Overview

Dataset statistics

Number of variables11
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.4 KiB
Average record size in memory96.3 B

Variable types

Categorical1
Text2
Numeric6
DateTime2

Alerts

race_cnt is highly overall correlated with rank_1_cnt and 3 other fieldsHigh correlation
rank_1_cnt is highly overall correlated with race_cnt and 3 other fieldsHigh correlation
rank_2_cnt is highly overall correlated with race_cnt and 3 other fieldsHigh correlation
rank_3_cnt is highly overall correlated with race_cntHigh correlation
avg_rank_scr is highly overall correlated with rank_1_cnt and 2 other fieldsHigh correlation
high_rank_ratio is highly overall correlated with rank_1_cnt and 2 other fieldsHigh correlation
stnd_year is highly overall correlated with race_cntHigh correlation
mot_no has unique valuesUnique
rank_1_cnt has 3 (3.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:07:34.369052
Analysis finished2023-12-10 10:07:41.824801
Duration7.46 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
2002
67 
2003
30 
2021
 
3

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2002
2nd row2021
3rd row2002
4th row2002
5th row2002

Common Values

ValueCountFrequency (%)
2002 67
67.0%
2003 30
30.0%
2021 3
 
3.0%

Length

2023-12-10T19:07:41.956728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:07:42.139558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2002 67
67.0%
2003 30
30.0%
2021 3
 
3.0%

mot_no
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:07:42.643993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters800
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowM2002066
2nd rowM2020005
3rd rowM2002063
4th rowM2002034
5th rowM2002014
ValueCountFrequency (%)
m2002066 1
 
1.0%
m2002004 1
 
1.0%
m2003013 1
 
1.0%
m2003009 1
 
1.0%
m2003076 1
 
1.0%
m2003077 1
 
1.0%
m2002068 1
 
1.0%
m2002010 1
 
1.0%
m2002009 1
 
1.0%
m2002008 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:07:43.420165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 328
41.0%
2 196
24.5%
M 100
 
12.5%
3 52
 
6.5%
1 30
 
3.8%
5 21
 
2.6%
4 19
 
2.4%
6 18
 
2.2%
7 15
 
1.9%
8 12
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700
87.5%
Uppercase Letter 100
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 328
46.9%
2 196
28.0%
3 52
 
7.4%
1 30
 
4.3%
5 21
 
3.0%
4 19
 
2.7%
6 18
 
2.6%
7 15
 
2.1%
8 12
 
1.7%
9 9
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
M 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 700
87.5%
Latin 100
 
12.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 328
46.9%
2 196
28.0%
3 52
 
7.4%
1 30
 
4.3%
5 21
 
3.0%
4 19
 
2.7%
6 18
 
2.6%
7 15
 
2.1%
8 12
 
1.7%
9 9
 
1.3%
Latin
ValueCountFrequency (%)
M 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 328
41.0%
2 196
24.5%
M 100
 
12.5%
3 52
 
6.5%
1 30
 
3.8%
5 21
 
2.6%
4 19
 
2.4%
6 18
 
2.2%
7 15
 
1.9%
8 12
 
1.5%

race_cnt
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.81
Minimum20
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:43.685733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile28.95
Q135
median38
Q352
95-th percentile79
Maximum82
Range62
Interquartile range (IQR)17

Descriptive statistics

Standard deviation16.717314
Coefficient of variation (CV)0.37307105
Kurtosis-0.36226387
Mean44.81
Median Absolute Deviation (MAD)3.5
Skewness1.0315397
Sum4481
Variance279.46859
MonotonicityNot monotonic
2023-12-10T19:07:43.925017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
38 14
 
14.0%
36 10
 
10.0%
39 7
 
7.0%
35 7
 
7.0%
40 5
 
5.0%
33 4
 
4.0%
37 4
 
4.0%
31 3
 
3.0%
29 3
 
3.0%
69 3
 
3.0%
Other values (24) 40
40.0%
ValueCountFrequency (%)
20 2
 
2.0%
21 1
 
1.0%
28 2
 
2.0%
29 3
3.0%
30 1
 
1.0%
31 3
3.0%
32 3
3.0%
33 4
4.0%
34 3
3.0%
35 7
7.0%
ValueCountFrequency (%)
82 1
1.0%
81 1
1.0%
80 2
2.0%
79 2
2.0%
78 1
1.0%
75 2
2.0%
74 1
1.0%
73 1
1.0%
72 1
1.0%
71 2
2.0%

rank_1_cnt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.31
Minimum0
Maximum22
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:44.146599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median7
Q310
95-th percentile16.05
Maximum22
Range22
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.8942246
Coefficient of variation (CV)0.66952457
Kurtosis0.30220364
Mean7.31
Median Absolute Deviation (MAD)4
Skewness0.76548563
Sum731
Variance23.953434
MonotonicityNot monotonic
2023-12-10T19:07:44.378996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
6 11
11.0%
2 10
 
10.0%
8 10
 
10.0%
7 9
 
9.0%
3 8
 
8.0%
1 6
 
6.0%
5 5
 
5.0%
9 5
 
5.0%
4 5
 
5.0%
11 5
 
5.0%
Other values (12) 26
26.0%
ValueCountFrequency (%)
0 3
 
3.0%
1 6
6.0%
2 10
10.0%
3 8
8.0%
4 5
5.0%
5 5
5.0%
6 11
11.0%
7 9
9.0%
8 10
10.0%
9 5
5.0%
ValueCountFrequency (%)
22 1
 
1.0%
21 1
 
1.0%
19 1
 
1.0%
18 1
 
1.0%
17 1
 
1.0%
16 2
2.0%
15 2
2.0%
14 2
2.0%
13 4
4.0%
12 4
4.0%

rank_2_cnt
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6
Minimum0
Maximum21
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:44.579203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median7
Q311
95-th percentile15
Maximum21
Range21
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.5438381
Coefficient of variation (CV)0.59787343
Kurtosis-0.27121782
Mean7.6
Median Absolute Deviation (MAD)3.5
Skewness0.52354175
Sum760
Variance20.646465
MonotonicityNot monotonic
2023-12-10T19:07:44.793680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 14
14.0%
4 9
9.0%
7 8
 
8.0%
10 8
 
8.0%
9 8
 
8.0%
6 8
 
8.0%
13 8
 
8.0%
12 5
 
5.0%
5 5
 
5.0%
1 5
 
5.0%
Other values (9) 22
22.0%
ValueCountFrequency (%)
0 1
 
1.0%
1 5
 
5.0%
2 4
 
4.0%
3 14
14.0%
4 9
9.0%
5 5
 
5.0%
6 8
8.0%
7 8
8.0%
8 4
 
4.0%
9 8
8.0%
ValueCountFrequency (%)
21 1
 
1.0%
20 1
 
1.0%
16 1
 
1.0%
15 4
4.0%
14 3
 
3.0%
13 8
8.0%
12 5
5.0%
11 3
 
3.0%
10 8
8.0%
9 8
8.0%

rank_3_cnt
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.47
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:44.960068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median7
Q310
95-th percentile15
Maximum19
Range18
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.9426572
Coefficient of variation (CV)0.52779882
Kurtosis0.1164581
Mean7.47
Median Absolute Deviation (MAD)3
Skewness0.71690568
Sum747
Variance15.544545
MonotonicityNot monotonic
2023-12-10T19:07:45.134814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
7 11
11.0%
4 11
11.0%
5 11
11.0%
9 10
10.0%
8 9
9.0%
10 9
9.0%
3 8
8.0%
6 7
7.0%
14 6
6.0%
2 5
 
5.0%
Other values (8) 13
13.0%
ValueCountFrequency (%)
1 2
 
2.0%
2 5
5.0%
3 8
8.0%
4 11
11.0%
5 11
11.0%
6 7
7.0%
7 11
11.0%
8 9
9.0%
9 10
10.0%
10 9
9.0%
ValueCountFrequency (%)
19 1
 
1.0%
17 2
 
2.0%
16 1
 
1.0%
15 2
 
2.0%
14 6
6.0%
13 1
 
1.0%
12 1
 
1.0%
11 3
 
3.0%
10 9
9.0%
9 10
10.0%

avg_rank_scr
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3851
Minimum2.9
Maximum7.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:45.352465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile3.6075
Q14.615
median5.385
Q36.085
95-th percentile7
Maximum7.42
Range4.52
Interquartile range (IQR)1.47

Descriptive statistics

Standard deviation1.0527736
Coefficient of variation (CV)0.19549751
Kurtosis-0.59164598
Mean5.3851
Median Absolute Deviation (MAD)0.77
Skewness-0.19880257
Sum538.51
Variance1.1083323
MonotonicityNot monotonic
2023-12-10T19:07:45.629139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.87 3
 
3.0%
6.53 3
 
3.0%
7.0 3
 
3.0%
5.19 2
 
2.0%
6.43 2
 
2.0%
6.86 2
 
2.0%
5.71 2
 
2.0%
5.16 2
 
2.0%
5.37 2
 
2.0%
5.29 2
 
2.0%
Other values (76) 77
77.0%
ValueCountFrequency (%)
2.9 1
1.0%
2.95 1
1.0%
3.3 1
1.0%
3.46 1
1.0%
3.56 1
1.0%
3.61 1
1.0%
3.65 1
1.0%
3.69 1
1.0%
3.86 1
1.0%
3.96 1
1.0%
ValueCountFrequency (%)
7.42 1
 
1.0%
7.18 1
 
1.0%
7.03 1
 
1.0%
7.0 3
3.0%
6.95 1
 
1.0%
6.92 1
 
1.0%
6.89 1
 
1.0%
6.88 1
 
1.0%
6.86 2
2.0%
6.69 2
2.0%

high_rank_ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.474
Minimum5
Maximum66.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:07:45.898435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile9.7
Q120.075
median31.45
Q343.05
95-th percentile55.315
Maximum66.7
Range61.7
Interquartile range (IQR)22.975

Descriptive statistics

Standard deviation14.624724
Coefficient of variation (CV)0.45035179
Kurtosis-0.774574
Mean32.474
Median Absolute Deviation (MAD)11.6
Skewness0.14312171
Sum3247.4
Variance213.88255
MonotonicityNot monotonic
2023-12-10T19:07:46.193001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.7 2
 
2.0%
33.3 2
 
2.0%
55.3 2
 
2.0%
18.4 2
 
2.0%
31.6 2
 
2.0%
41.3 2
 
2.0%
37.8 2
 
2.0%
31.4 2
 
2.0%
15.6 2
 
2.0%
15.4 2
 
2.0%
Other values (71) 80
80.0%
ValueCountFrequency (%)
5.0 2
2.0%
8.3 1
1.0%
9.1 1
1.0%
9.7 2
2.0%
10.3 1
1.0%
10.7 1
1.0%
11.8 1
1.0%
13.8 1
1.0%
14.3 1
1.0%
15.2 1
1.0%
ValueCountFrequency (%)
66.7 1
1.0%
63.9 1
1.0%
59.0 1
1.0%
57.1 1
1.0%
55.6 1
1.0%
55.3 2
2.0%
53.8 1
1.0%
53.3 1
1.0%
52.8 1
1.0%
52.5 1
1.0%

min_itrdt_run_tm
Date

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2023-12-10 01:13:43
Maximum2023-12-10 02:02:58
2023-12-10T19:07:46.464350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:46.735266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

rank_best_2
Date

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2023-12-10 01:13:43
Maximum2023-12-10 02:02:58
2023-12-10T19:07:46.950434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:47.176550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

rank_best_3
Text

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:07:47.797806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.82
Min length2

Characters and Unicode

Total characters782
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique97 ?
Unique (%)97.0%

Sample

1st row1:59.350
2nd row:.
3rd row1:54.185
4th row1:54.680
5th row1:54.681
ValueCountFrequency (%)
3
 
3.0%
1:58.391 1
 
1.0%
1:52.346 1
 
1.0%
1:50.723 1
 
1.0%
1:50.611 1
 
1.0%
1:48.882 1
 
1.0%
2:02.969 1
 
1.0%
1:55.625 1
 
1.0%
1:54.465 1
 
1.0%
1:58.975 1
 
1.0%
Other values (88) 88
88.0%
2023-12-10T19:07:48.601262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 136
17.4%
1 121
15.5%
: 100
12.8%
. 100
12.8%
9 45
 
5.8%
6 44
 
5.6%
8 43
 
5.5%
2 43
 
5.5%
7 41
 
5.2%
0 40
 
5.1%
Other values (2) 69
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 582
74.4%
Other Punctuation 200
 
25.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 136
23.4%
1 121
20.8%
9 45
 
7.7%
6 44
 
7.6%
8 43
 
7.4%
2 43
 
7.4%
7 41
 
7.0%
0 40
 
6.9%
4 39
 
6.7%
3 30
 
5.2%
Other Punctuation
ValueCountFrequency (%)
: 100
50.0%
. 100
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 782
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 136
17.4%
1 121
15.5%
: 100
12.8%
. 100
12.8%
9 45
 
5.8%
6 44
 
5.6%
8 43
 
5.5%
2 43
 
5.5%
7 41
 
5.2%
0 40
 
5.1%
Other values (2) 69
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 782
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 136
17.4%
1 121
15.5%
: 100
12.8%
. 100
12.8%
9 45
 
5.8%
6 44
 
5.6%
8 43
 
5.5%
2 43
 
5.5%
7 41
 
5.2%
0 40
 
5.1%
Other values (2) 69
8.8%

Interactions

2023-12-10T19:07:40.208516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:35.322623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:36.337775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:37.188245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:38.022335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:39.293266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:40.417135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:35.550484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:36.490699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:37.339149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:38.595665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:39.442962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:40.630925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:35.715791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:36.632421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:37.478881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:38.730924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:39.598813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:40.760472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:35.845311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:36.773616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:37.602822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:38.871442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:39.731137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:41.022689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:36.024856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:36.915679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:37.756295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:39.018493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:39.881343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:41.171993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:36.170237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:37.042559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:37.890674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:39.153715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:40.023336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:07:48.802360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
stnd_yearmot_norace_cntrank_1_cntrank_2_cntrank_3_cntavg_rank_scrhigh_rank_ratiomin_itrdt_run_tmrank_best_2rank_best_3
stnd_year1.0001.0000.7380.4270.6620.6200.3300.3361.0001.0001.000
mot_no1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
race_cnt0.7381.0001.0000.4390.6190.5140.5190.3921.0001.0000.933
rank_1_cnt0.4271.0000.4391.0000.5920.5620.7070.7981.0001.0000.984
rank_2_cnt0.6621.0000.6190.5921.0000.5230.5730.6161.0001.0000.946
rank_3_cnt0.6201.0000.5140.5620.5231.0000.5070.5461.0001.0000.952
avg_rank_scr0.3301.0000.5190.7070.5730.5071.0000.8871.0001.0000.949
high_rank_ratio0.3361.0000.3920.7980.6160.5460.8871.0001.0001.0000.949
min_itrdt_run_tm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
rank_best_21.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
rank_best_31.0001.0000.9330.9840.9460.9520.9490.9491.0001.0001.000
2023-12-10T19:07:49.493202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
race_cntrank_1_cntrank_2_cntrank_3_cntavg_rank_scrhigh_rank_ratiostnd_year
race_cnt1.0000.6010.6900.6100.2500.2510.619
rank_1_cnt0.6011.0000.6140.2700.7870.7670.279
rank_2_cnt0.6900.6141.0000.3960.6050.6570.363
rank_3_cnt0.6100.2700.3961.0000.107-0.0640.438
avg_rank_scr0.2500.7870.6050.1071.0000.9500.193
high_rank_ratio0.2510.7670.657-0.0640.9501.0000.204
stnd_year0.6190.2790.3630.4380.1930.2041.000

Missing values

2023-12-10T19:07:41.388750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:07:41.715134image/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_yearmot_norace_cntrank_1_cntrank_2_cntrank_3_cntavg_rank_scrhigh_rank_ratiomin_itrdt_run_tmrank_best_2rank_best_3
02002M2002066373384.4316.21:59.3501:59.3501:59.350
12021M2020005386695.4231.61:13.7301:13.730:.
22002M20020633612777.052.81:54.1851:54.1851:54.185
32002M2002034386344.5823.71:54.6801:54.6801:54.680
42002M20020143991276.6953.81:54.6811:54.6811:54.681
52002M2002015341384.0611.81:59.7871:59.7871:59.787
62002M2002016417685.4631.71:57.7731:57.7731:57.773
72021M2020007316334.7129.01:14.0271:14.027:.
82002M2002018403494.9817.51:57.3901:57.3901:57.390
92002M2002019364374.8919.41:55.0661:55.0661:55.066
stnd_yearmot_norace_cntrank_1_cntrank_2_cntrank_3_cntavg_rank_scrhigh_rank_ratiomin_itrdt_run_tmrank_best_2rank_best_3
902003M200301270514145.1927.11:53.7081:53.7081:53.708
912003M200301164119115.3631.31:53.5821:53.5821:53.582
922003M2003010811012105.1627.21:54.0091:54.0091:54.009
932003M200300878151595.7138.51:51.3251:51.3251:51.325
942003M2003007792214146.6245.61:52.4241:52.4241:52.424
952003M2003005691110145.5530.41:52.0341:52.0341:52.034
962003M200300473815155.4831.51:49.1361:49.1361:49.136
972003M2003002652112146.8850.81:50.1011:50.1011:50.101
982003M2003001751613146.038.71:52.7751:52.7751:52.775
992003M20030536779175.2423.91:52.5261:52.5261:52.526