Overview

Dataset statistics

Number of variables12
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory113.0 B

Variable types

Numeric11
Categorical1

Dataset

Description119 신고 전화에 대한 유/무선 신고 현황(2011년 ~ 2021년)에 대한 자료로 연도별, 유형, 화재, 구조, 구급, 대민출동 및 기타, 유관기관 이첩, 안내 및 민원, 장난전화, 무응답, 오접속, 기타 항목을 제공함
Author소방청
URLhttps://www.data.go.kr/data/15061186/fileData.do

Alerts

연도별 is highly overall correlated with 대민출동 및 기타 and 3 other fieldsHigh correlation
화 재 is highly overall correlated with 구 조 and 5 other fieldsHigh correlation
구 조 is highly overall correlated with 화 재 and 7 other fieldsHigh correlation
구 급 is highly overall correlated with 화 재 and 7 other fieldsHigh correlation
대민출동 및 기타 is highly overall correlated with 연도별 and 3 other fieldsHigh correlation
유관기관 이첩 is highly overall correlated with 화 재 and 7 other fieldsHigh correlation
안내 및 민원 is highly overall correlated with 화 재 and 6 other fieldsHigh correlation
장난전화 is highly overall correlated with 연도별 and 3 other fieldsHigh correlation
무응답 is highly overall correlated with 화 재 and 7 other fieldsHigh correlation
오접속 is highly overall correlated with 연도별 and 7 other fieldsHigh correlation
기 타 is highly overall correlated with 연도별 and 9 other fieldsHigh correlation
유형 is highly overall correlated with 화 재 and 6 other fieldsHigh correlation
화 재 has unique valuesUnique
구 조 has unique valuesUnique
구 급 has unique valuesUnique
대민출동 및 기타 has unique valuesUnique
유관기관 이첩 has unique valuesUnique
안내 및 민원 has unique valuesUnique
장난전화 has unique valuesUnique
무응답 has unique valuesUnique
오접속 has unique valuesUnique
기 타 has unique valuesUnique

Reproduction

Analysis started2023-12-13 00:00:34.776933
Analysis finished2023-12-13 00:00:44.705071
Duration9.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도별
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016
Minimum2011
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T09:00:44.747742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011.05
Q12013.25
median2016
Q32018.75
95-th percentile2020.95
Maximum2021
Range10
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.2366944
Coefficient of variation (CV)0.0016055032
Kurtosis-1.2191053
Mean2016
Median Absolute Deviation (MAD)3
Skewness0
Sum44352
Variance10.47619
MonotonicityIncreasing
2023-12-13T09:00:44.835082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2011 2
9.1%
2012 2
9.1%
2013 2
9.1%
2014 2
9.1%
2015 2
9.1%
2016 2
9.1%
2017 2
9.1%
2018 2
9.1%
2019 2
9.1%
2020 2
9.1%
ValueCountFrequency (%)
2011 2
9.1%
2012 2
9.1%
2013 2
9.1%
2014 2
9.1%
2015 2
9.1%
2016 2
9.1%
2017 2
9.1%
2018 2
9.1%
2019 2
9.1%
2020 2
9.1%
ValueCountFrequency (%)
2021 2
9.1%
2020 2
9.1%
2019 2
9.1%
2018 2
9.1%
2017 2
9.1%
2016 2
9.1%
2015 2
9.1%
2014 2
9.1%
2013 2
9.1%
2012 2
9.1%

유형
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size308.0 B
유선
11 
무선
11 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row유선
2nd row무선
3rd row유선
4th row무선
5th row유선

Common Values

ValueCountFrequency (%)
유선 11
50.0%
무선 11
50.0%

Length

2023-12-13T09:00:44.923597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:00:45.003090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유선 11
50.0%
무선 11
50.0%

화 재
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139798.14
Minimum40323
Maximum274940
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T09:00:45.079146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40323
5-th percentile49084.7
Q160829.75
median137091
Q3193842.25
95-th percentile274588.15
Maximum274940
Range234617
Interquartile range (IQR)133012.5

Descriptive statistics

Standard deviation87129.872
Coefficient of variation (CV)0.62325489
Kurtosis-1.4312519
Mean139798.14
Median Absolute Deviation (MAD)76036.5
Skewness0.39423813
Sum3075559
Variance7.5916146 × 109
MonotonicityNot monotonic
2023-12-13T09:00:45.176055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
61504 1
 
4.5%
48962 1
 
4.5%
256237 1
 
4.5%
119172 1
 
4.5%
252939 1
 
4.5%
76373 1
 
4.5%
274590 1
 
4.5%
66167 1
 
4.5%
274940 1
 
4.5%
60605 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
40323 1
4.5%
48962 1
4.5%
51416 1
4.5%
51847 1
4.5%
54003 1
4.5%
60605 1
4.5%
61504 1
4.5%
61905 1
4.5%
66167 1
4.5%
76373 1
4.5%
ValueCountFrequency (%)
274940 1
4.5%
274590 1
4.5%
274553 1
4.5%
256237 1
4.5%
252939 1
4.5%
194475 1
4.5%
191944 1
4.5%
187580 1
4.5%
160905 1
4.5%
160109 1
4.5%

구 조
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean269102.05
Minimum83514
Maximum605676
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T09:00:45.265238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum83514
5-th percentile88762.95
Q1106721.25
median192045
Q3458637
95-th percentile542254.3
Maximum605676
Range522162
Interquartile range (IQR)351915.75

Descriptive statistics

Standard deviation182031.88
Coefficient of variation (CV)0.67644183
Kurtosis-1.3111211
Mean269102.05
Median Absolute Deviation (MAD)105959
Skewness0.56400911
Sum5920245
Variance3.3135605 × 1010
MonotonicityNot monotonic
2023-12-13T09:00:45.360707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
137825 1
 
4.5%
100046 1
 
4.5%
605676 1
 
4.5%
94829 1
 
4.5%
504580 1
 
4.5%
83514 1
 
4.5%
544194 1
 
4.5%
91802 1
 
4.5%
500892 1
 
4.5%
88603 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
83514 1
4.5%
88603 1
4.5%
91802 1
4.5%
94829 1
4.5%
100046 1
4.5%
103896 1
4.5%
115197 1
4.5%
131932 1
4.5%
132055 1
4.5%
137825 1
4.5%
ValueCountFrequency (%)
605676 1
4.5%
544194 1
4.5%
505400 1
4.5%
504580 1
4.5%
500892 1
4.5%
490960 1
4.5%
361668 1
4.5%
341507 1
4.5%
301058 1
4.5%
300521 1
4.5%

구 급
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1235739.9
Minimum254383
Maximum2575304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T09:00:45.473903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum254383
5-th percentile261116.25
Q1508870.25
median1033984
Q32033684.8
95-th percentile2371670.1
Maximum2575304
Range2320921
Interquartile range (IQR)1524814.5

Descriptive statistics

Standard deviation813987.06
Coefficient of variation (CV)0.6587042
Kurtosis-1.5193219
Mean1235739.9
Median Absolute Deviation (MAD)667699.5
Skewness0.3197233
Sum27186278
Variance6.6257494 × 1011
MonotonicityNot monotonic
2023-12-13T09:00:45.564554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
747385 1
 
4.5%
432855 1
 
4.5%
2575304 1
 
4.5%
254383 1
 
4.5%
2260522 1
 
4.5%
258428 1
 
4.5%
2372582 1
 
4.5%
312193 1
 
4.5%
2354344 1
 
4.5%
370942 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
254383 1
4.5%
258428 1
4.5%
312193 1
4.5%
370942 1
4.5%
432855 1
4.5%
466356 1
4.5%
636413 1
4.5%
664883 1
4.5%
734678 1
4.5%
747385 1
4.5%
ValueCountFrequency (%)
2575304 1
4.5%
2372582 1
4.5%
2354344 1
4.5%
2260522 1
4.5%
2234678 1
4.5%
2103004 1
4.5%
1825727 1
4.5%
1706341 1
4.5%
1449000 1
4.5%
1358292 1
4.5%

대민출동 및 기타
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean321278.23
Minimum218646
Maximum434511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T09:00:45.662301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum218646
5-th percentile228811.1
Q1278468
median317146
Q3355956
95-th percentile415966.25
Maximum434511
Range215865
Interquartile range (IQR)77488

Descriptive statistics

Standard deviation57955.561
Coefficient of variation (CV)0.18039057
Kurtosis-0.52288077
Mean321278.23
Median Absolute Deviation (MAD)39391
Skewness0.085975907
Sum7068121
Variance3.3588471 × 109
MonotonicityNot monotonic
2023-12-13T09:00:45.745887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
317208 1
 
4.5%
356344 1
 
4.5%
384355 1
 
4.5%
434511 1
 
4.5%
380684 1
 
4.5%
417630 1
 
4.5%
340335 1
 
4.5%
373462 1
 
4.5%
317084 1
 
4.5%
354792 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
218646 1
4.5%
227808 1
4.5%
247870 1
4.5%
270275 1
4.5%
271719 1
4.5%
277562 1
4.5%
281186 1
4.5%
287005 1
4.5%
310251 1
4.5%
315911 1
4.5%
ValueCountFrequency (%)
434511 1
4.5%
417630 1
4.5%
384355 1
4.5%
380684 1
4.5%
373462 1
4.5%
356344 1
4.5%
354792 1
4.5%
346849 1
4.5%
340335 1
4.5%
336634 1
4.5%

유관기관 이첩
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55793.091
Minimum4778
Maximum119596
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T09:00:45.830452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4778
5-th percentile6080.65
Q113577
median55328
Q383403
95-th percentile117749.2
Maximum119596
Range114818
Interquartile range (IQR)69826

Descriptive statistics

Standard deviation40014.236
Coefficient of variation (CV)0.71718981
Kurtosis-1.4504325
Mean55793.091
Median Absolute Deviation (MAD)36519.5
Skewness0.13510078
Sum1227448
Variance1.6011391 × 109
MonotonicityNot monotonic
2023-12-13T09:00:45.925063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
48941 1
 
4.5%
11757 1
 
4.5%
80417 1
 
4.5%
4778 1
 
4.5%
81618 1
 
4.5%
5997 1
 
4.5%
97320 1
 
4.5%
7670 1
 
4.5%
119596 1
 
4.5%
11040 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
4778 1
4.5%
5997 1
4.5%
7670 1
4.5%
11040 1
4.5%
11757 1
4.5%
13092 1
4.5%
15032 1
4.5%
28086 1
4.5%
37245 1
4.5%
39297 1
4.5%
ValueCountFrequency (%)
119596 1
4.5%
117947 1
4.5%
113991 1
4.5%
97320 1
4.5%
88071 1
4.5%
83532 1
4.5%
83016 1
4.5%
81618 1
4.5%
80417 1
4.5%
77290 1
4.5%

안내 및 민원
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1607820.8
Minimum544691
Maximum2984958
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T09:00:46.022454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum544691
5-th percentile553258.75
Q1635036.75
median1529101
Q32564720.5
95-th percentile2777901
Maximum2984958
Range2440267
Interquartile range (IQR)1929683.8

Descriptive statistics

Standard deviation926572.11
Coefficient of variation (CV)0.57629066
Kurtosis-1.7303174
Mean1607820.8
Median Absolute Deviation (MAD)948830.5
Skewness0.14017697
Sum35372058
Variance8.5853588 × 1011
MonotonicityNot monotonic
2023-12-13T09:00:46.123509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
733607 1
 
4.5%
544691 1
 
4.5%
2984958 1
 
4.5%
557760 1
 
4.5%
2669843 1
 
4.5%
602180 1
 
4.5%
2775071 1
 
4.5%
553096 1
 
4.5%
2778050 1
 
4.5%
558361 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
544691 1
4.5%
553096 1
4.5%
556351 1
4.5%
557760 1
4.5%
558361 1
4.5%
602180 1
4.5%
733607 1
4.5%
835236 1
4.5%
1043680 1
4.5%
1117417 1
4.5%
ValueCountFrequency (%)
2984958 1
4.5%
2778050 1
4.5%
2775071 1
4.5%
2763069 1
4.5%
2669843 1
4.5%
2659969 1
4.5%
2278975 1
4.5%
2205184 1
4.5%
2189825 1
4.5%
1906533 1
4.5%

장난전화
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2570.7273
Minimum33
Maximum11996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T09:00:46.222660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile108.65
Q1338.25
median832.5
Q32957
95-th percentile10916.35
Maximum11996
Range11963
Interquartile range (IQR)2618.75

Descriptive statistics

Standard deviation3618.8449
Coefficient of variation (CV)1.4077125
Kurtosis1.9812753
Mean2570.7273
Median Absolute Deviation (MAD)628
Skewness1.7551942
Sum56556
Variance13096039
MonotonicityNot monotonic
2023-12-13T09:00:46.312463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
8301 1
 
4.5%
657 1
 
4.5%
216 1
 
4.5%
33 1
 
4.5%
441 1
 
4.5%
224 1
 
4.5%
304 1
 
4.5%
103 1
 
4.5%
481 1
 
4.5%
272 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
33 1
4.5%
103 1
4.5%
216 1
4.5%
224 1
4.5%
272 1
4.5%
304 1
4.5%
441 1
4.5%
481 1
4.5%
622 1
4.5%
657 1
4.5%
ValueCountFrequency (%)
11996 1
4.5%
11054 1
4.5%
8301 1
4.5%
6863 1
4.5%
3800 1
4.5%
3398 1
4.5%
1634 1
4.5%
1598 1
4.5%
1472 1
4.5%
1422 1
4.5%

무응답
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean919806.23
Minimum171726
Maximum2117829
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T09:00:46.408502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum171726
5-th percentile210155.65
Q1310461.25
median772097.5
Q31532615.8
95-th percentile1769767.8
Maximum2117829
Range1946103
Interquartile range (IQR)1222154.5

Descriptive statistics

Standard deviation643251.83
Coefficient of variation (CV)0.69933406
Kurtosis-1.4843139
Mean919806.23
Median Absolute Deviation (MAD)535610
Skewness0.31380632
Sum20235737
Variance4.1377291 × 1011
MonotonicityNot monotonic
2023-12-13T09:00:46.503013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
309378 1
 
4.5%
346381 1
 
4.5%
1661242 1
 
4.5%
171726 1
 
4.5%
1676702 1
 
4.5%
209317 1
 
4.5%
1774666 1
 
4.5%
226090 1
 
4.5%
1473293 1
 
4.5%
274893 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
171726 1
4.5%
209317 1
4.5%
226090 1
4.5%
246885 1
4.5%
274893 1
4.5%
309378 1
4.5%
313711 1
4.5%
346381 1
4.5%
483034 1
4.5%
558732 1
4.5%
ValueCountFrequency (%)
2117829 1
4.5%
1774666 1
4.5%
1676702 1
4.5%
1661242 1
4.5%
1628968 1
4.5%
1552390 1
4.5%
1473293 1
4.5%
1347383 1
4.5%
1200070 1
4.5%
1118852 1
4.5%

오접속
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1380234.6
Minimum103039
Maximum7783721
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T09:00:46.602854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum103039
5-th percentile117878.1
Q1232232.25
median769555
Q31309926.2
95-th percentile5295304.6
Maximum7783721
Range7680682
Interquartile range (IQR)1077694

Descriptive statistics

Standard deviation1880082
Coefficient of variation (CV)1.3621466
Kurtosis6.5330524
Mean1380234.6
Median Absolute Deviation (MAD)591476.5
Skewness2.5143657
Sum30365162
Variance3.5347082 × 1012
MonotonicityNot monotonic
2023-12-13T09:00:46.719237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2643278 1
 
4.5%
180277 1
 
4.5%
868293 1
 
4.5%
103039 1
 
4.5%
811437 1
 
4.5%
120084 1
 
4.5%
773972 1
 
4.5%
117762 1
 
4.5%
726086 1
 
4.5%
146962 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
103039 1
4.5%
117762 1
4.5%
120084 1
4.5%
139667 1
4.5%
146962 1
4.5%
180277 1
4.5%
388098 1
4.5%
726086 1
4.5%
735291 1
4.5%
736029 1
4.5%
ValueCountFrequency (%)
7783721 1
4.5%
5434885 1
4.5%
2643278 1
4.5%
2629413 1
4.5%
1960473 1
4.5%
1363230 1
4.5%
1150015 1
4.5%
868293 1
4.5%
811437 1
4.5%
788012 1
4.5%

기 타
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean694648.86
Minimum231577
Maximum2401061
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T09:00:46.832889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum231577
5-th percentile237333.5
Q1301103.25
median627344.5
Q3713734
95-th percentile1752689.2
Maximum2401061
Range2169484
Interquartile range (IQR)412630.75

Descriptive statistics

Standard deviation527597.13
Coefficient of variation (CV)0.75951629
Kurtosis4.8625648
Mean694648.86
Median Absolute Deviation (MAD)250103.5
Skewness2.0759039
Sum15282275
Variance2.7835873 × 1011
MonotonicityNot monotonic
2023-12-13T09:00:46.942578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1182085 1
 
4.5%
307572 1
 
4.5%
687298 1
 
4.5%
231577 1
 
4.5%
611430 1
 
4.5%
250616 1
 
4.5%
629055 1
 
4.5%
236739 1
 
4.5%
691360 1
 
4.5%
281925 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
231577 1
4.5%
236739 1
4.5%
248629 1
4.5%
250616 1
4.5%
281925 1
4.5%
298947 1
4.5%
307572 1
4.5%
446910 1
4.5%
510845 1
4.5%
611430 1
4.5%
ValueCountFrequency (%)
2401061 1
4.5%
1782721 1
4.5%
1182085 1
4.5%
1011228 1
4.5%
749262 1
4.5%
721192 1
4.5%
691360 1
4.5%
691338 1
4.5%
687298 1
4.5%
684851 1
4.5%

Interactions

2023-12-13T09:00:43.636933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:35.076262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:35.806817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:36.499390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:37.448396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:38.179118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:38.886262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:39.837985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:40.782169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:41.622883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:42.772933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:43.715838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:35.141814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:35.867997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:36.561499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:37.516160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:38.234488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:38.959108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:39.921615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:40.858776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:41.720083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:42.839143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:43.792953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:35.203756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:35.928498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:36.624325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:37.582530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:38.289366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:39.038342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:40.025541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:40.926686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:41.789861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:42.911908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:43.864151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:35.268921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:35.988888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:36.703267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:37.641585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:38.348779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:39.205214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:40.127659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:40.996629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:41.863952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:42.997530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:43.949149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:35.335292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:36.050368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:36.768671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:37.707170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:38.406375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:39.304083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:40.197304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:41.062716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:41.934520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:43.068247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:44.013364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:35.394274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:36.108519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:36.825844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:37.762779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:38.462692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:39.366762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:40.262482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:41.135187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:42.009664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:43.148593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:44.086380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:35.470302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:36.172741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:36.893899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:37.823999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:38.532303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:39.440918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:40.355094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:41.220451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:42.102631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:43.231638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:44.154690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:35.537107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:36.241548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:36.956365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:37.890008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:38.599222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:39.514751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:40.429802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:41.302219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:42.185829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:43.313126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:44.224001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:35.602765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:36.303398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:37.019014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:37.963907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:38.672738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:39.591892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:40.520951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:41.382094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:42.256705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:43.392149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:44.305053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:35.673554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:36.370291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:37.306689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:38.051546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:38.747404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:39.673785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:40.628250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:41.470827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:42.335121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:43.480837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:44.386388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:35.744725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:36.439516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:37.375710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:38.120454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:38.823217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:39.766352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:40.714875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:41.552005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:42.425753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:00:43.563582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T09:00:47.023041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도별유형화 재구 조구 급대민출동 및 기타유관기관 이첩안내 및 민원장난전화무응답오접속기 타
연도별1.0000.0000.0000.0000.0000.6430.0000.0000.6230.0000.4090.000
유형0.0001.0001.0001.0001.0000.2221.0001.0000.2701.0000.0000.825
화 재0.0001.0001.0000.5760.9810.5510.7630.7280.9110.8310.7630.566
구 조0.0001.0000.5761.0000.7850.0000.8800.7960.5430.8450.6710.792
구 급0.0001.0000.9810.7851.0000.5000.8130.8230.8330.8120.6520.603
대민출동 및 기타0.6430.2220.5510.0000.5001.0000.3690.6620.0000.8750.6180.509
유관기관 이첩0.0001.0000.7630.8800.8130.3691.0000.8040.7730.9300.4250.668
안내 및 민원0.0001.0000.7280.7960.8230.6620.8041.0000.7940.9200.8560.793
장난전화0.6230.2700.9110.5430.8330.0000.7730.7941.0000.8200.8880.937
무응답0.0001.0000.8310.8450.8120.8750.9300.9200.8201.0000.8530.803
오접속0.4090.0000.7630.6710.6520.6180.4250.8560.8880.8531.0000.922
기 타0.0000.8250.5660.7920.6030.5090.6680.7930.9370.8030.9221.000
2023-12-13T09:00:47.128810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도별화 재구 조구 급대민출동 및 기타유관기관 이첩안내 및 민원장난전화무응답오접속기 타유형
연도별1.0000.3060.007-0.0050.782-0.1990.066-0.967-0.136-0.707-0.6190.000
화 재0.3061.0000.8220.814-0.0510.7590.866-0.1600.7230.2520.4520.866
구 조0.0070.8221.0000.988-0.3000.8980.9490.1540.8790.5300.6220.837
구 급-0.0050.8140.9881.000-0.3120.9090.9580.1600.8870.5350.6360.806
대민출동 및 기타0.782-0.051-0.300-0.3121.000-0.482-0.267-0.794-0.369-0.608-0.6590.000
유관기관 이첩-0.1990.7590.8980.909-0.4821.0000.8840.3450.8590.5610.6860.837
안내 및 민원0.0660.8660.9490.958-0.2670.8841.0000.1010.8520.4760.5830.775
장난전화-0.967-0.1600.1540.160-0.7940.3450.1011.0000.3150.7910.7100.224
무응답-0.1360.7230.8790.887-0.3690.8590.8520.3151.0000.6520.6890.775
오접속-0.7070.2520.5300.535-0.6080.5610.4760.7910.6521.0000.8670.000
기 타-0.6190.4520.6220.636-0.6590.6860.5830.7100.6890.8671.0000.779
유형0.0000.8660.8370.8060.0000.8370.7750.2240.7750.0000.7791.000

Missing values

2023-12-13T09:00:44.495571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:00:44.654882image/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

연도별유형화 재구 조구 급대민출동 및 기타유관기관 이첩안내 및 민원장난전화무응답오접속기 타
02011유선6150413782574738531720848941733607830130937826432781182085
12011무선160109221549129290528118677290175295011996162896877837212401061
22012유선61905162541775063218646372451117417686362685319604731011228
32012무선160905300521135829222780883532190653311054211782954348851782721
42013유선5184713205573467827171939297130525238005587321150015510845
52013무선1550103010581449000277562880712205184339812000702629413721192
62014유선540031319326648833468492808610436801634483034736029446910
72014무선1875803415071706341287005830162189825159811188521363230625634
82015유선5141611519763641333663415032835236845313711388098298947
92015무선19194436166818257272478706171522789751422917342788012749262
연도별유형화 재구 조구 급대민출동 및 기타유관기관 이첩안내 및 민원장난전화무응답오접속기 타
122017유선4896210004643285535634411757544691657346381180277307572
132017무선274553505400223467831591111794727630698201552390765138691338
142018유선606058860337094235479211040558361272274893146962281925
152018무선274940500892235434431708411959627780504811473293726086691360
162019유선66167918023121933734627670553096103226090117762236739
172019무선27459054419423725823403359732027750713041774666773972629055
182020유선76373835142584284176305997602180224209317120084250616
192020무선25293950458022605223806848161826698434411676702811437611430
202021유선11917294829254383434511477855776033171726103039231577
212021무선25623760567625753043843558041729849582161661242868293687298