Overview

Dataset statistics

Number of variables10
Number of observations27
Missing cells39
Missing cells (%)14.4%
Duplicate rows1
Duplicate rows (%)3.7%
Total size in memory2.3 KiB
Average record size in memory88.9 B

Variable types

Categorical3
Text3
Numeric3
Unsupported1

Alerts

Dataset has 1 (3.7%) duplicate rowsDuplicates
계(백만원) is highly overall correlated with 국비 and 4 other fieldsHigh correlation
국비 is highly overall correlated with 계(백만원) and 4 other fieldsHigh correlation
시군비 is highly overall correlated with 계(백만원) and 4 other fieldsHigh correlation
구분 is highly overall correlated with 계(백만원) and 2 other fieldsHigh correlation
시군 is highly overall correlated with 계(백만원) and 2 other fieldsHigh correlation
자담 is highly overall correlated with 계(백만원) and 2 other fieldsHigh correlation
축제명 has 2 (7.4%) missing valuesMissing
기간 has 2 (7.4%) missing valuesMissing
장소 has 2 (7.4%) missing valuesMissing
계(백만원) has 2 (7.4%) missing valuesMissing
국비 has 2 (7.4%) missing valuesMissing
시군비 has 2 (7.4%) missing valuesMissing
Unnamed: 9 has 27 (100.0%) missing valuesMissing
Unnamed: 9 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-13 23:50:30.966593
Analysis finished2024-03-13 23:50:32.678721
Duration1.71 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size348.0 B
2014
6 
2013
4 
2012
4 
2011
3 
2010
3 
Other values (4)
7 

Length

Max length4
Median length4
Mean length3.9259259
Min length2

Unique

Unique1 ?
Unique (%)3.7%

Sample

1st row연도
2nd row2014
3rd row2014
4th row2014
5th row2014

Common Values

ValueCountFrequency (%)
2014 6
22.2%
2013 4
14.8%
2012 4
14.8%
2011 3
11.1%
2010 3
11.1%
2009 2
 
7.4%
2008 2
 
7.4%
<NA> 2
 
7.4%
연도 1
 
3.7%

Length

2024-03-14T08:50:32.748822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T08:50:32.863341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2014 6
22.2%
2013 4
14.8%
2012 4
14.8%
2011 3
11.1%
2010 3
11.1%
2009 2
 
7.4%
2008 2
 
7.4%
na 2
 
7.4%
연도 1
 
3.7%

시군
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
진안
6 
익산
5 
고창
4 
남원
3 
완주
3 
Other values (5)
6 

Length

Max length4
Median length2
Mean length2.1851852
Min length2

Unique

Unique4 ?
Unique (%)14.8%

Sample

1st row합 계
2nd row남원
3rd row진안
4th row고창
5th row장수

Common Values

ValueCountFrequency (%)
진안 6
22.2%
익산 5
18.5%
고창 4
14.8%
남원 3
11.1%
완주 3
11.1%
<NA> 2
 
7.4%
합 계 1
 
3.7%
장수 1
 
3.7%
김제 1
 
3.7%
정읍 1
 
3.7%

Length

2024-03-14T08:50:32.985494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T08:50:33.084563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
진안 6
21.4%
익산 5
17.9%
고창 4
14.3%
남원 3
10.7%
완주 3
10.7%
na 2
 
7.1%
합 1
 
3.6%
계 1
 
3.6%
장수 1
 
3.6%
김제 1
 
3.6%

축제명
Text

MISSING 

Distinct21
Distinct (%)84.0%
Missing2
Missing (%)7.4%
Memory size348.0 B
2024-03-14T08:50:33.244296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length17
Mean length10.92
Min length4

Characters and Unicode

Total characters273
Distinct characters86
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)72.0%

Sample

1st row26개 축제
2nd row용궁마을 산수유축제
3rd row원연장마을 꽃잔디축제
4th row하전마을 생생갯벌축제
5th row“월야의 흥”축제
ValueCountFrequency (%)
축제 16
23.2%
송천 3
 
4.3%
하전마을 3
 
4.3%
블루베리 3
 
4.3%
귀농?귀촌체험축제 2
 
2.9%
원연장 2
 
2.9%
꽃잔디 2
 
2.9%
생생 2
 
2.9%
산수유 2
 
2.9%
용궁마을 2
 
2.9%
Other values (32) 32
46.4%
2024-03-14T08:50:33.508030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
44
 
16.1%
축 25
 
9.2%
제 25
 
9.2%
마 9
 
3.3%
을 9
 
3.3%
생 8
 
2.9%
산 6
 
2.2%
리 6
 
2.2%
수 5
 
1.8%
하 4
 
1.5%
Other values (76) 132
48.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 219
80.2%
Space Separator 44
 
16.1%
Other Punctuation 2
 
0.7%
Open Punctuation 2
 
0.7%
Close Punctuation 2
 
0.7%
Decimal Number 2
 
0.7%
Initial Punctuation 1
 
0.4%
Final Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
축 25
 
11.4%
제 25
 
11.4%
마 9
 
4.1%
을 9
 
4.1%
생 8
 
3.7%
산 6
 
2.7%
리 6
 
2.7%
수 5
 
2.3%
하 4
 
1.8%
천 4
 
1.8%
Other values (68) 118
53.9%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
6 1
50.0%
Space Separator
ValueCountFrequency (%)
44
100.0%
Other Punctuation
ValueCountFrequency (%)
? 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Initial Punctuation
ValueCountFrequency (%)
“ 1
100.0%
Final Punctuation
ValueCountFrequency (%)
” 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 219
80.2%
Common 54
 
19.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
축 25
 
11.4%
제 25
 
11.4%
마 9
 
4.1%
을 9
 
4.1%
생 8
 
3.7%
산 6
 
2.7%
리 6
 
2.7%
수 5
 
2.3%
하 4
 
1.8%
천 4
 
1.8%
Other values (68) 118
53.9%
Common
ValueCountFrequency (%)
44
81.5%
? 2
 
3.7%
( 2
 
3.7%
) 2
 
3.7%
“ 1
 
1.9%
” 1
 
1.9%
2 1
 
1.9%
6 1
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 219
80.2%
ASCII 52
 
19.0%
Punctuation 2
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
44
84.6%
? 2
 
3.8%
( 2
 
3.8%
) 2
 
3.8%
2 1
 
1.9%
6 1
 
1.9%
Hangul
ValueCountFrequency (%)
축 25
 
11.4%
제 25
 
11.4%
마 9
 
4.1%
을 9
 
4.1%
생 8
 
3.7%
산 6
 
2.7%
리 6
 
2.7%
수 5
 
2.3%
하 4
 
1.8%
천 4
 
1.8%
Other values (68) 118
53.9%
Punctuation
ValueCountFrequency (%)
“ 1
50.0%
” 1
50.0%

기간
Text

MISSING 

Distinct25
Distinct (%)100.0%
Missing2
Missing (%)7.4%
Memory size348.0 B
2024-03-14T08:50:33.661949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length20
Mean length18
Min length1

Characters and Unicode

Total characters450
Distinct characters18
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row-
2nd row3.29. ~ 3.30.(2일간)
3rd row5.3. ~ 5.6. (4일간)
4th row8.15. ~ 8.16. (2일간)
5th row10.10. ~ 10.11. (2일간)
ValueCountFrequency (%)
25
26.0%
2일간 12
 
12.5%
3일간 4
 
4.2%
9.9 2
 
2.1%
10.26 2
 
2.1%
10일간 2
 
2.1%
5.5 2
 
2.1%
4.7 2
 
2.1%
8.16 2
 
2.1%
4일간 2
 
2.1%
Other values (40) 41
42.7%
2024-03-14T08:50:33.921993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 95
21.1%
72
16.0%
1 34
 
7.6%
2 29
 
6.4%
간 24
 
5.3%
~ 24
 
5.3%
( 24
 
5.3%
일 24
 
5.3%
) 24
 
5.3%
0 16
 
3.6%
Other values (8) 84
18.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 162
36.0%
Other Punctuation 95
21.1%
Space Separator 72
16.0%
Other Letter 48
 
10.7%
Math Symbol 24
 
5.3%
Open Punctuation 24
 
5.3%
Close Punctuation 24
 
5.3%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 34
21.0%
2 29
17.9%
0 16
9.9%
5 15
9.3%
3 15
9.3%
9 13
 
8.0%
6 12
 
7.4%
4 10
 
6.2%
7 10
 
6.2%
8 8
 
4.9%
Other Letter
ValueCountFrequency (%)
간 24
50.0%
일 24
50.0%
Other Punctuation
ValueCountFrequency (%)
. 95
100.0%
Space Separator
ValueCountFrequency (%)
72
100.0%
Math Symbol
ValueCountFrequency (%)
~ 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 402
89.3%
Hangul 48
 
10.7%

Most frequent character per script

Common
ValueCountFrequency (%)
. 95
23.6%
72
17.9%
1 34
 
8.5%
2 29
 
7.2%
~ 24
 
6.0%
( 24
 
6.0%
) 24
 
6.0%
0 16
 
4.0%
5 15
 
3.7%
3 15
 
3.7%
Other values (6) 54
13.4%
Hangul
ValueCountFrequency (%)
간 24
50.0%
일 24
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 402
89.3%
Hangul 48
 
10.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 95
23.6%
72
17.9%
1 34
 
8.5%
2 29
 
7.2%
~ 24
 
6.0%
( 24
 
6.0%
) 24
 
6.0%
0 16
 
4.0%
5 15
 
3.7%
3 15
 
3.7%
Other values (6) 54
13.4%
Hangul
ValueCountFrequency (%)
간 24
50.0%
일 24
50.0%

장소
Text

MISSING 

Distinct19
Distinct (%)76.0%
Missing2
Missing (%)7.4%
Memory size348.0 B
2024-03-14T08:50:34.104048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length11.44
Min length1

Characters and Unicode

Total characters286
Distinct characters88
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)56.0%

Sample

1st row-
2nd row남원시 주천면 용궁마을
3rd row원연장마을 및 꽃잔디동산
4th row심원면 하전리 하전갯벌마을
5th row장수군 천천면 구신마을
ValueCountFrequency (%)
및 4
 
5.9%
원연장마을 3
 
4.4%
꽃잔디동산 3
 
4.4%
주천면 3
 
4.4%
하전갯벌마을 3
 
4.4%
마을만들기 2
 
2.9%
용궁마을 2
 
2.9%
남원시 2
 
2.9%
지구 2
 
2.9%
20개 2
 
2.9%
Other values (38) 42
61.8%
2024-03-14T08:50:34.426122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
46
 
16.1%
마 16
 
5.6%
을 16
 
5.6%
면 12
 
4.2%
원 11
 
3.8%
산 8
 
2.8%
천 7
 
2.4%
장 6
 
2.1%
전 6
 
2.1%
하 5
 
1.7%
Other values (78) 153
53.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 232
81.1%
Space Separator 46
 
16.1%
Decimal Number 6
 
2.1%
Dash Punctuation 1
 
0.3%
Other Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
마 16
 
6.9%
을 16
 
6.9%
면 12
 
5.2%
원 11
 
4.7%
산 8
 
3.4%
천 7
 
3.0%
장 6
 
2.6%
전 6
 
2.6%
하 5
 
2.2%
리 4
 
1.7%
Other values (72) 141
60.8%
Decimal Number
ValueCountFrequency (%)
2 3
50.0%
0 2
33.3%
1 1
 
16.7%
Space Separator
ValueCountFrequency (%)
46
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 232
81.1%
Common 54
 
18.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
마 16
 
6.9%
을 16
 
6.9%
면 12
 
5.2%
원 11
 
4.7%
산 8
 
3.4%
천 7
 
3.0%
장 6
 
2.6%
전 6
 
2.6%
하 5
 
2.2%
리 4
 
1.7%
Other values (72) 141
60.8%
Common
ValueCountFrequency (%)
46
85.2%
2 3
 
5.6%
0 2
 
3.7%
- 1
 
1.9%
, 1
 
1.9%
1 1
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 232
81.1%
ASCII 54
 
18.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
46
85.2%
2 3
 
5.6%
0 2
 
3.7%
- 1
 
1.9%
, 1
 
1.9%
1 1
 
1.9%
Hangul
ValueCountFrequency (%)
마 16
 
6.9%
을 16
 
6.9%
면 12
 
5.2%
원 11
 
4.7%
산 8
 
3.4%
천 7
 
3.0%
장 6
 
2.6%
전 6
 
2.6%
하 5
 
2.2%
리 4
 
1.7%
Other values (72) 141
60.8%

계(백만원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)76.0%
Missing2
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean76.688
Minimum20
Maximum958.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-14T08:50:34.570703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile22.6
Q130
median38.5
Q345
95-th percentile100
Maximum958.6
Range938.6
Interquartile range (IQR)15

Descriptive statistics

Standard deviation184.56525
Coefficient of variation (CV)2.4067032
Kurtosis24.491776
Mean76.688
Median Absolute Deviation (MAD)8.5
Skewness4.9290873
Sum1917.2
Variance34064.333
MonotonicityNot monotonic
2024-03-14T08:50:34.683612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
40.0 4
14.8%
33.0 3
 
11.1%
25.0 2
 
7.4%
38.5 1
 
3.7%
110.0 1
 
3.7%
47.6 1
 
3.7%
45.0 1
 
3.7%
44.0 1
 
3.7%
30.0 1
 
3.7%
28.0 1
 
3.7%
Other values (9) 9
33.3%
(Missing) 2
 
7.4%
ValueCountFrequency (%)
20.0 1
 
3.7%
22.0 1
 
3.7%
25.0 2
7.4%
28.0 1
 
3.7%
29.0 1
 
3.7%
30.0 1
 
3.7%
33.0 3
11.1%
36.5 1
 
3.7%
37.0 1
 
3.7%
38.5 1
 
3.7%
ValueCountFrequency (%)
958.6 1
 
3.7%
110.0 1
 
3.7%
60.0 1
 
3.7%
54.0 1
 
3.7%
48.0 1
 
3.7%
47.6 1
 
3.7%
45.0 1
 
3.7%
44.0 1
 
3.7%
40.0 4
14.8%
38.5 1
 
3.7%

국비
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)44.0%
Missing2
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean31.376
Minimum10
Maximum392.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-14T08:50:34.785865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q114
median17
Q320
95-th percentile24
Maximum392.2
Range382.2
Interquartile range (IQR)6

Descriptive statistics

Standard deviation75.283006
Coefficient of variation (CV)2.3993819
Kurtosis24.833224
Mean31.376
Median Absolute Deviation (MAD)3
Skewness4.9759065
Sum784.4
Variance5667.5311
MonotonicityNot monotonic
2024-03-14T08:50:34.900771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
20.0 8
29.6%
10.0 4
14.8%
16.0 2
 
7.4%
12.0 2
 
7.4%
14.0 2
 
7.4%
15.0 2
 
7.4%
392.2 1
 
3.7%
18.0 1
 
3.7%
17.0 1
 
3.7%
18.2 1
 
3.7%
(Missing) 2
 
7.4%
ValueCountFrequency (%)
10.0 4
14.8%
12.0 2
 
7.4%
14.0 2
 
7.4%
15.0 2
 
7.4%
16.0 2
 
7.4%
17.0 1
 
3.7%
18.0 1
 
3.7%
18.2 1
 
3.7%
20.0 8
29.6%
25.0 1
 
3.7%
ValueCountFrequency (%)
392.2 1
 
3.7%
25.0 1
 
3.7%
20.0 8
29.6%
18.2 1
 
3.7%
18.0 1
 
3.7%
17.0 1
 
3.7%
16.0 2
 
7.4%
15.0 2
 
7.4%
14.0 2
 
7.4%
12.0 2
 
7.4%

시군비
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)48.0%
Missing2
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean34.416
Minimum5
Maximum430.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-14T08:50:34.995010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10
Q114
median17
Q320
95-th percentile45.6
Maximum430.2
Range425.2
Interquartile range (IQR)6

Descriptive statistics

Standard deviation82.894449
Coefficient of variation (CV)2.4086021
Kurtosis24.404418
Mean34.416
Median Absolute Deviation (MAD)3
Skewness4.9164944
Sum860.4
Variance6871.4897
MonotonicityNot monotonic
2024-03-14T08:50:35.085701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
20.0 7
25.9%
10.0 3
11.1%
28.0 2
 
7.4%
16.0 2
 
7.4%
12.0 2
 
7.4%
14.0 2
 
7.4%
15.0 2
 
7.4%
430.2 1
 
3.7%
17.0 1
 
3.7%
5.0 1
 
3.7%
Other values (2) 2
 
7.4%
(Missing) 2
 
7.4%
ValueCountFrequency (%)
5.0 1
 
3.7%
10.0 3
11.1%
12.0 2
 
7.4%
14.0 2
 
7.4%
15.0 2
 
7.4%
16.0 2
 
7.4%
17.0 1
 
3.7%
18.2 1
 
3.7%
20.0 7
25.9%
28.0 2
 
7.4%
ValueCountFrequency (%)
430.2 1
 
3.7%
50.0 1
 
3.7%
28.0 2
 
7.4%
20.0 7
25.9%
18.2 1
 
3.7%
17.0 1
 
3.7%
16.0 2
 
7.4%
15.0 2
 
7.4%
14.0 2
 
7.4%
12.0 2
 
7.4%

자담
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Memory size348.0 B
5
7 
-
4 
8
2 
2
2 
4
2 
Other values (8)
10 

Length

Max length5
Median length1
Mean length1.7037037
Min length1

Unique

Unique6 ?
Unique (%)22.2%

Sample

1st row136.2
2nd row5
3rd row8
4th row8
5th row2

Common Values

ValueCountFrequency (%)
5 7
25.9%
- 4
14.8%
8 2
 
7.4%
2 2
 
7.4%
4 2
 
7.4%
3 2
 
7.4%
<NA> 2
 
7.4%
136.2 1
 
3.7%
2.5 1
 
3.7%
12 1
 
3.7%
Other values (3) 3
11.1%

Length

2024-03-14T08:50:35.194654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5 7
25.9%
4
14.8%
8 2
 
7.4%
2 2
 
7.4%
4 2
 
7.4%
3 2
 
7.4%
na 2
 
7.4%
136.2 1
 
3.7%
2.5 1
 
3.7%
12 1
 
3.7%
Other values (3) 3
11.1%

Unnamed: 9
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing27
Missing (%)100.0%
Memory size375.0 B

Interactions

2024-03-14T08:50:31.971216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T08:50:31.308139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T08:50:31.766273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T08:50:32.036733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T08:50:31.367611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T08:50:31.836308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T08:50:32.130798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T08:50:31.704645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T08:50:31.907323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T08:50:35.269233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분시군축제명기간장소계(백만원)국비시군비자담
구분1.0000.0000.0001.0000.6331.0001.0000.8480.764
시군0.0001.0001.0001.0001.0001.0001.0000.9090.818
축제명0.0001.0001.0001.0000.9841.0001.0001.0000.964
기간1.0001.0001.0001.0001.0001.0001.0001.0001.000
장소0.6331.0000.9841.0001.0001.0001.0001.0000.927
계(백만원)1.0001.0001.0001.0001.0001.0000.6431.0001.000
국비1.0001.0001.0001.0001.0000.6431.0001.0001.000
시군비0.8480.9091.0001.0001.0001.0001.0001.0001.000
자담0.7640.8180.9641.0000.9271.0001.0001.0001.000
2024-03-14T08:50:35.368484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분자담시군
구분1.0000.3670.000
자담0.3671.0000.452
시군0.0000.4521.000
2024-03-14T08:50:35.449505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계(백만원)국비시군비구분시군자담
계(백만원)1.0000.9280.9680.8600.8340.752
국비0.9281.0000.9600.8600.8340.752
시군비0.9680.9601.0000.6990.5480.769
구분0.8600.8600.6991.0000.0000.367
시군0.8340.8340.5480.0001.0000.452
자담0.7520.7520.7690.3670.4521.000

Missing values

2024-03-14T08:50:32.269624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T08:50:32.415481image/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.
2024-03-14T08:50:32.572394image/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

구분시군축제명기간장소계(백만원)국비시군비자담Unnamed: 9
0연도합 계26개 축제--958.6392.2430.2136.2<NA>
12014남원용궁마을 산수유축제3.29. ~ 3.30.(2일간)남원시 주천면 용궁마을25.010.010.05<NA>
22014진안원연장마을 꽃잔디축제5.3. ~ 5.6. (4일간)원연장마을 및 꽃잔디동산48.020.020.08<NA>
32014고창하전마을 생생갯벌축제8.15. ~ 8.16. (2일간)심원면 하전리 하전갯벌마을54.018.028.08<NA>
42014장수“월야의 흥”축제10.10. ~ 10.11. (2일간)장수군 천천면 구신마을22.010.010.02<NA>
52014김제수류문화축제10.25. ~ 10.26. (2일간)김제시 금산면 수류로36.517.017.02.5<NA>
62014익산억새축제11.7. ~ 11.9. (3일간)익산시 용안면 난포리37.016.016.05<NA>
72013남원용궁마을 산수유 축제4.6. ~ 4.7. (2일간)남원시 주천면 용궁마을20.010.05.05<NA>
82013완주경천이 애인이와 함께 하는 생태나들이 축제10.26. ~ 10.27. (2일간)경천면농촌사랑학교, 12개 체험마을29.012.012.05<NA>
92013진안원연장 꽃잔디 축제5.3. ~ 5.5. (2일간)원연장마을 및 꽃잔디동산33.014.014.05<NA>
구분시군축제명기간장소계(백만원)국비시군비자담Unnamed: 9
172011진안원연장 꽃잔디 축제5.5. ~ 5.8. (4일간)원연장마을 및 꽃잔디동산45.020.020.05<NA>
182010익산송천 블루베리 축제6.19 ~ 6.20. (2일간)웅포문화센터33.015.015.03<NA>
192010완주물고기 마을 축제10.9. ~ 10.13. (5일간)이서 물고기마을47.618.218.211.2<NA>
202010진안생명의 땅 금지(배넘실) 마을축제12.3. ~ 12.5. (3일간)상전면 금지마을 및 광장40.020.020.0-<NA>
212009익산송천 블루베리 축제6.12. ~ 6.14. (3일간)웅포면 송천마을40.020.020.0-<NA>
222009진안귀농?귀촌체험축제7.31. ~ 8.9. (10일간)진안군내 20개 마을만들기 지구40.020.020.0-<NA>
232008진안귀농?귀촌체험축제8.7. ~ 8.16. (10일간)진안군내 20개 마을만들기 지구40.020.020.0-<NA>
242008고창청보리밭 축제4.12. ~ 5.12. (31일간)공음면 학원농장 청보리밭 일원110.025.050.035<NA>
25<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
26<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

구분시군축제명기간장소계(백만원)국비시군비자담# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA>2