Overview

Dataset statistics

Number of variables16
Number of observations100
Missing cells300
Missing cells (%)18.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.0 KiB
Average record size in memory143.3 B

Variable types

Unsupported3
Categorical7
Text1
Numeric5

Alerts

acdnt_year has constant value ""Constant
fclty_manage_no is highly overall correlated with caslt_co and 2 other fieldsHigh correlation
acdnt_ty_nm is highly overall correlated with caslt_co and 2 other fieldsHigh correlation
caslt_co is highly overall correlated with sinjpsn_co and 4 other fieldsHigh correlation
sinjpsn_co is highly overall correlated with caslt_coHigh correlation
fclty_lo is highly overall correlated with rate_valueHigh correlation
rate_value is highly overall correlated with fclty_loHigh correlation
acdnt_cas_co is highly overall correlated with caslt_co and 3 other fieldsHigh correlation
death_co is highly overall correlated with caslt_co and 1 other fieldsHigh correlation
death_co is highly imbalanced (71.4%)Imbalance
esntl_id has 100 (100.0%) missing valuesMissing
signgu_nm has 100 (100.0%) missing valuesMissing
signgu_accto_popltn_co has 100 (100.0%) missing valuesMissing
esntl_id is an unsupported type, check if it needs cleaning or further analysisUnsupported
signgu_nm is an unsupported type, check if it needs cleaning or further analysisUnsupported
signgu_accto_popltn_co is an unsupported type, check if it needs cleaning or further analysisUnsupported
sinjpsn_co has 8 (8.0%) zerosZeros

Reproduction

Analysis started2023-12-10 09:51:30.038210
Analysis finished2023-12-10 09:51:37.159054
Duration7.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

esntl_id
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

fclty_manage_no
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2020016
58 
2020015
39 
2020037
 
3

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020015
2nd row2020037
3rd row2020015
4th row2020015
5th row2020015

Common Values

ValueCountFrequency (%)
2020016 58
58.0%
2020015 39
39.0%
2020037 3
 
3.0%

Length

2023-12-10T18:51:37.356473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:37.641804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020016 58
58.0%
2020015 39
39.0%
2020037 3
 
3.0%

acdnt_year
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2019
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 100
100.0%

Length

2023-12-10T18:51:37.828340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:38.010403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 100
100.0%

acdnt_ty_nm
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
보행어린이
58 
스쿨존어린이
39 
자전거
 
3

Length

Max length6
Median length5
Mean length5.33
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row스쿨존어린이
2nd row자전거
3rd row스쿨존어린이
4th row스쿨존어린이
5th row스쿨존어린이

Common Values

ValueCountFrequency (%)
보행어린이 58
58.0%
스쿨존어린이 39
39.0%
자전거 3
 
3.0%

Length

2023-12-10T18:51:38.192341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:38.447214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
보행어린이 58
58.0%
스쿨존어린이 39
39.0%
자전거 3
 
3.0%

zone_cd
Text

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:51:38.760333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length28
Mean length23.73
Min length19

Characters and Unicode

Total characters2373
Distinct characters204
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)90.0%

Sample

1st row서울특별시 성북구 하월곡동(자보습학원 부근)
2nd row경상남도 사천시 동금동(동금2길32 부근)
3rd row서울특별시 은평구 역촌동(역촌초교 부근)
4th row서울특별시 은평구 불광동(연신초교 부근)
5th row서울특별시 양천구 신정동(신목초교 부근)
ValueCountFrequency (%)
부근 95
23.0%
경기도 23
 
5.6%
서울특별시 23
 
5.6%
대구광역시 11
 
2.7%
북구 6
 
1.5%
부산광역시 6
 
1.5%
충청남도 6
 
1.5%
울산광역시 5
 
1.2%
경상남도 5
 
1.2%
부천시 5
 
1.2%
Other values (174) 228
55.2%
2023-12-10T18:51:39.390470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
313
 
13.2%
부 117
 
4.9%
동 111
 
4.7%
시 110
 
4.6%
( 100
 
4.2%
근 96
 
4.0%
) 95
 
4.0%
구 92
 
3.9%
교 52
 
2.2%
서 50
 
2.1%
Other values (194) 1237
52.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1794
75.6%
Space Separator 313
 
13.2%
Open Punctuation 100
 
4.2%
Close Punctuation 95
 
4.0%
Decimal Number 71
 
3.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 117
 
6.5%
동 111
 
6.2%
시 110
 
6.1%
근 96
 
5.4%
구 92
 
5.1%
교 52
 
2.9%
서 50
 
2.8%
초 45
 
2.5%
도 44
 
2.5%
광 40
 
2.2%
Other values (181) 1037
57.8%
Decimal Number
ValueCountFrequency (%)
1 21
29.6%
3 12
16.9%
2 9
12.7%
7 9
12.7%
9 5
 
7.0%
0 5
 
7.0%
8 3
 
4.2%
5 3
 
4.2%
4 2
 
2.8%
6 2
 
2.8%
Space Separator
ValueCountFrequency (%)
313
100.0%
Open Punctuation
ValueCountFrequency (%)
( 100
100.0%
Close Punctuation
ValueCountFrequency (%)
) 95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1794
75.6%
Common 579
 
24.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 117
 
6.5%
동 111
 
6.2%
시 110
 
6.1%
근 96
 
5.4%
구 92
 
5.1%
교 52
 
2.9%
서 50
 
2.8%
초 45
 
2.5%
도 44
 
2.5%
광 40
 
2.2%
Other values (181) 1037
57.8%
Common
ValueCountFrequency (%)
313
54.1%
( 100
 
17.3%
) 95
 
16.4%
1 21
 
3.6%
3 12
 
2.1%
2 9
 
1.6%
7 9
 
1.6%
9 5
 
0.9%
0 5
 
0.9%
8 3
 
0.5%
Other values (3) 7
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1794
75.6%
ASCII 579
 
24.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
313
54.1%
( 100
 
17.3%
) 95
 
16.4%
1 21
 
3.6%
3 12
 
2.1%
2 9
 
1.6%
7 9
 
1.6%
9 5
 
0.9%
0 5
 
0.9%
8 3
 
0.5%
Other values (3) 7
 
1.2%
Hangul
ValueCountFrequency (%)
부 117
 
6.5%
동 111
 
6.2%
시 110
 
6.1%
근 96
 
5.4%
구 92
 
5.1%
교 52
 
2.9%
서 50
 
2.8%
초 45
 
2.5%
도 44
 
2.5%
광 40
 
2.2%
Other values (181) 1037
57.8%

signgu_nm
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

acdnt_cas_co
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
3
52 
2
29 
4
13 
5
 
3
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row5
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
3 52
52.0%
2 29
29.0%
4 13
 
13.0%
5 3
 
3.0%
1 3
 
3.0%

Length

2023-12-10T18:51:39.645263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:39.884856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 52
52.0%
2 29
29.0%
4 13
 
13.0%
5 3
 
3.0%
1 3
 
3.0%

caslt_co
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.05
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:40.194501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.95742711
Coefficient of variation (CV)0.31391053
Kurtosis0.096923877
Mean3.05
Median Absolute Deviation (MAD)1
Skewness0.46242589
Sum305
Variance0.91666667
MonotonicityNot monotonic
2023-12-10T18:51:40.386504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 41
41.0%
2 28
28.0%
4 22
22.0%
5 6
 
6.0%
1 2
 
2.0%
6 1
 
1.0%
ValueCountFrequency (%)
1 2
 
2.0%
2 28
28.0%
3 41
41.0%
4 22
22.0%
5 6
 
6.0%
6 1
 
1.0%
ValueCountFrequency (%)
6 1
 
1.0%
5 6
 
6.0%
4 22
22.0%
3 41
41.0%
2 28
28.0%
1 2
 
2.0%

death_co
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
95 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 95
95.0%
1 5
 
5.0%

Length

2023-12-10T18:51:40.581227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:40.771328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 95
95.0%
1 5
 
5.0%

swpsn_co
Categorical

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
39 
1
33 
2
25 
3
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 39
39.0%
1 33
33.0%
2 25
25.0%
3 3
 
3.0%

Length

2023-12-10T18:51:40.974459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:41.154964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 39
39.0%
1 33
33.0%
2 25
25.0%
3 3
 
3.0%

sinjpsn_co
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.82
Minimum0
Maximum5
Zeros8
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:41.321224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0766578
Coefficient of variation (CV)0.59157019
Kurtosis-0.21794447
Mean1.82
Median Absolute Deviation (MAD)1
Skewness0.41750127
Sum182
Variance1.1591919
MonotonicityNot monotonic
2023-12-10T18:51:41.504304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 36
36.0%
2 29
29.0%
3 21
21.0%
0 8
 
8.0%
4 5
 
5.0%
5 1
 
1.0%
ValueCountFrequency (%)
0 8
 
8.0%
1 36
36.0%
2 29
29.0%
3 21
21.0%
4 5
 
5.0%
5 1
 
1.0%
ValueCountFrequency (%)
5 1
 
1.0%
4 5
 
5.0%
3 21
21.0%
2 29
29.0%
1 36
36.0%
0 8
 
8.0%

injpsn_co
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
78 
1
18 
2
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 78
78.0%
1 18
 
18.0%
2 4
 
4.0%

Length

2023-12-10T18:51:41.731221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:41.906999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 78
78.0%
1 18
 
18.0%
2 4
 
4.0%

fclty_la
Real number (ℝ)

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.690548
Minimum33.514881
Maximum38.075399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:42.117860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.514881
5-th percentile35.112277
Q135.850682
median37.264902
Q337.512795
95-th percentile37.64633
Maximum38.075399
Range4.560518
Interquartile range (IQR)1.6621135

Descriptive statistics

Standard deviation0.99668313
Coefficient of variation (CV)0.027164575
Kurtosis-0.52724682
Mean36.690548
Median Absolute Deviation (MAD)0.404208
Skewness-0.75434753
Sum3669.0548
Variance0.99337726
MonotonicityNot monotonic
2023-12-10T18:51:42.452324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.490608 2
 
2.0%
37.604215 1
 
1.0%
35.85457 1
 
1.0%
37.249497 1
 
1.0%
35.536192 1
 
1.0%
36.434529 1
 
1.0%
36.319124 1
 
1.0%
35.200621 1
 
1.0%
37.547692 1
 
1.0%
37.504935 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
33.514881 1
1.0%
34.889823 1
1.0%
34.931136 1
1.0%
34.953731 1
1.0%
35.0761 1
1.0%
35.114181 1
1.0%
35.140933 1
1.0%
35.162354 1
1.0%
35.173722 1
1.0%
35.192471 1
1.0%
ValueCountFrequency (%)
38.075399 1
1.0%
37.773566 1
1.0%
37.716103 1
1.0%
37.676987 1
1.0%
37.661234 1
1.0%
37.645546 1
1.0%
37.629004 1
1.0%
37.628015 1
1.0%
37.626494 1
1.0%
37.624597 1
1.0%

fclty_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean400238.42
Minimum31623
Maximum853106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:42.720096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31623
5-th percentile170783.8
Q1274309
median405194
Q3493024.5
95-th percentile819990
Maximum853106
Range821483
Interquartile range (IQR)218715.5

Descriptive statistics

Standard deviation175785.8
Coefficient of variation (CV)0.43920271
Kurtosis0.32471817
Mean400238.42
Median Absolute Deviation (MAD)107224.5
Skewness0.5601461
Sum40023842
Variance3.0900646 × 1010
MonotonicityNot monotonic
2023-12-10T18:51:42.951360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
581909 5
 
5.0%
819990 5
 
5.0%
315279 4
 
4.0%
480617 4
 
4.0%
405194 4
 
4.0%
440736 4
 
4.0%
560354 3
 
3.0%
424947 3
 
3.0%
351790 2
 
2.0%
274309 2
 
2.0%
Other values (54) 64
64.0%
ValueCountFrequency (%)
31623 1
1.0%
65409 1
1.0%
108119 1
1.0%
111234 2
2.0%
173918 1
1.0%
175487 1
1.0%
189727 1
1.0%
195804 1
1.0%
197608 1
1.0%
209688 2
2.0%
ValueCountFrequency (%)
853106 1
 
1.0%
819990 5
5.0%
712900 1
 
1.0%
668498 2
 
2.0%
581909 5
5.0%
560354 3
3.0%
542350 2
 
2.0%
539838 1
 
1.0%
534343 1
 
1.0%
497925 1
 
1.0%

signgu_accto_popltn_co
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

rate_value
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2826124
Minimum0.10311
Maximum1.37596
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:43.227134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.10311
5-th percentile0.120438
Q10.1674725
median0.215515
Q30.3115925
95-th percentile0.5801085
Maximum1.37596
Range1.27285
Interquartile range (IQR)0.14412

Descriptive statistics

Standard deviation0.21387309
Coefficient of variation (CV)0.75677177
Kurtosis12.729566
Mean0.2826124
Median Absolute Deviation (MAD)0.06627
Skewness3.2258636
Sum28.26124
Variance0.045741697
MonotonicityNot monotonic
2023-12-10T18:51:43.492312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2042 3
 
3.0%
0.18726 3
 
3.0%
0.15466 3
 
3.0%
0.10976 3
 
3.0%
0.37019 2
 
2.0%
0.13415 2
 
2.0%
0.27511 2
 
2.0%
0.1963 2
 
2.0%
0.21179 2
 
2.0%
0.30609 1
 
1.0%
Other values (77) 77
77.0%
ValueCountFrequency (%)
0.10311 1
 
1.0%
0.1055 1
 
1.0%
0.10976 3
3.0%
0.121 1
 
1.0%
0.12193 1
 
1.0%
0.12624 1
 
1.0%
0.12784 1
 
1.0%
0.13415 2
2.0%
0.13463 1
 
1.0%
0.13614 1
 
1.0%
ValueCountFrequency (%)
1.37596 1
1.0%
1.34851 1
1.0%
0.94868 1
1.0%
0.83242 1
1.0%
0.72885 1
1.0%
0.57228 1
1.0%
0.54663 1
1.0%
0.5394 1
1.0%
0.47437 1
1.0%
0.46513 1
1.0%

Interactions

2023-12-10T18:51:35.250629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:31.660341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:32.960714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:33.763568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:34.472202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:35.407649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:32.299976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:33.142453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:33.913529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:34.630127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:35.588678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:32.462168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:33.280480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:34.079901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:34.782905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:35.798555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:32.640587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:33.430147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:34.215825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:34.938389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:36.032824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:32.792098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:33.579072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:34.337908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:35.089694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:51:44.068846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
fclty_manage_noacdnt_ty_nmzone_cdacdnt_cas_cocaslt_codeath_coswpsn_cosinjpsn_coinjpsn_cofclty_lafclty_lorate_value
fclty_manage_no1.0001.0000.5630.7550.9140.1530.2430.6320.2050.3890.3040.364
acdnt_ty_nm1.0001.0000.5630.7550.9140.1530.2430.6320.2050.3890.3040.364
zone_cd0.5630.5631.0000.0000.8251.0000.0000.8820.9651.0001.0000.995
acdnt_cas_co0.7550.7550.0001.0000.8500.6400.3090.3470.1740.3080.0000.536
caslt_co0.9140.9140.8250.8501.0000.8060.1580.7480.4080.1000.2370.754
death_co0.1530.1531.0000.6400.8061.0000.3480.2600.0000.3110.0000.496
swpsn_co0.2430.2430.0000.3090.1580.3481.0000.3270.0000.3190.0000.299
sinjpsn_co0.6320.6320.8820.3470.7480.2600.3271.0000.0000.2560.0000.222
injpsn_co0.2050.2050.9650.1740.4080.0000.0000.0001.0000.0000.4000.207
fclty_la0.3890.3891.0000.3080.1000.3110.3190.2560.0001.0000.5100.399
fclty_lo0.3040.3041.0000.0000.2370.0000.0000.0000.4000.5101.0000.650
rate_value0.3640.3640.9950.5360.7540.4960.2990.2220.2070.3990.6501.000
2023-12-10T18:51:44.284360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
injpsn_codeath_coacdnt_cas_coswpsn_cofclty_manage_noacdnt_ty_nm
injpsn_co1.0000.0000.1300.0000.0620.062
death_co0.0001.0000.7570.2290.2510.251
acdnt_cas_co0.1300.7571.0000.2550.7500.750
swpsn_co0.0000.2290.2551.0000.2310.231
fclty_manage_no0.0620.2510.7500.2311.0001.000
acdnt_ty_nm0.0620.2510.7500.2311.0001.000
2023-12-10T18:51:44.485461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
caslt_cosinjpsn_cofclty_lafclty_lorate_valuefclty_manage_noacdnt_ty_nmacdnt_cas_codeath_coswpsn_coinjpsn_co
caslt_co1.0000.5750.1530.2560.3620.6370.6370.7570.5970.0990.181
sinjpsn_co0.5751.0000.1180.0560.2670.3220.3220.2410.1820.2120.000
fclty_la0.1530.1181.0000.344-0.2150.2610.2610.1890.2250.1410.000
fclty_lo0.2560.0560.3441.000-0.7590.1580.1580.0760.0000.0000.241
rate_value0.3620.267-0.215-0.7591.0000.2410.2410.3590.3610.1320.127
fclty_manage_no0.6370.3220.2610.1580.2411.0001.0000.7500.2510.2310.062
acdnt_ty_nm0.6370.3220.2610.1580.2411.0001.0000.7500.2510.2310.062
acdnt_cas_co0.7570.2410.1890.0760.3590.7500.7501.0000.7570.2550.130
death_co0.5970.1820.2250.0000.3610.2510.2510.7571.0000.2290.000
swpsn_co0.0990.2120.1410.0000.1320.2310.2310.2550.2291.0000.000
injpsn_co0.1810.0000.0000.2410.1270.0620.0620.1300.0000.0001.000

Missing values

2023-12-10T18:51:36.349465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:51:37.023683image/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

esntl_idfclty_manage_noacdnt_yearacdnt_ty_nmzone_cdsigngu_nmacdnt_cas_cocaslt_codeath_coswpsn_cosinjpsn_coinjpsn_cofclty_lafclty_losigngu_accto_popltn_corate_value
0<NA>20200152019스쿨존어린이서울특별시 성북구 하월곡동(자보습학원 부근)<NA>22002037.604215438051<NA>0.13697
1<NA>20200372019자전거경상남도 사천시 동금동(동금2길32 부근)<NA>55022134.931136111234<NA>1.34851
2<NA>20200152019스쿨존어린이서울특별시 은평구 역촌동(역촌초교 부근)<NA>33011137.601703480617<NA>0.18726
3<NA>20200152019스쿨존어린이서울특별시 은평구 불광동(연신초교 부근)<NA>33011137.626494480617<NA>0.18726
4<NA>20200152019스쿨존어린이서울특별시 양천구 신정동(신목초교 부근)<NA>23102037.516687455058<NA>0.1758
5<NA>20200152019스쿨존어린이서울특별시 강서구 화곡동(우장초교 부근)<NA>33011137.548623581909<NA>0.15466
6<NA>20200152019스쿨존어린이서울특별시 강서구 화곡동(서울신정초교 부근)<NA>22001137.532872581909<NA>0.10311
7<NA>20200372019자전거경상남도 김해시 봉황동(가락로13 부근)<NA>55032035.227932542350<NA>0.27657
8<NA>20200152019스쿨존어린이서울특별시 구로구 구로동(성은어린이집 부근)<NA>33021037.490608405194<NA>0.22212
9<NA>20200152019스쿨존어린이서울특별시 구로구 오류동(오류초교 부근)<NA>22011037.498372405194<NA>0.14808
esntl_idfclty_manage_noacdnt_yearacdnt_ty_nmzone_cdsigngu_nmacdnt_cas_cocaslt_codeath_coswpsn_cosinjpsn_coinjpsn_cofclty_lafclty_losigngu_accto_popltn_corate_value
90<NA>20200162019보행어린이경기도 화성시 병점동(늘벗미션힐1차아파트 113동앞 부<NA>33001237.205288853106<NA>0.1055
91<NA>20200162019보행어린이경기도 광주시 경안동(중앙로113 부근)<NA>33012037.409829381324<NA>0.23602
92<NA>20200162019보행어린이강원도 강릉시 포남동(강릉시문화센터 부근)<NA>33003037.773566213189<NA>0.42216
93<NA>20200162019보행어린이강원도 삼척시 교동(정상로95 부근)<NA>33003037.45049465409<NA>1.37596
94<NA>20200162019보행어린이충청북도 청주시 상당구 용암동(중흥로181 부근)<NA>33021036.61923189727<NA>0.47437
95<NA>20200162019보행어린이충청북도 청주시 서원구 분평동(분평로21 부근)<NA>33012036.608603195804<NA>0.45964
96<NA>20200162019보행어린이충청남도 천안시 서북구 백석동(환서중학교 부근)<NA>34004036.827412400708<NA>0.27451
97<NA>20200162019보행어린이충청남도 아산시 배방읍(연화마을휴먼시아7단지 703동앞<NA>44013036.785905315279<NA>0.38062
98<NA>20200162019보행어린이충청남도 아산시 배미동(삼정백조아파트105동앞 부근)<NA>33021036.78858315279<NA>0.28546
99<NA>20200162019보행어린이충청남도 아산시 신창면(서부남로838 부근)<NA>34013036.779554315279<NA>0.3489