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How to penalize for empty fields in a DataFrame?
2019 Community Moderator ElectionPandas: access fields within field in a DataFrameHow duplicated items can be deleted from dataframe in pandaslengthy criteria in dataframe selectorResampling pandas Dataframe keeping other columnsHow to group this dataframe in python?Pandas DataFrame Rollup ErrorDataframe size is null?Pivot reshape dataframeHow to get a dataframe values in one single column for the following dataset?Manipulating multi-indices for a pandas dataframe
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I have to calculate the consistency of racing car drivers during the whole season. My DataFrame consists of 10 columns (10 circuit names) and for each of those columns I have the standard deviation in lap time the driver posted in that circuit. In other words, how consistent the driver is from lap to lap. In races the driver did not finish the field is blank.
So far I have calculated their average season consistency by averaging all 10 columns. However, not finishing a race should affect a driver's consistency negatively and I do not know how to implement that.
pandas data
New contributor
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add a comment |
$begingroup$
I have to calculate the consistency of racing car drivers during the whole season. My DataFrame consists of 10 columns (10 circuit names) and for each of those columns I have the standard deviation in lap time the driver posted in that circuit. In other words, how consistent the driver is from lap to lap. In races the driver did not finish the field is blank.
So far I have calculated their average season consistency by averaging all 10 columns. However, not finishing a race should affect a driver's consistency negatively and I do not know how to implement that.
pandas data
New contributor
$endgroup$
add a comment |
$begingroup$
I have to calculate the consistency of racing car drivers during the whole season. My DataFrame consists of 10 columns (10 circuit names) and for each of those columns I have the standard deviation in lap time the driver posted in that circuit. In other words, how consistent the driver is from lap to lap. In races the driver did not finish the field is blank.
So far I have calculated their average season consistency by averaging all 10 columns. However, not finishing a race should affect a driver's consistency negatively and I do not know how to implement that.
pandas data
New contributor
$endgroup$
I have to calculate the consistency of racing car drivers during the whole season. My DataFrame consists of 10 columns (10 circuit names) and for each of those columns I have the standard deviation in lap time the driver posted in that circuit. In other words, how consistent the driver is from lap to lap. In races the driver did not finish the field is blank.
So far I have calculated their average season consistency by averaging all 10 columns. However, not finishing a race should affect a driver's consistency negatively and I do not know how to implement that.
pandas data
pandas data
New contributor
New contributor
New contributor
asked 2 days ago
jatrp5jatrp5
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$begingroup$
This heavily depends on the domain knowledge. A general approach would be to place
A multiplicative of the worst or average consistency at each circuit $c$, i.e. $(1 + m)textmax(sigma_c)$ or $(1 + m)textavg(sigma_c)$ respectively, for the null values at that circuit, or
A multiplicative of the worst or average consistency of each driver $d$, i.e. $(1 + m)textmax(sigma_d)$ or $(1 + m)textavg(sigma_d)$ respectively, for their unfinished races, or
A multiplicative of average of driver and circuit average consistencies, i.e. $(1 + m)[textavg(sigma_d) + textavg(sigma_c)]/2$, for unfinished race of driver $d$ at circuit $c$, or some other combinations.
No matter which approach to choose, the choice of coefficient $m$ affects the final ranking and could be determined either
Subjectively by looking at the rankings from an expert point of view and selecting the one that makes more sense, or
By trying a range of values like $m in -0.2, -0.1, 0, 0.1, 0.2, .., 0.5$ and averaging the consistencies $sigma_d$ or rankings $R_d$ for each driver $d$. An advantage of this approach would be that when rank of a driver has a low variance over different values of $m$, it implies that driver's rank is insensitive to the choice of $m$, i.e. it is less controversial, and when rank changes a lot with different choices of $m$, the average rank is more controversial.
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$begingroup$
This heavily depends on the domain knowledge. A general approach would be to place
A multiplicative of the worst or average consistency at each circuit $c$, i.e. $(1 + m)textmax(sigma_c)$ or $(1 + m)textavg(sigma_c)$ respectively, for the null values at that circuit, or
A multiplicative of the worst or average consistency of each driver $d$, i.e. $(1 + m)textmax(sigma_d)$ or $(1 + m)textavg(sigma_d)$ respectively, for their unfinished races, or
A multiplicative of average of driver and circuit average consistencies, i.e. $(1 + m)[textavg(sigma_d) + textavg(sigma_c)]/2$, for unfinished race of driver $d$ at circuit $c$, or some other combinations.
No matter which approach to choose, the choice of coefficient $m$ affects the final ranking and could be determined either
Subjectively by looking at the rankings from an expert point of view and selecting the one that makes more sense, or
By trying a range of values like $m in -0.2, -0.1, 0, 0.1, 0.2, .., 0.5$ and averaging the consistencies $sigma_d$ or rankings $R_d$ for each driver $d$. An advantage of this approach would be that when rank of a driver has a low variance over different values of $m$, it implies that driver's rank is insensitive to the choice of $m$, i.e. it is less controversial, and when rank changes a lot with different choices of $m$, the average rank is more controversial.
$endgroup$
add a comment |
$begingroup$
This heavily depends on the domain knowledge. A general approach would be to place
A multiplicative of the worst or average consistency at each circuit $c$, i.e. $(1 + m)textmax(sigma_c)$ or $(1 + m)textavg(sigma_c)$ respectively, for the null values at that circuit, or
A multiplicative of the worst or average consistency of each driver $d$, i.e. $(1 + m)textmax(sigma_d)$ or $(1 + m)textavg(sigma_d)$ respectively, for their unfinished races, or
A multiplicative of average of driver and circuit average consistencies, i.e. $(1 + m)[textavg(sigma_d) + textavg(sigma_c)]/2$, for unfinished race of driver $d$ at circuit $c$, or some other combinations.
No matter which approach to choose, the choice of coefficient $m$ affects the final ranking and could be determined either
Subjectively by looking at the rankings from an expert point of view and selecting the one that makes more sense, or
By trying a range of values like $m in -0.2, -0.1, 0, 0.1, 0.2, .., 0.5$ and averaging the consistencies $sigma_d$ or rankings $R_d$ for each driver $d$. An advantage of this approach would be that when rank of a driver has a low variance over different values of $m$, it implies that driver's rank is insensitive to the choice of $m$, i.e. it is less controversial, and when rank changes a lot with different choices of $m$, the average rank is more controversial.
$endgroup$
add a comment |
$begingroup$
This heavily depends on the domain knowledge. A general approach would be to place
A multiplicative of the worst or average consistency at each circuit $c$, i.e. $(1 + m)textmax(sigma_c)$ or $(1 + m)textavg(sigma_c)$ respectively, for the null values at that circuit, or
A multiplicative of the worst or average consistency of each driver $d$, i.e. $(1 + m)textmax(sigma_d)$ or $(1 + m)textavg(sigma_d)$ respectively, for their unfinished races, or
A multiplicative of average of driver and circuit average consistencies, i.e. $(1 + m)[textavg(sigma_d) + textavg(sigma_c)]/2$, for unfinished race of driver $d$ at circuit $c$, or some other combinations.
No matter which approach to choose, the choice of coefficient $m$ affects the final ranking and could be determined either
Subjectively by looking at the rankings from an expert point of view and selecting the one that makes more sense, or
By trying a range of values like $m in -0.2, -0.1, 0, 0.1, 0.2, .., 0.5$ and averaging the consistencies $sigma_d$ or rankings $R_d$ for each driver $d$. An advantage of this approach would be that when rank of a driver has a low variance over different values of $m$, it implies that driver's rank is insensitive to the choice of $m$, i.e. it is less controversial, and when rank changes a lot with different choices of $m$, the average rank is more controversial.
$endgroup$
This heavily depends on the domain knowledge. A general approach would be to place
A multiplicative of the worst or average consistency at each circuit $c$, i.e. $(1 + m)textmax(sigma_c)$ or $(1 + m)textavg(sigma_c)$ respectively, for the null values at that circuit, or
A multiplicative of the worst or average consistency of each driver $d$, i.e. $(1 + m)textmax(sigma_d)$ or $(1 + m)textavg(sigma_d)$ respectively, for their unfinished races, or
A multiplicative of average of driver and circuit average consistencies, i.e. $(1 + m)[textavg(sigma_d) + textavg(sigma_c)]/2$, for unfinished race of driver $d$ at circuit $c$, or some other combinations.
No matter which approach to choose, the choice of coefficient $m$ affects the final ranking and could be determined either
Subjectively by looking at the rankings from an expert point of view and selecting the one that makes more sense, or
By trying a range of values like $m in -0.2, -0.1, 0, 0.1, 0.2, .., 0.5$ and averaging the consistencies $sigma_d$ or rankings $R_d$ for each driver $d$. An advantage of this approach would be that when rank of a driver has a low variance over different values of $m$, it implies that driver's rank is insensitive to the choice of $m$, i.e. it is less controversial, and when rank changes a lot with different choices of $m$, the average rank is more controversial.
edited 2 days ago
answered 2 days ago
EsmailianEsmailian
2,487318
2,487318
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jatrp5 is a new contributor. Be nice, and check out our Code of Conduct.
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