Overview

Dataset statistics

Number of variables19
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.5 KiB
Average record size in memory169.3 B

Variable types

Categorical3
Text2
Numeric14

Alerts

race_cnt is highly overall correlated with rank_1_cnt and 7 other fieldsHigh correlation
rank_1_cnt is highly overall correlated with race_cnt and 9 other fieldsHigh correlation
rank_2_cnt is highly overall correlated with race_cnt and 6 other fieldsHigh correlation
rank_3_cnt is highly overall correlated with race_cnt and 6 other fieldsHigh correlation
rank_4_cnt is highly overall correlated with race_cnt and 6 other fieldsHigh correlation
rank_5_cnt is highly overall correlated with race_cnt and 6 other fieldsHigh correlation
rank_6_cnt is highly overall correlated with race_cnt and 5 other fieldsHigh correlation
avg_rank_scr is highly overall correlated with rank_1_cnt and 4 other fieldsHigh correlation
avg_scr is highly overall correlated with rank_1_cnt and 4 other fieldsHigh correlation
win_ratio is highly overall correlated with rank_1_cnt and 5 other fieldsHigh correlation
high_rank_ratio is highly overall correlated with rank_1_cnt and 5 other fieldsHigh correlation
high_3_rank_ratio is highly overall correlated with avg_rank_scr and 4 other fieldsHigh correlation
stnd_year is highly overall correlated with race_cntHigh correlation
racer_perio_no is highly overall correlated with race_cnt and 9 other fieldsHigh correlation
stnd_year is highly imbalanced (53.0%)Imbalance
rank_1_cnt has 30 (30.0%) zerosZeros
rank_2_cnt has 26 (26.0%) zerosZeros
rank_3_cnt has 23 (23.0%) zerosZeros
rank_4_cnt has 22 (22.0%) zerosZeros
rank_5_cnt has 26 (26.0%) zerosZeros
rank_6_cnt has 29 (29.0%) zerosZeros
avg_acdnt_scr has 15 (15.0%) zerosZeros
win_ratio has 30 (30.0%) zerosZeros
high_rank_ratio has 19 (19.0%) zerosZeros
high_3_rank_ratio has 10 (10.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:07:51.984088
Analysis finished2023-12-10 10:08:32.148520
Duration40.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

stnd_year
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2002
84 
2003
13 
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 84
84.0%
2003 13
 
13.0%
2021 3
 
3.0%

Length

2023-12-10T19:08:32.265853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:32.542915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2002 84
84.0%
2003 13
 
13.0%
2021 3
 
3.0%
Distinct86
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:08:33.116385image/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

Unique72 ?
Unique (%)72.0%

Sample

1st row01-010
2nd row02-038
3rd row01-008
4th row01-007
5th row01-006
ValueCountFrequency (%)
01-010 2
 
2.0%
02-037 2
 
2.0%
01-002 2
 
2.0%
01-008 2
 
2.0%
01-007 2
 
2.0%
01-006 2
 
2.0%
01-004 2
 
2.0%
01-005 2
 
2.0%
01-011 2
 
2.0%
01-001 2
 
2.0%
Other values (76) 80
80.0%
2023-12-10T19:08:33.923600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 234
39.0%
- 100
16.7%
1 94
15.7%
2 72
 
12.0%
3 31
 
5.2%
4 20
 
3.3%
8 11
 
1.8%
7 11
 
1.8%
5 10
 
1.7%
6 10
 
1.7%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 234
46.8%
1 94
18.8%
2 72
 
14.4%
3 31
 
6.2%
4 20
 
4.0%
8 11
 
2.2%
7 11
 
2.2%
5 10
 
2.0%
6 10
 
2.0%
9 7
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 234
39.0%
- 100
16.7%
1 94
15.7%
2 72
 
12.0%
3 31
 
5.2%
4 20
 
3.3%
8 11
 
1.8%
7 11
 
1.8%
5 10
 
1.7%
6 10
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 234
39.0%
- 100
16.7%
1 94
15.7%
2 72
 
12.0%
3 31
 
5.2%
4 20
 
3.3%
8 11
 
1.8%
7 11
 
1.8%
5 10
 
1.7%
6 10
 
1.7%
Distinct86
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:08:34.432470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.02
Min length3

Characters and Unicode

Total characters302
Distinct characters99
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)72.0%

Sample

1st row김덕환
2nd row최재원
3rd row김국흠
4th row길현태
5th row권현기
ValueCountFrequency (%)
김덕환 2
 
2.0%
최광성 2
 
2.0%
2
 
2.0%
강창효 2
 
2.0%
김국흠 2
 
2.0%
길현태 2
 
2.0%
권오현 2
 
2.0%
권명호 2
 
2.0%
권현기 2
 
2.0%
김명진 2
 
2.0%
Other values (77) 82
80.4%
2023-12-10T19:08:35.198277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31
 
10.3%
13
 
4.3%
12
 
4.0%
10
 
3.3%
9
 
3.0%
7
 
2.3%
7
 
2.3%
6
 
2.0%
6
 
2.0%
6
 
2.0%
Other values (89) 195
64.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 298
98.7%
Space Separator 4
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
31
 
10.4%
13
 
4.4%
12
 
4.0%
10
 
3.4%
9
 
3.0%
7
 
2.3%
7
 
2.3%
6
 
2.0%
6
 
2.0%
6
 
2.0%
Other values (88) 191
64.1%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 298
98.7%
Common 4
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
31
 
10.4%
13
 
4.4%
12
 
4.0%
10
 
3.4%
9
 
3.0%
7
 
2.3%
7
 
2.3%
6
 
2.0%
6
 
2.0%
6
 
2.0%
Other values (88) 191
64.1%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 298
98.7%
ASCII 4
 
1.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
31
 
10.4%
13
 
4.4%
12
 
4.0%
10
 
3.4%
9
 
3.0%
7
 
2.3%
7
 
2.3%
6
 
2.0%
6
 
2.0%
6
 
2.0%
Other values (88) 191
64.1%
ASCII
ValueCountFrequency (%)
4
100.0%

race_cnt
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.39
Minimum2
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:35.488713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median41.5
Q353.25
95-th percentile66.15
Maximum95
Range93
Interquartile range (IQR)51.25

Descriptive statistics

Standard deviation26.286646
Coefficient of variation (CV)0.81156673
Kurtosis-1.2692302
Mean32.39
Median Absolute Deviation (MAD)18.5
Skewness0.063052423
Sum3239
Variance690.98778
MonotonicityNot monotonic
2023-12-10T19:08:35.776249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
2 29
29.0%
4 9
 
9.0%
55 5
 
5.0%
60 5
 
5.0%
50 4
 
4.0%
44 4
 
4.0%
41 3
 
3.0%
59 3
 
3.0%
46 3
 
3.0%
53 3
 
3.0%
Other values (23) 32
32.0%
ValueCountFrequency (%)
2 29
29.0%
4 9
 
9.0%
8 1
 
1.0%
12 2
 
2.0%
37 1
 
1.0%
38 1
 
1.0%
39 2
 
2.0%
40 2
 
2.0%
41 3
 
3.0%
42 3
 
3.0%
ValueCountFrequency (%)
95 1
 
1.0%
88 1
 
1.0%
86 1
 
1.0%
74 1
 
1.0%
69 1
 
1.0%
66 1
 
1.0%
65 1
 
1.0%
63 1
 
1.0%
60 5
5.0%
59 3
3.0%

rank_1_cnt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1
Minimum0
Maximum44
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:36.077622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q310
95-th percentile22.05
Maximum44
Range44
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.9308883
Coefficient of variation (CV)1.3001456
Kurtosis4.6614229
Mean6.1
Median Absolute Deviation (MAD)3
Skewness1.8830517
Sum610
Variance62.89899
MonotonicityNot monotonic
2023-12-10T19:08:36.348053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 30
30.0%
1 12
 
12.0%
3 9
 
9.0%
2 7
 
7.0%
7 5
 
5.0%
10 4
 
4.0%
9 4
 
4.0%
16 4
 
4.0%
6 3
 
3.0%
15 3
 
3.0%
Other values (12) 19
19.0%
ValueCountFrequency (%)
0 30
30.0%
1 12
 
12.0%
2 7
 
7.0%
3 9
 
9.0%
4 2
 
2.0%
6 3
 
3.0%
7 5
 
5.0%
8 2
 
2.0%
9 4
 
4.0%
10 4
 
4.0%
ValueCountFrequency (%)
44 1
 
1.0%
26 1
 
1.0%
25 2
2.0%
23 1
 
1.0%
22 2
2.0%
21 1
 
1.0%
16 4
4.0%
15 3
3.0%
14 2
2.0%
13 1
 
1.0%

rank_2_cnt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.63
Minimum0
Maximum26
Zeros26
Zeros (%)26.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:36.571209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.5
Q39
95-th percentile15
Maximum26
Range26
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.7448352
Coefficient of variation (CV)1.020397
Kurtosis0.8268052
Mean5.63
Median Absolute Deviation (MAD)4.5
Skewness1.0265307
Sum563
Variance33.003131
MonotonicityNot monotonic
2023-12-10T19:08:36.949311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 26
26.0%
1 10
 
10.0%
9 9
 
9.0%
2 8
 
8.0%
8 7
 
7.0%
5 6
 
6.0%
12 4
 
4.0%
3 4
 
4.0%
13 4
 
4.0%
10 3
 
3.0%
Other values (10) 19
19.0%
ValueCountFrequency (%)
0 26
26.0%
1 10
 
10.0%
2 8
 
8.0%
3 4
 
4.0%
4 2
 
2.0%
5 6
 
6.0%
6 3
 
3.0%
7 3
 
3.0%
8 7
 
7.0%
9 9
 
9.0%
ValueCountFrequency (%)
26 1
 
1.0%
22 1
 
1.0%
20 1
 
1.0%
18 1
 
1.0%
15 3
3.0%
14 2
2.0%
13 4
4.0%
12 4
4.0%
11 2
2.0%
10 3
3.0%

rank_3_cnt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.28
Minimum0
Maximum20
Zeros23
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:37.279022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q39
95-th percentile13.1
Maximum20
Range20
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.8908903
Coefficient of variation (CV)0.92630498
Kurtosis-0.25786052
Mean5.28
Median Absolute Deviation (MAD)4
Skewness0.6352481
Sum528
Variance23.920808
MonotonicityNot monotonic
2023-12-10T19:08:37.625839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 23
23.0%
1 18
18.0%
8 10
10.0%
9 9
 
9.0%
7 7
 
7.0%
6 5
 
5.0%
10 5
 
5.0%
11 4
 
4.0%
4 4
 
4.0%
5 4
 
4.0%
Other values (8) 11
11.0%
ValueCountFrequency (%)
0 23
23.0%
1 18
18.0%
2 1
 
1.0%
3 1
 
1.0%
4 4
 
4.0%
5 4
 
4.0%
6 5
 
5.0%
7 7
 
7.0%
8 10
10.0%
9 9
 
9.0%
ValueCountFrequency (%)
20 1
 
1.0%
17 2
 
2.0%
16 1
 
1.0%
15 1
 
1.0%
13 2
 
2.0%
12 2
 
2.0%
11 4
 
4.0%
10 5
5.0%
9 9
9.0%
8 10
10.0%

rank_4_cnt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.94
Minimum0
Maximum16
Zeros22
Zeros (%)22.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:37.855736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q38
95-th percentile12
Maximum16
Range16
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.2422121
Coefficient of variation (CV)0.85874739
Kurtosis-1.0920062
Mean4.94
Median Absolute Deviation (MAD)4
Skewness0.29036537
Sum494
Variance17.996364
MonotonicityNot monotonic
2023-12-10T19:08:38.162237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 22
22.0%
1 18
18.0%
8 12
12.0%
5 8
 
8.0%
9 8
 
8.0%
6 6
 
6.0%
7 6
 
6.0%
10 5
 
5.0%
4 4
 
4.0%
11 4
 
4.0%
Other values (4) 7
 
7.0%
ValueCountFrequency (%)
0 22
22.0%
1 18
18.0%
2 1
 
1.0%
4 4
 
4.0%
5 8
 
8.0%
6 6
 
6.0%
7 6
 
6.0%
8 12
12.0%
9 8
 
8.0%
10 5
 
5.0%
ValueCountFrequency (%)
16 1
 
1.0%
13 2
 
2.0%
12 3
 
3.0%
11 4
 
4.0%
10 5
5.0%
9 8
8.0%
8 12
12.0%
7 6
6.0%
6 6
6.0%
5 8
8.0%

rank_5_cnt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.84
Minimum0
Maximum21
Zeros26
Zeros (%)26.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:38.366384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q38
95-th percentile13.05
Maximum21
Range21
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.6833468
Coefficient of variation (CV)0.96763364
Kurtosis0.7210776
Mean4.84
Median Absolute Deviation (MAD)4
Skewness0.94767184
Sum484
Variance21.933737
MonotonicityNot monotonic
2023-12-10T19:08:38.602409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 26
26.0%
8 12
12.0%
1 10
 
10.0%
7 8
 
8.0%
4 7
 
7.0%
6 6
 
6.0%
2 5
 
5.0%
3 5
 
5.0%
10 4
 
4.0%
5 4
 
4.0%
Other values (8) 13
13.0%
ValueCountFrequency (%)
0 26
26.0%
1 10
 
10.0%
2 5
 
5.0%
3 5
 
5.0%
4 7
 
7.0%
5 4
 
4.0%
6 6
 
6.0%
7 8
 
8.0%
8 12
12.0%
9 2
 
2.0%
ValueCountFrequency (%)
21 1
 
1.0%
18 1
 
1.0%
17 1
 
1.0%
14 2
 
2.0%
13 1
 
1.0%
12 3
 
3.0%
11 2
 
2.0%
10 4
 
4.0%
9 2
 
2.0%
8 12
12.0%

rank_6_cnt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.98
Minimum0
Maximum16
Zeros29
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:38.798667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile12
Maximum16
Range16
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.2449733
Coefficient of variation (CV)1.0665762
Kurtosis-0.19061461
Mean3.98
Median Absolute Deviation (MAD)2
Skewness0.92611011
Sum398
Variance18.019798
MonotonicityNot monotonic
2023-12-10T19:08:39.005712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 29
29.0%
1 12
12.0%
2 11
 
11.0%
4 9
 
9.0%
9 8
 
8.0%
5 6
 
6.0%
7 5
 
5.0%
10 4
 
4.0%
3 4
 
4.0%
11 3
 
3.0%
Other values (6) 9
 
9.0%
ValueCountFrequency (%)
0 29
29.0%
1 12
12.0%
2 11
 
11.0%
3 4
 
4.0%
4 9
 
9.0%
5 6
 
6.0%
7 5
 
5.0%
8 3
 
3.0%
9 8
 
8.0%
10 4
 
4.0%
ValueCountFrequency (%)
16 1
 
1.0%
15 1
 
1.0%
14 1
 
1.0%
13 1
 
1.0%
12 2
 
2.0%
11 3
 
3.0%
10 4
4.0%
9 8
8.0%
8 3
 
3.0%
7 5
5.0%

avg_rank_scr
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2624
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:39.620960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5
Q14.1425
median5.48
Q36.455
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2.3125

Descriptive statistics

Standard deviation1.8067389
Coefficient of variation (CV)0.34332982
Kurtosis0.14125336
Mean5.2624
Median Absolute Deviation (MAD)1.07
Skewness-0.25750156
Sum526.24
Variance3.2643053
MonotonicityNot monotonic
2023-12-10T19:08:39.879231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.0 7
 
7.0%
6.0 4
 
4.0%
7.0 4
 
4.0%
1.5 4
 
4.0%
3.0 3
 
3.0%
6.5 3
 
3.0%
1.0 2
 
2.0%
5.92 2
 
2.0%
6.2 2
 
2.0%
5.33 2
 
2.0%
Other values (60) 67
67.0%
ValueCountFrequency (%)
1.0 2
2.0%
1.5 4
4.0%
2.0 2
2.0%
3.0 3
3.0%
3.24 1
 
1.0%
3.25 1
 
1.0%
3.26 1
 
1.0%
3.28 1
 
1.0%
3.32 1
 
1.0%
3.5 1
 
1.0%
ValueCountFrequency (%)
10.0 1
 
1.0%
9.0 2
2.0%
8.25 1
 
1.0%
8.0 2
2.0%
7.89 1
 
1.0%
7.82 1
 
1.0%
7.42 1
 
1.0%
7.14 2
2.0%
7.05 1
 
1.0%
7.0 4
4.0%

avg_acdnt_scr
Real number (ℝ)

ZEROS 

Distinct56
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6866
Minimum0
Maximum6.5
Zeros15
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:40.151914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.285
median0.5
Q30.8925
95-th percentile1.7845
Maximum6.5
Range6.5
Interquartile range (IQR)0.6075

Descriptive statistics

Standard deviation0.81027146
Coefficient of variation (CV)1.1801216
Kurtosis26.913649
Mean0.6866
Median Absolute Deviation (MAD)0.295
Skewness4.2939857
Sum68.66
Variance0.65653984
MonotonicityNot monotonic
2023-12-10T19:08:40.418441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 15
 
15.0%
0.5 13
 
13.0%
0.98 4
 
4.0%
0.17 3
 
3.0%
2.5 2
 
2.0%
1.25 2
 
2.0%
0.45 2
 
2.0%
0.49 2
 
2.0%
0.82 2
 
2.0%
0.44 2
 
2.0%
Other values (46) 53
53.0%
ValueCountFrequency (%)
0.0 15
15.0%
0.13 1
 
1.0%
0.17 3
 
3.0%
0.18 1
 
1.0%
0.2 1
 
1.0%
0.24 1
 
1.0%
0.25 2
 
2.0%
0.27 1
 
1.0%
0.29 1
 
1.0%
0.3 1
 
1.0%
ValueCountFrequency (%)
6.5 1
1.0%
3.0 1
1.0%
2.5 2
2.0%
2.25 1
1.0%
1.76 1
1.0%
1.42 1
1.0%
1.41 1
1.0%
1.4 1
1.0%
1.32 1
1.0%
1.25 2
2.0%

avg_scr
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5763
Minimum-3.5
Maximum10
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.0%
Memory size1.0 KiB
2023-12-10T19:08:40.721474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.5
5-th percentile1
Q13.385
median4.75
Q35.91
95-th percentile7.524
Maximum10
Range13.5
Interquartile range (IQR)2.525

Descriptive statistics

Standard deviation2.0394071
Coefficient of variation (CV)0.4456454
Kurtosis1.8365952
Mean4.5763
Median Absolute Deviation (MAD)1.25
Skewness-0.52059726
Sum457.63
Variance4.1591811
MonotonicityNot monotonic
2023-12-10T19:08:41.072349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.0 5
 
5.0%
3.0 5
 
5.0%
4.5 4
 
4.0%
1.0 3
 
3.0%
6.5 3
 
3.0%
4.75 3
 
3.0%
2.0 2
 
2.0%
2.5 2
 
2.0%
6.38 2
 
2.0%
7.0 2
 
2.0%
Other values (62) 69
69.0%
ValueCountFrequency (%)
-3.5 1
 
1.0%
0.5 2
2.0%
1.0 3
3.0%
1.49 1
 
1.0%
1.5 2
2.0%
2.0 2
2.0%
2.25 1
 
1.0%
2.5 2
2.0%
2.8 1
 
1.0%
2.88 1
 
1.0%
ValueCountFrequency (%)
10.0 1
1.0%
8.75 1
1.0%
8.5 1
1.0%
8.0 1
1.0%
7.98 1
1.0%
7.5 1
1.0%
7.4 1
1.0%
7.35 1
1.0%
7.24 1
1.0%
7.0 2
2.0%

avg_strt_tm
Real number (ℝ)

Distinct37
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4256
Minimum0.17
Maximum1.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:41.346009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.17
5-th percentile0.2695
Q10.3575
median0.42
Q30.47
95-th percentile0.6505
Maximum1.03
Range0.86
Interquartile range (IQR)0.1125

Descriptive statistics

Standard deviation0.11840113
Coefficient of variation (CV)0.27819815
Kurtosis6.4480691
Mean0.4256
Median Absolute Deviation (MAD)0.06
Skewness1.5610977
Sum42.56
Variance0.014018828
MonotonicityNot monotonic
2023-12-10T19:08:41.574886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.43 9
 
9.0%
0.4 8
 
8.0%
0.42 7
 
7.0%
0.33 7
 
7.0%
0.49 6
 
6.0%
0.39 6
 
6.0%
0.44 6
 
6.0%
0.35 3
 
3.0%
0.41 3
 
3.0%
0.45 3
 
3.0%
Other values (27) 42
42.0%
ValueCountFrequency (%)
0.17 1
 
1.0%
0.18 1
 
1.0%
0.22 1
 
1.0%
0.26 2
 
2.0%
0.27 2
 
2.0%
0.28 2
 
2.0%
0.29 1
 
1.0%
0.3 1
 
1.0%
0.32 3
3.0%
0.33 7
7.0%
ValueCountFrequency (%)
1.03 1
1.0%
0.7 2
2.0%
0.69 1
1.0%
0.66 1
1.0%
0.65 2
2.0%
0.6 1
1.0%
0.56 1
1.0%
0.54 1
1.0%
0.53 1
1.0%
0.52 2
2.0%

win_ratio
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.287
Minimum0
Maximum100
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:41.872870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12.35
Q325
95-th percentile50
Maximum100
Range100
Interquartile range (IQR)25

Descriptive statistics

Standard deviation18.323582
Coefficient of variation (CV)1.1250434
Kurtosis3.9342225
Mean16.287
Median Absolute Deviation (MAD)12.35
Skewness1.6367488
Sum1628.7
Variance335.75367
MonotonicityNot monotonic
2023-12-10T19:08:42.136869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 30
30.0%
50.0 6
 
6.0%
25.0 4
 
4.0%
2.4 2
 
2.0%
27.3 2
 
2.0%
23.1 2
 
2.0%
2.3 2
 
2.0%
26.7 1
 
1.0%
27.6 1
 
1.0%
13.3 1
 
1.0%
Other values (49) 49
49.0%
ValueCountFrequency (%)
0.0 30
30.0%
2.0 1
 
1.0%
2.3 2
 
2.0%
2.4 2
 
2.0%
4.8 1
 
1.0%
4.9 1
 
1.0%
5.0 1
 
1.0%
5.4 1
 
1.0%
5.6 1
 
1.0%
5.7 1
 
1.0%
ValueCountFrequency (%)
100.0 1
 
1.0%
75.0 1
 
1.0%
50.0 6
6.0%
46.3 1
 
1.0%
45.6 1
 
1.0%
40.0 1
 
1.0%
36.7 1
 
1.0%
35.0 1
 
1.0%
34.1 1
 
1.0%
33.8 1
 
1.0%

high_rank_ratio
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct62
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.734
Minimum0
Maximum100
Zeros19
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:42.379356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112.125
median35.2
Q350
95-th percentile65.785
Maximum100
Range100
Interquartile range (IQR)37.875

Descriptive statistics

Standard deviation24.019673
Coefficient of variation (CV)0.73378362
Kurtosis0.08705008
Mean32.734
Median Absolute Deviation (MAD)14.85
Skewness0.3999106
Sum3273.4
Variance576.94469
MonotonicityNot monotonic
2023-12-10T19:08:42.656096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 19
 
19.0%
50.0 15
 
15.0%
100.0 3
 
3.0%
12.2 2
 
2.0%
11.9 2
 
2.0%
75.0 2
 
2.0%
25.0 2
 
2.0%
5.0 1
 
1.0%
18.0 1
 
1.0%
22.2 1
 
1.0%
Other values (52) 52
52.0%
ValueCountFrequency (%)
0.0 19
19.0%
5.0 1
 
1.0%
8.1 1
 
1.0%
9.8 1
 
1.0%
11.3 1
 
1.0%
11.9 2
 
2.0%
12.2 2
 
2.0%
12.5 1
 
1.0%
15.8 1
 
1.0%
16.0 1
 
1.0%
ValueCountFrequency (%)
100.0 3
3.0%
75.0 2
2.0%
65.3 1
 
1.0%
63.6 1
 
1.0%
63.2 1
 
1.0%
58.3 1
 
1.0%
57.0 1
 
1.0%
56.9 1
 
1.0%
52.3 1
 
1.0%
51.7 1
 
1.0%

high_3_rank_ratio
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.008
Minimum0
Maximum100
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:42.932251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q134.7
median50
Q362.55
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)27.85

Descriptive statistics

Standard deviation24.630414
Coefficient of variation (CV)0.50257946
Kurtosis0.10533912
Mean49.008
Median Absolute Deviation (MAD)13.45
Skewness-0.21288936
Sum4900.8
Variance606.65731
MonotonicityNot monotonic
2023-12-10T19:08:43.198449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.0 17
 
17.0%
0.0 10
 
10.0%
100.0 6
 
6.0%
75.0 4
 
4.0%
58.2 3
 
3.0%
58.3 3
 
3.0%
25.0 2
 
2.0%
58.7 2
 
2.0%
23.8 1
 
1.0%
30.6 1
 
1.0%
Other values (51) 51
51.0%
ValueCountFrequency (%)
0.0 10
10.0%
20.0 1
 
1.0%
21.4 1
 
1.0%
22.0 1
 
1.0%
23.8 1
 
1.0%
24.5 1
 
1.0%
25.0 2
 
2.0%
26.3 1
 
1.0%
27.0 1
 
1.0%
27.5 1
 
1.0%
ValueCountFrequency (%)
100.0 6
6.0%
83.2 1
 
1.0%
80.0 1
 
1.0%
77.2 1
 
1.0%
75.6 1
 
1.0%
75.0 4
4.0%
70.9 1
 
1.0%
70.8 1
 
1.0%
70.5 1
 
1.0%
70.0 1
 
1.0%

racer_grd_cd
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
B2
49 
B1
27 
A2
16 
A1
<NA>
 
1

Length

Max length4
Median length2
Mean length2.02
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st rowB2
2nd rowA2
3rd rowB1
4th rowA2
5th rowB1

Common Values

ValueCountFrequency (%)
B2 49
49.0%
B1 27
27.0%
A2 16
 
16.0%
A1 7
 
7.0%
<NA> 1
 
1.0%

Length

2023-12-10T19:08:43.467539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:43.697309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b2 49
49.0%
b1 27
27.0%
a2 16
 
16.0%
a1 7
 
7.0%
na 1
 
1.0%

racer_perio_no
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
59 
2
41 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 59
59.0%
2 41
41.0%

Length

2023-12-10T19:08:43.886032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:44.140386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 59
59.0%
2 41
41.0%

Interactions

2023-12-10T19:08:28.677043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:53.471753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:55.815060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:58.209137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:00.825945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:03.369734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:06.400060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:09.715640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:12.722598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:15.713510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:18.131410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:20.708792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:23.656155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:26.324473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:28.824948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:53.613360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:55.945991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:58.332643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:00.979902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:03.541755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:06.641381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:09.914961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:12.886585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:15.895050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:18.351034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:20.854451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:23.837979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:26.454834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:28.957092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:53.816646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:56.097310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:58.503650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:01.116563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:03.719786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:06.984994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:10.121256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:13.022220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:16.108546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:18.540808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:21.011404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:24.037105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:26.602036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:29.101679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:54.053233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:56.255277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:58.700245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:01.266441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:03.885185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:07.327638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:10.398575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:13.236339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:16.275135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:18.748789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:21.154429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:24.218951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:26.741571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:29.390177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:54.301300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:56.569067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:58.873837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:01.492232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:04.094473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:07.588638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:10.678200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:13.387577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:16.432528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:18.906492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:21.330925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:24.392293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:26.913215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:29.659520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:54.476726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:56.753755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:59.048574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:01.700168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:04.317395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:07.840539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:10.893588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:13.663055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:16.591279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:19.066196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:21.501565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:24.550638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:27.067565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:29.807601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:54.610671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:56.952115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:59.190941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:01.854176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:04.522514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:08.046124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:11.092289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:13.822310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:16.749657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:19.233740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:21.640313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:24.693618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:27.200313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:30.008356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:54.800164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:57.107127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:59.340344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:02.034918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:04.898475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:08.244426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:11.426003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:14.028897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:16.977053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:19.440510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:21.821579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:24.907071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:27.353037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:30.264291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:54.952438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:57.246461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:59.512972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:02.183155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:05.132496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:08.415598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:11.633682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:14.226088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:17.128987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:19.579358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:21.991250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:25.056058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:27.514616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:30.412221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:55.105777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:57.390794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:59.782132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:02.327556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:05.417533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:08.674414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:11.848996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:14.412820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:17.273765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:19.764024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:22.373910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:25.203851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:27.673880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:30.650244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:55.271807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:57.591490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:59.966393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:02.784150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:05.653655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:08.923157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:12.015854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:14.579849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:17.419092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:19.953649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:22.608663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:25.362621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:27.817443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:30.802552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:55.409362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:57.785006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:00.140099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:02.910881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:05.843058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:09.149277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:12.180427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:14.705831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:17.574678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:20.107774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:22.878201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:25.510137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:28.059355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:30.961101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:55.534695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:57.932148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:00.334298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:03.066071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:06.006222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:09.356054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:12.315107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:15.439271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:17.803943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:20.280753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:23.158734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:25.667059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:28.288381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:31.205068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:55.679022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:07:58.061092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:00.661784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:03.220888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:06.190467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:09.543611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:12.561584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:15.573550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:17.972216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:20.446778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:23.401704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:25.836046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:28.503459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:08:44.284438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
stnd_yearracer_noracer_nmrace_cntrank_1_cntrank_2_cntrank_3_cntrank_4_cntrank_5_cntrank_6_cntavg_rank_scravg_acdnt_scravg_scravg_strt_tmwin_ratiohigh_rank_ratiohigh_3_rank_ratioracer_grd_cdracer_perio_no
stnd_year1.0000.0000.0000.8090.5160.5130.4480.5680.5080.0000.2960.0000.0000.5770.1270.6480.4720.1450.213
racer_no0.0001.0001.0000.0000.0000.0000.0000.0000.0000.6950.6350.9880.9160.8550.0000.0000.0001.0001.000
racer_nm0.0001.0001.0000.0000.0000.0000.0000.0000.0000.6950.6350.9880.9160.8550.0000.0000.0001.0001.000
race_cnt0.8090.0000.0001.0000.6210.7850.8220.7550.6740.6660.2620.0000.2910.6380.3650.5580.6390.2561.000
rank_1_cnt0.5160.0000.0000.6211.0000.7960.6680.4880.5790.4380.6160.1860.4360.0000.8730.7240.7560.0870.608
rank_2_cnt0.5130.0000.0000.7850.7961.0000.8640.7990.6400.6450.6290.0000.3780.0000.3710.6510.7480.2380.970
rank_3_cnt0.4480.0000.0000.8220.6680.8641.0000.7970.6310.7980.0000.0940.0000.0000.4090.5160.6450.3331.000
rank_4_cnt0.5680.0000.0000.7550.4880.7990.7971.0000.6690.8340.0000.0000.0000.3870.3690.4970.4390.0001.000
rank_5_cnt0.5080.0000.0000.6740.5790.6400.6310.6691.0000.7230.3490.0000.3140.0000.3900.7880.7100.1310.880
rank_6_cnt0.0000.6950.6950.6660.4380.6450.7980.8340.7231.0000.2790.0000.0680.0000.3370.3690.5580.0000.979
avg_rank_scr0.2960.6350.6350.2620.6160.6290.0000.0000.3490.2791.0000.0000.8750.4270.8060.8060.8560.2020.416
avg_acdnt_scr0.0000.9880.9880.0000.1860.0000.0940.0000.0000.0000.0001.0000.7060.3380.0000.0000.2910.2080.465
avg_scr0.0000.9160.9160.2910.4360.3780.0000.0000.3140.0680.8750.7061.0000.2780.7190.8890.8990.2630.390
avg_strt_tm0.5770.8550.8550.6380.0000.0000.0000.3870.0000.0000.4270.3380.2781.0000.0000.3070.3950.0000.405
win_ratio0.1270.0000.0000.3650.8730.3710.4090.3690.3900.3370.8060.0000.7190.0001.0000.7600.6820.0000.484
high_rank_ratio0.6480.0000.0000.5580.7240.6510.5160.4970.7880.3690.8060.0000.8890.3070.7601.0000.9010.2540.663
high_3_rank_ratio0.4720.0000.0000.6390.7560.7480.6450.4390.7100.5580.8560.2910.8990.3950.6820.9011.0000.1610.683
racer_grd_cd0.1451.0001.0000.2560.0870.2380.3330.0000.1310.0000.2020.2080.2630.0000.0000.2540.1611.0000.000
racer_perio_no0.2131.0001.0001.0000.6080.9701.0001.0000.8800.9790.4160.4650.3900.4050.4840.6630.6830.0001.000
2023-12-10T19:08:44.602283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
stnd_yearracer_perio_noracer_grd_cd
stnd_year1.0000.3480.135
racer_perio_no0.3481.0000.000
racer_grd_cd0.1350.0001.000
2023-12-10T19:08:44.765381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
race_cntrank_1_cntrank_2_cntrank_3_cntrank_4_cntrank_5_cntrank_6_cntavg_rank_scravg_acdnt_scravg_scravg_strt_tmwin_ratiohigh_rank_ratiohigh_3_rank_ratiostnd_yearracer_grd_cdracer_perio_no
race_cnt1.0000.8610.9090.8920.8250.7450.6670.3380.1670.335-0.1990.4770.3010.2840.7190.1110.969
rank_1_cnt0.8611.0000.8250.7480.6360.5360.4940.5610.2140.521-0.3570.7750.5130.4860.4030.0450.638
rank_2_cnt0.9090.8251.0000.8150.7550.6390.5570.4430.1470.442-0.2400.4540.4270.3920.3180.1360.815
rank_3_cnt0.8920.7480.8151.0000.7810.7120.6500.2740.2360.267-0.1190.3510.1440.2880.3700.1340.937
rank_4_cnt0.8250.6360.7550.7811.0000.7840.6880.1030.1410.122-0.1030.2330.040-0.0230.3980.0000.958
rank_5_cnt0.7450.5360.6390.7120.7841.0000.800-0.1560.124-0.1110.0200.135-0.137-0.2340.2500.0760.883
rank_6_cnt0.6670.4940.5570.6500.6880.8001.000-0.2270.177-0.1930.0820.117-0.165-0.2620.0650.0000.838
avg_rank_scr0.3380.5610.4430.2740.103-0.156-0.2271.000-0.0400.943-0.4680.7440.9090.9320.1910.0740.323
avg_acdnt_scr0.1670.2140.1470.2360.1410.1240.177-0.0401.000-0.2920.0590.093-0.0910.0430.0000.1130.316
avg_scr0.3350.5210.4420.2670.122-0.111-0.1930.943-0.2921.000-0.4910.6880.8620.8620.0000.1640.375
avg_strt_tm-0.199-0.357-0.240-0.119-0.1030.0200.082-0.4680.059-0.4911.000-0.459-0.467-0.4140.4320.0000.324
win_ratio0.4770.7750.4540.3510.2330.1350.1170.7440.0930.688-0.4591.0000.7390.6360.0910.0000.507
high_rank_ratio0.3010.5130.4270.1440.040-0.137-0.1650.909-0.0910.862-0.4670.7391.0000.8340.3510.1580.646
high_3_rank_ratio0.2840.4860.3920.288-0.023-0.234-0.2620.9320.0430.862-0.4140.6360.8341.0000.2080.0620.659
stnd_year0.7190.4030.3180.3700.3980.2500.0650.1910.0000.0000.4320.0910.3510.2081.0000.1350.348
racer_grd_cd0.1110.0450.1360.1340.0000.0760.0000.0740.1130.1640.0000.0000.1580.0620.1351.0000.000
racer_perio_no0.9690.6380.8150.9370.9580.8830.8380.3230.3160.3750.3240.5070.6460.6590.3480.0001.000

Missing values

2023-12-10T19:08:31.430494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:08:31.987281image/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_yearracer_noracer_nmrace_cntrank_1_cntrank_2_cntrank_3_cntrank_4_cntrank_5_cntrank_6_cntavg_rank_scravg_acdnt_scravg_scravg_strt_tmwin_ratiohigh_rank_ratiohigh_3_rank_ratioracer_grd_cdracer_perio_no
0200201-010김덕환4114981184.150.23.950.492.412.234.1B21
1202102-038최재원122502306.170.176.00.1716.758.358.3A22
2200201-008김국흠4811894845.790.795.00.4222.939.658.3B11
3200201-007길현태55109208446.510.516.00.4518.234.570.9A21
4200201-006권현기563691210124.160.593.570.355.416.132.1B11
5200201-005권오현4417981444.480.643.840.442.318.238.6B21
6200201-004권명호602161061026.570.576.00.4335.045.061.7B11
7202102-037최광성123311315.920.175.750.2225.050.058.3A22
8200201-002강창효4412974346.21.145.070.3327.347.763.6B21
9200201-001강지환4910785785.330.984.350.3920.434.751.0B11
stnd_yearracer_noracer_nmrace_cntrank_1_cntrank_2_cntrank_3_cntrank_4_cntrank_5_cntrank_6_cntavg_rank_scravg_acdnt_scravg_scravg_strt_tmwin_ratiohigh_rank_ratiohigh_3_rank_ratioracer_grd_cdracer_perio_no
90200301-010김덕환53337111893.830.433.40.435.711.324.5B21
91200301-009김대선598101288105.190.484.70.4313.630.550.8B11
92200301-008김국흠396946855.460.584.880.3915.438.548.7B11
93200301-007길현태8825201713847.140.186.960.3328.451.170.5A21
94200301-006권현기8623261610657.420.177.240.3426.757.075.6B11
95200301-005권오현50081181294.240.573.670.420.016.038.0B21
96200301-004권명호954418179418.250.277.980.3846.365.383.2B11
97200301-003곽현성57261087227.890.497.40.3945.663.277.2A11
98200301-002강창효399575825.671.14.560.4123.135.953.8B21
99200301-001강지환742513116796.740.546.20.3333.851.466.2B11