Overview

Dataset statistics

Number of variables16
Number of observations60
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.4 KiB
Average record size in memory144.2 B

Variable types

Categorical3
Numeric13

Dataset

Description경기도 포천시 자동기상현황관측시스템에서 제공하는 강우현황 입니다.
Author경기도 포천시
URLhttps://www.data.go.kr/data/15061913/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
1월 is highly overall correlated with 9월High correlation
2월 is highly overall correlated with 3월 and 1 other fieldsHigh correlation
3월 is highly overall correlated with 2월 and 5 other fieldsHigh correlation
4월 is highly overall correlated with 8월 and 3 other fieldsHigh correlation
5월 is highly overall correlated with 3월 and 3 other fieldsHigh correlation
6월 is highly overall correlated with 7월 and 3 other fieldsHigh correlation
7월 is highly overall correlated with 6월 and 2 other fieldsHigh correlation
8월 is highly overall correlated with 4월 and 2 other fieldsHigh correlation
9월 is highly overall correlated with 1월 and 3 other fieldsHigh correlation
10월 is highly overall correlated with 2월 and 3 other fieldsHigh correlation
11월 is highly overall correlated with 3월 and 1 other fieldsHigh correlation
12월 is highly overall correlated with 4월 and 4 other fieldsHigh correlation
누계 is highly overall correlated with 5월 and 1 other fieldsHigh correlation
연도 is highly overall correlated with 3월 and 9 other fieldsHigh correlation
1월 has 15 (25.0%) zerosZeros
6월 has 7 (11.7%) zerosZeros

Reproduction

Analysis started2023-12-12 18:33:01.436151
Analysis finished2023-12-12 18:33:24.657604
Duration23.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size612.0 B
2015
12 
2016
12 
2017
12 
2018
12 
2019
12 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2015 12
20.0%
2016 12
20.0%
2017 12
20.0%
2018 12
20.0%
2019 12
20.0%

Length

2023-12-13T03:33:24.760217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:33:24.919672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2015 12
20.0%
2016 12
20.0%
2017 12
20.0%
2018 12
20.0%
2019 12
20.0%

구분
Categorical

Distinct12
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
내리(한)
소흘읍
군내면
창수면
이동면
Other values (7)
35 

Length

Max length6
Median length5
Mean length4.4166667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row내리(한)
2nd row소흘읍
3rd row군내면
4th row창수면
5th row이동면

Common Values

ValueCountFrequency (%)
내리(한) 5
8.3%
소흘읍 5
8.3%
군내면 5
8.3%
창수면 5
8.3%
이동면 5
8.3%
관인(한) 5
8.3%
내촌2(한) 5
8.3%
송우(한) 5
8.3%
삼정(한) 5
8.3%
노곡(한) 5
8.3%
Other values (2) 10
16.7%

Length

2023-12-13T03:33:25.121964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
내리(한 5
8.3%
소흘읍 5
8.3%
군내면 5
8.3%
창수면 5
8.3%
이동면 5
8.3%
관인(한 5
8.3%
내촌2(한 5
8.3%
송우(한 5
8.3%
삼정(한 5
8.3%
노곡(한 5
8.3%
Other values (2) 10
16.7%

1월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.966667
Minimum0
Maximum207
Zeros15
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T03:33:25.260091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median14.5
Q348.25
95-th percentile143.05
Maximum207
Range207
Interquartile range (IQR)47.5

Descriptive statistics

Standard deviation52.540853
Coefficient of variation (CV)1.3838679
Kurtosis1.8807598
Mean37.966667
Median Absolute Deviation (MAD)14.5
Skewness1.6365822
Sum2278
Variance2760.5412
MonotonicityNot monotonic
2023-12-13T03:33:25.406330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 15
25.0%
1 2
 
3.3%
4 2
 
3.3%
16 2
 
3.3%
7 2
 
3.3%
8 2
 
3.3%
18 2
 
3.3%
61 1
 
1.7%
31 1
 
1.7%
77 1
 
1.7%
Other values (30) 30
50.0%
ValueCountFrequency (%)
0 15
25.0%
1 2
 
3.3%
2 1
 
1.7%
3 1
 
1.7%
4 2
 
3.3%
7 2
 
3.3%
8 2
 
3.3%
9 1
 
1.7%
10 1
 
1.7%
11 1
 
1.7%
ValueCountFrequency (%)
207 1
1.7%
190 1
1.7%
163 1
1.7%
142 1
1.7%
140 1
1.7%
128 1
1.7%
118 1
1.7%
109 1
1.7%
104 1
1.7%
100 1
1.7%

2월
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.333333
Minimum9
Maximum121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T03:33:25.546364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile14
Q121.75
median25
Q331
95-th percentile50.1
Maximum121
Range112
Interquartile range (IQR)9.25

Descriptive statistics

Standard deviation15.967835
Coefficient of variation (CV)0.54435801
Kurtosis17.708572
Mean29.333333
Median Absolute Deviation (MAD)4
Skewness3.4223598
Sum1760
Variance254.97175
MonotonicityNot monotonic
2023-12-13T03:33:25.732652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
22 9
 
15.0%
21 4
 
6.7%
27 4
 
6.7%
18 3
 
5.0%
23 3
 
5.0%
26 3
 
5.0%
30 3
 
5.0%
42 2
 
3.3%
14 2
 
3.3%
28 2
 
3.3%
Other values (16) 25
41.7%
ValueCountFrequency (%)
9 1
 
1.7%
13 1
 
1.7%
14 2
 
3.3%
16 2
 
3.3%
18 3
 
5.0%
20 2
 
3.3%
21 4
6.7%
22 9
15.0%
23 3
 
5.0%
24 2
 
3.3%
ValueCountFrequency (%)
121 1
1.7%
52 2
3.3%
50 1
1.7%
46 2
3.3%
45 1
1.7%
44 2
3.3%
43 2
3.3%
42 2
3.3%
41 1
1.7%
34 1
1.7%

3월
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.15
Minimum5
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T03:33:25.906454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7
Q111
median24.5
Q338.5
95-th percentile53
Maximum57
Range52
Interquartile range (IQR)27.5

Descriptive statistics

Standard deviation15.813345
Coefficient of variation (CV)0.60471681
Kurtosis-1.1166283
Mean26.15
Median Absolute Deviation (MAD)13.5
Skewness0.41199215
Sum1569
Variance250.06186
MonotonicityNot monotonic
2023-12-13T03:33:26.081877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
10 4
 
6.7%
26 3
 
5.0%
9 3
 
5.0%
7 3
 
5.0%
24 3
 
5.0%
29 3
 
5.0%
53 3
 
5.0%
43 2
 
3.3%
37 2
 
3.3%
33 2
 
3.3%
Other values (25) 32
53.3%
ValueCountFrequency (%)
5 1
 
1.7%
6 1
 
1.7%
7 3
5.0%
8 2
3.3%
9 3
5.0%
10 4
6.7%
11 2
3.3%
12 2
3.3%
13 2
3.3%
14 2
3.3%
ValueCountFrequency (%)
57 1
 
1.7%
56 1
 
1.7%
53 3
5.0%
51 1
 
1.7%
50 1
 
1.7%
48 2
3.3%
46 1
 
1.7%
44 1
 
1.7%
43 2
3.3%
41 1
 
1.7%

4월
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.766667
Minimum4
Maximum114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T03:33:26.267460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4.95
Q136.75
median61.5
Q391.25
95-th percentile108.15
Maximum114
Range110
Interquartile range (IQR)54.5

Descriptive statistics

Standard deviation33.099422
Coefficient of variation (CV)0.54469702
Kurtosis-1.0129352
Mean60.766667
Median Absolute Deviation (MAD)29
Skewness-0.23085433
Sum3646
Variance1095.5718
MonotonicityNot monotonic
2023-12-13T03:33:26.444093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
96 3
 
5.0%
4 3
 
5.0%
77 3
 
5.0%
5 2
 
3.3%
101 2
 
3.3%
56 2
 
3.3%
53 2
 
3.3%
46 2
 
3.3%
10 1
 
1.7%
112 1
 
1.7%
Other values (39) 39
65.0%
ValueCountFrequency (%)
4 3
5.0%
5 2
3.3%
7 1
 
1.7%
8 1
 
1.7%
10 1
 
1.7%
11 1
 
1.7%
25 1
 
1.7%
28 1
 
1.7%
29 1
 
1.7%
30 1
 
1.7%
ValueCountFrequency (%)
114 1
 
1.7%
112 1
 
1.7%
111 1
 
1.7%
108 1
 
1.7%
105 1
 
1.7%
104 1
 
1.7%
101 2
3.3%
100 1
 
1.7%
96 3
5.0%
94 1
 
1.7%

5월
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.6
Minimum12
Maximum265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T03:33:26.623230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile18.95
Q128
median35.5
Q3152.25
95-th percentile249.1
Maximum265
Range253
Interquartile range (IQR)124.25

Descriptive statistics

Standard deviation86.915934
Coefficient of variation (CV)0.90916249
Kurtosis-1.022977
Mean95.6
Median Absolute Deviation (MAD)14
Skewness0.76046627
Sum5736
Variance7554.3797
MonotonicityNot monotonic
2023-12-13T03:33:26.815609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
29 5
 
8.3%
26 3
 
5.0%
28 3
 
5.0%
32 2
 
3.3%
152 2
 
3.3%
229 2
 
3.3%
30 2
 
3.3%
38 2
 
3.3%
145 2
 
3.3%
35 2
 
3.3%
Other values (32) 35
58.3%
ValueCountFrequency (%)
12 1
 
1.7%
15 1
 
1.7%
18 1
 
1.7%
19 1
 
1.7%
21 1
 
1.7%
22 1
 
1.7%
24 1
 
1.7%
25 2
3.3%
26 3
5.0%
27 1
 
1.7%
ValueCountFrequency (%)
265 1
1.7%
263 1
1.7%
251 1
1.7%
249 2
3.3%
243 1
1.7%
241 1
1.7%
237 1
1.7%
229 2
3.3%
220 1
1.7%
206 1
1.7%

6월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.8
Minimum0
Maximum176
Zeros7
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T03:33:27.005581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131.75
median59
Q375
95-th percentile142.8
Maximum176
Range176
Interquartile range (IQR)43.25

Descriptive statistics

Standard deviation44.486448
Coefficient of variation (CV)0.70838293
Kurtosis0.07835903
Mean62.8
Median Absolute Deviation (MAD)22
Skewness0.74113373
Sum3768
Variance1979.0441
MonotonicityNot monotonic
2023-12-13T03:33:27.210788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 7
 
11.7%
42 3
 
5.0%
28 3
 
5.0%
31 3
 
5.0%
132 3
 
5.0%
46 3
 
5.0%
142 2
 
3.3%
62 2
 
3.3%
49 2
 
3.3%
59 2
 
3.3%
Other values (28) 30
50.0%
ValueCountFrequency (%)
0 7
11.7%
1 1
 
1.7%
28 3
5.0%
29 1
 
1.7%
31 3
5.0%
32 1
 
1.7%
37 1
 
1.7%
39 1
 
1.7%
42 3
5.0%
46 3
5.0%
ValueCountFrequency (%)
176 1
 
1.7%
164 1
 
1.7%
158 1
 
1.7%
142 2
3.3%
132 3
5.0%
130 1
 
1.7%
123 1
 
1.7%
112 1
 
1.7%
102 1
 
1.7%
85 1
 
1.7%

7월
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean348.58333
Minimum164
Maximum782
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T03:33:27.403010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum164
5-th percentile187
Q1288.75
median335
Q3419.5
95-th percentile497.5
Maximum782
Range618
Interquartile range (IQR)130.75

Descriptive statistics

Standard deviation122.1683
Coefficient of variation (CV)0.35047087
Kurtosis3.5351768
Mean348.58333
Median Absolute Deviation (MAD)73.5
Skewness1.2766159
Sum20915
Variance14925.095
MonotonicityNot monotonic
2023-12-13T03:33:27.574844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
187 3
 
5.0%
316 3
 
5.0%
340 3
 
5.0%
391 2
 
3.3%
315 2
 
3.3%
292 2
 
3.3%
305 2
 
3.3%
212 2
 
3.3%
460 2
 
3.3%
289 1
 
1.7%
Other values (38) 38
63.3%
ValueCountFrequency (%)
164 1
 
1.7%
173 1
 
1.7%
187 3
5.0%
188 1
 
1.7%
190 1
 
1.7%
194 1
 
1.7%
201 1
 
1.7%
212 2
3.3%
241 1
 
1.7%
262 1
 
1.7%
ValueCountFrequency (%)
782 1
1.7%
779 1
1.7%
507 1
1.7%
497 1
1.7%
477 1
1.7%
460 2
3.3%
456 1
1.7%
455 1
1.7%
449 1
1.7%
441 1
1.7%

8월
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean227.68333
Minimum47
Maximum527
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T03:33:27.765902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile65.85
Q191.25
median149
Q3388
95-th percentile467.2
Maximum527
Range480
Interquartile range (IQR)296.75

Descriptive statistics

Standard deviation154.87409
Coefficient of variation (CV)0.68021706
Kurtosis-1.494244
Mean227.68333
Median Absolute Deviation (MAD)78
Skewness0.46244832
Sum13661
Variance23985.983
MonotonicityNot monotonic
2023-12-13T03:33:27.938759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
405 3
 
5.0%
97 2
 
3.3%
75 2
 
3.3%
145 2
 
3.3%
95 2
 
3.3%
471 2
 
3.3%
149 2
 
3.3%
155 2
 
3.3%
464 1
 
1.7%
412 1
 
1.7%
Other values (41) 41
68.3%
ValueCountFrequency (%)
47 1
1.7%
48 1
1.7%
63 1
1.7%
66 1
1.7%
69 1
1.7%
70 1
1.7%
72 1
1.7%
75 2
3.3%
78 1
1.7%
79 1
1.7%
ValueCountFrequency (%)
527 1
1.7%
471 2
3.3%
467 1
1.7%
464 1
1.7%
455 1
1.7%
449 1
1.7%
435 1
1.7%
434 1
1.7%
432 1
1.7%
412 1
1.7%

9월
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.233333
Minimum4
Maximum271
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T03:33:28.135352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile11
Q127.75
median44
Q362
95-th percentile200.8
Maximum271
Range267
Interquartile range (IQR)34.25

Descriptive statistics

Standard deviation63.030089
Coefficient of variation (CV)0.93748273
Kurtosis2.0394397
Mean67.233333
Median Absolute Deviation (MAD)17
Skewness1.7036418
Sum4034
Variance3972.7921
MonotonicityNot monotonic
2023-12-13T03:33:28.318225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
11 4
 
6.7%
52 3
 
5.0%
42 3
 
5.0%
27 3
 
5.0%
39 2
 
3.3%
48 2
 
3.3%
35 2
 
3.3%
43 2
 
3.3%
60 2
 
3.3%
155 2
 
3.3%
Other values (31) 35
58.3%
ValueCountFrequency (%)
4 1
 
1.7%
11 4
6.7%
15 1
 
1.7%
19 1
 
1.7%
20 1
 
1.7%
21 1
 
1.7%
23 1
 
1.7%
24 1
 
1.7%
25 1
 
1.7%
27 3
5.0%
ValueCountFrequency (%)
271 1
1.7%
235 2
3.3%
199 1
1.7%
189 1
1.7%
182 1
1.7%
158 1
1.7%
155 2
3.3%
143 1
1.7%
136 1
1.7%
132 1
1.7%

10월
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.716667
Minimum8
Maximum174
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T03:33:28.487440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile13.95
Q135.5
median77
Q3107.75
95-th percentile165.05
Maximum174
Range166
Interquartile range (IQR)72.25

Descriptive statistics

Standard deviation45.425331
Coefficient of variation (CV)0.59993834
Kurtosis-0.7655023
Mean75.716667
Median Absolute Deviation (MAD)35
Skewness0.34371003
Sum4543
Variance2063.4607
MonotonicityNot monotonic
2023-12-13T03:33:28.682688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 3
 
5.0%
48 3
 
5.0%
135 3
 
5.0%
17 2
 
3.3%
76 2
 
3.3%
86 2
 
3.3%
110 1
 
1.7%
24 1
 
1.7%
11 1
 
1.7%
130 1
 
1.7%
Other values (41) 41
68.3%
ValueCountFrequency (%)
8 1
 
1.7%
11 1
 
1.7%
13 1
 
1.7%
14 1
 
1.7%
17 2
3.3%
20 1
 
1.7%
22 1
 
1.7%
23 3
5.0%
24 1
 
1.7%
29 1
 
1.7%
ValueCountFrequency (%)
174 1
 
1.7%
170 1
 
1.7%
166 1
 
1.7%
165 1
 
1.7%
135 3
5.0%
131 1
 
1.7%
130 1
 
1.7%
128 1
 
1.7%
122 1
 
1.7%
121 1
 
1.7%

11월
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.633333
Minimum9
Maximum1123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T03:33:28.905973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile15
Q121
median55
Q374.75
95-th percentile117.1
Maximum1123
Range1114
Interquartile range (IQR)53.75

Descriptive statistics

Standard deviation143.40579
Coefficient of variation (CV)1.9475661
Kurtosis50.688393
Mean73.633333
Median Absolute Deviation (MAD)28
Skewness6.8804696
Sum4418
Variance20565.219
MonotonicityNot monotonic
2023-12-13T03:33:29.159109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
16 5
 
8.3%
21 3
 
5.0%
29 3
 
5.0%
19 3
 
5.0%
15 2
 
3.3%
55 2
 
3.3%
80 2
 
3.3%
58 2
 
3.3%
52 2
 
3.3%
49 2
 
3.3%
Other values (33) 34
56.7%
ValueCountFrequency (%)
9 1
 
1.7%
14 1
 
1.7%
15 2
 
3.3%
16 5
8.3%
17 1
 
1.7%
18 1
 
1.7%
19 3
5.0%
21 3
5.0%
24 1
 
1.7%
29 3
5.0%
ValueCountFrequency (%)
1123 1
1.7%
246 1
1.7%
119 1
1.7%
117 1
1.7%
116 1
1.7%
115 1
1.7%
103 1
1.7%
100 1
1.7%
97 1
1.7%
93 1
1.7%

12월
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.083333
Minimum2
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T03:33:29.337852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q114.75
median21
Q326.25
95-th percentile67.15
Maximum78
Range76
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation20.969948
Coefficient of variation (CV)0.77427499
Kurtosis0.075871574
Mean27.083333
Median Absolute Deviation (MAD)6
Skewness1.1827678
Sum1625
Variance439.7387
MonotonicityNot monotonic
2023-12-13T03:33:29.930344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
21 8
 
13.3%
15 6
 
10.0%
22 5
 
8.3%
8 4
 
6.7%
25 3
 
5.0%
67 3
 
5.0%
35 2
 
3.3%
5 2
 
3.3%
70 2
 
3.3%
64 2
 
3.3%
Other values (20) 23
38.3%
ValueCountFrequency (%)
2 1
 
1.7%
3 1
 
1.7%
5 2
3.3%
6 1
 
1.7%
8 4
6.7%
9 1
 
1.7%
10 2
3.3%
11 1
 
1.7%
13 1
 
1.7%
14 1
 
1.7%
ValueCountFrequency (%)
78 1
 
1.7%
70 2
3.3%
67 3
5.0%
66 1
 
1.7%
64 2
3.3%
63 1
 
1.7%
61 1
 
1.7%
56 1
 
1.7%
35 2
3.3%
27 1
 
1.7%

누계
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1132.55
Minimum768
Maximum2058
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T03:33:30.203699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum768
5-th percentile857.7
Q1953.75
median1043.5
Q31303.25
95-th percentile1477.9
Maximum2058
Range1290
Interquartile range (IQR)349.5

Descriptive statistics

Standard deviation247.5782
Coefficient of variation (CV)0.21860244
Kurtosis2.880024
Mean1132.55
Median Absolute Deviation (MAD)133
Skewness1.4009096
Sum67953
Variance61294.964
MonotonicityNot monotonic
2023-12-13T03:33:30.416278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
932 3
 
5.0%
887 2
 
3.3%
966 2
 
3.3%
1384 2
 
3.3%
1042 1
 
1.7%
1323 1
 
1.7%
1476 1
 
1.7%
953 1
 
1.7%
954 1
 
1.7%
768 1
 
1.7%
Other values (45) 45
75.0%
ValueCountFrequency (%)
768 1
 
1.7%
830 1
 
1.7%
833 1
 
1.7%
859 1
 
1.7%
869 1
 
1.7%
887 2
3.3%
917 1
 
1.7%
919 1
 
1.7%
932 3
5.0%
935 1
 
1.7%
ValueCountFrequency (%)
2058 1
1.7%
1889 1
1.7%
1514 1
1.7%
1476 1
1.7%
1437 1
1.7%
1431 1
1.7%
1406 1
1.7%
1384 2
3.3%
1350 1
1.7%
1347 1
1.7%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size612.0 B
2020-06-30
60 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-06-30
2nd row2020-06-30
3rd row2020-06-30
4th row2020-06-30
5th row2020-06-30

Common Values

ValueCountFrequency (%)
2020-06-30 60
100.0%

Length

2023-12-13T03:33:30.532925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:33:30.629206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-06-30 60
100.0%

Interactions

2023-12-13T03:33:21.635139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:02.076226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:03.309815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:04.627218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:06.331597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:07.690321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:09.028944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:10.430922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:11.817454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:13.932688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:15.580125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:17.218140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:19.211739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:21.792730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:02.158235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:03.392206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:04.715101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:06.412314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:07.785234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:09.138624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:10.515571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:11.926420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:14.060000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:15.695424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:17.390907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:19.434570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:21.989629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:02.234064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:03.504222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:04.817161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:06.504866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:07.896687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:09.240859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:10.646330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:12.011761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:14.210320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:15.822750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:17.581719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:19.664083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:22.164652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:02.304647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:03.611048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:04.914051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:06.591371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:08.002719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:09.352110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:10.772466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:12.087091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:14.334153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:15.946526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:17.700930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:19.852724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:22.325942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:02.383944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:03.714659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:05.022628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:06.715164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:08.103045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:09.447720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:10.879371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:12.199472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:14.465005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:16.070661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:17.829304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:20.141504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:22.474996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:02.459564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:03.823663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:05.128830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:06.823898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:08.202353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:09.562453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:10.996339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:12.360955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:14.590666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:16.226395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:17.980112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:20.308130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:22.646378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:02.607965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:03.932699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:05.248245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:06.956160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:08.313798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:09.665629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:11.121509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:12.526507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:14.714258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:16.353255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:18.093651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:20.480130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:23.189948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:02.718100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:04.029564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:05.335297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:07.063312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:08.409385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:09.761847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:11.215808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:12.750391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:14.845022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:16.462809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:18.198916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:20.645542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:23.334624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:02.812105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:04.114645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:05.426875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:07.164705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:08.499026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:09.871366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:11.313575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:12.908569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:14.950226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:16.565100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:18.324176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:20.810654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:23.464392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:02.920148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:04.218022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:05.532394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:07.274501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:08.621915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:09.970213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:11.403338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:13.032493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:15.069634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:16.671821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:18.455124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:20.968406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:23.622629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:03.023998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:04.318125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:05.654205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:07.375745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:08.723063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:10.078153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:11.512543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:13.155879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:15.201480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:16.811894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:18.597908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:21.171159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:23.765860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:03.107205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:04.413871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:05.754764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:07.472050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:08.808470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:10.180482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:11.611686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:13.665084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:15.311688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:16.937218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:18.834493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:21.322374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:23.937675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:03.214536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:04.536309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:05.853016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:07.579762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:08.913248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:10.301995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:11.717106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:13.796614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:15.453636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:17.075592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:19.041877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:33:21.471182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:33:30.730769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도구분1월2월3월4월5월6월7월8월9월10월11월12월누계
연도1.0000.0000.5530.8390.9540.9810.8070.9700.7600.8080.7410.8980.0660.7910.711
구분0.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1320.0000.000
1월0.5530.0001.0000.0000.1800.3750.6020.6930.2940.5270.0000.6680.0000.4660.430
2월0.8390.0000.0001.0000.7150.6290.8110.8640.4150.4890.0000.6740.0000.5820.456
3월0.9540.0000.1800.7151.0000.8500.7360.7490.5480.4150.5640.7340.4790.6490.528
4월0.9810.0000.3750.6290.8501.0000.6480.7670.7020.6680.6610.7980.0000.6530.484
5월0.8070.0000.6020.8110.7360.6481.0000.7420.6810.5000.4700.6610.0000.9170.734
6월0.9700.0000.6930.8640.7490.7670.7421.0000.7470.6850.0000.7640.0000.6000.794
7월0.7600.0000.2940.4150.5480.7020.6810.7471.0000.5640.2170.5640.4310.6800.728
8월0.8080.0000.5270.4890.4150.6680.5000.6850.5641.0000.7860.7750.6600.5180.492
9월0.7410.0000.0000.0000.5640.6610.4700.0000.2170.7861.0000.8040.0000.0000.197
10월0.8980.0000.6680.6740.7340.7980.6610.7640.5640.7750.8041.0000.0000.5600.505
11월0.0660.1320.0000.0000.4790.0000.0000.0000.4310.6600.0000.0001.0000.0000.774
12월0.7910.0000.4660.5820.6490.6530.9170.6000.6800.5180.0000.5600.0001.0000.723
누계0.7110.0000.4300.4560.5280.4840.7340.7940.7280.4920.1970.5050.7740.7231.000
2023-12-13T03:33:30.895256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도구분
연도1.0000.000
구분0.0001.000
2023-12-13T03:33:31.002939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1월2월3월4월5월6월7월8월9월10월11월12월누계연도구분
1월1.000-0.080-0.3290.3160.1550.0260.184-0.120-0.5800.202-0.0160.1390.3130.3440.000
2월-0.0801.0000.584-0.0610.326-0.2840.162-0.2800.2690.514-0.4270.4180.1730.4710.000
3월-0.3290.5841.000-0.1400.515-0.1100.043-0.1580.6040.506-0.5240.1690.3100.6710.000
4월0.316-0.061-0.1401.000-0.117-0.1100.339-0.539-0.5950.2100.0530.506-0.0520.7650.000
5월0.1550.3260.515-0.1171.0000.465-0.2030.0680.3140.760-0.004-0.1870.6670.6500.000
6월0.026-0.284-0.110-0.1100.4651.000-0.5330.2600.1260.3540.583-0.5400.2560.7210.000
7월0.1840.1620.0430.339-0.203-0.5331.000-0.492-0.311-0.066-0.3080.7010.0880.6140.000
8월-0.120-0.280-0.158-0.5390.0680.260-0.4921.0000.233-0.2690.159-0.6840.3420.6120.000
9월-0.5800.2690.604-0.5950.3140.126-0.3110.2331.0000.042-0.182-0.2600.0420.5260.000
10월0.2020.5140.5060.2100.7600.354-0.066-0.2690.0421.000-0.1090.1410.5000.7420.000
11월-0.016-0.427-0.5240.053-0.0040.583-0.3080.159-0.182-0.1091.000-0.499-0.0220.0350.000
12월0.1390.4180.1690.506-0.187-0.5400.701-0.684-0.2600.141-0.4991.000-0.1260.6260.000
누계0.3130.1730.310-0.0520.6670.2560.0880.3420.0420.500-0.022-0.1261.0000.5200.000
연도0.3440.4710.6710.7650.6500.7210.6140.6120.5260.7420.0350.6260.5201.0000.000
구분0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-13T03:33:24.152675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:33:24.523017image/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

연도구분1월2월3월4월5월6월7월8월9월10월11월12월누계데이터기준일자
02015내리(한)20718101043281289971190812210422020-06-30
12015소흘읍16228963074301631583103228332020-06-30
22015군내면152191013066316862182116248872020-06-30
32015창수면18257903678391754071117239712020-06-30
42015이동면20268962967387145117693259832020-06-30
52015관인(한)12225782269374100244257258302020-06-30
62015내촌2(한)1902111100367134495489912210742020-06-30
72015송우(한)14020610529853308511861001010072020-06-30
82015삼정(한)18241010134654419238791152610432020-06-30
92015노곡(한)382479126424331492066972110142020-06-30
연도구분1월2월3월4월5월6월7월8월9월10월11월12월누계데이터기준일자
502019군내면028434638593161551554863159662020-06-30
512019창수면023282831603091482353849219702020-06-30
522019이동면022303025563401961363046219322020-06-30
532019관인(한)0222125115463051321822952219502020-06-30
542019내촌2(한)028434638593161551554863159662020-06-30
552019송우(한)1304140296232713319965591510012020-06-30
562019삼정(한)0232437296331513627145442110082020-06-30
572019노곡(한)025333226523151881433451189172020-06-30
582019화현(한)023374129662411451584852198592020-06-30
592019영중(한)022262933422921532353649159322020-06-30