Overview

Dataset statistics

Number of variables19
Number of observations23
Missing cells17
Missing cells (%)3.9%
Duplicate rows1
Duplicate rows (%)4.3%
Total size in memory4.0 KiB
Average record size in memory176.7 B

Variable types

Numeric1
Categorical18

Dataset

Description각 시도경찰청별로 최근 6년간의 학대예방경찰관(APO)에 대한 운영(정원 기준) 등 현황 데이터입니다.연도, 서울, 부산, 대구, 인천...제주
Author경찰청
URLhttps://www.data.go.kr/data/15037063/fileData.do

Alerts

Dataset has 1 (4.3%) duplicate rowsDuplicates
울산 is highly overall correlated with 연도 and 15 other fieldsHigh correlation
부산 is highly overall correlated with 연도 and 15 other fieldsHigh correlation
경기북 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
제주 is highly overall correlated with 연도 and 15 other fieldsHigh correlation
충남 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
대전 is highly overall correlated with 연도 and 15 other fieldsHigh correlation
경북 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
광주 is highly overall correlated with 연도 and 15 other fieldsHigh correlation
서울 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
강원 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
대구 is highly overall correlated with 연도 and 15 other fieldsHigh correlation
전남 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
경남 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
충북 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
경기남 is highly overall correlated with 연도 and 16 other fieldsHigh correlation
연도 is highly overall correlated with 서울 and 17 other fieldsHigh correlation
인천 is highly overall correlated with 연도 and 15 other fieldsHigh correlation
세종 is highly overall correlated with 연도 and 1 other fieldsHigh correlation
전북 is highly overall correlated with 연도 and 10 other fieldsHigh correlation
연도 has 17 (73.9%) missing valuesMissing

Reproduction

Analysis started2023-12-12 17:21:01.475619
Analysis finished2023-12-12 17:21:04.179060
Duration2.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)100.0%
Missing17
Missing (%)73.9%
Infinite0
Infinite (%)0.0%
Mean2019.5
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T02:21:04.239988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017.25
Q12018.25
median2019.5
Q32020.75
95-th percentile2021.75
Maximum2022
Range5
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation1.8708287
Coefficient of variation (CV)0.00092638212
Kurtosis-1.2
Mean2019.5
Median Absolute Deviation (MAD)1.5
Skewness0
Sum12117
Variance3.5
MonotonicityStrictly increasing
2023-12-13T02:21:04.365277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 1
 
4.3%
2018 1
 
4.3%
2019 1
 
4.3%
2020 1
 
4.3%
2021 1
 
4.3%
2022 1
 
4.3%
(Missing) 17
73.9%
ValueCountFrequency (%)
2017 1
4.3%
2018 1
4.3%
2019 1
4.3%
2020 1
4.3%
2021 1
4.3%
2022 1
4.3%
ValueCountFrequency (%)
2022 1
4.3%
2021 1
4.3%
2020 1
4.3%
2019 1
4.3%
2018 1
4.3%
2017 1
4.3%

서울
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
17 
107
3 
62
 
1
87
 
1
99
 
1

Length

Max length4
Median length4
Mean length3.6086957
Min length2

Unique

Unique3 ?
Unique (%)13.0%

Sample

1st row62
2nd row87
3rd row99
4th row107
5th row107

Common Values

ValueCountFrequency (%)
<NA> 17
73.9%
107 3
 
13.0%
62 1
 
4.3%
87 1
 
4.3%
99 1
 
4.3%

Length

2023-12-13T02:21:04.534956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:04.700060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
73.9%
107 3
 
13.0%
62 1
 
4.3%
87 1
 
4.3%
99 1
 
4.3%

부산
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
17 
42
4 
31
 
1
39
 
1

Length

Max length4
Median length4
Mean length3.4782609
Min length2

Unique

Unique2 ?
Unique (%)8.7%

Sample

1st row31
2nd row39
3rd row42
4th row42
5th row42

Common Values

ValueCountFrequency (%)
<NA> 17
73.9%
42 4
 
17.4%
31 1
 
4.3%
39 1
 
4.3%

Length

2023-12-13T02:21:04.856900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:04.976171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
73.9%
42 4
 
17.4%
31 1
 
4.3%
39 1
 
4.3%

대구
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
17 
34
4 
21
 
1
28
 
1

Length

Max length4
Median length4
Mean length3.4782609
Min length2

Unique

Unique2 ?
Unique (%)8.7%

Sample

1st row21
2nd row28
3rd row34
4th row34
5th row34

Common Values

ValueCountFrequency (%)
<NA> 17
73.9%
34 4
 
17.4%
21 1
 
4.3%
28 1
 
4.3%

Length

2023-12-13T02:21:05.115033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:05.232985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
73.9%
34 4
 
17.4%
21 1
 
4.3%
28 1
 
4.3%

인천
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
17 
32
2 
33
2 
18
 
1
27
 
1

Length

Max length4
Median length4
Mean length3.4782609
Min length2

Unique

Unique2 ?
Unique (%)8.7%

Sample

1st row18
2nd row27
3rd row32
4th row32
5th row33

Common Values

ValueCountFrequency (%)
<NA> 17
73.9%
32 2
 
8.7%
33 2
 
8.7%
18 1
 
4.3%
27 1
 
4.3%

Length

2023-12-13T02:21:05.372631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:05.491013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
73.9%
32 2
 
8.7%
33 2
 
8.7%
18 1
 
4.3%
27 1
 
4.3%

광주
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
17 
15
4 
11
 
1
14
 
1

Length

Max length4
Median length4
Mean length3.4782609
Min length2

Unique

Unique2 ?
Unique (%)8.7%

Sample

1st row11
2nd row14
3rd row15
4th row15
5th row15

Common Values

ValueCountFrequency (%)
<NA> 17
73.9%
15 4
 
17.4%
11 1
 
4.3%
14 1
 
4.3%

Length

2023-12-13T02:21:05.624182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:05.737890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
73.9%
15 4
 
17.4%
11 1
 
4.3%
14 1
 
4.3%

대전
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
17 
20
4 
13
 
1
19
 
1

Length

Max length4
Median length4
Mean length3.4782609
Min length2

Unique

Unique2 ?
Unique (%)8.7%

Sample

1st row13
2nd row19
3rd row20
4th row20
5th row20

Common Values

ValueCountFrequency (%)
<NA> 17
73.9%
20 4
 
17.4%
13 1
 
4.3%
19 1
 
4.3%

Length

2023-12-13T02:21:05.874496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:06.019104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
73.9%
20 4
 
17.4%
13 1
 
4.3%
19 1
 
4.3%

울산
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
17 
15
4 
9
 
1
13
 
1

Length

Max length4
Median length4
Mean length3.4347826
Min length1

Unique

Unique2 ?
Unique (%)8.7%

Sample

1st row9
2nd row13
3rd row15
4th row15
5th row15

Common Values

ValueCountFrequency (%)
<NA> 17
73.9%
15 4
 
17.4%
9 1
 
4.3%
13 1
 
4.3%

Length

2023-12-13T02:21:06.149752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:06.255848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
73.9%
15 4
 
17.4%
9 1
 
4.3%
13 1
 
4.3%

세종
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
17 
0
2 
5
2 
2
 
1
3
 
1

Length

Max length4
Median length4
Mean length3.2173913
Min length1

Unique

Unique2 ?
Unique (%)8.7%

Sample

1st row0
2nd row0
3rd row2
4th row3
5th row5

Common Values

ValueCountFrequency (%)
<NA> 17
73.9%
0 2
 
8.7%
5 2
 
8.7%
2 1
 
4.3%
3 1
 
4.3%

Length

2023-12-13T02:21:06.367059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:06.472216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
73.9%
0 2
 
8.7%
5 2
 
8.7%
2 1
 
4.3%
3 1
 
4.3%

경기남
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
17 
119
3 
62
 
1
87
 
1
110
 
1

Length

Max length4
Median length4
Mean length3.6521739
Min length2

Unique

Unique3 ?
Unique (%)13.0%

Sample

1st row62
2nd row87
3rd row110
4th row119
5th row119

Common Values

ValueCountFrequency (%)
<NA> 17
73.9%
119 3
 
13.0%
62 1
 
4.3%
87 1
 
4.3%
110 1
 
4.3%

Length

2023-12-13T02:21:06.598005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:06.715531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
73.9%
119 3
 
13.0%
62 1
 
4.3%
87 1
 
4.3%
110 1
 
4.3%

경기북
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
17 
40
3 
23
 
1
31
 
1
38
 
1

Length

Max length4
Median length4
Mean length3.4782609
Min length2

Unique

Unique3 ?
Unique (%)13.0%

Sample

1st row23
2nd row31
3rd row38
4th row40
5th row40

Common Values

ValueCountFrequency (%)
<NA> 17
73.9%
40 3
 
13.0%
23 1
 
4.3%
31 1
 
4.3%
38 1
 
4.3%

Length

2023-12-13T02:21:06.863508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:07.262069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
73.9%
40 3
 
13.0%
23 1
 
4.3%
31 1
 
4.3%
38 1
 
4.3%

강원
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
17 
29
3 
18
 
1
21
 
1
22
 
1

Length

Max length4
Median length4
Mean length3.4782609
Min length2

Unique

Unique3 ?
Unique (%)13.0%

Sample

1st row18
2nd row21
3rd row22
4th row29
5th row29

Common Values

ValueCountFrequency (%)
<NA> 17
73.9%
29 3
 
13.0%
18 1
 
4.3%
21 1
 
4.3%
22 1
 
4.3%

Length

2023-12-13T02:21:07.375258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:07.488051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
73.9%
29 3
 
13.0%
18 1
 
4.3%
21 1
 
4.3%
22 1
 
4.3%

충북
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
17 
24
3 
18
 
1
21
 
1
22
 
1

Length

Max length4
Median length4
Mean length3.4782609
Min length2

Unique

Unique3 ?
Unique (%)13.0%

Sample

1st row18
2nd row21
3rd row22
4th row24
5th row24

Common Values

ValueCountFrequency (%)
<NA> 17
73.9%
24 3
 
13.0%
18 1
 
4.3%
21 1
 
4.3%
22 1
 
4.3%

Length

2023-12-13T02:21:07.620450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:07.730232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
73.9%
24 3
 
13.0%
18 1
 
4.3%
21 1
 
4.3%
22 1
 
4.3%

충남
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
17 
28
3 
24
 
1
27
 
1
26
 
1

Length

Max length4
Median length4
Mean length3.4782609
Min length2

Unique

Unique3 ?
Unique (%)13.0%

Sample

1st row24
2nd row27
3rd row26
4th row28
5th row28

Common Values

ValueCountFrequency (%)
<NA> 17
73.9%
28 3
 
13.0%
24 1
 
4.3%
27 1
 
4.3%
26 1
 
4.3%

Length

2023-12-13T02:21:07.852360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:07.970740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
73.9%
28 3
 
13.0%
24 1
 
4.3%
27 1
 
4.3%
26 1
 
4.3%

전북
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
17 
24
3 
18
2 
19
 
1

Length

Max length4
Median length4
Mean length3.4782609
Min length2

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st row18
2nd row18
3rd row19
4th row24
5th row24

Common Values

ValueCountFrequency (%)
<NA> 17
73.9%
24 3
 
13.0%
18 2
 
8.7%
19 1
 
4.3%

Length

2023-12-13T02:21:08.088479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:08.193458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
73.9%
24 3
 
13.0%
18 2
 
8.7%
19 1
 
4.3%

전남
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
17 
36
3 
22
 
1
25
 
1
26
 
1

Length

Max length4
Median length4
Mean length3.4782609
Min length2

Unique

Unique3 ?
Unique (%)13.0%

Sample

1st row22
2nd row25
3rd row26
4th row36
5th row36

Common Values

ValueCountFrequency (%)
<NA> 17
73.9%
36 3
 
13.0%
22 1
 
4.3%
25 1
 
4.3%
26 1
 
4.3%

Length

2023-12-13T02:21:08.322581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:08.449524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
73.9%
36 3
 
13.0%
22 1
 
4.3%
25 1
 
4.3%
26 1
 
4.3%

경북
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
17 
44
3 
25
 
1
27
 
1
32
 
1

Length

Max length4
Median length4
Mean length3.4782609
Min length2

Unique

Unique3 ?
Unique (%)13.0%

Sample

1st row25
2nd row27
3rd row32
4th row44
5th row44

Common Values

ValueCountFrequency (%)
<NA> 17
73.9%
44 3
 
13.0%
25 1
 
4.3%
27 1
 
4.3%
32 1
 
4.3%

Length

2023-12-13T02:21:08.580551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:08.718727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
73.9%
44 3
 
13.0%
25 1
 
4.3%
27 1
 
4.3%
32 1
 
4.3%

경남
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
17 
44
3 
29
 
1
35
 
1
36
 
1

Length

Max length4
Median length4
Mean length3.4782609
Min length2

Unique

Unique3 ?
Unique (%)13.0%

Sample

1st row29
2nd row35
3rd row36
4th row44
5th row44

Common Values

ValueCountFrequency (%)
<NA> 17
73.9%
44 3
 
13.0%
29 1
 
4.3%
35 1
 
4.3%
36 1
 
4.3%

Length

2023-12-13T02:21:08.844471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:09.001611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
73.9%
44 3
 
13.0%
29 1
 
4.3%
35 1
 
4.3%
36 1
 
4.3%

제주
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
17 
13
4 
7
 
1
10
 
1

Length

Max length4
Median length4
Mean length3.4347826
Min length1

Unique

Unique2 ?
Unique (%)8.7%

Sample

1st row7
2nd row10
3rd row13
4th row13
5th row13

Common Values

ValueCountFrequency (%)
<NA> 17
73.9%
13 4
 
17.4%
7 1
 
4.3%
10 1
 
4.3%

Length

2023-12-13T02:21:09.134181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:21:09.255883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
73.9%
13 4
 
17.4%
7 1
 
4.3%
10 1
 
4.3%

Interactions

2023-12-13T02:21:03.530086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:21:09.364127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도서울부산대구인천광주대전울산세종경기남경기북강원충북충남전북전남경북경남제주
연도1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
서울1.0001.0001.0001.0000.9641.0001.0001.0000.8951.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
부산1.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0000.8271.0001.0001.0001.000
대구1.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0000.8271.0001.0001.0001.000
인천1.0000.9641.0001.0001.0001.0001.0001.0000.8950.9640.9640.9640.9640.9640.5680.9640.9640.9641.000
광주1.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0000.8271.0001.0001.0001.000
대전1.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0000.8271.0001.0001.0001.000
울산1.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0000.8271.0001.0001.0001.000
세종1.0000.8950.0000.0000.8950.0000.0000.0001.0000.8950.8950.8950.8950.8951.0000.8950.8950.8950.000
경기남1.0001.0001.0001.0000.9641.0001.0001.0000.8951.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
경기북1.0001.0001.0001.0000.9641.0001.0001.0000.8951.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
강원1.0001.0001.0001.0000.9641.0001.0001.0000.8951.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
충북1.0001.0001.0001.0000.9641.0001.0001.0000.8951.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
충남1.0001.0001.0001.0000.9641.0001.0001.0000.8951.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
전북1.0001.0000.8270.8270.5680.8270.8270.8271.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.827
전남1.0001.0001.0001.0000.9641.0001.0001.0000.8951.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
경북1.0001.0001.0001.0000.9641.0001.0001.0000.8951.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
경남1.0001.0001.0001.0000.9641.0001.0001.0000.8951.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
제주1.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0000.8271.0001.0001.0001.000
2023-12-13T02:21:09.565238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
울산부산경기북제주충남대전경북전북광주서울강원대구세종전남인천경남충북경기남
울산1.0001.0000.8161.0000.8161.0000.8160.4081.0000.8160.8161.0000.0000.8160.8160.8160.8160.816
부산1.0001.0000.8161.0000.8161.0000.8160.4081.0000.8160.8161.0000.0000.8160.8160.8160.8160.816
경기북0.8160.8161.0000.8161.0000.8161.0000.8160.8161.0001.0000.8160.4081.0000.6671.0001.0001.000
제주1.0001.0000.8161.0000.8161.0000.8160.4081.0000.8160.8161.0000.0000.8160.8160.8160.8160.816
충남0.8160.8161.0000.8161.0000.8161.0000.8160.8161.0001.0000.8160.4081.0000.6671.0001.0001.000
대전1.0001.0000.8161.0000.8161.0000.8160.4081.0000.8160.8161.0000.0000.8160.8160.8160.8160.816
경북0.8160.8161.0000.8161.0000.8161.0000.8160.8161.0001.0000.8160.4081.0000.6671.0001.0001.000
전북0.4080.4080.8160.4080.8160.4080.8161.0000.4080.8160.8160.4080.8160.8160.3330.8160.8160.816
광주1.0001.0000.8161.0000.8161.0000.8160.4081.0000.8160.8161.0000.0000.8160.8160.8160.8160.816
서울0.8160.8161.0000.8161.0000.8161.0000.8160.8161.0001.0000.8160.4081.0000.6671.0001.0001.000
강원0.8160.8161.0000.8161.0000.8161.0000.8160.8161.0001.0000.8160.4081.0000.6671.0001.0001.000
대구1.0001.0000.8161.0000.8161.0000.8160.4081.0000.8160.8161.0000.0000.8160.8160.8160.8160.816
세종0.0000.0000.4080.0000.4080.0000.4080.8160.0000.4080.4080.0001.0000.4080.4080.4080.4080.408
전남0.8160.8161.0000.8161.0000.8161.0000.8160.8161.0001.0000.8160.4081.0000.6671.0001.0001.000
인천0.8160.8160.6670.8160.6670.8160.6670.3330.8160.6670.6670.8160.4080.6671.0000.6670.6670.667
경남0.8160.8161.0000.8161.0000.8161.0000.8160.8161.0001.0000.8160.4081.0000.6671.0001.0001.000
충북0.8160.8161.0000.8161.0000.8161.0000.8160.8161.0001.0000.8160.4081.0000.6671.0001.0001.000
경기남0.8160.8161.0000.8161.0000.8161.0000.8160.8161.0001.0000.8160.4081.0000.6671.0001.0001.000
2023-12-13T02:21:09.821617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도서울부산대구인천광주대전울산세종경기남경기북강원충북충남전북전남경북경남제주
연도1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
서울1.0001.0000.8160.8160.6670.8160.8160.8160.4081.0001.0001.0001.0001.0000.8161.0001.0001.0000.816
부산1.0000.8161.0001.0000.8161.0001.0001.0000.0000.8160.8160.8160.8160.8160.4080.8160.8160.8161.000
대구1.0000.8161.0001.0000.8161.0001.0001.0000.0000.8160.8160.8160.8160.8160.4080.8160.8160.8161.000
인천1.0000.6670.8160.8161.0000.8160.8160.8160.4080.6670.6670.6670.6670.6670.3330.6670.6670.6670.816
광주1.0000.8161.0001.0000.8161.0001.0001.0000.0000.8160.8160.8160.8160.8160.4080.8160.8160.8161.000
대전1.0000.8161.0001.0000.8161.0001.0001.0000.0000.8160.8160.8160.8160.8160.4080.8160.8160.8161.000
울산1.0000.8161.0001.0000.8161.0001.0001.0000.0000.8160.8160.8160.8160.8160.4080.8160.8160.8161.000
세종1.0000.4080.0000.0000.4080.0000.0000.0001.0000.4080.4080.4080.4080.4080.8160.4080.4080.4080.000
경기남1.0001.0000.8160.8160.6670.8160.8160.8160.4081.0001.0001.0001.0001.0000.8161.0001.0001.0000.816
경기북1.0001.0000.8160.8160.6670.8160.8160.8160.4081.0001.0001.0001.0001.0000.8161.0001.0001.0000.816
강원1.0001.0000.8160.8160.6670.8160.8160.8160.4081.0001.0001.0001.0001.0000.8161.0001.0001.0000.816
충북1.0001.0000.8160.8160.6670.8160.8160.8160.4081.0001.0001.0001.0001.0000.8161.0001.0001.0000.816
충남1.0001.0000.8160.8160.6670.8160.8160.8160.4081.0001.0001.0001.0001.0000.8161.0001.0001.0000.816
전북1.0000.8160.4080.4080.3330.4080.4080.4080.8160.8160.8160.8160.8160.8161.0000.8160.8160.8160.408
전남1.0001.0000.8160.8160.6670.8160.8160.8160.4081.0001.0001.0001.0001.0000.8161.0001.0001.0000.816
경북1.0001.0000.8160.8160.6670.8160.8160.8160.4081.0001.0001.0001.0001.0000.8161.0001.0001.0000.816
경남1.0001.0000.8160.8160.6670.8160.8160.8160.4081.0001.0001.0001.0001.0000.8161.0001.0001.0000.816
제주1.0000.8161.0001.0000.8161.0001.0001.0000.0000.8160.8160.8160.8160.8160.4080.8160.8160.8161.000

Missing values

2023-12-13T02:21:03.752254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:21:04.064543image/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

연도서울부산대구인천광주대전울산세종경기남경기북강원충북충남전북전남경북경남제주
02017623121181113906223181824182225297
1201887392827141913087312121271825273510
22019994234321520152110382222261926323613
320201074234321520153119402924282436444413
420211074234331520155119402924282436444413
520221074234331520155119402924282436444413
6<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
7<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
8<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
9<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
연도서울부산대구인천광주대전울산세종경기남경기북강원충북충남전북전남경북경남제주
13<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
14<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
15<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
16<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
17<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
18<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
19<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
20<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
21<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
22<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

연도서울부산대구인천광주대전울산세종경기남경기북강원충북충남전북전남경북경남제주# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>17