Computing np.diff in Pandas after using groupby leads to unexpected result

Nice easy to reproduce example!! more questions should be like this!

Just pass a lambda to transform (this is tantamount to passing afuncton object, e.g. np.diff (or Series.diff) directly. So this equivalent to data1/data2

In [32]: data3['diffs'] = data3.groupby('ticker')['value'].transform(Series.diff)

In [34]: data3.sort_index(inplace=True)

In [25]: data3
Out[25]: 
         date    ticker     value     diffs
0  2013-10-03  ticker_2  0.435995  0.015627
1  2013-10-04  ticker_2  0.025926 -0.410069
2  2013-10-02  ticker_1  0.549662       NaN
3  2013-10-01  ticker_0  0.435322       NaN
4  2013-10-02  ticker_2  0.420368  0.120713
5  2013-10-03  ticker_0  0.330335 -0.288936
6  2013-10-04  ticker_1  0.204649 -0.345014
7  2013-10-02  ticker_0  0.619271  0.183949
8  2013-10-01  ticker_2  0.299655       NaN

[9 rows x 4 columns]

I believe that np.diff doesn't follow numpy's own unfunc guidelines to process array inputs (whereby it tries various methods to coerce input and send output, e.g. __array__ on input __array_wrap__ on output). I am not really sure why, see a bit more info here. So bottom line is that np.diff is not dealing with the index properly and doing its own calculation (which in this case is wrong).

Pandas has a lot of methods where they don't just call the numpy function, mainly because they handle different dtypes, handle nans, and in this case, handle 'special' diffs. e.g. you can pass a time frequency to a datelike-index where it calculates how many n to actually diff.

You can see that the Series .diff() method is different to np.diff():

In [11]: data.value.diff()  # Note the NaN
Out[11]: 
0         NaN
1   -0.410069
2    0.523736
3   -0.114340
4   -0.014955
5   -0.090033
6   -0.125686
7    0.414622
8   -0.319616
Name: value, dtype: float64

In [12]: np.diff(data.value.values)  # the values array of the column
Out[12]: 
array([-0.41006867,  0.52373625, -0.11434009, -0.01495459, -0.09003298,
       -0.12568619,  0.41462233, -0.31961629])

In [13]: np.diff(data.value) # on the column (Series)
Out[13]: 
0   NaN
1     0
2     0
3     0
4     0
5     0
6     0
7     0
8   NaN
Name: value, dtype: float64

In [14]: np.diff(data.value.index)  # er... on the index
Out[14]: Int64Index([8], dtype=int64)

In [15]: np.diff(data.value.index.values)
Out[15]: array([1, 1, 1, 1, 1, 1, 1, 1])