Overview

Dataset statistics

Number of variables10
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.6 KiB
Average record size in memory88.3 B

Variable types

Categorical5
Numeric4
Text1

Alerts

racer_no is highly overall correlated with day_ordHigh correlation
body_wght is highly overall correlated with jacket_add_wght and 1 other fieldsHigh correlation
stnd_year is highly overall correlated with tiltHigh correlation
day_ord is highly overall correlated with racer_noHigh correlation
tilt is highly overall correlated with stnd_yearHigh correlation
jacket_add_wght is highly overall correlated with body_wght and 1 other fieldsHigh correlation
boat_add_wght is highly overall correlated with body_wght and 1 other fieldsHigh correlation
stnd_year is highly imbalanced (80.6%)Imbalance
jacket_add_wght is highly imbalanced (74.7%)Imbalance
boat_add_wght is highly imbalanced (76.6%)Imbalance

Reproduction

Analysis started2023-12-10 09:52:47.358712
Analysis finished2023-12-10 09:52:51.698603
Duration4.34 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

2023-12-10T18:52:51.822543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:52.040594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 97
97.0%
2021 3
 
3.0%

tms
Real number (ℝ)

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.57
Minimum7
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:52.204725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile12
Q115.75
median18
Q319.25
95-th percentile29.05
Maximum46
Range39
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation6.6806747
Coefficient of variation (CV)0.34137326
Kurtosis1.8928697
Mean19.57
Median Absolute Deviation (MAD)2
Skewness1.2772574
Sum1957
Variance44.631414
MonotonicityNot monotonic
2023-12-10T18:52:52.511658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
18 28
28.0%
15 19
19.0%
29 18
18.0%
16 18
18.0%
19 4
 
4.0%
30 3
 
3.0%
9 3
 
3.0%
12 2
 
2.0%
7 1
 
1.0%
40 1
 
1.0%
Other values (3) 3
 
3.0%
ValueCountFrequency (%)
7 1
 
1.0%
9 3
 
3.0%
12 2
 
2.0%
15 19
19.0%
16 18
18.0%
18 28
28.0%
19 4
 
4.0%
20 1
 
1.0%
28 1
 
1.0%
29 18
18.0%
ValueCountFrequency (%)
46 1
 
1.0%
40 1
 
1.0%
30 3
 
3.0%
29 18
18.0%
28 1
 
1.0%
20 1
 
1.0%
19 4
 
4.0%
18 28
28.0%
16 18
18.0%
15 19
19.0%

day_ord
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2
51 
1
49 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 51
51.0%
1 49
49.0%

Length

2023-12-10T18:52:52.752532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:52.969593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 51
51.0%
1 49
49.0%

racer_no
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.34
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:53.152399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q311
95-th percentile17
Maximum17
Range16
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.8204531
Coefficient of variation (CV)0.65673747
Kurtosis-0.98149633
Mean7.34
Median Absolute Deviation (MAD)4
Skewness0.29920927
Sum734
Variance23.236768
MonotonicityNot monotonic
2023-12-10T18:52:53.366671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
10 17
17.0%
1 15
15.0%
11 13
13.0%
6 10
10.0%
5 9
9.0%
14 7
7.0%
2 7
7.0%
17 6
 
6.0%
3 6
 
6.0%
4 6
 
6.0%
Other values (3) 4
 
4.0%
ValueCountFrequency (%)
1 15
15.0%
2 7
7.0%
3 6
 
6.0%
4 6
 
6.0%
5 9
9.0%
6 10
10.0%
9 2
 
2.0%
10 17
17.0%
11 13
13.0%
12 1
 
1.0%
ValueCountFrequency (%)
17 6
 
6.0%
15 1
 
1.0%
14 7
7.0%
12 1
 
1.0%
11 13
13.0%
10 17
17.0%
9 2
 
2.0%
6 10
10.0%
5 9
9.0%
4 6
 
6.0%

race_no
Text

Distinct78
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:52:54.063169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters600
Distinct characters11
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

Unique61 ?
Unique (%)61.0%

Sample

1st row01-009
2nd row01-008
3rd row05-011
4th row04-007
5th row11-006
ValueCountFrequency (%)
02-037 4
 
4.0%
12-010 3
 
3.0%
02-038 3
 
3.0%
02-029 3
 
3.0%
05-011 2
 
2.0%
01-043 2
 
2.0%
14-004 2
 
2.0%
14-010 2
 
2.0%
03-008 2
 
2.0%
01-037 2
 
2.0%
Other values (68) 75
75.0%
2023-12-10T18:52:55.356979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 226
37.7%
- 100
16.7%
1 93
15.5%
2 47
 
7.8%
4 29
 
4.8%
3 28
 
4.7%
5 20
 
3.3%
8 17
 
2.8%
7 16
 
2.7%
6 15
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 500
83.3%
Dash Punctuation 100
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 226
45.2%
1 93
18.6%
2 47
 
9.4%
4 29
 
5.8%
3 28
 
5.6%
5 20
 
4.0%
8 17
 
3.4%
7 16
 
3.2%
6 15
 
3.0%
9 9
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 226
37.7%
- 100
16.7%
1 93
15.5%
2 47
 
7.8%
4 29
 
4.8%
3 28
 
4.7%
5 20
 
3.3%
8 17
 
2.8%
7 16
 
2.7%
6 15
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 226
37.7%
- 100
16.7%
1 93
15.5%
2 47
 
7.8%
4 29
 
4.8%
3 28
 
4.7%
5 20
 
3.3%
8 17
 
2.8%
7 16
 
2.7%
6 15
 
2.5%

race_reg_no
Real number (ℝ)

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.51
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:55.766406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7203183
Coefficient of variation (CV)0.49011917
Kurtosis-1.2643968
Mean3.51
Median Absolute Deviation (MAD)1.5
Skewness-0.017288608
Sum351
Variance2.9594949
MonotonicityNot monotonic
2023-12-10T18:52:56.006306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 18
18.0%
1 17
17.0%
6 17
17.0%
3 16
16.0%
2 16
16.0%
5 16
16.0%
ValueCountFrequency (%)
1 17
17.0%
2 16
16.0%
3 16
16.0%
4 18
18.0%
5 16
16.0%
6 17
17.0%
ValueCountFrequency (%)
6 17
17.0%
5 16
16.0%
4 18
18.0%
3 16
16.0%
2 16
16.0%
1 17
17.0%

tilt
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0.5
58 
1.0
23 
0.0
17 
1.5
 
1
-0.5
 
1

Length

Max length4
Median length3
Mean length3.01
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row0.5
2nd row1.0
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 58
58.0%
1.0 23
 
23.0%
0.0 17
 
17.0%
1.5 1
 
1.0%
-0.5 1
 
1.0%

Length

2023-12-10T18:52:56.245288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:56.451103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.5 59
59.0%
1.0 23
 
23.0%
0.0 17
 
17.0%
1.5 1
 
1.0%

jacket_add_wght
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
없음
92 
500g
 
5
2Kg
 
2
1Kg
 
1

Length

Max length4
Median length2
Mean length2.13
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row1Kg
2nd row없음
3rd row없음
4th row없음
5th row없음

Common Values

ValueCountFrequency (%)
없음 92
92.0%
500g 5
 
5.0%
2Kg 2
 
2.0%
1Kg 1
 
1.0%

Length

2023-12-10T18:52:56.699571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:56.911971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
없음 92
92.0%
500g 5
 
5.0%
2kg 2
 
2.0%
1kg 1
 
1.0%

boat_add_wght
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
없음
92 
2Kg
 
4
7Kg
 
2
4Kg
 
1
5Kg
 
1

Length

Max length3
Median length2
Mean length2.08
Min length2

Unique

Unique2 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
없음 92
92.0%
2Kg 4
 
4.0%
7Kg 2
 
2.0%
4Kg 1
 
1.0%
5Kg 1
 
1.0%

Length

2023-12-10T18:52:57.109975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:57.325008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
없음 92
92.0%
2kg 4
 
4.0%
7kg 2
 
2.0%
4kg 1
 
1.0%
5kg 1
 
1.0%

body_wght
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.128
Minimum41.2
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:57.568253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41.2
5-th percentile49
Q155.1
median56.25
Q357.8
95-th percentile62.405
Maximum65
Range23.8
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation3.6963012
Coefficient of variation (CV)0.065854853
Kurtosis4.3166974
Mean56.128
Median Absolute Deviation (MAD)1.2
Skewness-1.1588663
Sum5612.8
Variance13.662642
MonotonicityNot monotonic
2023-12-10T18:52:57.941893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55.1 8
 
8.0%
55.2 6
 
6.0%
55.0 4
 
4.0%
58.0 3
 
3.0%
55.4 3
 
3.0%
56.5 3
 
3.0%
57.2 3
 
3.0%
51.2 3
 
3.0%
55.9 3
 
3.0%
56.2 3
 
3.0%
Other values (44) 61
61.0%
ValueCountFrequency (%)
41.2 1
 
1.0%
42.1 1
 
1.0%
47.1 1
 
1.0%
48.5 1
 
1.0%
49.0 2
2.0%
49.6 1
 
1.0%
51.2 3
3.0%
52.5 1
 
1.0%
53.3 2
2.0%
54.0 1
 
1.0%
ValueCountFrequency (%)
65.0 1
1.0%
64.0 1
1.0%
63.6 2
2.0%
62.5 1
1.0%
62.4 1
1.0%
60.6 1
1.0%
60.5 1
1.0%
60.1 1
1.0%
59.8 1
1.0%
59.5 1
1.0%

Interactions

2023-12-10T18:52:50.557753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:48.138920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:48.742880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:49.748100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:50.746991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:48.305282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:49.222613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:49.914283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:50.928670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:48.464919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:49.455157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:50.121064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:51.089521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:48.603243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:49.610590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:50.388325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:52:58.139475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
stnd_yeartmsday_ordracer_norace_norace_reg_notiltjacket_add_wghtboat_add_wghtbody_wght
stnd_year1.0000.2150.0850.6710.0000.0000.4550.0000.0000.076
tms0.2151.0000.2600.6010.8780.0000.2220.3290.6510.648
day_ord0.0850.2601.0000.6800.0000.0000.1280.0000.0000.000
racer_no0.6710.6010.6801.0000.0000.0000.0610.0000.0000.152
race_no0.0000.8780.0000.0001.0000.0000.7641.0001.0000.993
race_reg_no0.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
tilt0.4550.2220.1280.0610.7640.0001.0000.0000.0000.000
jacket_add_wght0.0000.3290.0000.0001.0000.0000.0001.0000.7210.763
boat_add_wght0.0000.6510.0000.0001.0000.0000.0000.7211.0000.919
body_wght0.0760.6480.0000.1520.9930.0000.0000.7630.9191.000
2023-12-10T18:52:58.412974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
boat_add_wghttiltjacket_add_wghtday_ordstnd_year
boat_add_wght1.0000.0000.6570.0000.000
tilt0.0001.0000.0000.1530.544
jacket_add_wght0.6570.0001.0000.0000.000
day_ord0.0000.1530.0001.0000.054
stnd_year0.0000.5440.0000.0541.000
2023-12-10T18:52:58.653977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
tmsracer_norace_reg_nobody_wghtstnd_yearday_ordtiltjacket_add_wghtboat_add_wght
tms1.000-0.144-0.039-0.1100.2230.2700.1400.2270.488
racer_no-0.1441.0000.027-0.0100.4840.5260.0000.0000.296
race_reg_no-0.0390.0271.000-0.0680.0000.0000.0000.0000.000
body_wght-0.110-0.010-0.0681.0000.0670.0000.0000.5950.814
stnd_year0.2230.4840.0000.0671.0000.0540.5440.0000.000
day_ord0.2700.5260.0000.0000.0541.0000.1530.0000.000
tilt0.1400.0000.0000.0000.5440.1531.0000.0000.000
jacket_add_wght0.2270.0000.0000.5950.0000.0000.0001.0000.657
boat_add_wght0.4880.2960.0000.8140.0000.0000.0000.6571.000

Missing values

2023-12-10T18:52:51.312387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:52:51.598516image/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_ordracer_norace_norace_reg_notiltjacket_add_wghtboat_add_wghtbody_wght
02019151101-00910.51Kg없음54.0
12021302501-00831.0없음없음60.6
220191521505-01120.5없음없음56.8
320191521104-00760.5없음없음55.1
420191521111-00630.5없음없음55.1
52019122107-00840.0없음없음60.5
62019721215-00660.52Kg7Kg42.1
72021302514-00441.5없음없음59.1
820191521104-01640.5없음없음59.1
920194011102-01560.0500g없음54.5
stnd_yeartmsday_ordracer_norace_norace_reg_notiltjacket_add_wghtboat_add_wghtbody_wght
902019182415-00320.5없음없음53.3
912019182401-04731.0없음없음56.7
922019182402-02940.5없음없음55.7
932019182402-01050.5없음없음55.1
942019182408-00960.0없음없음57.0
952019182603-00810.5없음2Kg49.0
962019182605-01220.5없음없음57.7
972019182612-00240.0없음없음51.2
982019182612-00950.5없음없음56.2
992019201202-03710.0없음없음57.6