Overview

Dataset statistics

Number of variables5
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 KiB
Average record size in memory43.3 B

Variable types

Categorical2
Text1
Numeric2

Alerts

부족량 has constant value ""Constant
강수량(mm) is highly overall correlated with 평년(mm)High correlation
평년(mm) is highly overall correlated with 강수량(mm) and 1 other fieldsHigh correlation
시도명 is highly overall correlated with 평년(mm)High correlation
평년(mm) has unique valuesUnique

Reproduction

Analysis started2024-04-17 13:04:35.123895
Analysis finished2024-04-17 13:04:35.641074
Duration0.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기도
31 
경상북도
23 
강원도
18 
경상남도
18 
전라남도
10 

Length

Max length4
Median length4
Mean length3.51
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강원도
2nd row강원도
3rd row강원도
4th row강원도
5th row강원도

Common Values

ValueCountFrequency (%)
경기도 31
31.0%
경상북도 23
23.0%
강원도 18
18.0%
경상남도 18
18.0%
전라남도 10
 
10.0%

Length

2024-04-17T22:04:35.691041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T22:04:35.775306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 31
31.0%
경상북도 23
23.0%
강원도 18
18.0%
경상남도 18
18.0%
전라남도 10
 
10.0%

시군명
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-17T22:04:36.012119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.03
Min length3

Characters and Unicode

Total characters303
Distinct characters85
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)98.0%

Sample

1st row정선군
2nd row평창군
3rd row영월군
4th row횡성군
5th row홍천군
ValueCountFrequency (%)
고성군 2
 
2.0%
경산시 1
 
1.0%
김해시 1
 
1.0%
영천시 1
 
1.0%
영주시 1
 
1.0%
구미시 1
 
1.0%
안동시 1
 
1.0%
경주시 1
 
1.0%
포항시 1
 
1.0%
김천시 1
 
1.0%
Other values (89) 89
89.0%
2024-04-17T22:04:36.368541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 54
17.8%
군 49
 
16.2%
천 14
 
4.6%
양 12
 
4.0%
주 11
 
3.6%
성 9
 
3.0%
영 8
 
2.6%
안 6
 
2.0%
산 5
 
1.7%
남 5
 
1.7%
Other values (75) 130
42.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 303
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 54
17.8%
군 49
 
16.2%
천 14
 
4.6%
양 12
 
4.0%
주 11
 
3.6%
성 9
 
3.0%
영 8
 
2.6%
안 6
 
2.0%
산 5
 
1.7%
남 5
 
1.7%
Other values (75) 130
42.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 303
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 54
17.8%
군 49
 
16.2%
천 14
 
4.6%
양 12
 
4.0%
주 11
 
3.6%
성 9
 
3.0%
영 8
 
2.6%
안 6
 
2.0%
산 5
 
1.7%
남 5
 
1.7%
Other values (75) 130
42.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 303
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 54
17.8%
군 49
 
16.2%
천 14
 
4.6%
양 12
 
4.0%
주 11
 
3.6%
성 9
 
3.0%
영 8
 
2.6%
안 6
 
2.0%
산 5
 
1.7%
남 5
 
1.7%
Other values (75) 130
42.9%

강수량(mm)
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.872
Minimum52.4
Maximum180.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T22:04:36.494978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum52.4
5-th percentile79.13
Q193.6
median100.2
Q3111.225
95-th percentile153.64
Maximum180.2
Range127.8
Interquartile range (IQR)17.625

Descriptive statistics

Standard deviation21.74233
Coefficient of variation (CV)0.20536431
Kurtosis1.6506497
Mean105.872
Median Absolute Deviation (MAD)7.55
Skewness1.1694069
Sum10587.2
Variance472.72891
MonotonicityNot monotonic
2024-04-17T22:04:36.617920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98.9 2
 
2.0%
94.1 2
 
2.0%
101.6 2
 
2.0%
95.6 2
 
2.0%
93.6 2
 
2.0%
90.1 2
 
2.0%
101.3 2
 
2.0%
147.9 1
 
1.0%
101.2 1
 
1.0%
102.2 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
52.4 1
1.0%
70.7 1
1.0%
71.2 1
1.0%
77.0 1
1.0%
77.8 1
1.0%
79.2 1
1.0%
87.2 1
1.0%
87.3 1
1.0%
87.9 1
1.0%
88.2 1
1.0%
ValueCountFrequency (%)
180.2 1
1.0%
166.7 1
1.0%
156.4 1
1.0%
154.9 1
1.0%
154.4 1
1.0%
153.6 1
1.0%
150.5 1
1.0%
147.9 1
1.0%
146.8 1
1.0%
146.0 1
1.0%

평년(mm)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.289423
Minimum25.774622
Maximum107.66615
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T22:04:36.982868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25.774622
5-th percentile27.316923
Q134.470639
median45.258741
Q356.611035
95-th percentile80.641303
Maximum107.66615
Range81.891533
Interquartile range (IQR)22.140396

Descriptive statistics

Standard deviation18.16408
Coefficient of variation (CV)0.37615029
Kurtosis1.1850923
Mean48.289423
Median Absolute Deviation (MAD)11.044982
Skewness1.1246035
Sum4828.9423
Variance329.93382
MonotonicityNot monotonic
2024-04-17T22:04:37.106605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.8773618300655 1
 
1.0%
77.8318722952222 1
 
1.0%
49.9306954320286 1
 
1.0%
48.0830420895047 1
 
1.0%
46.6992867134315 1
 
1.0%
44.3657418643781 1
 
1.0%
43.6295528018822 1
 
1.0%
60.1220585798623 1
 
1.0%
54.8354867988857 1
 
1.0%
48.3618515133258 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
25.774621816353 1
1.0%
25.8344134900118 1
1.0%
26.8101857950199 1
1.0%
27.2210237976548 1
1.0%
27.3128148061475 1
1.0%
27.3171391424834 1
1.0%
27.5164851608996 1
1.0%
27.9557561588718 1
1.0%
28.4133333333333 1
1.0%
28.4786155304235 1
1.0%
ValueCountFrequency (%)
107.666154484586 1
1.0%
104.610658651183 1
1.0%
100.070041280454 1
1.0%
90.5771118908256 1
1.0%
80.8179209881452 1
1.0%
80.6320077467095 1
1.0%
79.2042491723884 1
1.0%
78.7731579265829 1
1.0%
77.8318722952222 1
1.0%
75.64337508191 1
1.0%

부족량
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 100
100.0%

Length

2024-04-17T22:04:37.211304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T22:04:37.283493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
100
100.0%

Interactions

2024-04-17T22:04:35.400023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:04:35.273067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:04:35.464759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:04:35.342754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T22:04:37.333198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명시군명강수량(mm)평년(mm)
시도명1.0000.8370.6670.755
시군명0.8371.0000.7700.904
강수량(mm)0.6670.7701.0000.676
평년(mm)0.7550.9040.6761.000
2024-04-17T22:04:37.413730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강수량(mm)평년(mm)시도명
강수량(mm)1.0000.6420.328
평년(mm)0.6421.0000.553
시도명0.3280.5531.000

Missing values

2024-04-17T22:04:35.536175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T22:04:35.610101image/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

시도명시군명강수량(mm)평년(mm)부족량
0강원도정선군101.838.877362-
1강원도평창군98.441.870559-
2강원도영월군99.334.532331-
3강원도횡성군96.037.423503-
4강원도홍천군100.334.724629-
5강원도삼척시104.049.921944-
6강원도양양군94.149.644382-
7강원도고성군118.241.977318-
8강원도인제군93.833.784414-
9강원도양구군88.225.774622-
시도명시군명강수량(mm)평년(mm)부족량
90전라남도화순군117.159.663592-
91전라남도장흥군166.779.204249-
92전라남도강진군146.878.773158-
93전라남도해남군131.475.643375-
94전라남도영암군126.663.646736-
95전라남도무안군105.159.293499-
96전라남도함평군99.656.375528-
97전라남도영광군96.255.411035-
98전라남도장성군101.657.317555-
99전라남도완도군129.2100.070041-