Overview

Dataset statistics

Number of variables16
Number of observations100
Missing cells401
Missing cells (%)25.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.3 KiB
Average record size in memory136.3 B

Variable types

Categorical3
Numeric3
Text8
Unsupported2

Alerts

meet is highly overall correlated with stnd_yearHigh correlation
stnd_year is highly overall correlated with meetHigh correlation
race_day is highly overall correlated with tmsHigh correlation
tms is highly overall correlated with race_dayHigh correlation
stnd_year is highly imbalanced (80.6%)Imbalance
meet is highly imbalanced (86.0%)Imbalance
rank3 has 97 (97.0%) missing valuesMissing
pool002 has 3 (3.0%) missing valuesMissing
pool005 has 3 (3.0%) missing valuesMissing
pool006 has 97 (97.0%) missing valuesMissing
pool007 has 100 (100.0%) missing valuesMissing
pool008 has 100 (100.0%) missing valuesMissing
pool007 is an unsupported type, check if it needs cleaning or further analysisUnsupported
pool008 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 10:08:21.179997
Analysis finished2023-12-10 10:08:26.846647
Duration5.67 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
2003
97 
2021
 
3

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2003
2nd row2021
3rd row2003
4th row2003
5th row2003

Common Values

ValueCountFrequency (%)
2003 97
97.0%
2021 3
 
3.0%

Length

2023-12-10T19:08:26.952916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:27.122763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2003 97
97.0%
2021 3
 
3.0%

race_day
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean683.29
Minimum219
Maximum1214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:27.310227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum219
5-th percentile321
Q1426
median624.5
Q3830
95-th percentile1212
Maximum1214
Range995
Interquartile range (IQR)404

Descriptive statistics

Standard deviation299.31988
Coefficient of variation (CV)0.43805687
Kurtosis-0.6589091
Mean683.29
Median Absolute Deviation (MAD)199.5
Skewness0.79851224
Sum68329
Variance89592.39
MonotonicityNot monotonic
2023-12-10T19:08:27.607459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1212 11
 
11.0%
627 5
 
5.0%
1207 5
 
5.0%
629 4
 
4.0%
628 4
 
4.0%
830 4
 
4.0%
622 4
 
4.0%
726 4
 
4.0%
725 4
 
4.0%
419 3
 
3.0%
Other values (28) 52
52.0%
ValueCountFrequency (%)
219 1
 
1.0%
309 1
 
1.0%
314 1
 
1.0%
315 1
 
1.0%
321 3
3.0%
410 2
2.0%
412 2
2.0%
413 2
2.0%
418 3
3.0%
419 3
3.0%
ValueCountFrequency (%)
1214 1
 
1.0%
1212 11
11.0%
1207 5
5.0%
1205 3
 
3.0%
906 2
 
2.0%
831 2
 
2.0%
830 4
 
4.0%
726 4
 
4.0%
725 4
 
4.0%
719 1
 
1.0%

meet
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
광명
97 
창원
 
2
부산
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row광명
2nd row창원
3rd row광명
4th row광명
5th row광명

Common Values

ValueCountFrequency (%)
광명 97
97.0%
창원 2
 
2.0%
부산 1
 
1.0%

Length

2023-12-10T19:08:27.849123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:28.070619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광명 97
97.0%
창원 2
 
2.0%
부산 1
 
1.0%

tms
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.45
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:28.397484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q18
median16.5
Q326
95-th percentile40
Maximum40
Range39
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.333231
Coefficient of variation (CV)0.6684678
Kurtosis-0.79530582
Mean18.45
Median Absolute Deviation (MAD)8.5
Skewness0.68597082
Sum1845
Variance152.10859
MonotonicityNot monotonic
2023-12-10T19:08:28.627784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
17 13
13.0%
40 12
12.0%
7 8
 
8.0%
21 8
 
8.0%
8 8
 
8.0%
10 8
 
8.0%
39 8
 
8.0%
26 6
 
6.0%
16 5
 
5.0%
6 4
 
4.0%
Other values (9) 20
20.0%
ValueCountFrequency (%)
1 2
 
2.0%
2 2
 
2.0%
3 3
 
3.0%
6 4
4.0%
7 8
8.0%
8 8
8.0%
9 3
 
3.0%
10 8
8.0%
11 2
 
2.0%
12 3
 
3.0%
ValueCountFrequency (%)
40 12
12.0%
39 8
8.0%
27 2
 
2.0%
26 6
6.0%
21 8
8.0%
20 1
 
1.0%
17 13
13.0%
16 5
 
5.0%
15 2
 
2.0%
12 3
 
3.0%

day_ord
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
40 
3
30 
2
30 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
1 40
40.0%
3 30
30.0%
2 30
30.0%

Length

2023-12-10T19:08:28.885505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:08:29.067974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 40
40.0%
3 30
30.0%
2 30
30.0%

race_no
Real number (ℝ)

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.34
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:08:29.246787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q311
95-th percentile13
Maximum14
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.8826732
Coefficient of variation (CV)0.52897456
Kurtosis-1.2232927
Mean7.34
Median Absolute Deviation (MAD)3
Skewness-0.0070823866
Sum734
Variance15.075152
MonotonicityNot monotonic
2023-12-10T19:08:29.444038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
12 10
10.0%
5 9
9.0%
4 9
9.0%
7 8
 
8.0%
10 8
 
8.0%
3 7
 
7.0%
1 7
 
7.0%
13 7
 
7.0%
11 7
 
7.0%
9 7
 
7.0%
Other values (4) 21
21.0%
ValueCountFrequency (%)
1 7
7.0%
2 6
6.0%
3 7
7.0%
4 9
9.0%
5 9
9.0%
6 6
6.0%
7 8
8.0%
8 6
6.0%
9 7
7.0%
10 8
8.0%
ValueCountFrequency (%)
14 3
 
3.0%
13 7
7.0%
12 10
10.0%
11 7
7.0%
10 8
8.0%
9 7
7.0%
8 6
6.0%
7 8
8.0%
6 6
6.0%
5 9
9.0%

rank1
Text

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:08:29.999749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.02
Min length4

Characters and Unicode

Total characters402
Distinct characters102
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)96.0%

Sample

1st row⑥권정국
2nd row③김병도
3rd row②김동환
4th row③김동옥
5th row⑥김재인
ValueCountFrequency (%)
④홍석한 4
 
3.9%
③최정헌 1
 
1.0%
③한정훈 1
 
1.0%
②박종현 1
 
1.0%
②고병수 1
 
1.0%
⑦장보규 1
 
1.0%
④김치범 1
 
1.0%
⑦김종훈 1
 
1.0%
⑥이유진 1
 
1.0%
③곽종헌 1
 
1.0%
Other values (89) 89
87.3%
2023-12-10T19:08:30.957112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 26
 
6.5%
③ 18
 
4.5%
⑦ 17
 
4.2%
⑥ 17
 
4.2%
④ 17
 
4.2%
② 13
 
3.2%
이 13
 
3.2%
정 11
 
2.7%
① 10
 
2.5%
석 8
 
2.0%
Other values (92) 252
62.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 298
74.1%
Other Number 100
 
24.9%
Space Separator 4
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 26
 
8.7%
이 13
 
4.4%
정 11
 
3.7%
석 8
 
2.7%
박 7
 
2.3%
종 7
 
2.3%
동 7
 
2.3%
원 7
 
2.3%
진 7
 
2.3%
우 7
 
2.3%
Other values (84) 198
66.4%
Other Number
ValueCountFrequency (%)
③ 18
18.0%
⑦ 17
17.0%
⑥ 17
17.0%
④ 17
17.0%
② 13
13.0%
① 10
10.0%
⑤ 8
8.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 298
74.1%
Common 104
 
25.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 26
 
8.7%
이 13
 
4.4%
정 11
 
3.7%
석 8
 
2.7%
박 7
 
2.3%
종 7
 
2.3%
동 7
 
2.3%
원 7
 
2.3%
진 7
 
2.3%
우 7
 
2.3%
Other values (84) 198
66.4%
Common
ValueCountFrequency (%)
③ 18
17.3%
⑦ 17
16.3%
⑥ 17
16.3%
④ 17
16.3%
② 13
12.5%
① 10
9.6%
⑤ 8
7.7%
4
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 298
74.1%
Enclosed Alphanum 100
 
24.9%
ASCII 4
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 26
 
8.7%
이 13
 
4.4%
정 11
 
3.7%
석 8
 
2.7%
박 7
 
2.3%
종 7
 
2.3%
동 7
 
2.3%
원 7
 
2.3%
진 7
 
2.3%
우 7
 
2.3%
Other values (84) 198
66.4%
Enclosed Alphanum
ValueCountFrequency (%)
③ 18
18.0%
⑦ 17
17.0%
⑥ 17
17.0%
④ 17
17.0%
② 13
13.0%
① 10
10.0%
⑤ 8
8.0%
ASCII
ValueCountFrequency (%)
4
100.0%

rank2
Text

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:08:31.524053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.02
Min length4

Characters and Unicode

Total characters402
Distinct characters98
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)94.0%

Sample

1st row④박현수
2nd row⑥박정욱
3rd row⑥임승빈
4th row⑤이영주
5th row③서동형
ValueCountFrequency (%)
⑥박민수 2
 
2.0%
①박 2
 
2.0%
①최용진 2
 
2.0%
⑤정점식 2
 
2.0%
③정점식 1
 
1.0%
⑤송대호 1
 
1.0%
①고광종 1
 
1.0%
①김우년 1
 
1.0%
②박학규 1
 
1.0%
③박일호 1
 
1.0%
Other values (88) 88
86.3%
2023-12-10T19:08:32.310422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
박 19
 
4.7%
김 19
 
4.7%
③ 17
 
4.2%
① 16
 
4.0%
⑤ 16
 
4.0%
⑥ 14
 
3.5%
② 14
 
3.5%
수 13
 
3.2%
⑦ 12
 
3.0%
정 12
 
3.0%
Other values (88) 250
62.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 298
74.1%
Other Number 100
 
24.9%
Space Separator 4
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
박 19
 
6.4%
김 19
 
6.4%
수 13
 
4.4%
정 12
 
4.0%
이 11
 
3.7%
진 11
 
3.7%
민 9
 
3.0%
성 8
 
2.7%
석 7
 
2.3%
최 7
 
2.3%
Other values (80) 182
61.1%
Other Number
ValueCountFrequency (%)
③ 17
17.0%
① 16
16.0%
⑤ 16
16.0%
⑥ 14
14.0%
② 14
14.0%
⑦ 12
12.0%
④ 11
11.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 298
74.1%
Common 104
 
25.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
박 19
 
6.4%
김 19
 
6.4%
수 13
 
4.4%
정 12
 
4.0%
이 11
 
3.7%
진 11
 
3.7%
민 9
 
3.0%
성 8
 
2.7%
석 7
 
2.3%
최 7
 
2.3%
Other values (80) 182
61.1%
Common
ValueCountFrequency (%)
③ 17
16.3%
① 16
15.4%
⑤ 16
15.4%
⑥ 14
13.5%
② 14
13.5%
⑦ 12
11.5%
④ 11
10.6%
4
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 298
74.1%
Enclosed Alphanum 100
 
24.9%
ASCII 4
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
박 19
 
6.4%
김 19
 
6.4%
수 13
 
4.4%
정 12
 
4.0%
이 11
 
3.7%
진 11
 
3.7%
민 9
 
3.0%
성 8
 
2.7%
석 7
 
2.3%
최 7
 
2.3%
Other values (80) 182
61.1%
Enclosed Alphanum
ValueCountFrequency (%)
③ 17
17.0%
① 16
16.0%
⑤ 16
16.0%
⑥ 14
14.0%
② 14
14.0%
⑦ 12
12.0%
④ 11
11.0%
ASCII
ValueCountFrequency (%)
4
100.0%

rank3
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing97
Missing (%)97.0%
Memory size932.0 B
2023-12-10T19:08:32.563856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters12
Distinct characters12
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

Unique3 ?
Unique (%)100.0%

Sample

1st row②박창순
2nd row⑦홍미웅
3rd row①엄재천
ValueCountFrequency (%)
②박창순 1
33.3%
⑦홍미웅 1
33.3%
①엄재천 1
33.3%
2023-12-10T19:08:33.047418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
② 1
8.3%
박 1
8.3%
창 1
8.3%
순 1
8.3%
⑦ 1
8.3%
홍 1
8.3%
미 1
8.3%
웅 1
8.3%
① 1
8.3%
엄 1
8.3%
Other values (2) 2
16.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9
75.0%
Other Number 3
 
25.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
박 1
11.1%
창 1
11.1%
순 1
11.1%
홍 1
11.1%
미 1
11.1%
웅 1
11.1%
엄 1
11.1%
재 1
11.1%
천 1
11.1%
Other Number
ValueCountFrequency (%)
② 1
33.3%
⑦ 1
33.3%
① 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9
75.0%
Common 3
 
25.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
박 1
11.1%
창 1
11.1%
순 1
11.1%
홍 1
11.1%
미 1
11.1%
웅 1
11.1%
엄 1
11.1%
재 1
11.1%
천 1
11.1%
Common
ValueCountFrequency (%)
② 1
33.3%
⑦ 1
33.3%
① 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9
75.0%
Enclosed Alphanum 3
 
25.0%

Most frequent character per block

Enclosed Alphanum
ValueCountFrequency (%)
② 1
33.3%
⑦ 1
33.3%
① 1
33.3%
Hangul
ValueCountFrequency (%)
박 1
11.1%
창 1
11.1%
순 1
11.1%
홍 1
11.1%
미 1
11.1%
웅 1
11.1%
엄 1
11.1%
재 1
11.1%
천 1
11.1%

pool001
Text

Distinct82
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:08:33.607044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.06
Min length6

Characters and Unicode

Total characters606
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)68.0%

Sample

1st row(6)5.8
2nd row(3)4.9
3rd row(2)5.0
4th row(3)10.1
5th row(6)8.2
ValueCountFrequency (%)
4)1.0 4
 
4.0%
3)1.1 3
 
3.0%
4)1.1 3
 
3.0%
6)1.0 2
 
2.0%
7)1.0 2
 
2.0%
3)1.3 2
 
2.0%
1)2.0 2
 
2.0%
4)1.6 2
 
2.0%
6)1.2 2
 
2.0%
1)1.5 2
 
2.0%
Other values (72) 76
76.0%
2023-12-10T19:08:34.484344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 100
16.5%
) 100
16.5%
. 100
16.5%
1 84
13.9%
2 46
7.6%
3 34
 
5.6%
6 33
 
5.4%
4 27
 
4.5%
7 26
 
4.3%
0 25
 
4.1%
Other values (3) 31
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 306
50.5%
Open Punctuation 100
 
16.5%
Close Punctuation 100
 
16.5%
Other Punctuation 100
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 84
27.5%
2 46
15.0%
3 34
11.1%
6 33
 
10.8%
4 27
 
8.8%
7 26
 
8.5%
0 25
 
8.2%
5 20
 
6.5%
9 6
 
2.0%
8 5
 
1.6%
Open Punctuation
ValueCountFrequency (%)
( 100
100.0%
Close Punctuation
ValueCountFrequency (%)
) 100
100.0%
Other Punctuation
ValueCountFrequency (%)
. 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 606
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
( 100
16.5%
) 100
16.5%
. 100
16.5%
1 84
13.9%
2 46
7.6%
3 34
 
5.6%
6 33
 
5.4%
4 27
 
4.5%
7 26
 
4.3%
0 25
 
4.1%
Other values (3) 31
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 606
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 100
16.5%
) 100
16.5%
. 100
16.5%
1 84
13.9%
2 46
7.6%
3 34
 
5.6%
6 33
 
5.4%
4 27
 
4.5%
7 26
 
4.3%
0 25
 
4.1%
Other values (3) 31
 
5.1%

pool002
Text

MISSING 

Distinct97
Distinct (%)100.0%
Missing3
Missing (%)3.0%
Memory size932.0 B
2023-12-10T19:08:35.133565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length13.051546
Min length13

Characters and Unicode

Total characters1266
Distinct characters14
Distinct categories5 ?
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 (%)100.0%

Sample

1st row(6)2.4 (4)2.6
2nd row(2)1.6 (6)1.1
3rd row(3)2.8 (5)1.8
4th row(6)2.0 (3)1.0
5th row(2)1.2 (5)1.9
ValueCountFrequency (%)
4)1.0 10
 
5.2%
3)1.1 9
 
4.6%
7)1.0 7
 
3.6%
6)1.0 5
 
2.6%
2)1.2 4
 
2.1%
7)1.3 4
 
2.1%
6)1.3 4
 
2.1%
3)1.0 4
 
2.1%
1)1.0 4
 
2.1%
5)1.9 3
 
1.5%
Other values (107) 140
72.2%
2023-12-10T19:08:36.190537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 194
15.3%
) 194
15.3%
. 194
15.3%
1 168
13.3%
97
7.7%
2 89
7.0%
3 72
 
5.7%
4 51
 
4.0%
0 48
 
3.8%
7 46
 
3.6%
Other values (4) 113
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 587
46.4%
Open Punctuation 194
 
15.3%
Close Punctuation 194
 
15.3%
Other Punctuation 194
 
15.3%
Space Separator 97
 
7.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 168
28.6%
2 89
15.2%
3 72
12.3%
4 51
 
8.7%
0 48
 
8.2%
7 46
 
7.8%
6 45
 
7.7%
5 38
 
6.5%
8 17
 
2.9%
9 13
 
2.2%
Open Punctuation
ValueCountFrequency (%)
( 194
100.0%
Close Punctuation
ValueCountFrequency (%)
) 194
100.0%
Other Punctuation
ValueCountFrequency (%)
. 194
100.0%
Space Separator
ValueCountFrequency (%)
97
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1266
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
( 194
15.3%
) 194
15.3%
. 194
15.3%
1 168
13.3%
97
7.7%
2 89
7.0%
3 72
 
5.7%
4 51
 
4.0%
0 48
 
3.8%
7 46
 
3.6%
Other values (4) 113
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1266
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 194
15.3%
) 194
15.3%
. 194
15.3%
1 168
13.3%
97
7.7%
2 89
7.0%
3 72
 
5.7%
4 51
 
4.0%
0 48
 
3.8%
7 46
 
3.6%
Other values (4) 113
8.9%

pool004
Text

Distinct99
Distinct (%)100.0%
Missing1
Missing (%)1.0%
Memory size932.0 B
2023-12-10T19:08:36.798614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length8.4343434
Min length8

Characters and Unicode

Total characters835
Distinct characters14
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique99 ?
Unique (%)100.0%

Sample

1st row(6-4)17.7
2nd row(3-6)5.5
3rd row(2-6)13.1
4th row(3-5)19.3
5th row(6-3)15.0
ValueCountFrequency (%)
6-4)17.7 1
 
1.0%
5-4)15.4 1
 
1.0%
2-5)14.1 1
 
1.0%
2-6)2.2 1
 
1.0%
7-4)2.5 1
 
1.0%
4-1)10.2 1
 
1.0%
4-1)1.7 1
 
1.0%
7-2)54.1 1
 
1.0%
6-3)3.0 1
 
1.0%
3-7)98.4 1
 
1.0%
Other values (89) 89
89.9%
2023-12-10T19:08:37.607161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 99
11.9%
- 99
11.9%
) 99
11.9%
. 99
11.9%
1 63
7.5%
3 59
7.1%
4 58
6.9%
6 56
6.7%
2 52
6.2%
5 51
6.1%
Other values (4) 100
12.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 439
52.6%
Open Punctuation 99
 
11.9%
Dash Punctuation 99
 
11.9%
Close Punctuation 99
 
11.9%
Other Punctuation 99
 
11.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 63
14.4%
3 59
13.4%
4 58
13.2%
6 56
12.8%
2 52
11.8%
5 51
11.6%
7 46
10.5%
9 20
 
4.6%
0 18
 
4.1%
8 16
 
3.6%
Open Punctuation
ValueCountFrequency (%)
( 99
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 99
100.0%
Close Punctuation
ValueCountFrequency (%)
) 99
100.0%
Other Punctuation
ValueCountFrequency (%)
. 99
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 835
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
( 99
11.9%
- 99
11.9%
) 99
11.9%
. 99
11.9%
1 63
7.5%
3 59
7.1%
4 58
6.9%
6 56
6.7%
2 52
6.2%
5 51
6.1%
Other values (4) 100
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 99
11.9%
- 99
11.9%
) 99
11.9%
. 99
11.9%
1 63
7.5%
3 59
7.1%
4 58
6.9%
6 56
6.7%
2 52
6.2%
5 51
6.1%
Other values (4) 100
12.0%

pool005
Text

MISSING 

Distinct96
Distinct (%)99.0%
Missing3
Missing (%)3.0%
Memory size932.0 B
2023-12-10T19:08:38.170152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length8.257732
Min length8

Characters and Unicode

Total characters801
Distinct characters14
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95 ?
Unique (%)97.9%

Sample

1st row(4-6)10.3
2nd row(2-6)5.3
3rd row(3-5)7.5
4th row(3-6)3.6
5th row(2-5)2.8
ValueCountFrequency (%)
4-6)4.8 2
 
2.1%
5-7)35.1 1
 
1.0%
5-6)33.5 1
 
1.0%
3-5)1.8 1
 
1.0%
2-5)9.2 1
 
1.0%
2-6)1.9 1
 
1.0%
4-7)2.2 1
 
1.0%
1-4)9.2 1
 
1.0%
1-4)1.6 1
 
1.0%
2-7)44.6 1
 
1.0%
Other values (86) 86
88.7%
2023-12-10T19:08:38.990347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 97
12.1%
- 97
12.1%
) 97
12.1%
. 97
12.1%
3 68
8.5%
1 59
7.4%
2 59
7.4%
4 49
6.1%
6 49
6.1%
5 45
5.6%
Other values (4) 84
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 413
51.6%
Open Punctuation 97
 
12.1%
Dash Punctuation 97
 
12.1%
Close Punctuation 97
 
12.1%
Other Punctuation 97
 
12.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 68
16.5%
1 59
14.3%
2 59
14.3%
4 49
11.9%
6 49
11.9%
5 45
10.9%
7 44
10.7%
8 16
 
3.9%
9 16
 
3.9%
0 8
 
1.9%
Open Punctuation
ValueCountFrequency (%)
( 97
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 97
100.0%
Close Punctuation
ValueCountFrequency (%)
) 97
100.0%
Other Punctuation
ValueCountFrequency (%)
. 97
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 801
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
( 97
12.1%
- 97
12.1%
) 97
12.1%
. 97
12.1%
3 68
8.5%
1 59
7.4%
2 59
7.4%
4 49
6.1%
6 49
6.1%
5 45
5.6%
Other values (4) 84
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 801
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 97
12.1%
- 97
12.1%
) 97
12.1%
. 97
12.1%
3 68
8.5%
1 59
7.4%
2 59
7.4%
4 49
6.1%
6 49
6.1%
5 45
5.6%
Other values (4) 84
10.5%

pool006
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing97
Missing (%)97.0%
Memory size932.0 B
2023-12-10T19:08:39.255493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters30
Distinct characters12
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row(2-3-6)6.9
2nd row(1-5-7)2.0
3rd row(1-3-7)1.5
ValueCountFrequency (%)
2-3-6)6.9 1
33.3%
1-5-7)2.0 1
33.3%
1-3-7)1.5 1
33.3%
2023-12-10T19:08:39.745433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 6
20.0%
( 3
10.0%
) 3
10.0%
. 3
10.0%
1 3
10.0%
2 2
 
6.7%
3 2
 
6.7%
6 2
 
6.7%
5 2
 
6.7%
7 2
 
6.7%
Other values (2) 2
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15
50.0%
Dash Punctuation 6
 
20.0%
Open Punctuation 3
 
10.0%
Close Punctuation 3
 
10.0%
Other Punctuation 3
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3
20.0%
2 2
13.3%
3 2
13.3%
6 2
13.3%
5 2
13.3%
7 2
13.3%
9 1
 
6.7%
0 1
 
6.7%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 6
20.0%
( 3
10.0%
) 3
10.0%
. 3
10.0%
1 3
10.0%
2 2
 
6.7%
3 2
 
6.7%
6 2
 
6.7%
5 2
 
6.7%
7 2
 
6.7%
Other values (2) 2
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 6
20.0%
( 3
10.0%
) 3
10.0%
. 3
10.0%
1 3
10.0%
2 2
 
6.7%
3 2
 
6.7%
6 2
 
6.7%
5 2
 
6.7%
7 2
 
6.7%
Other values (2) 2
 
6.7%

pool007
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

pool008
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

Interactions

2023-12-10T19:08:25.070515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:23.835823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:24.501186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:25.218005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:24.083214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:24.668781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:25.440495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:24.338359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:08:24.869299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:08:39.943278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
stnd_yearrace_daymeettmsday_ordrace_norank1rank2rank3pool001pool002pool004pool005pool006
stnd_year1.0000.3871.0000.5230.0360.2521.0001.000NaN1.000NaN1.000NaNNaN
race_day0.3871.0000.3870.9840.4070.2790.9860.982NaN0.8841.0001.0001.000NaN
meet1.0000.3871.0000.5110.3140.0001.0001.0001.0001.000NaN1.000NaN1.000
tms0.5230.9840.5111.0000.4560.1760.9760.9211.0000.8701.0001.0000.5771.000
day_ord0.0360.4070.3140.4561.0000.0000.3700.9461.0000.6921.0001.0000.7881.000
race_no0.2520.2790.0000.1760.0001.0001.0000.9521.0000.7921.0001.0000.9481.000
rank11.0000.9861.0000.9760.3701.0001.0000.9931.0000.9991.0001.0000.9971.000
rank21.0000.9821.0000.9210.9460.9520.9931.0001.0000.9911.0001.0000.9971.000
rank3NaNNaN1.0001.0001.0001.0001.0001.0001.0001.000NaN0.000NaN1.000
pool0011.0000.8841.0000.8700.6920.7920.9990.9911.0001.0001.0001.0000.9941.000
pool002NaN1.000NaN1.0001.0001.0001.0001.000NaN1.0001.0001.0001.000NaN
pool0041.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0000.000
pool005NaN1.000NaN0.5770.7880.9480.9970.997NaN0.9941.0001.0001.000NaN
pool006NaNNaN1.0001.0001.0001.0001.0001.0001.0001.000NaN0.000NaN1.000
2023-12-10T19:08:40.225116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
day_ordmeetstnd_year
day_ord1.0000.1060.058
meet0.1061.0000.995
stnd_year0.0580.9951.000
2023-12-10T19:08:40.382789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
race_daytmsrace_nostnd_yearmeetday_ord
race_day1.0000.9810.2010.3160.3570.275
tms0.9811.0000.1700.3810.3670.317
race_no0.2010.1701.0000.1830.0000.000
stnd_year0.3160.3810.1831.0000.9950.058
meet0.3570.3670.0000.9951.0000.106
day_ord0.2750.3170.0000.0580.1061.000

Missing values

2023-12-10T19:08:26.074778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:08:26.457084image/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.
2023-12-10T19:08:26.711508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

stnd_yearrace_daymeettmsday_ordrace_norank1rank2rank3pool001pool002pool004pool005pool006pool007pool008
02003629광명1737⑥권정국④박현수<NA>(6)5.8(6)2.4 (4)2.6(6-4)17.7(4-6)10.3<NA><NA><NA>
12021410창원1523③김병도⑥박정욱②박창순(3)4.9<NA>(3-6)5.5<NA>(2-3-6)6.9<NA><NA>
22003629광명1735②김동환⑥임승빈<NA>(2)5.0(2)1.6 (6)1.1(2-6)13.1(2-6)5.3<NA><NA><NA>
32003627광명1714③김동옥⑤이영주<NA>(3)10.1(3)2.8 (5)1.8(3-5)19.3(3-5)7.5<NA><NA><NA>
42003629광명1733⑥김재인③서동형<NA>(6)8.2(6)2.0 (3)1.0(6-3)15.0(3-6)3.6<NA><NA><NA>
52003629광명1731②최완수⑤이석훈<NA>(2)1.6(2)1.2 (5)1.9(2-5)4.6(2-5)2.8<NA><NA><NA>
62003628광명17213④홍석한②고병수<NA>(4)1.0(4)1.0 (2)2.8(4-2)2.9(2-4)2.8<NA><NA><NA>
72021410창원1522⑤함동주①이찬우⑦홍미웅(5)1.5<NA>(5-1)1.5<NA>(1-5-7)2.0<NA><NA>
82003628광명1724①임승빈⑤박성범<NA>(1)1.5(1)1.3 (5)1.6(1-5)6.2(1-5)5.3<NA><NA><NA>
92003628광명1722③박석채①서동형<NA>(3)1.5(3)1.7 (1)1.6(3-1)5.9(1-3)2.4<NA><NA><NA>
stnd_yearrace_daymeettmsday_ordrace_norank1rank2rank3pool001pool002pool004pool005pool006pool007pool008
902003419광명7213②이경곤④박종현<NA>(2)1.0(2)1.3 (4)2.0(2-4)3.6(2-4)3.1<NA><NA><NA>
912003511광명1034⑦김응수③최동철<NA>(7)2.1(7)1.3 (3)3.8(7-3)18.1(3-7)13.7<NA><NA><NA>
922003511광명1038⑦홍석헌⑤김수연<NA>(7)2.0(7)1.3 (5)1.3(7-5)3.5(5-7)1.7<NA><NA><NA>
932003830광명2623⑥박범진⑦김응수<NA>(6)6.2(6)4.9 (7)1.5(6-7)33.8(6-7)13.5<NA><NA><NA>
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