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 >
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