amesh | Everyone loves Tokyo Amesh

I am trying to access a static class from a mono-behaviour script. I am declaring the class with an instance but I get the error Cannot declare a variable of static type 'MeshExtension'. Even though creating an instance of the static class should work right? What am I doing wrong here?

I need help with the specific code I will paste below. I am using the Ames Housing data set collected by Dean De Cock. I am using a Python notebook and editing thru Anaconda's Jupyter Lab 2.1.5. The code below is supposed to replace all np.nan or "None" values. For some reason, after repeatedly calling a hand-made function inside a for loop, the columns of the resulting data frame get swapped around.

Note: I am aware I could do this with an "imputer." I plan to select numeric and object type features, impute them separately then put them back together. As a side-note, is there any way I can do that while having the details I output manually using text displayed or otherwise verified?

In the cell in question, the flow is:

1. Get and assign the number of data points in the data frame df_train.
2. Get and assign a series that lists the count of null values in df_train. The syntax is sr_null_counts = df_train.isnull().sum().
3. Create an empty list to which names of features that have 5% of their values equal to null are appended. They will be dropped later, outside the for loop. I thought at first that this was the problem since the command to drop the columns of df_train in-place used to be within the for-loop.
4. Repeatedly call a hand-made function to impute columns with null values not exceeding 5% of the row count for df_train.

I used a function that has a for-loop and nested try-except statements to:

1. Accept a series and, optionally, the series' name when it was a column in a dataframe. It assigns a copy of the passed series to a local variable.
2. In the exact order, (a) try to replace all null (NaN or None) values with the mean of the passed series. (b) If that fails, try to replace all null values with the median of the series. (c) If even that fails, replace all null values with the mode of the series.
3. Return the edited copy of the series with all null values replaced. It should also print out strings that tell me what feature was modified and what summary statistic was used to replace/impute the missing values.

The final line is to drop all the columns marked as having more than 5% missing values.

Here is the full code:

The full data-set was loaded thru df_housing = pd.read_csv(sep = '\t', filepath_or_buffer = "AmesHousing.tsv").

I'm loading a *.js model which has a texture. The texture itself is a separate file located in the same directory as the model, so the loader adds this texture to the *.js model.

I am calculating the boosting gradient to identify the importance of variables in the model, however I am performing resamples to identify how the importance of each variable behaves.

But I can't correctly save the variable name with it's importance calculated in each bootstrap resampling.

I'm doing this using a function, which is called within the bootstrap package boost command.

Below is a minimally reproducible example adapted for AmesHousing data:

I used the Ames data set to create the following code:

I'm relatively new at R, so please bear with me.

I'm using the Ames dataset (full description of dataset here; link to dataset download here).

I'm trying to create a subset data frame that will allow me to run a linear regression analysis, and I'm trying to remove the outliers using the boxplot.stats function. I created a frame that will include my samples using the following code:

I have dataframe with below values and I want to remove last characters i.e - from all the row. How can I do it?

df:

I have a .txt dataset with about 80 features, where it appears that "NA" is used as both an indicator of a missing value, as well as an actual value for particular ordinal string features, such as:

What is the best way to handle this?

By default, pandas converts these "NA" values to "nan". I read how to stop this by removing "NA" from the list na_vals like so:

Actually I have made small programme in php using simple array and search name from array and my programe given below :