I'm using df.columns.values to make a list of column names which I then iterate over and make charts, etc... but when I set this up I overlooked the non-numeric columns in the df. Now, I'd much rather not simply drop those columns from the df (or a copy of it). Instead, I would like to find a slick way to eliminate them from the list of column names.
Now I have:
names = df.columns.values
what I'd like to get to is something that behaves like:
names = df.columns.values(column_type=float64)
Is there any slick way to do this? I suppose I could make a copy of the df, and drop those non-numeric columns before doing columns.values, but that strikes me as clunky.
Welcome any inputs/suggestions. Thanks.
Someone will give you a better answe than this possibly, but one thing I tend to do is if all my numeric data are int64 or float64 objects, then you can create a dict of the column data types and then use the values to create your list of columns.
So for example, in a dataframe where I have columns of type float64, int64 and object firstly you can look at the data types as so:
DF.dtypes
and if they conform to the standard whereby the non-numeric columns of data are all object types (as they are in my dataframes), then you can do the following to get a list of the numeric columns:
[key for key in dict(DF.dtypes) if dict(DF.dtypes)[key] in ['float64', 'int64']]
Its just a simple list comprehension. Nothing fancy. Again, though whether this works for you will depend upon how you set up you dataframe...
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