converting daily stock data to weekly-based via pandas in Python

Question

I\'ve got a DataFrame storing daily-based data which is as below:

Date              Open        High         Low       Close   Volume
2010-01-04         


        

Answer 1

In general, assuming that you have the dataframe in the form you specified, you need to do the following steps:

1. put Date in the index
2. resample the index.

What you have is a case of applying different functions to different columns. See.

You can resample in various ways. for e.g. you can take the mean of the values or count or so on. check pandas resample.

You can also apply custom aggregators (check the same link). With that in mind, the code snippet for your case can be given as:

f['Date'] = pd.to_datetime(f['Date'])
f.set_index('Date', inplace=True)
f.sort_index(inplace=True)

def take_first(array_like):
    return array_like[0]

def take_last(array_like):
    return array_like[-1]

output = f.resample('W',                                 # Weekly resample
                    how={'Open': take_first, 
                         'High': 'max',
                         'Low': 'min',
                         'Close': take_last,
                         'Volume': 'sum'}, 
                    loffset=pd.offsets.timedelta(days=-6))  # to put the labels to Monday

output = output[['Open', 'High', 'Low', 'Close', 'Volume']]

Here, W signifies a weekly resampling which by default spans from Monday to Sunday. To keep the labels as Monday, loffset is used. There are several predefined day specifiers. Take a look at pandas offsets. You can even define custom offsets (see).

Coming back to the resampling method. Here for Open and Close you can specify custom methods to take the first value or so on and pass the function handle to the how argument.

This answer is based on the assumption that the data seems to be daily, i.e. for each day you have only 1 entry. Also, no data is present for the non-business days. i.e. Sat and Sun. So taking the last data point for the week as the one for Friday is ok. If you so want you can use business week instead of 'W'. Also, for more complex data you may want to use groupby to group the weekly data and then work on the time indices within them.

btw a gist for the solution can be found at: https://gist.github.com/prithwi/339f87bf9c3c37bb3188