Select all rows in Python pandas

Question

I have a function that aims at printing the sum along a column of a pandas DataFrame after filtering on some rows to be defined; and the percentage this quantity makes up in the same sum without any filter:

def my_function(df, filter_to_apply, col):
    my_sum = np.sum(df[filter_to_apply][col])
    print(my_sum)
    print(my_sum/np.sum(df[col]))

Now I am wondering if there is any way to have a filter_to_apply that actually doesn't do any filter (ie keeps all rows), to keep using my function (that is actually a bit more complex and convenient) even when I don't want any filter.

So, some filter_f1 that would do: df[filter_f1] = df and could be used with other filters: filter_f1 & filter_f2 .

One possible answer is: df.index.isin(df.index) but I am wondering if there is anything easier to understand (eg I tried to use just True but it didn't work).

Answer 1

This is a way to select all rows:

df[range(0, len(df))]

this is also

df[:]

But I haven't figured out a way to pass : as an argument.

Answer 2

Theres a function called loc on pandas that filters rows. You could do something like this:

df2 = df.loc[<Filter here>]

#Filter can be something like df['price']>500 or df['name'] == 'Brian'
#basically something that for each row returns a boolean

total = df2['ColumnToSum'].sum()

Answer 3

A Python slice object, ie slice(-1) , acts as an object that selects all indexes in a indexable object. So df[slice(-1)] would select all rows in the DataFrame . You can store that in a variable an an initial value which you can further refine in your logic:

filter_to_apply = slice(-1)  # initialize to select all rows

... # logic that may set `filter_to_apply` to something more restrictive

my_function(df, filter_to_apply, col)