![]() ![]() One can have a look at Python3 Wiki Built-In_Changes to get more details on it. Note that the method iteritems() was removed from Python 3. It uses iterators so it is fast and allows traversing the dictionary while editing. In Python 3, there is only one method named items(). There are good explanation in PEP 469, PEP 3106 and Views And Iterators Instead Of Lists In my opinion, it doesn't cost too much until the data we are working on is huge, where we have to be selective in our approach rest for small dataset either approach will be fine as mentioned below. I believe, the more important is to understand the requirement over cosmetics while looking around a solution for an individual requirement. Its easier and quicker when you make them numpy arrays and work on it. Loops are super expensive when it comes to bigdata. The best way in terms of memory and computation is to use the columns as vectors and performing vector computations using numpy arrays. If you want to iterate through rows of dataframe rather than the series, we could use iterrows, itertuple and iteritems. Lambda reduces the lines of code and can be used along side filter, reduce or map. You also use traditional for Loop and also a while Loop x=0 ![]() For very large n-dimensional lists it is advisable to use numpy. You could use np.ndenumerate() to mimic the behavior of enumerate for numpy arrays. It reduces the overhead of keeping a count of the elements while the iteration operation. List comprehension can work with and can identify whether the input is a list, string or tuple #Using Python enumerate() methodĮnumerate is very widely used as enumerate adds a counter to the list or any other iterable and returns it as an enumerate object by the function. Range doesn’t include the end value in the sequence #List Comprehension Ways to iterate through pandas/python arr = pandas.Series() Period_series = pd.Series(pd.period_range(end='', periods=n, freq='s')) Interval_series = pd.Series(pd._arrays((size=n), np.random.random(size=n))) String_series = pd.Series(np.random.randint(10000000000000000, size=n)).astype('string')ĭatetime_series = pd.Series(np.random.choice(pd.date_range('', ''), size=n))ĭatetimetz_series = pd.Series(np.random.choice(pd.date_range('', '', tz='CET'), size=n))Ĭategorical_series = pd.Series(np.random.randint(100, size=n)).astype('category') Int_series = pd.Series(np.random.randint(1000000000, size=n))įloat_series = pd.Series(np.random.randn(size=n))įloatnan_series = pd.Series(np.random.choice(*n + np.random.randn(n).tolist(), size=n)) The snippet is just to avoid cluttering the main post with supplemental code. Note: This is python code in a js snippet, so "run code snippet" will not work. ![]()
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