Overview

Dataset statistics

Number of variables4
Number of observations53
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory37.5 B

Variable types

Text1
Numeric3

Dataset

Description한국장애인고용공단의 연간 녹색제품(친환경) 구매 실적에 대한 데이터로 제품에 대한 분류, 총 구매금액, 녹색 구매금액, 비율 등 데이터를 제공합니다.
URLhttps://www.data.go.kr/data/15118354/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(품목) has unique valuesUnique
총구매금액(원) has unique valuesUnique
녹색구매금액(원) has 2 (3.8%) zerosZeros
비율 has 2 (3.8%) zerosZeros

Reproduction

Analysis started2023-12-12 15:36:20.341506
Analysis finished2023-12-12 15:36:21.896459
Duration1.55 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분류명(품목)
Text

UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size556.0 B
2023-12-13T00:36:22.130537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length10
Mean length5.5283019
Min length2

Characters and Unicode

Total characters293
Distinct characters127
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)100.0%

Sample

1st row합계
2nd row복사기
3rd row책상(탁자)
4th row의자
5th row보관용 가구
ValueCountFrequency (%)
기타 7
 
8.4%
및 7
 
8.4%
의복 2
 
2.4%
가구 2
 
2.4%
합계 1
 
1.2%
쓰레기봉투 1
 
1.2%
봉투 1
 
1.2%
식품용기 1
 
1.2%
방향·소취제 1
 
1.2%
비누 1
 
1.2%
Other values (59) 59
71.1%
2023-12-13T00:36:22.855440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
30
 
10.2%
기 16
 
5.5%
용 13
 
4.4%
타 11
 
3.8%
지 7
 
2.4%
및 7
 
2.4%
구 6
 
2.0%
터 6
 
2.0%
장 6
 
2.0%
재 5
 
1.7%
Other values (117) 186
63.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 253
86.3%
Space Separator 30
 
10.2%
Other Punctuation 4
 
1.4%
Close Punctuation 2
 
0.7%
Open Punctuation 2
 
0.7%
Uppercase Letter 2
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 16
 
6.3%
용 13
 
5.1%
타 11
 
4.3%
지 7
 
2.8%
및 7
 
2.8%
구 6
 
2.4%
터 6
 
2.4%
장 6
 
2.4%
재 5
 
2.0%
제 5
 
2.0%
Other values (109) 171
67.6%
Other Punctuation
ValueCountFrequency (%)
/ 2
50.0%
· 1
25.0%
, 1
25.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
50.0%
O 1
50.0%
Space Separator
ValueCountFrequency (%)
30
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 253
86.3%
Common 38
 
13.0%
Latin 2
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 16
 
6.3%
용 13
 
5.1%
타 11
 
4.3%
지 7
 
2.8%
및 7
 
2.8%
구 6
 
2.4%
터 6
 
2.4%
장 6
 
2.4%
재 5
 
2.0%
제 5
 
2.0%
Other values (109) 171
67.6%
Common
ValueCountFrequency (%)
30
78.9%
/ 2
 
5.3%
) 2
 
5.3%
( 2
 
5.3%
· 1
 
2.6%
, 1
 
2.6%
Latin
ValueCountFrequency (%)
A 1
50.0%
O 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 253
86.3%
ASCII 39
 
13.3%
None 1
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30
76.9%
/ 2
 
5.1%
) 2
 
5.1%
( 2
 
5.1%
A 1
 
2.6%
O 1
 
2.6%
, 1
 
2.6%
Hangul
ValueCountFrequency (%)
기 16
 
6.3%
용 13
 
5.1%
타 11
 
4.3%
지 7
 
2.8%
및 7
 
2.8%
구 6
 
2.4%
터 6
 
2.4%
장 6
 
2.4%
재 5
 
2.0%
제 5
 
2.0%
Other values (109) 171
67.6%
None
ValueCountFrequency (%)
· 1
100.0%

총구매금액(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0645342 × 108
Minimum300000
Maximum8.1210157 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-13T00:36:23.154817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300000
5-th percentile980400
Q15077000
median32834820
Q394847000
95-th percentile1.0054667 × 109
Maximum8.1210157 × 109
Range8.1207157 × 109
Interquartile range (IQR)89770000

Descriptive statistics

Standard deviation1.198363 × 109
Coefficient of variation (CV)3.9104244
Kurtosis36.610985
Mean3.0645342 × 108
Median Absolute Deviation (MAD)30101320
Skewness5.8403222
Sum1.6242031 × 1010
Variance1.4360738 × 1018
MonotonicityNot monotonic
2023-12-13T00:36:23.463586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8121015740 1
 
1.9%
9382000 1
 
1.9%
12696800 1
 
1.9%
300000 1
 
1.9%
23679600 1
 
1.9%
71223520 1
 
1.9%
92859200 1
 
1.9%
58833000 1
 
1.9%
76159600 1
 
1.9%
42351800 1
 
1.9%
Other values (43) 43
81.1%
ValueCountFrequency (%)
300000 1
1.9%
693000 1
1.9%
891000 1
1.9%
1040000 1
1.9%
1140000 1
1.9%
1320000 1
1.9%
1403600 1
1.9%
1714500 1
1.9%
2733500 1
1.9%
3504270 1
1.9%
ValueCountFrequency (%)
8121015740 1
1.9%
3186839405 1
1.9%
1701624690 1
1.9%
541361300 1
1.9%
220020500 1
1.9%
212086000 1
1.9%
208063000 1
1.9%
207896400 1
1.9%
169398200 1
1.9%
165158080 1
1.9%

녹색구매금액(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct52
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3528811 × 108
Minimum0
Maximum6.235135 × 109
Zeros2
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-13T00:36:23.784638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile535800
Q14818385
median31014000
Q389237000
95-th percentile3.331968 × 108
Maximum6.235135 × 109
Range6.235135 × 109
Interquartile range (IQR)84418615

Descriptive statistics

Standard deviation9.4748057 × 108
Coefficient of variation (CV)4.0268952
Kurtosis33.559998
Mean2.3528811 × 108
Median Absolute Deviation (MAD)28280500
Skewness5.6699729
Sum1.247027 × 1010
Variance8.9771943 × 1017
MonotonicityNot monotonic
2023-12-13T00:36:24.040203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
3.8%
6235134950 1
 
1.9%
12696800 1
 
1.9%
300000 1
 
1.9%
23679600 1
 
1.9%
71223520 1
 
1.9%
92859200 1
 
1.9%
58833000 1
 
1.9%
76159600 1
 
1.9%
42351800 1
 
1.9%
Other values (42) 42
79.2%
ValueCountFrequency (%)
0 2
3.8%
300000 1
1.9%
693000 1
1.9%
891000 1
1.9%
1024600 1
1.9%
1140000 1
1.9%
1320000 1
1.9%
1714500 1
1.9%
2733500 1
1.9%
3504270 1
1.9%
ValueCountFrequency (%)
6235134950 1
1.9%
3186839405 1
1.9%
522957300 1
1.9%
206689800 1
1.9%
191693000 1
1.9%
183901500 1
1.9%
176632400 1
1.9%
169378200 1
1.9%
165158080 1
1.9%
148238000 1
1.9%

비율
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)37.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.211321
Minimum0
Maximum100
Zeros2
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-13T00:36:24.296732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile64.68
Q194.6
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)5.4

Descriptive statistics

Standard deviation20.382597
Coefficient of variation (CV)0.22104224
Kurtosis15.263648
Mean92.211321
Median Absolute Deviation (MAD)0
Skewness-3.8438855
Sum4887.2
Variance415.45025
MonotonicityNot monotonic
2023-12-13T00:36:24.452666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
100.0 32
60.4%
96.6 2
 
3.8%
0.0 2
 
3.8%
99.3 1
 
1.9%
52.2 1
 
1.9%
82.2 1
 
1.9%
91.5 1
 
1.9%
92.1 1
 
1.9%
91.6 1
 
1.9%
94.6 1
 
1.9%
Other values (10) 10
 
18.9%
ValueCountFrequency (%)
0.0 2
3.8%
52.2 1
1.9%
73.0 1
1.9%
76.8 1
1.9%
82.2 1
1.9%
83.6 1
1.9%
85.0 1
1.9%
88.6 1
1.9%
91.5 1
1.9%
91.6 1
1.9%
ValueCountFrequency (%)
100.0 32
60.4%
99.3 1
 
1.9%
98.8 1
 
1.9%
97.5 1
 
1.9%
97.4 1
 
1.9%
96.6 2
 
3.8%
95.7 1
 
1.9%
94.6 1
 
1.9%
94.1 1
 
1.9%
92.1 1
 
1.9%

Interactions

2023-12-13T00:36:21.356639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:20.545789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:21.008244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:21.463735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:20.742874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:21.130480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:21.580485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:20.890573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:21.247266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:36:24.572600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류명(품목)총구매금액(원)녹색구매금액(원)비율
분류명(품목)1.0001.0001.0001.000
총구매금액(원)1.0001.0001.0000.594
녹색구매금액(원)1.0001.0001.0000.496
비율1.0000.5940.4961.000
2023-12-13T00:36:24.705451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총구매금액(원)녹색구매금액(원)비율
총구매금액(원)1.0000.897-0.416
녹색구매금액(원)0.8971.000-0.262
비율-0.416-0.2621.000

Missing values

2023-12-13T00:36:21.709346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:36:21.849330image/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

분류명(품목)총구매금액(원)녹색구매금액(원)비율
0합계8121015740623513495076.8
1복사기948470008923700094.1
2책상(탁자)22002050018390150083.6
3의자20789640017663240085.0
4보관용 가구21208600020668980097.5
5주방가구225837002231570098.8
6기타 가구 및 부속품1403600102460073.0
7OA칸막이15617950013843740088.6
8사무용지583778505589075095.7
9기타 사무용품232439002263390097.4
분류명(품목)총구매금액(원)녹색구매금액(원)비율
43소변기693000693000100.0
44샤워헤드 및 샤워기80080008008000100.0
45타일35042703504270100.0
46페인트39380003938000100.0
47건설용 방수재366644003642350099.3
48바닥장식재5425280054252800100.0
49접착제27335002733500100.0
50울타리 및 휀스11400001140000100.0
51기타48183854818385100.0
52에너지3283482032834820100.0