.8%
17 1
2.8%
18 2
5.6%
21 1
2.8%
26 1
2.8%
34 2
5.6%
35 1
2.8%
ValueCountFrequency (%)
26392 1
2.8%
14993 1
2.8%
9820 1
2.8%
9736 1
2.8%
9051 1
2.8%
8357 1
2.8%
7560 1
2.8%
7072 1
2.8%
6377 1
2.8%
6136 1
2.8%

2종보통
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean473387.97
Minimum34247
Maximum2327886
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T11:42:00.642280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34247
5-th percentile77422
Q1177577.75
median310655.5
Q3476359.25
95-th percentile1714396
Maximum2327886
Range2293639
Interquartile range (IQR)298781.5

Descriptive statistics

Standard deviation543647.38
Coefficient of variation (CV)1.1484182
Kurtosis5.9010586
Mean473387.97
Median Absolute Deviation (MAD)136726.5
Skewness2.4738297
Sum17041967
Variance2.9555247 × 1011
MonotonicityNot monotonic
2023-12-12T11:42:00.784357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1536235 1
 
2.8%
310167 1
 
2.8%
315063 1
 
2.8%
219217 1
 
2.8%
385614 1
 
2.8%
189445 1
 
2.8%
336552 1
 
2.8%
178522 1
 
2.8%
299225 1
 
2.8%
187873 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
34247 1
2.8%
75673 1
2.8%
78005 1
2.8%
111288 1
2.8%
128813 1
2.8%
140804 1
2.8%
160900 1
2.8%
173113 1
2.8%
174745 1
2.8%
178522 1
2.8%
ValueCountFrequency (%)
2327886 1
2.8%
2248879 1
2.8%
1536235 1
2.8%
1259023 1
2.8%
808462 1
2.8%
671835 1
2.8%
640157 1
2.8%
606033 1
2.8%
485510 1
2.8%
473309 1
2.8%

2종소형
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15769.667
Minimum71
Maximum108959
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T11:42:00.925227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum71
5-th percentile159.5
Q1406.25
median3186.5
Q320953.5
95-th percentile57806.25
Maximum108959
Range108888
Interquartile range (IQR)20547.25

Descriptive statistics

Standard deviation25598.086
Coefficient of variation (CV)1.6232484
Kurtosis7.59694
Mean15769.667
Median Absolute Deviation (MAD)3076.5
Skewness2.6495114
Sum567708
Variance6.5526199 × 108
MonotonicityNot monotonic
2023-12-12T11:42:01.086372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
425 2
 
5.6%
108959 1
 
2.8%
214 1
 
2.8%
348 1
 
2.8%
26556 1
 
2.8%
435 1
 
2.8%
16168 1
 
2.8%
232 1
 
2.8%
14883 1
 
2.8%
31385 1
 
2.8%
Other values (25) 25
69.4%
ValueCountFrequency (%)
71 1
2.8%
149 1
2.8%
163 1
2.8%
189 1
2.8%
214 1
2.8%
232 1
2.8%
279 1
2.8%
348 1
2.8%
350 1
2.8%
425 2
5.6%
ValueCountFrequency (%)
108959 1
2.8%
103914 1
2.8%
42437 1
2.8%
34773 1
2.8%
33476 1
2.8%
31385 1
2.8%
28655 1
2.8%
28203 1
2.8%
26556 1
2.8%
19086 1
2.8%

원자
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50384.333
Minimum1202
Maximum241749
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T11:42:01.215847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1202
5-th percentile2244.5
Q16180
median20199.5
Q381023.75
95-th percentile166666.5
Maximum241749
Range240547
Interquartile range (IQR)74843.75

Descriptive statistics

Standard deviation61365.169
Coefficient of variation (CV)1.2179415
Kurtosis2.6797861
Mean50384.333
Median Absolute Deviation (MAD)17402.5
Skewness1.6802636
Sum1813836
Variance3.7656839 × 109
MonotonicityNot monotonic
2023-12-12T11:42:01.355457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
223938 1
 
2.8%
5085 1
 
2.8%
9150 1
 
2.8%
109210 1
 
2.8%
15267 1
 
2.8%
83975 1
 
2.8%
8964 1
 
2.8%
93223 1
 
2.8%
12082 1
 
2.8%
147576 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
1202 1
2.8%
2153 1
2.8%
2275 1
2.8%
3319 1
2.8%
3538 1
2.8%
4203 1
2.8%
4505 1
2.8%
4879 1
2.8%
5085 1
2.8%
6545 1
2.8%
ValueCountFrequency (%)
241749 1
2.8%
223938 1
2.8%
147576 1
2.8%
138937 1
2.8%
109210 1
2.8%
95207 1
2.8%
93223 1
2.8%
84702 1
2.8%
83975 1
2.8%
80040 1
2.8%

Interactions

2023-12-12T11:41:57.196993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:50.025225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:50.784348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:51.632824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:52.525176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:53.408457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:54.142924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:55.066498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:55.951576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:57.283102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:50.094289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:50.883696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:51.800863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:52.622004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:53.489370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:54.230755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:55.164817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:56.069351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:57.382037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:50.197097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:50.976819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:51.898609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:52.734544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:53.566301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:54.339788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:55.284508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:56.171073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:57.481621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:50.274037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:51.070179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:51.997957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:52.833679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:53.648647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:54.434311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:55.382102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:56.259381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:57.567363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:50.358247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:51.164212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:52.091818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:52.930575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:53.745802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:54.521908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:55.470658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:56.367246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:57.657263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:50.428628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:51.242549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:52.174953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:53.026975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:53.834852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:54.634559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:55.564610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:56.817268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:57.735809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:50.518550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:51.329142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:52.271646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:53.120869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:53.914328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:54.736796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:55.656428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:56.918330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:57.805137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:50.600821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:51.425254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:52.360387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:53.215857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:53.989681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:54.823287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:55.748937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:57.011991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:57.877912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:50.693488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:51.517556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:52.443347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:53.310893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:54.063694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:54.968299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:55.845728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:41:57.101729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:42:01.455565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역별성별1종대형1종보통1종소형대형견인소형견인구난2종보통2종소형원자
지역별1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
성별0.0001.0000.8620.7900.3640.9770.9580.9810.5040.7460.976
1종대형0.0000.8621.0000.8820.8160.8700.8730.9240.9190.8820.865
1종보통0.0000.7900.8821.0000.7890.7920.9670.9820.8060.9150.776
1종소형0.0000.3640.8160.7891.0000.7990.8110.8030.6730.9040.716
대형견인0.0000.9770.8700.7920.7991.0000.8430.8860.5690.8190.934
소형견인0.0000.9580.8730.9670.8110.8431.0000.9800.7710.8390.827
구난0.0000.9810.9240.9820.8030.8860.9801.0000.7960.9090.864
2종보통0.0000.5040.9190.8060.6730.5690.7710.7961.0000.6840.433
2종소형0.0000.7460.8820.9150.9040.8190.8390.9090.6841.0000.893
원자0.0000.9760.8650.7760.7160.9340.8270.8640.4330.8931.000
2023-12-12T11:42:01.612700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1종대형1종보통1종소형대형견인소형견인구난2종보통2종소형원자성별
1종대형1.0000.8850.8350.9780.9510.9700.0500.9690.9310.863
1종보통0.8851.0000.6910.8670.8650.8830.3800.8920.8390.572
1종소형0.8350.6911.0000.8270.8470.857-0.1000.8770.8060.420
대형견인0.9780.8670.8271.0000.9160.9630.0280.9570.9240.786
소형견인0.9510.8650.8470.9161.0000.9650.0250.9700.9070.767
구난0.9700.8830.8570.9630.9651.0000.0530.9780.9130.822
2종보통0.0500.380-0.1000.0280.0250.0531.0000.081-0.0200.512
2종소형0.9690.8920.8770.9570.9700.9780.0811.0000.9030.840
원자0.9310.8390.8060.9240.9070.913-0.0200.9031.0000.786
성별0.8630.5720.4200.7860.7670.8220.5120.8400.7861.000

Missing values

2023-12-12T11:41:57.980006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:41:58.134169image/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

지역별성별1종대형1종보통1종소형대형견인소형견인구난2종보통2종소형원자
0서울남253607255763997243624143149931536235108959223938
1서울여7503769668014117380232788627999767
2부산남1299269462323236015210983573730813347684702
3부산여2936288345017061496718355214505
4대구남1084667525643212037188155892013102865580040
5대구여2543293607049108184855104256545
6인천남1483078724751222566216470723284352820373753
7인천여4193279637014087346060336173538
8광주남7923743069509300109636861288131115538526
9광주여264317621005652172883051892153
지역별성별1종대형1종보통1종소형대형견인소형견인구난2종보통2종소형원자
26전남남128208530016021325138652471785221488393223
27전남여36801883910130512129922521412082
28경북남16202984161718250562968973618787331385147576
29경북여33453626610881333539233742520684
30경남남175148100518419278142530905135077334773138937
31경남여48063649500183754264015752619715
32제주남4347920322342778797197675673715223577
33제주여11419616901512101408041494879
34세종남151671015202184362184234247357410266
35세종여551419080930878005711202