# Python Itertools Tutorial – Part III

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QuantInsti
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See Part I and Part II in this series to get familiar with itertools.

The chain() iterator

As you might have guessed, we can use the chain() itertool to combine two lists together. Here it is in action below:

# Chain() itertool
stocks_NYSE = [‘TSLA’, ‘MSFT’, ‘NVDA’, ‘GOOGL’ , ‘AAPL’ , ‘INTC’]
stocks_NSE = [‘HDFC’, ‘RELIANCE’, ‘INFY’, ‘ICICIBANK’]
result = itertools.chain(stocks_NYSE, stocks_NSE)
for each in result:
print(each)

The output will be as follows:

TSLA
MSFT
NVDA
GOOGL
AAPL
INTC
HDFC
RELIANCE
INFY
ICICIBANK

The compress() iterator

While the chain() iterator is used to combine more than one list (or rather any element), the compress() iterator can be used to select a few elements in the list. We will understand it by seeing the code.

# Compress() itertool
stocks_NYSE = [‘TSLA’, ‘MSFT’, ‘NVDA’, ‘GOOGL’ , ‘AAPL’ , ‘INTC’]
stocks_NSE = [‘HDFC’, ‘RELIANCE’, ‘INFY’, ‘ICICIBANK’]
selections = [1,0,0,1,0,1]
result = itertools.compress(stocks_NYSE, selections)
for each in result:
print(each)

The output is as follows:

TSLA
GOOGL
INTC

Thus, only those elements were printed which were associated with 1 in the selections list. You can also use ‘True’ and ‘False’ in place of 1 and 0.

The dropwhile() iterator

You can use this iterator to filter your list, but return only those elements after the condition has been false. For example, in our example below, we want to list only those closing prices after the stock price went below \$700. Thus, we write the code as follows:

# Dropwhile() itertool
data = tesla[‘Close’]
result = itertools.dropwhile(lambda x: x>700, data)
for each in result:
print(each)

The output is as follows:

608.0
645.3300170898438
634.22998046875
560.5499877929688
546.6199951171875
445.07000732421875
430.20001220703125
361.2200012207031
427.6400146484375
427.5299987792969
434.2900085449219
505.0
539.25
528.1599731445312
514.3599853515625

In the next installment, the author will discuss the takewhile() iterator.

Visit https://www.quantinsti.com/ for ready-to-use Python functions as applied in trading and data analysis.

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