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@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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