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
평년(mm) is highly overall correlated with 시도명High correlation
시도명 is highly overall correlated with 평년(mm)High correlation
평년(mm) has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:53:40.950887
Analysis finished2023-12-10 10:53:42.176508
Duration1.23 second
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

2023-12-10T19:53:42.293772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:53:42.490374image/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
2023-12-10T19:53:42.979759image/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%
2023-12-10T19:53:43.669322image/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)

Distinct87
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.962
Minimum42.8
Maximum104.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:43.911439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42.8
5-th percentile51.795
Q159.325
median63.7
Q370.525
95-th percentile85.68
Maximum104.7
Range61.9
Interquartile range (IQR)11.2

Descriptive statistics

Standard deviation11.009204
Coefficient of variation (CV)0.16690222
Kurtosis2.5892729
Mean65.962
Median Absolute Deviation (MAD)5.2
Skewness1.2815962
Sum6596.2
Variance121.20258
MonotonicityNot monotonic
2023-12-10T19:53:44.152321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.5 6
 
6.0%
63.7 3
 
3.0%
57.3 2
 
2.0%
67.5 2
 
2.0%
73.4 2
 
2.0%
67.7 2
 
2.0%
59.0 2
 
2.0%
63.1 2
 
2.0%
60.9 1
 
1.0%
54.0 1
 
1.0%
Other values (77) 77
77.0%
ValueCountFrequency (%)
42.8 1
1.0%
47.9 1
1.0%
49.2 1
1.0%
50.8 1
1.0%
51.7 1
1.0%
51.8 1
1.0%
53.1 1
1.0%
53.6 1
1.0%
54.0 1
1.0%
54.1 1
1.0%
ValueCountFrequency (%)
104.7 1
1.0%
103.0 1
1.0%
99.4 1
1.0%
94.9 1
1.0%
91.0 1
1.0%
85.4 1
1.0%
84.8 1
1.0%
81.6 1
1.0%
81.1 1
1.0%
78.4 1
1.0%

평년(mm)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.011796
Minimum17.111271
Maximum77.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:44.388501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17.111271
5-th percentile18.955575
Q121.33445
median28.444326
Q338.687673
95-th percentile46.79377
Maximum77.38
Range60.268729
Interquartile range (IQR)17.353223

Descriptive statistics

Standard deviation11.107351
Coefficient of variation (CV)0.35816536
Kurtosis2.0266731
Mean31.011796
Median Absolute Deviation (MAD)7.6975906
Skewness1.1750801
Sum3101.1796
Variance123.37325
MonotonicityNot monotonic
2023-12-10T19:53:44.572161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.9497725351256 1
 
1.0%
46.7715373728539 1
 
1.0%
31.0222746548802 1
 
1.0%
30.6564403164712 1
 
1.0%
27.5551936767039 1
 
1.0%
27.2375160657999 1
 
1.0%
24.1277098725656 1
 
1.0%
34.6638531240184 1
 
1.0%
35.7738275381787 1
 
1.0%
31.6612262573872 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
17.1112709916502 1
1.0%
18.4274914603401 1
1.0%
18.5067512780002 1
1.0%
18.7224466717472 1
1.0%
18.8043366161531 1
1.0%
18.9635351204019 1
1.0%
19.1549877124064 1
1.0%
19.4392697061898 1
1.0%
19.5263532932975 1
1.0%
19.6808224306974 1
1.0%
ValueCountFrequency (%)
77.38 1
1.0%
60.0842373771975 1
1.0%
59.5805822856357 1
1.0%
51.9266718267235 1
1.0%
47.216182108547 1
1.0%
46.7715373728539 1
1.0%
46.4682584505133 1
1.0%
45.5170123234086 1
1.0%
45.3758706401262 1
1.0%
45.3586577941773 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

2023-12-10T19:53:44.772049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:53:44.919259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
100
100.0%

Interactions

2023-12-10T19:53:41.558972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:41.259006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:41.721972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:41.406946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:53:45.027239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명시군명강수량(mm)평년(mm)
시도명1.0000.8370.5300.700
시군명0.8371.0001.0000.955
강수량(mm)0.5301.0001.0000.667
평년(mm)0.7000.9550.6671.000
2023-12-10T19:53:45.176372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강수량(mm)평년(mm)시도명
강수량(mm)1.0000.1970.239
평년(mm)0.1971.0000.519
시도명0.2390.5191.000

Missing values

2023-12-10T19:53:41.941816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:53:42.107507image/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
0강원도정선군66.725.949773-
1강원도평창군64.729.520301-
2강원도영월군66.620.975397-
3강원도횡성군69.823.303127-
4강원도홍천군73.421.499525-
5강원도삼척시60.235.423412-
6강원도양양군59.539.459605-
7강원도고성군75.329.571352-
8강원도인제군61.120.194997-
9강원도양구군63.717.111271-
시도명시군명강수량(mm)평년(mm)부족량
90전라남도화순군63.738.59704-
91전라남도장흥군85.445.517012-
92전라남도강진군81.145.375871-
93전라남도해남군66.045.358658-
94전라남도영암군67.343.914049-
95전라남도무안군58.041.62559-
96전라남도함평군74.840.564131-
97전라남도영광군67.740.546346-
98전라남도장성군73.143.056502-
99전라남도완도군71.751.926672-