Overview

Dataset statistics

Number of variables13
Number of observations22
Missing cells55
Missing cells (%)19.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory120.8 B

Variable types

Categorical5
Text1
Numeric7

Dataset

Description대전광역시 공원관리사업소의 시설 및 장비에 관한 데이터로 현재 관리하고 있는 시설물, 장비에 대한 정보를 제공합니다.
Author대전광역시
URLhttps://www.data.go.kr/data/15077433/fileData.do

Alerts

구 분 is highly overall correlated with 세천 and 3 other fieldsHigh correlation
계족산 is highly overall correlated with 계 and 7 other fieldsHigh correlation
장동문화 is highly overall correlated with 계 and 7 other fieldsHigh correlation
연축 is highly overall correlated with 계 and 7 other fieldsHigh correlation
용전근린 is highly overall correlated with 계 and 6 other fieldsHigh correlation
계 is highly overall correlated with 보문산 and 9 other fieldsHigh correlation
보문산 is highly overall correlated with 계 and 9 other fieldsHigh correlation
세천 is highly overall correlated with 계 and 8 other fieldsHigh correlation
읍내 is highly overall correlated with 계 and 8 other fieldsHigh correlation
장동산림 is highly overall correlated with 계 and 9 other fieldsHigh correlation
장태산 is highly overall correlated with 계 and 9 other fieldsHigh correlation
만인산 is highly overall correlated with 계 and 8 other fieldsHigh correlation
계족산 is highly imbalanced (60.5%)Imbalance
용전근린 is highly imbalanced (60.5%)Imbalance
구 분.1 has 1 (4.5%) missing valuesMissing
세천 has 8 (36.4%) missing valuesMissing
읍내 has 14 (63.6%) missing valuesMissing
장동산림 has 10 (45.5%) missing valuesMissing
장태산 has 11 (50.0%) missing valuesMissing
만인산 has 11 (50.0%) missing valuesMissing

Reproduction

Analysis started2024-03-14 12:24:51.434983
Analysis finished2024-03-14 12:25:05.506653
Duration14.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구 분
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Memory size304.0 B
편의시설
6 
안내시설
4 
체육시설
4 
등산로
3 
관리시설
2 
Other values (3)
3 

Length

Max length11
Median length4
Mean length4.1818182
Min length3

Unique

Unique3 ?
Unique (%)13.6%

Sample

1st row안내시설
2nd row안내시설
3rd row안내시설
4th row안내시설
5th row편의시설

Common Values

ValueCountFrequency (%)
편의시설 6
27.3%
안내시설 4
18.2%
체육시설 4
18.2%
등산로 3
13.6%
관리시설 2
 
9.1%
시각장애인산책로(㎞) 1
 
4.5%
약 수 터 1
 
4.5%
공원등 1
 
4.5%

Length

2024-03-14T21:25:05.748842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:25:06.114356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
편의시설 6
25.0%
안내시설 4
16.7%
체육시설 4
16.7%
등산로 3
12.5%
관리시설 2
 
8.3%
시각장애인산책로(㎞ 1
 
4.2%
약 1
 
4.2%
수 1
 
4.2%
터 1
 
4.2%
공원등 1
 
4.2%

구 분.1
Text

MISSING 

Distinct21
Distinct (%)100.0%
Missing1
Missing (%)4.5%
Memory size304.0 B
2024-03-14T21:25:06.726000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.6666667
Min length3

Characters and Unicode

Total characters98
Distinct characters56
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row종합안내판
2nd row방향표지판
3rd row등산로안내판
4th row기 타
5th row정 자
ValueCountFrequency (%)
장 4
 
9.8%
구 3
 
7.3%
수 2
 
4.9%
자 2
 
4.9%
관 1
 
2.4%
목계단(단 1
 
2.4%
로프(m 1
 
2.4%
약 1
 
2.4%
터 1
 
2.4%
공원등 1
 
2.4%
Other values (24) 24
58.5%
2024-03-14T21:25:07.628964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20
 
20.4%
장 6
 
6.1%
) 3
 
3.1%
구 3
 
3.1%
판 3
 
3.1%
( 3
 
3.1%
소 2
 
2.0%
자 2
 
2.0%
수 2
 
2.0%
단 2
 
2.0%
Other values (46) 52
53.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 69
70.4%
Space Separator 20
 
20.4%
Close Punctuation 3
 
3.1%
Open Punctuation 3
 
3.1%
Lowercase Letter 3
 
3.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 6
 
8.7%
구 3
 
4.3%
판 3
 
4.3%
소 2
 
2.9%
자 2
 
2.9%
수 2
 
2.9%
단 2
 
2.9%
로 2
 
2.9%
등 2
 
2.9%
배 2
 
2.9%
Other values (41) 43
62.3%
Lowercase Letter
ValueCountFrequency (%)
m 2
66.7%
k 1
33.3%
Space Separator
ValueCountFrequency (%)
20
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 69
70.4%
Common 26
 
26.5%
Latin 3
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 6
 
8.7%
구 3
 
4.3%
판 3
 
4.3%
소 2
 
2.9%
자 2
 
2.9%
수 2
 
2.9%
단 2
 
2.9%
로 2
 
2.9%
등 2
 
2.9%
배 2
 
2.9%
Other values (41) 43
62.3%
Common
ValueCountFrequency (%)
20
76.9%
) 3
 
11.5%
( 3
 
11.5%
Latin
ValueCountFrequency (%)
m 2
66.7%
k 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 69
70.4%
ASCII 29
29.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20
69.0%
) 3
 
10.3%
( 3
 
10.3%
m 2
 
6.9%
k 1
 
3.4%
Hangul
ValueCountFrequency (%)
장 6
 
8.7%
구 3
 
4.3%
판 3
 
4.3%
소 2
 
2.9%
자 2
 
2.9%
수 2
 
2.9%
단 2
 
2.9%
로 2
 
2.9%
등 2
 
2.9%
배 2
 
2.9%
Other values (41) 43
62.3%

계
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean761.81818
Minimum1
Maximum8164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-14T21:25:07.838593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median18.5
Q396
95-th percentile5259.1
Maximum8164
Range8163
Interquartile range (IQR)90

Descriptive statistics

Standard deviation2035.9064
Coefficient of variation (CV)2.6724309
Kurtosis9.4055476
Mean761.81818
Median Absolute Deviation (MAD)16.5
Skewness3.1181744
Sum16760
Variance4144915
MonotonicityNot monotonic
2024-03-14T21:25:08.057647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 3
 
13.6%
16 2
 
9.1%
12 2
 
9.1%
87 1
 
4.5%
3 1
 
4.5%
1 1
 
4.5%
4 1
 
4.5%
1594 1
 
4.5%
8164 1
 
4.5%
5452 1
 
4.5%
Other values (8) 8
36.4%
ValueCountFrequency (%)
1 1
 
4.5%
2 3
13.6%
3 1
 
4.5%
4 1
 
4.5%
12 2
9.1%
16 2
9.1%
18 1
 
4.5%
19 1
 
4.5%
32 1
 
4.5%
41 1
 
4.5%
ValueCountFrequency (%)
8164 1
4.5%
5452 1
4.5%
1594 1
4.5%
907 1
4.5%
218 1
4.5%
99 1
4.5%
87 1
4.5%
59 1
4.5%
41 1
4.5%
32 1
4.5%

보문산
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean664.68182
Minimum1
Maximum7886
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-14T21:25:08.320561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.05
Q12.25
median12.5
Q355.5
95-th percentile4476.85
Maximum7886
Range7885
Interquartile range (IQR)53.25

Descriptive statistics

Standard deviation1901.9066
Coefficient of variation (CV)2.8613791
Kurtosis10.92918
Mean664.68182
Median Absolute Deviation (MAD)10.5
Skewness3.3116672
Sum14623
Variance3617248.9
MonotonicityNot monotonic
2024-03-14T21:25:08.589258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 4
18.2%
1 2
 
9.1%
69 1
 
4.5%
3 1
 
4.5%
7 1
 
4.5%
1282 1
 
4.5%
12 1
 
4.5%
7886 1
 
4.5%
4645 1
 
4.5%
10 1
 
4.5%
Other values (8) 8
36.4%
ValueCountFrequency (%)
1 2
9.1%
2 4
18.2%
3 1
 
4.5%
7 1
 
4.5%
10 1
 
4.5%
11 1
 
4.5%
12 1
 
4.5%
13 1
 
4.5%
14 1
 
4.5%
17 1
 
4.5%
ValueCountFrequency (%)
7886 1
4.5%
4645 1
4.5%
1282 1
4.5%
511 1
4.5%
69 1
4.5%
59 1
4.5%
45 1
4.5%
29 1
4.5%
17 1
4.5%
14 1
4.5%

세천
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)78.6%
Missing8
Missing (%)36.4%
Infinite0
Infinite (%)0.0%
Mean60.285714
Minimum1
Maximum223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-14T21:25:08.804719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.25
median5.5
Q3119.75
95-th percentile214.55
Maximum223
Range222
Interquartile range (IQR)117.5

Descriptive statistics

Standard deviation84.122099
Coefficient of variation (CV)1.3953903
Kurtosis-0.44596267
Mean60.285714
Median Absolute Deviation (MAD)4.5
Skewness1.0776547
Sum844
Variance7076.5275
MonotonicityNot monotonic
2024-03-14T21:25:08.981659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 2
 
9.1%
3 2
 
9.1%
1 2
 
9.1%
5 1
 
4.5%
6 1
 
4.5%
127 1
 
4.5%
11 1
 
4.5%
152 1
 
4.5%
223 1
 
4.5%
210 1
 
4.5%
(Missing) 8
36.4%
ValueCountFrequency (%)
1 2
9.1%
2 2
9.1%
3 2
9.1%
5 1
4.5%
6 1
4.5%
11 1
4.5%
98 1
4.5%
127 1
4.5%
152 1
4.5%
210 1
4.5%
ValueCountFrequency (%)
223 1
4.5%
210 1
4.5%
152 1
4.5%
127 1
4.5%
98 1
4.5%
11 1
4.5%
6 1
4.5%
5 1
4.5%
3 2
9.1%
2 2
9.1%

계족산
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size304.0 B
<NA>
19 
1
 
1
2
 
1
23
 
1

Length

Max length4
Median length4
Mean length3.6363636
Min length1

Unique

Unique3 ?
Unique (%)13.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 19
86.4%
1 1
 
4.5%
2 1
 
4.5%
23 1
 
4.5%

Length

2024-03-14T21:25:09.197381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:25:09.396547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
86.4%
1 1
 
4.5%
2 1
 
4.5%
23 1
 
4.5%

읍내
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing14
Missing (%)63.6%
Infinite0
Infinite (%)0.0%
Mean76.625
Minimum1
Maximum553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-14T21:25:09.646174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.35
Q12.75
median8
Q317.5
95-th percentile367.15
Maximum553
Range552
Interquartile range (IQR)14.75

Descriptive statistics

Standard deviation192.63284
Coefficient of variation (CV)2.5139685
Kurtosis7.9629771
Mean76.625
Median Absolute Deviation (MAD)6.5
Skewness2.8197766
Sum613
Variance37107.411
MonotonicityNot monotonic
2024-03-14T21:25:09.821979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 1
 
4.5%
4 1
 
4.5%
12 1
 
4.5%
1 1
 
4.5%
16 1
 
4.5%
553 1
 
4.5%
22 1
 
4.5%
2 1
 
4.5%
(Missing) 14
63.6%
ValueCountFrequency (%)
1 1
4.5%
2 1
4.5%
3 1
4.5%
4 1
4.5%
12 1
4.5%
16 1
4.5%
22 1
4.5%
553 1
4.5%
ValueCountFrequency (%)
553 1
4.5%
22 1
4.5%
16 1
4.5%
12 1
4.5%
4 1
4.5%
3 1
4.5%
2 1
4.5%
1 1
4.5%

연축
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size304.0 B
<NA>
17 
2
2 
1
2 
3
 
1

Length

Max length4
Median length4
Mean length3.3181818
Min length1

Unique

Unique1 ?
Unique (%)4.5%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 17
77.3%
2 2
 
9.1%
1 2
 
9.1%
3 1
 
4.5%

Length

2024-03-14T21:25:10.049626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:25:10.260509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 17
77.3%
2 2
 
9.1%
1 2
 
9.1%
3 1
 
4.5%

장동문화
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Memory size304.0 B
<NA>
13 
2
4 
1
3 
6
 
1
4
 
1

Length

Max length4
Median length4
Mean length2.7727273
Min length1

Unique

Unique2 ?
Unique (%)9.1%

Sample

1st row2
2nd row<NA>
3rd row<NA>
4th row6
5th row1

Common Values

ValueCountFrequency (%)
<NA> 13
59.1%
2 4
 
18.2%
1 3
 
13.6%
6 1
 
4.5%
4 1
 
4.5%

Length

2024-03-14T21:25:10.471485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:25:10.715513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 13
59.1%
2 4
 
18.2%
1 3
 
13.6%
6 1
 
4.5%
4 1
 
4.5%

장동산림
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)75.0%
Missing10
Missing (%)45.5%
Infinite0
Infinite (%)0.0%
Mean12.833333
Minimum2
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-14T21:25:11.053494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14.5
median5.5
Q312.75
95-th percentile45.45
Maximum46
Range44
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation15.741279
Coefficient of variation (CV)1.2265932
Kurtosis1.9505401
Mean12.833333
Median Absolute Deviation (MAD)3
Skewness1.8014171
Sum154
Variance247.78788
MonotonicityNot monotonic
2024-03-14T21:25:11.399339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 3
 
13.6%
2 2
 
9.1%
6 1
 
4.5%
12 1
 
4.5%
3 1
 
4.5%
45 1
 
4.5%
8 1
 
4.5%
46 1
 
4.5%
15 1
 
4.5%
(Missing) 10
45.5%
ValueCountFrequency (%)
2 2
9.1%
3 1
 
4.5%
5 3
13.6%
6 1
 
4.5%
8 1
 
4.5%
12 1
 
4.5%
15 1
 
4.5%
45 1
 
4.5%
46 1
 
4.5%
ValueCountFrequency (%)
46 1
 
4.5%
45 1
 
4.5%
15 1
 
4.5%
12 1
 
4.5%
8 1
 
4.5%
6 1
 
4.5%
5 3
13.6%
3 1
 
4.5%
2 2
9.1%

용전근린
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size304.0 B
<NA>
19 
13
 
1
1
 
1
19
 
1

Length

Max length4
Median length4
Mean length3.6818182
Min length1

Unique

Unique3 ?
Unique (%)13.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 19
86.4%
13 1
 
4.5%
1 1
 
4.5%
19 1
 
4.5%

Length

2024-03-14T21:25:11.801792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:25:12.154057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
86.4%
13 1
 
4.5%
1 1
 
4.5%
19 1
 
4.5%

장태산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)81.8%
Missing11
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean21.454545
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-14T21:25:12.465121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.5
median6
Q319
95-th percentile88
Maximum93
Range92
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation33.637371
Coefficient of variation (CV)1.5678436
Kurtosis1.76535
Mean21.454545
Median Absolute Deviation (MAD)5
Skewness1.7786068
Sum236
Variance1131.4727
MonotonicityNot monotonic
2024-03-14T21:25:12.831385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 3
 
13.6%
20 1
 
4.5%
18 1
 
4.5%
2 1
 
4.5%
6 1
 
4.5%
93 1
 
4.5%
3 1
 
4.5%
8 1
 
4.5%
83 1
 
4.5%
(Missing) 11
50.0%
ValueCountFrequency (%)
1 3
13.6%
2 1
 
4.5%
3 1
 
4.5%
6 1
 
4.5%
8 1
 
4.5%
18 1
 
4.5%
20 1
 
4.5%
83 1
 
4.5%
93 1
 
4.5%
ValueCountFrequency (%)
93 1
 
4.5%
83 1
 
4.5%
20 1
 
4.5%
18 1
 
4.5%
8 1
 
4.5%
6 1
 
4.5%
3 1
 
4.5%
2 1
 
4.5%
1 3
13.6%

만인산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)54.5%
Missing11
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean18.181818
Minimum1
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-14T21:25:13.169462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q311
95-th percentile83.5
Maximum97
Range96
Interquartile range (IQR)10

Descriptive statistics

Standard deviation33.120441
Coefficient of variation (CV)1.8216243
Kurtosis2.8965729
Mean18.181818
Median Absolute Deviation (MAD)2
Skewness2.001575
Sum200
Variance1096.9636
MonotonicityNot monotonic
2024-03-14T21:25:13.373230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 5
22.7%
3 2
 
9.1%
15 1
 
4.5%
70 1
 
4.5%
7 1
 
4.5%
97 1
 
4.5%
(Missing) 11
50.0%
ValueCountFrequency (%)
1 5
22.7%
3 2
 
9.1%
7 1
 
4.5%
15 1
 
4.5%
70 1
 
4.5%
97 1
 
4.5%
ValueCountFrequency (%)
97 1
 
4.5%
70 1
 
4.5%
15 1
 
4.5%
7 1
 
4.5%
3 2
 
9.1%
1 5
22.7%

Interactions

2024-03-14T21:25:02.608878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:52.345602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:54.117541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:55.867961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:57.546484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:59.256901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:25:00.915817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:25:03.069542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:52.615537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:54.384966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:56.122845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:57.811167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:59.511688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:25:01.165277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:25:03.211054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:52.881949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:54.652910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:56.376689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:58.069651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:59.768995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:25:01.406869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:25:03.364957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:53.134519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:54.893985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:56.603853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:58.296821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:59.995798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:25:01.649199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:25:03.520916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:53.384683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:55.143526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:56.835366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:58.529373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:25:00.236280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:25:01.912094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:25:03.694516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:53.629635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:55.387591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:57.062566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:58.759250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:25:00.460056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:25:02.146152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:25:03.922646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:53.869472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:55.627413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:57.298211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:24:58.996313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:25:00.685589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:25:02.372995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T21:25:13.527603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구 분구 분.1계보문산세천계족산읍내연축장동문화장동산림용전근린장태산만인산
구 분1.0001.0000.6280.8850.8751.0000.4350.8980.0000.6311.0000.6330.435
구 분.11.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
계0.6281.0001.0000.9940.7781.0001.0001.0001.0000.9591.0001.0001.000
보문산0.8851.0000.9941.0000.7781.0001.000NaNNaN0.9541.0001.0001.000
세천0.8751.0000.7780.7781.0001.0000.4350.8271.0000.8231.0001.0001.000
계족산1.0001.0001.0001.0001.0001.0000.000NaNNaN1.000NaN0.0000.000
읍내0.4351.0001.0001.0000.4350.0001.000NaNNaN1.000NaNNaNNaN
연축0.8981.0001.000NaN0.827NaNNaN1.0001.0001.000NaN1.0001.000
장동문화0.0001.0001.000NaN1.000NaNNaN1.0001.0001.000NaN1.0001.000
장동산림0.6311.0000.9590.9540.8231.0001.0001.0001.0001.0001.0001.0001.000
용전근린1.0001.0001.0001.0001.000NaNNaNNaNNaN1.0001.0001.0001.000
장태산0.6331.0001.0001.0001.0000.000NaN1.0001.0001.0001.0001.0001.000
만인산0.4351.0001.0001.0001.0000.000NaN1.0001.0001.0001.0001.0001.000
2024-03-14T21:25:13.770227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구 분계족산장동문화연축용전근린
구 분1.0001.0000.0000.5001.000
계족산1.0001.000NaNNaNNaN
장동문화0.000NaN1.0001.000NaN
연축0.500NaN1.0001.000NaN
용전근린1.000NaNNaNNaN1.000
2024-03-14T21:25:14.152586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계보문산세천읍내장동산림장태산만인산구 분계족산연축장동문화용전근린
계1.0000.9500.7650.8980.9280.9730.8770.2551.0000.8160.8451.000
보문산0.9501.0000.6900.8810.8850.9450.8030.4961.0001.0001.0001.000
세천0.7650.6901.0000.8570.6960.6540.3500.5011.0000.0000.8941.000
읍내0.8980.8810.8571.0000.7500.8000.8940.0001.0001.0001.000NaN
장동산림0.9280.8850.6960.7501.0000.9270.9570.2371.0001.0000.8161.000
장태산0.9730.9450.6540.8000.9271.0000.8430.1541.0001.0000.8661.000
만인산0.8770.8030.3500.8940.9570.8431.0000.2671.0001.0000.8941.000
구 분0.2550.4960.5010.0000.2370.1540.2671.0001.0000.5000.0001.000
계족산1.0001.0001.0001.0001.0001.0001.0001.0001.000NaN0.0000.000
연축0.8161.0000.0001.0001.0001.0001.0000.500NaN1.0001.000NaN
장동문화0.8451.0000.8941.0000.8160.8660.8940.0000.0001.0001.000NaN
용전근린1.0001.0001.000NaN1.0001.0001.0001.0000.000NaNNaN1.000

Missing values

2024-03-14T21:25:04.275731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T21:25:04.819892image/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.
2024-03-14T21:25:05.233802image/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

구 분구 분.1계보문산세천계족산읍내연축장동문화장동산림용전근린장태산만인산
0안내시설종합안내판1625<NA>3<NA>22<NA>11
1안내시설방향표지판99456142<NA>6<NA>2015
2안내시설등산로안내판592922<NA><NA><NA>5<NA>183
3안내시설기 타21859127<NA>122612<NA><NA><NA>
4편의시설정 자1914<NA><NA><NA>11<NA><NA>21
5편의시설파 고 라411711<NA>1<NA>23<NA>61
6편의시설의 자907511152<NA>163445139370
7편의시설매 점1211<NA><NA><NA><NA><NA><NA><NA>1<NA>
8편의시설화 장 실32132<NA><NA><NA>15<NA>37
9편의시설음 수 대18103<NA><NA><NA>22<NA><NA>1
구 분구 분.1계보문산세천계족산읍내연축장동문화장동산림용전근린장태산만인산
12등산로로프(m)81647886210<NA>22<NA><NA>46<NA><NA><NA>
13시각장애인산책로(㎞)<NA>22<NA><NA><NA><NA><NA><NA><NA><NA><NA>
14약 수 터약 수 터1212<NA><NA><NA><NA><NA><NA><NA><NA><NA>
15공원등공원등1594128298<NA><NA><NA><NA>15198397
16관리시설관 리 소411<NA><NA><NA>1<NA><NA>1<NA>
17관리시설차량통제소1673<NA>212<NA><NA><NA>1
18체육시설축 구 장11<NA><NA><NA><NA><NA><NA><NA><NA><NA>
19체육시설배 구 장22<NA><NA><NA><NA><NA><NA><NA><NA><NA>
20체육시설족 구 장22<NA><NA><NA><NA><NA><NA><NA><NA><NA>
21체육시설배드민턴장33<NA><NA><NA><NA><NA><NA><NA><NA><NA>