Summing given columns by row in DataFrame

user_616349 0
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I have a DataFrame defined by:

df = pd.DataFrame({
    'id':[1,2,3], 
    'activity':['A1', 'A2', 'A2'], 
    'prep_hours':[None,None,1], 
    'delivery_hours':[10,10,15]})

I want to create a column total_hours which is the sum of all columns matching the pattern *_hours

For the time being, I simply add the columns I want into a new column:

df.fillna(0, inplace=True)
df['total_hours'] = df['prep_hours'] + df['delivery_hours']

But it does not scale easily. For the sake of example, I only have 2 columns called *_hours but in the real DataFrame, it contains more than 30 columns that should be added.

Is there a smarter way to do it?

pandas dataframe
2 Answers

Use DataFrame.filter with like parameter and sum, convert missing values to 0 is not necessary:

df["total_hours"] = df.filter(like='_hours').sum(axis=1)
print (df)
   id activity  prep_hours  delivery_hours  total_hours
0   1       A1         NaN              10         10.0
1   2       A2         NaN              10         10.0
2   3       A2         1.0              15         16.0
user_29010020

You could select a subset of your original dataframe using a list_comprehension and add content horizontally like this:

columns = [col for col in df.columns if "_hours" in col]
df["total_hours"] = df[columns].sum(axis=1)

If the pattern you are looking for is more complicated you could use regex matching as well :)

user_63033770
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