Overview

Dataset statistics

Number of variables18
Number of observations54
Missing cells47
Missing cells (%)4.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.1 KiB
Average record size in memory154.4 B

Variable types

Categorical11
Text3
Numeric4

Dataset

Description국립공원내 도유재산(건물) 현황(위치,용도,구조, 건축면적, 사용허가면적, 감정단가, 대장가액, 사용허가요청사유등) 제공
Author전북특별자치도
URLhttps://www.data.go.kr/data/15055536/fileData.do

Alerts

번호 is highly overall correlated with 사용허가면적-유상High correlation
구조 is highly overall correlated with 건축면적 and 4 other fieldsHigh correlation
읍면 is highly overall correlated with 공원명 and 4 other fieldsHigh correlation
리동 is highly overall correlated with 공원명 and 3 other fieldsHigh correlation
공원명 is highly overall correlated with 시군 and 4 other fieldsHigh correlation
시군 is highly overall correlated with 공원명 and 4 other fieldsHigh correlation
사용허가면적-유상 is highly overall correlated with 사용허가면적-계 and 7 other fieldsHigh correlation
용도 is highly overall correlated with 사용허가면적-계 and 2 other fieldsHigh correlation
건축면적 is highly overall correlated with 사용허가면적-계 and 2 other fieldsHigh correlation
사용허가면적-계 is highly overall correlated with 건축면적 and 3 other fieldsHigh correlation
사용허가면적-무상 is highly overall correlated with 건축면적 and 2 other fieldsHigh correlation
사용허가면적-유상 is highly imbalanced (83.2%)Imbalance
감정단가 is highly imbalanced (86.7%)Imbalance
09년평가액 is highly imbalanced (86.7%)Imbalance
10사용료 is highly imbalanced (86.7%)Imbalance
건축면적 has 16 (29.6%) missing valuesMissing
사용허가면적-계 has 2 (3.7%) missing valuesMissing
사용허가면적-무상 has 6 (11.1%) missing valuesMissing
대장가액(원) has 23 (42.6%) missing valuesMissing

Reproduction

Analysis started2024-03-15 01:44:11.603323
Analysis finished2024-03-15 01:44:19.832987
Duration8.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공원명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size560.0 B
덕유산
25 
내장산
18 
지리산
11 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
덕유산 25
46.3%
내장산 18
33.3%
지리산 11
20.4%

Length

2024-03-15T10:44:20.041805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:44:20.357932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
덕유산 25
46.3%
내장산 18
33.3%
지리산 11
20.4%

번호
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Memory size560.0 B
1
4 
3
4 
2
 
3
4
 
3
7
 
3
Other values (21)
37 

Length

Max length2
Median length1
Mean length1.462963
Min length1

Unique

Unique9 ?
Unique (%)16.7%

Sample

1st row1
2nd row2
3rd row3
4th row4
5th row3

Common Values

ValueCountFrequency (%)
1 4
 
7.4%
3 4
 
7.4%
2 3
 
5.6%
4 3
 
5.6%
7 3
 
5.6%
6 3
 
5.6%
8 3
 
5.6%
9 3
 
5.6%
10 3
 
5.6%
5 2
 
3.7%
Other values (16) 23
42.6%

Length

2024-03-15T10:44:20.728046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 4
 
7.5%
3 4
 
7.5%
2 3
 
5.7%
4 3
 
5.7%
7 3
 
5.7%
6 3
 
5.7%
8 3
 
5.7%
9 3
 
5.7%
10 3
 
5.7%
14 2
 
3.8%
Other values (15) 22
41.5%

시군
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size560.0 B
무주
25 
정읍시
18 
남원시
11 

Length

Max length4
Median length3
Mean length2.7407407
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
무주 25
46.3%
정읍시 18
33.3%
남원시 11
20.4%

Length

2024-03-15T10:44:21.157198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:44:21.512024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
무주 25
46.3%
정읍시 18
33.3%
남원시 11
20.4%

읍면
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size560.0 B
설천면
25 
내장동
17 
산내면
8 
주천면
3 
신정동
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)1.9%

Sample

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

Common Values

ValueCountFrequency (%)
설천면 25
46.3%
내장동 17
31.5%
산내면 8
 
14.8%
주천면 3
 
5.6%
신정동 1
 
1.9%

Length

2024-03-15T10:44:21.865536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:44:22.194364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
설천면 25
46.3%
내장동 17
31.5%
산내면 8
 
14.8%
주천면 3
 
5.6%
신정동 1
 
1.9%

리동
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Memory size560.0 B
삼공리
23 
<NA>
17 
부운리
6 
호경리
3 
덕동리
 
2
Other values (3)
3 

Length

Max length4
Median length3
Mean length3.3148148
Min length3

Unique

Unique3 ?
Unique (%)5.6%

Sample

1st row덕동리
2nd row덕동리
3rd row부운리
4th row부운리
5th row부운리

Common Values

ValueCountFrequency (%)
삼공리 23
42.6%
<NA> 17
31.5%
부운리 6
 
11.1%
호경리 3
 
5.6%
덕동리 2
 
3.7%
내장동 1
 
1.9%
두길리 1
 
1.9%
심곡리 1
 
1.9%

Length

2024-03-15T10:44:22.593637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:44:22.957537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
삼공리 23
42.6%
na 17
31.5%
부운리 6
 
11.1%
호경리 3
 
5.6%
덕동리 2
 
3.7%
내장동 1
 
1.9%
두길리 1
 
1.9%
심곡리 1
 
1.9%

지번
Text

Distinct38
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Memory size560.0 B
2024-03-15T10:44:23.788395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.8333333
Min length2

Characters and Unicode

Total characters207
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)51.9%

Sample

1st row402
2nd row산3
3rd row256
4th row431
5th row223
ValueCountFrequency (%)
4118 4
 
7.4%
41824 3
 
5.6%
산605 3
 
5.6%
산196 3
 
5.6%
산231 3
 
5.6%
633 2
 
3.7%
산109 2
 
3.7%
산120 2
 
3.7%
산541 2
 
3.7%
산655 2
 
3.7%
Other values (28) 28
51.9%
2024-03-15T10:44:25.187506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 36
17.4%
산 33
15.9%
2 26
12.6%
6 23
11.1%
4 21
10.1%
5 17
8.2%
3 16
7.7%
0 16
7.7%
8 11
 
5.3%
9 6
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 174
84.1%
Other Letter 33
 
15.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 36
20.7%
2 26
14.9%
6 23
13.2%
4 21
12.1%
5 17
9.8%
3 16
9.2%
0 16
9.2%
8 11
 
6.3%
9 6
 
3.4%
7 2
 
1.1%
Other Letter
ValueCountFrequency (%)
산 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 174
84.1%
Hangul 33
 
15.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 36
20.7%
2 26
14.9%
6 23
13.2%
4 21
12.1%
5 17
9.8%
3 16
9.2%
0 16
9.2%
8 11
 
6.3%
9 6
 
3.4%
7 2
 
1.1%
Hangul
ValueCountFrequency (%)
산 33
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 174
84.1%
Hangul 33
 
15.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 36
20.7%
2 26
14.9%
6 23
13.2%
4 21
12.1%
5 17
9.8%
3 16
9.2%
0 16
9.2%
8 11
 
6.3%
9 6
 
3.4%
7 2
 
1.1%
Hangul
ValueCountFrequency (%)
산 33
100.0%

용도
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Memory size560.0 B
화장실
32 
관사
4 
산장
 
2
매표소
 
2
휴게소
 
2
Other values (10)
12 

Length

Max length3
Median length3
Mean length2.8148148
Min length2

Unique

Unique8 ?
Unique (%)14.8%

Sample

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

Common Values

ValueCountFrequency (%)
화장실 32
59.3%
관사 4
 
7.4%
산장 2
 
3.7%
매표소 2
 
3.7%
휴게소 2
 
3.7%
사무실 2
 
3.7%
창고 2
 
3.7%
육각정 1
 
1.9%
취수장 1
 
1.9%
소독실 1
 
1.9%
Other values (5) 5
 
9.3%

Length

2024-03-15T10:44:25.529494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
화장실 32
59.3%
관사 4
 
7.4%
산장 2
 
3.7%
매표소 2
 
3.7%
휴게소 2
 
3.7%
사무실 2
 
3.7%
창고 2
 
3.7%
육각정 1
 
1.9%
취수장 1
 
1.9%
소독실 1
 
1.9%
Other values (5) 5
 
9.3%

구조
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)35.2%
Missing0
Missing (%)0.0%
Memory size560.0 B
시멘트벽돌조
9 
벽돌기와
7 
벽돌스라브기와
5 
연화조
4 
철근콘크리트조
4 
Other values (14)
25 

Length

Max length7
Median length6
Mean length5.3888889
Min length2

Unique

Unique8 ?
Unique (%)14.8%

Sample

1st row벽돌조 기와
2nd row벽돌 기와
3rd row벽돌 기와
4th row벽돌조 기와
5th row벽돌조 기와

Common Values

ValueCountFrequency (%)
시멘트벽돌조 9
16.7%
벽돌기와 7
13.0%
벽돌스라브기와 5
9.3%
연화조 4
 
7.4%
철근콘크리트조 4
 
7.4%
벽돌조 기와 4
 
7.4%
벽돌 기와 3
 
5.6%
벽돌조 스라브 3
 
5.6%
슬라브기와 3
 
5.6%
벽돌스레트 2
 
3.7%
Other values (9) 10
18.5%

Length

2024-03-15T10:44:25.948106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
시멘트벽돌조 9
13.2%
기와 8
11.8%
벽돌기와 7
10.3%
벽돌조 7
10.3%
벽돌 6
8.8%
스라브 6
8.8%
벽돌스라브기와 5
7.4%
연화조 4
5.9%
철근콘크리트조 4
5.9%
슬라브기와 3
 
4.4%
Other values (8) 9
13.2%

건축면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct31
Distinct (%)81.6%
Missing16
Missing (%)29.6%
Infinite0
Infinite (%)0.0%
Mean87.043684
Minimum4.6
Maximum369.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T10:44:26.416299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.6
5-th percentile21.5065
Q153.94
median66.9
Q379.2125
95-th percentile225.46
Maximum369.92
Range365.32
Interquartile range (IQR)25.2725

Descriptive statistics

Standard deviation75.104241
Coefficient of variation (CV)0.86283389
Kurtosis5.9666149
Mean87.043684
Median Absolute Deviation (MAD)13.03
Skewness2.3348803
Sum3307.66
Variance5640.647
MonotonicityNot monotonic
2024-03-15T10:44:26.848969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
33.05 3
 
5.6%
114.75 2
 
3.7%
55.62 2
 
3.7%
76.85 2
 
3.7%
70.45 2
 
3.7%
53.94 2
 
3.7%
43.5 1
 
1.9%
59.4 1
 
1.9%
60.0 1
 
1.9%
70.48 1
 
1.9%
Other values (21) 21
38.9%
(Missing) 16
29.6%
ValueCountFrequency (%)
4.6 1
 
1.9%
12.25 1
 
1.9%
23.14 1
 
1.9%
33.05 3
5.6%
33.75 1
 
1.9%
43.5 1
 
1.9%
48.6 1
 
1.9%
53.94 2
3.7%
54.7 1
 
1.9%
55.6 1
 
1.9%
ValueCountFrequency (%)
369.92 1
1.9%
304.0 1
1.9%
211.6 1
1.9%
207.06 1
1.9%
150.7 1
1.9%
135.0 1
1.9%
125.6 1
1.9%
114.75 2
3.7%
80.0 1
1.9%
76.85 2
3.7%

사용허가면적-계
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct39
Distinct (%)75.0%
Missing2
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean91.180577
Minimum4.6
Maximum369.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T10:44:27.232768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.6
5-th percentile33.05
Q153.895
median64.245
Q394.22
95-th percentile253.18
Maximum369.92
Range365.32
Interquartile range (IQR)40.325

Descriptive statistics

Standard deviation75.296669
Coefficient of variation (CV)0.82579724
Kurtosis5.5557231
Mean91.180577
Median Absolute Deviation (MAD)11.865
Skewness2.3292284
Sum4741.39
Variance5669.5883
MonotonicityNot monotonic
2024-03-15T10:44:27.529942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
53.76 4
 
7.4%
61.44 3
 
5.6%
53.94 2
 
3.7%
70.45 2
 
3.7%
94.22 2
 
3.7%
80.0 2
 
3.7%
114.75 2
 
3.7%
55.62 2
 
3.7%
76.85 2
 
3.7%
33.05 2
 
3.7%
Other values (29) 29
53.7%
(Missing) 2
 
3.7%
ValueCountFrequency (%)
4.6 1
 
1.9%
12.25 1
 
1.9%
33.05 2
3.7%
33.67 1
 
1.9%
33.75 1
 
1.9%
43.5 1
 
1.9%
48.6 1
 
1.9%
53.32 1
 
1.9%
53.76 4
7.4%
53.94 2
3.7%
ValueCountFrequency (%)
369.92 1
1.9%
342.72 1
1.9%
304.0 1
1.9%
211.6 1
1.9%
207.06 1
1.9%
174.0 1
1.9%
153.27 1
1.9%
150.7 1
1.9%
135.0 1
1.9%
125.6 1
1.9%

사용허가면적-무상
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct35
Distinct (%)72.9%
Missing6
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean80.853125
Minimum4.6
Maximum369.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T10:44:27.935482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.6
5-th percentile33.05
Q153.895
median62.395
Q380
95-th percentile190.442
Maximum369.92
Range365.32
Interquartile range (IQR)26.105

Descriptive statistics

Standard deviation59.774233
Coefficient of variation (CV)0.73929404
Kurtosis11.369338
Mean80.853125
Median Absolute Deviation (MAD)10.89
Skewness2.919142
Sum3880.95
Variance3572.959
MonotonicityNot monotonic
2024-03-15T10:44:28.368567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
53.76 4
 
7.4%
61.44 3
 
5.6%
76.85 2
 
3.7%
94.22 2
 
3.7%
80.0 2
 
3.7%
114.75 2
 
3.7%
55.62 2
 
3.7%
53.94 2
 
3.7%
70.45 2
 
3.7%
33.05 2
 
3.7%
Other values (25) 25
46.3%
(Missing) 6
 
11.1%
ValueCountFrequency (%)
4.6 1
 
1.9%
12.25 1
 
1.9%
33.05 2
3.7%
33.67 1
 
1.9%
33.75 1
 
1.9%
43.5 1
 
1.9%
53.32 1
 
1.9%
53.76 4
7.4%
53.94 2
3.7%
54.7 1
 
1.9%
ValueCountFrequency (%)
369.92 1
1.9%
211.6 1
1.9%
207.06 1
1.9%
159.58 1
1.9%
153.27 1
1.9%
135.0 1
1.9%
125.6 1
1.9%
114.75 2
3.7%
94.22 2
3.7%
80.0 2
3.7%

사용허가면적-유상
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size560.0 B
<NA>
52 
144.42
 
1
342.72
 
1

Length

Max length6
Median length4
Mean length4.0740741
Min length4

Unique

Unique2 ?
Unique (%)3.7%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 52
96.3%
144.42 1
 
1.9%
342.72 1
 
1.9%

Length

2024-03-15T10:44:28.825216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:44:29.168189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 52
96.3%
144.42 1
 
1.9%
342.72 1
 
1.9%

감정단가
Categorical

IMBALANCE 

Distinct2
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size560.0 B
<NA>
53 
230000
 
1

Length

Max length6
Median length4
Mean length4.037037
Min length4

Unique

Unique1 ?
Unique (%)1.9%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 53
98.1%
230000 1
 
1.9%

Length

2024-03-15T10:44:29.551371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:44:29.886735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 53
98.1%
230000 1
 
1.9%

09년평가액
Categorical

IMBALANCE 

Distinct2
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size560.0 B
<NA>
53 
72519552
 
1

Length

Max length8
Median length4
Mean length4.0740741
Min length4

Unique

Unique1 ?
Unique (%)1.9%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 53
98.1%
72519552 1
 
1.9%

Length

2024-03-15T10:44:30.239453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:44:30.481344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 53
98.1%
72519552 1
 
1.9%

대장가액(원)
Real number (ℝ)

MISSING 

Distinct27
Distinct (%)87.1%
Missing23
Missing (%)42.6%
Infinite0
Infinite (%)0.0%
Mean9869098.4
Minimum490000
Maximum90000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size614.0 B
2024-03-15T10:44:30.803791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum490000
5-th percentile1764550
Q13018840
median3719600
Q34459175
95-th percentile44059600
Maximum90000000
Range89510000
Interquartile range (IQR)1440335

Descriptive statistics

Standard deviation18431263
Coefficient of variation (CV)1.8675731
Kurtosis12.625788
Mean9869098.4
Median Absolute Deviation (MAD)756500
Skewness3.4468695
Sum3.0594205 × 108
Variance3.3971144 × 1014
MonotonicityNot monotonic
2024-03-15T10:44:31.202829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
4438350 2
 
3.7%
3170340 2
 
3.7%
4226750 2
 
3.7%
3074580 2
 
3.7%
2478750 1
 
1.9%
22195200 1
 
1.9%
2963100 1
 
1.9%
2640000 1
 
1.9%
3453520 1
 
1.9%
53119200 1
 
1.9%
Other values (17) 17
31.5%
(Missing) 23
42.6%
ValueCountFrequency (%)
490000 1
1.9%
1504100 1
1.9%
2025000 1
1.9%
2478750 1
1.9%
2513350 1
1.9%
2640000 1
1.9%
2644000 1
1.9%
2963100 1
1.9%
3074580 2
3.7%
3170340 2
3.7%
ValueCountFrequency (%)
90000000 1
1.9%
53119200 1
1.9%
35000000 1
1.9%
22195200 1
1.9%
14080080 1
1.9%
8032500 1
1.9%
5967000 1
1.9%
4480000 1
1.9%
4438350 2
3.7%
4226750 2
3.7%

10사용료
Categorical

IMBALANCE 

Distinct2
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size560.0 B
<NA>
53 
1809561
 
1

Length

Max length7
Median length4
Mean length4.0555556
Min length4

Unique

Unique1 ?
Unique (%)1.9%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 53
98.1%
1809561 1
 
1.9%

Length

2024-03-15T10:44:31.625059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:44:31.888543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 53
98.1%
1809561 1
 
1.9%

명칭(사용현황)
Text

Distinct37
Distinct (%)68.5%
Missing0
Missing (%)0.0%
Memory size560.0 B
2024-03-15T10:44:32.644947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length4.9814815
Min length2

Characters and Unicode

Total characters269
Distinct characters81
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)64.8%

Sample

1st row제3주차장 화장실
2nd row제1주차장 화장실
3rd row제2야영장 화장실
4th row제1야영장 화장실
5th row반선주차장변소
ValueCountFrequency (%)
화장실 20
33.3%
관사 3
 
5.0%
변소 2
 
3.3%
옹호정변소 1
 
1.7%
제3주차장 1
 
1.7%
케이블카화장실 1
 
1.7%
전망대(2층 1
 
1.7%
내장사입구변소 1
 
1.7%
휴게소(우화정자 1
 
1.7%
사무실(안내소 1
 
1.7%
Other values (28) 28
46.7%
2024-03-15T10:44:33.605983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 38
 
14.1%
화 26
 
9.7%
실 26
 
9.7%
소 17
 
6.3%
사 9
 
3.3%
변 9
 
3.3%
관 6
 
2.2%
6
 
2.2%
정 5
 
1.9%
차 5
 
1.9%
Other values (71) 122
45.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 246
91.4%
Space Separator 6
 
2.2%
Decimal Number 6
 
2.2%
Close Punctuation 4
 
1.5%
Open Punctuation 4
 
1.5%
Other Punctuation 3
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 38
 
15.4%
화 26
 
10.6%
실 26
 
10.6%
소 17
 
6.9%
사 9
 
3.7%
변 9
 
3.7%
관 6
 
2.4%
정 5
 
2.0%
차 5
 
2.0%
주 5
 
2.0%
Other values (64) 100
40.7%
Decimal Number
ValueCountFrequency (%)
2 3
50.0%
1 2
33.3%
3 1
 
16.7%
Space Separator
ValueCountFrequency (%)
6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Other Punctuation
ValueCountFrequency (%)
· 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 246
91.4%
Common 23
 
8.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 38
 
15.4%
화 26
 
10.6%
실 26
 
10.6%
소 17
 
6.9%
사 9
 
3.7%
변 9
 
3.7%
관 6
 
2.4%
정 5
 
2.0%
차 5
 
2.0%
주 5
 
2.0%
Other values (64) 100
40.7%
Common
ValueCountFrequency (%)
6
26.1%
) 4
17.4%
( 4
17.4%
· 3
13.0%
2 3
13.0%
1 2
 
8.7%
3 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 246
91.4%
ASCII 20
 
7.4%
None 3
 
1.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 38
 
15.4%
화 26
 
10.6%
실 26
 
10.6%
소 17
 
6.9%
사 9
 
3.7%
변 9
 
3.7%
관 6
 
2.4%
정 5
 
2.0%
차 5
 
2.0%
주 5
 
2.0%
Other values (64) 100
40.7%
ASCII
ValueCountFrequency (%)
6
30.0%
) 4
20.0%
( 4
20.0%
2 3
15.0%
1 2
 
10.0%
3 1
 
5.0%
None
ValueCountFrequency (%)
· 3
100.0%

사용허가요청사유
Text

Distinct29
Distinct (%)53.7%
Missing0
Missing (%)0.0%
Memory size560.0 B
2024-03-15T10:44:34.476642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length36
Mean length17.444444
Min length4

Characters and Unicode

Total characters942
Distinct characters92
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)38.9%

Sample

1st row탐방객 편의제공
2nd row탐방객 편의제공
3rd row탐방객 편의제공
4th row탐방객 편의제공
5th row물환경관리과 이관 110823
ValueCountFrequency (%)
탐방객편의제공 10
 
5.7%
탐방객 10
 
5.7%
편의제공 10
 
5.7%
국립공원관리 8
 
4.6%
승인 8
 
4.6%
용도폐지 7
 
4.0%
철거 5
 
2.9%
110823 5
 
2.9%
이관 5
 
2.9%
예정 5
 
2.9%
Other values (64) 101
58.0%
2024-03-15T10:44:35.934332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
120
 
12.7%
0 96
 
10.2%
2 51
 
5.4%
1 49
 
5.2%
- 40
 
4.2%
공 29
 
3.1%
제 24
 
2.5%
탐 21
 
2.2%
방 21
 
2.2%
의 21
 
2.2%
Other values (82) 470
49.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 474
50.3%
Decimal Number 287
30.5%
Space Separator 120
 
12.7%
Dash Punctuation 40
 
4.2%
Other Punctuation 17
 
1.8%
Close Punctuation 2
 
0.2%
Open Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 29
 
6.1%
제 24
 
5.1%
탐 21
 
4.4%
방 21
 
4.4%
의 21
 
4.4%
편 21
 
4.4%
객 21
 
4.4%
관 17
 
3.6%
승 12
 
2.5%
지 12
 
2.5%
Other values (66) 275
58.0%
Decimal Number
ValueCountFrequency (%)
0 96
33.4%
2 51
17.8%
1 49
17.1%
4 18
 
6.3%
3 16
 
5.6%
5 15
 
5.2%
8 12
 
4.2%
9 11
 
3.8%
6 10
 
3.5%
7 9
 
3.1%
Other Punctuation
ValueCountFrequency (%)
, 10
58.8%
/ 7
41.2%
Space Separator
ValueCountFrequency (%)
120
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 474
50.3%
Common 468
49.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 29
 
6.1%
제 24
 
5.1%
탐 21
 
4.4%
방 21
 
4.4%
의 21
 
4.4%
편 21
 
4.4%
객 21
 
4.4%
관 17
 
3.6%
승 12
 
2.5%
지 12
 
2.5%
Other values (66) 275
58.0%
Common
ValueCountFrequency (%)
120
25.6%
0 96
20.5%
2 51
10.9%
1 49
10.5%
- 40
 
8.5%
4 18
 
3.8%
3 16
 
3.4%
5 15
 
3.2%
8 12
 
2.6%
9 11
 
2.4%
Other values (6) 40
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 474
50.3%
ASCII 468
49.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
120
25.6%
0 96
20.5%
2 51
10.9%
1 49
10.5%
- 40
 
8.5%
4 18
 
3.8%
3 16
 
3.4%
5 15
 
3.2%
8 12
 
2.6%
9 11
 
2.4%
Other values (6) 40
 
8.5%
Hangul
ValueCountFrequency (%)
공 29
 
6.1%
제 24
 
5.1%
탐 21
 
4.4%
방 21
 
4.4%
의 21
 
4.4%
편 21
 
4.4%
객 21
 
4.4%
관 17
 
3.6%
승 12
 
2.5%
지 12
 
2.5%
Other values (66) 275
58.0%

Interactions

2024-03-15T10:44:17.118817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:44:14.314424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:44:15.242899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:44:16.282593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:44:17.335659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:44:14.481051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:44:15.500406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:44:16.500862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:44:17.787236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:44:14.641162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:44:15.759146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:44:16.761970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:44:18.048875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:44:14.997278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:44:16.015851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:44:16.974375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T10:44:36.195897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공원명번호시군읍면리동지번용도구조건축면적사용허가면적-계사용허가면적-무상사용허가면적-유상대장가액(원)명칭(사용현황)사용허가요청사유
공원명1.0000.0001.0001.0001.0001.0000.0000.9730.3610.0560.000NaN0.0000.9270.896
번호0.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.7140.0000.852
시군1.0000.0001.0001.0001.0001.0000.0000.9730.3610.0560.000NaN0.0000.9270.896
읍면1.0000.0001.0001.0001.0001.0000.6190.8730.0000.0000.000NaN0.0000.9770.901
리동1.0000.0001.0001.0001.0001.0000.0000.7630.0000.0000.375NaN0.0000.7580.000
지번1.0000.0001.0001.0001.0001.0000.0000.9710.9310.0000.000NaN0.8790.0000.910
용도0.0000.0000.0000.6190.0000.0001.0000.8390.7740.8670.7730.0000.0001.0000.893
구조0.9730.0000.9730.8730.7630.9710.8391.0000.8950.7670.5960.0000.7960.8550.948
건축면적0.3610.0000.3610.0000.0000.9310.7740.8951.0001.0001.000NaN0.6060.0000.842
사용허가면적-계0.0560.0000.0560.0000.0000.0000.8670.7671.0001.0001.0000.0000.6280.8510.845
사용허가면적-무상0.0000.0000.0000.0000.3750.0000.7730.5961.0001.0001.000NaN0.6280.7460.615
사용허가면적-유상NaN0.000NaNNaNNaNNaN0.0000.000NaN0.000NaN1.000NaN0.0000.000
대장가액(원)0.0000.7140.0000.0000.0000.8790.0000.7960.6060.6280.628NaN1.0001.0000.710
명칭(사용현황)0.9270.0000.9270.9770.7580.0001.0000.8550.0000.8510.7460.0001.0001.0000.000
사용허가요청사유0.8960.8520.8960.9010.0000.9100.8930.9480.8420.8450.6150.0000.7100.0001.000
2024-03-15T10:44:36.677084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
09년평가액번호구조감정단가읍면리동공원명시군사용허가면적-유상10사용료용도
09년평가액1.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
번호NaN1.0000.000NaN0.0000.0000.0000.0001.000NaN0.000
구조NaN0.0001.000NaN0.5610.3920.7790.7791.000NaN0.426
감정단가NaNNaNNaN1.000NaNNaNNaNNaNNaNNaNNaN
읍면NaN0.0000.561NaN1.0000.9530.9800.9801.000NaN0.271
리동NaN0.0000.392NaN0.9531.0000.9390.9391.000NaN0.000
공원명NaN0.0000.779NaN0.9800.9391.0001.0001.000NaN0.000
시군NaN0.0000.779NaN0.9800.9391.0001.0001.000NaN0.000
사용허가면적-유상NaN1.0001.000NaN1.0001.0001.0001.0001.000NaN1.000
10사용료NaNNaNNaNNaNNaNNaNNaNNaNNaN1.000NaN
용도NaN0.0000.426NaN0.2710.0000.0000.0001.000NaN1.000
2024-03-15T10:44:37.033456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건축면적사용허가면적-계사용허가면적-무상대장가액(원)공원명번호시군읍면리동용도구조사용허가면적-유상감정단가09년평가액10사용료
건축면적1.0001.0000.9990.4950.2360.0000.2360.0000.0000.4420.558NaN0.0000.0000.000
사용허가면적-계1.0001.0001.0000.4550.0000.0000.0000.0000.0000.5980.3881.000NaNNaNNaN
사용허가면적-무상0.9991.0001.0000.4700.0000.0000.0000.0000.1130.5150.274NaN0.0000.0000.000
대장가액(원)0.4950.4550.4701.0000.0000.3160.0000.0000.0000.0000.464NaN0.0000.0000.000
공원명0.2360.0000.0000.0001.0000.0001.0000.9800.9390.0000.7791.000NaNNaNNaN
번호0.0000.0000.0000.3160.0001.0000.0000.0000.0000.0000.0001.000NaNNaNNaN
시군0.2360.0000.0000.0001.0000.0001.0000.9800.9390.0000.7791.000NaNNaNNaN
읍면0.0000.0000.0000.0000.9800.0000.9801.0000.9530.2710.5611.000NaNNaNNaN
리동0.0000.0000.1130.0000.9390.0000.9390.9531.0000.0000.3921.000NaNNaNNaN
용도0.4420.5980.5150.0000.0000.0000.0000.2710.0001.0000.4261.000NaNNaNNaN
구조0.5580.3880.2740.4640.7790.0000.7790.5610.3920.4261.0001.000NaNNaNNaN
사용허가면적-유상NaN1.000NaNNaN1.0001.0001.0001.0001.0001.0001.0001.000NaNNaNNaN
감정단가0.000NaN0.0000.000NaNNaNNaNNaNNaNNaNNaNNaN1.000NaNNaN
09년평가액0.000NaN0.0000.000NaNNaNNaNNaNNaNNaNNaNNaNNaN1.000NaN
10사용료0.000NaN0.0000.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.000

Missing values

2024-03-15T10:44:18.420643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T10:44:19.065905image/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.
2024-03-15T10:44:19.575650image/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

공원명번호시군읍면리동지번용도구조건축면적사용허가면적-계사용허가면적-무상사용허가면적-유상감정단가09년평가액대장가액(원)10사용료명칭(사용현황)사용허가요청사유
0지리산1남원시산내면덕동리402화장실벽돌조 기와53.9453.9453.94<NA><NA><NA>3074580<NA>제3주차장 화장실탐방객 편의제공
1지리산2남원시산내면덕동리산3화장실벽돌 기와53.9453.9453.94<NA><NA><NA>3074580<NA>제1주차장 화장실탐방객 편의제공
2지리산3남원시산내면부운리256화장실벽돌 기와55.6255.6255.62<NA><NA><NA>3170340<NA>제2야영장 화장실탐방객 편의제공
3지리산4남원시산내면부운리431화장실벽돌조 기와55.6255.6255.62<NA><NA><NA>3170340<NA>제1야영장 화장실탐방객 편의제공
4지리산3남원시산내면부운리223화장실벽돌조 기와59.459.459.4<NA><NA><NA><NA><NA>반선주차장변소물환경관리과 이관 110823
5지리산7남원시주천면호경리산26육각정목조 기와4.64.64.6<NA><NA><NA><NA><NA>육각정2010-04-27 용도폐지 승인
6지리산6남원시주천면호경리산25화장실시멘트기와55.655.655.6<NA><NA><NA><NA><NA>옹호정변소2009-02-25 용도폐지 철거 결정
7지리산8남원시산내면부운리산120산장벽돌스라브150.7150.7<NA><NA><NA><NA><NA><NA>뱀사골산장2004-04-23 매각/ 환경부에 2건 매각 345696000원
8지리산9남원시산내면부운리산120산장돌담스레트48.648.6<NA><NA><NA><NA><NA><NA>화천산장(주택)2004-04-23 매각
9지리산10남원시주천면호경리산40화장실벽돌조 기와43.543.543.5<NA><NA><NA><NA><NA>구룡계곡 변소2007-03-29 용도폐지(철거 )결정 대장가액 2479500원/ 대장 살아있음
공원명번호시군읍면리동지번용도구조건축면적사용허가면적-계사용허가면적-무상사용허가면적-유상감정단가09년평가액대장가액(원)10사용료명칭(사용현황)사용허가요청사유
44덕유산16무주설천면삼공리산109화장실벽돌스라브기와<NA>94.2294.22<NA><NA><NA><NA><NA>화장실2010-05-17 용도폐지 승인/ 2010-07-20 철거
45덕유산17무주설천면삼공리산109화장실벽돌스라브기와<NA>94.2294.22<NA><NA><NA><NA><NA>화장실2010-05-17 용도폐지 승인/ 2010-07-21 철거
46덕유산18무주설천면삼공리산605집회장슬라브기와<NA>342.72<NA>342.7223000072519552<NA>1809561수련장2011-10-25 환경부에 매각(덕유산사무소)/ 금액 68715360원
47덕유산19무주설천면삼공리4562화장실벽돌기와<NA>53.7653.76<NA><NA><NA><NA><NA>화장실2009-06-08 용도폐지 승인
48덕유산20무주설천면삼공리4611화장실벽돌기와<NA>53.7653.76<NA><NA><NA><NA><NA>화장실2009-06-08 용도폐지 승인
49덕유산21무주설천면삼공리산541화장실벽돌조 스라브<NA>53.7653.76<NA><NA><NA><NA><NA>화장실1997년 철거 승인/ 2009-01-28 멸실 신고 완료
50덕유산22무주설천면삼공리산632화장실스라브기와<NA>53.7653.76<NA><NA><NA><NA><NA>화장실2009-06-08 용도폐지 승인
51덕유산23무주설천면삼공리산605화장실슬라브기와<NA>61.4461.44<NA><NA><NA><NA><NA>화장실2005-04-15 철거승인
52덕유산24무주설천면삼공리산635화장실벽돌스라브기와<NA>61.4461.44<NA><NA><NA><NA><NA>화장실2005-04-16 철거승인
53덕유산25무주설천면삼공리산655화장실벽돌스라브기와<NA>61.4461.44<NA><NA><NA><NA><NA>화장실2008-10-22 철거승인