Overview

Dataset statistics

Number of variables6
Number of observations302
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.2 KiB
Average record size in memory51.4 B

Variable types

Numeric3
Categorical1
Text2

Dataset

Description경상북도 내에 있는 소방서와 산하의 출동안전센터 및 지역대 ID와 출동소방서 명칭 및 안전센터 혹은 지역대의 명칭과 보유 차량 정보를 나타냅니다.
URLhttps://www.data.go.kr/data/15114371/fileData.do

Alerts

소방서_ID is highly overall correlated with 센터ID and 1 other fieldsHigh correlation
센터ID is highly overall correlated with 소방서_ID and 1 other fieldsHigh correlation
출동소방서 is highly overall correlated with 소방서_ID and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 10:04:32.341036
Analysis finished2023-12-12 10:04:34.081363
Duration1.74 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

소방서_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4708.7781
Minimum4700
Maximum4723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-12T19:04:34.149232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4700
5-th percentile4701
Q14703
median4707
Q34714
95-th percentile4720.95
Maximum4723
Range23
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.4391183
Coefficient of variation (CV)0.0013674712
Kurtosis-0.99352927
Mean4708.7781
Median Absolute Deviation (MAD)6
Skewness0.45913977
Sum1422051
Variance41.462245
MonotonicityNot monotonic
2023-12-12T19:04:34.285503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
4701 56
18.5%
4705 31
 
10.3%
4703 23
 
7.6%
4706 23
 
7.6%
4714 21
 
7.0%
4715 16
 
5.3%
4707 16
 
5.3%
4716 15
 
5.0%
4709 11
 
3.6%
4708 10
 
3.3%
Other values (13) 80
26.5%
ValueCountFrequency (%)
4700 2
 
0.7%
4701 56
18.5%
4703 23
7.6%
4704 9
 
3.0%
4705 31
10.3%
4706 23
7.6%
4707 16
 
5.3%
4708 10
 
3.3%
4709 11
 
3.6%
4710 6
 
2.0%
ValueCountFrequency (%)
4723 2
 
0.7%
4722 6
 
2.0%
4721 8
 
2.6%
4720 8
 
2.6%
4719 5
 
1.7%
4718 7
 
2.3%
4717 4
 
1.3%
4716 15
5.0%
4715 16
5.3%
4714 21
7.0%

구급대_ID
Real number (ℝ)

Distinct36
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean171.33775
Minimum101
Maximum510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-12T19:04:34.467402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile102
Q1104
median105
Q3302
95-th percentile401
Maximum510
Range409
Interquartile range (IQR)198

Descriptive statistics

Standard deviation107.39293
Coefficient of variation (CV)0.62679082
Kurtosis-0.1236763
Mean171.33775
Median Absolute Deviation (MAD)2
Skewness1.1720399
Sum51744
Variance11533.241
MonotonicityNot monotonic
2023-12-12T19:04:34.648345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
105 65
21.5%
103 35
11.6%
104 32
10.6%
102 26
 
8.6%
303 18
 
6.0%
107 13
 
4.3%
106 12
 
4.0%
108 8
 
2.6%
304 8
 
2.6%
401 8
 
2.6%
Other values (26) 77
25.5%
ValueCountFrequency (%)
101 6
 
2.0%
102 26
 
8.6%
103 35
11.6%
104 32
10.6%
105 65
21.5%
106 12
 
4.0%
107 13
 
4.3%
108 8
 
2.6%
109 5
 
1.7%
110 6
 
2.0%
ValueCountFrequency (%)
510 1
 
0.3%
502 2
 
0.7%
403 2
 
0.7%
402 7
2.3%
401 8
2.6%
337 3
 
1.0%
336 2
 
0.7%
330 1
 
0.3%
323 1
 
0.3%
322 3
 
1.0%

센터ID
Real number (ℝ)

HIGH CORRELATION 

Distinct158
Distinct (%)52.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4710488.8
Minimum4700106
Maximum4723502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-12T19:04:34.834710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4700106
5-th percentile4703103
Q14705336
median4709105
Q34715108.8
95-th percentile4721256.1
Maximum4723502
Range23396
Interquartile range (IQR)9772.75

Descriptive statistics

Standard deviation5953.0841
Coefficient of variation (CV)0.0012637933
Kurtosis-1.0316855
Mean4710488.8
Median Absolute Deviation (MAD)4998
Skewness0.31813483
Sum1.4225676 × 109
Variance35439211
MonotonicityIncreasing
2023-12-12T19:04:35.013056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4706112 5
 
1.7%
4706104 5
 
1.7%
4714104 5
 
1.7%
4714103 5
 
1.7%
4704106 4
 
1.3%
4715308 4
 
1.3%
4708104 4
 
1.3%
4718104 4
 
1.3%
4716103 4
 
1.3%
4718101 4
 
1.3%
Other values (148) 258
85.4%
ValueCountFrequency (%)
4700106 2
0.7%
4701102 1
 
0.3%
4701103 2
0.7%
4701104 1
 
0.3%
4701105 2
0.7%
4701106 2
0.7%
4701107 1
 
0.3%
4701302 2
0.7%
4703103 3
1.0%
4703104 2
0.7%
ValueCountFrequency (%)
4723502 2
0.7%
4722402 1
0.3%
4722401 1
0.3%
4722303 1
0.3%
4722302 2
0.7%
4722301 1
0.3%
4721403 2
0.7%
4721402 1
0.3%
4721401 1
0.3%
4721302 1
0.3%

출동소방서
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
칠곡소방서
56 
안동소방서
31 
경주소방서
25 
구미소방서
23 
의성소방서
21 
Other values (17)
146 

Length

Max length8
Median length5
Mean length5.2450331
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row119특수대응단
2nd row119특수대응단
3rd row칠곡소방서
4th row칠곡소방서
5th row칠곡소방서

Common Values

ValueCountFrequency (%)
칠곡소방서 56
18.5%
안동소방서 31
 
10.3%
경주소방서 25
 
8.3%
구미소방서 23
 
7.6%
의성소방서 21
 
7.0%
영주소방서 16
 
5.3%
포항북부소방서 16
 
5.3%
포항남부소방서 15
 
5.0%
상주소방서 11
 
3.6%
김천소방서 9
 
3.0%
Other values (12) 79
26.2%

Length

2023-12-12T19:04:35.193716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
칠곡소방서 56
18.5%
안동소방서 31
 
10.3%
경주소방서 25
 
8.3%
구미소방서 23
 
7.6%
의성소방서 21
 
7.0%
영주소방서 16
 
5.3%
포항북부소방서 16
 
5.3%
포항남부소방서 15
 
5.0%
상주소방서 11
 
3.6%
김천소방서 9
 
3.0%
Other values (12) 79
26.2%

출동안전센터_지역대
Text

Distinct139
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2023-12-12T19:04:35.565373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length8.9337748
Min length5

Characters and Unicode

Total characters2698
Distinct characters132
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)19.9%

Sample

1st row소방항공구조대
2nd row소방항공구조대
3rd row북삼119안전센터
4th row금산119안전센터
5th row금산119안전센터
ValueCountFrequency (%)
119구조구급센터 47
 
15.6%
군위119안전센터 5
 
1.7%
봉곡119안전센터 5
 
1.7%
인동119안전센터 5
 
1.7%
용흥119구급대 4
 
1.3%
의흥119안전센터 4
 
1.3%
해도119안전센터 4
 
1.3%
동부119안전센터 4
 
1.3%
영해119안전센터 4
 
1.3%
구)청송119안전센터 3
 
1.0%
Other values (129) 217
71.9%
2023-12-12T19:04:36.124052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 598
22.2%
9 299
11.1%
센 220
 
8.2%
터 220
 
8.2%
안 180
 
6.7%
전 175
 
6.5%
구 127
 
4.7%
대 85
 
3.2%
지 77
 
2.9%
역 73
 
2.7%
Other values (122) 644
23.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1763
65.3%
Decimal Number 897
33.2%
Open Punctuation 19
 
0.7%
Close Punctuation 19
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
센 220
12.5%
터 220
12.5%
안 180
 
10.2%
전 175
 
9.9%
구 127
 
7.2%
대 85
 
4.8%
지 77
 
4.4%
역 73
 
4.1%
급 54
 
3.1%
조 52
 
2.9%
Other values (118) 500
28.4%
Decimal Number
ValueCountFrequency (%)
1 598
66.7%
9 299
33.3%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1763
65.3%
Common 935
34.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
센 220
12.5%
터 220
12.5%
안 180
 
10.2%
전 175
 
9.9%
구 127
 
7.2%
대 85
 
4.8%
지 77
 
4.4%
역 73
 
4.1%
급 54
 
3.1%
조 52
 
2.9%
Other values (118) 500
28.4%
Common
ValueCountFrequency (%)
1 598
64.0%
9 299
32.0%
( 19
 
2.0%
) 19
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1763
65.3%
ASCII 935
34.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 598
64.0%
9 299
32.0%
( 19
 
2.0%
) 19
 
2.0%
Hangul
ValueCountFrequency (%)
센 220
12.5%
터 220
12.5%
안 180
 
10.2%
전 175
 
9.9%
구 127
 
7.2%
대 85
 
4.8%
지 77
 
4.4%
역 73
 
4.1%
급 54
 
3.1%
조 52
 
2.9%
Other values (118) 500
28.4%

차량번호
Text

Distinct202
Distinct (%)66.9%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2023-12-12T19:04:36.507987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length7.8940397
Min length6

Characters and Unicode

Total characters2384
Distinct characters47
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique139 ?
Unique (%)46.0%

Sample

1st rowHL9463
2nd rowHL9462
3rd row998보2777
4th row998보2717
5th row72두1790
ValueCountFrequency (%)
998오3019 7
 
2.3%
998러9807 5
 
1.7%
70두1951 5
 
1.7%
71머5803 5
 
1.7%
998러9849 5
 
1.7%
998노8165 4
 
1.3%
998우8410 4
 
1.3%
998러2500 4
 
1.3%
998마8140 3
 
1.0%
998마8127(영천 3
 
1.0%
Other values (192) 257
85.1%
2023-12-12T19:04:37.006708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 637
26.7%
8 426
17.9%
0 198
 
8.3%
1 150
 
6.3%
3 135
 
5.7%
5 120
 
5.0%
7 116
 
4.9%
2 101
 
4.2%
4 90
 
3.8%
6 85
 
3.6%
Other values (37) 326
13.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2058
86.3%
Other Letter 310
 
13.0%
Uppercase Letter 8
 
0.3%
Close Punctuation 4
 
0.2%
Open Punctuation 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
누 37
11.9%
러 35
11.3%
노 33
10.6%
오 28
 
9.0%
무 23
 
7.4%
가 21
 
6.8%
마 17
 
5.5%
주 17
 
5.5%
부 15
 
4.8%
서 12
 
3.9%
Other values (23) 72
23.2%
Decimal Number
ValueCountFrequency (%)
9 637
31.0%
8 426
20.7%
0 198
 
9.6%
1 150
 
7.3%
3 135
 
6.6%
5 120
 
5.8%
7 116
 
5.6%
2 101
 
4.9%
4 90
 
4.4%
6 85
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
L 4
50.0%
H 4
50.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2066
86.7%
Hangul 310
 
13.0%
Latin 8
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
누 37
11.9%
러 35
11.3%
노 33
10.6%
오 28
 
9.0%
무 23
 
7.4%
가 21
 
6.8%
마 17
 
5.5%
주 17
 
5.5%
부 15
 
4.8%
서 12
 
3.9%
Other values (23) 72
23.2%
Common
ValueCountFrequency (%)
9 637
30.8%
8 426
20.6%
0 198
 
9.6%
1 150
 
7.3%
3 135
 
6.5%
5 120
 
5.8%
7 116
 
5.6%
2 101
 
4.9%
4 90
 
4.4%
6 85
 
4.1%
Other values (2) 8
 
0.4%
Latin
ValueCountFrequency (%)
L 4
50.0%
H 4
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2074
87.0%
Hangul 310
 
13.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 637
30.7%
8 426
20.5%
0 198
 
9.5%
1 150
 
7.2%
3 135
 
6.5%
5 120
 
5.8%
7 116
 
5.6%
2 101
 
4.9%
4 90
 
4.3%
6 85
 
4.1%
Other values (4) 16
 
0.8%
Hangul
ValueCountFrequency (%)
누 37
11.9%
러 35
11.3%
노 33
10.6%
오 28
 
9.0%
무 23
 
7.4%
가 21
 
6.8%
마 17
 
5.5%
주 17
 
5.5%
부 15
 
4.8%
서 12
 
3.9%
Other values (23) 72
23.2%

Interactions

2023-12-12T19:04:33.284631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:04:32.614216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:04:32.920440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:04:33.367131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:04:32.698655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:04:33.038087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:04:33.466065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:04:32.824347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:04:33.161993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:04:37.161132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소방서_ID구급대_ID센터ID출동소방서
소방서_ID1.0000.6900.9880.999
구급대_ID0.6901.0000.6310.751
센터ID0.9880.6311.0000.966
출동소방서0.9990.7510.9661.000
2023-12-12T19:04:37.286640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소방서_ID구급대_ID센터ID출동소방서
소방서_ID1.0000.1440.7100.968
구급대_ID0.1441.0000.1440.479
센터ID0.7100.1441.0000.805
출동소방서0.9680.4790.8051.000

Missing values

2023-12-12T19:04:33.601300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:04:34.032306image/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

소방서_ID구급대_ID센터ID출동소방서출동안전센터_지역대차량번호
047001064700106119특수대응단소방항공구조대HL9463
147001064700106119특수대응단소방항공구조대HL9462
247011024701102칠곡소방서북삼119안전센터998보2777
347011034701103칠곡소방서금산119안전센터998보2717
447011034701103칠곡소방서금산119안전센터72두1790
547011044701104칠곡소방서가산119안전센터998보2737
647011054701105칠곡소방서119구조구급센터998보2725
747011054701105칠곡소방서119구조구급센터998보2727
847011064701106칠곡소방서석적119안전센터998보2789
947011064701106칠곡소방서석적119안전센터998보2736
소방서_ID구급대_ID센터ID출동소방서출동안전센터_지역대차량번호
29247213014721403청송소방서주왕산119지역대998노8147
29347213014721403청송소방서주왕산119지역대998서5603
29447223014722301봉화소방서봉화119안전센터998주5002
29547223024722302봉화소방서춘양119안전센터998주5008
29647223024722302봉화소방서춘양119안전센터998노8165
29747223034722303봉화소방서명호119안전센터998주5007
29847224014722401봉화소방서석포119지역대998주5009
29947224024722402봉화소방서재산119지역대998노8165
30047235024723502119특수대응단119항공대HL9462
30147235024723502119특수대응단119항공대HL9463