Overview

Dataset statistics

Number of variables4
Number of observations60
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory37.2 B

Variable types

Text1
Numeric3

Dataset

Description한국수력원자력 연간 녹색제품 구매실적에 대한 데이터로, 품목별 총 구매금액, 녹색제품 구매금액, 비율을 포함하고 있습니다.
URLhttps://www.data.go.kr/data/15083404/fileData.do

Alerts

총구매금액(원) is highly overall correlated with 녹색구매금액(원)High correlation
녹색구매금액(원) is highly overall correlated with 총구매금액(원)High correlation
분류명 has unique valuesUnique
녹색구매금액(원) has 3 (5.0%) zerosZeros
비율 has 3 (5.0%) zerosZeros

Reproduction

Analysis started2023-12-12 16:51:34.808476
Analysis finished2023-12-12 16:51:36.568410
Duration1.76 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분류명
Text

UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
2023-12-13T01:51:36.778498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length5
Min length2

Characters and Unicode

Total characters300
Distinct characters138
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)100.0%

Sample

1st row합계
2nd row복사기
3rd row식기세척기
4th row책상(탁자)
5th row의자
ValueCountFrequency (%)
합계 1
 
1.7%
복사기 1
 
1.7%
에어컨디셔너 1
 
1.7%
타일 1
 
1.7%
벽돌 1
 
1.7%
창호 1
 
1.7%
페인트 1
 
1.7%
건설용방수재 1
 
1.7%
바닥장식재 1
 
1.7%
고무바닥재 1
 
1.7%
Other values (50) 50
83.3%
2023-12-13T01:51:37.244691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 14
 
4.7%
용 12
 
4.0%
타 10
 
3.3%
및 8
 
2.7%
재 7
 
2.3%
장 7
 
2.3%
지 7
 
2.3%
제 6
 
2.0%
터 6
 
2.0%
품 6
 
2.0%
Other values (128) 217
72.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 294
98.0%
Open Punctuation 2
 
0.7%
Close Punctuation 2
 
0.7%
Uppercase Letter 2
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 14
 
4.8%
용 12
 
4.1%
타 10
 
3.4%
및 8
 
2.7%
재 7
 
2.4%
장 7
 
2.4%
지 7
 
2.4%
제 6
 
2.0%
터 6
 
2.0%
품 6
 
2.0%
Other values (124) 211
71.8%
Uppercase Letter
ValueCountFrequency (%)
A 1
50.0%
O 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 294
98.0%
Common 4
 
1.3%
Latin 2
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 14
 
4.8%
용 12
 
4.1%
타 10
 
3.4%
및 8
 
2.7%
재 7
 
2.4%
장 7
 
2.4%
지 7
 
2.4%
제 6
 
2.0%
터 6
 
2.0%
품 6
 
2.0%
Other values (124) 211
71.8%
Common
ValueCountFrequency (%)
( 2
50.0%
) 2
50.0%
Latin
ValueCountFrequency (%)
A 1
50.0%
O 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 294
98.0%
ASCII 6
 
2.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 14
 
4.8%
용 12
 
4.1%
타 10
 
3.4%
및 8
 
2.7%
재 7
 
2.4%
장 7
 
2.4%
지 7
 
2.4%
제 6
 
2.0%
터 6
 
2.0%
품 6
 
2.0%
Other values (124) 211
71.8%
ASCII
ValueCountFrequency (%)
( 2
33.3%
) 2
33.3%
A 1
16.7%
O 1
16.7%

총구매금액(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2722213 × 109
Minimum79500
Maximum3.816664 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T01:51:37.389521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum79500
5-th percentile1705980
Q115929550
median1.66349 × 108
Q34.954876 × 108
95-th percentile3.4469305 × 109
Maximum3.816664 × 1010
Range3.8166561 × 1010
Interquartile range (IQR)4.7955805 × 108

Descriptive statistics

Standard deviation5.0821548 × 109
Coefficient of variation (CV)3.9947096
Kurtosis49.127088
Mean1.2722213 × 109
Median Absolute Deviation (MAD)1.5475705 × 108
Skewness6.7975558
Sum7.633328 × 1010
Variance2.5828297 × 1019
MonotonicityNot monotonic
2023-12-13T01:51:37.538044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22643500 2
 
3.3%
38166640154 1
 
1.7%
1495554000 1
 
1.7%
486405259 1
 
1.7%
9350400 1
 
1.7%
1787677446 1
 
1.7%
1013282597 1
 
1.7%
14212000 1
 
1.7%
252462600 1
 
1.7%
201603880 1
 
1.7%
Other values (49) 49
81.7%
ValueCountFrequency (%)
79500 1
1.7%
818500 1
1.7%
1154600 1
1.7%
1735000 1
1.7%
5300560 1
1.7%
6371000 1
1.7%
7674000 1
1.7%
7688000 1
1.7%
9350400 1
1.7%
10221360 1
1.7%
ValueCountFrequency (%)
38166640154 1
1.7%
9878195860 1
1.7%
5806941252 1
1.7%
3322719375 1
1.7%
2296411800 1
1.7%
1988743900 1
1.7%
1787677446 1
1.7%
1495554000 1
1.7%
1309967559 1
1.7%
1054665200 1
1.7%

녹색구매금액(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2442796 × 108
Minimum0
Maximum2.7732839 × 1010
Zeros3
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T01:51:37.688759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75525
Q110003620
median72122800
Q32.7124036 × 108
95-th percentile2.2201574 × 109
Maximum2.7732839 × 1010
Range2.7732839 × 1010
Interquartile range (IQR)2.6123674 × 108

Descriptive statistics

Standard deviation3.778058 × 109
Coefficient of variation (CV)4.0869144
Kurtosis45.005897
Mean9.2442796 × 108
Median Absolute Deviation (MAD)70678000
Skewness6.4923216
Sum5.5465677 × 1010
Variance1.4273722 × 1019
MonotonicityNot monotonic
2023-12-13T01:51:37.851390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
5.0%
27732838659 1
 
1.7%
139436000 1
 
1.7%
5961950 1
 
1.7%
9350400 1
 
1.7%
1634614336 1
 
1.7%
22643500 1
 
1.7%
511988497 1
 
1.7%
168384600 1
 
1.7%
55114000 1
 
1.7%
Other values (48) 48
80.0%
ValueCountFrequency (%)
0 3
5.0%
79500 1
 
1.7%
818500 1
 
1.7%
1154600 1
 
1.7%
1735000 1
 
1.7%
5162000 1
 
1.7%
5961950 1
 
1.7%
6008000 1
 
1.7%
6371000 1
 
1.7%
6871000 1
 
1.7%
ValueCountFrequency (%)
27732838659 1
1.7%
9866348660 1
1.7%
3296139175 1
1.7%
2163526800 1
1.7%
1634614336 1
1.7%
1523897500 1
1.7%
1487887000 1
1.7%
1309967559 1
1.7%
956518200 1
1.7%
567455740 1
1.7%

비율
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)51.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.5
Minimum0
Maximum100
Zeros3
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T01:51:37.993896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.95
Q164
median91
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)36

Descriptive statistics

Standard deviation31.155433
Coefficient of variation (CV)0.40726056
Kurtosis0.68115941
Mean76.5
Median Absolute Deviation (MAD)9
Skewness-1.3438816
Sum4590
Variance970.66102
MonotonicityNot monotonic
2023-12-13T01:51:38.116427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
100 22
36.7%
0 3
 
5.0%
99 3
 
5.0%
77 2
 
3.3%
78 2
 
3.3%
76 2
 
3.3%
91 2
 
3.3%
73 1
 
1.7%
27 1
 
1.7%
51 1
 
1.7%
Other values (21) 21
35.0%
ValueCountFrequency (%)
0 3
5.0%
1 1
 
1.7%
5 1
 
1.7%
22 1
 
1.7%
27 1
 
1.7%
37 1
 
1.7%
38 1
 
1.7%
40 1
 
1.7%
48 1
 
1.7%
49 1
 
1.7%
ValueCountFrequency (%)
100 22
36.7%
99 3
 
5.0%
97 1
 
1.7%
96 1
 
1.7%
94 1
 
1.7%
92 1
 
1.7%
91 2
 
3.3%
88 1
 
1.7%
86 1
 
1.7%
85 1
 
1.7%

Interactions

2023-12-13T01:51:35.645039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:51:34.980816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:51:35.293559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:51:35.776835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:51:35.102552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:51:35.436893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:51:35.905713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:51:35.194778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:51:35.534822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:51:38.195939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류명총구매금액(원)녹색구매금액(원)비율
분류명1.0001.0001.0001.000
총구매금액(원)1.0001.0000.9800.000
녹색구매금액(원)1.0000.9801.0000.000
비율1.0000.0000.0001.000
2023-12-13T01:51:38.293364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총구매금액(원)녹색구매금액(원)비율
총구매금액(원)1.0000.834-0.353
녹색구매금액(원)0.8341.000-0.034
비율-0.353-0.0341.000

Missing values

2023-12-13T01:51:36.048248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:51:36.503588image/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

분류명총구매금액(원)녹색구매금액(원)비율
0합계381666401542773283865973
1복사기14955540001487887000100
2식기세척기407000001635000040
3책상(탁자)31789219016998309054
4의자73382024056745574077
5보관용가구52273461240724565278
6주방가구7688000600800078
7기타가구및부속품14213000687100048
8OA칸막이27149080022461650083
9인쇄용지7950079500100
분류명총구매금액(원)녹색구매금액(원)비율
50램프용안정기22643500870000038
51침대및침대매트릭스24046200011856800049
52방향소취제1022136010221360100
53식품용기1627290016272900100
54봉투818500818500100
55기타포장재료17350001735000100
56식음료품11546001154600100
57정보기술통신장비및부품5375700053757000100
58축전지3289925232899252100
59운송서비스275976700275976700100