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

평년(mm) is highly overall correlated with 부족량High correlation
부족량 is highly overall correlated with 평년(mm)High correlation
평년(mm) has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:53:29.673739
Analysis finished2023-12-10 10:53:31.333601
Duration1.66 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

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

2023-12-10T19:53:31.457299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:53:31.647676image/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
2023-12-10T19:53:32.089705image/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%
2023-12-10T19:53:32.814191image/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)

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.372
Minimum35.5
Maximum155.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:33.080039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.5
5-th percentile42.87
Q162.025
median93.4
Q3107.8
95-th percentile121.15
Maximum155.5
Range120
Interquartile range (IQR)45.775

Descriptive statistics

Standard deviation26.757418
Coefficient of variation (CV)0.30979273
Kurtosis-0.8671556
Mean86.372
Median Absolute Deviation (MAD)19.35
Skewness-0.20746424
Sum8637.2
Variance715.95941
MonotonicityNot monotonic
2023-12-10T19:53:33.344079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.4 2
 
2.0%
107.8 2
 
2.0%
100.5 2
 
2.0%
93.4 2
 
2.0%
115.9 2
 
2.0%
71.3 2
 
2.0%
69.9 1
 
1.0%
79.7 1
 
1.0%
68.1 1
 
1.0%
62.1 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
35.5 1
1.0%
35.7 1
1.0%
38.6 1
1.0%
39.5 1
1.0%
40.4 1
1.0%
43.0 1
1.0%
43.6 1
1.0%
44.4 1
1.0%
48.2 1
1.0%
49.4 1
1.0%
ValueCountFrequency (%)
155.5 1
1.0%
128.9 1
1.0%
128.6 1
1.0%
127.2 1
1.0%
124.0 1
1.0%
121.0 1
1.0%
119.9 1
1.0%
116.9 1
1.0%
115.9 2
2.0%
115.6 1
1.0%

평년(mm)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.320225
Minimum54.643333
Maximum169.23835
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:33.585131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum54.643333
5-th percentile60.961367
Q167.901802
median74.278189
Q384.034312
95-th percentile121.76981
Maximum169.23835
Range114.59501
Interquartile range (IQR)16.13251

Descriptive statistics

Standard deviation20.824307
Coefficient of variation (CV)0.25926605
Kurtosis5.6474762
Mean80.320225
Median Absolute Deviation (MAD)7.875622
Skewness2.1607485
Sum8032.0225
Variance433.65178
MonotonicityNot monotonic
2023-12-10T19:53:33.819832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74.2513410434012 1
 
1.0%
121.442131103088 1
 
1.0%
78.6542525531454 1
 
1.0%
76.7775595825786 1
 
1.0%
80.4509705268832 1
 
1.0%
71.3185667724777 1
 
1.0%
71.8512790807098 1
 
1.0%
82.8714846860069 1
 
1.0%
82.1764351231332 1
 
1.0%
75.8972214145966 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
54.6433333333333 1
1.0%
57.9012845688745 1
1.0%
59.3309154444639 1
1.0%
60.6526149579802 1
1.0%
60.8842975210294 1
1.0%
60.9654231274856 1
1.0%
61.4192002359967 1
1.0%
61.4724640085882 1
1.0%
61.5859112992145 1
1.0%
62.2397158221326 1
1.0%
ValueCountFrequency (%)
169.238346568896 1
1.0%
166.785624441012 1
1.0%
136.584777707479 1
1.0%
130.743310311542 1
1.0%
127.995689905614 1
1.0%
121.442131103088 1
1.0%
121.148912617298 1
1.0%
112.967199089686 1
1.0%
112.76634213932 1
1.0%
111.85200607762 1
1.0%

부족량
Categorical

HIGH CORRELATION 

Distinct46
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
55 
42.2956899056138
 
1
12.2764351231332
 
1
29.4922411492079
 
1
66.7856244410121
 
1
Other values (41)
41 

Length

Max length16
Median length1
Mean length7.67
Min length1

Unique

Unique45 ?
Unique (%)45.0%

Sample

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

Common Values

ValueCountFrequency (%)
- 55
55.0%
42.2956899056138 1
 
1.0%
12.2764351231332 1
 
1.0%
29.4922411492079 1
 
1.0%
66.7856244410121 1
 
1.0%
19.3575475138861 1
 
1.0%
31.3612060466234 1
 
1.0%
35.8916229407915 1
 
1.0%
23.660446584861 1
 
1.0%
32.548912617298 1
 
1.0%
Other values (36) 36
36.0%

Length

2023-12-10T19:53:34.041908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
55
55.0%
25.154662572619 1
 
1.0%
49.0628321383356 1
 
1.0%
10.5542525531454 1
 
1.0%
1.32118066259586 1
 
1.0%
24.0050360955315 1
 
1.0%
15.927840463159 1
 
1.0%
3.50735709305856 1
 
1.0%
4.23054383370794 1
 
1.0%
3.86325043898873 1
 
1.0%
Other values (36) 36
36.0%

Interactions

2023-12-10T19:53:30.813282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:30.502031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:30.954702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:30.673077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:53:34.176378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명시군명강수량(mm)평년(mm)부족량
시도명1.0000.8370.6460.7030.865
시군명0.8371.0000.7970.7600.990
강수량(mm)0.6460.7971.0000.6820.690
평년(mm)0.7030.7600.6821.0000.972
부족량0.8650.9900.6900.9721.000
2023-12-10T19:53:34.698626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부족량시도명
부족량1.0000.458
시도명0.4581.000
2023-12-10T19:53:34.853551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강수량(mm)평년(mm)시도명부족량
강수량(mm)1.000-0.4500.4340.242
평년(mm)-0.4501.0000.4930.626
시도명0.4340.4931.0000.458
부족량0.2420.6260.4581.000

Missing values

2023-12-10T19:53:31.120431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:53:31.273796image/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
0강원도정선군108.874.251341-
1강원도평창군110.777.753236-
2강원도영월군107.869.779382-
3강원도횡성군106.273.066271-
4강원도홍천군102.974.021836-
5강원도삼척시96.576.407668-
6강원도양양군113.279.293318-
7강원도고성군128.671.811509-
8강원도인제군116.972.928961-
9강원도양구군105.963.755484-
시도명시군명강수량(mm)평년(mm)부족량
90전라남도화순군35.586.76539851.2653977548776
91전라남도장흥군70.0111.85200641.8520060776198
92전라남도강진군60.5105.88646445.3864643298722
93전라남도해남군51.895.43476543.6347653794746
94전라남도영암군44.484.28606539.8860646665071
95전라남도무안군38.680.03919241.4391915745195
96전라남도함평군39.581.99333742.4933372675452
97전라남도영광군40.481.75652741.3565266374901
98전라남도장성군35.784.76283249.0628321383356
99전라남도완도군74.5130.7433156.2433103115416