I have a dataframe with directions:
Direction:
2/01/19 None
1/31/19 Upward
1/30/19 None
1/29/19 None
1/28/19 Downward
1/27/19 None
1/26/19 None
1/25/19 Upward
I want to create a "Momentum" column based on the following conditions (starting at 1/25/19):
1. If the Direction for the corresponding date is 'Upward', then set the value to 'Upward'
2. If the row one below in Momentum is "Upward" set it to 'Upward'
3. If the Direction for the corresponding date is 'Downward', then set it to 'None'
4. Otherwise, set it to 'None'
Said differently, once you reach the 'Upward' status, it should stay that way until you hit 'Downward'
The result should look like:
Direction: Momentum:
2/01/19 None Upward
1/31/19 Upward Upward
1/30/19 None None
1/29/19 None None
1/28/19 Downward None
1/27/19 None Upward
1/26/19 None Upward
1/25/19 Upward Upward
Is there a way to accomplish this without using loop?
EDITED answer by new data first back fill None values and then replace Downward to Nones:
#first replace strings Nones to None type
df['Direction:'] = df['Direction:'].mask(df['Direction:'] == 'None', None)
df['Momentum:'] = df['Direction:'].bfill().mask(lambda x: x == 'Downward', None)
Or:
s = df['Direction:'].bfill()
df['Momentum:'] = s.mask(s == 'Downward', None)
print (df)
Direction: Momentum:
2/01/19 None Upward
1/31/19 Upward Upward
1/30/19 None None
1/29/19 None None
1/28/19 Downward None
1/27/19 None Upward
1/26/19 None Upward
1/25/19 Upward Upward
Old answer:
Use numpy.where with chained boolean mask compared shifted values and also original by | for bitwise OR:
mask = df['Direction:'].eq('Upward') | df['Direction:'].shift(-1).eq('Upward')
df['Momentum:'] = np.where(mask, 'Upward', None)
print (df)
Direction: Momentum:
1/31/19 None Upward
1/30/19 Upward Upward
1/29/19 None None
1/28/19 None None
1/27/19 Downward None
1/26/19 None Upward
1/25/19 Upward Upward