Overview

Dataset statistics

Number of variables9
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory86.0 B

Variable types

Text1
Categorical3
Numeric5

Dataset

Description울산소방본부 및 산하기관(6개 소방서, 안전체험관) 차량 현황 데이터입니다. 소방차량의 분류는 소방장비 분류등에 관한 기준(소방청훈령) 별표1의 기동장비를 참고하시면 자세히 알수있습니다.
URLhttps://www.data.go.kr/data/15054879/fileData.do

Alerts

중부 소방서 is highly overall correlated with 남부 소방서 and 3 other fieldsHigh correlation
남부 소방서 is highly overall correlated with 중부 소방서 and 3 other fieldsHigh correlation
북부 소방서 is highly overall correlated with 중부 소방서 and 4 other fieldsHigh correlation
남울주 소방서 is highly overall correlated with 중부 소방서 and 4 other fieldsHigh correlation
서울주 소방서 is highly overall correlated with 중부 소방서 and 4 other fieldsHigh correlation
동부 소방서 is highly overall correlated with 북부 소방서 and 2 other fieldsHigh correlation
구분 has unique valuesUnique
중부 소방서 has 7 (31.8%) zerosZeros
남부 소방서 has 5 (22.7%) zerosZeros
북부 소방서 has 8 (36.4%) zerosZeros
남울주 소방서 has 6 (27.3%) zerosZeros
서울주 소방서 has 9 (40.9%) zerosZeros

Reproduction

Analysis started2023-12-11 23:43:02.111491
Analysis finished2023-12-11 23:43:04.427301
Duration2.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T08:43:04.540258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.9545455
Min length3

Characters and Unicode

Total characters87
Distinct characters59
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row펌프차
2nd row물탱크
3rd row무인방수차
4th row화학차
5th row분석차
ValueCountFrequency (%)
펌프차 1
 
4.3%
구급차 1
 
4.3%
순찰차 1
 
4.3%
진단차 1
 
4.3%
행정차 1
 
4.3%
이동체험 1
 
4.3%
소화약제탱크차 1
 
4.3%
장비운반 1
 
4.3%
유조차 1
 
4.3%
트레일러 1
 
4.3%
Other values (13) 13
56.5%
2023-12-12T08:43:04.833121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
차 17
 
19.5%
조 4
 
4.6%
소 3
 
3.4%
화 2
 
2.3%
방 2
 
2.3%
제 2
 
2.3%
구 2
 
2.3%
사 2
 
2.3%
크 2
 
2.3%
탱 2
 
2.3%
Other values (49) 49
56.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 84
96.6%
Other Punctuation 2
 
2.3%
Space Separator 1
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
차 17
 
20.2%
조 4
 
4.8%
소 3
 
3.6%
화 2
 
2.4%
방 2
 
2.4%
제 2
 
2.4%
구 2
 
2.4%
사 2
 
2.4%
크 2
 
2.4%
탱 2
 
2.4%
Other values (46) 46
54.8%
Other Punctuation
ValueCountFrequency (%)
. 1
50.0%
, 1
50.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 84
96.6%
Common 3
 
3.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
차 17
 
20.2%
조 4
 
4.8%
소 3
 
3.6%
화 2
 
2.4%
방 2
 
2.4%
제 2
 
2.4%
구 2
 
2.4%
사 2
 
2.4%
크 2
 
2.4%
탱 2
 
2.4%
Other values (46) 46
54.8%
Common
ValueCountFrequency (%)
. 1
33.3%
, 1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 84
96.6%
ASCII 3
 
3.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
차 17
 
20.2%
조 4
 
4.8%
소 3
 
3.6%
화 2
 
2.4%
방 2
 
2.4%
제 2
 
2.4%
구 2
 
2.4%
사 2
 
2.4%
크 2
 
2.4%
탱 2
 
2.4%
Other values (46) 46
54.8%
ASCII
ValueCountFrequency (%)
. 1
33.3%
, 1
33.3%
1
33.3%

소방본부
Categorical

Distinct5
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Memory size308.0 B
0
12 
1
5 
5
2 
2
2 
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)4.5%

Sample

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

Common Values

ValueCountFrequency (%)
0 12
54.5%
1 5
22.7%
5 2
 
9.1%
2 2
 
9.1%
4 1
 
4.5%

Length

2023-12-12T08:43:04.938005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:43:05.031615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12
54.5%
1 5
22.7%
5 2
 
9.1%
2 2
 
9.1%
4 1
 
4.5%

중부 소방서
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7727273
Minimum0
Maximum6
Zeros7
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T08:43:05.119376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile4.95
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8498859
Coefficient of variation (CV)1.0435254
Kurtosis-0.35491917
Mean1.7727273
Median Absolute Deviation (MAD)1
Skewness0.86266153
Sum39
Variance3.4220779
MonotonicityNot monotonic
2023-12-12T08:43:05.220223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 7
31.8%
1 6
27.3%
4 3
13.6%
3 2
 
9.1%
2 2
 
9.1%
5 1
 
4.5%
6 1
 
4.5%
ValueCountFrequency (%)
0 7
31.8%
1 6
27.3%
2 2
 
9.1%
3 2
 
9.1%
4 3
13.6%
5 1
 
4.5%
6 1
 
4.5%
ValueCountFrequency (%)
6 1
 
4.5%
5 1
 
4.5%
4 3
13.6%
3 2
 
9.1%
2 2
 
9.1%
1 6
27.3%
0 7
31.8%

남부 소방서
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7272727
Minimum0
Maximum10
Zeros5
Zeros (%)22.7%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T08:43:05.334357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33.75
95-th percentile8.9
Maximum10
Range10
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation2.8979766
Coefficient of variation (CV)1.0625914
Kurtosis1.0648179
Mean2.7272727
Median Absolute Deviation (MAD)1.5
Skewness1.3058525
Sum60
Variance8.3982684
MonotonicityNot monotonic
2023-12-12T08:43:05.440073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 5
22.7%
0 5
22.7%
3 3
13.6%
2 3
13.6%
5 2
 
9.1%
9 1
 
4.5%
7 1
 
4.5%
10 1
 
4.5%
4 1
 
4.5%
ValueCountFrequency (%)
0 5
22.7%
1 5
22.7%
2 3
13.6%
3 3
13.6%
4 1
 
4.5%
5 2
 
9.1%
7 1
 
4.5%
9 1
 
4.5%
10 1
 
4.5%
ValueCountFrequency (%)
10 1
 
4.5%
9 1
 
4.5%
7 1
 
4.5%
5 2
 
9.1%
4 1
 
4.5%
3 3
13.6%
2 3
13.6%
1 5
22.7%
0 5
22.7%

동부 소방서
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Memory size308.0 B
0
7 
2
5 
1
5 
4
3 
3
2 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 7
31.8%
2 5
22.7%
1 5
22.7%
4 3
13.6%
3 2
 
9.1%

Length

2023-12-12T08:43:05.584451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:43:05.704832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7
31.8%
2 5
22.7%
1 5
22.7%
4 3
13.6%
3 2
 
9.1%

북부 소방서
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2
Minimum0
Maximum9
Zeros8
Zeros (%)36.4%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T08:43:05.831454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q33.5
95-th percentile5.9
Maximum9
Range9
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.3502786
Coefficient of variation (CV)1.1751393
Kurtosis2.445214
Mean2
Median Absolute Deviation (MAD)1.5
Skewness1.5009226
Sum44
Variance5.5238095
MonotonicityNot monotonic
2023-12-12T08:43:05.926552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 8
36.4%
2 5
22.7%
4 4
18.2%
1 3
 
13.6%
9 1
 
4.5%
6 1
 
4.5%
ValueCountFrequency (%)
0 8
36.4%
1 3
 
13.6%
2 5
22.7%
4 4
18.2%
6 1
 
4.5%
9 1
 
4.5%
ValueCountFrequency (%)
9 1
 
4.5%
6 1
 
4.5%
4 4
18.2%
2 5
22.7%
1 3
 
13.6%
0 8
36.4%

남울주 소방서
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2272727
Minimum0
Maximum7
Zeros6
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T08:43:06.021302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.25
median1.5
Q33
95-th percentile6.9
Maximum7
Range7
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation2.2239351
Coefficient of variation (CV)0.99850149
Kurtosis-0.04368457
Mean2.2272727
Median Absolute Deviation (MAD)1.5
Skewness0.91398696
Sum49
Variance4.9458874
MonotonicityNot monotonic
2023-12-12T08:43:06.126899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 6
27.3%
1 5
22.7%
3 4
18.2%
7 2
 
9.1%
2 2
 
9.1%
5 2
 
9.1%
4 1
 
4.5%
ValueCountFrequency (%)
0 6
27.3%
1 5
22.7%
2 2
 
9.1%
3 4
18.2%
4 1
 
4.5%
5 2
 
9.1%
7 2
 
9.1%
ValueCountFrequency (%)
7 2
 
9.1%
5 2
 
9.1%
4 1
 
4.5%
3 4
18.2%
2 2
 
9.1%
1 5
22.7%
0 6
27.3%

서울주 소방서
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6818182
Minimum0
Maximum9
Zeros9
Zeros (%)40.9%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T08:43:06.256941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5.85
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2336467
Coefficient of variation (CV)1.3281142
Kurtosis4.9187952
Mean1.6818182
Median Absolute Deviation (MAD)1
Skewness2.0480307
Sum37
Variance4.9891775
MonotonicityNot monotonic
2023-12-12T08:43:06.361900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 9
40.9%
2 5
22.7%
1 3
 
13.6%
3 3
 
13.6%
9 1
 
4.5%
6 1
 
4.5%
ValueCountFrequency (%)
0 9
40.9%
1 3
 
13.6%
2 5
22.7%
3 3
 
13.6%
6 1
 
4.5%
9 1
 
4.5%
ValueCountFrequency (%)
9 1
 
4.5%
6 1
 
4.5%
3 3
 
13.6%
2 5
22.7%
1 3
 
13.6%
0 9
40.9%

안전체험관
Categorical

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size308.0 B
0
18 
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 18
81.8%
1 4
 
18.2%

Length

2023-12-12T08:43:06.473227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:43:06.575980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18
81.8%
1 4
 
18.2%

Interactions

2023-12-12T08:43:03.876528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:43:02.435858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:43:02.815608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:43:03.173737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:43:03.502339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:43:03.946442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:43:02.521349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:43:02.881481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:43:03.247697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:43:03.590650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:43:04.014940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:43:02.605301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:43:02.957653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:43:03.314811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:43:03.673081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:43:04.077299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:43:02.673991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:43:03.029881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:43:03.373326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:43:03.743348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:43:04.151203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:43:02.751564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:43:03.116172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:43:03.443933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:43:03.811343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:43:06.650031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분소방본부중부 소방서남부 소방서동부 소방서북부 소방서남울주 소방서서울주 소방서안전체험관
구분1.0001.0001.0001.0001.0001.0001.0001.0001.000
소방본부1.0001.0000.0000.5590.7310.2530.3940.0000.340
중부 소방서1.0000.0001.0000.8680.6560.8770.9190.8790.092
남부 소방서1.0000.5590.8681.0000.8360.9520.8930.8970.428
동부 소방서1.0000.7310.6560.8361.0000.8180.8450.7400.000
북부 소방서1.0000.2530.8770.9520.8181.0000.7910.9610.000
남울주 소방서1.0000.3940.9190.8930.8450.7911.0000.7910.000
서울주 소방서1.0000.0000.8790.8970.7400.9610.7911.0000.000
안전체험관1.0000.3400.0920.4280.0000.0000.0000.0001.000
2023-12-12T08:43:06.777310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소방본부안전체험관동부 소방서
소방본부1.0000.3730.342
안전체험관0.3731.0000.000
동부 소방서0.3420.0001.000
2023-12-12T08:43:06.878671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중부 소방서남부 소방서북부 소방서남울주 소방서서울주 소방서소방본부동부 소방서안전체험관
중부 소방서1.0000.8590.8980.8170.9010.0000.4550.000
남부 소방서0.8591.0000.9180.9580.8330.2470.4810.162
북부 소방서0.8980.9181.0000.9350.9030.1250.6870.000
남울주 소방서0.8170.9580.9351.0000.8670.2170.7010.000
서울주 소방서0.9010.8330.9030.8671.0000.0000.5870.000
소방본부0.0000.2470.1250.2170.0001.0000.3420.373
동부 소방서0.4550.4810.6870.7010.5870.3421.0000.000
안전체험관0.0000.1620.0000.0000.0000.3730.0001.000

Missing values

2023-12-12T08:43:04.259240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:43:04.380602image/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

구분소방본부중부 소방서남부 소방서동부 소방서북부 소방서남울주 소방서서울주 소방서안전체험관
0펌프차05949790
1물탱크03524310
2무인방수차00110100
3화학차01312310
4분석차10000000
5제독차10000000
6소방고가차02222220
7소형사다리01100000
8지휘, 조사차52222220
9구조차23334330
구분소방본부중부 소방서남부 소방서동부 소방서북부 소방서남울주 소방서서울주 소방서안전체험관
12조.배연차00111100
13트레일러01322520
14유조차10000000
15장비운반54422331
16소화약제탱크차00200100
17이동체험00000001
18행정차44544521
19진단차21111110
20순찰차11111120
21홍보차11000001