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
Text1
Numeric6
DateTime3

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 5 other fieldsHigh correlation
rank_2_cnt is highly overall correlated with race_cnt and 4 other fieldsHigh correlation
rank_3_cnt is highly overall correlated with race_cnt and 3 other fieldsHigh correlation
avg_rank_scr is highly overall correlated with rank_1_cnt and 1 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_cnt and 3 other fieldsHigh correlation
boat_no has unique valuesUnique
rank_1_cnt has 5 (5.0%) zerosZeros
rank_2_cnt has 2 (2.0%) zerosZeros
high_rank_ratio has 2 (2.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:12:27.938055
Analysis finished2023-12-10 10:12:35.960095
Duration8.02 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

2023-12-10T19:12:36.073261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:12:36.260319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2002 67
67.0%
2003 30
30.0%
2021 3
 
3.0%

boat_no
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:12:36.802292image/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 rowB2002063
2nd rowB2018016
3rd rowB2002047
4th rowB2002050
5th rowB2002053
ValueCountFrequency (%)
b2002063 1
 
1.0%
b2002024 1
 
1.0%
b2003012 1
 
1.0%
b2003014 1
 
1.0%
b2003015 1
 
1.0%
b2003017 1
 
1.0%
b2002061 1
 
1.0%
b2002064 1
 
1.0%
b2002019 1
 
1.0%
b2002020 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:12:37.541829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 320
40.0%
2 190
23.8%
B 100
 
12.5%
3 53
 
6.6%
1 29
 
3.6%
5 24
 
3.0%
6 23
 
2.9%
4 21
 
2.6%
8 15
 
1.9%
7 14
 
1.8%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 320
45.7%
2 190
27.1%
3 53
 
7.6%
1 29
 
4.1%
5 24
 
3.4%
6 23
 
3.3%
4 21
 
3.0%
8 15
 
2.1%
7 14
 
2.0%
9 11
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
B 100
100.0%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
0 320
45.7%
2 190
27.1%
3 53
 
7.6%
1 29
 
4.1%
5 24
 
3.4%
6 23
 
3.3%
4 21
 
3.0%
8 15
 
2.1%
7 14
 
2.0%
9 11
 
1.6%
Latin
ValueCountFrequency (%)
B 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 320
40.0%
2 190
23.8%
B 100
 
12.5%
3 53
 
6.6%
1 29
 
3.6%
5 24
 
3.0%
6 23
 
2.9%
4 21
 
2.6%
8 15
 
1.9%
7 14
 
1.8%

race_cnt
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.76
Minimum3
Maximum198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:12:37.817259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile21.8
Q135.5
median41.5
Q363
95-th percentile79.05
Maximum198
Range195
Interquartile range (IQR)27.5

Descriptive statistics

Standard deviation28.281814
Coefficient of variation (CV)0.5800208
Kurtosis11.821246
Mean48.76
Median Absolute Deviation (MAD)8.5
Skewness2.8983204
Sum4876
Variance799.86101
MonotonicityNot monotonic
2023-12-10T19:12:38.091185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
43 7
 
7.0%
36 7
 
7.0%
38 6
 
6.0%
33 5
 
5.0%
46 4
 
4.0%
40 4
 
4.0%
63 4
 
4.0%
41 4
 
4.0%
47 3
 
3.0%
39 3
 
3.0%
Other values (39) 53
53.0%
ValueCountFrequency (%)
3 1
1.0%
14 2
2.0%
17 1
1.0%
18 1
1.0%
22 1
1.0%
23 1
1.0%
24 2
2.0%
26 1
1.0%
27 2
2.0%
28 2
2.0%
ValueCountFrequency (%)
198 1
1.0%
167 1
1.0%
164 1
1.0%
87 1
1.0%
80 1
1.0%
79 1
1.0%
78 1
1.0%
75 1
1.0%
74 1
1.0%
73 1
1.0%

rank_1_cnt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.14
Minimum0
Maximum41
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:12:38.309477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.95
Q14
median7
Q310.25
95-th percentile17
Maximum41
Range41
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation6.2052763
Coefficient of variation (CV)0.76231896
Kurtosis8.3515667
Mean8.14
Median Absolute Deviation (MAD)3
Skewness2.1659646
Sum814
Variance38.505455
MonotonicityNot monotonic
2023-12-10T19:12:38.521886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3 10
10.0%
6 10
10.0%
9 9
 
9.0%
10 8
 
8.0%
4 8
 
8.0%
8 7
 
7.0%
5 7
 
7.0%
2 6
 
6.0%
13 6
 
6.0%
0 5
 
5.0%
Other values (12) 24
24.0%
ValueCountFrequency (%)
0 5
5.0%
1 1
 
1.0%
2 6
6.0%
3 10
10.0%
4 8
8.0%
5 7
7.0%
6 10
10.0%
7 4
 
4.0%
8 7
7.0%
9 9
9.0%
ValueCountFrequency (%)
41 1
 
1.0%
31 1
 
1.0%
23 1
 
1.0%
19 1
 
1.0%
17 3
3.0%
16 2
 
2.0%
15 2
 
2.0%
14 1
 
1.0%
13 6
6.0%
12 3
3.0%

rank_2_cnt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.93
Minimum0
Maximum35
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:12:38.744539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median7
Q39
95-th percentile17
Maximum35
Range35
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.4924282
Coefficient of variation (CV)0.6926139
Kurtosis8.0904825
Mean7.93
Median Absolute Deviation (MAD)3
Skewness2.3146442
Sum793
Variance30.166768
MonotonicityNot monotonic
2023-12-10T19:12:38.995986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
6 16
16.0%
8 13
13.0%
4 11
11.0%
7 9
9.0%
3 9
9.0%
9 8
8.0%
10 5
 
5.0%
2 5
 
5.0%
11 4
 
4.0%
13 4
 
4.0%
Other values (9) 16
16.0%
ValueCountFrequency (%)
0 2
 
2.0%
2 5
 
5.0%
3 9
9.0%
4 11
11.0%
5 3
 
3.0%
6 16
16.0%
7 9
9.0%
8 13
13.0%
9 8
8.0%
10 5
 
5.0%
ValueCountFrequency (%)
35 1
 
1.0%
31 1
 
1.0%
24 1
 
1.0%
17 3
3.0%
16 3
3.0%
15 1
 
1.0%
13 4
4.0%
12 1
 
1.0%
11 4
4.0%
10 5
5.0%

rank_3_cnt
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.19
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:12:39.256440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14.75
median7
Q310
95-th percentile16.15
Maximum33
Range32
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation5.4098582
Coefficient of variation (CV)0.66054435
Kurtosis7.0840997
Mean8.19
Median Absolute Deviation (MAD)3
Skewness2.1677525
Sum819
Variance29.266566
MonotonicityNot monotonic
2023-12-10T19:12:39.519961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
6 12
12.0%
4 11
11.0%
8 11
11.0%
5 8
8.0%
10 8
8.0%
3 8
8.0%
9 7
7.0%
11 7
7.0%
7 6
 
6.0%
2 5
 
5.0%
Other values (11) 17
17.0%
ValueCountFrequency (%)
1 1
 
1.0%
2 5
5.0%
3 8
8.0%
4 11
11.0%
5 8
8.0%
6 12
12.0%
7 6
6.0%
8 11
11.0%
9 7
7.0%
10 8
8.0%
ValueCountFrequency (%)
33 1
 
1.0%
32 1
 
1.0%
22 1
 
1.0%
21 1
 
1.0%
19 1
 
1.0%
16 1
 
1.0%
15 3
3.0%
14 2
2.0%
13 2
2.0%
12 3
3.0%

avg_rank_scr
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3773
Minimum2.65
Maximum7.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:12:39.839714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.65
5-th percentile4.212
Q14.785
median5.355
Q35.8775
95-th percentile6.742
Maximum7.71
Range5.06
Interquartile range (IQR)1.0925

Descriptive statistics

Standard deviation0.82284043
Coefficient of variation (CV)0.15302111
Kurtosis0.6049953
Mean5.3773
Median Absolute Deviation (MAD)0.57
Skewness-0.035823609
Sum537.73
Variance0.67706637
MonotonicityNot monotonic
2023-12-10T19:12:40.142024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.03 2
 
2.0%
5.97 2
 
2.0%
5.3 2
 
2.0%
5.29 2
 
2.0%
5.24 2
 
2.0%
4.0 2
 
2.0%
5.34 2
 
2.0%
5.05 2
 
2.0%
5.44 2
 
2.0%
5.75 2
 
2.0%
Other values (78) 80
80.0%
ValueCountFrequency (%)
2.65 1
1.0%
3.78 1
1.0%
4.0 2
2.0%
4.06 1
1.0%
4.22 1
1.0%
4.23 1
1.0%
4.26 2
2.0%
4.3 1
1.0%
4.44 1
1.0%
4.48 1
1.0%
ValueCountFrequency (%)
7.71 1
1.0%
7.03 1
1.0%
7.0 1
1.0%
6.88 1
1.0%
6.78 1
1.0%
6.74 1
1.0%
6.68 1
1.0%
6.58 1
1.0%
6.57 1
1.0%
6.45 1
1.0%

high_rank_ratio
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct84
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.03
Minimum0
Maximum64.3
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:12:40.403282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.965
Q125.3
median32
Q338.9
95-th percentile51.125
Maximum64.3
Range64.3
Interquartile range (IQR)13.6

Descriptive statistics

Standard deviation11.12388
Coefficient of variation (CV)0.34729566
Kurtosis0.91175438
Mean32.03
Median Absolute Deviation (MAD)6.9
Skewness0.019739955
Sum3203
Variance123.74071
MonotonicityNot monotonic
2023-12-10T19:12:40.666545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.3 3
 
3.0%
33.3 3
 
3.0%
31.6 2
 
2.0%
36.1 2
 
2.0%
33.8 2
 
2.0%
40.4 2
 
2.0%
29.2 2
 
2.0%
0.0 2
 
2.0%
31.9 2
 
2.0%
38.9 2
 
2.0%
Other values (74) 78
78.0%
ValueCountFrequency (%)
0.0 2
2.0%
12.9 1
1.0%
13.9 1
1.0%
14.3 1
1.0%
15.0 1
1.0%
17.1 1
1.0%
17.4 1
1.0%
18.5 1
1.0%
18.6 1
1.0%
18.8 1
1.0%
ValueCountFrequency (%)
64.3 1
1.0%
58.3 1
1.0%
56.5 1
1.0%
55.8 1
1.0%
51.6 1
1.0%
51.1 1
1.0%
47.5 1
1.0%
45.5 1
1.0%
44.7 1
1.0%
44.4 1
1.0%

min_itrdt_run_tm
Date

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2023-12-10 01:13:43
Maximum2023-12-10 02:05:16
2023-12-10T19:12:40.935659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:41.175692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

rank_best_2
Date

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2023-12-10 01:13:43
Maximum2023-12-10 02:05:16
2023-12-10T19:12:41.496158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:41.766248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

rank_best_3
Date

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2023-12-10 01:47:45
Maximum2023-12-10 02:05:16
2023-12-10T19:12:42.032912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:42.298517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-10T19:12:34.329358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:28.671178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:29.726991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:30.588941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:31.647043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:32.715647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:34.539845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:28.878938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:29.889368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:30.757652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:31.828631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:32.887232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:34.705279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:29.053584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:30.018365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:30.892353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:32.018157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:33.073292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:34.909204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:29.239338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:30.167206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:31.045649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:32.274486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:33.643898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:35.105519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:29.414444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:30.313728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:31.226750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:32.422851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:33.857035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:35.268391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:29.565017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:30.441835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:31.410964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:32.562478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:12:34.128784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:12:42.444042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
stnd_yearboat_norace_cntrank_1_cntrank_2_cntrank_3_cntavg_rank_scrhigh_rank_ratiomin_itrdt_run_tmrank_best_2rank_best_3
stnd_year1.0001.0000.9220.8520.8530.8340.1760.4281.0001.0001.000
boat_no1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
race_cnt0.9221.0001.0000.8150.8320.7870.4370.4121.0001.0001.000
rank_1_cnt0.8521.0000.8151.0000.9620.8670.5180.3741.0001.0001.000
rank_2_cnt0.8531.0000.8320.9621.0000.8680.3520.3981.0001.0001.000
rank_3_cnt0.8341.0000.7870.8670.8681.0000.2110.0571.0001.0001.000
avg_rank_scr0.1761.0000.4370.5180.3520.2111.0000.9081.0001.0001.000
high_rank_ratio0.4281.0000.4120.3740.3980.0570.9081.0001.0001.0001.000
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_31.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T19:12:42.998715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
race_cntrank_1_cntrank_2_cntrank_3_cntavg_rank_scrhigh_rank_ratiostnd_year
race_cnt1.0000.7450.7620.7300.2340.2280.917
rank_1_cnt0.7451.0000.6190.5050.6650.6650.784
rank_2_cnt0.7620.6191.0000.5100.4260.5370.800
rank_3_cnt0.7300.5050.5101.0000.2030.0380.756
avg_rank_scr0.2340.6650.4260.2031.0000.9050.095
high_rank_ratio0.2280.6650.5370.0380.9051.0000.201
stnd_year0.9170.7840.8000.7560.0950.2011.000

Missing values

2023-12-10T19:12:35.529959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:12:35.855888image/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_yearboat_norace_cntrank_1_cntrank_2_cntrank_3_cntavg_rank_scrhigh_rank_ratiomin_itrdt_run_tmrank_best_2rank_best_3
02002B20020634391056.1244.21:53.3421:53.3421:53.342
12021B20180161984131325.4436.41:14.4311:14.4311:54.459
22002B2002047224446.1436.41:59.1191:59.1191:59.119
32002B2002050232234.2617.41:56.6401:56.6401:56.640
42002B2002053396765.3133.31:53.7851:53.7851:53.785
52002B2002056435634.7425.61:55.0661:55.0661:55.066
62002B20020594611795.8539.11:54.1851:54.1851:54.185
72021B20180171672335224.9634.71:15.4471:15.4471:54.029
82002B20020134011866.6847.51:56.0981:56.0981:56.098
92002B20020124210485.9333.31:54.1451:54.1451:54.145
stnd_yearboat_norace_cntrank_1_cntrank_2_cntrank_3_cntavg_rank_scrhigh_rank_ratiomin_itrdt_run_tmrank_best_2rank_best_3
902003B200303874916105.5533.81:50.2681:50.2681:50.268
912003B2003035631013165.9736.51:50.6311:50.6311:50.631
922003B20030325213885.8340.41:51.4741:51.4741:51.474
932003B20030296394134.8720.61:50.9041:50.9041:50.904
942003B2003025691213105.7536.21:52.2081:52.2081:52.208
952003B200300668101295.2632.41:50.4121:50.4121:50.412
962003B200300572139155.4430.61:50.0051:50.0051:50.005
972003B20030036366114.7919.01:52.4241:52.4241:52.424
982003B20030026798145.5225.41:51.5131:51.5131:51.513
992003B200302863149145.7536.51:48.8821:48.8821:48.882