Overview

Dataset statistics

Number of variables12
Number of observations65
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 KiB
Average record size in memory109.0 B

Variable types

Text1
Numeric5
Categorical6

Dataset

Description국토안전관리원에서 제공하는 공공시설물 분류별 인적사고(사망자 내국인, 사망자 외국인, 부상자 내국인, 부상자 외국인), 피해금액 등의 목록으로 구성된 데이터입니다.
URLhttps://www.data.go.kr/data/3069935/fileData.do

Alerts

사망자(내국인) is highly overall correlated with 사망자(외국인) and 8 other fieldsHigh correlation
사망자(외국인) is highly overall correlated with 사망자(내국인) and 9 other fieldsHigh correlation
부상자(내국인) is highly overall correlated with 사망자(내국인) and 8 other fieldsHigh correlation
부상자(외국인) is highly overall correlated with 사망자(내국인) and 7 other fieldsHigh correlation
1000만원미만 is highly overall correlated with 사망자(내국인) and 8 other fieldsHigh correlation
1000만원-2000만원 is highly overall correlated with 사망자(내국인) and 9 other fieldsHigh correlation
2000만원-5000만원 is highly overall correlated with 사망자(내국인) and 7 other fieldsHigh correlation
5000만원-1억 is highly overall correlated with 사망자(내국인) and 4 other fieldsHigh correlation
1억-2억 is highly overall correlated with 사망자(외국인) and 1 other fieldsHigh correlation
2억-5억 is highly overall correlated with 사망자(내국인) and 7 other fieldsHigh correlation
5억이상 is highly overall correlated with 사망자(내국인) and 7 other fieldsHigh correlation
1000만원-2000만원 is highly imbalanced (57.9%)Imbalance
2000만원-5000만원 is highly imbalanced (64.9%)Imbalance
5000만원-1억 is highly imbalanced (70.6%)Imbalance
1억-2억 is highly imbalanced (66.6%)Imbalance
2억-5억 is highly imbalanced (85.6%)Imbalance
5억이상 is highly imbalanced (61.8%)Imbalance
소분류 has unique valuesUnique
사망자(내국인) has 30 (46.2%) zerosZeros
사망자(외국인) has 51 (78.5%) zerosZeros
부상자(내국인) has 2 (3.1%) zerosZeros
부상자(외국인) has 27 (41.5%) zerosZeros

Reproduction

Analysis started2023-12-12 00:38:18.808893
Analysis finished2023-12-12 00:38:22.281025
Duration3.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

소분류
Text

UNIQUE 

Distinct65
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size652.0 B
2023-12-12T09:38:22.453774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length4.6461538
Min length1

Characters and Unicode

Total characters302
Distinct characters125
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

Unique65 ?
Unique (%)100.0%

Sample

1st row숙박시설
2nd row노유자시설
3rd row정원
4th row공동구
5th row갑문
ValueCountFrequency (%)
및 5
 
6.3%
관련시설 2
 
2.5%
근린생활시설 1
 
1.3%
공공폐수처리시설 1
 
1.3%
군사시설 1
 
1.3%
교정 1
 
1.3%
수문/통문 1
 
1.3%
도로교량 1
 
1.3%
종교시설 1
 
1.3%
숙박시설 1
 
1.3%
Other values (64) 64
81.0%
2023-12-12T09:38:22.808622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
설 29
 
9.6%
시 28
 
9.3%
14
 
4.6%
수 11
 
3.6%
도 9
 
3.0%
장 8
 
2.6%
공 8
 
2.6%
관 5
 
1.7%
및 5
 
1.7%
물 5
 
1.7%
Other values (115) 180
59.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 284
94.0%
Space Separator 14
 
4.6%
Other Punctuation 2
 
0.7%
Close Punctuation 1
 
0.3%
Open Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
설 29
 
10.2%
시 28
 
9.9%
수 11
 
3.9%
도 9
 
3.2%
장 8
 
2.8%
공 8
 
2.8%
관 5
 
1.8%
및 5
 
1.8%
물 5
 
1.8%
동 5
 
1.8%
Other values (111) 171
60.2%
Space Separator
ValueCountFrequency (%)
14
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 284
94.0%
Common 18
 
6.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
설 29
 
10.2%
시 28
 
9.9%
수 11
 
3.9%
도 9
 
3.2%
장 8
 
2.8%
공 8
 
2.8%
관 5
 
1.8%
및 5
 
1.8%
물 5
 
1.8%
동 5
 
1.8%
Other values (111) 171
60.2%
Common
ValueCountFrequency (%)
14
77.8%
/ 2
 
11.1%
) 1
 
5.6%
( 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 284
94.0%
ASCII 18
 
6.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
설 29
 
10.2%
시 28
 
9.9%
수 11
 
3.9%
도 9
 
3.2%
장 8
 
2.8%
공 8
 
2.8%
관 5
 
1.8%
및 5
 
1.8%
물 5
 
1.8%
동 5
 
1.8%
Other values (111) 171
60.2%
ASCII
ValueCountFrequency (%)
14
77.8%
/ 2
 
11.1%
) 1
 
5.6%
( 1
 
5.6%

사망자(내국인)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6153846
Minimum0
Maximum40
Zeros30
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-12T09:38:22.939587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile18.4
Maximum40
Range40
Interquartile range (IQR)2

Descriptive statistics

Standard deviation7.3690491
Coefficient of variation (CV)2.0382476
Kurtosis10.317059
Mean3.6153846
Median Absolute Deviation (MAD)1
Skewness3.0255393
Sum235
Variance54.302885
MonotonicityNot monotonic
2023-12-12T09:38:23.050752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 30
46.2%
1 12
 
18.5%
2 7
 
10.8%
3 2
 
3.1%
13 2
 
3.1%
4 2
 
3.1%
28 1
 
1.5%
8 1
 
1.5%
21 1
 
1.5%
5 1
 
1.5%
Other values (6) 6
 
9.2%
ValueCountFrequency (%)
0 30
46.2%
1 12
 
18.5%
2 7
 
10.8%
3 2
 
3.1%
4 2
 
3.1%
5 1
 
1.5%
7 1
 
1.5%
8 1
 
1.5%
11 1
 
1.5%
13 2
 
3.1%
ValueCountFrequency (%)
40 1
1.5%
28 1
1.5%
21 1
1.5%
19 1
1.5%
16 1
1.5%
14 1
1.5%
13 2
3.1%
11 1
1.5%
8 1
1.5%
7 1
1.5%

사망자(외국인)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69230769
Minimum0
Maximum11
Zeros51
Zeros (%)78.5%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-12T09:38:23.164108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3.8
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.9196604
Coefficient of variation (CV)2.7728428
Kurtosis15.365902
Mean0.69230769
Median Absolute Deviation (MAD)0
Skewness3.7501035
Sum45
Variance3.6850962
MonotonicityNot monotonic
2023-12-12T09:38:23.275792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 51
78.5%
1 6
 
9.2%
7 2
 
3.1%
2 2
 
3.1%
3 2
 
3.1%
4 1
 
1.5%
11 1
 
1.5%
ValueCountFrequency (%)
0 51
78.5%
1 6
 
9.2%
2 2
 
3.1%
3 2
 
3.1%
4 1
 
1.5%
7 2
 
3.1%
11 1
 
1.5%
ValueCountFrequency (%)
11 1
 
1.5%
7 2
 
3.1%
4 1
 
1.5%
3 2
 
3.1%
2 2
 
3.1%
1 6
 
9.2%
0 51
78.5%

부상자(내국인)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)64.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.384615
Minimum0
Maximum1488
Zeros2
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-12T09:38:23.387361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median12
Q352
95-th percentile308.2
Maximum1488
Range1488
Interquartile range (IQR)47

Descriptive statistics

Standard deviation201.07521
Coefficient of variation (CV)2.7031828
Kurtosis39.174054
Mean74.384615
Median Absolute Deviation (MAD)10
Skewness5.80568
Sum4835
Variance40431.24
MonotonicityNot monotonic
2023-12-12T09:38:23.854955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
2 4
 
6.2%
5 4
 
6.2%
3 3
 
4.6%
4 3
 
4.6%
1 3
 
4.6%
6 3
 
4.6%
11 3
 
4.6%
52 2
 
3.1%
9 2
 
3.1%
0 2
 
3.1%
Other values (32) 36
55.4%
ValueCountFrequency (%)
0 2
3.1%
1 3
4.6%
2 4
6.2%
3 3
4.6%
4 3
4.6%
5 4
6.2%
6 3
4.6%
7 2
3.1%
8 2
3.1%
9 2
3.1%
ValueCountFrequency (%)
1488 1
1.5%
467 1
1.5%
364 1
1.5%
322 1
1.5%
253 1
1.5%
245 1
1.5%
230 1
1.5%
175 1
1.5%
123 1
1.5%
89 1
1.5%

부상자(외국인)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.307692
Minimum0
Maximum379
Zeros27
Zeros (%)41.5%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-12T09:38:23.974735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile50.6
Maximum379
Range379
Interquartile range (IQR)4

Descriptive statistics

Standard deviation49.286764
Coefficient of variation (CV)4.0045496
Kurtosis49.667014
Mean12.307692
Median Absolute Deviation (MAD)1
Skewness6.7638433
Sum800
Variance2429.1851
MonotonicityNot monotonic
2023-12-12T09:38:24.086432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 27
41.5%
1 11
16.9%
3 4
 
6.2%
2 4
 
6.2%
4 4
 
6.2%
5 4
 
6.2%
7 2
 
3.1%
37 1
 
1.5%
102 1
 
1.5%
379 1
 
1.5%
Other values (6) 6
 
9.2%
ValueCountFrequency (%)
0 27
41.5%
1 11
16.9%
2 4
 
6.2%
3 4
 
6.2%
4 4
 
6.2%
5 4
 
6.2%
7 2
 
3.1%
11 1
 
1.5%
15 1
 
1.5%
16 1
 
1.5%
ValueCountFrequency (%)
379 1
1.5%
102 1
1.5%
69 1
1.5%
54 1
1.5%
37 1
1.5%
36 1
1.5%
16 1
1.5%
15 1
1.5%
11 1
1.5%
7 2
3.1%

1000만원미만
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)64.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.784615
Minimum1
Maximum1870
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-12T09:38:24.238488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.2
Q15
median14
Q367
95-th percentile394
Maximum1870
Range1869
Interquartile range (IQR)62

Descriptive statistics

Standard deviation250.70815
Coefficient of variation (CV)2.8237792
Kurtosis40.90416
Mean88.784615
Median Absolute Deviation (MAD)12
Skewness5.9607146
Sum5771
Variance62854.578
MonotonicityNot monotonic
2023-12-12T09:38:24.439187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
5 5
 
7.7%
1 4
 
6.2%
8 3
 
4.6%
3 3
 
4.6%
2 3
 
4.6%
11 3
 
4.6%
7 3
 
4.6%
10 2
 
3.1%
39 2
 
3.1%
6 2
 
3.1%
Other values (32) 35
53.8%
ValueCountFrequency (%)
1 4
6.2%
2 3
4.6%
3 3
4.6%
4 2
 
3.1%
5 5
7.7%
6 2
 
3.1%
7 3
4.6%
8 3
4.6%
9 1
 
1.5%
10 2
 
3.1%
ValueCountFrequency (%)
1870 1
1.5%
515 1
1.5%
481 1
1.5%
415 1
1.5%
310 1
1.5%
291 1
1.5%
254 1
1.5%
190 1
1.5%
132 1
1.5%
95 1
1.5%

1000만원-2000만원
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size652.0 B
0
53 
1
7 
2
 
3
3
 
1
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)3.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 53
81.5%
1 7
 
10.8%
2 3
 
4.6%
3 1
 
1.5%
5 1
 
1.5%

Length

2023-12-12T09:38:24.616848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:38:24.748009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 53
81.5%
1 7
 
10.8%
2 3
 
4.6%
3 1
 
1.5%
5 1
 
1.5%

2000만원-5000만원
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size652.0 B
0
58 
1
6 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.5%

Sample

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

Common Values

ValueCountFrequency (%)
0 58
89.2%
1 6
 
9.2%
2 1
 
1.5%

Length

2023-12-12T09:38:24.886330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:38:25.032917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 58
89.2%
1 6
 
9.2%
2 1
 
1.5%

5000만원-1억
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size652.0 B
0
60 
1
 
3
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 60
92.3%
1 3
 
4.6%
3 2
 
3.1%

Length

2023-12-12T09:38:25.162584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:38:25.275047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 60
92.3%
1 3
 
4.6%
3 2
 
3.1%

1억-2억
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size652.0 B
0
61 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 61
93.8%
1 4
 
6.2%

Length

2023-12-12T09:38:25.398933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:38:25.525445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 61
93.8%
1 4
 
6.2%

2억-5억
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size652.0 B
0
63 
1
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)3.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 63
96.9%
1 1
 
1.5%
3 1
 
1.5%

Length

2023-12-12T09:38:25.649508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:38:25.772417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 63
96.9%
1 1
 
1.5%
3 1
 
1.5%

5억이상
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size652.0 B
0
57 
1
7 
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.5%

Sample

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

Common Values

ValueCountFrequency (%)
0 57
87.7%
1 7
 
10.8%
4 1
 
1.5%

Length

2023-12-12T09:38:25.901639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:38:26.014458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 57
87.7%
1 7
 
10.8%
4 1
 
1.5%

Interactions

2023-12-12T09:38:21.566719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:19.457938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:20.040632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:20.568481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:21.090694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:21.676300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:19.581013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:20.146741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:20.675933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:21.186736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:21.760619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:19.704042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:20.245281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:20.770268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:21.286541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:21.853041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:19.805050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:20.347526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:20.883326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:21.379127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:21.955270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:19.919108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:20.459692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:20.988234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:38:21.468808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:38:26.099307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소분류사망자(내국인)사망자(외국인)부상자(내국인)부상자(외국인)1000만원미만1000만원-2000만원2000만원-5000만원5000만원-1억1억-2억2억-5억5억이상
소분류1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사망자(내국인)1.0001.0000.8960.9390.9990.9980.8740.8530.7540.6290.8410.786
사망자(외국인)1.0000.8961.0000.8930.9790.9160.8620.9590.9520.7370.9850.946
부상자(내국인)1.0000.9390.8931.0000.8091.0000.9850.7550.7180.4020.7640.731
부상자(외국인)1.0000.9990.9790.8091.0000.9550.7580.7260.4640.6460.6630.678
1000만원미만1.0000.9980.9161.0000.9551.0000.8360.7120.5640.7050.7310.694
1000만원-2000만원1.0000.8740.8620.9850.7580.8361.0000.7270.7350.4420.7360.713
2000만원-5000만원1.0000.8530.9590.7550.7260.7120.7271.0000.8080.2890.9590.976
5000만원-1억1.0000.7540.9520.7180.4640.5640.7350.8081.0000.2360.8050.808
1억-2억1.0000.6290.7370.4020.6460.7050.4420.2890.2361.0000.2860.289
2억-5억1.0000.8410.9850.7640.6630.7310.7360.9590.8050.2861.0000.956
5억이상1.0000.7860.9460.7310.6780.6940.7130.9760.8080.2890.9561.000
2023-12-12T09:38:26.268236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2000만원-5000만원2억-5억5000만원-1억1000만원-2000만원5억이상1억-2억
2000만원-5000만원1.0000.7500.4740.7070.8090.463
2억-5억0.7501.0000.4710.7200.7420.459
5000만원-1억0.4740.4711.0000.7180.4750.381
1000만원-2000만원0.7070.7200.7181.0000.6890.524
5억이상0.8090.7420.4750.6891.0000.464
1억-2억0.4630.4590.3810.5240.4641.000
2023-12-12T09:38:26.413031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사망자(내국인)사망자(외국인)부상자(내국인)부상자(외국인)1000만원미만1000만원-2000만원2000만원-5000만원5000만원-1억1억-2억2억-5억5억이상
사망자(내국인)1.0000.5230.6510.6270.6910.8250.8010.6970.4250.7250.695
사망자(외국인)0.5231.0000.5910.6550.5980.7720.7340.7160.5280.8300.701
부상자(내국인)0.6510.5911.0000.7410.9920.8200.7450.6950.4770.7580.712
부상자(외국인)0.6270.6550.7411.0000.7740.6990.7630.4550.4430.6840.703
1000만원미만0.6910.5980.9920.7741.0000.8020.7460.5670.4930.7690.724
1000만원-2000만원0.8250.7720.8200.6990.8021.0000.7070.7180.5240.7200.689
2000만원-5000만원0.8010.7340.7450.7630.7460.7071.0000.4740.4630.7500.809
5000만원-1억0.6970.7160.6950.4550.5670.7180.4741.0000.3810.4710.475
1억-2억0.4250.5280.4770.4430.4930.5240.4630.3811.0000.4590.464
2억-5억0.7250.8300.7580.6840.7690.7200.7500.4710.4591.0000.742
5억이상0.6950.7010.7120.7030.7240.6890.8090.4750.4640.7421.000

Missing values

2023-12-12T09:38:22.079738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:38:22.223249image/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

소분류사망자(내국인)사망자(외국인)부상자(내국인)부상자(외국인)1000만원미만1000만원-2000만원2000만원-5000만원5000만원-1억1억-2억2억-5억5억이상
0숙박시설31521167000001
1노유자시설1133539000001
2정원10203000000
3공동구00505000000
4갑문00222000000
5수처리설비시설01405000000
6지하차도00505000000
7판매시설0131436000000
8환경오염방지시설00404000000
9상수도7074484000000
소분류사망자(내국인)사망자(외국인)부상자(내국인)부상자(외국인)1000만원미만1000만원-2000만원2000만원-5000만원5000만원-1억1억-2억2억-5억5억이상
55업무시설213364102481210000
56부지조성8185595201000
57석유화학공장0019018000000
58하수처리시설2020122000000
59도로터널1031133000000
60방송통신시설0014317000000
61간척매립20608000000
62공원0012114000100
63철도교량209011000000
64숲10203000000