Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory869.1 KiB
Average record size in memory89.0 B

Variable types

Numeric3
Categorical6

Dataset

Description울산광역시 행정동을 기준으로 100m 격자별 소방대상물 급수별 구분 현황 및 대형화재취약대상수 정보를 데이터로 제공
Author울산광역시
URLhttps://www.data.go.kr/data/15109132/fileData.do

Alerts

특급대상 수 is highly imbalanced (98.0%)Imbalance
1급대상 수 is highly imbalanced (75.7%)Imbalance
기타 수 is highly imbalanced (92.1%)Imbalance
대형화재취약대상 수 is highly imbalanced (89.2%)Imbalance
격자아이디 has unique valuesUnique

Reproduction

Analysis started2024-03-14 08:34:54.536645
Analysis finished2024-03-14 08:34:57.528054
Duration2.99 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

격자아이디
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5780.0988
Minimum1
Maximum11566
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T17:34:57.657709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile580.9
Q12887.75
median5784.5
Q38672.75
95-th percentile10998.15
Maximum11566
Range11565
Interquartile range (IQR)5785

Descriptive statistics

Standard deviation3338.7564
Coefficient of variation (CV)0.57762964
Kurtosis-1.1985773
Mean5780.0988
Median Absolute Deviation (MAD)2893.5
Skewness0.0031179803
Sum57800988
Variance11147294
MonotonicityNot monotonic
2024-03-14T17:34:57.937660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11024 1
 
< 0.1%
10998 1
 
< 0.1%
3012 1
 
< 0.1%
4343 1
 
< 0.1%
5697 1
 
< 0.1%
3793 1
 
< 0.1%
3196 1
 
< 0.1%
8662 1
 
< 0.1%
3127 1
 
< 0.1%
2905 1
 
< 0.1%
Other values (9990) 9990
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 (%)
11566 1
< 0.1%
11565 1
< 0.1%
11564 1
< 0.1%
11563 1
< 0.1%
11562 1
< 0.1%
11561 1
< 0.1%
11560 1
< 0.1%
11559 1
< 0.1%
11558 1
< 0.1%
11556 1
< 0.1%

격자좌표(X)
Real number (ℝ)

Distinct404
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean406621.38
Minimum382023
Maximum423223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T17:34:58.268850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum382023
5-th percentile389923
Q1400123
median409423
Q3412623
95-th percentile420023
Maximum423223
Range41200
Interquartile range (IQR)12500

Descriptive statistics

Standard deviation8898.9813
Coefficient of variation (CV)0.021885178
Kurtosis-0.44403004
Mean406621.38
Median Absolute Deviation (MAD)4600
Skewness-0.57827869
Sum4.0662138 × 109
Variance79191869
MonotonicityNot monotonic
2024-03-14T17:34:58.515818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
409423 106
 
1.1%
409523 100
 
1.0%
411923 95
 
0.9%
412423 92
 
0.9%
409723 91
 
0.9%
411823 89
 
0.9%
412323 87
 
0.9%
412223 87
 
0.9%
412523 86
 
0.9%
409623 86
 
0.9%
Other values (394) 9081
90.8%
ValueCountFrequency (%)
382023 2
 
< 0.1%
382123 2
 
< 0.1%
382223 10
0.1%
382323 8
0.1%
382423 7
0.1%
382523 8
0.1%
382623 8
0.1%
382723 3
 
< 0.1%
382923 1
 
< 0.1%
383023 1
 
< 0.1%
ValueCountFrequency (%)
423223 2
 
< 0.1%
423123 9
0.1%
422923 4
 
< 0.1%
422823 9
0.1%
422723 13
0.1%
422623 16
0.2%
422523 19
0.2%
422423 7
 
0.1%
422323 5
 
0.1%
422223 13
0.1%

격자좌표(Y)
Real number (ℝ)

Distinct426
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean328987.6
Minimum306047
Maximum349047
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T17:34:58.763819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum306047
5-th percentile314147
Q1324147
median329747
Q3333747
95-th percentile341947
Maximum349047
Range43000
Interquartile range (IQR)9600

Descriptive statistics

Standard deviation8360.6051
Coefficient of variation (CV)0.025413131
Kurtosis-0.03129432
Mean328987.6
Median Absolute Deviation (MAD)4800
Skewness-0.28632012
Sum3.289876 × 109
Variance69899718
MonotonicityNot monotonic
2024-03-14T17:34:59.012144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
331747 90
 
0.9%
331247 86
 
0.9%
330847 86
 
0.9%
331347 85
 
0.9%
330947 83
 
0.8%
331647 82
 
0.8%
331847 82
 
0.8%
331547 81
 
0.8%
331047 81
 
0.8%
331447 80
 
0.8%
Other values (416) 9164
91.6%
ValueCountFrequency (%)
306047 1
 
< 0.1%
306247 2
 
< 0.1%
306347 1
 
< 0.1%
306447 1
 
< 0.1%
306547 1
 
< 0.1%
306747 1
 
< 0.1%
306847 1
 
< 0.1%
307047 1
 
< 0.1%
307147 5
0.1%
307247 3
< 0.1%
ValueCountFrequency (%)
349047 1
 
< 0.1%
348947 4
< 0.1%
348847 4
< 0.1%
348747 4
< 0.1%
348647 3
< 0.1%
348547 3
< 0.1%
348447 5
0.1%
348347 4
< 0.1%
348247 7
0.1%
348147 6
0.1%

특급대상 수
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9981 
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 9981
99.8%
1 19
 
0.2%

Length

2024-03-14T17:34:59.324946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T17:34:59.484308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9981
99.8%
1 19
 
0.2%

1급대상 수
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9599 
1
 
401

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 9599
96.0%
1 401
 
4.0%

Length

2024-03-14T17:34:59.654444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T17:34:59.828624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9599
96.0%
1 401
 
4.0%

2급대상 수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
6943 
1
3057 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 6943
69.4%
1 3057
30.6%

Length

2024-03-14T17:35:00.041112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T17:35:00.346836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6943
69.4%
1 3057
30.6%

일반대상 수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
7770 
0
2230 

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 7770
77.7%
0 2230
 
22.3%

Length

2024-03-14T17:35:00.668952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T17:35:00.967643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 7770
77.7%
0 2230
 
22.3%

기타 수
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9833 
1
 
162
2
 
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 row0

Common Values

ValueCountFrequency (%)
0 9833
98.3%
1 162
 
1.6%
2 5
 
0.1%

Length

2024-03-14T17:35:01.296721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T17:35:01.791373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9833
98.3%
1 162
 
1.6%
2 5
 
< 0.1%

대형화재취약대상 수
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9754 
1
 
239
2
 
7

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 9754
97.5%
1 239
 
2.4%
2 7
 
0.1%

Length

2024-03-14T17:35:02.088048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T17:35:02.258628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9754
97.5%
1 239
 
2.4%
2 7
 
0.1%

Interactions

2024-03-14T17:34:56.579397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:34:55.330750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:34:56.115808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:34:56.765273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:34:55.598331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:34:56.279692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:34:56.982092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:34:55.858851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:34:56.427109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T17:35:02.375370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
격자아이디격자좌표(X)격자좌표(Y)특급대상 수1급대상 수2급대상 수일반대상 수기타 수대형화재취약대상 수
격자아이디1.0000.0450.0000.0000.0000.0000.0000.0170.000
격자좌표(X)0.0451.0000.5990.0370.1690.2980.2810.3460.123
격자좌표(Y)0.0000.5991.0000.0680.1590.2620.1070.1630.085
특급대상 수0.0000.0370.0681.0000.0280.0000.0000.0000.105
1급대상 수0.0000.1690.1590.0281.0000.0920.1640.0140.251
2급대상 수0.0000.2980.2620.0000.0921.0000.6790.0380.057
일반대상 수0.0000.2810.1070.0000.1640.6791.0000.0640.088
기타 수0.0170.3460.1630.0000.0140.0380.0641.0000.013
대형화재취약대상 수0.0000.1230.0850.1050.2510.0570.0880.0131.000
2024-03-14T17:35:02.584372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1급대상 수2급대상 수기타 수특급대상 수대형화재취약대상 수일반대상 수
1급대상 수1.0000.0590.0230.0180.4090.105
2급대상 수0.0591.0000.0630.0000.0940.475
기타 수0.0230.0631.0000.0000.0040.106
특급대상 수0.0180.0000.0001.0000.1730.000
대형화재취약대상 수0.4090.0940.0040.1731.0000.145
일반대상 수0.1050.4750.1060.0000.1451.000
2024-03-14T17:35:02.861858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
격자아이디격자좌표(X)격자좌표(Y)특급대상 수1급대상 수2급대상 수일반대상 수기타 수대형화재취약대상 수
격자아이디1.000-0.006-0.0040.0000.0000.0000.0000.0100.000
격자좌표(X)-0.0061.000-0.0280.0280.1290.2290.2150.2230.073
격자좌표(Y)-0.004-0.0281.0000.0520.1220.2010.0820.0980.050
특급대상 수0.0000.0280.0521.0000.0180.0000.0000.0000.173
1급대상 수0.0000.1290.1220.0181.0000.0590.1050.0230.409
2급대상 수0.0000.2290.2010.0000.0591.0000.4750.0630.094
일반대상 수0.0000.2150.0820.0000.1050.4751.0000.1060.145
기타 수0.0100.2230.0980.0000.0230.0630.1061.0000.004
대형화재취약대상 수0.0000.0730.0500.1730.4090.0940.1450.0041.000

Missing values

2024-03-14T17:34:57.185823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T17:34:57.427451image/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)특급대상 수1급대상 수2급대상 수일반대상 수기타 수대형화재취약대상 수
858711024412523331747001100
84469525412423326747001100
23627518397923331247000100
53568742408723331247001000
32319780401423338347000100
41602395405423330647001100
63829386410023307047000100
26867332399423320247000100
31588122401223327047000100
82726174412223329147001100
격자아이디격자좌표(X)격자좌표(Y)특급대상 수1급대상 수2급대상 수일반대상 수기타 수대형화재취약대상 수
9582492413723332847001000
111609915420323326447001100
88724596412823333047000100
2471376398423325047000100
324710688401523322647000100
8443601412423325047010000
71517190411023309447000100
54931534409023323947000100
29358635400323335647000100
834810525412323326747001000