spore | ReSTful extensions for Slim

For the part: "many of the columns the values are hidden by "...". How can I see these values"

It is because by default it hides the columns if they are too many to display. I am not sure which of the print(df.head()), df.head() and print(correlation_df) is your output image related to but you will need to see the columns separated using .iloc.

Example:

The first string (with FD) is not UTF-8-encoded. It is likely ISO-8859-1 or Windows-1252. The byte representing ý happens to match the Unicode code point value, but it is not using "[U]nicode code points to represent characters".

The LEGO string is UTF-8-encoded. If you are hacking strings from files and don't have a specification, you just have to guess. UTF-8 has to follow specific rules for its multi-byte encoding, so decoding is likely to fail if you try UTF-8 first and it isn't UTF-8. You could then fallback to ISO-8859-1. The latter will decode anything, even if it isn't that encoding. You may end up with garbage.

Example for UTF-8 encoding:

The comment "and I'm not even sure it reports all the variables" has me a bit confused about what you're asking for when you say "variable names used during the fit", so I've answered both interpretations here.

Both answers assume this slightly-smaller version of your reproducible example.

What you are trying to do is a bit tricky, most naive bayes implementation or at least the one you are using (from kLAR which is derived from e1071) uses a normal distribution. You can see under the details of naiveBayes help page from e1071:

The standard naive Bayes classifier (at least this implementation) assumes independence of the predictor variables, and Gaussian distribution (given the target class) of metric predictors. For attributes with missing values, the corresponding table entries are omitted for prediction.

And your predictors are categorical so this might be problematic. You can try to set kernel=TRUE and adjust=1 to force it towards normal, and avoid kernel=FALSE which will throw the error.

Before that we remove columns with only 1 level and sort out the column names, also in this case it's easier to use the formula and avoid the making dummy variables :

• pandas.DataFrame.plot returns an Axes, and has a figsize parameter.
• This option won't work if facecolor is used as a parameter inside the DataFrame.plot

We group by 'SPECIES' and then use summarise with across on the numeric columns, subset the column values were 'LOCATION' is 'Gauteng' or the other one, apply the t.test and extract the pvalue