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 <pmhob...@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 <tmrs...@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! >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@python.org >> https://mail.python.org/mailman/listinfo/numpy-discussion >> > _______________________________________________ > SciPy-User mailing list > scipy-u...@python.org > https://mail.python.org/mailman/listinfo/scipy-user >
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