Overview

Dataset statistics

Number of variables16
Number of observations48
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 KiB
Average record size in memory146.7 B

Variable types

Categorical6
Numeric10

Dataset

Description국립생태원에서 운영중인 국제생태정보종합은행(EcoBank) 시스템의 데이터별 다운로드 통계를 제공합니다.
Author국립생태원
URLhttps://www.data.go.kr/data/15120243/fileData.do

Alerts

디엠지(DMZ) 정밀조사 has constant value ""Constant
백두대간 정밀조사 has constant value ""Constant
생태통로 has constant value ""Constant
멸종위기종 has constant value ""Constant
생태자연도 is highly overall correlated with 습지 and 3 other fieldsHigh correlation
전국자연환경조사 is highly overall correlated with 로드킬 and 2 other fieldsHigh correlation
특정지역 자연환경조사 is highly overall correlated with 외래생물 and 3 other fieldsHigh correlation
외래생물 is highly overall correlated with 특정지역 자연환경조사 and 4 other fieldsHigh correlation
습지 is highly overall correlated with 생태자연도 and 4 other fieldsHigh correlation
로드킬 is highly overall correlated with 생태자연도 and 2 other fieldsHigh correlation
기타 is highly overall correlated with 생태자연도 and 4 other fieldsHigh correlation
계 is highly overall correlated with 생태자연도 and 6 other fieldsHigh correlation
년 is highly overall correlated with 전국자연환경조사 and 3 other fieldsHigh correlation
생태자연도 has unique valuesUnique
계 has unique valuesUnique
전국자연환경조사 has 4 (8.3%) zerosZeros
특정지역 자연환경조사 has 32 (66.7%) zerosZeros
외래생물 has 22 (45.8%) zerosZeros
습지 has 5 (10.4%) zerosZeros
로드킬 has 13 (27.1%) zerosZeros
조류충돌 has 24 (50.0%) zerosZeros

Reproduction

Analysis started2024-03-14 17:18:56.762347
Analysis finished2024-03-14 17:19:21.798512
Duration25.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size512.0 B
2023
12 
2022
12 
2021
12 
2020
12 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2023 12
25.0%
2022 12
25.0%
2021 12
25.0%
2020 12
25.0%

Length

2024-03-15T02:19:22.021376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:19:22.355760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 12
25.0%
2022 12
25.0%
2021 12
25.0%
2020 12
25.0%

월
Real number (ℝ)

Distinct12
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2024-03-15T02:19:22.715614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median6.5
Q39.25
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.4885832
Coefficient of variation (CV)0.53670511
Kurtosis-1.2175129
Mean6.5
Median Absolute Deviation (MAD)3
Skewness0
Sum312
Variance12.170213
MonotonicityNot monotonic
2024-03-15T02:19:23.457929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 4
8.3%
11 4
8.3%
10 4
8.3%
9 4
8.3%
8 4
8.3%
7 4
8.3%
6 4
8.3%
5 4
8.3%
4 4
8.3%
3 4
8.3%
Other values (2) 8
16.7%
ValueCountFrequency (%)
1 4
8.3%
2 4
8.3%
3 4
8.3%
4 4
8.3%
5 4
8.3%
6 4
8.3%
7 4
8.3%
8 4
8.3%
9 4
8.3%
10 4
8.3%
ValueCountFrequency (%)
12 4
8.3%
11 4
8.3%
10 4
8.3%
9 4
8.3%
8 4
8.3%
7 4
8.3%
6 4
8.3%
5 4
8.3%
4 4
8.3%
3 4
8.3%

생태자연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean385.20833
Minimum8
Maximum2788
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2024-03-15T02:19:23.849547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile92.3
Q1217.25
median338
Q3438.5
95-th percentile586.7
Maximum2788
Range2780
Interquartile range (IQR)221.25

Descriptive statistics

Standard deviation387.58302
Coefficient of variation (CV)1.0061647
Kurtosis32.609925
Mean385.20833
Median Absolute Deviation (MAD)111.5
Skewness5.2373958
Sum18490
Variance150220.59
MonotonicityNot monotonic
2024-03-15T02:19:24.359888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
334 1
 
2.1%
342 1
 
2.1%
222 1
 
2.1%
543 1
 
2.1%
539 1
 
2.1%
290 1
 
2.1%
331 1
 
2.1%
427 1
 
2.1%
438 1
 
2.1%
384 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
8 1
2.1%
77 1
2.1%
86 1
2.1%
104 1
2.1%
110 1
2.1%
156 1
2.1%
161 1
2.1%
163 1
2.1%
187 1
2.1%
193 1
2.1%
ValueCountFrequency (%)
2788 1
2.1%
785 1
2.1%
600 1
2.1%
562 1
2.1%
546 1
2.1%
543 1
2.1%
539 1
2.1%
519 1
2.1%
491 1
2.1%
456 1
2.1%

전국자연환경조사
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean232.10417
Minimum0
Maximum1069
Zeros4
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size560.0 B
2024-03-15T02:19:24.846345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q178.5
median228.5
Q3305.75
95-th percentile548.45
Maximum1069
Range1069
Interquartile range (IQR)227.25

Descriptive statistics

Standard deviation204.1142
Coefficient of variation (CV)0.87940773
Kurtosis4.7943342
Mean232.10417
Median Absolute Deviation (MAD)98.5
Skewness1.5578802
Sum11141
Variance41662.606
MonotonicityNot monotonic
2024-03-15T02:19:25.354008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 4
 
8.3%
4 2
 
4.2%
2 2
 
4.2%
3 2
 
4.2%
99 1
 
2.1%
567 1
 
2.1%
1069 1
 
2.1%
293 1
 
2.1%
299 1
 
2.1%
286 1
 
2.1%
Other values (32) 32
66.7%
ValueCountFrequency (%)
0 4
8.3%
2 2
4.2%
3 2
4.2%
4 2
4.2%
7 1
 
2.1%
17 1
 
2.1%
99 1
 
2.1%
116 1
 
2.1%
126 1
 
2.1%
164 1
 
2.1%
ValueCountFrequency (%)
1069 1
2.1%
619 1
2.1%
567 1
2.1%
514 1
2.1%
486 1
2.1%
455 1
2.1%
401 1
2.1%
387 1
2.1%
381 1
2.1%
323 1
2.1%

특정지역 자연환경조사
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.5
Minimum0
Maximum133
Zeros32
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size560.0 B
2024-03-15T02:19:25.756523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q353.25
95-th percentile88.75
Maximum133
Range133
Interquartile range (IQR)53.25

Descriptive statistics

Standard deviation35.309573
Coefficient of variation (CV)1.6423057
Kurtosis0.93219328
Mean21.5
Median Absolute Deviation (MAD)0
Skewness1.4086825
Sum1032
Variance1246.766
MonotonicityNot monotonic
2024-03-15T02:19:26.166851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 32
66.7%
70 2
 
4.2%
65 2
 
4.2%
53 1
 
2.1%
55 1
 
2.1%
54 1
 
2.1%
64 1
 
2.1%
79 1
 
2.1%
75 1
 
2.1%
94 1
 
2.1%
Other values (5) 5
 
10.4%
ValueCountFrequency (%)
0 32
66.7%
1 1
 
2.1%
17 1
 
2.1%
40 1
 
2.1%
53 1
 
2.1%
54 1
 
2.1%
55 1
 
2.1%
64 1
 
2.1%
65 2
 
4.2%
70 2
 
4.2%
ValueCountFrequency (%)
133 1
2.1%
97 1
2.1%
94 1
2.1%
79 1
2.1%
75 1
2.1%
70 2
4.2%
65 2
4.2%
64 1
2.1%
55 1
2.1%
54 1
2.1%

디엠지(DMZ) 정밀조사
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size512.0 B
0
48 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 48
100.0%

Length

2024-03-15T02:19:26.622530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:19:26.958644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 48
100.0%

백두대간 정밀조사
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size512.0 B
0
48 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 48
100.0%

Length

2024-03-15T02:19:27.273279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:19:27.598674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 48
100.0%

장기생태연구
Categorical

Distinct5
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Memory size512.0 B
0
29 
1
11 
2
5 
3
 
2
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)2.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29
60.4%
1 11
 
22.9%
2 5
 
10.4%
3 2
 
4.2%
6 1
 
2.1%

Length

2024-03-15T02:19:28.020324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:19:28.360117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 29
60.4%
1 11
 
22.9%
2 5
 
10.4%
3 2
 
4.2%
6 1
 
2.1%

생태통로
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size512.0 B
0
48 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 48
100.0%

Length

2024-03-15T02:19:28.730554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:19:29.023824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 48
100.0%

외래생물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.708333
Minimum0
Maximum55
Zeros22
Zeros (%)45.8%
Negative0
Negative (%)0.0%
Memory size560.0 B
2024-03-15T02:19:29.186624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11.5
Q321.25
95-th percentile50.5
Maximum55
Range55
Interquartile range (IQR)21.25

Descriptive statistics

Standard deviation16.178471
Coefficient of variation (CV)1.1801924
Kurtosis0.60547862
Mean13.708333
Median Absolute Deviation (MAD)11.5
Skewness1.1276542
Sum658
Variance261.74291
MonotonicityNot monotonic
2024-03-15T02:19:29.369194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 22
45.8%
35 3
 
6.2%
18 3
 
6.2%
21 2
 
4.2%
11 2
 
4.2%
55 2
 
4.2%
16 2
 
4.2%
12 2
 
4.2%
17 2
 
4.2%
23 1
 
2.1%
Other values (7) 7
 
14.6%
ValueCountFrequency (%)
0 22
45.8%
11 2
 
4.2%
12 2
 
4.2%
13 1
 
2.1%
16 2
 
4.2%
17 2
 
4.2%
18 3
 
6.2%
21 2
 
4.2%
22 1
 
2.1%
23 1
 
2.1%
ValueCountFrequency (%)
55 2
4.2%
54 1
 
2.1%
44 1
 
2.1%
35 3
6.2%
28 1
 
2.1%
27 1
 
2.1%
24 1
 
2.1%
23 1
 
2.1%
22 1
 
2.1%
21 2
4.2%

멸종위기종
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size512.0 B
0
48 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 48
100.0%

Length

2024-03-15T02:19:29.644338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:19:29.942994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 48
100.0%

습지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)77.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.729167
Minimum0
Maximum288
Zeros5
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size560.0 B
2024-03-15T02:19:30.267880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q124
median58
Q3104.25
95-th percentile190.55
Maximum288
Range288
Interquartile range (IQR)80.25

Descriptive statistics

Standard deviation62.197947
Coefficient of variation (CV)0.87938188
Kurtosis2.3088222
Mean70.729167
Median Absolute Deviation (MAD)35.5
Skewness1.4087744
Sum3395
Variance3868.5847
MonotonicityNot monotonic
2024-03-15T02:19:30.680201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 5
 
10.4%
59 2
 
4.2%
50 2
 
4.2%
111 2
 
4.2%
20 2
 
4.2%
24 2
 
4.2%
30 2
 
4.2%
19 2
 
4.2%
21 1
 
2.1%
79 1
 
2.1%
Other values (27) 27
56.2%
ValueCountFrequency (%)
0 5
10.4%
11 1
 
2.1%
19 2
 
4.2%
20 2
 
4.2%
21 1
 
2.1%
24 2
 
4.2%
26 1
 
2.1%
28 1
 
2.1%
30 2
 
4.2%
37 1
 
2.1%
ValueCountFrequency (%)
288 1
2.1%
217 1
2.1%
193 1
2.1%
186 1
2.1%
165 1
2.1%
134 1
2.1%
131 1
2.1%
126 1
2.1%
111 2
4.2%
110 1
2.1%

로드킬
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9791667
Minimum0
Maximum27
Zeros13
Zeros (%)27.1%
Negative0
Negative (%)0.0%
Memory size560.0 B
2024-03-15T02:19:31.042726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q37
95-th percentile15.65
Maximum27
Range27
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.6737154
Coefficient of variation (CV)1.139491
Kurtosis4.1311445
Mean4.9791667
Median Absolute Deviation (MAD)3
Skewness1.8165068
Sum239
Variance32.191046
MonotonicityNot monotonic
2024-03-15T02:19:31.413560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 13
27.1%
2 6
12.5%
5 5
 
10.4%
7 5
 
10.4%
4 4
 
8.3%
3 2
 
4.2%
1 2
 
4.2%
12 2
 
4.2%
8 2
 
4.2%
19 1
 
2.1%
Other values (6) 6
12.5%
ValueCountFrequency (%)
0 13
27.1%
1 2
 
4.2%
2 6
12.5%
3 2
 
4.2%
4 4
 
8.3%
5 5
 
10.4%
6 1
 
2.1%
7 5
 
10.4%
8 2
 
4.2%
9 1
 
2.1%
ValueCountFrequency (%)
27 1
 
2.1%
19 1
 
2.1%
16 1
 
2.1%
15 1
 
2.1%
12 2
 
4.2%
11 1
 
2.1%
9 1
 
2.1%
8 2
 
4.2%
7 5
10.4%
6 1
 
2.1%

조류충돌
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2083333
Minimum0
Maximum7
Zeros24
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2024-03-15T02:19:31.751917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q32
95-th percentile4.65
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7498733
Coefficient of variation (CV)1.448171
Kurtosis3.5600697
Mean1.2083333
Median Absolute Deviation (MAD)0.5
Skewness1.8795593
Sum58
Variance3.0620567
MonotonicityNot monotonic
2024-03-15T02:19:32.097406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 24
50.0%
1 10
20.8%
2 5
 
10.4%
3 5
 
10.4%
7 2
 
4.2%
5 1
 
2.1%
4 1
 
2.1%
ValueCountFrequency (%)
0 24
50.0%
1 10
20.8%
2 5
 
10.4%
3 5
 
10.4%
4 1
 
2.1%
5 1
 
2.1%
7 2
 
4.2%
ValueCountFrequency (%)
7 2
 
4.2%
5 1
 
2.1%
4 1
 
2.1%
3 5
 
10.4%
2 5
 
10.4%
1 10
20.8%
0 24
50.0%

기타
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean623.20833
Minimum109
Maximum2985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2024-03-15T02:19:32.488384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum109
5-th percentile121.35
Q1204.25
median431
Q3631.25
95-th percentile2316.95
Maximum2985
Range2876
Interquartile range (IQR)427

Descriptive statistics

Standard deviation671.91615
Coefficient of variation (CV)1.0781566
Kurtosis5.5663289
Mean623.20833
Median Absolute Deviation (MAD)224
Skewness2.3971211
Sum29914
Variance451471.32
MonotonicityNot monotonic
2024-03-15T02:19:32.857686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
586 2
 
4.2%
1020 1
 
2.1%
261 1
 
2.1%
181 1
 
2.1%
303 1
 
2.1%
360 1
 
2.1%
416 1
 
2.1%
613 1
 
2.1%
870 1
 
2.1%
1252 1
 
2.1%
Other values (37) 37
77.1%
ValueCountFrequency (%)
109 1
2.1%
113 1
2.1%
121 1
2.1%
122 1
2.1%
136 1
2.1%
163 1
2.1%
168 1
2.1%
170 1
2.1%
181 1
2.1%
191 1
2.1%
ValueCountFrequency (%)
2985 1
2.1%
2887 1
2.1%
2423 1
2.1%
2120 1
2.1%
1252 1
2.1%
1163 1
2.1%
1020 1
2.1%
889 1
2.1%
870 1
2.1%
794 1
2.1%

계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1353.3333
Minimum121
Maximum4397
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size560.0 B
2024-03-15T02:19:33.297617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum121
5-th percentile233.7
Q1694.75
median1204
Q31665.5
95-th percentile3739.6
Maximum4397
Range4276
Interquartile range (IQR)970.75

Descriptive statistics

Standard deviation976.55206
Coefficient of variation (CV)0.72159019
Kurtosis2.7267968
Mean1353.3333
Median Absolute Deviation (MAD)495
Skewness1.5488472
Sum64960
Variance953653.93
MonotonicityNot monotonic
2024-03-15T02:19:33.574798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1659 1
 
2.1%
995 1
 
2.1%
695 1
 
2.1%
1017 1
 
2.1%
1184 1
 
2.1%
1287 1
 
2.1%
1081 1
 
2.1%
1685 1
 
2.1%
1891 1
 
2.1%
1930 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
121 1
2.1%
186 1
2.1%
212 1
2.1%
274 1
2.1%
335 1
2.1%
355 1
2.1%
379 1
2.1%
420 1
2.1%
434 1
2.1%
442 1
2.1%
ValueCountFrequency (%)
4397 1
2.1%
4072 1
2.1%
4063 1
2.1%
3139 1
2.1%
2819 1
2.1%
1930 1
2.1%
1907 1
2.1%
1905 1
2.1%
1891 1
2.1%
1888 1
2.1%

Interactions

2024-03-15T02:19:18.208465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:18:57.409565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:00.079831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:02.346428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:04.861722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:06.916860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:09.188344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:11.512600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:13.669503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:15.970559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:18.440093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:18:57.656488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:00.318070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:02.672024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:05.065442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:07.159988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:09.436798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:11.754705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:13.814215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:16.210634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:18.674072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:18:57.915214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:00.555295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:02.912830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:05.207637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:07.465188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:09.696141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:12.011173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:13.961328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:16.450405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:18.907743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:18:58.152629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:00.791738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:03.148778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:05.353521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:07.727571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:09.935359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:12.178001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:14.151421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:16.697907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:19.149874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:18:58.611633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:00.990643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:03.394757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:05.594331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:07.981294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:10.125550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:12.326676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:14.474750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:16.927511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:19.387594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:18:58.851021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:01.131328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:03.635150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:05.839125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:08.228875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:10.288003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:12.463960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:14.702626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:17.134678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:19.625041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:18:59.098139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:01.369520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:03.882799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:06.097594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:08.484066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:10.435792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:12.765727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:14.969676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:17.337077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:19.863022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:18:59.340685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:01.609715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:04.121344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:06.324437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:08.688210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:10.586401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:13.003468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:15.216658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:17.476492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:20.187920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:18:59.592501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:01.865780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:04.376442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:06.582459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:08.841012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:11.035012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:13.335717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:15.472911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:17.727259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:20.457893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:18:59.843817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:02.113479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:04.684001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:06.735909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:08.987153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:11.279363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:13.483321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:15.730134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:19:17.972076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T02:19:33.854580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월생태자연도전국자연환경조사특정지역 자연환경조사장기생태연구외래생물습지로드킬조류충돌기타계
년1.0000.0000.7660.7320.6630.0000.9350.6580.7130.4920.5920.865
월0.0001.0000.0000.0000.0000.0000.3570.0000.0000.2520.0000.000
생태자연도0.7660.0001.0000.3910.0000.0000.6110.4710.8020.7110.4480.798
전국자연환경조사0.7320.0000.3911.0000.5670.0000.5370.4740.4990.6610.6140.639
특정지역 자연환경조사0.6630.0000.0000.5671.0000.0000.7560.7330.0000.0000.4660.110
장기생태연구0.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
외래생물0.9350.3570.6110.5370.7560.0001.0000.5840.0000.0000.3640.221
습지0.6580.0000.4710.4740.7330.0000.5841.0000.0000.1730.6540.527
로드킬0.7130.0000.8020.4990.0000.0000.0000.0001.0000.4610.6130.834
조류충돌0.4920.2520.7110.6610.0000.0000.0000.1730.4611.0000.3480.337
기타0.5920.0000.4480.6140.4660.0000.3640.6540.6130.3481.0000.905
계0.8650.0000.7980.6390.1100.0000.2210.5270.8340.3370.9051.000
2024-03-15T02:19:34.080037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
장기생태연구년
장기생태연구1.0000.000
년0.0001.000
2024-03-15T02:19:34.246455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월생태자연도전국자연환경조사특정지역 자연환경조사외래생물습지로드킬조류충돌기타계년장기생태연구
월1.000-0.0240.0740.1770.0670.131-0.0470.048-0.154-0.1350.0000.000
생태자연도-0.0241.0000.4910.3070.3630.5240.5310.2910.6020.7210.4020.000
전국자연환경조사0.0740.4911.0000.2270.2990.3350.6370.4950.4240.6580.5830.000
특정지역 자연환경조사0.1770.3070.2271.0000.7420.6930.082-0.1110.5350.4750.5050.000
외래생물0.0670.3630.2990.7421.0000.7590.3000.0730.5700.5670.6060.000
습지0.1310.5240.3350.6930.7591.0000.3130.1890.6940.6630.4570.000
로드킬-0.0470.5310.6370.0820.3000.3131.0000.4530.4260.6240.3640.000
조류충돌0.0480.2910.495-0.1110.0730.1890.4531.0000.1960.2220.3440.000
기타-0.1540.6020.4240.5350.5700.6940.4260.1961.0000.8850.4340.000
계-0.1350.7210.6580.4750.5670.6630.6240.2220.8851.0000.5170.000
년0.0000.4020.5830.5050.6060.4570.3640.3440.4340.5171.0000.000
장기생태연구0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-03-15T02:19:20.847485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T02:19:21.522698image/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

년월생태자연도전국자연환경조사특정지역 자연환경조사디엠지(DMZ) 정밀조사백두대간 정밀조사장기생태연구생태통로외래생물멸종위기종습지로드킬조류충돌기타계
0202312334995300002101264210201659
12023115622515500001801938029854072
220231029211670000022086336531245
320239235126540000350103206241179
420238258323650000440111214031207
520237412281640000550186205861586
620236344201790000270111505901357
720235421401750010550134707941888
820234519240650000240165508891907
920233546387940020540911128874063
년월생태자연도전국자연환경조사특정지역 자연환경조사디엠지(DMZ) 정밀조사백두대간 정밀조사장기생태연구생태통로외래생물멸종위기종습지로드킬조류충돌기타계
38202010110200000002800239379
3920209193700060002600202434
4020208187400000002400205420
4120207161200010005000121335
4220206199400010004700191442
432020510400000000000170274
44202047700000000000109186
452020315600000000000199355
46202028630001000000122212
4720201800000000000113121