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Home/python

WikiQuora Latest Questions

Saralyn
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SaralynTeacher
Asked: June 5, 2025

How do I start my journey to learn machine learning, having acquired some knowledge on programming with Python?

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I’ve been studying data science, math, and machine learning for about 1 year now, and have put about 500-1000 hours in (large range since I also spend a lot of time studying for my role as a resident physician and ...

AImachine learningNatural Processingpython
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Saralyn
  • 1
SaralynTeacher
Asked: May 27, 2025

How Does Python Memory Management Work?

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Okay, I got this concept of a class that would allow other classes to import classes on as basis versus if you use it you must import it. How ...

CMemorypython
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Saralyn
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SaralynTeacher
Asked: May 21, 2025

How does a dictionary work? What type of program and algorithm is used?

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A Python dictionary is a data structure that stores the value in key: value pairs. Values in a dictionary can be of any data type and can be duplicated, whereas keys can’t be repeated and must be immutable. Example: Here, The data is ...

dictionaryhashmapkey value pairpython
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Saralyn
  • 1
SaralynTeacher
Asked: May 12, 2025

Between Java and Python, which one is better to learn first and why?

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If you’d have asked me a couple of years ago to write an answer to this question, I would have said, without any hesitation, that you should learn Python as a first language.

BegginerJavalanguageprogrammerpython
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W3spoint99
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W3spoint99Begginer
Asked: December 27, 2024In: Python

How to split a list into equally-sized chunks in Python?

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How to split a list into equally-sized chunks in Python?

chunkslistpythonsplit
  1. Saralyn
    Saralyn Teacher
    Added an answer on December 27, 2024 at 6:25 am

    Here's a generator that yields evenly-sized chunks: def chunks(lst, n): """Yield successive n-sized chunks from lst.""" for i in range(0, len(lst), n): yield lst[i:i + n] import pprint pprint.pprint(list(chunks(range(10, 75), 10))) [[10, 11, 12, 13, 14, 15, 16, 17, 18, 19], [20, 21, 22, 23, 24, 25,Read more

    Here’s a generator that yields evenly-sized chunks:

    def chunks(lst, n):
        """Yield successive n-sized chunks from lst."""
        for i in range(0, len(lst), n):
            yield lst[i:i + n]
    
    import pprint
    pprint.pprint(list(chunks(range(10, 75), 10)))
    [[10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
     [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
     [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
     [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
     [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],
     [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],
     [70, 71, 72, 73, 74]]
    

    For Python 2, using xrange instead of range:

    def chunks(lst, n):
        """Yield successive n-sized chunks from lst."""
        for i in xrange(0, len(lst), n):
            yield lst[i:i + n]
    

    Below is a list comprehension one-liner. The method above is preferable, though, since using named functions makes code easier to understand. For Python 3:

    [lst[i:i + n] for i in range(0, len(lst), n)]
    

    For Python 2:

    [lst[i:i + n] for i in xrange(0, len(lst), n)]
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W3spoint99
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W3spoint99Begginer
Asked: December 26, 2024In: Python

How to make good reproducible pandas examples?

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Having spent a decent amount of time watching both the r and pandas tags on SO, the impression that I get is that pandas questions are less likely to contain reproducible data. This is ...

examplehowpandaspython
  1. Saralyn
    Saralyn Teacher
    Added an answer on December 26, 2024 at 2:02 pm

    The Good: Do include a small example DataFrame, either as runnable code: In [1]: df = pd.DataFrame([[1, 2], [1, 3], [4, 6]], columns=['A', 'B']) or make it "copy and pasteable" using pd.read_clipboard(sep=r'\s\s+'). In [2]: df Out[2]: A B 0 1 2 1 1 3 2 4 6 Test it yourself to make sure it works andRead more

    The Good:

    • Do include a small example DataFrame, either as runnable code:
      In [1]: df = pd.DataFrame([[1, 2], [1, 3], [4, 6]], columns=['A', 'B'])
      

      or make it “copy and pasteable” using pd.read_clipboard(sep=r'\s\s+').

      In [2]: df
      Out[2]:
         A  B
      0  1  2
      1  1  3
      2  4  6
      

      Test it yourself to make sure it works and reproduces the issue.

      • You can format the text for Stack Overflow by highlighting and using Ctrl+K (or prepend four spaces to each line), or place three backticks (“`) above and below your code with your code unindented.
      • I really do mean small. The vast majority of example DataFrames could be fewer than 6 rows,[citation needed] and I bet I can do it in 5. Can you reproduce the error with df = df.head()? If not, fiddle around to see if you can make up a small DataFrame which exhibits the issue you are facing.

        But every rule has an exception, the obvious one being for performance issues (in which case definitely use %timeit and possibly %prun to profile your code), where you should generate:

        df = pd.DataFrame(np.random.randn(100000000, 10))
        

        Consider using np.random.seed so we have the exact same frame. Having said that, “make this code fast for me” is not strictly on topic for the site.

      • For getting runnable code, df.to_dict is often useful, with the different orient options for different cases. In the example above, I could have grabbed the data and columns from df.to_dict('split').
    • Write out the outcome you desire (similarly to above)
      In [3]: iwantthis
      Out[3]:
         A  B
      0  1  5
      1  4  6
      

      Explain where the numbers come from:

      The 5 is the sum of the B column for the rows where A is 1.

    • Do show the code you’ve tried:
      In [4]: df.groupby('A').sum()
      Out[4]:
         B
      A
      1  5
      4  6
      

      But say what’s incorrect:

      The A column is in the index rather than a column.

    • Do show you’ve done some research (search the documentation, search Stack Overflow), and give a summary:

      The docstring for sum simply states “Compute sum of group values”

      The groupby documentation doesn’t give any examples for this.

      Aside: the answer here is to use df.groupby('A', as_index=False).sum().

    • If it’s relevant that you have Timestamp columns, e.g. you’re resampling or something, then be explicit and apply pd.to_datetime to them for good measure.
      df['date'] = pd.to_datetime(df['date']) # this column ought to be date.
      

      Sometimes this is the issue itself: they were strings.

    The Bad:

    • Don’t include a MultiIndex, which we can’t copy and paste (see above). This is kind of a grievance with Pandas’ default display, but nonetheless annoying:
      In [11]: df
      Out[11]:
           C
      A B
      1 2  3
        2  6
      

      The correct way is to include an ordinary DataFrame with a set_index call:

      In [12]: df = pd.DataFrame([[1, 2, 3], [1, 2, 6]], columns=['A', 'B', 'C'])
      
      In [13]: df = df.set_index(['A', 'B'])
      
      In [14]: df
      Out[14]:
           C
      A B
      1 2  3
        2  6
      
    • Do provide insight to what it is when giving the outcome you want:
         B
      A
      1  1
      5  0
      

      Be specific about how you got the numbers (what are they)… double check they’re correct.

    • If your code throws an error, do include the entire stack trace. This can be edited out later if it’s too noisy. Show the line number and the corresponding line of your code which it’s raising against.
    • Pandas 2.0 introduced a number of changes, and Pandas 1.0 before that, so if you’re getting unexpected output, include the version:
      pd.__version__
      

      On that note, you might also want to include the version of Python, your OS, and any other libraries. You could use pd.show_versions() or the session_info package (which shows loaded libraries and Jupyter/IPython environment).

    The Ugly:

    • Don’t link to a CSV file we don’t have access to (and ideally don’t link to an external source at all).
      df = pd.read_csv('my_secret_file.csv') # ideally with lots of parsing options
      

      Most data is proprietary, we get that. Make up similar data and see if you can reproduce the problem (something small).

    • Don’t explain the situation vaguely in words, like you have a DataFrame which is “large”, mention some of the column names in passing (be sure not to mention their dtypes). Try and go into lots of detail about something which is completely meaningless without seeing the actual context. Presumably no one is even going to read to the end of this paragraph.

      Essays are bad; it’s easier with small examples.

    • Don’t include 10+ (100+??) lines of data munging before getting to your actual question.

      Please, we see enough of this in our day jobs. We want to help, but not like this…. Cut the intro, and just show the relevant DataFrames (or small versions of them) in the step which is causing you trouble.

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W3spoint99
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W3spoint99Begginer
Asked: December 26, 2024In: Python

How Slicing in Python works?

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How does Python’s slice notation (Slicing) work? That is: when I write code like a[x:y:z], a[:], a[::2] etc., how can I understand which elements end up in the slice?

howpythonsequenceslice
  1. Saralyn
    Saralyn Teacher
    Added an answer on December 26, 2024 at 1:59 pm
    This answer was edited.

    The syntax is: a[start:stop] # items start through stop-1 a[start:] # items start through the rest of the array a[:stop] # items from the beginning through stop-1 a[:] # a copy of the whole array There is also the step value, which can be used with any of the above: a[start:stop:step] # start througRead more

    The syntax is:

    a[start:stop]  # items start through stop-1
    a[start:]      # items start through the rest of the array
    a[:stop]       # items from the beginning through stop-1
    a[:]           # a copy of the whole array
    

    There is also the step value, which can be used with any of the above:

    a[start:stop:step] # start through not past stop, by step
    

    The key point to remember is that the :stop value represents the first value that is not in the selected slice. So, the difference between stop and start is the number of elements selected (if step is 1, the default).

    The other feature is that start or stop may be a negative number, which means it counts from the end of the array instead of the beginning. So:

    a[-1]    # last item in the array
    a[-2:]   # last two items in the array
    a[:-2]   # everything except the last two items
    

    Similarly, step may be a negative number:

    a[::-1]    # all items in the array, reversed
    a[1::-1]   # the first two items, reversed
    a[:-3:-1]  # the last two items, reversed
    a[-3::-1]  # everything except the last two items, reversed
    

    Python is kind to the programmer if there are fewer items than you ask for. For example, if you ask for a[:-2] and a only contains one element, you get an empty list instead of an error. Sometimes you would prefer the error, so you have to be aware that this may happen.

    Relationship with the slice object

    A slice object can represent a slicing operation, i.e.:

    a[start:stop:step]
    

    is equivalent to:

    a[slice(start, stop, step)]
    

    Slice objects also behave slightly differently depending on the number of arguments, similar to range(), i.e. both slice(stop) and slice(start, stop[, step]) are supported. To skip specifying a given argument, one might use None, so that e.g. a[start:] is equivalent to a[slice(start, None)] or a[::-1] is equivalent to a[slice(None, None, -1)].

    While the :-based notation is very helpful for simple slicing, the explicit use of slice() objects simplifies the programmatic generation of slicing.

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