Question

I have a DataFrame with the following structure:

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 3333 entries, 2000-01-03 00:00:00+00:00 to 2012-11-21 00:00:00+00:00
Data columns:
open          3333  non-null values
high          3333  non-null values
low           3333  non-null values
close         3333  non-null values
volume        3333  non-null values
amount        3333  non-null values
pct_change    3332  non-null values
dtypes: float64(7)

The pct_change column contains percent change data.

Given a filtered DatetimeIndex from the DataFrame above:

<class 'pandas.tseries.index.DatetimeIndex'>
[2000-03-01 00:00:00, ..., 2012-11-01 00:00:00]
Length: 195, Freq: None, Timezone: UTC

I want to filter starting each date entry and return the first row where pct_change column is below 0.015.

I came up with this solution but it is very slow:

stops = []
#dates = DatetimeIndex
for d in dates:
    #check if pct_change is below -0.015 starting from date of signal. return date of first match
    match = df[df["pct_change"] < -0.015].ix[d:][:1].index

    stops.append([df.ix[d]["close"], df.ix[match]["close"].values[0]])

Any suggestions on how I can improve this?

Answer 1

How about this:

result = df[df.pct_change < -0.015].reindex(filtered_dates, method='bfill')

The only problem with this is that if an interval does NOT contain a value below -0.015, it will retrieve one from a future interval. If you add a column containing the date you can see the time each row came from, then set rows to NA if the retrieved timestamp exceeds the next “bin edge”.