Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells20736
Missing cells (%)17.3%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory1.1 MiB
Average record size in memory111.0 B

Variable types

Numeric7
DateTime1
Categorical1
Text3

Dataset

Description한국교통안전공단 운수안전컨설팅지원시스템에서 관리하고 있는 사고정보를 분석한 POI 정보
Author한국교통안전공단
URLhttps://www.data.go.kr/data/15066932/fileData.do

Alerts

기관코드 has constant value ""Constant
소분류명 has constant value ""Constant
상세분류명 has constant value ""Constant
Dataset has 1 (< 0.1%) duplicate rowsDuplicates
사고x좌표 is highly overall correlated with poix좌표High correlation
사고y좌표 is highly overall correlated with poiy좌표High correlation
poix좌표 is highly overall correlated with 사고x좌표High correlation
poiy좌표 is highly overall correlated with 사고y좌표High correlation
관심지점id has 123 (1.2%) missing valuesMissing
사고x좌표 has 123 (1.2%) missing valuesMissing
사고y좌표 has 123 (1.2%) missing valuesMissing
poix좌표 has 123 (1.2%) missing valuesMissing
poiy좌표 has 123 (1.2%) missing valuesMissing
거리 has 123 (1.2%) missing valuesMissing
소분류명 has 9999 (> 99.9%) missing valuesMissing
상세분류명 has 9999 (> 99.9%) missing valuesMissing

Reproduction

Analysis started2023-12-12 07:33:38.324769
Analysis finished2023-12-12 07:33:46.835325
Duration8.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

링크id
Real number (ℝ)

Distinct9190
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2010379 × 109
Minimum1.0000003 × 109
Maximum4.0801784 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:33:46.911503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.0000003 × 109
5-th percentile1.0700032 × 109
Q11.500038 × 109
median2.1000334 × 109
Q32.8100404 × 109
95-th percentile3.8500318 × 109
Maximum4.0801784 × 109
Range3.0801781 × 109
Interquartile range (IQR)1.3100024 × 109

Descriptive statistics

Standard deviation8.6477485 × 108
Coefficient of variation (CV)0.39289411
Kurtosis-0.76625699
Mean2.2010379 × 109
Median Absolute Deviation (MAD)6.401794 × 108
Skewness0.51279794
Sum2.2010379 × 1013
Variance7.4783555 × 1017
MonotonicityNot monotonic
2023-12-12T16:33:47.157262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2270030300 6
 
0.1%
1500008800 5
 
0.1%
1850156800 5
 
0.1%
2280071601 5
 
0.1%
1200005700 5
 
0.1%
1570010001 4
 
< 0.1%
1190000200 4
 
< 0.1%
2130033700 4
 
< 0.1%
1520000200 4
 
< 0.1%
2050005401 4
 
< 0.1%
Other values (9180) 9954
99.5%
ValueCountFrequency (%)
1000000302 1
< 0.1%
1000000502 2
< 0.1%
1000000903 1
< 0.1%
1000001202 1
< 0.1%
1000001403 1
< 0.1%
1000001901 1
< 0.1%
1000002100 2
< 0.1%
1000002201 2
< 0.1%
1000002302 1
< 0.1%
1000002401 1
< 0.1%
ValueCountFrequency (%)
4080178400 1
< 0.1%
4080175500 1
< 0.1%
4080171700 1
< 0.1%
4080171200 1
< 0.1%
4080158700 1
< 0.1%
4080121900 1
< 0.1%
4080116400 1
< 0.1%
4080106100 1
< 0.1%
4080079700 1
< 0.1%
4080077300 1
< 0.1%

사고년월
Date

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2015-01-01 00:00:00
Maximum2015-12-01 00:00:00
2023-12-12T16:33:47.281015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:47.381338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)

기관코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경찰청
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경찰청 10000
100.0%

Length

2023-12-12T16:33:47.521298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:33:47.642165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경찰청 10000
100.0%

경찰청사고번호
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0150669 × 1015
Minimum2.01501 × 1015
Maximum2.01512 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:33:47.749699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.01501 × 1015
5-th percentile2.01501 × 1015
Q12.01504 × 1015
median2.01507 × 1015
Q32.0151 × 1015
95-th percentile2.01512 × 1015
Maximum2.01512 × 1015
Range1.1 × 1011
Interquartile range (IQR)6 × 1010

Descriptive statistics

Standard deviation3.4156255 × 1010
Coefficient of variation (CV)1.6950432 × 10-5
Kurtosis-1.1937456
Mean2.0150669 × 1015
Median Absolute Deviation (MAD)3 × 1010
Skewness-0.066324701
Sum1.7039254 × 1018
Variance1.1666497 × 1021
MonotonicityNot monotonic
2023-12-12T16:33:47.865106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2015110000000000 932
9.3%
2015090000000000 913
9.1%
2015100000000000 880
8.8%
2015050000000000 871
8.7%
2015120000000000 848
8.5%
2015040000000000 848
8.5%
2015060000000000 845
8.5%
2015030000000000 834
8.3%
2015070000000000 817
8.2%
2015080000000000 803
8.0%
Other values (2) 1409
14.1%
ValueCountFrequency (%)
2015010000000000 779
7.8%
2015020000000000 630
6.3%
2015030000000000 834
8.3%
2015040000000000 848
8.5%
2015050000000000 871
8.7%
2015060000000000 845
8.5%
2015070000000000 817
8.2%
2015080000000000 803
8.0%
2015090000000000 913
9.1%
2015100000000000 880
8.8%
ValueCountFrequency (%)
2015120000000000 848
8.5%
2015110000000000 932
9.3%
2015100000000000 880
8.8%
2015090000000000 913
9.1%
2015080000000000 803
8.0%
2015070000000000 817
8.2%
2015060000000000 845
8.5%
2015050000000000 871
8.7%
2015040000000000 848
8.5%
2015030000000000 834
8.3%

관심지점id
Text

MISSING 

Distinct7478
Distinct (%)75.7%
Missing123
Missing (%)1.2%
Memory size156.2 KiB
2023-12-12T16:33:48.112267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters118524
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5885 ?
Unique (%)59.6%

Sample

1st rowAA0008110700
2nd rowAA0000085043
3rd rowAA0000074468
4th rowAA0000067855
5th rowAA0000051172
ValueCountFrequency (%)
aa0000042942 13
 
0.1%
aa0000054561 11
 
0.1%
aa0000050440 9
 
0.1%
aa0000047889 9
 
0.1%
aa0000044869 9
 
0.1%
aa0000072132 9
 
0.1%
aa0000043443 8
 
0.1%
aa0000048513 8
 
0.1%
aa0000041770 8
 
0.1%
aa0000073144 8
 
0.1%
Other values (7468) 9785
99.1%
2023-12-12T16:33:48.546653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 48092
40.6%
A 19754
16.7%
6 7421
 
6.3%
5 6608
 
5.6%
4 6287
 
5.3%
7 5969
 
5.0%
8 5935
 
5.0%
2 5142
 
4.3%
1 4931
 
4.2%
3 4467
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98770
83.3%
Uppercase Letter 19754
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 48092
48.7%
6 7421
 
7.5%
5 6608
 
6.7%
4 6287
 
6.4%
7 5969
 
6.0%
8 5935
 
6.0%
2 5142
 
5.2%
1 4931
 
5.0%
3 4467
 
4.5%
9 3918
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
A 19754
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 98770
83.3%
Latin 19754
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 48092
48.7%
6 7421
 
7.5%
5 6608
 
6.7%
4 6287
 
6.4%
7 5969
 
6.0%
8 5935
 
6.0%
2 5142
 
5.2%
1 4931
 
5.0%
3 4467
 
4.5%
9 3918
 
4.0%
Latin
ValueCountFrequency (%)
A 19754
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118524
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 48092
40.6%
A 19754
16.7%
6 7421
 
6.3%
5 6608
 
5.6%
4 6287
 
5.3%
7 5969
 
5.0%
8 5935
 
5.0%
2 5142
 
4.3%
1 4931
 
4.2%
3 4467
 
3.8%

사고x좌표
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9874
Distinct (%)> 99.9%
Missing123
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean127.50662
Minimum125.93429
Maximum129.57775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:33:48.754019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125.93429
5-th percentile126.65865
Q1126.9077
median127.10186
Q3128.18868
95-th percentile129.16076
Maximum129.57775
Range3.6434592
Interquartile range (IQR)1.280983

Descriptive statistics

Standard deviation0.84778078
Coefficient of variation (CV)0.006648916
Kurtosis-0.58585504
Mean127.50662
Median Absolute Deviation (MAD)0.3069431
Skewness0.92098076
Sum1259382.8
Variance0.71873225
MonotonicityNot monotonic
2023-12-12T16:33:48.924310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0614276 2
 
< 0.1%
127.7321499 2
 
< 0.1%
127.1318755 2
 
< 0.1%
126.4246677 1
 
< 0.1%
126.7708666 1
 
< 0.1%
126.9014913 1
 
< 0.1%
126.9023445 1
 
< 0.1%
128.6257597 1
 
< 0.1%
126.7005077 1
 
< 0.1%
128.6430904 1
 
< 0.1%
Other values (9864) 9864
98.6%
(Missing) 123
 
1.2%
ValueCountFrequency (%)
125.9342899 1
< 0.1%
126.126924 1
< 0.1%
126.2483459 1
< 0.1%
126.2520692 1
< 0.1%
126.2569681 1
< 0.1%
126.2615108 1
< 0.1%
126.2638746 1
< 0.1%
126.2639091 1
< 0.1%
126.2642849 1
< 0.1%
126.2649784 1
< 0.1%
ValueCountFrequency (%)
129.5777491 1
< 0.1%
129.5707593 1
< 0.1%
129.5590562 1
< 0.1%
129.5576425 1
< 0.1%
129.549443 1
< 0.1%
129.5257481 1
< 0.1%
129.5168045 1
< 0.1%
129.5039538 1
< 0.1%
129.4903631 1
< 0.1%
129.4708424 1
< 0.1%

사고y좌표
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9876
Distinct (%)> 99.9%
Missing123
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean36.578146
Minimum33.220761
Maximum38.378644
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:33:49.097647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.220761
5-th percentile34.894024
Q135.814476
median37.023209
Q337.503493
95-th percentile37.673487
Maximum38.378644
Range5.1578825
Interquartile range (IQR)1.6890173

Descriptive statistics

Standard deviation1.0712729
Coefficient of variation (CV)0.029287239
Kurtosis-0.34027474
Mean36.578146
Median Absolute Deviation (MAD)0.59654943
Skewness-0.7517739
Sum361282.35
Variance1.1476257
MonotonicityNot monotonic
2023-12-12T16:33:49.248389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.89927638 2
 
< 0.1%
34.80592414 1
 
< 0.1%
35.0368525 1
 
< 0.1%
37.65284884 1
 
< 0.1%
35.13858792 1
 
< 0.1%
37.46884866 1
 
< 0.1%
35.88438201 1
 
< 0.1%
37.65017571 1
 
< 0.1%
35.24762484 1
 
< 0.1%
37.49915137 1
 
< 0.1%
Other values (9866) 9866
98.7%
(Missing) 123
 
1.2%
ValueCountFrequency (%)
33.22076114 1
< 0.1%
33.2215291 1
< 0.1%
33.22819886 1
< 0.1%
33.23398681 1
< 0.1%
33.24306297 1
< 0.1%
33.24312032 1
< 0.1%
33.24526326 1
< 0.1%
33.24556122 1
< 0.1%
33.24664445 1
< 0.1%
33.24687394 1
< 0.1%
ValueCountFrequency (%)
38.37864368 1
< 0.1%
38.32831557 1
< 0.1%
38.28138377 1
< 0.1%
38.24912284 1
< 0.1%
38.23836574 1
< 0.1%
38.23556735 1
< 0.1%
38.2087593 1
< 0.1%
38.20380025 1
< 0.1%
38.20031268 1
< 0.1%
38.19877896 1
< 0.1%

poix좌표
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7477
Distinct (%)75.7%
Missing123
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean127.50675
Minimum125.93592
Maximum129.5809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:33:49.386627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125.93592
5-th percentile126.65943
Q1126.90801
median127.10079
Q3128.18781
95-th percentile129.1601
Maximum129.5809
Range3.6449815
Interquartile range (IQR)1.2798013

Descriptive statistics

Standard deviation0.84776977
Coefficient of variation (CV)0.0066488225
Kurtosis-0.58595349
Mean127.50675
Median Absolute Deviation (MAD)0.305672
Skewness0.92099692
Sum1259384.2
Variance0.71871358
MonotonicityNot monotonic
2023-12-12T16:33:49.523097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0286525 13
 
0.1%
126.8531241 11
 
0.1%
126.9288261 9
 
0.1%
127.1092687 9
 
0.1%
128.5977718 9
 
0.1%
127.9290267 9
 
0.1%
129.205797 8
 
0.1%
129.0233554 8
 
0.1%
128.5561343 8
 
0.1%
127.0307035 8
 
0.1%
Other values (7467) 9785
97.9%
(Missing) 123
 
1.2%
ValueCountFrequency (%)
125.935918 1
 
< 0.1%
126.121115 1
 
< 0.1%
126.2538166 1
 
< 0.1%
126.2542277 1
 
< 0.1%
126.2579846 1
 
< 0.1%
126.2605434 1
 
< 0.1%
126.2640218 3
< 0.1%
126.2649943 2
< 0.1%
126.267722 1
 
< 0.1%
126.2684057 1
 
< 0.1%
ValueCountFrequency (%)
129.5808995 1
< 0.1%
129.5749918 1
< 0.1%
129.5621167 1
< 0.1%
129.5528082 1
< 0.1%
129.5485349 1
< 0.1%
129.5204425 1
< 0.1%
129.5139211 1
< 0.1%
129.5074303 1
< 0.1%
129.4915942 1
< 0.1%
129.4729141 1
< 0.1%

poiy좌표
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7478
Distinct (%)75.7%
Missing123
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean36.578453
Minimum33.222922
Maximum38.38155
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:33:49.669461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.222922
5-th percentile34.894819
Q135.813928
median37.021566
Q337.503295
95-th percentile37.67289
Maximum38.38155
Range5.1586275
Interquartile range (IQR)1.6893668

Descriptive statistics

Standard deviation1.0712065
Coefficient of variation (CV)0.029285177
Kurtosis-0.33998613
Mean36.578453
Median Absolute Deviation (MAD)0.59714421
Skewness-0.75183508
Sum361285.38
Variance1.1474833
MonotonicityNot monotonic
2023-12-12T16:33:49.808910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.60605449 13
 
0.1%
35.16237544 11
 
0.1%
35.86698982 9
 
0.1%
37.5511425 9
 
0.1%
37.34588455 9
 
0.1%
37.51700275 9
 
0.1%
35.85780434 8
 
0.1%
35.09524568 8
 
0.1%
35.83711158 8
 
0.1%
37.57197628 8
 
0.1%
Other values (7468) 9785
97.9%
(Missing) 123
 
1.2%
ValueCountFrequency (%)
33.22292201 1
 
< 0.1%
33.2231517 1
 
< 0.1%
33.23105726 1
 
< 0.1%
33.23604853 1
 
< 0.1%
33.24370125 1
 
< 0.1%
33.24444796 3
< 0.1%
33.24659525 1
 
< 0.1%
33.24670491 2
< 0.1%
33.24951666 3
< 0.1%
33.2502247 1
 
< 0.1%
ValueCountFrequency (%)
38.38154952 1
< 0.1%
38.32889187 1
< 0.1%
38.27620582 1
< 0.1%
38.24732381 1
< 0.1%
38.24160478 1
< 0.1%
38.23546738 1
< 0.1%
38.20890841 1
< 0.1%
38.20398645 1
< 0.1%
38.19876727 1
< 0.1%
38.1986544 1
< 0.1%

거리
Real number (ℝ)

MISSING 

Distinct587
Distinct (%)5.9%
Missing123
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean229.84469
Minimum2
Maximum599
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:33:49.962917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile43
Q1107
median194
Q3328
95-th percentile533
Maximum599
Range597
Interquartile range (IQR)221

Descriptive statistics

Standard deviation150.77348
Coefficient of variation (CV)0.65597984
Kurtosis-0.48857544
Mean229.84469
Median Absolute Deviation (MAD)102
Skewness0.69875866
Sum2270176
Variance22732.643
MonotonicityNot monotonic
2023-12-12T16:33:50.131437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113 47
 
0.5%
99 45
 
0.4%
88 44
 
0.4%
70 44
 
0.4%
72 43
 
0.4%
136 43
 
0.4%
105 41
 
0.4%
107 41
 
0.4%
128 41
 
0.4%
97 40
 
0.4%
Other values (577) 9448
94.5%
(Missing) 123
 
1.2%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 3
 
< 0.1%
4 1
 
< 0.1%
5 2
 
< 0.1%
6 3
 
< 0.1%
7 8
0.1%
8 3
 
< 0.1%
9 4
< 0.1%
10 1
 
< 0.1%
11 2
 
< 0.1%
ValueCountFrequency (%)
599 4
 
< 0.1%
598 7
0.1%
597 7
0.1%
596 12
0.1%
595 14
0.1%
594 12
0.1%
593 4
 
< 0.1%
592 7
0.1%
591 6
0.1%
590 9
0.1%

소분류명
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing9999
Missing (%)> 99.9%
Memory size156.2 KiB
2023-12-12T16:33:50.263417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
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

Unique1 ?
Unique (%)100.0%

Sample

1st row아파트
ValueCountFrequency (%)
아파트 1
100.0%
2023-12-12T16:33:50.506603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 1
33.3%
파 1
33.3%
트 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 1
33.3%
파 1
33.3%
트 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 1
33.3%
파 1
33.3%
트 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 1
33.3%
파 1
33.3%
트 1
33.3%

상세분류명
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing9999
Missing (%)> 99.9%
Memory size156.2 KiB
2023-12-12T16:33:50.646483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
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

Unique1 ?
Unique (%)100.0%

Sample

1st row동번호
ValueCountFrequency (%)
동번호 1
100.0%
2023-12-12T16:33:50.957972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 1
33.3%
번 1
33.3%
호 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1
33.3%
번 1
33.3%
호 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1
33.3%
번 1
33.3%
호 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 1
33.3%
번 1
33.3%
호 1
33.3%

Interactions

2023-12-12T16:33:45.054719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:40.329731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:41.054827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:41.889113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:42.729394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:43.453730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:44.212091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:45.191833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:40.429953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:41.171909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:42.061774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:42.838528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:43.548992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:44.321189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:45.302646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:40.537651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:41.272641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:42.204210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:42.958611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:43.643916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:44.436581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:45.424570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:40.636030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:41.383243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:42.334114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:43.080564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:43.747141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:44.556293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:45.554537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:40.767698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:41.488993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:42.440011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:43.166788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:43.844186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:44.657366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:45.692954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:40.872378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:41.624784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:42.540580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:43.267776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:43.969975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:44.778257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:45.829265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:40.965108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:41.757508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:42.630538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:43.356955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:44.090455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:33:44.942361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:33:51.061079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
링크id사고년월경찰청사고번호사고x좌표사고y좌표poix좌표poiy좌표거리
링크id1.0000.0190.0330.8560.7910.8550.7920.228
사고년월0.0191.0001.0000.0000.0000.0000.0000.027
경찰청사고번호0.0331.0001.0000.0000.0170.0000.0130.046
사고x좌표0.8560.0000.0001.0000.7191.0000.7190.147
사고y좌표0.7910.0000.0170.7191.0000.7191.0000.138
poix좌표0.8550.0000.0001.0000.7191.0000.7190.148
poiy좌표0.7920.0000.0130.7191.0000.7191.0000.138
거리0.2280.0270.0460.1470.1380.1480.1381.000
2023-12-12T16:33:51.177243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
링크id경찰청사고번호사고x좌표사고y좌표poix좌표poiy좌표거리
링크id1.0000.0110.145-0.4380.145-0.4380.183
경찰청사고번호0.0111.0000.0030.0050.0030.0050.012
사고x좌표0.1450.0031.000-0.3561.000-0.3560.064
사고y좌표-0.4380.005-0.3561.000-0.3561.000-0.111
poix좌표0.1450.0031.000-0.3561.000-0.3560.065
poiy좌표-0.4380.005-0.3561.000-0.3561.000-0.111
거리0.1830.0120.064-0.1110.065-0.1111.000

Missing values

2023-12-12T16:33:46.006727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:33:46.221547image/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.
2023-12-12T16:33:46.405399image/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

링크id사고년월기관코드경찰청사고번호관심지점id사고x좌표사고y좌표poix좌표poiy좌표거리소분류명상세분류명
514530700139002015-09경찰청2015090000000000AA0008110700126.70584235.941659126.70318635.946353574<NA><NA>
944940700277002015-05경찰청2015050000000000AA0000085043126.33221133.462676126.33144833.461041195<NA><NA>
738628500647022015-06경찰청2015060000000000AA0000074468127.12524836.919844127.13035236.922494541<NA><NA>
1709222600452002015-03경찰청2015030000000000AA0000067855126.94993237.326564126.94589837.325887364<NA><NA>
1727315400373002015-09경찰청2015090000000000AA0000051172128.59711335.890985128.59656635.8903487<NA><NA>
443615600648002015-03경찰청2015030000000000AA0000051674128.53732535.857672128.53674335.8580869<NA><NA>
1766328505157002015-06경찰청2015060000000000AA0000074331127.13346636.81887127.13408136.81913162<NA><NA>
461821900272002015-12경찰청2015120000000000AA0000058388126.78019637.642379126.78468337.643651420<NA><NA>
329910000035062015-12경찰청2015120000000000AA0000041770126.96061337.571766126.96616237.571976490<NA><NA>
888015400784002015-12경찰청2015120000000000AA0000051096128.54057235.934024128.54183235.935164170<NA><NA>
링크id사고년월기관코드경찰청사고번호관심지점id사고x좌표사고y좌표poix좌표poiy좌표거리소분류명상세분류명
1671020300330002015-12경찰청2015120000000000AA0000062066127.05987637.265603127.0563937.266599328<NA><NA>
724322200093022015-06경찰청2015060000000000AA0005655115127.19806237.593524127.19926637.595321226<NA><NA>
1279732401601002015-09경찰청2015090000000000AA0002117282126.3841734.78922126.37971134.788649412<NA><NA>
721815600771002015-09경찰청2015090000000000AA0000051744128.51098635.859194128.51222835.859179112<NA><NA>
1282820600026002015-07경찰청2015070000000000AA0000063895127.14681537.372718127.14694637.37346384<NA><NA>
354721700162002015-09경찰청2015090000000000AA0006681787126.79187237.340308126.79028637.339156190<NA><NA>
585310200170022015-09경찰청2015090000000000AA0000042016126.98537637.545792126.98585937.546995140<NA><NA>
507832601674002015-06경찰청2015060000000000AA0000078462127.48892534.968059127.4833634.96893516<NA><NA>
1235117901841002015-12경찰청2015120000000000AA0006684071126.84043535.223175126.84061535.22248179<NA><NA>
1109810700352002015-11경찰청2015110000000000AA0006685984127.0430637.600344127.04232537.60082184<NA><NA>

Duplicate rows

Most frequently occurring

링크id사고년월기관코드경찰청사고번호관심지점id사고x좌표사고y좌표poix좌표poiy좌표거리소분류명상세분류명# duplicates
029600210012015-06경찰청2015060000000000<NA><NA><NA><NA><NA><NA><NA><NA>2