Overview

Dataset statistics

Number of variables14
Number of observations23
Missing cells13
Missing cells (%)4.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory130.7 B

Variable types

Text1
Categorical5
Numeric8

Dataset

Description경상북도 문화재 통계자료입니다. 국가지정문화재(국보, 보물, 사적, 명승, 천연기념물, 국가무형문화재, 중요민속문화재, 등록문화재), 도지정문화재 등
Author경상북도
URLhttps://www.data.go.kr/data/15063339/fileData.do

Alerts

보물(국가지정문화재) is highly overall correlated with 유형문화재(도지정문화재) and 2 other fieldsHigh correlation
국가민속문화재(국가지정문화재) is highly overall correlated with 문화재자료 and 1 other fieldsHigh correlation
유형문화재(도지정문화재) is highly overall correlated with 보물(국가지정문화재) and 3 other fieldsHigh correlation
기념물(도지정문화재) is highly overall correlated with 보물(국가지정문화재) and 4 other fieldsHigh correlation
민속문화재(도지정문화재) is highly overall correlated with 기념물(도지정문화재) and 2 other fieldsHigh correlation
문화재자료 is highly overall correlated with 국가민속문화재(국가지정문화재) and 2 other fieldsHigh correlation
국보(국가지정문화재) is highly overall correlated with 보물(국가지정문화재) and 5 other fieldsHigh correlation
사적(국가지정문화재) is highly overall correlated with 국보(국가지정문화재)High correlation
국가무형문화재(국가지정문화재) is highly overall correlated with 유형문화재(도지정문화재)High correlation
보물(국가지정문화재) has 1 (4.3%) missing valuesMissing
천연기념물(국가지정문화재) has 2 (8.7%) missing valuesMissing
국가민속문화재(국가지정문화재) has 4 (17.4%) missing valuesMissing
등록문화재 has 3 (13.0%) missing valuesMissing
유형문화재(도지정문화재) has 1 (4.3%) missing valuesMissing
민속문화재(도지정문화재) has 2 (8.7%) missing valuesMissing
시군별 has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:37:09.580788
Analysis finished2023-12-12 09:37:19.321632
Duration9.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군별
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-12T18:37:19.466875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters69
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row포항시
2nd row경주시
3rd row김천시
4th row안동시
5th row구미시
ValueCountFrequency (%)
포항시 1
 
4.3%
청송군 1
 
4.3%
울진군 1
 
4.3%
봉화군 1
 
4.3%
예천군 1
 
4.3%
칠곡군 1
 
4.3%
성주군 1
 
4.3%
고령군 1
 
4.3%
청도군 1
 
4.3%
영덕군 1
 
4.3%
Other values (13) 13
56.5%
2023-12-12T18:37:19.835394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 14
20.3%
시 10
14.5%
주 4
 
5.8%
영 4
 
5.8%
경 3
 
4.3%
천 3
 
4.3%
청 2
 
2.9%
울 2
 
2.9%
성 2
 
2.9%
포 1
 
1.4%
Other values (24) 24
34.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 69
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 14
20.3%
시 10
14.5%
주 4
 
5.8%
영 4
 
5.8%
경 3
 
4.3%
천 3
 
4.3%
청 2
 
2.9%
울 2
 
2.9%
성 2
 
2.9%
포 1
 
1.4%
Other values (24) 24
34.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 69
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 14
20.3%
시 10
14.5%
주 4
 
5.8%
영 4
 
5.8%
경 3
 
4.3%
천 3
 
4.3%
청 2
 
2.9%
울 2
 
2.9%
성 2
 
2.9%
포 1
 
1.4%
Other values (24) 24
34.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 69
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 14
20.3%
시 10
14.5%
주 4
 
5.8%
영 4
 
5.8%
경 3
 
4.3%
천 3
 
4.3%
청 2
 
2.9%
울 2
 
2.9%
성 2
 
2.9%
포 1
 
1.4%
Other values (24) 24
34.8%

국보(국가지정문화재)
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Memory size316.0 B
1
9 
<NA>
9 
2
2 
36
1 
5
1 

Length

Max length4
Median length1
Mean length2.2173913
Min length1

Unique

Unique3 ?
Unique (%)13.0%

Sample

1st row1
2nd row36
3rd row1
4th row5
5th row1

Common Values

ValueCountFrequency (%)
1 9
39.1%
<NA> 9
39.1%
2 2
 
8.7%
36 1
 
4.3%
5 1
 
4.3%
7 1
 
4.3%

Length

2023-12-12T18:37:19.996264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:37:20.114554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9
39.1%
na 9
39.1%
2 2
 
8.7%
36 1
 
4.3%
5 1
 
4.3%
7 1
 
4.3%

보물(국가지정문화재)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)59.1%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean17.818182
Minimum3
Maximum102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T18:37:20.238050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q15.25
median10.5
Q321
95-th percentile47.9
Maximum102
Range99
Interquartile range (IQR)15.75

Descriptive statistics

Standard deviation21.740102
Coefficient of variation (CV)1.2201078
Kurtosis11.123554
Mean17.818182
Median Absolute Deviation (MAD)6.5
Skewness3.0928766
Sum392
Variance472.63203
MonotonicityNot monotonic
2023-12-12T18:37:20.376613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
9 3
13.0%
21 3
13.0%
3 3
13.0%
5 3
13.0%
23 2
8.7%
102 1
 
4.3%
49 1
 
4.3%
15 1
 
4.3%
27 1
 
4.3%
14 1
 
4.3%
Other values (3) 3
13.0%
ValueCountFrequency (%)
3 3
13.0%
5 3
13.0%
6 1
 
4.3%
7 1
 
4.3%
9 3
13.0%
12 1
 
4.3%
14 1
 
4.3%
15 1
 
4.3%
21 3
13.0%
23 2
8.7%
ValueCountFrequency (%)
102 1
 
4.3%
49 1
 
4.3%
27 1
 
4.3%
23 2
8.7%
21 3
13.0%
15 1
 
4.3%
14 1
 
4.3%
12 1
 
4.3%
9 3
13.0%
7 1
 
4.3%

사적(국가지정문화재)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
9 
2
6 
1
5 
4
2 
78
1 

Length

Max length4
Median length1
Mean length2.2173913
Min length1

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st row2
2nd row78
3rd row<NA>
4th row2
5th row2

Common Values

ValueCountFrequency (%)
<NA> 9
39.1%
2 6
26.1%
1 5
21.7%
4 2
 
8.7%
78 1
 
4.3%

Length

2023-12-12T18:37:20.536916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:37:20.682858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9
39.1%
2 6
26.1%
1 5
21.7%
4 2
 
8.7%
78 1
 
4.3%

명승(국가지정문화재)
Categorical

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
12 
2
5 
1
5 
3
 
1

Length

Max length4
Median length4
Mean length2.5652174
Min length1

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st row2
2nd row<NA>
3rd row<NA>
4th row2
5th row1

Common Values

ValueCountFrequency (%)
<NA> 12
52.2%
2 5
21.7%
1 5
21.7%
3 1
 
4.3%

Length

2023-12-12T18:37:20.867247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:37:20.989600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 12
52.2%
2 5
21.7%
1 5
21.7%
3 1
 
4.3%

천연기념물(국가지정문화재)
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)38.1%
Missing2
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean3.4285714
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T18:37:21.110779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q35
95-th percentile7
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.292846
Coefficient of variation (CV)0.66874675
Kurtosis0.033153832
Mean3.4285714
Median Absolute Deviation (MAD)2
Skewness0.78931712
Sum72
Variance5.2571429
MonotonicityNot monotonic
2023-12-12T18:37:21.270469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 6
26.1%
3 5
21.7%
5 3
13.0%
2 2
 
8.7%
6 2
 
8.7%
7 1
 
4.3%
4 1
 
4.3%
9 1
 
4.3%
(Missing) 2
 
8.7%
ValueCountFrequency (%)
1 6
26.1%
2 2
 
8.7%
3 5
21.7%
4 1
 
4.3%
5 3
13.0%
6 2
 
8.7%
7 1
 
4.3%
9 1
 
4.3%
ValueCountFrequency (%)
9 1
 
4.3%
7 1
 
4.3%
6 2
 
8.7%
5 3
13.0%
4 1
 
4.3%
3 5
21.7%
2 2
 
8.7%
1 6
26.1%

국가무형문화재(국가지정문화재)
Categorical

HIGH CORRELATION 

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

Length

Max length4
Median length4
Mean length3.2173913
Min length1

Unique

Unique2 ?
Unique (%)8.7%

Sample

1st row<NA>
2nd row5
3rd row1
4th row3
5th row<NA>

Common Values

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

Length

2023-12-12T18:37:21.435076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:37:21.597214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
73.9%
1 2
 
8.7%
3 2
 
8.7%
5 1
 
4.3%
2 1
 
4.3%

국가민속문화재(국가지정문화재)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)47.4%
Missing4
Missing (%)17.4%
Infinite0
Infinite (%)0.0%
Mean5.1052632
Minimum1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T18:37:21.714840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34.5
95-th percentile17
Maximum35
Range34
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation8.0408315
Coefficient of variation (CV)1.5750082
Kurtosis11.478607
Mean5.1052632
Median Absolute Deviation (MAD)1
Skewness3.2448483
Sum97
Variance64.654971
MonotonicityNot monotonic
2023-12-12T18:37:21.866564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 6
26.1%
2 5
21.7%
4 2
 
8.7%
15 1
 
4.3%
35 1
 
4.3%
3 1
 
4.3%
6 1
 
4.3%
5 1
 
4.3%
9 1
 
4.3%
(Missing) 4
17.4%
ValueCountFrequency (%)
1 6
26.1%
2 5
21.7%
3 1
 
4.3%
4 2
 
8.7%
5 1
 
4.3%
6 1
 
4.3%
9 1
 
4.3%
15 1
 
4.3%
35 1
 
4.3%
ValueCountFrequency (%)
35 1
 
4.3%
15 1
 
4.3%
9 1
 
4.3%
6 1
 
4.3%
5 1
 
4.3%
4 2
 
8.7%
3 1
 
4.3%
2 5
21.7%
1 6
26.1%

등록문화재
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)35.0%
Missing3
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean3.3
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T18:37:22.024642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.75
median2
Q33.5
95-th percentile8.25
Maximum13
Range12
Interquartile range (IQR)1.75

Descriptive statistics

Standard deviation2.957595
Coefficient of variation (CV)0.89624092
Kurtosis5.4938061
Mean3.3
Median Absolute Deviation (MAD)1
Skewness2.19685
Sum66
Variance8.7473684
MonotonicityNot monotonic
2023-12-12T18:37:22.165130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 6
26.1%
1 5
21.7%
3 4
17.4%
5 2
 
8.7%
8 1
 
4.3%
6 1
 
4.3%
13 1
 
4.3%
(Missing) 3
13.0%
ValueCountFrequency (%)
1 5
21.7%
2 6
26.1%
3 4
17.4%
5 2
 
8.7%
6 1
 
4.3%
8 1
 
4.3%
13 1
 
4.3%
ValueCountFrequency (%)
13 1
 
4.3%
8 1
 
4.3%
6 1
 
4.3%
5 2
 
8.7%
3 4
17.4%
2 6
26.1%
1 5
21.7%

유형문화재(도지정문화재)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)72.7%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean22.227273
Minimum6
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T18:37:22.291893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile7.05
Q19
median21
Q329
95-th percentile41.8
Maximum78
Range72
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.402473
Coefficient of variation (CV)0.73794358
Kurtosis5.4796263
Mean22.227273
Median Absolute Deviation (MAD)10.5
Skewness1.9532387
Sum489
Variance269.04113
MonotonicityNot monotonic
2023-12-12T18:37:22.457096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
9 3
13.0%
22 2
 
8.7%
20 2
 
8.7%
8 2
 
8.7%
24 2
 
8.7%
7 1
 
4.3%
30 1
 
4.3%
6 1
 
4.3%
11 1
 
4.3%
31 1
 
4.3%
Other values (6) 6
26.1%
ValueCountFrequency (%)
6 1
 
4.3%
7 1
 
4.3%
8 2
8.7%
9 3
13.0%
11 1
 
4.3%
13 1
 
4.3%
20 2
8.7%
22 2
8.7%
24 2
8.7%
26 1
 
4.3%
ValueCountFrequency (%)
78 1
4.3%
42 1
4.3%
38 1
4.3%
32 1
4.3%
31 1
4.3%
30 1
4.3%
26 1
4.3%
24 2
8.7%
22 2
8.7%
20 2
8.7%

무형문화재(도지정문화재)
Categorical

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
1
7 
<NA>
6 
3
4 
5
3 
2
3 

Length

Max length4
Median length1
Mean length1.7826087
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row5
3rd row2
4th row5
5th row2

Common Values

ValueCountFrequency (%)
1 7
30.4%
<NA> 6
26.1%
3 4
17.4%
5 3
13.0%
2 3
13.0%

Length

2023-12-12T18:37:22.640475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:37:22.779145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 7
30.4%
na 6
26.1%
3 4
17.4%
5 3
13.0%
2 3
13.0%

기념물(도지정문화재)
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6086957
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T18:37:22.900815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q37.5
95-th percentile17.9
Maximum21
Range20
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation5.3405748
Coefficient of variation (CV)0.80811329
Kurtosis2.019194
Mean6.6086957
Median Absolute Deviation (MAD)2
Skewness1.6207442
Sum152
Variance28.521739
MonotonicityNot monotonic
2023-12-12T18:37:23.067640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
4 3
13.0%
5 3
13.0%
2 3
13.0%
3 3
13.0%
7 2
8.7%
6 2
8.7%
18 1
 
4.3%
21 1
 
4.3%
8 1
 
4.3%
17 1
 
4.3%
Other values (3) 3
13.0%
ValueCountFrequency (%)
1 1
 
4.3%
2 3
13.0%
3 3
13.0%
4 3
13.0%
5 3
13.0%
6 2
8.7%
7 2
8.7%
8 1
 
4.3%
9 1
 
4.3%
10 1
 
4.3%
ValueCountFrequency (%)
21 1
 
4.3%
18 1
 
4.3%
17 1
 
4.3%
10 1
 
4.3%
9 1
 
4.3%
8 1
 
4.3%
7 2
8.7%
6 2
8.7%
5 3
13.0%
4 3
13.0%

민속문화재(도지정문화재)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)52.4%
Missing2
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean7.3333333
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T18:37:23.220021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q38
95-th percentile12
Maximum52
Range51
Interquartile range (IQR)6

Descriptive statistics

Standard deviation10.781156
Coefficient of variation (CV)1.4701577
Kurtosis16.439178
Mean7.3333333
Median Absolute Deviation (MAD)3
Skewness3.8686183
Sum154
Variance116.23333
MonotonicityNot monotonic
2023-12-12T18:37:23.351919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 4
17.4%
1 3
13.0%
5 2
8.7%
3 2
8.7%
7 2
8.7%
8 2
8.7%
6 2
8.7%
52 1
 
4.3%
12 1
 
4.3%
10 1
 
4.3%
(Missing) 2
8.7%
ValueCountFrequency (%)
1 3
13.0%
2 4
17.4%
3 2
8.7%
5 2
8.7%
6 2
8.7%
7 2
8.7%
8 2
8.7%
10 1
 
4.3%
11 1
 
4.3%
12 1
 
4.3%
ValueCountFrequency (%)
52 1
 
4.3%
12 1
 
4.3%
11 1
 
4.3%
10 1
 
4.3%
8 2
8.7%
7 2
8.7%
6 2
8.7%
5 2
8.7%
3 2
8.7%
2 4
17.4%

문화재자료
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.782609
Minimum3
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T18:37:23.479890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile10.1
Q114
median22
Q331
95-th percentile47.9
Maximum72
Range69
Interquartile range (IQR)17

Descriptive statistics

Standard deviation15.991351
Coefficient of variation (CV)0.62023791
Kurtosis1.7307766
Mean25.782609
Median Absolute Deviation (MAD)8
Skewness1.2284981
Sum593
Variance255.72332
MonotonicityNot monotonic
2023-12-12T18:37:23.632476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
13 2
 
8.7%
14 2
 
8.7%
43 2
 
8.7%
26 2
 
8.7%
18 1
 
4.3%
3 1
 
4.3%
10 1
 
4.3%
30 1
 
4.3%
32 1
 
4.3%
17 1
 
4.3%
Other values (9) 9
39.1%
ValueCountFrequency (%)
3 1
4.3%
10 1
4.3%
11 1
4.3%
13 2
8.7%
14 2
8.7%
16 1
4.3%
17 1
4.3%
18 1
4.3%
19 1
4.3%
22 1
4.3%
ValueCountFrequency (%)
72 1
4.3%
48 1
4.3%
47 1
4.3%
43 2
8.7%
32 1
4.3%
30 1
4.3%
29 1
4.3%
27 1
4.3%
26 2
8.7%
22 1
4.3%

Interactions

2023-12-12T18:37:17.495031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:10.692127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:11.616906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:12.537926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:13.565987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:14.512968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:15.470793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:16.549858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:17.598092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:10.801316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:11.729766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:12.634557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:13.672315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:14.622169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:15.590996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:16.664462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:17.742602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:10.917785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:11.844602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:12.760623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:13.815556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:14.730053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:15.747228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:16.796438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:18.190840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:11.029216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:11.956428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:12.872800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:13.926803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:14.854536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:15.885231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:16.919670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:18.302238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:11.136743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:12.054683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:12.971911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:14.039107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:14.980197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:15.996046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:17.030818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:18.436367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:11.250466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:12.164511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:13.124764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:14.157242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:15.121845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:16.114505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:17.139031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:18.560632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:11.379332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:12.289082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:13.271911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:14.281766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:15.254805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:16.276938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:17.261923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:18.678024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:11.495860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:12.427332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:13.409525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:14.387962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:15.369268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:16.392588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:37:17.381027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:37:23.758115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군별국보(국가지정문화재)보물(국가지정문화재)사적(국가지정문화재)명승(국가지정문화재)천연기념물(국가지정문화재)국가무형문화재(국가지정문화재)국가민속문화재(국가지정문화재)등록문화재유형문화재(도지정문화재)무형문화재(도지정문화재)기념물(도지정문화재)민속문화재(도지정문화재)문화재자료
시군별1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
국보(국가지정문화재)1.0001.0000.9500.8020.0000.5720.7710.7560.8560.6960.0000.7170.7140.613
보물(국가지정문화재)1.0000.9501.0000.5440.2040.6660.7590.9350.4610.7550.3210.7340.6630.711
사적(국가지정문화재)1.0000.8020.5441.0000.0000.1020.8270.8530.2420.6380.0000.3100.0000.158
명승(국가지정문화재)1.0000.0000.2040.0001.0000.0000.0000.0000.0000.0000.5010.0000.0000.000
천연기념물(국가지정문화재)1.0000.5720.6660.1020.0001.0000.5730.6400.0000.5990.4410.5690.8310.885
국가무형문화재(국가지정문화재)1.0000.7710.7590.8270.0000.5731.0000.4160.0001.0000.5010.6880.8270.573
국가민속문화재(국가지정문화재)1.0000.7560.9350.8530.0000.6400.4161.0000.5860.7490.2970.6380.7050.745
등록문화재1.0000.8560.4610.2420.0000.0000.0000.5861.0000.6150.6150.0000.9120.543
유형문화재(도지정문화재)1.0000.6960.7550.6380.0000.5991.0000.7490.6151.0000.0300.7320.7740.666
무형문화재(도지정문화재)1.0000.0000.3210.0000.5010.4410.5010.2970.6150.0301.0000.4940.7250.000
기념물(도지정문화재)1.0000.7170.7340.3100.0000.5690.6880.6380.0000.7320.4941.0000.7010.426
민속문화재(도지정문화재)1.0000.7140.6630.0000.0000.8310.8270.7050.9120.7740.7250.7011.0000.962
문화재자료1.0000.6130.7110.1580.0000.8850.5730.7450.5430.6660.0000.4260.9621.000
2023-12-12T18:37:23.953333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국가무형문화재(국가지정문화재)명승(국가지정문화재)사적(국가지정문화재)무형문화재(도지정문화재)국보(국가지정문화재)
국가무형문화재(국가지정문화재)1.0000.0000.0000.2040.000
명승(국가지정문화재)0.0001.0000.0000.4270.000
사적(국가지정문화재)0.0000.0001.0000.0000.653
무형문화재(도지정문화재)0.2040.4270.0001.0000.000
국보(국가지정문화재)0.0000.0000.6530.0001.000
2023-12-12T18:37:24.088430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
보물(국가지정문화재)천연기념물(국가지정문화재)국가민속문화재(국가지정문화재)등록문화재유형문화재(도지정문화재)기념물(도지정문화재)민속문화재(도지정문화재)문화재자료국보(국가지정문화재)사적(국가지정문화재)명승(국가지정문화재)국가무형문화재(국가지정문화재)무형문화재(도지정문화재)
보물(국가지정문화재)1.0000.1890.3790.3010.8270.6830.2890.4250.6710.4220.0000.3540.221
천연기념물(국가지정문화재)0.1891.0000.118-0.4300.2840.037-0.262-0.0870.3540.0000.0000.0000.231
국가민속문화재(국가지정문화재)0.3790.1181.0000.2340.3280.1960.3950.5690.6310.4860.0000.0000.180
등록문화재0.301-0.4300.2341.0000.3290.3830.3580.4310.4580.0000.0000.0000.371
유형문화재(도지정문화재)0.8270.2840.3280.3291.0000.7370.2800.4430.5020.3830.0000.8660.000
기념물(도지정문화재)0.6830.0370.1960.3830.7371.0000.6140.5590.5250.3060.0000.3820.000
민속문화재(도지정문화재)0.289-0.2620.3950.3580.2800.6141.0000.7610.6150.0000.0000.0000.342
문화재자료0.425-0.0870.5690.4310.4430.5590.7611.0000.3500.0000.0000.0000.000
국보(국가지정문화재)0.6710.3540.6310.4580.5020.5250.6150.3501.0000.6530.0000.0000.000
사적(국가지정문화재)0.4220.0000.4860.0000.3830.3060.0000.0000.6531.0000.0000.0000.000
명승(국가지정문화재)0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.427
국가무형문화재(국가지정문화재)0.3540.0000.0000.0000.8660.3820.0000.0000.0000.0000.0001.0000.204
무형문화재(도지정문화재)0.2210.2310.1800.3710.0000.0000.3420.0000.0000.0000.4270.2041.000

Missing values

2023-12-12T18:37:18.805405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:37:19.005457image/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.
2023-12-12T18:37:19.182255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시군별국보(국가지정문화재)보물(국가지정문화재)사적(국가지정문화재)명승(국가지정문화재)천연기념물(국가지정문화재)국가무형문화재(국가지정문화재)국가민속문화재(국가지정문화재)등록문화재유형문화재(도지정문화재)무형문화재(도지정문화재)기념물(도지정문화재)민속문화재(도지정문화재)문화재자료
0포항시19225<NA>122217519
1경주시3610278<NA>5515242518347
2김천시121<NA><NA>11<NA>51324<NA>22
3안동시5492273355785215272
4구미시115212<NA>1<NA>2627729
5영주시727413<NA>383216848
6영천시121<NA><NA>1<NA>433818516
7상주시<NA>232<NA>3<NA>2224117726
8문경시2141233263155227
9경산시<NA>92<NA>2112935<NA>11
시군별국보(국가지정문화재)보물(국가지정문화재)사적(국가지정문화재)명승(국가지정문화재)천연기념물(국가지정문화재)국가무형문화재(국가지정문화재)국가민속문화재(국가지정문화재)등록문화재유형문화재(도지정문화재)무형문화재(도지정문화재)기념물(도지정문화재)민속문화재(도지정문화재)문화재자료
13영양군13<NA><NA>5<NA>216<NA>4632
14영덕군<NA>3<NA>11<NA>5138241243
15청도군<NA>21<NA><NA>6<NA>233039614
16고령군<NA>54<NA><NA><NA>128<NA>3114
17성주군<NA>52<NA>1<NA>1<NA>201101026
18칠곡군<NA>7111<NA><NA>3712113
19예천군123<NA>332412235830
20봉화군112121<NA>9320161143
21울진군25<NA>16<NA><NA>29<NA>3110
22울릉군<NA><NA><NA><NA>9<NA>21<NA><NA>223