Overview

Dataset statistics

Number of variables9
Number of observations33
Missing cells22
Missing cells (%)7.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory81.9 B

Variable types

Numeric6
Text2
Categorical1

Dataset

Description전북특별자치도 향토산업 육성사업 안내(시군명, 향토산업, 산업 규모 등)향토산업 육성사업이 진행된전북특별자치도 내 시군 구분우리나라 행정 구역의 하나, 전북특별자치도 향토산업 육성사업을 일률적으로 연속되어 나타낸 번호
Author전북특별자치도
URLhttps://www.data.go.kr/data/15055547/fileData.do

Alerts

연번 is highly overall correlated with 도비 and 1 other fieldsHigh correlation
총사업비(백만원) is highly overall correlated with 국비 High correlation
국비 is highly overall correlated with 총사업비(백만원) High correlation
도비 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
시군비 is highly overall correlated with 도비 and 1 other fieldsHigh correlation
기타 is highly overall correlated with 시군비 High correlation
사업기간 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
도비 has 20 (60.6%) missing valuesMissing
기타 has 2 (6.1%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-15 01:18:34.742061
Analysis finished2024-03-15 01:18:44.660201
Duration9.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size425.0 B
2024-03-15T10:18:44.772083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.6
Q19
median17
Q325
95-th percentile31.4
Maximum33
Range32
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.6695398
Coefficient of variation (CV)0.56879646
Kurtosis-1.2
Mean17
Median Absolute Deviation (MAD)8
Skewness0
Sum561
Variance93.5
MonotonicityStrictly increasing
2024-03-15T10:18:45.012002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1 1
 
3.0%
26 1
 
3.0%
20 1
 
3.0%
21 1
 
3.0%
22 1
 
3.0%
23 1
 
3.0%
24 1
 
3.0%
25 1
 
3.0%
27 1
 
3.0%
2 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
1 1
3.0%
2 1
3.0%
3 1
3.0%
4 1
3.0%
5 1
3.0%
6 1
3.0%
7 1
3.0%
8 1
3.0%
9 1
3.0%
10 1
3.0%
ValueCountFrequency (%)
33 1
3.0%
32 1
3.0%
31 1
3.0%
30 1
3.0%
29 1
3.0%
28 1
3.0%
27 1
3.0%
26 1
3.0%
25 1
3.0%
24 1
3.0%

시군명
Text

Distinct19
Distinct (%)57.6%
Missing0
Missing (%)0.0%
Memory size392.0 B
2024-03-15T10:18:45.602720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.5151515
Min length3

Characters and Unicode

Total characters149
Distinct characters24
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

Unique9 ?
Unique (%)27.3%

Sample

1st row완주군
2nd row남원시
3rd row김제시
4th row 완주군
5th row 장수군
ValueCountFrequency (%)
완주군 5
15.2%
김제시 4
12.1%
군산시 3
9.1%
고창군 3
9.1%
정읍시 3
9.1%
임실군 3
9.1%
남원시 3
9.1%
무주군 2
 
6.1%
전주시 2
 
6.1%
순창군 2
 
6.1%
Other values (3) 3
9.1%
2024-03-15T10:18:46.713700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
50
33.6%
군 20
 
13.4%
시 16
 
10.7%
주 9
 
6.0%
완 5
 
3.4%
창 5
 
3.4%
산 4
 
2.7%
제 4
 
2.7%
김 4
 
2.7%
원 3
 
2.0%
Other values (14) 29
19.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99
66.4%
Space Separator 50
33.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 20
20.2%
시 16
16.2%
주 9
 
9.1%
완 5
 
5.1%
창 5
 
5.1%
산 4
 
4.0%
제 4
 
4.0%
김 4
 
4.0%
원 3
 
3.0%
남 3
 
3.0%
Other values (13) 26
26.3%
Space Separator
ValueCountFrequency (%)
50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 99
66.4%
Common 50
33.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 20
20.2%
시 16
16.2%
주 9
 
9.1%
완 5
 
5.1%
창 5
 
5.1%
산 4
 
4.0%
제 4
 
4.0%
김 4
 
4.0%
원 3
 
3.0%
남 3
 
3.0%
Other values (13) 26
26.3%
Common
ValueCountFrequency (%)
50
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 99
66.4%
ASCII 50
33.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
50
100.0%
Hangul
ValueCountFrequency (%)
군 20
20.2%
시 16
16.2%
주 9
 
9.1%
완 5
 
5.1%
창 5
 
5.1%
산 4
 
4.0%
제 4
 
4.0%
김 4
 
4.0%
원 3
 
3.0%
남 3
 
3.0%
Other values (13) 26
26.3%

향토자원
Text

Distinct32
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size392.0 B
2024-03-15T10:18:47.466776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.1212121
Min length2

Characters and Unicode

Total characters103
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)93.9%

Sample

1st row봉동생강
2nd row오디뽕
3rd row수박
4th row이서관상어
5th row오미자
ValueCountFrequency (%)
수박 2
 
6.1%
봉동생강 1
 
3.0%
허브 1
 
3.0%
친환경쌀 1
 
3.0%
구절초 1
 
3.0%
과수 1
 
3.0%
양념채소 1
 
3.0%
고구마 1
 
3.0%
박대 1
 
3.0%
곰소젓갈 1
 
3.0%
Other values (22) 22
66.7%
2024-03-15T10:18:48.755560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 4
 
3.9%
수 3
 
2.9%
박 3
 
2.9%
마 3
 
2.9%
소 3
 
2.9%
통 2
 
1.9%
구 2
 
1.9%
고 2
 
1.9%
쌀 2
 
1.9%
보 2
 
1.9%
Other values (67) 77
74.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 103
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 4
 
3.9%
수 3
 
2.9%
박 3
 
2.9%
마 3
 
2.9%
소 3
 
2.9%
통 2
 
1.9%
구 2
 
1.9%
고 2
 
1.9%
쌀 2
 
1.9%
보 2
 
1.9%
Other values (67) 77
74.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 103
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 4
 
3.9%
수 3
 
2.9%
박 3
 
2.9%
마 3
 
2.9%
소 3
 
2.9%
통 2
 
1.9%
구 2
 
1.9%
고 2
 
1.9%
쌀 2
 
1.9%
보 2
 
1.9%
Other values (67) 77
74.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 103
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 4
 
3.9%
수 3
 
2.9%
박 3
 
2.9%
마 3
 
2.9%
소 3
 
2.9%
통 2
 
1.9%
구 2
 
1.9%
고 2
 
1.9%
쌀 2
 
1.9%
보 2
 
1.9%
Other values (67) 77
74.8%

사업기간
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)48.5%
Missing0
Missing (%)0.0%
Memory size392.0 B
2009~2011
5 
2010~2012
5 
2011~2013
4 
2013~2015
4 
2008~2010
3 
Other values (11)
12 

Length

Max length9
Median length9
Mean length8.969697
Min length8

Unique

Unique10 ?
Unique (%)30.3%

Sample

1st row2007~2009
2nd row2008~2010
3rd row2008~2009
4th row2008~2010
5th row2008~2010

Common Values

ValueCountFrequency (%)
2009~2011 5
15.2%
2010~2012 5
15.2%
2011~2013 4
12.1%
2013~2015 4
12.1%
2008~2010 3
9.1%
2012~2014 2
 
6.1%
2007~2009 1
 
3.0%
2008~2009 1
 
3.0%
2010~012 1
 
3.0%
2014-2017 1
 
3.0%
Other values (6) 6
18.2%

Length

2024-03-15T10:18:49.205486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2009~2011 5
15.2%
2010~2012 5
15.2%
2011~2013 4
12.1%
2013~2015 4
12.1%
2008~2010 3
9.1%
2012~2014 2
 
6.1%
2007~2009 1
 
3.0%
2008~2009 1
 
3.0%
2010~012 1
 
3.0%
2014-2017 1
 
3.0%
Other values (6) 6
18.2%

총사업비(백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2906.3333
Minimum868
Maximum3800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size425.0 B
2024-03-15T10:18:49.703929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum868
5-th percentile1204.8
Q13000
median3000
Q33200
95-th percentile3400
Maximum3800
Range2932
Interquartile range (IQR)200

Descriptive statistics

Standard deviation632.83433
Coefficient of variation (CV)0.2177432
Kurtosis5.463543
Mean2906.3333
Median Absolute Deviation (MAD)0
Skewness-2.3620491
Sum95909
Variance400479.29
MonotonicityNot monotonic
2024-03-15T10:18:50.019590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3000 20
60.6%
3400 2
 
6.1%
868 1
 
3.0%
2400 1
 
3.0%
1008 1
 
3.0%
1336 1
 
3.0%
3388 1
 
3.0%
3307 1
 
3.0%
3300 1
 
3.0%
3280 1
 
3.0%
Other values (3) 3
 
9.1%
ValueCountFrequency (%)
868 1
 
3.0%
1008 1
 
3.0%
1336 1
 
3.0%
2400 1
 
3.0%
3000 20
60.6%
3200 1
 
3.0%
3222 1
 
3.0%
3280 1
 
3.0%
3300 1
 
3.0%
3307 1
 
3.0%
ValueCountFrequency (%)
3800 1
 
3.0%
3400 2
 
6.1%
3388 1
 
3.0%
3307 1
 
3.0%
3300 1
 
3.0%
3280 1
 
3.0%
3222 1
 
3.0%
3200 1
 
3.0%
3000 20
60.6%
2400 1
 
3.0%

국비
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1388.6061
Minimum350
Maximum1900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size425.0 B
2024-03-15T10:18:50.346771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum350
5-th percentile444.8
Q11500
median1500
Q31500
95-th percentile1720
Maximum1900
Range1550
Interquartile range (IQR)0

Descriptive statistics

Standard deviation385.19005
Coefficient of variation (CV)0.27739332
Kurtosis2.3990265
Mean1388.6061
Median Absolute Deviation (MAD)0
Skewness-1.7765465
Sum45824
Variance148371.37
MonotonicityNot monotonic
2024-03-15T10:18:50.773097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1500 19
57.6%
1700 2
 
6.1%
362 1
 
3.0%
1200 1
 
3.0%
500 1
 
3.0%
682 1
 
3.0%
1690 1
 
3.0%
1750 1
 
3.0%
1650 1
 
3.0%
1640 1
 
3.0%
Other values (4) 4
 
12.1%
ValueCountFrequency (%)
350 1
 
3.0%
362 1
 
3.0%
500 1
 
3.0%
682 1
 
3.0%
900 1
 
3.0%
1200 1
 
3.0%
1300 1
 
3.0%
1500 19
57.6%
1640 1
 
3.0%
1650 1
 
3.0%
ValueCountFrequency (%)
1900 1
 
3.0%
1750 1
 
3.0%
1700 2
 
6.1%
1690 1
 
3.0%
1650 1
 
3.0%
1640 1
 
3.0%
1500 19
57.6%
1300 1
 
3.0%
1200 1
 
3.0%
900 1
 
3.0%

도비
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)69.2%
Missing20
Missing (%)60.6%
Infinite0
Infinite (%)0.0%
Mean193.61538
Minimum26
Maximum1150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size425.0 B
2024-03-15T10:18:51.245153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile33.8
Q150
median50
Q3120
95-th percentile820
Maximum1150
Range1124
Interquartile range (IQR)70

Descriptive statistics

Standard deviation325.8746
Coefficient of variation (CV)1.6831028
Kurtosis6.6861267
Mean193.61538
Median Absolute Deviation (MAD)11
Skewness2.6000114
Sum2517
Variance106194.26
MonotonicityNot monotonic
2024-03-15T10:18:51.629549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
50 5
 
15.2%
26 1
 
3.0%
92 1
 
3.0%
120 1
 
3.0%
39 1
 
3.0%
40 1
 
3.0%
200 1
 
3.0%
600 1
 
3.0%
1150 1
 
3.0%
(Missing) 20
60.6%
ValueCountFrequency (%)
26 1
 
3.0%
39 1
 
3.0%
40 1
 
3.0%
50 5
15.2%
92 1
 
3.0%
120 1
 
3.0%
200 1
 
3.0%
600 1
 
3.0%
1150 1
 
3.0%
ValueCountFrequency (%)
1150 1
 
3.0%
600 1
 
3.0%
200 1
 
3.0%
120 1
 
3.0%
92 1
 
3.0%
50 5
15.2%
40 1
 
3.0%
39 1
 
3.0%
26 1
 
3.0%

시군비
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1025.6061
Minimum199
Maximum1702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size425.0 B
2024-03-15T10:18:52.045442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum199
5-th percentile326.8
Q1900
median1100
Q31220
95-th percentile1534
Maximum1702
Range1503
Interquartile range (IQR)320

Descriptive statistics

Standard deviation370.11214
Coefficient of variation (CV)0.36087164
Kurtosis-0.039071893
Mean1025.6061
Median Absolute Deviation (MAD)200
Skewness-0.49130513
Sum33845
Variance136983
MonotonicityNot monotonic
2024-03-15T10:18:52.457904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
900 6
 
18.2%
1220 2
 
6.1%
1100 2
 
6.1%
1200 2
 
6.1%
888 1
 
3.0%
1410 1
 
3.0%
1360 1
 
3.0%
1500 1
 
3.0%
1702 1
 
3.0%
1202 1
 
3.0%
Other values (15) 15
45.5%
ValueCountFrequency (%)
199 1
 
3.0%
247 1
 
3.0%
380 1
 
3.0%
450 1
 
3.0%
614 1
 
3.0%
720 1
 
3.0%
750 1
 
3.0%
888 1
 
3.0%
900 6
18.2%
941 1
 
3.0%
ValueCountFrequency (%)
1702 1
3.0%
1585 1
3.0%
1500 1
3.0%
1458 1
3.0%
1410 1
3.0%
1400 1
3.0%
1360 1
3.0%
1330 1
3.0%
1220 2
6.1%
1202 1
3.0%

기타
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)80.6%
Missing2
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean442.67742
Minimum20
Maximum1198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size425.0 B
2024-03-15T10:18:52.906501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile95
Q1275
median401
Q3600
95-th percentile935
Maximum1198
Range1178
Interquartile range (IQR)325

Descriptive statistics

Standard deviation266.82921
Coefficient of variation (CV)0.60276218
Kurtosis1.0529097
Mean442.67742
Median Absolute Deviation (MAD)199
Skewness0.85276002
Sum13723
Variance71197.826
MonotonicityNot monotonic
2024-03-15T10:18:53.368670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
600 6
 
18.2%
400 2
 
6.1%
520 1
 
3.0%
90 1
 
3.0%
140 1
 
3.0%
20 1
 
3.0%
298 1
 
3.0%
500 1
 
3.0%
360 1
 
3.0%
280 1
 
3.0%
Other values (15) 15
45.5%
(Missing) 2
 
6.1%
ValueCountFrequency (%)
20 1
3.0%
90 1
3.0%
100 1
3.0%
140 1
3.0%
142 1
3.0%
170 1
3.0%
233 1
3.0%
270 1
3.0%
280 1
3.0%
298 1
3.0%
ValueCountFrequency (%)
1198 1
 
3.0%
980 1
 
3.0%
890 1
 
3.0%
700 1
 
3.0%
600 6
18.2%
520 1
 
3.0%
509 1
 
3.0%
500 1
 
3.0%
477 1
 
3.0%
430 1
 
3.0%

Interactions

2024-03-15T10:18:42.528015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:35.295038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:36.462203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:37.973587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:39.521868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:41.131931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:42.759644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:35.447481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:36.696435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:38.227666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:39.869309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:41.361141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:43.015222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:35.608994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:36.943597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:38.492093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:40.075348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:41.511662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:43.226742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:35.767070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:37.201542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:38.749871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:40.333335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:41.766804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:43.412671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:36.160471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:37.466291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:39.004303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:40.582549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:42.017472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:43.586294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:36.315824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:37.726810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:39.264307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:40.886598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:18:42.274444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T10:18:53.647218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군명향토자원사업기간총사업비(백만원)국비도비시군비기타
연번1.0000.0000.9320.9220.1490.3150.7260.6080.237
시군명0.0001.0000.9800.4430.2180.9370.6470.7720.705
향토자원0.9320.9801.0000.0000.0000.0001.0000.8540.000
사업기간0.9220.4430.0001.0000.3560.8491.0000.7330.129
총사업비(백만원)0.1490.2180.0000.3561.0000.9590.0000.8440.693
국비0.3150.9370.0000.8490.9591.0001.0000.8530.706
도비0.7260.6471.0001.0000.0001.0001.0000.6450.161
시군비0.6080.7720.8540.7330.8440.8530.6451.0000.943
기타0.2370.7050.0000.1290.6930.7060.1610.9431.000
2024-03-15T10:18:53.969869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번총사업비(백만원)국비도비시군비기타사업기간
연번1.0000.144-0.0970.6160.496-0.1310.550
총사업비(백만원)0.1441.0000.8760.3600.3990.1440.249
국비-0.0970.8761.000-0.0110.2460.2970.471
도비0.6160.360-0.0111.0000.565-0.1160.816
시군비0.4960.3990.2460.5651.000-0.7120.187
기타-0.1310.1440.297-0.116-0.7121.0000.000
사업기간0.5500.2490.4710.8160.1870.0001.000

Missing values

2024-03-15T10:18:43.816312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T10:18:44.275280image/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.
2024-03-15T10:18:44.577546image/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

연번시군명향토자원사업기간총사업비(백만원)국비도비시군비기타
01완주군봉동생강2007~200986836226247233
12남원시오디뽕2008~20103000150092888520
23김제시수박2008~200924001200120380700
34완주군이서관상어2008~2010100850039199270
45장수군오미자2008~2010133668240614<NA>
56전주시전통모주2009~201133881690504501198
67남원시추어탕2009~20113000150050973477
78완주군소양철쭉2009~20113000150050941509
89무주군천마2009~201133071700501156401
910고창군황토2009~201134001750501458142
연번시군명향토자원사업기간총사업비(백만원)국비도비시군비기타
2324정읍시귀리2013~201532001500<NA>1200500
2425임실군치즈2013~201530001500<NA>1202298
2526부안군곰소젓갈2013~201530001500<NA>900600
2627군산시박대2014-201730001500<NA>900600
2728고창군고구마2014~201730001500<NA>900600
2829임실군양념채소2015~201832221500<NA>170220
2930임실군과수2016~201930001500<NA>1500<NA>
3031정읍시구절초2017~2020300013002001360140
3132순창군친환경쌀2018~20213000900600141090
3233김제시로컬푸드2019~2022300035011501100400