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Revenue per order using itertools¶ Get revenue per order using order_items data set. In [1]: %run 02_preparing_data_sets.ipynb In [2]: order_items[:4] Out[2]: [‘1,1,957,1,299.98,299.98’, ‘2,2,1073,1,199.99,199.99’, ‘3,2,502,5,250.0,50.0’, ‘4,2,403,1,129.99,129.99’] In [3]: order_subtotals = map(lambda oi: (int(oi.split(‘,’)[1]), float(oi.split(‘,’)[4])), order_items) In [4]: list(order_subtotals)[:3] Out[4]: [(1, 299.98), (2, 199.99), (2, 250.0)] In [5]: order_subtotals = map(lambda oi: (int(oi.split(‘,’)[1]), float(oi.split(‘,’)[4])), order_items) order_subtotals_sorted = sorted(order_subtotals) In [6]: order_subtotals_sorted[:3] Out[6]: [(1, …

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Order Count by Status using itertools¶ Get count by order status using orders data set. In [1]: %run 02_preparing_data_sets.ipynb In [2]: orders[:3] Out[2]: [‘1,2013-07-25 00:00:00.0,11599,CLOSED’, ‘2,2013-07-25 00:00:00.0,256,PENDING_PAYMENT’, ‘3,2013-07-25 00:00:00.0,12111,COMPLETE’] In [3]: orders_sorted = sorted(orders, key=lambda k: k.split(‘,’)[3]) In [4]: orders_sorted[:3] Out[4]: [‘50,2013-07-25 00:00:00.0,5225,CANCELED’, ‘112,2013-07-26 00:00:00.0,5375,CANCELED’, ‘527,2013-07-28 00:00:00.0,5426,CANCELED’] In [5]: import itertools as iter In [6]: orders_grouped = iter.groupby(orders_sorted, lambda order: order.split(‘,’)[3]) …

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Overview of itertools groupby¶ Let us understand how we can use itertools.groupby to take care of aggregations by key. itertools.groupby can be used to get the data grouped by a key. It can be used to take care of use cases similar to following by using aggregate functions after grouping by key. Get count by …

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Using itertools starmap¶ Let us understand the usage of starmap. We will first create a collection with sales and commission percentage. Using that collection compute total commission amount. If the commission percent is None or not present, treat it as 0. Each element in the collection should be a tuple. First element is the sales …

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Cumulative Operations using itertools¶ Get cumulative sales from list of transactions. In [1]: ns = [1, 2, 3, 4] # Cumulative totals [1, 3, 6, 10] # Cumulative product [1, 2, 6, 24] In [2]: import itertools as iter In [3]: iter.accumulate? Init signature: iter.accumulate(iterable, func=None, *, initial=None) Docstring: Return series of accumulated sums (or other binary function …

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Overview of itertools¶ Let us go through one of the important library to manipulate collections called as itertools. Functions such as filter and map are part of core Python libraries. reduce is part of functools. It is not possible to use these functions to perform advanced operations such as grouped aggregations, joins etc. Python have …

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