Overview

Dataset statistics

Number of variables20
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.7 KiB
Average record size in memory181.3 B

Variable types

Categorical14
Numeric6

Alerts

bf_strt4 has constant value ""Constant
bf_strt5 has constant value ""Constant
now_str4 has constant value ""Constant
now_str5 has constant value ""Constant
af_str1 has constant value ""Constant
af_str2 has constant value ""Constant
af_str4 has constant value ""Constant
af_str5 has constant value ""Constant
now_str1 is highly overall correlated with max_race_day and 2 other fieldsHigh correlation
now_str2 is highly overall correlated with max_race_day and 2 other fieldsHigh correlation
now_str3 is highly overall correlated with max_race_day and 3 other fieldsHigh correlation
tms is highly overall correlated with stnd_yearHigh correlation
bf_strt1 is highly overall correlated with bf_strt2 and 1 other fieldsHigh correlation
bf_strt2 is highly overall correlated with bf_strt1 and 1 other fieldsHigh correlation
max_race_day is highly overall correlated with stnd_year and 3 other fieldsHigh correlation
stnd_year is highly overall correlated with tms and 4 other fieldsHigh correlation
dmag_cd is highly overall correlated with bf_strt1 and 1 other fieldsHigh correlation
now_str1 is highly imbalanced (85.9%)Imbalance
now_str2 is highly imbalanced (91.9%)Imbalance
now_str3 is highly imbalanced (89.8%)Imbalance
af_str3 is highly imbalanced (89.8%)Imbalance
cfm_insp has 25 (25.0%) zerosZeros
bf_strt1 has 56 (56.0%) zerosZeros
bf_strt2 has 55 (55.0%) zerosZeros
bf_strt3 has 81 (81.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:05:17.364551
Analysis finished2023-12-10 10:05:25.884628
Duration8.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

stnd_year
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2019
81 
2020
16 
2021
 
3

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2021
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 81
81.0%
2020 16
 
16.0%
2021 3
 
3.0%

Length

2023-12-10T19:05:26.011622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:26.181351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 81
81.0%
2020 16
 
16.0%
2021 3
 
3.0%

tms
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.79
Minimum1
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:05:26.423481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.95
Q118
median30
Q341.25
95-th percentile49
Maximum51
Range50
Interquartile range (IQR)23.25

Descriptive statistics

Standard deviation14.156624
Coefficient of variation (CV)0.49172018
Kurtosis-1.007687
Mean28.79
Median Absolute Deviation (MAD)12
Skewness-0.2602447
Sum2879
Variance200.41
MonotonicityNot monotonic
2023-12-10T19:05:26.793237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
43 4
 
4.0%
30 4
 
4.0%
46 4
 
4.0%
45 3
 
3.0%
29 3
 
3.0%
7 2
 
2.0%
35 2
 
2.0%
23 2
 
2.0%
24 2
 
2.0%
25 2
 
2.0%
Other values (39) 72
72.0%
ValueCountFrequency (%)
1 1
1.0%
2 2
2.0%
3 2
2.0%
4 1
1.0%
5 2
2.0%
7 2
2.0%
8 1
1.0%
10 2
2.0%
11 1
1.0%
12 2
2.0%
ValueCountFrequency (%)
51 2
2.0%
50 2
2.0%
49 2
2.0%
48 2
2.0%
47 2
2.0%
46 4
4.0%
45 3
3.0%
44 2
2.0%
43 4
4.0%
42 2
2.0%

dmag_cd
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2
52 
1
48 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 52
52.0%
1 48
48.0%

Length

2023-12-10T19:05:27.184715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:27.481472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 52
52.0%
1 48
48.0%

cfm_insp
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.41
Minimum0
Maximum13
Zeros25
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:05:27.739617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median2
Q33.25
95-th percentile7.05
Maximum13
Range13
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.4826468
Coefficient of variation (CV)1.0301439
Kurtosis3.4591766
Mean2.41
Median Absolute Deviation (MAD)1.5
Skewness1.6311416
Sum241
Variance6.1635354
MonotonicityNot monotonic
2023-12-10T19:05:27.960255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 25
25.0%
2 20
20.0%
1 17
17.0%
3 13
13.0%
4 11
11.0%
5 4
 
4.0%
6 3
 
3.0%
7 2
 
2.0%
9 2
 
2.0%
8 1
 
1.0%
Other values (2) 2
 
2.0%
ValueCountFrequency (%)
0 25
25.0%
1 17
17.0%
2 20
20.0%
3 13
13.0%
4 11
11.0%
5 4
 
4.0%
6 3
 
3.0%
7 2
 
2.0%
8 1
 
1.0%
9 2
 
2.0%
ValueCountFrequency (%)
13 1
 
1.0%
10 1
 
1.0%
9 2
 
2.0%
8 1
 
1.0%
7 2
 
2.0%
6 3
 
3.0%
5 4
 
4.0%
4 11
11.0%
3 13
13.0%
2 20
20.0%

bf_strt1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.32
Minimum0
Maximum7
Zeros56
Zeros (%)56.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:05:28.207145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9430269
Coefficient of variation (CV)1.4719901
Kurtosis0.67314494
Mean1.32
Median Absolute Deviation (MAD)0
Skewness1.3824813
Sum132
Variance3.7753535
MonotonicityNot monotonic
2023-12-10T19:05:28.474538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 56
56.0%
1 13
 
13.0%
2 10
 
10.0%
5 6
 
6.0%
6 5
 
5.0%
4 5
 
5.0%
3 4
 
4.0%
7 1
 
1.0%
ValueCountFrequency (%)
0 56
56.0%
1 13
 
13.0%
2 10
 
10.0%
3 4
 
4.0%
4 5
 
5.0%
5 6
 
6.0%
6 5
 
5.0%
7 1
 
1.0%
ValueCountFrequency (%)
7 1
 
1.0%
6 5
 
5.0%
5 6
 
6.0%
4 5
 
5.0%
3 4
 
4.0%
2 10
 
10.0%
1 13
 
13.0%
0 56
56.0%

bf_strt2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.33
Minimum0
Maximum6
Zeros55
Zeros (%)55.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:05:28.678159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.25
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation1.8370761
Coefficient of variation (CV)1.3812602
Kurtosis0.0056678345
Mean1.33
Median Absolute Deviation (MAD)0
Skewness1.1548231
Sum133
Variance3.3748485
MonotonicityNot monotonic
2023-12-10T19:05:28.859420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 55
55.0%
1 12
 
12.0%
2 8
 
8.0%
3 8
 
8.0%
4 7
 
7.0%
5 7
 
7.0%
6 3
 
3.0%
ValueCountFrequency (%)
0 55
55.0%
1 12
 
12.0%
2 8
 
8.0%
3 8
 
8.0%
4 7
 
7.0%
5 7
 
7.0%
6 3
 
3.0%
ValueCountFrequency (%)
6 3
 
3.0%
5 7
 
7.0%
4 7
 
7.0%
3 8
 
8.0%
2 8
 
8.0%
1 12
 
12.0%
0 55
55.0%

bf_strt3
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47
Minimum0
Maximum7
Zeros81
Zeros (%)81.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:05:29.167188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1930353
Coefficient of variation (CV)2.5383731
Kurtosis11.90724
Mean0.47
Median Absolute Deviation (MAD)0
Skewness3.2409092
Sum47
Variance1.4233333
MonotonicityNot monotonic
2023-12-10T19:05:29.381089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 81
81.0%
2 8
 
8.0%
1 5
 
5.0%
3 3
 
3.0%
5 2
 
2.0%
7 1
 
1.0%
ValueCountFrequency (%)
0 81
81.0%
1 5
 
5.0%
2 8
 
8.0%
3 3
 
3.0%
5 2
 
2.0%
7 1
 
1.0%
ValueCountFrequency (%)
7 1
 
1.0%
5 2
 
2.0%
3 3
 
3.0%
2 8
 
8.0%
1 5
 
5.0%
0 81
81.0%

bf_strt4
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
100 

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 (%)
0 100
100.0%

Length

2023-12-10T19:05:29.656732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:29.830569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

bf_strt5
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
100 

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 (%)
0 100
100.0%

Length

2023-12-10T19:05:30.005084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:30.185562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

now_str1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
98 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 98
98.0%
1 2
 
2.0%

Length

2023-12-10T19:05:30.377586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:30.596000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 98
98.0%
1 2
 
2.0%

now_str2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
99 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row0
2nd row2
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 99
99.0%
2 1
 
1.0%

Length

2023-12-10T19:05:30.767936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:30.936504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 99
99.0%
2 1
 
1.0%

now_str3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
98 
2
 
1
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row0
2nd row2
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 98
98.0%
2 1
 
1.0%
1 1
 
1.0%

Length

2023-12-10T19:05:31.105024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:31.279214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 98
98.0%
2 1
 
1.0%
1 1
 
1.0%

now_str4
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
100 

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 (%)
0 100
100.0%

Length

2023-12-10T19:05:31.479608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:31.654224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

now_str5
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
100 

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 (%)
0 100
100.0%

Length

2023-12-10T19:05:31.814893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:31.975611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

af_str1
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
100 

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 (%)
0 100
100.0%

Length

2023-12-10T19:05:32.141500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:32.308295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

af_str2
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
100 

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 (%)
0 100
100.0%

Length

2023-12-10T19:05:32.499458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:32.713467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

af_str3
Categorical

IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
98 
8
 
1
22
 
1

Length

Max length2
Median length1
Mean length1.01
Min length1

Unique

Unique2 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 98
98.0%
8 1
 
1.0%
22 1
 
1.0%

Length

2023-12-10T19:05:32.904737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:33.124533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 98
98.0%
8 1
 
1.0%
22 1
 
1.0%

af_str4
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
100 

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 (%)
0 100
100.0%

Length

2023-12-10T19:05:33.433576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:33.701019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

af_str5
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
100 

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 (%)
0 100
100.0%

Length

2023-12-10T19:05:33.870816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:05:34.071673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

max_race_day
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20192933
Minimum20190310
Maximum20210725
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:05:34.660360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190310
5-th percentile20190331
Q120190614
median20190908
Q320191210
95-th percentile20201115
Maximum20210725
Range20415
Interquartile range (IQR)595.5

Descriptive statistics

Standard deviation4754.214
Coefficient of variation (CV)0.0002354395
Kurtosis4.3594553
Mean20192933
Median Absolute Deviation (MAD)299.5
Skewness2.2000406
Sum2.0192933 × 109
Variance22602551
MonotonicityNot monotonic
2023-12-10T19:05:34.947673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20190526 2
 
2.0%
20191103 2
 
2.0%
20190609 2
 
2.0%
20190616 2
 
2.0%
20190623 2
 
2.0%
20190630 2
 
2.0%
20190707 2
 
2.0%
20190714 2
 
2.0%
20190825 2
 
2.0%
20190901 2
 
2.0%
Other values (44) 80
80.0%
ValueCountFrequency (%)
20190310 2
2.0%
20190317 1
1.0%
20190324 2
2.0%
20190331 2
2.0%
20190407 1
1.0%
20190414 2
2.0%
20190421 2
2.0%
20190428 1
1.0%
20190505 2
2.0%
20190512 2
2.0%
ValueCountFrequency (%)
20210725 2
2.0%
20210718 1
1.0%
20201122 2
2.0%
20201115 1
1.0%
20201101 2
2.0%
20200222 1
1.0%
20200216 2
2.0%
20200202 2
2.0%
20200127 1
1.0%
20200119 2
2.0%

Interactions

2023-12-10T19:05:23.935308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:18.628873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:19.851228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:20.891996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:22.159081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:23.017567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:24.103315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:18.825511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:20.030544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:21.045263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:22.289661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:23.165330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:24.250153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:19.008015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:20.203576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:21.199882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:22.427130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:23.321962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:24.446632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:19.182647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:20.377535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:21.362497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:22.574388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:23.483366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:24.653446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:19.406641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:20.544876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:21.510152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:22.710702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:23.619852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:24.858832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:19.669602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:20.709904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:21.680837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:22.849442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:05:23.768946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:05:35.141411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
stnd_yeartmsdmag_cdcfm_inspbf_strt1bf_strt2bf_strt3now_str1now_str2now_str3af_str3max_race_day
stnd_year1.0000.7440.0000.4650.0000.0000.0000.5410.3500.8690.4701.000
tms0.7441.0000.0000.1880.2080.2080.0000.2950.0000.0000.0000.831
dmag_cd0.0000.0001.0000.2290.8670.6620.5770.0000.0000.0000.0280.000
cfm_insp0.4650.1880.2291.0000.3910.4890.0000.3440.5160.6400.0000.326
bf_strt10.0000.2080.8670.3911.0000.5740.3430.0000.2000.1560.0000.000
bf_strt20.0000.2080.6620.4890.5741.0000.5490.0000.2280.0710.0000.000
bf_strt30.0000.0000.5770.0000.3430.5491.0000.0000.0000.0000.0000.124
now_str10.5410.2950.0000.3440.0000.0000.0001.0000.4971.0000.0000.954
now_str20.3500.0000.0000.5160.2000.2280.0000.4971.0001.0000.0000.761
now_str30.8690.0000.0000.6400.1560.0710.0001.0001.0001.0000.0000.550
af_str30.4700.0000.0280.0000.0000.0000.0000.0000.0000.0001.0000.342
max_race_day1.0000.8310.0000.3260.0000.0000.1240.9540.7610.5500.3421.000
2023-12-10T19:05:35.373979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
now_str1af_str3now_str2now_str3dmag_cdstnd_year
now_str11.0000.0000.3310.9950.0000.804
af_str30.0001.0000.0000.0000.0440.185
now_str20.3310.0001.0000.9950.0000.556
now_str30.9950.0000.9951.0000.0000.562
dmag_cd0.0000.0440.0000.0001.0000.000
stnd_year0.8040.1850.5560.5620.0001.000
2023-12-10T19:05:35.582646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
tmscfm_inspbf_strt1bf_strt2bf_strt3max_race_daystnd_yeardmag_cdnow_str1now_str2now_str3af_str3
tms1.0000.2500.0650.0550.1040.4290.5800.0000.2150.0000.0000.000
cfm_insp0.2501.0000.1750.3220.1890.3350.2230.2180.3300.4980.3440.000
bf_strt10.0650.1751.0000.5260.4110.1880.0000.6670.0000.1430.0930.000
bf_strt20.0550.3220.5261.0000.4580.0500.0000.6960.0000.2370.0390.000
bf_strt30.1040.1890.4110.4581.0000.0760.0000.4100.0000.0000.0000.000
max_race_day0.4290.3350.1880.0500.0761.0000.9950.0000.7970.5470.5530.330
stnd_year0.5800.2230.0000.0000.0000.9951.0000.0000.8040.5560.5620.185
dmag_cd0.0000.2180.6670.6960.4100.0000.0001.0000.0000.0000.0000.044
now_str10.2150.3300.0000.0000.0000.7970.8040.0001.0000.3310.9950.000
now_str20.0000.4980.1430.2370.0000.5470.5560.0000.3311.0000.9950.000
now_str30.0000.3440.0930.0390.0000.5530.5620.0000.9950.9951.0000.000
af_str30.0000.0000.0000.0000.0000.3300.1850.0440.0000.0000.0001.000

Missing values

2023-12-10T19:05:25.140092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:05:25.683552image/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

stnd_yeartmsdmag_cdcfm_inspbf_strt1bf_strt2bf_strt3bf_strt4bf_strt5now_str1now_str2now_str3now_str4now_str5af_str1af_str2af_str3af_str4af_str5max_race_day
02019211400000000000000020190526
12021292822000122000000020210718
22019142401200000000000020190407
32019151000000000000000020190414
42019152204000000000000020190414
52019161101000000000000020190421
62019162052000000000000020190421
72021301100000000000000020210725
82019172115000000000000020190428
92019181400000000000000020190505
stnd_yeartmsdmag_cdcfm_inspbf_strt1bf_strt2bf_strt3bf_strt4bf_strt5now_str1now_str2now_str3now_str4now_str5af_str1af_str2af_str3af_str4af_str5max_race_day
90202072233200000000000020200216
91202011000000000000000020200105
922020221012000000000000020200112
932020821365000000000000020200222
94202051210000000000000020200202
95202052220000000000000020200202
96202031000000000000000020200119
97202032354200000000000020200119
9820204615000000000000220020201122
992020462603100000000000020201122