CODEWITHSUNDEEP
pythonpandas
Leon Kyriacou
Leon Kyriacou

Reputation: 402

Pandas apply based on conditional from another column

I'm looking to adjust values of one column based on a conditional in another column.

I'm using np.busday_count, but I don't want the weekend values to behave like a Monday (Sat to Tues is given 1 working day, I'd like that to be 2)

dispdf = df[(df.dispatched_at.isnull()==False) & (df.sold_at.isnull()==False)]

dispdf["dispatch_working_days"] = np.busday_count(dispdf.sold_at.tolist(), dispdf.dispatched_at.tolist())

for i in range(len(dispdf)):
    if dispdf.dayofweek.iloc[i] == 5 or dispdf.dayofweek.iloc[i] == 6:
        dispdf.dispatch_working_days.iloc[i] +=1

Sample:

            dayofweek   dispatch_working_days
    43159   1.0 3
    48144   3.0 3
    45251   6.0 1
    49193   3.0 0
    42470   3.0 1
    47874   6.0 1
    44500   3.0 1
    43031   6.0 3
    43193   0.0 4
    43591   6.0 3

Expected Results:

        dayofweek   dispatch_working_days
43159   1.0 3
48144   3.0 3
45251   6.0 2
49193   3.0 0
42470   3.0 1
47874   6.0 2
44500   3.0 1
43031   6.0 2
43193   0.0 4
43591   6.0 4

At the moment I'm using this for loop to add a working day to Saturday and Sunday values. It's slow!

Can I use a vectorization instead to speed this up. I tried using .apply but to no avail.

Upvotes: 4

Views: 3749

Answers (2)

ilia timofeev
ilia timofeev

Reputation: 1119

for in you code could be replaced by that line: 

dispdf.loc[dispdf.dayofweek>5,'dispatch_working_days']+=1

or you could use numpy.where

https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html

Upvotes: 2

joaoavf
joaoavf

Reputation: 1383

Pretty sure this works, but there are more optimized implementations:

def adjust_dispatch(df_line):
    if df_line['dayofweek'] >= 5:
        return df_line['dispatch_working_days'] + 1
    else:
        return df_line['dispatch_working_days']         

df['dispatch_working_days'] = df.apply(adjust_dispatch, axis=1)

Upvotes: 3

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