Overview

Dataset statistics

Number of variables11
Number of observations54
Missing cells96
Missing cells (%)16.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.2 KiB
Average record size in memory99.4 B

Variable types

Unsupported1
Text1
Categorical1
Numeric8

Dataset

Description전국(서울,부산,대구,인천,광주,대전,울산,세종,경기,강원,충북,충남,전북,전남,경북,경남,제주) 무단횡단 교통사고 현황
Author도로교통공단
URLhttps://www.data.go.kr/data/15094179/fileData.do

Alerts

2010 is highly overall correlated with 2011 and 6 other fieldsHigh correlation
2011 is highly overall correlated with 2010 and 6 other fieldsHigh correlation
2012 is highly overall correlated with 2010 and 6 other fieldsHigh correlation
2013 is highly overall correlated with 2010 and 6 other fieldsHigh correlation
2014 is highly overall correlated with 2010 and 6 other fieldsHigh correlation
2015 is highly overall correlated with 2010 and 6 other fieldsHigh correlation
2016 is highly overall correlated with 2010 and 6 other fieldsHigh correlation
2017 is highly overall correlated with 2010 and 6 other fieldsHigh correlation
Unnamed: 0 has 54 (100.0%) missing valuesMissing
발생지_시도 has 36 (66.7%) missing valuesMissing
2010 has 3 (5.6%) missing valuesMissing
2011 has 3 (5.6%) missing valuesMissing
Unnamed: 0 is an unsupported type, check if it needs cleaning or further analysisUnsupported
2012 has 1 (1.9%) zerosZeros
2015 has 1 (1.9%) zerosZeros
2017 has 1 (1.9%) zerosZeros

Reproduction

Analysis started2023-12-12 19:49:19.101470
Analysis finished2023-12-12 19:49:27.185204
Duration8.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing54
Missing (%)100.0%
Memory size618.0 B

발생지_시도
Text

MISSING 

Distinct18
Distinct (%)100.0%
Missing36
Missing (%)66.7%
Memory size564.0 B
2023-12-13T04:49:27.307866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters36
Distinct characters23
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

Unique18 ?
Unique (%)100.0%

Sample

1st row서울
2nd row부산
3rd row대구
4th row인천
5th row광주
ValueCountFrequency (%)
경기 1
 
5.6%
부산 1
 
5.6%
강원 1
 
5.6%
제주 1
 
5.6%
경남 1
 
5.6%
경북 1
 
5.6%
전남 1
 
5.6%
전북 1
 
5.6%
충남 1
 
5.6%
서울 1
 
5.6%
Other values (8) 8
44.4%
2023-12-13T04:49:27.627113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 3
 
8.3%
남 3
 
8.3%
북 3
 
8.3%
전 3
 
8.3%
주 2
 
5.6%
충 2
 
5.6%
울 2
 
5.6%
대 2
 
5.6%
산 2
 
5.6%
천 1
 
2.8%
Other values (13) 13
36.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 36
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 3
 
8.3%
남 3
 
8.3%
북 3
 
8.3%
전 3
 
8.3%
주 2
 
5.6%
충 2
 
5.6%
울 2
 
5.6%
대 2
 
5.6%
산 2
 
5.6%
천 1
 
2.8%
Other values (13) 13
36.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 36
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 3
 
8.3%
남 3
 
8.3%
북 3
 
8.3%
전 3
 
8.3%
주 2
 
5.6%
충 2
 
5.6%
울 2
 
5.6%
대 2
 
5.6%
산 2
 
5.6%
천 1
 
2.8%
Other values (13) 13
36.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 36
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 3
 
8.3%
남 3
 
8.3%
북 3
 
8.3%
전 3
 
8.3%
주 2
 
5.6%
충 2
 
5.6%
울 2
 
5.6%
대 2
 
5.6%
산 2
 
5.6%
천 1
 
2.8%
Other values (13) 13
36.1%

발생년
Categorical

Distinct3
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size564.0 B
발생건수
18 
사망자수
18 
부상자수
18 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row발생건수
2nd row사망자수
3rd row부상자수
4th row발생건수
5th row사망자수

Common Values

ValueCountFrequency (%)
발생건수 18
33.3%
사망자수 18
33.3%
부상자수 18
33.3%

Length

2023-12-13T04:49:27.754277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:49:27.845325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
발생건수 18
33.3%
사망자수 18
33.3%
부상자수 18
33.3%

2010
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct50
Distinct (%)98.0%
Missing3
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean1349.8039
Minimum16
Maximum16984
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T04:49:27.959316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile25
Q180
median583
Q3952
95-th percentile3375
Maximum16984
Range16968
Interquartile range (IQR)872

Descriptive statistics

Standard deviation3250.2056
Coefficient of variation (CV)2.4079095
Kurtosis19.566307
Mean1349.8039
Median Absolute Deviation (MAD)465
Skewness4.4166301
Sum68840
Variance10563837
MonotonicityNot monotonic
2023-12-13T04:49:28.098677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57 2
 
3.7%
1048 1
 
1.9%
44 1
 
1.9%
547 1
 
1.9%
583 1
 
1.9%
543 1
 
1.9%
788 1
 
1.9%
61 1
 
1.9%
746 1
 
1.9%
763 1
 
1.9%
Other values (40) 40
74.1%
(Missing) 3
 
5.6%
ValueCountFrequency (%)
16 1
1.9%
18 1
1.9%
24 1
1.9%
26 1
1.9%
33 1
1.9%
34 1
1.9%
44 1
1.9%
46 1
1.9%
57 2
3.7%
60 1
1.9%
ValueCountFrequency (%)
16984 1
1.9%
16505 1
1.9%
3387 1
1.9%
3363 1
1.9%
3159 1
1.9%
3058 1
1.9%
1292 1
1.9%
1263 1
1.9%
1153 1
1.9%
1108 1
1.9%

2011
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct49
Distinct (%)96.1%
Missing3
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean1295.8824
Minimum20
Maximum16321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T04:49:28.235446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile28.5
Q170.5
median563
Q3850
95-th percentile3329
Maximum16321
Range16301
Interquartile range (IQR)779.5

Descriptive statistics

Standard deviation3124.8808
Coefficient of variation (CV)2.4113923
Kurtosis19.389422
Mean1295.8824
Median Absolute Deviation (MAD)400
Skewness4.3926431
Sum66090
Variance9764879.7
MonotonicityNot monotonic
2023-12-13T04:49:28.626608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
63 2
 
3.7%
912 2
 
3.7%
57 1
 
1.9%
511 1
 
1.9%
50 1
 
1.9%
478 1
 
1.9%
563 1
 
1.9%
70 1
 
1.9%
510 1
 
1.9%
785 1
 
1.9%
Other values (39) 39
72.2%
(Missing) 3
 
5.6%
ValueCountFrequency (%)
20 1
1.9%
24 1
1.9%
26 1
1.9%
31 1
1.9%
36 1
1.9%
38 1
1.9%
40 1
1.9%
49 1
1.9%
50 1
1.9%
57 1
1.9%
ValueCountFrequency (%)
16321 1
1.9%
15812 1
1.9%
3346 1
1.9%
3312 1
1.9%
3137 1
1.9%
3048 1
1.9%
1315 1
1.9%
1300 1
1.9%
1053 1
1.9%
1001 1
1.9%

2012
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1200.2963
Minimum0
Maximum16003
Zeros1
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T04:49:28.763303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q163.25
median524
Q3870.75
95-th percentile3136.9
Maximum16003
Range16003
Interquartile range (IQR)807.5

Descriptive statistics

Standard deviation2984.0594
Coefficient of variation (CV)2.4861023
Kurtosis20.702814
Mean1200.2963
Median Absolute Deviation (MAD)446.5
Skewness4.5291444
Sum64816
Variance8904610.6
MonotonicityNot monotonic
2023-12-13T04:49:28.910499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 2
 
3.7%
57 2
 
3.7%
35 2
 
3.7%
3172 1
 
1.9%
739 1
 
1.9%
540 1
 
1.9%
43 1
 
1.9%
514 1
 
1.9%
565 1
 
1.9%
64 1
 
1.9%
Other values (41) 41
75.9%
ValueCountFrequency (%)
0 1
1.9%
12 1
1.9%
13 2
3.7%
25 1
1.9%
30 1
1.9%
34 1
1.9%
35 2
3.7%
43 1
1.9%
47 1
1.9%
57 2
3.7%
ValueCountFrequency (%)
16003 1
1.9%
15474 1
1.9%
3172 1
1.9%
3118 1
1.9%
3051 1
1.9%
2959 1
1.9%
1372 1
1.9%
1346 1
1.9%
1091 1
1.9%
1049 1
1.9%

2013
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1166.3704
Minimum1
Maximum15559
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T04:49:29.048394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile21.3
Q154.25
median498.5
Q3770.5
95-th percentile3164.2
Maximum15559
Range15558
Interquartile range (IQR)716.25

Descriptive statistics

Standard deviation2907.7438
Coefficient of variation (CV)2.492985
Kurtosis20.612276
Mean1166.3704
Median Absolute Deviation (MAD)421.5
Skewness4.5185179
Sum62984
Variance8454974.2
MonotonicityNot monotonic
2023-12-13T04:49:29.181470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 2
 
3.7%
30 2
 
3.7%
20 2
 
3.7%
3185 1
 
1.9%
603 1
 
1.9%
480 1
 
1.9%
524 1
 
1.9%
61 1
 
1.9%
478 1
 
1.9%
687 1
 
1.9%
Other values (41) 41
75.9%
ValueCountFrequency (%)
1 1
1.9%
20 2
3.7%
22 1
1.9%
24 1
1.9%
27 1
1.9%
29 1
1.9%
30 2
3.7%
33 1
1.9%
47 1
1.9%
48 1
1.9%
ValueCountFrequency (%)
15559 1
1.9%
15081 1
1.9%
3185 1
1.9%
3153 1
1.9%
2979 1
1.9%
2887 1
1.9%
1196 1
1.9%
1175 1
1.9%
1123 1
1.9%
1090 1
1.9%

2014
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1150.4815
Minimum1
Maximum15337
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T04:49:29.328602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile24.65
Q153
median478.5
Q3817.5
95-th percentile3128.35
Maximum15337
Range15336
Interquartile range (IQR)764.5

Descriptive statistics

Standard deviation2870.663
Coefficient of variation (CV)2.495184
Kurtosis20.515124
Mean1150.4815
Median Absolute Deviation (MAD)401.5
Skewness4.506478
Sum62126
Variance8240706
MonotonicityNot monotonic
2023-12-13T04:49:29.483528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
29 3
 
5.6%
603 2
 
3.7%
50 2
 
3.7%
31 2
 
3.7%
66 2
 
3.7%
477 1
 
1.9%
518 1
 
1.9%
59 1
 
1.9%
470 1
 
1.9%
675 1
 
1.9%
Other values (38) 38
70.4%
ValueCountFrequency (%)
1 1
 
1.9%
22 1
 
1.9%
24 1
 
1.9%
25 1
 
1.9%
27 1
 
1.9%
29 3
5.6%
31 2
3.7%
48 1
 
1.9%
50 2
3.7%
51 1
 
1.9%
ValueCountFrequency (%)
15337 1
1.9%
14882 1
1.9%
3142 1
1.9%
3121 1
1.9%
3054 1
1.9%
2946 1
1.9%
1211 1
1.9%
1198 1
1.9%
1016 1
1.9%
976 1
1.9%

2015
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1192.7407
Minimum0
Maximum15907
Zeros1
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T04:49:29.640368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.25
Q156.5
median505
Q3802.5
95-th percentile3235.6
Maximum15907
Range15907
Interquartile range (IQR)746

Descriptive statistics

Standard deviation2983.0253
Coefficient of variation (CV)2.5009838
Kurtosis20.536972
Mean1192.7407
Median Absolute Deviation (MAD)443
Skewness4.509787
Sum64408
Variance8898439.8
MonotonicityNot monotonic
2023-12-13T04:49:29.800301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 2
 
3.7%
3124 1
 
1.9%
995 1
 
1.9%
31 1
 
1.9%
470 1
 
1.9%
616 1
 
1.9%
55 1
 
1.9%
580 1
 
1.9%
643 1
 
1.9%
44 1
 
1.9%
Other values (43) 43
79.6%
ValueCountFrequency (%)
0 1
1.9%
14 1
1.9%
16 1
1.9%
21 1
1.9%
25 2
3.7%
26 1
1.9%
27 1
1.9%
31 1
1.9%
36 1
1.9%
39 1
1.9%
ValueCountFrequency (%)
15907 1
1.9%
15499 1
1.9%
3285 1
1.9%
3209 1
1.9%
3124 1
1.9%
3079 1
1.9%
1165 1
1.9%
1158 1
1.9%
1073 1
1.9%
1024 1
1.9%

2016
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1108.4074
Minimum4
Maximum14791
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T04:49:29.947868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile17.95
Q148.75
median513
Q3715
95-th percentile2962.3
Maximum14791
Range14787
Interquartile range (IQR)666.25

Descriptive statistics

Standard deviation2772.3792
Coefficient of variation (CV)2.5012276
Kurtosis20.638604
Mean1108.4074
Median Absolute Deviation (MAD)435.5
Skewness4.5233566
Sum59854
Variance7686086.6
MonotonicityNot monotonic
2023-12-13T04:49:30.146585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1089 2
 
3.7%
337 2
 
3.7%
24 2
 
3.7%
2867 1
 
1.9%
877 1
 
1.9%
591 1
 
1.9%
48 1
 
1.9%
566 1
 
1.9%
621 1
 
1.9%
51 1
 
1.9%
Other values (41) 41
75.9%
ValueCountFrequency (%)
4 1
1.9%
10 1
1.9%
16 1
1.9%
19 1
1.9%
20 1
1.9%
21 1
1.9%
24 2
3.7%
28 1
1.9%
30 1
1.9%
33 1
1.9%
ValueCountFrequency (%)
14791 1
1.9%
14427 1
1.9%
3000 1
1.9%
2942 1
1.9%
2867 1
1.9%
2832 1
1.9%
1089 2
3.7%
1019 1
1.9%
979 1
1.9%
877 1
1.9%

2017
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean718.62963
Minimum0
Maximum9590
Zeros1
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T04:49:30.360645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.2
Q137
median326
Q3447.5
95-th percentile1995.95
Maximum9590
Range9590
Interquartile range (IQR)410.5

Descriptive statistics

Standard deviation1791.1816
Coefficient of variation (CV)2.4924961
Kurtosis20.446255
Mean718.62963
Median Absolute Deviation (MAD)265.5
Skewness4.4977905
Sum38806
Variance3208331.4
MonotonicityNot monotonic
2023-12-13T04:49:30.561492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
26 3
 
5.6%
37 2
 
3.7%
20 2
 
3.7%
184 2
 
3.7%
2020 1
 
1.9%
527 1
 
1.9%
431 1
 
1.9%
35 1
 
1.9%
408 1
 
1.9%
423 1
 
1.9%
Other values (39) 39
72.2%
ValueCountFrequency (%)
0 1
 
1.9%
7 1
 
1.9%
11 1
 
1.9%
19 1
 
1.9%
20 2
3.7%
24 1
 
1.9%
26 3
5.6%
27 1
 
1.9%
28 1
 
1.9%
35 1
 
1.9%
ValueCountFrequency (%)
9590 1
1.9%
9251 1
1.9%
2020 1
1.9%
1983 1
1.9%
1929 1
1.9%
1855 1
1.9%
653 1
1.9%
648 1
1.9%
646 1
1.9%
618 1
1.9%

Interactions

2023-12-13T04:49:26.031620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:19.508178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:20.587678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:21.409350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:22.303365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:23.422387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:24.295305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:25.198688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:26.146536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:19.653758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:20.690263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:21.538011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:22.394226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:23.535850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:24.409768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:25.299506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:26.259796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:19.765999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:20.807584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:21.648619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:22.787886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:23.635989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:24.514423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:25.389056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:26.350269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:19.888427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:20.913656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:21.767626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:22.904989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:23.768689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:24.644076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:25.496564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:26.433505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:20.021728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:21.023192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:21.881006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:23.014762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:23.892347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:24.767995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:25.633771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:26.545946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:20.153694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:21.119018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:21.972149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:23.120478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:23.999671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:24.865534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:25.739685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:26.656020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:20.313254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:21.229116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:22.089901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:23.225519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:24.109532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:24.978129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:25.846424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:26.739830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:20.444837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:21.321348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:22.206879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:23.335402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:24.203666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:25.098573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:49:25.935202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:49:30.683797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생지_시도발생년20102011201220132014201520162017
발생지_시도1.000NaN1.0001.0001.0001.0001.0001.0001.0001.000
발생년NaN1.0000.0000.0000.0000.0000.0000.0000.0000.000
20101.0000.0001.0001.0001.0001.0001.0001.0001.0001.000
20111.0000.0001.0001.0001.0001.0001.0001.0000.9920.992
20121.0000.0001.0001.0001.0001.0001.0001.0001.0001.000
20131.0000.0001.0001.0001.0001.0001.0001.0000.9920.992
20141.0000.0001.0001.0001.0001.0001.0001.0000.9920.992
20151.0000.0001.0001.0001.0001.0001.0001.0000.9920.992
20161.0000.0001.0000.9921.0000.9920.9920.9921.0000.984
20171.0000.0001.0000.9921.0000.9920.9920.9920.9841.000
2023-12-13T04:49:30.853088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
20102011201220132014201520162017발생년
20101.0000.9970.9960.9970.9950.9930.9920.9820.000
20110.9971.0000.9950.9970.9960.9920.9890.9820.000
20120.9960.9951.0000.9950.9900.9920.9930.9830.000
20130.9970.9970.9951.0000.9910.9890.9890.9780.000
20140.9950.9960.9900.9911.0000.9920.9870.9840.000
20150.9930.9920.9920.9890.9921.0000.9960.9890.000
20160.9920.9890.9930.9890.9870.9961.0000.9870.000
20170.9820.9820.9830.9780.9840.9890.9871.0000.000
발생년0.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-13T04:49:26.857013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:49:27.027061image/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.
2023-12-13T04:49:27.135079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0발생지_시도발생년20102011201220132014201520162017
0<NA>서울발생건수33873346317231853142312428672020
1<NA><NA>사망자수101111122102991029175
2<NA><NA>부상자수33633312311831533121307928321983
3<NA>부산발생건수1292131513721196121111651089653
4<NA><NA>사망자수5749635250413028
5<NA><NA>부상자수1263130013461175119811581089648
6<NA>대구발생건수962831875766832833729450
7<NA><NA>사망자수3431343048393420
8<NA><NA>부상자수942814858753801804717439
9<NA>인천발생건수845829726772685695622379
Unnamed: 0발생지_시도발생년20102011201220132014201520162017
44<NA><NA>부상자수1008869920886823949824493
45<NA>경남발생건수1153105310911123101610731019646
46<NA><NA>사망자수8371716666636237
47<NA><NA>부상자수11081001104910909761024979618
48<NA>제주발생건수320277248288300271285154
49<NA><NA>사망자수1820132422142011
50<NA><NA>부상자수311263236268285265268146
51<NA>합계발생건수169841632116003155591533715907147919590
52<NA><NA>사망자수931912931852844798709562
53<NA><NA>부상자수165051581215474150811488215499144279251