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
>
_______________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@python.org
https://mail.python.org/mailman/listinfo/numpy-discussion
