Blonde | Blonde , A simple theme using Tailwind CSS | Theme library
kandi X-RAY | Blonde Summary
kandi X-RAY | Blonde Summary
Blonde, A simple theme using Tailwind CSS. The demo can be seen here:
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Trending Discussions on Blonde
QUESTION
How could i make a rule that test if all facts from a deftemplate but one matches an specific condition?
Example: with
...ANSWER
Answered 2022-Mar-16 at 18:50 CLIPS (6.4 2/9/21)
CLIPS>
(deftemplate person
(slot name)
(slot hair-color))
CLIPS>
(defrule only-one-not-blonde
(person (name ?name-not-blonde)
(hair-color ~blonde))
(not (person (name ~?name-not-blonde)
(hair-color ~blonde)))
=>
(println "Only " ?name-not-blonde " is not blonde."))
CLIPS>
(deffacts initial
(person (name Sue) (hair-color blonde))
(person (name Frank) (hair-color blonde))
(person (name Josh) (hair-color brown)))
CLIPS> (reset)
CLIPS> (facts)
f-1 (person (name Sue) (hair-color blonde))
f-2 (person (name Frank) (hair-color blonde))
f-3 (person (name Josh) (hair-color brown))
For a total of 3 facts.
CLIPS> (agenda)
0 only-one-not-blonde: f-3,*
For a total of 1 activation.
CLIPS> (assert (person (name Anne) (hair-color red)))
CLIPS> (agenda)
CLIPS>
QUESTION
I have a Dataframe df
containing information about people. I need to compute the standard deviation of the weight of people whose name starts with N. This is my code to create the Dataframe:
ANSWER
Answered 2022-Feb-26 at 19:40You are almost there. Casting df.index
to pd.Series is unnecessary and leads to an error as indices get misaligned form the original df. Try:
QUESTION
I am a newbie here. English is not my native language so excuse any grammatical mistakes. I need to compute the average BMI per hair colour using the df
.
ANSWER
Answered 2022-Feb-26 at 08:02You can use df.groupby()
which is a functionality within Pandas
For your particular case, you may use
QUESTION
I am a newbie here. English is not my native language, so excuse any grammatical mistakes. I'm trying to compute the mean age of blonde people from the data in df
:
ANSWER
Answered 2022-Feb-24 at 19:56You get this error because you use df(..)
. This is the python syntax to call a function. You probably want df[..]
instead.
To answer your question:
QUESTION
I have the following dataset:
...ANSWER
Answered 2022-Feb-23 at 21:12This is not the more efficient answer, neither the more general solution, but may satisfy a solution:
QUESTION
I'm trying to print a raster visualisation. It renders fine in RStudio, but when I save it using the base svg device it comes out super blurry - as though each square of the raster is a massive pixel and they've been interpolated at some stage in the process.
Here's a reprex:
...ANSWER
Answered 2022-Feb-08 at 11:35The geom_raster
is being interpreted as a low-res raster, so it seems that the svg device is attempting to interpolate it (you will see the same image in the plotting window if you use geom_raster(interpolate = TRUE)
). One way round this is to use geom_tile
instead:
QUESTION
I want to find a document in my db and replace it with a document that has a new name and new key.
Here is my Schema
...ANSWER
Answered 2022-Jan-29 at 13:02There are two issues that I can see.
First, you should pass null as the third argument in your findOneAndReplace call. This will set the options to null and should get your code running. This is in my opinion a strange behavior of mongoose.
QUESTION
I am trying to present a table showing demographic information for multiple dichotomous outcomes asked in a survey.
This is an example of what I am starting with:
...ANSWER
Answered 2022-Jan-16 at 20:29Here's an example of how to get what you're after.
QUESTION
I tried to cluster my dataset using K-mean, but there is a categorical data in column 9; so when I ran k-mean it had an error like this:
...ANSWER
Answered 2021-Dec-17 at 17:31To solve your specific issue, you can generate dummy variables to run your desired clustering.
One way to do it is using the dummy_columns()
function from the fastDummies
package.
QUESTION
I ran multiple imputation to impute missing data for 2 variables of a data frame, then I got a new data frame (with 2 columns for 2 imputed variables).
Now, I want to replace the 2 columns in the original data frame with the two newly imputed columns from my new dataframe. What should I do?
Original data frame new data frame for imputed variables
This is the code I used. Only 2 columns in this data frame are missing data, so I only imputed those two. Is that ok? Can you please suggest me a better way?
...ANSWER
Answered 2021-Dec-14 at 22:53Updated
As @dcarlson recommended, you can run mice
on the entire dataframe, then you can use complete
to get the whole output dataframe. Then, you can join the new data with your original dataframe.
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