Overview

Dataset statistics

Number of variables13
Number of observations7219
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory825.0 KiB
Average record size in memory117.0 B

Variable types

Numeric3
Categorical10

Dataset

Description울산광역시 행정동을 기준으로 100m 격자별 화재분류별(고층건물, 대형화재, 차량화재, 산불 등) 현황 통계 정보를 데이터로 제공
Author울산광역시
URLhttps://www.data.go.kr/data/15109133/fileData.do

Alerts

격자아이디 is highly overall correlated with 격자좌표(X)High correlation
격자좌표(X) is highly overall correlated with 격자아이디High correlation
고층건물 수 is highly imbalanced (61.2%)Imbalance
대형화재 수 is highly imbalanced (63.2%)Imbalance
주택화재 수 is highly imbalanced (66.3%)Imbalance
차량화재 수 is highly imbalanced (71.1%)Imbalance
산불 수 is highly imbalanced (85.9%)Imbalance
특수화재 수 is highly imbalanced (97.7%)Imbalance
지하화재 수 is highly imbalanced (97.4%)Imbalance
붕괴사고 수 is highly imbalanced (99.5%)Imbalance
폭발사고 수 is highly imbalanced (99.6%)Imbalance
격자아이디 has unique valuesUnique

Reproduction

Analysis started2024-03-14 22:55:31.528393
Analysis finished2024-03-14 22:55:37.398733
Duration5.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

격자아이디
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct7219
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3610
Minimum1
Maximum7219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2024-03-15T07:55:37.632191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile361.9
Q11805.5
median3610
Q35414.5
95-th percentile6858.1
Maximum7219
Range7218
Interquartile range (IQR)3609

Descriptive statistics

Standard deviation2084.0901
Coefficient of variation (CV)0.57731029
Kurtosis-1.2
Mean3610
Median Absolute Deviation (MAD)1805
Skewness0
Sum26060590
Variance4343431.7
MonotonicityStrictly increasing
2024-03-15T07:55:38.036399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
4852 1
 
< 0.1%
4822 1
 
< 0.1%
4821 1
 
< 0.1%
4820 1
 
< 0.1%
4819 1
 
< 0.1%
4818 1
 
< 0.1%
4817 1
 
< 0.1%
4816 1
 
< 0.1%
4815 1
 
< 0.1%
Other values (7209) 7209
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
7219 1
< 0.1%
7218 1
< 0.1%
7217 1
< 0.1%
7216 1
< 0.1%
7215 1
< 0.1%
7214 1
< 0.1%
7213 1
< 0.1%
7212 1
< 0.1%
7211 1
< 0.1%
7210 1
< 0.1%

격자좌표(X)
Real number (ℝ)

HIGH CORRELATION 

Distinct398
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean408975.4
Minimum382123
Maximum423223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2024-03-15T07:55:38.322169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum382123
5-th percentile391523
Q1405823
median410823
Q3413423
95-th percentile420323
Maximum423223
Range41100
Interquartile range (IQR)7600

Descriptive statistics

Standard deviation8121.9288
Coefficient of variation (CV)0.019859211
Kurtosis0.54623627
Mean408975.4
Median Absolute Deviation (MAD)3200
Skewness-0.94479898
Sum2.9523934 × 109
Variance65965728
MonotonicityIncreasing
2024-03-15T07:55:38.683843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
412223 88
 
1.2%
412623 88
 
1.2%
412923 83
 
1.1%
411823 83
 
1.1%
411923 80
 
1.1%
412723 80
 
1.1%
409523 77
 
1.1%
409623 76
 
1.1%
412023 76
 
1.1%
413023 75
 
1.0%
Other values (388) 6413
88.8%
ValueCountFrequency (%)
382123 1
 
< 0.1%
382323 2
< 0.1%
382423 3
< 0.1%
382623 4
0.1%
383123 3
< 0.1%
383323 2
< 0.1%
383423 1
 
< 0.1%
383623 2
< 0.1%
383723 1
 
< 0.1%
383823 1
 
< 0.1%
ValueCountFrequency (%)
423223 2
 
< 0.1%
423123 3
 
< 0.1%
423023 2
 
< 0.1%
422923 1
 
< 0.1%
422823 3
 
< 0.1%
422723 8
0.1%
422623 9
0.1%
422523 7
0.1%
422423 7
0.1%
422323 5
0.1%

격자좌표(Y)
Real number (ℝ)

Distinct401
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean329318.24
Minimum306247
Maximum349247
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2024-03-15T07:55:39.092587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum306247
5-th percentile315547
Q1325747
median330047
Q3333047
95-th percentile340847
Maximum349247
Range43000
Interquartile range (IQR)7300

Descriptive statistics

Standard deviation7219.9881
Coefficient of variation (CV)0.021924045
Kurtosis0.44278369
Mean329318.24
Median Absolute Deviation (MAD)3600
Skewness-0.42390878
Sum2.3773484 × 109
Variance52128228
MonotonicityNot monotonic
2024-03-15T07:55:39.538684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
331747 85
 
1.2%
331147 81
 
1.1%
331347 77
 
1.1%
330147 77
 
1.1%
330247 77
 
1.1%
330047 77
 
1.1%
330947 76
 
1.1%
330347 74
 
1.0%
331547 73
 
1.0%
330847 73
 
1.0%
Other values (391) 6449
89.3%
ValueCountFrequency (%)
306247 1
 
< 0.1%
306347 1
 
< 0.1%
306447 1
 
< 0.1%
306647 1
 
< 0.1%
306847 1
 
< 0.1%
307247 1
 
< 0.1%
307347 2
< 0.1%
307447 4
0.1%
307547 4
0.1%
307647 3
< 0.1%
ValueCountFrequency (%)
349247 1
 
< 0.1%
348947 2
 
< 0.1%
348547 1
 
< 0.1%
348447 2
 
< 0.1%
348347 5
0.1%
348247 5
0.1%
348147 2
 
< 0.1%
348047 2
 
< 0.1%
347947 1
 
< 0.1%
347847 1
 
< 0.1%

고층건물 수
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.5 KiB
0
6671 
1
 
548

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 6671
92.4%
1 548
 
7.6%

Length

2024-03-15T07:55:39.883527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:55:40.207864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6671
92.4%
1 548
 
7.6%

대형화재 수
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.5 KiB
0
6709 
1
 
510

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 6709
92.9%
1 510
 
7.1%

Length

2024-03-15T07:55:40.552397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:55:40.885205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6709
92.9%
1 510
 
7.1%

주택화재 수
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.5 KiB
0
6769 
1
 
450

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 6769
93.8%
1 450
 
6.2%

Length

2024-03-15T07:55:41.233092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:55:41.555761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6769
93.8%
1 450
 
6.2%

차량화재 수
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.5 KiB
0
6853 
1
 
366

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 6853
94.9%
1 366
 
5.1%

Length

2024-03-15T07:55:41.910701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:55:42.242007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6853
94.9%
1 366
 
5.1%

산불 수
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.5 KiB
0
7075 
1
 
144

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 7075
98.0%
1 144
 
2.0%

Length

2024-03-15T07:55:42.582994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:55:42.970108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7075
98.0%
1 144
 
2.0%

특수화재 수
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.5 KiB
0
7203 
1
 
16

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 7203
99.8%
1 16
 
0.2%

Length

2024-03-15T07:55:43.300099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:55:43.490877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7203
99.8%
1 16
 
0.2%

지하화재 수
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.5 KiB
0
7200 
1
 
19

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 7200
99.7%
1 19
 
0.3%

Length

2024-03-15T07:55:43.672498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:55:43.934222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7200
99.7%
1 19
 
0.3%

붕괴사고 수
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.5 KiB
0
7216 
1
 
3

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 7216
> 99.9%
1 3
 
< 0.1%

Length

2024-03-15T07:55:44.279297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:55:44.607502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7216
> 99.9%
1 3
 
< 0.1%

폭발사고 수
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.5 KiB
0
7217 
1
 
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 7217
> 99.9%
1 2
 
< 0.1%

Length

2024-03-15T07:55:44.969743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:55:45.296475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7217
> 99.9%
1 2
 
< 0.1%

안전사고 수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.5 KiB
1
6271 
0
948 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 6271
86.9%
0 948
 
13.1%

Length

2024-03-15T07:55:45.635557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:55:45.846323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 6271
86.9%
0 948
 
13.1%

Interactions

2024-03-15T07:55:35.308758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:55:33.373783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:55:34.441292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:55:35.593154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:55:33.653521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:55:34.747367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:55:35.845639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:55:34.164877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:55:35.034882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T07:55:45.994853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
격자아이디격자좌표(X)격자좌표(Y)고층건물 수대형화재 수주택화재 수차량화재 수산불 수특수화재 수지하화재 수붕괴사고 수폭발사고 수안전사고 수
격자아이디1.0000.9720.5990.1360.2010.0750.0750.0890.0620.0380.0000.0000.177
격자좌표(X)0.9721.0000.5990.1100.2330.0520.0730.1050.0800.0000.0000.0000.200
격자좌표(Y)0.5990.5991.0000.1550.2770.0660.0340.1120.0560.0000.0000.0000.177
고층건물 수0.1360.1100.1551.0000.0000.0300.0250.0520.0000.0000.0000.0000.111
대형화재 수0.2010.2330.2770.0001.0000.0580.0610.0560.0000.0000.0000.0000.524
주택화재 수0.0750.0520.0660.0300.0581.0000.0510.0510.0000.0000.0000.0000.355
차량화재 수0.0750.0730.0340.0250.0610.0511.0000.0290.0000.0000.0000.0000.492
산불 수0.0890.1050.1120.0520.0560.0510.0291.0000.0000.0000.0000.0000.467
특수화재 수0.0620.0800.0560.0000.0000.0000.0000.0001.0000.0000.0000.0000.168
지하화재 수0.0380.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
붕괴사고 수0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
폭발사고 수0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
안전사고 수0.1770.2000.1770.1110.5240.3550.4920.4670.1680.0000.0000.0001.000
2024-03-15T07:55:46.448213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
안전사고 수차량화재 수고층건물 수특수화재 수지하화재 수주택화재 수산불 수대형화재 수폭발사고 수붕괴사고 수
안전사고 수1.0000.3280.0710.1080.0000.2310.3100.3510.0000.000
차량화재 수0.3281.0000.0160.0000.0000.0330.0180.0390.0000.000
고층건물 수0.0710.0161.0000.0000.0000.0190.0330.0000.0000.000
특수화재 수0.1080.0000.0001.0000.0000.0000.0000.0000.0000.000
지하화재 수0.0000.0000.0000.0001.0000.0000.0000.0000.0000.000
주택화재 수0.2310.0330.0190.0000.0001.0000.0330.0370.0000.000
산불 수0.3100.0180.0330.0000.0000.0331.0000.0360.0000.000
대형화재 수0.3510.0390.0000.0000.0000.0370.0361.0000.0000.000
폭발사고 수0.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
붕괴사고 수0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000
2024-03-15T07:55:46.783034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
격자아이디격자좌표(X)격자좌표(Y)고층건물 수대형화재 수주택화재 수차량화재 수산불 수특수화재 수지하화재 수붕괴사고 수폭발사고 수안전사고 수
격자아이디1.0001.0000.0090.1040.1540.0570.0570.0680.0480.0290.0000.0000.136
격자좌표(X)1.0001.0000.0050.0840.1790.0400.0560.0800.0620.0000.0000.0000.153
격자좌표(Y)0.0090.0051.0000.1190.2130.0510.0260.0860.0430.0000.0000.0000.136
고층건물 수0.1040.0840.1191.0000.0000.0190.0160.0330.0000.0000.0000.0000.071
대형화재 수0.1540.1790.2130.0001.0000.0370.0390.0360.0000.0000.0000.0000.351
주택화재 수0.0570.0400.0510.0190.0371.0000.0330.0330.0000.0000.0000.0000.231
차량화재 수0.0570.0560.0260.0160.0390.0331.0000.0180.0000.0000.0000.0000.328
산불 수0.0680.0800.0860.0330.0360.0330.0181.0000.0000.0000.0000.0000.310
특수화재 수0.0480.0620.0430.0000.0000.0000.0000.0001.0000.0000.0000.0000.108
지하화재 수0.0290.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
붕괴사고 수0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
폭발사고 수0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
안전사고 수0.1360.1530.1360.0710.3510.2310.3280.3100.1080.0000.0000.0001.000

Missing values

2024-03-15T07:55:36.296879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T07:55:37.129734image/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

격자아이디격자좌표(X)격자좌표(Y)고층건물 수대형화재 수주택화재 수차량화재 수산불 수특수화재 수지하화재 수붕괴사고 수폭발사고 수안전사고 수
013821233279470000000001
123823233280470000000001
233823233282470000000001
343824233285470000100000
453824233294470000000001
563824233295470000000001
673826233270470000000001
783826233298470010000000
893826233300470000000001
9103826233316470000000001
격자아이디격자좌표(X)격자좌표(Y)고층건물 수대형화재 수주택화재 수차량화재 수산불 수특수화재 수지하화재 수붕괴사고 수폭발사고 수안전사고 수
720972104228233316470000000001
721072114228233370470000000001
721172124229233356470000000001
721272134230233346470000000001
721372144230233369470000000001
721472154231233357470000000001
721572164231233358470000000001
721672174231233361470010000000
721772184232233362470000000001
721872194232233363470000000001