Overview

Dataset statistics

Number of variables6
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.4 KiB
Average record size in memory55.3 B

Variable types

Categorical1
Numeric5

Alerts

tms is highly overall correlated with max_race_dayHigh correlation
day_ord is highly overall correlated with cycle_keepHigh correlation
cycle_keep is highly overall correlated with day_ord and 1 other fieldsHigh correlation
max_race_day is highly overall correlated with tms and 1 other fieldsHigh correlation
stnd_year is highly overall correlated with cycle_keep and 1 other fieldsHigh correlation
stnd_year is highly imbalanced (80.6%)Imbalance
day_ord has 17 (17.0%) zerosZeros
cycle_keep has 32 (32.0%) zerosZeros
cycle_out has 77 (77.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:07:34.250654
Analysis finished2023-12-10 10:07:39.145359
Duration4.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

stnd_year
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2019
97 
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 97
97.0%
2021 3
 
3.0%

Length

2023-12-10T19:07:39.269533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:07:39.440582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 97
97.0%
2021 3
 
3.0%

tms
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.71
Minimum10
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:39.627529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q113
median40
Q345
95-th percentile50.05
Maximum51
Range41
Interquartile range (IQR)32

Descriptive statistics

Standard deviation15.956567
Coefficient of variation (CV)0.51958863
Kurtosis-1.8313145
Mean30.71
Median Absolute Deviation (MAD)10
Skewness-0.20259673
Sum3071
Variance254.61202
MonotonicityNot monotonic
2023-12-10T19:07:39.816542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
12 9
 
9.0%
45 6
 
6.0%
13 6
 
6.0%
15 6
 
6.0%
39 6
 
6.0%
40 6
 
6.0%
47 6
 
6.0%
14 6
 
6.0%
41 6
 
6.0%
43 6
 
6.0%
Other values (7) 37
37.0%
ValueCountFrequency (%)
10 6
6.0%
11 6
6.0%
12 9
9.0%
13 6
6.0%
14 6
6.0%
15 6
6.0%
16 4
4.0%
39 6
6.0%
40 6
6.0%
41 6
6.0%
ValueCountFrequency (%)
51 5
5.0%
50 5
5.0%
47 6
6.0%
46 6
6.0%
45 6
6.0%
43 6
6.0%
42 5
5.0%
41 6
6.0%
40 6
6.0%
39 6
6.0%

day_ord
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.49
Minimum0
Maximum5
Zeros17
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:40.003062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2.5
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6966693
Coefficient of variation (CV)0.68139331
Kurtosis-1.2179
Mean2.49
Median Absolute Deviation (MAD)1.5
Skewness-0.007808584
Sum249
Variance2.8786869
MonotonicityNot monotonic
2023-12-10T19:07:40.226432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 18
18.0%
3 18
18.0%
0 17
17.0%
4 16
16.0%
5 16
16.0%
1 15
15.0%
ValueCountFrequency (%)
0 17
17.0%
1 15
15.0%
2 18
18.0%
3 18
18.0%
4 16
16.0%
5 16
16.0%
ValueCountFrequency (%)
5 16
16.0%
4 16
16.0%
3 18
18.0%
2 18
18.0%
1 15
15.0%
0 17
17.0%

cycle_keep
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.16
Minimum0
Maximum132
Zeros32
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:40.439849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median111
Q3125
95-th percentile132
Maximum132
Range132
Interquartile range (IQR)125

Descriptive statistics

Standard deviation55.815301
Coefficient of variation (CV)0.71411593
Kurtosis-1.5239029
Mean78.16
Median Absolute Deviation (MAD)14.5
Skewness-0.62628711
Sum7816
Variance3115.3479
MonotonicityNot monotonic
2023-12-10T19:07:40.672178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 32
32.0%
111 16
16.0%
125 12
 
12.0%
132 10
 
10.0%
112 6
 
6.0%
119 5
 
5.0%
105 5
 
5.0%
126 4
 
4.0%
106 3
 
3.0%
46 2
 
2.0%
Other values (5) 5
 
5.0%
ValueCountFrequency (%)
0 32
32.0%
42 1
 
1.0%
46 2
 
2.0%
105 5
 
5.0%
106 3
 
3.0%
108 1
 
1.0%
110 1
 
1.0%
111 16
16.0%
112 6
 
6.0%
119 5
 
5.0%
ValueCountFrequency (%)
132 10
10.0%
130 1
 
1.0%
126 4
 
4.0%
125 12
12.0%
124 1
 
1.0%
119 5
 
5.0%
112 6
 
6.0%
111 16
16.0%
110 1
 
1.0%
108 1
 
1.0%

cycle_out
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.03
Minimum0
Maximum6
Zeros77
Zeros (%)77.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:40.897267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0619448
Coefficient of variation (CV)2.0018881
Kurtosis1.2270033
Mean1.03
Median Absolute Deviation (MAD)0
Skewness1.7084716
Sum103
Variance4.2516162
MonotonicityNot monotonic
2023-12-10T19:07:41.113130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 77
77.0%
6 10
 
10.0%
4 4
 
4.0%
5 3
 
3.0%
1 2
 
2.0%
2 2
 
2.0%
3 2
 
2.0%
ValueCountFrequency (%)
0 77
77.0%
1 2
 
2.0%
2 2
 
2.0%
3 2
 
2.0%
4 4
 
4.0%
5 3
 
3.0%
6 10
 
10.0%
ValueCountFrequency (%)
6 10
 
10.0%
5 3
 
3.0%
4 4
 
4.0%
3 2
 
2.0%
2 2
 
2.0%
1 2
 
2.0%
0 77
77.0%

max_race_day
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20191382
Minimum20190310
Maximum20210321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:41.357668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190310
5-th percentile20190310
Q120190407
median20191013
Q320191119
95-th percentile20191229
Maximum20210321
Range20011
Interquartile range (IQR)711.75

Descriptive statistics

Standard deviation3367.9001
Coefficient of variation (CV)0.00016679889
Kurtosis29.109376
Mean20191382
Median Absolute Deviation (MAD)209
Skewness5.4868994
Sum2.0191382 × 109
Variance11342751
MonotonicityNot monotonic
2023-12-10T19:07:41.582061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
20191117 6
 
6.0%
20190324 6
 
6.0%
20190414 6
 
6.0%
20191006 6
 
6.0%
20191013 6
 
6.0%
20191201 6
 
6.0%
20190407 6
 
6.0%
20191020 6
 
6.0%
20191103 6
 
6.0%
20190331 6
 
6.0%
Other values (8) 40
40.0%
ValueCountFrequency (%)
20190310 6
6.0%
20190317 6
6.0%
20190324 6
6.0%
20190331 6
6.0%
20190407 6
6.0%
20190414 6
6.0%
20190421 4
4.0%
20191006 6
6.0%
20191013 6
6.0%
20191020 6
6.0%
ValueCountFrequency (%)
20210321 3
3.0%
20191229 5
5.0%
20191222 5
5.0%
20191201 6
6.0%
20191124 6
6.0%
20191117 6
6.0%
20191103 6
6.0%
20191027 5
5.0%
20191020 6
6.0%
20191013 6
6.0%

Interactions

2023-12-10T19:07:37.942169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:34.595193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:35.519691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:36.350598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:37.143709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:38.098551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:34.760950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:35.650978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:36.503606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:37.300886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:38.249501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:34.966308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:35.785668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:36.660828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:37.438585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:38.471936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:35.219136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:35.919845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:36.814007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:37.592374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:38.649219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:35.361304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:36.128155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:36.966969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:37.761026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:07:42.156439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
stnd_yeartmsday_ordcycle_keepcycle_outmax_race_day
stnd_year1.0000.1260.0001.0000.1090.963
tms0.1261.0000.0000.7350.0000.166
day_ord0.0000.0001.0000.6700.6350.000
cycle_keep1.0000.7350.6701.0000.1991.000
cycle_out0.1090.0000.6350.1991.0000.089
max_race_day0.9630.1660.0001.0000.0891.000
2023-12-10T19:07:42.359680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
tmsday_ordcycle_keepcycle_outmax_race_daystnd_year
tms1.0000.0500.269-0.0110.8740.150
day_ord0.0501.000-0.751-0.2270.0240.000
cycle_keep0.269-0.7511.0000.3590.2100.985
cycle_out-0.011-0.2270.3591.0000.0040.111
max_race_day0.8740.0240.2100.0041.0000.826
stnd_year0.1500.0000.9850.1110.8261.000

Missing values

2023-12-10T19:07:38.878376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:07:39.058238image/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_yeartmsday_ordcycle_keepcycle_outmax_race_day
02019420132020191027
1202112146020210321
22019422132620191027
32019423126020191027
420194240020191027
520194250020191027
62019510125020191229
7202112246420210321
82019512125620191229
92019513119020191229
stnd_yeartmsday_ordcycle_keepcycle_outmax_race_day
902019150112020190414
912019151112020190414
922019152112620190414
932019153106020190414
9420191540020190414
9520191550020190414
962019160111020190421
972019161111020190421
982019162111420190421
992019163106020190421