Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 KiB
Average record size in memory68.3 B

Variable types

Categorical5
Numeric3

Alerts

피해유형2() is highly overall correlated with 피해인구() and 6 other fieldsHigh correlation
읍면동명() is highly overall correlated with 피해인구() and 5 other fieldsHigh correlation
피해유형1() is highly overall correlated with 피해인구() and 6 other fieldsHigh correlation
시도명() is highly overall correlated with 시군구주소() and 3 other fieldsHigh correlation
시군구주소() is highly overall correlated with 피해종료일자() and 4 other fieldsHigh correlation
피해인구() is highly overall correlated with 읍면동명() and 2 other fieldsHigh correlation
피해시작일자() is highly overall correlated with 피해종료일자() and 2 other fieldsHigh correlation
피해종료일자() is highly overall correlated with 피해시작일자() and 4 other fieldsHigh correlation
피해유형1() is highly imbalanced (53.1%)Imbalance

Reproduction

Analysis started2023-12-10 13:21:29.786912
Analysis finished2023-12-10 13:21:32.507364
Duration2.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명()
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
전라남도
51 
강원도
40 
경상남도
6 
경상북도
 
3

Length

Max length4
Median length4
Mean length3.6
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강원도
2nd row강원도
3rd row강원도
4th row강원도
5th row강원도

Common Values

ValueCountFrequency (%)
전라남도 51
51.0%
강원도 40
40.0%
경상남도 6
 
6.0%
경상북도 3
 
3.0%

Length

2023-12-10T22:21:32.661967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:21:32.907948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 51
51.0%
강원도 40
40.0%
경상남도 6
 
6.0%
경상북도 3
 
3.0%

시군구주소()
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
완도군
33 
속초시
16 
신안군
15 
태백시
8 
남해군
6 
Other values (9)
22 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique5 ?
Unique (%)5.0%

Sample

1st row속초시
2nd row속초시
3rd row속초시
4th row속초시
5th row속초시

Common Values

ValueCountFrequency (%)
완도군 33
33.0%
속초시 16
16.0%
신안군 15
15.0%
태백시 8
 
8.0%
남해군 6
 
6.0%
강릉시 6
 
6.0%
삼척시 5
 
5.0%
영덕군 3
 
3.0%
동해시 3
 
3.0%
정선군 1
 
1.0%
Other values (4) 4
 
4.0%

Length

2023-12-10T22:21:33.199033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
완도군 33
33.0%
속초시 16
16.0%
신안군 15
15.0%
태백시 8
 
8.0%
남해군 6
 
6.0%
강릉시 6
 
6.0%
삼척시 5
 
5.0%
영덕군 3
 
3.0%
동해시 3
 
3.0%
정선군 1
 
1.0%
Other values (4) 4
 
4.0%

읍면동명()
Categorical

HIGH CORRELATION 

Distinct49
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
노화읍
10 
보길면
7 
청산면
 
6
가곡면
 
5
금일읍
 
5
Other values (44)
67 

Length

Max length10
Median length3
Mean length3.07
Min length2

Unique

Unique28 ?
Unique (%)28.0%

Sample

1st row교동
2nd row교동
3rd row금호동
4th row금호동
5th row노학동

Common Values

ValueCountFrequency (%)
노화읍 10
 
10.0%
보길면 7
 
7.0%
청산면 6
 
6.0%
가곡면 5
 
5.0%
금일읍 5
 
5.0%
팔금면 4
 
4.0%
임자면 4
 
4.0%
안좌면 4
 
4.0%
소안면 3
 
3.0%
대포동 2
 
2.0%
Other values (39) 50
50.0%

Length

2023-12-10T22:21:33.509011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
노화읍 10
 
10.0%
보길면 7
 
7.0%
청산면 6
 
6.0%
가곡면 5
 
5.0%
금일읍 5
 
5.0%
팔금면 4
 
4.0%
임자면 4
 
4.0%
안좌면 4
 
4.0%
소안면 3
 
3.0%
교동 2
 
2.0%
Other values (39) 50
50.0%

피해유형1()
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
제한급수
90 
<NA>
10 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row제한급수
2nd row제한급수
3rd row제한급수
4th row제한급수
5th row제한급수

Common Values

ValueCountFrequency (%)
제한급수 90
90.0%
<NA> 10
 
10.0%

Length

2023-12-10T22:21:33.858689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:21:34.023529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제한급수 90
90.0%
na 10
 
10.0%

피해유형2()
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
88 
운반급수
12 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 88
88.0%
운반급수 12
 
12.0%

Length

2023-12-10T22:21:34.179630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:21:34.319317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 88
88.0%
운반급수 12
 
12.0%

피해인구()
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3454.27
Minimum7
Maximum25016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:34.510505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile59.6
Q11109
median1800
Q33691.75
95-th percentile11862.4
Maximum25016
Range25009
Interquartile range (IQR)2582.75

Descriptive statistics

Standard deviation4989.5681
Coefficient of variation (CV)1.4444638
Kurtosis9.7305318
Mean3454.27
Median Absolute Deviation (MAD)1473
Skewness3.0473073
Sum345427
Variance24895790
MonotonicityNot monotonic
2023-12-10T22:21:34.726538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200 9
 
9.0%
1800 6
 
6.0%
1500 5
 
5.0%
3316 3
 
3.0%
391 3
 
3.0%
3694 2
 
2.0%
193 2
 
2.0%
2988 2
 
2.0%
1109 2
 
2.0%
1115 2
 
2.0%
Other values (59) 64
64.0%
ValueCountFrequency (%)
7 1
1.0%
25 2
2.0%
46 1
1.0%
52 1
1.0%
60 2
2.0%
69 1
1.0%
76 1
1.0%
77 1
1.0%
93 2
2.0%
99 1
1.0%
ValueCountFrequency (%)
25016 1
1.0%
23996 1
1.0%
23541 1
1.0%
22874 1
1.0%
16259 1
1.0%
11631 1
1.0%
11161 1
1.0%
10384 1
1.0%
7566 1
1.0%
7417 1
1.0%

피해시작일자()
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20133950
Minimum20071015
Maximum20190116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:34.950046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20071015
5-th percentile20080514
Q120091102
median20140472
Q320170546
95-th percentile20180206
Maximum20190116
Range119101
Interquartile range (IQR)79443.5

Descriptive statistics

Standard deviation36269.885
Coefficient of variation (CV)0.0018014292
Kurtosis-1.3642594
Mean20133950
Median Absolute Deviation (MAD)30342.5
Skewness-0.24438872
Sum2.013395 × 109
Variance1.3155045 × 109
MonotonicityNot monotonic
2023-12-10T22:21:35.199794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20150617 8
 
8.0%
20090106 8
 
8.0%
20180206 8
 
8.0%
20160101 4
 
4.0%
20180101 3
 
3.0%
20150601 3
 
3.0%
20090211 3
 
3.0%
20110117 2
 
2.0%
20180305 2
 
2.0%
20170807 2
 
2.0%
Other values (48) 57
57.0%
ValueCountFrequency (%)
20071015 1
 
1.0%
20071210 2
 
2.0%
20080212 1
 
1.0%
20080218 1
 
1.0%
20080530 1
 
1.0%
20081013 1
 
1.0%
20081031 1
 
1.0%
20081115 2
 
2.0%
20081201 1
 
1.0%
20090106 8
8.0%
ValueCountFrequency (%)
20190116 1
 
1.0%
20180305 2
 
2.0%
20180206 8
8.0%
20180126 1
 
1.0%
20180113 1
 
1.0%
20180112 1
 
1.0%
20180101 3
 
3.0%
20170815 2
 
2.0%
20170807 2
 
2.0%
20170713 1
 
1.0%

피해종료일자()
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19935505
Minimum0
Maximum20190502
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:35.434310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20080528
Q120100292
median20140861
Q320170628
95-th percentile20180326
Maximum20190502
Range20190502
Interquartile range (IQR)70335.5

Descriptive statistics

Standard deviation2013982.3
Coefficient of variation (CV)0.10102489
Kurtosis99.939646
Mean19935505
Median Absolute Deviation (MAD)30594
Skewness-9.9955193
Sum1.9935505 × 109
Variance4.0561247 × 1012
MonotonicityNot monotonic
2023-12-10T22:21:36.222554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20150626 11
 
11.0%
20180305 8
 
8.0%
20090325 8
 
8.0%
20151231 4
 
4.0%
20090415 3
 
3.0%
20171231 3
 
3.0%
20080528 3
 
3.0%
20140317 3
 
3.0%
20150616 2
 
2.0%
20140525 2
 
2.0%
Other values (42) 53
53.0%
ValueCountFrequency (%)
0 1
 
1.0%
20080421 1
 
1.0%
20080526 1
 
1.0%
20080528 3
 
3.0%
20080620 1
 
1.0%
20090210 1
 
1.0%
20090218 1
 
1.0%
20090323 1
 
1.0%
20090325 8
8.0%
20090330 2
 
2.0%
ValueCountFrequency (%)
20190502 1
 
1.0%
20180513 1
 
1.0%
20180423 2
 
2.0%
20180408 1
 
1.0%
20180322 1
 
1.0%
20180320 2
 
2.0%
20180311 1
 
1.0%
20180305 8
8.0%
20180126 1
 
1.0%
20180119 1
 
1.0%

Interactions

2023-12-10T22:21:31.641270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:30.486531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:31.128680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:31.780152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:30.768699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:31.328476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:32.000266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:30.898172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:31.466878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:21:36.382554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명()시군구주소()읍면동명()피해인구()피해시작일자()피해종료일자()
시도명()1.0001.0001.0000.3010.6740.000
시군구주소()1.0001.0001.0000.6430.7371.000
읍면동명()1.0001.0001.0001.0000.0001.000
피해인구()0.3010.6431.0001.0000.0000.000
피해시작일자()0.6740.7370.0000.0001.0000.000
피해종료일자()0.0001.0001.0000.0000.0001.000
2023-12-10T22:21:36.564325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
피해유형2()읍면동명()피해유형1()시도명()시군구주소()
피해유형2()1.0001.0001.0001.0001.000
읍면동명()1.0001.0001.0000.7290.770
피해유형1()1.0001.0001.0001.0001.000
시도명()1.0000.7291.0001.0000.946
시군구주소()1.0000.7701.0000.9461.000
2023-12-10T22:21:36.735513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
피해인구()피해시작일자()피해종료일자()시도명()시군구주소()읍면동명()피해유형1()피해유형2()
피해인구()1.0000.1420.1200.2060.2800.7251.0001.000
피해시작일자()0.1421.0000.9790.4630.4010.0001.0001.000
피해종료일자()0.1200.9791.0000.0000.9370.7211.0001.000
시도명()0.2060.4630.0001.0000.9460.7291.0001.000
시군구주소()0.2800.4010.9370.9461.0000.7701.0001.000
읍면동명()0.7250.0000.7210.7290.7701.0001.0001.000
피해유형1()1.0001.0001.0001.0001.0001.0001.0001.000
피해유형2()1.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-10T22:21:32.234596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:21:32.424788image/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

시도명()시군구주소()읍면동명()피해유형1()피해유형2()피해인구()피해시작일자()피해종료일자()
0강원도속초시교동제한급수<NA>111612015061720150626
1강원도속초시교동제한급수<NA>103842018020620180305
2강원도속초시금호동제한급수<NA>74172015061720150626
3강원도속초시금호동제한급수<NA>68342018020620180305
4강원도속초시노학동제한급수<NA>235412015061720150626
5강원도속초시노학동제한급수<NA>228742018020620180305
6강원도속초시대포동제한급수<NA>33822015061720150626
7강원도속초시대포동제한급수<NA>31682018020620180305
8강원도속초시동명동제한급수<NA>36912015061720150626
9강원도속초시동명동제한급수<NA>43412018020620180305
시도명()시군구주소()읍면동명()피해유형1()피해유형2()피해인구()피해시작일자()피해종료일자()
90강원도강릉시왕산면<NA>운반급수772017062620170627
91강원도고성군거진읍제한급수<NA>692017052420170630
92강원도동해시망상동<NA>운반급수1602015052720150626
93강원도동해시삼화동<NA>운반급수252018011320180119
94강원도동해시삼화동<NA>운반급수252018012620180126
95강원도삼척시가곡면<NA>운반급수932011011720110120
96강원도삼척시가곡면<NA>운반급수992011011720110120
97강원도삼척시가곡면<NA>운반급수602014072820140818
98강원도삼척시가곡면<NA>운반급수602015052620150626
99강원도삼척시가곡면<NA>운반급수932018011220180112