Overview

Dataset statistics

Number of variables3
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory964.0 B
Average record size in memory30.1 B

Variable types

Categorical1
Numeric2

Dataset

Description2021년 9월 기준 국내통상요금에 대한 정보입니다. 해당 데이터가 보유한 컬럼은 다음과 같습니다. 컬럼명 : 구분, 중량, 요금
Author과학기술정보통신부 우정사업본부
URLhttps://www.data.go.kr/data/15090577/fileData.do

Alerts

중량(g) is highly overall correlated with 요금(원)High correlation
요금(원) is highly overall correlated with 중량(g)High correlation
구분 is highly imbalanced (55.1%)Imbalance
요금(원) has unique valuesUnique

Reproduction

Analysis started2023-12-12 10:11:31.583826
Analysis finished2023-12-12 10:11:32.331028
Duration0.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
규격외
29 
규격
3 

Length

Max length3
Median length3
Mean length2.90625
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row규격
2nd row규격
3rd row규격
4th row규격외
5th row규격외

Common Values

ValueCountFrequency (%)
규격외 29
90.6%
규격 3
 
9.4%

Length

2023-12-12T19:11:32.417599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:11:32.553998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
규격외 29
90.6%
규격 3
 
9.4%

중량(g)
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1143.125
Minimum5
Maximum6000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T19:11:32.693778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile38.75
Q1287.5
median675
Q31250
95-th percentile4450
Maximum6000
Range5995
Interquartile range (IQR)962.5

Descriptive statistics

Standard deviation1443.1444
Coefficient of variation (CV)1.2624555
Kurtosis4.5275656
Mean1143.125
Median Absolute Deviation (MAD)450
Skewness2.183206
Sum36580
Variance2082665.7
MonotonicityIncreasing
2023-12-12T19:11:32.869206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
50 2
 
6.2%
5 1
 
3.1%
800 1
 
3.1%
6000 1
 
3.1%
5000 1
 
3.1%
4000 1
 
3.1%
3000 1
 
3.1%
2000 1
 
3.1%
1800 1
 
3.1%
1600 1
 
3.1%
Other values (21) 21
65.6%
ValueCountFrequency (%)
5 1
3.1%
25 1
3.1%
50 2
6.2%
100 1
3.1%
150 1
3.1%
200 1
3.1%
250 1
3.1%
300 1
3.1%
350 1
3.1%
400 1
3.1%
ValueCountFrequency (%)
6000 1
3.1%
5000 1
3.1%
4000 1
3.1%
3000 1
3.1%
2000 1
3.1%
1800 1
3.1%
1600 1
3.1%
1400 1
3.1%
1200 1
3.1%
1000 1
3.1%

요금(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2121.25
Minimum400
Maximum5000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T19:11:33.054679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum400
5-th percentile441
Q11090
median2020
Q32950
95-th percentile4380
Maximum5000
Range4600
Interquartile range (IQR)1860

Descriptive statistics

Standard deviation1264.362
Coefficient of variation (CV)0.59604573
Kurtosis-0.4652563
Mean2121.25
Median Absolute Deviation (MAD)960
Skewness0.49427915
Sum67880
Variance1598611.3
MonotonicityStrictly increasing
2023-12-12T19:11:33.217633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
400 1
 
3.1%
2200 1
 
3.1%
5000 1
 
3.1%
4600 1
 
3.1%
4200 1
 
3.1%
3800 1
 
3.1%
3400 1
 
3.1%
3280 1
 
3.1%
3160 1
 
3.1%
3040 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
400 1
3.1%
430 1
3.1%
450 1
3.1%
520 1
3.1%
640 1
3.1%
760 1
3.1%
880 1
3.1%
1000 1
3.1%
1120 1
3.1%
1240 1
3.1%
ValueCountFrequency (%)
5000 1
3.1%
4600 1
3.1%
4200 1
3.1%
3800 1
3.1%
3400 1
3.1%
3280 1
3.1%
3160 1
3.1%
3040 1
3.1%
2920 1
3.1%
2800 1
3.1%

Interactions

2023-12-12T19:11:31.954867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:31.681206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:32.052362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:11:31.780209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:11:33.661659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분중량(g)요금(원)
구분1.0000.0000.404
중량(g)0.0001.0000.907
요금(원)0.4040.9071.000
2023-12-12T19:11:33.781038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중량(g)요금(원)구분
중량(g)1.0001.0000.000
요금(원)1.0001.0000.404
구분0.0000.4041.000

Missing values

2023-12-12T19:11:32.184007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:11:32.285811image/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

구분중량(g)요금(원)
0규격5400
1규격25430
2규격50450
3규격외50520
4규격외100640
5규격외150760
6규격외200880
7규격외2501000
8규격외3001120
9규격외3501240
구분중량(g)요금(원)
22규격외10002800
23규격외12002920
24규격외14003040
25규격외16003160
26규격외18003280
27규격외20003400
28규격외30003800
29규격외40004200
30규격외50004600
31규격외60005000