Overview

Dataset statistics

Number of variables7
Number of observations154
Missing cells750
Missing cells (%)69.6%
Duplicate rows1
Duplicate rows (%)0.6%
Total size in memory9.3 KiB
Average record size in memory61.9 B

Variable types

Categorical1
Text1
Numeric5

Dataset

Description한국조폐공사 연도별(2011~2021) 제품류별로 발생한 매출과 관련된 데이터.구성항목 : 연도, 제품, 금액 등
Author한국조폐공사
URLhttps://www.data.go.kr/data/15067438/fileData.do

Alerts

Dataset has 1 (0.6%) duplicate rowsDuplicates
2017년 is highly overall correlated with 2018년 and 3 other fieldsHigh correlation
2018년 is highly overall correlated with 2017년 and 3 other fieldsHigh correlation
2019년 is highly overall correlated with 2017년 and 3 other fieldsHigh correlation
2020년 is highly overall correlated with 2017년 and 3 other fieldsHigh correlation
2021년 is highly overall correlated with 2017년 and 3 other fieldsHigh correlation
구분 is highly imbalanced (59.9%)Imbalance
세부 사업명 has 125 (81.2%) missing valuesMissing
2017년 has 125 (81.2%) missing valuesMissing
2018년 has 125 (81.2%) missing valuesMissing
2019년 has 125 (81.2%) missing valuesMissing
2020년 has 125 (81.2%) missing valuesMissing
2021년 has 125 (81.2%) missing valuesMissing
2017년 has 3 (1.9%) zerosZeros
2018년 has 3 (1.9%) zerosZeros
2019년 has 2 (1.3%) zerosZeros
2020년 has 3 (1.9%) zerosZeros

Reproduction

Analysis started2023-12-12 22:46:22.576283
Analysis finished2023-12-12 22:46:25.525077
Duration2.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

IMBALANCE 

Distinct8
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
<NA>
125 
보안 인쇄용지사업
 
7
특수압인사업
 
5
해외 사업
 
5
화폐 사업
 
4
Other values (3)
 
8

Length

Max length9
Median length4
Mean length4.4415584
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row화폐 사업
2nd row화폐 사업
3rd row화폐 사업
4th row화폐 사업
5th rowI D 사업

Common Values

ValueCountFrequency (%)
<NA> 125
81.2%
보안 인쇄용지사업 7
 
4.5%
특수압인사업 5
 
3.2%
해외 사업 5
 
3.2%
화폐 사업 4
 
2.6%
ICT 사업 4
 
2.6%
I D 사업 2
 
1.3%
기타 사업 2
 
1.3%

Length

2023-12-13T07:46:25.584410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:46:25.712682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 125
69.4%
사업 17
 
9.4%
보안 7
 
3.9%
인쇄용지사업 7
 
3.9%
특수압인사업 5
 
2.8%
해외 5
 
2.8%
화폐 4
 
2.2%
ict 4
 
2.2%
i 2
 
1.1%
d 2
 
1.1%

세부 사업명
Text

MISSING 

Distinct29
Distinct (%)100.0%
Missing125
Missing (%)81.2%
Memory size1.3 KiB
2023-12-13T07:46:25.935478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length7
Mean length4.8275862
Min length2

Characters and Unicode

Total characters140
Distinct characters78
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)100.0%

Sample

1st row은행권
2nd row국내주화
3rd row기념화폐
4th row소계
5th row여권류
ValueCountFrequency (%)
모바일 2
 
5.1%
등 2
 
5.1%
상품권 2
 
5.1%
특수잉크안료 1
 
2.6%
신분증 1
 
2.6%
전사서명공통기반 1
 
2.6%
보안모듈 1
 
2.6%
주화 1
 
2.6%
보안인쇄용지 1
 
2.6%
국가신분증 1
 
2.6%
Other values (26) 26
66.7%
2023-12-13T07:46:26.267806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10
 
7.1%
기 5
 
3.6%
권 5
 
3.6%
증 4
 
2.9%
국 3
 
2.1%
바 3
 
2.1%
인 3
 
2.1%
품 3
 
2.1%
일 3
 
2.1%
수 3
 
2.1%
Other values (68) 98
70.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 126
90.0%
Space Separator 10
 
7.1%
Uppercase Letter 2
 
1.4%
Open Punctuation 1
 
0.7%
Close Punctuation 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 5
 
4.0%
권 5
 
4.0%
증 4
 
3.2%
국 3
 
2.4%
바 3
 
2.4%
인 3
 
2.4%
품 3
 
2.4%
일 3
 
2.4%
수 3
 
2.4%
달 3
 
2.4%
Other values (63) 91
72.2%
Uppercase Letter
ValueCountFrequency (%)
D 1
50.0%
I 1
50.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 126
90.0%
Common 12
 
8.6%
Latin 2
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 5
 
4.0%
권 5
 
4.0%
증 4
 
3.2%
국 3
 
2.4%
바 3
 
2.4%
인 3
 
2.4%
품 3
 
2.4%
일 3
 
2.4%
수 3
 
2.4%
달 3
 
2.4%
Other values (63) 91
72.2%
Common
ValueCountFrequency (%)
10
83.3%
( 1
 
8.3%
) 1
 
8.3%
Latin
ValueCountFrequency (%)
D 1
50.0%
I 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 126
90.0%
ASCII 14
 
10.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10
71.4%
( 1
 
7.1%
) 1
 
7.1%
D 1
 
7.1%
I 1
 
7.1%
Hangul
ValueCountFrequency (%)
기 5
 
4.0%
권 5
 
4.0%
증 4
 
3.2%
국 3
 
2.4%
바 3
 
2.4%
인 3
 
2.4%
품 3
 
2.4%
일 3
 
2.4%
수 3
 
2.4%
달 3
 
2.4%
Other values (63) 91
72.2%

2017년
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct27
Distinct (%)93.1%
Missing125
Missing (%)81.2%
Infinite0
Infinite (%)0.0%
Mean2.1847 × 1010
Minimum0
Maximum1.55803 × 1011
Zeros3
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-13T07:46:26.423459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.118 × 109
median9.233 × 109
Q32.1846 × 1010
95-th percentile9.5379 × 1010
Maximum1.55803 × 1011
Range1.55803 × 1011
Interquartile range (IQR)2.0728 × 1010

Descriptive statistics

Standard deviation3.5481917 × 1010
Coefficient of variation (CV)1.6241094
Kurtosis7.3316943
Mean2.1847 × 1010
Median Absolute Deviation (MAD)8.849 × 109
Skewness2.6486574
Sum6.33563 × 1011
Variance1.2589664 × 1021
MonotonicityNot monotonic
2023-12-13T07:46:26.575913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 3
 
1.9%
50124000000 1
 
0.6%
121000000 1
 
0.6%
1473000000 1
 
0.6%
13988000000 1
 
0.6%
1118000000 1
 
0.6%
9233000000 1
 
0.6%
16426000000 1
 
0.6%
8214000000 1
 
0.6%
248000000 1
 
0.6%
Other values (17) 17
 
11.0%
(Missing) 125
81.2%
ValueCountFrequency (%)
0 3
1.9%
121000000 1
 
0.6%
248000000 1
 
0.6%
384000000 1
 
0.6%
734000000 1
 
0.6%
1118000000 1
 
0.6%
1473000000 1
 
0.6%
2704000000 1
 
0.6%
3409000000 1
 
0.6%
6417000000 1
 
0.6%
ValueCountFrequency (%)
155803000000 1
0.6%
101859000000 1
0.6%
85659000000 1
0.6%
50124000000 1
0.6%
30176000000 1
0.6%
29311000000 1
0.6%
24277000000 1
0.6%
21846000000 1
0.6%
20020000000 1
0.6%
17127000000 1
0.6%

2018년
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct27
Distinct (%)93.1%
Missing125
Missing (%)81.2%
Infinite0
Infinite (%)0.0%
Mean2.0498586 × 1010
Minimum0
Maximum1.13854 × 1011
Zeros3
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-13T07:46:26.730327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.28 × 108
median8.202 × 109
Q32.679 × 1010
95-th percentile9.18846 × 1010
Maximum1.13854 × 1011
Range1.13854 × 1011
Interquartile range (IQR)2.6362 × 1010

Descriptive statistics

Standard deviation2.9541686 × 1010
Coefficient of variation (CV)1.4411572
Kurtosis4.1151157
Mean2.0498586 × 1010
Median Absolute Deviation (MAD)8.108 × 109
Skewness2.1263849
Sum5.94459 × 1011
Variance8.7271118 × 1020
MonotonicityNot monotonic
2023-12-13T07:46:26.886970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 3
 
1.9%
24147000000 1
 
0.6%
94000000 1
 
0.6%
873000000 1
 
0.6%
13089000000 1
 
0.6%
4016000000 1
 
0.6%
153000000 1
 
0.6%
7036000000 1
 
0.6%
33472000000 1
 
0.6%
393000000 1
 
0.6%
Other values (17) 17
 
11.0%
(Missing) 125
81.2%
ValueCountFrequency (%)
0 3
1.9%
94000000 1
 
0.6%
153000000 1
 
0.6%
378000000 1
 
0.6%
393000000 1
 
0.6%
428000000 1
 
0.6%
873000000 1
 
0.6%
3350000000 1
 
0.6%
3412000000 1
 
0.6%
4016000000 1
 
0.6%
ValueCountFrequency (%)
113854000000 1
0.6%
95611000000 1
0.6%
86295000000 1
0.6%
33472000000 1
0.6%
30485000000 1
0.6%
30094000000 1
0.6%
28188000000 1
0.6%
26790000000 1
0.6%
26769000000 1
0.6%
24147000000 1
0.6%

2019년
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct28
Distinct (%)96.6%
Missing125
Missing (%)81.2%
Infinite0
Infinite (%)0.0%
Mean2.1826552 × 1010
Minimum0
Maximum1.08179 × 1011
Zeros2
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-13T07:46:27.063110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9600000
Q15.21 × 108
median8.973 × 109
Q33.4091 × 1010
95-th percentile8.17718 × 1010
Maximum1.08179 × 1011
Range1.08179 × 1011
Interquartile range (IQR)3.357 × 1010

Descriptive statistics

Standard deviation2.8895422 × 1010
Coefficient of variation (CV)1.3238657
Kurtosis2.3427451
Mean2.1826552 × 1010
Median Absolute Deviation (MAD)8.678 × 109
Skewness1.7135977
Sum6.3297 × 1011
Variance8.3494542 × 1020
MonotonicityNot monotonic
2023-12-13T07:46:27.199717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 2
 
1.3%
7172000000 1
 
0.6%
24000000 1
 
0.6%
5613000000 1
 
0.6%
15090000000 1
 
0.6%
414000000 1
 
0.6%
521000000 1
 
0.6%
8973000000 1
 
0.6%
34091000000 1
 
0.6%
442000000 1
 
0.6%
Other values (18) 18
 
11.7%
(Missing) 125
81.2%
ValueCountFrequency (%)
0 2
1.3%
24000000 1
0.6%
295000000 1
0.6%
379000000 1
0.6%
414000000 1
0.6%
442000000 1
0.6%
521000000 1
0.6%
1203000000 1
0.6%
3467000000 1
0.6%
4736000000 1
0.6%
ValueCountFrequency (%)
108179000000 1
0.6%
88587000000 1
0.6%
71549000000 1
0.6%
70492000000 1
0.6%
37952000000 1
0.6%
37741000000 1
0.6%
34288000000 1
0.6%
34091000000 1
0.6%
24309000000 1
0.6%
21641000000 1
0.6%

2020년
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct27
Distinct (%)93.1%
Missing125
Missing (%)81.2%
Infinite0
Infinite (%)0.0%
Mean2.2316345 × 1010
Minimum0
Maximum1.15429 × 1011
Zeros3
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-13T07:46:27.399243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.723 × 109
median6.584 × 109
Q32.5638 × 1010
95-th percentile8.62184 × 1010
Maximum1.15429 × 1011
Range1.15429 × 1011
Interquartile range (IQR)2.3915 × 1010

Descriptive statistics

Standard deviation3.2003397 × 1010
Coefficient of variation (CV)1.4340788
Kurtosis1.7564672
Mean2.2316345 × 1010
Median Absolute Deviation (MAD)6.525 × 109
Skewness1.6372231
Sum6.47174 × 1011
Variance1.0242174 × 1021
MonotonicityNot monotonic
2023-12-13T07:46:27.552544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 3
 
1.9%
18192000000 1
 
0.6%
59000000 1
 
0.6%
1723000000 1
 
0.6%
18200000000 1
 
0.6%
1737000000 1
 
0.6%
8971000000 1
 
0.6%
2037000000 1
 
0.6%
467000000 1
 
0.6%
3607000000 1
 
0.6%
Other values (17) 17
 
11.0%
(Missing) 125
81.2%
ValueCountFrequency (%)
0 3
1.9%
59000000 1
 
0.6%
168000000 1
 
0.6%
467000000 1
 
0.6%
644000000 1
 
0.6%
1723000000 1
 
0.6%
1737000000 1
 
0.6%
2037000000 1
 
0.6%
3073000000 1
 
0.6%
3607000000 1
 
0.6%
ValueCountFrequency (%)
115429000000 1
0.6%
90062000000 1
0.6%
80453000000 1
0.6%
76461000000 1
0.6%
55362000000 1
0.6%
50774000000 1
0.6%
39635000000 1
0.6%
25638000000 1
0.6%
21960000000 1
0.6%
18200000000 1
0.6%

2021년
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)100.0%
Missing125
Missing (%)81.2%
Infinite0
Infinite (%)0.0%
Mean2.3787138 × 1010
Minimum0
Maximum1.39182 × 1011
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-13T07:46:27.706628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.782 × 108
Q12.327 × 109
median7.259 × 109
Q31.9523 × 1010
95-th percentile9.91726 × 1010
Maximum1.39182 × 1011
Range1.39182 × 1011
Interquartile range (IQR)1.7196 × 1010

Descriptive statistics

Standard deviation3.6320152 × 1010
Coefficient of variation (CV)1.526882
Kurtosis3.2704154
Mean2.3787138 × 1010
Median Absolute Deviation (MAD)6.849 × 109
Skewness1.980479
Sum6.89827 × 1011
Variance1.3191535 × 1021
MonotonicityNot monotonic
2023-12-13T07:46:27.888841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
16774000000 1
 
0.6%
107000000 1
 
0.6%
1472000000 1
 
0.6%
19523000000 1
 
0.6%
4356000000 1
 
0.6%
1618000000 1
 
0.6%
11486000000 1
 
0.6%
2327000000 1
 
0.6%
366000000 1
 
0.6%
410000000 1
 
0.6%
Other values (19) 19
 
12.3%
(Missing) 125
81.2%
ValueCountFrequency (%)
0 1
0.6%
107000000 1
0.6%
285000000 1
0.6%
366000000 1
0.6%
410000000 1
0.6%
1472000000 1
0.6%
1618000000 1
0.6%
2327000000 1
0.6%
3050000000 1
0.6%
3620000000 1
0.6%
ValueCountFrequency (%)
139182000000 1
0.6%
111173000000 1
0.6%
81172000000 1
0.6%
75643000000 1
0.6%
61977000000 1
0.6%
57296000000 1
0.6%
21925000000 1
0.6%
19523000000 1
0.6%
19152000000 1
0.6%
16774000000 1
0.6%

Interactions

2023-12-13T07:46:24.507483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:22.824523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:23.287305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:23.732244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:24.154337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:24.602783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:22.915159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:23.387773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:23.818897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:24.233405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:24.687468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:22.998501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:23.473174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:23.894689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:24.296879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:24.772774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:23.093898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:23.556164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:23.995637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:24.362281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:25.118736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:23.180854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:23.648036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:24.067146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:24.428559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:46:28.004432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분세부 사업명2017년2018년2019년2020년2021년
구분1.0001.0000.4660.5470.6010.5300.242
세부 사업명1.0001.0001.0001.0001.0001.0001.000
2017년0.4661.0001.0000.9750.8840.9100.965
2018년0.5471.0000.9751.0000.9260.9400.969
2019년0.6011.0000.8840.9261.0000.9140.862
2020년0.5301.0000.9100.9400.9141.0000.957
2021년0.2421.0000.9650.9690.8620.9571.000
2023-12-13T07:46:28.120743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2017년2018년2019년2020년2021년구분
2017년1.0000.8420.9050.8520.8060.279
2018년0.8421.0000.9460.8720.7130.346
2019년0.9050.9461.0000.9270.8150.228
2020년0.8520.8720.9271.0000.8730.295
2021년0.8060.7130.8150.8731.0000.108
구분0.2790.3460.2280.2950.1081.000

Missing values

2023-12-13T07:46:25.240512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:46:25.355417image/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.
2023-12-13T07:46:25.452375image/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

구분세부 사업명2017년2018년2019년2020년2021년
0화폐 사업은행권85659000000862950000007154900000090062000000111173000000
1화폐 사업국내주화5012400000024147000000243090000001819200000016774000000
2화폐 사업기념화폐20020000000341200000012321000000717500000011235000000
3화폐 사업소계155803000000113854000000108179000000115429000000139182000000
4I D 사업여권류10185900000095611000000885870000003963500000061977000000
5I D 사업ID카드류2427700000021793000000210630000002196000000021925000000
6보안 인쇄용지사업수표 우표171270000001560100000014258000000103020000008334000000
7보안 인쇄용지사업증채권7340000004280000003790000001680000000
8보안 인쇄용지사업상품권2184600000030094000000379520000008045300000075643000000
9보안 인쇄용지사업정품인증1092500000015342000000817000000047550000006289000000
구분세부 사업명2017년2018년2019년2020년2021년
144<NA><NA><NA><NA><NA><NA><NA>
145<NA><NA><NA><NA><NA><NA><NA>
146<NA><NA><NA><NA><NA><NA><NA>
147<NA><NA><NA><NA><NA><NA><NA>
148<NA><NA><NA><NA><NA><NA><NA>
149<NA><NA><NA><NA><NA><NA><NA>
150<NA><NA><NA><NA><NA><NA><NA>
151<NA><NA><NA><NA><NA><NA><NA>
152<NA><NA><NA><NA><NA><NA><NA>
153<NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

구분세부 사업명2017년2018년2019년2020년2021년# duplicates
0<NA><NA><NA><NA><NA><NA><NA>125