熊猫系列的循环移位

Question

I am using the shift method for a data series in pandas (documentation) .

Is it possible do a cyclic shift, ie the first value become the last value, in one step?

>>> input
Out[20]: 
5     0.995232
15    0.999794
25    1.006853
35    0.997781
45    0.981553
Name: vRatio, dtype: float64

>>> input.shift()
Out[21]: 
5          NaN
15    0.995232
25    0.999794
35    1.006853
45    0.997781
Name: vRatio, dtype: float64

desired output:

Out[21]: 
5     0.981553
15    0.995232
25    0.999794
35    1.006853
45    0.997781
Name: vRatio, dtype: float64

Answer 1

You can use np.roll to cycle the index values and pass this as the values to reindex :

In [23]:
df.reindex(index=np.roll(df.index,1))

Out[23]:
         vRatio
index          
45     0.981553
5      0.995232
15     0.999794
25     1.006853
35     0.997781

If you want to preserve your index then you can just overwrite the values again using np.roll :

In [25]:
df['vRatio'] = np.roll(df['vRatio'],1)
df

Out[25]:
         vRatio
index          
5      0.981553
15     0.995232
25     0.999794
35     1.006853
45     0.997781

Answer 2

Here's a slight modification of @EdChum 's great answer, which I find more useful in situations where I want to avoid an assignment:

pandas.DataFrame(np.roll(df.values, 1), index=df.index)

or for Series:

pandas.Series(np.roll(ser.values, 1), index=ser.index)

Answer 3

To do this without using a single step:

>>> output = input.shift()
>>> output.loc[output.index.min()] = input.loc[input.index.max()]
>>> output
Out[32]: 
5     0.981553
15    0.995232
25    0.999794
35    1.006853
45    0.997781
Name: vRatio, dtype: float64