Overview

Dataset statistics

Number of variables12
Number of observations107
Missing cells750
Missing cells (%)58.4%
Duplicate rows14
Duplicate rows (%)13.1%
Total size in memory10.3 KiB
Average record size in memory98.2 B

Variable types

Text5
Unsupported4
Categorical3

Dataset

Description2016신재생에너지산업주요예산
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202325

Alerts

Unnamed: 1 has constant value ""Constant
Unnamed: 2 has constant value ""Constant
Unnamed: 11 has constant value ""Constant
Dataset has 14 (13.1%) duplicate rowsDuplicates
Unnamed: 6 is highly overall correlated with Unnamed: 5High correlation
Unnamed: 7 is highly overall correlated with Unnamed: 5High correlation
Unnamed: 5 is highly overall correlated with Unnamed: 6 and 1 other fieldsHigh correlation
Unnamed: 6 is highly imbalanced (77.0%)Imbalance
2016 신재생에너지산업 관련예산 has 105 (98.1%) missing valuesMissing
Unnamed: 1 has 106 (99.1%) missing valuesMissing
Unnamed: 2 has 106 (99.1%) missing valuesMissing
Unnamed: 3 has 92 (86.0%) missing valuesMissing
Unnamed: 4 has 107 (100.0%) missing valuesMissing
Unnamed: 8 has 24 (22.4%) missing valuesMissing
Unnamed: 9 has 52 (48.6%) missing valuesMissing
Unnamed: 10 has 52 (48.6%) missing valuesMissing
Unnamed: 11 has 106 (99.1%) missing valuesMissing
Unnamed: 4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 9 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 10 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-13 23:53:23.200460
Analysis finished2024-03-13 23:53:23.801821
Duration0.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

2016 신재생에너지산업 관련예산
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing105
Missing (%)98.1%
Memory size988.0 B
2024-03-14T08:53:23.894194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length13
Mean length13
Min length5

Characters and Unicode

Total characters26
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row부서ㆍ정책ㆍ단위(회계)ㆍ세부사업ㆍ편성목
2nd row산업진흥과
ValueCountFrequency (%)
부서ㆍ정책ㆍ단위(회계)ㆍ세부사업ㆍ편성목 1
50.0%
산업진흥과 1
50.0%
2024-03-14T08:53:24.135615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
ㆍ 4
 
15.4%
부 2
 
7.7%
업 2
 
7.7%
세 1
 
3.8%
흥 1
 
3.8%
진 1
 
3.8%
산 1
 
3.8%
목 1
 
3.8%
성 1
 
3.8%
편 1
 
3.8%
Other values (11) 11
42.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24
92.3%
Close Punctuation 1
 
3.8%
Open Punctuation 1
 
3.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ㆍ 4
16.7%
부 2
 
8.3%
업 2
 
8.3%
세 1
 
4.2%
흥 1
 
4.2%
진 1
 
4.2%
산 1
 
4.2%
목 1
 
4.2%
성 1
 
4.2%
편 1
 
4.2%
Other values (9) 9
37.5%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24
92.3%
Common 2
 
7.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
ㆍ 4
16.7%
부 2
 
8.3%
업 2
 
8.3%
세 1
 
4.2%
흥 1
 
4.2%
진 1
 
4.2%
산 1
 
4.2%
목 1
 
4.2%
성 1
 
4.2%
편 1
 
4.2%
Other values (9) 9
37.5%
Common
ValueCountFrequency (%)
) 1
50.0%
( 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20
76.9%
Compat Jamo 4
 
15.4%
ASCII 2
 
7.7%

Most frequent character per block

Compat Jamo
ValueCountFrequency (%)
ㆍ 4
100.0%
Hangul
ValueCountFrequency (%)
부 2
 
10.0%
업 2
 
10.0%
세 1
 
5.0%
흥 1
 
5.0%
진 1
 
5.0%
산 1
 
5.0%
목 1
 
5.0%
성 1
 
5.0%
편 1
 
5.0%
사 1
 
5.0%
Other values (8) 8
40.0%
ASCII
ValueCountFrequency (%)
) 1
50.0%
( 1
50.0%

Unnamed: 1
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing106
Missing (%)99.1%
Memory size988.0 B
2024-03-14T08:53:24.251282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters10
Distinct characters10
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

Unique1 ?
Unique (%)100.0%

Sample

1st row녹색에너지산업 육성
ValueCountFrequency (%)
녹색에너지산업 1
50.0%
육성 1
50.0%
2024-03-14T08:53:24.463013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
녹 1
10.0%
색 1
10.0%
에 1
10.0%
너 1
10.0%
지 1
10.0%
산 1
10.0%
업 1
10.0%
1
10.0%
육 1
10.0%
성 1
10.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9
90.0%
Space Separator 1
 
10.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
녹 1
11.1%
색 1
11.1%
에 1
11.1%
너 1
11.1%
지 1
11.1%
산 1
11.1%
업 1
11.1%
육 1
11.1%
성 1
11.1%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9
90.0%
Common 1
 
10.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
녹 1
11.1%
색 1
11.1%
에 1
11.1%
너 1
11.1%
지 1
11.1%
산 1
11.1%
업 1
11.1%
육 1
11.1%
성 1
11.1%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9
90.0%
ASCII 1
 
10.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
녹 1
11.1%
색 1
11.1%
에 1
11.1%
너 1
11.1%
지 1
11.1%
산 1
11.1%
업 1
11.1%
육 1
11.1%
성 1
11.1%
ASCII
ValueCountFrequency (%)
1
100.0%

Unnamed: 2
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing106
Missing (%)99.1%
Memory size988.0 B
2024-03-14T08:53:24.584739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters11
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

Unique1 ?
Unique (%)100.0%

Sample

1st row신재생에너지산업 육성
ValueCountFrequency (%)
신재생에너지산업 1
50.0%
육성 1
50.0%
2024-03-14T08:53:24.827516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
신 1
9.1%
재 1
9.1%
생 1
9.1%
에 1
9.1%
너 1
9.1%
지 1
9.1%
산 1
9.1%
업 1
9.1%
1
9.1%
육 1
9.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10
90.9%
Space Separator 1
 
9.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
신 1
10.0%
재 1
10.0%
생 1
10.0%
에 1
10.0%
너 1
10.0%
지 1
10.0%
산 1
10.0%
업 1
10.0%
육 1
10.0%
성 1
10.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10
90.9%
Common 1
 
9.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
신 1
10.0%
재 1
10.0%
생 1
10.0%
에 1
10.0%
너 1
10.0%
지 1
10.0%
산 1
10.0%
업 1
10.0%
육 1
10.0%
성 1
10.0%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10
90.9%
ASCII 1
 
9.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
신 1
10.0%
재 1
10.0%
생 1
10.0%
에 1
10.0%
너 1
10.0%
지 1
10.0%
산 1
10.0%
업 1
10.0%
육 1
10.0%
성 1
10.0%
ASCII
ValueCountFrequency (%)
1
100.0%

Unnamed: 3
Text

MISSING 

Distinct15
Distinct (%)100.0%
Missing92
Missing (%)86.0%
Memory size988.0 B
2024-03-14T08:53:25.043557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length24
Mean length19.8
Min length8

Characters and Unicode

Total characters297
Distinct characters80
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

Unique15 ?
Unique (%)100.0%

Sample

1st row신재생에너지 지역지원사업
2nd row신재생에너지 주택지원사업(국가직접지원)
3rd row신재생에너지설비 실태조사를 통한 유지보수 사업
4th row유무기 하이브리드 태양전지 고급인력 양성(국가직접지원)
5th row신재생에너지산업 전문인력양성센터 지원(국가직접지원)
ValueCountFrequency (%)
신재생에너지단지 4
 
7.7%
부안 4
 
7.7%
신재생에너지 3
 
5.8%
운영 3
 
5.8%
양성(국가직접지원 2
 
3.8%
풍력발전소 2
 
3.8%
시설보강 1
 
1.9%
풍력에너지 1
 
1.9%
전력망 1
 
1.9%
적응기술 1
 
1.9%
Other values (30) 30
57.7%
2024-03-14T08:53:25.375666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37
 
12.5%
지 29
 
9.8%
력 10
 
3.4%
에 10
 
3.4%
너 10
 
3.4%
신 9
 
3.0%
재 9
 
3.0%
원 9
 
3.0%
생 9
 
3.0%
전 6
 
2.0%
Other values (70) 159
53.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 250
84.2%
Space Separator 37
 
12.5%
Close Punctuation 5
 
1.7%
Open Punctuation 5
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 29
 
11.6%
력 10
 
4.0%
에 10
 
4.0%
너 10
 
4.0%
신 9
 
3.6%
재 9
 
3.6%
원 9
 
3.6%
생 9
 
3.6%
전 6
 
2.4%
국 6
 
2.4%
Other values (67) 143
57.2%
Space Separator
ValueCountFrequency (%)
37
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 250
84.2%
Common 47
 
15.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 29
 
11.6%
력 10
 
4.0%
에 10
 
4.0%
너 10
 
4.0%
신 9
 
3.6%
재 9
 
3.6%
원 9
 
3.6%
생 9
 
3.6%
전 6
 
2.4%
국 6
 
2.4%
Other values (67) 143
57.2%
Common
ValueCountFrequency (%)
37
78.7%
) 5
 
10.6%
( 5
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 250
84.2%
ASCII 47
 
15.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37
78.7%
) 5
 
10.6%
( 5
 
10.6%
Hangul
ValueCountFrequency (%)
지 29
 
11.6%
력 10
 
4.0%
에 10
 
4.0%
너 10
 
4.0%
신 9
 
3.6%
재 9
 
3.6%
원 9
 
3.6%
생 9
 
3.6%
전 6
 
2.4%
국 6
 
2.4%
Other values (67) 143
57.2%

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing107
Missing (%)100.0%
Memory size1.1 KiB

Unnamed: 5
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Memory size988.0 B
<NA>
49 
02
10 
307
9 
01
6 
03
 
3
Other values (21)
30 

Length

Max length37
Median length35
Mean length7.0093458
Min length2

Unique

Unique14 ?
Unique (%)13.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 49
45.8%
02 10
 
9.3%
307 9
 
8.4%
01 6
 
5.6%
03 3
 
2.8%
401 3
 
2.8%
○풍력발전소 시설물 보완사업 3
 
2.8%
05 2
 
1.9%
○부안 신재생에너지단지 시설보강 2
 
1.9%
403 2
 
1.9%
Other values (16) 18
 
16.8%

Length

2024-03-14T08:53:25.496622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 49
30.2%
02 10
 
6.2%
307 9
 
5.6%
01 6
 
3.7%
○부안 6
 
3.7%
신재생에너지단지 6
 
3.7%
○풍력발전소 4
 
2.5%
지원 4
 
2.5%
03 3
 
1.9%
401 3
 
1.9%
Other values (48) 62
38.3%

Unnamed: 6
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size988.0 B
<NA>
103 
 
4

Length

Max length5
Median length4
Mean length4.0373832
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 103
96.3%
4
 
3.7%

Length

2024-03-14T08:53:25.589488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T08:53:25.671935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 103
100.0%

Unnamed: 7
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Memory size988.0 B
<NA>
70 
민간이전
9 
민간경상사업보조
 
7
시설비및부대비
 
3
시설비
 
3
Other values (8)
15 

Length

Max length9
Median length4
Mean length4.5700935
Min length3

Unique

Unique2 ?
Unique (%)1.9%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 70
65.4%
민간이전 9
 
8.4%
민간경상사업보조 7
 
6.5%
시설비및부대비 3
 
2.8%
시설비 3
 
2.8%
시설부대비 3
 
2.8%
자치단체등자본이전 2
 
1.9%
자치단체자본보조 2
 
1.9%
일반운영비 2
 
1.9%
공공운영비 2
 
1.9%
Other values (3) 4
 
3.7%

Length

2024-03-14T08:53:25.751962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 70
65.4%
민간이전 9
 
8.4%
민간경상사업보조 7
 
6.5%
시설비및부대비 3
 
2.8%
시설비 3
 
2.8%
시설부대비 3
 
2.8%
자치단체등자본이전 2
 
1.9%
자치단체자본보조 2
 
1.9%
일반운영비 2
 
1.9%
공공운영비 2
 
1.9%
Other values (3) 4
 
3.7%

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing24
Missing (%)22.4%
Memory size988.0 B

Unnamed: 9
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)48.6%
Memory size988.0 B

Unnamed: 10
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)48.6%
Memory size988.0 B

Unnamed: 11
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing106
Missing (%)99.1%
Memory size988.0 B
2024-03-14T08:53:25.849060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
Distinct characters7
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

Unique1 ?
Unique (%)100.0%

Sample

1st row(단위:천원)
ValueCountFrequency (%)
단위:천원 1
100.0%
2024-03-14T08:53:26.057662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 1
14.3%
단 1
14.3%
위 1
14.3%
: 1
14.3%
천 1
14.3%
원 1
14.3%
) 1
14.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4
57.1%
Open Punctuation 1
 
14.3%
Other Punctuation 1
 
14.3%
Close Punctuation 1
 
14.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
단 1
25.0%
위 1
25.0%
천 1
25.0%
원 1
25.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Other Punctuation
ValueCountFrequency (%)
: 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4
57.1%
Common 3
42.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
단 1
25.0%
위 1
25.0%
천 1
25.0%
원 1
25.0%
Common
ValueCountFrequency (%)
( 1
33.3%
: 1
33.3%
) 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4
57.1%
ASCII 3
42.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 1
33.3%
: 1
33.3%
) 1
33.3%
Hangul
ValueCountFrequency (%)
단 1
25.0%
위 1
25.0%
천 1
25.0%
원 1
25.0%

Correlations

2024-03-14T08:53:26.145096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2016 신재생에너지산업 관련예산Unnamed: 3Unnamed: 5Unnamed: 7
2016 신재생에너지산업 관련예산1.000NaNNaNNaN
Unnamed: 3NaN1.000NaNNaN
Unnamed: 5NaNNaN1.0001.000
Unnamed: 7NaNNaN1.0001.000
2024-03-14T08:53:26.228191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 6Unnamed: 7Unnamed: 5
Unnamed: 61.000NaN1.000
Unnamed: 7NaN1.0000.928
Unnamed: 51.0000.9281.000
2024-03-14T08:53:26.308103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 5Unnamed: 6Unnamed: 7
Unnamed: 51.0001.0000.928
Unnamed: 61.0001.0000.000
Unnamed: 70.9280.0001.000

Missing values

2024-03-14T08:53:23.450699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T08:53:23.584278image/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.
2024-03-14T08:53:23.704404image/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

2016 신재생에너지산업 관련예산Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11
0<NA><NA><NA><NA><NA><NA><NA><NA>NaNNaNNaN(단위:천원)
1<NA><NA><NA><NA><NA><NA><NA><NA>NaNNaNNaN<NA>
2부서ㆍ정책ㆍ단위(회계)ㆍ세부사업ㆍ편성목<NA><NA><NA><NA><NA><NA><NA>예산액전년도\n예산액비교증감<NA>
3산업진흥과<NA><NA><NA><NA><NA><NA><NA>NaNNaNNaN<NA>
4<NA><NA><NA><NA><NA><NA><NA><NA>NaNNaNNaN<NA>
5<NA><NA><NA><NA><NA><NA><NA><NA>NaNNaNNaN<NA>
6<NA><NA><NA><NA><NA><NA><NA><NA>NaNNaNNaN<NA>
7<NA>녹색에너지산업 육성<NA><NA><NA><NA><NA><NA>43142335037400-723167<NA>
8<NA><NA><NA><NA><NA><NA><NA><NA>국 2,048,000NaNNaN<NA>
9<NA><NA><NA><NA><NA><NA><NA><NA>도 2,266,233NaNNaN<NA>
2016 신재생에너지산업 관련예산Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11
97<NA><NA><NA>풍력발전소 시설물 보완사업<NA><NA><NA><NA>1838610183861<NA>
98<NA><NA><NA><NA><NA>401<NA>시설비및부대비1838610183861<NA>
99<NA><NA><NA><NA><NA>01<NA>시설비1800000180000<NA>
100<NA><NA><NA><NA><NA>○풍력발전소 시설물 보완사업<NA><NA>180,000NaNNaN<NA>
101<NA><NA><NA><NA><NA><NA><NA><NA>NaNNaNNaN<NA>
102<NA><NA><NA><NA><NA>02<NA>감리비256502565<NA>
103<NA><NA><NA><NA><NA>○풍력발전소 시설물 보완사업<NA><NA>2,565NaNNaN<NA>
104<NA><NA><NA><NA><NA><NA><NA><NA>NaNNaNNaN<NA>
105<NA><NA><NA><NA><NA>03<NA>시설부대비129601296<NA>
106<NA><NA><NA><NA><NA>○풍력발전소 시설물 보완사업<NA><NA>1,296NaNNaN<NA>

Duplicate rows

Most frequently occurring

2016 신재생에너지산업 관련예산Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 11# duplicates
13<NA><NA><NA><NA><NA><NA><NA><NA>29
10<NA><NA><NA><NA>307<NA>민간이전<NA>9
6<NA><NA><NA><NA>02<NA>민간경상사업보조<NA>7
2<NA><NA><NA><NA>○풍력발전소 시설물 보완사업<NA><NA><NA>3
3<NA><NA><NA><NA>01<NA>시설비<NA>3
7<NA><NA><NA><NA>03<NA>시설부대비<NA>3
11<NA><NA><NA><NA>401<NA>시설비및부대비<NA>3
0<NA><NA><NA><NA>○부안 신재생에너지단지 시설보강<NA><NA><NA>2
1<NA><NA><NA><NA>○부안 신재생에너지단지 유수지 준설<NA><NA><NA>2
4<NA><NA><NA><NA>01<NA>자치단체자본보조<NA>2