Overview

Dataset statistics

Number of variables14
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory128.7 B

Variable types

Categorical1
Numeric12
Text1

Dataset

Description기타 가축통계(산양)
Author농림축산식품부
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220216000000001949

Alerts

2013 has constant value ""Constant
1 is highly overall correlated with 82575 and 9 other fieldsHigh correlation
82575 is highly overall correlated with 1 and 10 other fieldsHigh correlation
58853 is highly overall correlated with 1 and 10 other fieldsHigh correlation
12411 is highly overall correlated with 1 and 10 other fieldsHigh correlation
5343 is highly overall correlated with 1 and 10 other fieldsHigh correlation
2076 is highly overall correlated with 1 and 10 other fieldsHigh correlation
1757 is highly overall correlated with 1 and 10 other fieldsHigh correlation
1431 is highly overall correlated with 1 and 10 other fieldsHigh correlation
635 is highly overall correlated with 1 and 10 other fieldsHigh correlation
55 is highly overall correlated with 1 and 10 other fieldsHigh correlation
12 is highly overall correlated with 1 and 10 other fieldsHigh correlation
2 is highly overall correlated with 82575 and 9 other fieldsHigh correlation
1 has unique valuesUnique
'94 has unique valuesUnique
82575 has 2 (5.6%) zerosZeros
58853 has 3 (8.3%) zerosZeros
12411 has 3 (8.3%) zerosZeros
5343 has 3 (8.3%) zerosZeros
2076 has 2 (5.6%) zerosZeros
1757 has 3 (8.3%) zerosZeros
1431 has 3 (8.3%) zerosZeros
635 has 3 (8.3%) zerosZeros
55 has 8 (22.2%) zerosZeros
12 has 10 (27.8%) zerosZeros
2 has 13 (36.1%) zerosZeros

Reproduction

Analysis started2023-12-11 03:32:46.650627
Analysis finished2023-12-11 03:33:02.066359
Duration15.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

2013
Categorical

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size420.0 B
2013
36 

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 36
100.0%

Length

2023-12-11T12:33:02.162560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:33:02.293740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2013 36
100.0%

1
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.5
Minimum2
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T12:33:02.413665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3.75
Q110.75
median19.5
Q328.25
95-th percentile35.25
Maximum37
Range35
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation10.535654
Coefficient of variation (CV)0.54028994
Kurtosis-1.2
Mean19.5
Median Absolute Deviation (MAD)9
Skewness0
Sum702
Variance111
MonotonicityStrictly increasing
2023-12-11T12:33:02.562827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
2 1
 
2.8%
21 1
 
2.8%
23 1
 
2.8%
24 1
 
2.8%
25 1
 
2.8%
26 1
 
2.8%
27 1
 
2.8%
28 1
 
2.8%
29 1
 
2.8%
30 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
2 1
2.8%
3 1
2.8%
4 1
2.8%
5 1
2.8%
6 1
2.8%
7 1
2.8%
8 1
2.8%
9 1
2.8%
10 1
2.8%
11 1
2.8%
ValueCountFrequency (%)
37 1
2.8%
36 1
2.8%
35 1
2.8%
34 1
2.8%
33 1
2.8%
32 1
2.8%
31 1
2.8%
30 1
2.8%
29 1
2.8%
28 1
2.8%

'94
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-11T12:33:02.797252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4.5
Mean length2.6111111
Min length2

Characters and Unicode

Total characters94
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)100.0%

Sample

1st row'95
2nd row'96
3rd row'97
4th row'98
5th row'99
ValueCountFrequency (%)
95 1
 
2.8%
96 1
 
2.8%
경기 1
 
2.8%
부산 1
 
2.8%
대구 1
 
2.8%
인천 1
 
2.8%
광주 1
 
2.8%
대전 1
 
2.8%
울산 1
 
2.8%
강원 1
 
2.8%
Other values (26) 26
72.2%
2023-12-11T12:33:03.157736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 18
19.1%
0 12
 
12.8%
1 7
 
7.4%
9 7
 
7.4%
3
 
3.2%
3
 
3.2%
2 3
 
3.2%
3
 
3.2%
3
 
3.2%
8 2
 
2.1%
Other values (24) 33
35.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40
42.6%
Other Letter 34
36.2%
Other Punctuation 19
20.2%
Initial Punctuation 1
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3
 
8.8%
3
 
8.8%
3
 
8.8%
3
 
8.8%
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
1
 
2.9%
Other values (11) 11
32.4%
Decimal Number
ValueCountFrequency (%)
0 12
30.0%
1 7
17.5%
9 7
17.5%
2 3
 
7.5%
8 2
 
5.0%
7 2
 
5.0%
3 2
 
5.0%
6 2
 
5.0%
5 2
 
5.0%
4 1
 
2.5%
Other Punctuation
ValueCountFrequency (%)
' 18
94.7%
. 1
 
5.3%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60
63.8%
Hangul 34
36.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3
 
8.8%
3
 
8.8%
3
 
8.8%
3
 
8.8%
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
1
 
2.9%
Other values (11) 11
32.4%
Common
ValueCountFrequency (%)
' 18
30.0%
0 12
20.0%
1 7
 
11.7%
9 7
 
11.7%
2 3
 
5.0%
8 2
 
3.3%
7 2
 
3.3%
3 2
 
3.3%
6 2
 
3.3%
5 2
 
3.3%
Other values (3) 3
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59
62.8%
Hangul 34
36.2%
Punctuation 1
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 18
30.5%
0 12
20.3%
1 7
 
11.9%
9 7
 
11.9%
2 3
 
5.1%
8 2
 
3.4%
7 2
 
3.4%
3 2
 
3.4%
6 2
 
3.4%
5 2
 
3.4%
Other values (2) 2
 
3.4%
Hangul
ValueCountFrequency (%)
3
 
8.8%
3
 
8.8%
3
 
8.8%
3
 
8.8%
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
2
 
5.9%
1
 
2.9%
Other values (11) 11
32.4%
Punctuation
ValueCountFrequency (%)
1
100.0%

82575
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21448.361
Minimum0
Maximum82719
Zeros2
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T12:33:03.315506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q1202.25
median11316.5
Q341988.75
95-th percentile67157.75
Maximum82719
Range82719
Interquartile range (IQR)41786.5

Descriptive statistics

Standard deviation25422.555
Coefficient of variation (CV)1.1852913
Kurtosis-0.4070645
Mean21448.361
Median Absolute Deviation (MAD)11282
Skewness0.91906433
Sum772141
Variance6.463063 × 108
MonotonicityNot monotonic
2023-12-11T12:33:03.444821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 2
 
5.6%
76037 1
 
2.8%
4 1
 
2.8%
79 1
 
2.8%
65 1
 
2.8%
18 1
 
2.8%
101 1
 
2.8%
236 1
 
2.8%
624 1
 
2.8%
82719 1
 
2.8%
Other values (25) 25
69.4%
ValueCountFrequency (%)
0 2
5.6%
4 1
2.8%
17 1
2.8%
18 1
2.8%
51 1
2.8%
65 1
2.8%
79 1
2.8%
101 1
2.8%
236 1
2.8%
624 1
2.8%
ValueCountFrequency (%)
82719 1
2.8%
76037 1
2.8%
64198 1
2.8%
59775 1
2.8%
54171 1
2.8%
51585 1
2.8%
50824 1
2.8%
45231 1
2.8%
43008 1
2.8%
41649 1
2.8%

58853
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13424.194
Minimum0
Maximum57020
Zeros3
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T12:33:03.902345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145
median4918.5
Q324018.75
95-th percentile45699.75
Maximum57020
Range57020
Interquartile range (IQR)23973.75

Descriptive statistics

Standard deviation17149.336
Coefficient of variation (CV)1.2774946
Kurtosis0.10017784
Mean13424.194
Median Absolute Deviation (MAD)4916
Skewness1.1253857
Sum483271
Variance2.9409972 × 108
MonotonicityNot monotonic
2023-12-11T12:33:04.039411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 3
 
8.3%
424 1
 
2.8%
13 1
 
2.8%
16 1
 
2.8%
3 1
 
2.8%
18 1
 
2.8%
54 1
 
2.8%
233 1
 
2.8%
57020 1
 
2.8%
4356 1
 
2.8%
Other values (24) 24
66.7%
ValueCountFrequency (%)
0 3
8.3%
2 1
 
2.8%
3 1
 
2.8%
13 1
 
2.8%
15 1
 
2.8%
16 1
 
2.8%
18 1
 
2.8%
54 1
 
2.8%
233 1
 
2.8%
424 1
 
2.8%
ValueCountFrequency (%)
57020 1
2.8%
52458 1
2.8%
43447 1
2.8%
40614 1
2.8%
36874 1
2.8%
34733 1
2.8%
34058 1
2.8%
28834 1
2.8%
25854 1
2.8%
23407 1
2.8%

12411
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3768.2222
Minimum0
Maximum13297
Zeros3
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T12:33:04.185931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q133.25
median2378
Q37984.75
95-th percentile10526.5
Maximum13297
Range13297
Interquartile range (IQR)7951.5

Descriptive statistics

Standard deviation4192.8962
Coefficient of variation (CV)1.1126988
Kurtosis-0.95574202
Mean3768.2222
Median Absolute Deviation (MAD)2373.5
Skewness0.68795998
Sum135656
Variance17580379
MonotonicityNot monotonic
2023-12-11T12:33:04.307436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 3
 
8.3%
8391 2
 
5.6%
13297 1
 
2.8%
341 1
 
2.8%
7 1
 
2.8%
1 1
 
2.8%
16 1
 
2.8%
37 1
 
2.8%
163 1
 
2.8%
220 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
0 3
8.3%
1 1
 
2.8%
2 1
 
2.8%
7 1
 
2.8%
12 1
 
2.8%
16 1
 
2.8%
22 1
 
2.8%
37 1
 
2.8%
163 1
 
2.8%
220 1
 
2.8%
ValueCountFrequency (%)
13297 1
2.8%
11803 1
2.8%
10101 1
2.8%
9394 1
2.8%
8803 1
2.8%
8391 2
5.6%
8276 1
2.8%
8137 1
2.8%
7934 1
2.8%
7923 1
2.8%

5343
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1843.0556
Minimum0
Maximum5591
Zeros3
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T12:33:04.425534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q139.25
median1572.5
Q33811
95-th percentile4670.75
Maximum5591
Range5591
Interquartile range (IQR)3771.75

Descriptive statistics

Standard deviation1925.4586
Coefficient of variation (CV)1.04471
Kurtosis-1.403146
Mean1843.0556
Median Absolute Deviation (MAD)1562
Skewness0.48497421
Sum66350
Variance3707391
MonotonicityNot monotonic
2023-12-11T12:33:04.579031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 3
 
8.3%
229 2
 
5.6%
87 1
 
2.8%
14 1
 
2.8%
13 1
 
2.8%
4 1
 
2.8%
25 1
 
2.8%
44 1
 
2.8%
5591 1
 
2.8%
5099 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
0 3
8.3%
3 1
 
2.8%
4 1
 
2.8%
8 1
 
2.8%
13 1
 
2.8%
14 1
 
2.8%
25 1
 
2.8%
44 1
 
2.8%
87 1
 
2.8%
119 1
 
2.8%
ValueCountFrequency (%)
5591 1
2.8%
5099 1
2.8%
4528 1
2.8%
4506 1
2.8%
4505 1
2.8%
4220 1
2.8%
4063 1
2.8%
3876 1
2.8%
3820 1
2.8%
3808 1
2.8%

2076
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean758.02778
Minimum0
Maximum2229
Zeros2
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T12:33:04.731610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.75
Q119
median701
Q31539
95-th percentile1873.75
Maximum2229
Range2229
Interquartile range (IQR)1520

Descriptive statistics

Standard deviation768.27556
Coefficient of variation (CV)1.013519
Kurtosis-1.4874863
Mean758.02778
Median Absolute Deviation (MAD)693
Skewness0.39998876
Sum27289
Variance590247.34
MonotonicityNot monotonic
2023-12-11T12:33:04.872580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 2
 
5.6%
4 2
 
5.6%
37 1
 
2.8%
1 1
 
2.8%
12 1
 
2.8%
16 1
 
2.8%
3 1
 
2.8%
13 1
 
2.8%
20 1
 
2.8%
2229 1
 
2.8%
Other values (24) 24
66.7%
ValueCountFrequency (%)
0 2
5.6%
1 1
2.8%
3 1
2.8%
4 2
5.6%
12 1
2.8%
13 1
2.8%
16 1
2.8%
20 1
2.8%
37 1
2.8%
70 1
2.8%
ValueCountFrequency (%)
2229 1
2.8%
2020 1
2.8%
1825 1
2.8%
1770 1
2.8%
1748 1
2.8%
1707 1
2.8%
1610 1
2.8%
1579 1
2.8%
1578 1
2.8%
1526 1
2.8%

1757
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean687.77778
Minimum0
Maximum1870
Zeros3
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T12:33:05.007674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q129
median630.5
Q31396
95-th percentile1764
Maximum1870
Range1870
Interquartile range (IQR)1367

Descriptive statistics

Standard deviation701.53766
Coefficient of variation (CV)1.0200063
Kurtosis-1.5375534
Mean687.77778
Median Absolute Deviation (MAD)624
Skewness0.40754379
Sum24760
Variance492155.09
MonotonicityNot monotonic
2023-12-11T12:33:05.152831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 3
 
8.3%
90 2
 
5.6%
74 2
 
5.6%
1870 1
 
2.8%
708 1
 
2.8%
5 1
 
2.8%
67 1
 
2.8%
80 1
 
2.8%
52 1
 
2.8%
35 1
 
2.8%
Other values (22) 22
61.1%
ValueCountFrequency (%)
0 3
8.3%
1 1
 
2.8%
3 1
 
2.8%
5 1
 
2.8%
8 1
 
2.8%
9 1
 
2.8%
11 1
 
2.8%
35 1
 
2.8%
52 1
 
2.8%
67 1
 
2.8%
ValueCountFrequency (%)
1870 1
2.8%
1860 1
2.8%
1732 1
2.8%
1655 1
2.8%
1629 1
2.8%
1621 1
2.8%
1531 1
2.8%
1479 1
2.8%
1435 1
2.8%
1383 1
2.8%

1431
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean555.97222
Minimum0
Maximum1703
Zeros3
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T12:33:05.325493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q119.75
median485
Q31108.5
95-th percentile1541.5
Maximum1703
Range1703
Interquartile range (IQR)1088.75

Descriptive statistics

Standard deviation581.1805
Coefficient of variation (CV)1.0453409
Kurtosis-1.2268825
Mean555.97222
Median Absolute Deviation (MAD)481.5
Skewness0.54226288
Sum20015
Variance337770.77
MonotonicityNot monotonic
2023-12-11T12:33:05.481686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 3
 
8.3%
78 1
 
2.8%
7 1
 
2.8%
4 1
 
2.8%
2 1
 
2.8%
10 1
 
2.8%
27 1
 
2.8%
23 1
 
2.8%
1703 1
 
2.8%
496 1
 
2.8%
Other values (24) 24
66.7%
ValueCountFrequency (%)
0 3
8.3%
1 1
 
2.8%
2 1
 
2.8%
3 1
 
2.8%
4 1
 
2.8%
7 1
 
2.8%
10 1
 
2.8%
23 1
 
2.8%
27 1
 
2.8%
47 1
 
2.8%
ValueCountFrequency (%)
1703 1
2.8%
1651 1
2.8%
1505 1
2.8%
1333 1
2.8%
1331 1
2.8%
1263 1
2.8%
1156 1
2.8%
1153 1
2.8%
1149 1
2.8%
1095 1
2.8%

635
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean348.19444
Minimum0
Maximum1032
Zeros3
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T12:33:05.629060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median323.5
Q3645.5
95-th percentile939.75
Maximum1032
Range1032
Interquartile range (IQR)631.5

Descriptive statistics

Standard deviation347.0733
Coefficient of variation (CV)0.99678012
Kurtosis-1.3155014
Mean348.19444
Median Absolute Deviation (MAD)311.5
Skewness0.43328588
Sum12535
Variance120459.88
MonotonicityNot monotonic
2023-12-11T12:33:05.800832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2 3
 
8.3%
0 3
 
8.3%
4 2
 
5.6%
937 1
 
2.8%
1032 1
 
2.8%
44 1
 
2.8%
93 1
 
2.8%
66 1
 
2.8%
48 1
 
2.8%
35 1
 
2.8%
Other values (21) 21
58.3%
ValueCountFrequency (%)
0 3
8.3%
2 3
8.3%
4 2
5.6%
8 1
 
2.8%
16 1
 
2.8%
25 1
 
2.8%
35 1
 
2.8%
44 1
 
2.8%
48 1
 
2.8%
51 1
 
2.8%
ValueCountFrequency (%)
1032 1
2.8%
948 1
2.8%
937 1
2.8%
789 1
2.8%
764 1
2.8%
757 1
2.8%
753 1
2.8%
736 1
2.8%
692 1
2.8%
630 1
2.8%

55
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.472222
Minimum0
Maximum103
Zeros8
Zeros (%)22.2%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T12:33:05.942962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.75
median56.5
Q380.25
95-th percentile96
Maximum103
Range103
Interquartile range (IQR)78.5

Descriptive statistics

Standard deviation39.073912
Coefficient of variation (CV)0.89882482
Kurtosis-1.7633627
Mean43.472222
Median Absolute Deviation (MAD)39.5
Skewness0.083602179
Sum1565
Variance1526.7706
MonotonicityNot monotonic
2023-12-11T12:33:06.128295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 8
22.2%
59 2
 
5.6%
91 2
 
5.6%
2 2
 
5.6%
68 1
 
2.8%
5 1
 
2.8%
13 1
 
2.8%
24 1
 
2.8%
16 1
 
2.8%
8 1
 
2.8%
Other values (16) 16
44.4%
ValueCountFrequency (%)
0 8
22.2%
1 1
 
2.8%
2 2
 
5.6%
5 1
 
2.8%
8 1
 
2.8%
10 1
 
2.8%
13 1
 
2.8%
16 1
 
2.8%
24 1
 
2.8%
56 1
 
2.8%
ValueCountFrequency (%)
103 1
2.8%
99 1
2.8%
95 1
2.8%
92 1
2.8%
91 2
5.6%
89 1
2.8%
86 1
2.8%
81 1
2.8%
80 1
2.8%
79 1
2.8%

12
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.722222
Minimum0
Maximum53
Zeros10
Zeros (%)27.8%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T12:33:06.283916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12
Q325
95-th percentile45.5
Maximum53
Range53
Interquartile range (IQR)25

Descriptive statistics

Standard deviation15.882805
Coefficient of variation (CV)1.0102137
Kurtosis-0.38366975
Mean15.722222
Median Absolute Deviation (MAD)12
Skewness0.78182432
Sum566
Variance252.26349
MonotonicityNot monotonic
2023-12-11T12:33:06.434957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 10
27.8%
12 3
 
8.3%
23 2
 
5.6%
35 2
 
5.6%
2 2
 
5.6%
24 1
 
2.8%
6 1
 
2.8%
7 1
 
2.8%
14 1
 
2.8%
1 1
 
2.8%
Other values (12) 12
33.3%
ValueCountFrequency (%)
0 10
27.8%
1 1
 
2.8%
2 2
 
5.6%
6 1
 
2.8%
7 1
 
2.8%
9 1
 
2.8%
12 3
 
8.3%
14 1
 
2.8%
16 1
 
2.8%
17 1
 
2.8%
ValueCountFrequency (%)
53 1
2.8%
50 1
2.8%
44 1
2.8%
39 1
2.8%
35 2
5.6%
32 1
2.8%
31 1
2.8%
28 1
2.8%
24 1
2.8%
23 2
5.6%

2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)36.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7222222
Minimum0
Maximum12
Zeros13
Zeros (%)36.1%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-11T12:33:06.610633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.5
Q37.25
95-th percentile11
Maximum12
Range12
Interquartile range (IQR)7.25

Descriptive statistics

Standard deviation4.0398806
Coefficient of variation (CV)1.085341
Kurtosis-1.0146705
Mean3.7222222
Median Absolute Deviation (MAD)2.5
Skewness0.68218102
Sum134
Variance16.320635
MonotonicityNot monotonic
2023-12-11T12:33:06.791191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 13
36.1%
1 4
 
11.1%
3 3
 
8.3%
9 3
 
8.3%
4 2
 
5.6%
6 2
 
5.6%
11 2
 
5.6%
8 2
 
5.6%
2 1
 
2.8%
5 1
 
2.8%
Other values (3) 3
 
8.3%
ValueCountFrequency (%)
0 13
36.1%
1 4
 
11.1%
2 1
 
2.8%
3 3
 
8.3%
4 2
 
5.6%
5 1
 
2.8%
6 2
 
5.6%
7 1
 
2.8%
8 2
 
5.6%
9 3
 
8.3%
ValueCountFrequency (%)
12 1
 
2.8%
11 2
5.6%
10 1
 
2.8%
9 3
8.3%
8 2
5.6%
7 1
 
2.8%
6 2
5.6%
5 1
 
2.8%
4 2
5.6%
3 3
8.3%

Interactions

2023-12-11T12:33:00.488697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:47.214611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:48.163563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:49.295160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:50.933761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:52.016195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:53.272217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:54.453629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:55.506308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:56.563634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:58.105060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:59.264577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:33:00.580180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:47.283329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:48.252880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:49.396406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:51.045233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:52.113355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:53.379671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:54.545555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:55.591855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:56.971879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:58.199633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:59.373008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:33:00.677979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:47.348313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:48.339154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:49.499315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:51.132809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:52.194638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:53.475810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:54.647967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:55.676557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:57.067543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:58.281601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:59.471336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:33:00.771794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:47.422922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:48.433303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:49.597087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:51.217048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:52.296921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:53.599132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:54.734891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:55.755333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:57.172557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:58.349868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:59.572676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:33:00.885280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:47.522458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:48.536399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:49.700851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:51.307055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:52.405473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:53.739335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:54.824727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:55.834966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:57.291695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:58.443028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:59.691111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:33:00.986847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:47.608534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:48.631395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:49.816845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:51.406673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:52.515699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:53.858295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:54.909595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:55.915645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:57.390497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:58.527135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:59.779555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:33:01.063757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:47.679344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:48.714572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:49.921802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:51.486473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:52.642817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:53.939059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:54.991097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:55.992701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:57.492566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:58.603866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:59.875403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:33:01.189100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:47.759468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:48.850925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:50.041551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:51.583799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:52.796639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:54.036602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:55.080954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:56.089086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:57.612457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:58.707680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:59.986784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:33:01.278101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:47.840257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:48.945226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:50.150087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:51.664248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:52.930052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:54.129334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:55.169518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:56.183597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:57.731242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:58.825272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:33:00.118683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:33:01.369505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:47.919127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:49.031280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:50.272900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:51.749446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:53.012805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:54.205488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:55.249583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:56.269063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:57.820565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:58.936695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:33:00.210470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:33:01.463333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:47.994100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:49.116618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:50.380623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:51.830547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:53.086637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:54.279353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:55.331662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:56.357965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:57.913365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:59.027402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:33:00.298093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:33:01.560300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:48.069402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:49.206084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:50.828199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:51.916801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:53.176857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:54.359755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:55.413356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:56.461824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:58.004867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:32:59.138520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:33:00.388914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:33:06.902785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1'94825755885312411534320761757143163555122
11.0001.0000.8560.8440.7750.8050.7410.8650.7640.8560.4840.8730.826
'941.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
825750.8561.0001.0000.9880.9840.9160.9230.9290.9180.9280.7320.8210.856
588530.8441.0000.9881.0000.9600.8750.8920.8950.8690.9230.7310.7770.882
124110.7751.0000.9840.9601.0000.9240.9080.8910.9170.8790.6420.8280.847
53430.8051.0000.9160.8750.9241.0000.9790.9410.9870.9020.8060.8350.793
20760.7411.0000.9230.8920.9080.9791.0000.9300.9920.8910.8250.7440.810
17570.8651.0000.9290.8950.8910.9410.9301.0000.9490.9430.6710.7460.837
14310.7641.0000.9180.8690.9170.9870.9920.9491.0000.9110.7810.7830.764
6350.8561.0000.9280.9230.8790.9020.8910.9430.9111.0000.7320.7620.870
550.4841.0000.7320.7310.6420.8060.8250.6710.7810.7321.0000.7300.780
120.8731.0000.8210.7770.8280.8350.7440.7460.7830.7620.7301.0000.808
20.8261.0000.8560.8820.8470.7930.8100.8370.7640.8700.7800.8081.000
2023-12-11T12:33:07.054519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1825755885312411534320761757143163555122
11.000-0.837-0.834-0.831-0.821-0.812-0.822-0.818-0.789-0.697-0.552-0.454
82575-0.8371.0000.9970.9960.9820.9720.9730.9750.9540.8310.6940.557
58853-0.8340.9971.0000.9930.9780.9680.9690.9710.9500.8290.6970.560
12411-0.8310.9960.9931.0000.9860.9750.9740.9790.9550.8420.7010.560
5343-0.8210.9820.9780.9861.0000.9910.9910.9880.9660.8770.7340.598
2076-0.8120.9720.9680.9750.9911.0000.9920.9890.9670.8880.7440.592
1757-0.8220.9730.9690.9740.9910.9921.0000.9940.9730.8840.7480.602
1431-0.8180.9750.9710.9790.9880.9890.9941.0000.9750.8800.7390.593
635-0.7890.9540.9500.9550.9660.9670.9730.9751.0000.8800.7520.584
55-0.6970.8310.8290.8420.8770.8880.8840.8800.8801.0000.8760.740
12-0.5520.6940.6970.7010.7340.7440.7480.7390.7520.8761.0000.846
2-0.4540.5570.5600.5600.5980.5920.6020.5930.5840.7400.8461.000

Missing values

2023-12-11T12:33:01.748547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:33:01.964447image/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

20131'94825755885312411534320761757143163555122
020132'95827195702013297559122291870170393759121
120133'967603752458118035099202018601651103210392
220134'97641984344710101452818251732150594892200
320135'9859775406149394422017071621133178980163
420136'9954171368748803380814371383115663064124
520137'0051585347338391382015781348106457157194
620138'0150824340588391387615151325104053859175
720139'0245231288347934374015261435109558156237
8201310'0343008258547923406316101531114975789239
9201311'04416492340781374506174816291333753913510
20131'94825755885312411534320761757143163555122
26201328경기23654374420352716201
27201329강원6242331638737522325220
28201330충북13254243412291118078511010
29201331충남127659326717770744735823
30201332전북11124982201197567574816120
31201333전남17587683542818990666624146
32201334경북139545727123411890111931371
33201335경남22301262448229102746044560
34201336제주172234014001
35201337세종51151284534000