[SciPy-User] [Numpy-discussion] Why slicing Pandas column and then subtract gives NaN?

[Thomas Kluyver]

> I don’t have index when I read in the data. I just want to slice two
series to the same length, and subtract. That’s it!

That sounds like you want Numpy. Pandas objects always have an index, even
if it's the default integer index. You've already found how to extract a
Numpy array from a pandas series.

On Fri, 15 Feb 2019 at 06:02, Mike C <tmrsg11 at gmail.com> wrote:

> Thanks a lot, Thomas.
>
> I don’t have index when I read in the data. I just want to slice two
> series to the same length, and subtract. That’s it!
>
> I also don’t what numpy methods wrapped within methods. They work, but
> hard do understand.
>
> How would you do it? In Matlab or R, it’s very simple, one line.
>
>
>
>
> ------------------------------
> *From:* SciPy-User <scipy-user-bounces+tmrsg11=gmail.com at python.org> on
> behalf of Thomas Kluyver <takowl at gmail.com>
> *Sent:* Thursday, February 14, 2019 4:54 PM
> *To:* SciPy Users List
> *Cc:* Discussion of Numerical Python
> *Subject:* Re: [SciPy-User] [Numpy-discussion] Why slicing Pandas column
> and then subtract gives NaN?
>
> Maybe it's useful to look a bit more at what pandas is doing and why. The
> 'index' on a series or dataframe labels each row - e.g. if your series is
> measuring total sales for each day, its index would be the dates. When you
> combine (e.g. subtract) two series, pandas automatically lines up the
> indices. So it will join up the numbers for February 14th, even if they're
> not in the same position in the data.
>
> In your example, you haven't specified an index, so pandas generates an
> integer index which doesn't really mean anything, and aligning on it
> doesn't do what you want.
>
> What are you trying to do? If Numpy does exactly what you want, then the
> answer might be to use Numpy.
>
> > Isn't Numpy built on top of Pandas?
>
> It's the other way round: pandas is built on Numpy. Pandas indices are an
> extra layer of functionality on top of what Numpy does.
>
> On Thu, 14 Feb 2019 at 20:22, C W <tmrsg11 at gmail.com> wrote:
>
>> Hi Paul,
>>
>> Thanks for your response! I did not find a Pandas list for users, only
>> for developers. I'd love to be on there.
>>
>> result = a.subtract(b.shift()).dropna()
>>
>> This seems verbose, several layers of parenthesis follow by a dot method.
>> I'm new to Python, I thought Python code would be pity and short. Is this
>> what everyone will write?
>>
>> Thank you!
>>
>>
>>
>> On Wed, Feb 13, 2019 at 6:50 PM Paul Hobson <pmhobson at gmail.com> wrote:
>>
>>> This is more a question for the pandas list, but since i'm here i'll
>>> take a crack.
>>>
>>>
>>>    - numpy aligns arrays by position.
>>>    - pandas aligns by label.
>>>
>>> So what you did in pandas is roughly equivalent to the following:
>>>
>>> a = pandas.Series([85, 86, 87, 86], name='a').iloc[1:4].to_frame()
>>> b = pandas.Series([15, 72, 2, 3], name='b').iloc[0:3].to_frame()
>>> result = a.join(b,how='outer').assign(diff=lambda df: df['a'] - df['b'])
>>> print(result)
>>>
>>>       a     b  diff
>>> 0   NaN  15.0   NaN
>>> 1  86.0  72.0  14.0
>>> 2  87.0   2.0  85.0
>>> 3  86.0   NaN   NaN
>>>
>>> So what I think you want would be the following:
>>>
>>> a = pandas.Series([85, 86, 87, 86], name='a')
>>> b = pandas.Series([15, 72, 2, 3], name='b')
>>> result = a.subtract(b.shift()).dropna()
>>> print(result)
>>> 1    71.0
>>> 2    15.0
>>> 3    84.0
>>> dtype: float64
>>>
>>>
>>>
>>> On Wed, Feb 13, 2019 at 2:51 PM C W <tmrsg11 at gmail.com> wrote:
>>>
>>>> Dear list,
>>>>
>>>> I have the following to Pandas Series: a, b. I want to slice and then
>>>> subtract. Like this: a[1:4] - b[0:3]. Why does it give me NaN? But it works
>>>> in Numpy.
>>>>
>>>> Example 1: did not work
>>>> >>>a = pd.Series([85, 86, 87, 86])
>>>> >>>b = pd.Series([15, 72, 2, 3])
>>>> >>> a[1:4]-b[0:3] 0   NaN 1   14.0 2   85.0 3   NaN
>>>> >>> type(a[1:4])
>>>> <class 'pandas.core.series.Series'>
>>>>
>>>> Example 2: worked
>>>> If I use values() method, it's converted to a Numpy object. And it
>>>> works!
>>>> >>> a.values[1:4]-b.values[0:3]
>>>> array([71, 15, 84])
>>>> >>> type(a.values[1:4])
>>>> <class 'numpy.ndarray'>
>>>>
>>>> What's the reason that Pandas in example 1 did not work? Isn't Numpy
>>>> built on top of Pandas? So, why is everything ok in Numpy, but not in
>>>> Pandas?
>>>>
>>>> Thanks in advance!
>>>> _______________________________________________
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>>>>
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