Overview

Dataset statistics

Number of variables11
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory101.7 B

Variable types

Text1
Numeric10

Dataset

Description소방청 119신고 지령시스템 운영 현황(2017)
Author소방청
URLhttps://www.data.go.kr/data/15052715/fileData.do

Alerts

화재출동 is highly overall correlated with 구조출동 and 6 other fieldsHigh correlation
구조출동 is highly overall correlated with 화재출동 and 5 other fieldsHigh correlation
구급출동 is highly overall correlated with 화재출동 and 6 other fieldsHigh correlation
대민출동 및 기타 is highly overall correlated with 화재출동 and 1 other fieldsHigh correlation
유관기관이첩 is highly overall correlated with 화재출동 and 5 other fieldsHigh correlation
안내및민원 is highly overall correlated with 화재출동 and 6 other fieldsHigh correlation
장난전화 is highly overall correlated with 구급출동 and 1 other fieldsHigh correlation
무응답 is highly overall correlated with 화재출동 and 6 other fieldsHigh correlation
오접속 is highly overall correlated with 화재출동 and 5 other fieldsHigh correlation
화재출동 has unique valuesUnique
구조출동 has unique valuesUnique
구급출동 has unique valuesUnique
대민출동 및 기타 has unique valuesUnique
유관기관이첩 has unique valuesUnique
안내및민원 has unique valuesUnique
무응답 has unique valuesUnique
오접속 has unique valuesUnique
기타 has unique valuesUnique
장난전화 has 9 (25.0%) zerosZeros

Reproduction

Analysis started2023-12-12 16:05:16.026297
Analysis finished2023-12-12 16:05:27.148791
Duration11.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

본부
Text

Distinct35
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-13T01:05:27.283413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters216
Distinct characters27
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

Unique34 ?
Unique (%)94.4%

Sample

1st row서울(유선)
2nd row서울(무선)
3rd row부산(유선)
4th row부산(무선)
5th row인천(유선)
ValueCountFrequency (%)
전북(유선 2
 
5.6%
강원(유선 1
 
2.8%
전남(무선 1
 
2.8%
충북(유선 1
 
2.8%
충북(무선 1
 
2.8%
충남(유선 1
 
2.8%
충남(무선 1
 
2.8%
전남(유선 1
 
2.8%
강원(무선 1
 
2.8%
서울(유선 1
 
2.8%
Other values (25) 25
69.4%
2023-12-13T01:05:27.607070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 36
16.7%
선 36
16.7%
) 36
16.7%
유 19
8.8%
무 17
 
7.9%
전 6
 
2.8%
북 6
 
2.8%
남 6
 
2.8%
경 6
 
2.8%
원 4
 
1.9%
Other values (17) 44
20.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 144
66.7%
Open Punctuation 36
 
16.7%
Close Punctuation 36
 
16.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
선 36
25.0%
유 19
13.2%
무 17
11.8%
전 6
 
4.2%
북 6
 
4.2%
남 6
 
4.2%
경 6
 
4.2%
원 4
 
2.8%
충 4
 
2.8%
울 4
 
2.8%
Other values (15) 36
25.0%
Open Punctuation
ValueCountFrequency (%)
( 36
100.0%
Close Punctuation
ValueCountFrequency (%)
) 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 144
66.7%
Common 72
33.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
선 36
25.0%
유 19
13.2%
무 17
11.8%
전 6
 
4.2%
북 6
 
4.2%
남 6
 
4.2%
경 6
 
4.2%
원 4
 
2.8%
충 4
 
2.8%
울 4
 
2.8%
Other values (15) 36
25.0%
Common
ValueCountFrequency (%)
( 36
50.0%
) 36
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 144
66.7%
ASCII 72
33.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 36
50.0%
) 36
50.0%
Hangul
ValueCountFrequency (%)
선 36
25.0%
유 19
13.2%
무 17
11.8%
전 6
 
4.2%
북 6
 
4.2%
남 6
 
4.2%
경 6
 
4.2%
원 4
 
2.8%
충 4
 
2.8%
울 4
 
2.8%
Other values (15) 36
25.0%

화재출동
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8986.5278
Minimum117
Maximum83133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T01:05:27.732344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum117
5-th percentile396
Q11324.25
median3856
Q311742
95-th percentile22672.25
Maximum83133
Range83016
Interquartile range (IQR)10417.75

Descriptive statistics

Standard deviation14595.482
Coefficient of variation (CV)1.6241515
Kurtosis19.463201
Mean8986.5278
Median Absolute Deviation (MAD)3039
Skewness3.9920995
Sum323515
Variance2.130281 × 108
MonotonicityNot monotonic
2023-12-13T01:05:27.863363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
2907 1
 
2.8%
11068 1
 
2.8%
13764 1
 
2.8%
3415 1
 
2.8%
21856 1
 
2.8%
964 1
 
2.8%
8235 1
 
2.8%
3468 1
 
2.8%
19713 1
 
2.8%
5452 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
117 1
2.8%
243 1
2.8%
447 1
2.8%
517 1
2.8%
670 1
2.8%
964 1
2.8%
983 1
2.8%
1062 1
2.8%
1085 1
2.8%
1404 1
2.8%
ValueCountFrequency (%)
83133 1
2.8%
25121 1
2.8%
21856 1
2.8%
19713 1
2.8%
19490 1
2.8%
19148 1
2.8%
16790 1
2.8%
15188 1
2.8%
13764 1
2.8%
11068 1
2.8%

구조출동
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16817.944
Minimum489
Maximum133922
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T01:05:28.001944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum489
5-th percentile1507.25
Q13942.75
median9379.5
Q324611
95-th percentile36252
Maximum133922
Range133433
Interquartile range (IQR)20668.25

Descriptive statistics

Standard deviation23165.854
Coefficient of variation (CV)1.3774486
Kurtosis19.000036
Mean16817.944
Median Absolute Deviation (MAD)6842
Skewness3.8818086
Sum605446
Variance5.3665679 × 108
MonotonicityNot monotonic
2023-12-13T01:05:28.117965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
8467 1
 
2.8%
28419 1
 
2.8%
19647 1
 
2.8%
6059 1
 
2.8%
28922 1
 
2.8%
4107 1
 
2.8%
22787 1
 
2.8%
5477 1
 
2.8%
26267 1
 
2.8%
9903 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
489 1
2.8%
875 1
2.8%
1718 1
2.8%
1914 1
2.8%
2233 1
2.8%
2842 1
2.8%
3369 1
2.8%
3402 1
2.8%
3450 1
2.8%
4107 1
2.8%
ValueCountFrequency (%)
133922 1
2.8%
36306 1
2.8%
36234 1
2.8%
34428 1
2.8%
33163 1
2.8%
28922 1
2.8%
28419 1
2.8%
27460 1
2.8%
26267 1
2.8%
24059 1
2.8%

구급출동
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74098.139
Minimum1449
Maximum516147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T01:05:28.243032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1449
5-th percentile6439.25
Q117649
median36152.5
Q392476
95-th percentile218775
Maximum516147
Range514698
Interquartile range (IQR)74827

Descriptive statistics

Standard deviation107739.21
Coefficient of variation (CV)1.454007
Kurtosis10.925982
Mean74098.139
Median Absolute Deviation (MAD)28801.5
Skewness3.2006511
Sum2667533
Variance1.1607738 × 1010
MonotonicityNot monotonic
2023-12-13T01:05:28.404749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
86451 1
 
2.8%
89821 1
 
2.8%
75020 1
 
2.8%
21795 1
 
2.8%
101400 1
 
2.8%
19373 1
 
2.8%
86920 1
 
2.8%
23440 1
 
2.8%
93361 1
 
2.8%
27065 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
1449 1
2.8%
4913 1
2.8%
6948 1
2.8%
7754 1
2.8%
9527 1
2.8%
11233 1
2.8%
11300 1
2.8%
13995 1
2.8%
16410 1
2.8%
18062 1
2.8%
ValueCountFrequency (%)
516147 1
2.8%
440190 1
2.8%
144970 1
2.8%
125510 1
2.8%
119779 1
2.8%
101400 1
2.8%
98757 1
2.8%
94396 1
2.8%
93361 1
2.8%
92181 1
2.8%

대민출동 및 기타
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18673.75
Minimum30
Maximum95404
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T01:05:28.542873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile214.25
Q12689.25
median12009
Q321936.25
95-th percentile58860.5
Maximum95404
Range95374
Interquartile range (IQR)19247

Descriptive statistics

Standard deviation22213.058
Coefficient of variation (CV)1.1895338
Kurtosis3.1536305
Mean18673.75
Median Absolute Deviation (MAD)9413.5
Skewness1.82312
Sum672255
Variance4.9341992 × 108
MonotonicityNot monotonic
2023-12-13T01:05:28.667296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
14165 1
 
2.8%
10674 1
 
2.8%
9128 1
 
2.8%
55605 1
 
2.8%
24712 1
 
2.8%
51617 1
 
2.8%
19168 1
 
2.8%
16383 1
 
2.8%
23941 1
 
2.8%
58403 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
30 1
2.8%
119 1
2.8%
246 1
2.8%
486 1
2.8%
1025 1
2.8%
1592 1
2.8%
1717 1
2.8%
2232 1
2.8%
2441 1
2.8%
2772 1
2.8%
ValueCountFrequency (%)
95404 1
2.8%
60233 1
2.8%
58403 1
2.8%
55807 1
2.8%
55605 1
2.8%
51617 1
2.8%
27901 1
2.8%
24712 1
2.8%
23941 1
2.8%
21268 1
2.8%

유관기관이첩
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3602.8889
Minimum49
Maximum44317
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T01:05:28.788513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile83
Q1421
median1345.5
Q33987.75
95-th percentile8718.25
Maximum44317
Range44268
Interquartile range (IQR)3566.75

Descriptive statistics

Standard deviation7485.5127
Coefficient of variation (CV)2.0776418
Kurtosis26.454898
Mean3602.8889
Median Absolute Deviation (MAD)1161
Skewness4.8572019
Sum129704
Variance56032900
MonotonicityNot monotonic
2023-12-13T01:05:28.932343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1296 1
 
2.8%
6251 1
 
2.8%
1588 1
 
2.8%
488 1
 
2.8%
9328 1
 
2.8%
382 1
 
2.8%
3931 1
 
2.8%
539 1
 
2.8%
6599 1
 
2.8%
434 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
49 1
2.8%
77 1
2.8%
85 1
2.8%
97 1
2.8%
128 1
2.8%
130 1
2.8%
239 1
2.8%
347 1
2.8%
382 1
2.8%
434 1
2.8%
ValueCountFrequency (%)
44317 1
2.8%
9328 1
2.8%
8515 1
2.8%
7961 1
2.8%
6599 1
2.8%
6586 1
2.8%
6251 1
2.8%
5519 1
2.8%
4158 1
2.8%
3931 1
2.8%

안내및민원
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91882.222
Minimum2648
Maximum553147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T01:05:29.062328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2648
5-th percentile7998.75
Q121697
median51131.5
Q3114910.75
95-th percentile293328.75
Maximum553147
Range550499
Interquartile range (IQR)93213.75

Descriptive statistics

Standard deviation113429.74
Coefficient of variation (CV)1.2345124
Kurtosis8.3686193
Mean91882.222
Median Absolute Deviation (MAD)35766.5
Skewness2.6828039
Sum3307760
Variance1.2866306 × 1010
MonotonicityNot monotonic
2023-12-13T01:05:29.223227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
99728 1
 
2.8%
113973 1
 
2.8%
159246 1
 
2.8%
44670 1
 
2.8%
130384 1
 
2.8%
34640 1
 
2.8%
169964 1
 
2.8%
19579 1
 
2.8%
75587 1
 
2.8%
37475 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
2648 1
2.8%
7578 1
2.8%
8139 1
2.8%
11130 1
2.8%
14513 1
2.8%
16981 1
2.8%
17782 1
2.8%
18174 1
2.8%
19579 1
2.8%
22403 1
2.8%
ValueCountFrequency (%)
553147 1
2.8%
410490 1
2.8%
254275 1
2.8%
169964 1
2.8%
159246 1
2.8%
152655 1
2.8%
130384 1
2.8%
129934 1
2.8%
116566 1
2.8%
114359 1
2.8%

장난전화
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.027778
Minimum0
Maximum470
Zeros9
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T01:05:29.354976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median8
Q322
95-th percentile226.75
Maximum470
Range470
Interquartile range (IQR)21.25

Descriptive statistics

Standard deviation108.48844
Coefficient of variation (CV)2.6442681
Kurtosis13.04578
Mean41.027778
Median Absolute Deviation (MAD)8
Skewness3.6861672
Sum1477
Variance11769.742
MonotonicityNot monotonic
2023-12-13T01:05:29.462444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 9
25.0%
1 5
13.9%
14 3
 
8.3%
8 2
 
5.6%
20 2
 
5.6%
5 1
 
2.8%
33 1
 
2.8%
34 1
 
2.8%
12 1
 
2.8%
466 1
 
2.8%
Other values (10) 10
27.8%
ValueCountFrequency (%)
0 9
25.0%
1 5
13.9%
3 1
 
2.8%
4 1
 
2.8%
5 1
 
2.8%
8 2
 
5.6%
9 1
 
2.8%
12 1
 
2.8%
14 3
 
8.3%
18 1
 
2.8%
ValueCountFrequency (%)
470 1
2.8%
466 1
2.8%
147 1
2.8%
70 1
2.8%
40 1
2.8%
35 1
2.8%
34 1
2.8%
33 1
2.8%
28 1
2.8%
20 2
5.6%

무응답
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52743.639
Minimum231
Maximum328752
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T01:05:29.588036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum231
5-th percentile723
Q18232.5
median24949
Q366514.75
95-th percentile172112.25
Maximum328752
Range328521
Interquartile range (IQR)58282.25

Descriptive statistics

Standard deviation74223.79
Coefficient of variation (CV)1.4072558
Kurtosis7.8151365
Mean52743.639
Median Absolute Deviation (MAD)22817.5
Skewness2.6806817
Sum1898771
Variance5.509171 × 109
MonotonicityNot monotonic
2023-12-13T01:05:29.718609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
128212 1
 
2.8%
56383 1
 
2.8%
55299 1
 
2.8%
19462 1
 
2.8%
78518 1
 
2.8%
11500 1
 
2.8%
66841 1
 
2.8%
9912 1
 
2.8%
67078 1
 
2.8%
18230 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
231 1
2.8%
543 1
2.8%
783 1
2.8%
2143 1
2.8%
4022 1
2.8%
5028 1
2.8%
5788 1
2.8%
6387 1
2.8%
6539 1
2.8%
8797 1
2.8%
ValueCountFrequency (%)
328752 1
2.8%
303813 1
2.8%
128212 1
2.8%
123633 1
2.8%
110111 1
2.8%
86796 1
2.8%
78518 1
2.8%
67078 1
2.8%
66841 1
2.8%
66406 1
2.8%

오접속
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26261.528
Minimum793
Maximum214881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T01:05:29.880209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum793
5-th percentile1963
Q16631.5
median12871
Q334774
95-th percentile67196
Maximum214881
Range214088
Interquartile range (IQR)28142.5

Descriptive statistics

Standard deviation37855.692
Coefficient of variation (CV)1.4414886
Kurtosis17.843456
Mean26261.528
Median Absolute Deviation (MAD)9124.5
Skewness3.7931205
Sum945415
Variance1.4330534 × 109
MonotonicityNot monotonic
2023-12-13T01:05:30.024326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
14918 1
 
2.8%
41258 1
 
2.8%
33137 1
 
2.8%
8898 1
 
2.8%
25479 1
 
2.8%
1997 1
 
2.8%
8500 1
 
2.8%
13169 1
 
2.8%
62903 1
 
2.8%
3956 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
793 1
2.8%
1861 1
2.8%
1997 1
2.8%
2149 1
2.8%
3537 1
2.8%
3956 1
2.8%
5526 1
2.8%
6239 1
2.8%
6327 1
2.8%
6733 1
2.8%
ValueCountFrequency (%)
214881 1
2.8%
72992 1
2.8%
65264 1
2.8%
62903 1
2.8%
51780 1
2.8%
45268 1
2.8%
44865 1
2.8%
41258 1
2.8%
39685 1
2.8%
33137 1
2.8%

기타
Real number (ℝ)

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27747.5
Minimum40
Maximum296121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T01:05:30.154307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile643.25
Q16043
median11592
Q326764.25
95-th percentile80947.25
Maximum296121
Range296081
Interquartile range (IQR)20721.25

Descriptive statistics

Standard deviation54020.045
Coefficient of variation (CV)1.9468437
Kurtosis18.733576
Mean27747.5
Median Absolute Deviation (MAD)8624.5
Skewness4.14329
Sum998910
Variance2.9181653 × 109
MonotonicityNot monotonic
2023-12-13T01:05:30.298003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
162722 1
 
2.8%
10647 1
 
2.8%
8702 1
 
2.8%
7600 1
 
2.8%
21479 1
 
2.8%
6953 1
 
2.8%
31746 1
 
2.8%
9194 1
 
2.8%
34217 1
 
2.8%
39926 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
40 1
2.8%
272 1
2.8%
767 1
2.8%
953 1
2.8%
2064 1
2.8%
2878 1
2.8%
2914 1
2.8%
3914 1
2.8%
4369 1
2.8%
6601 1
2.8%
ValueCountFrequency (%)
296121 1
2.8%
162722 1
2.8%
53689 1
2.8%
46912 1
2.8%
46230 1
2.8%
39926 1
2.8%
34217 1
2.8%
31746 1
2.8%
30305 1
2.8%
25584 1
2.8%

Interactions

2023-12-13T01:05:25.991002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:16.381923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:17.462227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:18.720101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:19.517188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:20.443433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:21.302278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:22.309473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:23.467212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:24.668857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:26.075006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:16.491883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:17.558231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:18.793904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:19.593808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:20.529283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:21.394991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:22.417394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:23.559273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:24.782047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:26.164815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:16.654721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:17.675206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:18.887683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:19.673903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:20.609427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:21.487794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:22.537253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:23.687786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:24.920866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:26.239730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:16.818540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:17.784311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:18.959267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:19.768284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:20.694582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:21.585564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:22.668712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:23.792283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:25.025961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:26.316265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:16.915668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:17.887618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:19.038627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:19.856362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:20.774931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:21.687853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:22.789101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:23.926996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:25.149574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:26.390162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:17.031002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:17.977709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:19.110051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:19.953394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:20.849526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:21.785872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:22.909081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:24.039908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:25.260131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:26.480358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:17.127400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:18.058972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:19.195877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:20.055218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:20.960202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:21.877440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:23.029954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:24.151744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:25.356360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:26.589088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:17.207921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:18.141971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:19.287307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:20.147899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:21.052973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:21.990900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:23.116689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:24.275359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:25.454216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:26.724205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:17.296336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:18.542069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:19.369159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:20.268423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:21.151339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:22.119622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:23.234529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:24.398654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:25.840119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:26.810392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:17.369494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:18.628641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:19.439605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:20.353239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:21.218016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:22.206338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:23.342614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:24.526913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:05:25.911655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:05:30.423556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
본부화재출동구조출동구급출동대민출동 및 기타유관기관이첩안내및민원장난전화무응답오접속기타
본부1.0001.0000.8450.9600.4261.0000.0001.0000.8921.0000.885
화재출동1.0001.0000.7870.9340.3750.7510.7360.4750.8510.9050.177
구조출동0.8450.7871.0000.8040.5210.9430.8470.7580.6770.8060.276
구급출동0.9600.9340.8041.0000.5630.7510.9180.7400.9650.9170.669
대민출동 및 기타0.4260.3750.5210.5631.0000.4460.5760.5350.4310.2240.590
유관기관이첩1.0000.7510.9430.7510.4461.0000.7640.6510.5810.7390.000
안내및민원0.0000.7360.8470.9180.5760.7641.0000.8070.8140.7840.752
장난전화1.0000.4750.7580.7400.5350.6510.8071.0000.6120.4630.979
무응답0.8920.8510.6770.9650.4310.5810.8140.6121.0000.8440.648
오접속1.0000.9050.8060.9170.2240.7390.7840.4630.8441.0000.000
기타0.8850.1770.2760.6690.5900.0000.7520.9790.6480.0001.000
2023-12-13T01:05:30.609470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
화재출동구조출동구급출동대민출동 및 기타유관기관이첩안내및민원장난전화무응답오접속기타
화재출동1.0000.9590.9290.5300.8800.8610.4680.8160.7820.455
구조출동0.9591.0000.9630.4900.9110.8940.4750.8470.7920.446
구급출동0.9290.9631.0000.4990.8810.9320.5380.9230.8180.437
대민출동 및 기타0.5300.4900.4991.0000.3810.5260.3350.4400.3150.267
유관기관이첩0.8800.9110.8810.3811.0000.7610.3840.7720.7960.373
안내및민원0.8610.8940.9320.5260.7611.0000.4880.9050.6780.431
장난전화0.4680.4750.5380.3350.3840.4881.0000.5960.3810.296
무응답0.8160.8470.9230.4400.7720.9050.5961.0000.6830.411
오접속0.7820.7920.8180.3150.7960.6780.3810.6831.0000.254
기타0.4550.4460.4370.2670.3730.4310.2960.4110.2541.000

Missing values

2023-12-13T01:05:26.932064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:05:27.089337image/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

본부화재출동구조출동구급출동대민출동 및 기타유관기관이첩안내및민원장난전화무응답오접속기타
0서울(유선)2907846786451141651296997287012821214918162722
1서울(무선)194903316344019060233384455314714732875225993296121
2부산(유선)151954352564754036123736931555011649953
3부산(무선)7551344281449702790165862542758110111652646601
4인천(유선)106234021641015922391778220114671411015236
5인천(무선)8145206019218112126176611656628664064486546230
6대구(유선)362642022132927721395224031824645159984369
7대구(무선)151882405912551081737961114359401236335178020163
8광주(유선)5173369952730130145131916455262064
9광주(무선)4777149065869110252439831849595772400512226
본부화재출동구조출동구급출동대민출동 및 기타유관기관이첩안내및민원장난전화무응답오접속기타
26전남(유선)3468547723440163835391957919912131699194
27전남(무선)197132626793361239416599755871670786290334217
28경북(유선)545299032706558403434374753418230395639926
29경북(무선)251213623411977910783551915265533867961257353689
30경남(유선)39157654186409540482827812550281150317338
31경남(무선)19148363069439611907851511300014426497299246912
32창원(유선)670223377544622347757802311091312001
33창원(무선)3797938432973699018313242007833968519931
34제주(유선)243875694811949111300214321492914
35제주(무선)340793754126817171294793110252531123516093