Using np.where with a pandas column. How do you fill the column with the previous value until condition is met again

Question

I have this code:

df['cross'] = np.where((df['mean_close_spread'].shift(1) < 0) & (df['mean_close_spread'] > 0), 'cross', 'none')

df['cross_price'] = np.where((df['cross'] == 'cross'), df['close'], 'none')

The above code gives me the below dataframe

close	cross	cross_price
0.3434	none	none
0.3435	none	none
0.3433	none	none
0.3434	cross	0.3434
0.3433	none	none
0.3432	none	none
0.3431	cross	0.3431
0.4330	none	none

Instead of using the "none" string in the cross_price column when there is no cross in the cross column I would like to use the last price from the last cross until the next cross happens.

Here is an example of what i want:

close	cross	cross_price
0.3434	none	none
0.3435	none	none
0.3433	none	none
0.3434	cross	0.3434
0.3433	none	0.3434
0.3432	none	0.3434
0.3431	cross	0.3431
0.4330	none	0.3431

Asked By: Jack Mahon

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Source

Answer 1

try this one:

df['cross'] = np.where((df['mean_close_spread'].shift(1) < 0) & (df['mean_close_spread'] > 0), 'cross', 'none')    
df['cross_price'] = np.where((df['cross'] == 'cross'), df['close'], None) # not 'none'

df['cross_price'] = df['cross_price'].ffill().fillna('none')

Answered By: Panda Kim

Answer 2

Use Series.where for NaNs if not match cross, so possible forward missing values:

df['cross_price'] = df['close'].where(df['cross'] == 'cross').ffill() 
print (df)
    close  cross  cross_price
0  0.3434   none          NaN
1  0.3435   none          NaN
2  0.3433   none          NaN
3  0.3434  cross       0.3434
4  0.3433   none       0.3434
5  0.3432   none       0.3434
6  0.3431  cross       0.3431
7  0.4330   none       0.3431

Then replacement to none not recommended, because get mixed numeric and strings values.

Alternative without cross column:

m = (df['mean_close_spread'].shift(1) < 0) & (df['mean_close_spread'] > 0)
df['cross_price'] = df['close'].where(m).ffill()

Answered By: jezrael

Using np.where with a pandas column. How do you fill the column with the previous value until condition is met again

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