Overview

Dataset statistics

Number of variables14
Number of observations179
Missing cells31
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.0 KiB
Average record size in memory125.7 B

Variable types

Text1
Numeric13

Dataset

Description전북특별자치도 14개 시군(전주, 군산, 익산, 정읍, 남원, 김제, 완주, 진안, 무주, 장수, 임실, 순창, 고창, 부안)의 강수량 정보입니다.
Author전북특별자치도
URLhttps://www.data.go.kr/data/15042173/fileData.do

Alerts

1월 강수량 is highly overall correlated with 합계High correlation
2월 강수량 is highly overall correlated with 3월 강수량 and 1 other fieldsHigh correlation
3월 강수량 is highly overall correlated with 2월 강수량 and 4 other fieldsHigh correlation
5월 강수량 is highly overall correlated with 6월 강수량 and 4 other fieldsHigh correlation
6월 강수량 is highly overall correlated with 2월 강수량 and 6 other fieldsHigh correlation
7월 강수량 is highly overall correlated with 3월 강수량 and 5 other fieldsHigh correlation
10월 강수량 is highly overall correlated with 3월 강수량 and 6 other fieldsHigh correlation
11월 강수량 is highly overall correlated with 10월 강수량High correlation
12월 강수량 is highly overall correlated with 5월 강수량 and 4 other fieldsHigh correlation
합계 is highly overall correlated with 1월 강수량 and 6 other fieldsHigh correlation
1월 강수량 has 2 (1.1%) missing valuesMissing
2월 강수량 has 6 (3.4%) missing valuesMissing
3월 강수량 has 3 (1.7%) missing valuesMissing
4월 강수량 has 2 (1.1%) missing valuesMissing
5월 강수량 has 2 (1.1%) missing valuesMissing
6월 강수량 has 2 (1.1%) missing valuesMissing
7월 강수량 has 2 (1.1%) missing valuesMissing
8월 강수량 has 2 (1.1%) missing valuesMissing
9월 강수량 has 2 (1.1%) missing valuesMissing
10월 강수량 has 3 (1.7%) missing valuesMissing
11월 강수량 has 3 (1.7%) missing valuesMissing
12월 강수량 has 2 (1.1%) missing valuesMissing
관측소 has unique valuesUnique
1월 강수량 has 4 (2.2%) zerosZeros
2월 강수량 has 3 (1.7%) zerosZeros
3월 강수량 has 2 (1.1%) zerosZeros
6월 강수량 has 2 (1.1%) zerosZeros

Reproduction

Analysis started2024-03-14 13:16:08.374188
Analysis finished2024-03-14 13:16:50.816943
Duration42.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관측소
Text

UNIQUE 

Distinct179
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-03-14T22:16:51.985016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length6.0446927
Min length5

Characters and Unicode

Total characters1082
Distinct characters149
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique179 ?
Unique (%)100.0%

Sample

1st row전주 대성동
2nd row전주 송천2동
3rd row전주 원당동
4th row전주 장동
5th row전주 진북동
ValueCountFrequency (%)
익산 19
 
5.3%
남원 17
 
4.7%
김제 16
 
4.5%
고창 16
 
4.5%
정읍 15
 
4.2%
완주 14
 
3.9%
군산 14
 
3.9%
임실 12
 
3.4%
진안 11
 
3.1%
부안 10
 
2.8%
Other values (181) 214
59.8%
2024-03-14T22:16:53.860065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
180
 
16.6%
137
 
12.7%
59
 
5.5%
33
 
3.0%
32
 
3.0%
28
 
2.6%
28
 
2.6%
28
 
2.6%
22
 
2.0%
20
 
1.8%
Other values (139) 515
47.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 898
83.0%
Space Separator 180
 
16.6%
Decimal Number 4
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
137
 
15.3%
59
 
6.6%
33
 
3.7%
32
 
3.6%
28
 
3.1%
28
 
3.1%
28
 
3.1%
22
 
2.4%
20
 
2.2%
20
 
2.2%
Other values (136) 491
54.7%
Decimal Number
ValueCountFrequency (%)
2 3
75.0%
1 1
 
25.0%
Space Separator
ValueCountFrequency (%)
180
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 898
83.0%
Common 184
 
17.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
137
 
15.3%
59
 
6.6%
33
 
3.7%
32
 
3.6%
28
 
3.1%
28
 
3.1%
28
 
3.1%
22
 
2.4%
20
 
2.2%
20
 
2.2%
Other values (136) 491
54.7%
Common
ValueCountFrequency (%)
180
97.8%
2 3
 
1.6%
1 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 898
83.0%
ASCII 184
 
17.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
180
97.8%
2 3
 
1.6%
1 1
 
0.5%
Hangul
ValueCountFrequency (%)
137
 
15.3%
59
 
6.6%
33
 
3.7%
32
 
3.6%
28
 
3.1%
28
 
3.1%
28
 
3.1%
22
 
2.4%
20
 
2.2%
20
 
2.2%
Other values (136) 491
54.7%

1월 강수량
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct36
Distinct (%)20.3%
Missing2
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean8.0920904
Minimum0
Maximum21
Zeros4
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-03-14T22:16:54.263274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median7
Q311
95-th percentile15.1
Maximum21
Range21
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.2284809
Coefficient of variation (CV)0.52254494
Kurtosis-0.045642295
Mean8.0920904
Median Absolute Deviation (MAD)3
Skewness0.53069673
Sum1432.3
Variance17.880051
MonotonicityNot monotonic
2024-03-14T22:16:54.656982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
5.0 20
 
11.2%
6.0 17
 
9.5%
7.0 14
 
7.8%
8.0 14
 
7.8%
10.0 13
 
7.3%
4.0 11
 
6.1%
12.0 10
 
5.6%
11.0 8
 
4.5%
9.0 6
 
3.4%
2.0 6
 
3.4%
Other values (26) 58
32.4%
ValueCountFrequency (%)
0.0 4
 
2.2%
1.0 2
 
1.1%
1.3 1
 
0.6%
2.0 6
 
3.4%
2.5 1
 
0.6%
3.0 3
 
1.7%
3.5 1
 
0.6%
4.0 11
6.1%
4.5 3
 
1.7%
5.0 20
11.2%
ValueCountFrequency (%)
21.0 1
 
0.6%
19.5 1
 
0.6%
19.0 1
 
0.6%
18.0 1
 
0.6%
17.0 2
1.1%
16.5 1
 
0.6%
16.0 1
 
0.6%
15.5 1
 
0.6%
15.0 4
2.2%
14.5 4
2.2%

2월 강수량
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct62
Distinct (%)35.8%
Missing6
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean37.120231
Minimum0
Maximum65
Zeros3
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-03-14T22:16:55.039927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21.3
Q132
median36
Q343.5
95-th percentile54.2
Maximum65
Range65
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation10.605406
Coefficient of variation (CV)0.28570421
Kurtosis2.3209399
Mean37.120231
Median Absolute Deviation (MAD)5.5
Skewness-0.48971801
Sum6421.8
Variance112.47465
MonotonicityNot monotonic
2024-03-14T22:16:55.475435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.0 15
 
8.4%
32.0 9
 
5.0%
45.0 9
 
5.0%
36.0 8
 
4.5%
34.0 7
 
3.9%
39.0 7
 
3.9%
46.0 6
 
3.4%
33.0 6
 
3.4%
37.0 6
 
3.4%
43.0 6
 
3.4%
Other values (52) 94
52.5%
(Missing) 6
 
3.4%
ValueCountFrequency (%)
0.0 3
1.7%
2.8 1
 
0.6%
15.5 1
 
0.6%
17.5 1
 
0.6%
18.0 1
 
0.6%
19.5 1
 
0.6%
21.0 1
 
0.6%
21.5 1
 
0.6%
22.0 1
 
0.6%
22.5 1
 
0.6%
ValueCountFrequency (%)
65.0 1
 
0.6%
63.0 1
 
0.6%
62.0 1
 
0.6%
61.0 1
 
0.6%
59.5 1
 
0.6%
57.0 1
 
0.6%
55.0 2
1.1%
54.5 1
 
0.6%
54.0 2
1.1%
53.0 3
1.7%

3월 강수량
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct64
Distinct (%)36.4%
Missing3
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean28.809091
Minimum0
Maximum66
Zeros2
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-03-14T22:16:55.889712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.75
Q124
median27.75
Q333
95-th percentile48.625
Maximum66
Range66
Interquartile range (IQR)9

Descriptive statistics

Standard deviation10.704244
Coefficient of variation (CV)0.37155784
Kurtosis1.8884028
Mean28.809091
Median Absolute Deviation (MAD)4.25
Skewness0.42415449
Sum5070.4
Variance114.58083
MonotonicityNot monotonic
2024-03-14T22:16:56.315957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.0 12
 
6.7%
28.0 11
 
6.1%
24.0 10
 
5.6%
31.0 10
 
5.6%
25.0 9
 
5.0%
30.0 9
 
5.0%
27.0 6
 
3.4%
29.0 5
 
2.8%
34.0 5
 
2.8%
35.0 5
 
2.8%
Other values (54) 94
52.5%
ValueCountFrequency (%)
0.0 2
1.1%
1.0 1
0.6%
2.4 1
0.6%
3.0 1
0.6%
4.5 1
0.6%
5.5 1
0.6%
8.0 1
0.6%
12.5 1
0.6%
15.5 1
0.6%
16.0 1
0.6%
ValueCountFrequency (%)
66.0 1
0.6%
60.5 1
0.6%
59.0 1
0.6%
56.0 2
1.1%
54.0 1
0.6%
52.5 1
0.6%
50.0 1
0.6%
49.0 1
0.6%
48.5 1
0.6%
48.0 2
1.1%

4월 강수량
Real number (ℝ)

MISSING 

Distinct79
Distinct (%)44.6%
Missing2
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean87.459322
Minimum0
Maximum147.5
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-03-14T22:16:56.722290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile55.8
Q179
median89
Q397
95-th percentile119.6
Maximum147.5
Range147.5
Interquartile range (IQR)18

Descriptive statistics

Standard deviation20.491232
Coefficient of variation (CV)0.23429444
Kurtosis4.6931365
Mean87.459322
Median Absolute Deviation (MAD)8.5
Skewness-1.0916856
Sum15480.3
Variance419.89061
MonotonicityNot monotonic
2024-03-14T22:16:57.173073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.0 7
 
3.9%
89.0 7
 
3.9%
84.0 7
 
3.9%
94.0 6
 
3.4%
92.0 6
 
3.4%
97.0 6
 
3.4%
79.0 5
 
2.8%
93.0 5
 
2.8%
87.0 5
 
2.8%
85.0 5
 
2.8%
Other values (69) 118
65.9%
ValueCountFrequency (%)
0.0 1
0.6%
4.8 1
0.6%
6.0 1
0.6%
27.0 1
0.6%
35.0 1
0.6%
36.0 1
0.6%
39.5 1
0.6%
52.5 1
0.6%
55.0 1
0.6%
56.0 1
0.6%
ValueCountFrequency (%)
147.5 1
0.6%
136.0 1
0.6%
132.0 1
0.6%
130.0 1
0.6%
127.5 1
0.6%
126.5 1
0.6%
126.0 2
1.1%
120.0 1
0.6%
119.5 2
1.1%
118.5 2
1.1%

5월 강수량
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct79
Distinct (%)44.6%
Missing2
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean53.507345
Minimum0
Maximum93
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-03-14T22:16:57.601115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34.7
Q143
median53.5
Q363.5
95-th percentile80
Maximum93
Range93
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation15.627296
Coefficient of variation (CV)0.2920589
Kurtosis1.0996069
Mean53.507345
Median Absolute Deviation (MAD)10.5
Skewness-0.17270832
Sum9470.8
Variance244.21239
MonotonicityNot monotonic
2024-03-14T22:16:57.892388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.0 9
 
5.0%
44.0 7
 
3.9%
42.0 7
 
3.9%
41.0 6
 
3.4%
50.0 6
 
3.4%
57.0 5
 
2.8%
51.0 4
 
2.2%
64.0 4
 
2.2%
45.0 4
 
2.2%
40.0 4
 
2.2%
Other values (69) 121
67.6%
ValueCountFrequency (%)
0.0 1
 
0.6%
2.0 1
 
0.6%
3.5 1
 
0.6%
20.0 1
 
0.6%
23.5 1
 
0.6%
26.0 1
 
0.6%
29.5 1
 
0.6%
31.0 1
 
0.6%
33.5 1
 
0.6%
35.0 3
1.7%
ValueCountFrequency (%)
93.0 1
 
0.6%
92.0 1
 
0.6%
89.0 1
 
0.6%
86.0 1
 
0.6%
85.5 1
 
0.6%
82.0 1
 
0.6%
81.5 1
 
0.6%
81.0 1
 
0.6%
80.0 3
1.7%
79.5 1
 
0.6%

6월 강수량
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct107
Distinct (%)60.5%
Missing2
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean97.330508
Minimum0
Maximum822
Zeros2
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-03-14T22:16:58.248012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36.8
Q173.5
median83
Q3117
95-th percentile175
Maximum822
Range822
Interquartile range (IQR)43.5

Descriptive statistics

Standard deviation67.175225
Coefficient of variation (CV)0.69017645
Kurtosis76.995248
Mean97.330508
Median Absolute Deviation (MAD)13.5
Skewness7.2612386
Sum17227.5
Variance4512.5109
MonotonicityNot monotonic
2024-03-14T22:16:58.674202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.0 7
 
3.9%
76.0 6
 
3.4%
79.0 6
 
3.4%
75.0 6
 
3.4%
71.0 5
 
2.8%
70.0 5
 
2.8%
85.0 3
 
1.7%
64.0 3
 
1.7%
139.0 3
 
1.7%
80.0 3
 
1.7%
Other values (97) 130
72.6%
ValueCountFrequency (%)
0.0 2
1.1%
1.0 1
0.6%
3.0 1
0.6%
10.5 1
0.6%
11.5 1
0.6%
22.5 1
0.6%
23.0 1
0.6%
34.0 1
0.6%
37.5 1
0.6%
38.0 1
0.6%
ValueCountFrequency (%)
822.0 1
0.6%
212.0 1
0.6%
199.0 1
0.6%
198.0 1
0.6%
192.0 1
0.6%
189.0 1
0.6%
188.0 1
0.6%
181.0 1
0.6%
179.0 1
0.6%
174.0 1
0.6%

7월 강수량
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct133
Distinct (%)75.1%
Missing2
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean221.14407
Minimum33.5
Maximum470
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-03-14T22:16:59.082637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.5
5-th percentile145.5
Q1171
median204
Q3253
95-th percentile365
Maximum470
Range436.5
Interquartile range (IQR)82

Descriptive statistics

Standard deviation71.571963
Coefficient of variation (CV)0.32364406
Kurtosis1.4617183
Mean221.14407
Median Absolute Deviation (MAD)39
Skewness0.9627113
Sum39142.5
Variance5122.5459
MonotonicityNot monotonic
2024-03-14T22:16:59.436360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
170.0 5
 
2.8%
243.0 4
 
2.2%
166.0 3
 
1.7%
157.0 3
 
1.7%
183.0 3
 
1.7%
156.0 3
 
1.7%
255.0 3
 
1.7%
198.0 3
 
1.7%
182.0 3
 
1.7%
208.0 2
 
1.1%
Other values (123) 145
81.0%
ValueCountFrequency (%)
33.5 1
0.6%
38.0 1
0.6%
86.0 1
0.6%
120.0 1
0.6%
125.0 1
0.6%
138.5 1
0.6%
141.5 1
0.6%
142.0 1
0.6%
143.5 1
0.6%
146.0 1
0.6%
ValueCountFrequency (%)
470.0 1
0.6%
450.5 1
0.6%
425.0 1
0.6%
422.0 1
0.6%
411.0 1
0.6%
388.0 1
0.6%
387.5 1
0.6%
384.0 1
0.6%
377.0 1
0.6%
362.0 1
0.6%

8월 강수량
Real number (ℝ)

MISSING 

Distinct120
Distinct (%)67.8%
Missing2
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean135.73729
Minimum6
Maximum256.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-03-14T22:16:59.681217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile84.6
Q1105
median130
Q3157
95-th percentile230.1
Maximum256.5
Range250.5
Interquartile range (IQR)52

Descriptive statistics

Standard deviation44.112655
Coefficient of variation (CV)0.32498553
Kurtosis0.63120823
Mean135.73729
Median Absolute Deviation (MAD)26
Skewness0.57340068
Sum24025.5
Variance1945.9263
MonotonicityNot monotonic
2024-03-14T22:16:59.941139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112.0 5
 
2.8%
100.0 4
 
2.2%
136.0 4
 
2.2%
95.0 4
 
2.2%
144.0 3
 
1.7%
130.0 3
 
1.7%
127.0 3
 
1.7%
131.0 3
 
1.7%
123.0 3
 
1.7%
90.0 3
 
1.7%
Other values (110) 142
79.3%
ValueCountFrequency (%)
6.0 1
0.6%
15.5 1
0.6%
43.0 1
0.6%
65.5 1
0.6%
72.0 1
0.6%
73.0 1
0.6%
76.0 1
0.6%
80.0 1
0.6%
83.0 1
0.6%
85.0 1
0.6%
ValueCountFrequency (%)
256.5 1
0.6%
248.0 1
0.6%
245.0 1
0.6%
238.0 1
0.6%
236.0 2
1.1%
234.5 1
0.6%
233.5 1
0.6%
230.5 1
0.6%
230.0 1
0.6%
222.0 1
0.6%

9월 강수량
Real number (ℝ)

MISSING 

Distinct127
Distinct (%)71.8%
Missing2
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean211.00565
Minimum25.5
Maximum384
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-03-14T22:17:00.215523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25.5
5-th percentile133.8
Q1179
median217.5
Q3235.5
95-th percentile290.7
Maximum384
Range358.5
Interquartile range (IQR)56.5

Descriptive statistics

Standard deviation48.596056
Coefficient of variation (CV)0.2303069
Kurtosis1.9236265
Mean211.00565
Median Absolute Deviation (MAD)30
Skewness-0.031290093
Sum37348
Variance2361.5767
MonotonicityNot monotonic
2024-03-14T22:17:00.623339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
220.0 4
 
2.2%
249.0 4
 
2.2%
237.0 3
 
1.7%
203.0 3
 
1.7%
207.0 3
 
1.7%
228.0 3
 
1.7%
226.0 3
 
1.7%
182.0 3
 
1.7%
223.0 3
 
1.7%
235.0 3
 
1.7%
Other values (117) 145
81.0%
ValueCountFrequency (%)
25.5 1
0.6%
75.0 1
0.6%
85.0 1
0.6%
121.0 2
1.1%
127.0 1
0.6%
130.0 1
0.6%
132.0 1
0.6%
133.0 1
0.6%
134.0 1
0.6%
138.0 1
0.6%
ValueCountFrequency (%)
384.0 1
0.6%
355.0 1
0.6%
349.0 1
0.6%
316.0 1
0.6%
308.0 1
0.6%
307.0 1
0.6%
301.0 1
0.6%
294.0 1
0.6%
293.5 1
0.6%
290.0 1
0.6%

10월 강수량
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct110
Distinct (%)62.5%
Missing3
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean108.53977
Minimum0
Maximum308
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-03-14T22:17:01.265441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile66
Q180.875
median97.75
Q3125
95-th percentile194.5
Maximum308
Range308
Interquartile range (IQR)44.125

Descriptive statistics

Standard deviation41.554798
Coefficient of variation (CV)0.38285319
Kurtosis3.0455703
Mean108.53977
Median Absolute Deviation (MAD)20
Skewness1.369638
Sum19103
Variance1726.8013
MonotonicityNot monotonic
2024-03-14T22:17:01.594332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85.0 5
 
2.8%
73.0 5
 
2.8%
80.0 4
 
2.2%
68.0 4
 
2.2%
82.0 4
 
2.2%
133.0 4
 
2.2%
109.5 3
 
1.7%
115.0 3
 
1.7%
120.0 3
 
1.7%
100.5 3
 
1.7%
Other values (100) 138
77.1%
ValueCountFrequency (%)
0.0 1
 
0.6%
44.0 1
 
0.6%
55.0 1
 
0.6%
61.0 1
 
0.6%
63.0 2
1.1%
64.0 2
1.1%
66.0 2
1.1%
67.0 1
 
0.6%
67.5 1
 
0.6%
68.0 4
2.2%
ValueCountFrequency (%)
308.0 1
0.6%
242.0 1
0.6%
218.0 1
0.6%
208.0 1
0.6%
205.0 1
0.6%
201.0 1
0.6%
200.0 1
0.6%
198.0 1
0.6%
196.0 1
0.6%
194.0 1
0.6%

11월 강수량
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct85
Distinct (%)48.3%
Missing3
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean50.704545
Minimum11
Maximum167
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-03-14T22:17:01.996281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile26
Q137.375
median48
Q357.5
95-th percentile93.75
Maximum167
Range156
Interquartile range (IQR)20.125

Descriptive statistics

Standard deviation21.104859
Coefficient of variation (CV)0.41623209
Kurtosis5.3953877
Mean50.704545
Median Absolute Deviation (MAD)10.25
Skewness1.6959704
Sum8924
Variance445.41506
MonotonicityNot monotonic
2024-03-14T22:17:02.427662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.0 9
 
5.0%
41.0 7
 
3.9%
62.0 6
 
3.4%
28.0 6
 
3.4%
29.0 5
 
2.8%
57.0 5
 
2.8%
43.0 5
 
2.8%
44.5 4
 
2.2%
50.0 4
 
2.2%
52.0 4
 
2.2%
Other values (75) 121
67.6%
ValueCountFrequency (%)
11.0 1
 
0.6%
17.0 1
 
0.6%
19.0 1
 
0.6%
20.0 2
 
1.1%
23.0 2
 
1.1%
26.0 3
1.7%
27.5 1
 
0.6%
28.0 6
3.4%
28.5 1
 
0.6%
29.0 5
2.8%
ValueCountFrequency (%)
167.0 1
 
0.6%
112.0 1
 
0.6%
109.0 3
1.7%
102.0 1
 
0.6%
101.0 2
1.1%
96.0 1
 
0.6%
93.0 1
 
0.6%
87.0 1
 
0.6%
86.0 1
 
0.6%
84.5 1
 
0.6%

12월 강수량
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct39
Distinct (%)22.0%
Missing2
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean18.435028
Minimum0
Maximum54
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-03-14T22:17:02.847280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q115
median18
Q322
95-th percentile27.2
Maximum54
Range54
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.9482678
Coefficient of variation (CV)0.37690573
Kurtosis7.4325402
Mean18.435028
Median Absolute Deviation (MAD)3.5
Skewness1.348783
Sum3263
Variance48.278425
MonotonicityNot monotonic
2024-03-14T22:17:03.277870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
15.0 18
 
10.1%
14.0 16
 
8.9%
18.0 12
 
6.7%
16.0 10
 
5.6%
13.0 9
 
5.0%
19.0 9
 
5.0%
21.0 8
 
4.5%
23.0 8
 
4.5%
22.0 7
 
3.9%
20.5 6
 
3.4%
Other values (29) 74
41.3%
ValueCountFrequency (%)
0.0 1
 
0.6%
1.5 1
 
0.6%
3.0 1
 
0.6%
4.0 4
 
2.2%
5.0 3
 
1.7%
11.5 1
 
0.6%
12.0 2
 
1.1%
13.0 9
5.0%
13.5 3
 
1.7%
14.0 16
8.9%
ValueCountFrequency (%)
54.0 2
 
1.1%
41.0 1
 
0.6%
31.0 1
 
0.6%
30.0 2
 
1.1%
28.0 3
1.7%
27.0 5
2.8%
26.0 4
2.2%
25.5 1
 
0.6%
25.0 4
2.2%
24.5 1
 
0.6%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct158
Distinct (%)88.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1044.1849
Minimum3
Maximum1871
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-03-14T22:17:03.678534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile775.9
Q1938.5
median1030
Q31174
95-th percentile1387
Maximum1871
Range1868
Interquartile range (IQR)235.5

Descriptive statistics

Standard deviation224.307
Coefficient of variation (CV)0.2148154
Kurtosis5.3091439
Mean1044.1849
Median Absolute Deviation (MAD)113
Skewness-0.55454238
Sum186909.1
Variance50313.63
MonotonicityNot monotonic
2024-03-14T22:17:04.119829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
980.0 3
 
1.7%
945.0 3
 
1.7%
1022.0 2
 
1.1%
1030.0 2
 
1.1%
1064.5 2
 
1.1%
1184.0 2
 
1.1%
1218.0 2
 
1.1%
1020.0 2
 
1.1%
955.0 2
 
1.1%
1331.0 2
 
1.1%
Other values (148) 157
87.7%
ValueCountFrequency (%)
3.0 1
0.6%
4.0 1
0.6%
536.5 1
0.6%
542.5 1
0.6%
583.5 1
0.6%
692.0 1
0.6%
722.0 1
0.6%
724.5 1
0.6%
766.0 1
0.6%
777.0 1
0.6%
ValueCountFrequency (%)
1871.0 1
0.6%
1649.0 1
0.6%
1605.0 1
0.6%
1596.0 1
0.6%
1478.0 1
0.6%
1477.0 1
0.6%
1437.5 1
0.6%
1397.0 1
0.6%
1387.0 2
1.1%
1377.0 1
0.6%

Interactions

2024-03-14T22:16:45.918054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:09.061820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:12.198652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:15.076609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:17.800903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:20.923818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:23.577967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:26.891766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:29.637640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:32.812397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:36.089481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:39.508459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:42.753977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:46.155364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:09.292931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:12.439593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:15.213692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:18.041237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:21.058158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:23.810074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:27.123701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:29.874782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:33.055381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:36.328137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:39.752161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:42.989392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:46.410432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:09.535548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:12.696121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:15.363533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:18.295804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:21.202527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:24.055649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:27.365059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:30.125618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:33.310186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:36.582065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:40.011008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:43.235718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:46.666114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:09.780615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:12.847157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:15.517074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:18.548487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:21.350608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:24.304872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:27.612860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:30.371308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:33.570021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:36.834174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:40.268547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:43.480456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:46.927560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:10.031184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:13.218468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:15.672655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:18.809465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:21.501149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:24.553590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:27.864480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:30.625561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:33.830624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:37.095849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:40.530886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:43.733654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:47.166469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:10.264540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:13.354678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:15.812531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:19.049847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:21.639236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:24.782714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:28.093271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:30.857673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:34.073009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:37.332045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:40.771961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:43.965828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:47.406224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:10.496126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:13.520611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:16.048253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:19.291917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:21.868333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:25.198036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:28.276227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:31.092468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:34.312169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:37.567231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:41.011411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:44.200646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:47.642170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:10.720916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:13.754969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:16.281550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:19.530075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:22.097392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:25.424240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:28.396393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:31.322284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:34.547907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:37.803649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:41.247670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:44.430070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:47.891418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:10.958968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:14.000112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:16.524729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:19.781372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:22.335450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:25.661359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:28.531607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:31.561860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:34.797933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:38.044914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:41.483805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:44.668391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:48.149031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:11.211689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:14.258588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:16.785563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:20.044986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:22.587011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:25.911070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:28.678084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:31.814621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:35.062368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:38.494725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:41.734182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:44.923088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:48.400697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:11.461649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:14.513470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:17.038387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:20.301981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:22.837819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:26.155202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:28.910488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:32.061438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:35.314829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:38.742747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:41.989160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:45.168282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:48.658292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:11.713433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:14.776000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:17.300509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:20.564108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:23.093219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:26.404820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:29.158679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:32.316744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:35.577852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:39.003106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:42.249284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:45.426830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:48.909600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:11.953971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:14.922190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:17.544249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:20.767382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:23.333099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:26.648274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:29.396058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:32.560836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:35.827004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:39.248652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:42.496311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:16:45.664116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T22:17:04.408492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1월 강수량2월 강수량3월 강수량4월 강수량5월 강수량6월 강수량7월 강수량8월 강수량9월 강수량10월 강수량11월 강수량12월 강수량합계
1월 강수량1.0000.4050.5250.5700.4390.3820.5120.5680.5510.3080.4320.4020.393
2월 강수량0.4051.0000.6730.6510.7420.3690.4240.3090.4240.6090.3200.5730.588
3월 강수량0.5250.6731.0000.6510.6140.3980.6830.3100.5040.5680.3810.5260.594
4월 강수량0.5700.6510.6511.0000.6130.0000.1790.3460.3570.1210.0000.1800.309
5월 강수량0.4390.7420.6140.6131.0000.4210.5120.3250.0020.7310.3160.5510.607
6월 강수량0.3820.3690.3980.0000.4211.0000.5270.4160.1600.5900.5950.5150.796
7월 강수량0.5120.4240.6830.1790.5120.5271.0000.5750.6640.6040.0780.5400.707
8월 강수량0.5680.3090.3100.3460.3250.4160.5751.0000.6800.2560.1310.0980.507
9월 강수량0.5510.4240.5040.3570.0020.1600.6640.6801.0000.6570.2160.6180.654
10월 강수량0.3080.6090.5680.1210.7310.5900.6040.2560.6571.0000.3960.7030.758
11월 강수량0.4320.3200.3810.0000.3160.5950.0780.1310.2160.3961.0000.6950.342
12월 강수량0.4020.5730.5260.1800.5510.5150.5400.0980.6180.7030.6951.0000.705
합계0.3930.5880.5940.3090.6070.7960.7070.5070.6540.7580.3420.7051.000
2024-03-14T22:17:04.767390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1월 강수량2월 강수량3월 강수량4월 강수량5월 강수량6월 강수량7월 강수량8월 강수량9월 강수량10월 강수량11월 강수량12월 강수량합계
1월 강수량1.0000.2330.2970.2030.4840.4780.4560.145-0.0440.379-0.3890.4870.505
2월 강수량0.2331.0000.5740.2020.2790.5160.441-0.132-0.2260.287-0.2140.4030.376
3월 강수량0.2970.5741.0000.1490.4440.5460.641-0.033-0.1770.509-0.1800.4340.541
4월 강수량0.2030.2020.1491.0000.0880.1900.103-0.0020.395-0.1050.095-0.0080.257
5월 강수량0.4840.2790.4440.0881.0000.6260.5950.4190.0680.712-0.4100.6600.811
6월 강수량0.4780.5160.5460.1900.6261.0000.631-0.001-0.1590.553-0.4180.5730.638
7월 강수량0.4560.4410.6410.1030.5950.6311.0000.151-0.1000.676-0.2890.6210.753
8월 강수량0.145-0.132-0.033-0.0020.419-0.0010.1511.0000.3300.319-0.0870.2740.460
9월 강수량-0.044-0.226-0.1770.3950.068-0.159-0.1000.3301.000-0.1100.236-0.0800.261
10월 강수량0.3790.2870.509-0.1050.7120.5530.6760.319-0.1101.000-0.5060.6860.715
11월 강수량-0.389-0.214-0.1800.095-0.410-0.418-0.289-0.0870.236-0.5061.000-0.292-0.271
12월 강수량0.4870.4030.434-0.0080.6600.5730.6210.274-0.0800.686-0.2921.0000.703
합계0.5050.3760.5410.2570.8110.6380.7530.4600.2610.715-0.2710.7031.000

Missing values

2024-03-14T22:16:49.288337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T22:16:49.862742image/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.
2024-03-14T22:16:50.506896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

관측소1월 강수량2월 강수량3월 강수량4월 강수량5월 강수량6월 강수량7월 강수량8월 강수량9월 강수량10월 강수량11월 강수량12월 강수량합계
0전주 대성동17.037.525.077.045.571.0120.06.025.585.028.05.0542.5
1전주 송천2동7.040.018.577.529.572.538.015.5152.576.042.014.5583.5
2전주 원당동5.5<NA>17.083.039.538.0153.5121.0157.080.028.51.5724.5
3전주 장동10.041.028.080.536.066.0125.0103.0198.563.039.513.5804.0
4전주 진북동11.038.024.074.531.022.5156.076.0140.070.556.022.5722.0
5전주 호성동6.543.521.076.535.579.0157.593.5132.068.053.020.5786.5
6전주 효자1동8.035.026.076.035.577.5141.594.0145.067.557.526.0789.5
7군산 나운2동<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>4.04.0
8군산 나포면3.035.031.089.040.081.0229.0100.0182.081.093.014.0978.0
9군산 대야면2.036.029.095.045.081.0204.0102.0227.082.062.015.0980.0
관측소1월 강수량2월 강수량3월 강수량4월 강수량5월 강수량6월 강수량7월 강수량8월 강수량9월 강수량10월 강수량11월 강수량12월 강수량합계
169부안 변산면9.022.523.577.066.576.0156.0132.5223.0110.549.515.0961.0
170부안 진서면8.530.019.079.056.079.0189.5200.5227.0105.048.516.01058.0
171부안 계화면6.523.55.575.555.585.0175.5122.0198.083.047.516.0893.5
172부안 상서면11.028.534.581.066.596.5189.5163.0222.0101.071.518.51083.5
173부안 주산면11.030.527.578.563.597.0235.0167.0235.593.559.019.51117.5
174부안 백산면9.029.029.082.057.587.0260.0154.0263.586.549.516.01123.0
175부안 부안읍5.531.026.085.059.090.5241.0118.0250.584.557.016.51064.5
176부안 동진면10.029.524.584.058.588.5226.5130.0235.090.054.518.51049.5
177부안 하서면3.519.526.076.562.088.0152.5146.0212.597.553.016.0953.0
178부안 보안면11.035.021.076.055.087.0255.0182.0217.590.543.023.51096.5