See Part I in this series to get familiar with itertools.
Terminating iterators
In contrast to the infinite iterators, this type of iterator does not keep going endlessly. Terminating iterators produce a short output and are used for fast processing of the elements in a collection. Let’s go through a few of them now.
The accumulate() iterator
This iterator can be used to perform algebraic operations on the elements of a collection. For example, let’s say we have the daily percentage returns of the closing price of Tesla, Inc. (TSLA) and we want to see how it adds up. Well we will use the accumulate function.
Since we need the data of a stock, we will import yahoo finance libraries and retrieve the data of Tesla Inc. for this example.
The code is as follows:
# Importing libraries
import yfinance as yf
import pandas as pd
# Import Tesla data
tesla = yf.download(‘TSLA’,’2020-03-01′, ‘2020-03-30’)
tesla[‘daily_returns’] = tesla[‘Close’].pct_change()
Now, we will use the accumulate function.
# accumulate function
tesla[‘daily_returns’].dropna(inplace=True)
result = itertools.accumulate(tesla[‘daily_returns’], operator.add)
for each in result:
print(each)
In the next installment, the author will discuss the chain() iterator.
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