Setting category and type for multiple columns possible?-CodePudding

I have a dataset which contains 6 columns TIME1 to TIME6, amongst others. For each of these I need to apply the code below (which is shown for 2 columns). LISTED is a prepared list of the possible elements to be seen in these columns.

Is there a way to do this without writing the same 2 lines 6 times?

df['PART1'] = df['TIME1'].astype('category')
df['PART1'].cat.set_categories(LISTED, inplace=True)

df['PART2'] = df['TIME2'].astype('category')
df['PART2'].cat.set_categories(LISTED, inplace=True)

For astype(first line of code), I tried the following:

for col in ['TIME1', 'TIME2', 'TIME3', 'TIME4', 'TIME5', 'TIME6']:
    df_col = df[col].astype('category')

I think this works (not sure how to check without the whole code working). But how could I do something similar for the second line of code with the set_categories etc?

In short, I'm looking for something short/more elegant that just copying and modifying the same 2 lines 6 times. I am new to python, any help is greatly appreciated.

Using python 2.7 and pandas 0.24.2

CodePudding user response：

Yes it is possible! We can change the dtype of multiple columns to categorical is one go by creating CategoricalDtype

i = pd.RangeIndex(1, 7).astype(str)
df['PART'   i] = df['TIME'   i].astype(pd.CategoricalDtype(LISTED))