Overview

Dataset statistics

Number of variables9
Number of observations64
Missing cells3
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 KiB
Average record size in memory79.0 B

Variable types

Numeric5
Categorical1
Text3

Dataset

Description전북특별자치도내 주요 농촌 축제 현황 데이터입니다. 시군, 축제명, 기간, 장소, 국비, 시군비, 자담 등에 관한 데이터 입니다.
Author전북특별자치도
URLhttps://www.data.go.kr/data/15011481/fileData.do

Alerts

구분 is highly overall correlated with 계(백만원) and 3 other fieldsHigh correlation
계(백만원) is highly overall correlated with 구분 and 3 other fieldsHigh correlation
국비 is highly overall correlated with 구분 and 3 other fieldsHigh correlation
시군비 is highly overall correlated with 구분 and 3 other fieldsHigh correlation
자담 is highly overall correlated with 구분 and 3 other fieldsHigh correlation
장소 has 3 (4.7%) missing valuesMissing
자담 has 36 (56.2%) zerosZeros

Reproduction

Analysis started2024-03-14 14:14:46.847204
Analysis finished2024-03-14 14:14:54.403179
Duration7.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.5312
Minimum2008
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size704.0 B
2024-03-14T23:14:54.571537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2009.15
Q12013
median2016
Q32018.25
95-th percentile2020
Maximum2020
Range12
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation3.4776783
Coefficient of variation (CV)0.00172544
Kurtosis-0.80405026
Mean2015.5312
Median Absolute Deviation (MAD)3
Skewness-0.49627949
Sum128994
Variance12.094246
MonotonicityDecreasing
2024-03-14T23:14:54.957495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2020 8
12.5%
2019 8
12.5%
2018 8
12.5%
2017 6
9.4%
2014 6
9.4%
2016 5
7.8%
2015 5
7.8%
2013 4
6.2%
2012 4
6.2%
2011 3
 
4.7%
Other values (3) 7
10.9%
ValueCountFrequency (%)
2008 2
 
3.1%
2009 2
 
3.1%
2010 3
4.7%
2011 3
4.7%
2012 4
6.2%
2013 4
6.2%
2014 6
9.4%
2015 5
7.8%
2016 5
7.8%
2017 6
9.4%
ValueCountFrequency (%)
2020 8
12.5%
2019 8
12.5%
2018 8
12.5%
2017 6
9.4%
2016 5
7.8%
2015 5
7.8%
2014 6
9.4%
2013 4
6.2%
2012 4
6.2%
2011 3
 
4.7%

시군
Categorical

Distinct13
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Memory size640.0 B
익산
9 
정읍
8 
장수
8 
임실
7 
남원
7 
Other values (8)
25 

Length

Max length3
Median length2
Mean length2.03125
Min length2

Unique

Unique2 ?
Unique (%)3.1%

Sample

1st row익산
2nd row정읍
3rd row김제
4th row장수
5th row장수

Common Values

ValueCountFrequency (%)
익산 9
14.1%
정읍 8
12.5%
장수 8
12.5%
임실 7
10.9%
남원 7
10.9%
진안 6
9.4%
김제 4
6.2%
완주 4
6.2%
고창 4
6.2%
부안 3
 
4.7%
Other values (3) 4
6.2%

Length

2024-03-14T23:14:55.377044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
익산 9
14.1%
임실 9
14.1%
정읍 8
12.5%
장수 8
12.5%
남원 7
10.9%
진안 6
9.4%
김제 4
6.2%
완주 4
6.2%
고창 4
6.2%
부안 3
 
4.7%
Other values (2) 2
 
3.1%

축제명
Text

Distinct45
Distinct (%)70.3%
Missing0
Missing (%)0.0%
Memory size640.0 B
2024-03-14T23:14:56.318841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length15
Mean length11.5625
Min length4

Characters and Unicode

Total characters740
Distinct characters167
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)48.4%

Sample

1st row성당포구 “배드러온다‘
2nd row다 같이 노~올자! 동네한바퀴
3rd row수록골마을 정월대보름축제
4th row추수감사 축제
5th row더위야 물러가라!! 복달음축제
ValueCountFrequency (%)
축제 38
 
19.6%
다 6
 
3.1%
같이 6
 
3.1%
노~올자 5
 
2.6%
동네한바퀴 5
 
2.6%
한마당 5
 
2.6%
둔데기 4
 
2.1%
말천방 4
 
2.1%
들노래 4
 
2.1%
블루베리 3
 
1.5%
Other values (80) 114
58.8%
2024-03-14T23:14:57.463702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
130
 
17.6%
축 49
 
6.6%
제 49
 
6.6%
마 20
 
2.7%
을 19
 
2.6%
이 13
 
1.8%
노 12
 
1.6%
한 10
 
1.4%
다 9
 
1.2%
리 8
 
1.1%
Other values (157) 421
56.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 578
78.1%
Space Separator 130
 
17.6%
Other Punctuation 7
 
0.9%
Math Symbol 5
 
0.7%
Initial Punctuation 5
 
0.7%
Lowercase Letter 4
 
0.5%
Open Punctuation 3
 
0.4%
Close Punctuation 3
 
0.4%
Uppercase Letter 3
 
0.4%
Final Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
축 49
 
8.5%
제 49
 
8.5%
마 20
 
3.5%
을 19
 
3.3%
이 13
 
2.2%
노 12
 
2.1%
한 10
 
1.7%
다 9
 
1.6%
리 8
 
1.4%
천 8
 
1.4%
Other values (141) 381
65.9%
Lowercase Letter
ValueCountFrequency (%)
o 2
50.0%
d 1
25.0%
g 1
25.0%
Uppercase Letter
ValueCountFrequency (%)
G 1
33.3%
U 1
33.3%
N 1
33.3%
Other Punctuation
ValueCountFrequency (%)
! 4
57.1%
, 3
42.9%
Initial Punctuation
ValueCountFrequency (%)
‘ 3
60.0%
“ 2
40.0%
Space Separator
ValueCountFrequency (%)
130
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 576
77.8%
Common 155
 
20.9%
Latin 7
 
0.9%
Han 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
축 49
 
8.5%
제 49
 
8.5%
마 20
 
3.5%
을 19
 
3.3%
이 13
 
2.3%
노 12
 
2.1%
한 10
 
1.7%
다 9
 
1.6%
리 8
 
1.4%
천 8
 
1.4%
Other values (139) 379
65.8%
Common
ValueCountFrequency (%)
130
83.9%
~ 5
 
3.2%
! 4
 
2.6%
, 3
 
1.9%
‘ 3
 
1.9%
( 3
 
1.9%
) 3
 
1.9%
“ 2
 
1.3%
’ 1
 
0.6%
- 1
 
0.6%
Latin
ValueCountFrequency (%)
o 2
28.6%
d 1
14.3%
g 1
14.3%
G 1
14.3%
U 1
14.3%
N 1
14.3%
Han
ValueCountFrequency (%)
月 1
50.0%
夜 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 576
77.8%
ASCII 156
 
21.1%
Punctuation 6
 
0.8%
CJK 2
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
130
83.3%
~ 5
 
3.2%
! 4
 
2.6%
, 3
 
1.9%
( 3
 
1.9%
) 3
 
1.9%
o 2
 
1.3%
d 1
 
0.6%
g 1
 
0.6%
- 1
 
0.6%
Other values (3) 3
 
1.9%
Hangul
ValueCountFrequency (%)
축 49
 
8.5%
제 49
 
8.5%
마 20
 
3.5%
을 19
 
3.3%
이 13
 
2.3%
노 12
 
2.1%
한 10
 
1.7%
다 9
 
1.6%
리 8
 
1.4%
천 8
 
1.4%
Other values (139) 379
65.8%
Punctuation
ValueCountFrequency (%)
‘ 3
50.0%
“ 2
33.3%
’ 1
 
16.7%
CJK
ValueCountFrequency (%)
月 1
50.0%
夜 1
50.0%

기간
Text

Distinct59
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Memory size640.0 B
2024-03-14T23:14:58.337387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length13.859375
Min length8

Characters and Unicode

Total characters887
Distinct characters21
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)84.4%

Sample

1st row11-16~11-18(3일간)
2nd row10-31(1일간)
3rd row02-08(1일간)
4th row10-31(1일간)
5th row07-24(1일간)
ValueCountFrequency (%)
10-31(1일간 2
 
3.1%
10-09~10-10(2일간 2
 
3.1%
11-11(1일간 2
 
3.1%
11-05~11-06(2일간 2
 
3.1%
10-24(1일간 2
 
3.1%
11-07~11-09(3일간 1
 
1.6%
05-30~05-31(2일간 1
 
1.6%
04-26～04-27(2일간 1
 
1.6%
03-29~03-30(2일간 1
 
1.6%
05-03~05-06(4일간 1
 
1.6%
Other values (49) 49
76.6%
2024-03-14T23:14:59.662186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 139
15.7%
1 128
14.4%
- 103
11.6%
( 64
7.2%
일 64
7.2%
간 64
7.2%
) 64
7.2%
2 62
7.0%
~ 40
 
4.5%
3 29
 
3.3%
Other values (11) 130
14.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 481
54.2%
Other Letter 133
 
15.0%
Dash Punctuation 103
 
11.6%
Open Punctuation 64
 
7.2%
Close Punctuation 64
 
7.2%
Math Symbol 41
 
4.6%
Space Separator 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 139
28.9%
1 128
26.6%
2 62
12.9%
3 29
 
6.0%
5 25
 
5.2%
6 23
 
4.8%
9 23
 
4.8%
4 20
 
4.2%
8 16
 
3.3%
7 16
 
3.3%
Other Letter
ValueCountFrequency (%)
일 64
48.1%
간 64
48.1%
월 2
 
1.5%
중 2
 
1.5%
순 1
 
0.8%
Math Symbol
ValueCountFrequency (%)
~ 40
97.6%
～ 1
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 103
100.0%
Open Punctuation
ValueCountFrequency (%)
( 64
100.0%
Close Punctuation
ValueCountFrequency (%)
) 64
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 754
85.0%
Hangul 133
 
15.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 139
18.4%
1 128
17.0%
- 103
13.7%
( 64
8.5%
) 64
8.5%
2 62
8.2%
~ 40
 
5.3%
3 29
 
3.8%
5 25
 
3.3%
6 23
 
3.1%
Other values (6) 77
10.2%
Hangul
ValueCountFrequency (%)
일 64
48.1%
간 64
48.1%
월 2
 
1.5%
중 2
 
1.5%
순 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 753
84.9%
Hangul 133
 
15.0%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 139
18.5%
1 128
17.0%
- 103
13.7%
( 64
8.5%
) 64
8.5%
2 62
8.2%
~ 40
 
5.3%
3 29
 
3.9%
5 25
 
3.3%
6 23
 
3.1%
Other values (5) 76
10.1%
Hangul
ValueCountFrequency (%)
일 64
48.1%
간 64
48.1%
월 2
 
1.5%
중 2
 
1.5%
순 1
 
0.8%
None
ValueCountFrequency (%)
～ 1
100.0%

장소
Text

MISSING 

Distinct47
Distinct (%)77.0%
Missing3
Missing (%)4.7%
Memory size640.0 B
2024-03-14T23:15:00.651736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length13.52459
Min length6

Characters and Unicode

Total characters825
Distinct characters153
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)60.7%

Sample

1st row성당면 성당포구 금강체험관 일원
2nd row입암면 대흥권역 무지개센터 광장 일원
3rd row백산면 수록골길 50-10
4th row계북면 산촌마을(땡야지 생태마을)
5th row산서면 쌍계리 마평마을 공터
ValueCountFrequency (%)
일원 14
 
7.2%
입암면 6
 
3.1%
임실군 6
 
3.1%
남원 5
 
2.6%
두월리 4
 
2.1%
천천면 4
 
2.1%
및 4
 
2.1%
오수면 4
 
2.1%
사매면 4
 
2.1%
삼계면 4
 
2.1%
Other values (95) 140
71.8%
2024-03-14T23:15:02.257802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
134
 
16.2%
면 48
 
5.8%
마 34
 
4.1%
을 33
 
4.0%
원 32
 
3.9%
리 19
 
2.3%
일 16
 
1.9%
산 15
 
1.8%
천 13
 
1.6%
군 13
 
1.6%
Other values (143) 468
56.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 645
78.2%
Space Separator 134
 
16.2%
Decimal Number 25
 
3.0%
Close Punctuation 8
 
1.0%
Open Punctuation 8
 
1.0%
Other Punctuation 2
 
0.2%
Lowercase Letter 2
 
0.2%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 48
 
7.4%
마 34
 
5.3%
을 33
 
5.1%
원 32
 
5.0%
리 19
 
2.9%
일 16
 
2.5%
산 15
 
2.3%
천 13
 
2.0%
군 13
 
2.0%
장 12
 
1.9%
Other values (127) 410
63.6%
Decimal Number
ValueCountFrequency (%)
0 5
20.0%
2 5
20.0%
1 4
16.0%
6 3
12.0%
3 2
 
8.0%
5 2
 
8.0%
4 2
 
8.0%
7 1
 
4.0%
9 1
 
4.0%
Lowercase Letter
ValueCountFrequency (%)
h 1
50.0%
a 1
50.0%
Space Separator
ValueCountFrequency (%)
134
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 645
78.2%
Common 178
 
21.6%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 48
 
7.4%
마 34
 
5.3%
을 33
 
5.1%
원 32
 
5.0%
리 19
 
2.9%
일 16
 
2.5%
산 15
 
2.3%
천 13
 
2.0%
군 13
 
2.0%
장 12
 
1.9%
Other values (127) 410
63.6%
Common
ValueCountFrequency (%)
134
75.3%
) 8
 
4.5%
( 8
 
4.5%
0 5
 
2.8%
2 5
 
2.8%
1 4
 
2.2%
6 3
 
1.7%
, 2
 
1.1%
3 2
 
1.1%
5 2
 
1.1%
Other values (4) 5
 
2.8%
Latin
ValueCountFrequency (%)
h 1
50.0%
a 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 645
78.2%
ASCII 180
 
21.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
134
74.4%
) 8
 
4.4%
( 8
 
4.4%
0 5
 
2.8%
2 5
 
2.8%
1 4
 
2.2%
6 3
 
1.7%
, 2
 
1.1%
3 2
 
1.1%
5 2
 
1.1%
Other values (6) 7
 
3.9%
Hangul
ValueCountFrequency (%)
면 48
 
7.4%
마 34
 
5.3%
을 33
 
5.1%
원 32
 
5.0%
리 19
 
2.9%
일 16
 
2.5%
산 15
 
2.3%
천 13
 
2.0%
군 13
 
2.0%
장 12
 
1.9%
Other values (127) 410
63.6%

계(백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.74375
Minimum10
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size704.0 B
2024-03-14T23:15:02.641295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q113.5
median20
Q333
95-th percentile47.94
Maximum110
Range100
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation16.453811
Coefficient of variation (CV)0.63913808
Kurtosis9.7689975
Mean25.74375
Median Absolute Deviation (MAD)8
Skewness2.4035477
Sum1647.6
Variance270.7279
MonotonicityNot monotonic
2024-03-14T23:15:03.025463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
12.0 9
14.1%
10.0 7
10.9%
16.0 6
 
9.4%
20.0 6
 
9.4%
18.0 5
 
7.8%
40.0 4
 
6.2%
33.0 4
 
6.2%
25.0 2
 
3.1%
30.0 2
 
3.1%
22.0 2
 
3.1%
Other values (16) 17
26.6%
ValueCountFrequency (%)
10.0 7
10.9%
12.0 9
14.1%
14.0 1
 
1.6%
16.0 6
9.4%
18.0 5
7.8%
20.0 6
9.4%
22.0 2
 
3.1%
25.0 2
 
3.1%
28.0 2
 
3.1%
29.0 1
 
1.6%
ValueCountFrequency (%)
110.0 1
 
1.6%
60.0 1
 
1.6%
54.0 1
 
1.6%
48.0 1
 
1.6%
47.6 1
 
1.6%
45.0 1
 
1.6%
44.0 1
 
1.6%
40.0 4
6.2%
38.5 1
 
1.6%
37.0 1
 
1.6%

국비
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.323438
Minimum5
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size704.0 B
2024-03-14T23:15:03.386656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q16.75
median10
Q315
95-th percentile20
Maximum25
Range20
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation5.2241728
Coefficient of variation (CV)0.46135927
Kurtosis-0.63257646
Mean11.323438
Median Absolute Deviation (MAD)4
Skewness0.64149504
Sum724.7
Variance27.291982
MonotonicityNot monotonic
2024-03-14T23:15:03.986009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
6.0 9
14.1%
10.0 9
14.1%
20.0 8
12.5%
5.0 7
10.9%
9.0 6
9.4%
8.0 6
9.4%
15.0 5
7.8%
14.0 3
 
4.7%
16.0 2
 
3.1%
12.0 2
 
3.1%
Other values (7) 7
10.9%
ValueCountFrequency (%)
5.0 7
10.9%
6.0 9
14.1%
7.0 1
 
1.6%
8.0 6
9.4%
9.0 6
9.4%
10.0 9
14.1%
12.0 2
 
3.1%
12.5 1
 
1.6%
13.0 1
 
1.6%
14.0 3
 
4.7%
ValueCountFrequency (%)
25.0 1
 
1.6%
20.0 8
12.5%
18.2 1
 
1.6%
18.0 1
 
1.6%
17.0 1
 
1.6%
16.0 2
 
3.1%
15.0 5
7.8%
14.0 3
 
4.7%
13.0 1
 
1.6%
12.5 1
 
1.6%

시군비
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.917188
Minimum5
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size704.0 B
2024-03-14T23:15:04.362963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q16
median10
Q315
95-th percentile20
Maximum50
Range45
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.4325017
Coefficient of variation (CV)0.62367917
Kurtosis10.023339
Mean11.917188
Median Absolute Deviation (MAD)4
Skewness2.5037728
Sum762.7
Variance55.242081
MonotonicityNot monotonic
2024-03-14T23:15:04.716319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
6.0 9
14.1%
5.0 8
12.5%
10.0 8
12.5%
20.0 7
10.9%
9.0 6
9.4%
8.0 6
9.4%
15.0 5
7.8%
14.0 3
 
4.7%
16.0 2
 
3.1%
28.0 2
 
3.1%
Other values (7) 8
12.5%
ValueCountFrequency (%)
5.0 8
12.5%
6.0 9
14.1%
7.0 1
 
1.6%
8.0 6
9.4%
9.0 6
9.4%
10.0 8
12.5%
12.0 2
 
3.1%
12.5 1
 
1.6%
13.0 1
 
1.6%
14.0 3
 
4.7%
ValueCountFrequency (%)
50.0 1
 
1.6%
28.0 2
 
3.1%
20.0 7
10.9%
18.2 1
 
1.6%
17.0 1
 
1.6%
16.0 2
 
3.1%
15.0 5
7.8%
14.0 3
4.7%
13.0 1
 
1.6%
12.5 1
 
1.6%

자담
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.503125
Minimum0
Maximum35
Zeros36
Zeros (%)56.2%
Negative0
Negative (%)0.0%
Memory size704.0 B
2024-03-14T23:15:05.040938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile8
Maximum35
Range35
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.0189156
Coefficient of variation (CV)2.0050599
Kurtosis28.02624
Mean2.503125
Median Absolute Deviation (MAD)0
Skewness4.6411804
Sum160.2
Variance25.189514
MonotonicityNot monotonic
2024-03-14T23:15:05.397860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.0 36
56.2%
5.0 9
 
14.1%
2.0 6
 
9.4%
3.0 4
 
6.2%
8.0 2
 
3.1%
4.0 2
 
3.1%
2.5 1
 
1.6%
12.0 1
 
1.6%
6.5 1
 
1.6%
11.2 1
 
1.6%
ValueCountFrequency (%)
0.0 36
56.2%
2.0 6
 
9.4%
2.5 1
 
1.6%
3.0 4
 
6.2%
4.0 2
 
3.1%
5.0 9
 
14.1%
6.5 1
 
1.6%
8.0 2
 
3.1%
11.2 1
 
1.6%
12.0 1
 
1.6%
ValueCountFrequency (%)
35.0 1
 
1.6%
12.0 1
 
1.6%
11.2 1
 
1.6%
8.0 2
 
3.1%
6.5 1
 
1.6%
5.0 9
14.1%
4.0 2
 
3.1%
3.0 4
6.2%
2.5 1
 
1.6%
2.0 6
9.4%

Interactions

2024-03-14T23:14:52.649461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:14:47.670009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:14:48.927752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:14:50.144960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:14:51.432783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:14:52.827031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:14:47.923645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:14:49.175231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:14:50.402295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:14:51.678722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:14:52.967084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:14:48.163196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:14:49.403896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:14:50.647452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:14:51.908227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:14:53.194010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:14:48.428189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:14:49.666660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:14:50.921286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:14:52.171593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:14:53.436121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:14:48.670765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:14:49.895991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:14:51.169125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:14:52.399357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T23:15:05.650691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분시군축제명기간장소계(백만원)국비시군비자담
구분1.0000.0000.6580.9810.0000.8810.6420.8690.616
시군0.0001.0001.0000.1701.0000.6420.6600.7150.370
축제명0.6581.0001.0000.9410.9990.9600.9570.9660.795
기간0.9810.1700.9411.0000.9060.9930.9220.9880.991
장소0.0001.0000.9990.9061.0000.9410.8760.9520.854
계(백만원)0.8810.6420.9600.9930.9411.0000.9030.9920.789
국비0.6420.6600.9570.9220.8760.9031.0000.9490.783
시군비0.8690.7150.9660.9880.9520.9920.9491.0000.754
자담0.6160.3700.7950.9910.8540.7890.7830.7541.000
2024-03-14T23:15:05.960475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분계(백만원)국비시군비자담시군
구분1.000-0.878-0.878-0.832-0.6840.000
계(백만원)-0.8781.0000.9930.9780.7460.318
국비-0.8780.9931.0000.9770.6880.269
시군비-0.8320.9780.9771.0000.6530.437
자담-0.6840.7460.6880.6531.0000.188
시군0.0000.3180.2690.4370.1881.000

Missing values

2024-03-14T23:14:53.793481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T23:14:54.233004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

구분시군축제명기간장소계(백만원)국비시군비자담
02020익산성당포구 “배드러온다‘11-16~11-18(3일간)성당면 성당포구 금강체험관 일원12.06.06.00.0
12020정읍다 같이 노~올자! 동네한바퀴10-31(1일간)입암면 대흥권역 무지개센터 광장 일원10.05.05.00.0
22020김제수록골마을 정월대보름축제02-08(1일간)백산면 수록골길 50-1010.05.05.00.0
32020장수추수감사 축제10-31(1일간)계북면 산촌마을(땡야지 생태마을)10.05.05.00.0
42020장수더위야 물러가라!! 복달음축제07-24(1일간)산서면 쌍계리 마평마을 공터10.05.05.00.0
52020장수가야의 혼, 충절 타루비 한마당10-24(1일간)천천면 타루비 앞 광장(장척마을 회관)12.06.06.00.0
62020임실둔데기 백중술멕이 축제09-24(1일간)오수면 이웅재 고가, 둔데기마을 일원18.09.09.00.0
72020순창전원누리 역사문화 축제10-24(1일간)팔덕면 전원마을 일원10.05.05.00.0
82019익산성당포구 “배드러온다‘11-11(1일간)성당면 성당로 762 일원12.06.06.00.0
92019정읍다 같이 노~올자! 동네한바퀴06-29(1일간)입암면 대흥권역 무지개센터12.06.06.00.0
구분시군축제명기간장소계(백만원)국비시군비자담
542011익산송천 블루베리 축제07-01~07-03(3일간)웅포문화센터44.020.020.04.0
552011완주소양 꽃 축제10-29~10-30(2일간)소양초등학교33.015.015.03.0
562011진안원연장 꽃잔디 축제05-05~05-08(4일간)원연장마을 및 꽃잔디동산45.020.020.05.0
572010익산송천 블루베리 축제06-19~06-20(2일간)웅포문화센터33.015.015.03.0
582010완주물고기 마을 축제10-09~10-13(5일간)이서 물고기마을47.618.218.211.2
592010진안금지(배넘실) 마을축제12-03~12-05(3일간)상전면 금지마을 및 광장40.020.020.00.0
602009익산송천 블루베리 축제06-12~06-14(3일간)웅포면 송천마을40.020.020.00.0
612009진안귀농귀촌체험축제07-31~08-09(10일간)진안군내 20개 마을만들기 지구40.020.020.00.0
622008진안귀농귀촌체험축제08-07~08-16(10일간)진안군내 20개 마을만들기 지구40.020.020.00.0
632008고창청보리밭 축제04-12~05-12(31일간)공음면 학원농장 청보리밭 일원110.025.050.035.0