Overview

Dataset statistics

Number of variables14
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory129.5 B

Variable types

Text2
Categorical12

Alerts

11월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
6월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
7월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
4월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
9월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
2월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
5월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
1월 is highly overall correlated with 2월 and 10 other fieldsHigh correlation
10월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
8월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
3월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
12월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
1월 is highly imbalanced (62.9%)Imbalance
2월 is highly imbalanced (68.6%)Imbalance
3월 is highly imbalanced (68.6%)Imbalance
4월 is highly imbalanced (68.6%)Imbalance
5월 is highly imbalanced (68.6%)Imbalance
6월 is highly imbalanced (68.6%)Imbalance
7월 is highly imbalanced (68.6%)Imbalance
8월 is highly imbalanced (68.6%)Imbalance
9월 is highly imbalanced (68.6%)Imbalance
10월 is highly imbalanced (68.6%)Imbalance
11월 is highly imbalanced (68.6%)Imbalance
12월 is highly imbalanced (68.6%)Imbalance

Reproduction

Analysis started2024-03-18 03:21:40.062393
Analysis finished2024-03-18 03:21:42.694947
Duration2.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

Distinct13
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
2024-03-18T12:21:42.802777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length8
Mean length7.875
Min length4

Characters and Unicode

Total characters189
Distinct characters50
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)33.3%

Sample

1st row열차사고
2nd row열차사고
3rd row열차사고
4th row열차사고
5th row건널목사고
ValueCountFrequency (%)
열차사고 4
14.3%
철도안전사상 3
10.7%
철도안전사상사고 3
10.7%
인명피해(명 3
10.7%
열차운행거리(100만km 3
10.7%
수송 2
 
7.1%
km 2
 
7.1%
건널목사고 1
 
3.6%
철도화재사고 1
 
3.6%
철도시설파손사고 1
 
3.6%
Other values (5) 5
17.9%
2024-03-18T12:21:43.067067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 17
 
9.0%
고 11
 
5.8%
( 9
 
4.8%
철 9
 
4.8%
도 9
 
4.8%
) 9
 
4.8%
0 8
 
4.2%
차 7
 
3.7%
열 7
 
3.7%
전 7
 
3.7%
Other values (40) 96
50.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 144
76.2%
Decimal Number 13
 
6.9%
Lowercase Letter 10
 
5.3%
Open Punctuation 9
 
4.8%
Close Punctuation 9
 
4.8%
Space Separator 4
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 17
 
11.8%
고 11
 
7.6%
철 9
 
6.2%
도 9
 
6.2%
차 7
 
4.9%
열 7
 
4.9%
전 7
 
4.9%
안 7
 
4.9%
상 6
 
4.2%
명 6
 
4.2%
Other values (33) 58
40.3%
Decimal Number
ValueCountFrequency (%)
0 8
61.5%
1 5
38.5%
Lowercase Letter
ValueCountFrequency (%)
m 5
50.0%
k 5
50.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 144
76.2%
Common 35
 
18.5%
Latin 10
 
5.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 17
 
11.8%
고 11
 
7.6%
철 9
 
6.2%
도 9
 
6.2%
차 7
 
4.9%
열 7
 
4.9%
전 7
 
4.9%
안 7
 
4.9%
상 6
 
4.2%
명 6
 
4.2%
Other values (33) 58
40.3%
Common
ValueCountFrequency (%)
( 9
25.7%
) 9
25.7%
0 8
22.9%
1 5
14.3%
4
11.4%
Latin
ValueCountFrequency (%)
m 5
50.0%
k 5
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 144
76.2%
ASCII 45
 
23.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 17
 
11.8%
고 11
 
7.6%
철 9
 
6.2%
도 9
 
6.2%
차 7
 
4.9%
열 7
 
4.9%
전 7
 
4.9%
안 7
 
4.9%
상 6
 
4.2%
명 6
 
4.2%
Other values (33) 58
40.3%
ASCII
ValueCountFrequency (%)
( 9
20.0%
) 9
20.0%
0 8
17.8%
m 5
11.1%
k 5
11.1%
1 5
11.1%
4
8.9%

종류별
Text

Distinct16
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
2024-03-18T12:21:43.256491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.375
Min length1

Characters and Unicode

Total characters57
Distinct characters29
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)50.0%

Sample

1st row열차충돌
2nd row열차탈선
3rd row열차화재
4th row기타열차사고
5th row건널목사고
ValueCountFrequency (%)
여객 3
15.8%
공종 2
 
10.5%
직원 2
 
10.5%
열차충돌 1
 
5.3%
열차탈선 1
 
5.3%
열차화재 1
 
5.3%
기타열차사고 1
 
5.3%
건널목사고 1
 
5.3%
화재 1
 
5.3%
사망 1
 
5.3%
Other values (5) 5
26.3%
2024-03-18T12:21:43.512070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6
 
10.5%
열 4
 
7.0%
차 4
 
7.0%
화 3
 
5.3%
객 3
 
5.3%
사 3
 
5.3%
여 3
 
5.3%
상 2
 
3.5%
고 2
 
3.5%
타 2
 
3.5%
Other values (19) 25
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 49
86.0%
Space Separator 6
 
10.5%
Lowercase Letter 2
 
3.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
열 4
 
8.2%
차 4
 
8.2%
화 3
 
6.1%
객 3
 
6.1%
사 3
 
6.1%
여 3
 
6.1%
상 2
 
4.1%
고 2
 
4.1%
타 2
 
4.1%
기 2
 
4.1%
Other values (16) 21
42.9%
Lowercase Letter
ValueCountFrequency (%)
m 1
50.0%
k 1
50.0%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 49
86.0%
Common 6
 
10.5%
Latin 2
 
3.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
열 4
 
8.2%
차 4
 
8.2%
화 3
 
6.1%
객 3
 
6.1%
사 3
 
6.1%
여 3
 
6.1%
상 2
 
4.1%
고 2
 
4.1%
타 2
 
4.1%
기 2
 
4.1%
Other values (16) 21
42.9%
Latin
ValueCountFrequency (%)
m 1
50.0%
k 1
50.0%
Common
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 49
86.0%
ASCII 8
 
14.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6
75.0%
m 1
 
12.5%
k 1
 
12.5%
Hangul
ValueCountFrequency (%)
열 4
 
8.2%
차 4
 
8.2%
화 3
 
6.1%
객 3
 
6.1%
사 3
 
6.1%
여 3
 
6.1%
상 2
 
4.1%
고 2
 
4.1%
타 2
 
4.1%
기 2
 
4.1%
Other values (16) 21
42.9%

1월
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
0.0
21 
73.8
 
1
0.761
 
1
0.22
 
1

Length

Max length5
Median length3
Mean length3.1666667
Min length3

Unique

Unique3 ?
Unique (%)12.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 21
87.5%
73.8 1
 
4.2%
0.761 1
 
4.2%
0.22 1
 
4.2%

Length

2024-03-18T12:21:43.623474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T12:21:43.709193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 21
87.5%
73.8 1
 
4.2%
0.761 1
 
4.2%
0.22 1
 
4.2%

2월
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
0.0
22 
0.684
 
1
0.187
 
1

Length

Max length5
Median length3
Mean length3.1666667
Min length3

Unique

Unique2 ?
Unique (%)8.3%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 22
91.7%
0.684 1
 
4.2%
0.187 1
 
4.2%

Length

2024-03-18T12:21:43.792975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T12:21:43.901265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 22
91.7%
0.684 1
 
4.2%
0.187 1
 
4.2%

3월
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
0.0
22 
0.763
 
1
0.22
 
1

Length

Max length5
Median length3
Mean length3.125
Min length3

Unique

Unique2 ?
Unique (%)8.3%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 22
91.7%
0.763 1
 
4.2%
0.22 1
 
4.2%

Length

2024-03-18T12:21:43.994514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T12:21:44.091979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 22
91.7%
0.763 1
 
4.2%
0.22 1
 
4.2%

4월
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
0.0
22 
0.743
 
1
0.25
 
1

Length

Max length5
Median length3
Mean length3.125
Min length3

Unique

Unique2 ?
Unique (%)8.3%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 22
91.7%
0.743 1
 
4.2%
0.25 1
 
4.2%

Length

2024-03-18T12:21:44.184744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T12:21:44.266647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 22
91.7%
0.743 1
 
4.2%
0.25 1
 
4.2%

5월
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
0.0
22 
0.763
 
1
0.277
 
1

Length

Max length5
Median length3
Mean length3.1666667
Min length3

Unique

Unique2 ?
Unique (%)8.3%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 22
91.7%
0.763 1
 
4.2%
0.277 1
 
4.2%

Length

2024-03-18T12:21:44.358429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T12:21:44.442504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 22
91.7%
0.763 1
 
4.2%
0.277 1
 
4.2%

6월
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
0.0
22 
0.743
 
1
0.257
 
1

Length

Max length5
Median length3
Mean length3.1666667
Min length3

Unique

Unique2 ?
Unique (%)8.3%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 22
91.7%
0.743 1
 
4.2%
0.257 1
 
4.2%

Length

2024-03-18T12:21:44.531137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T12:21:44.611720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 22
91.7%
0.743 1
 
4.2%
0.257 1
 
4.2%

7월
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
0.0
22 
0.775
 
1
0.258
 
1

Length

Max length5
Median length3
Mean length3.1666667
Min length3

Unique

Unique2 ?
Unique (%)8.3%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 22
91.7%
0.775 1
 
4.2%
0.258 1
 
4.2%

Length

2024-03-18T12:21:44.725725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T12:21:44.825893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 22
91.7%
0.775 1
 
4.2%
0.258 1
 
4.2%

8월
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
0.0
22 
0.782
 
1
0.25
 
1

Length

Max length5
Median length3
Mean length3.125
Min length3

Unique

Unique2 ?
Unique (%)8.3%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 22
91.7%
0.782 1
 
4.2%
0.25 1
 
4.2%

Length

2024-03-18T12:21:44.912466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T12:21:44.992747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 22
91.7%
0.782 1
 
4.2%
0.25 1
 
4.2%

9월
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
0.0
22 
0.717
 
1
0.267
 
1

Length

Max length5
Median length3
Mean length3.1666667
Min length3

Unique

Unique2 ?
Unique (%)8.3%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 22
91.7%
0.717 1
 
4.2%
0.267 1
 
4.2%

Length

2024-03-18T12:21:45.074133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T12:21:45.154913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 22
91.7%
0.717 1
 
4.2%
0.267 1
 
4.2%

10월
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
0.0
22 
0.765
 
1
0.28
 
1

Length

Max length5
Median length3
Mean length3.125
Min length3

Unique

Unique2 ?
Unique (%)8.3%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 22
91.7%
0.765 1
 
4.2%
0.28 1
 
4.2%

Length

2024-03-18T12:21:45.257117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T12:21:45.374499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 22
91.7%
0.765 1
 
4.2%
0.28 1
 
4.2%

11월
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
0.0
22 
0.762
 
1
0.282
 
1

Length

Max length5
Median length3
Mean length3.1666667
Min length3

Unique

Unique2 ?
Unique (%)8.3%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 22
91.7%
0.762 1
 
4.2%
0.282 1
 
4.2%

Length

2024-03-18T12:21:45.464818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T12:21:45.543242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 22
91.7%
0.762 1
 
4.2%
0.282 1
 
4.2%

12월
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
0.0
22 
0.79
 
1
0.277
 
1

Length

Max length5
Median length3
Mean length3.125
Min length3

Unique

Unique2 ?
Unique (%)8.3%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 22
91.7%
0.79 1
 
4.2%
0.277 1
 
4.2%

Length

2024-03-18T12:21:45.630726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T12:21:45.713359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 22
91.7%
0.79 1
 
4.2%
0.277 1
 
4.2%

Correlations

2024-03-18T12:21:45.773146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분종류별1월2월3월4월5월6월7월8월9월10월11월12월
구분1.0000.0000.8740.7190.7190.7190.7190.7190.7190.7190.7190.7190.7190.719
종류별0.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
1월0.8740.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2월0.7190.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
3월0.7190.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
4월0.7190.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
5월0.7190.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
6월0.7190.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
7월0.7190.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
8월0.7190.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
9월0.7190.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
10월0.7190.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
11월0.7190.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
12월0.7190.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-03-18T12:21:45.890336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
11월6월7월4월9월2월5월1월10월8월3월12월
11월1.0001.0001.0001.0001.0001.0001.0000.9761.0001.0001.0001.000
6월1.0001.0001.0001.0001.0001.0001.0000.9761.0001.0001.0001.000
7월1.0001.0001.0001.0001.0001.0001.0000.9761.0001.0001.0001.000
4월1.0001.0001.0001.0001.0001.0001.0000.9761.0001.0001.0001.000
9월1.0001.0001.0001.0001.0001.0001.0000.9761.0001.0001.0001.000
2월1.0001.0001.0001.0001.0001.0001.0000.9761.0001.0001.0001.000
5월1.0001.0001.0001.0001.0001.0001.0000.9761.0001.0001.0001.000
1월0.9760.9760.9760.9760.9760.9760.9761.0000.9760.9760.9760.976
10월1.0001.0001.0001.0001.0001.0001.0000.9761.0001.0001.0001.000
8월1.0001.0001.0001.0001.0001.0001.0000.9761.0001.0001.0001.000
3월1.0001.0001.0001.0001.0001.0001.0000.9761.0001.0001.0001.000
12월1.0001.0001.0001.0001.0001.0001.0000.9761.0001.0001.0001.000
2024-03-18T12:21:46.001673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1월2월3월4월5월6월7월8월9월10월11월12월
1월1.0000.9760.9760.9760.9760.9760.9760.9760.9760.9760.9760.976
2월0.9761.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
3월0.9761.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
4월0.9761.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
5월0.9761.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
6월0.9761.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
7월0.9761.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
8월0.9761.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
9월0.9761.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
10월0.9761.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
11월0.9761.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
12월0.9761.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-03-18T12:21:42.416203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T12:21:42.634222image/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

구분종류별1월2월3월4월5월6월7월8월9월10월11월12월
0열차사고열차충돌0.00.00.00.00.00.00.00.00.00.00.00.0
1열차사고열차탈선0.00.00.00.00.00.00.00.00.00.00.00.0
2열차사고열차화재0.00.00.00.00.00.00.00.00.00.00.00.0
3열차사고기타열차사고0.00.00.00.00.00.00.00.00.00.00.00.0
4건널목사고건널목사고0.00.00.00.00.00.00.00.00.00.00.00.0
5철도안전사상여객0.00.00.00.00.00.00.00.00.00.00.00.0
6철도안전사상공종0.00.00.00.00.00.00.00.00.00.00.00.0
7철도안전사상직원0.00.00.00.00.00.00.00.00.00.00.00.0
8철도화재사고화재0.00.00.00.00.00.00.00.00.00.00.00.0
9철도안전사상사고여객0.00.00.00.00.00.00.00.00.00.00.00.0
구분종류별1월2월3월4월5월6월7월8월9월10월11월12월
14인명피해(명)사망0.00.00.00.00.00.00.00.00.00.00.00.0
15인명피해(명)중상0.00.00.00.00.00.00.00.00.00.00.00.0
16인명피해(명)경상0.00.00.00.00.00.00.00.00.00.00.00.0
17재산피해(백만원)0.00.00.00.00.00.00.00.00.00.00.00.0
18선로연장km73.80.00.00.00.00.00.00.00.00.00.00.0
19열차운행거리(100만km)여객0.7610.6840.7630.7430.7630.7430.7750.7820.7170.7650.7620.79
20열차운행거리(100만km)화물0.00.00.00.00.00.00.00.00.00.00.00.0
21열차운행거리(100만km)기타0.00.00.00.00.00.00.00.00.00.00.00.0
22수송 여객(10억인 km)0.220.1870.220.250.2770.2570.2580.250.2670.280.2820.277
23수송 화물(10억톤 km)0.00.00.00.00.00.00.00.00.00.00.00.0