Overview

Dataset statistics

Number of variables20
Number of observations110
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.2 KiB
Average record size in memory179.2 B

Variable types

Categorical3
Numeric17

Dataset

Description기타 가축통계(거위/사슴/칠면조) 정보제공
Author농림축산식품부
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220216000000001946

Alerts

2013 has constant value ""Constant
1 is highly overall correlated with 951 and 16 other fieldsHigh correlation
951 is highly overall correlated with 1 and 15 other fieldsHigh correlation
887 is highly overall correlated with 1 and 15 other fieldsHigh correlation
53 is highly overall correlated with 1 and 14 other fieldsHigh correlation
8 is highly overall correlated with 1 and 14 other fieldsHigh correlation
2 is highly overall correlated with 1 and 15 other fieldsHigh correlation
0 is highly overall correlated with 1 and 14 other fieldsHigh correlation
0.1 is highly overall correlated with 1 and 15 other fieldsHigh correlation
1.1 is highly overall correlated with 1 and 15 other fieldsHigh correlation
5158 is highly overall correlated with 1 and 15 other fieldsHigh correlation
2575 is highly overall correlated with 1 and 15 other fieldsHigh correlation
858 is highly overall correlated with 1 and 14 other fieldsHigh correlation
470 is highly overall correlated with 1 and 14 other fieldsHigh correlation
255 is highly overall correlated with 1 and 15 other fieldsHigh correlation
0.2 is highly overall correlated with 1 and 14 other fieldsHigh correlation
0.3 is highly overall correlated with 1 and 15 other fieldsHigh correlation
10 is highly overall correlated with 1 and 9 other fieldsHigh correlation
'94 is highly overall correlated with 1High correlation
951 has 3 (2.7%) zerosZeros
887 has 3 (2.7%) zerosZeros
53 has 12 (10.9%) zerosZeros
8 has 33 (30.0%) zerosZeros
2 has 34 (30.9%) zerosZeros
0 has 51 (46.4%) zerosZeros
0.1 has 62 (56.4%) zerosZeros
1.1 has 46 (41.8%) zerosZeros
5158 has 3 (2.7%) zerosZeros
2575 has 3 (2.7%) zerosZeros
858 has 12 (10.9%) zerosZeros
470 has 33 (30.0%) zerosZeros
255 has 34 (30.9%) zerosZeros
0.2 has 51 (46.4%) zerosZeros
0.3 has 62 (56.4%) zerosZeros
10 has 46 (41.8%) zerosZeros

Reproduction

Analysis started2023-12-11 03:08:46.744248
Analysis finished2023-12-11 03:09:21.314478
Duration34.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

2013
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1012.0 B
2013
110 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2013 110
100.0%

Length

2023-12-11T12:09:21.397869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:09:21.502946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2013 110
100.0%

거위
Categorical

Distinct3
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1012.0 B
사슴
37 
칠면조
37 
거위
36 

Length

Max length3
Median length2
Mean length2.3363636
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
사슴 37
33.6%
칠면조 37
33.6%
거위 36
32.7%

Length

2023-12-11T12:09:21.636290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:09:21.764913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사슴 37
33.6%
칠면조 37
33.6%
거위 36
32.7%

1
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)33.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.163636
Minimum1
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:09:21.885803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q110
median19
Q328
95-th percentile35.55
Maximum37
Range36
Interquartile range (IQR)18

Descriptive statistics

Standard deviation10.634484
Coefficient of variation (CV)0.55493036
Kurtosis-1.1992616
Mean19.163636
Median Absolute Deviation (MAD)9
Skewness-0.0017088642
Sum2108
Variance113.09224
MonotonicityNot monotonic
2023-12-11T12:09:22.104984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
2 3
 
2.7%
3 3
 
2.7%
23 3
 
2.7%
24 3
 
2.7%
25 3
 
2.7%
26 3
 
2.7%
27 3
 
2.7%
28 3
 
2.7%
29 3
 
2.7%
30 3
 
2.7%
Other values (27) 80
72.7%
ValueCountFrequency (%)
1 2
1.8%
2 3
2.7%
3 3
2.7%
4 3
2.7%
5 3
2.7%
6 3
2.7%
7 3
2.7%
8 3
2.7%
9 3
2.7%
10 3
2.7%
ValueCountFrequency (%)
37 3
2.7%
36 3
2.7%
35 3
2.7%
34 3
2.7%
33 3
2.7%
32 3
2.7%
31 3
2.7%
30 3
2.7%
29 3
2.7%
28 3
2.7%

'94
Categorical

HIGH CORRELATION 

Distinct37
Distinct (%)33.6%
Missing0
Missing (%)0.0%
Memory size1012.0 B
'95
 
3
'96
 
3
'97
 
3
'98
 
3
'99
 
3
Other values (32)
95 

Length

Max length6
Median length3
Mean length2.6181818
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row'95
2nd row'96
3rd row'97
4th row'98
5th row'99

Common Values

ValueCountFrequency (%)
'95 3
 
2.7%
'96 3
 
2.7%
'97 3
 
2.7%
'98 3
 
2.7%
'99 3
 
2.7%
'00 3
 
2.7%
'01 3
 
2.7%
'02 3
 
2.7%
'03 3
 
2.7%
'04 3
 
2.7%
Other values (27) 80
72.7%

Length

2023-12-11T12:09:22.340904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
95 3
 
2.7%
서울 3
 
2.7%
대구 3
 
2.7%
인천 3
 
2.7%
광주 3
 
2.7%
대전 3
 
2.7%
울산 3
 
2.7%
경기 3
 
2.7%
강원 3
 
2.7%
충북 3
 
2.7%
Other values (27) 80
72.7%

951
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct100
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1999.2455
Minimum0
Maximum12564
Zeros3
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:09:22.489870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q137
median622.5
Q31283.75
95-th percentile10873.55
Maximum12564
Range12564
Interquartile range (IQR)1246.75

Descriptive statistics

Standard deviation3447.6459
Coefficient of variation (CV)1.7244736
Kurtosis2.6205216
Mean1999.2455
Median Absolute Deviation (MAD)595.5
Skewness2.002169
Sum219917
Variance11886263
MonotonicityNot monotonic
2023-12-11T12:09:22.642046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4
 
3.6%
0 3
 
2.7%
37 2
 
1.8%
5 2
 
1.8%
9 2
 
1.8%
7 2
 
1.8%
3 2
 
1.8%
1035 1
 
0.9%
26 1
 
0.9%
838 1
 
0.9%
Other values (90) 90
81.8%
ValueCountFrequency (%)
0 3
2.7%
1 4
3.6%
2 1
 
0.9%
3 2
1.8%
4 1
 
0.9%
5 2
1.8%
6 1
 
0.9%
7 2
1.8%
8 1
 
0.9%
9 2
1.8%
ValueCountFrequency (%)
12564 1
0.9%
12337 1
0.9%
12137 1
0.9%
11501 1
0.9%
11369 1
0.9%
10874 1
0.9%
10873 1
0.9%
10192 1
0.9%
9892 1
0.9%
9451 1
0.9%

887
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct95
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1363.2
Minimum0
Maximum7784
Zeros3
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:09:22.795317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q130.25
median521.5
Q31174.75
95-th percentile6671.1
Maximum7784
Range7784
Interquartile range (IQR)1144.5

Descriptive statistics

Standard deviation2132.7246
Coefficient of variation (CV)1.5644987
Kurtosis2.3037423
Mean1363.2
Median Absolute Deviation (MAD)511.5
Skewness1.8894088
Sum149952
Variance4548514.4
MonotonicityNot monotonic
2023-12-11T12:09:22.967259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 5
 
4.5%
6 4
 
3.6%
1 4
 
3.6%
0 3
 
2.7%
4 3
 
2.7%
7 2
 
1.8%
963 1
 
0.9%
952 1
 
0.9%
1073 1
 
0.9%
860 1
 
0.9%
Other values (85) 85
77.3%
ValueCountFrequency (%)
0 3
2.7%
1 4
3.6%
2 1
 
0.9%
3 5
4.5%
4 3
2.7%
6 4
3.6%
7 2
 
1.8%
11 1
 
0.9%
14 1
 
0.9%
16 1
 
0.9%
ValueCountFrequency (%)
7784 1
0.9%
7672 1
0.9%
7591 1
0.9%
7160 1
0.9%
7089 1
0.9%
6744 1
0.9%
6582 1
0.9%
6246 1
0.9%
6074 1
0.9%
5880 1
0.9%

53
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)64.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean568.46364
Minimum0
Maximum4318
Zeros12
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:09:23.116774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.25
median61.5
Q3107.75
95-th percentile3830.45
Maximum4318
Range4318
Interquartile range (IQR)104.5

Descriptive statistics

Standard deviation1200.4997
Coefficient of variation (CV)2.111832
Kurtosis2.9911234
Mean568.46364
Median Absolute Deviation (MAD)55.5
Skewness2.1180759
Sum62531
Variance1441199.5
MonotonicityNot monotonic
2023-12-11T12:09:23.290486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12
 
10.9%
2 8
 
7.3%
3 4
 
3.6%
1 4
 
3.6%
6 4
 
3.6%
92 3
 
2.7%
87 2
 
1.8%
117 2
 
1.8%
111 2
 
1.8%
81 2
 
1.8%
Other values (61) 67
60.9%
ValueCountFrequency (%)
0 12
10.9%
1 4
 
3.6%
2 8
7.3%
3 4
 
3.6%
4 1
 
0.9%
5 2
 
1.8%
6 4
 
3.6%
8 2
 
1.8%
10 1
 
0.9%
12 1
 
0.9%
ValueCountFrequency (%)
4318 1
0.9%
4194 1
0.9%
4097 1
0.9%
3893 1
0.9%
3889 1
0.9%
3875 1
0.9%
3776 1
0.9%
3617 1
0.9%
3303 1
0.9%
3232 1
0.9%

8
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)33.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.318182
Minimum0
Maximum414
Zeros33
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:09:23.438454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q311
95-th percentile330.15
Maximum414
Range414
Interquartile range (IQR)11

Descriptive statistics

Standard deviation106.67441
Coefficient of variation (CV)2.1199973
Kurtosis3.5178891
Mean50.318182
Median Absolute Deviation (MAD)5
Skewness2.2047781
Sum5535
Variance11379.43
MonotonicityNot monotonic
2023-12-11T12:09:23.619465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 33
30.0%
9 10
 
9.1%
5 6
 
5.5%
2 6
 
5.5%
1 6
 
5.5%
8 5
 
4.5%
10 4
 
3.6%
7 4
 
3.6%
12 3
 
2.7%
3 3
 
2.7%
Other values (27) 30
27.3%
ValueCountFrequency (%)
0 33
30.0%
1 6
 
5.5%
2 6
 
5.5%
3 3
 
2.7%
4 2
 
1.8%
5 6
 
5.5%
6 2
 
1.8%
7 4
 
3.6%
8 5
 
4.5%
9 10
 
9.1%
ValueCountFrequency (%)
414 1
0.9%
379 1
0.9%
371 1
0.9%
368 1
0.9%
365 1
0.9%
336 1
0.9%
323 1
0.9%
318 1
0.9%
286 1
0.9%
282 1
0.9%

2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.236364
Minimum0
Maximum108
Zeros34
Zeros (%)30.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:09:23.774455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q37
95-th percentile76.55
Maximum108
Range108
Interquartile range (IQR)7

Descriptive statistics

Standard deviation23.295835
Coefficient of variation (CV)1.9038201
Kurtosis4.4115866
Mean12.236364
Median Absolute Deviation (MAD)3
Skewness2.3019718
Sum1346
Variance542.69591
MonotonicityNot monotonic
2023-12-11T12:09:23.930301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 34
30.9%
3 11
 
10.0%
1 11
 
10.0%
2 8
 
7.3%
4 7
 
6.4%
7 5
 
4.5%
5 5
 
4.5%
6 4
 
3.6%
29 2
 
1.8%
80 2
 
1.8%
Other values (17) 21
19.1%
ValueCountFrequency (%)
0 34
30.9%
1 11
 
10.0%
2 8
 
7.3%
3 11
 
10.0%
4 7
 
6.4%
5 5
 
4.5%
6 4
 
3.6%
7 5
 
4.5%
8 2
 
1.8%
9 1
 
0.9%
ValueCountFrequency (%)
108 1
0.9%
81 1
0.9%
80 2
1.8%
77 2
1.8%
76 1
0.9%
69 1
0.9%
60 1
0.9%
54 1
0.9%
50 1
0.9%
49 2
1.8%

0
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0272727
Minimum0
Maximum17
Zeros51
Zeros (%)46.4%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:09:24.056928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.3577491
Coefficient of variation (CV)1.6562888
Kurtosis5.3707225
Mean2.0272727
Median Absolute Deviation (MAD)1
Skewness2.3203119
Sum223
Variance11.274479
MonotonicityNot monotonic
2023-12-11T12:09:24.461841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 51
46.4%
1 22
20.0%
2 12
 
10.9%
4 7
 
6.4%
3 3
 
2.7%
5 2
 
1.8%
8 2
 
1.8%
13 2
 
1.8%
6 2
 
1.8%
10 2
 
1.8%
Other values (5) 5
 
4.5%
ValueCountFrequency (%)
0 51
46.4%
1 22
20.0%
2 12
 
10.9%
3 3
 
2.7%
4 7
 
6.4%
5 2
 
1.8%
6 2
 
1.8%
7 1
 
0.9%
8 2
 
1.8%
9 1
 
0.9%
ValueCountFrequency (%)
17 1
0.9%
13 2
1.8%
12 1
0.9%
11 1
0.9%
10 2
1.8%
9 1
0.9%
8 2
1.8%
7 1
0.9%
6 2
1.8%
5 2
1.8%

0.1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3545455
Minimum0
Maximum10
Zeros62
Zeros (%)56.4%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:09:24.650069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile6
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1655618
Coefficient of variation (CV)1.5987369
Kurtosis3.7158218
Mean1.3545455
Median Absolute Deviation (MAD)0
Skewness1.9601574
Sum149
Variance4.689658
MonotonicityNot monotonic
2023-12-11T12:09:24.785063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 62
56.4%
1 15
 
13.6%
2 10
 
9.1%
4 7
 
6.4%
3 6
 
5.5%
5 3
 
2.7%
6 3
 
2.7%
8 2
 
1.8%
10 1
 
0.9%
9 1
 
0.9%
ValueCountFrequency (%)
0 62
56.4%
1 15
 
13.6%
2 10
 
9.1%
3 6
 
5.5%
4 7
 
6.4%
5 3
 
2.7%
6 3
 
2.7%
8 2
 
1.8%
9 1
 
0.9%
10 1
 
0.9%
ValueCountFrequency (%)
10 1
 
0.9%
9 1
 
0.9%
8 2
 
1.8%
6 3
 
2.7%
5 3
 
2.7%
4 7
 
6.4%
3 6
 
5.5%
2 10
 
9.1%
1 15
 
13.6%
0 62
56.4%

1.1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6454545
Minimum0
Maximum8
Zeros46
Zeros (%)41.8%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:09:24.913182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32.75
95-th percentile7
Maximum8
Range8
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation2.0966831
Coefficient of variation (CV)1.2742273
Kurtosis1.3761543
Mean1.6454545
Median Absolute Deviation (MAD)1
Skewness1.4431858
Sum181
Variance4.3960801
MonotonicityNot monotonic
2023-12-11T12:09:25.054005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 46
41.8%
1 23
20.9%
2 13
 
11.8%
4 10
 
9.1%
3 8
 
7.3%
7 5
 
4.5%
6 2
 
1.8%
8 2
 
1.8%
5 1
 
0.9%
ValueCountFrequency (%)
0 46
41.8%
1 23
20.9%
2 13
 
11.8%
3 8
 
7.3%
4 10
 
9.1%
5 1
 
0.9%
6 2
 
1.8%
7 5
 
4.5%
8 2
 
1.8%
ValueCountFrequency (%)
8 2
 
1.8%
7 5
 
4.5%
6 2
 
1.8%
5 1
 
0.9%
4 10
 
9.1%
3 8
 
7.3%
2 13
 
11.8%
1 23
20.9%
0 46
41.8%

5158
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct107
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24049.455
Minimum0
Maximum156076
Zeros3
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:09:25.196094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.45
Q1381.75
median7172
Q315480.25
95-th percentile137839.85
Maximum156076
Range156076
Interquartile range (IQR)15098.5

Descriptive statistics

Standard deviation42724.025
Coefficient of variation (CV)1.776507
Kurtosis2.8188063
Mean24049.455
Median Absolute Deviation (MAD)6846.5
Skewness2.0476546
Sum2645440
Variance1.8253423 × 109
MonotonicityNot monotonic
2023-12-11T12:09:25.374571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
2.7%
2 2
 
1.8%
6006 1
 
0.9%
3820 1
 
0.9%
13159 1
 
0.9%
8912 1
 
0.9%
8141 1
 
0.9%
7616 1
 
0.9%
8096 1
 
0.9%
10648 1
 
0.9%
Other values (97) 97
88.2%
ValueCountFrequency (%)
0 3
2.7%
2 2
1.8%
3 1
 
0.9%
4 1
 
0.9%
6 1
 
0.9%
9 1
 
0.9%
11 1
 
0.9%
23 1
 
0.9%
25 1
 
0.9%
28 1
 
0.9%
ValueCountFrequency (%)
156076 1
0.9%
153438 1
0.9%
150466 1
0.9%
144926 1
0.9%
140740 1
0.9%
138302 1
0.9%
137275 1
0.9%
127816 1
0.9%
125653 1
0.9%
111413 1
0.9%

2575
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct104
Distinct (%)94.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5572.2273
Minimum0
Maximum34980
Zeros3
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:09:25.579761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.45
Q197.5
median1652
Q33680
95-th percentile29836
Maximum34980
Range34980
Interquartile range (IQR)3582.5

Descriptive statistics

Standard deviation9735.0458
Coefficient of variation (CV)1.7470655
Kurtosis2.5100969
Mean5572.2273
Median Absolute Deviation (MAD)1597
Skewness1.992878
Sum612945
Variance94771117
MonotonicityNot monotonic
2023-12-11T12:09:25.726919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
2.7%
8 3
 
2.7%
21 2
 
1.8%
2 2
 
1.8%
2665 1
 
0.9%
470 1
 
0.9%
2329 1
 
0.9%
2230 1
 
0.9%
2150 1
 
0.9%
2457 1
 
0.9%
Other values (94) 94
85.5%
ValueCountFrequency (%)
0 3
2.7%
2 2
1.8%
3 1
 
0.9%
4 1
 
0.9%
6 1
 
0.9%
8 3
2.7%
9 1
 
0.9%
10 1
 
0.9%
11 1
 
0.9%
13 1
 
0.9%
ValueCountFrequency (%)
34980 1
0.9%
33980 1
0.9%
33517 1
0.9%
32618 1
0.9%
31062 1
0.9%
30628 1
0.9%
28868 1
0.9%
28846 1
0.9%
28793 1
0.9%
28528 1
0.9%

858
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct95
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10888.1
Minimum0
Maximum83712
Zeros12
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:09:25.907786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q159.25
median1020.5
Q31822
95-th percentile73272.2
Maximum83712
Range83712
Interquartile range (IQR)1762.75

Descriptive statistics

Standard deviation23151.314
Coefficient of variation (CV)2.1262951
Kurtosis3.0417798
Mean10888.1
Median Absolute Deviation (MAD)954
Skewness2.1237694
Sum1197691
Variance5.3598333 × 108
MonotonicityNot monotonic
2023-12-11T12:09:26.063019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12
 
10.9%
73 2
 
1.8%
42 2
 
1.8%
1721 2
 
1.8%
43 2
 
1.8%
1035 1
 
0.9%
1711 1
 
0.9%
1620 1
 
0.9%
1640 1
 
0.9%
1988 1
 
0.9%
Other values (85) 85
77.3%
ValueCountFrequency (%)
0 12
10.9%
10 1
 
0.9%
13 1
 
0.9%
21 1
 
0.9%
29 1
 
0.9%
30 1
 
0.9%
36 1
 
0.9%
37 1
 
0.9%
39 1
 
0.9%
40 1
 
0.9%
ValueCountFrequency (%)
83712 1
0.9%
81474 1
0.9%
79606 1
0.9%
75690 1
0.9%
74785 1
0.9%
74642 1
0.9%
71598 1
0.9%
70977 1
0.9%
64392 1
0.9%
61080 1
0.9%

470
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct76
Distinct (%)69.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3104.4545
Minimum0
Maximum24018
Zeros33
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:09:26.193651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median303.5
Q3705
95-th percentile20823.3
Maximum24018
Range24018
Interquartile range (IQR)705

Descriptive statistics

Standard deviation6570.8755
Coefficient of variation (CV)2.1165958
Kurtosis3.4342949
Mean3104.4545
Median Absolute Deviation (MAD)303.5
Skewness2.1919776
Sum341490
Variance43176405
MonotonicityNot monotonic
2023-12-11T12:09:26.387523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33
30.0%
520 3
 
2.7%
306 1
 
0.9%
447 1
 
0.9%
792 1
 
0.9%
926 1
 
0.9%
166 1
 
0.9%
126 1
 
0.9%
648 1
 
0.9%
771 1
 
0.9%
Other values (66) 66
60.0%
ValueCountFrequency (%)
0 33
30.0%
50 1
 
0.9%
57 1
 
0.9%
60 1
 
0.9%
62 1
 
0.9%
70 1
 
0.9%
80 1
 
0.9%
100 1
 
0.9%
126 1
 
0.9%
130 1
 
0.9%
ValueCountFrequency (%)
24018 1
0.9%
23652 1
0.9%
23077 1
0.9%
22707 1
0.9%
22496 1
0.9%
21060 1
0.9%
20534 1
0.9%
20105 1
0.9%
18111 1
0.9%
17189 1
0.9%

255
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct68
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1415.4455
Minimum0
Maximum10071
Zeros34
Zeros (%)30.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:09:26.560364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median303.5
Q3813.75
95-th percentile8142.35
Maximum10071
Range10071
Interquartile range (IQR)813.75

Descriptive statistics

Standard deviation2634.0496
Coefficient of variation (CV)1.8609333
Kurtosis3.4983215
Mean1415.4455
Median Absolute Deviation (MAD)303.5
Skewness2.1617145
Sum155699
Variance6938217.5
MonotonicityNot monotonic
2023-12-11T12:09:26.706743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34
30.9%
100 4
 
3.6%
302 2
 
1.8%
750 2
 
1.8%
810 2
 
1.8%
390 2
 
1.8%
290 2
 
1.8%
160 2
 
1.8%
364 1
 
0.9%
305 1
 
0.9%
Other values (58) 58
52.7%
ValueCountFrequency (%)
0 34
30.9%
100 4
 
3.6%
103 1
 
0.9%
106 1
 
0.9%
110 1
 
0.9%
130 1
 
0.9%
150 1
 
0.9%
160 2
 
1.8%
201 1
 
0.9%
204 1
 
0.9%
ValueCountFrequency (%)
10071 1
0.9%
9982 1
0.9%
9895 1
0.9%
9419 1
0.9%
9298 1
0.9%
8321 1
0.9%
7924 1
0.9%
7238 1
0.9%
6382 1
0.9%
6358 1
0.9%

0.2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)37.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean442.02727
Minimum0
Maximum2921
Zeros51
Zeros (%)46.4%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:09:26.856066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median200
Q3497.5
95-th percentile2063.05
Maximum2921
Range2921
Interquartile range (IQR)497.5

Descriptive statistics

Standard deviation695.51994
Coefficient of variation (CV)1.5734774
Kurtosis3.4774805
Mean442.02727
Median Absolute Deviation (MAD)200
Skewness2.0173671
Sum48623
Variance483747.99
MonotonicityNot monotonic
2023-12-11T12:09:26.979218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 51
46.4%
200 12
 
10.9%
450 3
 
2.7%
850 3
 
2.7%
400 2
 
1.8%
300 2
 
1.8%
950 2
 
1.8%
250 2
 
1.8%
630 1
 
0.9%
1557 1
 
0.9%
Other values (31) 31
28.2%
ValueCountFrequency (%)
0 51
46.4%
200 12
 
10.9%
202 1
 
0.9%
210 1
 
0.9%
215 1
 
0.9%
220 1
 
0.9%
240 1
 
0.9%
250 2
 
1.8%
300 2
 
1.8%
400 2
 
1.8%
ValueCountFrequency (%)
2921 1
0.9%
2870 1
0.9%
2629 1
0.9%
2423 1
0.9%
2351 1
0.9%
2068 1
0.9%
2057 1
0.9%
1993 1
0.9%
1945 1
0.9%
1894 1
0.9%

0.3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)35.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean467.38182
Minimum0
Maximum3501
Zeros62
Zeros (%)56.4%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:09:27.137546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3687.5
95-th percentile2176.4
Maximum3501
Range3501
Interquartile range (IQR)687.5

Descriptive statistics

Standard deviation767.41222
Coefficient of variation (CV)1.6419385
Kurtosis4.1258876
Mean467.38182
Median Absolute Deviation (MAD)0
Skewness2.0797209
Sum51412
Variance588921.52
MonotonicityNot monotonic
2023-12-11T12:09:27.307721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 62
56.4%
300 4
 
3.6%
350 4
 
3.6%
600 3
 
2.7%
450 2
 
1.8%
700 2
 
1.8%
790 1
 
0.9%
359 1
 
0.9%
1800 1
 
0.9%
1500 1
 
0.9%
Other values (29) 29
26.4%
ValueCountFrequency (%)
0 62
56.4%
300 4
 
3.6%
313 1
 
0.9%
350 4
 
3.6%
352 1
 
0.9%
359 1
 
0.9%
380 1
 
0.9%
400 1
 
0.9%
450 2
 
1.8%
600 3
 
2.7%
ValueCountFrequency (%)
3501 1
0.9%
3076 1
0.9%
3040 1
0.9%
2910 1
0.9%
2190 1
0.9%
2180 1
0.9%
2172 1
0.9%
2096 1
0.9%
1960 1
0.9%
1800 1
0.9%

10
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1907
Minimum0
Maximum16500
Zeros46
Zeros (%)41.8%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T12:09:27.487899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median500
Q32345
95-th percentile8679.5
Maximum16500
Range16500
Interquartile range (IQR)2345

Descriptive statistics

Standard deviation3259.5757
Coefficient of variation (CV)1.7092689
Kurtosis6.7556138
Mean1907
Median Absolute Deviation (MAD)500
Skewness2.4763154
Sum209770
Variance10624834
MonotonicityNot monotonic
2023-12-11T12:09:27.677144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46
41.8%
500 6
 
5.5%
7500 2
 
1.8%
20 2
 
1.8%
2500 2
 
1.8%
550 2
 
1.8%
1200 2
 
1.8%
10 2
 
1.8%
1500 2
 
1.8%
1850 2
 
1.8%
Other values (40) 42
38.2%
ValueCountFrequency (%)
0 46
41.8%
10 2
 
1.8%
20 2
 
1.8%
30 1
 
0.9%
500 6
 
5.5%
530 1
 
0.9%
550 2
 
1.8%
600 1
 
0.9%
610 1
 
0.9%
650 1
 
0.9%
ValueCountFrequency (%)
16500 1
0.9%
16300 1
0.9%
11110 1
0.9%
10950 1
0.9%
9250 1
0.9%
8900 1
0.9%
8410 1
0.9%
7700 1
0.9%
7500 2
1.8%
6600 1
0.9%

Interactions

2023-12-11T12:09:18.864410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:47.715363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:49.840557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:51.820430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:54.321361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:56.162490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:58.107639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:00.245361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:02.385910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:04.351418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:06.105469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:08.135381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:09.752846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:11.311884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:13.639412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:15.416266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:16.897809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:18.974196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:47.853201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:49.960445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:51.972603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:54.437978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:56.291018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:58.230903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:00.357576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:02.514444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:04.457486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:06.199928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:08.244227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:09.832670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:11.430949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:13.756525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:15.511708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:17.006659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:19.084461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:47.976085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:50.068520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:52.105074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:54.536067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:56.426964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:58.330375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:00.480430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:02.637436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:04.564560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:06.646944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:08.334364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:09.910666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:11.558295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:13.877095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:15.592877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:17.106974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:19.229455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:48.099080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:50.179691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-11T12:09:13.309584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:15.153148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:16.599022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:18.256706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:20.603913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:49.585752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:51.592452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:54.017864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:55.936182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:57.915059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:00.020998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:02.097683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:04.154133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:05.901240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:07.925340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:09.564904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:11.072358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:13.403873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:15.229616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:16.686601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:18.359253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:20.719980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:49.712802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:51.716711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:54.183521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:56.053189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:08:58.009821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:00.134135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:02.232344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:04.250496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:06.007384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:08.013847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:09.655891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:11.183547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:13.529730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:15.322217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:16.802059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:09:18.753259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:09:27.852855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
거위1'94951887538200.11.1515825758584702550.20.310
거위1.0000.0000.0000.7860.7570.6880.6880.5420.5760.5120.4150.5630.5970.6880.6880.7230.4520.5120.326
10.0001.0001.0000.4770.5980.4970.4100.3910.3610.5420.5750.5340.6900.3320.3720.3160.5820.4420.471
'940.0001.0001.0000.0000.4430.0000.0000.0000.3850.5090.6480.0000.0000.0000.0000.0000.5720.5920.589
9510.7860.4770.0001.0000.9870.9770.9440.8700.9100.8240.0400.9540.9330.9930.9580.9220.8400.8730.000
8870.7570.5980.4430.9871.0000.9700.9400.8820.9420.8390.6340.9410.9030.9720.9680.9380.8560.8870.100
530.6880.4970.0000.9770.9701.0000.9580.8910.9330.8600.0000.9000.9370.9910.9840.9500.8180.8820.000
80.6880.4100.0000.9440.9400.9581.0000.8780.9190.8690.0000.8820.8730.9550.9820.9460.8040.8910.000
20.5420.3910.0000.8700.8820.8910.8781.0000.8780.7300.0000.9030.8810.8830.8990.9400.8210.7480.000
00.5760.3610.3850.9100.9420.9330.9190.8781.0000.8380.4630.8180.8400.9350.9400.9220.9480.8600.000
0.10.5120.5420.5090.8240.8390.8600.8690.7300.8381.0000.6230.7460.7280.8280.8960.8310.6850.9820.433
1.10.4150.5750.6480.0400.6340.0000.0000.0000.4630.6231.0000.4670.0000.0000.0000.1920.2630.5870.748
51580.5630.5340.0000.9540.9410.9000.8820.9030.8180.7460.4671.0000.9740.9330.8980.8570.9390.7890.281
25750.5970.6900.0000.9330.9030.9370.8730.8810.8400.7280.0000.9741.0000.9200.9080.8550.9390.8130.000
8580.6880.3320.0000.9930.9720.9910.9550.8830.9350.8280.0000.9330.9201.0000.9670.9460.8500.8800.000
4700.6880.3720.0000.9580.9680.9840.9820.8990.9400.8960.0000.8980.9080.9671.0000.9620.8470.9180.000
2550.7230.3160.0000.9220.9380.9500.9460.9400.9220.8310.1920.8570.8550.9460.9621.0000.8040.8470.000
0.20.4520.5820.5720.8400.8560.8180.8040.8210.9480.6850.2630.9390.9390.8500.8470.8041.0000.6640.000
0.30.5120.4420.5920.8730.8870.8820.8910.7480.8600.9820.5870.7890.8130.8800.9180.8470.6641.0000.451
100.3260.4710.5890.0000.1000.0000.0000.0000.0000.4330.7480.2810.0000.0000.0000.0000.0000.4511.000
2023-12-11T12:09:28.056369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
거위'94
거위1.0000.000
'940.0001.000
2023-12-11T12:09:28.196363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1951887538200.11.1515825758584702550.20.310거위'94
11.000-0.745-0.748-0.620-0.647-0.697-0.691-0.620-0.672-0.690-0.752-0.604-0.650-0.692-0.680-0.627-0.6380.0000.854
951-0.7451.0000.9980.9230.9100.9350.8660.7740.6530.9500.9920.9120.9070.9360.8720.7880.6080.4770.000
887-0.7480.9981.0000.9110.8990.9300.8640.7730.6530.9450.9920.9000.8960.9310.8700.7860.6100.4480.140
53-0.6200.9230.9111.0000.9430.9110.8310.7270.5480.9200.9160.9920.9410.9110.8280.7480.4900.3850.000
8-0.6470.9100.8990.9431.0000.9050.8450.7430.5340.8890.9040.9450.9970.9110.8510.7580.4940.3850.000
2-0.6970.9350.9300.9110.9051.0000.8750.7690.6220.9170.9300.8960.9050.9950.8750.7770.5660.3970.000
0-0.6910.8660.8640.8310.8450.8751.0000.7020.5220.8050.8660.8250.8470.8730.9430.7160.4720.2980.114
0.1-0.6200.7740.7730.7270.7430.7690.7021.0000.5220.7980.7830.7170.7390.7730.7250.9740.5370.2480.113
1.1-0.6720.6530.6530.5480.5340.6220.5220.5221.0000.7320.6420.5160.5240.6200.5310.5290.9660.1940.247
5158-0.6900.9500.9450.9200.8890.9170.8050.7980.7321.0000.9450.9040.8810.9180.8200.8070.6930.3920.000
2575-0.7520.9920.9920.9160.9040.9300.8660.7830.6420.9451.0000.9040.9020.9280.8720.7920.6040.4260.000
858-0.6040.9120.9000.9920.9450.8960.8250.7170.5160.9040.9041.0000.9440.8970.8230.7350.4580.3850.000
470-0.6500.9070.8960.9410.9970.9050.8470.7390.5240.8810.9020.9441.0000.9100.8470.7580.4800.3850.000
255-0.6920.9360.9310.9110.9110.9950.8730.7730.6200.9180.9280.8970.9101.0000.8790.7780.5590.4160.000
0.2-0.6800.8720.8700.8280.8510.8750.9430.7250.5310.8200.8720.8230.8470.8791.0000.7040.4840.2940.196
0.3-0.6270.7880.7860.7480.7580.7770.7160.9740.5290.8070.7920.7350.7580.7780.7041.0000.5310.2540.214
10-0.6380.6080.6100.4900.4940.5660.4720.5370.9660.6930.6040.4580.4800.5590.4840.5311.0000.2130.224
거위0.0000.4770.4480.3850.3850.3970.2980.2480.1940.3920.4260.3850.3850.4160.2940.2540.2131.0000.000
'940.8540.0000.1400.0000.0000.0000.1140.1130.2470.0000.0000.0000.0000.0000.1960.2140.2240.0001.000

Missing values

2023-12-11T12:09:20.917478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:09:21.202176image/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

2013거위1'94951887538200.11.1515825758584702550.20.310
02013거위2'9510359636153201600626651035306370630010
12013거위3'9611041034568410161762878960531607200010
22013거위4'971174110459433016191304710012613027800800
32013거위5'9812861175881071149955327412047128152004503300
42013거위6'9915381433839623211722377214805207504006004200
52013거위7'0016381517968841411843417914904308849103003650
62013거위8'0118071690821710413121624696146392411499803502600
72013거위9'0216561542929812210321425417215729342407501850
82013거위10'031574147278123522101904009144179045010506001850
92013거위11'04150113879311620290873744175068376045001700
2013거위1'94951887538200.11.1515825758584702550.20.310
1002013칠면조28경기2316301003788751360100007700
1012013칠면조29강원1131085000003843117300000
1022013칠면조30충북59554000002071485900000
1032013칠면조31충남555220000113821532900001200
1042013칠면조32전북585061010054416611662020000
1052013칠면조33전남32292100002038043800000
1062013칠면조34경북63566010003871361010150000
1072013칠면조35경남373420100023078420110000
1082013칠면조36제주1100000099000000
1092013칠면조37세종1100000022000000