Overview

Dataset statistics

Number of variables14
Number of observations79
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.8 KiB
Average record size in memory126.7 B

Variable types

Numeric13
Categorical1

Dataset

Description2007년 ~ 2015년 경상북도 영주시 관내 관측지점별, 월별 강수량 현황
Author경상북도 영주시
URLhttps://www.data.go.kr/data/15014587/fileData.do

Alerts

관측년도 is highly overall correlated with 2월강수량 and 3 other fieldsHigh correlation
2월강수량 is highly overall correlated with 관측년도 and 3 other fieldsHigh correlation
3월강수량 is highly overall correlated with 6월강수량 and 2 other fieldsHigh correlation
5월강수량 is highly overall correlated with 관측년도 and 3 other fieldsHigh correlation
6월강수량 is highly overall correlated with 3월강수량High correlation
7월강수량 is highly overall correlated with 관측년도 and 2 other fieldsHigh correlation
8월강수량 is highly overall correlated with 9월강수량 and 1 other fieldsHigh correlation
9월강수량 is highly overall correlated with 3월강수량 and 2 other fieldsHigh correlation
10월강수량 is highly overall correlated with 관측년도 and 3 other fieldsHigh correlation
11월강수량 is highly overall correlated with 3월강수량 and 2 other fieldsHigh correlation
12월강수량 is highly overall correlated with 2월강수량High correlation
1월강수량 has 11 (13.9%) zerosZeros
2월강수량 has 2 (2.5%) zerosZeros

Reproduction

Analysis started2023-12-12 23:17:06.016140
Analysis finished2023-12-12 23:17:22.534100
Duration16.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관측년도
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.8861
Minimum2007
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-13T08:17:22.572964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2009
Q12010
median2012
Q32014
95-th percentile2015
Maximum2015
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1121586
Coefficient of variation (CV)0.0010498401
Kurtosis-1.0471789
Mean2011.8861
Median Absolute Deviation (MAD)2
Skewness-0.097652147
Sum158939
Variance4.4612139
MonotonicityIncreasing
2023-12-13T08:17:22.727276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2009 11
13.9%
2010 11
13.9%
2011 11
13.9%
2012 11
13.9%
2013 11
13.9%
2014 11
13.9%
2015 11
13.9%
2007 1
 
1.3%
2008 1
 
1.3%
ValueCountFrequency (%)
2007 1
 
1.3%
2008 1
 
1.3%
2009 11
13.9%
2010 11
13.9%
2011 11
13.9%
2012 11
13.9%
2013 11
13.9%
2014 11
13.9%
2015 11
13.9%
ValueCountFrequency (%)
2015 11
13.9%
2014 11
13.9%
2013 11
13.9%
2012 11
13.9%
2011 11
13.9%
2010 11
13.9%
2009 11
13.9%
2008 1
 
1.3%
2007 1
 
1.3%

관측지
Categorical

Distinct13
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Memory size764.0 B
풍기읍
이산면
평은면
문수면
장수면
Other values (8)
44 

Length

Max length3
Median length3
Mean length2.9240506
Min length2

Unique

Unique1 ?
Unique (%)1.3%

Sample

1st row영주시
2nd row영주시
3rd row풍기읍
4th row이산면
5th row평은면

Common Values

ValueCountFrequency (%)
풍기읍 7
8.9%
이산면 7
8.9%
평은면 7
8.9%
문수면 7
8.9%
장수면 7
8.9%
안정면 7
8.9%
봉현면 7
8.9%
순흥면 7
8.9%
단산면 7
8.9%
부석면 7
8.9%
Other values (3) 9
11.4%

Length

2023-12-13T08:17:22.839904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
풍기읍 7
8.9%
이산면 7
8.9%
평은면 7
8.9%
문수면 7
8.9%
장수면 7
8.9%
안정면 7
8.9%
봉현면 7
8.9%
순흥면 7
8.9%
단산면 7
8.9%
부석면 7
8.9%
Other values (3) 9
11.4%

1월강수량
Real number (ℝ)

ZEROS 

Distinct36
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.449367
Minimum0
Maximum39.5
Zeros11
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-13T08:17:23.284065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q317.25
95-th percentile33.14
Maximum39.5
Range39.5
Interquartile range (IQR)14.25

Descriptive statistics

Standard deviation10.707604
Coefficient of variation (CV)1.0247132
Kurtosis0.1651557
Mean10.449367
Median Absolute Deviation (MAD)5
Skewness1.1042959
Sum825.5
Variance114.65279
MonotonicityNot monotonic
2023-12-13T08:17:23.409230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0.0 11
 
13.9%
4.0 7
 
8.9%
3.0 7
 
8.9%
18.5 3
 
3.8%
4.5 3
 
3.8%
15.5 3
 
3.8%
2.5 3
 
3.8%
3.5 3
 
3.8%
5.5 3
 
3.8%
5.0 2
 
2.5%
Other values (26) 34
43.0%
ValueCountFrequency (%)
0.0 11
13.9%
1.0 1
 
1.3%
1.5 1
 
1.3%
2.0 2
 
2.5%
2.5 3
 
3.8%
3.0 7
8.9%
3.5 3
 
3.8%
4.0 7
8.9%
4.5 3
 
3.8%
4.9 1
 
1.3%
ValueCountFrequency (%)
39.5 1
1.3%
37.5 1
1.3%
33.5 2
2.5%
33.1 1
1.3%
33.0 1
1.3%
29.5 2
2.5%
28.5 1
1.3%
27.5 1
1.3%
24.5 1
1.3%
22.0 2
2.5%

2월강수량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)60.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.302532
Minimum0
Maximum92
Zeros2
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-13T08:17:23.538220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q12.25
median16.5
Q351.5
95-th percentile84.15
Maximum92
Range92
Interquartile range (IQR)49.25

Descriptive statistics

Standard deviation29.069197
Coefficient of variation (CV)0.99203704
Kurtosis-0.97702549
Mean29.302532
Median Absolute Deviation (MAD)16
Skewness0.59746883
Sum2314.9
Variance845.0182
MonotonicityNot monotonic
2023-12-13T08:17:23.687319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.5 7
 
8.9%
4.0 6
 
7.6%
1.0 5
 
6.3%
1.5 4
 
5.1%
16.5 3
 
3.8%
50.5 3
 
3.8%
55.5 2
 
2.5%
44.5 2
 
2.5%
51.5 2
 
2.5%
54.0 2
 
2.5%
Other values (38) 43
54.4%
ValueCountFrequency (%)
0.0 2
 
2.5%
0.5 7
8.9%
1.0 5
6.3%
1.5 4
5.1%
2.0 2
 
2.5%
2.5 1
 
1.3%
3.5 1
 
1.3%
4.0 6
7.6%
4.3 1
 
1.3%
4.5 1
 
1.3%
ValueCountFrequency (%)
92.0 1
1.3%
91.0 1
1.3%
87.0 1
1.3%
85.5 1
1.3%
84.0 1
1.3%
82.5 1
1.3%
80.0 1
1.3%
73.0 1
1.3%
72.0 1
1.3%
71.0 1
1.3%

3월강수량
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.575949
Minimum14
Maximum277
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-13T08:17:23.842476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile21.95
Q132.5
median39
Q367.5
95-th percentile95.75
Maximum277
Range263
Interquartile range (IQR)35

Descriptive statistics

Standard deviation35.446428
Coefficient of variation (CV)0.68726662
Kurtosis20.537532
Mean51.575949
Median Absolute Deviation (MAD)14
Skewness3.6623029
Sum4074.5
Variance1256.4493
MonotonicityNot monotonic
2023-12-13T08:17:23.974750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.0 4
 
5.1%
32.5 3
 
3.8%
36.5 3
 
3.8%
35.0 3
 
3.8%
70.0 2
 
2.5%
33.0 2
 
2.5%
71.5 2
 
2.5%
74.5 2
 
2.5%
65.0 2
 
2.5%
22.5 2
 
2.5%
Other values (46) 54
68.4%
ValueCountFrequency (%)
14.0 1
1.3%
18.0 1
1.3%
21.5 2
2.5%
22.0 1
1.3%
22.5 2
2.5%
23.0 1
1.3%
23.5 1
1.3%
25.0 1
1.3%
26.0 1
1.3%
28.0 2
2.5%
ValueCountFrequency (%)
277.0 1
1.3%
146.0 1
1.3%
98.5 1
1.3%
98.0 1
1.3%
95.5 1
1.3%
87.5 1
1.3%
86.5 1
1.3%
84.5 1
1.3%
84.0 1
1.3%
83.5 1
1.3%

4월강수량
Real number (ℝ)

Distinct67
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.670886
Minimum14.5
Maximum142.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-13T08:17:24.114562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14.5
5-th percentile43.95
Q157.25
median67.5
Q391.25
95-th percentile126.5
Maximum142.5
Range128
Interquartile range (IQR)34

Descriptive statistics

Standard deviation25.779445
Coefficient of variation (CV)0.34524091
Kurtosis0.36434443
Mean74.670886
Median Absolute Deviation (MAD)14.5
Skewness0.52572757
Sum5899
Variance664.57978
MonotonicityNot monotonic
2023-12-13T08:17:24.265841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56.5 2
 
2.5%
82.5 2
 
2.5%
57.5 2
 
2.5%
84.5 2
 
2.5%
73.5 2
 
2.5%
55.0 2
 
2.5%
64.5 2
 
2.5%
54.5 2
 
2.5%
63.0 2
 
2.5%
60.0 2
 
2.5%
Other values (57) 59
74.7%
ValueCountFrequency (%)
14.5 1
1.3%
20.0 1
1.3%
27.2 1
1.3%
43.5 1
1.3%
44.0 1
1.3%
44.5 1
1.3%
48.0 2
2.5%
48.5 1
1.3%
51.5 1
1.3%
53.0 1
1.3%
ValueCountFrequency (%)
142.5 1
1.3%
136.0 1
1.3%
132.5 1
1.3%
131.0 1
1.3%
126.0 1
1.3%
125.5 1
1.3%
110.5 1
1.3%
109.0 1
1.3%
104.5 1
1.3%
101.5 1
1.3%

5월강수량
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.11519
Minimum23
Maximum217
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-13T08:17:24.395853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile28
Q141.5
median120.5
Q3139.5
95-th percentile198
Maximum217
Range194
Interquartile range (IQR)98

Descriptive statistics

Standard deviation58.353659
Coefficient of variation (CV)0.57710082
Kurtosis-1.3559922
Mean101.11519
Median Absolute Deviation (MAD)66.5
Skewness0.13568204
Sum7988.1
Variance3405.1495
MonotonicityNot monotonic
2023-12-13T08:17:24.524426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.5 4
 
5.1%
45.5 3
 
3.8%
45.0 2
 
2.5%
198.0 2
 
2.5%
40.0 2
 
2.5%
54.0 2
 
2.5%
136.0 2
 
2.5%
139.5 2
 
2.5%
41.5 2
 
2.5%
43.0 2
 
2.5%
Other values (54) 56
70.9%
ValueCountFrequency (%)
23.0 1
1.3%
25.5 1
1.3%
26.5 1
1.3%
28.0 2
2.5%
28.5 1
1.3%
29.5 1
1.3%
31.0 1
1.3%
32.0 1
1.3%
33.0 1
1.3%
36.0 1
1.3%
ValueCountFrequency (%)
217.0 1
1.3%
205.0 1
1.3%
201.0 1
1.3%
198.0 2
2.5%
195.0 1
1.3%
191.5 1
1.3%
187.0 1
1.3%
177.0 1
1.3%
172.0 1
1.3%
169.5 1
1.3%

6월강수량
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)87.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.77848
Minimum19
Maximum406.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-13T08:17:24.689333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile32.45
Q157.5
median104.5
Q3170.5
95-th percentile378.5
Maximum406.5
Range387.5
Interquartile range (IQR)113

Descriptive statistics

Standard deviation105.09632
Coefficient of variation (CV)0.77402778
Kurtosis1.0086477
Mean135.77848
Median Absolute Deviation (MAD)50.5
Skewness1.4156322
Sum10726.5
Variance11045.236
MonotonicityNot monotonic
2023-12-13T08:17:24.841902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.5 3
 
3.8%
118.0 2
 
2.5%
116.5 2
 
2.5%
100.0 2
 
2.5%
52.0 2
 
2.5%
93.0 2
 
2.5%
39.5 2
 
2.5%
54.0 2
 
2.5%
356.0 2
 
2.5%
145.5 1
 
1.3%
Other values (59) 59
74.7%
ValueCountFrequency (%)
19.0 1
1.3%
21.5 1
1.3%
24.0 1
1.3%
32.0 1
1.3%
32.5 1
1.3%
33.0 1
1.3%
35.0 1
1.3%
39.5 2
2.5%
41.0 1
1.3%
43.5 1
1.3%
ValueCountFrequency (%)
406.5 1
1.3%
386.5 1
1.3%
384.0 1
1.3%
383.0 1
1.3%
378.0 1
1.3%
367.0 1
1.3%
362.5 1
1.3%
358.5 1
1.3%
356.0 2
2.5%
331.5 1
1.3%

7월강수량
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195.03418
Minimum42.5
Maximum421.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-13T08:17:25.010212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42.5
5-th percentile49.9
Q186.25
median145.5
Q3334.75
95-th percentile402.35
Maximum421.5
Range379
Interquartile range (IQR)248.5

Descriptive statistics

Standard deviation126.68644
Coefficient of variation (CV)0.64956022
Kurtosis-1.2208619
Mean195.03418
Median Absolute Deviation (MAD)66.5
Skewness0.59342748
Sum15407.7
Variance16049.455
MonotonicityNot monotonic
2023-12-13T08:17:25.187478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.5 2
 
2.5%
42.5 2
 
2.5%
142.0 2
 
2.5%
145.5 1
 
1.3%
203.5 1
 
1.3%
208.5 1
 
1.3%
169.0 1
 
1.3%
169.5 1
 
1.3%
138.5 1
 
1.3%
146.0 1
 
1.3%
Other values (66) 66
83.5%
ValueCountFrequency (%)
42.5 2
2.5%
43.5 1
1.3%
49.0 1
1.3%
50.0 1
1.3%
52.5 1
1.3%
54.5 1
1.3%
62.5 1
1.3%
63.0 1
1.3%
66.0 1
1.3%
67.5 1
1.3%
ValueCountFrequency (%)
421.5 1
1.3%
417.0 1
1.3%
411.5 1
1.3%
410.0 1
1.3%
401.5 1
1.3%
398.0 1
1.3%
391.5 1
1.3%
391.0 1
1.3%
386.5 1
1.3%
382.0 1
1.3%

8월강수량
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean206.89241
Minimum13.5
Maximum412.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-13T08:17:25.401230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13.5
5-th percentile36.8
Q1115.5
median215
Q3309.5
95-th percentile361.4
Maximum412.5
Range399
Interquartile range (IQR)194

Descriptive statistics

Standard deviation111.73881
Coefficient of variation (CV)0.54008175
Kurtosis-1.2947188
Mean206.89241
Median Absolute Deviation (MAD)97.5
Skewness0.014505551
Sum16344.5
Variance12485.562
MonotonicityNot monotonic
2023-12-13T08:17:25.558458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
317.0 2
 
2.5%
327.5 2
 
2.5%
309.5 2
 
2.5%
383.0 1
 
1.3%
88.0 1
 
1.3%
346.0 1
 
1.3%
91.0 1
 
1.3%
82.5 1
 
1.3%
405.0 1
 
1.3%
87.0 1
 
1.3%
Other values (66) 66
83.5%
ValueCountFrequency (%)
13.5 1
1.3%
30.5 1
1.3%
34.0 1
1.3%
35.0 1
1.3%
37.0 1
1.3%
43.0 1
1.3%
44.5 1
1.3%
50.5 1
1.3%
67.5 1
1.3%
74.0 1
1.3%
ValueCountFrequency (%)
412.5 1
1.3%
405.0 1
1.3%
383.5 1
1.3%
383.0 1
1.3%
359.0 1
1.3%
357.0 1
1.3%
354.0 1
1.3%
353.0 1
1.3%
349.0 1
1.3%
346.0 1
1.3%

9월강수량
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)87.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.77089
Minimum8
Maximum344.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-13T08:17:25.712490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14.9
Q172
median143
Q3162.75
95-th percentile253.8
Maximum344.5
Range336.5
Interquartile range (IQR)90.75

Descriptive statistics

Standard deviation71.248383
Coefficient of variation (CV)0.54903211
Kurtosis0.15446844
Mean129.77089
Median Absolute Deviation (MAD)38.5
Skewness0.30946796
Sum10251.9
Variance5076.3321
MonotonicityNot monotonic
2023-12-13T08:17:25.884878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161.5 3
 
3.8%
71.0 2
 
2.5%
154.0 2
 
2.5%
27.5 2
 
2.5%
160.0 2
 
2.5%
143.0 2
 
2.5%
161.0 2
 
2.5%
106.5 2
 
2.5%
137.0 2
 
2.5%
185.0 1
 
1.3%
Other values (59) 59
74.7%
ValueCountFrequency (%)
8.0 1
1.3%
9.0 1
1.3%
13.8 1
1.3%
14.0 1
1.3%
15.0 1
1.3%
20.0 1
1.3%
27.5 2
2.5%
28.5 1
1.3%
29.5 1
1.3%
30.0 1
1.3%
ValueCountFrequency (%)
344.5 1
1.3%
271.5 1
1.3%
269.0 1
1.3%
265.5 1
1.3%
252.5 1
1.3%
249.0 1
1.3%
248.5 1
1.3%
247.0 1
1.3%
209.5 1
1.3%
209.0 1
1.3%

10월강수량
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)77.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.918987
Minimum1
Maximum166.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-13T08:17:26.061292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q127.25
median39.5
Q351.75
95-th percentile153.2
Maximum166.5
Range165.5
Interquartile range (IQR)24.5

Descriptive statistics

Standard deviation44.157983
Coefficient of variation (CV)0.85051703
Kurtosis1.6698671
Mean51.918987
Median Absolute Deviation (MAD)12.5
Skewness1.6805087
Sum4101.6
Variance1949.9275
MonotonicityNot monotonic
2023-12-13T08:17:26.199817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.0 3
 
3.8%
37.0 3
 
3.8%
61.5 3
 
3.8%
23.5 2
 
2.5%
150.5 2
 
2.5%
47.0 2
 
2.5%
34.0 2
 
2.5%
31.0 2
 
2.5%
36.0 2
 
2.5%
152.5 2
 
2.5%
Other values (51) 56
70.9%
ValueCountFrequency (%)
1.0 1
1.3%
8.5 1
1.3%
9.0 1
1.3%
10.0 2
2.5%
11.5 1
1.3%
12.5 1
1.3%
13.0 2
2.5%
13.5 1
1.3%
14.5 1
1.3%
15.0 1
1.3%
ValueCountFrequency (%)
166.5 1
1.3%
165.0 2
2.5%
159.5 1
1.3%
152.5 2
2.5%
150.5 2
2.5%
149.5 1
1.3%
147.0 1
1.3%
144.0 1
1.3%
68.5 1
1.3%
62.0 1
1.3%

11월강수량
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)69.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.873418
Minimum0.3
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-13T08:17:26.364040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile4.5
Q131.25
median44.5
Q356.5
95-th percentile81
Maximum92
Range91.7
Interquartile range (IQR)25.25

Descriptive statistics

Standard deviation23.235642
Coefficient of variation (CV)0.52960638
Kurtosis-0.44605608
Mean43.873418
Median Absolute Deviation (MAD)13
Skewness-0.106689
Sum3466
Variance539.89505
MonotonicityNot monotonic
2023-12-13T08:17:26.485590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.0 3
 
3.8%
4.5 3
 
3.8%
39.0 3
 
3.8%
41.0 3
 
3.8%
5.5 3
 
3.8%
47.0 2
 
2.5%
44.5 2
 
2.5%
30.0 2
 
2.5%
73.5 2
 
2.5%
58.0 2
 
2.5%
Other values (45) 54
68.4%
ValueCountFrequency (%)
0.3 1
 
1.3%
4.0 1
 
1.3%
4.5 3
3.8%
5.0 1
 
1.3%
5.5 3
3.8%
6.5 1
 
1.3%
8.0 1
 
1.3%
9.5 1
 
1.3%
10.0 1
 
1.3%
10.7 1
 
1.3%
ValueCountFrequency (%)
92.0 1
1.3%
89.0 2
2.5%
81.0 2
2.5%
79.0 1
1.3%
77.5 1
1.3%
75.5 1
1.3%
73.5 2
2.5%
73.0 2
2.5%
72.5 1
1.3%
71.0 1
1.3%

12월강수량
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.906329
Minimum0.8
Maximum58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-13T08:17:26.622171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile1.95
Q14.75
median13.5
Q330.75
95-th percentile44.05
Maximum58
Range57.2
Interquartile range (IQR)26

Descriptive statistics

Standard deviation14.749353
Coefficient of variation (CV)0.82369497
Kurtosis-0.35424053
Mean17.906329
Median Absolute Deviation (MAD)10
Skewness0.77054671
Sum1414.6
Variance217.54342
MonotonicityNot monotonic
2023-12-13T08:17:26.761169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.0 7
 
8.9%
11.5 4
 
5.1%
12.5 3
 
3.8%
4.0 3
 
3.8%
2.5 3
 
3.8%
6.0 2
 
2.5%
13.5 2
 
2.5%
18.0 2
 
2.5%
19.0 2
 
2.5%
22.0 2
 
2.5%
Other values (42) 49
62.0%
ValueCountFrequency (%)
0.8 1
 
1.3%
1.0 1
 
1.3%
1.5 2
 
2.5%
2.0 7
8.9%
2.5 3
3.8%
3.0 1
 
1.3%
3.5 1
 
1.3%
4.0 3
3.8%
4.5 1
 
1.3%
5.0 1
 
1.3%
ValueCountFrequency (%)
58.0 1
1.3%
52.0 1
1.3%
50.5 1
1.3%
49.0 1
1.3%
43.5 1
1.3%
41.5 1
1.3%
39.0 2
2.5%
38.0 1
1.3%
37.0 1
1.3%
36.0 1
1.3%

Interactions

2023-12-13T08:17:21.195826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:06.495368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:08.123837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:09.197415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:10.175700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:11.131349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:12.519654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:13.736172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:14.920903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:16.114649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:17.311058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:18.787278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:20.149104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:21.281445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:06.606446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:08.209431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:09.274914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:10.256314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:11.209088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:12.613124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:13.834416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:15.013642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:16.216319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:17.409650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:18.873768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:20.233048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:21.364170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:06.710068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:08.299528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:09.355604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:10.340467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:11.288212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:12.717371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:13.924625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:15.095548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:16.317418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:17.741776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:18.988851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:20.334612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:21.437011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:06.807647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:08.368758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:09.428141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:10.416260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:11.354581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:12.808751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:14.003967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:15.171938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:16.393857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:17.818166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:19.107035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:20.412118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:21.510835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:06.895093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:08.443966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:09.522579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:10.486034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:11.425954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:12.900458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:14.081047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:15.244162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:16.465032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:17.899798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:19.201025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:20.492924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:21.591903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:07.325100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:08.521909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:09.591724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:10.554870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:11.502974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:12.980983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:14.162019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:15.332272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:16.549691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:17.977543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:19.329805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:20.569147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:21.673219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:07.452007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:08.606720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:09.665426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:10.635154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:11.585106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:13.068874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:14.260356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:15.458633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:16.660095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:18.070202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:19.428763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:20.650962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:21.787176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:07.551807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:08.702083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:09.746371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:10.706330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:11.667775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:13.180238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:14.343284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:15.575714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:16.769893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:18.174103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:19.540223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:20.731298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:21.872507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:07.657854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:08.787305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:09.815181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:10.776961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:11.752676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:13.279930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:14.451862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:15.674084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:16.854761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:18.262756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:19.653239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:20.809223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:21.964028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:07.772801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:08.872683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:09.891250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:10.854301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:11.848292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:13.398505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:14.545073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:15.769686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:16.939398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:18.355217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:19.775859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:20.894122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:22.040775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:07.870212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:08.952586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:09.964359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:10.924830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:11.938897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:13.481334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:14.639418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:15.853910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:17.029381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:18.477628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:19.882804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:20.984458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:22.128591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:07.954191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:09.034589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:10.038046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:10.998182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:12.031643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:13.564960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:14.728564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:15.949909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:17.113159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:18.585741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:19.964638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:21.061421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:22.203210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:08.030848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:09.110815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:10.099967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:11.063456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:12.104525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:13.649195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:14.806363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:16.030022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:17.202547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:18.689735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:20.057218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:17:21.123690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:17:26.861908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측년도관측지1월강수량2월강수량3월강수량4월강수량5월강수량6월강수량7월강수량8월강수량9월강수량10월강수량11월강수량12월강수량
관측년도1.0000.1080.7590.8530.7030.7260.8690.8520.9150.8530.9490.8630.8560.847
관측지0.1081.0000.0000.0000.4170.5390.2220.0000.2970.0000.0000.0000.0000.000
1월강수량0.7590.0001.0000.6370.3930.0130.6740.6250.5520.6140.6120.4750.6320.583
2월강수량0.8530.0000.6371.0000.7630.5750.7230.7630.6730.6880.7530.7040.5500.621
3월강수량0.7030.4170.3930.7631.0000.5700.0000.5470.4570.6100.6820.4410.5900.406
4월강수량0.7260.5390.0130.5750.5701.0000.5580.5530.4640.6680.2620.2190.4910.563
5월강수량0.8690.2220.6740.7230.0000.5581.0000.7490.6400.8270.6680.5820.7320.737
6월강수량0.8520.0000.6250.7630.5470.5530.7491.0000.5790.6710.5970.6890.6090.602
7월강수량0.9150.2970.5520.6730.4570.4640.6400.5791.0000.6550.8050.6110.6320.619
8월강수량0.8530.0000.6140.6880.6100.6680.8270.6710.6551.0000.7400.7230.8630.850
9월강수량0.9490.0000.6120.7530.6820.2620.6680.5970.8050.7401.0000.7180.6800.685
10월강수량0.8630.0000.4750.7040.4410.2190.5820.6890.6110.7230.7181.0000.7770.718
11월강수량0.8560.0000.6320.5500.5900.4910.7320.6090.6320.8630.6800.7771.0000.759
12월강수량0.8470.0000.5830.6210.4060.5630.7370.6020.6190.8500.6850.7180.7591.000
2023-12-13T08:17:27.319611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측년도1월강수량2월강수량3월강수량4월강수량5월강수량6월강수량7월강수량8월강수량9월강수량10월강수량11월강수량12월강수량관측지
관측년도1.0000.196-0.516-0.098-0.151-0.5430.099-0.599-0.193-0.1350.8650.350-0.0410.000
1월강수량0.1961.0000.1710.360-0.250-0.025-0.188-0.333-0.3600.239-0.085-0.211-0.1020.000
2월강수량-0.5160.1711.000-0.0160.1280.8050.1940.228-0.0290.378-0.547-0.311-0.5690.000
3월강수량-0.0980.360-0.0161.0000.160-0.268-0.690-0.4830.4710.5980.015-0.697-0.1830.206
4월강수량-0.151-0.2500.1280.1601.0000.307-0.1070.2640.3830.407-0.0510.0660.1670.254
5월강수량-0.543-0.0250.805-0.2680.3071.0000.4020.586-0.1430.215-0.5810.011-0.2200.079
6월강수량0.099-0.1880.194-0.690-0.1070.4021.0000.477-0.430-0.3740.0290.480-0.0610.000
7월강수량-0.599-0.3330.228-0.4830.2640.5860.4771.000-0.243-0.292-0.6120.3830.4210.125
8월강수량-0.193-0.360-0.0290.4710.383-0.143-0.430-0.2431.0000.5960.151-0.528-0.2090.000
9월강수량-0.1350.2390.3780.5980.4070.215-0.374-0.2920.5961.000-0.008-0.651-0.3690.000
10월강수량0.865-0.085-0.5470.015-0.051-0.5810.029-0.6120.151-0.0081.0000.148-0.1080.000
11월강수량0.350-0.211-0.311-0.6970.0660.0110.4800.383-0.528-0.6510.1481.0000.4710.000
12월강수량-0.041-0.102-0.569-0.1830.167-0.220-0.0610.421-0.209-0.369-0.1080.4711.0000.000
관측지0.0000.0000.0000.2060.2540.0790.0000.1250.0000.0000.0000.0000.0001.000

Missing values

2023-12-13T08:17:22.321633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:17:22.479208image/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

관측년도관측지1월강수량2월강수량3월강수량4월강수량5월강수량6월강수량7월강수량8월강수량9월강수량10월강수량11월강수량12월강수량
02007영주시4.940.6146.014.5133.5118.0341.513.513.81.00.30.8
12008영주시33.14.365.027.262.1168.0312.2257.580.630.110.712.3
22009풍기읍5.522.037.573.0117.097.0410.0145.071.013.045.039.0
32009이산면4.025.039.053.5131.5122.5377.0149.576.514.548.534.0
42009평은면3.516.532.551.5139.0105.0325.5129.579.59.051.037.0
52009문수면3.019.531.048.0123.5120.5358.0143.070.511.550.033.5
62009장수면4.516.529.043.5120.593.0376.0133.067.512.547.529.5
72009안정면3.516.528.058.0114.0100.5382.0139.073.015.041.034.0
82009봉현면3.014.528.060.096.093.5353.0134.564.010.041.036.0
92009순흥면4.016.034.574.0126.593.0398.0144.065.513.040.539.0
관측년도관측지1월강수량2월강수량3월강수량4월강수량5월강수량6월강수량7월강수량8월강수량9월강수량10월강수량11월강수량12월강수량
692015풍기읍8.04.035.066.054.0100.0127.543.030.061.589.016.5
702015이산면14.01.536.554.528.590.567.574.028.549.070.018.0
712015평은면18.51.034.057.531.0129.580.599.014.040.073.022.0
722015문수면18.51.034.055.026.5115.073.574.59.044.073.019.5
732015장수면16.51.533.553.028.087.581.035.015.053.081.024.0
742015안정면8.00.532.566.545.5100.582.534.020.061.575.528.5
752015봉현면7.51.032.563.545.095.5128.544.527.560.092.019.0
762015순흥면12.02.036.566.023.0116.5128.050.529.562.089.021.5
772015단산면10.51.034.060.040.0111.5119.030.527.561.577.520.0
782015부석면14.01.035.063.041.583.0142.037.032.068.581.022.0