Overview

Dataset statistics

Number of variables9
Number of observations36
Missing cells4
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory83.6 B

Variable types

Text1
Numeric8

Dataset

Description전북특별자치도 사회참여 통합 관련 조사전북특별자치도 사회조사 도정중점추진사업 중 기업/투자유치 등우리기관에서는 더 이상 생성 불가 데이터입니다.
Author전북특별자치도
URLhttps://www.data.go.kr/data/3042697/fileData.do

Alerts

기업 투자유치 is highly overall correlated with 신재생에너지 산업 and 2 other fieldsHigh correlation
새만금 개발 is highly overall correlated with 식품산업 and 4 other fieldsHigh correlation
식품산업 is highly overall correlated with 새만금 개발High correlation
신재생에너지 산업 is highly overall correlated with 기업 투자유치 and 2 other fieldsHigh correlation
방사선 융복합 기술산업 is highly overall correlated with 새만금 개발High correlation
미생물 융복합 기술 산업 is highly overall correlated with 새만금 개발High correlation
항공 우주산업 is highly overall correlated with 기업 투자유치 and 2 other fieldsHigh correlation
기타 is highly overall correlated with 기업 투자유치High correlation
항공 우주산업 has 1 (2.8%) missing valuesMissing
기타 has 3 (8.3%) missing valuesMissing
구분 has unique valuesUnique

Reproduction

Analysis started2024-03-14 14:50:14.708848
Analysis finished2024-03-14 14:50:30.421465
Duration15.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size416.0 B
2024-03-14T23:50:31.147384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.0277778
Min length2

Characters and Unicode

Total characters145
Distinct characters58
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)100.0%

Sample

1st row남자
2nd row여자
3rd row15-19세
4th row20-29세
5th row30-39세
ValueCountFrequency (%)
남자 1
 
2.8%
여자 1
 
2.8%
단독주택 1
 
2.8%
미혼 1
 
2.8%
기혼 1
 
2.8%
사별 1
 
2.8%
이혼 1
 
2.8%
가구주 1
 
2.8%
가구원 1
 
2.8%
아파트 1
 
2.8%
Other values (26) 26
72.2%
2024-03-14T23:50:32.142415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 10
 
6.9%
구 8
 
5.5%
가 8
 
5.5%
대 7
 
4.8%
학 6
 
4.1%
- 5
 
3.4%
9 5
 
3.4%
0 5
 
3.4%
기 4
 
2.8%
자 4
 
2.8%
Other values (48) 83
57.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 112
77.2%
Decimal Number 26
 
17.9%
Dash Punctuation 5
 
3.4%
Other Punctuation 2
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 10
 
8.9%
구 8
 
7.1%
가 8
 
7.1%
대 7
 
6.2%
학 6
 
5.4%
기 4
 
3.6%
자 4
 
3.6%
사 4
 
3.6%
교 4
 
3.6%
주 4
 
3.6%
Other values (38) 53
47.3%
Decimal Number
ValueCountFrequency (%)
9 5
19.2%
0 5
19.2%
1 4
15.4%
2 3
11.5%
5 3
11.5%
3 3
11.5%
4 2
 
7.7%
6 1
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 112
77.2%
Common 33
 
22.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 10
 
8.9%
구 8
 
7.1%
가 8
 
7.1%
대 7
 
6.2%
학 6
 
5.4%
기 4
 
3.6%
자 4
 
3.6%
사 4
 
3.6%
교 4
 
3.6%
주 4
 
3.6%
Other values (38) 53
47.3%
Common
ValueCountFrequency (%)
- 5
15.2%
9 5
15.2%
0 5
15.2%
1 4
12.1%
2 3
9.1%
5 3
9.1%
3 3
9.1%
/ 2
 
6.1%
4 2
 
6.1%
6 1
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 112
77.2%
ASCII 33
 
22.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 10
 
8.9%
구 8
 
7.1%
가 8
 
7.1%
대 7
 
6.2%
학 6
 
5.4%
기 4
 
3.6%
자 4
 
3.6%
사 4
 
3.6%
교 4
 
3.6%
주 4
 
3.6%
Other values (38) 53
47.3%
ASCII
ValueCountFrequency (%)
- 5
15.2%
9 5
15.2%
0 5
15.2%
1 4
12.1%
2 3
9.1%
5 3
9.1%
3 3
9.1%
/ 2
 
6.1%
4 2
 
6.1%
6 1
 
3.0%

기업 투자유치
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.661111
Minimum12.5
Maximum27.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2024-03-14T23:50:32.354486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12.5
5-th percentile17
Q119.9
median21.6
Q323.325
95-th percentile26.775
Maximum27.9
Range15.4
Interquartile range (IQR)3.425

Descriptive statistics

Standard deviation3.2066251
Coefficient of variation (CV)0.14803604
Kurtosis0.99474043
Mean21.661111
Median Absolute Deviation (MAD)1.7
Skewness-0.35972392
Sum779.8
Variance10.282444
MonotonicityNot monotonic
2024-03-14T23:50:32.588290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
25.9 2
 
5.6%
22.5 2
 
5.6%
19.9 2
 
5.6%
23.4 1
 
2.8%
26.3 1
 
2.8%
19.2 1
 
2.8%
20.9 1
 
2.8%
22.8 1
 
2.8%
22.2 1
 
2.8%
21.2 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
12.5 1
2.8%
15.5 1
2.8%
17.5 1
2.8%
18.1 1
2.8%
18.8 1
2.8%
18.9 1
2.8%
19.2 1
2.8%
19.7 1
2.8%
19.9 2
5.6%
20.1 1
2.8%
ValueCountFrequency (%)
27.9 1
2.8%
27.0 1
2.8%
26.7 1
2.8%
26.3 1
2.8%
25.9 2
5.6%
24.3 1
2.8%
23.5 1
2.8%
23.4 1
2.8%
23.3 1
2.8%
22.8 1
2.8%

새만금 개발
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.688889
Minimum26.6
Maximum46.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2024-03-14T23:50:32.895178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26.6
5-th percentile29.575
Q136.375
median39.3
Q341.4
95-th percentile45.2
Maximum46.8
Range20.2
Interquartile range (IQR)5.025

Descriptive statistics

Standard deviation4.5163688
Coefficient of variation (CV)0.11673555
Kurtosis0.88554567
Mean38.688889
Median Absolute Deviation (MAD)2.35
Skewness-0.75726505
Sum1392.8
Variance20.397587
MonotonicityNot monotonic
2024-03-14T23:50:33.131972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
42.8 2
 
5.6%
41.4 2
 
5.6%
40.2 2
 
5.6%
40.6 1
 
2.8%
46.4 1
 
2.8%
32.2 1
 
2.8%
39.4 1
 
2.8%
39.2 1
 
2.8%
37.9 1
 
2.8%
46.8 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
26.6 1
2.8%
28.6 1
2.8%
29.9 1
2.8%
32.2 1
2.8%
34.2 1
2.8%
35.4 1
2.8%
35.6 1
2.8%
36.2 1
2.8%
36.3 1
2.8%
36.4 1
2.8%
ValueCountFrequency (%)
46.8 1
2.8%
46.4 1
2.8%
44.8 1
2.8%
43.1 1
2.8%
43.0 1
2.8%
42.8 2
5.6%
41.9 1
2.8%
41.4 2
5.6%
41.3 1
2.8%
40.6 1
2.8%

식품산업
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.111111
Minimum2.5
Maximum18.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2024-03-14T23:50:33.362511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile9.5
Q112.375
median13.5
Q314.225
95-th percentile16.45
Maximum18.7
Range16.2
Interquartile range (IQR)1.85

Descriptive statistics

Standard deviation2.6632716
Coefficient of variation (CV)0.20313089
Kurtosis7.0621778
Mean13.111111
Median Absolute Deviation (MAD)0.95
Skewness-1.7821134
Sum472
Variance7.0930159
MonotonicityNot monotonic
2024-03-14T23:50:33.581703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
13.4 2
 
5.6%
13.8 2
 
5.6%
13.7 2
 
5.6%
14.3 2
 
5.6%
14.0 2
 
5.6%
12.0 2
 
5.6%
17.5 1
 
2.8%
18.7 1
 
2.8%
13.2 1
 
2.8%
10.8 1
 
2.8%
Other values (20) 20
55.6%
ValueCountFrequency (%)
2.5 1
2.8%
7.4 1
2.8%
10.2 1
2.8%
10.8 1
2.8%
11.5 1
2.8%
11.7 1
2.8%
12.0 2
5.6%
12.3 1
2.8%
12.4 1
2.8%
12.5 1
2.8%
ValueCountFrequency (%)
18.7 1
2.8%
17.5 1
2.8%
16.1 1
2.8%
15.3 1
2.8%
14.9 1
2.8%
14.6 1
2.8%
14.5 1
2.8%
14.3 2
5.6%
14.2 1
2.8%
14.1 1
2.8%

신재생에너지 산업
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.758333
Minimum9.5
Maximum24.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2024-03-14T23:50:33.802620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.5
5-th percentile12.05
Q116.05
median17.4
Q321.1
95-th percentile22.325
Maximum24.1
Range14.6
Interquartile range (IQR)5.05

Descriptive statistics

Standard deviation3.4748381
Coefficient of variation (CV)0.19567366
Kurtosis-0.32331755
Mean17.758333
Median Absolute Deviation (MAD)2.75
Skewness-0.32677142
Sum639.3
Variance12.0745
MonotonicityNot monotonic
2024-03-14T23:50:34.190620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
21.1 3
 
8.3%
18.0 2
 
5.6%
21.5 2
 
5.6%
17.2 2
 
5.6%
17.1 2
 
5.6%
16.1 1
 
2.8%
9.5 1
 
2.8%
14.2 1
 
2.8%
17.8 1
 
2.8%
17.3 1
 
2.8%
Other values (20) 20
55.6%
ValueCountFrequency (%)
9.5 1
2.8%
11.6 1
2.8%
12.2 1
2.8%
12.4 1
2.8%
13.1 1
2.8%
14.2 1
2.8%
15.0 1
2.8%
15.7 1
2.8%
15.9 1
2.8%
16.1 1
2.8%
ValueCountFrequency (%)
24.1 1
 
2.8%
23.0 1
 
2.8%
22.1 1
 
2.8%
21.7 1
 
2.8%
21.5 2
5.6%
21.4 1
 
2.8%
21.1 3
8.3%
21.0 1
 
2.8%
20.5 1
 
2.8%
19.2 1
 
2.8%

방사선 융복합 기술산업
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7527778
Minimum1.5
Maximum14.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2024-03-14T23:50:34.567161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.125
Q12.775
median3.1
Q33.7
95-th percentile9.4
Maximum14.8
Range13.3
Interquartile range (IQR)0.925

Descriptive statistics

Standard deviation2.4967964
Coefficient of variation (CV)0.66531953
Kurtosis11.56384
Mean3.7527778
Median Absolute Deviation (MAD)0.55
Skewness3.2370157
Sum135.1
Variance6.2339921
MonotonicityNot monotonic
2024-03-14T23:50:34.967260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2.3 5
13.9%
2.8 4
 
11.1%
3.5 2
 
5.6%
9.4 2
 
5.6%
3.7 2
 
5.6%
2.9 2
 
5.6%
4.0 2
 
5.6%
3.4 2
 
5.6%
3.0 2
 
5.6%
3.1 2
 
5.6%
Other values (11) 11
30.6%
ValueCountFrequency (%)
1.5 1
 
2.8%
1.9 1
 
2.8%
2.2 1
 
2.8%
2.3 5
13.9%
2.7 1
 
2.8%
2.8 4
11.1%
2.9 2
 
5.6%
3.0 2
 
5.6%
3.1 2
 
5.6%
3.2 1
 
2.8%
ValueCountFrequency (%)
14.8 1
2.8%
9.4 2
5.6%
4.8 1
2.8%
4.5 1
2.8%
4.0 2
5.6%
3.9 1
2.8%
3.7 2
5.6%
3.6 1
2.8%
3.5 2
5.6%
3.4 2
5.6%

미생물 융복합 기술 산업
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3083333
Minimum0.7
Maximum7.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2024-03-14T23:50:35.330225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1.325
Q11.8
median2.15
Q32.425
95-th percentile3.475
Maximum7.9
Range7.2
Interquartile range (IQR)0.625

Descriptive statistics

Standard deviation1.146018
Coefficient of variation (CV)0.49646987
Kurtosis16.353204
Mean2.3083333
Median Absolute Deviation (MAD)0.35
Skewness3.4187689
Sum83.1
Variance1.3133571
MonotonicityNot monotonic
2024-03-14T23:50:35.712290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1.8 5
13.9%
2.4 4
 
11.1%
2.2 3
 
8.3%
2.0 2
 
5.6%
1.5 2
 
5.6%
2.3 2
 
5.6%
1.6 2
 
5.6%
1.9 2
 
5.6%
2.1 2
 
5.6%
3.4 2
 
5.6%
Other values (10) 10
27.8%
ValueCountFrequency (%)
0.7 1
 
2.8%
0.8 1
 
2.8%
1.5 2
 
5.6%
1.6 2
 
5.6%
1.7 1
 
2.8%
1.8 5
13.9%
1.9 2
 
5.6%
2.0 2
 
5.6%
2.1 2
 
5.6%
2.2 3
8.3%
ValueCountFrequency (%)
7.9 1
 
2.8%
3.7 1
 
2.8%
3.4 2
5.6%
2.9 1
 
2.8%
2.8 1
 
2.8%
2.7 1
 
2.8%
2.6 1
 
2.8%
2.5 1
 
2.8%
2.4 4
11.1%
2.3 2
5.6%

항공 우주산업
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)54.3%
Missing1
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean2.0742857
Minimum0.8
Maximum4.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2024-03-14T23:50:36.073907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile1
Q11.7
median2.1
Q32.45
95-th percentile2.83
Maximum4.6
Range3.8
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.70641716
Coefficient of variation (CV)0.34055924
Kurtosis3.7161082
Mean2.0742857
Median Absolute Deviation (MAD)0.4
Skewness0.9481172
Sum72.6
Variance0.49902521
MonotonicityNot monotonic
2024-03-14T23:50:36.461944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2.0 5
13.9%
2.2 4
11.1%
1.0 3
 
8.3%
2.5 3
 
8.3%
1.7 2
 
5.6%
2.7 2
 
5.6%
2.3 2
 
5.6%
1.4 2
 
5.6%
2.4 2
 
5.6%
1.2 1
 
2.8%
Other values (9) 9
25.0%
ValueCountFrequency (%)
0.8 1
 
2.8%
1.0 3
8.3%
1.2 1
 
2.8%
1.4 2
 
5.6%
1.6 1
 
2.8%
1.7 2
 
5.6%
1.8 1
 
2.8%
1.9 1
 
2.8%
2.0 5
13.9%
2.1 1
 
2.8%
ValueCountFrequency (%)
4.6 1
 
2.8%
2.9 1
 
2.8%
2.8 1
 
2.8%
2.7 2
5.6%
2.6 1
 
2.8%
2.5 3
8.3%
2.4 2
5.6%
2.3 2
5.6%
2.2 4
11.1%
2.1 1
 
2.8%

기타
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)36.4%
Missing3
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean0.78181818
Minimum0.2
Maximum3.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.0 B
2024-03-14T23:50:36.807200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.32
Q10.5
median0.7
Q30.8
95-th percentile1.34
Maximum3.8
Range3.6
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.61311426
Coefficient of variation (CV)0.78421591
Kurtosis19.003788
Mean0.78181818
Median Absolute Deviation (MAD)0.2
Skewness3.9433377
Sum25.8
Variance0.37590909
MonotonicityNot monotonic
2024-03-14T23:50:37.165999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.7 7
19.4%
0.5 6
16.7%
0.6 5
13.9%
0.4 3
8.3%
0.8 3
8.3%
0.2 2
 
5.6%
1.2 2
 
5.6%
1.1 1
 
2.8%
1.3 1
 
2.8%
3.8 1
 
2.8%
Other values (2) 2
 
5.6%
(Missing) 3
8.3%
ValueCountFrequency (%)
0.2 2
 
5.6%
0.4 3
8.3%
0.5 6
16.7%
0.6 5
13.9%
0.7 7
19.4%
0.8 3
8.3%
0.9 1
 
2.8%
1.1 1
 
2.8%
1.2 2
 
5.6%
1.3 1
 
2.8%
ValueCountFrequency (%)
3.8 1
 
2.8%
1.4 1
 
2.8%
1.3 1
 
2.8%
1.2 2
 
5.6%
1.1 1
 
2.8%
0.9 1
 
2.8%
0.8 3
8.3%
0.7 7
19.4%
0.6 5
13.9%
0.5 6
16.7%

Interactions

2024-03-14T23:50:27.681371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:15.042243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:16.576297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:18.503120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:20.579185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:22.570705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:24.181195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:25.552686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:27.835263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:15.343272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:16.758521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:18.771670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:20.843928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:22.775131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:24.346746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:25.721938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:27.996025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:15.514139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:16.934876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:19.042130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:21.114882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:23.040493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:24.516281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:25.979401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:28.157664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:15.683007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:17.316441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:19.306091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:21.379779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:23.299165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:24.768028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:26.174566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:28.411376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:15.843978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:17.483680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:19.565430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:21.639227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:23.559938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:24.930395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:26.436481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:28.669461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:16.001885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:17.713684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:19.820261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:21.891634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:23.705917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:25.084227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:26.700094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:28.911631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:16.165589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:17.979606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:20.084237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:22.153330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:23.863432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:25.245826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:26.967165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:29.168360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:16.332511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:18.245794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:20.339110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:22.416945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:24.024136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:25.407933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:50:27.426418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T23:50:37.432439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분기업 투자유치새만금 개발식품산업신재생에너지 산업방사선 융복합 기술산업미생물 융복합 기술 산업항공 우주산업기타
구분1.0001.0001.0001.0001.0001.0001.0001.0001.000
기업 투자유치1.0001.0000.0000.7350.6240.6850.6990.0000.322
새만금 개발1.0000.0001.0000.5590.8340.6400.7610.8090.924
식품산업1.0000.7350.5591.0000.5860.8400.5710.5730.431
신재생에너지 산업1.0000.6240.8340.5861.0000.7220.6600.0000.214
방사선 융복합 기술산업1.0000.6850.6400.8400.7221.0000.3880.4140.000
미생물 융복합 기술 산업1.0000.6990.7610.5710.6600.3881.0000.5790.772
항공 우주산업1.0000.0000.8090.5730.0000.4140.5791.0000.332
기타1.0000.3220.9240.4310.2140.0000.7720.3321.000
2024-03-14T23:50:37.765378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기업 투자유치새만금 개발식품산업신재생에너지 산업방사선 융복합 기술산업미생물 융복합 기술 산업항공 우주산업기타
기업 투자유치1.0000.257-0.377-0.515-0.499-0.270-0.5220.549
새만금 개발0.2571.000-0.560-0.718-0.591-0.516-0.5690.126
식품산업-0.377-0.5601.0000.2690.3030.2640.428-0.326
신재생에너지 산업-0.515-0.7180.2691.0000.4760.3450.546-0.369
방사선 융복합 기술산업-0.499-0.5910.3030.4761.0000.2930.491-0.419
미생물 융복합 기술 산업-0.270-0.5160.2640.3450.2931.0000.375-0.118
항공 우주산업-0.522-0.5690.4280.5460.4910.3751.000-0.065
기타0.5490.126-0.326-0.369-0.419-0.118-0.0651.000

Missing values

2024-03-14T23:50:29.517561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T23:50:29.973638image/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.
2024-03-14T23:50:30.285992image/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

구분기업 투자유치새만금 개발식품산업신재생에너지 산업방사선 융복합 기술산업미생물 융복합 기술 산업항공 우주산업기타
0남자21.238.512.419.23.52.52.20.6
1여자22.140.114.615.92.81.82.00.7
215-19세22.529.913.621.03.73.74.61.1
320-29세19.735.614.522.12.92.72.20.5
430-39세17.538.115.320.54.01.82.40.5
540-49세20.141.313.715.73.62.32.50.7
650-59세21.443.111.517.92.31.81.40.6
760세이상27.041.912.612.42.71.61.00.7
8무학27.943.012.011.62.30.71.21.3
9초등학교25.941.413.413.12.32.31.00.7
구분기업 투자유치새만금 개발식품산업신재생에너지 산업방사선 융복합 기술산업미생물 융복합 기술 산업항공 우주산업기타
26단독주택22.842.812.016.12.81.51.40.5
27아파트22.237.913.817.33.51.82.70.8
28연립주택23.446.810.812.21.53.40.81.2
29다세대주택24.328.617.521.54.02.41.7<NA>
30기타12.536.47.424.19.47.92.3<NA>
311인가구25.936.313.416.63.12.02.00.7
321세대가구21.740.214.116.42.82.42.00.5
332세대가구21.140.212.818.03.91.62.00.5
343세대가구20.744.810.217.52.21.51.81.4
35비혈연가구22.435.42.523.014.81.9<NA><NA>